CN118731352B - Automated analysis and detection method and system for cell surface markers - Google Patents
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Abstract
本申请提供了用于细胞表面标志物的自动化分析检测方法及系统，涉及生物检测技术领域，包括：分离多个目标细胞群；对多种标志物进行同时标记；自动采集细胞信号，进行实时分析，识别细胞表面标志物表达模式；对多个目标细胞群中任意一个目标细胞群的M个随机细胞进行基因表达与表面蛋白的联合分析；确定细胞表面标志物表达水平、细胞亚群分布，与高通量细胞表型分析技术相结合，进行自动化分析检测。通过本申请可以解决现有技术中由于细胞分离和标记过程繁琐，易出错，导致细胞表面标志物检测的效率低的技术问题，通过高维流式细胞术，结合自动化分析检测，实现了对多种细胞标志物的同时检测，提高了细胞表面标志物检测的准确性和效率。
The present application provides an automated analysis and detection method and system for cell surface markers, which relates to the field of biological detection technology, including: separating multiple target cell populations; simultaneously marking multiple markers; automatically collecting cell signals, performing real-time analysis, and identifying cell surface marker expression patterns; performing joint analysis of gene expression and surface proteins on M random cells of any one of the multiple target cell populations; determining the cell surface marker expression level and cell subpopulation distribution, and combining with high-throughput cell phenotyping technology for automated analysis and detection. This application can solve the technical problem of low efficiency of cell surface marker detection in the prior art due to the cumbersome and error-prone cell separation and labeling process. Through high-dimensional flow cytometry, combined with automated analysis and detection, it is possible to achieve simultaneous detection of multiple cell markers, thereby improving the accuracy and efficiency of cell surface marker detection.
Description
Technical Field
The application relates to the technical field of biological detection, in particular to an automatic analysis detection method and system for cell surface markers.
Background
Cell surface markers are important components of cells and play a critical role in cell recognition, signaling and function. In the field of biological detection technology, accurate analysis and detection of cell surface markers is critical for understanding cell function, disease mechanism and developing new therapeutic methods. However, existing methods for cell surface marker analysis and detection present some technical challenges and limitations. Sample pretreatment processes the raw biological sample into a form suitable for subsequent analysis, including cell separation, washing, immobilization, or other necessary processing steps. The existing sample pretreatment method is tedious and easy to make mistakes, so that the sample is lost or polluted, and the accuracy and reliability of subsequent analysis are affected. Furthermore, existing cell separation techniques may not be accurate enough, resulting in loss of target cell populations or contamination of non-target cell populations in the sample. This limits the accuracy and reliability of subsequent analysis.
In summary, the prior art has the technical problem that the efficiency of detecting the cell surface markers is low due to the complicated cell separation and labeling processes and easy error.
Disclosure of Invention
The application aims to provide an automatic analysis detection method and an automatic analysis detection system for cell surface markers, which are used for solving the technical problem that the efficiency of cell surface marker detection is low due to complex cell separation and marking processes and easy error in the prior art.
In view of the foregoing, the present application provides automated analytical detection methods and systems for cell surface markers.
The application provides an automatic analysis detection method for cell surface markers, which is realized by an automatic analysis detection system for the cell surface markers, wherein the automatic analysis detection method for the cell surface markers comprises the steps of preprocessing a target biological sample, separating a plurality of target cell groups by utilizing a microfluidic technology, adopting a high-dimensional flow cytometry, combining a metal isotope labeled antibody and/or a fluorescence labeled antibody, simultaneously labeling a plurality of markers on the cell surfaces of the target cell groups, automatically acquiring the labeled cell signals based on the plurality of markers on the cell surfaces of the target cell groups, analyzing the acquired cell signals in real time through a deep learning model, identifying a cell surface marker expression mode, combining a single cell sequencing technology on the basis of the cell surface marker expression mode, carrying out gene expression and surface protein combined analysis on M random cells in any one of the target cell groups, acquiring single cell level phenotype information, and determining the cell surface phenotype information combined with the single cell phenotype analysis technology based on the single cell surface phenotype expression mode, and carrying out automatic analysis on the cell surface phenotype information combined with the single cell surface phenotype information.
In a second aspect, the present application also provides an automated analytical test system for cell surface markers for performing the automated analytical test method for cell surface markers according to the first aspect, wherein the automated analytical test system for cell surface markers comprises a sample pretreatment module for pretreating a target biological sample for separating a plurality of target cell populations using microfluidic technology; the system comprises a marker marking module, a real-time analysis module, a single cell sequencing module, an automatic analysis module, a cell surface marker analysis module and a cell flux analysis module, wherein the marker marking module is used for simultaneously marking a plurality of markers on the cell surfaces of a plurality of target cell groups by adopting a high-dimensional flow cytometry and combining a metal isotope marked antibody and/or a fluorescent marked antibody, the real-time analysis module is used for automatically collecting marked cell signals based on the plurality of markers on the cell surfaces of the plurality of target cell groups by utilizing a high content screening technology and carrying out real-time analysis on the collected cell signals through a deep learning model to identify a cell surface marker expression mode, the single cell sequencing module is used for carrying out joint analysis of gene expression and surface proteins on M random cells of any one of the plurality of target cell groups based on the cell surface marker expression mode and single cell level phenotype information to obtain single cell level phenotype information, the automatic analysis module is used for determining cell surface marker expression level and cell distribution and carrying out joint analysis technology with high cell flux phenotype, and (5) performing automatic analysis and detection.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The method comprises the steps of preprocessing a target biological sample, separating a plurality of target cell groups by utilizing a microfluidic technology, simultaneously marking a plurality of markers on the cell surfaces of the target cell groups by adopting a high-dimensional flow cytometry and combining a metal isotope-marked antibody and/or a fluorescent-marked antibody, automatically collecting marked cell signals based on the plurality of markers on the cell surfaces of the target cell groups by utilizing a high-content screening technology, analyzing the collected cell signals in real time through a deep learning model, identifying a cell surface marker expression mode, carrying out joint analysis of gene expression and surface proteins on M random cells of any one of the target cell groups based on the cell surface marker expression mode and combining a single cell sequencing technology, obtaining single cell level phenotype information, determining cell surface marker expression level and cell distribution subpopulation the basis of the cell surface marker expression mode and the single cell level phenotype information, and carrying out automatic analysis and detection by combining the high-throughput cell phenotype analysis technology. That is, through high-dimensional flow cytometry and combined with automatic analysis and detection, simultaneous detection of various cell markers is realized, and the accuracy and efficiency of cell surface marker detection are improved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent. It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following brief description will be given of the drawings used in the description of the embodiments or the prior art, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained from the drawings provided without the inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an automated analytical detection method for cell surface markers according to the present application;
FIG. 2 is a schematic structural diagram of an automated assay detection system for cell surface markers according to the present application.
Reference numerals illustrate a sample preprocessing module 11, a marker labeling module 12, a real-time analysis module 13, a single cell sequencing module 14 and an automated analysis module 15.
Detailed Description
The application solves the technical problems of low efficiency of cell surface marker detection caused by complex cell separation and marking processes and easy error in the prior art by providing the automatic analysis and detection method and the system for the cell surface markers. Through high-dimensional flow cytometry and automatic analysis and detection, the simultaneous detection of various cell markers is realized, and the accuracy and the efficiency of cell surface marker detection are improved.
In the following, the technical solutions of the present application will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application, and that the present application is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present application are shown.
Referring to fig. 1, the present application provides an automated analysis and detection method for a cell surface marker, wherein the automated analysis and detection method for a cell surface marker specifically comprises the following steps:
Firstly, preprocessing a target biological sample, and separating a plurality of target cell groups by utilizing a microfluidic technology.
Specifically, according to the research requirements, samples are collected from tissues, blood or other biological fluids, the samples are subjected to steps such as cell washing, centrifugation and the like, impurities in the samples such as unattached cells, cell fragments, culture medium residues and the like are removed, and the quality of subsequent analysis is ensured. Microfluidic technology is a technology that manipulates fluids on a microscopic scale, often used for separation and analysis of cells. By designing the micro-fluidic chip, accurate control and separation of cell populations are realized. Different cell populations are separated according to physical or chemical characteristics (such as size, shape, density, surface markers, etc.) of cells, so that specific separation of target cell populations is realized, and the separated target cell populations are collected in specific collection tubes or specific areas of microfluidic chips. Through pretreatment, impurities in a sample are removed, and different cell populations are separated according to physical or chemical characteristics of cells by utilizing a microfluidic technology, so that specific separation of target cell populations is realized.
And secondly, adopting high-dimensional flow cytometry, combining a metal isotope labeled antibody and/or a fluorescent labeled antibody, and simultaneously labeling a plurality of markers on the cell surfaces of the target cell groups.
In particular, high-dimensional flow cytometry (such as CyTOF or advanced flow cytometry) is used to collect cell data while simultaneously detecting multiple parameters, i.e., multiple different cell surface markers. High-dimensional flow cytometry is an advanced cell analysis technique capable of simultaneously detecting multiple cell surface markers at the single cell level, and simultaneously analyzing multiple parameters by using multiple lasers and detectors of different wavelengths, thereby achieving detailed characterization of cells. CyTOF uses mass spectrometry to detect metal isotope labeled antibodies, without limitation of spectral overlap, while labeling and detecting more than 30 parameters. Conventional flow cytometry uses fluorescently labeled antibodies, but because of the overlapping fluorescence spectra, the number of parameters detected simultaneously is limited. Metal isotope labeled antibodies are commonly used in mass spectrometry (CyTOF) using metal isotopes (e.g., cadmium, indium, iron, etc.) as labels. The fluorescently labeled antibodies are labeled with different fluorescent dyes for use in conventional flow cytometry. Antibodies directed against specific cell surface markers, including monoclonal or polyclonal, and directed against different cell types and markers, are selected according to the study objectives. The labeling of cell surface markers is achieved by binding a metal isotope or fluorescent dye to an antibody, specifically to a specific antigen on the cell surface. Through high-dimensional flow cytometry, a plurality of cell surface markers are detected simultaneously, and the metal isotope labeled antibodies and/or fluorescent labeled antibodies are used for specifically labeling the plurality of cell surface markers, so that the accuracy and the flux of analysis are improved.
And thirdly, automatically collecting the marked cell signals based on a plurality of markers on the cell surfaces of the target cell groups by utilizing a high content screening technology, and analyzing the collected cell signals in real time through a deep learning model to identify the expression mode of the cell surface markers.
Specifically, the high content screening technique is used to automatically obtain an image of the labeled cells, which includes cell morphology, and labeling information, such as fluorescent signals, mass spectrum signals, and the like, within and on the cell surface. The high content screening technology (HCS) is a high-throughput cell imaging and analysis technology, and automatically acquires images and data of a large number of cells, thereby realizing detailed analysis of cell populations. The HCS system is capable of simultaneously detecting a plurality of parameters such as cell number, size, shape, texture, and fluorescence intensity and distribution of cell surface markers, etc., thereby providing high resolution of cell characteristic information. The acquired cell images are preprocessed by automated software, including background subtraction, contrast enhancement, image segmentation, etc., to extract features of cells and cell surface markers. The labeled dataset is used to train a deep learning model, such as a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), or other model suitable for image analysis. The trained model is applied to cell signals collected in real time, the expression mode of cell surface markers is identified, complex modes and characteristics in images are identified, cells are classified, different cell subsets are identified, or a new cell phenotype is found through cluster analysis. The deep learning model is capable of identifying the expression pattern of cell surface markers from a large amount of data, thereby revealing the state and function of the cells. Through high content screening technology, automatically collect image and data of a large amount of cells, realize high flux cell analysis, use the deep learning model, the real-time analysis of cell signal of collection to the expression pattern of cell surface marker is discerned fast.
And step four, carrying out joint analysis of gene expression and surface protein on M random cells in the target cell groups based on the cell surface marker expression mode and combining a single cell sequencing technology, so as to obtain single cell level phenotype information.
Specifically, cell surface markers were labeled by high-dimensional flow cytometry. And identifying the expression mode of the cell surface markers by using a high content screening technology, and knowing the characteristics and functions of different cell subsets. Single cell sequencing techniques, such as single cell RNA sequencing (scRNA-seq) and single cell proteomics techniques, analyze both gene expression and protein composition within a single cell. M cells are randomly selected from a plurality of target cell populations, and single cell level gene expression and surface protein analysis are performed to provide unique gene expression and protein composition information for each cell. The gene expression profile and the protein composition of each cell are obtained through a single cell sequencing technology, single cell gene expression data and cell surface protein marker data are combined, and the relationship between the cell phenotype and the gene expression pattern is analyzed. And carrying out joint analysis of gene expression and surface proteins on random cells in a plurality of target cell groups by utilizing a single cell sequencing technology, thereby obtaining single cell level phenotype information and further knowing the heterogeneity inside the cells.
And fifthly, determining the expression level of the cell surface markers and the distribution of cell subpopulations based on the expression mode of the cell surface markers and the single cell level phenotype information, and combining with a high-throughput cell phenotype analysis technology to perform automatic analysis and detection.
Specifically, based on the cell surface marker expression pattern, and combining the single cell level gene expression and protein composition information, the unique characteristics of each cell are known, and the expression level of the cell surface markers and the distribution of different cell subsets in the sample are determined. The phenotypic characteristics of a large number of cells, such as cell morphology, intracellular protein expression and localization, etc., are detected and analyzed simultaneously using high throughput cellular phenotypic analysis techniques, such as high content screening systems. The cell surface marker expression mode and single cell level phenotype information are combined with a high-throughput cell phenotype analysis technology, so that automation, high throughput and accurate analysis and detection of a large number of cell samples are realized. The high-throughput cell phenotype analysis technology is combined with automatic analysis detection, so that a large number of cell samples can be rapidly and accurately analyzed. By utilizing single cell sequencing technology and high throughput cell phenotype analysis technology and combining with automatic analysis and detection, the deep understanding of cell heterogeneity and phenotype is realized.
Further, the first step of the present application includes:
And simultaneously, using a microfluidic chip integrated with a micro-column array technology and combining cell physical characteristics, wherein the cell physical characteristics comprise cell size and electrophoresis mobility.
In particular, tumor tissue, including tumor cells, immune cells, vascular endothelial cells, etc., is obtained from a patient by surgical resection, biopsy, or other methods. Tumor tissues are selected as samples, biological characteristics and pathological mechanisms of tumors are studied, and immune cell functions in tumor microenvironments are understood. Microfluidic chips are devices that manipulate fluids on a microscopic scale, and are typically composed of microchannels, micropumps, microvalves, and the like. The micro-column array technology is a technology integrated on a micro-fluidic chip, and by arranging the micro-column array in a micro-channel, accurate control and separation of cells are realized. The micropillar arrays are typically designed with different sizes and spacings to separate cells according to their size. Smaller cells can pass through the microcolumn interstices, while larger cells are trapped. Cell separation is performed according to physical characteristics of cells, such as cell size and electrophoretic mobility. Cell size refers to the volume or diameter of a cell in three dimensions, while electrophoretic mobility refers to the rate of movement of a cell under the influence of an electric field. By adjusting parameters and electric field conditions of the micro-column array, separation of cells with different sizes and electrophoretic mobility is realized. The cell suspension was screened according to the size of the cells as it flowed through the array of micropillars. For example, tumor stem cells are typically smaller than other tumor cells, and are isolated by adjusting the size of the microcolumn interstices. If the microfluidic chip is integrated with an electrophoresis module, an electric field is applied during the flow process to separate according to the electrophoretic mobility (charge and size) of the cells, further subdividing the cell population, especially when the cells are similar in size. The separated cell population is collected through an outlet of the microfluidic chip and is directly used for subsequent analysis, such as cell counting, gene expression analysis, protein expression analysis and the like. By automatic analysis and detection, the research process of tumor immune microenvironment is accelerated, and the development of a novel immunotherapy method is facilitated.
Further, the application also comprises the following steps:
And simultaneously, treating the target biological sample by using EDTA anticoagulant, and carrying out cell separation by combining with a leukocyte heterogenous antibody, wherein the leukocyte heterogenous antibody comprises a CD34 specific antibody, a CD38 specific antibody and an HLA-DR specific antibody.
In particular, a blood sample is collected from a patient, typically by venipuncture. White blood cells are important components in blood and play a critical role in immune response and disease progression. A blood sample is selected as a target biological sample to study the immune status and disease progression of the patient. By utilizing a microfluidic technology, different leukocyte populations are separated according to physical or chemical characteristics (such as size, density, charge and the like) of the leukocytes, so that accurate control and separation of specific leukocyte populations are realized, and a foundation is provided for subsequent analysis of cell surface markers. Blood samples were treated with EDTA (ethylenediamine tetraacetic acid) as an anticoagulant to prevent blood clotting. Typically, 1.5-2.0 ml of EDTA solution is added to every 10ml of blood. During blood sample treatment, EDTA is added to maintain the liquid state of blood and prevent blood cells from aggregating and coagulating, so that the original state of the sample is maintained.
Density gradient centrifugation is a commonly used cell separation technique that efficiently separates cell populations having large differences in density, such as red blood cells, white blood cells, platelets, etc., from blood by adjusting the centrifugation speed and gradient media to separate different cell populations according to the differences in cell density. Ficoll separation was placed in a centrifuge tube to form a density gradient. Lightly superposing the anticoagulated blood sample on the Ficoll separating liquid. Centrifugation is carried out at a suitable speed and temperature, typically 400-1200 g at room temperature, for 20-30 minutes. After centrifugation, the blood sample will be separated into several different layers, with the leukocyte layer between the Ficoll layer and the plasma layer. Specific leukocyte heterogeneous antibodies, such as CD34 specific antibodies, CD38 specific antibodies, HLA-DR specific antibodies, are used to identify and isolate different leukocyte subpopulations. Through automatic analysis and detection, the accuracy and efficiency of blood disease diagnosis are improved.
Further, the third step of the present application includes:
And simultaneously, tracking dynamic changes of the expression modes of cell surface markers in the virus infection process at different time points by using a virus culture technology, and determining a virus invasion mechanism.
Specifically, virus-infected cells are an important model for studying the mechanism of virus infection, and by selecting virus-infected cells as a sample, the effect of the virus on the cells is observed and studied, and the mechanism of virus infection is understood. Cell lines suitable for viral growth are selected and the cells are cultured under appropriate culture conditions. The virus is inoculated into the cells, typically by adsorption, centrifugation or the use of transfection reagents, and the like, under appropriate temperature and conditions, allowing the virus to replicate within the cells. After viral infection, samples are taken at various time points (e.g., hours, days, etc. after infection). If the virus or viral protein has a unique label, specific antibodies or dyes are used to label the infected cells and are separated by flow cytometry or microfluidic techniques to separate different cell populations according to their physical or chemical characteristics (e.g., size, density, charge, etc.), thereby achieving precise manipulation and separation of the specific cell populations. Viral infection may alter the expression of cell surface markers and these changes are used to isolate the population of infected cells. Cell surface markers are labeled with metal isotope-labeled antibodies and/or fluorescent-labeled antibodies and cells are analyzed by high-dimensional flow cytometry for different time periods. Cell images were acquired using HCS technology and analyzed for changes in cell surface markers by automated software. By comparing the data at different time points, the dynamic change of the expression of the cell surface markers is monitored, so that how the virus enters the cells, replicates in the cells and influences the physiological functions of the cells is revealed, and the invasion mechanism of the virus is determined. The method selects proper samples (virus infected cells), and utilizes virus culture technology and high content screening technology to track dynamic change of cell surface marker expression mode so as to determine invasion mechanism of virus, and through automatic analysis and detection, progress of virology research is accelerated, and development of antiviral strategy is facilitated.
Further, the application also comprises the following steps:
The method comprises the steps of quantitatively analyzing the expression level of a specific viral gene in an infected cell group by utilizing a PCR technology, monitoring the virus infection progress and the virus infection efficiency, adopting an immunofluorescent staining technology, combining antibodies of specific viral proteins, carrying out visual analysis on the expression and the virus protein distribution of the virus proteins in the infected cell group, carrying out a cell proliferation experiment and an apoptosis detection technology, evaluating the influence of the virus infection on the growth and death of the cells, determining the pathogenicity of the virus, combining transcriptomics and proteomics technologies, analyzing the influence of the virus infection on the gene expression and the protein synthesis of host cells, determining the molecular mechanism of the interaction of the virus and the host cells, constructing a cell response network model corresponding to the virus infection by utilizing a bioinformatics tool, developing a diagnosis marker and a potential treatment target aiming at the specific virus according to the virus invasion mechanism based on the cell response network model corresponding to the virus infection.
Specifically, total RNA is extracted from an infected cell population, RNA is transcribed into cDNA using reverse transcriptase, and the expression level of a specific viral gene is quantitatively analyzed by a real-time quantitative PCR (qPCR) technique. Polymerase Chain Reaction (PCR) is a molecular biological technique for amplifying specific DNA fragments by which millions to billions of copies of a specific DNA sequence are produced in a short period of time. The expression level of a specific viral gene is quantitatively analyzed, the replication and diffusion processes of the virus in cells are monitored, and the infection process of the virus is known. The infection efficiency of the virus, i.e. the replication rate and the infection range of the virus in the cells, was evaluated by comparing the expression levels of the viral genes at different time points or under different conditions. The viral infection process refers to the replication and spread process of the virus in the cell, and the viral infection efficiency refers to the replication rate and infection range of the virus in the cell. The infected cells were fixed on slides and immunofluorescent stained with antibodies against specific viral proteins. Immunofluorescent staining is a common cell imaging technique that detects proteins of interest by using specific antibodies to bind to the proteins and then using fluorescently labeled secondary antibodies. Antibodies are key reagents in immunofluorescent staining techniques that bind specifically to viral proteins. By selecting specific antibodies, viral proteins are precisely detected and localized. Expression and distribution of viral proteins within the infected cell population was observed.
The effect of viral infection on cell growth was assessed using cell proliferation assays such as MTS colorimetric, brdU incorporation assays, and the like. Apoptosis is detected by flow cytometry, ELISA, TUNEL staining or fluorescent staining techniques. Apoptosis is the process of apoptosis, and detection of apoptosis is used to assess the effect of viral infection on cell survival. Transcriptomics and proteomics data are integrated and analyzed using bioinformatics tools. Transcriptomics is a technique for studying gene expression patterns by analyzing the transcription products (RNAs) of all genes in a cell to understand the expression of the genes, and is generally implemented by RNA sequencing (RNA-seq), providing information about the expression level of the genes, splicing and transcriptional variation of the genes, and the like. Proteomics is a technique for studying protein composition and function by analyzing the expression and modification of all proteins in cells to understand the composition and function of proteins, and is generally implemented by mass spectrometry techniques (e.g., LC-MS/MS) to provide information about the expression level of proteins, post-translational modifications, protein-protein interactions, and the like. The host cells were analyzed for gene expression and protein synthesis before and after viral infection by transcriptomic and proteomic techniques. By comparing the gene expression and protein synthesis of the host cell before and after infection by the virus, the molecular mechanism of the interaction of the virus and the host cell is revealed, including the expression of the virus gene, the regulation of the host cell gene and the change of the protein synthesis pathway.
The bioinformatics tools analyze a large amount of gene expression data, proteomic data, and other biological information in constructing a network model of the cellular response corresponding to viral infection. Bioinformatics is a discipline that utilizes computer science and information processing technology to process biological data. By analyzing gene expression and protein synthesis data before and after viral infection, a biological network model describing the response of cells to viral infection is constructed, including gene regulation networks, signal transduction pathways, protein interaction networks, etc., for describing how cells respond to viral infection.
Analysis of the corresponding cellular response network model for viral infection reveals how the virus invades the host cell, including key molecules and pathways for the virus to interact with the host cell. Based on the virus invasion mechanism, the molecule with the expression level changed obviously in the virus infection process is identified and used as a diagnosis marker for rapidly and accurately detecting the virus infection. And analyzing the cell response network model to find key molecules and pathways in the viral replication and infection process, and using the key molecules and pathways as potential therapeutic targets for developing antiviral drugs. The viral gene expression level was quantitatively analyzed by PCR technique, and the progress and efficiency of viral infection were monitored. The effect of the virus on cell growth and death was assessed by visual analysis of viral protein expression and distribution by immunofluorescent staining techniques. The effect of viral infection on gene expression and protein synthesis was analyzed by transcriptomic and proteomic techniques. Based on the cell response network model, diagnostic markers and potential therapeutic targets for specific viruses are developed. By utilizing various technical means, the virus infection process is comprehensively analyzed from the molecular level to the cellular level, the interaction mechanism of the virus and host cells is revealed, and scientific basis is provided for virus research and clinical application.
Further, the application also comprises the following steps:
Screening known antiviral drugs by using a drug screening technology, evaluating the treatment effect of the known antiviral drugs on virus infected cells, developing a personalized medical strategy, constructing a customized treatment constraint space according to the cell surface marker expression mode and gene expression characteristics of a patient, and continuously optimizing and updating the customized treatment constraint space by using a machine learning technology.
Specifically, known antiviral drugs are collected, and a drug library is established. Host cells are cultured in vitro and infected with a particular strain of virus. Different concentrations of antiviral drugs were added to the infected cells, and a control group and an experimental group were set. Drugs known to be useful in the treatment of viral infections are screened by drug screening techniques to assess the efficacy of the drugs against a particular virus-infected cell, including testing the concentration and duration of action of various drugs to determine the optimal treatment regimen. Drug screening is a high throughput assay for assessing the effect of a variety of drugs on a particular cell or biological process. In antiviral drug screening, the effect of drugs on viral replication, viral protein expression, and viability of virus-infected cells was evaluated. The therapeutic effect of known antiviral drugs on virus-infected cells, including the effect of the drugs on virus replication, cell proliferation and apoptosis, is evaluated by methods such as cell proliferation experiments, virus replication experiments, immunofluorescence staining and the like.
Cell samples of the patient were collected and analyzed for their cell surface marker expression pattern and gene expression profile. And analyzing the expression pattern of the cell surface markers of the patient by using high-dimensional flow cytometry and other methods to know the immune state and pathological characteristics of the patient. By means of transcriptomics, the expression profile of the patient's genes is analyzed to understand the patient's gene expression pattern and possible disease risk. Based on the cell surface marker expression pattern and gene expression characteristics, a customized treatment constraint space is constructed, which comprises a treatment scheme and treatment parameters suitable for patients. Personalized medicine is a medical method based on individual characteristics of patients, aiming at providing each patient with a treatment regimen that is most appropriate for its particular situation.
The customized treatment constraint space is optimized by utilizing a machine learning technology, and the treatment scheme and parameters are adjusted according to the treatment response and treatment result data of the patient so as to improve the treatment effect and adaptability. The machine learning model is trained using techniques such as supervised learning, unsupervised learning, or reinforcement learning. As new data is accumulated and treatment experience increases, machine learning models continue to learn and adapt, thereby updating the treatment constraint space to more accurately reflect the patient's specific needs and treatment effects. Machine learning is an artificial intelligence technique that allows a computer system to learn from experience and make predictions or decisions through algorithms and data. In the medical field, machine learning is used to analyze large amounts of patient data to find relationships between therapeutic effects and patient characteristics. The more accurate and personalized medical strategy is realized by utilizing a drug screening technology, a personalized medical strategy, a customized treatment constraint space and a machine learning technology, and a more effective treatment scheme is provided for patients.
Further, the fifth step of the present application comprises:
Quantifying cell surface marker expression levels Wherein, the method comprises the steps of, wherein,Is the fluorescence or luminescence intensity of the surface marker of the ith cell,Is the background noise intensity of the ith cell, M is the total number of analyzed cells of any one target cell group in the target cell groups, and the cell subgroup distribution is quantifiedWherein, For characterizing the proportion of the jth cell subpopulation, N is the total number of cell subpopulations.
In particular, the method comprises the steps of,For quantifying the expression level of a cell surface marker,Represents the expression level of the cell surface marker,Is the fluorescence or luminescence intensity of the surface marker of the ith cell,Is the background noise intensity of the ith cell, M represents the total number of analyzed cells of any one of the plurality of target cell populations. The sum of the differences between fluorescence or luminescence intensity and background noise intensity of all cells is divided by the total number of cells M to obtain average expression level, and the average expression level is used for comparing the expression level of the cell surface markers of different cell groups or different time points.
Entropy values representing the distribution of cell subsets, for quantifying the complexity of the distribution of cell subsets,Represents the distribution entropy of a cell subpopulation,For characterizing the proportion of the jth cell subpopulation, N is the total number of cell subpopulations. The complexity of the cell subpopulation distribution is quantified by calculating the ratio of each cell subpopulation multiplied by the negative of its natural logarithm, and then summing. The higher the distribution entropy, the more uniform the distribution of the cell subpopulations, i.e. the more diverse the composition of the cell subpopulations. These two formulas are used in the fields of cell biology and bioinformatics to quantify the expression levels of cell surface markers and the distribution of cell subsets, helping to gain insight into the biological properties and functions of cells.
In summary, the automatic analysis and detection method for cell surface markers provided by the application has the following technical effects:
The method comprises the steps of preprocessing a target biological sample, separating a plurality of target cell groups by utilizing a microfluidic technology, simultaneously marking a plurality of markers on the cell surfaces of the target cell groups by adopting a high-dimensional flow cytometry and combining a metal isotope-marked antibody and/or a fluorescent-marked antibody, automatically collecting marked cell signals based on the plurality of markers on the cell surfaces of the target cell groups by utilizing a high-content screening technology, analyzing the collected cell signals in real time through a deep learning model, identifying a cell surface marker expression mode, carrying out joint analysis of gene expression and surface proteins on M random cells of any one of the target cell groups based on the cell surface marker expression mode and combining a single cell sequencing technology, obtaining single cell level phenotype information, determining cell surface marker expression level and cell distribution subpopulation the basis of the cell surface marker expression mode and the single cell level phenotype information, and carrying out automatic analysis and detection by combining the high-throughput cell phenotype analysis technology. That is, through high-dimensional flow cytometry and combined with automatic analysis and detection, simultaneous detection of various cell markers is realized, and the accuracy and efficiency of cell surface marker detection are improved.
In a second embodiment, based on the same inventive concept as the automated analysis and detection method for cell surface markers in the previous embodiment, the present application further provides an automated analysis and detection system for cell surface markers, referring to fig. 2, the automated analysis and detection system for cell surface markers includes:
A sample preprocessing module 11, wherein the sample preprocessing module 11 is used for preprocessing a target biological sample and separating a plurality of target cell groups by utilizing a microfluidic technology.
A marker labelling module 12, wherein the marker labelling module 12 is used for simultaneously labelling a plurality of markers on the cell surfaces of the plurality of target cell populations by high-dimensional flow cytometry in combination with a metal isotope labelled antibody and/or a fluorescent labelled antibody.
And the real-time analysis module 13 is used for automatically collecting the marked cell signals based on various markers on the cell surfaces of the target cell groups by utilizing a high content screening technology, and carrying out real-time analysis on the collected cell signals through a deep learning model to identify the expression mode of the cell surface markers.
And the single-cell sequencing module 14 is used for carrying out joint analysis of gene expression and surface protein on M random cells of any one target cell group in the target cell groups based on the cell surface marker expression mode and combining a single-cell sequencing technology, so as to obtain single-cell level phenotype information.
And the automatic analysis module 15 is used for determining the expression level of the cell surface marker and the distribution of cell subpopulations based on the expression mode of the cell surface marker and the single cell level phenotype information, and combining with a high-throughput cell phenotype analysis technology to perform automatic analysis detection.
Further, the sample preprocessing module 11 in the automated analysis detection system for cell surface markers is further configured to:
And simultaneously, using a microfluidic chip integrated with a micro-column array technology and combining cell physical characteristics, wherein the cell physical characteristics comprise cell size and electrophoresis mobility.
Further, the sample preprocessing module 11 in the automated analysis detection system for cell surface markers is further configured to:
And simultaneously, treating the target biological sample by using EDTA anticoagulant, and carrying out cell separation by combining with a leukocyte heterogenous antibody, wherein the leukocyte heterogenous antibody comprises a CD34 specific antibody, a CD38 specific antibody and an HLA-DR specific antibody.
Further, the real-time analysis module 13 in the automated analysis detection system for cell surface markers is further configured to:
And simultaneously, tracking dynamic changes of the expression modes of cell surface markers in the virus infection process at different time points by using a virus culture technology, and determining a virus invasion mechanism.
Further, the real-time analysis module 13 in the automated analysis detection system for cell surface markers is further configured to:
The method comprises the steps of quantitatively analyzing the expression level of a specific viral gene in an infected cell group by utilizing a PCR technology, monitoring the virus infection progress and the virus infection efficiency, adopting an immunofluorescent staining technology, combining antibodies of specific viral proteins, carrying out visual analysis on the expression and the virus protein distribution of the virus proteins in the infected cell group, carrying out a cell proliferation experiment and an apoptosis detection technology, evaluating the influence of the virus infection on the growth and death of the cells, determining the pathogenicity of the virus, combining transcriptomics and proteomics technologies, analyzing the influence of the virus infection on the gene expression and the protein synthesis of host cells, determining the molecular mechanism of the interaction of the virus and the host cells, constructing a cell response network model corresponding to the virus infection by utilizing a bioinformatics tool, developing a diagnosis marker and a potential treatment target aiming at the specific virus according to the virus invasion mechanism based on the cell response network model corresponding to the virus infection.
Further, the real-time analysis module 13 in the automated analysis detection system for cell surface markers is further configured to:
Screening known antiviral drugs by using a drug screening technology, evaluating the treatment effect of the known antiviral drugs on virus infected cells, developing a personalized medical strategy, constructing a customized treatment constraint space according to the cell surface marker expression mode and gene expression characteristics of a patient, and continuously optimizing and updating the customized treatment constraint space by using a machine learning technology.
Further, the automated analysis module 15 in the automated analysis detection system for cell surface markers is further configured to:
Quantifying cell surface marker expression levels Wherein, the method comprises the steps of, wherein,Is the fluorescence or luminescence intensity of the surface marker of the ith cell,Is the background noise intensity of the ith cell, M is the total number of analyzed cells of any one target cell group in the target cell groups, and the cell subgroup distribution is quantifiedWherein, the method comprises the steps of, wherein,For characterizing the proportion of the jth cell subpopulation, N is the total number of cell subpopulations.
The embodiments in this specification are described in a progressive manner, and each embodiment focuses on the difference from the other embodiments, so that the automated analysis and detection method for cell surface markers in the first embodiment of fig. 1 and the specific example described above are equally applicable to the automated analysis and detection system for cell surface markers in this embodiment, and by the detailed description of the automated analysis and detection method for cell surface markers described above, those skilled in the art will clearly know the automated analysis and detection system for cell surface markers in this embodiment, and therefore, for the sake of brevity of this specification, will not be described in detail herein. For the system disclosed in the embodiment, since the system corresponds to the method disclosed in the embodiment, the description is simpler, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalent techniques thereof, the present application is also intended to include such modifications and variations.
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