Proteomic analysis of extracellular matrices used in stem cell culture
Bibliographic record
Abstract
Numerous matrices for the growth of human embryonic stem cells (hESC) in vitro have been described. However, their exact composition is typically unknown. Information on the components of these matrices will aid in the development of a fully defined growth surface for hESCs. These matrices typically consist of mixture of proteins present in a wide range of abundance making their characterization challenging. In this study, we performed the proteomic analysis of five previously uncharacterized matrices: CellStart, Human Basement Membrane Extract (Human BME), StemXVivo, Bridge Human Extracellular Matrix (BridgeECM), and mouse embryonic fibroblast conditioned matrix (MEF-CMTX). Based on a proteomics protocol optimized using lysates from HeLa cells, we undertook the analysis of the five complex extracellular matrix (ECM) samples using a combination of strong anion and cation exchange chromatography and SDS-PAGE. For each of these matrices, we identify numerous proteins, indicating their complex nature. We also compared these results with a similar proteomics analysis of the growth matrix, Matrigel™. From these analyses, we observed that fibronectin is a primary component of nearly all hESC supportive matrices. This observation led to the investigation of the suitability of fibronectin as a defined ECM for the growth of hESCs. We found that fibronectin promotes the maintenance of pluripotent H9 and CA1 hESCs in an undifferentiated state using mTeSR1 medium. This finding validates the utility of characterizing matrices used for hESC growth in revealing ECM components required for culturing hESCs in a universally applicable defined system.
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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) | 0.001 | 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".