Expression profiling by high throughput sequencing
Summary
We combined CRISPR genome editing with single-cell RNA sequencing to assess complex phenotypes in pooled cellular screens. Our method for CRISPR droplet sequencing (CROP-seq) comprises four key components: a gRNA vector that makes individual gRNAs detectable in single-cell transcriptomes, a high-throughput assay for single-cell RNA-seq, a computational pipeline for assigning single-cell transcriptomes to gRNAs, and a bioinformatic method for analyzing and interpreting gRNA-induced transcriptional profiles. CROP-seq allowed us to link gRNA expression to the associated transcriptome responses in thousands of single cells using a straightforward and broadly applicable screening workflow. Additional information are available from the CROP-seq website http://crop-seq.computational-epigenetics.org
Overall design
Drop-seq species mixing experiment was performed with human HEK293T and mouse 3T3 cells in a 1:1 proportion as described by Macosko et al.
For CROP-seq, Jurkat cells were transduced with a gRNA library targeting high-level regulators of T cell receptor signaling and a set of transcription factors. After 10 days of antibiotic selection and expansion, cells were stimulated with anti-CD3 and anti-CD28 antibodies or left untreated. Both conditions were analyzed using CROP-seq, measuring TCR activation for each gene knockout. Our dataset comprises 5,905 high-quality single-cell transcriptomes with uniquely assigned gRNAs.
All CROP-seq raw data files are multiplexed with single-cell reads. Each read 1 contains the cell barcode (12 bp) and a molecule barcode (8 bp) and read 2 contains the transcriptome read. The libraries are pooled by nature but also intrinsically labelled. The file CROP-seq_Jurkat_TCR.digital_expression.csv.gz contains gene level expression quantifications of each gene for each cell which corresponds to the cell barcode in read1.
For the Drop-seq_HEK293T-3T3 sample (Drop-seq species mixing), reads aligning to two genomes were used to quantify for each cell barcode the amount of reads coming from each genome. In a similar way, in the CROP-seq_HEK293T sample (CROP-seq gRNA mixing), the number of gRNA molecules detected per cell barcode (which is possible due to the polyadenylation of these gRNA-containing transcripts when expressed from a Pol2 promoter as engineered) were counted.
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