WO2020071501A1 - Cell-information processing method - Google Patents

Abstract

The purpose of the present invention is to provide a cell-information processing method for efficiently analyzing mechanisms of action of changes in individual cell populations in the presence of a mixture of different 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; and 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.

Description

Cell information processing method

The present invention relates to a cell information processing method. In particular, the present invention relates to a method for identifying each cell type, which is performed in a target cell group including a plurality of different cell types, or a method for screening the efficacy and pharmacological of each cell type.

When searching for the optimal indication or usage / capacity of a drug, determine the effect of cell stimulation by the drug etc. on multiple cells, and select the drug and the optimal indication from the results of analyzing the mechanism of action. .
Conventionally, as a screening method for such a drug efficacy and pharmacology, as shown in FIG. 7, a drug is given to a cell population consisting of only individual cell types 54, 56, and 58 arranged in each well 52, and cultured. Then, a method of analyzing the efficacy 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 efficacy is determined using the averaged data of the cell population. Analysis methods have 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. 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 molecular biological analysis (single cell) at the level of individual cells (single cells) has been performed. Analysis) has been performed. Such single-cell analysis has enabled analysis of individual cells, rather than analysis of averaged data of cell populations. Changes can be detected. Such single cell analysis is used, for example, to classify cell subspecies present in cancer tissue 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 1 has been proposed.

Patent Literature 1 discloses a method for evaluating cell activity, in which (a) a step of setting a cell population containing a plurality of different types of cells on a region, that is, a different plurality of cells are placed 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 the dynamic behavior; (d) Characterizing the molecular profile of the identified at least one cell of interest, i.e., mass spectrometry, genetic analysis, protein analysis at the single cell level for the identified at least one cell of interest. Analyze, etc., 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). Proposed.
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-T-2018-514195

However, according to the method described in Patent Document 1, 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 behaviors. 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 and identify and select the cells to be analyzed, there is a problem that it is not possible to obtain highly accurate analysis results. .
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 analysis is performed while repeatedly selecting and culturing cells to be analyzed), when the method described in Patent Document 1 is employed, all the cells are administered within at least 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 Literature 1 requires time and cost for cell identification and selection of target cells, 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.

In the initial stage of drug search using the drug efficacy and pharmacological screening method as shown in FIG. 7, interaction analysis with different cells is performed based only on the judgment of no cell death caused by the 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.

Therefore, the present invention has been made in view of the above-described problems of the related art, and when a plurality of different types of cell populations are mixed, cell information for efficiently analyzing the mechanism of change of each cell population. It is an object to provide a processing method.
More specifically, identification of each cell type, or drug efficacy and pharmacological screening performed in a cell population in which a plurality of cell types are present, can be evaluated and analyzed with a simpler operation, with high accuracy and speed. It is an object to provide a method that 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 tumor by comparing the clustering results at the respective time points, based on the detection result, Analyzing the mechanism of the temporal change in the cell population of the same cell tumor.

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. Preferably, it is 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 detects 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. Is preferred.

In the mechanism of action analysis step, the molecular target of the cell stimulation may be analyzed.
In the mechanism-of-action analysis step, the molecular target of the cell stimulation may be analyzed to identify an indication for treatment.
It is preferable that the indication example assuming the above treatment is a disease or condition that can be improved by cell stimulation, or a disease or condition related to a molecular target.
Preferably, the molecular target is a molecule inside or outside a cell on which cell stimulation acts directly or indirectly.

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 an n-dimensional cell feature included in the single cell data by 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 cell growth, repair, metabolism, and information exchange between cells.
The above-mentioned cell state is preferably the state of gene expression, the state of protein expression, and the enzymatic activity.

From the single cell data, values representing the amounts or states of a plurality of biological substances, time-series data acquired at a plurality of time points for each biological substance are created in advance, and the time change of the time-series data for each biological substance, Based on the similarity of the biological function of the biological material, the cells from which the single cell data was acquired are grouped into a cell population having a common first cell characteristic, and the similarity of the biological function is common. At least one selected from the group consisting of having a 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 the following.

In the above-mentioned cell collection step and the above-mentioned single cell forming step, all cells are collected, and as a method for forming a single cell, manual, flow cytometry, magnetic separation, laser capture microdissection, micro channel, micro droplet, nanowell, At least one selected from the group consisting of micropipette microneedle aspiration, laser tweezers, label arrays, surface plasmon response, and nanobiodevices can be used.
In the above method of collecting all cells and converting them into 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 can be used as a cell label.
As the target cell group including 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 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.

Advantageous Effects of Invention According to the present invention, it is possible to provide a cell information processing method for efficiently analyzing an action mechanism of a change in a cell population of each cell type when a plurality of different 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 an efficient method that can be used on an industrial scale.

According to the present invention, since humans do not arbitrarily judge the dynamic behavior of cells over time, the identification of each cell type, the action mechanism of drugs or cell stimulation on various cells, with high accuracy , Can be analyzed.
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. FIG. 2B is a diagram showing cells after a predetermined time has elapsed after cell stimulation. FIG. 3A is a diagram showing a result of the clustering according to FIG. 2A. FIG. 3B is a diagram showing a result of the clustering according to FIG. 2B. FIG. 4A is a diagram showing a cell population immediately after cell stimulation. FIG. 4B is a diagram showing a cell after a predetermined time has elapsed after cell stimulation. FIG. 5A is a diagram showing a result of the clustering according to FIG. 4A. FIG. 5B is a diagram showing a result of the clustering according to FIG. 4B. FIG. 6 is a diagram showing the change over time of the cell population based on FIG. 5B. FIG. 7 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
In the first embodiment, a screening analysis of an anticancer agent 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 cancer cells 2, 4, and 6 are prepared. Next, an anticancer agent is added to the target cell group seeded in the prepared container 1 (that is, cell stimulation is applied) and cultured, and at two or more time points (for example, immediately after the addition of the drug, for 1 hour after the addition of the drug) , 6 hours, 12 hours, 24 hours, 48 hours, 72 hours...), And collected all cells in one container 1 among at least two or more containers 1. The number of containers 1 used at each time is not limited to one as long as the number of containers used for cell collection at each time is the same, and may be plural.
FIG. 2A shows cells immediately after the cell stimulation (after the addition of the drug), and FIG. 2B shows cells after a predetermined time (1 hour) after the cell stimulation.

《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 analysis is performed using cancer cells, but the type of cells included in the target cell group is 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.

Also, 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.

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, 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 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 time, and then given a drug and cell stimulation.

<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 and 2B, the fluorescent labels 12, 14 and 18 have been applied to the cancer cells 2, 4 and 6, respectively.
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), DNA sequence information, a fluorescent label, and a gene expression amount of the gene are acquired as single cell data from each cell collected at each time point and made into a single cell. Single cell data is acquired for all single cells that have been converted into single cells and collected.
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》
Note that the single cell data means information of a biological substance indicating a function, property, or 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 information such as phosphorylation, oxidation, glycation, amino acid sequence information (proteome), metabolite information (metabolome), intracellular hydrogen ion concentration index (pH), The intracellular 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 The cell type of each cell population is divided into groups based on a second cell characteristic capable of discriminating each cell type included in the single cell data. Identify.
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 constituting each cell population and the fluorescent label (second cell characteristic), a process of identifying the cell type of each grouped cell population (clustering analysis) is performed.
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, the cell characteristics of each cell population and the number of cells are visualized in association with each other, so that it is possible to easily confirm or detect the relationship between cell death and cell characteristics simultaneously and with high accuracy.

Note that the number of first cell features used for grouping 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.

3A shows a clustering result based on single cell data obtained from the target cell group immediately after the cell stimulation of FIG. 2A, and FIG. 3B shows target cell after a predetermined time (one hour) after the cell stimulation of FIG. 2B. 9 shows a clustering result based on single cell data obtained from a group.
Principal component analysis is performed on the single cell data (gene expression amount data) obtained from the target cell group immediately after the cell stimulation in FIG. 2A, and the cells are compressed two-dimensionally, so that all cells are divided into a plurality of cell populations 20, 22, and 24. , And based on the single cell data (DNA sequence and fluorescent labeling 12, 14, and 18), the cell type constituting the cell population 20 is the cancer cell 2, and the cell type constituting the cell population 22 is the cancer cell 2. It is identified that the cell type is cell 4 and the cell type constituting the cell population 24 is cancer cell 6.
Similarly, by performing principal component analysis on single cell data (gene expression level data) obtained from the target cell group after a predetermined time (one hour) after the cell stimulation in FIG. 2B, and compressing the two-dimensional data, All cells are grouped into a plurality of cell populations 26, 28 and 30, and based on single cell data (DNA sequence and fluorescent labeling 12, 14 and 18), the cell type constituting the cell population 26 is cancer cell 2 It is identified that the cell type constituting the cell population 28 is the cancer cell 4 and the cell type constituting the cell population 30 is the cancer cell 6.

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) Information on biological materials of cells that can be used (included in single data), such as DNA sequence information of genes (genome) and epigenetic information that controls gene expression (DNA methylation, histone methylation, 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, Thorium, 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 included.

<Cell change detection step (S5)>
Next, in the cell change detecting step (S5), the clustering result of the cells immediately after the cell stimulation acquired in the grouping step (S4) is compared with the clustering result of the cells that have passed a predetermined time after the cell stimulation. , The change over 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 cell characteristics that have changed over time and calculate the amount of the temporal change.
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 and 3B, 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 (the main components 1 and 2, which are the first cell characteristics) Detect changes in Specifically, a comparison of the cell population 20 of FIG. 3A with the cell population 26 of FIG. 3B, a comparison of the cell population 22 of FIG. 2A with the cell population 28 of FIG. 3B, a cell population 24 of FIG. The comparison with the population 30 detects that there is a change over time in the cell characteristics of each cell population.
3A and 3B, the numbers of cells constituting the cell populations 20 and 26 of the same cell type were compared, and the number of cells 1 hour after the cell stimulation in FIG. That is, the number of cell deaths of the cancer cells 2) is detected as a change with time of the anticancer agent. Similarly, the numbers of cells constituting the cell populations 24 and 30 of the same cell type are compared from FIGS. 3A and 3B, and the number of cells one hour after the cell stimulation in FIG. (That is, the number of cell deaths of the cancer cells 6) is detected as a change over time of the anticancer agent. On the other hand, comparing the numbers of cells constituting the cell populations 22 and 28 of the same cell type from FIGS. 3A and 3B, there is no change in the number of cells before and after the cell stimulation in FIG. And the cell death of the cancer cells 4 is not affected by the anticancer drug).

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 / absence of cell death but also changes in cell death and cell characteristics over time, and The relationship between death and 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.

Examples of the mechanism of action analysis include analysis of a gene expression profile and analysis of a molecular target.
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.

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. Generating a value representing the state of the cell population from one or more first cell characteristics included in each of the plurality of cell populations at each of the plurality of time points. Generated values representing the state of a plurality of cell populations at a plurality of time points are extracted from a data set consisting of time-series data obtained at a plurality of time points for each biological material. State dependencies between cell types of the cell population can be estimated.

The estimation of the state dependency between cell populations can be performed, for example, as follows.
For each type of cell, the change in the number of cells and the change in the amount of gene expression over time are found, and the mechanism of action is estimated based on the time change pattern of the gene. Genes having a similar pattern of temporal change in gene expression level are identified as, for example, three out of three states in which the transition of the state value at two adjacent two time points is increased, unchanged, or decreased according to a certain threshold value. Is determined, and classified into 9 ³ = 27 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.

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 possible to identify each cell type and analyze the mechanism of action of drugs or cell stimulation on various cells with higher accuracy than the conventional method of artificially identifying the cell type of each cell from the target cell group where it can.
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. In addition, as a result, in the action mechanism analysis step (S6), the cell type, gene, and the like that need to be analyzed can be easily grasped.
Further, in the conventional screening method, only the presence or absence of cell death can be confirmed, but the cause of cell death cannot be simultaneously confirmed. However, according to the first embodiment, the grouping step (S4) and the detection of cell change are performed. By the step (S5), the presence or absence of cell death and the presence or absence of a change in other cell characteristics can be detected.

Modified Example of Embodiment 1 In Embodiment 1, in the 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 The analysis based on the comparison of the single cell data is performed, but is not limited thereto. In the cell collection step (S1), a cell without cell stimulation (control sample) and a cell with cell stimulation are prepared. After culturing for a predetermined time, the cells may be collected, and a screening analysis based on 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 At this point, the operation is the same as that of the first embodiment, except for the operation of collecting all cells in one container.

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.

Embodiment 2
In the above-described first embodiment and the cell change detection step of the modification of the first embodiment, the clustering result (FIG. 3A) relating to the cells collected immediately after the cell stimulation and the clustering result after a predetermined time has elapsed after the cell stimulation (FIG. 3A). Although screening analysis for cell death was performed in comparison with FIG. 3B), the present invention is not limited to this. For example, detection and analysis can also be performed on changes over time in the shape of cells and the like.

The second embodiment will be described as an example of monitoring a change in shape of a cell over time as shown in FIGS. 4A and 4B. In the second embodiment, the cells 2, 4 and 6 are not the cancer cells used in the first embodiment, but the cell 2 is a dendritic cell, the cell 4 is a CD4-positive T cell, and the cell 6 is a CD8 Positive T cells.
In the second embodiment, the processes in the cell collection step (S1), the single cell conversion step (S2), the single cell data acquisition step (S3), the grouping step (S4), and the action mechanism analysis step (S6) are as follows. The description of these steps is omitted because it is the same as in the first embodiment.

4A and 4B are diagrams showing cells at the time of collecting all cells in the cell collection step (S1), and FIG. 4A shows cells immediately after cell stimulation (after addition of a drug). Indicates cells that have passed a predetermined time (1 hour) after cell stimulation. The cell 32 in the figure shows the cell 2 whose shape has changed, and the cell 34 has the cell 6 whose shape has changed.

5A and 5B show the clustering results obtained in the grouping step (S4). FIG. 5A shows a clustering result based on the single cell data obtained from the target cell group immediately after the cell stimulation in FIG. 4A, and FIG. 5B shows the single cell data obtained from the target cell group one hour after the cell stimulation in FIG. 4B. 2 shows a clustering result based on.
Principal component analysis is performed on the single cell data (gene expression amount data) obtained from the target cell group immediately after the cell stimulation shown in FIG. 4A, and the two-dimensional compression is performed, so that all cells become a plurality of cell populations 36, 38, and 40. , And based on the single cell data (DNA sequence and fluorescent labels 12, 14, and 18), the cell type constituting the cell population 36 is the cell 2, and the cell type constituting the cell population 38 is the cell 4 And that the cell type constituting the cell population 40 is the cell 6.
Similarly, by performing principal component analysis on single cell data (gene expression amount data) obtained from the target cell group one hour after the cell stimulation in FIG. And cell groups 42, 44, 46, 48, and 50 (5 cell populations), and based on single cell data (DNA sequence and fluorescent labels 12, 14, and 18), constitute cell populations 42 and 44. The cell type that forms the cell population 46 is the cell 4, and the cell type that forms the cell populations 48 and 50 is the cell 6.

<Cell change detection step (S5)>
In the first embodiment, since the number of the grouped cell populations was the same immediately after the cell stimulation and after the lapse of the predetermined time, the clustering results of the cells immediately after the cell stimulation and the cells that passed the predetermined time after the cell stimulation Only the change with time of the cell population was detected by comparison with the clustering result according to the above. In the second embodiment, the number of grouped cell populations differs immediately after the cell stimulation and after a predetermined time has elapsed. Therefore, a change over time of the cell population of the same cell type is detected from each clustering result.

5A and FIG. 5B, it is confirmed that there is no change in the cell characteristics (first cell characteristics) of the cell population 36 composed of the cells 2 of FIG. 5A and the cell population 42 composed of the cells 2 of FIG. 5B. As a result, it is detected that the cell population 42 in FIG. 5B is the cell population before the change. That is, it can be estimated (or confirmed) that the cell population 42 in FIG. 5B is a cell population composed of the cells 2 before the shape of the cells in FIGS. 4A and 4B is changed.
Similarly, FIG. 5A and FIG. 5B are compared, and changes are found in the cell characteristics (first cell characteristics) of the cell population 38 composed of the cells 4 of FIG. 5A and the cell population 46 composed of the cells 4 of FIG. 5B. Detect that there is no. That is, it can be estimated (or confirmed) that the cell population 46 of FIG. 5B is a cell population composed of the cells 4 of FIGS. 4A and 4B.
Similarly, FIG. 5A and FIG. 5B are compared, and a change is found in the cell characteristics (first cell characteristics) of the cell population 40 composed of the cells 6 of FIG. 5A and the cell population 48 composed of the cells 6 of FIG. 5B. Is detected, and 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 a cell population composed of the cells 6 before the shape of the cells in FIGS. 4A and 4B is changed.

Subsequently, in the clustering result shown in FIG. 5B, a cell population showing the same cell type as the cell population 42 is searched to detect the cell population 44, and as a result, the change over time from the cell population 42 to the cell population 44 ( (See the solid line in FIG. 6). That is, detecting that the cell population 44 is a cell population composed of the cells 32 after the shape of the cell 2 in FIGS. 4A and 4B has changed is avoided by avoiding confusion with a cell population of a different cell type. In addition, it is estimated (or confirmed) without being linked to the time-dependent change from the cell population of the different cell type or the time-dependent change to the cell population of the different cell type (for example, see the broken line in FIG. 6). can do.
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, the fact that the cell population 50 is a cell population composed of the cells 34 after the shape of the cell 6 in FIGS. 4A and 4B has changed is avoided by avoiding confusion with the cell populations of different cell types, It can be estimated (or confirmed) without being linked to the change over time from the cell population of the cell type or the change over time to the cell population of the different cell type (for example, see the broken line in FIG. 6).

The method of Embodiment 2 also 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. Thus, as shown in FIG. 7, without performing drug screening for cell types, identification of each cell type, and change of cells due to drug or cell stimulation for each cell type can be easily and accurately performed. Can be detected.

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 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 (cell number) and seeded on a 6-well plate. For 24 hours.
After confirming the engraftment, physiological saline, doxorubicin solution, paclitaxel solution, carboplatin solution, fluorouracil solution, and epirubicin solution were added to each well.
At the time of addition (after 0 hour), 6 hours, 12 hours, and 24 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).
cDNA preparation kit ^{(SMARTer (R) Ultra (R} ) Low RNA kit, Clontech) was added to target primers 500-610 and 750-870 of the TP53 gene, lysis of the cells, reverse transcription and cDNA preamplification of mRNA 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 by next-generation sequencing ^(HiSeq (R) manufactured by 2500 system, Illumina) and sequenced using terminal reading of 2 × 100 bp. 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 in which 524A (the 524th base is A) were detected from the results of the target sequence of TP53 were replaced with SK-Br-3 strain clusters, and cells in which 580T (the 580th base was T) were detected as T cells. The cells in which 839A (base 839 is A) were detected in the -47D strain cluster and those which did not correspond to any of the MDA-MB-231 strain clusters (524G (the 524th base is G), 580C (580 The cell in which the 9th base was C) or 839G (the cell in which the 839th base was G) was grouped into the MCF-7 strain cluster, respectively.

(4) For each type of cell, we found changes in cell number and gene expression over time, and estimated the mechanism of action based on the time-varying pattern of genes. Genes having a similar pattern of temporal change in gene expression level, specifically, the transition of state values at three adjacent two time points were increased, unchanged, or decreased according to a certain threshold. It was determined which of the three was the case, and was classified into 27 (= 3 to the third power) groups. Genes having similar biological functions were grouped by using the functional {Annotation} Clustering of public web tool DAVID (https://david.ncifcrf.gov/), and genes having similar gene ontology. Grouping based on similarity in temporal changes and similarity in biological function resulted in 82 groups. When the temporal dependence between the state values of the 82 groups was estimated in the light of a Bayesian network model, the mechanism of the anticancer action could be captured. As an effect of fluorouracil on the MCF-7 strain, it was observed that the apoptotic effect was induced in a time series from the decrease in DNA replication effect. It was also observed that the sensitivity and mechanism of action of the drug differed from cell to cell.

[Example 2]
Human primary hepatocytes, human Kupffer cells, and immortalized human hepatic stellate cells into which hTERT (human telomerase reverse transcriptase) has been introduced are mixed and seeded at a ratio of 2: 1: 1 (cell number ratio). For 24 hours.
After confirming engraftment, physiological saline, acetaminophen, carbamazepine, amiodarone, rosiglitazone, benzbromarone, and isoniazid were exposed, and at the time of exposure (0 hour), 6 hours, 12 hours, and 24 hours after exposure At 48, and 72 hours later, cells were trypsinized and collected.
The collected cells were immunostained with an ASGPR1 antibody and an EpCAM antibody, and made into single cells using FACS (Fluorescence Activated Cell Sorting: fluorescence-activated cell sorting).

The recovered single cell, cDNA preparation kit ^{(SMARTer (R) Ultra (R} ) Low RNA kit, Clontech) lysis of cells using, 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 by next-generation sequencing ^(HiSeq (R) manufactured by 2500 system, Illumina) and sequenced using terminal reading of 2 × 100 bp.
After the cells were lysed, the amount of albumin, the amount of LDH (lactate dehydrogenase), the amount of CD68, the amount of CD11b, and the amount of CD14 in the residual solution from which the mRNA was collected were measured.
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 in which albumin and LDH were detected were in human primary hepatocyte clusters, cells in which CD68, CD11b, and CD14 were detected were in human Kupffer cell clusters, and cells in which hTERT gene was detected were immortalized human hepatic stellate cells Each was grouped into clusters.
For each cell type, we found changes in cell number and gene expression over time, and estimated the mechanism of action based on the pattern of gene expression over time.

[Example 3]
Mouse embryo-derived fibroblasts were seeded on a 6-well plate, and 24 hours later, ROCK (Rho-associated coiled-coil forming kinase / Rho binding kinase) inhibitor Y-27632 (10 μL; Fujifilm Wako Pure Chemical Industries, Ltd.) was added to human iPS cells. (Supplier) was further 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 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 by next-generation sequencing ^(HiSeq (R) manufactured by 2500 system, Illumina) and sequenced using terminal reading of 2 × 100 bp. 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.
From the sequence data, the cells were identified as human iPS cells or mouse embryo-derived fibroblasts, cell number fluctuations and gene expression level fluctuations over time were determined, and gene time fluctuation patterns were obtained. The time-varying patterns of each gene of human iPS cells were grouped and patterned collectively, and the temporal dependence was calculated, whereby changes from iPS cells to embryoid bodies were observed.

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 (25)

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 tumor,
   Analyzing the mechanism of the temporal change in the cell population of the same cell tumor based on the detection result.
2. 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, wherein the cell information is detected.
3. 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 2.
4. 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. 4. The cell information processing method according to any one of items 3 to 3.
5. The cell information processing method according to any one of claims 1 to 4, wherein in the action mechanism analyzing 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 specify an application example for which treatment is assumed.
7. 7. The cell information processing method according to claim 6, wherein the indication example assuming the treatment is a disease or condition that can be improved by cell stimulation, or a disease or condition related to a molecular target.
8. 細胞 The cell information processing method according to claim 6 or 7, wherein the molecular target is a molecule inside or outside a cell that acts directly or indirectly on cell stimulation.
9. 細胞 The cell information processing method 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 cell information processing method according to claim 9, wherein the chemical stimulus is caused by the addition of an agent that induces a biological response to the cell.
11. The 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 and outside the cell by the cell stimulation. The cell information processing method according to the above.
12. 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 11, wherein the method is at least one.
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 method according to any one of claims 1 to 13, wherein in the grouping step, the first cell feature is obtained by reducing an n-dimensional cell feature included in the single cell data by two or three dimensions. The cell information processing method according to the above.
15. 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 14, which is at least one selected.
16. The method according to any one of claims 13 to 15, 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.
17. 17. The method according to claim 1, wherein, in the grouping step, the second cell feature is at least one cell information capable of identifying each cell type from a function or a state of the cell. 3. The cell information processing method according to item 1.
18. 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 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 17, 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.
19. 19. The cell information processing method according to claim 17, wherein the function of the cell is at least one selected from cell growth, repair, metabolism, and information exchange between cells.
20. 19. The cell information processing method according to claim 17, wherein the cell state is at least one selected from a gene expression state, a protein expression state, and an enzyme activity.
21. 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 was obtained are grouped into a cell population having a common first cell characteristic,
    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 The cell information processing method according to any one of claims 1 to 20, wherein the method is evaluated based on at least one selected from the group consisting of:
22. 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 21, which is a method using at least one selected from the group consisting of micropipette fine needle aspiration, laser tweezers, a label array, a surface plasmon response, and a nanobiodevice. Processing method.
23. 23. The method according to claim 22, wherein in the method of collecting all 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.
24. 24. The cell according to claim 1, wherein the target cell group including the plurality of 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. Cell information processing method.
25. The cell information processing method according to claim 24, 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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