Quality management method of stem cells based on image analysis using ai

Abstract

The present application provides a standard for quality evaluation or determination of a subject cell, comprising: a high-speed proliferative mesenchymal stem cell population characterized by positive or negative of at least one marker of LNGFR (CD271) and Thy-1 (CD90).

Description

Technical Field

[0001] This invention relates to image analysis, and in particular to a method for quality management of stem cells based on image analysis using AI. Background Technology

[0002] Regenerative medicine refers to medical treatment that regenerates damaged organs. As examples of practical regenerative medicine, there are heart transplantation, liver transplantation, kidney transplantation, and hematopoietic stem cell transplantation.

[0003] The three types of stem cells that are expected to be used in regenerative medicine are embryonic stem cells (ES cells) obtained from fertilized eggs, iPS cells obtained by introducing four Yamanaka factors into skin fibroblasts, and adult stem cells that exist in tissues and organs of organisms. Currently, research and development of these cells for drug discovery and clinical applications are being actively carried out worldwide.

[0004] Stem cells exist in all tissues of the body, and mesenchymal stem cells (MSCs) are one of them. MSCs are mainly found in bone marrow and participate in bone and cartilage regeneration. In recent years, in particular, cell products using MSCs have been approved for regenerative medicine and other applications. By using allogeneic cells to mass-produce cells, the possibility of reducing costs while addressing a huge market is increasing, thus accelerating the industrialization of regenerative medicine.

[0005] The inventors developed a technique for directly isolating human MSCs from bone marrow using two antibodies, LNGFR (CD271) and Thy1 (CD90), and a cell sorting instrument. This allowed them to successfully screen and obtain ultra-high purity human MSCs, a cell population with extremely high proliferation and differentiation capacity. These ultra-high purity human MSCs were named REC (Rapidly Expanding Cell). Furthermore, it was demonstrated that even when transplanted with a cell number more than 100 times that of MSCs cultured using conventional methods, RECs did not cause side effects such as pulmonary embolism (Patent Documents 1-4, Non-Patent Document 1).

[0006] However, in the current field of regenerative medicine, the price of a single product of regenerative medicine and other products with continuous clinical research progress is estimated to be hundreds to tens of millions of yen, which is difficult to benefit many patients and still poses a problem of increasing medical finances.

[0007] The high cost of regenerative medicine stems primarily from the inefficiency of its cell manufacturing processes. Currently, cells used in regenerative medicine are manufactured manually, one cell at a time, requiring highly skilled professionals. A particularly serious issue is the lack of cell quality control technology; even in the world's leading research facilities, hospitals, and cell banks, cell quality evaluation relies heavily on the expertise of skilled technicians.

[0008] On the other hand, the inventors have developed the following technology: for various cells used in human cell therapy, by integrating image processing technology and AI (artificial intelligence) and machine learning technology (cell morphology information analysis), a technology is developed that enables non-destructive, real-time, low-cost and quantitative inspection and quality prediction of cultured cells (Patent Documents 5-8, Non-Patent Document 2).

[0009] Cell morphology information analysis refers to a technology that integrates image processing, data mining, and AI analysis techniques to predict the biological evaluation results (quality) of cells from images alone. When using cell morphology information analysis, quality-related information that was previously only obtainable through long-term culture and staining can be detected up to 3 hours after inoculation.

[0010] However, the development of the aforementioned cell morphology information analysis technology requires a large amount of high-quality data (images and quality data of functional stem cells), making it extremely expensive even when using commercially available cells. Previously, analysis was limited to data from only 10-20 cells. Furthermore, the quality of commercially available cells is inconsistent between batches, and they are mixtures of cells with various properties, thus raising concerns about cell conformity.

[0011] Existing technical documents

[0012] Patent documents

[0013] Patent Document 1: Japanese Patent No. 6463029

[0014] Patent Document 2: Japanese Patent No. 6850944

[0015] Patent Document 3: Japanese Patent No. 6932390

[0016] Patent Document 4: WO2021 / 145002

[0017] Patent Document 5: Japanese Patent Application Publication No. 2016-189701

[0018] Patent Document 6: Japanese Patent Application Publication No. 2016-189702

[0019] Patent Document 7: Japanese Patent Application Publication No. 2016-191565

[0020] Patent Document 8: Japanese Patent No. 6721895

[0021] Non-patent literature

[0022] Non-patent literature 1: Mabuchi Y et al., "LNGFR+ Thy-1+ Vcam-1hi+ cells reveal functionally distinct subpopulations in mesenchymal stem cells". Stem CellReports 1(2): 152-165, 2013

[0023] Non-Patent Document 2: NEDO 2018-2019 Annual Report / Development of Core Technologies for Next-Generation Artificial Intelligence and Robotics / Research on the Enhancement of Quality Inspection of High-Purity Mesenchymal Stem Cells Based on AI in the Field of Next-Generation Artificial Intelligence Summary of the Invention

[0024] The problem that the invention aims to solve

[0025] In view of the above, there is a need to develop cells suitable for cell morphology information analysis technology.

[0026] Problem Solving Methods

[0027] In order to solve the above problems, the inventors conducted in-depth research and found that REC is useful for cell morphology information analysis technology, thus completing the present invention.

[0028] That is, the present invention is as follows. [1]

[0030] A standard for evaluating or determining the quality of tested cells, comprising:

[0031] Highly proliferating mesenchymal stem cell populations characterized by the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90). [2]

[0033] According to the standard product described in [1], wherein,

[0034] The characteristics of the cell population are selected from any of the following (a) to (d):

[0035] (a) At least one type of cell information selected from the cell size, extensibility, contour complexity, texture and brightness of each cell in the cell population, and their image information;

[0036] (b) The information obtained by accumulating and statistically processing the cell information from (a) above in multiple cells within the cell population;

[0037] (c) Combined information obtained from the information in (b) above for multiple cell populations;

[0038] (d) The information obtained over time from (a) to (c) above. [3]

[0040] According to the standard product described in [2], wherein,

[0041] The statistically processed information is selected from at least one of the mean, standard deviation, and distribution shape. [4]

[0043] According to the standard product described in [1], wherein,

[0044] Quality is selected from at least one of cell proliferation rate, cell recovery count, degree of undifferentiation, degree of differentiation, amount of produced factors, and cell heterogeneity. [5]

[0046] According to the standard product described in [1], wherein,

[0047] The cells examined were stem cells. [6]

[0049] According to the standard product described in [5], wherein,

[0050] The stem cells examined were the mesenchymal stem cells examined. [7]

[0052] A method for providing information for quality assessment of examined stem cell populations, the method comprising the following steps:

[0053] (a) A process of characterizing each cell of a rapidly proliferating mesenchymal stem cell population using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtaining image data of each cell (reference image information), or obtaining parameter information of the characteristics of the cell population (reference parameter information) from the reference image information.

[0054] (b) A process of obtaining image data (examined image information) of each cell of the examined stem cell population provided by the user, or obtaining parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information.

[0055] (c) The process of comparing the inspected image information or inspected parameter information with the reference image information or reference parameter information; and

[0056] (d) The process of providing all or part of the information obtained through the above comparisons to the user for use in the quality evaluation of the tested stem cell population. [8]

[0058] A method for quality assessment of a tested stem cell population, comprising the following steps:

[0059] (a) A process of characterizing each cell of a rapidly proliferating mesenchymal stem cell population using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtaining image data of each cell (reference image information), or obtaining parameter information of the characteristics of the cell population (reference parameter information) from the reference image information.

[0060] (b) A process of obtaining image data (examined image information) of each cell of the examined stem cell population provided by the user, or obtaining parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information.

[0061] (c) The process of comparing the inspected image information or inspected parameter information with the reference image information or reference parameter information; and

[0062] (d) A process for evaluating the quality of the tested stem cells using all or part of the information obtained through the above comparisons as indicators. [9]

[0064] An information provision system for quality assessment of examined stem cell populations, the system comprising the following units:

[0065] (a) A unit that characterizes each cell of a rapidly proliferating mesenchymal stem cell population by using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtains image data of each cell (reference image information), or obtains parameter information (reference parameter information) of the characteristics of the cell population from the reference image information.

[0066] (b) A unit that acquires image data (examined image information) of each cell of the examined stem cell population provided by the user, or acquires parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information;

[0067] (c) A unit that compares the above-mentioned inspected image information or inspected parameter information with the above-mentioned reference image information or reference parameter information; and

[0068] (d) A unit that provides all or part of the information obtained through the above comparisons to the user for use in the quality assessment of the tested stem cell population.

[10]

[0070] A quality assessment system for examined stem cell populations, comprising the following units:

[0071] (a) A unit that characterizes each cell of a rapidly proliferating mesenchymal stem cell population by using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtains image data of each cell (reference image information), or obtains parameter information (reference parameter information) of the characteristics of the cell population from the reference image information.

[0072] (b) A unit that acquires image data (examined image information) of each cell of the examined stem cell population provided by the user, or acquires parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information;

[0073] (c) A unit that compares the above-mentioned inspected image information or inspected parameter information with the above-mentioned reference image information or reference parameter information; and

[0074] (d) A unit that evaluates the quality of the tested stem cells using all or part of the information obtained through the above comparisons as indicators.

[11]

[0076] According to the system described in [9] or

[10] , wherein,

[0077] The characteristics of the cell population are selected from any of the following (a) to (d):

[0078] (a) At least one type of cell information selected from the cell size, extensibility, contour complexity, texture and brightness of each cell in the cell population, and their image information;

[0079] (b) The information obtained by accumulating and statistically processing the cell information from (a) above in multiple cells within the cell population;

[0080] (c) Combined information obtained from the information in (b) above for multiple cell populations;

[0081] (d) The information obtained over time from (a) to (c) above.

[12]

[0083] According to the system described in

[11] , wherein,

[0084] The statistically processed information is selected from at least one of the mean, standard deviation, and distribution shape.

[13]

[0086] According to the system described in [9] or

[10] , wherein,

[0087] Quality is selected from at least one of cell proliferation rate, cell recovery count, degree of undifferentiation, degree of differentiation, amount of produced factors, and cell heterogeneity.

[14]

[0089] According to the system described in [9] or

[10] , wherein,

[0090] The stem cells examined were the mesenchymal stem cells examined.

[15]

[0092] An information-providing program for quality assessment of examined stem cell populations, which enables a computer to function as a unit:

[0093] (a) A unit that characterizes each cell of a rapidly proliferating mesenchymal stem cell population by using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtains image data of each cell (reference image information), or obtains parameter information (reference parameter information) of the characteristics of the cell population from the reference image information.

[0094] (b) A unit that acquires image data (examined image information) of each cell of the examined stem cell population provided by the user, or acquires parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information;

[0095] (c) A unit that compares the above-mentioned inspected image information or inspected parameter information with the above-mentioned reference image information or reference parameter information; and

[0096] (d) A unit that provides all or part of the information obtained through the above comparisons to the user for use in the quality assessment of the tested stem cell population.

[16]

[0098] A quality assessment procedure for examined stem cell populations, which enables a computer to function as a unit:

[0099] (a) A unit that characterizes each cell of a rapidly proliferating mesenchymal stem cell population by using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtains image data of each cell (reference image information), or obtains parameter information (reference parameter information) of the characteristics of the cell population from the reference image information.

[0100] (b) A unit that acquires image data (examined image information) of each cell of the examined stem cell population provided by the user, or acquires parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information;

[0101] (c) A unit that compares the above-mentioned inspected image information or inspected parameter information with the above-mentioned reference image information or reference parameter information; and

[0102] (d) A unit that evaluates the quality of the tested stem cells using all or part of the information obtained through the above comparisons as indicators.

[17]

[0104] According to the procedure described in

[15] or

[16] , wherein,

[0105] The characteristics of the cell population are selected from any of the following (a) to (d):

[0106] (a) At least one type of cell information selected from the cell size, extensibility, contour complexity, texture and brightness of each cell in the cell population, and their image information;

[0107] (b) The information obtained by accumulating and statistically processing the cell information from (a) above in multiple cells within the cell population;

[0108] (c) Combined information obtained from the information in (b) above for multiple cell populations;

[0109] (d) The information obtained over time from (a) to (c) above.

[18]

[0111] According to the procedure described in

[17] , wherein,

[0112] The statistically processed information is selected from at least one of the mean, standard deviation, and distribution shape.

[19]

[0114] According to the procedure described in

[15] or

[16] , wherein,

[0115] Quality is selected from at least one of cell proliferation rate, cell recovery count, degree of undifferentiation, degree of differentiation, amount of produced factors, and cell heterogeneity.

[20]

[0117] According to the procedure described in

[15] or

[16] , wherein,

[0118] The stem cells examined were the mesenchymal stem cells examined. [twenty one]

[0120] A computer-readable recording medium having the program described in

[15] or

[16] recorded thereon. [twenty two]

[0122] An image processing method for rapidly proliferating mesenchymal stem cells, comprising the following steps:

[0123] (a) The process of obtaining image data of the standard specimens described in [1]; and

[0124] (b) The process of removing noise from the image data obtained above and / or standardizing the image data above. [twenty three]

[0126] A method is provided for using image data processed by the method described in

[22] for machine learning in a computer. [twenty four]

[0128] A method for predicting the quality of a cell by comparing image data processed by the method described in

[22] with image data of the cell under examination.

[0129] The effects of the invention

[0130] According to the present invention, a standard comprising a population of rapidly proliferating mesenchymal stem cells can be provided for the quality evaluation or determination of test cells. The standard of the present invention enables efficient and cost-effective quality management when manufacturing stem cell drugs, thereby reducing the manufacturing cost of stem cell drugs. Attached Figure Description

[0131] Figure 1 This is a conceptual diagram of continuous passage capability prediction using a morphological profile for selecting high-capacity cell banks. Target RECs are screened from MSCs via clonal selection processes (P1 and P2). Cells are cryopreserved in P3 to form an initial cell bank for the preparation of initial passaged cells for further experiments. Continuous passage experiments are performed from P3 until the passage limit. For the manufacture of practical cell-based therapeutics, candidate cell banks can be formed between such continuous passages. However, for example, if cells exhibit unexpected proliferation arrest, there is a risk of cell bank establishment failure. Moreover, it is desirable to form a lower-risk candidate cell bank with higher-capacity cells, even with further reduced proliferation capacity—that is, a higher-quality candidate cell bank. Such continuous passage capability is predicted based on the morphological profile of cell images from P2. The morphological profile consists of time elapsed × 12 morphological descriptors × cell population information (mean and SD).

[0132] Figure 2This is a graph showing the profiles of 15 clones of REC. a) Results of successive passage experiments with REC. b) Representative morphology of REC. c) Growth curves of REC. d) Correlation between proliferation rate and the limit number of successive passages. 2 This represents the coefficient of determination.

[0133] Figure 3 In the figures, a) shows a comparison of the size distribution of 15 cloned RECs with previously treated bulk MSCs (cMSCs, 9 batches) and their changes over time, as well as the morphological characterization of the 15 RECs. Only adherent and elongating cells were counted. The dashed vertical lines represent the average cell size. b) shows a representative image of RECs and bulk MSCs changing over time. Yellow and blue arrows represent proliferating cells and most recently proliferating cells, respectively. c) shows the size distribution among the 15 clones and their changes over time. d) shows the correlation between "SD of area" and passage number, R 2 The coefficient of determination is denoted as (e) and (f). These are PCA plots showing the 15 RECs analyzed using 24 morphological descriptors. (e) is the PCA plot including the clonal color tag (e), and (f) is the PCA plot colored using a heatmap of successive passage ability.

[0134] Figure 4 The charts above provide a comprehensive evaluation of the data utilization effectiveness and the performance of successive passaging prediction models. Chart a shows the evaluation of LASSO-based data utilization effectiveness. Row values ​​represent the number of fields of view (FOV) used, and column values ​​represent the size of the time window used in the training data. The heatmap represents the RMSE. An RMSE < 1.0 is considered a well-performing model. Chart b is a scatter plot used to visualize the performance of successive passaging prediction models, with each point representing one clone. Chart c compares the model structure between two pairs of constructed models, selecting time windows of 6, 6–30, 6–60, and 6–90 hours. The heatmap represents the correlation coefficients among all weights on all selected morphological descriptors within the model. Higher correlation coefficients are observed when the combinations of descriptors used are similar. Chart d shows the evaluation of RF-based data utilization effectiveness. Rows represent the number of FOVs used, and columns represent the size of the time window used in the training data. The heatmap represents the RMSE. An RMSE < 1.0 is considered a well-performing model.

[0135] Figure 5 This is a graph illustrating the robustness of the continuous passage forecasting model in terms of the data variation included through bootstrap FOV selection (50 repetitions) and its data utilization effectiveness.

[0136] Figure 6 This is a block diagram of the system of the present invention.

[0137] Figure 7 This is a flowchart illustrating the transportation of the system of the present invention.

[0138] Figure 8 This graph illustrates information extracted from image data of stem cells. Red represents REC cloned MSCs, showing homogeneous and well-organized information. The horizontal axis represents numerical indicators. The mountain-shaped structures in the graph are histograms of thousands of cell populations.

[0139] Figure 9 This is a graph illustrating information extracted from image data of stem cells. Information extracted from REC image data shows that the cells are regular even when observed across multiple metrics. Blue dots represent batches of MSCs, scattered across various locations, while red dots represent REC clones. The dots collectively form a clear ellipse.

[0140] Figure 10 This is a graph illustrating information extracted from image data of stem cells. The left graph shows REC clones, and the right graph shows a batch (commercially available MSCs) of stem cells. The vertical axis represents time. REC clones maintain a distribution that changes over time. The batch shows two peaks observed in the early stages of culture, transitioning over time in a fused or mixed state. In other words, the cellular information is intricately complex.

[0141] Figure 11 This is a graph showing the results of flow cytometry-based cell screening. Through the flow cytometry-based selection process, the population becomes clones, resulting in a variety of clones. This indicates that, in addition to RECs (Rejected Cells), clones that are discarded can also be fully utilized as cells with certain characteristics for machine learning.

[0142] Figure 12 This is a graph showing the proliferation rate (top), adipose differentiation (middle), and bone differentiation (bottom) of each MSC.

[0143] Figure 13 This is a diagram showing the adipogenic differentiation prediction model for each MSC. REC clone data has high accuracy, while batch data has low accuracy.

[0144] Figure 14 This is a graph showing the results of predicting batch data using a cloning model.

[0145] Figure 15 It is a diagram that integrates information about cell morphology.

[0146] Figure 16 It is a diagram that integrates information about cell morphology.

[0147] Figure 17 This is a diagram showing the morphological characteristics of a cell population.

[0148] Figure 18This is a graph showing the proliferation curves of a batch of MSCs. Detailed Implementation

[0149] 1. Overview

[0150] This invention relates to standards comprising a population of rapidly proliferating mesenchymal stem cells (RECs) for the quality evaluation or determination of tested cells.

[0151] In this invention, cell image information analysis of each clone of REC is performed, and based on the comparison with actual culture data, a learning model using data from each clone of REC is constructed, thus completing the quality evaluation system.

[0152] The cell image information analysis used more than 100 clones, including commercially viable clones, clones identified as abnormal, clones from different donor batches, and clones with altered culture environments. Furthermore, detailed culture process data were compiled along with the image data into metadata and databased. Additionally, experimental results regarding the osteodifferentiation, adipogenesis, and proliferation capacities of each clone were also added to the database.

[0153] Furthermore, in one aspect of this invention, AI is fully utilized, and in addition to linear regression models, which are conventional machine learning models, nonlinear regression models (three types) were selected for source code development and environment setup. Additionally, in this invention, the learning model using REC clone data was validated, and by referencing and comparing REC data, other methods for evaluating the quality of mesenchymal stem cells were established. For example, it was found that the cell recovery rate of REC after 5 days can be predicted with high accuracy based on data from a minimum of 2 days of proliferation culture, and abnormalities in differentiation capacity after 1 month can be predicted based on images from proliferation culture. Furthermore, it was clarified that using a quality database fully validated through REC cells, the heterogeneity of MSCs from other companies can be quantitatively evaluated based on REC data, and the quality of MSCs from other companies can be predicted with substantially the same performance.

[0154] 2. Standards used for quality evaluation or determination of the tested cells.

[0155] In this invention, the standard used is a rapidly proliferating mesenchymal stem cell population characterized by the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90).

[0156] At least one of the markers LNGFR (CD271) and Thy-1 (CD90) shows positive or negative high-velocity proliferative mesenchymal stem cells, for example, according to the method described in WO2009 / 31678.

[0157] The method is summarized below.

[0158] First, mesenchymal stem cells are highly concentrated from a cell population containing human mesenchymal stem cells by screening for cell fractions that are positive for at least one of CD271 and CD90 (CD271+ / CD90+). It should be noted that if the cell population containing human mesenchymal stem cells includes hematopoietic cells, a step can be added to screen for cells that are double-negative for both CD45 and CD235a (CD45-CD235a-) in order to screen for non-hematopoietic cells.

[0159] Cell populations containing mesenchymal stem cells can be prepared by flow cytometry or affinity chromatography. The materials used to obtain these cell populations are not particularly limited, and examples include bone marrow and peripheral blood (including peripheral blood after G-CSF administration). It should be noted that bone marrow from the vertebrae, sternum, iliac crest, etc., can be used. When preparing cells, if the material is a cell mass mixed with mesenchymal stem cells, the material can be subjected to physical treatments such as pipetting, or enzymatic treatments such as trypsin or collagenase, as needed. Furthermore, if the material contains red blood cells, it is preferable to pre-hemolyze the red blood cells.

[0160] Using the cell population prepared as described above, CD271+ / CD90+ cells were screened. For example, an antibody-based method can be used to screen CD271+ / CD90+ cells. The antibodies are anti-CD271 antibodies and anti-CD90 antibodies capable of screening CD271+ / CD90+ cells. When using flow cytometry for screening, by appropriately combining anti-CD271 antibodies and anti-CD90 antibodies labeled with different fluorescent dyes such as FITC, PE, and APC, viable cells can be screened in a short time. In addition to flow cytometry, CD271+ / CD90+ cells can also be screened using magnetic beads or affinity chromatography.

[0161] Next, the selected LNGFR and Thy1 double-positive cells were cultured as single cells (clones). By selecting the batches with rapid proliferation, high-purity human mesenchymal stem cells (REC: Rapidly Expanding Clone) with excellent proliferation, differentiation, and migration abilities could be obtained.

[0162] Monocytes were prepared from human bone marrow or adipose / placental chorionic villus membrane, and stained with anti-LNGFR and anti-Thy1 antibodies. Then, LNGFR-positive and / or Thy1-positive cell clones were sorted into 96-well culture plates using flow cytometry (cell sorting system), i.e., one cell per well. After two weeks of single-cell culture, the culture plate was photographed under a microscope to screen for confluent or subconfluent wells, and the cells contained in these wells were designated as RECs.

[0163] Here, "rapid proliferation" and "high-speed proliferation" refer to the proliferation rate at which the culture plate reaches confluence or sub-confluence after two weeks of culture or before the start of culture when the cells are seeded in one well of a 96-well culture plate at a rate of one cell per well. The doubling time is 26 ± 1 hours.

[0164] Consolidation refers to a state where cultured cells cover more than 90% of the surface of the culture container (culture surface). Sub-consolidation refers to a state where cultured cells cover 70-90% of the surface of the culture container (culture surface). The size and type of culture equipment used can be appropriately changed according to the cell proliferation rate. Moderately / Slowly Expanding Cells, i.e., single cells that have not reached sub-consolidation or confluence after 2 weeks of culture, are discarded. RECs recovered from each well as RECs are transferred to a culture flask and cultured further until confluence (expansion culture). Then, the expanded cultured cells are recovered individually. RECs from one well are considered as one batch (one clone in the examples described later) and used for the screening described later.

[0165] The cells of this invention are obtained by clonal sorting, where one cell is seeded into each well. Therefore, the proliferated cells have identical genetic traits. Thus, in this invention, the cell population as a whole is sometimes referred to as a "clone," and sometimes the individual cells constituting the cell population are also referred to as "clones."

[0166] It should be noted that, in this invention, the RECs used for screening and quality evaluation can also be evaluated in advance using REC markers (anti-Ror2). For example, after the above-mentioned expansion culture, attached and proliferating cells are recovered from all batches, and a portion of each batch (approximately 1~3 × 10⁻⁶ cells) is taken. 3 (Number) cells were stained with a monoclonal antibody against Ror2. The method of single staining with a monoclonal antibody against Ror2 is well-known (WO2016 / 17795). Briefly, the proportion of REC-positive cells in the recovered cells was determined by flow cytometry analysis using the REC marker. This proportion can be quantified using quantitative PCR to measure Ror2 mRNA expression, or it can be determined manually using a microscope. Batches (cell populations) with a positive proportion of 65% or higher are considered acceptable.

[0167] 2. Characteristics of cell populations

[0168] In this invention, the characteristics of the cell population used as a standard for evaluating or determining the quality of the tested cells can be obtained from any of the information in (a) to (d) below. The tested cells are not particularly limited, but are preferably mesenchymal stem cells, embryonic stem cells (ES cells), iPS cells, etc.

[0169] (a) At least one type of cell information selected from the cell size, extensibility, contour complexity, texture and brightness of each cell within the cell population, and their image information.

[0170] Information about cell size, extensibility, and contour complexity can be obtained by measuring or calculating indicators such as area, cell diameter, width, length, perimeter, arc length, and combinations thereof, as well as their proportion in the cell image (e.g., number of pixels).

[0171] Texture and brightness can be obtained, for example, by the number of pixels that display a specific hue in an image.

[0172] (b) Information obtained by accumulating and statistically processing the cell information from (a) above across multiple cells in the cell population.

[0173] The cell information in (a) above is accumulated in multiple cells within the cell population and statistically processed.

[0174] There are no particular restrictions on the methods used for statistical processing.

[0175] Information obtained after statistical processing may include, for example, the mean, standard deviation, and distribution shape.

[0176] (c) Combined information obtained from the information in (b) above for multiple cell populations

[0177] The information in (b) above can be obtained from multiple cell populations and combined.

[0178] (d) The information obtained over time from (a) to (c) above.

[0179] "Over time" refers to information taken at any point in time after the preparation of the rapidly proliferating mesenchymal stem cell population, or continuously after a given time interval. For example, information after 1 hour, 2 hours, 3 hours, 5 hours, 10 hours, etc.

[0180] In this invention, the quality indicators used to evaluate or determine the tested cells include cell proliferation rate, cell recovery count, degree of undifferentiation, degree of differentiation, amount of factors produced, and cell heterogeneity, but are not limited to these.

[0181] Cell proliferation rate refers to the momentum of cell proliferation after culturing the tested cells or their progeny. It can be evaluated or determined by indicators such as the cell proliferation rate, the number of passages until cell proliferation ceases, and the total number of cells obtained from a single cell through proliferation. Cell proliferation rate is positively correlated with these indicators. A high proliferation rate, i.e., high values ​​of these indicators, can be evaluated or determined as high quality (high proliferative capacity).

[0182] Cell recovery count refers to the number of live cells obtained after culturing the tested cells or their progeny for a certain period of time. When high proliferative capacity is important, cells with a high cell recovery count can be evaluated or judged as "cells that meet the target quality".

[0183] Undifferentiated degree refers to the immaturity of the differentiation stage of the tested cells during the functional differentiation of stem cells, or the diversity of cell types that can be differentiated from the tested cells. In cases where it is important to prepare / store a large number of stem cells in an undifferentiated state, cells with high undifferentiated degree can be evaluated or judged as "cells that meet the target quality".

[0184] Differentiation degree refers to the extent or speed at which the tested cells differentiate normally into the target type of cells, tissues or organs. In cases where it is important to differentiate stem cells to the target state, cells with a high degree of differentiation can be evaluated or judged as "cells that meet the target quality".

[0185] The quantity of produced factors refers to the amount (concentration, etc.) or number of specific cytokines produced by the tested cells. The quantity of produced factors can be used to evaluate or determine whether the tested cells possess the target function (e.g., proliferative capacity and migration capacity) or its degree, as well as the aging level of the tested cells. For example, a high quantity of cell proliferation factors produced by the tested cells can be considered as high proliferative capacity, and a high quantity of migration-promoting factors can be considered as high migration capacity. In either case, this can be used to evaluate or determine the conformity of the target cell quality.

[0186] Cellular heterogeneity refers to the fact that the phenotype of the tested cells (including cell morphology, migration ability, responsiveness, and other cell states) is not homogeneous, indicating that it may be impossible to manufacture cell populations that meet the target quality with high purity (risk of preparation failure). Therefore, low heterogeneity indicates that process control parameters can be controlled to achieve the preparation of cells with the target quality, while high heterogeneity indicates the risk of not being able to adequately perform these culture controls. When heterogeneity is sufficiently low, it can be evaluated or determined as a state of high cell preparation capability (adequate process control) or high cell preparation success rate (low culture risk, resulting in economical and efficient manufacturing).

[0187] 3. Information delivery methods, quality assessment methods, and the systems and procedures used in these methods.

[0188] (1) Information provision methods and quality evaluation methods

[0189] This invention provides a method for providing information for the quality evaluation of tested stem cell populations, and a method for evaluating the quality of tested stem cell populations, which can use the cell standards of this invention.

[0190] The information providing method of the present invention includes the following steps.

[0191] (a) A process of characterizing each cell of a rapidly proliferating mesenchymal stem cell population using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtaining image data of each cell (reference image information), or obtaining parameter information of the characteristics of the cell population (reference parameter information) from the reference image information.

[0192] (b) A process of obtaining image data (examined image information) of each cell of the examined stem cell population provided by the user, or obtaining parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information.

[0193] (c) The process of comparing the inspected image information or inspected parameter information with the reference image information or reference parameter information; and

[0194] (d) The process of providing all or part of the information obtained through the above comparisons to the user for use in the quality evaluation of the tested stem cell population.

[0195] In addition, the present invention provides a method for evaluating the quality of tested cells, the method comprising: (d) evaluating the quality of tested stem cells using all or part of the information obtained through comparison in the above step (c) as an indicator.

[0196] In these methods, a system and program comprising units for implementing the steps (a) to (d) above can be used. The system of the present invention is described below.

[0197] (2) Information processing devices and programs

[0198] An information processing apparatus and program for quality evaluation according to one embodiment of the present invention will be described with reference to the accompanying drawings.

[0199] Figure 6 This is a schematic diagram of the structure of the test stem cell population quality evaluation system 1, which includes the information processing device 10 of this embodiment. It should be noted that, hereinafter, the institutions that process the test cells and the institutions (testing companies, hospitals, universities, etc.) or individuals who wish to obtain stem cell information are referred to as "users".

[0200] The information processing device 10 is a server-type computer equipped with a CPU, ROM, RAM, and input / output interfaces. The information processing device 10 connects to the user device 30 via a network 20 such as the Internet or a LAN. a1 30 a2 30 an (Collectively referred to as "user equipment 30") can be communicatively connected together. It should be noted that user equipment 30 can be a single device or multiple devices.

[0201] The information processing device 10 uses the positivity or negativity of at least one of the markers LNGFR (CD271) and Thy-1 (CD90) as indicators to characterize each cell in a rapidly proliferating mesenchymal stem cell population that serves as a cell standard, and obtains image data (reference image information) of each cell, or obtains parameter information (reference parameter information) of the characteristics of the cell population from the reference image information.

[0202] Next, the information processing device 10 compares the stem cell information (including image information) of the user (referred to as the "target user") who is processing the tested cells with the stem cell information of the REC standard stored in the database, and calculates and obtains parameter information based on the comparison results.

[0203] Then, the information processing device 10 compares the calculated parameter information to evaluate and determine the quality of the tested stem cell population of the user. The information processing device 10 then sends the user's stem cell information, evaluation, and determination results to the user device 30.

[0204] User equipment 30 is a computer, smartphone, or tablet computer equipped with an information input unit, display unit, etc., and is communicatively connected to information processing device 10 via network 20. User equipment 30 displays various information received from information processing device 10 on a screen through a browser or application, or provides an interface for inputting information sent to information processing device 10.

[0205] As a functional unit implemented by a program stored in ROM or RAM in cooperation with the CPU, the information processing device 10 includes an information acquisition unit 101, a stem cell information extraction unit 102, an evaluation benchmark setting unit 103, a comparison unit 104, an image information calculation unit 105, and a determination unit 106. In addition, the information processing device 10 includes a storage unit 110 provided in ROM (or RAM), which includes a stem cell information database (DB) 111, a parameter information database (DB) 112, and an evaluation benchmark database (DB) 113.

[0206] The information acquisition unit 101 acquires various information, such as image information of stem cells, from the user device 30 via the network 20 and stores it in the storage unit 110. The information received from the user device 30 includes information about the stem cells obtained by the user, as well as user identification information (user's name, information about the stem cells being examined (source of acquisition, information about the provider of the stem cells being examined), or the user's set ID, etc.).

[0207] When the information acquisition unit 101 receives the user's tested stem cell data, it associates it with the user's identification information and stores it in the stem cell information DB111. The stem cell information DB111 stores stem cell data collected from multiple users in this manner. When the information acquisition unit 101 receives the user's stem cell information, it obtains parameter information describing the characteristics of the tested stem cell population from that information and stores it in the parameter information DB112. The evaluation benchmark DB113 stores information obtained through statistical analysis processing that extracts features using the comparison unit 104. This information is associated with and stored with information identifying the user's parent sample as the object of the statistical analysis processing.

[0208] The statistically processed information includes the mean, standard deviation, any information about the shape of the distribution, or a combination of these. Details of this information are as described above.

[0209] Figure 7 This is a flowchart of the processing of the information processing apparatus 10 based on this embodiment.

[0210] First, the information acquisition unit 101 of the information processing device 10 requests the user to input basic information and cell images and other information of the stem cells under examination, acquires this information (S1), and stores it in the stem cell information DB111 (S2).

[0211] The stem cell information extraction unit 102 extracts information related to quality from the information stored in the stem cell information DB111, such as at least one indicator selected from cell proliferation rate, cell recovery count, undifferentiated degree, amount of produced factors, and cell heterogeneity, and stores it in the parameter information DB112 (S3). In addition, the evaluation benchmark setting unit 103 sets the judgment benchmark values ​​for each quality evaluation item based on the information stored in the parameter information DB112, and stores them in the evaluation benchmark DB113.

[0212] The comparison unit 104 extracts the stem cell population information (reference parameter information) of the standard and the stem cell information (tested parameter information) of the user from the stem cell information DB111 and parameter information DB112, and compares the stem cell information between the reference parameter information and the tested parameter information (S4).

[0213] The image information calculation unit 105 calculates the quality evaluation value of the examined stem cells based on the comparison results of reference parameter information and test parameter information, and obtains information for quality evaluation and judgment (S5). The judgment unit 106 judges the quality of the examined stem cells of the target user. The information processing device 10 refers to the data stored in the judgment benchmark evaluation benchmark DB, and judges whether the quality of the cells is good or bad with the benchmark value as the boundary, or displays the evaluation score. The display results are displayed on the user device through the display unit (not shown).

[0214] The program of this invention can be stored on a computer-readable recording medium or a storage unit that can be connected to a computer. Computer recording media or storage units containing the program of this invention are also included in this invention. Examples of recording media or storage units include magnetic media (floppy disks, hard disks, etc.), optical media (CDs, DVDs, etc.), magneto-optical media, flash memory, etc., but are not limited thereto.

[0215] Example

[0216] The present invention will be further described in detail below through embodiments. However, the scope of the present invention is not limited to these embodiments.

[0217] [Example 1]

[0218] Mesenchymal stem cells (MSCs) are the most extensively studied stem cells for applications in cell therapy [1-3]. Various cell types are known to exist in MSCs [4-7] because previous MSC preparations only separated adherent cell components from a mixture of cell suspensions [8, 9]. LNGFR (CD271) and THY-1 (CD90) double-positive cells (LT cells) exist in a specific subset of MSCs in the bone marrow, and they have shown unparalleled characteristics that can help advance MSC-based cell therapy: (1) high proliferative capacity; (2) diverse differentiation capacity (adipogenic, osteogenic, and chondrogenic); (3) low expression of the aging marker SA-β-ga1; and (4) uniform and small size, thus avoiding pulmonary capillary interception after intravenous administration in mouse models

[10] .

[0219] In LT cells obtained by single-cell ablation, a rapidly proliferating clone was observed and named “rapidly proliferating clone (REC)”. The proliferating ability of REC is very useful for the establishment of cell banks with less heterogeneity, and therefore REC is currently used in clinical trials for hypophosphatase disease and spinal stenosis

[11] .

[0220] MSCs have already been used in numerous studies for clinical applications [3, 12, 13, 14], leading to increasing expectations for advancements in MSC-based cell therapies. Therefore, there is a strong demand for the development of technologies that can support MSC manufacturing [15, 16]. REC is characterized by reducing two major risks in current MSC manufacturing processes and increasing the likelihood of constructing efficient and robust cell preparation processes, thus offering significant potential benefits.

[0221] (1) Difficulty in controlling quality deviations between donors

[0222] MSCs exhibit significant donor-to-donor variation [17, 18, 19], and it is practically impossible to fully investigate donor cell variation until robust manufacturing processes are developed [20-22]. Therefore, handling various unknown donor cells is a high-risk operation in MSC preparation and a major cause of unexpected and difficult-to-solve errors.

[0223] Establishing allogeneic cell banks is a practical method for controlling the quality of starting cells, enabling efficient MSC preparation. Recombinant cell lines (RECs) are highly helpful in establishing allogeneic cell banks. Because RECs can be mass-produced through cell ablation, choosing RECs is more feasible and efficient compared to the difficulty of large-scale donor selection until an ideal cell bank is obtained. Furthermore, the proliferative capacity of RECs can significantly improve the success rate of cell bank establishment while maintaining banked cells during the initial culture period. Therefore, preparation using RECs as starting cells enables more feasible process development and stable quality management.

[0224] (2) Quality degradation during cell proliferation culture

[0225] MSCs lose their proliferative capacity after several passages, and other important quality characteristics also decline during expansion culture [23-31]. However, achieving a certain cell number is an essential quality requirement in the manufacture of MSC-based therapeutic products. From a therapeutic point of view, general protocols for MSC-based therapies require more than 1 billion cells per treatment to ensure efficacy

[32] . From a manufacturing point of view, since most quality tests are invasive and cells are consumed with each test, the final cell bank must have a large cell number to allow for comprehensive inspection of the final product according to multiple quality standards

[33] .

[0226] However, such cell preparation processes cannot eliminate the possibility of quality degradation, and therefore offer no guarantee of success. In fact, cell bank establishment failures are only discovered as a "fact" after all operations have been completed, making it difficult to avoid such failures beforehand. Against this backdrop, the high and sustained proliferation capacity of RECs, capable of more than 10 consecutive passages, can significantly reduce the risk of cell bank establishment failure, enabling maximum culture efficiency to establish a large reserve in the initial passages.

[0227] While RECs offer the advantage of low-risk and high-efficiency manufacturing, a dilemma exists in establishing cell banks for RECs. RECs can be efficiently manufactured even from small cell sources through cell screening. Therefore, the diversity of starting clones can be greater than in allogeneic cell banks. However, expanding and culturing large quantities of RECs to the final cell bank stage is costly; furthermore, the final quality testing to ensure the established cell bank may require additional costs and labor.

[0228] While biases in the initial clone of RECs lead to the expectation of selecting "better RECs" during the cell bank establishment process, such expectations increase the number of cell banks that require significant cost and labor. In practice, for RECs, screening from LT cells should be based on the initial proliferation rate of a single clone in a 96-well plate. All candidates should be further expanded and cultured to form multiple "candidate cell banks," ultimately selecting the "better REC cell bank" with higher quality.

[0229] For RECs (Reproductive Cells), a greedy selection can be made for products of higher quality (i.e., end products with lower risk and higher capability), but this increases the overall cost of the process. Therefore, it is necessary to identify such candidate RECs as early as possible to minimize unnecessary operations. However, it is extremely difficult to use conventional cell evaluation methods to examine cells in the early stages of cell bank establishment, especially RECs from single-clonal sources, as these are mostly invasive and waste valuable cell resources.

[0230] This study developed a morphology-based, non-invasive capability prediction method for selecting “better RECs” in the early stages of establishing a cell bank with extremely limited cell counts. Building upon the authors’ previous insights into early prediction based on morphology associated with declining MSC quality [34-37], the authors attempted to enhance the selection of better RECs for forming a better REC cell bank by using only their early morphological information to predict “further successive passage capability” (SCL). Figure 1 ).

[0231] Furthermore, such capability prediction is expected to help achieve a balance between "library size" and "library cell capacity" in cell library construction processes, addressing a significant dilemma in cell library management. While large-scale expansion of the cell library can improve preparation efficiency, it also increases the risk of losing the proliferative capacity of the library cells. Therefore, it is hypothesized that if future continuous passage capacity can be predicted in advance, a low-risk timing can be set for achieving a cell library of the maximum size with high capacity.

[0232] For the training data used to develop the predictive model, the inventors established 15 cloned RECs from bone marrow and experimentally confirmed the number of consecutive passages up to the passage limit (defined as "consecutive passage capacity"). In this embodiment, a machine learning model for quantitatively predicting such capacity was developed using morphological descriptors from passage stage 1 (P1). During the development of the predictive model, a detailed analysis of data utilization efficiency was performed to robustly obtain a high-performance predictive model. Thus, the inventors' data revealed the most efficient time for data usage and the minimum number of images required to implement the predictive model in a practical manner. The inventors' concept of predicting the future capacity of RECs based on cell morphology suggests that image-based, data-driven cell bank construction processes in MSC manufacturing may achieve both efficiency and robustness.

[0233] Materials and methods

[0234] <Cells and Culture>

[0235] To obtain RECs, bone marrow mononuclear cells were prepared from bone marrow aspirate fluid collected from healthy donors (AllCells, Alameda, USA) using density gradient centrifugation with Ficoll (GE Healthcare, Chicago, USA). The bone marrow mononuclear cells were stained at 37°C for 1 hour with anti-human rabbit anti-CD90 IgG (BD Biosciences, Cat#559,869, Flanklin Lakes, USA) and anti-human mouse anti-CD271 IgG (Thermo Fisher).

[0236] Single-cell screening of CD90 and CD271 double-positive cells was performed using a cell sorter (JSAN, BayBioscience, Kobe, Japan). LT cells were further cultured as single clones (passage number = P0) in 96-well plates (Thermo Fisher Scientific, Waltham, USA) using maintenance medium (low-glucose Duchenne modified Eagle medium [DMEM] (Wako, Osaka)) containing 20% ​​fetal bovine serum [FBS] (CytivaHyClone, Marlborough, USA), 20 ng / ml basic fibroblast growth factor [bFGF] (KAKENPHARMACEUTICAL, Tokyo, Japan), 0.01 M 4-(2-hydroxyethyl)-1-piperazine ethanesulfonic acid [HEPES] (Thermo Fisher Scientific, Waltham, USA), and 1% penicillin / streptomycin (Meiji Seika Pharma, Tokyo, Japan). After 2 weeks, the clones were cultured in 96-well plates to subconfluence, recovered, and passaged in single wells of 6-well plates (Thermo Fisher Scientific) (passage number = P1) for image acquisition.

[0237] Images were acquired at this stage for further morphological analysis. Cells were then recovered when they reached subconfluence in 6-well plates (Thermo Fisher Scientific) and passaged in T75 flasks (Thermo Fisher Scientific) (passage number: P2). After incubation at 37°C for 3–5 minutes, cells were recovered using TrypLESelect (Thermo Fisher Scientific).

[0238] P2 cells in T75 culture flasks that had reached sub-confluence were cultured at 1.0 × 10⁶ cells per flask using CP-1 (KYOKUTO PHARMACEUTICAL, Tokyo, Japan) and 25% human serum albumin (CSLBehring, Tokyo, Japan). 6 Cells / ml were cryopreserved and named "Initial Cell Bank (P3)". To preserve cells for continuous passage experiments, cells suitable for various in vitro and in vivo experiments were excluded from the Initial Cell Bank.

[0239] For morphological comparison, previously processed batches of MSCs (BMC: Lot 0,000,394,413, 0,000,411,107, 0,000,413,042, 0,000,422,610, 0,000,423,370, 0,000,429,365, 0,000,446,319, 0,000,451,491, 0,000,458,207) purchased from Lonza Japan, Ltd. (Tokyo, Japan) were cultured to P3 in MSCGM (Lonza Japan, Ltd.) supplemented with Bullet Kit (Lonza Japan, Ltd.). The culture medium was maintained at 37°C and 5% CO2, and was changed every 3 days.

[0240] <Continuous Subculturing Capacity Test>

[0241] Thaw the initial cell bank (P3) vials at 2.0 × 10⁻⁶. 5 Cells were seeded in 100 mm culture dishes (Thermo Fisher Scientific), designated as P3 data. Cells were retrieved from the 100 mm dishes using TrypLE Select (Thermo Fisher Scientific) and passaged at the same seeding density into new 100 mm culture dishes when cells reached subconfluence. This passaged culture process was repeated until cell proliferation ceased. The number of passages was counted, and the final number of passages was defined as the "continuous passage limit," which was defined as "continuous passage capacity." Figure 1 At each passage, the total number of recovered cells was counted using a Cellometer Auto T4 (Nexcelom Bioscience, Lawrence, USA), and the results were recorded as the total number of recovered cells.

[0242] Image Acquisition and Processing

[0243] In a 6-well plate (Thermo Fisher Scientific), for P1 REC, an automated image acquisition device, BioStation CT (Nikon Corporation, Japan), was used every 6 hours at ×4 magnification (64 images per well, covering 16mm). 2 Phase-contrast microscopy images were obtained at 1000 pixels per image. Each cloning batch was imaged in one well. The cell count was extremely limited at this stage.

[0244] For batch MSCs, MSCs are stored at 2000 cells / cm³. 2Cells were seeded in 6-well plates (Corning Incorporated, NY, USA) (n=1, 3 wells per batch). Time points were designated as time points 1 (6 hours post-seeding) to 20 (120 hours post-seeding). Time point 15 was selected as the final time point for morphology and proliferation determination because some batches had reached a sub-confluent state, making it difficult to accurately identify individual cells in the images.

[0245] To obtain a summary of morphological feature spectra of cell populations for processing the original images, the morphology of each cell was determined (from a minimum of 300 cells to a maximum of 100,000 cells recovered from each well covered by 64 images). Image processing was performed in raw code using Python version 3.7.3, NumPy package version 1.20.0, and OpenCV version 4.4.0. The following techniques were designed: 1) background adjustment, 2) texture enhancement, 3) binarization, 4) small object removal, 5) erosion, 6) small object removal, 7) well filling, and 8) contact border object removal. Figure 17 These are the eight processes. After image processing, 12 basic morphological descriptors were determined for each cell location in each image (Table 1).

[0246]

[0247] The cell population was summarized by calculating the mean and standard deviation (SD) using all single-cell-based morphological descriptor data. This summary represents approximately 1 × 10⁻⁶ cells from a single well covered by 64 images. 4 ~1×10 5 Each cell.

[0248] The inventors accumulated the summaries of these morphological descriptors over time (6-90 hours) and designated them as the "morphological feature spectrum" of the specimen. A complete morphological feature spectrum for each condition (1 well) contains 360 parameters (12 descriptors × (average value and SD) × 15 time points). All data processing was performed using R version 4.0.2.

[0249] Visualization of Morphological Feature Spectra

[0250] Gaussian kernel density estimation was performed using single-cell data from each time point, and the cell population was visualized using a single morphological descriptor. The distribution was estimated using a kernel function in R. For distribution estimation, the raw data was transformed with log10 and described as “area”. Measurement data of single cells with an area > 200 pixels were collected to enable a detailed discussion of “area” between REC and previously processed batches of MSCs.

[0251] Since objects smaller than 200 pixels mostly appear in proliferating "round cells," the characteristics of larger cells are reduced in such data, making it difficult to discuss size differences. Principal component analysis (PCA) was used to analyze the morphological feature spectra of all batches, and the relative similarity of clones was visualized using multiple descriptors.

[0252] In the PCA of comparative morphological feature spectra of clones, points were colored using clone labels (15 colors) and successive subculturing limits (7-level gradient: 7~13). All data with different FOV numbers and the same time window size were integrated to define all principal components covering the full diversity of the data, visualize the data utilization effect, and change their time windows and FOV numbers.

[0253] Next, data using different FOV numbers were plotted on a fixed PCA axis. In the comparative PCA of data utilization, one data point represents "one clone". All single-cell assay data based on various data sizes (changing combinations of time window sizes and FOV numbers) were resampled using a bootstrap method (allowing 50 iterations of repetition) to visualize the explanatory power of different data uses, and new means and SDs were obtained for each resampled data. Using PCA and such resampled data, a total of 50 data points were plotted for each clone. The difference between morphological descriptors and their population distribution was tested using a Student's t-test. All data processing was performed using R (version 4.0.2).

[0254] Construction of a continuous passaging capacity prediction model

[0255] The time-evolved morphological feature spectrum was used as the explanatory parameter, and the experimentally determined successive passage limit was used as the target parameter for the machine learning dataset. Two types of machine learning models were validated: Lasso Regression (LASSO), a linear regression model, and Random Forest (RF), a nonlinear machine learning model. In LASSO, the reduced Gini index was used for parameter selection. Model performance was validated using leave-one-out cross-validation and compared using root mean square error (RMSE).

[0256] The effectiveness of morphological data utilization was validated through a comprehensive combination of two parameters: the length of the time window (ranging from 6 to 90 hours) and the number of fields of view (FOV) (ranging from 1 to 64). To vary the time window length, the total window was shortened by 6 to 90 hours from the last time point (90 hours) of each time point (6 hours). Therefore, the maximum value of the total morphological descriptors consisted of 360 parameters, and the minimum value consisted of 24 parameters. Images from 64 images were randomly selected, and the number of FOVs was varied. A bootstrap method was introduced to resample the FOVs of the 64 images 50 times, increasing the dataset size from 15 to 750 samples. The effects of these data variations under different data size conditions (6 hours, 6–30 hours, and 6–60 hours corresponding to FOVs of 15, 40, and 60, respectively) were evaluated. All data processing and machine learning were performed using R version 4.0.2.

[0257] result

[0258] <Obtaining and Representing Learning Data Using REC>

[0259] To develop a machine learning model for predicting “continuous passage capacity” based on initial cell morphology, the process of obtaining REC clones from 15 bone marrow mononuclear cells (BMMCs) from a single donor was initiated. Figure 1 (The protocol). Fifteen clones were obtained from the same donor sample and screened according to the basic conditions of REC.

[0260] (1) LNGFR and THY-1 double positive during cell screening, and (2) after single clones are sorted into 96-well plates, they reach sub-merging (P0 stage) within 2 weeks.

[0261] Next, the clones that were proliferated in 6-well plates and subsequently in T-75 culture flasks were cryopreserved as a temporary cell bank (P3) to preserve the initial passaged cells.

[0262] To further select "better RECs," the serial passage capability of candidate clones from P2 was evaluated. Cell performance capable of multiple repeated passages can be considered an ideal benchmark for selecting "higher quality cell banks" for subsequent use. The results of the serial passage capability assay showed that RECs maintained high proliferative capacity, with an average of 9.8 serial passages recorded. Figure 2 a) It can be assumed that this performance is superior to that of commercially available MSCs processed using conventional methods.

[0263] Next, the morphological characteristics of the clones were evaluated. Figure 2b). At the P1 stage (very early stage) of a 6-well plate, the morphology contains important markers for the cells used in these evaluations. However, it is difficult to identify the differences among RECs through manual morphological observation (without quantification).

[0264] The proliferation curves of RECs at the P1 stage were analyzed using time-lapse images ( Figure 2 c). Among 15 clones, 12 showed a proliferation rate of more than 4-fold. This proliferation rate was significantly higher than that of multiple batches of MSCs processed previously in the study by the present inventors ( Figure 18 ). The clone with the lowest continuous passage ability (Clone 13) showed a low proliferation rate, and the clone with the highest continuous passage ability (Clone 4) showed a high proliferation rate. However, the coefficient of determination between "proliferation rate" and "maximum number of passages" was low (R 2 = 0.09) ( Figure 2 d). Therefore, this data indicates that the measurement of the proliferation rate at the P1 stage cannot predict the future continuous passage ability.

[0265] <Morphological characteristics of RECs>

[0266] The present inventors et al. attempted to quantitatively characterize RECs (at the P1 time point) through image-based morphological analysis according to known analysis methods [33 - 36]. Compared with the previously processed batches of MSCs (cMSCs, 9 batches), RECs were more uniform and remained smaller after attachment. Figure 3 a, b). Both RECs and cMSCs started from cell populations of the same size (median area = 358 μm 2 , 335 μm respectively) at the very early stage of attachment (6 hours, T-test p < 0.22). The median size of RECs remained small (363 μm 2 ), while the size of batch cMSCs increased after 30 hours of culture (median area = 473 μm 2 , T-test p < 0.000001). In addition, in this series of time-lapse images, more proliferating cells (white and round under phase contrast microscopy) were observed in RECs. Figure 3 b).

[0267] By visualizing the population changes of RECs at P1, it was found that the cell area of the adherent cell population of the clone with reduced continuous passage ability remained large. Figure 3 c). Thus, high-performance cells form a uniform population of cell sizes, and such size data analysis is instructive. The coefficient of determination between "SD of area" and "maximum number of passages" is low. Figure 3 d). Analysis based on a single morphological descriptor is not effective enough for quantitative prediction.

[0268] The clones were characterized using a variety of morphological information described by 24 descriptors derived from the basic descriptors (Table 1) [33-36]. Principal component analysis (PCA) was used to visualize the morphological similarity of the RECs. Figure 3 e is represented by the clone number. Figure 3 f is expressed as the successive passage ability of each clone.

[0269] These results indicate the existence of specific clusters of clones with similar morphological profiles, which slightly separate clones with low successive subculturing ability from those with high successive subculturing ability. Specifically, clones with low successive subculturing ability cluster at the lower part of the PC2 axis, while clones with high successive subculturing ability cluster at the higher part of the PC2 axis and the center of the PC1 axis. The descriptors contributing to each axis of the PCA plot, particularly the most explanatory PC2 axis, can be interpreted as follows (Table 2).

[0270]

[0271] Clones exhibited higher continuous passage capacity (allowing for long-term continuous passage) when cells displayed more uniform and spindle-shaped morphology during the 24-30 hour proliferation process; however, this continuous passage capacity decreased when cell morphological uniformity was disordered. This unsupervised model analysis suggests that combinations of multiple descriptors can provide a better explanation for morphological characterization of performance.

[0272] Morphological Machine Learning for Predicting Successive Successive Generation Capacity

[0273] Next, the construction of machine learning models using morphological information was studied. Figure 1 This study aimed to quantitatively predict the "successive subculturing ability" of RECs based solely on morphological information. The successive subculturing limit was predicted using a morphological feature spectrum (24 descriptors × 15 time points). Based on the construction of this prediction model, an attempt was made to understand the extent or part of the effective contribution of morphological information to the development of the prediction model.

[0274] Therefore, by altering the effects of the time window and the field of view (FOV) number, the effectiveness of utilizing morphological data was verified. The reason for studying these parameters is that, in the authors' previous research attempting to predict MSC proliferation rate and bone differentiation rate based on morphological descriptors, it was found that detailed study of these parameters could effectively reduce the workload of image data collection [33-36]. Shortening the time window and minimizing the FOV number not only saves time and effort in acquiring image data but also improves prediction efficiency.

[0275] The effects of time window and field of view (FOV) were thoroughly investigated using Lasso regression (LASSO). The results showed that even with morphological information of P1 cells, high-performance prediction models could be obtained under certain parameter combinations. Figure 4 (As shown in the green heatmap in section a, RMSE < 1.0). This data indicates that prediction performance can be maintained even by reducing the time window and the number of FOVs. Furthermore, it can be inferred that the effect of the "number of FOVs" is more important than that of the "time window." This is because, when collecting more than 15 FOVs, the time window can be shortened without degrading performance.

[0276] Scatter plots were drawn to further understand the performance of the predictive model regarding the successive passage ability of each clone. Figure 4 b). These data clearly demonstrate that the inventors' predictive model can predict the "quantitative value of the successive subgeneration limit number" (b). Figure 4 (a, b) Numerous high-performance prediction models have been developed using training data of varying sizes. However, because LASSO is an algorithm that generates the optimal combination of descriptors for each dataset, all model structures exhibit random differences due to the lack of a common structure, raising concerns that models with completely different structures but similar performance may be generated.

[0277] When the model structure varies under different data conditions, such modeling results lack robustness and are therefore impractical. Therefore, the correlation of all model structures was compared. Figure 4 c) The results confirm that high-performing models share the same model structure in terms of robustness. This result indicates that the relationship between morphological descriptor combinations and successive succession capability can be modeled through specific universal combinations of descriptors.

[0278] Comparisons of highly contributing descriptors shared across different models, such as “Correlation_SD(18h)”, “Correlation_SD(6h)”, and “Energy_SD(18h)”, have a positive contribution to the prediction of clones with high successive passage ability. Conversely, “Correlation_mean(6h)”, “Length_SD(18h)”, and “Compactness_SD(18h)” have a negative contribution to the prediction of clones with low successive passage ability (Table 3).

[0279]

[0280] Both "correlation" and "energy" are texture descriptors, and therefore, because they alter the intensity curves under phase-contrast microscopy, they often reflect the three-dimensional pattern and complexity of cells. In fact, the intensity curves change drastically when cells change their three-dimensional roundness during cell division. Therefore, an increase in the "SD of texture" indicates the presence of a larger population of proliferating cells. Consequently, when the "average of texture" has a greater influence, the cell population becomes more homogeneous in texture, meaning a decrease in the number of cellular events altering the texture.

[0281] Length and compactness are shape-related descriptors, and therefore typically reflect two-dimensional responses such as elongation and expansion. When such changes in shape occur without substantial changes in texture, it indicates that cell activity is primarily driven by elongation rather than proliferation. Therefore, information derived from the structure of such models suggests that the resulting sequential passage prediction models are useful not only for the early detection of future cell performance but also for the quantitative extraction of descriptively recorded morphological regularities. Thus, such a descriptive understanding of morphological feature spectra helps to move away from the old habit of relying on intuition to grasp morphological changes.

[0282] The same conditional matrix was validated using the nonlinear machine learning model RF, and the results showed that the model did not outperform LASSO under any data utilization conditions. Figure 4 d). These data reflect a linear relationship between successive subculturing ability and morphological information.

[0283] The inventors verified the combination of two parameters (time window and FOV number), but on the other hand, since the model learning is based on a small amount of raw sample data compared to machine learning applications in other fields, they ultimately attempted to confirm the predictive performance of the LASSO model in more detail.

[0284] To evaluate the robustness of the prediction model to the effects of data bias, a bootstrap method was introduced to account for the bias in the morphological feature spectrum of the image sources. The performance of the prediction model was validated by introducing 50 bootstrap iterations, collecting various combinations of field of view (FOV) from 64 mosaic images for each sample. Figure 5 ).

[0285] This result indicates that the image sampling bias caused by the bootstrap method leads to a performance degradation in some prediction models. However, although the range of "time window effect" and "FOV number effect" used to achieve high-performance models (RMSE < 1.0) is relatively small... Figure 4The range of a to c is narrowed compared to more robust models, but in the earliest predictions, it was able to minimize the data collection scale while maintaining prediction accuracy (RMSE<1.0) with a field of view (FOV) of 40 and 6 to 30 hours. In the predictions with minimal labor, it was able to minimize the data collection scale while maintaining prediction accuracy (RMSE<1.0) with a field of view (FOV) of 15 and 6 to 90 hours.

[0286] In evaluating the effectiveness of such bootstrapping methods in PCA, the number of fields of view (FOV) significantly improved the robustness of morphological feature spectra. Accumulation of morphological feature spectra over longer time windows improved the ability to distinguish clonal differences. Such a data-size-based effectiveness study helps design more efficient processes for incorporating image-based quality checks in cell bank construction.

[0287] Investigation

[0288] RECs are clonal MSCs selected from human BMMCs. They not only retain the excellent qualities of previously processed MSCs but also possess characteristics favorable for practical cell preparation processes used in therapeutic products. In particular, the high proliferative capacity of RECs is a significant advantage in developing efficient preparation processes for cell therapy products. Therefore, this study explores the possibility of predicting continuous passage performance based solely on initial morphological information and its most practical construction methods to enable the evaluation of REC performance from the early stages of cell bank construction.

[0289] Determining the optimal timing for preparing cryopreservations during expansion cultures has been a long-standing problem in any type of cell culture. Since most normal cells lose their proliferative capacity during in vitro culture [38, 39], the preparation of cryopreservations is a gamble on the passage number at which end the culture. This gamble carries significant risk when constructing manufacturing cell banks. High costs can result from cells failing to proliferate as expected during low-success-rate expansion cultures, while premature cryopreservation can lead to unsatisfactory production efficiency.

[0290] In reality, the bottleneck in actual cell bank construction lies in the effort to recruit valuable donors, rather than in the greedy selection of candidate cell banks. However, REC (Real Cell Banking) anticipates more rigorous and selective processes to complete candidate cell banks as "master cell banks." Our research proposes a new concept: using non-invasive morphological analysis as an "in-process analysis tool" to enhance and optimize the cell bank construction process. This concept considers both "cell yield" and "performance preservation of banked cells," and predicts future continuous passage capacity, thereby helping to determine the optimal timing for cryopreservation. This approach helps to abandon current cell bank design concepts that use passage numbers without data-driven logic to limit them.

[0291] The inventors have investigated methods for predicting the “passage capacity” of RECs, but the passage capacity of cMSCs may require some discussion. For artificial pluripotent stem cells (iPSCs), the uncontrolled proliferation capacity of iPSCs is known to have adverse effects on treatment (e.g., the risk of teratoma formation) [40, 41]. However, for MSCs, although their proliferation capacity is known to be limited, their passage capacity is still considered in some respects.

[0292] In this study, where the "candidate cell bank" was created as a master cell bank for producing further working cell banks, its continuous passage capability would be beneficial to the entire process. However, when created as the final cell bank for transplantation, the effectiveness of continuous passage capability should be carefully verified. This may be an advantage if it improves the efficacy of the final product, but it is a risk if it adversely affects efficacy.

[0293] In any case, making such future performance predictions from the very early stages of the process helps to optimize the quality of the final cell bank. This is because such predictions can only be evaluated through excessive and continuous evaluation of unsuitable REC candidates. Since RECs are currently moving towards clinical trials, our next task is to validate the effectiveness of such performance predictions and efficiently advance product manufacturing.

[0294] Finally, the future performance prediction based on REC morphology sparked a discussion about whether the model developed in this study could be applied to the establishment of other MSC cell banks. The inventors' detailed image-based morphological measurements showed that the main population of RECs consists of cells approximately twice the size of bulk MSCs. This large size difference makes the predictive model structure, which combines morphological descriptors, suitable for RECs. This is because the inventors used "mean and SD" to reflect the cell population distribution in the morphological feature spectrum.

[0295] [Example 2]

[0296] In Example 2, a comparison of the population distribution of the entire cell population was performed.

[0297] The results are shown in Figures 8-16 .

[0298] Figure 8 The results show a comparison of the overall population distribution of cell populations. Clonal MSCs (highly proliferating mesenchymal stem cells characterized by positivity or negativity of at least one of the markers LNGFR (CD271) and Thy-1 (CD90)) and bulk MSCs (mesenchymal stem cells not characterized by these markers) were cultured. Phase-contrast microscopy images obtained from the culture vessels were image-processed to describe the population distribution of thousands of cells in the field of view. Six figures show six cell morphological characteristics, with population distribution displayed using their one-dimensional characteristics. The results show that the population distribution of clonal and bulk MSCs differed significantly under all morphological characteristics. Figure 3 In comparison, the image quality, the time series compared, and the image processing methods differed, and measurements were performed based on more stable cell image data.

[0299] Figure 9 The results show the comparison of the population distribution of the cell population as a whole.

[0300] Three batches of clonal MSCs (highly proliferating mesenchymal stem cells characterized by positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) markers) and bulk MSCs (mesenchymal stem cells not characterized by these markers) were cultured. Phase contrast microscopy images obtained from the culture containers were processed to describe the population distribution of tens of thousands of cells in the field of view as a scatter plot combining two markers rather than a single marker. Figure 9 The differences between each batch (row differences) and the differences in time-series data (column differences) are displayed. By comparing cloned MSCs with batch MSCs using a two-dimensional (two-indicator) combined observation, it can be confirmed that multiple subpopulations (multiple distributions) exist within batch MSCs. Figure 3 In comparison, the image quality, the time series compared, and the image processing methods differed, and measurements were performed based on more stable cell image data.

[0301] Figure 10The results of a comparison of the overall population distribution of cell populations are shown. Cloned MSCs (highly proliferating mesenchymal stem cells characterized by positivity or negativity of at least one of the markers LNGFR (CD271) and Thy-1 (CD90)) and bulk MSCs (mesenchymal stem cells not characterized by these markers) were cultured. Phase-contrast microscopy images obtained from the culture vessels were processed to show the population distribution of tens of thousands of cells in the field of view over time. The horizontal axis represents cell size, and when compared over time, differences in the polymorphism of distribution and the time zones at which these differences occur are observed between the left (cloned MSCs) and right (bulk MSCs). The y-axis represents the changes over time (6-hour intervals). Cloned MSCs = REC maintained a consistent distribution over time (towards later stages of culture), while bulk MSCs = commercially available MSCs exhibited two initial bulges, showing the mixed expansion resulting from their fusion. In other words, the cell population is complex and contains multiple subpopulations.

[0302] Figure 11 The results were used to evaluate the proliferation rate, adipogenic differentiation, and osteogenic differentiation of each MSC.

[0303] Ninety-one batches of cloned MSCs (highly proliferating mesenchymal stem cells characterized by positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) markers) and 21 batches of bulk MSCs (mesenchymal stem cells not characterized by these markers) were cultured and evaluated for the following: (i) proliferation rate after 7 days of culture, (ii) adipogenic differentiation after 2 weeks, and (iii) osteogenic differentiation after 2 weeks. Proliferation rate was determined by cell counting based on image processing of images over time. For adipogenic differentiation, after 2 weeks of culture in differentiation-inducing medium, Oil Red O staining was performed, and the differentiation capacity score was calculated as the sum of the red stained areas in the images. For osteogenic differentiation, after 2 weeks of differentiation-inducing medium, Alizarin Red S staining was performed, and the score was calculated as the sum of the red stained areas in the images. The results of all samples were then statistically analyzed, showing the results arranged in ascending order of (i), (ii), and (iii). The results showed that cloned MSCs without enhanced selection could yield a large amount of data showing diverse qualities (proliferation and differentiation capabilities), while even if one wanted to purchase a large number of MSCs to obtain all kinds of diversity, the comprehensiveness was very low (the quality values ​​obtained from the blue bars were biased).

[0304] Figure 12 It is to maintain Figure 11 The data, arranged in ascending order of proliferation rate, are displayed as a bar chart showing the quality scores of fat differentiation and bone differentiation. According to... Figure 12It is evident that there is no simple correlation between high proliferation rate and high differentiation, nor between low proliferation rate and low differentiation, nor is there a simple data correlation where a high proliferation rate strongly influences the differentiation of either bone or fat. That is, accumulating various clones is effective for preparing a large volume of high-quality cells; collecting only rapidly proliferating clones cannot cover all qualities. Furthermore, it is known that since the quality of cells cannot usually be understood without actually purchasing them ((i)(ii)(iii)), it is impossible to obtain sufficient data diversity based solely on prior data. Therefore, cloning MSCs is effective for obtaining data diversity. Moreover, it is known that the diversity of qualities covered by cloned MSCs is far broader than that of bulk MSCs. Especially in terms of proliferative capacity, it can achieve a quality far exceeding that of bulk MSCs.

[0305] Figure 13 The results of measuring the proliferation rate of each MSC based on image analysis over time are shown.

[0306] Twenty batches of cloned MSCs (highly proliferating mesenchymal stem cells characterized by positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) markers) and twenty batches of bulk MSCs (mesenchymal stem cells not characterized by these markers) were cultured. The proliferation rate (days 1-5) was determined by image analysis over time (measured values ​​on the horizontal axis of a scatter plot). Then, using each cell batch as a sample, a machine learning model was constructed using a dataset with cell morphology information (360 parameters (equivalent to population statistics of 12 morphological indicators from thousands of cells, mean and SD = 2 types) × 15 time intervals (equivalent to 90 hours)) as explanatory variables and proliferation rate as the target variable. This model predicted the proliferation rate after 5 days. Figure 13 It can be seen that the machine learning model for the proliferation rate prediction model using cloned MSCs has a small error (RMSE value), which shows the effectiveness of the machine learning model construction efficiency using cloned MSC data.

[0307] Figure 14 The results of measuring the proliferation rate of each MSC based on image analysis over time are shown.

[0308] Seventy-five cloned MSCs (highly proliferating mesenchymal stem cells characterized by positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) markers) and 18 batches of bulk MSCs (mesenchymal stem cells not characterized by these markers) were cultured, and their proliferation rates (days 1-5) were determined based on image analysis over time (measured values ​​on the horizontal axis of a scatter plot). Then, using only the cloned MSC data, a machine learning model was constructed using a dataset with cell morphology information (360 parameters (equivalent to population statistics of 12 morphological indicators from thousands of cells, mean and SD = 2 types) × 15 time intervals (equivalent to 90 hours)) as explanatory variables and proliferation rate as the target variable. This model was used to predict the proliferation rate after 5 days (gray dots in the scatter plot). Then, the explanatory variables of 360 indicators obtained from the batch MSCs were used as input values ​​and substituted into the proliferation rate prediction model constructed with cloned MSCs. The results (black dots in the scatter plot) show that the proliferation rate of batch MSC samples that were not used for learning and were not cloned MSCs can be predicted with high accuracy based solely on morphological information.

[0309] Figure 15 The results were obtained by analyzing the proliferation rate of each MSC based on images that changed over time.

[0310] Cloned MSCs (highly proliferating mesenchymal stem cells characterized by the positivity or negativity of at least one biomarker, LNGFR (CD271) and Thy-1 (CD90)) were cultured (75 batches in total, with different batches cultured in each experiment). The proliferation rate (days 1-5) was measured using image analysis over time (resulting in measured values ​​on the horizontal axis of a scatter plot). A machine learning model was constructed for each batch (different experimental days). The model included morphological information (360 parameters = population statistics of 12 morphological indicators from thousands of cells (mean and SD = 2) × 15 time intervals (90 hours)) as the explanatory variable and proliferation rate as the target variable. The results showed low prediction error (RMSE value) for each experimental day (see above). However, simply mixing all the data to construct a proliferation prediction model for all 75 culture batches resulted in a significantly worse prediction error. This indicates that even if all image data are taken by the exact same automated culture observation device, data bias will be present on each experimental day, resulting in poor performance of machine learning models made from simply aggregated data.

[0311] Figure 16 The results of the data integration are shown. Figure 15In the data integration, each dataset underwent a preprocessing procedure combining data curation, noise removal, and data standardization. The result was that, compared to the case with this preprocessing (left figure), the machine learning model exhibited lower and more stable prediction errors (RMSE values) (right figure). This indicates that even when all image data were captured by the exact same automated culture observation device, data bias is present on each experimental day, leading to poor performance of the machine learning model based on simply integrated data.

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Claims

1. A standard for evaluating or determining the quality of tested cells, comprising: Highly proliferating mesenchymal stem cell populations characterized by the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90).

2. The standard product according to claim 1, wherein, The characteristics of the cell population are selected from any of the following (a) to (d): (a) At least one type of cell information selected from the cell size, extensibility, contour complexity, texture and brightness of each cell in the cell population, and their image information; (b) The information obtained by accumulating and statistically processing the cell information of (a) in multiple cells within the cell population; (c) Combined information obtained by acquiring the information in (b) from multiple cell populations; (d) The information obtained over time from (a) to (c).

3. The standard product according to claim 2, wherein, The statistically processed information is selected from at least one of the mean, standard deviation, and distribution shape.

4. The standard product according to claim 1, wherein, Quality is selected from at least one of cell proliferation rate, cell recovery count, degree of undifferentiation, degree of differentiation, amount of produced factors, and cell heterogeneity.

5. The standard product according to claim 1, wherein, The cells examined were stem cells.

6. The standard product according to claim 5, wherein, The stem cells examined were the mesenchymal stem cells examined.

7. A method for providing information for quality assessment of examined stem cell populations, the method comprising the following steps: (a) A step of characterizing each cell of a rapidly proliferating mesenchymal stem cell population using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtaining image data of each cell (reference image information), or obtaining parameter information of the characteristics of the cell population (reference parameter information) from the reference image information. (b) A process of obtaining image data (examined image information) of each cell of the examined stem cell population provided by the user, or obtaining parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information; (c) The step of comparing the inspected image information or inspected parameter information with the reference image information or reference parameter information; as well as (d) The process of providing all or part of the information obtained through the comparison to the user for use in the quality evaluation of the tested stem cell population.

8. A method for quality evaluation of a tested stem cell population, the method comprising the following steps: (a) A step of characterizing each cell of a rapidly proliferating mesenchymal stem cell population using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtaining image data of each cell (reference image information), or obtaining parameter information of the characteristics of the cell population (reference parameter information) from the reference image information. (b) A process of obtaining image data (examined image information) of each cell of the examined stem cell population provided by the user, or obtaining parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information; (c) The step of comparing the inspected image information or inspected parameter information with the reference image information or reference parameter information; as well as (d) A process for evaluating the quality of the tested stem cells using all or part of the information obtained through the comparison as an indicator.

9. An information provision system for quality assessment of examined stem cell populations, the system comprising the following units: (a) A unit that characterizes each cell of a rapidly proliferating mesenchymal stem cell population by using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtains image data of each cell (reference image information), or obtains parameter information (reference parameter information) of the characteristics of the cell population from the reference image information. (b) A unit that acquires image data (examined image information) of each cell of the examined stem cell population provided by the user, or acquires parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information; (c) A unit that compares the inspected image information or inspected parameter information with the reference image information or reference parameter information; and (d) A unit that provides all or part of the information obtained through the comparison to the user for use in the quality assessment of the examined stem cell population.

10. A quality assessment system for a tested stem cell population, the system comprising the following units: (a) A unit that characterizes each cell of a rapidly proliferating mesenchymal stem cell population by using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtains image data of each cell (reference image information), or obtains parameter information (reference parameter information) of the characteristics of the cell population from the reference image information. (b) A unit that acquires image data (examined image information) of each cell of the examined stem cell population provided by the user, or acquires parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information; (c) A unit that compares the inspected image information or inspected parameter information with the reference image information or reference parameter information; and (d) A unit that evaluates the quality of the tested stem cells using all or part of the information obtained through the comparison as indicators.

11. The system according to claim 9 or 10, wherein, The characteristics of the cell population are selected from any of the following (a) to (d): (a) At least one type of cell information selected from the cell size, extensibility, contour complexity, texture and brightness of each cell in the cell population, and their image information; (b) The information obtained by accumulating and statistically processing the cell information of (a) in multiple cells within the cell population; (c) Combined information obtained by acquiring the information in (b) from multiple cell populations; (d) The information obtained over time from (a) to (c).

12. The system according to claim 11, wherein, The statistically processed information is selected from at least one of the mean, standard deviation, and distribution shape.

13. The system according to claim 9 or 10, wherein, Quality is selected from at least one of cell proliferation rate, cell recovery count, degree of undifferentiation, degree of differentiation, amount of produced factors, and cell heterogeneity.

14. The system according to claim 9 or 10, wherein, The stem cells examined were the mesenchymal stem cells examined.

15. An information-providing program for quality assessment of examined stem cell populations, which enables a computer to function as a unit: (a) A unit that characterizes each cell of a rapidly proliferating mesenchymal stem cell population by using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtains image data of each cell (reference image information), or obtains parameter information (reference parameter information) of the characteristics of the cell population from the reference image information. (b) A unit that acquires image data (examined image information) of each cell of the examined stem cell population provided by the user, or acquires parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information; (c) A unit that compares the inspected image information or inspected parameter information with the reference image information or reference parameter information; and (d) A unit that provides all or part of the information obtained through the comparison to the user for use in the quality assessment of the examined stem cell population.

16. A quality assessment procedure for a tested stem cell population, wherein a computer functions as a unit: (a) A unit that characterizes each cell of a rapidly proliferating mesenchymal stem cell population by using the positivity or negativity of at least one of LNGFR (CD271) and Thy-1 (CD90) as indicators, and obtains image data of each cell (reference image information), or obtains parameter information (reference parameter information) of the characteristics of the cell population from the reference image information. (b) A unit that acquires image data (examined image information) of each cell of the examined stem cell population provided by the user, or acquires parameter information (examined parameter information) of the characteristics of the examined stem cell population from the examined image information; (c) A unit that compares the inspected image information or inspected parameter information with the reference image information or reference parameter information; and (d) A unit that evaluates the quality of the tested stem cells using all or part of the information obtained through the comparison as indicators.

17. The procedure according to claim 15 or 16, wherein, The characteristics of the cell population are selected from any of the following (a) to (d): (a) At least one type of cell information selected from the cell size, extensibility, contour complexity, texture and brightness of each cell in the cell population, and their image information; (b) The information obtained by accumulating and statistically processing the cell information of (a) in multiple cells within the cell population; (c) Combined information obtained by acquiring the information in (b) from multiple cell populations; (d) The information obtained over time from (a) to (c).

18. The procedure according to claim 17, wherein, The statistically processed information is selected from at least one of the mean, standard deviation, and distribution shape.

19. The procedure according to claim 15 or 16, wherein, Quality is selected from at least one of cell proliferation rate, cell recovery count, degree of undifferentiation, degree of differentiation, amount of produced factors, and cell heterogeneity.

20. The procedure according to claim 15 or 16, wherein, The stem cells examined were the mesenchymal stem cells examined.

21. A computer-readable recording medium having recorded the program as described in claim 15 or 16.

22. An image processing method for rapidly proliferating mesenchymal stem cells, the method comprising the following steps: (a) the process of obtaining image data of the standard as claimed in claim 1; and (b) The process of removing noise from the acquired image data and / or standardizing the image data.

23. A method for using image data processed by the method of claim 22 for machine learning in a computer.

24. A method for predicting the quality of a cell by comparing image data processed by the method of claim 22 with image data of the cell under examination.

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