Multivariate proteomic analysis of murine embryonic stem cell self-renewal versus differentiation signaling
Bibliographic record
Abstract
A number of extracellular stimuli, including soluble cytokines and insoluble matrix factors, are known to influence murine embryonic stem cell self-renewal and differentiation behavioral responses via intracellular signaling pathways, but their net effects in combination are difficult to understand. To gain insight concerning key intracellular signals governing these behavioral responses, we employ a multivariate systems analysis of proteomic data generated from combinatorial stimulation of mouse embryonic stem cells by fibronectin, laminin, leukemia-inhibitory factor, and fibroblast growth factor 4. Phosphorylation states of 31 intracellular signaling network components were obtained across 16 different stimulus conditions at three time points by quantitative Western blotting, and partial-least-squares modeling was used to determine which components were most strongly correlated with cell proliferation and differentiation rate constants obtained from flow cytometry measurements of Oct-4 expression levels. This data-driven, multivariate (16 conditions x 31 components x 3 time points = approximately 1,500 values) proteomic approach identified a set of signaling network components most critically associated (positively or negatively) with differentiation (Stat3, Raf1, MEK, and ERK), proliferation of undifferentiated cells (MEK and ERK), and proliferation of differentiated cells (PKB alpha, Stat3, Src, and PKC epsilon). These predictions were found to be consistent with previous in vivo literature, along with direct in vitro test here by a peptide inhibitor of PKC epsilon. Our results demonstrate how a computational systems biology approach can elucidate key sets of intracellular signaling protein activities that combine to govern cell phenotypic responses to extracellular cues.
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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 | 0.000 | 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) | 0.000 | 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".