PMC

a) Experimental design of a single-cell CRISPR screen for T cell receptor (TCR) pathway induction. b) Fold change of gRNA abundance between cell assignments from CROP-seq and gRNA counts from plasmid library sequencing. Values were normalized to the total of assigned cells or reads, respectively. c) Inference of pathway signature from CROP-seq data. Single-cell transcriptomes were aggregated by gRNA target genes, and principal component analysis separated naive and anti-CD3/CD28-stimulated cells. Genes with absolute loading values for principal component 1 that exceeded the 99^th percentile were included in the TCR induction signature (n = 165). The signature was enriched for genes with a known role in TCR signaling (inset). d) Median relative expression (column z-score) across the 165 pathway signature genes (columns), aggregating cells that express gRNAs targeting the same gene (rows). e) Distribution of signature intensity across single cells (left) and number of cells (right) for each gRNA target gene. The median is indicated with a white dot. f) Gene signature concordance between CROP-seq and bulk RNA-seq in an arrayed validation screen. Known positive and negative regulators of the TCR pathway are highlighted. g) Concordance of the CD69 marker of TCR induction between CROP-seq and an arrayed validation screen with flow cytometry readout. h) Changes in TCR pathway induction detected by CROP-seq mapped onto a schematic of the T-cell receptor with key downstream regulators. i) CD69 marker levels in control cells and knockouts for important TCR activators or repressors. j) Robustness of CROP-seq signatures in a downsampling analysis at the gene and gRNA levels, evaluated against bulk RNA-seq data.

a) Pooled screens detect changes in gRNA abundance among bulk populations of cells, which limits them to simple readouts based on cell frequencies. b) Arrayed screens support complex readouts such as transcriptome profiling, but cells transduced with different gRNAs have to be physically separated. c) CROP-seq uses droplet-based single-cell RNA-seq to profile each cell’s transcriptome together with the expressed gRNA, and knockout signatures are derived by averaging across cells that express gRNAs for the same target gene. d) Data analysis identifies pathway signature genes and quantifies the effect of specific gRNAs on these signatures. e) The CROP-seq lentiviral construct includes a gRNA cassette within the 3’ long terminal repeat (LTR), which is duplicated during viral integration. It expresses an RNA polymerase III transcript for genome editing and a polyadenylated RNA polymerase II transcript detected by single-cell RNA-seq. f) Cloning the hU6-gRNA cassette into the 3’ LTR to generate CROPseq-Guide-Puro does not compromise lentiviral function for gRNAs. In contrast, 1,885 bp of filler DNA result in a 98-fold reduction of the viral titer. g) Genome editing efficiencies and indel signatures are highly similar between LentiGuide-Puro and CROPseq-Guide-Puro. h) CROP-seq can detect gRNAs from single-cell transcriptomes. i) The rate of successful gRNA assignments is associated with single-cell transcriptome quality, expressed as the number of detected genes per cell. Most cells were assigned to one gRNA, except for a small fraction of cell doublets. Error bars, 95% CI. j) Performance statistics across all CROP-seq experiments.