Multiplex, single-cell CRISPRa screening in single cells to detect target gene upregulation

Multiplex, single-cell CRISPRa screening in single cells to detect target gene upregulation

Abstract

CRISPR-based gene activation (CRISPRa) is a promising approach for gene therapy, but ideally would enable specific genes to be upregulated in a cell type-specific manner, e.g. as might be mediated by its targeting of enhancers. Here, we describe an experimental framework that combines highly multiplexed perturbations with single-cell RNA sequencing (sc-RNA-seq) to identify cell-type-specific, CRISPRa-responsive cis-regulatory elements and the gene(s) they regulate. Random combinations of many gRNAs are introduced to each of many cells, and cells are computationally partitioned into test and control groups to test for effect(s) of CRISPRa perturbations of both enhancers and promoters on the expression of neighboring genes. Applying this method to regulatory elements in both K562 cells and iPSC-derived neurons, we identify gRNAs capable of specifically and potently upregulating target genes. A consistent pattern is that the “sensitivities” of individual enhancers to CRISPRa are consistently restricted by cell type, implying a dependency on either chromatin landscape and/or additional trans-acting factors for successful gene activation. This approach may facilitate large-scale screens for gRNAs that activate therapeutically relevant genes in a cell type-specific manner.

Processed files

We have made all processed files available for further analysis or visualization. The following are available:

1. gRNA designs and sequences (used for both the K562 and the iPSC-derived neuron CRISPRa screen)

  1. CRISPRa gRNA designs and sequences

2. K562 CRISPRa screening data and associated files:

  1. cellranger input files

  2. differential gene expression testing outputs

  3. ATAC and H3k27ac data tracks

  4. cellranger count outputs

3. iPSC-derived neuron CRISPRa screening data and associated files:

  1. cellranger input files

  2. differential gene expression testing outputs

  3. ATAC and H3k27ac data tracks

  4. cellranger count outputs

4. Processed data to use for single-cell dimensionality reduction visualization and analysis

  1. processed Seurat object

Data processing and visualization scripts

1. gRNA design (used for both the K562 and the iPSC-derived neuron CRISPRa screen)

2. Preprocessing of raw sequencing data

  1. K562 CRISPRa screen

  2. iPSC-derived neuron CRISPRa screen

*Note: the transcriptome reference used in cellranger count can be found here

3. Differential expression testing (used for both the K562 and the iPSC-derived neuron CRISPRa screen)

4. Data visualization (all main text and supplemental figures can be generated using these scripts)

UMAP projection of our CRISPRa neuron dataset (blue) onto a cross sectional single-cell RNA seq differentiation time course from a similar differentiation protocol and NGN2 iPSC line

UMAP projection of our CRISPRa neuron dataset (blue) onto a cross sectional single-cell RNA seq differentiation time course from a similar differentiation protocol and NGN2 iPSC line