CN116818634A

CN116818634A - Cell morphological variable model for visualizing mesenchymal stem cell state and method for predicting mesenchymal stem cell regeneration capacity

Info

Publication number: CN116818634A
Application number: CN202210287098.8A
Authority: CN
Inventors: 寇晓星; 吴迪; 施松涛
Original assignee: Sun Yat Sen University
Current assignee: Sun Yat Sen University
Priority date: 2022-03-22
Filing date: 2022-03-22
Publication date: 2023-09-29

Abstract

The invention belongs to the field of biological medicine, and relates to a cell morphological variable model for visualizing a mesenchymal stem cell state and a method for predicting the regeneration capacity of the mesenchymal stem cell. The invention provides a combination of distinguishing indexes of stem cell performance or quality or state, wherein one of indexes in the combination is SSC-H. Mesenchymal Stem Cells (MSCs) have been widely used for the treatment of various diseases. However, this hampers its clinical use due to the lack of reliable evaluation methods to evaluate the potential of predictive MSCs. The present study proposes a mathematical model containing a combination of the indices for predicting Regeneration Capacity (RC) and dryness of MSC. The mathematical model is used to evaluate and predict RC of MSC. The predictive power of the model was verified via screening experiments of these indicators by predicting RC of newly incorporated MSCs and chemical inhibitor treated MSCs. Further RNA sequencing analysis revealed that cell appearance-based indicators could be used as the primary markers to visualize the results of the integrated weighting signal inside and outside the cell and reflect MSC dryness.

Description

Cell morphological variable model for visualizing mesenchymal stem cell state and method for predicting mesenchymal stem cell regeneration capacity

Technical Field

The invention belongs to the field of biological medicine, and relates to a cell morphological variable model for visualizing a mesenchymal stem cell state and a method for predicting the regeneration capacity of the mesenchymal stem cell.

Background

Mesenchymal Stem Cells (MSCs) are adult stem cells which are derived from embryonic developmental early germ layers, have self-renewal and multidirectional differentiation potential and can still maintain the biological characteristics through in-vitro large-scale expansion, and have the characteristics of adherence and are fibrous. The mesenchymal stem cells may be derived from bone marrow, adipose tissue, umbilical cord, gum, cartilage tissue, skin tissue, dental pulp, etc. MSCs can differentiate into a variety of tissue-specific lineages including osteoblasts, chondrocytes, adipocytes, hepatocytes, myocytes, neuronal-like cells, and the like ^1,2,3 。

MSCs have a powerful and broad therapeutic potential, and more than 1300 clinical trials related to MSCs worldwide have been registered on clinical trimals. MSCs have also been used in the treatment of a variety of diseases including bone/cartilage repair, graft versus host disease, systemic lupus erythematosus, diabetes, regenerative medicine, and the like. MSCs and related fields have become a growing global industry. With the deep knowledge of MSCs characteristics, the definition standard of the pluripotent stem cells and the clinical transformation instruction standard of the human MSCs are continuously revised ^4,5,6 . However, even if MSCs are derived from the same tissue in the same algebra, accurate and objective therapeutic effect evaluation results are difficult to obtain due to donor differences, and the lack of standard quality control of the cell stem cells has limited effective clinical transformation application ^7,8,9,10 。

Furthermore, MSCs from different donors, tissues, clones and even different cells from the same clonal colony may be heterogeneous. The heterogeneity of MSCs may specifically reflect the morphology and function of cells, for reasons including, but not limited to, donor variability, tissue source variability, cell separation techniques, and differences in cell culture and preservation conditions ^{9,11,12,13,14,15} . Thus, research and development of surrogate biomarkers for assessing stem cell efficacy and use in stem cell productionRapid detection methods for agent quality control have become commonplace in the art.

Given that mesenchymal stem cells contain complex life state information, the lack of comprehensive understanding and in-depth evaluation of them is one of the main reasons why stem cell therapies are currently unable to break bottlenecks.

Disclosure of Invention

In some embodiments, the invention provides a combination of discrimination indicators of stem cell performance or quality or status, including SSC-H.

In some embodiments, the index combination further comprises nuclear roundness and phosphorylated ERK1/2.

In some embodiments, the index combination further comprises a nuclear to cytoplasmic ratio.

In some embodiments, the phosphorylated ERK1/2 comprises a gene, mRNA or protein that phosphorylates ERK1/2.

In some embodiments, the invention provides the use of a marker to identify or distinguish stem cell performance or quality or status; the marker comprises SSC-H.

In some embodiments, the markers further comprise nuclear roundness and phosphorylated ERK1/2.

In some embodiments, the marker further comprises a nuclear to cytoplasmic ratio.

In some embodiments, the stem cell property or quality or status comprises the regenerative capacity of the stem cell.

In some embodiments, the regenerative capacity of the stem cells comprises osteogenic capacity, bone regenerative capacity, or dryness.

In some embodiments, the stem cells comprise totipotent stem cells or pluripotent stem cells.

In some embodiments, the stem cells comprise mesenchymal stem cells or induced pluripotent stem cells.

In some embodiments, the mesenchymal stem cell source comprises bone marrow tissue, adipose tissue, gingival tissue, umbilical cord tissue, cartilage tissue, skin tissue, urine tissue, placenta tissue, periosteum tissue, or tendon tissue.

In some embodiments, the oral tissue comprises a tooth, dental pulp, gum, periodontal ligament, or ligament.

In some embodiments, the dental pulp comprises deciduous dental pulp mesenchymal stem cells or permanent dental pulp mesenchymal stem cells.

In some embodiments, the deciduous dental pulp mesenchymal stem cells are human deciduous dental pulp stem cells.

In some embodiments, the cell number of dental pulp mesenchymal stem cells comprises P1-P12.

In some embodiments, the cell number of umbilical cord mesenchymal stem cells comprises P1-P15.

In some embodiments, the cell number of the bone marrow mesenchymal stem cells comprises P1-P20.

In some embodiments, the cell number of adipose-derived mesenchymal stem cells comprises P1-P20.

In some embodiments, the invention provides a method for distinguishing the performance or quality or state of stem cells, comprising detecting the value of an index SSC-H.

In some embodiments, the discriminating method comprises: detecting the value of an index SSC-H; comparing the values of the index SSC-H between the stem cells under the same instrument; judging the performance or quality or state of the stem cells according to the value of the stem cell index SSC-H; when the value of the index SSC-H of a stem cell is increased relative to the value of the index SSC-H of another stem cell, it is judged that the performance or quality or state of the stem cell in which the value of the index SSC-H is increased is better.

In some embodiments, the method further comprises using an indicator of nuclear roundness and phosphorylation ERK1/2.

In some embodiments, the method further comprises using an indicator cytonuclear to cytoplasmic ratio.

In some embodiments, the discriminating method comprises: detecting the value of an index SSC-H, the value of the roundness of a cell nucleus, the value of the relative expression quantity of the phosphorylation ERK1/2 and the value of the nuclear-cytoplasmic ratio; comparing the value of the index SSC-H, the value of the roundness of the cell nucleus, the value of the relative expression quantity of the phosphorylation ERK1/2 and the value of the nuclear-cytoplasmic ratio; judging the performance, quality or state of the stem cells according to the values of the stem cell index SSC-H, the cell nucleus roundness, the relative expression quantity of the phosphorylated ERK1/2 and the cell nuclear mass ratio; when the value of SSC-H, the value of nuclear roundness, the value of relative expression of phosphorylated ERK1/2 and the value of nuclear cytoplasmic ratio of one stem cell are increased relative to the value of SSC-H, the value of relative expression of phosphorylated ERK1/2 and the value of nuclear cytoplasmic ratio of another stem cell, the performance, quality or state of the stem cell with the increased value of the index SSC-H, the value of nuclear roundness, the value of relative expression of phosphorylated ERK1/2 and the value of nuclear cytoplasmic ratio is judged to be good, otherwise, the performance, quality or state of the stem cell with the increased value of the index SSC-H, the value of nuclear roundness, the value of phosphorylated ERK1/2 and the value of nuclear cytoplasmic ratio is judged to be bad.

In some embodiments, the phosphorylated ERK1/2 comprises a gene, mRNA or protein that phosphorylates ERK 1/2.

In some embodiments, the relative amount of phosphorylated ERK1/2 is the ratio of the amount of phosphorylated ERK protein expressed divided by the amount of total ERK protein expressed.

In some embodiments, the invention provides a method for distinguishing the performance or quality or state of stem cells, comprising: acquiring the value of the index; inputting the value of the index into a judging model to obtain a judging result of the stem cell performance or quality or state; the judging model is a calculation model constructed according to the functional relation between the value of the index and the performance or quality or state of the stem cells.

In some embodiments, the index comprises SSC-H.

In some embodiments, the indicator further comprises nuclear roundness and phosphorylated ERK1/2.

In some embodiments, the indicator further comprises a nuclear to cytoplasmic ratio.

Mesenchymal Stem Cells (MSCs) have been widely used for the treatment of various diseases. However, this hampers its clinical use due to the lack of reliable evaluation methods to characterize MSC potential.

In some embodiments, the present study proposes a mathematical model containing a combination of the indices for predicting RC and dryness of MSCs. The mathematical model is used to evaluate and predict the Regeneration Capacity (RC) of MSC. The model excavates four best fit indices from a given index combination, including nuclear roundness, nuclear/cytoplasmic ratio (nuclear to cytoplasmic ratio), lateral scatter height (SSC-H), and phosphorylated ERK1/2. The predictive power of the model was verified via screening experiments of these indicators by predicting RC of newly incorporated MSCs and chemical inhibitor treated MSCs. Further RNA sequencing analysis revealed that cell appearance-based indicators could be used as the primary markers to visualize the results of the intracellular and extracellular integrated weighted signals and reflect MSC dryness.

In some embodiments, the computational model is: y=q1n+q2n/c+q3s+q4e; wherein N is a nuclear roundness value, N/C is a nuclear cytoplasm ratio, S is an SSC-H value, E is a value of relative expression quantity of phosphorylated ERK1/2, Y is a stem cell performance or quality or state degree value, and q1, q2, q3 and q4 are constant coefficients.

In some embodiments, the computational model is: y=1.34E-01n+5.68e-01N/c+1.17E-07s+6.36e-02E.

In some embodiments, the regenerative capacity of the stem cells comprises osteogenic capacity or stem.

In some embodiments, the mesenchymal stem cell source comprises bone marrow, fat, gum, umbilical cord, cartilage, skin, urine, placenta, periosteum or tendon.

In some embodiments, the oral cavity comprises a tooth, dental pulp, gum, periodontal ligament, or ligament.

In some embodiments, the invention provides a device for discriminating a property or quality or status of a stem cell, the device comprising: the data acquisition module is used for acquiring the value of the index; the judging module is used for inputting the value of the index into the judging model and outputting a judging result; the judging model is a calculation model constructed according to the functional relation between the value of the index and the performance or quality or state of the stem cells; the index includes SSC-H.

In some embodiments, the indicator further comprises nuclear roundness and ERK1/2.

In some embodiments, the ERK1/2 comprises a gene, mRNA or protein of ERK1/2.

In some embodiments, the invention provides a system for detecting a property or quality or status of a stem cell, comprising:

a detection member: the detection component is used for detecting the value of the index;

and a result judgment means: the result judging component is used for outputting the result of the stem cell performance or quality or state according to the value of the index obtained by the detecting component;

the index includes SSC-H.

In some embodiments, the ERK1/2 comprises a gene, mRNA or protein of ERK1/2.

In some embodiments, the detection component comprises one or more of qPCR kit, immunoblotting detection kit, immunochromatography detection kit, flow cytometry analysis kit, immunohistochemical detection kit, ELISA kit, electrochemiluminescence detection kit, qPCR instrument, immunoblotting detection device, flow cytometer, immunohistochemical detection device, ELISA detection device, electrochemiluminescence detection device.

Drawings

FIG. 1 shows the analysis of Mesenchymal Stem Cells (MSCs) of different origins by experimental screening of 30 biological indicators. (FIG. 1 a) A schematic diagram of a screening process. 11 groups of MSCs, labeled Cell 1 through Cell 11, from different sources and stages, including bone marrow, adipose tissue, umbilical cord, dental pulp, etc., were recruited for experimental screening of 30 biological indicators. (FIGS. 1 b-1 j) morphology, migration rate and proliferation screening of 11 MSCs based on a high content imaging system. (FIGS. 1 k-1 r) MSC surface labeling, forward Scattering height (FSC-H) and side Scattering height (SSC-H) analyses by single cell analysis. (FIG. 1 s) screening for protein expression of multiple molecular signals. (FIG. 1 t) alizarin red staining of 11 MSCs showed the ability to form mineralized nodules after 12 days osteoinduction. The upper right circle represents the entire alizarin red staining area. (FIG. 1 u) Western blot analysis of 11 MSCs, showing the expression levels of the osteogenic lineage proteins ALP and Runx 2. GAPDH was used as a protein loading control. (FIG. 1 v) 11 MSCs were evaluated for Regeneration Capability (RC). Alizarin red positive areas and osteogenic protein expression were analyzed using Image J software (NIH). The results are shown as percentage of alizarin red positive area to total area (red circles) and relative density (blue and purple circles) relative to the loading control, respectively. RC values (RCexp, black five-star) were calculated by mean alizarin red positive area and osteoblast protein expression.

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. (FIG. 2 a) 11 sets of microscopic images of 40X fluorescence mode (dead cells) MSCs. Green, F-Actin labeled with action Green 488; blue, DAPI stained nuclei. (FIG. 2 b) 11 sets of 10 XMSCs bright field microscopy images (live cells). (FIG. 2 c) schematic representation of the identification of living cells and three demonstration shapes, showing the read-out of different morphological parameters. Purplish red, nuclei stained with Hoechst 33342; malachite green, using Cellmask ^TM Cell plasma membrane stained with dark red plasma membrane. (FIG. 2 d) unlabeled live cell tracking was performed in Digital Phase Contrast (DPC) mode to monitor cell migration velocity. The green circle indicates the position of the cell at 0 hours, and the green line details the tracking of the cell over 24 hours. Cell migration was followed over 24 hours; each color line represents a mesenchymal stem cell. The velocity is calculated as the total distance of migration divided by the total time.

Figure 3. Evaluation of RC values fitted by 11 MSC biological characteristics predictive models as training data set. (FIG. 3 a) schematic of the algorithm. (fig. 3 b) increasing the number of arguments x (input index) from 1 to 5 will result in a corresponding decrease of the average offset value. (FIG. 3 c) increasing the argument x from 1 to 5 enhances the overlap of the predicted regeneration ability (RCpred, grey pentagram) value with the actual osteogenic potential (experimental RC value, RCexp, black pentagram) and increases RCpred and RCexp. The red line represents a linear regression fit of the RC data points, and the fitted R value represents the Pearson correlation. Each black dot represents one of eleven MSCs. (fig. 3 d) Pearson correlation coefficients for 30 input x-index and output y-value pairs (RCexp as output y). The gray pentagram represents the selected best four index combinations of the selected equation (Eq.) model. Among the four input x combinations, the selected indicators include core roundness, core/mass ratio, SSC-H and ERK1/2, mainly cell appearance related indicators. (FIG. 3 e) the NU/CY ratio is excluded from the selected equation. The model results in an increase in the deviation between the RCpred and RCexp values. A comparison of the actual RC value to the predicted RC value is calculated using the selected equation. A model that corresponds to (selected equation) or has no NU/CY ratio (selected equation has no NU/CY ratio). (FIG. 3 f) the NU/CY ratio is excluded from the selected equation. The decrease in the R value of the fit caused by the model represents Pearson correlation. Each black dot represents one of eleven MSCs. (FIG. 3 g) a five-dimensional plot represents the selected equation. A model of four equations is used. An indicator of the RC value is predicted. SSC-H, air bubbles; nuclear roundness, Y-axis; NU/CY ratio, X axis; p/t ERK1/2, Z axis. The size of the bubble represents the value size of SSC-H. The color scale shows the range of values of RCpred.

FIG. 4. Five sets of index combinations, ordered from small to large, of the first thirty exponential simulation model values listed by model offset. The table is divided into five columns representing five sets of index combinations, since the number of independent variables x varies from 1 to 5. Purple data represent indicators related to cell appearance; blue data represent indicators related to cell surface phenotypes; green represents an indicator related to cell viability; black represents an indicator related to protein expression of various molecular signals. The red box represents the best four index combinations selected, including x1, x7, x15, and x19, indicating the lowest offset value in the formula model fitted by the four input x indices (Colum x=4). The four selected Eq. indices appearing in each index combination are highlighted in a gray background. Blue boxes represent the only Eq. index combination in the Eq. model fitted with four input x-indices (Colum x=4), including x13 (CD 146), x14 (FSC-H), x16 (EdU), and x21 (EZH 2), which does not contain any selected Eq. indices. The heat map scale bar indicates the offset value, red for the minimum value and purple for the maximum value.

Fig. 5. Experimental verification of the predictive model by predicting RC of newly added mesenchymal stem cells. (FIG. 5 a) the experiment screened four selected Eq. indicators of newly added mesenchymal stem cells from different stages and tissues. Three different mesenchymal stem cells, designated as cells A, B and C, were registered in (fig. 5 a) to (fig. 5 e). Detailed information about these cells is provided in table 2. (FIG. 5 b) to exclude false signals (highly correlated with cell appearance) for this special combination, a Supplementary formula model (i.e., supplementary Eq.) was also validated by screening cells A, B and C. These four metrics, including CD146, FSC-H, edU + and EZH2, do not include any metrics related to the selected Eq. metric. See fig. 4 for details. (FIG. 5 c) the actual RC of newly added mesenchymal stem cells was detected by alizarin red staining and Western blot. (FIG. 5 d) the predictive model was validated by predicting RC of newly added mesenchymal stem cells. The accuracy of the predictive model using four selected formula indices (RCpred) or four supplemental formula indices (RCpred supplement) was verified by predicting the RC of newly added mesenchymal stem cells. (FIG. 5 e) RCexp is related to the linearity of RCpred, which is obtained from either the selected formula model or the supplemented formula model in newly added mesenchymal stem cells. The red line represents a linear regression fit of the RC data points. The fitted R values represent Pearson correlation. (FIG. 5 f) four selected Eq. indices were experimentally screened in umbilical mesenchymal stem cells (UCMSCs) of three different donors. Three different UCMSCs were added in (FIG. 5 f) to (FIG. 5 h), designated as donor A, S and Y during the same period. Details about these cells are shown in Table 2. (FIG. 5 g) the actual RC of three different UCMSCs at the same time period was detected by alizarin red staining and Western blot. (FIG. 5 h) the predictive model was validated by predicting RC of three UCMSCs from different donors. By comparing the predicted RC value with the actual RC value, the accuracy of the predictive model using the four selected Eq. indicators was verified. (FIG. 5 i) RCexp of three UCMSCs from different donors is linearly related to the RCpred value. The red line represents a linear regression fit of the RC data points. The fitted R values represent Pearson correlation. Each black dot in panels e, i represents cells A, B and C and donor A/S/Y, scale bar, 100 μm (FIGS. 5a and 5 f). RCpred represents the predicted RC value and RCexp represents the actual RC value.

Fig. 6 the efficiency of XAV939 was tested in cells A, B and C at the indicated doses.

Fig. 7. Predicting RC of mesenchymal stem cells treated with different concentrations and types of chemical inhibitors. (FIGS. 7a,7 e) four selected Eq. indices were screened experimentally in three different mesenchymal stem cells by increasing the dose of XAV939 or PD 98059. Three different mesenchymal stem cells, designated cells A, B and C, were treated with 0, 1, 5 and 10 μm doses of Wnt/β -catenin pathway inhibitor XAV939 (fig. 7 a-7 d) or 0, 10, 20 and 25 μm doses of MEK/ERK pathway inhibitor PD98059 (fig. 7 e-7 h). Details about these cells are shown in Table 2. Scale bar, 100 μm. Data are expressed as mean ± SD. (FIG. 7b, FIG. 7 f) the actual RC of three different mesenchymal stem cells treated with increasing doses of XAV939 or PD98059 were detected by alizarin red staining and Western blot. RC values of 10 μm XAV939 treated cells C (fig. 7 b) or 25 μm PD98059 treated mesenchymal stem cells (fig. 7 f) were excluded as cells died under osteoinductive conditions. (FIG. 7c, FIG. 7 g) the predictive model was validated by predicting RC of three different mesenchymal stem cells after treatment with increasing doses of XAV939 or PD 98059. By comparing the predicted RC value with the actual RC value, the accuracy of the predictive model using the four selected Eq. indicators was verified. (FIG. 7d, FIG. 7 h) in three different mesenchymal stem cells, the linear correlation of RCexp and RCpred values was achieved by increasing the dose of XAV939 or PD98059 treatments. The red line represents a linear regression fit of the RC data points. The fitted R values represent pearson correlations. Each black dot represents a group of mesenchymal stem cells treated with a specific concentration of XAV939 or PD 98059. RCpred represents the predicted RC value and RCexp represents the actual RC value.

FIG. 8 RNA sequencing of three UCMSCs from different donors. (FIG. 8 a) Venn diagram of unique and shared gene coding in a preselected population of mesenchymal stem cells. Three preselected UCMSCs from different donors, designated as donorA, S and Y, were included for RNA sequencing (n=3). The selected Eq. index reflecting the cellular appearance of donorS was significantly higher than that of donorA and Y (associated with FIG. 3 f). (FIG. 8 b) Venn diagram of Differentially Expressed Genes (DEGs) in a preselected population of mesenchymal stem cells. 818 DEGs of donor S were common to both the donor A and Y cell populations. Q value<0.05 and |log ₂ FC|gtoreq 1 is set as a cutoff criterion. (FIG. 8 c) functional classification of DEGs based on KEGG classification. (FIG. 8 d) gene expression of donor S and donorA or Y, respectively, was compared based on the Gene Set Enrichment Analysis (GSEA) of the KEGG database. NOM p value is selected<0.05 and FDR q values>A pathway of significant enrichment of 0.25. The Venn diagram shows the overlap of 27 KEGG pathways and their corresponding KEGG classifications. (FIG. 8 e) GSEA shows four features of common KEGG pathway and environmental information processing. (FIG. 8 f) GSEA shows that the immune system has 10 common KEGG pathway features. (FIG. 8 g) the KEGG pathway-based network is primarily a feature of the environmental information processing and immune system, showing a potential relationship between osteogenic differentiation-related pathways and significantly enriched pathways. (FIG. 8 h) thermal diagram shows the DEGs of stem cell related genes related to human adult tissue stem cell modules in donorA, Y and S. Q value <0.05 and |log ₂ FC|gtoreq 1 is set as a cutoff criterion. Gene expression data were normalized by z-score. Pie charts show that 57% of the DEGs of the genes associated with dryness are up-regulated by Donor S. (FIG. 8 i) the dominant expression of the dryness-related gene in donor S was confirmed by real-time quantitative polymerase chain reaction analysis (qPCR). Among the genes upregulated by donor S in the heat map (fig. 8 h), the first 10 genes and the other 8 genes with an average Transcripts Per Million (TPM) of greater than 30 were selected for qPCR analysis. Data are expressed as mean ± SD.

Detailed Description

The technical solution of the present invention is further illustrated by the following specific examples, which do not represent limitations on the scope of the present invention. Some insubstantial modifications and adaptations of the invention based on the inventive concept by others remain within the scope of the invention.

In the examples herein below, donorA, Y, S are also donors a, Y, S.

The phosphorylation/total ERK1/2 is also called "Phospho-p44/42MAPK (Erk 1/2)/p 44/42MAPK (Erk 1/2)" and also called "p/t ERK 1/2".

Antibodies and reagents

EZH2 (# 07-689), antibodies that activate beta-Catenin (# 05-665) and beta-Catenin (# 06-734) are available from Millipore (Merck Millipore, billerica, mass., USA). Antibodies to Tet1 (#ab 191698), oct4 (#ab 19857), SOX2 (#ab 93689) and alkaline phosphatase (ALP, #ab 108337) were purchased from Abcam (Cambridge, MA, USA). Antibodies to Nanog (# 4903T), TAZ (# 83669S), YAP (# 14074S), phospho-Smad3 (# 9520S), smad3 (# 9523S), clear Notch1 (# 4147S), notch1 (# 3608S), phospho-p44/42MAPK (Erk 1/2) (# 4370S), p44/42MAPK (Erk 1/2) (# 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, calif., USA). Anti-human CD34, CD45, CD73, CD90, CD105 and CD146 antibodies were purchased from BD Biosciences (San Jose, CA, USA).

1. Research method

Ethical approval

The ethical approval of collecting and using human mesenchymal stem cells from umbilical cord, gingival, skin, dental pulp was approved by the ethical committee of affiliated stomatology hospitals of the university of Zhongshan (project number: KQEC-2021-48-01). All donors obtained informed consent, and collection of material was without additional risk to them.

1. Isolated culture of 11 MSCs from multiple passages of different tissue sources from different individuals.

Isolated culture of umbilical cord mesenchymal stem cells:

umbilical cord tissue was washed 3 times with 70% ethanol and then 3 times with PBS to disinfect and remove blood. The tissue was cut into small pieces, the blood vessels were removed and transferred to a 50ml centrifuge tube, and then shaken with 3mg/ml type I collagenase and 4mg/ml dispase at 37℃for 1 hour. After digestion, PBS was diluted and centrifuged at 1500rpm for 5 minutes at 4℃to collect the cell pellet. The cell pellet was resuspended in alpha-MEM cell culture medium (containing 15% fetal calf serum, 2mM L-glutamine, a double antibody containing 100U ml-1 penicillin and 100. Mu.g ml-1 streptomycin and 10mM L-ascorbyl phosphate) and transferred to a cell culture dish for incubation at 37℃and 5% CO2 for cell expansion. The 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 pancreatin for the next passage. In the experiments, umbilical cord mesenchymal stem cells were used P3, P7, P12.

Isolation culture of dental pulp mesenchymal stem cells:

human deciduous dental pulp stem cells were isolated from normal deciduous teeth collected from four children aged 5 to 10 years, and dental pulp stem cells were extracted from healthy teeth of one young donor. Briefly, the cell culture method was as follows, in which dental pulp was isolated from root canal, and then digested with shaking in a mixed solution of 3mg/ml type I collagenase and 4mg/ml dispase at 37℃for 1 hour, and cultured in a cell culture dish using the above-mentioned alpha-MEM cell culture medium. Cells were expanded to 90% confluence and passaged with pancreatin for digestion. The generation P11, P12, P13 of human deciduous tooth pulp stem cells and P12 of pulp stem cells were obtained for in vitro experiments.

Isolated culture of mesenchymal stem cells of human gums, skin, bone marrow and adipose tissue:

gingival and skin tissue was taken from waste tissue of the oral hospital at the university of Zhongshan. Gingival and skin tissues were aseptically treated into pieces and incubated with 3mg/ml type I collagenase and 4mg/ml dispase for 1 hour at 37℃with shaking. The dissociated cell suspension is then diluted in PBS and centrifuged to obtain the cell pellet. The cell pellet was resuspended in a-MEM cell culture medium and plated on 10cm cell culture dishes and cultured in a incubator at 37℃with 5% CO 2. After 72h of incubation, the non-adherent cells were removed. The adherent cells were subcultured when reaching approximately 90% confluence density. MSCs of P6 and P11 gingival origin and MSCs of P8 skin origin were used in this study. In addition, two lines of bone marrow-derived mesenchymal stem cells and primary human adipose-derived stem cells were purchased from ScienCell (San Diego, CA, USA). Subculturing was performed under recommended conditions. P15 and P16 bone marrow mesenchymal stem cells and P10 adipose stem cells were used in our study.

2. Determination of 30 biological indicators and evaluation of actual osteogenic Capacity of MSCs

a) Cell morphology metrics (including cell area, cell roundness, cell nuclear area, cell nuclear roundness, cell cytoplasmic area, cell cytoplasmic roundness, and cell nuclear cytoplasmic ratio) of MSCs in the state of viable cells, proliferation rate (EdU detection), and migration rate (DPC mode detection) were collected and isolated using a high content imaging system.

High content image processing analysis:

cells were seeded in 96-well black plates (CellCarrier-96, PERGINElmer). After 24 hours, cells were washed with PBS and washed with CellMask ^TM Deep Red membrane fuel (dilution 1:2000) was incubated at 37℃for 15 minutes. The cells were then washed and the nuclei were observed for 5 minutes at room temperature with Hoechst33342 (dilution 1:2000). Finally, the cells were washed with PBS, covered in PBS and examined. The whole process should be kept in the dark.

The MSCs plates were subjected to high content analysis and image acquisition and data processing (objective x 20 magnification, PERKinElmer, USA). Morphological indexes such as cell area, cell roundness, cell nucleus area, cell nucleus roundness, cell cytoplasmic area, cell cytoplasm roundness, cell nucleus cytoplasmic ratio and the like are analyzed by adopting an image analysis module customized by Harmony 4.0 software. Each morphological parameter was quantified using an average of at least 5,000 selected cells.

EdU detection:

the determination was performed using the EDU kit (KeyGen Biotech, jiangsu, china). Cells were seeded in 24-well plates and incubated at 37℃for 24 hours. Cells were stained with 50 μm EdU in medium for 2 hours. Cells were then washed with PBS and fixed with 4% paraformaldehyde for 15 minutes at room temperature. Cells were counterstained with Hoechst 33342 (1:2000, 5. Mu.g/ml) for 15 minutes to stain nuclei and washed with PBS. Images of EdU positive cells were obtained and calculated using a high content screening system (objective x 10 magnification, PERKINELmer, USA).

b) Flow cytometry analyzed 11 MSCs physical properties and surface markers. Flow cytometry parameters reflecting the relative size and internal miscibility of the suspended living cells were collected and recorded as forward scatter height (FSC-H) and side scatter height (SSC-H), respectively. MSCs surface markers include CD73, CD90, CD105, CD146, CD34, CD45.

Flow cytometry analysis:

MSC-specific surface markers were assessed by flow cytometry analysis. Cells were washed three times, digested, washed and resuspended in PBS containing 0.5% bovine serum albumin, and then antibody conjugated with fluorochromes that bind human antigens (including anti-CD 34-PE, anti-CD 45-PE, anti-CD 73-PE, anti-CD 90-PE, anti-CD 105-PE and anti-CD 146-PE) were placed in the dark at 4℃for 30 minutes. After incubation, cells were rinsed with PBS containing 0.5% BSA, centrifuged at 1500 rpm for 5 minutes, and then resuspended for flow cytometry analysis. Samples were characterized using a novoCyte flow cytometer and analyzed using Novoexpress software (NovoCyte, ACEA Biosciences, USA).

c) Immunoblot reactions were assayed for protein expression levels of 13 molecular signals critical to stem cell function, including Tet1, phosphorylated/total ERK1/2, nanog, EZH2, oct4, YAP, phosphorylated/total mTOR, caveolin-1, sox2, phosphorylated/total Smad3, activated/total β -catenin, TAZ, and lytic/total Notch1, and their relative expression levels were calculated by GAPDH.

Western immunoblotting:

the sample was lysed using a protein extraction kit (# 78501,Thermo Fisher) containing protease and phosphatase inhibitors. After quantification by BCA kit, an equal amount of protein per sample was loaded onto SDS-PAGE gel and then transferred onto PVDF membrane (Millipore). The membrane was blocked with TBST containing 5% bsa for 2 hours and then incubated overnight at 4 ℃ with the following primary antibodies at 4 ℃ with shaking: EZH2, active beta-Catenin, tet1, oct4, SOX2, alkaline phosphatase (Alkaline Phosphatase), nanog, TAZ, YAP, phospho-Smad3, clear Notch1, phospho-p44/42MAPK (Erk 1/2) (Phospho-p 44/42MAPK (Erk 1/2)), p44/42MAPK (Erk 1/2), runx2, phospho-mTOR (Phospho-mTOR), mTOR, caveolin-1 or GAPDH. The membranes were then washed with TBST and incubated with the appropriate secondary antibodies for 1 hour at room temperature. After complete washing, protein bands were observed using Supersignal West Pico chemiluminescent substrate (Thermo Fisher) and evaluated using a gel imaging system (Bio-Rad, USA). The relative expression levels were calculated by NIH ImageJ software (Media Cybernetics, USA).

d) Calculation of the actual osteogenic regeneration capacity (RC value) of MSCs the alizing areas of alizarin red stained positive mineralization were quantified as a percentage of the total area using imageJ software. The relative expression levels of the osteogenic related proteins (ALP and Runx 2) were also calculated by imageJ software. RC values were calculated by averaging the mineralized area percentage and the osteoblast protein level.

Osteogenic differentiation induction: cells were seeded in 12-well plates. At 90% confluence, the medium was changed to medium containing 1.8mM potassium dihydrogen phosphate, 10nM dexamethasone, 100U/ml penicillin/streptomycin, 0.1mM L-ascorbyl phosphate, 2mM glutamine, 15% fetal bovine serum, and alpha-MEM. The osteogenic medium was changed every 2 days. After induction for 5 to 12 days, cultured cells were stained with alizarin red or the protein was lysed to assess the expression of osteogenic genes.

Alizarin red staining: osteogenic differentiation was assessed by the ability of cells to form mineralized nodules stained with alizarin red after 4 weeks of osteoinduction. Briefly, cells were washed with PBS, fixed with 60% isopropanol for 30 min at room temperature, and rehydrated with ddH2O for 5 min. Subsequently, the cells were incubated with alizarin red staining solution (1 g alizarin red S (#a5533, sigma) in 100mlddH2O and filtered) for 10 minutes at room temperature. Finally, cells were washed with PBS to remove non-specific binding, dried and observed under a microscope.

3. And (3) screening the optimal biological related index combination and establishing an MSCs osteogenic differentiation capacity prediction mathematical model.

The present study uses an exhaustive method and a multiple linear regression to screen prediction related independent variables and build a prediction model. Considering that when the number of independent variables is too large, not only overfitting can be generated, but also the practical application of the model is not facilitated, we mainly discuss the situation that the number of independent variables is within 5 (including 5). To increase the number of sample operations and sample tests to increase the accuracy of the fitting effect, we split the 11 cells into 8 MSCs in the training group and the remaining 3 MSCs in the test group with an 8/3 random combination.

The independent variable index values and the actual osteogenesis capacity RC values of 8 cells are used for establishing a fitting model, and parameters corresponding to the independent variable can be obtained from the step (1).

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Wherein y is a dependent variable representing the actual RC value; x is an argument representing a selected index; b1, b 2..and bn are unknown parameters of the corresponding independent variables; n represents the number of selected metrics. Multiple linear regression was performed to give b1, b2,.

Model test was performed on the remaining 3 cells, and from (2), we could obtain predicted RC values for the remaining 3 cells to compare with the actual RC values. Next, the difference between the experimental value and the predicted value is calculated. The predicted value can be obtained by the formula (2).

yy ⁹ 、yy ¹⁰ And yy ¹¹ Is the predicted RC value calculated according to equation (2).

The above procedure was repeated by extracting 20 sets from the 8/3 random combinations of 11 cell samples possible, fitting the independent variable combinations with the smallest bias. The corresponding parameters and the finally established multiple regression fit formula are taken as our prediction model, and the optimal multiple linear regression model shown as formula (3) can be constructed

y＝b ₁ x ₁ +…+b _n x _n (3)

It is worth mentioning that the calculated constant coefficients of b1, b2,..bn are based on the data obtained by our instrument. Data are expressed as mean ± SD.

4. And (5) verifying an MSCs osteogenesis regeneration prediction model through in vitro experiments.

a) The accuracy of the predictive model was verified by inclusion of new MSCs in vitro experiments.

b) The sensitivity of the predictive model was verified by in vitro experiments with MSCs treated with chemical inhibitors of WNT and ERK pathways.

Treatment of MSCs with chemical inhibitors: cells were seeded on culture plates to 70% -80% confluence and three concentration gradient ranges with XAV939 (13596,Cayman Chemical): 1 μm, 5 μm, 10 μm and PD98059 (#s1805_beyotidme, shanghai, china): cells were treated at 10. Mu.M, 20. Mu.M, 25. Mu.M. Chemical efficiency was assessed by western blotting. The treated cells were harvested and prepared for further experiments.

5. Internal links between screening criteria and the osteogenic capacity and dryness of MSCs were revealed by transcriptome sequencing analysis.

RNA sequencing analysis: umbilical cord mesenchymal stem cells collected from donor a, Y, S were analyzed by mRNA sequencing (RNA-Seq, china hua major biotechnology limited), three replicates for each cell. The BGISEQ-500 platform was used for the RNA-seq library. The clean reads were stored in FASYQ format and aligned with the reference genome and reference gene using HISAT and Bowtie2, respectively ^35-36 . The expression level of the gene was then calculated using RSEM (v1.2.12) ³⁷ . Essentially, DESeq2 (v1.4.5) (|log 2FC|) 1. Gtoreq.1 and Q value based on absolute multiple change of log2 is used<0.05 ³⁸ Differential expression analysis was performed. To gain insight into the change in phenotype, KEGG (https:// www.kegg.jp /) and GSEA (http:// www.broadinstitute.org/GSEA) were performed. And (5) analyzing.

Quantitative real-time polymerase chain reaction: total RNA was extracted using RNA-Quick Purification Kit. According to the instructions, using Primescript II RTase 1. Mu.g total RNA was transcribed into 20. Mu.l reaction mixture for cDNA synthesis. Using cDNA as template, specific primer and TB Green ^TM Premix Ex Taq ^TM The II kit amplified the target gene by qPCR on the LightCycler system (LightCycler, roche Diagnostics). Three biological replicates of gene expression were assessed and used 2- ^ΔΔCT The method calculates the relative transcript levels. The GAPDH gene was used for normalization. The sequences of qPCR primers used in this study are shown in table 1.

TABLE 1

2. Experimental results

1. The mesenchymal stem cells of 11 different tissue sources or different algebra are selected, and 30 biological indexes of each cell are measured to be included into variables.

We registered 11 different types of MSCs from different donors, sources and stages, designated Cell 1-11, and determined 30 biological indicators for each Cell to analyze their characteristics (FIG. 1a; see Table 2 for MSC details).

TABLE 2 details of cells in training set and validation set

First, cell morphology is one of the important indicators of cell status ^16-22 . MSCs cell morphology metrics for 11 viable cell states were collected and analyzed using the high content imaging system including cell area, cell roundness, cell nucleus area, cell nucleus roundness, cytoplasmic area, cell nucleus to cytoplasmic ratio (FIGS. 1 b-h).

Compared to the morphology of dead MSCs after fixation (fig. 2 a), most of the living stem cells were in a uniform fibroblast-like morphology with smooth boundaries, approximately circular nuclei (fig. 2b, fig. 2 c).

Cell proliferation rates were determined by EdU incorporation (figure 1 i). The migration velocity of MSCs can also be monitored by the DPC-mode high content imaging system and calculated as the total migration distance divided by the total time (fig. 1 j). The relative displacement of all MSCs was less than 10 μm within 24 hours (fig. 2 d).

The 11 MSCs physical properties and surface markers were then analyzed by flow cytometry. All 11 types of MSCs appeared positive for markers CD73, CD90, CD105, CD 146, while markers CD34, CD45 were negative (fig. 1 k-p).

Flow cytometry parameters reflecting the relative size and internal complexity of the suspended living cells were collected and recorded as forward scatter height (FSC-H) and side scatter height (SSC-H), respectively (FIGS. 1q,1 r).

Finally, protein expression levels of 13 molecular signals critical to stem cell function were determined by immunoblotting (Westernblot), including Tet1, phosphorylated/Total ERK1/2, nanog, EZH2, oct4, YAP, phosphorylated/Total mTOR (Phospho-mTOR), caveolin-

1. Sox2, phosphorylated/total Smad3 (Phospho-Smad 3), activated/total β -Catenin (activated β -Catenin), TAZ and cleaved/total Notch1 (clearedotch 1), and their relative expression levels were calculated by GAPDH (fig. 1 s).

To evaluate RC (osteogenic regeneration capacity) of 11 MSCs, alizarin red staining and western blot analysis of related osteogenic lineage proteins (ALP and Runx 2) were performed after osteoinduction (fig. 1t,1 u). The osteogenic regeneration RC value of each MSCs was calculated by averaging the percentage of mineralized area of alizarin red staining and the expression level of osteogenic protein (FIG. 1 v).

2. And establishing a predictive model of the osteoblast differentiation RC value of the mesenchymal stem cells.

Based on the above results, those features of the 11 types of MSCs that meet the conditions were screened out and integrated into the final dataset consisting of thirty different metrics as input x, and the actual RC value (RCexp) as output y (see table 3 for raw data of x and y). The input x of a given level leads to a corresponding output y, representing a predicted RC value (RCpred). Our predictive equation (Eq.) model is constructed by considering the set of indices that achieve the best fit. Specifically, we performed 8/3 random combinations of 11 cells, i.e., 11 MSCs were randomly divided into 8 training sets and 3 test sets at a distribution ratio of 8:3 (i.e., where 8 cells were modeled as fitted, leaving 3 modeled assays). 20 groups were drawn from the randomly combined groups of cell samples for fitting test operations (FIG. 3 a). The results suggest that as the number of independent variables involved in the fitting increases, the fitting offset value offset decreases and the fitting accuracy improves, but where the fitting offset change of X from 4 to 5 is less than 0.01 (fig. 3 b).

TABLE 3 Table 3

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For 11 types of MSCs, we compared the predicted result (RCpred value) obtained by fitting the least-deviated combination of fitting models of different self-variable numbers from 1 to 5 with the actual experimental result (RCexp), found that the predicted RC value and the actual RC value overlap better with increasing self-variable number X involved in fitting (the gray dotted line of RCpred of cells 1-11 closely overlaps with the black dotted line representing its corresponding RCexp), and calculated their linear correlation comparison, the R value increased from 0.582 to 0.986 (fig. 3 c). Thus, four arguments x can predict y with small offset values quite well. As the number of inputs x increases to five or more, the increased prediction cost does not result in a significantly stronger correlation between RCpred and RCexp (FIG. 3 c).

It follows that the selected equation model fitted by the four input x-indices is optimal. Among the four sets of inputs x, the best four index combinations of the Eq. model selected included nuclear roundness, nuclear/cytoplasmic ratio (or nuclear cytoplasmic ratio), SSC-H and ERK1/2 (FIG. 3d; FIG. 4). Notably, three of the four selected indicators, other than ERK1/2, were features related to the appearance of the cells, rather than surface markers or molecular signals (fig. 3 d).

Meanwhile, the pearson correlation coefficient (figure 3 d) of the prediction index and the actual RC value is calculated, and one of four indexes in the optimal independent variable fitting equation is found: the NU/CY ratio has very low correlation with the actual RC value (r= 0.0298), approaching 0, but when removed from the fit prediction formula, the overlap ratio of the predicted RC value and the actual RC value is reduced (fig. 3 e), the deviation of the model fit is reduced from 0.979 to 0.947 (fig. 3 f), indicating that the index has low correlation with the output result, but its contribution to the model fit accuracy is not negligible.

The results suggest that in the combined equation model with small fitting deviation, the cell shape is closely related to different degrees, and that they are nuclus Area, nucleus Roundness, cytoplasm roundness, NU/CY ratio, SSC-H, respectively, except that a set of equations consisting of CD146, FSC, EDU, EZH2 are not related to the above-mentioned index at all. In order to eliminate false signal interference with strong index, the group of data is verified when the formula is verified later, and the predicted result and the actual result are basically matched, so that the operation process is not interfered by signals, and the appearance of the cells has an important indication function on the osteogenic potential.

And the result is combined, four indexes are used for forming a fitting equation, so that the accuracy of the fitting equation is ensured, and the fitting equation has good feasibility. The best prediction index is nucleus roundness (Y axis), nucleic/cytoplasma (nu/cy) ratio (X axis), SSC-H (size), p/t ERK1/2 (Z axis) form four dimensions, which are balanced to define and compensate each other, we have five-dimensional graphic representation of the index combination, X, Y, Z represents one dimension, sphere size and color represent one dimension again, thus giving our predictive formula as follows: Y=q1N+q2N/C+q3S+q4E (q1:1.34E-01, q2:5.68E-01, q3:1.17E-07, q4:6.36E-02) (FIG. 3 g). Wherein, N refers to the value of the roundness (nucleus roundness) of the cell nucleus, and the closer to the value 1, the more round is the value of 0-1; N/C refers to the nuclear/cytoplasmic ratio (nuclear/cytoplasms (nu/cy)); s refers to SSC-H (lateral scattering height) values, generally with the same machine detection, the larger the value, indicating more complex cell contents; e refers to the relative expression of phosphorylated ERK1/2, which is the ratio of the expression of phosphorylated ERK protein divided by the expression of whole ERK protein.

3. And verifying the RC value prediction model through in vitro experiments.

To verify the accuracy of the predictive 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 biological indicators of nuclear roundness, nuclear-to-cytoplasmic ratio, SSC, ERK1/2 of 3 newly added MSCs, substituted into the formula to calculate the predicted RC values, where the osteogenic potential of cell B performed better than cell A than cell C (FIG. 5 a). To calculate RC _exp Values, after 12 days of osteoinductive culture, alizarin red assay and protein trace analysis were performed on ALP and Runx2 in cells A, B and C, and the actual osteogenic experimental results also showed that cell B exhibited better osteogenic potential than cell A than cell C (FIGS. 5C, 5 d), the trend of the predicted result is the same, but the numerical value has equal proportion deviation, which is possibly carried out under the experimental condition of different batches with 11 cells, and the numerical value deviation caused by experimental operation environment, such as the relation between the intensity of antibodies of western blot and the exposure intensity, influences the calculation of the numerical value of imageJ; such as deviations in values caused by cell staining time, nuances before and after the induction environment, and the like.

In addition, a new three-cell model was used to simultaneously detect a set of non-cellular profile related indicators, CD146, FSC-H, EDU, EZH2, and a fitting operation was performed using the indicators to find that the predicted outcome also matches the experimental outcome, but the predicted RC value deviates significantly from the actual RC value by more than the optimal combination (fig. 5b, fig. 5d, fig. 5 e). Of the three newly incorporated cells, cell a was the umbilical cord mesenchymal stem cell P3 generation of donor Y, cell B was the umbilical cord mesenchymal stem cell P11 generation of donor S (table 2), although the algebra of cell B was greater than that of cell a, the osteogenic potential of cell B was superior to that of cell a, suggesting the importance of our donor source for MSCs stem.

In fig. 5, supplementary Eq. refers to a complementary operation for eliminating high-intensity interference of a mathematical model operation signal, and is shown in blue circle in fig. 5, and the complementary formula includes indexes CD146, FSC-H, EDU, EZH2.

To make the formula model more theoretical basis for clinical application, we increased umbilical cord mesenchymal stem cells of donorA, and cultured three kinds of mesenchymal stem cells of the same species and the same algebra but different donor sources were pre-judged and compared, which were named donor a, donor S and donor Y (donor source as the only variable), respectively. The same procedure examined four predictors of donor A, donor S and donor Y, substituted into the formula for osteogenic RC value prediction, and the results suggested that donor S was the most osteogenic cell (FIG. 5 f) and that the RC value results after 12 days of experimental induction were highly matched (FIGS. 5g-5 i), with a pearson correlation of 0.947 (FIG. 5 i). These results indicate that this cell morphology variable model can accurately predict RC of MSC.

4. RC results of chemical inhibitor treated MSCs were predicted.

The state of some signal pathways reflects the vital state of the cell, and changing the signal pathways affects the vital state of the cell, thereby affecting the osteogenic potential of the cell. The sensitivity of the formula prediction was verified by blocking the classical pathway. It is well known that the Wnt/β -catenin (Wnt/β -catenin) signaling pathway is a key component involved in regulating cell stem and osteogenic capacity ^23-25 . We selected a classical wnt pathway, which is now considered significant for stem cells, to intervene, and an erk pathway in an important index, both of which selected classical drug inhibitors, to intervene, where wnt intervened with XAV939, and erk with PD 98059.

We demonstrated that a specific inhibitor of the Wnt/β -catenin signaling pathway, XAV939, reduced β -catenin expression in cells A, B and C (FIG. 6) ²⁶ . Most of the previous research reportsInhibition of Wnt/β -catenin results in negative regulation of osteogenic differentiation, but we cannot ignore that different cell states might be responsible for signaling pathways ^27-28 Is inconsistent.

Three newly incorporated cells were intervened with XAV939 (1 um 5um 10 um) and PD98059 (10 um 20um 25 um), respectively, and the drug could significantly inhibit the signaling pathway, the effect of which was detected with WB.

We first examined the nuclear roundness, nuclear-to-cytoplasmic ratio, SSC-H, ERK1/2 biological index of cells A, B, C after 24H of drug XAV939 interference, and the predicted results (RCpred values) showed that under WNT pathway inhibition, both nuclear roundness and nuclear-to-cytoplasmic ratio of cells A, B, and C decreased with increasing drug concentration, SSC of cells A and B decreased with increasing drug concentration, but cell C increased; ERK1/2 increased in AC, but showed a decreasing trend in cell B.

Osteoblast potential cells a and B predicted according to the formula were attenuated with increasing concentration, whereas cell C was not significantly attenuated or even slightly enhanced with increasing concentration (fig. 7 a). After 12 days of osteoinductive culture, cell C died on day 4 due to continuous stimulation with high concentration of drug. The trend of the rest of the cell experimental results was similar to the formula pre-judgment result (fig. 7 b). The deviation of the predicted values still occurs here, except for the possible experimental factors, where the trend values of unequal proportions occur during the drug concentration intervention, and the predicted value change amplitude is smaller than the actual experimental result change amplitude, which is probably because, in the 12-day induction process, in order to maintain the drug inhibition, each time the induced solution is changed, a new inhibitor is also changed, the cells receive continuous drug stimulation during the culture process, the reaction state may not be consistent with the state obtained after 24h, and the cells after 12-day drug stimulation and the same cells after 24h stimulation are different cell states, but we can still accurately trend the osteogenic potential according to the cell state change after 24h (fig. 7c and 7 d).

ERK1/2 is the only molecular signal equation index. To study the internal links between the ERK pathway and the other three selected formula indices, i amThey were tested with PD98059 (10. Mu.M, 20. Mu.M and 25. Mu.M) ²⁹ The concentration of (C) inhibits ERK pathway changes to nuclear roundness, NU/CY ratio and SSC of cells A, B and C. Overall, the nuclear roundness of all three cells decreased with increasing PD98059 concentration, except for cell a treated with 25 μm inhibitor (fig. 7 e). The NU/CY ratio and SSC analysis showed that cell A and cell B showed a decreasing trend and cell C mainly an increasing trend (FIG. 7 e). Furthermore, western blot analysis showed that ERK1/2 protein expression decreased in a concentration-dependent manner (FIG. 7 e). In summary, based on the results of the Eq. index, we predicted that the RCprep values for cells a and B become smaller with increasing concentration, but that for cell C become larger (fig. 7 g). To obtain the RCexp values, cells A, B and C were incubated under osteoinductive conditions for 12 days, treated with PD98059 every two days, and the RCexp values were calculated by alizarin red assay and Westernblot (fig. 7 f).

Although most cells of the high concentration 25 μm PD98059 treated group died 2 days after osteoinduction. As expected, we found that the trend of RC variation between the values of RCexp and RCpred was consistent in the other groups of cells (fig. 7 g). The pearson correlation coefficients for cells A/B/C were 0.999, 0.994 and 0.962, respectively (FIG. 7 h).

In summary, we have newly incorporated three cells, verifying the accuracy of the important index and formula screened by the operation. Meanwhile, interference is carried out by means of biological angle, and the sensitivity of the screened important indexes and formulas to capturing the complexity change of the cell life state and predicting the osteogenic potential is tested.

The anti-staling, among the four selected Eq. indices, including cell nucleus roundness, cell nucleus/cytoplasm ratio, SSC-H and ERK1/2, did not have a sufficient predictor to be an MSC-RC alone. However, in all validation experiments, the SSC-H values showed a trend consistent with the RCexp values (FIG. 5a, FIG. 5d, FIG. 5f, FIG. 5H; FIG. 7a, FIG. 7c, FIG. 7e, FIG. 7 g).

In addition, a higher SSC-H value of the mesenchymal stem cell population was most clearly associated with a higher osteogenic differentiation capacity (r= 0.89952) (fig. 3 d). These results inspire us to mine genetic information related to SSC-H. Thus, we compared the transcriptional profiles of the donorA, Y and S mesenchymal stem cells, showing an increasing trend of SSC-H (fig. 5 f). RNA sequencing analysis showed that the different genes for donor A, S and Y were 222, 389 and 268, respectively. Donor S contained more than 100 different genes than the other two cell populations, which means that Donor S had more abundant gene expression (fig. 8 a).

In addition, the total 818 genes in donorS were Differentially Expressed Genes (DEGs) compared to the other two cell populations. Q value<0.05 and |log ₂ FC|gtoreq 1 is set to the cutoff criteria (FIG. 8 b). Based on the above results, the selected equation index reflecting the cellular appearance of donor S was distinguishable from the other two cell populations, with RCpred values also higher than donorA/Y cells, which matched RCexp (fig. 5 f-5 h).

The donor S cells exhibit their uniqueness regardless of the appearance and intrinsic gene level of the cell. Then, a Kyoto gene and genome encyclopedia (KEGG) enrichment analysis was performed on 818 DEGs. Functional classification based on KEGG classification showed that these DEGs were found to be primarily involved in signal transduction, signal molecules and interactions, immune system, cancer and infectious disease (fig. 8 c).

These results may give some hint to us 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 may be considered a special environmental information capture.

Gene Set Enrichment Analysis (GSEA) was performed on the gene expression of donor S and donorA or Y, respectively, based on the KEGG database, and significantly enriched pathways with Nominal (NOM) p-value <0.05 and wig appearance ratio (FDR) q-value >0.25 were selected. The Venn diagram shows the overlap of 27 KEGG pathways with their corresponding KEGG classifications (FIG. 8 d).

Of the 27 generally markedly enriched pathways, 4 environmental information processing-related pathways, including Mitogen Activated Protein Kinase (MAPK) signaling, jak-STAT signaling, cell adhesion molecule and cytokine receptor interactions, and 10 immune-related pathways were upregulated in donor S (fig. 8e, fig. 8 f).

To investigate the potential relationship between the up-regulated pathway of donor S and osteogenic differentiation, we constructed a relevant network based on the significantly enriched KEGG pathway (fig. 8 g).

It is well known that Wnt signaling pathway, MAPK signaling pathway and Notch signaling pathway can dominate MSC osteogenic differentiation and play an important role in regulating stem cell self-renewal/proliferation ^30-32 . The KEGG-related network determines that the MAPK signaling pathway is a common node connecting other pathways, followed by the Jak-STAT pathway and immune-related pathways that can affect MSC osteogenic differentiation by activating the signaling pathway for environmental information processing (fig. 8 g).

We then focused on 553 genes associated with dryness, which have previously been demonstrated to be continuously up-regulated in human adult tissue stem cells ^33-34 And inquires whether they display different expression profiles of donor S and A/Y.

The heat map shows 76 DEGs, the cut-off criteria being q-value <0.05 and |log ₂ FC|gtoreq 1, 57% of the genes were significantly upregulated in donor S, supporting that donor S was better dry than donor Y/A (FIG. 8h; table 4).

Table 4. Donor S related to FIG. 8 was compared with 553 human adult tissue stem module related genes and up-or down-regulated differentially expressed genes in A/Y.

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The genes related to human adult tissue stem model in donor S and A/Y were significantly up-or down-regulated (|log 2Fc|.gtoreq.1 and Q < 0.05).

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Among the up-regulated genes of donor S in the heat map, the first 10 genes and the other 8 genes with an average of more than 30 Transcripts Per Million (TPM) in donor S were selected and analyzed by real-time quantitative polymerase chain reaction (qPCR). The results confirm that donor S showed an advantageous up-regulation of mRNA expression of these dryness-related genes (fig. 8 i).

Taken together, these data indicate that cell appearance-based indicators can serve as the primary indicator for visualizing the transcriptional status of mesenchymal stem cells and reflecting mesenchymal stem cells.

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sequence listing

<110> university of Zhongshan

<120> a cell morphology variable model for visualizing mesenchymal stem cell state and a method for predicting mesenchymal stem cell regeneration ability

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<170> SIPOSequenceListing 1.0

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<211> 21

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<213> Artificial sequence (Artificial Sequence)

<220>

<223> GAPDH F primer (5 'to 3')

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aggtcggtgt gaacggattt g 21

<210> 2

<211> 23

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> GAPDH R primer (5 'to 3')

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tgtagaccat gtagttgagg tca 23

<210> 3

<211> 22

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> CSF 2F primer (5 'to 3')

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tcctgaacct gagtagagac ac 22

<210> 4

<211> 19

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> CSF 2R primer (5 'to 3')

<400> 4

tgctgcttgt agtggctgg 19

<210> 5

<211> 23

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IL 6F primer (5 'to 3')

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actcacctct tcagaacgaa ttg 23

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<211> 23

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IL 6R primer (5 'to 3')

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ccatctttgg aaggttcagg ttg 23

<210> 7

<211> 19

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> CXCL 2F primer (5 'to 3')

<400> 7

aaagcttgtc tcaaccccg 19

<210> 8

<211> 22

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> CXCL 2R primer (5 'to 3')

<400> 8

ggtcagttgg atttgccatt tt 22

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<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> EHF F primer (5 'to 3')

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gctcagctat ggggtaaaaa gaa 23

<210> 10

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<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> EHF R primer (5 'to 3')

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atccacacgc tccagaattt c 21

<210> 11

<211> 20

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> ATF 3F primer (5 'to 3')

<400> 11

cctctgcgct ggaatcagtc 20

<210> 12

<211> 22

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> TF 3R primer (5 'to 3')

<400> 12

ttctttctcg tcgcctcttt tt 22

<210> 13

<211> 22

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IFIT 1F primer (5 'to 3')

<400> 13

agaagcaggc aatcacagaa aa 22

<210> 14

<211> 23

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IFIT 1R primer (5 'to 3')

<400> 14

ctgaaaccga ccatagtgga aat 23

<210> 15

<211> 21

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> STAC F primer (5 'to 3')

<400> 15

tacggagcaa aagtgctgac a 21

<210> 16

<211> 22

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> STAC R primer (5 'to 3')

<400> 16

tgttcccact atcatgtggt tg 22

<210> 17

<211> 21

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> ISG 15F primer (5 'to 3')

<400> 17

cgcagatcac ccagaagatc g 21

<210> 18

<211> 20

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> ISG 15R primer (5 'to 3')

<400> 18

ttcgtcgcat ttgtccacca 20

<210> 19

<211> 22

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IFIH 1F primer (5 'to 3')

<400> 19

tcgaatgggt attccacaga cg 22

<210> 20

<211> 19

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IFIH 1R primer (5 'to 3')

<400> 20

gtggcgactg tcctctgaa 19

<210> 21

<211> 23

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IL1B F primer (5 'to 3')

<400> 21

atgatggctt attacagtgg caa 23

<210> 22

<211> 20

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IL1B R primer (5 'to 3')

<400> 22

gtcggagatt cgtagctgga 20

<210> 23

<211> 21

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IRF 7F primer (5 'to 3')

<400> 23

gctggacgtg accatcatgt a 21

<210> 24

<211> 19

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IRF 7R primer (5 'to 3')

<400> 24

gggccgtata ggaacgtgc 19

<210> 25

<211> 21

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IFI 44F primer (5 'to 3')

<400> 25

atggcagtga caactcgttt g 21

<210> 26

<211> 23

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IFI 44R primer (5 'to 3')

<400> 26

tcctggtaac tctcttctgc ata 23

<210> 27

<211> 20

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> ICAM 1F primer (5 'to 3')

<400> 27

ttgggcatag agaccccgtt 20

<210> 28

<211> 23

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> ICAM 1R primer (5 'to 3')

<400> 28

gcacattgct cagttcatac acc 23

<210> 29

<211> 21

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> HBEGF F primer (5 'to 3')

<400> 29

atcgtggggc ttctcatgtt t 21

<210> 30

<211> 23

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> HBEGF R primer (5 'to 3')

<400> 30

ttagtcatgc ccaacttcac ttt 23

<210> 31

<211> 21

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> GREM 1F primer (5 'to 3')

<400> 31

tcatcaaccg cttctgttac g 21

<210> 32

<211> 20

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> GREM 1R primer (5 'to 3')

<400> 32

ggctgtagtt cagggcagtt 20

<210> 33

<211> 20

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> TNC F primer (5 'to 3')

<400> 33

tcccagtgtt cggtggatct 20

<210> 34

<211> 21

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> TNC R primer (5 'to 3')

<400> 34

ttgatgcgat gtgtgaagac a 21

<210> 35

<211> 20

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> CCND 1F primer (5 'to 3')

<400> 35

gctgcgaagt ggaaaccatc 20

<210> 36

<211> 22

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> CCND 1R primer (5 'to 3')

<400> 36

cctccttctg cacacatttg aa 22

<210> 37

<211> 20

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IFITM 3F primer (5 'to 3')

<400> 37

cgcctactcc gtgaagtcta 20

<210> 38

<211> 20

<212> DNA

<213> Artificial sequence (Artificial Sequence)

<220>

<223> IFITM 3R primer (5 'to 3')

<400> 38

atgacgatga gcagaatggt 20

Claims

1. A combination of criteria for stem cell performance or quality or status, comprising SSC-H.

2. The index combination of claim 1, further comprising nuclear roundness and phosphorylation ERK1/2;

preferably, the index combination further comprises a nuclear to cytoplasmic ratio;

preferably, the phosphorylated ERK1/2 comprises a gene, mRNA or protein of phosphorylated ERK 1/2.

3. The application of the marker in identifying or distinguishing the performance, quality or state of stem cells; the marker comprises SSC-H;

Preferably, the marker further comprises nuclear roundness and phosphorylation ERK1/2;

preferably, the marker further comprises a nuclear to cytoplasmic ratio;

Preferably, the stem cell property or quality or status comprises the regenerative capacity of stem cells;

preferably, the regenerative capacity of the stem cells includes osteogenic capacity, bone regenerative capacity or dryness.

4. The use of claim 3, wherein the stem cells comprise totipotent stem cells or pluripotent stem cells;

preferably, the stem cells comprise mesenchymal stem cells or induced pluripotent stem cells;

preferably, the mesenchymal stem cell source comprises bone marrow, fat, gum, umbilical cord, cartilage, skin, urine, placenta, periosteum or tendon;

preferably, the oral cavity comprises a tooth, dental pulp, gum, periodontal ligament or ligament;

preferably, the dental pulp comprises deciduous dental pulp mesenchymal stem cells or permanent dental pulp mesenchymal stem cells;

preferably, the deciduous tooth pulp mesenchymal stem cells are deciduous tooth pulp stem cells of a human;

preferably, the cell algebra of the dental pulp mesenchymal stem cells comprises P1-P12

Preferably, the cell algebra of the umbilical cord mesenchymal stem cells comprises P1-P15;

preferably, the cell algebra of the bone marrow mesenchymal stem cells comprises P1-P20;

preferably, the cell number of the adipose-derived mesenchymal stem cells comprises P1-P20.

5. The method for judging the performance, quality or state of the stem cells is characterized by comprising the steps of detecting the value of an index SSC-H;

preferably, the discriminating method includes:

detecting the value of an index SSC-H;

comparing the values of the index SSC-H between the stem cells under the same instrument;

judging the performance or quality or state of the stem cells according to the value of the stem cell index SSC-H;

when the value of the index SSC-H of the stem cell is increased relative to the value of the index SSC-H of another stem cell, judging that the performance or quality or state of the stem cell with the increased value of the index SSC-H is good;

preferably, the discrimination method further includes using index nuclear roundness and phosphorylation ERK1/2;

preferably, the method further comprises the step of using an index cell nuclear-cytoplasmic ratio;

preferably, the discriminating method includes:

detecting the value of an index SSC-H, the value of the roundness of a cell nucleus, the value of the relative expression quantity of the phosphorylation ERK1/2 and the value of the nuclear-cytoplasmic ratio;

Comparing the value of the index SSC-H, the value of the roundness of the cell nucleus, the value of the relative expression quantity of the phosphorylation ERK1/2 and the value of the nuclear-cytoplasmic ratio;

judging the performance, quality or state of the stem cells according to the values of the stem cell index SSC-H, the value of the cell nucleus roundness, the value of the relative expression quantity of the phosphorylation ERK1/2 and the value of the cell nucleus-cytoplasm ratio;

when the value of SSC-H, the value of nuclear roundness, the value of relative expression quantity of phosphorylated ERK1/2 and the value of nuclear cytoplasmic ratio of one stem cell are increased relative to the value of SSC-H, the value of nuclear roundness, the value of relative expression quantity of phosphorylated ERK1/2 and the value of nuclear cytoplasmic ratio of another stem cell, judging that the performance or quality or state of the stem cell with the increased value of the index SSC-H, the value of nuclear roundness, the value of relative expression quantity of phosphorylated ERK1/2 and the value of nuclear cytoplasmic ratio is good;

preferably, the phosphorylated ERK1/2 comprises a gene, mRNA or protein of phosphorylated ERK 1/2;

preferably, the relative expression level of phosphorylated ERK1/2 is the ratio of the expression level of phosphorylated ERK protein divided by the expression level of whole ERK protein;

Preferably, the regenerative capacity of the stem cells includes osteogenic capacity or dryness;

preferably, the stem cells comprise totipotent stem cells or pluripotent stem cells;

6. A method for determining the performance, quality or state of stem cells, comprising: acquiring the value of the index; inputting the value of the index into a judging model to obtain a judging result of the stem cell performance or quality or state; the judging model is a calculation model constructed according to the functional relation between the value of the index and the stem cell performance or quality or state;

The index includes SSC-H;

preferably, the index further comprises nuclear roundness and phosphorylation ERK1/2;

preferably, the index further comprises a nuclear to cytoplasmic ratio;

7. The discrimination method of claim 6, wherein the calculation model is: y=q1n+q2n/c+q3s+q4e; wherein N is the value of the roundness of the cell nucleus, N/C is the value of the nuclear-cytoplasmic ratio, S is the value of SSC-H, E is the value of the relative expression quantity of phosphorylated ERK1/2, Y is the value of the performance or quality or state degree of the stem cells, and q1, q2, q3 and q4 are constant coefficients;

preferably, the calculation model is: y=1.34E-01n+5.68e-01N/c+1.17E-07s+6.36e-02E;

8. A device for discriminating a stem cell property or quality or status, the device comprising:

the data acquisition module is used for acquiring the value of the index;

the judging module is used for inputting the value of the index into the judging model and outputting a judging result; wherein, the liquid crystal display device comprises a liquid crystal display device,

the judging model is a calculation model constructed according to the functional relation between the value of the index and the stem cell performance or quality or state;

the index includes SSC-H;

preferably, the index further comprises a nuclear to cytoplasmic ratio;

preferably, the calculation model is: y=q1n+q2n/c+q3s+q4e; wherein N is a nuclear roundness value, N/C is a nuclear cytoplasm ratio, S is an SSC-H value, E is a value of relative expression quantity of phosphorylated ERK1/2, Y is a stem cell performance or quality or state degree value, and q1, q2, q3 and q4 are constant coefficients;

9. A stem cell performance or quality or status detection system, comprising:

the index includes SSC-H;

preferably, the index further comprises a nuclear to cytoplasmic ratio;

10. The detection system of claim 9, wherein the detection means comprises one or more of qPCR kit, immunoblotting detection kit, immunochromatography detection kit, flow cytometry analysis kit, immunohistochemical detection kit, ELISA kit, electrochemiluminescence detection kit, qPCR instrument, immunoblotting detection device, flow cytometer, immunohistochemical detection device, ELISA detection device, electrochemiluminescence detection device;