Temporal control of sgRNA library activation unlocks large-scale <i>in vivo</i> CRISPR screens
Bibliographic record
Abstract
ABSTRACT Functional genomics screens have illuminated genetic dependencies in cancer, but conventional in vitro approaches fail to capture vulnerabilities shaped by the tumor microenvironment. Here, we implement CRISPR-StAR (Stochastic Activation by Recombination), a next-generation inducible CRISPR screening platform for large-scale in vivo applications. The system uses a dual lox-based recombination system to enable guide-level normalization and clonal knockout phenotyping. To analyze the rich (barcode-embedded) sequencing output, we developed UMIBB, a superior Bayesian statistical framework for quantifying gene-level dropout and enrichment compared to conventional software packages. Screening a 30,000-sgRNA library in A549 xenografts, followed by clone representation and dropout correlation analyses, showed high fidelity and reproducibility with dropout phenotypes resolvable using as few as 30 tumors for this size library. Validation across multiple tumor models demonstrated that a single tumor can provide reliable, functional annotation for ∼1,000 genes leveraging intra-tumor library controls for normalization. Comparing in vivo and in vitro screens revealed that a substantial subset of tumor suppressor genes exerts strong phenotypic effects only observable in vivo . For example, single-gene knockout and transcriptomic profiling confirmed that KMT2C and KMT2D have contrasting impacts on tumor growth - an insight that would have been overlooked in standard cell culture. Looking ahead, CRISPR-StAR screening, combined with our user-friendly analysis pipeline available on GitHub (R-package), offer an integrated framework for creating in vivo dependency maps that can complement existing vitro datasets like DepMap and Achilles. Critically, our approach reduces animal use by up to 7-fold compared to conventional in vivo dropout screens. This represents a significant ethical and methodological advancement - achieving genome-scale resolution with far fewer animals and greater reproducibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".