To better understand host-virus genetic dependencies and find potential therapeutic targets for COVID-19, we performed a genome-scale CRISPR loss-of-function screen to identify host factors required for SARS-CoV-2 viral infection of human alveolar epithelial cells. Top-ranked genes cluster into distinct pathways, including the vacuolar ATPase proton pump, Retromer, and Commander complexes. We validate these gene targets using several orthogonal methods such as CRISPR knock-out, RNA interference knock-down, and small-molecule inhibitors. Using single-cell RNA-sequencing, we identify shared transcriptional changes in cholesterol biosynthesis upon loss of top-ranked genes. In addition, given the key role of the ACE2 receptor in the early stages of viral entry, we show that loss of
RAB7A
reduces viral entry by sequestering the ACE2 receptor inside cells. Overall, this work provides a genome-scale, quantitative resource of the impact of the loss of each host gene on fitness/response to viral infection.
Type VI CRISPR enzymes are RNA-targeting proteins with nuclease activity that enable specific and robust target gene knock-down without altering the genome. To define rules for the design of Cas13d guide RNAs, we conducted massively-parallel screens targeting mRNAs of a green fluorescent protein (GFP) transgene and CD46, CD55 and CD71 cell surface proteins in human cells. In total, we measured the activity of 24,460 guide RNAs with and without mismatches relative to the target sequences. Knock-down efficacy is driven by guide RNA-specific features and target site context. Single mismatches generally reduce knock-down to a modest degree, but spacer nucleotides 15 – 21 are largely intolerant to target site mismatches. We developed a computational model to identify optimal guide RNAs and confirm its generalizability testing 3,979 guides targeting mRNAs of 48 endogenous genes. We show that Cas13 can be used in forward transcriptomic pooled screens and, using our model, predict optimized Cas13 guide RNAs for all protein-coding transcripts in the human genome.
Multi-modal single-cell assays provide high-resolution snapshots of complex cell populations but are mostly limited to transcriptome plus an additional modality. Here, we describe Expanded CRISPR-compatible Cellular Indexing of Transcriptomes and Epitopes by sequencing (ECCITE-seq) for the high-throughput characterization of at least five modalities of information from each single cell. We demonstrate application of ECCITE-seq to multimodal CRISPR screens with robust direct sgRNA capture and to clonotype-aware multimodal phenotyping of cancer samples.
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