Mass Spectrometry–based Proteomic Analysis of the Matrix Microenvironment in Pluripotent Stem Cell Culture
Bibliographic record
Abstract
The cellular microenvironment comprises soluble factors, support cells, and components of the extracellular matrix (ECM) that combine to regulate cellular behavior. Pluripotent stem cells utilize interactions between support cells and soluble factors in the microenvironment to assist in the maintenance of self-renewal and the process of differentiation. However, the ECM also plays a significant role in shaping the behavior of human pluripotent stem cells, including embryonic stem cells (hESCs) and induced pluripotent stem cells. Moreover, it has recently been observed that deposited factors in a hESC-conditioned matrix have the potential to contribute to the reprogramming of metastatic melanoma cells. Therefore, the ECM component of the pluripotent stem cell microenvironment necessitates further analysis. In this study we first compared the self-renewal and differentiation properties of hESCs grown on Matrigel™ pre-conditioned by hESCs to those on unconditioned Matrigel™. We determined that culture on conditioned Matrigel™ prevents differentiation when supportive growth factors are removed from the culture medium. To investigate and identify factors potentially responsible for this beneficial effect, we performed a defined SILAC MS-based proteomics screen of hESC-conditioned Matrigel™. From this proteomics screen, we identified over 80 extracellular proteins in matrix conditioned by hESCs and induced pluripotent stem cells. These included matrix-associated factors that participate in key stem cell pluripotency regulatory pathways, such as Nodal/Activin and canonical Wnt signaling. This work represents the first investigation of stem-cell-derived matrices from human pluripotent stem cells using a defined SILAC MS-based proteomics approach.
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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".