WO2020071500A1 - Cell-information processing method - Google Patents

Abstract

The purpose of the present invention is to provide a cell-information processing method for efficiently analyzing interactions among cell populations in the presence of a mixture of multiple types of cell populations. A cell-information processing method according to the present invention includes: a cell retrieving step for preparing at least two prescribed vessels in which target cell groups containing different multiple types of cells are seeded, subjecting the target cells to a prescribed cell stimulation to culture said cells, and retrieving all of the cells, one vessel at a time, at two or more points in time; a single-cell preparing step for preparing single cells from all of the cells retrieved at each point in time; a single-cell-data acquiring step for acquiring single-cell data from the individual cells prepared as single cells at each point in time; a grouping step for grouping, on the basis of the single-cell data at each point in time, all of the cells retrieved at each point in time into multiple cell populations having a common first cell characteristic to plot said cell populations on a two-dimensional plane or in a three-dimensional space, performing processing for identifying, on the basis of a second cell characteristic, cell types of the grouped individual cell populations, and acquiring a clustering result at each point in time; a cell-change detecting step for comparing the clustering results at the individual points in time, thereby detecting changes over time in the cell populations of the same cell types; a mechanism-of-action analyzing step for analyzing, on the basis of the detection results, mechanisms of action of the changes over time in the cell populations of the same cell types; and an interaction analyzing step for analyzing, on the basis of the mechanism-of-action analysis results, interactions occurring among the cell populations of different cell types.

Description

Cell information processing method

The present invention relates to a cell information processing method. In particular, to analyze the interaction between different cell types and the mechanism of action that occur when cell stimulation by a drug is given to a cell population cultured under a situation where different cell types are mixed. About the method.

There are many cell types in living organisms, they coexist depending on each other, and change by mutual stimulation. In addition, when it changes, many genes change while having a dependency in one cell, and the gene exerts a function in a living body through this dependency. Therefore, in the field of drug discovery and regenerative medicine, it is important to analyze the interaction between cells or cell populations and the mechanism of molecular behavior in cells in order to understand biological phenomena and biological phenomena. For example, when multiple types of cells are mixed and cultured, how do they mutually stimulate each other, and how much the gene expression level or gene state in the cells depends on the stimulation By analyzing whether it changes over time, a clue for elucidating the mechanism of the biological phenomenon can be obtained.

As a method that can use the above mechanism for analysis, Patent Literature 1 discloses “(1) calculating a feature amount reflecting similarity between data from data indicating a change in the expression level of a gene over time, (2) calculating the eigenvectors of the similarity matrix M for all combinations between genes from the calculated feature amounts; and (3) converting the similarity matrix M into a Boolean matrix N while maintaining the eigenvalues of the eigenvectors. A gene clustering method that performs at least a converting step and (4) a step of clustering each data based on the Boolean matrix N. ”That is, a plurality of genes are classified based on the similarity of the expression level change over time. A grouping method has been proposed (claim 9).

Also, Patent Document 2 discloses a method for clustering gene expression profiles, which includes selecting one or more GO terms from a gene ontology (GO) tree; receiving a gene expression data set; Classifying the sets into groups according to GO terms; first, clustering the gene expression data belonging to each group based on the similarity of the gene expression data; and, second, seeding the result of the first clustering. Clustering gene expression data sets using the same as a method of grouping multiple genes based on similarity of expression data and similarity of biological functions involved. Has been proposed (claim 1).

As a conventional method for analyzing the efficacy and pharmacology, as shown in FIG. 8, a drug is applied to a cell population consisting of only cells 30, 32 and 34 of the same kind arranged individually in each well 36 and cultured. Then, a method of analyzing the drug efficacy using the averaged data of each cell population, or culturing by giving a drug to a cell population containing a plurality of cell types extracted from an organ, and averaging the drug efficacy of the cell population A method of analyzing using has been used.

However, even in a cell population consisting of only cells of the same type, the dynamic behavior of individual cells over time (eg, cell activation, cell inhibition, cell interaction, protein expression, protein secretion, cell proliferation, cell morphology Change, cell death, etc.), conventional methods have not been able to perform sufficiently accurate analysis. Therefore, in recent years, in order to further improve the analysis accuracy, cells constituting a cell population consisting of only cells of the same type have been converted into single cells, and analysis at the level of individual cells (single cells) (single cell analysis) has been performed. ing. Such single-cell analysis has enabled analysis of individual cells instead of analysis of averaged data for cell populations. Changes can be detected. Such single cell analysis is used, for example, to classify cell subtypes present in cancer tissues by single cell transcriptome analysis of cancer cells. As a method for evaluating cell activity using single cell analysis, a method described in Patent Document 3 has been proposed.

Patent Document 3 discloses a method for evaluating cell activity, in which (a) a step of arranging a cell population containing a plurality of different types of cells on a region, that is, a different plurality of cells in each nanowell on a nanowell array plate. Setting up a cell population containing different types of cells; (b) assaying the dynamic behavior of the cell population as a function of time, i.e., measuring the dynamic behavior at the single cell level with a microscope, fluorescence microscope, etc. (C) identifying at least one cell to be analyzed from the cell population based on dynamic behavior; (d) identifying Characterizing the molecular profile of the identified at least one analyte cell, i.e., mass spectrometry, genetic analysis, protein analysis at the single cell level for the identified at least one analyte cell. Analysis, transcription activity, transcriptome profile, gene expression activity, genomic profile, protein expression activity, proteome profile, protein interaction activity, cell receptor expression activity, lipid profile, lipid activity, carbohydrate profile Acquiring microvesicle activity, glucose activity, metabolic profile, cell receptor expression activity, and the like; and (e) correlating the information obtained from steps (b) and (d). It has been proposed (claim 1).
That is, the method described in Patent Literature 1 evaluates the cell activity of each cell type in a cell population containing a plurality of different types of cells.

JP 2010-157214 A US Patent Application Publication No. 2009/0112480 JP-T-2018-514195

However, the methods described in Patent Documents 1 and 2 are methods for examining intracellular networks, and it is impossible to know interactions between cells and cell types.

In addition, the method described in Patent Document 3 is also a method for merely evaluating each cell activity, and thus cannot know the interaction between cells and between cell types.
In addition, the method described in Patent Document 3 discloses that a human observes the dynamic behavior of a cell population composed of at least 100 to 200 cells by image analysis, and uses a plurality of types of cells and cells having different dynamic behavior. In order to identify and select single cells to be analyzed from among them, and to analyze the mechanism of action and intercellular interaction of the dynamic behavior of single cells selected by humans, Since humans arbitrarily judge the morphology of the cell type and the dynamic change of each cell over time to identify and select the cells to be analyzed, there is a problem that it is not possible to obtain highly accurate analysis results. Was.
Further, as described as one embodiment of Patent Document 1, when a cell population to be analyzed contains immune cells, specifically, when a blood sample contains immune cells, a drug is added to the blood sample. There is a situation that cells are killed in about 24 hours after the application. In addition, the analysis method described in Patent Document 1 is for observing a cell population to which a drug has been applied over time and performing analysis (that is, observation of one sample is continued until the analysis is completed. In addition, since the method is a method in which the analysis is performed while repeatedly selecting and culturing cells to be analyzed), when the method described in Patent Literature 3 is employed, all the cells are administered at least within 24 hours after the drug is given to the cell population. There is a very severe time constraint that the analysis must be completed.

In addition, the method described in Patent Document 3 requires labor and cost for cell identification and selection of a target cell, and the method of calculating the evaluation by the analysis and the result thereof are complicated, so that the method is efficient and highly accurate. In addition, there is a problem that it is not practical as an industrial method that requires quick evaluation and analysis.
Furthermore, a method for identifying each cell type, which is performed in a cell population containing a plurality of different cell types, using a single-cell analysis technique, and culturing each of the cell populations containing a plurality of different types of cells by giving the agent thereto, At present, there is no report on how to efficiently analyze the efficacy and pharmacology of species. Therefore, the interaction between different cell types and the mechanism of action that occur when cell stimulation by a drug is given to a cell population cultured in a situation where multiple different cell types are mixed are analyzed by single-cell analysis. At present, there is no report on an efficient analysis based on this.

In the initial stage of drug search using the drug efficacy and pharmacological screening method as shown in FIG. 8, analysis of interaction with different cells based on only judgment of no cell death due to drug (cell stimulation), Since the analysis of the medicinal properties and pharmacological mechanisms is advanced, in the initial stage of analysis in which a substance that can be a drug is extracted from a huge number of candidate substances, for example, even if cell death is detected, Depending on the factors, it was not possible to become a drug candidate without knowing whether cell death was caused.

The present invention has been made in view of the above problems of the related art, and provides a cell information processing method for efficiently analyzing an interaction between cell populations when a plurality of different types of cell populations are mixed. With the goal.
More specifically, the interaction between different cell types that occurs when a drug or a cell stimulus is applied to a cell population cultured in a situation where different cell types are mixed, and the mechanism of action It is an object of the present invention to provide a method which can efficiently and quickly evaluate and analyze with high accuracy and quickness by a simpler operation, and can be used even on an industrial scale.

In the cell information processing method of the present invention, at least two or more predetermined containers in which a target cell group including a plurality of different types of cells are seeded are prepared, and the target cell group is subjected to predetermined cell stimulation and cultured. At the above time points, a cell collecting step of collecting all cells in one container, a single cell forming step of converting all cells collected at each time point into a single cell, and a single cell forming step of each cell at each time point A single cell data acquisition step of acquiring single cell data; and, based on the single cell data at each time point, grouping all cells collected at each time point into a plurality of cell populations having a common first cell characteristic. Plotted on a two-dimensional plane or three-dimensional space, and based on the second cell feature, perform a process of identifying the cell type of each grouped cell population, and at each time point A grouping step of obtaining a clustering result, and a cell change detection step of detecting a change over time of the cell population of the same cell type by comparing the clustering results at each time point, based on a result of the detection. Analyzing the mechanism of action of the temporal change of the cell population of the cell type, and analyzing the interaction occurring between the cell populations of different cell types based on the result of the mechanism of action analysis Interaction analysis step.

The interaction occurring between the cell populations in the interaction analysis step preferably includes an interaction occurring between cell populations of the same cell type.
The cell change detection step includes comparing a clustering result at each time point to determine a temporal change in the number of cells constituting the cell population of the same cell type and a temporal change in the first cell characteristic. It can also be detected.
The cell change detection step further includes detecting, with the clustering result at each time point, a temporal change in the number of cells constituting the cell population of the same cell type, and a temporal change in the first cell characteristic. Is also good.

The cell collection step further includes preparing a target cell group including the plurality of types of cells to be cultured without applying the predetermined cell stimulation, and, at one or more time points, all cells in one predetermined container. The cell change detection step evaluates the change over time of the same cell tumor cell population due to the presence or absence of the predetermined cell stimulation of the cell population by comparing the clustering results at the respective time points. You can also.
The cell stimulus is preferably at least one selected from the group consisting of a chemical stimulus and a physical stimulus.
The chemical stimulus is preferably due to the addition of an agent that induces a biological response to the cells.
Preferably, the action mechanism is a specific biochemical reaction or interaction for exerting a biological phenomenon induced inside and outside the cell by the cell stimulation.

The single cell data includes the DNA sequence information of the gene (genome), epigenetic information controlling the expression of the gene (DNA methylation, histone methylation, acetylation, phosphorylation), the primary transcript of the gene (mRNA, (Translated RNA, microRNA, etc.) information (transcriptome), protein translation and modification information such as phosphorylation, oxidation, glycation, amino acid sequence information (proteome), metabolite information (metabolome), intracellular hydrogen ion concentration index (PH), intracellular ATP concentration, ion concentration (calcium, magnesium, potassium, sodium, etc.), and at least one selected from the group consisting of intracellular temperature.
The single cell data is preferably a gene expression level and a gene DNA sequence.

In the grouping step, it is preferable that the first cell feature is obtained by reducing the n-dimensional cell feature included in the single cell data into two or three dimensions.
The dimension reduction method is based on the group consisting of principal component analysis (PCA), principal component analysis with kernel (Kernel-PCA), multidimensional scaling (MDS), t-SNE, and convolutional neural network (CNN). Preferably, it is at least one selected.
In the grouping step, the first cell feature is preferably obtained by performing a principal component analysis on the gene expression amount and reducing the dimension to two or three dimensions.
In the grouping step, it is preferable that the second cell feature is at least one piece of cell information capable of identifying each cell type from a cell function or a cell state.

The above-mentioned cell information includes DNA sequence information of a gene (genome), epigenetic information for controlling gene expression (DNA methylation, histone methylation, acetylation, phosphorylation), gene primary transcript (mRNA, (Translated RNA, microRNA, etc.) information (transcriptome), protein translation and modification information such as phosphorylation, oxidation, glycation, amino acid sequence information (proteome), metabolite information (metabolome), intracellular hydrogen ion concentration index (PH), intracellular ATP concentration, ion concentration (calcium, magnesium, potassium, sodium, etc.), and at least one selected from the group consisting of intracellular temperature.
The function of the cell is preferably at least one selected from cell growth, repair, metabolism, and information exchange between cells.
The state of the cell is preferably at least one selected from the state of gene expression, the state of protein expression, and the enzymatic activity.

In the interaction analysis step, the interaction occurring between the cell populations refers to a biological or physical action occurring between cell populations of different cell types, or the number of cells due to the action, the first cell Preferably, it is a change in characteristics.
From the single cell data, a value representing the amount or state of a plurality of biological substances, time-series data obtained at a plurality of time points for each biological substance is created in advance, and a time change of the time-series data for each biological substance, Based on the similarity in biological function of each biological material, the cells from which the single cell data has been obtained are grouped into cell populations having a common first cell characteristic, and in the interaction analysis step, For each of the time points, a value representing the state of the cell population is generated from one or more first cell characteristics included in each of the plurality of cell populations, and the generated plurality of time points of the plurality of cell populations are generated. It is preferable to estimate the state dependency between cell populations from a data set consisting of time-series data obtained at a plurality of time points for each biological material. .
Similarity of the above biological functions means having a common gene ontology, belonging to a common canonical pathway, having a common upstream factor, being involved in a common expression system, and being involved in a common disease Preferably, the evaluation is based on at least one selected from the group consisting of:

In the above-mentioned cell collection step and the above-mentioned single cell forming step, a method of collecting all cells and converting it into a single cell is manually operated, flow cytometry, magnetic separation, laser capture microdissection, micro flow path, micro droplet, nano well, Preferably, the method uses at least one selected from the group consisting of micropipette fine needle aspiration, laser tweezers, label arrays, surface plasmon response, and nanobiodevice.
In the above method of collecting all cells and forming a single cell, it is preferable to use at least one selected from the group consisting of a fluorescent label, a radioisotope label, an antibody label, and a magnetic label as a cell label.
The target cell group containing the plurality of types of cells is preferably cells obtained from at least one selected from the group consisting of a biological tissue sample, a blood sample, a culture sample, and an environmental sample.
The plurality of types of cells are preferably at least one selected from the group consisting of animal cells, plant cells, fungal cells, and bacterial cells.

According to the present invention, it is possible to provide a cell information processing method for efficiently analyzing an interaction between different cell populations when a plurality of types of cell populations are mixed.
More specifically, it is possible to identify and identify each cell type in a cell population in which a plurality of different cell types are present, or to evaluate and analyze drug efficacy and pharmacological screening with a simpler operation with high accuracy and speed. And provide a method that can be used on an industrial scale. Therefore, not only changes between cell populations composed of the same cell type, but also changes in cell populations composed of different cell types (interaction between cell populations) can be efficiently and simultaneously confirmed. Further, as a result, a wider interaction between cells and between cell types (interconnection network) can be analyzed with high accuracy and easily.
In addition, since humans do not arbitrarily judge the dynamic behavior of cells over time, it is necessary to identify each cell type and analyze the mechanism of action of drugs or cell stimulation on various cells with high accuracy. Can be.
In addition, the target cell can be identified without any trouble such as image analysis.
In addition, even when a drug efficacy screening for immune cells is performed, the analysis can be performed without particularly limiting the analysis time.
In addition, since it is not necessary to apply a drug or cell stimulus to each sample cultured for the same cell type and analyze it, it is possible to apply a drug or cell stimulus to a sample in which multiple types of cells are co-cultured and analyze. Therefore, it is possible to reduce the labor and cost of preparing and analyzing a large number of samples.
Further, according to the present invention, even if the cells to be analyzed cannot be cultured with only one type of cells for the purpose of maintaining cell growth and function, according to the present invention, a sample obtained by co-culturing a plurality of types of cells is used. The cells to be analyzed can be identified. In addition, the mechanism of action by co-culture with cells other than the analysis target, for example, the mechanism of action of a sample drug or cell stimulation that requires co-culture of the analysis target cell and immune cells can be appropriately analyzed.

FIG. 1 is a flowchart illustrating the cell information processing method of the present invention. FIG. 2A is a diagram showing cells immediately after cell stimulation (time point 0). FIG. 2B is a diagram showing cells one hour after the cell stimulation (time point 1). FIG. 2C is a diagram showing cells 6 hours after the cell stimulation (time point 2). FIG. 3A is a diagram illustrating a result of the clustering according to FIG. 2A (time point 0). FIG. 3B is a diagram illustrating a result of the clustering according to FIG. 2B (time point 1). FIG. 3C is a diagram showing a result of the clustering according to FIG. 2C (time point 2). FIG. 4 is a diagram showing the change over time of the cell population based on FIG. 3B. FIG. 5 is a diagram showing a time-dependent change of the cell population based on FIG. 3C. FIG. 6 is a diagram showing the results of measuring the amount of each component of nerve cells and glial cells over time. FIG. 7 is a diagram showing a time-dependent change of a cell population based on FIGS. 3C and 6. FIG. 8 is a diagram for explaining a conventional drug screening method.

[Cell information processing method]
Hereinafter, the cell information processing method of the present invention will be described in detail.
Embodiment 1
The first embodiment analyzes the interaction between nerve cells and glial cells, that is, the interaction network between nerve cells and glial cells generated when a drug is added to a target cell group consisting of nerve cells and glial cells and cultured. An analysis of the formation process (change over time) will be described as an example.
FIG. 1 is a flowchart showing a cell information processing method according to Embodiment 1 of the present invention.

<Cell collection step (S1)>
First, in this step, as shown in FIG. 2A, at least two or more predetermined containers 1 seeded with a plurality of different types of cell groups (cell groups including glial cells 2 and nerve cells 4) are prepared. Here, the cell group seeded in the predetermined container 1 is referred to as a target cell group. Next, with respect to the target cell group seeded in the prepared container 1, the cells are stimulated and cultured by adding a drug, and cultured at two or more time points (for example, immediately after drug addition, 1 hour after drug addition, and 6 hours). Time, 12 hours, 24 hours, 48 hours, 72 hours ...) Collect all cells in one container.

2A shows the cells immediately after the cell stimulation (time 0), FIG. 2B shows the cells 1 hour after the application of the cell stimulation (time 1), and FIG. 2C shows the cells after the application of the cell stimulation. , 6 hours later (time point 2). In this step, all cells in the container 1 are collected at each time point.
At time point 0, the shape of astrocytes 2 and nerve cells 4 was not changed, and at time point 1, the cells were changed to reactive astrocytes 10 in which the shape of astrocytes 2 was changed due to cell stimulation by the addition of a drug. At the time point 2, the nerve cell 4 comes into contact with the changed reactive astrocyte 10 and the nerve cell 4 includes the nerve cell 12 whose shape has changed.

《Target cell group》
Here, the target cell group is a cell group seeded in the predetermined container 1 and means a group of cells including a plurality of different types of cells. More specifically, “a group of cells containing a plurality of different types of cells” includes, for example, a plurality of cells having different cell types, such as a cell group composed of human iPS cells and mouse embryo-derived fibroblasts. A cell group composed of cells derived from the same species or a cell group composed of cells derived from different species.
In the present embodiment, the cells constituting the target cell group are described as glial cells and nerve cells, but the types of cells included in the target cell group are not particularly limited.

Examples of the “target cell group containing a plurality of types of cells” include a biological tissue sample, a blood sample, a culture sample, and an environmental sample.
Examples of the biological tissue sample include mouse brain tissue and human resected tumor tissue.
Examples of the blood sample include a human blood sample.
Examples of the culture sample include a co-culture sample of human iPS cells and mouse embryo-derived fibroblasts.
Examples of the environmental sample include a soil sample and a water sample collected from a seabed hydrothermal vent.

In addition, “cells” included in the “target cell group containing a plurality of cell types” include, for example, animal cells, plant cells, fungal cells, and bacterial cells.

Examples of the animal cells include vertebrate, notochord (excluding vertebrates) or insect cells.
Examples of the vertebrates include mammals such as humans, chimpanzees, rhesus monkeys, dogs, pigs, mice, rats, Chinese hamsters, and guinea pigs, Xenopus laevis, zebrafish, medaka, and tiger puffer.
The mammalian cells (mammalian cells) include, but are not limited to, tumor cells, hepatocytes, fibroblasts, stem cells, and immune cells.
As an example of the above chordates (excluding vertebrates), ascidians are exemplified.
Examples of the insects include Drosophila, silkworm, tobacco spider, and honeybee.

Examples of the plant cell include an angiosperm cell.
Examples of the angiosperm include Arabidopsis thaliana, rice, wheat, minatocamphor, Lotus japonicus, and tobacco.

Examples of the fungal cells include mold and yeast cells.
Examples of the mold include Neurospora crassa, Aspergillus oryzae, Aspergillus fumigatus, Aspergillus nidulans, Rhizopus oryzae and Rhizopus oryzae circumne, and Rhizopus oryzae or muciin in Rhozopus oryzae. Is exemplified.
Examples of the yeast include Saccharomyces cerevisiae, fission yeast (Schizosaccharomyces pombe), Candida albicans, Cryptococcus neoformans, and Trichosporon ovoides.

{Examples of the bacterial cells include cells of Escherichia coli, Salmonella enterica, Clostridium difficile, or Bacillus anthracis.

The cell stimulus is not particularly limited as long as it is at least one selected from the group consisting of a chemical stimulus (such as a chemical substance) and a physical stimulus (such as light, heat, or pressure).

Examples of the chemical stimulus include the addition of an agent that induces a biological response to cells. The drug may be one that induces a biological reaction on cells by adding it to a biological sample.
Specific examples of the biological reaction include proliferation, cell death, differentiation, antigen-antibody reaction, secretion of growth factors, and the like.
The above-mentioned drug is not particularly limited as long as it is a drug intended to have a measurable effect on body structure and function. Examples of such drugs include pharmaceuticals such as anticancer drugs, growth factors, cytokines and low-molecular-weight drugs. Specific examples of growth factors include epidermal growth factor (EGF). Specific examples include: tumor necrosis factor (TNF-α), interleukin 1β (IL-1β), insulin, glucagon-like peptide-1 (GLP-1), imatinib (imatinib), acetaminophen, adalimumab (Adalimumab) , And Nivolumab.

The container 1 for housing the target cell group is not particularly limited as long as it is a cell culture container for housing and culturing the target cell group. Further, as the culture solution to be used, a preferable one can be appropriately used depending on the cells and the analysis technique.
In each container, a target cell group (for example, composed of A cells, B cells, and C cells) composed of a plurality of cell types at a predetermined ratio, and the number of each cell is A cell: B cell: C cell = 2: (A target cell group consisting of 1: 1). A target cell group including a plurality of cell types is cultured for a predetermined period of time, and then a drug is added thereto to stimulate the cells.

<Single cell conversion step (S2)>
Next, in this step, the target cell group collected in the cell collection step (S1) is converted into a single cell (single cell).

The method and device for converting the target cell group into a single cell and collecting the single cell are not particularly limited, and known methods and devices can be used. For example, known methods include manual, flow cytometry, magnetic separation, laser capture microdissection, microdroplet method, micropipette fine needle suction method, and surface plasmon resonance method. For example, microchannels, nanowells, laser tweezers, label arrays, and nanobiodevices.
Among these known methods, it is preferable to use a microdroplet method, a microchannel, a nanowell, and flow cytometry. This is because a large amount of cells can be separated and recovered at a high speed without the need for skill in single cell formation, thereby improving analysis accuracy.

When recovering single cells, it is preferable to label each cell using a fluorescent label, a radioisotope (RI) label, an antibody label, and a magnetic label. This is because it can be used to identify the cell type of each cell population in the grouping step (S4) described later. 2A to 2C, the astrocytes 2 are labeled with a fluorescent label 6, and the nerve cells 4 are labeled with a fluorescent label 8. In the figure, the fluorescent label is attached to only one cell each of the astrocyte 2 and the nerve cell 4, but it is assumed that the fluorescent label is attached to all cells.
In particular, a combination of an antibody that binds to a protein expressed on the surface of a cell and a fluorescent, RI, or magnetic label is preferable because the specificity of the antibody increases.

It should be noted that an automated solution for single-cell genome research such as C1 ^™ Single-Cell Auto Prep system (made by Fluidime) can also be used. Since the solution can automatically perform single cell isolation, cell labeling, cell lysis, and genomic DNA or total RNA extraction performed in the single cell data acquisition step (S3) described below, For example, if it is used when acquiring single cell data using genomic DNA or total RNA, the working efficiency can be further improved.

<Single cell data acquisition step (S3)>
Next, in a single cell data acquisition step (S3), the DNA sequence information of the gene and the gene expression level are acquired as single cell data from each cell collected at each time and converted into a single cell. The single cell data is acquired for each single cell collected as a single cell.
The “gene expression level” in the present invention is the amount of mRNA that is a transcription product of a gene, and can be measured by examining the expression state of the gene by gene expression analysis. Alternatively, the amount of a protein that is an expression product of a gene may be analyzed.

《Single cell data》
The single cell data refers to information indicating the function, property, and state of a single cell, and is not limited to the above-described gene expression amount and DNA sequence information. For example, DNA sequence information of a gene (genome), epigenetic information controlling gene expression (DNA methylation, histone methylation, acetylation, phosphorylation), gene primary transcript (mRNA, untranslated RNA, micro RNA, etc.) (transcriptome), protein translation amount and modification such as phosphorylation, oxidation, glycation, amino acid sequence information (proteome), metabolite information (metabolome), intracellular hydrogen ion concentration index (pH), cell The internal ATP concentration, ion concentration (calcium, magnesium, potassium, sodium, etc.), intracellular temperature, etc. may be acquired as single cell data.
Further, when collecting single cells, if each cell is labeled with a fluorescent substance, an antibody, or the like, such information can also be obtained as single data.

<Grouping step (S4)>
Next, in the grouping step (S4), based on the single cell data (gene expression amount data, DNA sequence and fluorescent label) obtained in the single cell data obtaining step, the cells from which the single cell data has been Each cell grouped into a cell population having one cell characteristic, and further grouped by a first cell characteristic based on a second cell characteristic capable of discriminating each cell type included in the single cell data. Identify the cell type of the cell population.
Specifically, using the principal component analysis, the n (n-dimensional) cell features included in the gene expression amount data are compressed to a two-dimensional or three-dimensional that can be visualized, and all the collected cells are subjected to multiple processing. And plotted on a two-dimensional plane or three-dimensional space (that is, each principal component corresponds to the “first cell feature”). Further, based on the base sequence of the cells constituting each cell population and the fluorescent label (second cell characteristic), the cell type of each grouped cell is identified (clustering analysis).
Here, it is preferable to search for an axis that maximizes the variance of data (random variables) as the principal axis during principal component analysis.

In this step, since the cell characteristics of each cell population and the cell number are visualized in association with each other, it is possible to easily confirm or detect the relationship between the cell number and the cell characteristics at the same time and with high accuracy.

The number of cell features used to group the collected cells into a plurality of cell populations is not particularly limited. One or more of the n cell features may be used to group cells.
Further, in the present embodiment, cells obtained from single-cell data are visualized by two-dimensional or three-dimensional reduction using principal component analysis, and the cells are grouped into a cell population having a common first cell characteristic. However, the present invention is not limited to this, and methods for dimension reduction include, for example, principal component analysis (PCA), principal component analysis with kernel (Kernel-PCA), multidimensional scaling (MDS), t-SNE, Alternatively, a convolutional neural network (CNN) can be used.
In addition, from the single cell data of each cell (information on a plurality of biological materials), values representing the amounts or states of a plurality of biological materials, and time series data obtained at a plurality of time points for each biological material are prepared in advance. Grouping cells that have obtained single-cell data into a cell population having a common first cell characteristic based on the time change of time-series data for each biological material and the similarity of biological functions of each biological material You may divide.
Here, the similarity of biological functions means that they have a common gene ontology, belong to a common canonical pathway, have a common upstream factor, be involved in a common expression system, and have a common disease. It is preferable that the evaluation is based on at least one selected from the group consisting of related items.

FIG. 3A shows a clustering result (main component and) based on single cell data obtained from the target cell group immediately after the cell stimulation of FIG. 2A (time 0), and FIG. 3B shows one hour after the cell stimulation of FIG. 2B ( FIG. 3C shows the (principal component and) clustering results based on the single cell data obtained from the target cell group at time 1), and FIG. 3C shows a single cell obtained from the target cell group 6 times after the cell stimulation in FIG. 2C (time 2). 7 shows a (principal component and) clustering result based on cell data.
Principal component analysis is performed on the single cell data (gene expression level data) obtained from the target cell group immediately after the cell stimulation (time point 0) in FIG. And 16 (FIG. 3A), and based on the single-cell data (DNA sequence and fluorescent labels 6 and 8), the cell type constituting the cell population 14 is astrocyte 2, which constitutes the cell population 16. The cell type is identified as the nerve cell 4.
Similarly, the principal component analysis is performed on the single cell data (gene expression amount data) obtained from the target cell group one hour after the cell stimulation in FIG. 2B (time point 1), and the two-dimensional compression is performed. Cells are grouped into a plurality of cell populations 18, 20 and 22 (FIG. 3B), and based on single cell data (DNA sequence and fluorescent labels 6 and 8), cell types constituting cell populations 18 and 20 are determined. It is identified that the cell type which is the astrocyte 2 and which constitutes the cell population 22 is the nerve cell 4.
In addition, the principal component analysis is performed on the single cell data (gene expression amount data) obtained from the target cell group after 6 hours (time point 2) after the cell stimulation in FIG. Are grouped into a plurality of cell populations 24 and 26 (FIG. 3C), and based on single cell data (DNA sequence and fluorescent labels 6 and 8), the cell type constituting the cell population 24 is astrocyte 2. , The cell type constituting the cell population 26 is identified as the nerve cell 4.

In the present embodiment, the cell type of each cell population is identified using the DNA sequence and the fluorescent label as the second cell feature, but the present invention is not particularly limited thereto. Distinguishing each cell type from cell function (eg, cell growth, repair, metabolism, and information exchange between cells) or cell state (eg, gene expression status, protein expression status, and enzyme activity) Cell information that can be used (included in single data), for example, DNA sequence information of a gene (genome), epigenetic information that controls gene expression (DNA methylation, histone methylation, acetylation, phosphorylation) , Gene primary transcript (mRNA, untranslated RNA, microRNA, etc.) information (transcriptome), protein translation amount and modification such as phosphorylation, oxidation, glycation, amino acid sequence information (proteome), metabolite information ( Metabolome), intracellular hydrogen ion concentration index (pH), intracellular ATP concentration, ion concentration (calcium, magnesium, potassium, sodium, etc.) It can be utilized such as intracellular temperature.
The cell information capable of discriminating each cell type includes not only information such as genes, proteins, and metabolites originally possessed by cells, but also genes introduced from outside the cells (eg, immortalized genes). , Proteins and metabolites, and organic matter. Examples of the immortalizing gene include the hTERT gene (human telomerase reverse transcriptase gene) and the SV40T antigen (simian virus 40T antigen gene). In addition, when collecting single cells, if each cell is labeled with a fluorescent substance, an antibody, or the like, such information is also included.

<Cell change detection step (S5)>
Next, the cell change detection step (S5) is based on the clustering result obtained in the grouping step (S4), specifically, the clustering result of the cell immediately after the cell stimulation and the predetermined time after the cell stimulation. By comparing the result with the clustering result of the cells, a change with time of the cell population of the same cell type (change with respect to real time, or change with pseudo time estimated from the change) is detected. In addition, as for the temporal change of the cell population, it is preferable to extract the cell characteristics that have changed with time and detect the amount of the temporal change numerically.
Here, the “time-dependent change of the cell population” refers to the number of cells, cell characteristics (first cell characteristics), or other cell characteristics (that is, DNA sequence information (genome) of genes, control of gene expression). Epigenetic information (DNA methylation, histone methylation, acetylation, phosphorylation), gene primary transcript (mRNA, untranslated RNA, microRNA, etc.) information (transcriptome), protein translation and phosphorylation Modification information such as oxidation, glycation, amino acid sequence information (proteome), metabolite information (metabolome), intracellular hydrogen ion concentration index (pH), intracellular ATP concentration, ion concentration (calcium, magnesium, potassium, sodium, etc.) , Intracellular temperature, etc.), and the biological material of the cell over time.

3A (time 0) and FIG. 3B (time 1) which are the clustering results obtained in the grouping step (S4) are compared, and the cell characteristics (first cell characteristics) of the cell population composed of cells of the same cell type are compared. Of the principal components 1 and 2) are detected. Specifically, a comparison between the cell population 14 identified as astrocyte 2 in FIG. 3A (time 0) and the cell populations 18 and 20 identified as astrocyte 2 in FIG. 3B (time 1), and FIG. By comparing the cell characteristics of the cell population 16 identified as the nerve cell 4 at 3A (time 0) with the cell population 22 identified as the nerve cell 4 of FIG. 3B (time 1), the cell characteristics of each cell population were determined. The presence or absence of a change over time is detected.
Specifically, the cell characteristics of the cell population 14 identified as the astrocytes 2 in FIG. 3A (time point 0) (the main components 1 and 2 which are the first cell characteristics) and the astrocyte in FIG. 2 and the cell characteristics of the identified cell population 18 detected no change over time, and in the comparison of the astrocytes of FIG. 3A (time point 1) with the cell characteristics of the identified cell population 20, It detects that there has been a change over time. In addition, the cell characteristics of the cell population 16 identified as the nerve cells 4 in FIG. 3A (time point 0) (the main components 1 and 2 which are the first cell characteristics) and the nerve cells 4 in FIG. 3B (time point 1) are identified. The comparison with the cell characteristics of the cell population 22 detected detects no change with time.

The number of astrocytes 2 identified as constituting the cell population 18 in FIG. 3B (time point 1) was compared with the number of astrocytes 2 identified as constituting the cell population 14 in FIG. 3A (time point 0). 3B (time point 1), the number of astrocytes 2 identified as constituting the cell population 20 in FIG. 3B (point 1) is detected to increase. Furthermore, the total number of glial cells that make up the cell populations 18 and 20 of FIG. 3B (time point 1) should be equal to the number of astrocyte 2 cells identified as making up the cell population 14 of FIG. 3A (time point 0). Is also detected. Furthermore, the number of neurons 4 identified as constituting the cell population 22 of FIG. 3B (time 1) is the number of neurons 4 identified as constituting the cell population 16 of FIG. 3A (time 0). Detects no change.

From these detection results, one hour after the cell stimulation by the addition of the drug, changes in the cell characteristics and cell number of astrocytes 2 were observed, but it was easily detected that there was no change in the nerve cells 4. can do. Furthermore, it can be detected (estimated or confirmed) that the cell population 20 is composed of the reactive astrocytes 10 in which the shape of the astrocytes 2 has changed from the changes in the cell characteristics and the number of cells of the astrocytes 2. As a result, it is possible to easily detect (estimate or confirm) that the shape of the astrocyte 2 has changed from time 0 to time 1 and has become the reactive astrocyte 10 (solid arrow in FIG. 4).

Similarly, FIG. 3B (time point 1) and FIG. 3C (time point 2) which are the clustering results obtained in the grouping step (S4) are compared, and the cell characteristics of the cell population composed of cells of the same cell type (first The main components 1 and 2), which are the cell characteristics, detect the presence or absence of a temporal change from time 1 to time 2.
That is, in FIG. 3C (time point 2), in FIG. 3B (time point 1), the disappearance of the cell population 18 composed of astrocytes 2 and the reactive astrocyte present in FIG. It is detected that only the cell population 20 detected as composed of 10 remains. Further, in FIG. 3B (time point 1), the existing cell population 22 composed of the nerve cells 4 disappears, and in FIG. 3C (time point 2), the cell population 26 identified to be composed of the nerve cells 4 is newly added. Detect what is happening.
In addition, the cell characteristics of the cell population 20 detected as being composed of the reactive astrocytes of FIG. 3B (time point 1) (the main components 1 and 2, which are the first cell characteristics), and the astrocyte 2 of time point 2 The cell characteristics of the cell population 24 identified as being composed are compared, and no change over time is detected. Similarly, the cell characteristics (the main components 1 and 2 that are the first cell characteristics) of the cell population 22 detected to be composed of the nerve cells 4 in FIG. 3B (time point 1) and the cell characteristics in FIG. The cell characteristics of the cell population 26 identified as being composed of the nerve cells 4 are compared to detect the change with time.

Further, the number of cells of the reactive astrocyte 10 detected to constitute the cell population 24 of FIG. 3B (time point 2) is the total number of cells constituting the cell populations 20 and 18 of FIG. 3A (time point 1). It is detected that the number of nerve cells constituting the cell population 26 in FIG. 3C (time point 2) is the same as the number of cells in the cell population 22 in FIG. 3B (time point 1).
From these detection results, no change is seen in the cell characteristics and cell number of the reactive astrocytes 10 at 6 o'clock after the cell stimulation by the addition of the drug, that is, at 5 hours after time 1; 4 can easily detect a change. Further, it is detected from the change in the cell characteristics and the number of cells of the cell population 22 detected to be composed of the nerve cells 4 that the cell population 26 is composed of the nerve cells 12 in which the shape of the nerve cells 4 has changed (estimated). Or confirm). As a result, it is possible to easily detect (estimate or confirm) that the shape of the nerve cell 4 has changed from the time point 1 to the time point 2 and has become the nerve cell 12. (FIG. 5 solid line arrow).

As described above, in the present invention, since the cell characteristics of each cell population and the cell number are displayed in association with each other, not only the presence or absence of cell death, but also changes in the cell number and cell characteristics over time, and The relationship between the number and the cell characteristics can be easily or accurately confirmed or detected.

<Action mechanism analysis step (S6)>
Next, in the mechanism of action analysis step (S6), based on the cell change detection result obtained in the cell change detection step (S5), the mechanism of action of the change within the cell population of the same cell type or between the cell populations is determined. To analyze. Here, the mechanism of action refers to a specific action for cell stimulation by a drug to exert its pharmacological effect, and a specific action observed within or between cell populations of the same cell type. A biochemical reaction or interaction is meant.

In the present embodiment, the mechanism of action of a change in a cell population or between cell populations refers to a biological phenomenon (for example, proliferation, cell death, antigen-antibody reaction, growth factor Secretion, etc.), and more specifically, specific biochemical reactions or interactions (eg, metabolism of biological materials, gene expression, etc.) to exert biological phenomena induced inside and outside cells by cell stimulation. , Energy metabolism, signal transduction, etc.).
In the present embodiment, by analyzing the mechanism of action as described above from the cell change detection result obtained in the cell change detection step (S5), each cell population obtained in the cell change detection step (S5) (For example, biological substances, genes, etc.) related to the change in the number of cells and the change in cell characteristics.

FIG. 6 is considered to be related to the interaction between the astrocytes 2 (and the reactive astrocytes 10) and the nerve cells 4 and 12 together with the DNA sequence and the fluorescent label described above in the single cell data acquisition step (S3). The component amounts of components A to Z are also acquired as single cell data, and all cells are extracted along a pseudo time axis (pseudotime) estimated based on the similarity of expression profiles after dimension reduction by principal component analysis. It is the figure which plotted. By arranging the cells in this manner, pseudo temporal changes in cells and changes in gene expression can be found.
FIGS. 6 (1) to 6 (4) show changes in components A to C and Z relating to astrocytes 2 (and reactive astrocytes 10), and FIGS. 12 shows changes in components A to C and Z.

The components used in this step that are considered to be involved in the interaction between the astrocytes 2 (and the reactive astrocytes 10) and the nerve cells 4 and 12 are not particularly limited. In the single cell data acquisition step (S3), data that can be acquired as single cell data can be used.

In the present embodiment, from FIG. 6 (1) to (4), the cell population 20 (astrocytic 2) to the cell population 20 (astroblast 2) from the time point 0 to the time point 1 detected in the above-described cell change detection step (S5). The change over time in the cell characteristics and cell number into the reactive astrocytes 10) (solid arrow in FIG. 4) indicates that the detection result that the B component whose component amount changes from time 0 to time 1 is related. To win.
Also, from FIGS. 6 (5) to (8), the cell population 28 from the time point 1 to the time point 2 detected in the above-described cell change detection step (S5), that is, the cell population 22 (the nerve cell 4) to the cell population The change over time in cell characteristics and cell number to 26 (neural cell 12) (solid line arrow in FIG. 5) relates to the Z component and the B component whose component amounts change from time 1 to time 2. Get the detection result.

In the present embodiment, the method is used to analyze the process (time-dependent change) of the formation of a mutual network between glial cells and nerve cells. However, the present invention is not limited to this, and fungi such as Candida. It can also be used to analyze the process of infection with mucosal epithelial cells.

As the mechanism of action analysis, for example, gene expression profile analysis and molecular target analysis can also be used.
For analysis of the gene expression profile, for example, tensor decomposition can be used.

An example of analyzing a molecular target will be described.
First, a gene whose expression is changed by a drug (cell stimulation) and whose change is expected to be consistent with a disease is specified. Here, although the compound (drug) is actually bound to the protein, what is measured is the expression level of mRNA. Since it is unlikely that the amount of mRNA of the gene encoding the protein has changed due to the binding of the compound (drug) to the protein, the molecular target (target protein) is included in the gene whose expression level is changing. ) Is assumed not to exist.
The effect of the protein to which the compound is actually attached on other gene expression profiles is expected to be close to knocking out the gene in which the protein is encoded. Therefore, the target gene is estimated by referring to the gene expression profile when the gene is knocked out comprehensively.

In order to find the best indication of cell stimulation from multiple disease or case type candidates, a series of effects can be obtained by simultaneously performing effect determination and action mechanism analysis on multiple types of cells. . Based on a comparison or ranking of the effects, optimal indications or uses can be found. Here, the effect of cell stimulation is determined, for example, by comparing data without cell stimulation (drug) treatment with time-dependent data with cell stimulation (drug) treatment, and comparing cells derived from the same cell population with biological data. Alternatively, the cell characteristics and cell number are compared, and if there is a change, it can be determined that there is an effect.

<Interaction analysis step (S7)>
Next, in an interaction analysis step (S7), an interaction that occurs between cell populations of different cell types is analyzed based on the result of the action mechanism analysis obtained in the cell change detection step (S6). As a result, it is possible to analyze interactions that occur not only between cell populations of the same cell type but also between cell populations of different cell types. it can.

The interaction that occurs between the cell populations is a biological or physical action that occurs between different types of cell populations, or a change caused by those actions.
The biological or physical action is, for example, transfer of a humoral factor, cell adhesion, pulsation, or a weak current, and the change caused by the action is, for example, morphological change, differentiation, proliferation, Cell death, migration, increase of surface antigens, or release of humoral factors.
The humoral factor is, for example, an inorganic substance such as a hormone, a growth factor, a cytokine, an exosome, a metabolite, or an ion.

In the above grouping step (S4), when values representing the amounts or states of a plurality of biological materials are obtained at a plurality of time points for each biological material from the single cell data (information on a plurality of biological materials) of each cell. Sequence data is prepared in advance, and cells that have obtained single-cell data based on the time change of the time-series data for each biological material and the similarity of the biological function of each biological material are identified by a common first cell characteristic. When the cells are grouped into cell populations having the following, interaction analysis can be performed using the method described in International Publication No. 2018 / 150878A1.
In the present invention, in the interaction analysis step (S7), for each of the plurality of time points, a value representing the state of the cell population is generated from one or more first cell characteristics included in each of the plurality of cell populations. Then, the generated values representing the state of the plurality of cell populations at the plurality of time points are determined from the data set including the time-series data acquired at the plurality of time points for each biological material, and the state dependency between the cell populations is determined. The biological or physical effects that occur between the cell populations of different cell types by estimating and extracting the dependencies between the different cell populations within the inter-group (cell population) dependencies Or their effects (interactions that occur between cell populations of different cell types).

The estimation of the state dependency between cell populations can be performed, for example, as follows.
The cells were identified from the sequence data, and the genes whose temporal change patterns of the gene expression levels were similar to each other were, for example, the transition of the state value at two adjacent two time points increased according to a certain threshold value. It is determined which of the three was unchanged or decreased, and classified into 3 ⁷ = 2187 groups (cell populations).
Genes having similar biological functions are grouped using Functional Annotation Clustering of the public Web tool DAVID (https://david.ncifcrf.gov/), and genes having similar gene ontology are grouped. Grouping based on the similarity of the temporal change and the similarity of the biological function, and the temporal dependency between the state values of each group (cell population), for example, in the light of a Bayesian network model, or It can be estimated by linking between groups (between cell populations) based on time series or biological relationship.

In the present embodiment, the results of the mechanism of action analysis obtained in the cell change detection step (S5) described above, that is, the time-dependent changes in the cell characteristics and cell number of glial cells from time 0 to time 1 (FIG. 4) Solid arrows) indicate that the B component whose component amount changes from time 0 to time 1 is related, and that the time-dependent changes in the cell characteristics and cell number of the neurons from time 1 to time 2 ( The solid line arrow in FIG. 5) indicates that the Z component and the B component whose component amounts change from the time point 1 to the time point 2 are related, and further, the glial cells shown in FIGS. 6 (1) to (4) Cell population composed of glial cells at time point 1 based on the changes in the components A to C and Z according to the above and the changes in the components A to C and Z of the nerve cells shown in FIGS. 6 (5) to (8) 20 and a cell population 22 composed of nerve cells 4 The biological or physical actions (stimulations) that occur, or their actions (stimulations), that is, the interactions that occur between cell populations of different cell types, and from time 0 to time 2, First, the B component of glial cells increases due to cell stimulation by the addition of a drug, and the cellular characteristics of glial cells change (solid arrows in FIGS. 4 and 7), that is, astrocyte 2 changes to reactive astrocyte 10. Then, a biological or physical stimulus is given from the changed reactive astrocyte 10 to the nerve cell 4 (thick arrow in FIG. 7). Subsequently, as the component C of the nerve cell 4 stimulated by the reactive astrocyte 10 decreases, the Z component of the nerve cell 4 increases, thereby changing the cell characteristics of the nerve cell (FIG. 5). It is possible to estimate a mutual network between the glial cells and the nerve cells, that is, the change of the nerve cells 4 to the nerve cells 12.

According to the method of Embodiment 1, a single cell analysis is performed for all cells constituting a target cell group in which a plurality of different cell types are present, and the cell type of each cell is identified. Analyzes each cell type and the mechanism of action of drugs or cell stimulation on various cells with higher accuracy than conventional methods that artificially identify the cell type of each cell from the target cell group where the cell type exists be able to.
In addition, since the identification of each cell type and the mechanism of action of a drug or cell stimulation on each cell type can be analyzed with high accuracy, not only the interaction between the same cell types but also the interaction between different cell types can be performed. It is highly accurate and can be easily analyzed. Further, as a result, a wider interaction between cells and between cell types (interconnection network) can be analyzed with high accuracy and easily.
In addition, since identification and selection of cell types does not require labor such as image analysis, and even when drug efficacy screening is performed on immune cells, observation of one sample is not continued until analysis is completed. In particular, the analysis can be performed without any restriction on the analysis time.
Further, since the single cell analysis is used, the number of time points at which the cells are observed (collected) can be reduced as compared with the case where the change of the cells is artificially confirmed and the target cells are selected.

In the method of the first embodiment, the state of the cells at each time point can be visualized by the grouping step (S4). Therefore, in the cell change detection step (S5), the change of each cell (cell population) can be easily performed. You can check. As a result, in the action mechanism analysis step (S6), it is possible to easily extract cell types, genes, and the like that need to be further analyzed.

Modification of Embodiment 1 In Embodiment 1 described above, in the cell collection step (S1), the cells collected immediately after the cell stimulation and the cells cultured for a predetermined time after the cell stimulation are collected, and the single cells are collected. Although the analysis based on the comparison of the cell data is performed, the present invention is not limited to this. In the cell collection step (S1), a target cell group to which no cell stimulation is applied and a target cell group to which the cell stimulation is applied are prepared. After culturing the cells for a predetermined time, the cells may be collected and a screening analysis based on a comparison of single cell data of those cells may be performed.

Specifically, in the cell collection step (S1), the target cell group seeded in some of the containers 1 among the prepared containers 1 is cultured without applying cell stimulation, and the remaining cells are cultured. Embodiment 1 except that the target cell group seeded in the container 1 is subjected to cell stimulation to collect the cells in one container at two or more time points. Is the same as

As described above, by further preparing and analyzing a control sample, it is possible to confirm whether the effect is due to a drug (cell stimulation) or an effect due to other factors such as culture conditions.
More specifically, in the mechanism of action analysis step (S6), the mechanism of action of an agent (cell stimulation) within or between individual cell populations can be analyzed. It will also be possible to analyze and identify indications for treatment.
Here, examples of indications assuming treatment include, for example, diseases or conditions that can be improved by cell stimulation, or diseases or conditions related to molecular targets.

Hereinafter, the present invention will be described more specifically with reference to examples, but the present invention is not limited to these examples.

[Example 1]
Human iPS cells and fibroblasts derived from mouse embryos are mixed at a ratio of 1: 1 (cell number ratio), and ROCK (Rho-associated coiled-coil forming kinase / Rho binding kinase) inhibitor Y-27632 (10 μL; Fujifilm Wako Pure) (Yakusha Co., Ltd.) and seeded in a 6-well plate at 1000 cells / well / 200 μL, 3000 cells / well / 200 μL, and 9000 cells / well / 200 μL.
Cells were collected at the time of seeding (0 hour), 6 hours, 12 hours, 24 hours, 48 hours, 72 hours, 96 hours, and 120 hours after seeding.
The cell dispersion was diluted to 1000 cells / μL, and a single cell was captured using a C1 system (made by Fluidime). ^Then, using a ^{SMARTer (R) Ultra (R)} Low RNA kit, lysis of the cells, reverse transcription and cDNA preamplification of mRNA was carried out.
The resulting cDNA was recovered and concentrations higher than 0.05 ng / μL were selected for library preparation. Library preparation ^was performed using a ^Nextera (R) XT DNA sample preparation kit (manufactured by Illumina).

The prepared library was sequenced by a next-generation sequencer (HiSeq 2500 system, manufactured by Illumina) using 2 × 100 bp end reading. The gene expression level for each cell was calculated from the obtained data, time samples were collected for each drug, and clustering analysis was performed using principal component analysis.
The cells were identified as human iPS cells or mouse embryonic fibroblasts from the sequence data, and genes with similar temporal changes in the expression levels of each gene were identified. It is determined whether the transition of the state value at a certain two adjacent times is one of three, that is, increased, invariable, or decreased according to a certain threshold value, and each of the 2187 (= 3 to the seventh power) groups is determined. Classified. Genes having similar biological functions were grouped using Functional Annotation Clustering of the public Web tool DAVID (https://david.ncifcrf.gov/), and genes having similar gene ontology were grouped. As a result of grouping based on the similarity of the temporal change and the similarity of the biological function, 7305 groups were obtained. The time dependence between the status values of the 7305 groups was estimated in light of the Bayesian network model, and the activation of the undifferentiated maintenance mechanism of human iPS cells following the secretion of growth factors of mouse embryonic fibroblasts was estimated. Was able to capture the chronological mechanism.

[Example 2]
Human primary neurons, immortalized human microglia into which SV40T antigen has been introduced, and immortalized astrocytes into which hTERT gene has been introduced are mixed at a ratio of 2: 1: 1 (cell number ratio), seeded on a 6-well plate, and cultured for 24 hours. did.
After confirming engraftment, saline, TNF-α (tumor necrosis factor α), IL-1β (interleukin 1β), IL-1ra (interleukin 1 receptor antagonist), IL-12 (interleukin 12), IFNγ (interferon γ), IRF1 (interferon regulator 1), IRF2 (interferon regulator 2), IRF3 (interferon regulator 3), IRF4 (interferon regulator 4), IRF5 (interferon regulator 5), IRF6 (interferon regulation) Factor 6), IRF7 (interferon regulator 7), IRF8 (interferon regulator 8), IRF9 (interferon regulator 9), and LPS (lipopolysaccharide) were added to each well.
At the time of addition (0 hour), 6, 12, 24, and 48 hours after addition, the cells were trypsinized and collected.
The cell dispersion was diluted to 1000 cells / μL, and a single cell was captured using a C1 system (made by Fluidime).

A TP53 gene 500-600 and 750-870 target primers were added to a cDNA preparation kit (SMARTer ^(R) Ultra ^(R) Low RNA kit, manufactured by Clontech) to lyse cells, reverse transcribe mRNA and pre-amplify cDNA. Was done.
The resulting cDNA was recovered and concentrations higher than 0.05 ng / μL were selected for library preparation. Library preparation ^was performed using a ^Nextera (R) XT DNA sample preparation kit (manufactured by Illumina).
The prepared library was sequenced by a next-generation sequencer (HiSeq 2500 system, manufactured by Illumina) using 2 × 100 bp end reading.
The gene expression level for each cell was calculated from the obtained data, time samples were collected for each drug, and clustering analysis was performed using principal component analysis.

Cells were obtained by immobilizing cells in which the SV40T antigen gene was detected in immortalized microglia clusters, cells in which the hTERT gene was detected in immortalized astrocyte clusters, and cells in which neither the SV40T antigen gene nor the hTERT gene were detected in human primary neurons. Each was grouped into cell clusters.
For each type of cell, a change in the number of cells and a change in the amount of gene expression over time were found, and a time-varying pattern of the gene was obtained. Dependency between cells was obtained by grouping and patterning the gene temporal variation patterns of each cell collectively and further calculating temporal dependence.

As described above, various embodiments and examples of the cell information processing method of the present invention have been described in detail. However, the present invention is not limited to these embodiments and examples, and does not depart from the gist of the present invention. Of course, various improvements or changes may be made within the scope.

Claims (24)

1. At least two or more predetermined containers in which a target cell group including a plurality of different types of cells are seeded are prepared, and the target cell group is cultured by applying a predetermined cell stimulation to the target cell group. A cell collection step of collecting all cells in the cell,
   A single-celling step of converting all cells collected at each time point into a single cell,
   Single cell data acquisition step of acquiring single cell data from each cell that has been made into a single cell at each time point,
   Based on the single cell data at each time point, all cells collected at each time point are grouped into a plurality of cell populations having a common first cell characteristic and plotted on a two-dimensional plane or three-dimensional space, A grouping step of performing a process of identifying a cell type of each of the grouped cell populations based on the second cell feature, and acquiring a clustering result at each time point;
   By comparing the clustering results at each time point, a cell change detection step of detecting a change over time of the cell population of the same cell type,
   Based on the result of the detection, an action mechanism analysis step of analyzing the action mechanism of the change over time of the cell population of the same cell type,
   An interaction analysis step of analyzing an interaction occurring between the cell populations of different cell types based on a result of the action mechanism analysis.
2. The cell information processing method according to claim 1, wherein the interaction occurring between the cell populations in the interaction analysis step includes an interaction occurring between the cell populations of the same cell type.
3. The cell change detecting step includes, by comparing the clustering results at the respective time points, a change over time in the number of cells constituting the cell population of the same cell type, and a change in the first cell characteristic over time. The cell information processing method according to claim 1 or 2, wherein the cell information is detected.
4. The cell change detecting step further detects, with the clustering result at each time point, a temporal change in the number of cells constituting the cell population of the same cell type and a temporal change in the first cell characteristic. The cell information processing method according to claim 3.
5. The cell collection step further includes preparing a target cell group including the plurality of types of cells to be cultured without applying the predetermined cell stimulation, and, at one or more time points, all cells in one predetermined container. And collect
   The cell change detecting step evaluates a change over time of the same cell tumor cell population depending on the presence or absence of the predetermined cell stimulation of the cell population by comparing clustering results at the respective time points. 5. The cell information processing method according to any one of items 4 to 4.
6. The cell information processing method according to any one of claims 1 to 5, wherein the cell stimulus is at least one selected from the group consisting of a chemical stimulus and a physical stimulus.
7. 7. The cell information processing method according to claim 6, wherein the chemical stimulus is caused by the addition of an agent that induces a biological response to the cell.
8. The method according to any one of claims 1 to 7, wherein the mechanism of action is a specific biochemical reaction or interaction for exerting a biological phenomenon induced inside and outside the cell by the cell stimulation. The cell information processing method according to the above.
9. The single-cell data is information on biological material indicating the function, property, and state of a single cell, and includes DNA sequence information of a gene (genome) and epigenetic information that controls gene expression (DNA methylation, histone methyl). Acetylation, phosphorylation), gene primary transcripts (mRNA, untranslated RNA, microRNA, etc.) information (transcriptome), protein translation and modification information such as phosphorylation, oxidation, glycation, amino acid sequence Information (proteome), metabolite information (metabolome), intracellular hydrogen ion concentration index (pH), intracellular ATP concentration, ion concentration (calcium, magnesium, potassium, sodium, etc.), selected from the group consisting of intracellular temperature The cell information processing method according to any one of claims 1 to 8, wherein the method is at least one.
10. 10. The cell information processing method according to claim 9, wherein the single cell data is a gene expression level and a DNA sequence of the gene.
11. The method according to any one of claims 1 to 10, wherein in the grouping step, the first cell feature is obtained by reducing the n-dimensional cell feature included in the single cell data by two or three dimensions. The cell information processing method according to the above.
12. The dimension reduction method is based on a group consisting of principal component analysis (PCA), principal component analysis with kernel (Kernel-PCA), multidimensional scaling (MDS), t-SNE, and convolutional neural network (CNN). The cell information processing method according to claim 11, which is at least one selected.
13. 13. The method according to claim 10, wherein in the grouping step, the first cell feature is obtained by performing principal component analysis on the gene expression amount and reducing the dimension to two or three dimensions. Item 14. The cell information processing method according to Item.
14. The method according to any one of claims 1 to 13, wherein in the grouping step, the second cell feature is at least one piece of cell information capable of identifying each cell type from a cell function or a cell state. 3. The cell information processing method according to item 1.
15. The cell information includes DNA sequence information of a gene (genome), epigenetic information for controlling gene expression (DNA methylation, histone methylation, acetylation, phosphorylation), primary transcript of a gene (mRNA, (Translated RNA, microRNA, etc.) information (transcriptome), protein translation amount and modification information such as phosphorylation, oxidation, glycation, amino acid sequence information (proteome), metabolite information (metabolome), intracellular hydrogen ion concentration index The cell information processing method according to claim 14, which is at least one selected from the group consisting of (pH), intracellular ATP concentration, ion concentration (calcium, magnesium, potassium, sodium, etc.) and intracellular temperature.
16. 16. The cell information processing method according to claim 14 or 15, wherein the function of the cell is at least one selected from cell proliferation, repair, metabolism, and information exchange between cells.
17. 16. The cell information processing method according to claim 14, wherein the cell state is at least one selected from a gene expression state, a protein expression state, and an enzyme activity.
18. In the interaction analysis step, the interaction that occurs between the cell populations refers to a biological or physical action that occurs between the cell populations of the different cell types, or the number of cells due to the action, The cell information processing method according to any one of claims 1 to 17, which is a change in cell characteristics.
19. From the single cell data, a value representing the amount or state of a plurality of biological materials, time-series data acquired at a plurality of time points for each of the biological materials is created in advance, and a time change of the time-series data for each of the biological materials. , Based on the similarity of the biological function of each biological material, the cells obtained the single cell data, grouped into a cell population having the common first cell characteristics,
    In the interaction analysis step, for each of the plurality of time points, a value representing a state of the cell population is generated from one or more first cell characteristics included in each of the plurality of cell populations. At a plurality of time points, values representing the state of a plurality of cell populations, from a data set consisting of time-series data obtained at a plurality of time points for each biological material, to estimate the state dependency between the cell populations, 19. The cell information processing method according to any one of 1 to 18.
20. Similarity of the biological functions may have a common gene ontology, belong to a common canonical pathway, have a common upstream factor, be involved in a common expression system, and be involved in a common disease 20. The cell information processing method according to claim 19, wherein the method is evaluated based on at least one selected from the group consisting of:
21. In the cell collection step and the single cell forming step, a method of collecting all cells and forming a single cell is performed manually, flow cytometry, magnetic separation, laser capture microdissection, microchannel, micro droplet, nanowell, The cell information according to any one of claims 1 to 20, wherein the method is a method using at least one selected from the group consisting of a micropipette fine needle aspiration, a laser tweezer, a label array, a surface plasmon response, and a nanobiodevice. Processing method.
22. 22. The method according to claim 21, wherein in the method of collecting the whole cells and forming a single cell, at least one selected from the group consisting of a fluorescent label, a radioisotope label, an antibody label, and a magnetic label is used as a cell label. Cell information processing method.
23. 23. The cell according to claim 1, wherein the target cell group including the plurality of types of cells is a cell obtained from at least one selected from the group consisting of a biological tissue sample, a blood sample, a culture sample, and an environmental sample. The cell information processing method according to claim 1.
24. 24. The cell information processing method according to claim 23, wherein the plurality of types of cells are at least one selected from the group consisting of animal cells, plant cells, fungal cells, and bacterial cells.

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