Application of CRISPR screens to investigate mammalian cell competition
Bibliographic record
Abstract
Cell competition is defined as the context-dependent elimination of cells that is mediated by intercellular communication, such as paracrine or contact-dependent cell signaling, and/or mechanical stresses. It is considered to be a quality control mechanism that facilitates the removal of suboptimal cells from both adult and embryonic tissues. Cell competition, however, can also be hijacked by transformed cells to acquire a 'super-competitor' status and outcompete the normal epithelium to establish a precancerous field. To date, many genetic drivers of cell competition have been identified predominately through studies in Drosophila. Especially during the last couple of years, ethylmethanesulfonate-based genetic screens have been instrumental to our understanding of the molecular regulators behind some of the most common competition mechanisms in Drosophila, namely competition due to impaired ribosomal function (or anabolism) and mechanical sensitivity. Despite recent findings in Drosophila and in mammalian models of cell competition, the drivers of mammalian cell competition remain largely elusive. Since the discovery of CRISPR/Cas9, its use in functional genomics has been indispensable to uncover novel cancer vulnerabilities. We envision that CRISPR/Cas9 screens will enable systematic, genome-scale probing of mammalian cell competition to discover novel mutations that not only trigger cell competition but also identify novel molecular components that are essential for the recognition and elimination of less fit cells. In this review, we summarize recent contributions that further our understanding of the molecular mechanisms of cell competition by genetic screening in Drosophila, and provide our perspective on how similar and novel screening strategies made possible by whole-genome CRISPR/Cas9 screening can advance our understanding of mammalian cell competition in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".