Science AdvancesCancer BiologyPeer-reviewed journal article
A novel, high-density CRISPR activation platform for mapping cancer dependencies and resistance pathways ex vivo and in vivo
Sarah T. DiepstrateniD, Yexuan Deng, Margaret A. PottsiD, Amy J. HeidersbachiD, Christina KoenigiD, Kristel M. DorighiiD, Lin TaiiD, Andrew J. KuehiD, Lauren Whelan, Catherine W.T. ChangiD, Felix BrowniD, Gemma L. KellyiD, Jean‐Philippe FortiniD, Benjamin HaleyiD, John E. La MarcaiD, Marco J. HeroldiD
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- top 38%
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.
- organism: mouse
- assay: CRISPR screen
- research focus: drug response / drug discovery
- research focus: cancer & tumor microenvironment
- approach: deep learning
- approach: experimental assay
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