Direct-capture Perturb-seq – sequencing expressed sgRNAs alongside single-cell transcriptomes

Single-cell CRISPR screens enable the exploration of mammalian gene function and genetic regulatory networks. However, use of this technology has been limited by reliance on indirect indexing of single-guide RNAs (sgRNAs). A team led by researchers at UCSF and Princeton University present direct-capture Perturb-seq, a versatile screening approach in which expressed sgRNAs are sequenced alongside single-cell transcriptomes. Direct-capture Perturb-seq enables detection of multiple distinct sgRNA sequences from individual cells and thus allows pooled single-cell CRISPR screens to be easily paired with combinatorial perturbation libraries that contain dual-guide expression vectors. Th researchers demonstrate the utility of this approach for high-throughput investigations of genetic interactions and, leveraging this ability, dissect epistatic interactions between cholesterol biogenesis and DNA repair. Using direct capture Perturb-seq, they also show that targeting individual genes with multiple sgRNAs per cell improves efficacy of CRISPR interference and activation, facilitating the use of compact, highly active CRISPR libraries for single-cell screens. Last, the researchers show that hybridization-based target enrichment permits sensitive, specific sequencing of informative transcripts from single-cell RNA-seq experiments.

Design and validation of direct-capture Perturb-seq for 3′ and 5′ single-cell RNA-sequencing

Fig. 1

a, Schematic of sgRNA capture during 5′ scRNA-seq. An sgRNA containing a standard constant region (top) anneals to a guide-specific RT oligo. Indexing of reverse transcribed complementary DNA (bottom) occurs after template switch. This strategy is compatible with unmodified sgRNAs (shown) or with sgRNAs with an integrated capture sequence. b, Schematic of sgRNA capture via an integrated capture sequence by 3′ scRNA-seq. A capture sequence within the constant region of the sgRNA (top) anneals to a barcoded, target-specific RT primer. Indexed cDNA (bottom) is produced by RT. c, Index (GBC or guide) capture rates per cell across experiments conducted with GBC Perturb-seq and direct-capture Perturb-seq. Data represent median index UMI counts per cell for cells bearing each of n = 32 sgRNAs across platforms. Gray lines indicate median values. ‘sgRNA-CR1’ indicates standard sgRNAs without a capture sequence. d, Index (GBC or guide) assignment rates across experiments conducted with GBC Perturb-seq and direct-capture Perturb-seq. The total number of cells per experiment as well as the fractions of cells assigned no guide, a single guide or more than one guide are indicated. ‘sgRNA-CR1’ indicates standard sgRNAs without a capture sequence. e, Clustering of perturbations from UPR Perturb-seq experiments conducted with GBC Perturb-seq and direct-capture Perturb-seq. Heatmaps represent Spearman’s rank correlations between pseudo-bulk expression profiles for each of n = 32 perturbations. For visual comparison, the rows and columns of all three heatmaps are ordered identically based on the hierarchical clustering of GBC Perturb-seq data. Functional annotations are indicated. f, Hierarchical clustering of UPR-regulated genes based on coexpression in each of the indicated Perturb-seq experiments. Colors indicate membership in different UPR-regulated groups as determined by Adamson et al.4g, Single-cell projections are based on t-sne visualization of ten independent components (n = 1,795 cells for 3′ GBC Perturb-seq, n = 1,595 cells for 3′ sgRNA-CR1cs1 Perturb-seq and n = 1,424 cells for 5′ sgRNA-CR1 Perturb-seq). Colors indicate functional similarities among targeted genes.

Replogle JM, Norman TM, Xu A, Hussmann JA, Chen J, Cogan JZ, Meer EJ, Terry JM, Riordan DP, Srinivas N, Fiddes IT, Arthur JG, Alvarado LJ, Pfeiffer KA, Mikkelsen TS, Weissman JS, Adamson B. (2020) Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing. Nat Biotechnol [Epub ahead of print]. [article]

One comment

  1. A study conducted at the University of Tuebingen had utilized RNA-seq based transcriptomic analyses for the identification of potential modifiers of lung dysfunction in Cystic Fibrosis patients with the ultimate aim of severity characterization.

    find more actionable insight on RNA seq: https://bisresearch.com/requestsample?id=829&type=download

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