Nature CommunicationsGenomicsPeer-reviewed journal article
Multiplex, single-cell CRISPRa screening for cell type specific regulatory elements
Florence M. ChardoniD, Troy A. McDiarmidiD, Nicholas F. PageiD, Riza M. DazaiD, Beth MartiniD, Silvia DomckeiD, Samuel G. RegaladoiD, Jean‐Benoît LalanneiD, Diego CalderoniD, Xiaoyi LiiD, Lea M. StaritaiD, Stephan SandersiD, Nadav AhituviD, Jay ShendureiD
Abstract
CRISPR-based gene activation (CRISPRa) is a promising therapeutic approach for gene therapy, upregulating gene expression by targeting promoters or enhancers in a tissue/cell-type specific manner. Here, we describe an experimental framework that combines highly multiplexed perturbations with single-cell RNA sequencing (sc-RNA-seq) to identify cell-type-specific, CRISPRa-responsive cis-regulatory elements and the gene(s) they regulate. Random combinations of many gRNAs are introduced to each of many cells, which are then profiled and partitioned into test and control groups to test for effect(s) of CRISPRa perturbations of both enhancers and promoters on the expression of neighboring genes. Applying this method to a library of 493 gRNAs targeting candidate cis-regulatory elements in both K562 cells and iPSC-derived excitatory neurons, we identify gRNAs capable of specifically upregulating intended target genes and no other neighboring genes within 1 Mb, including gRNAs yielding upregulation of six autism spectrum disorder (ASD) and neurodevelopmental disorder (NDD) risk genes in neurons. A consistent pattern is that the responsiveness of individual enhancers to CRISPRa is restricted by cell type, implying a dependency on either chromatin landscape and/or additional trans-acting factors for successful gene activation. The approach outlined here may facilitate large-scale screens for gRNAs that activate therapeutically relevant genes in a cell type-specific manner.
- organism: human
- assay: scRNA-seq
- research focus: drug response / drug discovery
- approach: experimental assay
- Citations
- 48
- FWCI
- 5.45
- Field percentile
- top 2%
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