Abstract Background Despite many years of investigation into mesenchymal stem cells (MSCs) and their potential for treating inflammatory conditions such as COVID-19, clinical outcomes remain variable due to factors like donor variability, different tissue sources, and diversity within MSC populations. Variations in MSCs’ secretory and proliferation profiles, and their proteomic and transcriptional characteristics significantly influence their therapeutic potency, highlighting the need for enhanced characterization methods to better predict their efficacy. This study aimed to evaluate the biological characteristics of MSCs from different tissue origins, selecting the most promising line for further validation in a K18-hACE2 mouse model of SARS-CoV-2 infection. Methods We studied nine MSC lines sourced from either bone marrow (hBMMSC), dental pulp (hDPMSC), or umbilical cord tissue (hUCMSC). The cells were assessed for their proliferative capacity, immunophenotype, trilineage differentiation, proteomic profile, and in vitro immunomodulatory potential by co-culture with activated lymphocytes. The most promising MSC line was selected for further experimental validation using the K18-hACE2 mouse model of SARS-CoV-2 infection. Results The analyzed cells met the minimum criteria for defining MSCs, including the expression of surface molecules and differentiation capacity, showing genetic stability and proliferative potential. Proteomic analysis revealed distinct protein profiles that correlate with the tissue origin of MSCs. The immunomodulatory response exhibited variability, lacking a discernible pattern associated with their origin. In co-culture assays with lymphocytes activated with anti-CD3/CD28 beads, all MSC lines demonstrated the ability to inhibit TNF-α, to induce TGF-β and Indoleamine 2,3-dioxygenase (IDO), with varying degrees of inhibition observed for IFN-γ and IL-6, or induction of IL-10 expression. A module of proteins was found to statistically correlate with the potency of IL-6 modulation, leading to the selection of one of the hUCMSCs as the most promising line. Administration of hUCMSC to SARS-CoV-2-infected K18 mice expressing hACE2 was effective in improving lung histology and modulating of a panel of cytokines. Conclusions Our study assessed MSCs derived from various tissues, uncovering significant variability in their characteristics and immunomodulatory capacities. Particularly, hUCMSCs demonstrated potential in mitigating lung pathology in a SARS-CoV-2 infection model, suggesting their promising therapeutic efficacy. Supplementary Information The online version contains supplementary material available at 10.1186/s13287-024-04086-4. Keywords: Mesenchymal stem cells, Heterogeneity, Immunomodulation, COVID-19, K18-hACE2 Background Despite the extensive investigation of mesenchymal stem cells (MSCs) over many years and their potential for treating inflammatory conditions, including COVID-19, variability in clinical outcomes persists [[60]1–[61]3]. This inconsistency can be partially attributed to a complex interplay of factors that influence MSC efficacy, including donor variability, differing tissue sources, and the diversity of MSC populations within each isolate [[62]4–[63]7]. Understanding and addressing these underlying variables is critical to harnessing the full therapeutic potential of MSCs and achieving more uniform results in clinical applications. Two key variability factors for MSC-based product manufacturing are their tissue origin and donor characteristics [[64]8]. The isolation of MSCs from different origins, such as bone marrow, adipose tissue, umbilical cord, and dental pulp, presents a diversity that poses both a challenge and an opportunity for clinical therapeutics [[65]9]. Identifying the most suitable MSC source for COVID-19 treatment is critical, given their heterogeneity and the complexities of the disease [[66]10–[67]13]. In line with this, detailed cell profiling, standardized preparation, and continuous improvements in cell characterization and manipulation, are crucial for enhancing the efficacy of MSC-based therapies in clinical trials. The variations in the secretory profile of MSCs [[68]12, [69]14, [70]15], as well as disparities in proliferation, differentiation potential, surface markers, proteomic and transcriptional profile of different isolates influence their therapeutic potency [[71]16–[72]18]. Despite the attempt of the ISCT to standardize the basic characteristics of MSCs, there is a need for extended characterization methods to predict immunomodulatory potency and better understand the biology and the main factors that influence these cells as therapeutic agents [[73]19]. These enhanced methods are critical for accurately predicting the immunomodulatory potency of MSCs and for a deeper understanding of the key factors that influence their therapeutic efficacy. This study aimed to rigorously evaluate the biological characteristics of MSCs derived from three distinct tissue sources-umbilical cord Wharton’s Jelly (hUCMSCs), dental pulp (hDPMSCs), and bone marrow (hBMMSCs), including their proliferative capabilities, proteomic profile, and immunomodulatory capacities, to identify specific MSC profiles that correlated with enhanced therapeutic efficacy. Utilizing the results from these detailed analyses, the most promising MSC line was selected for further experimental validation using the K18-hACE2 mouse model of SARS-CoV-2 infection, which assessed the therapeutic potential of MSCs in a controlled, relevant model of severe COVID-19. Materials and methods Ethical approval All procedures involving human MSCs were approved by the Research Ethics Committee of São Rafael Hospital (CAAE: 09803819.30000.0048). The participants provided signed consent forms authorizing the use of their samples for this study. Animal experiments were approved by the Ethics Committee on Animal Experimentation of the Universidade Federal de Minas Gerais (UFMG/CEUA 191/2021) and less than five mice were housed in each cage under standard temperature and humidity conditions, according to the guidelines of the Biosafety Level 3 Laboratory ICB/UFMG. These animals were raised and maintained in the Immunopharmacology Vivarium of the Biological Sciences Institute of UFMG (ICB/UFMG) and during the experiments, were manipulated exclusively in the Biosafety Level 3 Laboratory (BSL-3). This study was conducted and reported in accordance with the ARRIVE guidelines 2.0. Cell culture Nine MSC lines were obtained from the biorepository at the Center for Biotechnology and Cell Therapy, Hospital São Rafael (D’OR Institute for Research and Education) or were provided by the Center for Cellular Technology of PUCPR. The MSC lines were previously obtained from different tissues: dental pulp (MSC DP), Wharton jelly of the umbilical cord (MSC UC) and bone marrow (MSC BM), with three samples from each source (Table [74]S1). The dental pulp donors were aged 6 and 7, while bone marrow donors were 28, 38, and 48. MSCs were cultured in low glucose DMEM (ThermoFisher Scientific, Waltham, MA, USA) supplemented with 10% fetal bovine serum (SBF) (Sigma-Aldrich, St. Louis, MO, USA), 1% penicillin/streptomycin (ThermoFisher Scientific) and L-glutamine (200mM) (ThermoFisher Scientific). Cultures were maintained up to passage 3 (p3) and were plated for analysis at p4, at the density of 4,000 cells/cm^2 for all experiments. Immunophenotype by flow cytometry The cells were harvested via enzymatic digestion using trypsin-edta (ThermoFisher Scientific) and subsequently rinsed with PBS. The cell suspension was centrifuged at 340 x g for 5 min at room temperature. Next, the cell pellet was resuspended in 100 µL of PBS containing the cocktail of antibodies for MSC immunophenotyping (Stemflow hMSC Analysis Kit, BD Biosciences, Franklin Lakes, NJ, USA) were added according to the manufacturer’s instructions. This kit includes antibodies for MSC immunophenotyping, featuring positive markers such as anti-CD73, CD90, and CD105, and negative markers including anti-CD11b, CD34, and CD45, along with isotype controls. Following a 15-minute incubation at room temperature, the samples were washed with PBS, centrifuged for 5 min at 340 x g, and the supernatants were discarded. The cells were then resuspended in 300 µL of PBS for acquisition. Analysis was performed using a BD LSRFortessa SORP flow cytometer (BD Biosciences), with data analyzed via FlowJo v.10 software. Trilineage differentiation assay Cells were plated at a density of 4,000 cells/cm² in duplicate across 24-well plates. Upon reaching 80% confluence, the cells were differentiated using commercially available adipogenic or osteogenic (StemPro media, ThermoFisher Scientific). For chondrogenic differentiation, cells at over 90% confluence were treated with chondrogenic (StemPro medium, ThermoFisher Scientific). After 14 days, cells undergoing adipogenic and osteogenic differentiation were stained with Oil Red O and Alizarin Red S, respectively. After 21 days, the cells submitted to chondrogenic differentiation were stained with Alcian blue. Imaging was performed using a Nikon Eclipse Ti - S microscope DS-U3 camera (Nikon, Tokyo, Japan) with NIS-Elements AR 4.30.01 software, utilizing a 20X objective lens. G-band karyotype MSCs were seeded at a density of 4,000 cells/cm², and upon reaching 80% confluence, the cells were treated with 0.1 µg/ml colcemid. Subsequently, they were exposed to a 0.075 M KCl hypotonic solution and fixed with Carnoy’s fixative (methanol: acetic acid, 3:1). The slides were then prepared, aged at 60 °C for 16 h, and subjected to GTG banding (G-banding with trypsin and Giemsa). At least 20 metaphase cells were analyzed (as per ISCN, 2009 guidelines), and the images were documented using Lucia Karyo software (Lucia Cytogenetics, Prague, Czech Republic). Growth curve MSCs were seeded at a density of 4,000 cells/cm² across nine wells of a 96-well plate for analysis at time points D0 through D5. Cell proliferation was quantified every 24 h using a CellTiter-Glo assay (Promega, Madison, WI, USA) following the manufacturer’s instructions. D0 measurements were performed immediately after cell seeding. Proliferation rates were inferred from luminescence intensity, and the results were expressed as means ± SD for comparative analysis. Cellular senescence MSCs were seeded at a density of 4,000 cells/cm² in quadruplicates in 96-well plates, achieving approximately 70% confluence. The cells were fixed with 4% paraformaldehyde for 20 min and washed three times with PBS. Senescence-associated β-galactosidase (SA-βGal) expression was evaluated by staining with CellEvent™ Senescence Green Detection Kit (ThermoFisher Scientific), following the manufacturer’s instructions. Cell nuclei were stained with Hoechst (diluted 1:1000). As a positive control for senescence, MSCs treated with hydrogen peroxide (H2O2) were used, following the previously described methodology [[75]20]. Cell quantification was performed in quadruplicates, assessing 20 quadrants per well using an Operetta high-performance microscope (PerkinElmer, Waltham, MA, USA) equipped with a 20X objective and Alexa 488 filters for SA-βGal and Hoechst for nuclei. Images were analyzed using Harmony 3.8 software (PerkinElmer), calculating the percentage of cells labeled in green. The means ± SD of the percentages of labeled cells were compared. In vitro evaluation of immunomodulatory potential The immunomodulatory potential of MSCs was evaluated by co-culturing them with activated lymphocytes, followed by measurements of lymphocyte proliferation, cytokine activation, and kynurenine measurements in the supernatant and qPCR. Peripheral blood mononuclear cells (PBMCs) were isolated from the venous blood of a healthy donor via density gradient centrifugation using Ficoll. The isolated cells were resuspended in RPMI 1640 medium (ThermoFisher Scientific) supplemented with 10% fetal bovine serum (FBS), 1% penicillin/streptomycin, and 200 mM GlutaMAX (ThermoFisher Scientific). Cell viability was assessed, and the cells were seeded at densities of 2 × 10^4 cells per well in 96-well plates for lymphoproliferation assays and 2 × 10^5 cells per well in 24-well plates for supernatant collection and further analysis. The experiments were conducted in triplicate, activating PBMCs with anti-CD3/anti-CD28 beads (ThermoFisher Scientific) at a 1:5 bead-to-cell ratio and co-culturing with mitomycin C-treated MSCs at a 1:10 MSC-to-PBMC ratio. After five days of co-culture, cellular responses and supernatants were harvested for analysis. Following five days of culture, suspension cells in 96-well plates were homogenized, and 100 µL of the cell suspension was transferred to a luminescence-compatible white plate. Cell viability was quantitatively assessed using the CellTiter-Glo luminescent cell viability assay (Promega), and luminescence was recorded with the GloMax Explorer version 3.2.3 (Promega), expressed as Relative Luminescence Units (RLU). Cytokine levels (TNF-α, IL-6, IFNγ, IL-10, TGFβ) were determined using DUOSET ELISA kits (R&D Systems, Minneapolis, MN, USA), adhering strictly to the manufacturer’s instructions. Absorbance was measured at dual wavelengths of 450 nm and 570 nm to correct for optical imperfections using the GloMax Explorer. Results were calculated in picograms per milliliter (pg/ml). The inhibition percentage in the co-culture was computed using the formula: % inhibition = 100 - ((nA x 100) / nB), where nA represents cytokine levels in the MSC: PBMC co-culture and nB denotes levels from the CD3/CD28-activated control PBMCs. For kynurenine measurement, 75 µl of 30% trichloroacetic acid was added to 100 µl of supernatant from various culture conditions, vortexed, and centrifuged at 10,000 × g for 5 min. Subsequently, 75 µl of the clear supernatant was mixed with an equal volume of Ehrlich’s reagent (100 mg of p-dimethylaminobenzaldehyde in 5 ml of glacial acetic acid) in a 96-well plate. The optical density was measured at 492 nm using the GloMax Explorer version 3.2.3 (Promega). A standard curve ranging from 0 to 100 µM kynurenine facilitated the determination of unknown concentrations [[76]21]. RT-qPCR analysis of IDO and TSG6 Activated peripheral blood mononuclear cells (PBMCs) were co-cultured with mitomycin C-treated MSCs at a 1:10 ratio under standard culture conditions (37 °C, 5% CO2). After five days, total RNA was extracted using Trizol^® (ThermoFisher Scientific), and cDNA was synthesized from 1 µg of RNA using the SuperScript™ VILO™ Master Mix kit (ThermoFisher Scientific), according to the manufacturer’s’ protocol. RNA integrity and quantity were assessed with the NanoDrop™ 2000 (ThermoFisher Scientific). Gene expression analyses were conducted in triplicates using an ABI7500 FAST thermocycler (ThermoFisher Scientific). TaqMan Gene Expression Assays (ThermoFisher Scientific) were used to measure the levels of IDO-1 (Hs00984148_m1) and TSG6 (Hs00200180_m1), with GAPDH (Hs02786624_g1) serving as an endogenous control. Relative mRNA expression levels were calculated using the 2-ΔΔCT method (Schmittgen & Livak, 2008), and data were analyzed using GraphPad Prism v9 (GraphPad Software, La Jolla, CA, United States). Study of proteomics Sample preparation and processing The protein extract was obtained from the MSCs in passage 4. The cells were washed with a phosphate buffer (PBS) and subjected to lysis with a buffer containing 4% SDS, 0.1 M DTT, 0.1 M Tris-HCl pH 7,5. The supernatants were sent for proteomic analysis on the mass spectrometry platform ([77]https://www.icc.fiocruz.br/plataformas/). Sample preparation for LC-MS/MS analysis followed an adapted Filter Aided Sample Preparation protocol (FASP) [[78]22]. In brief, 100 µg of proteins were denatured and reduced by exchanging the lysis buffer with urea 8 M and DTT 5mM. Solution exchange was done with 30 K centrifugal filter units (0.5 mL Amicon^® filter, 30 K). Samples were alkylated with Iodocetamide 15 mM for 20 min at room temperature in the dark. After reduction and alkylation, the sample volume was adjusted to 40 µL with ABC (0.05 M NH4HCO3). Trypsin was added in a 1:100 m/m ratio (trypsin: protein), and then incubated in a humid chamber for 16 to 18 h in an oven at 37 °C. The following day, the peptides were eluted in a new tube by centrifugation for 10 min (20 ºC, 14,000 x g. The elution was repeated by adding 50 µL of 0.5 mol L-1 NaCl and centrifuging for another 10 min (20ºC, 14,000 x g). Peptide concentration was measured by Nanodrop at 280 nm. Samples were acidified with 20% TFA solution, leaving a final concentration of 0.5%. The sample was desalted on Stage Tip-C18. The samples were analyzed in nanoLC Ultra 1D (Eksigent) coupled to a LTQ Orbitrap XL. The separation of peptides was as follows: phase A: 0.1% formic acid, 5% DMSO in water; phase B: 0.1% formic acid, 5% DMSO in acetonitrile. Flow rate 250 nL/min Linear gradient from 5 to 40% of phase B in 120 min. Analytical column of 15 cm with 75 μm of internal diameter, containing C18 particles of 3 micrometers in diameter. The MS/MS (MS2) parameters were: Ion Trap type analyzer, Scan Event 2 repeated for the 10 highest intensity peaks, dynamic exclusion was enabled, and the exclusion duration was 90 s. Normalization was performed using Perseus software, according to the manufacturer’s instructions. Proteomics differential expression The interpretation of proteomics data from samples of mesenchymal cells from bone marrow, umbilical cord, and dental pulp was conducted using bioinformatics tools. The proteome identified through quantitative mass spectrometry (MS) was submitted to the MaxQuant program to detect and quantify the proteins found in the samples. The quantification method was based on LFQ (label-free quantification) intensity values ​​to compare the abundance of proteins present in the samples. The results obtained were used to verify differentially expressed proteins (DEP) with the DEP R package version 1.24.0 [[79]23]. This package integrates task flow with robust and reproducible proteomics data analysis. Proteins or peptides were filtered for those detected in all replicates of at least one group (bone marrow, umbilical cord, and dental pulp). The data were adjusted for baseline, and the variance was normalized using the variance-stabilizing transformation. Missing data were imputed by applying random draws from a left-shifted Gaussian distribution, manually defined using the parameters fun: “man,” shift: 1.8, and scale: 0.1. The test for differential expressions between the sample groups was performed based on linear models and the empirical Bayes method. We considered an |lfc| > 1.0 and P-value < 0.05. Gene ontology (GO) annotations and pathways enrichment analysis were conducted using the Functional Annotation Tool DAVID Bioinformatics Resources 6.8 ([80]https://david.ncifcrf.gov/). Weighted gene co-expression network analysis The co-expressed protein modules in the proteomic analyses were obtained by calculating the co-expression network implemented in the WGCNA package in R [[81]24]. Following the WGCNA protocol, the normalized protein expression profile was used to obtain the adjacency matrix through Pearson correlation and the graphically determined optimal soft threshold. Additionally, a topological overlap matrix (TOM) was constructed to estimate the connectivity between network nodes, and then the hierarchical clustering method was applied to identify the different network modules. The module eigengenes, the first principal components representing individual modules’ overall gene expression level, were calculated, and correlated with clinical traits. To identify the most important proteins (hubs), we selected elements with the highest gene (proteins) significance (GS) values associated with module membership (MM). GS quantifies the individual associations of proteins with clinical characteristics, while MM is calculated based on the correlation between module eigengenes and protein expression profiles. Animals and in vivo SARS-CoV-2 infection The experimental model used consisted in the SARS-CoV-2 infection of the K18-hACE2 mice. Transgenic mice expressing the human ACE-2 receptor (K18-hACE2 mice, Mus musculus) on a C57BL/6 background were obtained from Jackson Laboratories and housed at the Immunopharmacology Laboratory at the Institute of Biological Sciences (ICB-UFMG). These K18-hACE2 mice express the human angiotensin-2 converting enzyme (hACE2), the primary cellular receptor facilitating SARS-CoV-2 virus entry, rendering them susceptible to infection by the virus. Male and female K18-hACE2 mice, aged 12–14 weeks and weighing 20–25 g, were maintained under controlled conditions (24 °C ± 2 °C) on a 12-hour light/12-hour dark cycle with ad libitum access to food and water. The experiments involving SARS-CoV-2 infection of mice were conducted at the Animal Biosafety Level 3 (BSL-3) multiuser facility at ICB-UFMG. K18-hACE2 mice were randomly assigned into three groups: (i) K18 hACE2 group (uninfected control, n = 6); 2 different groups infected with SARS-CoV-2 (3 × 10^4 PFU/animal), (ii) K18hACE2 + SARS-COV-2 + saline administration group (SARS-CoV-2, n = 6) and (iii) K18hACE2 + SARS-COV-2 + hUCMSCs (MSC, n = 6). Sample size was based on literature review and compliance with the 3Rs principles. Only the staff that performed the experiments were aware of the allocation groups. The infection protocol followed that described by Zheng et al. 2021. Briefly, the mice were anesthetized by an intraperitoneal administration of ketamine (80 mg/kg) and xylazine (15 mg/kg) solution. SARS-CoV-2 virus (3 × 10^4 PFU/animal) was then administered intranasally in a volume of 30µL diluted in saline, with 15µL instilled into each nostril. Control animals (uninfected) underwent the same anesthesia procedure, followed by instillation of the diluent medium (saline) [[82]25]. The mice were monitored daily, and, at 4 dpi, MSCs were inoculated into the tail vein in a single dose containing 2.5 × 10^5 cells in 100 uL. For necropsy, mice were anesthetized, and, after sedation, they were euthanized by cervical dislocation at five days post-SARS-CoV-2 infection to collect the lung to screen for viral loads and histopathology. The analyses were performed in all surviving mice, considering one death in the SARS-CoV-2 (control group). Histopathology evaluation The left lung was fixed (4% buffered formalin) and paraffin-embedded for microtomy into 4 μm-thick sections stained with hematoxylin and eosin. They were completely scanned to obtain a panoramic view of the lung parenchyma (3DHISTECH, Budapest, Hungary). Photomicrographs at × 25, × 100, and × 400 magnification were obtained from 10 non-overlapping fields of view per section in the digitalized images. Diffuse alveolar damage (DAD) was quantified using a weighted scoring system by two investigators (F.F.C., P.R.M.R) blinded to group allocation. Six features: hemorrhage, alveolar collapse, inflammation, and edema were graded on scales of 0 to 4 in terms of severity (0, no effect; 4, maximum severity) and extent (0, not visualized; 4, (complete involvement). Final scores, ranging from 0 to 16, were then calculated as the product of the severity and extent of each feature. The cumulative DAD score thus ranged from 0 to 64 [[83]26, [84]27]. Cytokine measurements by Luminex Mouse cytokines (IL-1a, IL-1b, TNF-a, IL-9, IFNg, IL-2, IL-3, IL-5, IL-4, IL-6, IL-13, MCP-1, G-CSF, RANTES, IL-10, KC, GM-CSF, Eotaxin, IL-12p40, IL-12p70, MIP-1a) were analyzed using Bio-Plex Pro Mouse Cytokine 21-plex Assay (Bio-Rad, California, USA) according to the manufacturer’s protocol. Each sample was assayed in duplicate and analyzed in Luminex 200 Instrument System (Milliplex 200, Millirep). RNA isolation and RT-qPCR analysis To extract total RNA from lung tissue fragments (approximately 50 mg) obtained from mice, 1 ml of TRIzol™ reagent (Thermo Fisher Scientific, Waltham, MA, USA) per sample was used following the manufacturer’s recommendations. Sample disruption and homogenization were facilitated using the TissueLyser III (Qiagen, Hilden, Mettmann, Germany). The Allplex™ SARS-CoV-2 Assay kit was utilized to confirm the detection of SARS-CoV-2 in the samples. The same total RNA was quantified by spectrometry Nanodrop 2000 (ThermoFisher Scientific), and cDNA was synthesized using the High-Capacity cDNA Reverse Transcription Kit (ThermoFisher Scientific). For the evaluation of mRNA expression levels, TaqMan probes were used: Il10 (Mm01288386_m1), Tnfa (Mm00443258_m1), Il6 (Mm00446190_m1), Ifng (Mm01168134_m1), Tgfb1 (Mm01178820_m1), Arg1 (Mm00475988_m1), according to the manufacturer’s recommendations (all ThermoFisher Scientific). And endogenous control Gapdh (Mm99999915_g1) and Hprt (Mm03024075_m1) were utilized for normalization purposes. All amplifications were carried out in an ABI7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) under standard thermal cycling conditions. The Threshold Cycle Method was used for RT-qPCR data analysis (Schmittgen and Livak, 2008). Graphs were generated using Graphpad Prism v9. Statistical analysis Statistical analysis was performed using GraphPad Prism 8.0.2 Software (GraphPad Software, USA). Means between groups are compared with the unpaired Student’s t-test or One-way analysis of variance (ANOVA). Tukey post-hoc tests were used for multiple comparisons. Data were presented as mean ± standard deviation (SD), and p < 0.05 were considered statistically significant. Results Characterization of MSCs According to the minimum criteria set by the International Society for Cellular Therapy (ISCT), MSCs must positively express the surface molecules CD73, CD90, and CD105, and negatively express CD34, CD45, CD11b, and HLA-DR, which are markers for hematopoietic lineages [[85]19]. All samples of hUCMSCs, hDPMSCs, and hBMMSCs expressed MSC-related surface molecules at levels higher than 95% and were negative for molecules related to hematopoietic origin cells (Fig. [86]1A). MSCs were also capable of undergoing adipogenic, chondrogenic and osteogenic differentiation (Fig. [87]1B) and maintained genetic stability, displaying a normal karyotype (Fig. [88]1C), either 46, XX or 46, XY, with no numerical or structural abnormalities. All samples exhibited AS-β-galactosidase staining at levels below 2%, suggesting low levels of senescence at passage 4 (Fig. [89]1D). Comparing the growth curve of MSCs, we observed different proliferative rates with no correlation with their tissue origin (Fig. [90]1E). Fig. 1. [91]Fig. 1 [92]Open in a new tab MSC characterization (according to ISCT criteria). (A) MSC surface molecules were assessed by flow cytometry; (B) Representative images of MSC lines: control, grown in culture medium (bright field); exposed to adipogenic differentiation medium, Oil Red staining; chondrogenic differentiation medium, Alcian blue staining; and osteogenic differentiation medium, Alizarin red staining (20X magnification) lineages; (C) representative G-band karyotypes of 46, XY and 46, XX; (D) growth curve from MSC lines; (E) β-gal senescence-associated dosages from MSC lines To expand the evaluation of MSC heterogeneity, we performed a proteomics analysis. Protein expression profiles of the three MSC samples per group were obtained using the Label-free LC-MS/MS quantitative proteomics analysis. A total of 3685 proteins were identified from all the nine groups of samples. Consequently, 2576 shared proteins were identified across all sample groups (Fig. [93]2A). The differential expression analysis between hUCMSC, hDPMSC, and hBMMSC identified 187 proteins differentially expressed (DEPs) after all pairwise comparisons (hUCMSC vs. hDPMSC, hDPMSC vs. hBMMSC, hUCMSC vs. hBMMSC) (Figure [94]S1A-C). Detailed information on the DEPs is provided in Table [95]S2. Hierarchical cluster heatmap analysis with DEPs was performed for complete linkage clustering using the Euclidean distance measurement method (Fig. [96]2B). Between hUCMSC and hDPMSC, 129 DEPs were found, of which 111 were down-regulated, and 18 were up-regulated in the hUCMSC. Regarding the comparison among hUCMSC and hBMMSC, 47 DEPs were found, of which 36 were down-regulated and 11 were up-regulated in the hUCMSC. Comparing hBMMSC and hDPMSC, 74 DEPS were found, of which 27 were down-regulated, and 47 were up-regulated in the hDPMSC. Fig. 2. [97]Fig. 2 [98]Open in a new tab Comparison of the proteomic profile and identification of differentially expressed proteins among the MSCs. (A) Venn diagram depicting the overlap in protein content among samples of hUCMSc, hDPMSCs, and hBMMSCs. (B) Hierarchical cluster heatmap of DEPs (n = 187) found in pairwise group comparisons (hUCMSc vs. hBMMSCs, hUCMSc vs. hDPMSCs and hDPMSCs vs. hBMMSCs). (C) The most relevant biological processes related to DEPs identified in each comparison The functional category of DEPs was predicted using DAVID ([99]https://david.ncifcrf.gov/). The findings of the GO analysis revealed significant biological processes associated with DEPs, presented in Fig. [100]2C (details in Table [101]S3). DEPs upregulated in hDPMSC were enriched in mannose metabolic process and translation compared to hUCMSC and in aerobic respiration and mitochondrial respiratory chain complex I assembly compared to hBMMSC. In hBMMSC, assays showed enriched proteins that are involved in RNA export from the nucleus and fructose-6-phosphate metabolic process when compared to hUCMSC, and terms enriched in negative regulation of NF-KappaB transcription factor activity and positive regulation of cell-substrate adhesion compared to hDPMSC. DEPs related to hUCMSC were enriched in phosphorylation in both comparisons and cellular response to hypoxia compared to hBMMSC and negative regulation of apoptotic processes compared to hDPMSC. GO annotation terms, which involve analysis of cell components (CC) and molecular functions (MF), are illustrated in Figure [102]S1B. Next, we aimed to investigate the aspects of functional heterogeneity related to the immunomodulatory potency of MSCs. By performing an in vitro immunomodulatory assay consisting of activated lymphocytes/MSCs co-culture, we found that the most consistent response across various MSC lines was the inhibition of TNF-α. The remaining parameters evaluated, including IDO, kynurenines, TSG6, TGF-β, IFN-γ, IL-6, IL-10, and inhibition of lymphocyte proliferation, exhibited a significant variation across the different MSC lines. HBMMSC2 exhibited a diminished response when co-cultured with activated lymphocytes, displaying reduced potency in modulating TNF-α and other inflammatory cytokines. Conversely, a cluster consisting of hDPMSC3 and hDPMSC2 displayed robust induction of IDO and TSG6 expression and modulation of kynurenines and IFN-γ, albeit with a comparatively weaker inhibition of lymphocyte proliferation. hUCMSC3 exhibited the highest potency in suppressing lymphocyte proliferation, accompanied by enhanced induction of IL-10 and lower production of IL-6 (Fig. [103]3A, Supplementary Figure [104]S2). Fig. 3. [105]Fig. 3 [106]Open in a new tab Weighted correlation network analysis based on expression profiles identified in proteomic analysis. Heatmap with immunomodulatory potency measurements (IDO, kynurenines, TSG6, TGF-β, IFN-γ, IL-6, IL-10, and inhibition of lymphocyte proliferation), obtained from a functional assay - coculture of MSCs with activated PBMCs - normalized by z-score for hUCMSCs, hDPMSCs, and hBMMSCs. (A) WGCNA cluster dendrogram and module assignment (B). The branches indicate clusters of highly connected proteins. The colors on the horizontal bar represent the modules. Analysis of module-trait relationships (C). Each row corresponds to a ME, and each column corresponds to immunomodulation indicators. Each cell contains a corresponding correlation and P-value of modules with various immunomodulation indicators. Scatterplot of MM and GS from the red module and potency of inhibition of IL-6 (D), inhibition of IFNy (E), inhibition of TNF-α (F), inhibition of Lymphocyte proliferation (G), and induction of IL-10 (H). Protein hubs were highlighted in yellow. Gene Ontology for enriched proteins in the red module: Molecular process (I), Component Cellular (J), and biological process (K) We proceeded to examine the correlation between the proteomic profiles of each MSC line and their respective immunomodulatory potency. Utilizing the weighted co-expression network analysis (WGCNA) with nine MSC samples (hUCMSCs, hDPMSCs, and hBMMSCs), a soft threshold of 7 (R² = 0.5) was applied, followed by dynamic module identification ensuring a minimum protein count of 100 per module. Clustering of selected proteins was accomplished using a dissimilarity measure based on a topological overlap matrix (TOM) with the Dynamic Tree Cut algorithm, yielding six co-expression modules (Fig. [107]3B): blue (MEblue: 339 proteins), brown (MEbrown: 321 proteins), green (MEgreen: 189 proteins), red (MEred: 176 proteins), turquoise (MEturquoise: 367 proteins), and yellow (MEyellow: 233 proteins). Each module was then correlated with immunomodulatory potency measurements (inhibition of TNF-α, IL-6, IFN-γ, and lymphocyte proliferation, and induction of IL-10, IDO, kynurenines, TSG6, and TGF-β) (Fig. [108]3A). Notably, the co-expression red module demonstrated the most significant positive association with lower IL-6 production (R = 0.89, p = 0.001) (Fig. [109]3C). Additionally, a positive correlation was observed between MEred and the inhibition of other inflammatory cytokines, including TNF-α (R = 0.39, p = 0.3), IFN-γ (R = 0.54, P = 0.1), and lymphocyte proliferation (R = 0.42, p = 0.3), alongside the induction of the anti-inflammatory cytokine IL-10 (R = 0.38, p = 0.3). While not statistically significant, these findings suggest a potential association between the MEred of correlated proteins to MSCs’ modulation of inflammatory responses. Due to the strong positive correlation between the red module and immunomodulatory potency measurements, this module was chosen to investigate potential biomarkers in the immunomodulation process. After selecting the module of interest, gene significance (GS) and module membership (MM) values were screened for all proteins and lower lymphocyte proliferation and production of IL-6, TNF-α, and IFN-γ (Fig. [110]3D-H). The top proteins with 30% highest values of GS and MM were highlighted as potential hubs (biomarkers). The proteins CCT6A, CAV2, SERBP1, ETF1, TCIRG1, EIF5, MIA2, and HNRNPA1 showed the highest GS and MM values regarding the potential inhibition of IL-6, which showed a significant correlation with the red module. The list of other hubs found for the analysis of inhibition of other inflammatory cytokines is in Table [111]S4. The biological functions of the proteins can be found in Table [112]S5. To further study the functional enrichment analysis of proteins in the red module, Gene ontology (GO) term analyses were performed. In the category of GO-MF (Molecular Function), the proteins were mainly involved in ATP-dependent protein folding chaperone and protein binding (Fig. [113]3I) and significantly enriched the functional items of the extracellular exosome, organelle membrane, and cell junction in the category of GO-CC (Component cellular) (Fig. [114]3J). Biological processes analysis showed enrichment of GO terms associated with organonitrogen compound metabolic process, macromolecule localization, and regulation of telomere maintenance (Fig. [115]3L). In vivo MSC-based therapy in COVID-19 model After the characterization of the MSC lines, including the results of potency evaluation, we selected hUCMSC3 for preclinical testing in a mouse model of SARS-CoV-2 infection. The K18-hACE2 model of COVID-19 is a murine model based on transgenic expression of the human ACE2 (angiotensin-converting enzyme 2) under the control of the K18 promoter, allowing the investigation into viral infection mechanisms and development of therapies aimed at reducing disease severity [[116]28]. K18-hACE2 mice were infected with SARS-CoV-2 (3 × 10^4 PFU/animal) and randomized on day 4 to receive either vehicle or MSC administration (2.5 × 10^5 MSCs in 100 µl, i.v.), followed by euthanasia on day 5 (Supplemental Figure [117]S3A). The MSC line utilized was hUCMSC3, based on its performance on the in vitro immunomodulatory assay, with increased IL-10 production and inhibition of inflammatory parameters (Fig. [118]3A). At 5 days post-infection, only one mouse from the control group had died due to the infection. By performing SARS-CoV-2 RT-PCR in lung tissue, we confirmed that the mice showed comparable infection levels, based on Ct values, regardless of the group allocation (Supplemental Figure [119]S3B). Representative photomicrographs of lung parenchyma showed an increased DAD score, representing the severity of hemorrhage, alveolar collapse, and edema (Fig. [120]4, left panels) in the SARS-CoV-2 group compared to the CTRL non-infected group. MSCs attenuated alveolar collapse and edema, resulting in decreased DAD score (Fig. [121]4, right panels). Fig. 4. [122]Fig. 4 [123]Open in a new tab Administration of hUCMSCs mitigates pathological damage in the lungs of K18-hACE mice inoculated with SARS-CoV-2. Left panels: Representative photomicrographs of lung parenchyma stained with hematoxylin-eosin. (A) in the CTRL group, lung architecture presents normal alveolar duct (D) and airways (AW), C: capillary; (B) in the SARS-CoV-2 group, lung damage was observed, with areas of alveolar collapse (asterisk), airway edema (ED), areas of hemorrhage (H), interstitial edema (arrows); (C) administration of MSCs decreased alveolar duct collapse (AD), airway inflammation (AW) and interstitial edema (arrows). Right panels: Cumulative diffuse alveolar damage (DAD) score (H) representing injury from hemorrhage (D), collapse (E), inflammation (F), and edema (G). Boxes show the interquartile range (P25–P75) range, whiskers denote the range (minimum-maximum), and horizontal lines represent the median of 5–6 animals per group. *Significantly different from the CTRL group (P < 0.05) To explore the immune response in the K18-hACE2 mouse model following SARS-CoV-2 infection, we analyzed the expression of 23 cytokines in the lung tissue. One day after the treatment with hUCMSCs, at 5 days post-infection (dpi), we observed a modulation of nine pro-inflammatory cytokines and chemokines in the lung tissue (Fig. [124]4). These included tumor necrosis factor-alpha (TNF-α), interleukin-1 alpha (IL-1α), interleukin-1 beta (IL-1β), interferon-gamma (IFNγ), interleukin-5 (IL-5), interleukin-2 (IL-2), keratinocyte-derived chemoattractant (KC, also known as CXCL1), and interleukin-17a (IL-17a), as illustrated in Fig. [125]5 (panels A-H). In this study, treatment with human umbilical cord-derived MSCs (hUCMSCs) in K18-hACE2 mice demonstrated a dual effect: it lowered the levels of the aforementioned pro-inflammatory markers (Fig. [126]5, panels A-H) and elevated the production of the anti-inflammatory cytokine interleukin-10 (IL-10, Fig. [127]5, panel I), along with eotaxin (Fig. [128]5, panel J), granulocyte colony-stimulating factor (G-CSF, Fig. [129]5, panel K), and interleukin-12p40 (IL-12p40, Fig. [130]5, panel L). By complementary RT-qPCR analysis of gene expression in the lung tissue, we observed significant reductions in the expression of IFNγ and Arg1 genes, increased TGF-β, and non-statistically significant trends towards reduced TNF-α, IL-6, IFNA, and IFNB transcripts (Fig. [131]6). Fig. 5. [132]Fig. 5 [133]Open in a new tab Treatment with MSCs reduced the amplification of inflammation-associated cytokines and chemokines in lung tissue from SAR-CoV-2-infected mice. Levels of the proinflammatory cytokine (A) TNF-α, (B) 1L-1 α, C) IL-1-β, (D) IFN-g, (E) IL-5, (F) IL-2, (G) chemokines KC, and (H) IL-17a, (I) antiinflammatory cytokine IL-10, (J) Eotaxin, (K) Granulocyte colony-stimulating factor (G-CSF), and (L) interleukin-12-p40 were measured by multiplex immunoassay. Group comparisons were analyzed by unpaired Student’ t-test. Values are mean ± SD (*P < 0.05, **P < 0.01, and ***P < 0.001 SAR-CoV-2/saline versus MSC-treated group, n = 4–6 per group) Fig. 6. [134]Fig. 6 [135]Open in a new tab Assessment of gene expression related to lung inflammation following hUCMSC treatment in SARS-CoV-2-infected mice. (A-E) Comparison of mRNA expression of TNF-α, IL-6, IL-1β, TGFB, Arg-1, IFN-y, IFN-A and IFN-B in the lung from SARS-CoV-2 mice with or without hUCMSC administration (n = 5–6/group). Data presented as mean ± SD of individuals included in each group. *P < 0.05, **P < 0.01 and # P < 0.05, significance levels for comparison between SARS-CoV-2-infected and MSC-treated groups Discussion The heterogeneity of mesenchymal stem cells (MSCs) extends beyond their tissue origin, and factors such as donor age, gender, disease exposure, inflammatory processes, and epigenetic modifications may influence their therapeutic functions [[136]29]. In this study, we explored the immunomodulatory capacities of MSCs derived from different sources and observed high heterogeneity in their immunomodulatory activities when these cells were co-cultured with activated lymphocytes. Following an integrative analysis of proteomics and functional parameters, one MSC line was selected for preclinical validation in an in vivo model of COVID-19 that, to our knowledge, has never been studied in the context of cell therapy. In our study, we comparatively analyzed MSC lines at the same passage, acknowledging that cellular behavior shifts as cultures progress from polyclonal to oligoclonal states with passages [[137]30]. However, we recognize the importance of studying temporal dynamics in gene expression and cellular responses, particularly for complex conditions like COVID-19. Future analyses across multiple time points could provide deeper insights into the functional plasticity and therapeutic potential of MSCs in varying conditions. Proteomic analysis comparing MSCs from different sources revealed grouping according to origin, indicating potential variations in functional characteristics. We investigated the main biological process related to each protein groups. When comparing hDPMSCs to hUCMSCs, DEPs in hDPMSCs were predominantly involved in metabolism and mitochondrial protein translation. Conversely, DEPs in hUCMSCs were associated with phosphorylation, negative regulation of apoptosis, and positive regulation of keratinocyte migration. In the comparison between hUCMSCs and hUCBMSCs, DEPs in hUCMSCs were notably linked to cellular response to hypoxia, which is crucial for their homing ability to hypoxic tissues [[138]31]. On the other hand, DEPs in hUCBMSCs were related to RNA export from the nucleus, fructose 6-phosphate metabolism, pentose phosphate pathway activity, cytoplasmic translational initiation, and RNA biogenesis. Finally, when contrasting hDPMSCs with hUCBMSCs, DEPs in hDPMSCs showed enrichment in aerobic respiration and mitochondrial ATP production. These processes are essential for MSCs’ adaptation to changing energy demands during tissue repair and inflammation [[139]32, [140]33]. In contrast, DEPs in hUCBMSCs were associated with negative regulation of NF-κB transcription factor, positive regulation of cell-substrate adhesion, negative regulation of coagulation, and neuron projection-related factors, which are crucial for their function in tissue repair and maintenance of tissue integrity [[141]34]. By correlating proteomics and functional data, we identified a hub of proteins (CCT6A, CAV2, SERBP1, ETF1, TCIRG1, EIF5, MIA2, and HNRNPA1), which showed the highest gene significance and module membership values regarding the lower IL-6 production in co-culture with activated PBMCs. These proteins are predominantly involved in cell adhesion, proliferation, differentiation, and migration. CCT6A (chaperonin containing TCP1, subunit 6 A) is a subunit of the chaperonin-containing T-complex protein-1 complex, which is involved in folding various proteins in the cell, especially those important for cellular structure and function [[142]35]. SERBP1 regulates the translation of mRNA [[143]36]; Eukaryotic Translation Termination Factor 1 (ETF1), also known as eRF1 (eukaryotic release factor 1), is a protein involved in the termination phase of protein translation [[144]37]. T cell immune regulator 1 (TCIRG1) is a subunit of the vacuolar proton pump, that participates in regulation of extracellular environment, autophagy, and lysosomal function [[145]38]. EIF5, or Eukaryotic Translation Initiation Factor 5, is a protein involved in the initiation phase of protein translation [[146]39]; MIA2, short for MIA SH3 domain ER export factor 2, is a protein involved in cellular processes related to the endoplasmic reticulum (ER), which is a critical organelle involved in protein synthesis, folding, and transport within the cell. The SH3 domain is a protein domain that mediates protein-protein interactions and is found in many signaling proteins [[147]40]. Heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1) is a multifunctional RNA-binding protein involved in various aspects of RNA metabolism, including pre-mRNA processing, alternative splicing, mRNA stability, and translation regulation. It is essential for the proper functioning of cells and is known to play critical roles in gene expression regulation [[148]41]; RNA-binding proteins such as HNRNPA1 could potentially play roles in modulating the expression of genes involved in MSC proliferation, differentiation, and response to stimuli. The immunomodulatory behavior of MSCs appears to be complex and heterogeneous, influenced by various factors, including tissue source and donor characteristics [[149]29]. Our study demonstrated that MSCs from different sources inhibited TNF-α production, but with varying effects on other cytokines and mediators. TNF-α plays a central role in the inflammatory microenvironment, affecting the immunomodulation capacity of MSCs [[150]42]. Preconditioning with TNF-α has been shown to enhance MSC potency in certain models [[151]43]. Additionally, the combination of TNF-α with other cytokines can significantly alter immunomodulation, as well as the composition of MSC-derived extracellular vesicles [[152]44]. In our study, one of the UC-derived MSCs exhibited potent inhibition of lymphocyte proliferation, induction of IL-10, and modulation of IL-6, and proteomic profiling revealed a strong association between a group of proteins and lower levels of IL-6. IL-6 has a well-documented role as a key mediator of the hyperinflammatory response, or “cytokine storm,” observed in severe COVID-19 cases. Elevated IL-6 levels have been consistently associated with poor outcomes in SARS-CoV-2 infection, making it a critical target for therapeutic intervention [[153]45]. MSCs themselves basally produce IL-6, and in co-culture conditions, IL-6 levels were further elevated, making it challenging to distinguish the exact source of IL-6 in the supernatant. IL-6 has a dual role in inflammatory regulation, and prior studies have highlighted its importance in the immunomodulatory effects of MSCs [[154]46, [155]47]. Our analysis revealed a correlation between lower overall IL-6 levels and a reduction in inflammatory cytokines, coupled with an increase in IL-10, within a specific protein module. We believe this relationship is critical in the context of COVID-19 treatment, where IL-6—despite its dual role as both antiviral and a driver of tissue damage—has been identified as an essential therapeutic target. Previously, integrated transcriptomic and proteomic analyses have shown distinct signatures in MSCs from different tissue origins, with Wharton’s jelly-derived MSCs exhibiting a robust immunomodulatory potential [[156]48]. Additionally, hUCMSCs offer advantages such as non-invasiveness, low immunogenicity, rapid self-renewal, and high proliferative efficacy, making them suitable candidates for evaluation in models such as SARS-CoV-2. The experimental model used in our study, transgenic mice expressing the human ACE2 receptor under control of cytokeratin-18 promoter (K18-hACE2), develops a severe viral disease after SARS-CoV-2 inoculation. These results agree with previous data obtained by other groups using this experimental model [[157]49]. Lung damage in COVID-19 involves the leakage of fluid from capillaries and the accumulation of immune cells such as lymphocytes, neutrophils, and macrophages, with the participation of chemokines and cytokines [[158]50]. Previous studies, including our own [[159]51, [160]52], have shown that MSCs can significantly reduce systemic inflammation and aid in the restoration of immune homeostasis in SARS-CoV-2 infected subjects. In humans, the cytokine response to SARS-CoV-2 includes increased expression of several proinflammatory cytokines and chemokines, including TNF-α, IL-1β, IL-1Rα, IL-6, IL-17, IL-18, IFN-γ, IFN-γ, MCP-3, M-CSF, MIP-1α, G-CSF, IP-10, and MCP-1 [[161]53, [162]54]. Interestingly, we demonstrated in the K18 SARS-CoV-2 infection model that important mediator of lung pathology and disease progression, such as TNF-α [[163]55], IL-1β [[164]56], and IL-17 [[165]57] were reduced after MSC treatment. While there was no reduction in IL-6 at the protein level, a trend toward decreased transcript levels was observed, suggesting that the levels of this critical mediator might be reduced at later time points. The reduction of inflammatory mediators was also accompanied by the induction of IL-10 and TGF-b, which are known regulatory cytokines with important roles in inflammation resolution and repair and elicit M2 macrophage differentiation [[166]58]. Interestingly, Arg1 was found to be increased in the control group, but significantly reduced by MSC treatment. Arg1 upregulation is found in severe COVID-19 cases and is associated with elevated viral load in different viral diseases [[167]59]. Furthermore, Arg1 has also been implied as a mediator of capillary endothelial hyperpermeability [[168]60]. IL-12p40, which was shown to inversely correlate with various inflammatory mediators in COVID-19, was induced by MSC therapy herein [[169]61]. Being part of the heterodimer that forms IL-12 (IL-12p40 + IL-12p35), an excess of IL-12p40 can antagonize and suppress the IL-12 pathway. IL-12 is a key inducer of Th1, and is involved in pro-inflammatory responses, being IL-12p70 correlated with severe disease [[170]62]. In our study, a trend towards decreased IL-12p70 was found in MSC-treated mice (data not shown). Interestingly, we found that MSC therapy increased the levels of two molecules associated with recruitment of eosinophils (eotaxin) and neutrophils (G-CSF). Eotaxin has a complex role in COVID-19, and has been associated with mild disease, along with IL-12p40 [[171]63]. G-CSF, when associated with other mediators of neutrophil activation (RETN, LCN2, HGF, and IL-8), has been associated with increased mortality in COVID-19 patients [[172]64]. However, G-CSF in association with MSCs was shown to have improved immune regulation and recruitment of regulatory immune cell populations [[173]65]. Overall, while there are similarities among MSCs of the same origin regarding biological characteristics, there is considerable individual variability in immunomodulatory response. This underscores the importance of considering various factors influencing MSC behavior when selecting cells for therapeutic applications. However, we acknowledge that SARS-CoV-2 induces a range of other pro-inflammatory cytokines, including Type I interferons (IFNs), and that T cells, especially CD8 + T cells, play a major role in the antiviral immune response. While our in vitro assays demonstrated that MSCs can modulate lymphocyte proliferation and cytokine production, we did not specifically examine T cell populations in our in vivo model. Future studies could address this gap by employing techniques such as flow cytometry or immunohistochemistry to analyze T cell subsets (e.g., CD4 + and CD8 + T cells) and their functional status—such as activation or exhaustion markers—in lung tissue post-MSC treatment. This additional data would provide a more comprehensive view of MSC therapy’s effects on both cytokine dynamics and cellular immunity in the context of SARS-CoV-2 infection. Conclusion In summary, our study thoroughly assessed MSCs from diverse tissue origins, revealing variability in their biological characteristics and therapeutic potential. All MSC lines exhibited immunomodulatory capabilities, albeit with varying response patterns. By studying the SARS-CoV-2 infection model, we demonstrated the potential of hUCMSCs in mitigating lung pathology and modulating cytokine responses, reinforcing their role in treating inflammatory conditions like COVID-19. A key finding of our study was the distinct proteomic profiles of MSC lines associated with their tissue source. This proteomic variation underpins the observed heterogeneity in their biological functions and therapeutic responses. These results reinforce the importance of standardized characterization methods and pave the way for further research aimed at optimizing MSC-based therapies for clinical use. Electronic supplementary material Below is the link to the electronic supplementary material. [174]Supplementary Material 1^ (25.8KB, xlsx) [175]Supplementary Material 2^ (40.6KB, xlsx) [176]Supplementary Material 3^ (9.7KB, xlsx) [177]Supplementary Material 4^ (311KB, docx) Acknowledgements