WO2024236537A1

WO2024236537A1 - Apparatus and methods for microscopic analysis of a biological sample

Info

Publication number: WO2024236537A1
Application number: PCT/IB2024/054798
Authority: WO
Inventors: Arnon Houri Yafin; Yishai Avior; Amir ZAIT; Evgeny YURKOVSKY; Yochay Shlomo ESHEL; Niv Steven Samuel MASTBOIM
Original assignee: S.D. Sight Diagnostics Ltd
Priority date: 2023-05-18
Filing date: 2024-05-17
Publication date: 2024-11-21

Abstract

Apparatus (20) and methods are provided for analyzing a blood sample. The blood sample is mixed with fluorescently-labeled antibodies and deposited within a sample chamber (52). The sample chamber, with the blood sample deposited therein is placed within an optical measurement unit (31). One or more of the fluorescent microscopic images of the blood sample within the sample chamber are acquired using a microscope of the optical measurement unit. A computer processor (28) performs immunophenotyping on the blood sample, by analyzing the one or more fluorescent microscopic images of the blood sample, and an output is generated at least partially in response thereto. Other applications are also described.

Description

APPARATUS AND METHODS FOR MICROSCOPIC ANALYSIS OF A BIOLOGICAL SAMPLE

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority from:

U.S. Provisional Patent Application No. 63/467,396 to Houri Yafin, filed May 18, 2023, entitled "Apparatus and methods for microscopic immunophenotyping",

U.S. Provisional Patent Application No. 63/467,415 to Pumerantz, filed May 18, 2023, entitled "Apparatus and methods for microscopic biomarker detection",

U.S. Provisional Patent Application No. 63/532,997 to Houri Yafin, filed August 16, 2023, entitled "Apparatus and methods for microscopic immunophenotyping," all of which are incorporated herein by reference.

FIELD OF EMBODIMENTS OF THE INVENTION

Some applications of the presently disclosed subject matter relate generally to analysis of biological samples, and, in particular, to microscopic analysis performed upon blood samples, including microscopic immunophenotyping and morphology-based biomarker detection.

BACKGROUND

In some optics-based methods (e.g., diagnostic, and/or analytic methods), a property of a biological sample, such as a blood sample, is determined by performing an optical measurement. For example, the density of a component (e.g., a count of the component per unit volume) may be determined by counting the component within a microscopic image. Similarly, the concentration and/or density of a component may be measured by performing optical absorption, transmittance, fluorescence, and/or luminescence measurements upon the sample. Typically, the sample is placed into a sample carrier and the measurements are performed with respect to a portion of the sample that is contained within a chamber of the sample carrier. The measurements that are performed upon the portion of the sample that is contained within the chamber of the sample carrier are analyzed in order to determine a property of the sample. SUMMARY OF EMBODIMENTS

In accordance with some applications of the present invention, a biological sample (e.g., a blood sample) analysis system is provided. For some applications, the blood-sample analysis system is configured for analyzing the blood sample by immunopheno typing, which is a technique whereby antibodies are used to identify cells based on the expression of surface proteins by the cells. For some applications, a blood sample is prepared for analysis by mixing fluorescently-labeled antibodies with the blood sample, and depositing the blood sample within a sample chamber. The sample chamber with the blood sample deposited therein is placed in an optical measurement unit that includes a microscope. One or more fluorescent microscopic images of the blood sample within the sample chamber are acquired using the microscope of the optical measurement unit. A computer processor performs immunophenotyping on the blood sample, by analyzing the one or more fluorescent microscopic images of the blood sample. An output is typically generated on an output device in response to the immunophenotyping.

In accordance with some applications of the present invention, analysis of the blood sample includes performing immunophenotyping of the blood sample in combination with a count of entities within the blood sample. For some such applications, the blood sample is deposited within the sample chamber, and the sample chamber is deposited within the optical measurement unit. Microscopic images of the blood sample within the sample chamber are acquired using the microscope of the optical measurement unit. The computer processor then performs a count of entities within the blood sample (e.g., red blood cells, neutrophils, eosinophils, basophils, lymphocytes, monocytes, and/or platelets) by analyzing the microscopic images of the blood sample. Additionally, by analyzing the microscopic images of the blood sample, the computer processor performs immunophenotyping on the blood sample, and determines a characteristic of the blood sample, based upon the count of entities within the sample and the immunophenotyping. An output is typically generated on an output device in response to determining the characteristic of the blood sample. Alternatively, for some applications, the immunophenotyping is only performed in cases in which, based upon the count of the entities within the blood sample, the computer processor determines whether immunophenotyping should be performed on the blood sample. In response to determining that immunophenotyping should be performed on the blood sample, the computer processor performs immunophenotyping on the blood sample, by analyzing the microscopic images of the blood sample. An output is typically generated on an output device in response to immunopheno typing .

In accordance with additional applications of the present invention, analysis of the blood sample includes performing immunophenotyping of the blood sample in combination with identifying morphological characteristics (e.g., nucleus shape and size, mitosis activity, viability, granulation, activation, cell age and other features) of entities within the sample. For some such applications, the blood sample is deposited within the sample chamber, and the sample chamber is deposited within the optical measurement unit. Microscopic images of the blood sample within the sample chamber are acquired using the microscope of the optical measurement unit. The computer processor then identifies morphological characteristics of entities within the sample, by analyzing the microscopic images of the blood sample (e.g., nucleus shape and size, mitosis activity, viability, granulation, activation, cell age and other features). Additionally, by analyzing the microscopic images of the blood sample, the computer processor performs immunophenotyping on the blood sample, and determines a characteristic of the blood sample, based upon the morphological characteristics of entities within the sample and the immunophenotyping. An output is typically generated on an output device in response to determining the characteristic of the blood sample. Alternatively, for some applications, the immunophenotyping is only performed in cases in which based upon the identifying of morphological characteristics of entities within the sample, the computer processor determines whether immunophenotyping should be performed on the blood sample. In response to determining that immunophenotyping should be performed on the blood sample, the computer processor performs immunophenotyping on the blood sample, by analyzing the microscopic images of the blood sample. An output is typically generated on an output device in response to immunophenotyping.

In accordance with some applications of the present invention, the blood sample analysis system is trained to perform immunophenotyping on blood samples. The training is typically done by mixing antibodies that are fluorescently labeled with a fluorescent stain with a plurality of blood samples and acquiring (a) fluorescent microscopic images of each of the blood samples under a fluorescent imaging modality that is configured to excite the fluorescent stain, and (b) additional microscopic images are acquired of each of the blood samples under a second imaging modality. The computer processor performs immunophenotyping on each of the blood samples, by analyzing the one or more fluorescent microscopic images of each of the blood samples, and identifies features of each of the blood samples, by analyzing the one or more additional microscopic images of each of the blood samples. The computer processor identifies correlations between results of the immunophenotyping and features of the blood sample that are identifiable in microscopic images that are acquired under the second imaging modality.

In accordance with additional applications of the present invention, the blood- sample analysis system is configured for analyzing the blood sample microscopically for indication of morphological and spectral features, and/or for biomarker detection. Hairy cell leukemia is a rare bone marrow cancer, which accounts for approximately 2% of all leukemias. It is typically classified as a subtype of chronic lymphocytic leukemia (CLL). In accordance with some applications of the present invention, hairy cells are identified using several morphological and spectral features extracted from images taken using four illumination models and six different heights. For some such applications, brightfield-based morphological features describing the outlines of the cell, brightfield-based spectrometric features describing the inner structure of the cell (i.e., absorption) and fluorescent-based spectrometric features describing the internal structure of the cytoplasm are used to identify hairy cells.

Lymphocytes in the blood are composed of mainly two types of cells: B- and T-cell lymphocytes, each of which has a unique role in the immune response cascade. It is challenging to distinguish between the two cell types using a traditional Giemsa stain and a blood smear. Cell quantification is thus usually done using flow cytometry and unique surface antibodies - specifically CD3 and CD19. In accordance with some applications of the present invention, morphological features of cells (e.g., nucleus shape and density, cytoplasm shape, cytoplasm granularity, and cell outlines) are used to distinguish between B- and T-cell lymphocytes. For some such application, brightfield-based spectrometric features describing the inner structure of the cell (i.e., absorption) and fluorescent-based spectrometric features describing the internal structure of the cytoplasm are used to distinguish between B- and T- cell lymphocytes.

Chronic lymphocytic leukemia (CLL) is a bone marrow malignancy which causes the bone marrow to produce an abnormally high number of lymphocytes. CLL is one of the most common types of leukemia in adults and its progression is typically gradual. It is typically diagnosed using a combination of tests, including complete blood counts (CBC), blood smear, flow cytometry and DNA sequencing. Alongside an elevation of lymphocytes, subsets of abnormal cells are typically found in CLL patients. In accordance with some applications of the present invention, such abnormal cells are detected, and in response thereto a biological sample analysis system determines that the subject is suffering from or is likely to be suffering from CLL. For some such applications, the biological sample analysis system determines this based upon several morphological and spectral features extracted from images taken using four illumination models and six different heights. For some such applications, the biological sample analysis system determines this based upon mean lymphocyte size exceeding a maximum threshold or being less than a minimum threshold, and/or mean lymphocyte cytoplasm internal complexity exceeding a maximum threshold or being less than a minimum threshold.

There is therefore provided, in accordance with some embodiments of the present invention, a method including: preparing a blood sample for analysis by: mixing fluorescently-labeled antibodies with the blood sample; depositing the blood sample within a sample chamber; placing the sample chamber, with the blood sample deposited therein, within an optical measurement unit; acquiring one or more fluorescent microscopic images of the blood sample within the sample chamber, using a microscope of the optical measurement unit; and using a computer processor: performing immunophenotyping on the blood sample, by analyzing the one or more fluorescent microscopic images of the blood sample; and generating an output, at least partially in response thereto.

In some embodiments, the method further includes performing a count of entities within the blood sample, by analyzing the one or more microscopic images of the blood sample, and determining a characteristic of the blood sample, based upon the count of entities within the sample and the immunophenotyping, and generating the output including generating an output in response to determining the characteristic of the blood sample.

In some embodiments, mixing the fluorescently-labeled antibodies with the blood sample includes mixing fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1, and performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample includes identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1. In some embodiments, performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample, includes determining an average fluorescence intensity level of cells conjugated to the fluorescently- labeled antibodies within the one or more microscopic images of the blood sample.

In some embodiments, mixing the fluorescently-labeled antibodies with the blood sample includes mixing fluorescently-labeled antibodies against at least one of CD 14 and CD16.

In some embodiments, depositing the blood sample within a sample chamber includes depositing the blood sample in the sample chamber in an unwashed state.

In some embodiments, mixing the fluorescently-labeled antibodies with the blood sample includes mixing fluorescently-labeled antibodies against CD3, and performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample includes identifying T cell lymphocytes in the blood sample.

In some embodiments, mixing the fluorescently-labeled antibodies with the blood sample includes mixing fluorescently-labeled antibodies against CD3, and performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample includes performing a count of T cell lymphocytes in the blood sample.

In some embodiments, mixing the fluorescently-labeled antibodies with the blood sample includes mixing fluorescently-labeled antibodies against CD 19, and performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample includes identifying B cell lymphocytes in the blood sample.

In some embodiments, mixing the fluorescently-labeled antibodies with the blood sample includes mixing fluorescently-labeled antibodies against CD64, and generating the output includes generating an output that a subject from whom the blood sample was drawn is suspected of suffering from an infection at least partially based on the immunophenotyping.

In some embodiments, generating the output includes generating an output that a subject from whom the blood sample was drawn is suspected of suffering from sepsis at least partially based on the immunophenotyping. In some embodiments, the method further includes identifying one or more biomarkers selected from the group consisting of: Procalcitonin (PCT), C-reactive protein (CRP), Interleukin-6 (IL-6), Interleukin-8 (IL-8), Interleukin- 10 (IL- 10), Interleukin- ip (IL-ip), Tumor Necrosis Factor-alpha (TNF-a), Presepsin, Thrombomodulin, Lactate Soluble urokinase-type plasminogen activator receptor (suPAR), Soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1), Lipopolysaccharide-binding protein (LBP), N- terminal pro-brain natriuretic peptide (NT -proBNP), Neutrophil gelatinase-associated lipocalin (NGAL), Adrenomedullin, Resistin, Pro-adrenomedullin (MR-proADM), D-dimer, Pancreatic and stone protein (PSP); and generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection includes generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the one or more biomarkers selected from the group.

In some embodiments, the method further includes receiving a result of one or more diagnostic tests selected from the group consisting of: measuring a vital sign, blood culture, urine culture, chest X-ray, CT Scan, ultrasound, lumbar puncture, sputum culture and gram stain, wound culture, echocardiography, arterial blood gas (ABG) analysis, complete blood count (CBC), basic metabolic panel (BMP), lactate level, coagulation profile, urinalysis, accessing electronic medical records (EMRs); performing an EMR-based sepsis algorithm, central venous pressure (CVP) monitoring, and a culture from a bodily site; and generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection includes generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the result of the one or more diagnostic tools selected from the group.

In some embodiments, the method further includes performing a blood count on the blood sample, and generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection includes generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the blood count.

In some embodiments, the method further includes identifying morphological characteristics of entities within the blood sample, and generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection includes generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the morphological characteristics of entities within the blood sample.

In some embodiments, identifying morphological characteristics of entities within the blood sample includes identifying at least one of nucleus shape, nucleus density, cytoplasm, and/or cell outlines.

In some embodiments, performing immunophenotyping on the blood sample includes detecting average fluorescence intensity levels of the fluorescently-labeled antibodies within the one or more fluorescent microscopic images of the blood sample.

In some embodiments, performing the immunophenotyping includes determining a count of neutrophils expressing CD64 within the blood sample, and generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, includes generating the output in response to the count of the neutrophils expressing CD64.

In some embodiments, performing the immunophenotyping includes determining a count of lymphocytes expressing CD64 within the blood sample, and generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, includes generating the output in response to the count of the lymphocytes expressing CD64.

In some embodiments, performing the immunophenotyping includes determining a count of monocytes expressing CD64 within the blood sample, and generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, includes generating the output in response to the count of the monocytes expressing CD64.

In some embodiments, performing the immunophenotyping includes determining a count of white blood cells expressing CD64 within the blood sample, and generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, includes generating the output in response to the count of the white blood cells expressing CD64.

In some embodiments, performing the immunophenotyping includes quantifying CD64 expression in neutrophils within the blood sample, and generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, includes generating the output in response to the CD64 expression in the neutrophils within the blood sample. In some embodiments, performing the immunophenotyping includes quantifying CD64 expression in lymphocytes within the blood sample, and generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, includes generating the output in response to the CD64 expression in the lymphocytes within the blood sample.

In some embodiments, performing the immunophenotyping includes quantifying CD64 expression in monocytes within the blood sample, and generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, includes generating the output in response to the CD64 expression in the monocytes within the blood sample.

In some embodiments, performing the immunophenotyping includes quantifying CD64 expression in white blood cells within the blood sample, and generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, includes generating the output in response to the CD64 expression in the white blood cells within the blood sample.

In some embodiments, mixing the fluorescently-labeled antibodies with the blood sample includes mixing fluorescently-labeled antibodies against CD 19, and performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample includes performing a count of B cell lymphocytes in the blood sample.

In some embodiments, mixing the fluorescently-labeled antibodies with the blood sample further includes mixing fluorescently-labeled antibodies against CD3, and performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample includes performing a count of T cell lymphocytes in the blood sample.

In some embodiments, generating the output includes generating an output indicating relative counts of B cell lymphocytes and T cell lymphocytes in the blood sample.

In some embodiments, the method further includes identifying morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample, and determining a characteristic of the blood sample, based upon the morphological characteristics of the entities within the sample and the immunophenotyping; and generating an output including generating an output in response to determining the characteristic of the blood sample.

In some embodiments, identifying morphological characteristics of entities within the sample includes identifying at least one of nucleus shape, nucleus density, cytoplasm, and/or cell outlines.

There is further provided, in accordance with some embodiments of the present invention, apparatus for use with a sample chamber containing a blood sample that has been mixed with fluorescently-labeled antibodies, the apparatus including: an optical measurement unit configured to receive the sample chamber, the optical measurement unit including a microscope configured to acquire one or more fluorescent microscopic images of the blood sample within the sample chamber; and at least one computer processor configured to: perform immunophenotyping on the blood sample, by analyzing the one or more fluorescent microscopic images of the blood sample, and generate an output, at least partially in response thereto.

In some embodiments, the computer processor is further configured to: perform a count of entities within the blood sample, by analyzing the one or more microscopic images of the blood sample, and determine a characteristic of the blood sample, based upon the count of entities within the sample and the immunophenotyping.

In some embodiments, the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1, and the at least one computer processor configured to perform immunophenotyping on the blood sample by identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1.

In some embodiments, the computer processor is configured to perform immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample, by determining an average fluorescence intensity level of cells conjugated to the fluorescently-labeled antibodies within the one or more microscopic images of the blood sample.

In some embodiments, the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD3, and the computer processor is configured to perform the immunophenotyping by identifying T cell lymphocytes in the blood sample.

In some embodiments, the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD3, and the computer processor is configured to perform the immunophenotyping by performing a count of T cell lymphocytes in the blood sample.

In some embodiments, the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD 19, and the computer processor is configured to perform the immunophenotyping by identifying B cell lymphocytes in the blood sample.

In some embodiments, the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD64, and the computer processor is configured to generate an output that a subject from whom the blood sample was drawn is suspected of suffering from an infection at least partially based on the immunophenotyping .

In some embodiments, the computer processor is configured to generate an output that a subject from whom the blood sample was drawn is suspected of suffering from sepsis at least partially based on the immunophenotyping.

In some embodiments, the computer processor is configured to: identify one or more biomarkers selected from the group consisting of: Procalcitonin (PCT), C-reactive protein (CRP), Interleukin-6 (IL-6), Interleukin-8 (IL-8), Interleukin- 10 (IL- 10), Interleukin- ip (IL-lp), Tumor Necrosis Factor-alpha (TNF-a), Presepsin, Thrombomodulin, Lactate Soluble urokinase-type plasminogen activator receptor (suPAR), Soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1), Lipopolysaccharide- binding protein (LBP), N-terminal pro-brain natriuretic peptide (NT-proBNP), Neutrophil gelatinase-associated lipocalin (NGAL), Adrenomedullin, Resistin, Pro-adrenomedullin (MR- proADM), D-dimer, Pancreatic and stone protein (PSP); and generate the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the one or more biomarkers selected from the group.

In some embodiments, the computer processor is configured to: receive a result of one or more diagnostic tests selected from the group consisting of: measuring a vital sign, blood culture, urine culture, chest X-ray, CT Scan, ultrasound, lumbar puncture, sputum culture and gram stain, wound culture, echocardiography, arterial blood gas (ABG) analysis, complete blood count (CBC), basic metabolic panel (BMP), lactate level, coagulation profile, urinalysis, accessing electronic medical records (EMRs); performing an EMR-based sepsis algorithm, central venous pressure (CVP) monitoring, and a culture from a bodily site, and generate the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the result of the one or more diagnostic tools selected from the group.

In some embodiments, the computer processor is configured to perform a blood count on the blood sample, and generate the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the blood count.

In some embodiments, the computer processor is configured to identify morphological characteristics of entities within the blood sample, and generate the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the morphological characteristics of entities within the blood sample.

In some embodiments, the computer processor is configured to identify morphological characteristics of entities within the blood sample by identifying at least one of nucleus shape, nucleus density, cytoplasm, and/or cell outlines.

In some embodiments, the computer processor is configured to perform immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample, by determining an average fluorescence intensity level of cells conjugated to the CD64 fluorescently-labeled antibodies within the one or more microscopic images of the blood sample.

In some embodiments, the computer processor is configured to determine a count of neutrophils expressing CD64 within the blood sample, and generate the output based on the count of the neutrophils expressing CD64. In some embodiments, the computer processor is configured to determine a count of lymphocytes expressing CD64 within the blood sample, and generate the output based on the count of the lymphocytes expressing CD64.

In some embodiments, the computer processor is configured to determine a count of monocytes expressing CD64 within the blood sample, and generate the output based on the count of the monocytes expressing CD64.

In some embodiments, the computer processor is configured to determine a count of white blood cells expressing CD64 within the blood sample, and generate the output based on the count of white blood cells expressing CD64.

In some embodiments, the computer processor is configured to quantify CD64 expression in neutrophils within the blood sample, and generate the output based on the CD64 expression in neutrophils within the blood sample.

In some embodiments, the computer processor is configured to quantify CD64 expression in lymphocytes within the blood sample, and generate the output based on the CD64 expression in lymphocytes within the blood sample.

In some embodiments, the computer processor is configured to quantify CD64 expression in monocytes within the blood sample, and generate the output based on the CD64 expression in monocytes within the blood sample.

In some embodiments, the computer processor is configured to determine CD64 expression in white blood cells within the blood sample, and generate the output based on the CD64 expression in white blood cells within the blood sample.

In some embodiments, the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD 19, and the computer processor is configured to perform the immunophenotyping by performing a count of B cell lymphocytes in the blood sample.

In some embodiments, the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD3 and fluorescently-labeled antibodies against CD 19, and the computer processor is configured to perform the immunophenotyping by performing counts of T cell lymphocytes and B cell lymphocytes in the blood sample. In some embodiments, the computer processor is configured to generate the output by generating an output indicating relative counts of B cell lymphocytes and T cell lymphocytes in the blood sample.

In some embodiments, the computer processor is further configured to: identify morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample, and determine a characteristic of the blood sample, based upon the morphological characteristics of entities within the sample and the immunopheno typing.

In some embodiments, the computer processor is configured to identify morphological characteristics of entities within the sample by identifying at least one of nucleus shape, nucleus density, cytoplasm, and/or cell outlines.

There is further provided, in accordance with some embodiments of the present invention, a method including: depositing a blood sample within a sample chamber; placing the sample chamber, with the blood sample deposited therein, within an optical measurement unit; acquiring one or more microscopic images of the blood sample within the sample chamber, using a microscope of the optical measurement unit; and using a computer processor: performing a count of entities within the blood sample, by analyzing the one or more microscopic images of the blood sample; performing immunophenotyping on the blood sample, by analyzing the one or more microscopic images of the blood sample; determining a characteristic of the blood sample, based upon the count of entities within the sample and the immunophenotyping; and generating an output, at least partially in response thereto.

In some embodiments, performing the immunophenotyping includes mixing with the sample fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1 and identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1.

In some embodiments, the method further includes mixing fluorescently-labeled antibodies with the blood sample. In some embodiments, performing a count of entities includes performing a count of at least one of: red blood cells, neutrophils, eosinophils, basophils, lymphocytes, monocytes, and/or platelets.

In some embodiments, performing immunophenotyping includes performing immunophenotyping for CD3, and generating the output includes generating an output indicating a count of T cell lymphocytes in the blood sample.

In some embodiments, performing immunophenotyping includes performing immunophenotyping for CD 19, and generating the output includes generating an output indicating a count of B cell lymphocytes in the blood sample.

In some embodiments, performing immunophenotyping includes performing immunophenotyping for CD3 and CD 19, and generating the output includes generating an output indicating counts of B cell lymphocytes and T cell lymphocytes in the blood sample.

In some embodiments, performing immunophenotyping includes performing immunophenotyping for at least one of: CD14 and CD16.

In some embodiments, performing immunophenotyping includes performing immunophenotyping for CD64, and generating the output includes generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from an infection.

In some embodiments, generating the output includes generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from sepsis.

There is further provided, in accordance with some embodiments of the present invention, apparatus for use with a sample chamber containing a blood sample, the apparatus including: an optical measurement unit configured to receive the sample chamber, the optical measurement unit including a microscope configured to acquire one or more microscopic images of the blood sample within the sample chamber; and at least one computer processor configured to: perform a count of entities within the blood sample, by analyzing the one or more microscopic images of the blood sample, perform immunophenotyping on the blood sample, by analyzing the one or more microscopic images of the blood sample, determine a characteristic of the blood sample, based upon the count of entities within the sample and the immunopheno typing, and generate an output, at least partially in response thereto.

In some embodiments, the computer processor is configured to perform a count of entities by performing a count of at least one of: red blood cells, neutrophils, eosinophils, basophils, lymphocytes, monocytes, and/or platelets.

In some embodiments, the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD3, and the computer processor is configured to generate the output by generating an output indicating a count of T cell lymphocytes in the blood sample.

In some embodiments, the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD 19, and the computer processor is configured to generate the output by generating an output indicating a count of B cell lymphocytes in the blood sample.

In some embodiments, the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD3 and CD 19, and the computer processor is configured to generate the output by generating an output indicating counts of B cell lymphocytes and T cell lymphocytes in the blood sample.

In some embodiments, the computer processor is configured to perform immunophenotyping by performing immunophenotyping for at least one of: CD 14 and CD 16.

In some embodiments, the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD64, and the computer processor is configured to generate the output by generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from an infection. In some embodiments, the computer processor is configured to generate the output by generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from sepsis.

There is further provided, in accordance with some embodiments of the present invention, a method including: depositing a blood sample within a sample chamber; placing the sample chamber, with the blood sample deposited therein, within an optical measurement unit; acquiring one or more microscopic images of the blood sample within the sample chamber, using a microscope of the optical measurement unit; and using a computer processor: identifying morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample; performing immunophenotyping on the blood sample, by analyzing the one or more microscopic images of the blood sample; determining a characteristic of the blood sample, based upon the morphological characteristics of entities within the sample and the immunophenotyping; and generating an output, at least partially in response thereto.

In some embodiments, the method further includes mixing fluorescently-labeled antibodies with the blood sample.

In some embodiments, identifying morphological characteristics of entities within the sample includes identifying at least one of nucleus shape, nucleus density, cytoplasm shape, cytoplasm granularity, and/or cell outlines.

In some embodiments, performing immunophenotyping includes performing immunophenotyping for CD3, and generating the output includes generating an output indicating a count of T cell lymphocytes in the blood sample. In some embodiments, performing immunophenotyping includes performing immunophenotyping for CD 19, and generating the output includes generating an output indicating a count of B cell lymphocytes in the blood sample.

There is further provided, in accordance with some embodiments of the present invention, apparatus for use with a sample chamber containing a blood sample, the apparatus including: an optical measurement unit configured to receive the sample chamber, the optical measurement unit including a microscope configured to acquire one or more microscopic images of the blood sample within the sample chamber; and at least one computer processor configured to: identify morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample, perform immunophenotyping on the blood sample, by analyzing the one or more microscopic images of the blood sample, determine a characteristic of the blood sample, based upon the morphological characteristics of entities within the sample and the immunophenotyping, and generate an output, at least partially in response thereto.

In some embodiments, the computer processor is configured to identify morphological characteristics of entities within the sample by identifying at least one of nucleus shape, nucleus density, cytoplasm shape, cytoplasm granularity, and/or cell outlines.

In some embodiments, the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD64, and the computer processor is configured to generate the output by generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from an infection.

In some embodiments, the computer processor is configured to generate the output by generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from sepsis.

There is further provided, in accordance with some embodiments of the present invention, a method including: training a blood-sample analysis system to perform immunophenotyping on a test blood sample without the use of antibodies, by, during a training stage: mixing antibodies that are fluorescently labeled with a fluorescent stain with a plurality of blood samples; acquiring one or more fluorescent microscopic images of each of the blood samples under a fluorescent imaging modality that is configured to excite the fluorescent stain; acquiring one or more additional microscopic images of each of the blood samples under a second imaging modality; and using a computer processor: performing immunophenotyping on each of the blood samples, by analyzing the one or more fluorescent microscopic images of each of the blood samples; identifying features of each of the blood samples, by analyzing the one or more additional microscopic images of each of the blood samples; and identifying correlations between results of the immunophenotyping and features of the blood sample that are identifiable in microscopic images that are acquired under the second imaging modality.

In some embodiments, mixing the antibodies that are fluorescently labeled with the fluorescent stain with the plurality of blood samples includes mixing fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1 with the plurality of blood samples, and performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of each of the blood samples includes identifying within at least some of the blood samples a corresponding indication to the one or more cell surface markers, as listed in Table 1.

In some embodiments, the method further includes, during a subsequent stage, performing immunophenotyping on the test blood sample without the use of antibodies.

In some embodiments: mixing antibodies that are fluorescently labeled with the fluorescent stain with the plurality of blood samples includes mixing antibodies against CD64 that are fluorescently labeled with a fluorescent stain with a plurality of blood samples; identifying features of each of the blood samples includes identifying morphological characteristics of entities within each of the blood samples; the method further including, in response to performing immunophenotyping on the test blood sample without the use of antibodies, determining that a subject from whom the test blood sample was drawn is suspected of suffering from an infection. In some embodiments, identifying morphological characteristics of entities within each of the blood samples includes identifying at least one of nucleus shape, nucleus density, cytoplasm shape, cytoplasm granularity, and/or cell outlines of entities within each of the blood samples.

There is further provided, in accordance with some embodiments of the present invention, apparatus including: a blood analysis system including a microscope; and at least one computer processor associated with the blood analysis system configured to train the blood-sample analysis system to perform immunophenotyping on a test blood sample without the use of antibodies, by, during a training stage: receiving one or more fluorescent microscopic images of each of a plurality of blood samples that have been mixed with antibodies that are fluorescently labeled with a fluorescent stain, the one or more fluorescent microscopic images having been acquired under a fluorescent imaging modality that is configured to excite the fluorescent stain, receiving one or more additional microscopic images of each of the blood samples, the one or more additional microscopic images having been acquired under a second imaging modality, performing immunophenotyping on each of the blood samples, by analyzing the one or more fluorescent microscopic images of each of the blood samples, identifying features of each of the blood samples, by analyzing the one or more additional microscopic images of each of the blood samples, and identifying correlations between results of the immunophenotyping and features of the blood sample that are identifiable in microscopic images that are acquired under the second imaging modality.

In some embodiments, the computer processor is configured to during a subsequent stage, to perform immunophenotyping on the test blood sample without the use of antibodies.

In some embodiments: the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD64 that are fluorescently labeled with a fluorescent stain with a plurality of blood samples; and the computer processor is configured to: identify features of each of the blood samples by identifying morphological characteristics of entities within each of the blood samples; and in response to performing the immunophenotyping on the test blood sample without the use of antibodies, determine that a subject from whom the test blood sample was drawn is suspected of suffering from an infection.

In some embodiments, the computer processor is configured to identify morphological characteristics of entities within each of the blood samples by identifying at least one of nucleus shape, nucleus density, cytoplasm shape, cytoplasm granularity, and/or cell outlines of entities within each of the blood samples.

There is further provided, in accordance with some embodiments of the present invention, a method for use with a computer processor that has been trained, during a training phase, to identify features that are correlated to an expression of antibodies within training blood samples, the method including: acquiring one or more microscopic images of a test blood sample to which no antibodies have been added; and using the computer processor: performing immunophenotyping on the test blood sample by analyzing the microscopic images and identifying features that were determined, during the training phase, to be correlated to the expression of antibodies within training blood samples; and generating an output in response thereto.

There is further provided, in accordance with some embodiments of the present invention, apparatus including: a computer processor that has been trained, during a training phase, to identify features that are correlated to an expression of antibodies within training blood samples, the computer processor being configured to: receive one or more microscopic images of a test blood sample to which no antibodies have been added; perform immunophenotyping on the test blood sample by analyzing the microscopic images and identifying features that were determined, during the training phase, to be correlated to the expression of antibodies within training blood samples; and generate an output in response thereto.

There is further provided, in accordance with some embodiments of the present invention, a method for use with a blood sample, the method including: acquiring one or more microscopic images of the blood sample; and using a computer processor: identifying neutrophils expressing CD64 within the blood sample by analyzing the one or more microscopic images; and generating an output in response thereto.

In some embodiments, the method is for use with a blood sample to which no antibodies have been added, and identifying neutrophils expressing CD64 within the blood sample includes identifying neutrophils expressing CD64 within the blood sample to which no antibodies have been added.

In some embodiments, the method is for use with a blood sample to which antibodies against CD64 that are fluorescently labeled with a fluorescent stain have been added, and identifying neutrophils expressing CD64 within the blood sample includes identifying the antibodies against CD64 that are fluorescently labeled with the fluorescent stain within the microscopic images.

In some embodiments, the method further includes determining a count of neutrophils expressing CD64 within the blood sample.

In some embodiments, generating the output includes outputting the count of neutrophils expressing CD64 within the blood sample.

In some embodiments, the method further includes determining a percentage of neutrophils expressing CD64 within the blood sample.

In some embodiments, generating the output includes outputting the percentage of neutrophils expressing CD64 within the blood sample.

In some embodiments, the method further includes using the computer processor determining a quantification of expression of CD64 in the neutrophils within the blood sample. In some embodiments, generating the output includes generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from an infection, based on the quantification of expression of CD64 in the neutrophils within the blood sample.

In some embodiments, the method further includes identifying one or more biomarkers selected from the group consisting of: Procalcitonin (PCT), C-reactive protein (CRP), Interleukin-6 (IL-6), Interleukin-8 (IL-8), Interleukin- 10 (IL- 10), Interleukin- ip (IL-lp), Tumor Necrosis Factor-alpha (TNF-a), Presepsin, Thrombomodulin, Lactate Soluble urokinase-type plasminogen activator receptor (suPAR), Soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1), Lipopolysaccharide-binding protein (LBP), N- terminal pro-brain natriuretic peptide (NT -proBNP), Neutrophil gelatinase-associated lipocalin (NGAL), Adrenomedullin, Resistin, Pro-adrenomedullin (MR-proADM), D-dimer, Pancreatic and stone protein (PSP); and generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection includes generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the quantification of expression of CD64 in the neutrophils within the blood sample and the one or more biomarkers selected from the group.

In some embodiments, the method further includes receiving a result of one or more diagnostic tests selected from the group consisting of: measuring a vital sign, blood culture, urine culture, chest X-ray, CT Scan, ultrasound, lumbar puncture, sputum culture and gram stain, wound culture, echocardiography, arterial blood gas (ABG) analysis, complete blood count (CBC), basic metabolic panel (BMP), lactate level, coagulation profile, urinalysis, accessing electronic medical records (EMRs); performing an EMR-based sepsis algorithm, central venous pressure (CVP) monitoring, and a culture from a bodily site; and generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection includes generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the quantification of expression of CD64 in the neutrophils within the blood sample and the result of the one or more diagnostic tools selected from the group.

In some embodiments, the method further includes performing a blood count on the blood sample, and generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection includes generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the quantification of expression of CD64 in the neutrophils within the blood sample and the blood count.

In some embodiments, the method further includes identifying morphological characteristics of entities within the blood sample, and generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection includes generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the quantification of expression of CD64 in the neutrophils within the blood sample and the morphological characteristics of entities within the blood sample.

There is further provided, in accordance with some embodiments of the present invention, a method including: depositing a blood sample within a sample chamber; placing the sample chamber, with the blood sample deposited therein, within an optical measurement unit; acquiring one or more microscopic images of the blood sample within the sample chamber, using a microscope of the optical measurement unit; and using a computer processor: identifying morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample; determining that a subject from whom the sample was drawn is suspected of suffering from an infection, based on identifying the morphological characteristics; and generating an output, at least partially in response thereto.

In some embodiments, the method further includes using the computer processor to identify one or more cell surface markers listed in Table 1, and determining that a subject from whom the sample was drawn is suspected of suffering from an infection is based on identifying the morphological characteristics and the one or more cell surface markers listed in Table 1.

In some embodiments, the method further includes using the computer processor to identify one or more cell markers listed in Table 2, and determining that a subject from whom the sample was drawn is suspected of suffering from an infection is based on identifying the morphological characteristics and the one or more cell markers listed in Table 2.

In some embodiments, the method further includes using the computer processor to identify expression levels of CD64 within the blood sample, and determining that a subject from whom the sample was drawn is suspected of suffering from an infection is based on identifying the morphological characteristics and the expression levels on CD64 within the sample.

There is further provided, in accordance with some embodiments of the present invention, a method including: preparing a blood sample for analysis by: depositing the blood sample in an unwashed state within a sample chamber; placing the sample chamber, with the blood sample deposited therein, within an optical measurement unit; acquiring one or more images of the blood sample within the sample chamber, using a microscope of the optical measurement unit; and using a computer processor: performing immunophenotyping on the blood sample, by analyzing the one or more microscopic images of the blood sample; and generating an output, at least partially in response thereto.

There is further provided, in accordance with some embodiments of the present invention, a method including: depositing a blood sample within a sample chamber; placing the sample chamber, with the blood sample deposited therein, within an optical measurement unit; acquiring one or more microscopic images of the blood sample within the sample chamber, using a microscope of the optical measurement unit; and using a computer processor: performing a count of entities within the blood sample, by analyzing the one or more microscopic images of the blood sample; based upon the count of the entities within the blood sample, determining that immunophenotyping should be performed on the blood sample; performing immunophenotyping on the blood sample, by analyzing one or more microscopic images of the blood sample; and generating an output, at least partially in response thereto.

In some embodiments, performing the immunophenotyping includes mixing with the sample fluorescently-labeled antibodies against CD64, and identifying neutrophils expressing CD64 within the blood sample by analyzing the one or more microscopic images.

In some embodiments, performing immunophenotyping on the blood sample, by analyzing the one or more fluorescent microscopic images of the blood sample, includes determining an average fluorescence intensity level of cells conjugated to the fluorescently- labeled CD64 antibodies within the one or more microscopic images of the blood sample.

In some embodiments, the method further includes determining a percentage of neutrophils within the blood sample that express CD64.

In some embodiments, generating the output includes outputting the percentage of neutrophils within the blood sample that express CD64.

In some embodiments, generating the output includes generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from an infection, based on the identified neutrophils expressing CD64.

In some embodiments, performing the immunophenotyping includes mixing with the sample with fluorescently-labeled antibodies against CD64, and identifying monocytes expressing CD64 within the blood sample by analyzing the one or more microscopic images. In some embodiments, performing the immunophenotyping includes mixing with the sample with fluorescently-labeled antibodies against CD64, and identifying lymphocytes expressing CD64 within the blood sample by analyzing the one or more microscopic images.

In some embodiments, performing the immunophenotyping includes mixing with the sample with fluorescently-labeled antibodies against CD64, and quantifying expression of CD64 in neutrophiles within the blood sample by analyzing the one or more microscopic images.

In some embodiments, performing the immunophenotyping includes mixing with the sample with fluorescently-labeled antibodies against CD64, and quantifying expression of CD64 in lymphocytes within the blood sample by analyzing the one or more microscopic images.

In some embodiments, performing the immunophenotyping includes mixing with the sample with fluorescently-labeled antibodies against CD64, and quantifying expression of CD64 in monocytes within the blood sample by analyzing the one or more microscopic images.

In some embodiments, performing immunophenotyping on the blood sample includes mixing with the sample fluorescently-labeled antibodies, subsequently to determining that immunophenotyping should be performed on the blood sample.

In some embodiments, performing immunophenotyping on the blood sample, by analyzing the one or more fluorescent microscopic images of the blood sample, includes determining an average fluorescence intensity level of cells conjugated to the fluorescently- labeled antibodies within the one or more microscopic images of the blood sample.

In some embodiments, the method includes based upon a count of the entities showing a presence of blast cells in the blood, determining that immunophenotyping should be performed on the blood sample using fluorescently-labeled antibodies against at least of: CD34, CD45 and/or CD117.

In some embodiments, the method includes based upon the count of the entities within the blood sample, determining that immunophenotyping should be performed on the blood sample using fluorescently-labeled antibodies against at least of: CD20, CD55, and/or CD59. In some embodiments, performing a count of entities includes performing a count of at least one of: red blood cells, neutrophils, eosinophils, basophils, lymphocytes, monocytes, and/or platelets.

There is further provided, in accordance with some applications of the present invention, apparatus for use with a sample chamber containing a blood sample, the apparatus including: an optical measurement unit configured to receive the sample chamber, the optical measurement unit including a microscope configured to acquire one or more microscopic images of the blood sample within the sample chamber; and at least one computer processor configured to: perform a count of entities within the blood sample, by analyzing the one or more microscopic images of the blood sample, based upon the count of the entities within the blood sample, determine that immunophenotyping should be performed on the blood sample, perform immunophenotyping on the blood sample, by analyzing one or more microscopic images of the blood sample, and generating an output, at least partially in response thereto.

In some embodiments, the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1 and the at least one computer processor is configured to perform immunophenotyping on the blood sample by identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1.

In some embodiments, the computer processor is configured to perform immunophenotyping on the blood sample, by analyzing one or more fluorescent microscopic images of the blood sample, by determining an average fluorescence intensity level of cells conjugated to the fluorescently-labeled antibodies within the one or more microscopic images of the blood sample.

In some embodiments, the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD64, and the computer processor is configured to perform immunophenotyping by identifying neutrophils expressing CD64 within the blood sample by analyzing the one or more microscopic images. In some embodiments, the computer processor is configured to perform immunophenotyping on the blood sample, by analyzing the one or more microscopic images of the blood sample, to determine an average fluorescence intensity level of cells conjugated to the fluorescently-labeled CD64 antibodies within the one or more microscopic images of the blood sample.

In some embodiments, the computer processor is configured to determine a count of neutrophils expressing CD64 within the blood sample.

In some embodiments, the computer processor is configured to generate the output indicating that a subject from whom the sample was drawn is suspected of suffering from an infection, based on identifying neutrophils expressing CD64.

In some embodiments, based upon a count of the entities showing a presence of blast cells in the blood, the computer processor is configured to determine that immunophenotyping should be performed on the blood sample using fluorescently-labeled antibodies against at least of: CD34, CD45 and/or CD117.

In some embodiments, based upon the count of the entities within the blood sample, the computer processor is configured to determine that immunophenotyping should be performed on the blood sample using fluorescently-labeled antibodies against at least of: CD20, CD55, and/or CD59.

There is further provided, in accordance with some applications of the present invention, a method including: depositing a blood sample within a sample chamber; placing the sample chamber, with the blood sample deposited therein, within an optical measurement unit; acquiring one or more microscopic images of the blood sample within the sample chamber, using a microscope of the optical measurement unit; and using a computer processor: identifying morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample; based upon the identified morphological characteristics of entities within the sample, determining that immunophenotyping should be performed on the blood sample; performing immunophenotyping on the blood sample, by analyzing one or more microscopic images of the blood sample; and generating an output, at least partially in response thereto.

In some embodiments, identifying morphological characteristics of entities within the sample includes identifying at least one of nucleus shape, nucleus density, cytoplasm shape, cytoplasm granularity and/or cell outlines.

There is further provided, in accordance with some applications of the present invention, apparatus for use with a sample chamber containing a blood sample, the apparatus including: an optical measurement unit configured to receive the sample chamber, the optical measurement unit including a microscope configured to acquire one or more microscopic images of the blood sample within the sample chamber; and at least one computer processor configured to: identify morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample, based upon the identified morphological characteristics of entities within the sample, determine that immunophenotyping should be performed on the blood sample; perform immunophenotyping on the blood sample, by analyzing one or more microscopic images of the blood sample, and generate an output, at least partially in response thereto.

In some embodiments, the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against one or more cell surface markers listed in Table l,and the at least one computer processor is configured to perform immunophenotyping on the blood sample by identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1.

In some embodiments, the computer processor is configured to identify morphological characteristics of entities within the sample by identifying at least one of nucleus shape, nucleus density, cytoplasm shape, cytoplasm granularity and/or cell outlines.

There is further provided, in accordance with some applications of the present invention, a method including: training a blood-sample analysis system to identify a biomarker within a blood sample without the use of antibodies by: mixing antibodies that are fluorescently labeled with a fluorescent stain with a plurality of blood samples; acquiring one or more fluorescent microscopic images of each of the blood samples under a fluorescent imaging modality that is configured to excite the fluorescent stain; acquiring one or more additional microscopic images of each of the blood samples under a second imaging modality; and using a computer processor: identifying a presence of a biomarker within at least a portion of the blood samples, by analyzing the one or more fluorescent microscopic images of each of the blood samples; identifying features of each of the blood samples, by analyzing the one or more additional microscopic images of each of the blood samples; and identifying correlations between a presence of a biomarker and features of the blood sample that are identifiable in microscopic images that are acquired under the second imaging modality.

There is further provided, in accordance with some applications of the present invention, apparatus including: a blood analysis system including a microscope; and at least one computer processor associated with the blood analysis system and configured to train the blood-sample analysis system to identify a biomarker within a test blood sample without the use of antibodies, by, during a training stage: receiving one or more fluorescent microscopic images of each of a plurality of blood samples that have been mixed with antibodies that are fluorescently labeled with a fluorescent stain, the one or more fluorescent microscopic images having been acquired under a fluorescent imaging modality that is configured to excite the fluorescent stain, receiving one or more additional microscopic images of each of the blood samples, the one or more additional microscopic images having been acquired under a second imaging modality, identifying a presence of a biomarker within at least a portion of the blood samples, by analyzing the one or more fluorescent microscopic images of each of the blood samples, identifying features of each of the blood samples, by analyzing the one or more additional microscopic images of each of the blood samples, and identifying correlations between a presence of a biomarker and features of the blood sample that are identifiable in microscopic images that are acquired under the second imaging modality.

The present invention will be more fully understood from the following detailed description of embodiments thereof, taken together with the drawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a block diagram showing components of a biological sample analysis system, in accordance some applications of the present invention;

Figs. 2A, 2B, and 2C are schematic illustrations of an optical measurement unit, in accordance with some applications of the present invention;

Figs. 3 A, 3B, and 3C are schematic illustrations of respective views of a sample carrier that is used for performing both microscopic measurements and optical density measurements, in accordance with some applications of the present invention;

Fig. 4 is a generalized flowchart showing steps of immunophenotyping performed by the biological sample analysis system, in accordance with some applications of the present invention;

Fig. 5 is a flowchart showing steps of immunophenotyping performed in combination with additional measurements by the biological sample analysis system, in accordance with some applications of the present invention; Fig. 6 is a flowchart of steps of sample analysis performed by the biological sample analysis system, in accordance with some applications of the present invention;

Fig. 7 is a flowchart showing steps of immunophenotyping performed in combination with additional measurements by the biological sample analysis system, in accordance with some applications of the present invention;

Fig. 8 is a flowchart of steps of sample analysis performed by the biological sample analysis system, in accordance with some applications of the present invention;

Fig. 9 is a flowchart of training steps performed on the biological sample analysis system, in accordance with some applications of the present invention;

Fig. 10A is a receiver operating characteristic (“ROC”) curve indicating the diagnostic ability of microscopically-extractable features of blood without the use of antibodies to distinguish between CD64 positive and negative neutrophil cell populations, in accordance with some applications of the present invention;

Fig. 10B is a receiver operating characteristic (“ROC”) curve indicating the diagnostic ability of microscopically-extractable features of blood without the use of antibodies to distinguish between T cell and B cell lymphocytes by detecting CD3 and CD19 expression, in accordance with some applications of the present invention;

Fig. 11A is a graphical representation showing correlation between detection of neutrophil CD64 expression levels in blood samples analyzed by flow cytometry techniques and by the biological sample analysis system, in accordance with some applications of the present invention;

Fig. 11B is a graphical representation showing correlation between detection of CD3 positive lymphocytes in blood samples analyzed by flow cytometry techniques and by the biological sample analysis system, in accordance with some applications of the present invention;

Fig. 11C is a graphical representation showing correlation between detection of CD 19 positive lymphocytes in blood samples analyzed by flow cytometry techniques and by the biological sample analysis system, in accordance with some applications of the present invention; Fig. 12 is a generalized flowchart showing steps of biomarker detection performed by the biological sample analysis system, in accordance with some applications of the present invention; and

Fig. 13 is a flowchart of training steps performed on the biological sample analysis system, in accordance with some applications of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

Biological sample analysis system

Reference is now made to Fig. 1A, which is a block diagram showing components of a biological sample analysis system 20, in accordance with some applications of the present invention. Typically, a biological sample (e.g., a blood sample) is placed into a sample carrier 22. While the sample is disposed in the sample carrier, optical measurements are performed upon the sample using one or more optical measurement devices 24. For example, the optical measurement devices may include a microscope (e.g., a digital microscope), a spectrophotometer, a photometer, a spectrometer, a camera, a spectral camera, a hyperspectral camera, a fluorometer, a spectrofluorometer, and/or a photodetector (such as a photodiode, a photoresistor, and/or a phototransistor). For some applications, the optical measurement devices include dedicated light sources (such as light emitting diodes, incandescent light sources, etc.) and/or optical elements for manipulating light collection and/or light emission (such as lenses, diffusers, filters, etc.).

A computer processor 28 typically receives and processes optical measurements that are performed by the optical measurement device. Further typically, the computer processor controls the acquisition of optical measurements that are performed by the one or more optical measurement devices. The computer processor communicates with a memory 30. A user (e.g., a laboratory technician, or an individual from whom the sample was drawn) sends instructions to the computer processor via a user interface 32. For some applications, the user interface includes a keyboard, a mouse, a joystick, a touchscreen device (such as a smartphone or a tablet computer), a touchpad, a trackball, a voice-command interface, and/or other types of user interfaces that are known in the art. Typically, the computer processor generates an output via an output device 34. Further typically, the output device includes a display, such as a monitor, and the output includes an output that is displayed on the display. For some applications, the processor generates an output on a different type of visual, text, graphics, tactile, audio, and/or video output device, e.g., speakers, headphones, a smartphone, or a tablet computer. For some applications, user interface 32 acts as both an input interface and an output interface, i.e., it acts as an input/output interface. For some applications, the processor generates an output on a computer-readable medium (e.g., a non-transitory computer-readable medium), such as a disk, or a portable USB drive, and/or generates an output on a printer.

Reference is now made to Figs. 2A, 2B, and 2C, which are schematic illustrations of an optical measurement unit 31, in accordance with some applications of the present invention. Fig. 2A shows an oblique view of the exterior of the fully assembled device, while Figs. 2B and 2C shows respective oblique views of the device with the cover having been made transparent, such components within the device are visible. For some applications, one or more optical measurement devices 24 (and/or computer processor 28 and memory 30) is housed inside optical measurement unit 31. In order to perform the optical measurements upon the sample, sample carrier 22 is placed inside the optical measurement unit. For example, the optical measurement unit may define a slot 36, via which the sample carrier is inserted into the optical measurement unit. Typically, the optical measurement unit includes a stage 64, which is configured to support sample carrier 22 within the optical measurement unit. For some applications, a screen 63 on the cover of the optical measurement unit (e.g., a screen on the front cover of the optical measurement unit, as shown) functions as user interface 32 and/or output device 34.

Typically, the optical measurement unit includes microscope system 37 (shown in Figs. 2B-C) configured to perform microscopic imaging of a portion of the sample. For some applications, in addition to a microscope, the microscope system includes a set of light sources 65 (which typically include a set of brightfield light sources (e.g. light emitting diodes) that are configured to be used for brightfield imaging of the sample, a set of fluorescent light sources (e.g. light emitting diodes) that are configured to be used for fluorescent imaging of the sample), and a camera (e.g., a CCD camera, or a CMOS camera) configured to image the sample. Typically, the optical measurement unit also includes an optical-density- measurement unit 39 (shown in Fig. 2C) configured to perform optical density measurements (e.g., optical absorption measurements) on a second portion of the sample. For some applications, the optical-density-measurement unit includes a set of optical-density- measurement light sources (e.g., light emitting diodes) and light detectors, which are configured for performing optical density measurements on the sample. For some applications, each of the aforementioned sets of light sources (i.e., the set of brightfield light sources, the set of fluorescent light sources, and the set optical-density-measurement light sources) includes a plurality of light sources (e.g. a plurality of light emitting diodes), each of which is configured to emit light at a respective wavelength or at a respective band of wavelengths.

Reference is now made to Figs. 3A and 3B, which are schematic illustrations of respective views of sample carrier 22, in accordance with some applications of the present invention. Fig. 3A shows a top view of the sample carrier (the top cover of the sample carrier being shown as being opaque in Fig. 3A, for illustrative purposes), and Fig. 3B shows a bottom view (in which the sample carrier has been rotated around its short edge with respect to the view shown in Fig. 3A). Typically, the sample carrier includes a first set 52 of one or more sample chambers, which are used for performing microscopic analysis upon the sample, and a second set 54 of sample chambers, which are used for performing optical density measurements upon the sample. Typically, the sample chambers of the sample carrier are filled with a biological sample, such as blood, via sample inlet holes 38. For some applications, the sample chambers define one or more outlet holes 40. The outlet holes are configured to facilitate filling of the sample chambers with the biological sample, by allowing air that is present in the sample chambers to be released from the sample chambers. Typically, as shown, the outlet holes are located longitudinally opposite the inlet holes (with respect to a sample chamber of the sample carrier). For some applications, the outlet holes thus provide a more efficient mechanism of air escape than if the outlet holes were to be disposed closer to the inlet holes.

Reference is made to Fig. 3C, which shows an exploded view of sample carrier 22, in accordance with some applications of the present invention. For some applications, the sample carrier includes at least three components: a molded component 42, a glass layer 44 (e.g., glass sheet), and an adhesive layer 46 configured to adhere the glass layer to an underside of the molded component. The molded component is typically made of a polymer (e.g., a plastic) that is molded (e.g., via injection molding) to provide the sample chambers with a desired geometrical shape. For example, as shown, the molded component is typically molded to define inlet holes 38, outlet holes 40, and gutters 48 which surround the central portion of each of the sample chambers. The gutters typically facilitate filling of the sample chambers with the biological sample, by allowing air to flow to the outlet holes, and/or by allowing the biological sample to flow around the central portion of the sample chamber.

For some applications, a sample carrier as shown in Figs. 3A-C is used when performing a complete blood count and/or immunopheno typing, and/or biomarker detection on a blood sample. For some such applications, the sample carrier is used with optical measurement unit 31 configured as generally shown and described with reference to Figs. 2A- C. For some applications, a first portion of the blood sample is placed inside first set 52 of sample chambers (which are used for performing microscopic analysis upon the sample, e.g., using microscope system 37 (shown in Figs. 2B-C)), and a second portion of the blood sample is placed inside second set 54 of sample chambers (which are used for performing optical density measurements upon the sample, e.g., using optical-density-measurement unit 39 (shown in Fig. 2C)). For some applications, first set 52 of sample chambers includes a plurality of sample chambers, while second set 54 of sample chambers includes only a single sample chamber, as shown. However, the scope of the present application, includes using any number of sample chambers (e.g., a single sample chamber or a plurality of sample chambers) within either the first set of sample chambers or within the second set of sample chambers, or any combination thereof. The first portion of the blood sample is typically diluted with respect to the second portion of the blood sample. For example, the diluent may contain pH buffers, stains, fluorescent stains, antibodies, sphering agents, lysing agents, etc. Typically, the second portion of the blood sample, which is placed inside second set 54 of sample chambers is a natural, undiluted blood sample. Alternatively or additionally, the second portion of the blood sample may be a sample that underwent some modification, including, for example, one or more of dilution (e.g., dilution in a controlled fashion), addition of a component or reagent, or fractionation.

For some applications, one or more staining substances are used to stain the first portion of the blood sample (which is placed inside first set 52 of sample chambers) before the sample is imaged microscopically. For example, the staining substance may be configured to stain DNA with preference over staining of other cellular components. Alternatively, the staining substance may be configured to stain all cellular nucleic acids with preference over staining of other cellular components. For example, the sample may be stained with Acridine Orange reagent, Hoechst reagent (i.e., a bis-benzimide dye and/or a blue fluorescent dye), and/or any other staining substance that is configured to preferentially stain DNA and/or RNA within the blood sample. Optionally, the staining substance is configured to stain all cellular nucleic acids but the staining of DNA and RNA are each more prominently visible under some lighting and filter conditions, as is known, for example, for Acridine Orange. Images of the sample may be acquired using imaging conditions that allow detection of cells (e.g., brightfield) and/or imaging conditions that allow visualization of stained bodies (e.g., appropriate fluorescent illumination). Typically, the first portion of the sample is stained with Acridine Orange and with a Hoechst reagent. For example, the first (diluted) portion of the blood sample may be prepared using techniques as described in US 9,329,129 to Pollak, which is incorporated herein by reference, and which describes a method for preparation of blood samples for analysis that involves a dilution step, the dilution step facilitating the identification and/or counting of components within microscopic images of the sample. For some applications, the first portion of the sample is stained with one or more stains that cause platelets within the sample to be visible under brightfield imaging conditions and/or under fluorescent imaging conditions, e.g., as described hereinabove. For example, the first portion of the sample may be stained with methylene blue and/or Romanowsky stains. For some applications, the sample is a fine needle aspirate sample, and the first portion of the sample is stained with stains that cause one or more of the following entities to fluoresce: macrophages, histiocytes, mast cells, plasma cells, melanocytes, epithelial cells, mesenchymal cells, mesothelial cells, bacteria, yeast, and/or parasites.

Referring again to Figs. 2B-C, typically, sample carrier 22 is supported within the optical measurement unit by stage 64. Further typically, the stage has a forked design, such that the sample carrier is supported by the stage around the edges of the sample carrier, but such that the stage does not interfere with the visibility of the sample chambers of the sample carrier by the optical measurement devices. For some applications, the sample carrier is held within the stage, such that molded component 42 of the sample carrier is disposed above the glass layer 44, and such that an objective lens 66 of a microscope unit of the optical measurement unit is disposed below the glass layer of the sample carrier. Typically, at least some light sources 65 that are used during microscopic measurements that are performed upon the sample (for example, light sources that are used during brightfield imaging) illuminate the sample carrier from above the molded component. Further typically, at least some additional light sources (not shown) illuminate the sample carrier from below the sample carrier (e.g., via the objective lens). For example, light sources that are used to excite the sample during fluorescent microscopy may illuminate the sample carrier from below the sample carrier (e.g., via the objective lens).

Typically, prior to being imaged microscopically, the first portion of blood (which is placed in first set 52 of sample chambers) is allowed to settle such as to form a monolayer of cells, e.g., using techniques as described in US 9,329,129 to Pollak, which is incorporated herein by reference. For some applications, the first portion of blood is a cell suspension and the chambers belonging to the first set 52 of chambers each define a cavity 55 that includes a base surface 57 (shown in Fig. 3C). Typically, the cells in the cell suspension are allowed to settle on the base surface of the sample chamber of the carrier to form a monolayer of cells on the base surface of the sample chamber. Subsequent to the cells having been left to settle on the base surface of the sample chamber (e.g., by having been left to settle for a predefined time interval), at least one microscopic image of at least a portion of the monolayer of cells is typically acquired. Typically, a plurality of images of the monolayer are acquired, each of the images corresponding to an imaging field that is located at a respective, different area within the imaging plane of the monolayer. Typically, an optimum depth level at which to focus the microscope in order to image the monolayer is determined, e.g., using techniques as described in US Patent US 10,176,565 to Greenfield, which is incorporated herein by reference. For some applications, respective imaging fields have different optimum depth levels from each other.

It is noted that, in the context of the present application, the term monolayer is used to mean a layer of cells that have settled, such as to be disposed within a single focus level of the microscope (referred to herein as "the monolayer focus level"). Within the monolayer there may be some overlap of cells, such that within certain areas there are two or more overlapping layers of cells. For example, red blood cells may overlap with each other within the monolayer, and/or platelets may overlap with, or be disposed above, red blood cells within the monolayer.

For some applications, the microscopic analysis of the first portion of the blood sample is performed with respect to the monolayer of cells. Typically, the first portion of the blood sample is imaged under brightfield imaging, i.e., under illumination from one or more light sources (e.g., one or more light emitting diodes, which typically emit light at respective spectral bands). Further typically, the first portion of the blood sample is additionally imaged under fluorescent imaging. Typically, the fluorescent imaging is performed by exciting stained objects (i.e., objects that have absorbed the stain(s)) within the sample by directing light toward the sample at known excitation wavelengths (i.e., wavelengths at which it is known that stained objects emit fluorescent light if excited with light at those wavelengths), and detecting the fluorescent light. Typically, for the fluorescent imaging, a separate set of light sources (e.g., one or more light emitting diodes) is used to illuminate the sample at the known excitation wavelengths. As described hereinabove, for some applications, the sample is stained with Acridine Orange reagent and Hoechst reagent. For some such applications, the sample is illuminated with light that is at least partially within the UV range (e.g., 300-400 nm), and/or with light that is at least partially within the blue light range (e.g., 450-520 nm), in order to excite the stained objects. For some applications, the sample is mixed with one or more fluorescently-labeled antibodies. Typically, for such applications, the sample is illuminated with light at a wavelength that excites the fluorescent stains with which the antibodies are labeled.

As described with reference to US 2019/0302099 to Pollak, which is incorporated herein by reference, for some applications, sample chambers belonging to set 52 (which is used for microscopy measurements) have different heights from each other, in order to facilitate different measurands being measured using microscope images of respective sample chambers, and/or different sample chambers being used for microscopic analysis of respective sample types. For example, if a blood sample, and/or a monolayer formed by the sample, has a relatively low density of red blood cells, then measurements may be performed within a sample chamber of the sample carrier having a greater height (i.e., a sample chamber of the sample carrier having a greater height relative to a different sample chamber having a relatively lower height), such that there is a sufficient density of cells, and/or such that there is a sufficient density of cells within the monolayer formed by the sample, to provide statistically reliable data. Such measurements may include, for example red blood cell density measurements, measurements of other cellular attributes, (such as counts of abnormal red blood cells, red blood cells that include intracellular bodies (e.g., pathogens, Howell-Jolly bodies), etc.), and/or hemoglobin concentration. Conversely, if a blood sample, and/or a monolayer formed by the sample, has a relatively high density of red blood cells, then such measurements may be performed upon a sample chamber of the sample carrier having a relatively low height, for example, such that there is a sufficient sparsity of cells, and/or such that there is a sufficient sparsity of cells within the monolayer of cells formed by the sample, that the cells can be identified within microscopic images. For some applications, such methods are performed even without the variation in height between the sample chambers belonging to set 52 being precisely known.

For some applications, based upon the measurand that is being measured, the sample chamber within the sample carrier upon which to perform optical measurements is selected. For example, a sample chamber of the sample carrier having a greater height may be used to perform a white blood cell count (e.g., to reduce statistical errors which may result from a low count in a shallower region), white blood cell differentiation, and/or to detect more rare forms of white blood cells. Conversely, in order to determine mean corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), red blood cell distribution width (RDW), red blood cell morphologic features, and/or red blood cell abnormalities, microscopic images may be obtained from a sample chamber of the sample carrier having a relatively low height, since in such sample chambers the cells are relatively sparsely distributed across the area of the region, and/or form a monolayer in which the cells are relatively sparsely distributed. Similarly, in order to count platelets, classify platelets, and/or extract any other attributes (such as volume) of platelets, microscopic images may be obtained from a sample chamber of the sample carrier having a relatively low height, since within such sample chambers there are fewer red blood cells which overlap (fully or partially) with the platelets in microscopic images, and/or in a monolayer.

In accordance with the above-described examples, it is preferable to use a sample chamber of the sample carrier having a lower height for performing optical measurements for measuring some measurands within a sample (such as a blood sample), whereas it is preferable to use a sample chamber of the sample carrier having a greater height for performing optical measurements for measuring other measurands within such a sample. Therefore, for some applications, a first measurand within a sample is measured, by performing a first optical measurement upon (e.g., by acquiring microscopic images of) a portion of the sample that is disposed within a first sample chamber belonging to set 52 of the sample carrier, and a second measurand of the same sample is measured, by performing a second optical measurement upon (e.g., by acquiring microscopic images of) a portion of the sample that is disposed within a second sample chamber of set 52 of the sample carrier. For some applications, the first and second measurands are normalized with respect to each other, for example, using techniques as described in US 2019/0145963 to Zait, which is incorporated herein by reference.

Typically, in order to perform optical density measurements upon the sample, it is desirable to know the optical path length, the volume, and/or the thickness of the portion of the sample upon which the optical measurements were performed, as precisely as possible. Typically, an optical density measurement is performed on the second portion of the sample (which is typically placed into second set 54 of sample chambers in an undiluted form). For example, the concentration and/or density of a component may be measured by performing optical absorption, transmittance, fluorescence, and/or luminescence measurements upon the sample. Referring again to Fig. 3B, for some applications, sample chambers belonging to set 54 (which is used for optical density measurements), define at least a first region 56 (which is typically deeper) and a second region 58 (which is typically shallower), the height of the sample chambers varying between the first and second regions in a predefined manner, e.g., as described in US 2019/0302099 to Pollak, which is incorporated herein by reference. The heights of first region 56 and second region 58 of the sample chamber are defined by a lower surface that is defined by the glass layer and by an upper surface that is defined by the molded component. The upper surface at the second region is stepped with respect to the upper surface at the first region. The step between the upper surface at the first and second regions, provides a predefined height difference Ah between the regions, such that even if the absolute height of the regions is not known to a sufficient degree of accuracy (for example, due to tolerances in the manufacturing process), the height difference Ah is known to a sufficient degree of accuracy to determine a parameter of the sample, using the techniques described herein, and as described in US 2019/0302099 to Pollak, which is incorporated herein by reference. For some applications, the height of the sample chamber varies from the first region 56 to the second region 58, and the height then varies again from the second region to a third region 59, such that, along the sample chamber, first region 56 defines a maximum height region, second region 58 defines a medium height region, and third region 59 defines a minimum height region. For some applications, additional variations in height occur along the length of the sample chamber, and/or the height varies gradually along the length of the sample chamber.

As described hereinabove, while the sample is disposed in the sample carrier, optical measurements are performed upon the sample using one or more optical measurement devices 24. Typically, the sample is viewed by the optical measurement devices via the glass layer, glass being transparent at least to wavelengths that are typically used by the optical measurement device. Typically, the sample carrier is inserted into optical measurement unit 31, which houses the optical measurement device while the optical measurements are performed. Typically, the optical measurement unit houses the sample carrier such that the molded layer is disposed above the glass layer, and such that the optical measurement unit is disposed below the glass layer of the sample carrier and is able to perform optical measurements upon the sample via the glass layer. The sample carrier is formed by adhering the glass layer to the molded component. For example, the glass layer and the molded component may be bonded to each other during manufacture or assembly (e.g. using thermal bonding, solvent-assisted bonding, ultrasonic welding, laser welding, heat staking, adhesive, mechanical clamping and/or additional substrates). For some applications, the glass layer and the molded component are bonded to each other during manufacture or assembly using adhesive layer 46.

For some applications, the apparatus and methods described herein are applied to a fine needle aspirate sample. For some such applications, one or more of the following entities within the sample are made to fluoresce: macrophages, histiocytes, mast cells, plasma cells, melanocytes, epithelial cells, mesenchymal cells, mesothelial cells, bacteria, yeast, and/or parasites.

Immunophenotyping

Reference is now made to Fig. 4 - Fig. 11C. In accordance with some applications of the present invention, biological sample analysis system 20 is configured to analyze a blood sample by performing immunophenotyping on at least a portion of the blood sample. Immunophenotyping is a technique whereby antibodies are used to identify cells based on the expression of proteins by the cells. Immunophenotyping is typically used in basic research and in clinical settings for diagnostic purposes, e.g., to assist in diagnosis of diseases, such as specific types of leukemia and lymphoma. Flow cytometry, and in particular using flow cytometry devices to perform fluorescence-activated cell sorting (FACS) analysis, is a technique commonly used in laboratory settings for immunophenotyping. However, this technique has several limitations, and has yet to find widespread use in point-of-care settings due to several reasons. First, flow cytometry measurement suffers from a lack of standardization. Reproducible protocols for sample preparation including RBC lysis, cell staining, gating strategies and acquisition protocols have proven difficult to be kept constant. Second, flow cytometry measurement requires both a well-equipped laboratory and significant technical expertise, which are difficult to maintain at point-of-care settings.

By contrast, in accordance with some applications of the present invention, cell surface proteins are identified via microscopic imaging of cells with or without the use of antibodies (by incorporating system training) and without performing flow cytometry.

Table 1 lists examples of cell surface protein markers that are detectable using the apparatus and methods of applications of the present invention, as well as examples of corresponding indications that are indicated by the presence of respective cell surface protein markers,.

Table 1: Cell surface markers and corresponding indications

Reference is still made to Fig. 4 - Fig. 11C, which are flowcharts showing steps of various procedures that are performed on the blood sample and that include immunopheno typing, in accordance with some applications of the present invention. In general, immunophenotyping is performed to identify cells in the blood sample based on proteins that are expressed by the cells. For some applications, the blood sample (or a portion thereof) is incubated with fluorescently-labeled antibodies that bind specific proteins in the sample thereby allowing identification of the cell populations within the sample, and/or provide information regarding cellular processes. For some applications, the system is trained to perform immunophenotyping even without the use of fluorescently-labeled antibodies, as described in further detail hereinbelow. It is noted that although particular configurations of the optical measurement unit and/or the sample carrier are described hereinabove, the scope of the present application includes performing the techniques described with reference to Figs. 4- 11C using an optical measurement unit and/or a sample carrier having different characteristics from those shown in and described with reference to Figs. 1-3C, mutatis mutandis.

Reference is first made to Fig. 4, which is a flowchart showing general steps of immunophenotyping performed by biological sample analysis system 20, in accordance with some applications of the present invention. As described hereinabove, a blood sample is deposited in a sample chamber (e.g., one or more of set 52 of sample chambers) of sample carrier 22, as described hereinabove with reference to Figs. 2B-C. As further described with reference to Figs. 2B-C, analysis of the blood sample within the sample chamber is typically performed with respect to the monolayer of cells that settled in the sample chamber.

As shown in Fig. 4, the blood sample is mixed with fluorescently-labeled antibodies for performing immunophenotyping analysis. For some applications, prior to placement of the blood sample in the sample chamber, the blood sample is mixed with fluorescently-labeled antibodies. Alternatively, the fluorescently-labeled antibodies are mixed with the blood sample when the blood sample is disposed within the sample chamber. Mixing of fluorescently-labeled antibodies with the blood sample is shown in step 402. The fluorescently-labeled antibodies selectively bind specific target proteins/antigens within the blood sample, causing these target proteins to fluoresce upon being excited by excitation light, thereby allowing detection of these proteins. The fluorescently-labeled antibodies are mixed with the blood sample in either a liquid form or a dried form, and are typically incubated for about 10-30 minutes with the sample. For some applications, the sample is diluted and/or mixed with other fluorescent stains (e.g. a Hoechst reagent and/or Acridine Orange) after the incubation. Alternatively, the sample is initially mixed and/or diluted with both fluorescently- labeled antibodies and other fluorescent stains (e.g. a Hoechst reagent and/or Acridine Orange) and is incubated with both.

The sample chamber, with the blood sample deposited therein, is placed inside optical measurement unit 31 for analysis by microscope system 37. One or more fluorescent microscopic images of the blood sample within the sample chamber are acquired, using microscope system 37 (step 404).

Computer processor 28 analyzes one or more fluorescent microscopic images of the blood sample to perform immunophenotyping on the blood sample (step 406). The computer processor is configured to identify fluorescent entities within the sample, perform analysis and generate an output with results of the immunophenotyping, thereby identifying cell populations within the blood cell (step 408).

Typically, immunophenotyping is performed with respect to fluorescently-labelled antibodies in a non-binary manner, with the expression of the antibody within a given entity within a sample typically being determined by detecting average (e.g., median and/or mean) fluorescence intensity levels of the given entity within one or more microscopic images of the sample. Typically, within a microscopic image in which fluorescently-labelled antibodies are detectable, the average fluorescence intensity level is detected at locations corresponding to a given entity, e.g., neutrophils, and/or lymphocytes, and/or monocytes, and/or white blood cells. For some applications, the locations corresponding to the given entity are identified based on microscopic images acquired in other imaging modalities, e.g., brightfield microscopic images and/or fluorescent images acquired under alternative fluorescent modalities (e.g., fluorescent imaging conditions that allow visualization of entities stained with Hoechst reagent and/or Acridine Orange).

For some applications, multiple fluorescently-labeled antibodies targeting multiple respective proteins in the blood sample are incubated with the blood sample, and microscope system 37 is configured to multiplex the fluorescent signals emitted from the fluorescent stains using a color camera of the microscope. For some applications, multiplexing is performed using a monochrome camera of microscope system 37 and a plurality of excitation channels. For some applications, microscope system 37 is configured to multiplex one-to- several pairs of fluorescently-labeled antibodies each in a separate one of set 52 of one or more sample chambers that each house portions of the blood sample.

In accordance with some applications of the present invention, preparation of the blood sample for immunophenotyping analysis by biological sample analysis system 20 generally does not involve washing of the sample. Typically, the sample is diluted and/or stained (with both fluorescently-labeled antibodies and/or other fluorescent stains (e.g. a Hoechst reagent and/or Acridine Orange)) prior to imaging. For some such applications, the blood sample is in an unwashed state when it is deposited within the sample chamber, thereby minimizing preparation processes of the sample prior to performing immunophenotyping analysis. This is in contrast to preparation of a sample for analysis by flow cytometry which typically requires multiple washing steps and centrifugation. For some applications, preparation of the blood sample for the immunophenotyping analysis by biological sample analysis system 20 includes red blood cell lysis. Alternatively, the blood sample is prepared for analysis without red blood cell lysis.

Reference is now made to Fig. 5, which is a flowchart showing steps of immunophenotyping performed by biological sample analysis system 20 in combination with additional measurements, in accordance with some applications of the present invention. For example, biological sample analysis system 20 is configured to perform immunophenotyping as well as other measurements on the sample such as a count of entities within the blood sample, e.g., by performing a complete blood count on the blood sample, for example using apparatus and methods described in US 11,099,175 to Zait, which is incorporated herein by reference.

For some such applications, a blood sample is deposited within the sample chamber. As described hereinabove, the blood sample is deposited in a sample chamber (e.g., one or more of set 52 of sample chambers) of sample carrier 22, as described hereinabove with reference to Figs. 2B-C. As further described with reference to Figs. 2B-C, analysis of the blood sample within the sample chamber is typically performed with respect to the monolayer of cells that settled in the sample chamber. One or more microscopic images of the blood sample within the sample chamber are acquired, using microscope system 37 (step 502). Typically, the blood sample (or portions thereof) is imaged under brightfield imaging, i.e., under illumination from one or more light sources, or under fluorescent imaging in cases in which the sample is stained to facilitate performing the blood count. Computer processor 28 analyzes the one or more microscopic images of the blood sample to count entities within the blood sample (e.g., red blood cells, white blood cells, neutrophils, eosinophils, basophils, lymphocytes, monocytes, platelets, anomalous white blood cells, circulating tumor cells, reticulocytes, Howell-Jolly bodies, etc.), in step 504a.

For some applications, in addition to performing the blood count, immunophenotyping is also performed on the blood sample, in step 504b. As described hereinabove, in order to perform immunophenotyping on the blood sample, the blood sample is incubated with fluorescently-labeled antibodies prior to/or while the blood sample is deposited in the sample chamber, and the blood sample is further imaged under fluorescent imaging. The computer processor is configured to identify fluorescent entities within the sample and perform immunophenotyping on the sample. A characteristic of the blood sample is determined, based upon the count of entities within the sample and the immunophenotyping (step 506), and an output is generated on output device 34, in response thereto (step 508).

For some applications, the blood sample is divided into first and second portions, the first portion deposited in a first sample chamber belonging to set 52 of the sample carrier, and the second portion of the sample is deposited within a second sample chamber of set 52 of the sample carrier (as described hereinabove with reference to Fig. 3B). Typically, the first portion of the blood sample is imaged under brightfield imaging, i.e., under illumination from one or more light sources, and/or under fluorescent imaging in cases in which the sample is stained (for example, by a Hoechst reagent and/or by Acridine Orange) to facilitate performing the blood count. Computer processor 28 analyzes the one or more microscopic images acquired from the first portion of the blood sample to count entities within the blood sample (e.g., red blood cells, neutrophils, eosinophils, basophils, lymphocytes, monocytes, and/or platelets). Additionally, the second portion of the blood sample is incubated with fluorescently-labeled antibodies prior to/or while the blood sample is deposited in the sample chamber, and the second portion of the blood sample is further imaged under fluorescent imaging using imaging conditions that allow visualization of fluorescently-labeled antibodies. The computer processor is configured to identify fluorescent entities within the sample and perform immunophenotyping on the sample. A characteristic of the blood sample is determined, based upon the count of entities within the sample and the immunophenotyping, and an output is generated on output device 34, in response thereto. Alternatively, for some applications, the same portion of the blood sample is used both for performing the blood count and for performing immunophenotyping on the sample.

Typically, the blood count together with immunophenotyping allows the provision of a more precise clinical predication of the subject from which the blood sample is taken, compared to performing only a blood count on the sample. For example, performing a blood count and immunophenotyping may assist in identifying the reason for a particular outcome in the blood count. Further additionally or alternatively, antibodies for immunophenotyping performed in combination with a blood count may be selected based on symptoms exhibited by a subject such that the combination of immunophenotyping with performing a blood count may confirm or rule out a symptom-based clinical assumption.

Advantageously, performing of the blood count in combination with immunophenotyping as described herein in accordance with some applications of the present invention, is done using the same device and does not require additional laboratory devices or preparation of samples that are processable by various laboratory devices.

It is noted that for applications in which the same sample portion is incubated with fluorescently-labeled antibodies for immunophenotyping, and is additionally stained for performing a count of entities within the sample (e.g., by being stained with Hoechst reagent and/or Acridine Orange), the sample is excited with light at wavelengths that allow differentiation between the stains and the fluorescently-labeled antibodies.

Example 1 detailing specific non-limiting applications of the method detailed above, with reference to Fig. 5, will now be provided in order to better understand the disclosed subject matter.

Example 1 : Combining leukocyte parameters derived from a blood count with identification of monocyte subpopulation by immunophenotyping for clinical prediction of a pathogenic infection.

In accordance with some applications of the present invention, using biological sample analysis system 20, and as described with reference to the method detailed in Fig. 5 hereinabove, a white blood cell count is performed in combination with immunophenotyping for specific monocyte subpopulations, which may be indicative of sepsis. For some such applications, information regarding white blood cells is obtained from the blood count. For example, leukocyte parameters are obtained such as leukocyte differential count (i.e., measuring the percentages of each type of leukocytes present in the sample). Additionally, or alternatively, leukocytes cell population metrics are obtained, such as monocyte distribution width (MDW) and/or IG fraction, which are both indicators of an infection, including an infection that has progressed to sepsis.

In addition, immunophenotyping is performed on the sample, using antibodies specific to CD14, CD16 and/or HLA-DR markers that are expressed by activated, pro- inflammatory monocytes.

For some applications, immunophenotyping is performed on the sample, using antibodies specific to a CD64 neutrophil subpopulation, which is typically indicative of an infection (including an infection that has progressed to sepsis), for identification of CD64 positive neutrophils and/or neutrophil CD64 expression levels (e.g., quantification of the CD64 expressed by the cells). In this manner, a more precise clinical prediction can be obtained confirming a pathogenic infection, compared to performing a blood count alone. It is noted that, in accordance with some applications of the present invention, immunophenotyping is performed on the sample, using antibodies specific to identification of other blood cells expressing CD64, for example, monocyte CD64, and/or lymphocyte CD64.

Although this example relates to identification and/or quantification of CD64 expression levels in neutrophils, it is noted that the scope of the present invention comprises applying the apparatus and methods disclosed herein to identify and quantify CD64 expression in other blood cells (e.g., expression of monocyte CD64 and/or lymphocyte CD64).

For some applications, CD64 expression levels in blood cells within a sample (e.g., expression levels of neutrophil CD64, lymphocyte CD64, and/or monocyte CD64) are detected by detecting average (e.g., median and/or mean) fluorescence intensity levels of those blood cells within one or more microscopic images of the sample. For some applications, within a microscopic image in which fluorescently-labelled CD64 antibodies are detectable, the average fluorescence intensity level is detected at locations corresponding to neutrophils (and/or lymphocytes and/or monocytes, and/or white blood cells). For some applications, the locations corresponding to neutrophils (and/or lymphocytes and/or monocytes, and/or white blood cells) are identified based on microscopic images acquired in other imaging modalities, e.g., brightfield microscopic images and/or fluorescent images acquired under alternative fluorescent modalities. It is further noted regarding immunophenotyping in accordance with some applications of the present invention, that, although some portions of the present disclosure describe the detection of CD64-positive neutrophils, typically, the identification of CD64 expression is not performed in a binary manner (i.e., with neutrophils being classified as being either CD64- positive or CD64-negative). Rather, the expression of CD64 within neutrophils within a sample is typically determined by detecting average (e.g., median and/or mean) fluorescence intensity levels of neutrophils within one or more microscopic images of the sample, with the average fluorescence intensity levels of neutrophils being indicative of the expression of CD64 within neutrophils within the sample. Similarly, the detection of CD64 within a given other type of blood cells (e.g., lymphocytes and/or monocytes, and/or white blood cells) is typically performed in a non-binary manner, with the expression of CD64 within the given type of blood cells within the sample typically being determined by detecting average (e.g., median and/or mean) fluorescence intensity levels of the given type of blood cells within one or more microscopic images of the sample. Similarly, immunophenotyping that is performed with respect to other fluorescently-labelled antibodies is typically performed in a non-binary manner, with the expression of the antibody within a given entity within a sample typically being determined by detecting average (e.g., median and/or mean) fluorescence intensity levels of the given entity within one or more microscopic images of the sample.

For some applications, immunophenotyping results are normalized based on results of the blood count. In this manner, based on the number of entities detected by the immunophenotyping, a count per unit volume of such entities can be determined. For some applications, the expression of the antibody within a given entity is determined based on the expression of the antibody per unit volume of the entity, and/or the expression of the antibody per occurrence of the entity. Typically, the expression of the antibody is determined based upon the fluorescence intensity levels of the fluorescently-labelled antibody, in accordance with the techniques described hereinabove.

In accordance with some applications of the present invention, correlations between results of the blood count and results of the immunophenotyping are identified such that by computer processor is trained to predict blood count results based on immunophenotyping of the sample.

Reference is now made to Fig. 6, which is a flowchart of steps of sample analysis performed by biological sample analysis system 20, in accordance with some applications of the present invention. For some applications, immunophenotyping is performed only in response to results of the count of the entities in the blood sample (i.e., a blood count, e.g., a complete blood count).

For some such applications, a blood sample is deposited within the sample chamber. For example, the blood sample is deposited in a sample chamber (e.g., one or more of set 52 of sample chambers) of sample carrier 22, as described hereinabove with reference to Figs. 2B-C. As further described with reference to Figs. 2B-C, analysis of the blood sample within the sample chamber is typically performed with respect to the monolayer of cells that settled in the sample chamber.

One or more microscopic images of the blood sample within the sample chamber are acquired, using a microscope of microscope system 37 (step 602). Typically, the blood sample (or portions thereof) is imaged under brightfield imaging, i.e., under illumination from one or more light sources, or under fluorescent imaging in cases in which the sample is stained to facilitate performing the blood count. Computer processor 28 analyzes the one or more microscopic images of the blood sample to count entities within the blood sample (e.g., red blood cells, neutrophils, eosinophils, basophils, lymphocytes, monocytes, and/or platelets) (step 604). Based upon the count of the entities within the blood sample, the computer processor (or user input) determines that immunophenotyping should be performed on the blood sample (step 606). In response to determining that immunophenotyping should be performed on the blood sample, immunophenotyping is performed on the blood sample, by analyzing one or more microscopic images of the blood sample (step 608), and an output is generated on output device 34, in response thereto (step 610). In cases in which it is determined that based on the results of the count of the entities within the blood sample, immunophenotyping should not be performed, immunophenotyping is not performed.

In accordance with some applications of the present invention, in response to determining that immunophenotyping should be performed on the blood sample, a signal is generated to the user that a separate portion of the same blood sample should be prepare for immunophenotyping by incubation with selected fluorescently-labeled antibodies. Following incubation of the portion of the blood sample with the fluorescently-labeled antibodies, one or more fluorescent microscopic images of the portion of the blood sample are acquired, and computer processor 28 performs immunophenotyping on the blood sample, by analyzing one or more microscopic images of the blood sample. In such a manner, advantageously, fluorescently-labeled antibodies are used only in cases in which based upon the count of the entities within the blood sample, the computer processor determines that immunophenotyping should be performed on the blood sample.

Alternatively, in accordance with other applications of the present invention, the blood sample is incubated with fluorescently-labeled antibodies prior to analysis of the sample. In response to determining that immunophenotyping should be performed on the blood sample, the computer processor is configured to acquire additional images, which are fluorescent microscopic images for identifying the fluorescent entities within the sample (i.e., the fluorescently-labeled antibodies that targeted specific proteins in the sample). In such a manner, additional images for identification of the fluorescent entities within the sample are only required in cases in which based upon the count of the entities within the blood sample, the computer processor determines that immunophenotyping should be performed on the blood sample, thereby reducing computational resources that are required for analysis of the blood sample.

Further alternatively, in accordance with additional applications of the present invention, the blood sample is incubated with fluorescently-labeled antibodies prior to analysis of the sample, and one or more fluorescent microscopic images of the sample are acquired for identifying the fluorescently-labeled antibodies that targeted specific proteins in the sample. Only in response to determining that immunophenotyping should be performed on the blood sample, the computer processor performs immunophenotyping analysis on the already acquired fluorescent microscopic images, thereby, reducing computational resources that are required for analysis of the blood sample.

Advantageously, performing of immunophenotyping in response to results of a blood count as described herein in accordance with some applications of the present invention, is done using the same device and does not require additional laboratory devices or preparation of samples that are processable by various laboratory devices.

Examples 2-4 detailing specific non-limiting applications of the method detailed above, with reference to Fig. 6, will now be provided in order to better understand the disclosed subject matter.

Example 2: Immunophenotyping analysis with markers indicative of leukemia in response to an elevated blast count.

In accordance with some applications of the present invention, using biological sample analysis system 20, and as described with reference to the method detailed in Fig. 6 hereinabove, immunophenotyping is performed in response to abnormal blood count results. For example, in response to detection of presence of blast cells (or an elevated count of blast cells) obtained by performing a blood count on the sample, immunophenotyping for specific markers which are indicators of leukemia is performed. For example, immunophenotyping is performed using antibodies specific for CD34, which is commonly expressed types of leukemias. Optionally, immunophenotyping with additional specific markers which are indicators of leukemia is performed, e.g., CD45/CD117. In this manner, a more precise clinical prediction can be obtained confirming a neoplastic disease, compared to performing a blood count alone. Typically, immunotyping with multiple specific antibodies for multiple markers allows a more precise clinical prediction.

Example 3: Immunophenotyping analysis for detection of lymphocyte subpopulation in response to high lymphocyte count.

In accordance with some applications of the present invention, using biological sample analysis system 20, and as described with reference to the method detailed in Fig. 6 hereinabove, immunophenotyping is performed in response to abnormal blood count results. For example, in response to an elevated lymphocyte count detected by performing a blood count on the sample (indicative of an infection or other inflammatory condition), immunophenotyping for specific markers of subpopulation of lymphocytes is performed. By identifying which subpopulations of lymphocytes are elevated in the sample, additional information is obtained regarding the nature of the infection, thereby, a more precise clinical prediction can be derived.

Example 4: Immunophenotyping analysis for detection of aplastic anemia in response to abnormal blood count.

In accordance with some applications of the present invention, using biological sample analysis system 20, and as described with reference to the method detailed in Fig. 6 hereinabove, immunophenotyping is performed in response to abnormal blood count results. For example, in response to receiving abnormal blood count results (e.g., exhibiting a low white blood cell count, a low red blood cell count, low hemoglobin, and/or a low platelet count), which may be indicative of hematological disorders such aplastic anemia, immunophenotyping is performed with antibodies for specific aplastic anemia markers (e.g., CD20), thereby confirming or ruling out the likelihood of aplastic anemia. In such a manner, a more precise clinical prediction can be obtained confirming the likelihood of aplastic anemia, compared to performing a blood count alone. For some applications, immunophenotyping is performed with antibodies for CD55 and/or CD59 and/or FLAER deficiency cells (e.g., red blood cells and/or white blood cells), which are indicative of Glucose phosphate isomerase deficiency (“GPI-deficiency”). For some applications, this is used to diagnose paroxysmal nocturnal hemoglobinuria (“PNH”).

Reference is now made to Fig. 7, which is a flowchart showing steps of immunophenotyping performed by biological sample analysis system 20 in combination with additional measurements, in accordance with some applications of the present invention. For example, biological sample analysis system 20 is configured to perform immunophenotyping in combination with obtaining information regarding morphological features of entities within the blood sample (e.g., nucleus shape and density, cytoplasm shape, cytoplasm granularity, and cell outlines). In such a manner, morphological information is combined with immunophenotyping by fluorescent tagging of cellular proteins to provide more detailed information regarding the blood sample.

As describe hereinabove, the immunophenotyping of the sample is performed using fluorescently-labeled antibodies that selectively bind proteins in the sample to facilitate identifying protein populations that are present in the sample, thereby providing information regarding the blood sample.

Analysis of morphological features of the sample is typically achieved by imaging the sample microscopically. For example, images of the sample are acquired using imaging conditions that allow detection of cells (e.g., brightfield imaging under illumination from one or more light sources, which typically emit light at respective spectral bands). Additionally, or alternatively, one or more staining substances are used to stain the blood sample before the sample is imaged microscopically. For example, the staining substance may be configured to stain DNA with preference over staining of other cellular components. Alternatively, the staining substance may be configured to stain all cellular nucleic acids with preference over staining of other cellular components. For example, the sample may be stained with Acridine Orange reagent, Hoechst reagent, and/or any other staining substance that is configured to preferentially stain DNA and/or RNA within the blood sample. Optionally, the staining substance is configured to stain all cellular nucleic acids but the staining of DNA and RNA are each more prominently visible under some lighting and filter conditions, as is known, for example, for Acridine Orange. Images of the sample may be acquired using imaging conditions that allow visualization of stained bodies (e.g., appropriate fluorescent illumination). For some applications, the sample is stained with one or more stains that cause other entities, e.g., platelets within the sample to be visible under brightfield imaging conditions and/or under fluorescent imaging conditions. For example, the blood sample may be stained with methylene blue and/or Romanowsky stains. For some applications, the sample is stained with stains that cause one or more of the following entities to fluoresce: macrophages, histiocytes, mast cells, plasma cells, melanocytes, epithelial cells, mesenchymal cells, mesothelial cells, bacteria, yeast, and/or parasites.

In accordance with some applications of the present invention, this type of microscopic analysis provides morphological information that is combined with immunophenotyping to provide a more detailed information of the state of the blood sample. For example, for each cell in the blood sample both (a) protein information (for surface proteins and/or inner cell proteins) is provided by immunophenotyping and (b) morphology information such as nucleus shape and size, mitosis activity, viability, granulation, activation, cell age and other features, are obtained.

It is noted that for applications in which the same sample portion is incubated with fluorescently-labeled antibodies for immunophenotyping, and is additionally stained for extracting morphological features of the entities within the sample, the sample is excited with light at wavelengths that allow differentiation between the stains and the fluorescently-labeled antibodies.

Reference is still made to Fig. 7. As described hereinabove, a blood sample is deposited in a sample chamber (e.g., one or more of set 52 of sample chambers) of sample carrier 22, as described hereinabove with reference to Figs. 2B-C. As further described with reference to Figs. 2B-C, analysis of the blood sample within the sample chamber is typically performed with respect to the monolayer of cells that settled in the sample chamber.

One or more microscopic images of the blood sample within the sample chamber are acquired, using a microscope of microscope system 37 (step 702). Typically, the blood sample (or portions thereof) is imaged under brightfield imaging, or under fluorescent imaging in cases in which the sample is stained to facilitate obtaining morphological information regarding the sample, as described hereinabove. Computer processor 28 analyzes one or more microscopic images of the blood sample to identify morphological characteristics of entities within the sample (step 704a). For some applications, computer processor 28 is configured to determine parameters relating to one or more of the components. For example, in relation to red blood cells, the computer processor determines parameters such as corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), red blood cell distribution width (RDW), red blood cell morphologic features, clumping, and/or red blood cell abnormalities. Alternatively or additionally, in relation to white blood cells, the computer processor determines parameters such as absolute and relative numbers of neutrophils, lymphocytes, monocytes, eosinophils and basophils. For some applications, the computer processor performs normal and abnormal leukocyte differentiation, including detecting the existence of immature or hyper segmented cells, white blood cell agglutination or fragmentation, blasts, and/or atypical or abnormal lymphocytes. For some applications, the computer processor detects leukocyte subpopulations (such as B, T-cells), and/or morphological characteristics.

For some applications, in addition to performing the morphological analysis, immunophenotyping is also performed on the blood sample, in step 704b. As described hereinabove, in order to perform immunophenotyping on the blood sample, the blood sample is incubated with fluorescently-labeled antibodies prior to/or while the blood sample is deposited in the sample chamber, and the blood sample is further imaged under fluorescent imaging. The computer processor is configured to identify fluorescent entities within the sample and perform immunophenotyping on the sample. A characteristic of the blood sample is determined, based upon the morphological data and the immunophenotyping (step 706), and an output is generated on output device 34, in response thereto (step 708).

For some applications, the blood sample is divided into first and second portions, the first portion is deposited in a first sample chamber belonging to set 52 of the sample carrier, and the second portion of the sample is deposited within a second sample chamber of set 52 of the sample carrier (as described hereinabove with reference to Fig. 3B). Typically, the first portion of the blood sample is imaged under brightfield imaging, i.e., under illumination from one or more light sources, and/or under fluorescent imaging in cases in which the sample is stained to facilitate obtaining morphological information of entities within the sample. Computer processor 28 analyzes one or more microscopic images acquired from the first portion of the blood sample to obtain the morphological information. Additionally, the second portion of the blood sample is incubated with fluorescently-labeled antibodies prior to/or while the blood sample is deposited in the sample chamber, and the second portion of the blood sample is further imaged under fluorescent imaging. The computer processor is configured to identify fluorescent entities within the sample and perform immunophenotyping on the sample. A characteristic of the blood sample is determined, based upon the count of entities within the sample and the immunopheno typing, and an output is generated on output device 34, in response thereto.

Typically, information regarding morphological features together with immunophenotyping allows providing of a more precise clinical predication of the subject from which the blood sample is taken, compared to morphological information alone. Additionally, the combination of morphological features together with immunophenotyping is useful for identification and classification of morphological features that are clinically relevant.

Advantageously, performing of the morphological analysis in combination with immunophenotyping as described herein in accordance with some applications of the present invention, is done using the same device and does not require additional laboratory devices or preparation of samples that are processable by various laboratory devices.

Example 5 detailing specific non-limiting applications of the method detailed above, with reference to Fig. 7, will now be provided in order to better understand the disclosed subject matter.

Example 5: Combining morphological features with immunophenotyping analysis for identification and classification of clinically relevant information.

In accordance with some applications of the present invention, using biological sample analysis system 20, and as described with reference to the method detailed in Fig. 7 hereinabove, immunophenotyping is performed in combination with morphological analysis of the sample. This combination may be useful for identification and classification of clinically relevant information. For example, an age of a cell is derived from the morphological features and together with immunophenotyping analysis can signal whether an abnormal cell population detected by immunophenotyping has temporal dynamics which can be clinically significant for providing disease prognosis, an assessment of treatment efficacy, or risk of adverse effects of a therapeutic treatment.

Reference is now made to Fig. 8, which is a flowchart of steps of sample analysis performed in accordance with some applications of the present invention. For some applications, immunophenotyping is performed only in response to results of morphological analysis of the blood sample (examples of morphological features analyzed in accordance with applications of the present invention are described hereinabove with reference to Fig. 7). For some such applications, a blood sample is deposited within the sample chamber. As described hereinabove, the blood sample is deposited in a sample chamber (e.g., one or more of set 52 of sample chambers) of sample carrier 22, as described hereinabove with reference to Figs. 2B-C. As further described with reference to Figs. 2B-C, analysis of the blood sample within the sample chamber is typically performed with respect to the monolayer of cells that settled in the sample chamber.

One or more microscopic images of the blood sample within the sample chamber are acquired, using a microscope of microscope system 37 (step 802). Typically, the blood sample (or portions thereof) is imaged under brightfield imaging, i.e., under illumination from one or more light sources, and/or under fluorescent imaging in cases in which the sample is stained to obtain the information regarding morphological characteristics of entities of the sample. Computer processor 28 analyzes the one or more microscopic images of the blood sample to obtain morphological information regarding the blood sample (as listed hereinabove with reference to Fig. 7) (step 804). Based upon the results of the morphological analysis the blood sample, the computer processor (or user input) determines that immunophenotyping should be performed on the blood sample (step 806). In response to determining that immunophenotyping should be performed on the blood sample, immunophenotyping is performed on the blood sample, by analyzing one or more microscopic images of the blood sample (step 808), and an output is generated on output device 34, in response thereto (step 810). In cases in which it is determined that based on the results of morphological analysis of the blood sample, immunophenotyping should not be performed, immunophenotyping is not performed.

In accordance with some applications of the present invention, in response to determining that immunophenotyping should be performed on the blood sample, a signal is generated to the user that a separate portion of the same blood sample should be prepare for immunophenotyping by incubation with selected fluorescently-labeled antibodies. Following incubation of the portion of the blood sample with the fluorescently-labeled antibodies, one or more fluorescent microscopic images of the portion of the blood sample are acquired, and computer processor 28 performs immunophenotyping on the blood sample, by analyzing one or more microscopic images of the blood sample. In such a manner, advantageously, fluorescently-labeled antibodies are used only in cases in which based upon the morphological analysis of the blood sample, the computer processor determines that immunophenotyping should be performed on the blood sample. Alternatively, in accordance with other applications of the present invention, the blood sample has been incubated with fluorescently-labeled antibodies prior to analysis of the sample. In response to determining that immunophenotyping should be performed on the blood sample, the computer processor is configured to acquire additional images, which are fluorescent microscopic images for identifying the fluorescent entities within the sample (i.e., the fluorescently-labeled antibodies that targeted specific proteins in the sample). In such a manner, additional images for identification of the fluorescent entities within the sample are only required in cases in which based upon the morphological analysis of the blood sample, the computer processor determines that immunophenotyping should be performed on the blood sample, thereby reducing computational resources that are required for analysis of the blood sample.

Further alternatively, in accordance with additional applications of the present invention, the blood sample has been incubated with fluorescently-labeled antibodies prior to analysis of the sample, and one or more fluorescent microscopic images of the sample are acquired for identifying the fluorescently-labeled antibodies that targeted specific proteins in the sample. Only in response to determining that immunophenotyping should be performed on the blood sample, the computer processor performs immunophenotyping analysis on the already acquired fluorescent microscopic images, thereby, reducing computational resources that are required for analysis of the blood sample.

Advantageously, performing of immunophenotyping in response to results of the morphological analysis as described herein in accordance with some applications of the present invention, is done using the same device and does not require additional laboratory devices or preparation of samples that are processable by various laboratory devices.

Reference is now made to Fig. 9, which is a flowchart of steps of sample analysis performed by biological sample analysis system 20, in accordance with some applications of the present invention. For some applications, biological sample analysis system 20 is trained to perform immunophenotyping on a blood sample. Typically, fluorescently-labeled antibodies are mixed with a plurality of blood samples (step 900). For some applications, each of the blood samples is deposited within a sample chamber (e.g., one or more of set 52 of sample chambers) of sample carrier 22, as described hereinabove with reference to Figs. 2B- C. As further described with reference to Figs. 2B-C, analysis of the blood sample within the sample chamber is typically performed with respect to the monolayer of cells that settled in the sample chamber. One or more fluorescent microscopic images of each of the blood samples are acquired under a fluorescent imaging modality that is configured to excite the fluorescent stain of the antibodies (step 902). Additionally, one or more additional microscope images of each of the blood samples are acquired under a second modality (step 904). Typically, when imaged under the second modality, the blood samples are imaged under brightfield imaging, i.e., under illumination from one or more light sources, and/or under fluorescent imaging in cases in which the samples are stained (the fluorescent imaging using excitation wavelengths that allow differentiation between the fluorescent staining of the antibodies and other staining). Typically, imaging of blood samples under the second modality allows analysis of the sample for obtaining additional features (e.g., morphological characteristics) of entities within the sample as described hereinabove.

Computer processor 28 analyzes the one or more fluorescent microscopic images of the blood samples to perform immunophenotyping on each of the blood samples (step 906), and additionally analyzes the one or more additional microscopic images of each of the blood samples (obtained by the second modality) to identify features (e.g., morphological characteristics of entities within the samples) of each of the blood samples (step 908). Computer processor 28 identifies correlations between results of the immunophenotyping and features of the blood sample that are identifiable in microscopic images that are acquired under the second imaging modality (step 910).

Typically, the above-described steps are performed during a training stage. Further typically, during a subsequent stage, immunophenotyping is performed on a test blood sample without the use of antibodies (by incorporating the previous training).

In such a manner, training of sample analysis system 20 to identify cell subpopulations differentiated by surface protein levels within the sample, is done using fluorescently-labeled antibodies. However, once the system is trained, use of antibodies is generally not required, as the system is trained to classify these proteins based on morphological features alone. For some applications, sample analysis system 20 comprises a dedicated hardware that is configured in the training stage to scan the fluorescently -labeled antibody images in addition to acquiring additional images, e.g., brightfield images and/or fluorescent images (such as images under Acridine Orange staining, and/or Hoechst reagent staining, as described herein). For some applications, the above-described training is performed in the reverse direction, such that the biological sample analysis system is trained to identify morphological features of blood cells based upon images that are acquired using fluorescently-labeled antibodies. For some applications, the above-described training is performed with respect to alternative or additional imaging modalities. For example, such that the biological sample analysis system is trained to identify features that are typically identifiable in images acquired using Acridine Orange staining based on brightfield images and/or images acquired under staining with a Hoechst reagent.

Example 6 detailing specific non-limiting applications of the method detailed above, with reference to Fig. 9, will now be provided in order to better understand the disclosed subject matter.

Example 6: Training the sample analysis system to predict or identify subpopulations of proteins (e.g., lymphocytes) within the sample through morphological information.

In accordance with some applications of the present invention, and as described with reference to the method detailed in Fig. 9 hereinabove, biological sample analysis system 20 can be trained to identify or predict, via morphological information, the presence of certain populations and subpopulations of proteins that are typically identified by immunophenotyping using fluorescently-labeled antibodies as described herein.

For example, using the steps described with reference to Fig. 9, sample analysis system 20 is trained to predict or identify subpopulations of lymphocytes within the blood sample by analyzing morphological features of entities within the samples that are obtained by analyzing images by brightfield and/or fluorescence imaging as described herein, thereby avoiding the need for performing immunophenotyping using fluorescently-labeled antibodies (such as antibodies against markers CD64 or CD3 and CD 19).

Example 6A: Training the sample analysis system to predict or identify CD64 positive neutrophils and/or neutrophil CD64 expression levels within the sample through morphological information.

Reference is still made to Fig. 9, as well as to Fig. 10.

As described hereinabove, for some applications, immunophenotyping is performed on the sample, using antibodies specific to a CD64 neutrophil subpopulation, which is typically indicative of infections, including severe infections that have progressed to sepsis. In this manner, a more precise clinical prediction can be obtained confirming a pathogenic infection, compared to performing a blood count alone. CD64, which is also known as the high-affinity IgG Fc receptor, is a glycoprotein primarily found on the surface of monocytes and macrophages and to a lesser extent on neutrophils. It plays an important role in the immune system's response by binding to the Fc portion of immunoglobulin G (IgG). Several studies have evaluated the potential of CD64 expression on neutrophils as a diagnostic marker for an infection including an infection that has progressed to sepsis. Some of these studies have shown that CD64 has a high sensitivity and specificity for diagnosing sepsis, especially when compared with other traditional markers such as C-reactive protein (CRP) or procalcitonin (PCT). Measuring the neutrophil CD64 index can aid in distinguishing between bacterial infections and other inflammatory conditions as well. Flow cytometry is the method that is currently used for the quantification and assessment of CD64 expression on neutrophils and other cells. However, this technique has yet to find widespread use in point-of-care settings due to several reasons. First, flow cytometry measurement suffers from a lack of standardization. Reproducible protocols for sample preparation including RBC lysis, cell staining, gating strategies and acquisition protocols have proven difficult to be kept constant. Second, flow cytometry measurement require both a well-equipped laboratory and significant technical expertise, which are difficult to maintain at point-of-care settings.

By contrast, in accordance with some applications of the present invention, neutrophils expressing CD64 are identified and/or neutrophil CD64 expression levels are detected, via microscopic imaging of cells without the use of antibodies (by incorporating previous training) and without performing flow cytometry. For some applications, a percentage of neutrophils that are CD64 positive is determined and/or outputted. For some applications, a count of neutrophils that are CD64 positive is determined and/or outputted. For some applications, a sample is determined to be CD64 positive based upon the identification of CD64 positive neutrophils (e.g., due to a count of CD64 positive neutrophils and/or a percentage of neutrophils that are CD64 positive being greater than a threshold). For some applications, a diagnosis of an infection (e.g., an infection that has progressed to sepsis) is determined based upon the identification of CD64 positive neutrophils (e.g., due to a count of CD64 positive neutrophils being greater than a threshold). For some applications, the diagnosis of infection (e.g., an infection that has progressed to sepsis) is determined based on the identification of the CD64 positive neutrophils in addition to one or more additional factors, as described in further detail hereinbelow. As described hereinabove with reference to Fig. 9, for some applications, biological sample analysis system 20 is trained to perform immunophenotyping on a blood sample. Typically, during a training stage, computer processor 28 identifies correlations between results of the immunophenotyping and features of the blood sample that are identifiable in microscopic images that are acquired under the second imaging modality. During a subsequent stage, immunophenotyping is performed on a test blood sample without the use of antibodies (by incorporating the previous training). In such a manner, training of sample analysis system 20 to identify cell subpopulations differentiated by surface protein levels within the sample, is done using fluorescently-labeled antibodies. However, once the system is trained, use of antibodies is generally not required, as the system is trained to classify these proteins based on microscopically-extractable features alone.

In a study in which inventors of the present application participated in conducting, the technique with reference to Fig. 9 was performed with respect to CD64. Residual blood samples were collected from Shaare Zedek Hospital (Jerusalem, Israel). For the purposes of this study, the sample were selected according to predefined complete blood count (CBC) criteria and origin. (It is noted that the scope of the present disclosure includes applying the apparatus and methods described herein to blood samples that are selected based upon predefined complete blood count (CBC) criteria and origin.) The samples were obtained from three hospital departments: internal medicine, geriatric care and intensive care (with such departments typically having a relatively high population of patients who are suspected of suffering from an infection including infections that have progressed to sepsis).

15 samples originating from 13 patients were obtained and scanned on a hematology system manufactured by Sysmex Corporation (Kobe, Japan). The samples were stained with a mouse monoclonal antibody conjugated to APC fluorophore against human CD64 (manufactured by BioLegend® (San Diego, CA, USA) under cat. no. 305014). Following a 15 minute incubation at room temperature, samples were scanned on two biological sample analysis systems of the type described herein (as biological sample analysis system 20). One biological sample analysis system was programmed to extract features (e.g., morphological features) of the blood by performing microscopic analysis of the blood without the use of antibodies. The other biological sample analysis system was configured to detect APC fluorescence. Scans from both devices were analyzed to detect white blood cell subtypes and to detect APC fluorescence (which was indicative of the presence of CD64). Thus, neutrophils that express CD64 were identified. Neutrophils that expressed CD64 were classified as CD64 positive, and neutrophils that did not express CD64 were classified as CD64 negative. In parallel to the 15 samples originating from 13 patients, control samples from healthy volunteers were collected and analyzed in a similar manner to that described hereinabove.

As mentioned hereinabove, one biological sample analysis system was programmed to extract features (e.g., morphological features) of the blood by performing microscopic analysis of the blood without the use of antibodies. Within this system, the blood was imaged under brightfield imaging and under fluorescent imaging. The blood was stained with Acridine Orange reagent and Hoechst reagent, and for the fluorescent imaging the blood was illuminated with light within the UV range and with light within the blue light range. During the training stage, features extracted in this manner from microscopic images were associated with the CD64 positive and the CD64 neutrophil identifications, in order to identify correlations between results of the immunophenotyping and features of the blood sample that are extractable from microscopic images acquired without the use of antibodies (in accordance with techniques described with reference to Fig. 9). For each CD64 positive/negative detected neutrophil as described above, a patch of 40x40 pixels was extracted in corresponding microscopic images acquired under 6 modalities (3 brightfield modalities and 3 fluorescent modalities) in the absence of antibodies, and features were extracted from those pixels. In order to improve results, a Convolutional Neural Network (CNN) with 3 convolutional layers and 2 fully connected layers was trained with binary cross entropy loss in order to classify the neutrophils.

A total of 920 CD64-negative cells and 1244 CD64-positive cells were used during the training stage. Subsequently, the trained algorithm was tested using 592 cells that had been independently determined to be CD64-negative and 1733 cells that had been independently determined to be CD64 positive.

Fig. 10A is a receiver operating characteristic (“ROC”) curve indicating the diagnostic ability of microscopically-extractable features of blood to distinguish between CD64 positive and negative neutrophils without the use of antibodies, in accordance with some applications of the present invention. The ROC curve in Fig. 10 indicates the true positive rate against the false positive rate of the trained algorithm for identifying CD64 positive neutrophils based on features (e.g., morphological features) that are microscopically extractable without the use of antibodies (by incorporating the previous training). The total Area Under the Curve (“AUC”) is 0.91, indicating that the microscopically-extractable features are effective for discriminating between CD64-negative and CD64-positive neutrophils without the use of antibodies (by incorporating the previous training). Therefore, in accordance with some applications of the present invention, CD64 positive neutrophils are identified based on features (e.g., morphological features) that are microscopically extractable without the use of antibodies (typically by incorporating the previous training). For some applications, CD64 expression levels within a sample (e.g., expression levels of CD64-positive neutrophils, CD64-positive lymphocytes, and/or CD64-positive monocytes) are detected by detecting median and/or mean fluorescence intensity levels of one or more fluorescent microscopic images of the sample.

Reference is still made to Figs. 9 and 10A. For some applications, a diagnosis of infection and/or a diagnosis of sepsis is determined based on the identification of CD64 positive neutrophils and/or neutrophil CD64 expression levels in addition to one or more additional factors. For some applications, the diagnosis of infection and/or a diagnosis of sepsis is determined based on the identification of the CD64 positive neutrophils and/or neutrophil CD64 expression levels in addition to identifying one or more additional biomarkers, for example one or more of the biomarkers listed in Table 2 below. For some applications, the diagnosis of infection and/or a diagnosis of sepsis is determined based on the identification of the CD64 positive neutrophils and/or neutrophil CD64 expression levels in addition to the results of one or more diagnostic tools that are applied to the subject from whom the blood sample was drawn, for example one or more of the diagnostic tools listed in Table 3 below.

Table 2: Additional biomarkers that are indicative of infection and/or sepsis and/or related indications

Table 3: Diagnostic tools applicable to a subject suspected of infection and/or sepsis and/or associated indications

Example 6B: Training the sample analysis system for detecting and distinguishing between B-cell and T-cell lymphocytes within the sample through morphological information.

Reference is still made to Fig. 9, as well as to Fig. 10B.

There are two main types of lymphocytes in the blood: B-cell and T-cell lymphocytes, each of which has a unique role in the immune response cascade. It is typically challenging to distinguish between the two cell types using a traditional Giemsa stain and a blood smear. Cell quantification is thus usually done using flow cytometry with unique surface antibodies - specifically CD3 (as a T cell marker) and CD 19 (as a B cell marker). However, as described hereinabove, this technique has yet to find widespread use in point-of-care settings due to several reasons. First, flow cytometry measurement suffers from a lack of standardization. Reproducible protocols for sample preparation including RBC lysis, cell staining, gating strategies and acquisition protocols have proven difficult to be kept constant. Second, flow cytometry measurement require both a well-equipped laboratory and significant technical expertise, which are difficult to maintain at point-of-care settings.

By contrast, in accordance with some applications of the present invention, CD3 and CD 19 positive lymphocytes are identified via microscopic imaging of cells without the use of antibodies (by incorporating previous training) and without performing flow cytometry. For some applications, a percentage and/or a count of CD3 and CD 19 positive lymphocytes is determined and/or outputted.

As described hereinabove with reference to Fig. 9, for some applications, biological sample analysis system 20 is trained to perform immunophenotyping on a blood sample. Typically, during a training stage, computer processor 28 identifies correlations between results of the immunophenotyping and features of the blood sample that are identifiable in microscopic images that are acquired under the second imaging modality. During a subsequent stage, immunophenotyping is performed on a test blood sample without the use of antibodies (by incorporating the previous training). In such a manner, training of sample analysis system 20 to identify cell subpopulations differentiated by surface protein levels within the sample, is done using fluorescently-labeled antibodies. However, once the system is trained, use of antibodies is generally not required, as the system is trained to classify these proteins based on microscopically-extractable features alone.

In a study in which inventors of the present application participated in conducting, the technique with reference to Fig. 9 was performed with respect to CD3 and CD19. 10 blood samples were collected from 8 patients from Shaare Zedek Hospital (Jerusalem, Israel), and 2 healthy volunteers. The samples were incubated with antibodies conjugated to APC fluorophore against CD3 (manufactured by eBioscience, under cat. no. 17-0038-42) and antibodies conjugated to APC fluorophore against CD 19 (manufactured by eBioscience, under cat. no. 17-0199-42)

Following incubation with the antibodies, the samples were scanned on a hematology system manufactured by Sysmex Corporation (Kobe, Japan). The samples were scanned on two biological sample analysis systems of the type described herein (as biological sample analysis system 20). One biological sample analysis system was programmed to extract features (e.g., morphological features) of the blood by performing microscopic analysis of the blood without the use of antibodies. The other biological sample analysis system was configured to detect APC fluorescence. Scans from both devices were analyzed to detect white blood cell subtypes and to detect APC fluorescence (which was indicative of the presence of CD3 and CD19). Thus, lymphocytes that express CD3 or CD19 were identified. Lymphocytes that expressed CD3 or CD 19 were classified as CD3 or CD 19-positive, respectively.

As mentioned hereinabove, one biological sample analysis system was programmed to extract features (e.g., morphological features) of the blood by performing microscopic analysis of the blood without the use of antibodies. Within this system, the blood was imaged under brightfield imaging and under fluorescent imaging. The blood was stained with Hoechst reagent (and in some cases also with Acridine Orange reagent), and for the fluorescent imaging the blood was illuminated with light within the UV range and with light within the blue light range. During the training stage, features extracted in this manner from microscopic images were associated with the CD3 and CD19 positive identifications, in order to identify correlations between results of the immunophenotyping and features of the blood sample that are extractable from microscopic images acquired without the use of antibodies (in accordance with techniques described with reference to Fig. 9, and similar to the techniques described hereinabove with refereed to identification of neutrophil CD64).

A total of 1456 lymphocytes were used during the training stage, of which 1306 lymphocytes were 1306 CD3-positive, and 150 lymphocytes were CD 19-positive. Subsequently, the trained algorithm was tested using 365 lymphocytes of which 342 lymphocytes have been determined to be CD3 -positive and 23 lymphocytes have been determined to be CD 19-positive. Fig. 1 OB is a receiver operating characteristic (“ROC”) curve indicating the diagnostic ability of microscopically-extractable features of blood to distinguish between CD3 positive lymphocytes and CD19 positive lymphocytes without the use of antibodies, in accordance with some applications of the present invention. The ROC curve in Fig. 10B indicates the true positive rate against the false positive rate of the trained algorithm for identifying CD3 and CD19 positive lymphocytes based on features (e.g., morphological features) that are microscopically extractable without the use of antibodies (by incorporating the previous training). The total Area Under the Curve (“AUC”) is 0.92, indicating that the microscopically-extractable features are effective for distinguishing between CD3-positive and CD 19-positive lymphocytes without the use of antibodies (by incorporating the previous training). Therefore, in accordance with some applications of the present invention, T cells and B cells are identified and distinguished between based on features (e.g., morphological features) that are microscopically extractable without the use of antibodies (typically by incorporating the previous training).

Comparison between flow cytometry and the Biological sample analysis system

Reference is now made to Figs. 11A, 11B, and 11C, which are all graphical representations of results obtained from a set of studies in which inventors of the present application participated in conducting, in order to correlate the detection of protein surface markers by flow cytometry techniques with the detection of protein surface markers by apparatus and methods provided herein, in accordance with applications of the present invention. The studies, the results of which are presented in Figs. 11A, 11B, and 11C are described in further detail in Examples 7A and 7B, below.

Example 7A: Experimental data showing a correlation between detection of neutrophil CD64 expression levels in blood samples by using the biological sample analysis system in accordance with some applications of the present invention and using flow cytometry techniques.

Reference is now made to Fig. 11 A, which is a graphical representation showing the correlation between detection of neutrophil CD64 expression levels in blood samples that were analyzed by flow cytometry techniques and those analyzed by biological sample analysis system 20, in accordance with some applications of the present invention.

As described hereinabove, flow cytometry techniques (e.g., fluorescence-activated cell sorting (FACS) analysis), are commonly used for immunophenotyping. For example, flow cytometry is a known method that is currently used for the quantification and assessment of CD64 expression on neutrophils and other cells. In a study in which inventors of the present application participated in conducting, a comparison between biological sample analysis system 20 of the present invention, and a clinical flow cytometry device for detection of CD64 expression on neutrophils, was performed. In other words, the ability of biological sample analysis system 20 to detect and quantify neutrophil CD64 expression levels in blood samples, was compared to detection of neutrophil CD64 expression levels by commonly used flow cytometry techniques and device.

In the study, 76 blood samples were collected from Shaare Zedek Hospital (Jerusalem, Israel). The samples were obtained from various hospital departments, excluding hematology and maternity wards. For example, the samples were obtained from hospital departments such as internal medicine, geriatric care and intensive care (with such departments typically having a relatively high population of patients who are suspected of suffering from sepsis). All blood samples were obtained from patients above the age of 18. This study obtained ethical approval per institutional guidelines, appropriate regulatory requirements, and was performed in accordance with the International Conference on Harmonization, the Guidelines for Good Clinical Practice, and the Declaration of Helsinki.

The blood samples were divided for analysis by biological sample analysis system 20 in accordance with some applications of the present invention, and for analysis by a flow cytometry device at Shaare Zedek Hospital (Jerusalem, Israel).

The blood samples that were analyzed by biological sample analysis system 20 in accordance with some applications of the present invention, were scanned on a hematology system manufactured by Sysmex Corporation (Kobe, Japan). The samples were stained with a mouse monoclonal antibody conjugated to APC fluorophore against human CD64 (manufactured by BioLegend® (San Diego, CA, USA) under cat. no. 305014). Following a 15 minute incubation at room temperature, samples were scanned on two biological sample analysis systems of the type described herein (as biological sample analysis system 20). One biological sample analysis system was programmed to extract features (e.g., morphological features) of the blood by performing microscopic analysis of the blood without the use of antibodies. The other biological sample analysis system was configured to detect APC fluorescence. Scans from both devices were analyzed to detect white blood cell subtypes and to detect APC fluorescence (which was indicative of the presence of CD64). Thus, the levels of CD64 expression by neutrophils were identified. The blood samples that were analyzed at Shaare Zedek Hospital (Jerusalem, Israel), were incubated with a mouse monoclonal antibody conjugated to APC fluorophore against human CD64 (manufactured by BioLegend® (San Diego, CA, USA) under cat. no. 305014). The samples were additionally incubated with an antibody against CD45 (manufactured by BD Bioscience, under cat. no. 345808). The blood samples were processed according to flow cytometry protocols, by undergoing RBC lysis prior to staining with the antibodies. The samples were analyzed using BD FACSCanto™ II Flow Cytometer (manufactured by BD Bio science).

As shown in Fig. 11 A, median fluorescence intensity of biological sample analysis system 20 and FACSCanto™ II Flow Cytometer, was compared for each of the blood samples, presenting a correlation of R2=0.96. Thus, biological sample analysis system 20 has been shown to correctly assess expression of neutrophil CD64 using antibodies, while providing the benefits of system 20 compared to flow cytometry, as described hereinabove.

In accordance with the data shown in Fig. 11 A, for some applications, CD64 expression levels in blood cells within a sample (e.g., expression levels of neutrophil CD64, lymphocyte CD64, and/or monocyte CD64) are detected by detecting average (e.g., median and/or mean) fluorescence intensity levels of those blood cells within one or more microscopic images of the sample. For some applications, within a microscopic image in which fluorescently-labelled CD64 antibodies are detectable, the average fluorescence intensity level is detected at locations corresponding to neutrophils (and/or lymphocytes and/or monocytes, and/or white blood cells). For some applications, the locations corresponding to neutrophils (and/or lymphocytes and/or monocytes, and/or white blood cells) are identified based on microscopic images acquired in other imaging modalities, e.g., brightfield microscopic images and/or fluorescent images acquired under alternative fluorescent modalities. For some applications, a diagnosis of infection, e.g., a diagnosis of sepsis, is made with respect to the subject from whom the blood sample was drawn, at least partially based on CD64 expression levels in blood cells within the sample (e.g., expression levels of neutrophil CD64, lymphocyte CD64, and/or monocyte CD64). For some applications, such a diagnosis is made based upon a combination of CD64 expression levels in blood cells within the sample and either (1) one or more of the biomarkers listed in Table 2, (2) one or more of the diagnostic tools listed in Table 3, (3) morphological features of one or more entities within the sample, (4) a result of a blood count performed on the sample, or (5) any combination of (l)-(4). For some applications, CD64 expression levels in blood cells within a sample (e.g., expression levels of neutrophil CD64, lymphocyte CD64, and/or monocyte CD64) are detected by detecting average (e.g., median and/or mean) fluorescence intensity levels of those blood cells within one or more microscopic images of the sample. For some applications, within a microscopic image in which fluorescently-labelled CD64 antibodies are detectable, the average fluorescence intensity level is detected at locations corresponding to neutrophils (and/or lymphocytes and/or monocytes, and/or white blood cells). For some applications, the locations corresponding to neutrophils (and/or lymphocytes and/or monocytes, and/or white blood cells) are identified based on microscopic images acquired in other imaging modalities, e.g., brightfield microscopic images and/or fluorescent images acquired under alternative fluorescent modalities.

It is noted regarding immunophenotyping in accordance with some applications of the present invention, that, although some portions of the present disclosure describe the detection of CD64-positive neutrophils, typically, the identification of CD64 expression is not performed in a binary manner (i.e., with neutrophils being classified as being either CD64- positive or CD64-negative). Rather, the expression of CD64 within neutrophils within a sample is typically determined by detecting average (e.g., median and/or mean) fluorescence intensity levels of neutrophils within one or more microscopic images of the sample, with the average fluorescence intensity levels of neutrophils being indicative of the expression of CD64 within neutrophils within the sample. Similarly, the detection of CD64 within a given other type of blood cells (e.g., lymphocytes and/or monocytes, and/or white blood cells) is typically performed in a non-binary manner, with the expression of CD64 within the given type of blood cells within the sample typically being determined by detecting average (e.g., median and/or mean) fluorescence intensity levels of the given type of blood cells within one or more microscopic images of the sample. Similarly, immunophenotyping that is performed with respect to other fluorescently-labelled antibodies is typically performed in a non-binary manner, with the expression of the antibody within a given entity within a sample typically being determined by detecting average (e.g., median and/or mean) fluorescence intensity levels of the given entity within one or more microscopic images of the sample.

Example 7B: Experimental data showing a correlation between detection of CD3 and CD19 positive lymphocytes in blood samples by using the biological sample analysis system in accordance with some applications of the present invention and using flow cytometry techniques. Reference is now made to Figs. 11B and 11C, which are graphical representation showing the correlation between detection of CD3/19 positive lymphocytes (CD3 being a marker for T cell lymphocytes, and CD 19 being a marker for B cell lymphocytes) by flow cytometry techniques and by biological sample analysis system 20, in accordance with some applications of the present invention.

As described hereinabove, flow cytometry techniques (e.g., fluorescence-activated cell sorting (FACS) analysis), are commonly used for immunopheno typing, and distinguishing between cell populations based on detection of cell surface markers. For example, flow cytometry can be used for the quantification and assessment of CD3 and CD 19 expression on lymphocytes (and thereby allowing differentiation between types of lymphocytes). In a study in which inventors of the present application participated in conducting, a comparison between biological sample analysis system 20 of the present invention, and a clinical flow cytometry device for detection of CD3 and CD 19 expression on lymphocytes, was performed. In other words, the ability of biological sample analysis system 20 to detect and quantify CD3 and CD 19 positive lymphocytes were compared with that of a flow cytometry system for all samples and antibodies.

In the study, 14 blood samples were collected from Shaare Zedek Hospital (Jerusalem, Israel). The blood samples were divided for analysis by biological sample analysis system 20 in accordance with some applications of the present invention, and for analysis by a flow cytometry device at the Hebrew University (Jerusalem, Israel). In order to detect CD3 and CD 19, the samples were stained with antibodies conjugated to APC fluorophore against CD3 (manufactured by eBioscience, under cat. no. 17-0038-42) and antibodies conjugated to APC fluorophore against CD19 (manufactured by eBioscience, under cat. no. 17-0199-42). The samples were additionally stained with Hoechst DNA stain such that a count of white blood cells could be performed based on the count of Hoechst positive cells, as observed using a UV filter.

The blood samples that were analyzed by biological sample analysis system 20 in accordance with some applications of the present invention, were scanned on a hematology system manufactured by Sysmex Corporation (Kobe, Japan), to detect APC fluorescence. The blood samples that were analyzed by flow cytometry were analyzed by FACSCanto™ II Flow Cytometer. Stained cell counts and percentages (out of all WBC detected) obtained by system 20 were compared with flow cytometry results (results which were positive to Hoechst and positive to APC fluorescence) for all samples, was performed. As described above, a total number of WBCs were estimated by the number of cells detected by the UV filter (Hoechst positive cells).

As shown in Fig. 11B, regarding CD3, median fluorescence intensity of biological sample analysis system 20 and FACSCanto™ II Flow Cytometer, was compared for each of the blood samples, to identify CD3-positive cells, presenting a correlation coefficient for CD3 positive lymphocyte percentages between system 20 and flow cytometry (FACS) was 0.9046, with an accompanying R value of 0.818. Thus, biological sample analysis system 20 has been shown to correctly assess expression of CD3 positive lymphocytes using antibodies, while providing the benefits of system 20 compared to flow cytometry, as described hereinabove.

As shown in Fig. 11C, regarding CD19, median fluorescence intensity of biological sample analysis system 20 and FACSCanto™ II Flow Cytometer, was compared for each of the blood samples, to identify CD 19-positive cells, presenting a correlation coefficient for CD19-positive lymphocyte percentages between system 20 and flow cytometry (FACS) was 0.994, with an R value of 0.988. Thus, biological sample analysis system 20 has been shown to correctly assess expression of CD19 positive lymphocytes using antibodies, while providing the benefits of system 20 compared to flow cytometry, as described hereinabove.

In accordance with the data shown in Fig. 11 A, for some applications, CD3 expression levels and/or CD 19 expression levels of lymphocytes within a sample are detected by detecting average (e.g., median and/or mean) fluorescence intensity levels of those lymphocytes within one or more microscopic images of the sample that are acquired after the sample has been incubated with fluorescently-labelled CD3 and or CD 19 antibodies. Based upon the CD3 expression levels and/or CD19 expression levels of lymphocytes within the sample T cell lymphocytes and B cell lymphocytes are distinguished from each other, respective counts of T cell lymphocytes and B cell lymphocytes are determined, and/or relative counts of T cell lymphocytes and B cell lymphocytes are determined.

Biomarkers

Reference is now made to Fig. 12 and Fig. 13. In accordance with some applications of the present invention, biological sample analysis system 20 is configured to identify a biomarker (i.e., a biological molecule that is a sign of a normal or abnormal process, or of a condition or disease) within a biological sample (such as a blood sample) by analyzing images of at least a portion of the blood sample that has been tagged with antibodies. Fig. 12 and Fig. 13 are flowcharts showing steps of various procedures that are performed on the blood sample and that include identification of biomarkers, in accordance with some applications of the present invention. In general, images of at least a portion of the blood sample that has been tagged with antibodies are analyzed to identify biomarkers within the blood sample based on proteins that are expressed by the cells. The blood sample (or a portion thereof) is incubated with fluorescently-labeled antibodies that bind specific proteins in the sample thereby allowing identification of biomarkers within the sample, and/or provide information regarding cellular processes. It is noted that although particular configurations of the optical measurement unit and/or the sample carrier are described hereinabove, the scope of the present application includes performing the techniques described with reference to Figs. 12-13 using an optical measurement unit and/or a sample carrier having different characteristics from those shown in and described with reference to Figs. 1-3C, mutatis mutandis.

Reference is first made to Fig. 12, which is a flowchart showing general steps of a procedure performed by biological sample analysis system 20, in accordance with some applications of the present invention. As described hereinabove, a blood sample is deposited in a sample chamber (e.g., one or more of set 52 of sample chambers) of sample carrier 22, as described hereinabove with reference to Figs. 2B-C. As further described with reference to Figs. 2B-C, analysis of the blood sample within the sample chamber is typically performed with respect to the monolayer of cells that settled in the sample chamber.

As shown in Fig. 12, the blood sample is mixed with fluorescently-labeled antibodies. For some applications, prior to placement of the blood sample in the sample chamber, the blood sample is mixed with fluorescently-labeled antibodies. Alternatively, the fluorescently- labeled antibodies are mixed with the blood sample when the blood sample is disposed within the sample chamber. Mixing of fluorescently-labeled antibodies with the blood sample is shown in step 1202. The fluorescently-labeled antibodies selectively bind specific target proteins/antigens within the blood sample, causing these target proteins to fluoresce upon being excited by excitation light, thereby allowing detection of these proteins. The fluorescently-labeled antibodies are mixed with the blood sample in either a liquid form or a dried form, and are typically incubated for about 10-30 minutes with the sample. For some applications, the sample is diluted and/or mixed with other fluorescent stains (e.g. a Hoechst reagent and/or Acridine Orange) after the incubation. Alternatively, the sample is initially mixed and/or diluted with both fluorescently-labeled antibodies and other fluorescent stains (e.g. a Hoechst reagent and/or Acridine Orange) and is incubated with both. The sample chamber, with the blood sample deposited therein, is placed inside optical measurement unit 31 for analysis by microscope system 37. One or more fluorescent microscopic images of the blood sample within the sample chamber are acquired, using microscope system 37 (step 1204).

Computer processor 28 identifies one or more biomarkers within the sample by analyzing one or more fluorescent microscopic images of the blood sample (step 1206). The computer processor is configured to identify fluorescent entities within the sample, perform analysis and generate an output (step 1208), e.g., an output indicating the presence and/or concentration of the biomarkers.

In accordance with some applications of the present invention, preparation of the blood sample for identification of biomarkers by biological sample analysis system 20 generally does not involve washing of the sample. Typically, the sample is diluted and/or stained (with both fluorescently-labeled antibodies and/or other fluorescent stains (e.g. a Hoechst reagent and/or Acridine Orange) prior to imaging. For some such applications, the blood sample is in an unwashed state when it is deposited within the sample chamber, thereby minimizing preparation processes of the sample prior to performing biomarker analysis. This is in contrast to preparation of a sample for analysis by flow cytometry which typically requires multiple washing steps and centrifugation. For some applications, preparation of the blood sample for analysis by biological sample analysis system 20 includes red blood cell lysis. Alternatively, the blood sample is prepared for analysis without red blood cell lysis.

Typically, in addition to performing the above-described analysis, biological sample analysis system 20 is further configured to perform a count of entities within the blood sample, e.g., by performing a complete blood count on the blood sample, for example using apparatus and methods described in US 11,099,175 to Zait, which is incorporated herein by reference. Typically, the blood sample (or portions thereof) is imaged under brightfield imaging, i.e., under illumination from one or more light sources, and/or or under fluorescent imaging in cases in which the sample is stained (for example with a Hoechst reagent and/or with Acridine Orange) to facilitate performing the blood count. Computer processor 28 analyzes the one or more microscopic images of the blood sample to count entities within the blood sample (e.g., red blood cells, white blood cells, neutrophils, eosinophils, basophils, lymphocytes, monocytes, platelets, anomalous white blood cells, circulating tumor cells, reticulocytes, Howell-Jolly bodies, etc.).

Reference is now made to Fig. 13, which is a flowchart of steps of sample analysis performed by biological sample analysis system 20, in accordance with some applications of the present invention. For some applications, biological sample analysis system 20 is trained to identify biomarkers within a blood sample even without the use of fluorescently-labeled antibodies. For some applications, the biological sample analysis system is trained to identify such biomarkers by initially training the system to associate features that are identifiable in microscopic images that are not acquired under fluorescently-labeled antibodies staining (e.g., brightfield images and/or fluorescent images that are acquired of a sample stained with a fluorescent stain such as Hoechst reagent and/or Acridine Orange) with features that are identifiable in images acquired under fluorescently-labeled antibodies staining, in accordance with the method shown in Fig. 13. Alternatively or additionally, the biological sample analysis system is configured to identify features within microscopic images of the blood sample (such as, brightfield images and/or fluorescent images that are acquired of a sample stained with a fluorescent stain such as Hoechst reagent and/or Acridine Orange) that are indicative of such biomarkers, even without having been trained to associate such features with features that are identifiable in images acquired under fluorescently-labeled antibodies staining.

For some such applications, during a training phase, fluorescently-labeled antibodies are mixed with a plurality of blood samples (step 1300). For some applications, each of the blood samples is deposited within a sample chamber (e.g., one or more of set 52 of sample chambers) of sample carrier 22, as described hereinabove with reference to Figs. 2B-C. As further described with reference to Figs. 2B-C, analysis of the blood sample within the sample chamber is typically performed with respect to the monolayer of cells that settled in the sample chamber.

One or more fluorescent microscopic images of each of the blood samples are acquired under a fluorescent imaging modality that is configured to excite the fluorescent stain of the antibodies (step 1302). Additionally, one or more additional microscope images of each of the blood samples are acquired under a second modality (step 1304). Typically, when imaged under the second modality, the blood samples are imaged under brightfield imaging, i.e., under illumination from one or more light sources, and/or under fluorescent imaging in cases in which the samples are stained (the fluorescent imaging using excitation wavelengths that allow differentiation between the fluorescent staining of the antibodies and other staining). Typically, imaging of blood samples under the second modality allows further analysis of the sample for obtaining additional features (such as morphological characteristics of entities within the sample).

For some applications, computer processor 28 is configured to obtain additional features relating to one or more of the components within the sample. For example, in relation to red blood cells, the computer processor determines parameters such as corpuscular hemoglobin (MCH), mean corpuscular volume (MCV), red blood cell distribution width (RDW), red blood cell morphologic features, clumping, and/or red blood cell abnormalities. Alternatively or additionally, in relation to white blood cells, the computer processor determines parameters such as absolute and relative numbers of neutrophils, lymphocytes, monocytes, eosinophils and basophils. For some applications, the computer processor performs normal and abnormal leukocyte differentiation, including detecting the existence of immature or hyper segmented cells, white blood cell agglutination or fragmentation, blasts, and/or atypical or abnormal lymphocytes. For some applications, the computer processor detects leukocyte subpopulations, and/or morphological characteristics.

Computer processor 28 analyzes the one or more fluorescent microscopic images of the blood samples to identify biomarkers within each of the blood samples (step 1306), and additionally analyzes the one or more additional microscopic images of each of the blood samples (obtained by the second modality) to identify features (e.g., morphological characteristics of entities within the samples) of each of the blood samples (step 1308). Computer processor 28 identifies correlations between the presence and/or concentration of biomarkers and additional features of the blood sample that are identifiable in microscopic images that are acquired under the second imaging modality (step 1310).

Typically, the above-described steps are performed during a training stage. Further typically, during a subsequent stage, biomarker detection is performed on a test blood sample without the use of antibodies (by incorporating the previous training).

In such a manner, training of sample analysis system 20 to identify biomarkers within the sample, is done using fluorescently-labeled antibodies. However, once the system is trained, use of antibodies is generally not required, as the system is trained to classify these biomarkers based on additional features (e.g., morphological features) alone. For some applications, sample analysis system 20 comprises a dedicated hardware that is configured in the training stage to scan the fluorescently-labeled antibody images in addition to acquiring additional images, e.g., brightfield images and/or fluorescent images (such as images under Acridine Orange staining, and/or Hoechst reagent staining, as described herein). For some applications, the above-described training is performed in the reverse direction, such that the biological sample analysis system is trained to identify morphological features of blood cells based upon images that are acquired using fluorescently-labeled antibodies. For some applications, the above-described training is performed with respect to alternative or additional imaging modalities. For example, such that the biological sample analysis system is trained to identify features that are typically identifiable in images acquired using Acridine Orange staining based on brightfield images and/or images acquired under staining with a Hoechst reagent.

Examples 8-10 detailing specific non-limiting applications of the present disclosure will now be provided in order to better understand the disclosed subject matter. As described hereinabove, for some applications, the biological sample analysis system is trained to identify such biomarkers by initially training the system to associate features that are identifiable in microscopic images that are not acquired under fluorescently-labeled antibodies staining (e.g., brightfield images and/or fluorescent images that are acquired of a sample stained with a fluorescent stain such as Hoechst reagent and/or Acridine Orange) with features that are identifiable in images acquired under fluorescently-labeled antibodies staining, in accordance with the method shown in Fig. 13. Alternatively or additionally, the biological sample analysis system is configured to identify features within microscopic images of the blood sample (such as, brightfield images and/or fluorescent images that are acquired of a sample stained with a fluorescent stain such as Hoechst reagent and/or Acridine Orange) that are indicative of such biomarkers, even without having been trained to associate such features with features that are identifiable in images acquired under fluorescently-labeled antibodies staining.

Example 8: Detecting hairy cells in a blood sample

Hairy cell leukemia is a rare bone marrow cancer, which accounts for approximately 2% of all leukemias. It is typically classified as a subtype of chronic lymphocytic leukemia (CLL). For some applications, using the techniques described hereinabove, hairy cells are identified using several morphological and spectral features extracted from images taken using four illumination models and six different heights. For some such applications, brightfieldbased morphological features describing the outlines of the cell, brightfield-based spectrometric features describing the inner structure of the cell (i.e., absorption) and fluorescent-based spectrometric features describing the internal structure of the cytoplasm are used to identify hairy cells.

Example 9: Detecting chronic lymphocytic leukemia (CLL)

Chronic lymphocytic leukemia (CLL) is a bone marrow malignancy which causes the bone marrow to produce an abnormally high number of lymphocytes. CLL is one of the most common types of leukemia in adults and its progression is typically gradual. It is typically diagnosed using a combination of tests, including complete blood counts (CBC), blood smear, flow cytometry and DNA sequencing. Alongside an elevation of lymphocytes, subsets of abnormal cells are typically found in CLL patients. For some applications, such abnormal cells are detected, and in response thereto the biological sample analysis system determines that the subject is suffering from or is likely to be suffering from CLL. For some such applications, the biological sample analysis system determines this based upon several morphological and spectral features extracted from images taken using four illumination models and six different heights. For some such applications, the biological sample analysis system determines this based upon mean lymphocyte size exceeding a maximum threshold or being less than a minimum threshold, and/or mean lymphocyte cytoplasm internal complexity exceeding a maximum threshold or being less than a minimum threshold.

Example 10: Distinguishing between B- and T-cell lymphocytes in a blood sample

Lymphocytes in the blood are composed of two types of cells: B- and T-cell lymphocytes, each of which has a unique role in the immune response cascade. It is challenging to distinguish between the two cell types using a traditional Giemsa stain and a blood smear. Cell quantification is thus usually done using flow cytometry and unique surface antibodies - specifically CD3 and CD19. For some applications, using the techniques described hereinabove, morphological features of cells (e.g., nucleus shape and density, cytoplasm and cell outlines) are used to distinguish between B- and T-cell lymphocytes. For some such application, brightfield-based spectrometric features describing the inner structure of the cell (i.e., absorption) and fluorescent-based spectrometric features describing the internal structure of the cytoplasm are used to distinguish between B- and T-cell lymphocytes. For some applications, the sample as described herein is a sample that includes blood or components thereof (e.g., a diluted or non-diluted whole blood sample, a sample including predominantly red blood cells, or a diluted sample including predominantly red blood cells), and parameters are determined relating to components in the blood such as platelets, white blood cells, anomalous white blood cells, circulating tumor cells, red blood cells, reticulocytes, Howell-Jolly bodies, sickle cells, tear-drop cells, etc. For some applications, the sample includes a fine needle aspirate sample. For some such applications, parameters are determined relating to components in the sample such as: macrophages, histiocytes, mast cells, plasma cells, melanocytes, epithelial cells, mesenchymal cells, mesothelial cells, bacteria, yeast, and/or parasites.

Applications of the invention described herein can take the form of a computer program product accessible from a computer-usable or computer-readable medium (e.g., a non- transitory computer-readable medium) providing program code for use by or in connection with a computer or any instruction execution system, such as computer processor 28. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Typically, the computer-usable or computer readable medium is a non-transitory computer-usable or computer readable medium.

Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random-access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing program code will include at least one processor (e.g., computer processor 28) coupled directly or indirectly to memory elements (e.g., memory 30) through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments of the invention. Network adapters may be coupled to the processor to enable the processor to become coupled to other processors or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.

Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object- oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the C programming language or similar programming languages.

It will be understood that algorithms described herein can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer (e.g., computer processor 28) or other programmable data processing apparatus, create means for implementing the functions/acts specified in the algorithms described in the present application. These computer program instructions may also be stored in a computer-readable medium (e.g., a non-transitory computer-readable medium) that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart blocks and algorithms. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the algorithms described in the present application.

Computer processor 28 is typically a hardware device programmed with computer program instructions to produce a special purpose computer. For example, when programmed to perform the algorithms described herein, computer processor 28 typically acts as a special purpose sample-analysis computer processor. Typically, the operations described herein that are performed by computer processor 28 transform the physical state of memory 30, which is a real physical article, to have a different magnetic polarity, electrical charge, or the like depending on the technology of the memory that is used.

The apparatus and methods described herein may be used in conjunction with apparatus and methods described in any one of the following patents or patent applications, all of which are incorporated herein by reference:

US 9,522,396 to Bachelet;

US 10,176,565 to Greenfield;

US 10,640,807 to Pollak;

US 9,329,129 to Pollak;

US 10,093,957 to Pollak;

US 10,831,013 to Yorav Raphael;

US 10,843,190 to Bachelet;

US 10,482,595 to Yorav Raphael;

US 10,488,644 to Eshel;

US 11,733,150 to Eshel;

US 11,307,196 to Pollak;

US 11,099,175 to Zait;

US 11,614,609 to Yorav-Raphael;

WO 21/079305 to Pecker;

WO 21/079306 to Pecker;

WO 21/116955 to Yafin;

WO 21/116957 to Gluck;

WO 21/116959 to Eshel;

WO 21/116960 to Zait;

WO 21/116962 to Halperin;

WO 22/009104 to Zait; and

WO 23/144713 to Pecker. It will be appreciated by persons skilled in the art that the present invention is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present invention includes both combinations and subcombinations of the various features described hereinabove, as well as variations and modifications thereof that are not in the prior art, which would occur to persons skilled in the art upon reading the foregoing description.

Claims

1. A method comprising: preparing a blood sample for analysis by: mixing fluorescently-labeled antibodies with the blood sample; depositing the blood sample within a sample chamber; placing the sample chamber, with the blood sample deposited therein, within an optical measurement unit; acquiring one or more fluorescent microscopic images of the blood sample within the sample chamber, using a microscope of the optical measurement unit; and using a computer processor: performing immunophenotyping on the blood sample, by analyzing the one or more fluorescent microscopic images of the blood sample; and generating an output, at least partially in response thereto.

2. The method according to claim 1, further comprising performing a count of entities within the blood sample, by analyzing the one or more microscopic images of the blood sample, and determining a characteristic of the blood sample, based upon the count of entities within the sample and the immunophenotyping, wherein generating the output comprising generating an output in response to determining the characteristic of the blood sample.

3. The method according to claim 1, wherein mixing the fluorescently-labeled antibodies with the blood sample comprises mixing fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1, and wherein performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample comprises identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1.

4. The method according to claim 1, wherein performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample, comprises determining an average fluorescence intensity level of cells conjugated to the fluorescently-labeled antibodies within the one or more microscopic images of the blood sample.

5. The method according to claim 1, wherein mixing the fluorescently-labeled antibodies with the blood sample comprises mixing fluorescently-labeled antibodies against at least one of CD 14 and CD16.

6. The method according to claim 1 , wherein depositing the blood sample within a sample chamber comprises depositing the blood sample in the sample chamber in an unwashed state.

7. The method according to claim 1, wherein mixing the fluorescently-labeled antibodies with the blood sample comprises mixing fluorescently-labeled antibodies against CD3, and wherein performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample comprises identifying T cell lymphocytes in the blood sample.

8. The method according to claim 1, wherein mixing the fluorescently-labeled antibodies with the blood sample comprises mixing fluorescently-labeled antibodies against CD3, and wherein performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample comprises performing a count of T cell lymphocytes in the blood sample.

9. The method according to claim 1, wherein mixing the fluorescently-labeled antibodies with the blood sample comprises mixing fluorescently-labeled antibodies against CD 19, and wherein performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample comprises identifying B cell lymphocytes in the blood sample.

10. The method according to any one of claims 1 or 3-9, wherein mixing the fluorescently- labeled antibodies with the blood sample comprises mixing fluorescently-labeled antibodies against CD64, and wherein generating the output comprises generating an output that a subject from whom the blood sample was drawn is suspected of suffering from an infection at least partially based on the immunophenotyping.

11. The method according to claim 10, wherein generating the output comprises generating an output that a subject from whom the blood sample was drawn is suspected of suffering from sepsis at least partially based on the immunophenotyping.

12. The method according to claim 10, further comprising identifying one or more biomarkers selected from the group consisting of: Procalcitonin (PCT), C-reactive protein (CRP), Interleukin-6 (IL-6), Interleukin-8 (IL-8), Interleukin- 10 (IL- 10), Interleukin- ip (IL- 1P), Tumor Necrosis Factor-alpha (TNF-a), Presepsin, Thrombomodulin, Lactate Soluble urokinase-type plasminogen activator receptor (suPAR), Soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1), Lipopolysaccharide-binding protein (LBP), N- terminal pro-brain natriuretic peptide (NT -proBNP), Neutrophil gelatinase-associated lipocalin (NGAL), Adrenomedullin, Resistin, Pro-adrenomedullin (MR-proADM), D-dimer, Pancreatic and stone protein (PSP); and wherein generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection comprises generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the one or more biomarkers selected from the group.

13. The method according to claim 10, further comprising receiving a result of one or more diagnostic tests selected from the group consisting of: measuring a vital sign, blood culture, urine culture, chest X-ray, CT Scan, ultrasound, lumbar puncture, sputum culture and gram stain, wound culture, echocardiography, arterial blood gas (ABG) analysis, complete blood count (CBC), basic metabolic panel (BMP), lactate level, coagulation profile, urinalysis, accessing electronic medical records (EMRs); performing an EMR-based sepsis algorithm, central venous pressure (CVP) monitoring, and a culture from a bodily site; and wherein generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection comprises generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the result of the one or more diagnostic tools selected from the group.

14. The method according to claim 10, further comprising performing a blood count on the blood sample, wherein generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection comprises generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the blood count.

15. The method according to claim 10, further comprising identifying morphological characteristics of entities within the blood sample, wherein generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection comprises generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the morphological characteristics of entities within the blood sample.

16. The method according to claim 15, wherein identifying morphological characteristics of entities within the blood sample comprises identifying at least one of nucleus shape, nucleus density, cytoplasm, and/or cell outlines.

17. The method according to claim 10, wherein performing immunophenotyping on the blood sample comprises detecting average fluorescence intensity levels of the fluorescently- labeled antibodies within the one or more fluorescent microscopic images of the blood sample.

18. The method according to claim 10, wherein performing the immunophenotyping comprises determining a count of neutrophils expressing CD64 within the blood sample, and wherein generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, comprises generating the output in response to the count of the neutrophils expressing CD64.

19. The method according to claim 10, wherein performing the immunophenotyping comprises determining a count of lymphocytes expressing CD64 within the blood sample, and wherein generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, comprises generating the output in response to the count of the lymphocytes expressing CD64.

20. The method according to claim 10, wherein performing the immunophenotyping comprises determining a count of monocytes expressing CD64 within the blood sample, and wherein generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, comprises generating the output in response to the count of the monocytes expressing CD64.

21. The method according to claim 10, wherein performing the immunophenotyping comprises determining a count of white blood cells expressing CD64 within the blood sample, and wherein generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, comprises generating the output in response to the count of the white blood cells expressing CD64.

22. The method according to claim 10, wherein performing the immunophenotyping comprises quantifying CD64 expression in neutrophils within the blood sample, and wherein generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, comprises generating the output in response to the CD64 expression in the neutrophils within the blood sample.

23. The method according to claim 10, wherein performing the immunophenotyping comprises quantifying CD64 expression in lymphocytes within the blood sample, and generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, comprises generating the output in response to the CD64 expression in the lymphocytes within the blood sample.

24. The method according to claim 10, wherein performing the immunophenotyping comprises quantifying CD64 expression in monocytes within the blood sample, and wherein generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, comprises generating the output in response to the CD64 expression in the monocytes within the blood sample.

25. The method according to claim 10, wherein performing the immunophenotyping comprises quantifying CD64 expression in white blood cells within the blood sample, and wherein generating the output that a subject from whom the blood sample was drawn is suspected of suffering from an infection, comprises generating the output in response to the CD64 expression in the white blood cells within the blood sample.

26. The method according to any one of claims 1-6, wherein mixing the fluorescently- labeled antibodies with the blood sample comprises mixing fluorescently-labeled antibodies against CD 19, and wherein performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample comprises performing a count of B cell lymphocytes in the blood sample.

27. The method according to claim 26, wherein mixing the fluorescently-labeled antibodies with the blood sample further comprises mixing fluorescently-labeled antibodies against CD3 wherein performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample comprises performing a count of T cell lymphocytes in the blood sample.

28. The method according to claim 27, wherein generating the output comprises generating an output indicating relative counts of B cell lymphocytes and T cell lymphocytes in the blood sample.

29. The method according to any one of claims 1-9, further comprising identifying morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample, and determining a characteristic of the blood sample, based upon the morphological characteristics of the entities within the sample and the immunophenotyping; and wherein generating an output comprising generating an output in response to determining the characteristic of the blood sample.

30. The method according to claim 29, wherein identifying morphological characteristics of entities within the sample comprises identifying at least one of nucleus shape, nucleus density, cytoplasm, and/or cell outlines.

31. Apparatus for use with a sample chamber containing a blood sample that has been mixed with fluorescently-labeled antibodies, the apparatus comprising: an optical measurement unit configured to receive the sample chamber, the optical measurement unit comprising a microscope configured to acquire one or more fluorescent microscopic images of the blood sample within the sample chamber; and at least one computer processor configured to: perform immunophenotyping on the blood sample, by analyzing the one or more fluorescent microscopic images of the blood sample, and generate an output, at least partially in response thereto.

32. The apparatus according to claim 31, wherein the computer processor is further configured to: perform a count of entities within the blood sample, by analyzing the one or more microscopic images of the blood sample, and determine a characteristic of the blood sample, based upon the count of entities within the sample and the immunophenotyping.

33. The apparatus according to claim 31, wherein the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1, and wherein the at least one computer processor configured to perform immunophenotyping on the blood sample by identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1.

34. The apparatus according to claim 31, wherein the computer processor is configured to perform immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample, by determining an average fluorescence intensity level of cells conjugated to the fluorescently-labeled antibodies within the one or more microscopic images of the blood sample.

35. The apparatus according to claim 31, wherein the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD3, and wherein the computer processor is configured to perform the immunophenotyping by identifying T cell lymphocytes in the blood sample.

36. The apparatus according to claim 31, wherein the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD3, and wherein the computer processor is configured to perform the immunophenotyping by performing a count of T cell lymphocytes in the blood sample.

37. The apparatus according to claim 31, wherein the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD 19, and wherein the computer processor is configured to perform the immunophenotyping by identifying B cell lymphocytes in the blood sample.

38. The apparatus according to claim any one of claims 31 or 33-37, wherein the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD64, and wherein the computer processor is configured to generate an output that a subject from whom the blood sample was drawn is suspected of suffering from an infection at least partially based on the immunophenotyping.

39. The apparatus according to claim 38, wherein the computer processor is configured to generate an output that a subject from whom the blood sample was drawn is suspected of suffering from sepsis at least partially based on the immunophenotyping.

40. The apparatus according to claim 38, wherein the computer processor is configured to: identify one or more biomarkers selected from the group consisting of: Procalcitonin

(PCT), C-reactive protein (CRP), Interleukin-6 (IL-6), Interleukin-8 (IL-8), Interleukin- 10 (IL- 10), Interleukin- ip (IL-lp), Tumor Necrosis Factor-alpha (TNF-a), Presepsin, Thrombomodulin, Lactate Soluble urokinase-type plasminogen activator receptor (suPAR), Soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1), Lipopolysaccharide- binding protein (LBP), N-terminal pro-brain natriuretic peptide (NT-proBNP), Neutrophil gelatinase-associated lipocalin (NGAL), Adrenomedullin, Resistin, Pro-adrenomedullin (MR- proADM), D-dimer, Pancreatic and stone protein (PSP); and generate the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the one or more biomarkers selected from the group.

41. The apparatus according to claim 38, wherein the computer processor is configured to: receive a result of one or more diagnostic tests selected from the group consisting of: measuring a vital sign, blood culture, urine culture, chest X-ray, CT Scan, ultrasound, lumbar puncture, sputum culture and gram stain, wound culture, echocardiography, arterial blood gas (ABG) analysis, complete blood count (CBC), basic metabolic panel (BMP), lactate level, coagulation profile, urinalysis, accessing electronic medical records (EMRs); performing an EMR-based sepsis algorithm, central venous pressure (CVP) monitoring, and a culture from a bodily site, and generate the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the result of the one or more diagnostic tools selected from the group.

42. The apparatus according to claim 38, wherein the computer processor is configured to perform a blood count on the blood sample, and generate the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the blood count.

43. The apparatus according to claim 38, wherein the computer processor is configured to identify morphological characteristics of entities within the blood sample, and generate the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the immunophenotyping and the morphological characteristics of entities within the blood sample.

44. The method according to claim 43, wherein the computer processor is configured to identify morphological characteristics of entities within the blood sample by identifying at least one of nucleus shape, nucleus density, cytoplasm, and/or cell outlines.

45. The apparatus according to claim 38, wherein the computer processor is configured to perform immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of the blood sample, by determining an average fluorescence intensity level of cells conjugated to the CD64 fluorescently-labeled antibodies within the one or more microscopic images of the blood sample.

46. The apparatus according to claim 38, wherein the computer processor is configured to determine a count of neutrophils expressing CD64 within the blood sample, and generate the output based on the count of the neutrophils expressing CD64.

47. The apparatus according to claim 38, wherein the computer processor is configured to determine a count of lymphocytes expressing CD64 within the blood sample, and generate the output based on the count of the lymphocytes expressing CD64.

48. The apparatus according to claim 38, wherein the computer processor is configured to determine a count of monocytes expressing CD64 within the blood sample, and generate the output based on the count of the monocytes expressing CD64.

49. The apparatus according to claim 38, wherein the computer processor is configured to determine a count of white blood cells expressing CD64 within the blood sample, and generate the output based on the count of white blood cells expressing CD64.

50. The apparatus according to claim 38, wherein the computer processor is configured to quantify CD64 expression in neutrophils within the blood sample, and generate the output based on the CD64 expression in neutrophils within the blood sample.

51. The apparatus according to claim 38, wherein the computer processor is configured to quantify CD64 expression in lymphocytes within the blood sample, and generate the output based on the CD64 expression in lymphocytes within the blood sample.

52. The apparatus according to claim 38, wherein the computer processor is configured to quantify CD64 expression in monocytes within the blood sample, and generate the output based on the CD64 expression in monocytes within the blood sample.

53. The apparatus according to claim 38, wherein the computer processor is configured to determine CD64 expression in white blood cells within the blood sample, and generate the output based on the CD64 expression in white blood cells within the blood sample.

54. The apparatus according to and one of claims 31-34, wherein the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently- labeled antibodies against CD 19, and wherein the computer processor is configured to perform the immunophenotyping by performing a count of B cell lymphocytes in the blood sample.

55. The apparatus according to claim 54, wherein the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD3 and fluorescently-labeled antibodies against CD 19, and wherein the computer processor is configured to perform the immunophenotyping by performing counts of T cell lymphocytes and B cell lymphocytes in the blood sample.

56. The apparatus according to claim 55, wherein the computer processor is configured to generate the output by generating an output indicating relative counts of B cell lymphocytes and T cell lymphocytes in the blood sample.

57. The apparatus according to any one of claims 31-37, wherein the computer processor is further configured to: identify morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample, and determine a characteristic of the blood sample, based upon the morphological characteristics of entities within the sample and the immunopheno typing.

58. The apparatus according to claim 57, wherein the computer processor is configured to identify morphological characteristics of entities within the sample by identifying at least one of nucleus shape, nucleus density, cytoplasm, and/or cell outlines.

59. A method comprising: depositing a blood sample within a sample chamber; placing the sample chamber, with the blood sample deposited therein, within an optical measurement unit; acquiring one or more microscopic images of the blood sample within the sample chamber, using a microscope of the optical measurement unit; and using a computer processor: performing a count of entities within the blood sample, by analyzing the one or more microscopic images of the blood sample; performing immunophenotyping on the blood sample, by analyzing the one or more microscopic images of the blood sample; determining a characteristic of the blood sample, based upon the count of entities within the sample and the immunophenotyping; and generating an output, at least partially in response thereto.

60. The method according to claim 59, wherein performing the immunophenotyping comprises mixing with the sample fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1 and identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1.

61. The method according to claim 59, further comprising mixing fluorescently-labeled antibodies with the blood sample.

62. The method according to claim 59, wherein performing a count of entities comprises performing a count of at least one of: red blood cells, neutrophils, eosinophils, basophils, lymphocytes, monocytes, and/or platelets.

63. The method according to claim 59, wherein performing immunophenotyping comprises performing immunophenotyping for CD3, and wherein generating the output comprises generating an output indicating a count of T cell lymphocytes in the blood sample.

64. The method according to claim 59, wherein performing immunophenotyping comprises performing immunophenotyping for CD 19, and wherein generating the output comprises generating an output indicating a count of B cell lymphocytes in the blood sample.

65. The method according to claim 59, wherein performing immunophenotyping comprises performing immunophenotyping for CD3 and CD 19, and wherein generating the output comprises generating an output indicating counts of B cell lymphocytes and T cell lymphocytes in the blood sample.

66. The method according to claim 59, wherein performing immunophenotyping comprises performing immunophenotyping for at least one of: CD14 and CD16.

67. The method according to any one of claims 59-66, wherein performing immunophenotyping comprises performing immunophenotyping for CD64, and wherein generating the output comprises generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from an infection.

68. The apparatus according to claim 67, wherein generating the output comprises generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from sepsis.

69. Apparatus for use with a sample chamber containing a blood sample, the apparatus comprising: an optical measurement unit configured to receive the sample chamber, the optical measurement unit comprising a microscope configured to acquire one or more microscopic images of the blood sample within the sample chamber; and at least one computer processor configured to: perform a count of entities within the blood sample, by analyzing the one or more microscopic images of the blood sample, perform immunophenotyping on the blood sample, by analyzing the one or more microscopic images of the blood sample, determine a characteristic of the blood sample, based upon the count of entities within the sample and the immunopheno typing, and generate an output, at least partially in response thereto.

70. The apparatus according to claim 69, wherein the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1, and wherein the at least one computer processor configured to perform immunophenotyping on the blood sample by identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1.

71. The apparatus according to claim 69, wherein the computer processor is configured to perform a count of entities by performing a count of at least one of: red blood cells, neutrophils, eosinophils, basophils, lymphocytes, monocytes, and/or platelets.

72. The apparatus according to claim 69, wherein the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD3, and wherein the computer processor is configured to generate the output by generating an output indicating a count of T cell lymphocytes in the blood sample.

73. The apparatus according to claim 69, wherein the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD 19, and wherein the computer processor is configured to generate the output by generating an output indicating a count of B cell lymphocytes in the blood sample.

74. The apparatus according to claim 69, wherein the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD 3 and CD 19, and wherein the computer processor is configured to generate the output by generating an output indicating counts of B cell lymphocytes and T cell lymphocytes in the blood sample.

75. The apparatus according to claim 69, wherein the computer processor is configured to perform immunophenotyping by performing immunophenotyping for at least one of: CD 14 and CD16.

76. The apparatus according to any one of claims 69-75, wherein the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD64, and wherein the computer processor is configured to generate the output by generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from an infection.

77. The apparatus according to claim 76, wherein the computer processor is configured to generate the output by generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from sepsis.

78. A method comprising: depositing a blood sample within a sample chamber; placing the sample chamber, with the blood sample deposited therein, within an optical measurement unit; acquiring one or more microscopic images of the blood sample within the sample chamber, using a microscope of the optical measurement unit; and using a computer processor: identifying morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample; performing immunophenotyping on the blood sample, by analyzing the one or more microscopic images of the blood sample; determining a characteristic of the blood sample, based upon the morphological characteristics of entities within the sample and the immunophenotyping; and generating an output, at least partially in response thereto.

79. The method according to claim 78, wherein performing the immunophenotyping comprises mixing with the sample fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1 and identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1.

80. The method according to claim 78, further comprising mixing fluorescently-labeled antibodies with the blood sample.

81. The method according to claim 78, wherein identifying morphological characteristics of entities within the sample comprises identifying at least one of nucleus shape, nucleus density, cytoplasm shape, cytoplasm granularity, and/or cell outlines.

82. The method according to claim 78, wherein performing immunophenotyping comprises performing immunophenotyping for CD3, and wherein generating the output comprises generating an output indicating a count of T cell lymphocytes in the blood sample.

83. The method according to claim 78, wherein performing immunophenotyping comprises performing immunophenotyping for CD 19, and wherein generating the output comprises generating an output indicating a count of B cell lymphocytes in the blood sample. Ill

84. The method according to claim 78, wherein performing immunophenotyping comprises performing immunophenotyping for CD3 and CD 19, and wherein generating the output comprises generating an output indicating counts of B cell lymphocytes and T cell lymphocytes in the blood sample.

85. The method according to claim 78, wherein performing immunophenotyping comprises performing immunophenotyping for at least one of: CD14 and CD16.

86. The method according to any one of claims 78-85, wherein performing immunophenotyping comprises performing immunophenotyping for CD64, and wherein generating the output comprises generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from an infection.

87. The apparatus according to claim 86, wherein generating the output comprises generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from sepsis.

88. Apparatus for use with a sample chamber containing a blood sample, the apparatus comprising: an optical measurement unit configured to receive the sample chamber, the optical measurement unit comprising a microscope configured to acquire one or more microscopic images of the blood sample within the sample chamber; and at least one computer processor configured to: identify morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample, perform immunophenotyping on the blood sample, by analyzing the one or more microscopic images of the blood sample, determine a characteristic of the blood sample, based upon the morphological characteristics of entities within the sample and the immunophenotyping, and generate an output, at least partially in response thereto.

89. The apparatus according to claim 88, wherein the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1, and wherein the at least one computer processor configured to perform immunophenotyping on the blood sample by identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1.

90. The apparatus according to claim 88, wherein the computer processor is configured to identify morphological characteristics of entities within the sample by identifying at least one of nucleus shape, nucleus density, cytoplasm shape, cytoplasm granularity, and/or cell outlines.

91. The apparatus according to claim 88, wherein the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD3, and wherein the computer processor is configured to generate the output by generating an output indicating a count of T cell lymphocytes in the blood sample.

92. The apparatus according to claim 88, wherein the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD 19, and wherein the computer processor is configured to generate the output by generating an output indicating a count of B cell lymphocytes in the blood sample.

93. The apparatus according to claim 88, wherein the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD 3 and CD 19, and wherein the computer processor is configured to generate the output by generating an output indicating counts of B cell lymphocytes and T cell lymphocytes in the blood sample.

94. The apparatus according to claim 88, wherein the computer processor is configured to perform immunophenotyping by performing immunophenotyping for at least one of: CD 14 and CD16.

95. The apparatus according to any one of claims 88-94, wherein the computer processor is configured to perform immunophenotyping by performing immunophenotyping for CD64, and wherein the computer processor is configured to generate the output by generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from an infection.

96. The apparatus according to claim 95, wherein the computer processor is configured to generate the output by generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from sepsis.

97. A method comprising: training a blood-sample analysis system to perform immunophenotyping on a test blood sample without the use of antibodies, by, during a training stage: mixing antibodies that are fluorescently labeled with a fluorescent stain with a plurality of blood samples; acquiring one or more fluorescent microscopic images of each of the blood samples under a fluorescent imaging modality that is configured to excite the fluorescent stain; acquiring one or more additional microscopic images of each of the blood samples under a second imaging modality; and using a computer processor: performing immunophenotyping on each of the blood samples, by analyzing the one or more fluorescent microscopic images of each of the blood samples; identifying features of each of the blood samples, by analyzing the one or more additional microscopic images of each of the blood samples; and identifying correlations between results of the immunophenotyping and features of the blood sample that are identifiable in microscopic images that are acquired under the second imaging modality.

98. The method according to claim 97, wherein mixing the antibodies that are fluorescently labeled with the fluorescent stain with the plurality of blood samples comprises mixing fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1 with the plurality of blood samples, wherein performing immunophenotyping on the blood sample by analyzing the one or more fluorescent microscopic images of each of the blood samples comprises identifying within at least some of the blood samples a corresponding indication to the one or more cell surface markers, as listed in Table 1.

99. The method according to claim 97, further comprising, during a subsequent stage, performing immunophenotyping on the test blood sample without the use of antibodies.

100. The method according to claim 99, wherein: mixing antibodies that are fluorescently labeled with the fluorescent stain with the plurality of blood samples comprises mixing antibodies against CD64 that are fluorescently labeled with a fluorescent stain with a plurality of blood samples; identifying features of each of the blood samples comprises identifying morphological characteristics of entities within each of the blood samples; the method further comprising, in response to performing immunophenotyping on the test blood sample without the use of antibodies, determining that a subject from whom the test blood sample was drawn is suspected of suffering from an infection.

101. The method according to claim 100, wherein identifying morphological characteristics of entities within each of the blood samples comprises identifying at least one of nucleus shape, nucleus density, cytoplasm shape, cytoplasm granularity, and/or cell outlines of entities within each of the blood samples.

102. Apparatus comprising: a blood analysis system comprising a microscope; and at least one computer processor associated with the blood analysis system configured to train the blood-sample analysis system to perform immunophenotyping on a test blood sample without the use of antibodies, by, during a training stage: receiving one or more fluorescent microscopic images of each of a plurality of blood samples that have been mixed with antibodies that are fluorescently labeled with a fluorescent stain, the one or more fluorescent microscopic images having been acquired under a fluorescent imaging modality that is configured to excite the fluorescent stain, receiving one or more additional microscopic images of each of the blood samples, the one or more additional microscopic images having been acquired under a second imaging modality, performing immunophenotyping on each of the blood samples, by analyzing the one or more fluorescent microscopic images of each of the blood samples, identifying features of each of the blood samples, by analyzing the one or more additional microscopic images of each of the blood samples, and identifying correlations between results of the immunophenotyping and features of the blood sample that are identifiable in microscopic images that are acquired under the second imaging modality.

103. The apparatus according to claim 102, wherein the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against one or more cell surface markers listed in Table 1, and wherein the at least one computer processor configured to perform immunophenotyping on the blood sample by identifying a corresponding indication to the one or more cell surface markers, as listed in Table 1.

104. The apparatus according to claim 102, wherein the computer processor is configured, during a subsequent stage, to perform immunophenotyping on the test blood sample without the use of antibodies.

105. The apparatus according to claim 104, wherein: the apparatus is for use with a sample chamber containing the blood sample that has been mixed with fluorescently-labeled antibodies against CD64 that are fluorescently labeled with a fluorescent stain with a plurality of blood samples; and wherein the computer processor is configured to: identify features of each of the blood samples by identifying morphological characteristics of entities within each of the blood samples; and in response to performing the immunophenotyping on the test blood sample without the use of antibodies, determine that a subject from whom the test blood sample was drawn is suspected of suffering from an infection.

106. The apparatus according to claim 105, wherein the computer processor is configured to identify morphological characteristics of entities within each of the blood samples by identifying at least one of nucleus shape, nucleus density, cytoplasm shape, cytoplasm granularity, and/or cell outlines of entities within each of the blood samples.

107. A method for use with a computer processor that has been trained, during a training phase, to identify features that are correlated to an expression of antibodies within training blood samples, the method comprising: acquiring one or more microscopic images of a test blood sample to which no antibodies have been added; and using the computer processor: performing immunophenotyping on the test blood sample by analyzing the microscopic images and identifying features that were determined, during the training phase, to be correlated to the expression of antibodies within training blood samples; and generating an output in response thereto.

108. Apparatus comprising: a computer processor that has been trained, during a training phase, to identify features that are correlated to an expression of antibodies within training blood samples, the computer processor being configured to: receive one or more microscopic images of a test blood sample to which no antibodies have been added; perform immunophenotyping on the test blood sample by analyzing the microscopic images and identifying features that were determined, during the training phase, to be correlated to the expression of antibodies within training blood samples; and generate an output in response thereto.

109. A method for use with a blood sample, the method comprising: acquiring one or more microscopic images of the blood sample; and using a computer processor: identifying neutrophils expressing CD64 within the blood sample by analyzing the one or more microscopic images; and generating an output in response thereto.

110. The method according to claim 109, wherein the method is for use with a blood sample to which no antibodies have been added, and identifying neutrophils expressing CD64 within the blood sample comprises identifying neutrophils expressing CD64 within the blood sample to which no antibodies have been added.

111. The method according to claim 109, wherein the method is for use with a blood sample to which antibodies against CD64 that are fluorescently labeled with a fluorescent stain have been added, and identifying neutrophils expressing CD64 within the blood sample comprises identifying the antibodies against CD64 that are fluorescently labeled with the fluorescent stain within the microscopic images.

112. The method according to any one of claims 109-111, further comprising determining a count of neutrophils expressing CD64 within the blood sample.

113. The method according to claim 112, wherein generating the output comprises outputting the count of neutrophils expressing CD64 within the blood sample.

114. The method according to any one of claims 109-111, further comprising determining a percentage of neutrophils expressing CD64 within the blood sample.

115. The method according to claim 8b, wherein generating the output comprises outputting the percentage of neutrophils expressing CD64 within the blood sample.

116. The method according to any one of claims 109-111, further comprising using the computer processor determining a quantification of expression of CD64 in the neutrophils within the blood sample.

117. The method according to 116, wherein generating the output comprises generating an output indicating that a subject from whom the sample was drawn is suspected of suffering from an infection, based on the quantification of expression of CD64 in the neutrophils within the blood sample.

118. The method according to claim 117, further comprising identifying one or more biomarkers selected from the group consisting of: Procalcitonin (PCT), C-reactive protein (CRP), Interleukin-6 (IL-6), Interleukin-8 (IL-8), Interleukin- 10 (IL- 10), Interleukin- ip (IL- 1P), Tumor Necrosis Factor-alpha (TNF-a), Presepsin, Thrombomodulin, Lactate Soluble urokinase-type plasminogen activator receptor (suPAR), Soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1), Lipopolysaccharide-binding protein (LBP), N- terminal pro-brain natriuretic peptide (NT -proBNP), Neutrophil gelatinase-associated lipocalin (NGAL), Adrenomedullin, Resistin, Pro-adrenomedullin (MR-proADM), D-dimer, Pancreatic and stone protein (PSP); and wherein generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection comprises generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the quantification of expression of CD64 in the neutrophils within the blood sample and the one or more bio markers selected from the group.

119. The method according to claim 117, further comprising receiving a result of one or more diagnostic tests selected from the group consisting of: measuring a vital sign, blood culture, urine culture, chest X-ray, CT Scan, ultrasound, lumbar puncture, sputum culture and gram stain, wound culture, echocardiography, arterial blood gas (ABG) analysis, complete blood count (CBC), basic metabolic panel (BMP), lactate level, coagulation profile, urinalysis, accessing electronic medical records (EMRs); performing an EMR-based sepsis algorithm, central venous pressure (CVP) monitoring, and a culture from a bodily site; and wherein generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection comprises generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the quantification of expression of CD64 in the neutrophils within the blood sample and the result of the one or more diagnostic tools selected from the group.

120. The method according to claim 117, further comprising performing a blood count on the blood sample, wherein generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection comprises generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the quantification of expression of CD64 in the neutrophils within the blood sample and the blood count.

121. The method according to claim 117, further comprising identifying morphological characteristics of entities within the blood sample, wherein generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from an infection comprises generating the output indicating that the subject from whom the sample was drawn is suspected of suffering from the infection based on the quantification of expression of CD64 in the neutrophils within the blood sample and the morphological characteristics of entities within the blood sample.

122. The method according to claim 121, wherein identifying morphological characteristics of entities within the blood sample comprises identifying at least one of nucleus shape, nucleus density, cytoplasm, and/or cell outlines.

123. A method comprising: depositing a blood sample within a sample chamber; placing the sample chamber, with the blood sample deposited therein, within an optical measurement unit; acquiring one or more microscopic images of the blood sample within the sample chamber, using a microscope of the optical measurement unit; and using a computer processor: identifying morphological characteristics of entities within the sample, by analyzing the one or more microscopic images of the blood sample; determining that a subject from whom the sample was drawn is suspected of suffering from an infection, based on identifying the morphological characteristics; and generating an output, at least partially in response thereto.

124. The method according to claim 123, further comprising using the computer processor to identify one or more cell surface markers listed in Table 1, and wherein determining that a subject from whom the sample was drawn is suspected of suffering from an infection is based on identifying the morphological characteristics and the one or more cell surface markers listed in Table 1.

125. The method according to claim 123 or claim 124, further comprising using the computer processor to identify one or more cell markers listed in Table 2, and wherein determining that a subject from whom the sample was drawn is suspected of suffering from an infection is based on identifying the morphological characteristics and the one or more cell markers listed in Table 2.

126. The method according to claim 125, further comprising using the computer processor to identify expression levels of CD64 within the blood sample, and wherein determining that a subject from whom the sample was drawn is suspected of suffering from an infection is based on identifying the morphological characteristics and the expression levels on CD64 within the sample.