Christoph Bock, PhD
Principal investigator, Research Center for Molecular Medicine of the Austrian Academy of Science
CROP-seq and scifi-RNA-seq: Single-cell CRISPR sequencing at scale
CRISPR screening is a powerful method for biomedical research and drug discovery. While the first genome-wide CRISPR screens focused on essential genes in cancer cells and on other cell survival / proliferation phenotypes, a new generation of “high-content CRISPR screens” now broaden the scope to a wide range of other applications [1]. Pooled CRISPR screens can be combined with single-cell sequencing and imaging readouts, to obtain detailed biological insights directly as part of the screen. Moreover, they are now routinely done in primary cells (e.g., T cells for immuno-oncology) and organoids [2].
We have developed the CROP-seq method for pooled CRISPR screening with single-cell RNA sequencing readout [3]. CROP-seq provides a broadly useful assay for investigating cell states, gene-regulatory processes, and drug responses at scale and with single-cell resolution. Cells are perturbed in bulk (as in a pooled CRISPR screen) and profiled with single-cell RNA-seq in way that measures both the CRISPR perturbation (based on the expressed guide RNA) and the induced changes in each cell’s transcriptome.
CROP-seq screens are typically applied to 100s or a few thousand target genes at a time, due to the high cost of single-cell sequencing. Genome-wide screens are possible but need single-cell RNA-seq assays that support cost-effective sequencing in millions of single cells. We developed the scifi-RNA-seq method for ultra-high throughput single-cell RNA-seq [4]. This assay can obtain >100,000 single-cell transcriptomes from a single channel of the 10x Genomics system, by pre-indexing and massive overloading. This method enables applications that require cost-effective single-cell RNA-seq 100,000s or millions of cells, with built-in multiplexing for example for high-content drug screens with single-cell RNA-seq readout.
In conclusion, single-cell CRISPR sequencing (with CROP-seq) and ultra-high throughput single-cell RNA-seq (with scifi-RNA-seq) facilitate functional biology and drug discovery at scale. Moreover, these methods can provide powerful training data for interpretable machine learning on biological systems [5].
[1] Bock et al. (2022) High-content CRISPR screening. https://nature.com/articles/s43586-021-00093-4
[2] Bock et al. (2021) The Organoid Cell Atlas. https://nature.com/articles/s41587-020-00762-x
[3] Datlinger et al. (2017) Pooled CRISPR screening with single-cell transcriptome readout. https://nature.com/articles/nmeth.4177
[4] Datlinger et al. (2021) Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing. https://nature.com/articles/s41592-021-01153-z
[5] Fortelny et al. (2020) Knowledge-primed neural networks enable biologically interpretable
