Cell-information processing metho

Abstract

To provide a cell-information processing method for efficiently analyzing mechanisms of action of changes in individual cell populations when multiple types of different cell populations coexist.SOLUTION: A cell-information processing method according to the present invention includes: a cell retrieving step; a single-cell preparing step; a single-cell-data acquiring step; a grouping step of acquiring a clustering result at each point in time; a cell-change detecting step of comparing the clustering results at the individual points in time, thereby detecting changes over time in the cell populations of the same cell types; and a mechanism-of-action analyzing step of 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.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a method for processing cell information. In particular, it relates to a method for identifying each cell type to be implemented in a target cell group containing a plurality of different cell types, or a method for screening the drug efficacy and pharmacology of each cell type.

Background Art

[0002] When exploring the optimal indication, usage, or dosage of a drug, the effect of cell stimulation by a drug or the like on a plurality of cells is determined, and from the results of further analyzing the mechanism of action, the drug or the like and the optimal indication are selected. Conventionally, as such a method for screening drug efficacy and pharmacology, as shown in FIG. 7, a drug is respectively given to a cell population composed only of individual cell types 54, 56, and 58 arranged in each well 52 and cultured, and the drug efficacy is analyzed from the averaged data of each cell population, or a drug is given to a cell population containing a plurality of cell types extracted from an organ and cultured, and the drug efficacy is analyzed using the averaged data of the cell population. Such methods have been used.

[0003] However, even in a cell population consisting of only the same type of cells, there is heterogeneity in the dynamic behavior of individual cells over time (e.g., cell activation, cell inhibition, cell interaction, protein expression, protein secretion, cell proliferation, cell morphology change, cell death, etc.). Therefore, with conventional methods, highly accurate analysis could not be performed sufficiently. Thus, in recent years, in order to further improve the analysis accuracy, cells constituting a cell population consisting of only the same type of cells are made into single cells, and molecular biological analysis (single-cell analysis) is performed at the level of individual cells (single cells). By such single-cell analysis, instead of analyzing the averaged data of the cell population, individual cells can be analyzed, so that cells that could not be detected by analysis using averaged data and small changes in dynamic behavior can be detected. In addition, such single-cell analysis is used, for example, to classify cell subtypes present in cancer tissue by single-cell transcriptome analysis of cancer cells. Further, as a method for evaluating cell activity using single-cell analysis, the method described in Patent Document 1 has been proposed.

[0004] Patent Document 1 proposes a method for evaluating cell activity, comprising: (a) a step of placing a cell population containing a plurality of different types of cells on a region, that is, a step of placing a cell population containing a plurality of different types of cells in each nanowell of a nanowell array plate; (b) a step of assaying the dynamic behavior of the cell population as a function of time, that is, a step of visualizing (i.e., by image analysis) the dynamic behavior at the single-cell level over time using a microscope, fluorescence microscope, etc.; (c) a step of identifying at least one cell to be analyzed from the cell population based on the dynamic behavior; (d) a step of characterizing the molecular profile of the identified at least one cell to be analyzed, that is, performing mass spectrometry, gene analysis, protein analysis, etc. at the single-cell level for the identified at least one cell to be analyzed, and obtaining transcriptional activity, transcriptome profile, gene expression activity, genome profile, protein expression activity, proteome profile, protein interaction activity, cell receptor expression activity, lipid profile, lipid activity, carbohydrate profile, microvesicle activity, glucose activity, metabolic profile, cell receptor expression activity, etc.; and (e) a step of correlating the information obtained from steps (b) and (d). That is, the method described in Patent Document 1 evaluates the cell activity of individual cell types in a cell population containing a plurality of different types of cells.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] However, the method described in Patent Document 1 involves a human observing the dynamic behavior of a cell population consisting of at least 100 to 200 cells through image analysis, identifying and selecting a single cell to be analyzed from among multiple types of cells and cells with different dynamic behaviors, and analyzing the mechanism of action of the dynamic behavior of the single cell selected by the human and cell-cell interactions. That is, since a human arbitrarily judges and analyzes the morphology of each cell type and the dynamic changes of each cell over time to identify and select cells for analysis, there is a problem that highly accurate analysis results cannot be obtained. Also, as described in one embodiment of Patent Document 1, when the cell population to be analyzed contains immune cells, specifically, when the blood sample contains immune cells, there is a situation where the cells will die within about 24 hours after a drug is administered to the blood sample. Further, since the analysis method described in Patent Document 1 observes the cell population administered with the drug over time and performs analysis (that is, the observation of one sample continues until the analysis is completed, and further, it is a method of performing analysis while repeating the selection and culture of the cells to be analyzed), when adopting the method described in Patent Document 1, there is a very strict time constraint that all analysis must be completed within at least 24 hours after the drug is administered to the cell population.

[0007] Moreover, the method described in Patent Document 1 is laborious and costly for cell identification and selection operations of target cells, and the calculation method of the evaluation by the analysis and the result are also complex. Therefore, there is a problem that it is not practical as an industrially used method that requires efficient, highly accurate, and rapid evaluation and analysis. Furthermore, a method for culturing a cell population containing a plurality of different types of cells by respectively administering a drug and efficiently analyzing the identification method of each cell type, the drug efficacy and pharmacology of each cell type in a cell population containing a plurality of different cell types using single-cell analysis technology has not been reported at present.

[0008] Also, in the initial stage of searching for drugs using the drug efficacy and pharmacological screening method as shown in FIG. 7, based only on the judgment of no cell death caused by the drug (cell stimulation), the analysis of the interaction with different cells and the analysis of the mechanisms of drug efficacy and pharmacology are carried out. Therefore, in the initial stage of analysis for extracting substances that can become drugs from a huge number of candidate substances, for example, even if cell death is detected, it remains unclear what factors caused the cell death, and there is a possibility that it cannot become a drug candidate.

[0009] Therefore, in view of the above problems of the prior art, the present invention aims to provide a cell information processing method for efficiently analyzing the action mechanism of changes in individual cell populations when a plurality of different types of cell populations are mixed. More specifically, it is an object to provide a method that can evaluate and analyze the identification of each cell type or drug efficacy and pharmacological screening implemented in a cell population in which a plurality of cell types exist with higher accuracy and speed by a simpler operation and can be used on an industrial scale.

Means for Solving the Problems

[0010] The cell information processing method of the present invention prepares at least two or more predetermined containers seeded with a target cell group containing a plurality of different types of cells, applies a predetermined cell stimulus to the target cell group and cultures it, and at two or more time points, a cell recovery step of recovering all the cells in one container, a single cell conversion step of converting all the cells recovered at each time point into single cells, a single cell data acquisition step of acquiring single cell data from each of the single cell-converted cells at each time point, and based on the single cell data at each time point, all the cells recovered at each time point are grouped into a plurality of cell populations having a common first cell feature and plotted on a two-dimensional plane or three-dimensional space, and based on the second cell feature, a process of identifying the cell types of each grouped cell population is performed to obtain a clustering result at each time point, a grouping step, 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 of the above time points, and a mechanism of action analysis step of analyzing the mechanism of action of the change over time of the cell population of the same cell type based on the detection result.

[0011] The cell change detection step preferably detects a change over time in the number of cells constituting the cell population of the same cell type and a change over time in the first cell feature by comparing the clustering results at each of the above time points. The cell change detection step may further detect a change over time in the number of cells constituting the cell population of the same cell type and a change over time in the first cell feature in the clustering results at each of the above time points. The cell recovery step further prepares a target cell group containing the plurality of types of cells cultured without applying the predetermined cell stimulus, recovers all the cells in one of the predetermined containers at one or more time points, and the cell change detection step preferably detects a change over time of the cell population of the same cell type depending on the presence or absence of the predetermined cell stimulus of the cell population by comparing the clustering results at each of the above time points.

[0012] In the above mechanism of action analysis step, the molecular target of the above cell stimulation may be analyzed. In the above mechanism of action analysis step, the molecular target of the above cell stimulation may be analyzed, and an indication example assumed for treatment may be specified. The indication example assumed for the above treatment is preferably a disease or symptom that can be expected to be improved by cell stimulation, or a disease or symptom related to the molecular target. The above molecular target is preferably a molecule inside and outside the cell to which cell stimulation directly or indirectly acts.

[0013] The above cell stimulation is preferably at least one selected from the group consisting of chemical stimulation and physical stimulation. The above chemical stimulation is preferably by adding a drug that induces a biological reaction to the cell. The above mechanism of action is preferably a specific biochemical reaction or interaction for exerting a biological phenomenon induced inside and outside the cell by the above cell stimulation.

[0014] The above single-cell data is preferably at least one selected from the group consisting of DNA sequence information (genome) of a gene, epigenetic information (DNA methylation, histone methylation, acetylation, phosphorylation) that controls gene expression, gene primary transcript (mRNA, non-coding RNA, microRNA, etc.) information (transcriptome), protein translation amount and modification information such as phosphorylation, oxidation, and glycosylation, 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 intracellular temperature. The above single-cell data is preferably the gene expression level and the DNA sequence of the gene.

[0015] In the above grouping step, the above first cell feature is preferably obtained by reducing the dimensionality of the n-dimensional cell feature included in the above single-cell data to two or three dimensions. The above-mentioned dimensionality reduction method is preferably at least one selected from the group consisting of principal component analysis (PCA), kernel principal component analysis (Kernel-PCA), multidimensional scaling (MDS), t-SNE, and convolutional neural network (CNN). In the above grouping step, it is preferable that the first cell feature is obtained by performing principal component analysis on the gene expression level and reducing the dimension to two or three dimensions.

[0016] In the above grouping step, it is preferable that the second cell feature is at least one cell information capable of identifying each cell type from cell functions or cell states. The above cell information is preferably at least one selected from the group consisting of DNA sequence information (genome) of genes, epigenetic information (DNA methylation, histone methylation, acetylation, phosphorylation) controlling gene expression, gene primary transcript (mRNA, non-coding RNA, microRNA, etc.) information (transcriptome), modification information such as protein translation amount and phosphorylation, oxidation, glycosylation, 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 intracellular temperature. The above cell functions are preferably cell proliferation, repair, metabolism, and information exchange between cells. The above cell states are preferably gene expression status, protein expression status, and enzyme activity.

[0017] Values representing the amounts or states of a plurality of biological substances are obtained from the above single-cell data at a plurality of time points for each biological substance to create time-series data in advance. Based on the time changes in the time-series data for each biological substance and the similarity of the biological functions of each biological substance, the cells from which the single-cell data was obtained are grouped into cell populations having a common first cell feature. The similarity of the biological functions is preferably evaluated based on at least one selected from the group consisting of 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.

[0018] In the above cell recovery step and the above single-cell formation step, as a method for recovering all cells and forming them into single cells, at least one selected from the group consisting of manual operation, flow cytometry, magnetic separation, laser capture microdissection, microfluidics, microdroplets, nanowells, micropipette aspiration, laser tweezers, labeled arrays, surface plasmon resonance, and nanobiodevices can be used. In the method for recovering all the above cells and forming them into single cells, at least one selected from the group consisting of a fluorescent label, a radioisotope label, an antibody label, and a magnetic label can be used as a cell label. As the target cell group containing a plurality of types of cells, at least one selected from the group consisting of a biological tissue sample, a blood sample, a culture sample, and an environmental sample can be used. As the above plurality of types of cells, at least one selected from the group consisting of animal cells, plant cells, fungal cells, and bacterial cells can be selected.

Advantages of the Invention

[0019] According to the present invention, when a plurality of different types of cell populations are mixed, a cell information processing method for efficiently analyzing the mechanism of action of changes in the cell populations of individual cell types can be provided. More specifically, in a cell population in which a plurality of different cell types are present, identification of each cell type, or evaluation and analysis of drug efficacy and pharmacological screening can be performed more easily, with high precision and quickly, and an efficient method that can be used even on an industrial scale can be provided.

[0020] According to the present invention, since a human does not arbitrarily judge the dynamic behavior of cells over time, etc., identification of each cell type and the mechanism of action of drugs or cell stimuli on various cells can be analyzed with high accuracy. In addition, target cells can be identified without taking the trouble of image analysis or the like. Also, even when performing drug efficacy screening on immune cells, analysis can be performed without particularly imposing a restriction on the analysis time. Moreover, it is not necessary to give drugs or cell stimuli to each sample cultured for each same cell type for analysis, and drugs or cell stimuli can be given to a sample in which a plurality of types of cells are co-cultured for analysis. Therefore, the labor and cost burden of preparing and analyzing a large amount of samples for analysis can be reduced. Also, even if the cells to be analyzed cannot be cultured with only one type of cell for cell growth and function maintenance, according to the present invention, the cells to be analyzed can be identified from a sample in which a plurality of types of cells are co-cultured. In addition, the mechanism of action by co-culture with cells other than the cells to be analyzed, for example, the mechanism of action of a sample drug or cell stimulus that requires co-culture of the cells to be analyzed and immune cells can also be appropriately analyzed.

Brief Description of the Drawings

[0021]

Figure 1

Figure 2A

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Figure 7

Embodiments for Carrying Out the Invention

[0022] [Cell Information Processing Method] Hereinafter, the cell information processing method of the present invention will be described in detail. Embodiment 1 Embodiment 1 will be described by taking the screening analysis of an anticancer agent as an example. Figure 1 is a flowchart showing the cell information processing method according to Embodiment 1 of the present invention.

[0023] [Cell Recovery Step (S1)] First, in this step, as shown in Figure 2A, at least two or more predetermined containers 1 seeded with a plurality of different types of cancer cells 2, 4, and 6 are prepared. Next, an anticancer agent is added to the target cell population seeded in the prepared container 1 (that is, cell stimulation is given) and cultured, and at two or more time points (for example, immediately after drug addition, after drug addition, 1 hour, 6 hours, 12 hours, 24 hours, 48 hours, 72 hours...), all the cells in one of at least two or more containers 1 are recovered. Note that the number of containers 1 used at each time point is not limited to one, and may be plural as long as the number of containers used for cell recovery at each time point is the same. FIG. 2A shows the cells immediately after cell stimulation (after drug addition), and FIG. 2B shows the cells after a predetermined time (1 hour) has elapsed after cell stimulation.

[0024] 《Target cell group》 Here, the target cell group is a group of cells seeded in a predetermined container 1 and means a group of cells containing a plurality of different types of cells. More specifically, the “group of cells containing a plurality of different types of cells” means, for example, a group of cells composed of a plurality of cells with different cell types, such as a group of cells composed of human iPS cells and mouse fetal-derived fibroblasts, and may be a group of cells composed of cells derived from the same species or a group of cells composed of cells derived from different species. In this embodiment, the analysis is performed using cancer cells, but the types of cells included in the target cell group are not particularly limited.

[0025] 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 above biological tissue sample include a mouse brain tissue and a human resected tumor tissue. Examples of the above blood sample include a human blood collection sample. Examples of the above culture sample include a co-culture sample of human iPS cells and mouse fetal-derived fibroblasts. Examples of the above environmental sample include a soil sample and a water sample collected from a hydrothermal vent on the sea floor.

[0026] In addition, examples of the “cells” included in the “target cell group containing a plurality of types of cells” include animal cells, plant cells, fungal cells, and bacterial cells.

[0027] Examples of the above animal cells include cells of vertebrates, chordates (excluding vertebrates), or insects. Examples of the vertebrates include mammals such as humans, chimpanzees, rhesus monkeys, dogs, pigs, mice, rats, Chinese hamsters, and guinea pigs, and African clawed frogs, zebrafish, medaka, and pufferfish. Examples of the mammalian cells (mammalian cells) include, but are not limited to, tumor cells, hepatocytes, fibroblasts, stem cells, and immune cells. Examples of the chordates (excluding vertebrates) include ascidians. Examples of the insects include Drosophila, silkworms, tobacco budworms, and honeybees.

[0028] Examples of the plant cells include cells of angiosperms. Examples of the angiosperms include Arabidopsis thaliana, rice, wheat, saltwater plantain, carpetweed, and tobacco.

[0029] Examples of the fungal cells include cells of molds or yeasts. Examples of the molds include Neurospora crassa, Aspergillus oryzae, Aspergillus fumigatus, Aspergillus nidulans, Rhizopus oryzae, and Mucor circinelloides. Examples of the yeasts include Saccharomyces cerevisiae, Schizosaccharomyces pombe, Candida albicans, Cryptococcus neoformans, and Trichosporon ovoides.

[0030] Examples of the above-mentioned bacterial cells include cells of Escherichia coli, Salmonella enterica, Clostridium difficile, or Bacillus anthracis.

[0031] Cell stimulation is not particularly limited as long as it is at least one selected from the group consisting of chemical stimuli (such as chemical substances) and physical stimuli (such as light, heat, or pressure).

[0032] Examples of the above-mentioned chemical stimuli include those by adding a drug that induces a biological reaction in cells. Note that the drug may also induce a biological reaction in cells by adding it to a biological sample. Specific examples of the above-mentioned biological reactions include proliferation, cell death, differentiation, antigen-antibody reaction, and secretion of growth factors. The above-mentioned drug is not particularly limited as long as it is a drug intended to have a measurable effect on the structure and function of the body. Such drugs include pharmaceuticals such as anticancer drugs, growth factors, cytokines, and small molecule drugs. Specific examples of growth factors include epidermal growth factor (EGF: Epidermal Growth Factor). Specific examples of cytokines include tumor necrosis factor (TNF-α), interleukin 1β (IL-1β), insulin, glucagon-like peptide-1 (GLP-1), imatinib, acetaminophen, adalimumab, and nivolumab.

[0033] Note that the container 1 for accommodating the target cell group may be any cell culture container that can accommodate and culture the target cell group, and is not particularly limited. Also, the culture medium to be used can be appropriately selected according to the cells and analysis methods. A target cell group composed of a plurality of cell types in a predetermined ratio (for example, a target cell group consisting of A cells, B cells, and C cells, and the number of each cell is configured as A cells: B cells: C cells = 2: 1: 1) is seeded in each container. The target cell group containing a plurality of cell types is cultured for a predetermined time and then given a drug and cell stimulation.

[0034] <Single cell conversion step (S2)> Next, in this step, the target cell group recovered in the cell recovery step (S1) is converted into single cells (single cell conversion).

[0035] The method and instrument for converting the target cell group into single cells and recovering single cells are not particularly limited, and known methods and instruments can be used. For example, known methods include manual, flow cytometry, magnetic separation, laser capture microdissection, microdroplet method, micropipette fine needle aspiration method, and surface plasmon resonance method, and known instruments include, for example, microfluidic channels, nanowells, laser tweezers, labeled arrays, and nanobiodevices. Among these known methods, it is preferable to use the microdroplet method, microfluidic channels, nanowells, and flow cytometry. This is because it does not require proficiency in single cell conversion and can separate and recover a large number of cells at high speed, thus improving the analysis accuracy.

[0036] When recovering single cells, it is preferable to label each cell using a fluorescent label, radioisotope (RI) label, antibody label, or magnetic label. This is because it can be used for identifying the cell types of each cell population in the grouping step (S4) described later. In FIGS. 2A and 2B, fluorescent labels 12, 14, and 18 are attached to cancer cells 2, 4, and 6, respectively. In particular, the combination of an antibody that binds to a protein expressed on the cell surface and a fluorescent, RI label, or magnetic label is preferable because the specificity of the antibody is increased.

[0037] Note that C1 TMAutomated solutions for single-cell genomics research, such as the Single-Cell Auto Prep system (manufactured by Fluidigm), can also be used. Since such solutions can automatically perform single-cell isolation, cell labeling, cell lysis, and extraction of genomic DNA or total RNA performed in the single-cell data acquisition step (S3) described below, for example, when attempting to acquire single-cell data using genomic DNA or total RNA, work efficiency can be further enhanced.

[0038] <Single-cell data acquisition step (S3)> Next, in the single-cell data acquisition step (S3), DNA sequence information of genes, fluorescence labels, and gene expression levels are acquired as single-cell data from each cell recovered at each time point and made single-cell. Single-cell data is acquired for all single cells that have been made single-cell and recovered. The "gene expression level" in the present invention refers to the amount of mRNA, which is a transcription product of a gene, and can be measured by examining the expression state of the gene by gene expression analysis. Alternatively, analysis of the amount of protein, which is the expression product of a gene, may be performed.

[0039] 《Single-cell data》 Note that single-cell data means information on biological substances indicating the functions, properties, and states of single cells, and is not limited to the gene expression levels and DNA sequence information described above. For example, DNA sequence information (genome) of genes, epigenetic information (DNA methylation, histone methylation, acetylation, phosphorylation) that controls gene expression, gene primary transcript (mRNA, non-coding RNA, microRNA, etc.) information (transcriptome), information on the translation amount of proteins and modifications such as phosphorylation, oxidation, and glycosylation, 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. may be acquired as single-cell data. In addition, when collecting single cells, if each cell is labeled with a fluorescent substance, an antibody, or the like, that information can also be obtained as single data.

[0040] <Grouping Step (S4)> Next, in the grouping step (S4), based on the single cell data (gene expression level data, DNA sequence, and fluorescence label) obtained in the single cell data acquisition step, the cells from which the single cell data was obtained are grouped into cell populations having a common first cell feature, and further, based on a second cell feature capable of discriminating each cell type included in the single cell data, the cell types of the grouped cell populations are identified. Specifically, using principal component analysis, the n (n-dimensional) cell features included in the gene expression level data are compressed to two or three dimensions that can be visualized, and all the collected cells are grouped into a plurality of cell populations and plotted on a two-dimensional plane or in three-dimensional space (that is, each principal component corresponds to the "first cell feature"). Further, based on the base sequence and fluorescence label (second cell feature) constituting each cell population, an identification process (clustering analysis) of the cell types of the grouped cell populations is performed. Here, it is preferable to search for a principal component axis in principal component analysis such that the variance of the data (random variable) is maximized.

[0041] By this step, since the cell features of each cell population and the number of cells are associated and visualized, it is possible to easily and accurately confirm or detect the relationship between cell death and cell features at the same time.

[0042] Note that the number of first cell features used to group all 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 the cells. In addition, in this embodiment, principal component analysis is used for visualization by reducing the dimensions to two or three dimensions, and cells from which single-cell data has been obtained are grouped into cell populations having a common first cell feature. However, the method is not limited to this. For example, methods for dimension reduction can also include principal component analysis (PCA), kernel principal component analysis (Kernel-PCA), multidimensional scaling (MDS), t-SNE, or convolutional neural network (CNN). In addition, from the single-cell data (information related to a plurality of biological substances) of each cell, values representing the amounts or states of the plurality of biological substances are prepared in advance as time-series data obtained at a plurality of time points for each biological substance. Based on the time change of the time-series data for each biological substance and the similarity of the biological functions of each biological substance, cells from which single-cell data has been obtained may be grouped into cell populations having a common first cell feature. Here, the similarity of biological functions is preferably evaluated based on at least one selected from the group consisting of 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.

[0043] FIG. 3A shows a clustering result based on single-cell data obtained from the target cell group immediately after cell stimulation in FIG. 2A, and FIG. 3B shows a clustering result based on single-cell data obtained from the target cell group after a predetermined time (1 hour) has elapsed after cell stimulation in FIG. 2B. Principal component analysis is performed on the single-cell data (gene expression level data) obtained from the target cell group immediately after cell stimulation in FIG. 2A, and by compressing it into two dimensions, all cells are grouped into a plurality of cell populations 20, 22, and 24. Based on the single-cell data (DNA sequence and fluorescent labels 12, 14, and 18), it is identified that the cell type constituting cell population 20 is cancer cell 2, the cell type constituting cell population 22 is cancer cell 4, and the cell type constituting cell population 24 is cancer cell 6. Similarly, after cell stimulation in Fig. 2B, principal component analysis was performed on single-cell data (gene expression level data) obtained from the target cell population after a predetermined time period (1 hour later), and by compressing it into two dimensions, all cells were grouped into a plurality of cell populations 26, 28, and 30. Further, based on the single-cell data (DNA sequences and fluorescent labels 12, 14, and 18), it was identified that the cell type constituting cell population 26 is cancer cell 2, the cell type constituting cell population 28 is cancer cell 4, and the cell type constituting cell population 30 is cancer cell 6.

[0044] In this embodiment, as the second cell feature, the cell types of each cell population were identified using DNA sequences and fluorescent labels, but it is not particularly limited thereto. Information on biological substances of cells (included in single data) that can distinguish each cell type from cell functions (e.g., cell proliferation, repair, metabolism, and information exchange between cells) or cell states (e.g., gene expression status, protein expression status, and enzyme activity) can be used, such as DNA sequence information (genome) of genes, epigenetic information (DNA methylation, histone methylation, acetylation, phosphorylation) that controls gene expression, information on gene primary transcripts (mRNA, non-coding RNA, microRNA, etc.) (transcriptome), modification information such as protein translation amount and phosphorylation, oxidation, and glycosylation, 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 intracellular temperature.

[0045] The cell information capable of distinguishing the above cell types includes not only information on genes, proteins, and metabolites inherently possessed by cells, but also information on genes (e.g., immortalization genes), proteins, metabolites, and organic substances introduced from outside the cells. Examples of immortalization genes include the hTERT gene (human telomerase reverse transcriptase gene) and the SV40T antigen (simian virus 40T antigen gene). Also, when collecting single cells, if each cell is labeled with a fluorescent substance, an antibody, etc., that information is also included.

[0046] <Cell change detection step (S5)> Next, the cell change detection step (S5) compares the clustering result related to the cells immediately after cell stimulation obtained in the grouping step (S4) with the clustering result related to the cells after a predetermined time has elapsed after cell stimulation, thereby detecting the change over time (change with respect to real time or pseudo time estimated from the change) of the cell population of the same cell type. It should be noted that it is preferable to extract the cell characteristics that have changed over time and calculate the amount of change over time of the cell population. Here, the "change over time of the cell population" means the number of cells, cell characteristics (first cell characteristics), or other cell characteristics (that is, DNA sequence information of genes (genome), epigenetic information that controls gene expression (DNA methylation, histone methylation, acetylation, phosphorylation), gene primary transcript (mRNA, non-coding RNA, microRNA, etc.) information (transcriptome), protein translation amount and modification information such as phosphorylation, oxidation, glycosylation, 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 change over time of the biological substances of the cells.

[0047] Compare FIGS. 3A and 3B, which are the clustering results obtained in the grouping step (S4), and detect the changes in the cell characteristics (principal components 1 and 2, which are the first cell characteristics) of the cell population composed of cells of the same cell type. Specifically, by comparing the cell population 20 in FIG. 3A with the cell population 26 in FIG. 3B, the cell population 22 in FIG. 2A with the cell population 28 in FIG. 3B, and the cell population 24 in FIG. 3A with the cell population 30 in FIG. 3B, it is detected that there are changes over time in the cell characteristics of each cell population. Further, compare the number of cells that make up cell populations 20 and 26 of the same cell type from FIGS. 3A and 3B, and detect, as a change over time of the anticancer agent, the decrease in the number of cells 1 hour after cell stimulation in FIG. 3B relative to the number of cells immediately after cell stimulation in FIG. 3A (i.e., the number of cancer cells 2 that have died). Similarly, compare the number of cells that make up cell populations 24 and 30 of the same cell type from FIGS. 3A and 3B, and detect, as a change over time of the anticancer agent, the decrease in the number of cells 1 hour after cell stimulation in FIG. 3B relative to the number of cells immediately after cell stimulation in FIG. 3A (i.e., the number of cancer cells 6 that have died). On the other hand, compare the number of cells that make up cell populations 22 and 28 of the same cell type from FIGS. 3A and 3B, and detect the fact that there is no change in the number of cells before and after cell stimulation in FIG. 3A (i.e., there is no cell death of cancer cells 4 and the anticancer agent has no effect on the cell death of cancer cells 4).

[0048] Thus, in the present invention, since the cell characteristics of each cell population are displayed in association with the number of cells, it is possible to easily and accurately confirm or detect not only the presence or absence of cell death, but also changes in cell death and cell characteristics over time, and the relationship between cell death and cell characteristics.

[0049] <Mechanism of action analysis step (S6)> Next, in the mechanism of action analysis step (S6), based on the cell change detection results obtained in the cell change detection step (S5), analyze the mechanism of action of the changes within or between cell populations of the same cell type. Here, the mechanism of action is a specific action for a cell stimulation by a drug to exhibit its pharmacological effect, and means a specific biochemical reaction or interaction observed within or between cell populations of the same cell type.

[0050] In the present embodiment, the mechanism of action of the changes within or between cell populations is a biological phenomenon (e.g., proliferation, cell death, antigen-antibody reaction, secretion of growth factors, etc.) induced inside and outside the cell by cell stimulation, and more specifically, a specific biochemical reaction or interaction (e.g., metabolism of biological substances, gene expression, energy metabolism, signal transduction, etc.) for exhibiting the biological phenomenon induced inside and outside the cell by cell stimulation. In this embodiment, from the cell change detection results obtained in the cell change detection step (S5), by analyzing the mechanism of action as described above, the change in the number of cells in each cell population obtained in the cell change detection step (S5) and the factors involved in the change in cell characteristics (for example, biological substances, genes, etc.) are extracted.

[0051] Examples of the above mechanism of action analysis include, for example, analysis of gene expression profiles and analysis of molecular targets. For the analysis of gene expression profiles, for example, tensor decomposition can be used.

[0052] An example of the analysis of molecular targets will be described. First, identify genes whose expression changes due to a drug (cell stimulation) and whose change can be expected to match that due to a disease. Here, although the compound (drug) actually binds to a protein, the measured value is the expression level of mRNA. Since it is unlikely that the amount of mRNA of the gene encoding the protein changes when the compound (drug) binds to the protein, it is presumed that there is no molecular target (target protein) among the genes whose expression levels have changed. The effect of the compound on the other gene expression profiles of the protein to which it actually binds is expected to be similar to the case where the gene encoding the protein is knocked out. Therefore, the target gene is estimated by referring to the gene expression profile when genes are comprehensively knocked out.

[0053] To find the optimal application examples of cell stimulation from multiple disease or case type candidates, the order of effects can be obtained by simultaneously performing effect determination and mechanism of action analysis on multiple types of cells. Based on the comparison or order of those effects, the optimal indication or use can be found. Here, for the effect determination of cell stimulation, for example, by comparing the data without cell stimulation (drug) treatment and the time-dependent data with cell stimulation (drug) treatment, cells derived from the same cell population are compared in terms of biological data or cell characteristics and cell number, and if there is a change, it can be determined that there is an effect.

[0054] In the above grouping step (S4), when, from the single-cell data of each cell (information related to a plurality of biological substances), time-series data in which values representing the amounts or states of a plurality of biological substances are obtained in a plurality of time points for each biological substance in advance, and the single-cell data is grouped into cell populations having a common first cell feature based on the temporal change of the time-series data for each biological substance and the similarity of the biological functions of each biological substance, 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 features included in each of the plurality of cell populations, and the values representing the states of the plurality of cell populations at the plurality of time points thus generated are used to estimate the dependency relationship of the states between cell populations of the same cell type from a data set consisting of time-series data obtained in a plurality of time points for each biological substance.

[0055] The estimation of the dependency relationship of the states between cell populations can be performed, for example, as follows. For each cell type, find the change in the number of cells and the change in the gene expression level over time, and estimate the mechanism of action based on the temporal variation pattern of the genes. Genes with similar temporal change patterns of gene expression levels are classified, for example, into 27 groups (cell populations) by determining which of the three cases of the transition of the state values at three adjacent time points is increased, unchanged, or decreased with respect to a certain threshold. 3 = 27 groups (cell populations). Genes with similar biological functions are grouped using the Functional Annotation Clustering of the public web tool DAVID (https: / / david.ncifcrf.gov / ) to group genes having similar gene ontologies. Based on the similarity of the temporal changes and the similarity of the biological functions, the groups are grouped, and the temporal dependency relationship between the state values of each group (cell population) is estimated, for example, in light of a Bayesian network model or by associating between groups (between cell populations) based on temporal or biological relationships.

[0056] Since the method of Embodiment 1 performs single-cell analysis on all cells constituting a target cell group in which a plurality of different cell types are present and identifies the cell type of each cell, it is more accurate than the conventional method of artificially identifying the cell type of each cell from a target cell group in which a plurality of different cell types are present, and can analyze the identification of each cell type and the mechanism of action of drugs or cell stimuli on various cells. In addition, it is not necessary to spend time on image analysis or the like for the identification and selection of cell types, and even when performing drug efficacy screening on immune cells, it is not necessary to continue observing one sample until the analysis is completed. Therefore, the analysis can be performed without particularly restricting the analysis time. In addition, since single-cell analysis is used, the number of time points for observing (collecting) cells can be reduced compared to the case of artificially confirming cell changes and selecting target cells.

[0057] In addition, since the method of Embodiment 1 can visualize the state of cells at each time point by the grouping step (S4), in the cell change detection step (S5), the changes in each cell (cell population) can be easily confirmed. As a result, in the mechanism of action analysis step (S6), the cell types and genes that need to be analyzed can also be easily grasped. In addition, in the conventional screening method, only the presence or absence of cell death could be confirmed, and the cause of cell death could not be confirmed at the same time. However, according to the present Embodiment 1, by the grouping step (S4) and the cell change detection step (S5), it is possible to detect the presence or absence of changes in other cell characteristics as well as the presence or absence of cell death.

[0058] Modification of Embodiment 1 In Embodiment 1, in the above cell collection step (S1), cells collected immediately after cell stimulation and cells collected after culturing for a predetermined time after cell stimulation are collected, and analysis based on comparison of single-cell data of these cells is performed. However, the present invention is not limited to this. In the above cell collection step (S1), cells without cell stimulation (control sample) and cells with cell stimulation are prepared, and after culturing each for a predetermined time, the cells are collected, and screening analysis based on comparison of single-cell data of these cells may be performed.

[0059] Specifically, in the above cell collection step (S1), among the plurality of prepared containers 1, for the target cell group seeded in some of the containers 1, the cells are cultured without cell stimulation, and at two or more time points, except for the operation of collecting all the cells in one container, it is the same as in Embodiment 1.

[0060] In this way, by further preparing a control sample and performing analysis, it is possible to confirm whether it is an effect by a drug (cell stimulation) or an effect by other factors such as culture conditions. More specifically, in the mechanism of action analysis step (S6), the mechanism of action of a drug (cell stimulation) within or between individual cell populations can be analyzed, and further, the molecular target of cell stimulation can be analyzed, or an indication example assumed for treatment can be specified. Here, as indication examples assumed for treatment, for example, diseases or symptoms that can be expected to be improved by cell stimulation, or diseases or symptoms related to the molecular target can be cited.

[0061] Embodiment 2 In the cell change detection step of the above-described Embodiment 1 and the modified example of Embodiment 1, the clustering result (FIG. 3A) related to the cells collected immediately after cell stimulation and the clustering result (FIG. 3B) after a predetermined time has elapsed after cell stimulation are compared, and screening analysis of cell death is performed. However, the present invention is not limited to this. For example, detection and analysis can also be performed on temporal changes such as the shape of cells.

[0062] Embodiment 2 will be described by taking the monitoring of the time-dependent shape change of cells as shown in FIGS. 4A and 4B as an example. In Embodiment 2, cells 2, 4, and 6 are not cancer cells used in Embodiment 1. Specifically, cell 2 is a dendritic cell, cell 4 is a CD4-positive T cell, and cell 6 is a CD8-positive T cell. In addition, in Embodiment 2, the processes in the cell recovery step (S1), the single-cell formation step (S2), the single-cell data acquisition step (S3), the grouping step (S4), and the mechanism of action analysis step (S6) are the same as those in Embodiment 1. Therefore, the description of these steps is omitted.

[0063] FIGS. 4A and 4B are diagrams showing cells at the time of collecting all cells in the cell recovery step (S1). FIG. 4A shows the cells immediately after cell stimulation (after drug addition), and FIG. 4B shows the cells after a predetermined time (1 hour) has elapsed after cell stimulation. Cell 32 in the figure indicates that the shape of cell 2 has changed, and cell 34 indicates that the shape of cell 6 has changed.

[0064] FIGS. 5A and 5B show the clustering results obtained in the grouping step (S4). FIG. 5A shows the clustering result based on the single-cell data obtained from the target cell group immediately after cell stimulation in FIG. 4A, and FIG. 5B shows the clustering result based on the single-cell data obtained from the target cell group 1 hour after cell stimulation in FIG. 4B. Principal component analysis is performed on the single-cell data (gene expression level data) obtained from the target cell group immediately after cell stimulation in FIG. 4A and compressed into two dimensions, so that all cells are grouped into a plurality of cell populations 36, 38, and 40. Based on the single-cell data (DNA sequences and fluorescent labels 12, 14, and 18), it is identified that the cell type constituting cell population 36 is cell 2, the cell type constituting cell population 38 is cell 4, and the cell type constituting cell population 40 is cell 6. Similarly, after cell stimulation in Fig. 4B, principal component analysis is performed on single-cell data (gene expression level data) obtained from the target cell population after 1 hour has elapsed, and by compressing it into two dimensions, all cells are grouped into a plurality of cell populations 42, 44, 46, 48, and 50 (five cell populations), and based on the single-cell data (DNA sequences and fluorescent labels 12, 14, and 18), it is identified that the cell types constituting cell populations 42 and 44 are cell 2, the cell type constituting cell population 46 is cell 4, and the cell types constituting cell populations 48 and 50 are cell 6.

[0065] <Cell change detection step (S5)> In Embodiment 1, immediately after cell stimulation and after a predetermined time has elapsed, since the number of grouped cell populations was the same, only the detection of the temporal change of the cell population was performed by comparing the clustering result related to the cells immediately after cell stimulation with the clustering result related to the cells after a predetermined time has elapsed after cell stimulation. However, in Embodiment 2, immediately after cell stimulation and after a predetermined time has elapsed, since the number of grouped cell populations is different, furthermore, the temporal change of the cell population of the same cell type is detected from each clustering result.

[0066] By comparing Fig. 5A and Fig. 5B, it is detected that there is no change in the cell characteristics (first cell characteristics) of cell population 36 composed of cell 2 in Fig. 5A and cell population 42 composed of cell 2 in Fig. 5B. As a result, it is detected that cell population 42 in Fig. 5B is the cell population before the change. That is, it can be estimated (or confirmed) that cell population 42 in Fig. 5B is the cell population composed of cell 2 before the cell shapes in Figs. 4A and 4B change. Similarly, by comparing Fig. 5A and Fig. 5B, it is detected that there is no change in the cell characteristics (first cell characteristics) of cell population 38 composed of cell 4 in Fig. 5A and cell population 46 composed of cell 4 in Fig. 5B. That is, it can be estimated (or confirmed) that cell population 46 in Fig. 5B is the cell population composed of cell 4 in Figs. 4A and 4B. Similarly, by comparing FIGS. 5A and 5B, it is detected that there is no change in the cell characteristics (first cell characteristics) of the cell population 40 composed of the cells 6 in FIG. 5A and the cell population 48 composed of the cells 6 in FIG. 5B. As a result, it is detected that the cell population 48 in FIG. 5B is the cell population before the change. That is, it can be estimated (or confirmed) that the cell population 48 in FIG. 5B is the cell population composed of the cells 6 before the shapes of the cells in FIGS. 4A and 4B change.

[0067] Subsequently, in the clustering result shown in FIG. 5B, a cell population showing the same cell type as the cell population 42 is searched for, and the cell population 44 is detected. As a result, a change over time from the cell population 42 to the cell population 44 (see the solid line in FIG. 6) is detected. That is, it is detected that the cell population 44 is the cell population composed of the cells 32 after the shapes of the cells 2 in FIGS. 4A and 4B change, avoiding confusion with cell populations of different cell types and without being associated with changes over time from those cell populations of different cell types or changes over time to those cell populations of different cell types (see, for example, the dashed line in FIG. 6), and it can be estimated (or confirmed). Similarly, a change over time from the cell population 48 to the cell population 50 (see the solid line in FIG. 6) is detected. That is, it is detected that the cell population 50 is the cell population composed of the cells 34 after the shapes of the cells 6 in FIGS. 4A and 4B change, avoiding confusion with cell populations of different cell types and without being associated with changes over time from those cell populations of different cell types or changes over time to those cell populations of different cell types (see, for example, the dashed line in FIG. 6), and it can be estimated (or confirmed).

[0068] The method of Embodiment 2 also performs single-cell analysis on all the cells constituting a target cell group in which a plurality of different cell types exist, and identifies the cell type of each cell. Therefore, like the conventional screening method, as shown in FIG. 7, without performing drug screening by cell type, the identification of each cell type and the change of cells due to a drug or cell stimulation can be easily and accurately detected for each cell type.

Example

[0069] Hereinafter, the present invention will be described more specifically by way of examples, but the present invention is not limited to these examples.

[0070] [Example 1] Human breast cancer cell lines MCF-7, T-47D, SK-Br-3, and MDA-MB-231 were mixed at a ratio of 1:1:1:1 (number of cells) and seeded in a 6-well plate, followed by culturing for 24 hours. After confirming cell attachment, physiological saline, doxorubicin solution, paclitaxel solution, carboplatin solution, fluorouracil solution, and epirubicin solution were added to each well. At the time of addition (0 hours later), 6 hours after addition, 12 hours after addition, and 24 hours after addition, the cells were trypsinized and collected. The cell dispersion was diluted to 1000 cells / μL, and single cells were captured using a C1 system (manufactured by Fluidigm). Target primers for 500-610 and 750-870 of the TP53 gene were added to a cDNA preparation kit (SMARTer (R) Ultra (R) Low RNA Kit, manufactured by Clontech), and cell lysis, reverse transcription of mRNA, and cDNA pre-amplification were performed. The obtained cDNA was recovered, and a concentration higher than 0.05 ng / μL was selected for library preparation. Library preparation was performed using the Nextera (R) XT DNA Sample Preparation Kit (manufactured by Illumina).

[0071] For the prepared library, sequencing was performed using a next-generation sequencer (HiSeq (R) 2500 System, manufactured by Illumina) with 2×100 bp paired-end reads. The gene expression level for each cell was calculated from the obtained data, the time-point samples were grouped for each drug, and clustering analysis was performed using principal component analysis. Cells were grouped into the SK-Br-3 strain cluster for cells in which 524A (the 524th base is A) was detected from the results of the TP53 target sequence, the T-47D strain cluster for cells in which 580T (the 580th base is T) was detected, the MDA-MB-231 strain cluster for cells in which 839A (the 839th base is A) was detected, and the MCF-7 strain cluster for those that did not fall into any of the above (cells in which 524G (the 524th base is G), 580C (the 580th base is C), or 839G (the 839th base is G) was detected).

[0072] For each cell type, changes in the number of cells and gene expression levels over time were examined, and the mechanism of action was estimated based on the temporal variation patterns of the genes. Genes with similar temporal change patterns of gene expression levels were specifically classified into 27 (= 3 to the 3rd power) groups by determining which of three states the transition of the state values at three adjacent time points fell into: increased, unchanged, or decreased, relative to a certain threshold. Genes with similar biological functions were grouped using the Functional Annotation Clustering of the public web tool DAVID (https: / / david.ncifcrf.gov / ) to group genes with similar gene ontologies. As a result of grouping based on the similarity of temporal changes and the similarity of biological functions, 82 groups were obtained. When the temporal dependence relationship between the state values of the 82 groups was estimated in light of the Bayesian network model, the mechanism of the anti-cancer effect could be grasped. As an effect of fluorouracil on the MCF-7 strain, it was possible to observe that apoptosis was induced from a decrease in DNA replication over time. In addition, it was observed that the drug sensitivity and mechanism of action differed for each cell.

[0073] [Example 2] Human primary hepatocytes, human Kupffer cells, and immortalized human hepatic stellate cells transfected with hTERT (human telomerase reverse transcriptase) were mixed at a ratio of 2:1:1 (cell number ratio) and seeded, followed by culturing for 24 hours. After confirming viability, the cells were exposed to physiological saline, acetaminophen, carbamazepine, amiodarone, rosiglitazone, benzbromarone, and isoniazid. At the time of exposure (0 hours), 6 hours, 12 hours, 24 hours, 48 hours, and 72 hours after exposure, the cells were trypsinized and collected. The collected cells were immunostained with ASGPR1 antibody and EpCAM antibody and made into single cells using FACS (Fluorescence Activated Cell Sorting).

[0074] For the collected single cells, cell lysis, reverse transcription of mRNA, and cDNA pre-amplification were performed using a cDNA preparation kit (SMARTer (R) Ultra (R) Low RNA Kit, manufactured by Clontech). The obtained cDNA was recovered, and a concentration higher than 0.05 ng / μL was selected for library preparation. Library preparation was performed using the Nextera (R) XT DNA Sample Preparation Kit (manufactured by Illumina). For the prepared library, sequencing was performed using a next-generation sequencer (HiSeq (R) 2500 System, manufactured by Illumina) with 2×100 bp paired-end reads. Also, after lysing the cells, the amounts of albumin, LDH (lactate dehydrogenase), CD68, CD11b, and CD14 in the residual solution from which mRNA was recovered were measured. The gene expression levels per cell were calculated from the obtained data, the time-point samples were grouped for each drug, and clustering analysis was performed using principal component analysis.

[0075] Cells with detected albumin and LDH were grouped into the human primary hepatocyte cluster, cells with detected CD68, CD11b, and CD14 were grouped into the human Kupffer cell cluster, and cells with detected hTERT gene were grouped into the immortalized human hepatic stellate cell cluster. For each cell type, changes in the number of cells and gene expression levels over time were determined, and the mechanism of action was estimated based on the temporal variation patterns of gene expression levels.

[0076] [Example 3] Mouse embryonic fibroblasts were seeded in 6-well plates, and after 24 hours, a suspension of human iPS cells supplemented with the ROCK (Rho-associated coiled-coil forming kinase / Rho-binding kinase) inhibitor Y-27632 (10 μL; manufactured by Fujifilm Wako Pure Chemical Corporation) was seeded 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 hours), 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 single cells were captured using a C1 system (manufactured by Fluidigm). Subsequently, SMARTer (R) Ultra (R) Low RNA Kit was used to lyse the cells, reverse-transcribe the mRNA, and pre-amplify the cDNA. The obtained cDNA was recovered, and a concentration higher than 0.05 ng / μL was selected for library preparation. Library preparation was performed using the Nextera (R) XT DNA Sample Preparation Kit (manufactured by Illumina).

[0077] For the prepared libraries, sequencing was performed using a next-generation sequencer (HiSeq (R) 2500 System, manufactured by Illumina) with 2 × 100 bp paired-end reads. The gene expression levels for each cell were calculated from the obtained data, and the time-point samples were grouped for each drug, followed by clustering analysis using principal component analysis. The cells identified whether they were human iPS cells or mouse embryonic fibroblast cells from the sequence data, found the variation in the number of cells and the variation in the gene expression level over time, and obtained the time-varying pattern of genes. By grouping and patterning the time-varying patterns of each gene of human iPS cells and further calculating the temporal dependence, the changes from iPS cells to embryoid bodies were observed.

[0078] 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 various improvements or modifications may of course be made without departing from the gist of the present invention.

Claims

1. A cell recovery step of preparing at least two or more predetermined containers in which a target cell group containing a plurality of different types of cells is seeded, culturing the target cell group by applying a predetermined cell stimulation to the target cell group, and recovering all the cells in one container at two or more time points; A single cell isolation step of isolating all the cells collected at each time point into single cells; A single cell data acquisition step of acquiring single cell data from each single cell at each time point; a grouping step of grouping all the cells collected at each time point into a plurality of cell populations having a common first cell characteristic based on the single cell data at each time point, plotting the grouped cell populations on a two-dimensional plane or a three-dimensional space, and identifying the cell type of each grouped cell population based on a second cell characteristic, thereby obtaining a clustering result at each time point; a cellular change detection step of detecting a change over time in the cell population of the same cell type by comparing the clustering results at each time point; an action mechanism analysis step of analyzing the action mechanism of the change over time of the cell population of the same cell type based on the detection result; Including, The mechanism of action analysis step is a cell information processing method in which, for each of the cell populations of the same cell type, at least one first cell population obtained by grouping all of the cells collected at each time point based on the similarity in the pattern of fluctuation in the gene expression level at each time point, and at least one second cell population obtained by grouping all of the cells collected at each time point based on the similarity in the biological function of the genes in the gene expression level, are linked by either a chronological relationship, a biological relationship, or a Bayesian network, thereby estimating the interaction of states between the cell populations.

2. The cell information processing method according to claim 1, wherein the cell change detection step detects changes over time in the number of cells constituting the cell population of the same cell type and changes over time in the first cell characteristic by comparing the clustering results at each time point.

3. The cell information processing method according to claim 2, wherein the cell change detection step further detects a change over time in the number of cells constituting the cell population of the same cell type and a change over time in the first cell characteristic in the clustering results at each time point.

4. The cell recovery 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 recovering all the cells in one of the predetermined containers at one or more time points; The cell information processing method according to any one of claims 1 to 3, wherein the cell change detection step evaluates changes over time in the cell population of the same cell type due to the presence or absence of the specified cell stimulation of the cell population by comparing the clustering results at each time point.

5. The cell information processing method according to any one of claims 1 to 4, wherein in the action mechanism analysis step, a molecular target of the cell stimulation is analyzed.

6. The cell information processing method according to any one of claims 1 to 4, wherein in the mechanism of action analysis step, a molecular target of the cell stimulation is analyzed to identify examples of application for potential treatment.

7. The cell information processing method according to claim 6 , wherein the anticipated therapeutic application is a disease or symptom that can be improved by cell stimulation, or a disease or symptom associated with a molecular target.

8. The method for processing cell information according to claim 6 or 7, wherein the molecular target is a molecule inside or outside a cell on which a cell stimulus acts directly or indirectly.

9. The method for processing cell information according to any one of claims 1 to 6, wherein the cell stimulus is at least one selected from the group consisting of a chemical stimulus and a physical stimulus.

10. The method for processing cell information according to claim 9 , wherein the chemical stimulus is achieved by adding a drug that induces a biological response in the cells.

11. The cell information processing method according to any one of claims 1 to 10, wherein the mechanism of action is a specific biochemical reaction or interaction for exerting a biological phenomenon induced inside or outside the cell by the cell stimulation.

12. The cell information processing method according to any one of claims 1 to 11, wherein the single cell data is information on biological materials that indicate the function, properties, and state of a single cell, and is at least one selected from the group consisting of DNA sequence information of genes, epigenetic information that controls gene expression, gene primary transcript information, protein translation amount and modification information on phosphorylation, oxidation, and glycation, amino acid sequence information, metabolic product information, intracellular hydrogen ion concentration exponent (pH), intracellular ATP concentration, ion concentration, and intracellular temperature.

13. The cell information processing method according to claim 12 , wherein the single-cell data is a gene expression level and a DNA sequence of the gene.

14. The cell information processing method according to claim 13 , wherein in the grouping step, the first cell feature is a dimensional reduction of an n-dimensional cell feature contained in the single cell data to two or three dimensions.

15. The cell information processing method according to claim 14, wherein the dimensionality reduction method is at least one selected from the group consisting of principal component analysis (PCA), kernel-based principal component analysis (Kernel-PCA), multidimensional scaling (MDS), t-SNE, and convolutional neural network (CNN).

16. The cell information processing method according to claim 14 or 15, wherein in the grouping step, the first cell feature is obtained by performing principal component analysis on the gene expression levels and reducing the dimensions to two or three dimensions.

17. The cell information processing method according to any one of claims 1 to 16, 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.

18. The cell information processing method according to claim 17, wherein the cell information is at least one selected from the group consisting of DNA sequence information of genes, epigenetic information controlling gene expression, gene primary transcript information, protein translation amount and modification information on phosphorylation, oxidation and glycation, amino acid sequence information, metabolic product information, intracellular hydrogen ion concentration exponent (pH), intracellular ATP concentration, ion concentration and intracellular temperature.

19. The method for processing cell information according to claim 17 or 18, wherein the cell function is at least one selected from cell proliferation, repair, metabolism, and intercellular information exchange.

20. The method for processing cell information according to claim 17 or 18, wherein the state of the cell is at least one selected from the group consisting of gene expression state, protein expression state, and enzyme activity.

21. creating time-series data for each biological substance at a plurality of time points from the single-cell data, and grouping the cells from which the single-cell data has been obtained into cell populations having a common first cell characteristic based on the time-series data for each biological substance and the similarity in biological function of each biological substance; The cell information processing method according to any one of claims 1 to 20, wherein the similarity in biological function is evaluated based on at least one selected from the group consisting of having a common gene ontology, belonging to a common canonical pathway, having a common upstream factor, being involved in a common phenotype, and being involved in a common disease.

22. The cell information processing method according to any one of claims 1 to 21, wherein in the cell recovery step and the single cell generation step, the method of recovering whole cells and generating single cells is a method using at least one selected from the group consisting of manual, flow cytometry, magnetic separation, laser capture microdissection, microchannel, microdroplet, nanowell, micropipette fine needle aspiration, laser tweezers, labeled array, surface plasmon response, and nanobiodevice.

23. The cell information processing method according to claim 22, wherein in the method of recovering whole cells and converting them into single cells, at least one selected from the group consisting of fluorescent labeling, radioisotope labeling, antibody labeling, and magnetic labeling is used as a cell label.

24. The cell information processing method according to any one of claims 1 to 23, wherein the target cell group containing multiple types of cells is at least one selected from the group consisting of a biological tissue sample, a blood sample, a culture sample, and an environmental sample.

25. The cell information processing method according to claim 24 , wherein the multiple 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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