Single-Cell RNA Sequencing to Guide Autologous Preterm Cord Mesenchymal Stromal Cell Therapy
Bibliographic record
Abstract
Abstract Rationale The chronic lung disease bronchopulmonary dysplasia (BPD) remains the most common complication of extreme prematurity (<28 wk of gestation). Umbilical cord–derived mesenchymal stromal cells (UC-MSCs) represent an opportunity for autologous cell therapy, as UC-MSCs have been shown to improve lung function and structure in experimental BPD. However, characterization and repair capacity of UC-MSCs derived from donors with pregnancy-related complications associated with prematurity remain unexplored. Objectives To characterize UC-MSCs’ transcriptome and determine if pregnancy-related complications (preeclampsia and chorioamnionitis) alter their therapeutic potential. Methods Single-cell RNA sequencing was used to compare the transcriptome of UC-MSCs derived from 5 term donors, 16 preterm donors, and human neonatal dermal fibroblasts (control cells of mesenchymal origin) and correlated with their therapeutic potential in experimental BPD. Using publicly available neonatal lung single-nucleus RNA sequencing data, we also determined putative communication networks between UC-MSCs and resident lung cell populations. Measurements and Main Results Most UC-MSCs displayed a similar transcriptome despite their pregnancy-related conditions and mitigated hyperoxia-induced lung injury in newborn rats. Conversely, human neonatal dermal fibroblasts and one term and two preterm with preeclampsia UC-MSC donors exhibited a distinct transcriptome enriched in genes related to fibroblast function and senescence and were devoid of therapeutic benefit in hyperoxia-induced BPD. Conversely, therapeutic UC-MSCs displayed a unique transcriptome active in cell proliferation and distinct cell–cell interactions with neonatal lung cell populations, including NEGR (neuronal growth regulator 1) and NRNX (neurexin) pathways. Conclusions Term and preterm UC-MSCs are lung protective in experimental BPD. Single-cell RNA sequencing allows us to identify donors with a distinct UC-MSC transcriptome characteristic of reduced therapeutic potential.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".