Computational modelling of stem cell–niche interactions facilitates discovery of strategies to enhance tissue regeneration and counteract ageing
Bibliographic record
Abstract
The stem cell niche is a specialized microenvironment for stem cells in an adult tissue. The niche provides cues for the maintenance and regulation of stem cell activities and thus presents a target for potential rejuvenating strategies. García‐Prat et al . found that in the heterogeneous population of quiescent stem cells of skeletal muscles, a fraction of cells responsible for regeneration and having genuine ‘stemness’ properties deteriorates only in extremely old age. An essential tool used in this analysis of stem cell–niche interactions is the computational tool, NicheHotSpotter, which proved to be instrumental for identifying niche and cell signalling factors that contribute to the maintenance of the pool of genuine quiescent stem cells. NicheHotSpotter predicts candidate factors by analysing signalling interactome and gene regulatory network data in combination with expression profiles. The effect of the niche environment on stem cells is modelled as a mean field of niche cues that induce sustained activation or inhibition of signalling pathways. In this way, NicheHotSpotter has been successful in delineating novel strategies to enhance stemness, which may rejuvenate skeletal muscle cells at the extreme old age.
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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".