CRISPR-StAR enables high-resolution genetic screening in complex in vivo models
Bibliographic record
Abstract
Pooled genetic screening with CRISPR-Cas9 has enabled genome-wide, high-resolution mapping of genes to phenotypes, but assessing the effect of a given genetic perturbation requires evaluation of each single guide RNA (sgRNA) in hundreds of cells to counter stochastic genetic drift and obtain robust results. However, resolution is limited in complex, heterogeneous models, such as organoids or tumors transplanted into mice, because achieving sufficient representation requires impractical scaling. This is due to bottleneck effects and biological heterogeneity of cell populations. Here we introduce CRISPR-StAR, a screening method that uses internal controls generated by activating sgRNAs in only half the progeny of each cell subsequent to re-expansion of the cell clone. Our method overcomes both intrinsic and extrinsic heterogeneity as well as genetic drift in bottlenecks by generating clonal, single-cell-derived intrinsic controls. We use CRISPR-StAR to identify in-vivo-specific genetic dependencies in a genome-wide screen in mouse melanoma. Benchmarking against conventional screening demonstrates the improved data quality provided by this technology.
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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 | 0.001 | 0.001 |
| 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".