The Field of Cell Competition Comes of Age: Semantics and Technological Synergy
Bibliographic record
Abstract
Stem cells experience many selective pressures which shape their cellular populations, potentially pushing them to skew towards dominance of a few break-through clones. An evolutionarily conserved answer to curb these aberrant selective pressures is cell competition, the elimination of a subset of cells by their neighbours in a seemingly homogenous population. Cell competition in mammalian systems is a relatively recent discovery that has now been observed across many tissue systems, such as embryonic, haematopoietic, intestinal, and epithelial compartments. With this rapidly growing field, there is a need to revisit and standardize the terminology used, much of which has been co-opted from evolutionary biology. Further, the implications of cell competition across biological scales in organisms have been difficult to capture. In this review, we make three key points. One, we propose new nomenclature to standardize concepts across dispersed studies of different types of competition, each of which currently use the same terminology to describe different phenomena. Second, we highlight the challenges in capturing information flow across biological scales. Third, we challenge the field to incorporate next generation technologies into the cell competition toolkit to bridge these gaps. As the field of cell competition matures, synergy between cutting edge tools will help elucidate the molecular events which shape cellular growth and death dynamics, allowing a deeper examination of this evolutionarily conserved mechanism at the core of multicellularity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".