Single-cell Transcriptomic Analysis Reveals the Cellular Heterogeneity of Mesenchymal Stem Cells
Bibliographic record
Abstract
Abstract Ex vivo -expanded mesenchymal stem cells (MSCs) have been demonstrated to be a heterogeneous mixture of cells exhibiting varying proliferative, multipotential, and immunomodulatory capacities. However, the exact characteristics of MSCs remain largely unknown. By single-cell RNA sequencing of 61,296 MSCs derived from bone marrow and Wharton’s jelly, we revealed five distinct subpopulations. The developmental trajectory of these five MSC subpopulations were mapped, revealing a differentiation path from stem-like active proliferative cells (APCs) to multipotent progenitor cells, followed by the branching into two paths – adipogenesis or osteochondrogenesis – and subsequent differentiation into unipotent prechondrocytes. The stem-like APCs, expressing the perivascular mesodermal progenitor markers CSPG4/MCAM/NES , uniquely exhibited strong proliferation and stemness signatures. Remarkably, the prechondrocyte subpopulation specifically expressed immunomodulatory genes and was able to suppress activated CD3 + T cell proliferation in vitro , supporting the role of this population in immunoregulation. In summary, our analysis mapped the heterogeneous subpopulations of MSCs and identified two subpopulations with potential functions in self-renewal and immunoregulation. Our findings advance the definition of MSCs by identifying the specific functions of its heterogeneous cellular composition, allowing for more specific and effective MSC application through the purification of its functional subpopulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".