Accumulating Transcriptome Drift Precedes Cell Aging in Human Umbilical Cord-Derived Mesenchymal Stromal Cells Serially Cultured to Replicative Senescence
Bibliographic record
Abstract
In preclinical studies, mesenchymal stromal cells (MSCs) exhibit robust potential for numerous applications. To capitalize on these benefits, cell manufacturing and delivery protocols have been scaled up to facilitate clinical trials without adequately addressing the impact of these processes on cell utility nor inevitable regulatory requirements for consistency. Growing evidence indicates that culture-aged MSCs, expanded to the limits of replicative exhaustion to generate human doses, are not equivalent to early passage cells, and their use may underpin reportedly underwhelming or inconsistent clinical outcomes. Here, we sought to define the maximum expansion boundaries for human umbilical cord-derived MSCs, cultured in chemically defined xeno- and serum-free media, that yield consistent cell batches comparable to early passage cells. Two male and two female donor populations, recovered from cryostorage at mean population doubling level (mPDL) 10, were serially cultivated until replicative exhaustion (senescence). At each passage, growth kinetics, cell morphology, and transcriptome profiles were analyzed. All MSC populations displayed comparable growth trajectories through passage 9 (P9; mPDL 45) and variably approached senescence after P10 (mPDL 49). Transcription profiles of 14,500 human genes, generated by microarray, revealed a nonlinear evolution of culture-adapted MSCs. Significant expression changes occurred only after P5 (mPDL 27) and accumulated rapidly after P9 (mPDL 45), preceding other cell aging metrics. We report that cryobanked umbilical cord-derived MSCs can be reliably expanded to clinical human doses by P4 (mPDL 23), before significant transcriptome drift, and thus represent a mesenchymal cell source suited for clinical translation of cellular therapies. Stem Cells Translational Medicine 2019;8:945&958.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".