WO2020138351A1 - 細胞並べ替え（階層化）処理システム。 - Google Patents

細胞並べ替え（階層化）処理システム。 Download PDF

Info

Publication number
WO2020138351A1
WO2020138351A1 PCT/JP2019/051270 JP2019051270W WO2020138351A1 WO 2020138351 A1 WO2020138351 A1 WO 2020138351A1 JP 2019051270 W JP2019051270 W JP 2019051270W WO 2020138351 A1 WO2020138351 A1 WO 2020138351A1
Authority
WO
WIPO (PCT)
Prior art keywords
cell
cells
spatial structure
data
processing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2019/051270
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
正憲 宮西
正和 中島
功 宮塚
マイ アン リー
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nextgem Inc
Original Assignee
Nextgem Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nextgem Inc filed Critical Nextgem Inc
Priority to JP2020562445A priority Critical patent/JPWO2020138351A1/ja
Priority to JP2021566786A priority patent/JPWO2021131110A1/ja
Priority to US17/789,279 priority patent/US20230039070A1/en
Priority to PCT/JP2020/025915 priority patent/WO2021131110A1/ja
Publication of WO2020138351A1 publication Critical patent/WO2020138351A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M1/00—Apparatus for enzymology or microbiology
    • C12M1/34—Measuring or testing with condition measuring or sensing means, e.g. colony counters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G01N33/487—Physical analysis of biological material of liquid biological material
    • G01N33/49—Blood
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
    • C12M41/46—Means for regulation, monitoring, measurement or control, e.g. flow regulation of cellular or enzymatic activity or functionality, e.g. cell viability
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12M—APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY; APPARATUS FOR CULTURING MICROORGANISMS FOR PRODUCING BIOMASS, FOR GROWING CELLS OR FOR OBTAINING FERMENTATION OR METABOLIC PRODUCTS, i.e. BIOREACTORS OR FERMENTERS
    • C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
    • C12M41/48—Automatic or computerized control
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B99/00—Subject matter not provided for in other groups of this subclass

Definitions

  • the present invention relates to an optimization (learning) method of a cell rearrangement (hierarchicalization) algorithm, a high-precision classification ability model learned by the optimization method, a rare cell identification method using the high-precision classification ability model, and , And a system having the same high-precision classification ability model.
  • ⁇ Cells in living organisms basically have the same gene set, and despite being produced by the division of a single cell, a fertilized egg cell, their biological functions are different.
  • a cell is distinguished from other cells as a cell (group) having a certain biological property based on its unique biological function. For example, cells found in various tissues in the living body, such as bone marrow and muscle, differ in morphology and biological function.
  • Stem cells such as hematopoietic stem cells (HSCs), are known to have the biological functions of self-renewal and pluripotency.
  • Non-patent document 1 Among stem cells having self-renewal and pluripotency, it is known that there are cells that maintain the biological functions that characterize these stem cells for a long period of time and stem cells that lose their functions in a relatively short period of time.
  • Stem cells that maintain self-renewal and pluripotency over a long period of time can be mass-cultured by utilizing their self-renewal ability, and also utilize their pluripotency. Then, they can be differentiated into various cells. Stem cells having such characteristics are considered to be a trump card when putting regenerative medicine into practical use. However, all of these stem cells are extremely rare in the living body and cannot be easily identified and isolated from the living body.
  • various cells of the living body are unique in various physical characteristics such as intracellular content of proteins expressed by each gene, types of proteins exposed on the cell surface, post-translational modification state of proteins, and cell morphology. It is possible to identify cells that have a state and the combination of these physical properties has a certain biological function.
  • fluorescently labeled monoclonal antibodies that recognize molecules such as proteins expressed on the surface of each cell, and bound antibodies A method using a flow cytometer capable of separating cells according to the combination of the fluorescent labels has been mainly used.
  • the present invention optimizes a cell sorting (stratification) algorithm that estimates a cell differentiation hierarchy based on quantitative physical property data of cells, thereby hierarchically classifying a target cell population according to a differentiation hierarchy.
  • An accurate classification ability model is provided, and by using the high accuracy classification ability model, a method for identifying and isolating cells located in a specific layer of a cell differentiation hierarchy and a system using the same are provided.
  • various cells in the living body have various physical properties such as intracellular content of proteins expressed by each gene, types of proteins exposed on the cell surface, post-translational modification state of proteins, and cell morphology. It is possible to identify cells that have a unique state and the combination of these physical properties has a unique biological function. These physical properties can be quantified based on a certain standard by various known methods, and therefore various cells having a certain biological function can be identified by a combination of the quantitative physical property data. can do.
  • cell differentiation is hierarchical, and it is understood that cells that are more closely related in the cell differentiation hierarchy are more similar in their quantitative physical property data.
  • the target cell population is similar to the cell differentiation hierarchy. It is considered possible to obtain a dendrogram obtained by Similarly, by performing a non-hierarchical clustering process that divides the processing target into a plurality of groups based on the similarity of the processing target, a plurality of cell groups (cell fraction ).
  • each cell cluster (fraction) and/or each cell has a mutual positional relationship based on the similarity of the quantitative physical property data set.
  • a cell fraction arrangement spatial structure and/or a cell arrangement spatial structure which are defined and include these positional relationships are obtained.
  • cell fractions and/or cells having a similar quantitative physical property data set occupy close positions.
  • a cell population obtained from a certain amount of living tissue contains cell groups belonging to various positions in the differentiation lineage.
  • the cells and/or cells occupy close positions in the cell fraction arrangement spatial structure and/or the cell arrangement spatial structure obtained by the clustering process. Therefore, the cell fraction arrangement spatial structure or the cell arrangement spatial structure obtained by the above clustering process has a structure similar to the cell differentiation lineage tree (FIG. 2).
  • the technique of the present invention in the cell population that changes along the time series, by arranging the cell population based on the quantitative physical property data obtained from the cell population at a specific time point, It is possible to reproduce changes that have been made.
  • -It is possible to express and/or analyze the temporal transition in the differentiation process of each cell by using the cell fraction arrangement spatial structure or the cell arrangement spatial structure (pseudo-cell lineage). For example, by visualizing the cell fraction arrangement spatial structure and/or the cell arrangement spatial structure by an arbitrary method, it is possible to identify the early stage of differentiation, that is, to identify cells close to stem cells and to grasp the relationship between branched differentiation lineages. It becomes possible to support.
  • the result of such clustering processing depends on setting parameters such as the number of clusters to be divided.
  • various tissue stem cells having the characteristics of stem cells and fractions containing the same have already been identified in each tissue.
  • the clustering process in the layering algorithm can be optimized based on the known fractionation information, and thereby a layering result that reflects a more biological characteristic difference can be obtained.
  • the cell groups showing similarities are the same or
  • the clustering process can be optimized so that it is hierarchically divided into neighboring fractions.
  • a discriminator that classifies known stem cells in a tissue to be examined based on the quantitative physical characteristic data can be configured by using general machine learning. Then, by analyzing the discriminator (learned model) trained so that the stem cells can be discriminated in this way, one or more quantitative physical characteristic data important for discriminating the stem cells can be obtained from the differentiation lineage. Identification Quantitative physical property data can be specified.
  • the clustering process is optimized by using the information about the known stem cells and/or one or more differentiation lineage quantitative quantitative physical property data specified by the trained model learned using the information. By categorizing, target cells can be rearranged (hierarchical) according to the cell differentiation lineage.
  • the clustering processing algorithm optimized in this manner is used for operating a computer to generate cell fraction arrangement spatial structure data or cell arrangement spatial structure data in which cells are grouped or hierarchized according to a cell differentiation lineage.
  • a high-accuracy cell classification model and a program that encodes it can be applied to a cell classification system (FIG. 3).
  • the present inventor actually applied the above method to a hematopoietic cell population, and by using a long-term hematopoietic stem cell as a known stem cell, extremely accurately reproduces a cell differentiation hierarchy, and is close to this in a hematopoietic stem cell and a differentiation lineage. It was confirmed that a cell classification system based on clustering processing capable of identifying cells could be constructed.
  • the cell classification method and the cell classification system using the same may include the following steps. (1) a step of acquiring physical property data of cells, (2) A step of layering cells based on the physical property data to generate cell fraction arrangement spatial structure data or cell arrangement spatial structure data, (2-1) a step of identifying a stratified fraction or cells containing a target cell population, (2-2) optionally optimizing the stratification process based on the target cell population or physical properties of the cells, A process including. Furthermore, the cell classification method and the cell classification system using the same may include the following steps.
  • the present invention provides the following in one aspect.
  • the isolated cell population containing a specific cell lineage is subjected to clustering processing based on the quantitative physical data of a plurality of types of individual cells of the cell population, and cells similar to the cell lineage are obtained.
  • Item 2 Item 2. The method according to Item 1, wherein the clustering process is optimized based on quantitative physical data of cells whose position on the cell differentiation lineage in the cell population is known.
  • the optimization is based on differentiation lineage quantitative physical property data obtained by analyzing a discriminator learned to classify cells whose position on the cell lineage in the cell population is known.
  • the method according to item 2 which is optimized.
  • (Item 4) 4. The method according to any one of Items 1 to 3, wherein (1) downsampling processing, (2) data normalization processing, and/or (3) dimension reduction processing are performed before the clustering processing.
  • (Item 5) After the clustering processing (4), (5) visualization data processing for generating image data for visually expressing the obtained cell fraction arrangement spatial structure or cell arrangement spatial structure, (6) using input analysis parameters An analysis process for analyzing and/or modifying the cell fraction arrangement spatial structure or the cell arrangement spatial structure based on the above, and/or (7) cell fraction arrangement spatial structure and/or cells included in a partial region of the cell arrangement spatial structure 5.
  • An automatic gating parameter set generation process for generating an automatic gating parameter set that is a combination of quantitative physical property data (quantitative physical property data set) that defines a fraction or a cell, and performs any one of items 1 to 4.
  • the method described in. (Item 6) 6.
  • the method according to any one of Items 1 to 6 is executed by being executed by at least one processor, and a cell fraction arrangement spatial structure or data expressing a cell arrangement spatial structure is generated as a clustering processing result.
  • a computer program causing the at least one processor to function.
  • a system including a computer (1) An input unit for inputting quantitative physical data (2) A processing unit for performing clustering processing (3) An output unit for outputting the cell fraction arrangement spatial structure and/or the cell arrangement spatial structure obtained by the clustering processing Then
  • the isolated cell population containing a specific cell differentiation lineage is subjected to clustering processing based on a plurality of types of quantitative physical data acquired for individual cells of the cell population, and a cell image is obtained.
  • the above system for obtaining a spatial arrangement of cells or a spatial arrangement of cells. Item 9
  • Item 10 Item 10.
  • an image output unit that outputs the cell fraction arrangement spatial structure and/or the cell arrangement spatial structure as image data.
  • a cell fraction arrangement spatial structure and/or a specific area of the cell arrangement spatial structure and an input unit that specifies processing to be performed on the area are input to the area.
  • the system according to Item 10 having a process input unit that specifies a process to be performed.
  • Item 13 7. A method according to items 1 to 6 executed by at least one processor executing computer readable instructions, said one processor being executed by executing said instructions.
  • the clustering optimization technique of the present invention it is possible to obtain a cell hierarchy (sorting) algorithm according to a biological rule, that is, a cell differentiation lineage.
  • a cell hierarchy sorting
  • the "cell rearrangement algorithm” to stratify a given cell population, it is possible to extremely rapidly locate a specific hierarchy of the differentiation lineage in the cell population, for example, the most upstream of the cell differentiation lineage. It is possible to identify or enrich cells.
  • tissue stem cells or cancer stem cells it becomes possible to identify, for example, tissue stem cells or cancer stem cells at a cost of several thousandth of a period as compared with the conventional method.
  • the “cell rearrangement algorithm” of the present invention identification of various cell states becomes easy, and current/future profiling (health, disease state, etc.) of each individual (individual) is performed, and individual Information can be provided to provide a specific treatment method. Further, by investigating the relationship between the identification of the cell state of the individual and the medication history and/or the content of the meal, it is possible to support the screening of the individualized therapeutic drug and/or the individualization guidance of the medication and the diet. .. Further, by applying the cell sorting algorithm of the present invention to a cell population obtained after culturing purified cells isolated from a cell population once obtained from a living body for a certain period, differentiation of the purified cells It becomes possible to evaluate the form. Furthermore, by clarifying the relationship between the culture conditions when the purified cells are cultured for a certain period of time and the evaluation of the differentiation mode, the optimum culture condition screening for enriching specific differentiated cells from the purified cells can be performed. I can help.
  • FIG. 1 is a schematic diagram showing a cell differentiation lineage in a living tissue.
  • FIG. 2 is a schematic diagram showing the similarity between a cell differentiation lineage in a biological tissue and a cell fraction arrangement spatial structure or a cell arrangement spatial structure generated by the high-accuracy classification ability model of the present invention.
  • FIG. 3 is a schematic diagram showing an aspect of a system having the high-precision classification ability model of the present invention.
  • FIG. 4 is a schematic diagram showing an aspect of processing contents included in the high-accuracy classifying ability model of the present invention.
  • FIG. 5 is a schematic diagram showing another aspect of the processing content included in the high-accuracy classification ability model of the present invention.
  • FIG. 6 is a schematic diagram showing an aspect of a system having the high-accuracy classification ability model of the present invention.
  • FIG. 7 is a schematic diagram showing an image displayed on the display unit of the interface in one mode of the system having the high-accuracy classification ability model of the present invention.
  • FIG. 8 is a schematic diagram showing another aspect of the system having the high-accuracy classification ability model of the present invention.
  • “differentiation” means a process in which a cell having a low degree of specialization changes into a more specialized cell such as a nerve cell or a muscle cell. "Differentiated” or “differentiated” is a relative term and “differentiated cell” or “differentiated cell” is further advanced in the developmental pathway than the cell with which it is being compared, Means that it is specialized.
  • stem cell means an undifferentiated cell that has the ability of self-renewal at the single cell level and the ability to produce two or more different differentiated cells. That is, stem cells are capable of dividing asymmetrically, where one daughter cell retains the stem cell state and the other daughter cell expresses some distinct and distinct biological function and phenotype. Alternatively, stem cells can divide symmetrically into two stem cells, so that some stem cells are maintained in a cell population with stem cells, while other cells in the population are differentiated into progeny. Cause only.
  • stem cells may include pluripotent stem cells, tissue stem cells, etc., where “pluripotent” means the ability to differentiate into cells of multiple but limited number of lineages. .. “Tissue stem cells” include, for example, ectodermal lineage stem cells, mesoderm lineage stem cells and endoderm lineage stem cells. Whether or not the obtained cells are various stem cells can be examined by the presence or absence of expression of a specific marker gene.
  • the “stem cell” also includes the “cancer stem cell” described below.
  • the “rare cell” means a cell having a small number in the living body.
  • cells located upstream of the hierarchy are generally considered to be rare cells because they are rare in the living body.
  • Such rare cells can be, but are not limited to, pluripotent stem cells, oligopotent stem cells, unipotent progenitor cells.
  • cancer stem cells there are cells that have self-renewal ability and ability to give rise to cells of various lineages that make up tumors (multipotency) in cancer tissues. Such cells are called “cancer stem cells”. “It is called. "Cancer stem cells” are sometimes called “tumor progenitor cells” or “tumorigenic/carcinogenic cells”.
  • cancer stem cells also have a differentiation lineage with cancer stem cells as the most upstream. Therefore, in the present specification, the “rare cells” also include cells in cancer tissues such as “cancer stem cells”.
  • hematopoietic stem cells may be “hematopoietic stem cells”.
  • the “hematopoietic stem cell” means a cell that has a pluripotency throughout the life capable of being finally differentiated into all blood cells (erythrocytes, leukocytes, megakaryocytes, platelets, etc.). Hematopoietic stem cells may include intermediate stages of differentiation into progenitor or blast cells. “Progenitor cells” or “blasts” are used interchangeably in the present invention and have a reduced differentiation potential, but the ability to still mature into different cells of a particular lineage (eg myeloid or lymphoid lineage). It means a cell that is maturing.
  • a particular lineage eg myeloid or lymphoid lineage
  • hematopoietic stem cells are identified by the markers Lin ⁇ , c-Kit + , Sca-1 + , CD150 + , CD34 ⁇ /low , Flk2 ⁇ , markers Lin ⁇ , CD48 ⁇ , CD41 ⁇ , CD150 +, etc. (Cell, 2005, Vol. 121, pp. 1109-1121).
  • rare cells may be “long-term hematopoietic stem cells”.
  • “Long-term hematopoietic stem cell” means a hematopoietic stem cell that maintains self-renewal ability for a long time even after cell division, and at least one of the two daughter cells produced by cell division is the same hematopoietic stem cell as the cell before division. Maintain the trait of.
  • the long-term hematopoietic stem cells do not lose any self-renewal ability by cell division in a normal culture environment in vivo or a culture environment in vitro.
  • the long-term hematopoietic stem cells maintain their self-renewal ability even after undergoing 100, 50, 20 or 10 cell divisions.
  • the maintenance of the self-replicating ability can be confirmed by various known methods. For example, it is possible to confirm that the self-renewal ability is maintained by measuring the content rate of the undifferentiated cell population contained in the cell group after culturing for a certain period.
  • the undifferentiated cell population can be identified by a positive/negative combination of known markers such as c-Kit, Sca-1, and CD11b.
  • isolated cell means a cell taken from the organism in which it is originally found or a progeny of such a cell. Such cells may have been cultured in vitro, for example in the presence of other cells. Further, such cells or their progeny cells may be introduced into a second organism later.
  • the term “isolated cell population” means a population of cells taken out and separated from a heterogeneous cell population.
  • the isolated population may be a substantially pure cell population relative to the heterogeneous cell population from which the cells were isolated or enriched, and is still heterogeneous. It may be a cell population.
  • a cell population is “substantially pure” for a particular cell type, in that cell population is at least about 75%, more preferably at least about 85%, 90% of the cells that make up the entire cell population. It means that 92%, 93%, most preferably at least about 95%, 96%, 97%, 98%, 99% of cells are composed of cells of the particular cell type.
  • a "substantially pure" stem cell population is less than about 25% non-stem cells, more preferably about 15%, 10%, 8%, less than 7%, most preferably about 5%, 4%, By a population of cells containing less than 3%, 2% or 1%.
  • the term “identification”, “separation”, “isolation”, “purification” or “screening” refers to a target such as an organism, cell, substance or data having a certain specific property of interest. It refers to selecting from a group containing many by the operation/evaluation method.
  • clustering process or simply “clustering” generally means a data process of dividing a set of classification targets into subsets in which internal coupling and external separation are achieved (B S. Everitt: Cluster Analysis, Edward Arnold, third edition (1993), Yasuo Ohashi: Introduction to classification method, measurement and control, Vol. 2015, Vol. 2, no. 2, pp165-193), and is generally classified into “non-hierarchical clustering” and “hierarchical clustering”.
  • Non-hierarchical clustering means data processing that determines the evaluation function of goodness of division and searches for a division that optimizes the evaluation function.
  • Non-hierarchical clustering is not particularly limited and can be performed using a method known to those skilled in the art.
  • the non-hierarchical clustering of the present invention can be performed using the k-means method, a mixed Gaussian model (Gaussian Mixture Model, GMM), etc., but is not limited thereto.
  • Hierarchical clustering means data processing that regards each target as a separate cluster and integrates clusters with a high degree of similarity sequentially based on certain criteria to obtain a final classification result.
  • the processing result is expressed as a dendrogram (dendrogram) that connects all the processing targets.
  • the hierarchical clustering analysis is not particularly limited and can be performed using a method known to those skilled in the art.
  • the hierarchical clustering of the present invention can be performed using, but is not limited to, the group averaging method, the Ward method, the UPGMA, the shortest distance method, the simple connection method, the longest distance method, the complete connection method, and the like.
  • quantitative physical property data means the intracellular content of proteins expressed from each gene, the types of proteins exposed on the cell surface, and the post-translational protein, measured on various cells of the living body. It refers to quantitative data of various physical properties such as the state of modification and cell morphology. Cells with unique biological functions can be identified by a combination of these quantitative physical property data. These physical properties can be quantified by various known methods on the basis of a certain standard, and for example, they can be obtained using a device such as a flow cytometer, a next-generation sequencer (NGS), or a technique such as imaging. However, it is not limited to these. ..
  • “differentiation lineage discrimination quantitative physical characteristic data” has the greatest effect on the classification when a classifier that classifies known stem cells in the tissue under consideration is configured using machine learning. Means one or more quantitative physical property data.
  • the quantitative physical characteristic data by analyzing the learned discriminator (learned model) by a method known to those skilled in the art, specifies the importance in the classification of each quantitative physical characteristic data used for classification, It can be specified by ranking these. For example, the quantitative physical characteristic data of the first rank or a plurality of quantitative physical characteristic data of higher ranks in the rank can be selected as the quantitative physical characteristic data of the differentiation line identification.
  • the number of the quantitative physical property data can appropriately select the number of the quantitative physical property data, depending on the number of the quantitative physical property data to be used, the number of objects to be classified, the required classification accuracy, and the like.
  • the distribution state of the importance of each quantitative physical characteristic data may be considered. For example, in terms of importance, 1st, 1st-2nd, 1st-3rd, 1st-4th, 1-5th, 1-6th, 1-7th, 1-8th, 1-9th or 1st It is possible to select the quantitative physical characteristic data of the 10th to 10th positions as the differentiation line identification quantitative physical characteristic data.
  • the present invention in a cell population that changes along a time series, based on quantitative physical property data obtained from the cell population at a specific time point, arranges the cell population to reproduce the change along the time series. Is what you can do.
  • the cell fraction arrangement space or the cell arrangement space is generated by using the quantitative physical property data of the cell population obtained from the subject at a plurality of time points.
  • quantitative physical property data of cell populations obtained from similar tissues of different subjects may be mixed and used. Therefore, the quantitative physical property data of the present invention may be obtained from a cell population obtained from a single or multiple subjects at multiple time points.
  • machine learning means finding out a pattern hidden in data (learning data) in a certain aspect by a computer according to a normal understanding by those skilled in the art. Further, in a certain aspect, it means a method of configuring a discriminator for discriminating data by learning data obtained previously, and thereby discriminating and interpreting newly acquired data.
  • the trained classifier is sometimes referred to as a "trained model”.
  • the method of performing machine learning can be selected from, but not limited to, artificial neural network learning, decision tree learning, support vector machine learning, Bayesian network learning, clustering, and regression analysis, AdaBoost, and the like.
  • the “flow cytometer” generally means a known technique for detecting scattered light and fluorescence of individual cells, which is constituted by three systems of a flow channel system, an optical system and an electrical system. Information such as the relative size and internal structure of the cell can be obtained from the detected scattered light, and information such as the amount of various antigens and nucleic acids present in the cell membrane, cytoplasm and nucleus can be obtained from the fluorescence signal.
  • the scattered light is further classified into two types of forward scattered light (FSC: Forward Scattered Light) and side scattered light (SSC: Side Scattered Light) depending on the direction of scattering.
  • the FSC is light detected ahead of the optical axis of the laser beam among scattered lights, and its intensity is approximately proportional to the surface area or size of the cell.
  • SSC is light detected at an angle of 90° with respect to the optical axis of the laser beam.
  • Most of SSC is a substance scattered by the light hitting the intracellular substance, and is almost proportional to the granular property and the internal structure of the cell.
  • a photodiode is used as the FSC signal detector
  • a highly sensitive photomultiplier tube is used as the SSC and fluorescence signal detector.
  • Each detector detects an optical signal generated when a cell crosses a laser beam and generates a voltage pulse in proportion to its intensity, whereby the height (Height: H) and area (Area) of the voltage pulse are detected. : A) and width (Width: W) values are recorded. These values for FSC and SSC are commonly denoted as FSC-H, FSC-A, FSC-W, SSC-H, SSC-A, SSC-W, and so on.
  • a flow cytometer is capable of simultaneously detecting fluorescence in multiple wavelength regions.For example, by labeling multiple types of cell surface molecules with multiple types of fluorescent labels each having a different emission wavelength region, each cell can be labeled. The amount of these cell surface molecules in can be quantified simultaneously. Analysis using the "flow cytometer" technique is called “flow cytometry”.
  • the cell fraction arrangement spatial structure or the cell arrangement spatial structure obtained as a result of cell stratification or rearrangement treatment has a structure similar to a cell differentiation lineage tree, or
  • the cell fraction arrangement spatial structure or each cell fraction arranged in the cell arrangement spatial structure or the positional relationship of each cell is , Has a positional relationship according to the order of biological cell differentiation.
  • the two cell fractions or cells that are close to each other in the spatial structure are more closely related to each other in the cell lineage than the farther cell fractions or cells.
  • the arrangement of cells in the arrangement spatial structure and the order of biological cell differentiation do not have to be 100% coincident.
  • the matching degree is 95%, 90%, 85%, 80%, 75% or 70%. Good.
  • the quantitative physical property data of the target cells used in the present invention can be obtained by various known methods that can quantify the physical property data for each cell.
  • the flow cytometer can be used as the method, but the method is not limited thereto.
  • the cell layering algorithm of the present invention is based on a clustering process based on quantitative physical property data of target cells and its optimization technique.
  • the clustering process is optimized for the target cell population in which the cells of a specific stratum in the cell differentiation stratum have already been identified.
  • a cell having a certain biological function is expressed by a combination of quantitative physical characteristic data, and a clustering process is performed on a target cell population having various different biological functions based on the quantitative physical characteristic data. It can be carried out.
  • these cells can be divided into multiple cell groups (fractions), and when hierarchical clustering is performed, each of these cells is represented by a hierarchical relationship such as a dendrogram. The result is obtained. Further, in both non-hierarchical clustering and hierarchical clustering, it is possible to obtain a positional relationship between cells based on the similarity of quantitative physical property data sets.
  • a positional relationship between each cell cluster (fraction) and/or each cell based on the similarity of the quantitative physical property data set is defined, and these positions are defined.
  • a cell fraction arrangement spatial structure and/or a cell arrangement spatial structure including a relationship is obtained.
  • cell fractions and/or cells having a similar quantitative physical property data set occupy close positions.
  • a cell population obtained from a certain amount of living tissue contains cell groups belonging to various positions in the differentiation lineage.
  • the cells and/or cells occupy close positions in the cell fraction arrangement spatial structure and/or the cell arrangement spatial structure obtained by the clustering process. Therefore, the cell fraction arrangement spatial structure or the cell arrangement spatial structure obtained by the above clustering process has a structure similar to the cell differentiation lineage tree.
  • the clustering process of the present invention comprises quantitative physical property data obtained by performing (1) downsampling process, (2) data normalization process and/or (3) dimension reduction process on the quantitative physical property data.
  • (4) Data clustering processing is performed on the set.
  • the (1) downsampling step the number of data to be subjected to the clustering process is reduced based on the quantitative physical property data of the analysis target, the distribution of cells, and the like.
  • a known method such as Density Sampling, SPADE, or Decimate can be applied to the step, but is not limited thereto.
  • the (2) data processing step a known method is used to normalize the quantitative physical property data, convert the unit, and the like.
  • the dimension reduction processing the dimension of the quantitative physical property data is reduced by using a known method. For example, known methods such as PCA, MDS, T-SNE, UMAP, PHAT, and Modified Locally Linear Embedding can be applied, but not limited thereto.
  • the quantitative physical property data of the target cell is not particularly limited, and various data obtained by known methods can be used. For example, it is possible to obtain quantitative data on the physical properties such as the intracellular content of the protein expressed from each gene, the type of the protein exposed on the cell surface, the post-translational modification state of the protein, and the cell morphology. Yes, these can be used as quantitative physical property data. In a specific embodiment, these quantitative physical property data can be obtained using a flow cytometer, and various antibodies and reporter proteins can also be used. Any number of quantitative physical property data can be used, for example 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17.
  • the classification ability in the clustering process depends on the combination of the quantitative physical property data set for each type of cell, the setting parameter of the clustering process, and the like.
  • the target cell population can be classified into some form by appropriately setting the type of quantitative physical property data.
  • the combination of quantitative physical property data may be such that the values have variations to the extent that each cell can be distinguished in the target cell population, and usually, by increasing the types of quantitative physical property data, Usually, it is possible to secure a sufficient variation.
  • such variation of quantitative physical characteristic data can be quantitatively evaluated by a value such as variance, standard deviation, or coefficient of variation
  • the combination of quantitative physical characteristic data used in the present invention is For example, in the target cell population, it can be obtained by combining two or more characteristic data in which the values of “variance”, “standard deviation” or “variation coefficient” among cells are constant or more. Specific numerical values of these values can be appropriately set by those skilled in the art based on the target cell population, the equipment used, the accuracy of target cell classification, and the like.
  • the coefficient of variation is 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.15, 0.
  • the quantitative physical property data having values of 0.2, 0.3, 0.4, 0.5, etc. can be used in combination, but is not limited thereto.
  • the expression level of cKit, Sca-1, CD11b, CD34, Slamf1 etc. on the cell surface can be used as quantitative physical property data.
  • the result of the clustering process depends not only on the type of clustering algorithm used, but also on the setting parameter at the time of clustering such as the number of clusters.
  • various tissue stem cells having the characteristics of stem cells and fractions containing the same have already been identified in each tissue.
  • the clustering process can be optimized based on the known fractional information, and thereby a hierarchical result that reflects a more biological characteristic difference can be obtained. For example, with respect to quantitative physical property data indicating a plurality of cell surface molecules that are known to be uniquely expressed in a cell group whose position in a cell differentiation line is known, for example, stem cells, the cell groups showing similarities are the same or
  • the clustering process can be optimized so that it is hierarchically divided into neighboring fractions.
  • a classifier that classifies known stem cells in the tissue under consideration based on the quantitative physical property data is configured using a general machine learning method. Then, by analyzing the discriminator (learned model) that has been trained to identify stem cells in this way, the importance of each quantitative physical property data used for classification in the classification is identified, and the stem cells are identified.
  • One or a plurality of quantitative physical characteristic data that are important for the identification are identified as the differentiation line identification quantitative physical characteristic data.
  • the optimization is performed by changing the setting parameter of the clustering process, and for example, it can be performed by setting the number of clusters so as to minimize the cluster including the differentiated lineage discrimination quantitative physical characteristic data. , But is not limited to this.
  • the optimization can also be performed by changing the setting parameters of the processing of (1) downsampling processing, (2) data processing and/or (3) dimension reduction processing combined with the clustering processing.
  • the clustering process optimized so as to more accurately reflect the cell differentiation hierarchy in the living body includes the above (1) downsampling process, (2)
  • the target cell group can be used as a processing system that causes a computer to perform cell layering processing for rearranging cell groups into cell differentiation layers.
  • the processing system used for cell layering processing using the computer is referred to as a "high-accuracy classification ability model" (FIG. 4).
  • the model is configured as a program instructing the computer to perform processing, and can be stored in a computer-readable storage medium.
  • the high-precision classification ability model of the present invention is optionally combined with the above (1) downsampling processing, (2) data processing and/or (3) dimension reduction processing, and then (4) clustering processing. , (5) Visualization data processing for generating image data for visualizing the cell fraction arrangement spatial structure or the cell arrangement spatial structure obtained by the high-accuracy classification ability model, (6) based on the input analysis parameter Analytical treatment for analyzing and/or modifying a cell fraction-arranged spatial structure or a cell-arranged spatial structure, and (7) a cell fraction-arranged spatial structure and/or a cell fraction or cells contained in a partial region of the cell-arranged spatial structure It may have an automatic gating parameter set generation process for generating an automatic gating parameter set made up of a combination of quantitative physical property data (quantitative physical property data set) that defines (FIG.
  • a publicly known method can be used for the above-mentioned visualization data processing, for example, Minimum Spanning Tree, ElPiGraph, etc. can be used, but not limited to these.
  • a known method can be used to generate the automatic gating parameter set, and for example, flow Density can be used, but the method is not limited thereto.
  • cells of a specific layer in the cell differentiation hierarchy of the cell group for example, stem cells, have a higher differentiation hierarchy. It is possible to specify a candidate for a rare cell located upstream, and to rapidly identify the rare cell.
  • the high-precision classification ability model can also be applied to cell groups obtained from tissues similar to the cell group used for optimization.
  • the position of the rare cells in the differentiation hierarchy of the identified cells using the highly accurate classification ability model of the present invention can be confirmed based on a known biological experiment method.
  • Such an experiment can be performed by an experimental method known to those skilled in the art, for example, an in vitro differentiation induction experiment or an in vivo passage transplantation experiment.
  • the high-precision classification ability model of the present invention By applying the high-precision classification ability model of the present invention to various tissues and confirming the results by biological experiments, it becomes possible to generate a high-precision classification ability model applicable to a wider range of cell populations. Ultimately, it is reasonably understood that the high-precision classification ability model of the present invention can be applied to all tissues in the living body.
  • cancer stem cells there are cells that have self-renewal ability and ability to give rise to cells of various lineages that make up tumors (multipotency) in cancer tissues. Such cells are called “cancer stem cells”. "It is called. It is understood that there is a cancer cell differentiation lineage that has cancer stem cells as the most upstream in cancer tissues as well as normal tissues, and the high-precision classification ability model of the present invention is used for cell classification in cancer cell differentiation lineages. It is reasonably understood that it is applicable to.
  • an image displayed on the image display unit of the interface is shown in FIG.
  • the image includes a graph (201) in which cells included in each cell fraction fractionated by the high-accuracy classification ability model are plotted in different colors in the three-dimensional space.
  • the graph can be rotated by operating the virtual joystick (202) with the mouse pointer (203). Further, by designating the area (204) in the graph (201) with the mouse pointer (203), the cells or cell fractions contained in the area can be specified.
  • the information display unit (205) can display numerical data regarding all cells used in the analysis, data regarding cells belonging to the region specified in the graph, and the like.
  • buttons (206) displayed in the image or the pull-down menu (207) with the mouse pointer by operating the button (206) displayed in the image or the pull-down menu (207) with the mouse pointer, various processing such as analysis of the entire analysis data or cells belonging to the specified area, data storage, etc. Can be specified.
  • the drawing is an example, and the present invention may include an interface having another configuration.
  • the analysis device 107 has a measurement unit such as a flow cytometer or NGS for detecting quantitative physical property data from a sample to be analyzed.
  • the analysis device 107 has a sample processing unit such as a cell sorter for isolating a cell population based on a quantitative physical property data set.
  • the measurement unit acquires a plurality of types of quantitative physical property data for each cell of the analysis target sample and provides the data to the computer.
  • the computer directly processes the quantitative physical property data with a high-accuracy classification ability model, and converts all or part of the quantitative physical property data obtained from a plurality of cells contained in the sample to be analyzed into data in an arbitrary format. It can be stored in the main memory as a structure.
  • the sample processing unit divides a sample cell group based on a quantitative physical property data set generated by an automatic gating parameter set generation process of a high-accuracy classification ability model of a computer, or A portion can be isolated.
  • the cell group obtained from the specimen may be the cell fraction arrangement spatial structure in the high-accuracy classification ability model or the cell group itself used to generate the cell arrangement spatial structure data, or obtained from the same kind of specimen as this. It may be a cell group.
  • a plurality of the above-mentioned computers can be used, and each computer may individually perform each process of the high-accuracy classifying ability model and exchange the process result via a network line.
  • the input/output device and the analysis device may be connected to the computer via a network line, and the entire system may have a Cloud network configuration (FIG. 8).
  • a system having the high-accuracy classifying ability model of the present invention a system having a learning unit for generating and expanding the high-precision classifying ability model, and an identification unit for classifying target cells using the learned high-precision classifying ability model Can be configured.
  • the system may include other functions such as a management function and a report function that are used for investigating the state of the high-precision classification ability model, if necessary (Fig. 3).
  • the system using the high-accuracy classifying ability model of the present invention performs clustering processing based on quantitative physical characteristic data to provide mutual analysis between cell fractions and/or cells based on the similarity of quantitative physical characteristic data sets.
  • the cell fraction and/or the cell arrangement spatial structure is generated.
  • the cell fraction arrangement spatial structure and/or the cell arrangement spatial structure has a structure similar to a cell differentiation phylogenetic tree, and by analyzing the structure, information that can be used in various fields is extracted. be able to.
  • the system using the high-precision classification ability model of the present invention can be used for identifying tissue stem cells or cancer stem cells.
  • tissue stem cells and cancer stem cells can be identified at a cost of several thousandth of the time as compared with the conventional method.
  • the system utilizing the high-accuracy classifying ability model of the present invention can support the screening of an individualized therapeutic drug. Specifically, for example, by identifying the cancer stem cells in a specific patient, the characteristics of the cancer stem cells unique to the patient are clarified, and the optimal therapeutic agent and/or treatment method for the treatment is selected. Can help.
  • the system utilizing the high-accuracy classification ability model of the present invention can be used to evaluate the differentiation pattern of a specific cell. Specifically, purified cells isolated from a cell population obtained from a living body are cultured for a certain period of time under conditions that allow their differentiation, and the cell population obtained after the culturing has a high accuracy classification ability of the present invention. By applying a system using a model, it becomes possible to evaluate the differentiation mode of the purified cells.
  • the system utilizing the highly accurate classification ability model of the present invention can be applied to screening of culture conditions for differentiating a specific cell into a specific differentiated cell. Specifically, when assessing the differentiation pattern of a particular cell, the results of changing the culture conditions were analyzed to elucidate the relationship between the culture condition and the differentiation pattern, and to differentiate from the particular cell. It is possible to support the optimal culture condition screening for enriching cells.
  • the system using the high-accuracy classifying ability model of the present invention can support the health prediction of each subject and the medication instruction and/or diet individualization instruction using the same. Specifically, for example, by analyzing the cell fraction spatial structure and/or the cell spatial structure in subjects in various health conditions, the relationship between the health condition and the cell fraction spatial structure, and/or the cell spatial structure is analyzed. It is possible to assist in predicting the health state of the individual based on the cell fraction spatial structure obtained from a specific individual and/or the cell spatial structure. Furthermore, by performing the same analysis on the medication history and the like, it is possible to support individualized guidance on medication, meals, and the like.
  • Example 1 Verification of optimization of stratification Collection of cells
  • Long-term hematopoietic stem cell-specific reporter mouse mouse in which Hoxb5 gene on the genome was replaced with Hoxb5 gene fused with a gene encoding 3 copies of mcherry fluorescent protein; nature, 2016, Vol. .530, pp.223-227
  • bone marrow cells were collected from the bone marrow.
  • Quantitative data was acquired for the following 16 physical properties.
  • bone marrow cells are suspended in an antibody staining buffer solution (PBS/2% FCS/2 mM EDTA or the like), and all cell staining is performed on ice. After staining with anti-c-kit antibody, the c-Kit positive cells were concentrated using MACS (Magnetic-activated cell sorting). Then, the remaining antibodies were sequentially added to the concentrated c-Kit positive cells for staining, and finally SYTOX-Red was added as a DNA staining reagent for removing dead cells. The stained cells were subjected to data collection using a flow cytometer and a dedicated software BD FACSDiva. The data for machine learning was analyzed and extracted using FlowJo.
  • an antibody staining buffer solution PBS/2% FCS/2 mM EDTA or the like
  • Step 1 Identification of Differentiated Lineage Identification Quantitative Physical Characteristic Data
  • HSC cell group that has already been identified based on Hoxb5 positivity is used as correct data
  • sample data 220,610 items are sampled by supervised learning by machine learning from HSC
  • AdaBoost machine learning model
  • Step 2 Optimization of Clustering and Clustering
  • GaussianMixtureModel non-hierarchical clustering
  • the clustering process was optimized by setting the number of clusters so that the number of clusters including Att12, 9 and 14 was minimized.
  • the clustering process was optimized to divide 220,610 pieces of sample data (each sample data represents an individual cell) into 101 clusters.
  • the number of clusters containing cells having Att12, 9 and 14 was 3 out of 101 clusters. A total of 1,198 sample data were classified into the three clusters.
  • mCherry-positive cells that is, Hoxb5-positive HSC cells were all classified into the above three clusters, and the clustering treatment was optimized so that the cells could be stratified according to the differentiation hierarchy of the cell differentiation lineage. was confirmed.
  • Example 2 High-speed identification of long-term hematopoietic stem cells by cell stratification
  • LT-HSC hematopoietic stem cell
  • Quantitative physical property data was obtained for all 16 bone marrow nucleated cells (250, 304 cases) from the cell group collected in the same manner as in Example 1 in the same manner as in Example 1, and the quantitative physical property was calculated. Based on the data, the results of cell sorting (hierarchicalization) processing were placed in a three-dimensional spatial structure and imaged.
  • the LT-HSC cell group (331 cases) that has already been identified based on Hoxb5 positivity and the ST-HSC cell group (1,081 case) that has already been identified based on Hoxb5 negative are the above-mentioned three-dimensional spatial structure. The location placed inside was verified.
  • the LT-HSC cell group (331 cases) that has been identified based on Hoxb5 positivity and the ST-HSC cell group (1,081 case) that has been identified based on Hoxb5 negative are in the above three-dimensional spatial structure. It was confirmed that they were concentrated in a specific area of and were arranged according to the order of biological cell differentiation. Specifically, it was confirmed that the LT-HSC cell group and then the ST-HSC cell group were arranged starting from one of the ends of the three-dimensional spatial structure.
  • the LT-HSC cell group requires optimization of the cell staining method, and cell function experiments requiring a long-term observation period of about 8 months after transplantation into mice etc. need to be repeated many times. As it was there, it took about 30 years. However, it was confirmed that the LT-HSC cell group can be identified in a few hours by processing with the high-precision classification ability model of the present invention. That is, the system using the highly accurate classification ability model of the present invention makes it possible to identify cells in a short time of tens of thousands to hundreds of thousands of times compared with the conventional system.
  • the “cell sorting algorithm” of the present invention rapidly sorts cells according to biological rules (hierarchization) to identify the cells located at the most upstream of the differentiation lineage in a given cell population. This makes it possible to identify cells in a short time of tens of thousands to hundreds of thousands of times compared with the conventional method.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Biomedical Technology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Organic Chemistry (AREA)
  • Wood Science & Technology (AREA)
  • Zoology (AREA)
  • General Health & Medical Sciences (AREA)
  • Biotechnology (AREA)
  • Physics & Mathematics (AREA)
  • Biochemistry (AREA)
  • Analytical Chemistry (AREA)
  • Genetics & Genomics (AREA)
  • General Engineering & Computer Science (AREA)
  • Hematology (AREA)
  • Microbiology (AREA)
  • Molecular Biology (AREA)
  • Medicinal Chemistry (AREA)
  • Immunology (AREA)
  • Biophysics (AREA)
  • Sustainable Development (AREA)
  • Pathology (AREA)
  • General Physics & Mathematics (AREA)
  • Food Science & Technology (AREA)
  • Urology & Nephrology (AREA)
  • Ecology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Medical Informatics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Toxicology (AREA)
  • Plant Pathology (AREA)
  • Cell Biology (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Micro-Organisms Or Cultivation Processes Thereof (AREA)
PCT/JP2019/051270 2018-12-28 2019-12-26 細胞並べ替え（階層化）処理システム。 Ceased WO2020138351A1 (ja)

Priority Applications (4)

Application Number Priority Date Filing Date Title
JP2020562445A JPWO2020138351A1 (https=) 2018-12-28 2019-12-26
JP2021566786A JPWO2021131110A1 (https=) 2018-12-28 2020-07-01
US17/789,279 US20230039070A1 (en) 2018-12-28 2020-07-01 Method for identifying cell having specific biological characteristics by cell classification process and application method thereof
PCT/JP2020/025915 WO2021131110A1 (ja) 2018-12-28 2020-07-01 細胞階層化処理により特定の生物学的特徴を有する細胞を同定する方法およびその応用方法

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2018-247567 2018-12-28
JP2018247567 2018-12-28

Publications (1)

Publication Number Publication Date
WO2020138351A1 true WO2020138351A1 (ja) 2020-07-02

Family

ID=71128786

Family Applications (2)

Application Number Title Priority Date Filing Date
PCT/JP2019/051270 Ceased WO2020138351A1 (ja) 2018-12-28 2019-12-26 細胞並べ替え（階層化）処理システム。
PCT/JP2020/025915 Ceased WO2021131110A1 (ja) 2018-12-28 2020-07-01 細胞階層化処理により特定の生物学的特徴を有する細胞を同定する方法およびその応用方法

Family Applications After (1)

Application Number Title Priority Date Filing Date
PCT/JP2020/025915 Ceased WO2021131110A1 (ja) 2018-12-28 2020-07-01 細胞階層化処理により特定の生物学的特徴を有する細胞を同定する方法およびその応用方法

Country Status (3)

Country Link
US (1) US20230039070A1 (https=)
JP (2) JPWO2020138351A1 (https=)
WO (2) WO2020138351A1 (https=)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008520247A (ja) * 2004-11-19 2008-06-19 トリリウム ダイアグノスティクス，エルエルシー 白血球ＦｃγＲＩ受容体（ＣＤ６４）の定量化のための、ソフトウェアを統合したフローサイトメトリーアッセイ
JP2014517313A (ja) * 2011-06-10 2014-07-17 ザ トラスティーズ オブ ザ ユニバーシティ オブ ペンシルバニア サイトミックな血管健康プロファイリングのシステムおよび方法

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008520247A (ja) * 2004-11-19 2008-06-19 トリリウム ダイアグノスティクス，エルエルシー 白血球ＦｃγＲＩ受容体（ＣＤ６４）の定量化のための、ソフトウェアを統合したフローサイトメトリーアッセイ
JP2014517313A (ja) * 2011-06-10 2014-07-17 ザ トラスティーズ オブ ザ ユニバーシティ オブ ペンシルバニア サイトミックな血管健康プロファイリングのシステムおよび方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"Gating strategy of AQUIOS Tetra application", AQUIOS TETRA, August 2016 (2016-08-01), pages 7, Retrieved from the Internet <URL:https://www.beckmancoulter.co.jp/product/product02/download/AQUIOSO1.pdf> [retrieved on 20200323] *
HWANG, B. ET AL.: "Single- cell RNA sequencing technologies and bioinformatics pipelines", EXPERIMENTAL & MOLECULAR MEDICINE, vol. 50, no. 96, 7 August 2018 (2018-08-07), XP055723644 *

Also Published As

Publication number Publication date
JPWO2021131110A1 (https=) 2021-07-01
JPWO2020138351A1 (https=) 2020-07-02
WO2021131110A1 (ja) 2021-07-01
US20230039070A1 (en) 2023-02-09

Similar Documents

Publication Publication Date Title
Wang et al. Molecular and cellular dynamics of the developing human neocortex
Zhang et al. In situ mapping identifies distinct vascular niches for myelopoiesis
Pellin et al. A comprehensive single cell transcriptional landscape of human hematopoietic progenitors
Bian et al. Deciphering human macrophage development at single-cell resolution
Tusi et al. Population snapshots predict early haematopoietic and erythroid hierarchies
Hislop et al. Modelling post-implantation human development to yolk sac blood emergence
Becher et al. High-dimensional analysis of the murine myeloid cell system
Yoshida et al. Early hematopoietic lineage restrictions directed by Ikaros
Wang et al. Morphological pseudotime ordering and fate mapping reveal diversification of cerebellar inhibitory interneurons
Dobbin et al. Isolation and characterization of potential cancer stem cells from solid human tumors—potential applications
WO2023134390A1 (en) Method for evaluating the quality of stem cells
Sommarin et al. Single-cell multiomics reveals distinct cell states at the top of the human hematopoietic hierarchy
Zhu et al. Preparation of whole bone marrow for mass cytometry analysis of neutrophil-lineage cells
Alcántara-Hernández et al. Mass cytometry profiling of human dendritic cells in blood and tissues
EP4177337A1 (en) Human long-term hematopoietic stem cell marker
Feng et al. Cortical arealization of interneurons defines shared and distinct molecular programs in developing human and macaque brains
Howell et al. Analysis of the Leishmania mexicana promastigote cell cycle using imaging flow cytometry provides new insights into cell cycle flexibility and events of short duration
Gómez‐López et al. Evaluation of myelin phagocytosis by microglia/macrophages in nervous tissue using flow cytometry
Guillot et al. Control of epiblast cell fate by mechanical cues
WO2020138351A1 (ja) 細胞並べ替え（階層化）処理システム。
Spiteri et al. High‐dimensional methods of single‐cell microglial profiling to enhance understanding of neuropathological disease
Hamey et al. Advancing haematopoietic stem and progenitor cell biology through single‐cell profiling
Harris et al. A meta-analytic single-cell atlas of mouse bone marrow hematopoietic development
JP2022066730A (ja) 細胞評価、分類または純化装置、システムおよび方法。
JP2023537260A (ja) バイオマーカーを使用する骨髄穿刺液の血液希釈の決定

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19903583

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2020562445

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 18.08.2021)

122 Ep: pct application non-entry in european phase

Ref document number: 19903583

Country of ref document: EP

Kind code of ref document: A1