Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs.

Autor: Jost M; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA, USA.; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA., Santos DA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA, USA., Saunders RA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA, USA., Horlbeck MA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA, USA., Hawkins JS; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA., Scaria SM; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA, USA., Norman TM; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA, USA.; Computational & Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Hussmann JA; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA, USA.; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA., Liem CR; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA, USA., Gross CA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA.; Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA, USA., Weissman JS; Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA. jonathan.weissman@ucsf.edu.; Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA. jonathan.weissman@ucsf.edu.; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA, USA. jonathan.weissman@ucsf.edu.
Jazyk: angličtina
Zdroj: Nature biotechnology [Nat Biotechnol] 2020 Mar; Vol. 38 (3), pp. 355-364. Date of Electronic Publication: 2020 Jan 13.
DOI: 10.1038/s41587-019-0387-5
Abstrakt: A lack of tools to precisely control gene expression has limited our ability to evaluate relationships between expression levels and phenotypes. Here, we describe an approach to titrate expression of human genes using CRISPR interference and series of single-guide RNAs (sgRNAs) with systematically modulated activities. We used large-scale measurements across multiple cell models to characterize activities of sgRNAs containing mismatches to their target sites and derived rules governing mismatched sgRNA activity using deep learning. These rules enabled us to synthesize a compact sgRNA library to titrate expression of ~2,400 genes essential for robust cell growth and to construct an in silico sgRNA library spanning the human genome. Staging cells along a continuum of gene expression levels combined with single-cell RNA-seq readout revealed sharp transitions in cellular behaviors at gene-specific expression thresholds. Our work provides a general tool to control gene expression, with applications ranging from tuning biochemical pathways to identifying suppressors for diseases of dysregulated gene expression.
Databáze: MEDLINE
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