A cell morphological variable model for visualizing mesenchymal stem cell status and a method for predicting mesenchymal stem cell regenerative capacity

Abstract

The present application belongs to the field of biological medicine, and relates to a cell morphological variable model for visualizing the state of mesenchymal stem cells and a method for predicting the regenerative capacity of mesenchymal stem cells. The present application provides a combination of discriminant indexes of stem cell performance or quality or state, one of which is SSC-H. Mesenchymal stem cells (MSC) have been widely used in the treatment of various diseases. However, due to the lack of reliable evaluation methods to assess the potential of MSC, it hinders its clinical application. This study proposes a mathematical model containing the index combination for predicting the regenerative capacity (RC) and stemness of MSC. The RC of MSC is evaluated and predicted by the mathematical model. By predicting the RC of newly included MSC and chemically inhibited MSC, the predictive ability of the model is verified through screening experiments of these indexes. Further RNA sequencing analysis reveals that the indexes based on the appearance of cells can be used as the main markers to visualize the results of integrated weighted signals inside and outside the cells and reflect the stemness of MSC.

Description

Technical Field

[0001] This invention belongs to the field of biomedicine and relates to a cell morphology variable model for visualizing the state of mesenchymal stem cells and a method for predicting the regenerative capacity of mesenchymal stem cells. Background Technology

[0002] Mesenchymal stem cells (MSCs) are adult stem cells derived from the mesoderm in early embryonic development. They possess self-renewal and multi-lineage differentiation potential and retain their biological characteristics even after large-scale in vitro expansion. They are adherent and fibrous. MSCs can be derived from bone marrow, adipose tissue, umbilical cord, gingiva, cartilage, skin, dental pulp, etc. MSCs can differentiate into various tissue-specific lineages, including osteoblasts, chondrocytes, adipocytes, hepatocytes, myocytes, and neuron-like cells. 1,2,3 .

[0003] MSCs possess strong and broad therapeutic potential, with over 1,300 MSC-related clinical trials registered globally on ClinicalTrials.gov. MSCs have also been used to treat various diseases, including bone / cartilage repair, graft-versus-host disease, systemic lupus erythematosus, diabetes, and regenerative medicine. MSCs and related fields have become a continuously developing global industry. As our understanding of the characteristics of MSCs deepens, the definition criteria for pluripotent stem cells and the clinical translation guidelines for human MSCs are constantly being revised. 4,5,6 However, even among MSCs from the same tissue source and of the same generation, accurate and objective efficacy evaluations are often difficult to obtain due to donor differences. The lack of standardized quality control for stem cells has hindered their effective clinical translation and application. 7,8,9,10 .

[0004] Furthermore, MSCs derived from different donors, tissues, clones, or even different cells from the same clonal colony may exhibit heterogeneity. This heterogeneity can be specifically reflected in cell morphology and function, and its causes include, but are not limited to, differences in donor and tissue origin, cell isolation techniques, and cell culture and preservation conditions. 9,11,12,13,14,15 Therefore, researching and developing alternative biomarkers for assessing stem cell efficacy and rapid detection methods for quality control of stem cell preparations has become a consensus in the field.

[0005] Given that mesenchymal stem cells contain complex information about their life state, the lack of a comprehensive understanding and in-depth evaluation of them is one of the main reasons why stem cell therapy has failed to break through current bottlenecks. Summary of the Invention

[0006] In some embodiments, the present invention provides a combination of indicators for judging stem cell performance, quality, or status, including SSC-H.

[0007] In some implementations, the combination of indicators also includes nuclear roundness and phosphorylated ERK1 / 2.

[0008] In some implementations, the combination of indicators also includes the nucleocytoplasmic ratio.

[0009] In some embodiments, the phosphorylated ERK1 / 2 includes the gene, mRNA, or protein of phosphorylated ERK1 / 2.

[0010] In some embodiments, the present invention provides the application of biomarkers in identifying or determining the performance, quality, or status of stem cells; said biomarkers include SSC-H.

[0011] In some implementations, the markers also include cell nuclear roundness and phosphorylated ERK1 / 2.

[0012] In some implementations, the biomarker also includes the nucleocytoplasmic ratio.

[0013] In some embodiments, the phosphorylated ERK1 / 2 includes the gene, mRNA, or protein of phosphorylated ERK1 / 2.

[0014] In some implementations, the stem cell performance or quality or status includes the stem cell's regenerative capacity.

[0015] In some implementations, the regenerative capacity of the stem cells includes osteogenic capacity, bone regeneration capacity, or stemness.

[0016] In some implementations, the stem cells include pluripotent stem cells or multipotent stem cells.

[0017] In some implementations, the stem cells include mesenchymal stem cells or induced pluripotent stem cells.

[0018] In some implementations, the mesenchymal stem cells are derived from bone marrow tissue, adipose tissue, gingival tissue, umbilical cord tissue, cartilage tissue, skin tissue, urine tissue, placental tissue, periosteum tissue, or tendon tissue.

[0019] In some implementations, the oral tissues include the tooth body, pulp, gingiva, periodontal ligament, or dental ligament.

[0020] In some implementations, the dental pulp includes mesenchymal stem cells from deciduous tooth pulp or permanent tooth pulp.

[0021] In some implementations, the deciduous tooth pulp mesenchymal stem cells are human exfoliated deciduous tooth pulp stem cells.

[0022] In some implementations, the cell generations of the dental pulp mesenchymal stem cells include P1-P12.

[0023] In some implementations, the cell passages of the umbilical cord mesenchymal stem cells include P1-P15.

[0024] In some implementations, the cell passages of the bone marrow mesenchymal stem cells include P1-P20.

[0025] In some implementations, the cell passages of the adipose-derived mesenchymal stem cells include P1-P20.

[0026] In some embodiments, the present invention provides a method for determining the performance, quality, or state of stem cells, including detecting the value of the indicator SSC-H.

[0027] In some implementations, the discrimination method includes: detecting the value of the SSC-H index; comparing the values ​​of the SSC-H index among the stem cells under the same instrument; judging the stem cell performance, quality, or status based on the value of the SSC-H index; and judging the stem cell performance, quality, or status as the stem cell with the increased SSC-H index value is better when the SSC-H index value of the stem cell is higher than that of another type of stem cell.

[0028] In some implementations, the method also includes the use of indicators such as cell nuclear roundness and phosphorylated ERK1 / 2.

[0029] In some implementations, the method also includes the use of an indicator cell nucleocytoplasmic ratio.

[0030] In some implementations, the discrimination method includes: detecting the values ​​of SSC-H, nuclear roundness, relative expression level of phosphorylated ERK1 / 2, and nuclear-cytoplasmic ratio; comparing the values ​​of SSC-H, nuclear roundness, relative expression level of phosphorylated ERK1 / 2, and nuclear-cytoplasmic ratio; judging the stem cell performance, quality, or status based on the values ​​of the stem cell indicators SSC-H, nuclear roundness, relative expression level of phosphorylated ERK1 / 2, and nuclear-cytoplasmic ratio; when the values ​​of SSC-H, nuclear roundness, relative expression level of phosphorylated ERK1 / 2, and nuclear-cytoplasmic ratio of one type of stem cell are higher than those of another type of stem cell, the stem cells with higher values ​​of SSC-H, nuclear roundness, relative expression level of phosphorylated ERK1 / 2, and nuclear-cytoplasmic ratio are judged to have better performance, quality, or status, and vice versa.

[0031] In some embodiments, the phosphorylated ERK1 / 2 includes the gene, mRNA, or protein of phosphorylated ERK1 / 2.

[0032] In some embodiments, the relative expression level of phosphorylated ERK1 / 2 is the ratio of the phosphorylated ERK protein expression level to the total ERK protein expression level.

[0033] In some implementations, the stem cell performance or quality or status includes the stem cell's regenerative capacity.

[0034] In some implementations, the regenerative capacity of the stem cells includes osteogenic capacity, bone regeneration capacity, or stemness.

[0035] In some implementations, the stem cells include pluripotent stem cells or multipotent stem cells.

[0036] In some implementations, the stem cells include mesenchymal stem cells or induced pluripotent stem cells.

[0037] In some implementations, the mesenchymal stem cells are derived from bone marrow tissue, adipose tissue, gingival tissue, umbilical cord tissue, cartilage tissue, skin tissue, urine tissue, placental tissue, periosteum tissue, or tendon tissue.

[0038] In some implementations, the oral tissues include the tooth body, pulp, gingiva, periodontal ligament, or dental ligament.

[0039] In some implementations, the dental pulp includes mesenchymal stem cells from deciduous tooth pulp or permanent tooth pulp.

[0040] In some implementations, the deciduous tooth pulp mesenchymal stem cells are human exfoliated deciduous tooth pulp stem cells.

[0041] In some implementations, the cell generations of the dental pulp mesenchymal stem cells include P1-P12.

[0042] In some implementations, the cell passages of the umbilical cord mesenchymal stem cells include P1-P15.

[0043] In some implementations, the cell passages of the bone marrow mesenchymal stem cells include P1-P20.

[0044] In some implementations, the cell passages of the adipose-derived mesenchymal stem cells include P1-P20.

[0045] In some embodiments, the present invention provides a method for judging the performance, quality, or state of stem cells, comprising: obtaining the value of an indicator; inputting the value of the indicator into a discrimination model to obtain the discrimination result of stem cell performance, quality, or state; wherein the discrimination model is a computational model constructed based on the functional relationship between the value of the indicator and the performance, quality, or state of stem cells.

[0046] In some implementations, the metrics include SSC-H.

[0047] In some implementations, the metrics also include cell nuclear roundness and phosphorylated ERK1 / 2.

[0048] In some implementations, the indicator also includes the nucleocytoplasmic ratio.

[0049] In some embodiments, the phosphorylated ERK1 / 2 includes the gene, mRNA, or protein of phosphorylated ERK1 / 2.

[0050] Mesenchymal stem cells (MSCs) have been widely used to treat various diseases. However, the lack of reliable evaluation methods to characterize the potential of MSCs hinders their clinical application.

[0051] In some implementations, this study proposes a mathematical model containing the aforementioned indices for predicting the regenerative capacity (RC) and stemness of MSCs. This mathematical model is used to assess and predict the regenerative capacity (RC) of MSCs. The model extracts four best-fit indices from a given combination of indices, including nuclear roundness, nucleus / cytoplasm ratio (nucleocytoplasmic ratio), side scattering height (SSC-H), and phosphorylated ERK1 / 2. The predictive ability of the model is validated through screening experiments using these indices, predicting the RC of newly included MSCs and MSCs treated with chemical inhibitors. Further RNA sequencing analysis reveals that cell appearance-based indices can serve as key biomarkers to visualize the results of integrated weighted signals inside and outside the cell and reflect MSC stemness.

[0052] In some implementations, the calculation model is: Y = q1N + q2N / C + q3S + q4E; ​​where N is the roundness value of the cell nucleus, N / C is the nucleocytoplasmic ratio value, S is the SSC-H value, E is the relative expression level of phosphorylated ERK1 / 2, Y is the stem cell performance, quality, or state value, and q1, q2, q3, and q4 are constant coefficients.

[0053] In some implementations, the calculation model is: Y = 1.34E-01N + 5.68E-01N / C + 1.17E-07S + 6.36E-02E.

[0054] In some implementations, the stem cell performance or quality or status includes the stem cell's regenerative capacity.

[0055] In some implementations, the regenerative capacity of the stem cells includes osteogenic capacity or stemness.

[0056] In some implementations, the stem cells include pluripotent stem cells or multipotent stem cells.

[0057] In some implementations, the stem cells include mesenchymal stem cells or induced pluripotent stem cells.

[0058] In some implementations, the mesenchymal stem cells are derived from bone marrow, fat, gums, umbilical cord, cartilage, skin, urine, placenta, periosteum, or tendon.

[0059] In some implementations, the oral cavity includes the tooth body, pulp, gingiva, periodontal ligament, or dental ligament.

[0060] In some implementations, the dental pulp includes mesenchymal stem cells from deciduous tooth pulp or permanent tooth pulp.

[0061] In some implementations, the deciduous tooth pulp mesenchymal stem cells are human exfoliated deciduous tooth pulp stem cells.

[0062] In some implementations, the cell generations of the dental pulp mesenchymal stem cells include P1-P12.

[0063] In some implementations, the cell passages of the umbilical cord mesenchymal stem cells include P1-P15.

[0064] In some implementations, the cell passages of the bone marrow mesenchymal stem cells include P1-P20.

[0065] In some implementations, the cell passages of the adipose-derived mesenchymal stem cells include P1-P20.

[0066] In some embodiments, the present invention provides a device for judging the performance, quality, or state of stem cells. The device includes: a data acquisition module for acquiring the value of an indicator; and a discrimination module for inputting the value of the indicator into a discrimination model and outputting a discrimination result. The discrimination model is a computational model constructed based on the functional relationship between the value of the indicator and the performance, quality, or state of stem cells. The indicator includes SSC-H.

[0067] In some implementations, the metrics also include cell nuclear roundness and ERK1 / 2.

[0068] In some implementations, the indicator also includes the nucleocytoplasmic ratio.

[0069] In some implementations, the ERK1 / 2 includes the gene, mRNA, or protein of ERK1 / 2.

[0070] In some implementations, the calculation model is: Y = q1N + q2N / C + q3S + q4E; ​​where N is the roundness value of the cell nucleus, N / C is the nucleocytoplasmic ratio value, S is the SSC-H value, E is the relative expression level of phosphorylated ERK1 / 2, Y is the stem cell performance, quality, or state value, and q1, q2, q3, and q4 are constant coefficients.

[0071] In some implementations, the calculation model is: Y = 1.34E-01N + 5.68E-01N / C + 1.17E-07S + 6.36E-02E.

[0072] In some implementations, the stem cell performance or quality or status includes the stem cell's regenerative capacity.

[0073] In some implementations, the regenerative capacity of the stem cells includes osteogenic capacity or stemness.

[0074] In some implementations, the stem cells include pluripotent stem cells or multipotent stem cells.

[0075] In some implementations, the stem cells include mesenchymal stem cells or induced pluripotent stem cells.

[0076] In some implementations, the mesenchymal stem cells are derived from bone marrow, fat, gums, umbilical cord, cartilage, skin, urine, placenta, periosteum, or tendon.

[0077] In some implementations, the oral cavity includes the tooth body, pulp, gingiva, periodontal ligament, or dental ligament.

[0078] In some implementations, the dental pulp includes mesenchymal stem cells from deciduous tooth pulp or permanent tooth pulp.

[0079] In some implementations, the deciduous tooth pulp mesenchymal stem cells are human exfoliated deciduous tooth pulp stem cells.

[0080] In some implementations, the cell generations of the dental pulp mesenchymal stem cells include P1-P12.

[0081] In some implementations, the cell passages of the umbilical cord mesenchymal stem cells include P1-P15.

[0082] In some implementations, the cell passages of the bone marrow mesenchymal stem cells include P1-P20.

[0083] In some implementations, the cell passages of the adipose-derived mesenchymal stem cells include P1-P20.

[0084] In some embodiments, the present invention provides a system for detecting stem cell performance, quality, or status, comprising:

[0085] Detection component: The detection component is used to detect the value of the index;

[0086] Result judgment component: The result judgment component is used to output the result of stem cell performance, quality or status based on the value of the indicator obtained by the detection component;

[0087] The indicators include SSC-H.

[0088] In some implementations, the metrics also include cell nuclear roundness and ERK1 / 2.

[0089] In some implementations, the indicator also includes the nucleocytoplasmic ratio.

[0090] In some implementations, the ERK1 / 2 includes the gene, mRNA, or protein of ERK1 / 2.

[0091] In some implementations, the detection component comprises one or more of the following: qPCR kit, immunoblotting kit, immunochromatographic kit, flow cytometry kit, immunohistochemistry kit, ELISA kit, electrochemiluminescence kit, qPCR instrument, immunoblotting device, flow cytometer, immunohistochemistry device, ELISA device, and electrochemiluminescence device.

[0092] In some implementations, the stem cell performance or quality or status includes the stem cell's regenerative capacity.

[0093] In some implementations, the regenerative capacity of the stem cells includes osteogenic capacity or stemness.

[0094] In some implementations, the stem cells include pluripotent stem cells or multipotent stem cells.

[0095] In some implementations, the stem cells include mesenchymal stem cells or induced pluripotent stem cells.

[0096] In some implementations, the mesenchymal stem cells are derived from bone marrow, fat, gums, umbilical cord, cartilage, skin, urine, placenta, periosteum, or tendon.

[0097] In some implementations, the oral cavity includes the tooth body, pulp, gingiva, periodontal ligament, or dental ligament.

[0098] In some implementations, the dental pulp includes mesenchymal stem cells from deciduous tooth pulp or permanent tooth pulp.

[0099] In some implementations, the deciduous tooth pulp mesenchymal stem cells are human exfoliated deciduous tooth pulp stem cells.

[0100] In some implementations, the cell generations of the dental pulp mesenchymal stem cells include P1-P12.

[0101] In some implementations, the cell passages of the umbilical cord mesenchymal stem cells include P1-P15.

[0102] In some implementations, the cell passages of the bone marrow mesenchymal stem cells include P1-P20.

[0103] In some implementations, the cell passages of the adipose-derived mesenchymal stem cells include P1-P20. Attached Figure Description

[0104] Figure 1. Analysis of mesenchymal stem cells (MSCs) from different sources through experimental screening based on 30 biological indicators. Figure 1a (A schematic diagram of the screening process.) Eleven groups of MSCs from different sources and stages, labeled Cell 1 to Cell 11, including bone marrow, adipose tissue, umbilical cord, and dental pulp, were recruited for experimental screening based on 30 biological indicators. Figures 1b-1j Screening of morphology, migration rate, and proliferation of 11 MSCs based on a high-content imaging system. Figure 1k-Figure 1r MSC surface labeling, forward scattering height (FSC-H), and side scattering height (SSC-H) analysis were performed using single-cell analysis. Figure 1s Screening for protein expression based on multiple molecular signals. Figure 1t Alizarin red staining of 11 MSCs shows their ability to form mineralized nodules after 12 days of osteogenic induction. The circle in the upper right corner represents the entire alizarin red stained area. Figure 1u Western blot analysis of 11 MSCs revealed the expression levels of osteogenic lineage proteins ALP and Runx2. GAPDH was used as a protein loading control. Figure 1v The regenerative capacity (RC) of 11 MSCs was assessed. Alizarin Red positive areas and osteogenic protein expression were analyzed using ImageJ software (NIH). Results are shown as the percentage of alizarin Red positive area to total area (red circles) and the relative density compared to the control (blue and purple circles). RC values ​​(RCexp, black pentagrams) were calculated using the average alizarin Red positive area and osteogenic protein expression.

[0105] Figure 2. High-content screening of MSC characteristics (morphology, migration rate, proliferation) and single-cell analysis of MSC physical properties and surface markers by flow cytometry. Figure 2a Microscopic images of 11 groups of MSCs in 40X fluorescence mode (dead cells). Green, F-actin labeled with Actin Green 488; blue, cell nuclei stained with DAPI. Figure 2b11 sets of bright-field micrographs of 10X MSCs (live cells). Figure 2c A schematic diagram of live cell identification and three demonstration shapes showcasing readout values ​​for different morphological parameters. Purple-red: cell nuclei stained with Hoechst 33342; Malachite green: cell nuclei stained with CellMask. TM Cell plasma membrane stained with deep red chromatin. Figure 2d Tag-free live cell tracking was performed in digital phase-contrast (DPC) mode to monitor cell migration speed. Green circles indicate the cell's position at hour 0, and green lines detail the cell's trajectory over 24 hours. Cell migration was tracked over 24 hours; each colored line represents a type of mesenchymal stem cell. Speed ​​was calculated as the total migration distance divided by the total time.

[0106] Figure 3. The RC value assessment prediction model fitted by 11 MSC biological characteristics is used as the training dataset. Figure 3a Algorithm diagram. Figure 3b Increasing the number of independent variables x (input exponents) from 1 to 5 will result in a decrease in the corresponding mean offset. Figure 3c Increasing the independent variable x from 1 to 5 enhances the overlap between the predicted regenerative capacity (RCpred, gray pentagram) and the actual osteogenic potential (experimental RC value, RCexp, black pentagram), and increases the correlation between RCpred and RCexp. The red line represents a linear regression fit of the RC data points, and the R-value represents the Pearson correlation. Each black dot represents one of the eleven MSCs. Figure 3d Pearson correlation coefficients (RCexp as output y) for 30 input x-index pairs and output y-value pairs. Gray pentagrams represent the four optimal indices selected for the chosen equation (Eq.) model. Among the numerous four input x-pairs, the selected indices, including nuclear roundness, nucleus / cytoplasm ratio, SSC-H, and ERK1 / 2, are primarily cell appearance-related indices. Figure 3e Exclude the NU / CY ratio from the selected equation. The model causes an increase in the deviation between RCpred and RCexp values. Compare the actual RC value with the predicted RC value using the selected equation. Models that conform to (the selected equation) or have no NU / CY ratio (the selected equation has no NU / CY ratio). Figure 3fThe NU / CY ratio is excluded from the selected equations. A decrease in the R-value resulting from the model indicates Pearson correlation. Each black dot represents one of the eleven MSCs. (Figure 3g) The five-dimensional plot represents the selected equations. The model uses four equations. Indicators predicting RC values: SSC-H, bubbles; nucleus roundness, Y-axis; NU / CY ratio, X-axis; p / t ERK1 / 2, Z-axis. The size of the bubbles represents the magnitude of the SSC-H value. The color scale shows the range of RCPred values.

[0107] Figure 4 The table lists the top thirty index combinations fitted to the model by model offset, sorted from smallest to largest. It is divided into five columns, representing the five index combinations, as the number of independent variables x ranges from 1 to 5. Purple data represents indicators related to cell appearance; blue data represents indicators related to cell surface phenotype; green represents indicators related to cell viability; and black represents indicators related to protein expression of various molecular signals. The red boxes indicate the four selected optimal index combinations, including x1, x7, x15, and x19, indicating the lowest offset values ​​in the formula model fitted by the four input x indices (Column x = 4). These four selected Eq. indices appearing in each index combination are highlighted with a gray background. The blue boxes indicate the unique Eq. index combination in the Eq. model fitted by the four input x indices (Column x = 4), including x13 (CD146), x14 (FSC-H), x16 (EdU), and x21 (EZH2), which does not contain any selected Eq. indices. The heatmap scale bar represents the offset value, with red representing the minimum value and purple representing the maximum value.

[0108] Figure 5. The predictive model was experimentally validated by predicting the RC of newly added mesenchymal stem cells. Figure 5a The experiment screened newly added mesenchymal stem cells from different stages and tissues using four selected Eq. indicators. Figure 5a )to( Figure 5e Three different mesenchymal stem cells were registered in the study, named cells A, B, and C. Detailed information about these cells is provided in Table 2. Figure 5b To eliminate erroneous signals from this particular combination of phenomena (highly correlated with cell appearance), the supplementary equation model was also validated through screening cells A, B, and C. These four indicators, including CD146, FSC-H, EdU+, and EZH2, do not include any indicators related to the selected equation. See details. Figure 4 . ( Figure 5c The actual renal capacity (RC) of newly added mesenchymal stem cells was detected by alizarin red staining and Western blot. Figure 5d The predictive model was validated by predicting the RC of newly added mesenchymal stem cells. The accuracy of the predictive model using four selected formula indicators (RCpred) or four supplementary formula indicators (RCpred supplement) was validated by predicting the RC of newly added mesenchymal stem cells. Figure 5e The linear correlation between RCexp and RCpred is derived from the selected or supplementary formulaic model in newly added mesenchymal stem cells. The red line represents the linear regression fit of the RC data points. The R-value of the fit represents the Pearson correlation. Figure 5f Experimental screening for four selected Eq. indices was conducted in umbilical cord mesenchymal stem cells (UCMSCs) from three different donors. Figure 5f )to( Figure 5h Three different UCMSCs were added to the cell line and named donor A, S, and Y at the same time. Detailed information about these cells is shown in Table 2. Figure 5g Actual retinopathy (RC) of three different UCMSCs at the same time point was detected by alizarin red staining and Western blot. Figure 5h The predictive model was validated by predicting the RC values ​​of three UCMSCs from different donors. The accuracy of the predictive model using four selected Eq. indicators was verified by comparing the predicted RC values ​​with the actual RC values. Figure 5i The linear correlation between RCexp and RCpred values ​​of three UCMSCs from different donors is shown in the red line, representing the linear regression fit of the RC data points. The R-value of the fit represents the Pearson correlation. Each black dot in Figures e and i represents cells A, B, and C, and donors A / S / Y. Scale bar, 100 μm. Figure 5a (See Figure 5f). RCpred represents the predicted RC value, and RCexp represents the actual RC value.

[0109] Figure 6 The efficiency of XAV939 was tested at specified doses in cells A, B, and C.

[0110] Figure 7. Prediction of RC in mesenchymal stem cells treated with different concentrations and types of chemical inhibitors. Figure 7a (7e) In three different mesenchymal stem cells, four selected Eq. indices were experimentally screened by increasing the dose of XAV939 or PD98059. The Wnt / β-catenin pathway inhibitor XAV939 was used at doses of 0, 1, 5, and 10 μM. Figures 7a to 7d ) or MEK / ERK pathway inhibitor PD98059 at doses of 0, 10, 20, and 25 μM ( Figures 7e to 7hThree different types of mesenchymal stem cells were treated and named cells A, B, and C. Detailed information about these cells is shown in Table 2. Scale bar, 100 μm. Data are expressed as mean ± SD. Figure 7b , Figure 7f Actual RC of three different mesenchymal stem cells treated with increased doses of XAV939 or PD98059 was detected by alizarin red staining and Western blot. Cells treated with 10 μM XAV939 showed C( Figure 7b or mesenchymal stem cells treated with 25 μM PD98059 ( Figure 7f The RC value was excluded because of cell death under bone-induced conditions. Figure 7c , Figure 7g The predictive model was validated by predicting the RC (recovery rate) of three different mesenchymal stem cells after treatment with increased doses of XAV939 or PD98059. The accuracy of the predictive model using four selected Eq. indices was verified by comparing the predicted RC values ​​with the actual RC values. Figure 7d , Figure 7h In three different types of mesenchymal stem cells, the linear correlation between RCexp and RCpred values ​​was established by increasing the dosage of XAV939 or PD98059. The red line represents the linear regression fit of the RC data points. The R-value represents the Pearson correlation. Each black dot represents a group of mesenchymal stem cells treated with a specific concentration of XAV939 or PD98059. RCpred represents the predicted RC value, and RCexp represents the actual RC value.

[0111] Figure 8. RNA sequencing of three UCMSCs from different donors. Figure 8a A Venn diagram of unique and shared gene IDs from the pre-selected mesenchymal stem cell group. Three pre-selected UCMSCs from different donors, named donor A, S, and Y, were included in RNA sequencing (n=3). The selection Eq. index, reflecting the cell appearance of donor S, was significantly higher than that of donor A and Y (with...). Figure 3f related).( Figure 8b Venn diagram of differentially expressed genes (DEGs) in the pre-selected mesenchymal stem cell population. 818 DEGs from donor S were common to both donor A and Y cell populations. Q value < 0.05 and |log2FC| ≥ 1 were set as cutoff criteria. Figure 8c Functional classification of DEGs based on KEGG classification. Figure 8dGene set enrichment analysis (GSEA) based on the KEGG database was used to compare gene expression of donor S with donor A or Y. Pathways with significantly enriched NOM p-values ​​< 0.05 and FDR q-values ​​> 0.25 were selected. Venn diagrams showed the overlap between 27 KEGG pathways and their corresponding KEGG classifications. Figure 8e GSEA reveals four common KEGG pathways and characteristics of environmental information processing. Figure 8f GSEA shows that the immune system shares 10 common KEGG pathway characteristics. Figure 8g Networks based on the KEGG pathway primarily exhibit characteristics of environmental information processing and the immune system, revealing a potential relationship between osteogenic differentiation-related pathways and significantly enriched pathways. Figure 8h The heatmap shows the DEGs of stem cell-related genes associated with the human adult tissue stem cell module in donors A, Y, and S. Q values ​​<0.05 and |log2FC| ≥1 were set as cutoff criteria. Gene expression data were normalized using z-scores. The pie chart shows that 57% of the DEGs associated with stemness were upregulated by donor S. Figure 8i The dominant expression of stemness-related genes in donor S was confirmed by real-time quantitative polymerase chain reaction (qPCR) analysis. (In the heatmap...) Figure 8h Among the upregulated genes of donor S, the top 10 genes and the other 8 genes with an average TPM greater than 30 for donor S were selected for qPCR analysis. Data are expressed as mean ± SD. Detailed Implementation

[0112] The following specific embodiments further illustrate the technical solution of the present invention. These specific embodiments do not represent a limitation on the scope of protection of the present invention. Non-essential modifications and adjustments made by others based on the concept of the present invention still fall within the scope of protection of the present invention.

[0113] In the following embodiments, donor A, Y, S are also donors A, Y, S.

[0114] Phosphorylated / total ERK1 / 2 is also known as "Phospho-p44 / 42MAPK(Erk1 / 2) / p44 / 42MAPK(Erk1 / 2)" or "p / t ERK 1 / 2".

[0115] Antibodies and reagents

[0116] Antibodies against EZH2 (#07-689), β-Catenin activation (#05-665), and β-Catenin (#06-734) were purchased from Millipore (Merck Millipore, Billerica, MA, USA). Antibodies against Tet1 (#ab191698), Oct4 (#ab19857), SOX2 (#ab93689), and alkaline phosphatase (ALP, #ab108337) were purchased from Abcam (Cambridge, MA, USA). The antibodies for Nanog (#4903T), TAZ (#83669S), YAP (#14074S), Phospho-Smad3 (#9520S), Smad3 (#9523S), Cleaved Notch1 (#4147S), Notch1 (#3608S), Phospho-p44 / 42MAPK(Erk1 / 2) (#4370S), p44 / 42MAPK(Erk1 / 2) (#4695S), Runx2 (#8486), Phospho-mTOR (#2971S), and mTOR (#2983S) were all purchased from Cell Signaling Technology (CST, Danvers, MA, USA). GAPDH was purchased from Sigma (St. Louis, MO, USA). Anti-Caveolin-1 antibody (#sc-53564) was purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Anti-human CD34, CD45, CD73, CD90, CD105, and CD146 antibodies were purchased from BDBiosciences (San Jose, CA, USA).

[0117] I. Research Methods

[0118] Ethical Approval

[0119] The Medical Ethics Committee of the Affiliated Stomatological Hospital of Sun Yat-sen University (Project No.: KQEC-2021-48-01) approved the ethical procedures for collecting and using human mesenchymal stem cells from the umbilical cord, gums, skin, and dental pulp. Informed consent was obtained from all donors, and the collection of materials posed no additional risk to them.

[0120] 1. Isolation and culture of 11 MSCs from different individuals and tissue sources through multiple passages.

[0121] Isolation and culture of umbilical cord mesenchymal stem cells:

[0122] Umbilical cord tissue was washed three times with 70% ethanol, followed by three washes with PBS to disinfect and remove blood. The tissue was cut into small pieces, blood vessels were removed, and the tissue was transferred to 50 ml centrifuge tubes. The tubes were then incubated with 3 mg / ml type I collagenase and 4 mg / ml dispersin at 37°C with shaking for 1 hour. After digestion, the cells were diluted with PBS and centrifuged at 1500 rpm for 5 minutes at 4°C to collect the cell pellet. The cell pellet was resuspended in α-MEM cell culture medium (containing 15% fetal bovine serum, 2 mM L-glutamine, a penicillin-1 solution containing 100 U / ml penicillin and 100 μg / ml streptomycin, and 10 mM L-ascorbic acid phosphate) and transferred to cell culture dishes for incubation at 37°C and 5% CO2 for cell expansion. The culture medium was changed every 3 days. When the cells reached 90% confluence (after 10-14 days) to establish P0 generation MSCs, the cells were digested with trypsin for the next passage. In the experiment, umbilical cord mesenchymal stem cells were used with P3, P7, and P12.

[0123] Isolation and culture of dental pulp mesenchymal stem cells:

[0124] Human deciduous tooth pulp stem cells were isolated from normal deciduous teeth collected from four children aged 5 to 10 years, and pulp stem cells were extracted from healthy teeth of a young donor. In short, the cell culture method was as follows: the dental pulp was isolated from the root canal and then digested with a mixture of 3 mg / ml type I collagenase and 4 mg / ml dispersant enzyme at 37°C with shaking for 1 hour, followed by culture in cell culture dishes using the aforementioned α-MEM cell culture medium. Cells were expanded to 90% confluence and passaged using trypsin. P11, P12, and P13 passages of human deciduous tooth pulp stem cells and P12 passage of pulp stem cells were obtained for in vitro experiments.

[0125] Isolation and culture of mesenchymal stem cells from human gingiva, skin, bone marrow, and adipose tissue:

[0126] Gingival and skin tissues were obtained from waste tissues at the Stomatological Hospital of Sun Yat-sen University. The gingival and skin tissues were aseptically fragmented and incubated with 3 mg / ml type I collagenase and 4 mg / ml dispersin at 37°C in a shaker for 1 hour. The dissociated cell suspension was then diluted in PBS and centrifuged to obtain cell sediment. The cell sediment was resuspended in α-MEM cell culture medium and seeded onto 10 cm cell culture dishes, and cultured in an incubator at 37°C and 5% CO2. After 72 hours of culture, non-adherent cells were removed. Adherent cells were passaged when they reached approximately 90% confluence. Gingival MSCs from P6 and P11, and skin MSCs from P8 were used in this study. In addition, two bone marrow-derived mesenchymal stem cells and primary human adipose-derived stem cells were purchased from ScienCell (San Diego, CA, USA). They were passaged under recommended conditions. Bone marrow mesenchymal stem cells from P15 and P16, and adipose-derived stem cells from P10 were used in our study.

[0127] 2. Measurement of 30 biological indicators and assessment of the actual osteogenic capacity of MSCs

[0128] a) Cell morphology parameters (including cell area, cell roundness, nuclear area, nuclear roundness, cytoplasmic area, cytoplasmic roundness, and nuclear-cytoplasmic ratio), proliferation rate (EdU detection), and migration speed (DPC mode detection) of MSCs in a live cell state were acquired using a high-content imaging system.

[0129] High-content image processing analysis:

[0130] Cells were seeded in 96-well black plates (CellCarrier-96, PERKinElmer). After 24 hours, the cells were washed with PBS and then treated with CellMask. TM Deep Red membrane dye (1:2000 dilution) was incubated at 37°C for 15 minutes. Cells were then washed and the nuclei were observed for 5 minutes at room temperature using Hoechst 33342 (1:2000 dilution). Finally, cells were rinsed with PBS, covered with PBS, and examined. The entire process should be conducted in the dark.

[0131] High-content analysis of MSCs plates was performed, along with image acquisition and data processing (objective lens × 20 magnification, PERKinElmer, USA). Morphological parameters such as cell area, cell roundness, nuclear area, nuclear roundness, cytoplasmic area, cytoplasmic roundness, and nucleocytoplasmic ratio were analyzed using a customized image analysis module in Harmony 4.0 software. Each morphological parameter was quantified using an average of at least 5,000 selected cells.

[0132] EdU detection:

[0133] The assay was performed using an EDU kit (KeyGen Biotech, Jiangsu, China). Cells were seeded in 24-well plates and cultured at 37°C for 24 hours. Cells were stained with 50 μM EdU in the culture medium for 2 hours. Then, the cells were washed with PBS and fixed with 4% paraformaldehyde for 15 minutes at room temperature. Cells were counterstained with Hoechst 33342 (1:2000, 5 μg / ml) for 15 minutes to stain the nuclei and washed with PBS. Images of EdU-positive cells were obtained and counted using a high-content screening system (objective × 10 magnification, PERKINELmer, USA).

[0134] b) Flow cytometry was used to analyze the physical properties and surface markers of 11 MSCs. Flow cytometry parameters reflecting the relative size and internal complexity of suspended live cells were collected and recorded as forward scatter height (FSC-H) and side scatter height (SSC-H), respectively. MSC surface markers included CD73, CD90, CD105, CD146, CD34, and CD45.

[0135] Flow cytometry analysis:

[0136] MSC-specific surface markers were assessed by flow cytometry. Cells were washed three times, digested, washed, and resuspended in PBS containing 0.5% bovine serum albumin, then conjugated with antibodies against fluorescent dyes binding to human antigens (including anti-CD34-PE, anti-CD45-PE, anti-CD73-PE, anti-CD90-PE, anti-CD105-PE, and anti-CD146-PE) and incubated in the dark at 4°C for 30 min. After incubation, cells were washed with PBS containing 0.5% BSA, centrifuged at 1500 rpm for 5 min, and then resuspended for flow cytometry analysis. Samples were characterized using a NovoCyte flow cytometer and analyzed using NovoExpress software (NovoCyte, ACEA Biosciences, USA).

[0137] c) Immunoblotting was used to determine the protein expression levels of 13 molecular signals that are crucial for stem cell function, including Tet1, phosphorylated / total ERK1 / 2, Nanog, EZH2, Oct4, YAP, phosphorylated / total mTOR, Caveolin-1, Sox2, phosphorylated / total Smad3, activation / total β-catenin, TAZ, and cleavage / total Notch1, and their relative expression levels were calculated using GAPDH.

[0138] Western blot of proteins:

[0139] Samples were lysed using a protein extraction kit containing protease and phosphatase inhibitors (#78501, Thermo Fisher). After quantification using a BCA kit, equal volumes of protein from each sample were loaded onto an SDS-PAGE gel and then transferred to a PVDF (Millipore) membrane. Block the membrane with TBST containing 5% BSA for 2 hours, then incubate overnight at 4°C with the following primary antibodies, shaking: EZH2, active β-Catenin, β-Catenin, Tet1, Oct4, SOX2, Alkaline Phosphatase, Nanog, TAZ, YAP, Phospho-Smad3, Smad3, Cleaved Notch1, Notch1, Phosphate-p44 / 42MAPK(Erk1 / 2), p44 / 42 MAPK(Erk1 / 2), Runx2, Phosphate-mTOR, mTOR, Caveolin-1, or GAPDH primary antibody. Then wash the membrane with TBST and incubate with an appropriate secondary antibody at room temperature for 1 hour. After complete washing, protein bands were observed using a Supersignal West Pico chemiluminescent substrate (Thermo Fisher) and evaluated using a gel imaging system (Bio-Rad, USA). Relative expression levels were calculated using NIH ImageJ software (Media Cybernetics, USA).

[0140] d) Calculation of the actual osteogenic regeneration capacity (RC value) of MSCs: Alizarin Red-stained positive mineralized areas were quantified as a percentage of the total area using ImageJ software. The relative expression levels of osteogenic-related proteins (ALP and Runx2) were also calculated using ImageJ software. The RC value was calculated by averaging the percentage of mineralized area and the osteogenic protein levels.

[0141] Osteogenic differentiation induction: Cells were seeded in 12-well plates. At 90% confluence, the medium was replaced with a solution containing 1.8 mM potassium dihydrogen phosphate, 10 nM dexamethasone, 100 U / ml penicillin / streptomycin, 0.1 ml M1-ascorbic acid phosphate, 2 mM glutamine, 15% fetal bovine serum, and α-MEM. The osteogenic induction medium was changed every 2 days. After 5 to 12 days of induction, the cultured cells were stained with Alizarin Red or had their proteins lysed to assess osteogenic gene expression.

[0142] Alizarin Red Staining: Osteogenic differentiation was assessed by the ability of cells to form alizarin red-stained mineralized nodules 4 weeks after osteogenic induction. Briefly, cells were washed with PBS, fixed with 60% isopropanol for 30 min at room temperature, and rehydrated with ddH2O for 5 min. Subsequently, cells were incubated with alizarin red staining solution (1 g alizarin red S (#A5533, Sigma) dissolved in 100 ml ddH2O and filtered) at room temperature for 10 min. Finally, cells were washed with PBS to remove non-specific binding, dried, and observed under a microscope.

[0143] 3. Screening of the optimal combination of biologically relevant indicators and establishment of a mathematical model for predicting the osteogenic differentiation capacity of MSCs.

[0144] This study employed exhaustive search and multiple linear regression to screen relevant independent variables and establish a predictive model. Considering that too many independent variables can lead to overfitting and hinder practical application, we primarily discussed the case where the number of independent variables was 5 or less (including 5). To increase the number of sample calculations and tests, thereby improving the accuracy of the fit, we randomly combined 11 cell types in an 8 / 3 ratio, dividing them into an 8-cell MSC training group and the remaining 3-cell MSCs in a test group.

[0145] The independent variable index values ​​of 8 types of cells and the actual osteogenic capacity RC value were used to establish a fitting model. From (1), we can obtain the parameters corresponding to the independent variables.

[0146]

[0147] Where y is the dependent variable, representing the actual RC value; x is the independent variable, representing the selected indicators; b1, b2, ..., bn are the unknown parameters of the corresponding independent variables; and n represents the number of selected indicators. Multiple linear regression is performed to obtain b1, b2, ..., bn.

[0148] The remaining three cell types were used for model testing. From step ②, we can compare the predicted RC values ​​with the actual RC values ​​for these three cell types. Next, the difference between the experimental and predicted values ​​was calculated. The predicted values ​​can be obtained using formula ②.

[0149]

[0150] yy 9 yy 10 and yy 11 It is the predicted RC value calculated according to formula (2).

[0151] From the possible 8 / 3 random combinations of 11 cell samples, 20 groups were randomly selected, and the above process was repeated. The combination of independent variables with the smallest fitting deviation was selected. The corresponding parameters and the final established multiple regression fitting formula are our prediction model, that is, the optimal multiple linear regression model as shown in formula (3) can be constructed.

[0152] y = b1x1 + ... + b n x n (3)

[0153] It is worth noting that the constant coefficients of b1, b2, ... bn were calculated based on data obtained from our instrument. The data are expressed as mean ± SD.

[0154] 4. Validate the MSC osteogenic regeneration prediction model through in vitro experiments.

[0155] a) Validate the accuracy of the predictive model by incorporating new MSCs into in vitro experiments.

[0156] b) Validate the sensitivity of the prediction model by in vitro experiments using chemical inhibitors of the WNT and ERK pathways to treat MSCs.

[0157] Chemical inhibitor treatment of MSCs: Cells were seeded on culture plates until 70%-80% confluence and treated with three concentration gradients of XAV939 (13596, Cayman Chemical): 1 μM, 5 μM, 10 μM, and PD98059 (#S1805, Beyotime, Shanghai, China): 10 μM, 20 μM, 25 μM. The chemical efficacy was assessed by Western blotting. The treated cells were harvested and prepared for further experiments.

[0158] 5. Transcriptome sequencing analysis revealed the intrinsic relationship between screening indicators and osteogenic capacity and stemness of MSCs.

[0159] RNA sequencing analysis: Umbilical cord mesenchymal stem cells collected from donors A, Y, and S were analyzed using mRNA sequencing (RNA-Seq, BGI Genomics Co., Ltd., China), with three replicates for each cell type. The BGISEQ-500 platform was used for the RNA-seq library. Clean reads were stored in FASYQ format and aligned with the reference genome and reference gene using HISAT and Bowtie2, respectively. 35-36 Then, RSEM (v1.2.12) was used to calculate gene expression levels. 37 Essentially, using DESeq2(v1.4.5)(|log2FC|)≥1 and Q-value <0.05 based on absolute multiples of log2. 38Differential expression analysis was performed. To gain a deeper understanding of phenotypic changes, KEGG (https: / / www.kegg.jp / ) and GSEA (http: / / www.broadinstitute.org / gsea) analyses were conducted.

[0160] Quantitative real-time polymerase chain reaction (PCR): Total RNA was extracted using the RNA-Quick Purification Kit. Following the instructions, 1 μg of total RNA was transcribed into a 20 μl reaction mixture using Primescript II RTase for cDNA synthesis. cDNA was used as a template, specific primers, and TB Green's reagent. TM Premix Ex Taq TM The II kit was used to amplify the target gene via qPCR on the LightCycler system (LightCycler, Roche Diagnostics). Gene expression was assessed three times with biological replicates and 2- ΔΔCT The relative transcription levels were calculated. The GAPDH gene was used for normalization. The sequences of the qPCR primers used in this study are shown in Table 1.

[0161] Table 1

[0162]

[0163]

[0164] II. Experimental Results

[0165] 1. Mesenchymal stem cells from 11 different tissue sources or different generations were selected, and 30 biological indicators of each cell type were included as variables.

[0166] We registered 11 different types of MSCs from different donors, sources, and stages, named Cell 1-11, and measured 30 biological indicators for each cell type to analyze their characteristics. Figure 1a (See Table 2 for MSC details).

[0167] Table 2. Details of cells in the training and validation sets.

[0168]

[0169]

[0170] First, cell morphology is one of the important indicators of cell state. 16-22The high-content imaging system was used to collect and analyze 11 live-cell MSCs morphology parameters, including cell area, cell roundness, nuclear area, nuclear roundness, cytoplasmic area, and nuclear-to-cytoplasmic ratio. Figure 1b - h).

[0171] Compared with the morphology of MSCs that died after fixation ( Figure 2a Most living stem cells have smooth borders, nearly round nuclei, and a uniform fibroblast-like morphology. Figure 2b , Figure 2c ).

[0172] Cell proliferation rate was determined by EdU incorporation. Figure 1i The migration velocity of MSCs can also be monitored using a high-content imaging system in DPC mode and calculated as the total migration distance divided by the total time. Figure 1j The relative displacement of all MSCs was less than 10 μm over 24 hours. Figure 2d ).

[0173] Then, the physical characteristics and surface markers of 11 MSCs were analyzed by flow cytometry. All 11 types of MSCs were positive for the markers CD73, CD90, CD105, and CD146, but negative for the markers CD34 and CD45. Figure 1k -p).

[0174] Flow cytometry parameters reflecting the relative size and internal complexity of suspended live cells were collected and recorded as forward scattering height (FSC-H) and side scattering height (SSC-H), respectively. Figure 1q ,1r).

[0175] Finally, the protein expression levels of 13 molecular signals crucial for stem cell function were determined by Western blot, including Tet1, phosphorylated / total ERK1 / 2, Nanog, EZH2, Oct4, YAP, phosphorylated / total mTOR (Phospho-mTOR), Caveolin-

[0176] 1. Sox2, phosphorylated / total Smad3 (Phospho-Smad3), activated / total β-catenin (activated β-Catenin), TAZ and cleaved / total Notch1 (Cleaved Notch1), and their relative expression levels were calculated using GAPDH. Figure 1s ).

[0177] To assess the osteogenic regeneration capacity (RC) of 11 MSCs, alizarin red staining and Western blot analysis of relevant osteogenic lineage proteins (ALP and Runx2) were performed after osteogenic induction. Figure 1t 1u). The osteogenic regeneration RC value of each MSC was calculated by the average percentage of mineralized area stained with alizarin red and the osteogenic protein expression level. Figure 1v ).

[0178] 2. Establish a predictive model for the RC value of osteogenic differentiation of mesenchymal stem cells.

[0179] Based on the above results, features of 11 types of MSCs that met the criteria were selected and integrated into the final dataset. This final dataset consists of thirty different indicators as input x and the actual RC values ​​(RCexp) as output y (the original data for x and y are shown in Table 3). A given level of input x yields a corresponding output y, representing the predicted RC value (RCpred). Our prediction equation (Eq.) model is constructed by considering this set of indicators that achieves the best fit. Specifically, we randomly combined the 11 cell types in an 8 / 3 ratio, that is, the 11 MSCs were randomly divided into 8 training sets and 3 test sets in an 8:3 allocation ratio (i.e., 8 cell types were used for model fitting, and the remaining 3 were used for model validation). Twenty groups were randomly selected from the randomly combined cell samples for fit testing. Figure 3a The results indicate that as the number of independent variables involved in the fitting increases, the fitting offset value decreases and the fitting accuracy improves, but the change in fitting offset for X from 4 to 5 is less than 0.01 (Figure 3b).

[0180] Table 3

[0181]

[0182]

[0183]

[0184] For 11 types of MSCs, we compared the predicted results (RCpred values) and actual experimental results (RCexp) obtained from the combination fitting models with the smallest deviation among the fitting models with different numbers of independent variables from 1 to 5. We found that the overlap between the predicted RC values ​​and the actual RC values ​​improved as the number of independent variables X involved in the fitting increased (the gray dashed line representing RCpred for cells 1-11 closely overlaps with the black dashed line representing their corresponding RCexp). Calculating their linear correlation, the R-value increased from 0.582 to 0.986. Figure 3cTherefore, four independent variables x can predict y with small offset values ​​quite well. As the number of input x increases to five or more, the increased prediction cost does not lead to a significantly stronger correlation between RCpred and RCexp. Figure 3c ).

[0185] Therefore, the selected equation model fitted by the four input x indices is optimal. Among the numerous sets of four input x, the optimal combination of four indices for the selected Eq. model includes nuclear roundness, nucleus / cytoplasm ratio (or nucleus-cytoplasm ratio), SSC-H, and ERK1 / 2 (…). Figure 3d ; Figure 4 It is noteworthy that, apart from ERK1 / 2, three of the four selected indicators are features related to cell appearance, rather than surface markers or molecular signals. Figure 3d ).

[0186] Simultaneously calculate the Pearson correlation coefficient between the predicted index and the actual RC value. Figure 3d It was found that one of the four indicators in the optimal independent variable fitting equation, the NU / CY ratio, had an extremely low correlation with the actual RC value (R = 0.0298), close to 0. However, removing it from the fitting prediction formula reduced the overlap between the predicted RC value and the actual RC value. Figure 3e This reduced the model fitting bias from 0.979 to 0.947. Figure 3f This indicates that although the index has a low correlation with the output results, its contribution to the model fitting accuracy cannot be ignored.

[0187] The results indicated that the combined equation models with relatively small fitting deviations were all closely related to cell morphology to varying degrees. These models included Nucleus Area, Nucleus Roundness, Cytoplasm Roundness, NU / CYratio, and SSC-H, except for one set of equations composed of CD146, FSC, EDU, and EZH2, which were completely unrelated to the above indicators. To eliminate strong spurious signal interference, this set of data was also verified during the subsequent formula validation. The predicted results and actual results were found to be largely consistent, indicating that the calculation process was not affected by signal interference, and that cell morphology does indeed play an important indicative role in osteogenic potential.

[0188] In summary, the fitting equation composed of four indices ensures both accuracy and feasibility. The optimal predictive indices are nucleus roundness (Y axis), nucleus / cytoplasm (nu / cy) ratio (X axis), SSC-H (size), and p / t ERK1 / 2 (Z axis), forming four dimensions that balance and compensate for each other. We have presented this indices in a five-dimensional diagram, where X, Y, and Z each represent one dimension, and the size and color of the sphere each represent another dimension. Therefore, our prediction formula is as follows: Y = q1N + q2N / C + q3S + q4E (q1: 1.34E-01, q2: 5.68E-01, q3: 1.17E-07, q4: 6.36E-02) Figure 3g Here, N refers to the nucleus roundness value, ranging from 0 to 1, with the closer to 1 indicating a more rounded cell; N / C refers to the nucleus / cytoplasm ratio (nucleus / cytoplasm, or nucleus / cytoplasm); S refers to the SSC-H (side scattering height) value, which generally indicates a more complex cell contents under the same instrument detection conditions; E refers to the relative expression level of phosphorylated ERK1 / 2, which is the ratio of phosphorylated ERK protein expression level to total ERK protein expression level.

[0189] 3. The RC value prediction model was validated through in vitro experiments.

[0190] To verify the accuracy of the prediction formula, three different types of MSCs from different donors or tissues were newly cultured and named cell A, cell B, and cell C, respectively (see Table 2 for details). We examined the nuclear roundness, nucleocytoplasmic ratio, SSC, and ERK1 / 2 biomarkers of the three newly added MSCs, substituted them into the formula, and obtained the predicted RC value. Among them, cell B showed better osteogenic potential than cell A, which in turn was better than cell C. Figure 5a To calculate RC exp After 12 days of osteogenic induction culture, Alizarin Red and Western blot analyses were performed on cells A, B, and C. Actual osteogenic experimental results also showed that cell B exhibited superior osteogenic potential compared to cell A, which in turn was superior to cell C. Figure 5c , Figure 5d The trend of the results was the same as the predicted results, but the values ​​deviated proportionally. This may be due to the experimental conditions of the 11 cell types being tested under different batch conditions, resulting in numerical deviations caused by the experimental environment. For example, the relationship between antibody strength and exposure intensity in Western blot affects the calculation of ImageJ values; or the subtle differences in cell staining time and induction environment before and after the induction process can also cause numerical deviations.

[0191] In addition, a group of non-cellular morphology-related indicators, CD146, FSC-H, EDU, and EZH2, were simultaneously detected using three new cell types. Fitting calculations were performed using these indicators, and the predicted results matched the experimental results. However, the deviation between the predicted RC values ​​and the actual RC values ​​was significantly greater than that of the optimal combination (Figure 5b). Figure 5d , Figure 5e Of the three newly included cell types, cell A was a P3 generation of umbilical cord mesenchymal stem cells from donor Y, and cell B was a P11 generation of umbilical cord mesenchymal stem cells from donor S (Table 2). Although cell B had a higher generation number than cell A, cell B had better osteogenic potential than cell A, suggesting the importance of donor source for the stemness of MSCs.

[0192] In Figure 5, Supplementary Eq. refers to the supplementary calculations performed to eliminate high-intensity interference in the mathematical model operation signal. See the blue circle in Figure 5 for details. This supplementary formula includes the indices CD146, FSC-H, EDU, and EZH2.

[0193] To provide a stronger theoretical basis for clinical applications, we added umbilical cord mesenchymal stem cells from donor A. We cultured three types of mesenchymal stem cells of the same species and passage number but from different donor sources for prediction and comparison, naming them donor A, donor S, and donor Y (donor source as the sole variable). Using the same method, we tested four predictive indicators for donor A, donor S, and donor Y, and substituted them into the formula to predict osteogenic RC values. The results indicated that donor S cells had the highest osteogenic potential. Figure 5f The results were highly consistent with the RC values ​​obtained 12 days after experimental induction. Figure 5g-5i ), of which the Pearson correlation coefficient was 0.947 ( Figure 5i These results demonstrate that this cell morphology variable model can accurately predict the renal function (RC) of MSCs.

[0194] 4. Predict the RC results of MSCs treated with chemical inhibitors.

[0195] The state of some signaling pathways reflects the cell's vital state; altering these pathways can also affect the cell's vital state, thereby influencing its osteogenic potential. This study verifies the sensitivity of the formula's predictions by blocking classical pathways. The Wnt / β-catenin signaling pathway is well-known as a key pathway involved in regulating cell stemness and osteogenic capacity. 23-25We selected the classic Wnt pathway, which is now considered important for stem cells, for intervention experiments. We also selected the ERK pathway, an important indicator, for intervention. For both, we used classic drug inhibitors for intervention experiments. Wnt was treated with XAV939, and ERK was treated with PD98059.

[0196] We demonstrated that XAV939, a specific inhibitor of the Wnt / β-catenin signaling pathway, can reduce the expression of β-catenin in cells A, B, and C. Figure 6 ) 26 Previous studies have mostly reported that inhibition of Wnt / β-catenin leads to negative regulation of osteogenic differentiation, but we cannot ignore the potential influence of different cellular states on signaling pathways. 27-28 The responses were inconsistent.

[0197] The three newly included cell types were treated with XAV 939 (1um, 5um, 10um) and PD98059 (10um, 20um, 25um), respectively. The drugs significantly inhibited the signaling pathways, and the effects of the intervention were detected by Western blotting.

[0198] We first examined the nuclear roundness, nucleocytoplasmic ratio, SSC-H, and ERK1 / 2 biomarkers of cells A, B, and C after 24 hours of XAV 939 interference. Predictive results (RCpred values) showed that under WNT pathway inhibition, the nuclear roundness and nucleocytoplasmic ratio of cells A, B, and C decreased with increasing drug concentration. The SSC of cells A and B decreased with increasing drug concentration, but increased in cell C. ERK1 / 2 showed an increasing trend in cells A and C, but a decreasing trend in cells B.

[0199] According to the formula, osteogenic potential cells A and B decrease with increasing concentration, while cell C shows no significant decrease or even a slight increase with increasing concentration. Figure 7a After 12 days of osteogenic induction culture, cell C died on day 4 due to continuous stimulation with high concentrations of the drug. The trends of the remaining cell experimental results were similar to the predicted results. Figure 7bHere, a deviation in the predicted values ​​still occurs. Besides the possible experimental factors mentioned above, a non-proportional trend appears in the drug concentration intervention; the predicted value changes less than the actual experimental result. This may be because, during the 12-day induction process, to maintain the inhibitory effect of the drug, a new inhibitor is introduced each time the induction medium is changed. The cells receive continuous drug stimulation during culture, and their resulting state may differ from the state obtained after 24 hours of sampling. In other words, the cells after 12 days of drug stimulation and the same type of cells after 24 hours of stimulation are in different cell states. However, we can still accurately predict the trend of osteogenic potential based on the changes in cell state after 24 hours. Figure 7c , Figure 7d ).

[0200] ERK1 / 2 is the only molecular signaling indices. To investigate the intrinsic relationship between the ERK pathway and three other selected indices, we tested with PD98059 (10 μM, 20 μM, and 25 μM). 29 The concentration of PD98059 inhibited the ERK pathway and affected the nuclear roundness, NU / CY ratio, and SSC of cells A, B, and C. Overall, except for cell A treated with 25 μM of the inhibitor, the nuclear roundness of all three cell types decreased with increasing PD98059 concentration. Figure 7e NU / CY ratio and SSC analysis showed that cells A and B exhibited a decreasing trend, while cell C mainly showed an increasing trend. Figure 7e Furthermore, Western blot analysis showed that ERK1 / 2 protein expression decreased in a concentration-dependent manner (Figure 7e). In summary, based on the Eq. index results, we predicted that the RCprep values ​​of cells A and B would decrease with increasing concentration, but the RCprep value of cell C would increase. Figure 7g To obtain RCexp values, cells A, B, and C were cultured for 12 days under osteogenic induction conditions and treated with PD98059 every two days. RCexp values ​​were calculated using the Alizarin Red assay and Western blot. Figure 7f ).

[0201] Although most cells in the high-concentration 25 μM PD98059 treatment group died 2 days after osteogenic induction, as expected, we found that the trend of RC change between RCexp and RCpred values ​​was consistent in cells from other groups. Figure 7g The Pearson correlation coefficients for cells A, B, and C were 0.999, 0.994, and 0.962, respectively. Figure 7h ).

[0202] In summary, we introduced three new cell types to verify the accuracy of the key indicators and formulas selected through computational screening. Simultaneously, we used biological methods to introduce interference and tested the sensitivity of the selected key indicators and formulas in capturing complex changes in cell life states and predicting osteogenic potential.

[0203] 5. Transcriptome sequencing analysis revealed the intrinsic relationship between screening indicators and osteogenic capacity and stemness of MSCs.

[0204] Intriguingly, none of the four selected Eq. indicators—including nuclear roundness, nucleus / cytoplasm ratio, SSC-H, and ERK1 / 2—could serve as a sufficient predictor of MSC-RC on its own. However, in all validation experiments, SSC-H values ​​showed a trend consistent with RCexp values. Figure 5a , Figure 5d , Figure 5f , Figure 5h ; Figure 7a , Figure 7c , Figure 7e , Figure 7g ).

[0205] Furthermore, a higher SSC-H value in the mesenchymal stem cell population was most significantly associated with a higher osteogenic differentiation capacity (R = 0.89952). Figure 3d These results inspired us to explore the genetic information associated with SSC-H. Therefore, we compared the transcriptional profiles of donor A, Y, and S mesenchymal stem cells, showing an increasing trend of SSC-H. Figure 5f RNA sequencing analysis showed that there were 222, 389, and 268 different genes in donor A, S, and Y, respectively. Donor S contained more than 100 more different genes than the other two cell populations, which means that donor S has richer gene expression (Figure 8a).

[0206] Furthermore, compared to the other two cell populations, donorS contained 818 differentially expressed genes (DEGs). Q values ​​< 0.05 and |log2FC| ≥ 1 were set as cutoff criteria. Figure 8b Based on the above results, the selected equation index reflecting the cell appearance of donor S can be distinguished from the other two cell populations, and its RCpred value is also higher than that of donor A / Y cells, which matches RCexp ( Figures 5f-5h ).

[0207] Regardless of cellular appearance or intrinsic gene levels, donor S cells exhibit uniqueness. Subsequently, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed on 818 DEGs. Functional classification based on KEGG classification revealed that these DEGs were primarily involved in signal transduction, signaling molecules and interactions, the immune system, cancer, and infectious diseases. Figure 8c ).

[0208] These results suggest that the unique appearance of mesenchymal stem cells from donor S may be due to differences in microenvironment information processing and immune system-related pathways, which can be viewed as a special form of environmental information capture.

[0209] Gene set enrichment analysis (GSEA) was performed on gene expression in donors S and donors A or Y based on the KEGG database. Pathways with significant enrichment (nominal mean squared error) p-value < 0.05 and false discovery ratio (FDR) q-value > 0.25 were selected. A Venn diagram shows the overlap between the 27 KEGG pathways and their corresponding KEGG classifications. Figure 8d ).

[0210] Of the 27 generally significantly enriched pathways, 4 were related to environmental information processing, including mitogen-activated protein kinase (MAPK) signaling, Jak-STAT signaling, and interactions between cell adhesion molecules and cytokine receptors; and 10 immune-related pathways were upregulated in donor S. Figure 8e , Figure 8f ).

[0211] To investigate the potential relationship between donor S upregulated pathways and osteogenic differentiation, we constructed a correlation network based on the significantly enriched KEGG pathway. Figure 8g ).

[0212] It is well known that the Wnt, MAPK, and Notch signaling pathways control osteogenic differentiation of MSCs and play important roles in regulating stem cell self-renewal / proliferation. 30-32 The KEGG-related network identifies the MAPK signaling pathway as a common node connecting other pathways, followed by the Jak-STAT pathway and immune-related pathways. These pathways can influence osteogenic differentiation of mesenchymal stem cells (MSCs) by activating environmental information processing signaling pathways. Figure 8g ).

[0213] We then focused on 553 stem cell-related genes that have previously been shown to be persistently upregulated in human adult tissue stem cells. 33-34 And ask them whether they show different expression profiles for donors S and A / Y.

[0214] The heatmap shows 76 DEGs, with cutoff criteria of q-value < 0.05 and |log2FC| ≥ 1. 57% of the genes were significantly upregulated in donor S, supporting the superior stemness of donor S compared to donor Y / A. Figure 8h (Table 4).

[0215] Table 4. Genes associated with 553 human adult tissue stem modules and differentially expressed genes that are upregulated or downregulated in donors S and A / Y, as shown in Figure 8.

[0216]

[0217]

[0218]

[0219]

[0220]

[0221] Genes associated with human adult tissue stem models in donors S and A / Y were significantly upregulated or downregulated (|log2FC|≥1 and Q value<0.05).

[0222]

[0223]

[0224]

[0225] Among the upregulated genes in donor S in the heatmap, the top 10 genes and 8 other genes with an average TPM greater than 30 in donor S were selected and analyzed by real-time quantitative polymerase chain reaction (qPCR). The results confirmed that donor S showed a dominant upregulation of the mRNA expression of these stemness-related genes. Figure 8i ).

[0226] In summary, these data suggest that cell appearance-based indicators can serve as key indicators for visualizing the transcriptional status of mesenchymal stem cells and reflecting their overall condition.

[0227] References:

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Genome Biol 15,550(2014). sequence list <110> Sun Yat-sen University <120> A model for visualizing the morphological variables of mesenchymal stem cell status and a method for predicting the regenerative capacity of mesenchymal stem cells. <160> 38 <170> SIPOSequenceListing 1.0 <210> 1 <211> twenty one <212> DNA <213> Artificial Sequence <220> <223> GAPDH F primers (5' to 3') <400> 1 aggtcggtgt gaacggattt g 21 <210> 2 <211> twenty three <212> DNA <213> Artificial Sequence <220> <223> GAPDH R primers (5' to 3') <400> 2 tgtagaccat gtagttgagg tca 23 <210> 3 <211> twenty two <212> DNA <213> Artificial Sequence <220> <223> CSF2 F primers (5' to 3') <400> 3 tcctgaacct gagtagagac ac 22 <210> 4 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> CSF2 R primers (5' to 3') <400> 4 tgctgcttgt agtggctgg 19 <210> 5 <211> twenty three <212> DNA <213> Artificial Sequence <220> <223> IL6 F primers (5' to 3') <400> 5 actcacctct tcagaacgaa ttg 23 <210> 6 <211> twenty three <212> DNA <213> Artificial Sequence <220> <223> IL6 R primers (5' to 3') <400> 6 ccatctttgg aaggttcagg ttg 23 <210> 7 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> CXCL2 F primers (5' to 3') <400> 7 aaagcttgtc tcaaccccg 19 <210> 8 <211> twenty two <212> DNA <213> Artificial Sequence <220> <223> CXCL2 R primers (5' to 3') <400> 8 ggtcagttgg atttgccatt tt 22 <210> 9 <211> twenty three <212> DNA <213> Artificial Sequence <220> <223> EHF F primers (5' to 3') <400> 9 gctcagctat ggggtaaaaa gaa 23 <210> 10 <211> twenty one <212> DNA <213> Artificial Sequence <220> <223> EHF R primers (5' to 3') <400> 10 atccacacgc tccagaattt c 21 <210> 11 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> ATF3 F primers (5' to 3') <400> 11 cctctgcgct ggaatcagtc 20 <210> 12 <211> twenty two <212> DNA <213> Artificial Sequence <220> <223> TF3 R primers (5' to 3') <400> 12 ttctttctcg tcgcctcttt tt 22 <210> 13 <211> twenty two <212> DNA <213> Artificial Sequence <220> <223> IFIT1 F primers (5' to 3') <400> 13 agaagcaggc aatcacagaa aa 22 <210> 14 <211> twenty three <212> DNA <213> Artificial Sequence <220> <223> IFIT1 R primers (5' to 3') <400> 14 ctgaaaccga ccatagtgga aat 23 <210> 15 <211> twenty one <212> DNA <213> Artificial Sequence <220> <223> STAC F primers (5' to 3') <400> 15 tacggagcaa aagtgctgac a 21 <210> 16 <211> twenty two <212> DNA <213> Artificial Sequence <220> <223> STAC R primers (5' to 3') <400> 16 tgttcccact atcatgtggt tg 22 <210> 17 <211> twenty one <212> DNA <213> Artificial Sequence <220> <223> ISG15 F primers (5' to 3') <400> 17 cgcagatcac ccagaagatc g 21 <210> 18 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> ISG15 R primers (5' to 3') <400> 18 ttcgtcgcat ttgtccacca 20 <210> 19 <211> twenty two <212> DNA <213> Artificial Sequence <220> <223> IFIH1 F primers (5' to 3') <400> 19 tcgaatgggt attccacaga cg 22 <210> 20 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> IFIH1 R primers (5' to 3') <400> 20 gtggcgactg tcctctgaa 19 <210> twenty one <211> twenty three <212> DNA <213> Artificial Sequence <220> <223> IL1B F primers (5' to 3') <400> twenty one atgatggctt attacagtgg caa 23 <210> twenty two <211> 20 <212> DNA <213> Artificial Sequence <220> <223> IL1B R primers (5' to 3') <400> twenty two gtcggagatt cgtagctgga 20 <210> twenty three <211> twenty one <212> DNA <213> Artificial Sequence <220> <223> IRF7 F primers (5' to 3') <400> twenty three gctggacgtg accatcatgt a 21 <210> twenty four <211> 19 <212> DNA <213> Artificial Sequence <220> <223> IRF7 R primers (5' to 3') <400> twenty four gggccgtata ggaacgtgc 19 <210> 25 <211> twenty one <212> DNA <213> Artificial Sequence <220> <223> IFI44 F primers (5' to 3') <400> 25 atggcagtga caactcgttt g 21 <210> 26 <211> twenty three <212> DNA <213> Artificial Sequence <220> <223> IFI44 R primers (5' to 3') <400> 26 tcctggtaac tctcttctgc ata 23 <210> 27 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> ICAM1 F primers (5' to 3') <400> 27 ttgggcatag agaccccgtt 20 <210> 28 <211> twenty three <212> DNA <213> Artificial Sequence <220> <223> ICAM1 R primers (5' to 3') <400> 28 gcacattgct cagttcatac acc 23 <210> 29 <211> twenty one <212> DNA <213> Artificial Sequence <220> <223> HBEGF F primers (5' to 3') <400> 29 atcgtggggc ttctcatgtt t 21 <210> 30 <211> twenty three <212> DNA <213> Artificial Sequence <220> <223> HBEGF R primers (5' to 3') <400> 30 ttagtcatgc ccaacttcac ttt 23 <210> 31 <211> twenty one <212> DNA <213> Artificial Sequence <220> <223> GREM1 F primers (5' to 3') <400> 31 tcatcaaccg cttctgttac g 21 <210> 32 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> GREM1 R primers (5' to 3') <400> 32 ggctgtagtt cagggcagtt 20 <210> 33 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> TNC F primers (5' to 3') <400> 33 tcccagtgtt cggtggatct 20 <210> 34 <211> twenty one <212> DNA <213> Artificial Sequence <220> <223> TNC R primers (5' to 3') <400> 34 ttgatgcgat gtgtgaagac a 21 <210> 35 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> CCND1 F primers (5' to 3') <400> 35 gctgcgaagt ggaaaccatc 20 <210> 36 <211> twenty two <212> DNA <213> Artificial Sequence <220> <223> CCND1 R primers (5' to 3') <400> 36 cctccttctg cacacatttg aa 22 <210> 37 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> IFITM3 F primers (5' to 3') <400> 37 cgcctactcc gtgaagtcta 20 <210> 38 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> IFITM3 R primers (5' to 3') <400> 38 atgacgatga gcagaatggt 20

Claims

1. A combination of parameters for determining the regenerative capacity of stem cells, characterized in that, The discrimination parameter combination is SSC-H, cell nuclear roundness, phosphorylated ERK1 / 2, and cell nucleus-to-cytoplasm ratio.

2. The parameter combination as described in claim 1, characterized in that, The phosphorylated ERK1 / 2 includes the gene, mRNA, or protein of phosphorylated ERK1 / 2.

3. The application of a combination of discriminant parameters in identifying or determining the regenerative capacity of stem cells; wherein the combination of discriminant parameters is SSC-H, nuclear roundness, phosphorylated ERK1 / 2, and nuclear-cytoplasmic ratio.

4. The application as described in claim 3, characterized in that, The phosphorylated ERK1 / 2 includes the gene, mRNA, or protein of phosphorylated ERK1 / 2.

5. The application as described in claim 3, characterized in that, The regenerative capacity of the stem cells includes osteogenic capacity, bone regeneration capacity, or stemness.

6. The application as described in claim 3, characterized in that, The stem cells include pluripotent stem cells or multipotent stem cells.

7. The application as described in claim 3, characterized in that, The stem cells include mesenchymal stem cells or induced pluripotent stem cells.

8. The application as described in claim 7, characterized in that, The mesenchymal stem cells are derived from sources including bone marrow, fat, gums, umbilical cord, cartilage, skin, urine, placenta, periosteum, or tendons.

9. The application as described in claim 8, characterized in that, The cell passages of the umbilical cord-derived mesenchymal stem cells include P1-P15.

10. The application as described in claim 8, characterized in that, The cell generations of the bone marrow-derived mesenchymal stem cells include P1-P20.

11. The application as described in claim 8, characterized in that, The cell generations of the adipose-derived mesenchymal stem cells include P1-P20.

12. A method for determining the regenerative capacity of stem cells, characterized in that, The detection parameters include SSC-H, cell nuclear roundness, phosphorylated ERK1 / 2, and nucleocytoplasmic ratio.

13. The discrimination method as described in claim 12, characterized in that, The discrimination method includes: The values ​​of SSC-H, cell nuclear roundness, relative expression levels of phosphorylated ERK1 / 2, and nucleocytoplasmic ratio were measured. Compare the values ​​of SSC-H, cell nuclear roundness, relative expression levels of phosphorylated ERK1 / 2, and cell nucleocytoplasmic ratio. The regenerative capacity of stem cells is determined based on the values ​​of SSC-H, cell nucleus roundness, relative expression levels of phosphorylated ERK1 / 2, and cell nucleocytoplasmic ratio. When the values ​​of SSC-H, nuclear roundness, relative expression level of phosphorylated ERK1 / 2, and nucleocytoplasmic ratio of one type of stem cell are higher than those of another type of stem cell, the stem cells with the higher values ​​of these parameters are considered to have better regenerative capacity.

14. The discrimination method as described in claim 12, characterized in that, The phosphorylated ERK1 / 2 includes the gene, mRNA, or protein of phosphorylated ERK1 / 2.

15. The discrimination method as described in claim 12, characterized in that, The relative expression level of phosphorylated ERK1 / 2 is the ratio of the phosphorylated ERK protein expression level to the total ERK protein expression level.

16. The discrimination method as described in claim 12, characterized in that, The regenerative capacity of the stem cells includes osteogenic capacity or stemness.

17. The discrimination method as described in claim 12, characterized in that, The stem cells include pluripotent stem cells or multipotent stem cells.

18. The discrimination method as described in claim 12, characterized in that, The stem cells include mesenchymal stem cells or induced pluripotent stem cells.

19. The discrimination method as described in claim 18, characterized in that, The mesenchymal stem cells are derived from sources including bone marrow, fat, gums, umbilical cord, cartilage, skin, urine, placenta, periosteum, or tendons.

20. The discrimination method as described in claim 19, characterized in that, The cell passages of the umbilical cord-derived mesenchymal stem cells include P1-P15.

21. The discrimination method as described in claim 19, characterized in that, The cell generations of the bone marrow-derived mesenchymal stem cells include P1-P20.

22. The discrimination method as described in claim 19, characterized in that, The cell generations of the adipose-derived mesenchymal stem cells include P1-P20.

23. A method for determining the regenerative capacity of stem cells, characterized in that, include: Get the value of the parameter; Input the values ​​of the parameters into the discriminant model to obtain the discriminant results of the stem cell regeneration capacity; The discrimination model is a computational model constructed based on the functional relationship between the parameter values ​​and the regenerative capacity of stem cells; The parameters are SSC-H, nuclear roundness, phosphorylated ERK1 / 2, and nucleocytoplasmic ratio.

24. The discrimination method as described in claim 23, characterized in that, The phosphorylated ERK1 / 2 includes the gene, mRNA, or protein of phosphorylated ERK1 / 2.

25. The discrimination method as described in claim 23, characterized in that, The calculation model is: Y = q1N + q2N / C + q3S + q4E; ​​where N is the value of cell nucleus roundness, N / C is the value of cell nucleus-cytoplasm ratio, S is the value of SSC-H, E is the value of relative expression level of phosphorylated ERK1 / 2, Y is the value of stem cell regeneration capacity, and q1, q2, q3 and q4 are constant coefficients.

26. The discrimination method as described in claim 23, characterized in that, The calculation model is: Y = 1.34E-01N +5.68E-01N / C +1.17E-07S + 6.36E-02E.

27. The discrimination method as described in claim 23, characterized in that, The regenerative capacity of the stem cells includes osteogenic capacity or stemness.

28. The discrimination method as described in claim 23, characterized in that, The stem cells include pluripotent stem cells or multipotent stem cells.

29. The discrimination method as described in claim 23, characterized in that, The stem cells include mesenchymal stem cells or induced pluripotent stem cells.

30. The discrimination method as described in claim 29, characterized in that, The mesenchymal stem cells are derived from sources including bone marrow, fat, gums, umbilical cord, cartilage, skin, urine, placenta, periosteum, or tendons.

31. The discrimination method as described in claim 30, characterized in that, The cell passages of the umbilical cord-derived mesenchymal stem cells include P1-P15.

32. The discrimination method as described in claim 30, characterized in that, The cell generations of the bone marrow-derived mesenchymal stem cells include P1-P20.

33. The discrimination method as described in claim 30, characterized in that, The cell generations of the adipose-derived mesenchymal stem cells include P1-P20.

34. A device for determining the regenerative capacity of stem cells, characterized in that, The device includes: The data acquisition module is used to obtain the values ​​of the parameters; The discrimination module is used to input the values ​​of the parameters into the discrimination model and output the discrimination result; wherein, The discrimination model is a computational model constructed based on the functional relationship between the parameter values ​​and the regenerative capacity of stem cells; The parameters are SSC-H, nuclear roundness, phosphorylated ERK1 / 2, and nucleocytoplasmic ratio.

35. The discrimination device as described in claim 34, characterized in that, The phosphorylated ERK1 / 2 includes the gene, mRNA, or protein of phosphorylated ERK1 / 2.

36. The discrimination device as described in claim 34, characterized in that, The calculation model is: Y = q1N + q2N / C + q3S + q4E; ​​where N is the roundness value of the cell nucleus, N / C is the nucleocytoplasmic ratio value, S is the SSC-H value, E is the relative expression level of phosphorylated ERK1 / 2, Y is the regenerative capacity value of stem cells, and q1, q2, q3 and q4 are constant coefficients.

37. The discrimination device as described in claim 34, characterized in that, The calculation model is: Y = 1.34E-01N +5.68E-01N / C +1.17E-07S + 6.36E-02E.

38. The discrimination device as described in claim 34, characterized in that, The regenerative capacity of the stem cells includes osteogenic capacity or stemness.

39. The discrimination device as described in claim 34, characterized in that, The stem cells include pluripotent stem cells or multipotent stem cells.

40. The discrimination device as described in claim 34, characterized in that, The stem cells include mesenchymal stem cells or induced pluripotent stem cells.

41. The discrimination device as described in claim 40, characterized in that, The mesenchymal stem cells are derived from sources including bone marrow, fat, gums, umbilical cord, cartilage, skin, urine, placenta, periosteum, or tendons.

42. The discrimination device as described in claim 41, characterized in that, The cell passages of the umbilical cord-derived mesenchymal stem cells include P1-P15.

43. The discrimination device as described in claim 41, characterized in that, The cell generations of the bone marrow-derived mesenchymal stem cells include P1-P20.

44. The discrimination device as described in claim 41, characterized in that, The cell generations of the adipose-derived mesenchymal stem cells include P1-P20.

45. A system for detecting the regenerative capacity of stem cells, characterized in that, include: Detection component: The detection component is used to detect the value of the parameter; Result judgment component: The result judgment component is used to output the result of the regenerative capacity of stem cells based on the value of the parameter obtained by the detection component; The parameters are SSC-H, nuclear roundness, phosphorylated ERK1 / 2, and nucleocytoplasmic ratio.

46. ​​The detection system as described in claim 45, characterized in that, The phosphorylated ERK1 / 2 includes the gene, mRNA, or protein of phosphorylated ERK1 / 2.

47. The detection system as described in claim 45, characterized in that, The detection component comprises one or more of the following: qPCR kit, immunoblotting detection kit, immunochromatographic detection kit, flow cytometry analysis kit, immunohistochemistry detection kit, ELISA kit, electrochemiluminescence detection kit, qPCR instrument, immunoblotting detection device, flow cytometer, immunohistochemistry detection device, ELISA detection device, and electrochemiluminescence detection device.

48. The detection system as described in claim 45, characterized in that, The regenerative capacity of the stem cells includes osteogenic capacity or stemness.

49. The detection system as described in claim 45, characterized in that, The stem cells include pluripotent stem cells or multipotent stem cells.

50. The detection system as described in claim 45, characterized in that, The stem cells include mesenchymal stem cells or induced pluripotent stem cells.

51. The detection system as described in claim 50, characterized in that, The mesenchymal stem cells are derived from sources including bone marrow, fat, gums, umbilical cord, cartilage, skin, urine, placenta, periosteum, or tendons.

52. The detection system as described in claim 51, characterized in that, The cell passages of the umbilical cord-derived mesenchymal stem cells include P1-P15.

53. The detection system as described in claim 51, characterized in that, The cell generations of the bone marrow-derived mesenchymal stem cells include P1-P20.

54. The detection system as described in claim 51, characterized in that, The cell generations of the adipose-derived mesenchymal stem cells include P1-P20.

Images