Transcriptome profiles acquired during cell expansion and licensing validate mesenchymal stromal cell lineage genes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Mesenchymal stromal cells (MSCs) are rapidly advancing as commercial therapeutics. However, there are still no adequate tools to validate the identity of MSCs and support standardization of MSC-based products. Currently accepted metrics include cell surface marker profiling and tri-lineage differentiation assays, neither of which is definitive. Transcript profiling represents a cost- and time-effective approach amenable to MSC manufacturing processes. Two independent labs recently reported non-overlapping MSC-specific transcriptomic signatures of 489 and 16 genes. METHODS: Here, we interrogated our repository of transcriptome data to determine whether routine culture manipulations including cell expansion and immune activation affect expression of the reported MSC lineage genes. These data sets comprise 4 donor populations of human umbilical cord (UC) MSCs serially cultured from cryopreservation thaw through pre-senescence, and 3 donor populations each of naïve UC and bone marrow (BM) MSCs and licensed by 3 different cytokines. RESULTS: Overall, 437 of 456 proposed signature genes assessed in these data sets were reliably expressed, representing an enduring lineage profile in 96% agreement with the previous studies. Serial passaging resulted in the downregulation of 3 signature genes, and one was silenced. Cytokine stimulation downregulated expression of 16 signature genes, and 3 were uniformly silenced in one or the other MSC type. Fifteen additional genes were unreliably detected, independent of culture manipulation. CONCLUSION: These results validate and refine the proposed transcriptomic tools for reliable identification of MSCs after isolation through cell expansion and after inflammatory activation. We propose a 24-gene signature to support standardized and accessible MSC characterization.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it