A novel, high-density CRISPR activation platform for mapping cancer dependencies and resistance pathways <i>ex vivo</i> and <i>in vivo</i>
Bibliographic record
Abstract
Abstract CRISPR activation (CRISPRa) enables precise, locus-specific upregulation of gene expression, offering potential for both ex vivo and in vivo applications. However, the lack of scalable, high-coverage tools has limited its use in comprehensive genetic screens, particularly in murine models. Here, we introduce Partita, a next-generation, whole-genome CRISPRa sgRNA platform designed for unparalleled efficiency and depth in gene activation studies. Partita employs a high-density targeting strategy, deploying 10 sgRNAs per transcription start site, structured into five gene family-specific sub-libraries to maximize transcriptional induction. To demonstrate its capabilities, we performed a series of large-scale screens: an in vitro enrichment/depletion screen in iBMDMs, whole-genome CRISPRa screens in a double-hit lymphoma model to uncover genes driving resistance to pro-apoptotic drugs (venetoclax, nutlin-3a, etoposide), and an in vivo whole-genome screen identifying accelerators of Myc-driven lymphomagenesis. Each experiment revealed both expected and novel regulators of cellular phenotypes, with a high validation rate in secondary assays. By enabling robust, high-throughput gain-of-function screening, Partita unlocks new avenues for functional genomics and expands the toolkit for discovering key drivers of biological processes across diverse research fields.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".