Quantitative Proteomics Evaluation of Human Multipotent Stromal Cell for β Cell Regeneration
Bibliographic record
Abstract
Human multipotent stromal cells (hMSCs) are one of the most versatile cell types used in regenerative medicine due to their ability to respond to injury. In the context of diabetes, it has been previously shown that the regenerative capacity of hMSCs is donor specific after transplantation into streptozotocin (STZ)-treated immunodeficient mice. However, in vivo transplantation models to determine regenerative potency of hMSCs are lengthy, costly, and low throughput. Therefore, a high-throughput quantitative proteomics assay was developed to screen β cell regenerative potency of donor-derived hMSC lines. Using proteomics, we identified 16 proteins within hMSC conditioned media that effectively identify β cell regenerative hMSCs. This protein signature was validated using human islet culture assay, ELISA, and the potency was confirmed by recovery of hyperglycemia in STZ-treated mice. Herein, we demonstrated that quantitative proteomics can determine sample-specific protein signatures that can be used to classify previously uncharacterized hMSC lines for β cell regenerative clinical applications.
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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 | 0.001 |
| 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".