BMP signaling: at the gate between activated melanocyte stem cells and differentiation
Bibliographic record
Abstract
Through recurrent bouts synchronous with the hair cycle, quiescent melanocyte stem cells (McSCs) become activated to generate proliferative progeny that differentiate into pigment-producing melanocytes. The signaling factors orchestrating these events remain incompletely understood. Here, we use single-cell RNA sequencing with comparative gene expression analysis to elucidate the transcriptional dynamics of McSCs through quiescence, activation, and melanocyte maturation. Unearthing converging signs of increased WNT and BMP signaling along this progression, we endeavored to understand how these pathways are integrated. Employing conditional lineage-specific genetic ablation studies in mice, we found that loss of BMP signaling in the lineage leads to hair graying due to a block in melanocyte maturation. We show that interestingly, BMP signaling functions downstream from activated McSCs and maintains WNT effector, transcription factor LEF1. Employing pseudotime analysis, genetics, and chromatin landscaping, we show that following WNT-mediated activation of McSCs, BMP and WNT pathways collaborate to trigger the commitment of proliferative progeny by fueling LEF1- and MITF-dependent differentiation. Our findings shed light upon the signaling interplay and timing of cues that orchestrate melanocyte lineage progression in the hair follicle and underscore a key role for BMP signaling in driving complete differentiation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".