Single-Cell RNA Sequencing Reveals Repair Features of Human Umbilical Cord Mesenchymal Stromal Cells
Bibliographic record
Abstract
Abstract Rationale The chronic lung disease bronchopulmonary dysplasia (BPD) is the most severe complication of extreme prematurity. BPD results in impaired lung alveolar and vascular development and long-term respiratory morbidity, for which only supportive therapies exist. Umbilical cord–derived mesenchymal stromal cells (UC-MSCs) improve lung structure and function in experimental BPD. Results of clinical trials with MSCs for many disorders do not yet match the promising preclinical studies. A lack of specific criteria to define functionally distinct MSCs persists. Objectives To determine and correlate single-cell UC-MSC transcriptomic profiles with therapeutic potential. Methods UC-MSCs from five term donors and human neonatal dermal fibroblasts (HNDFs; control cells of mesenchymal origin) transcriptomes were investigated using single-cell RNA sequencing (scRNA-seq) analysis. The lung-protective effect of UC-MSCs with a distinct transcriptome and control HNDFs was tested in vivo in hyperoxia-induced neonatal lung injury in rats. Measurements and Main Results UC-MSCs showed limited transcriptomic heterogeneity but were different from HNDFs. Gene Ontology enrichment analysis revealed distinct (progenitor-like and fibroblast-like) UC-MSC subpopulations. Only treatment with progenitor-like UC-MSCs improved lung function and structure and attenuated pulmonary hypertension in hyperoxia-exposed rat pups. Moreover, scRNA-seq identified major histocompatibility complex class I as a molecular marker of nontherapeutic cells and associated with decreased lung retention. Conclusions UC-MSCs with a progenitor-like transcriptome, but not with a fibroblast-like transcriptome, provide lung protection in experimental BPD. High expression of major histocompatibility complex class I is associated with reduced therapeutic benefit. scRNA-seq may be useful to identify subsets of MSCs with superior repair capacity for clinical application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".