Identification of a Candidate Proteomic Signature to Discriminate Multipotent and Non-Multipotent Stromal Cells
Bibliographic record
Abstract
Bone marrow stromal cell cultures contain multipotent cells that may have therapeutic utility for tissue restoration; however, the identity of the cell that maintains this function remains poorly characterized. We have utilized a unique model of murine bone marrow stroma in combination with liquid chromatography mass spectrometry to compare the nuclear, cytoplasmic and membrane associated proteomes of multipotent (MSC) (CD105+) and non-multipotent (CD105-) stromal cells. Among the 25 most reliably identified proteins, 10 were verified by both real-time PCR and Western Blot to be highly enriched, in CD105+ cells and were members of distinct biological pathways and functional networks. Five of these proteins were also identified as potentially expressed in human MSC derived from both standard and serum free human stromal cultures. The quantitative amount of each protein identified in human stromal cells was only minimally affected by media conditions but varied highly between bone marrow donors. This study provides further evidence of heterogeneity among cultured bone marrow stromal cells and identifies potential candidate proteins that may prove useful for identifying and quantifying both murine and human MSC in vitro.
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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 | 0.000 |
| 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".