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Profiling the genetic determinants of chromatin accessibility with scalable single-cell CRISPR screens

Nature Biotechnology volume 39, pages 1270–1277 (2021)Cite this article

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Abstract

CRISPR screens have been used to connect genetic perturbations with changes in gene expression and phenotypes. Here we describe a CRISPR-based, single-cell combinatorial indexing assay for transposase-accessible chromatin (CRISPR–sciATAC) to link genetic perturbations to genome-wide chromatin accessibility in a large number of cells. In human myelogenous leukemia cells, we apply CRISPR–sciATAC to target 105 chromatin-related genes, generating chromatin accessibility data for ~30,000 single cells. We correlate the loss of specific chromatin remodelers with changes in accessibility globally and at the binding sites of individual transcription factors (TFs). For example, we show that loss of the H3K27 methyltransferase EZH2 increases accessibility at heterochromatic regions involved in embryonic development and triggers expression of genes in the HOXA and HOXD clusters. At a subset of regulatory sites, we also analyze changes in nucleosome spacing following the loss of chromatin remodelers. CRISPR–sciATAC is a high-throughput, single-cell method for studying the effect of genetic perturbations on chromatin in normal and disease states.

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Fig. 1: CRISPR screens with CRISPR–sciATAC enable the joint capture of chromatin accessibility profiles and CRISPR perturbations.
Fig. 2: CRISPR–sciATAC reveals changes in accessibility at HOX genes following loss of EZH2.
Fig. 3: CRISPR–sciATAC screen targeting 17 chromatin remodeling complexes uncovers widespread disruptions in accessibility following SWI/SNF disruption.
Fig. 4: Nucleosome dynamics around TFBS following CRISPR targeting of chromatin remodelers.

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Data availability

Processed and raw data can be downloaded from NCBI GEO (PRJNA674902, GSE161002).

Code availability

The scripts and pipeline for the analysis can be found at https://gitlab.com/sanjanalab/crispr-sciatac.

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Acknowledgements

We thank the entire Sanjana laboratory for support and advice. We thank J. Morris for help with eQTL resources, M. Zaran and R. Satija for computational resources and the NYGC Sequencing Platform and NYU Biology Genomics Core for sequencing resources. BL21(DE3) cells transformed with pET-PfuX7 were kindly provided by J. Gregory. N.L.-B. is supported by a postdoctoral fellowship from the Human Frontier Science Program Organization (no. LT000672/2019-L), an EMBO long-term fellowship (no. ALTF 826-2018) and the Weizmann Institute of Science National Postdoctoral Award Program for Advancing Women in Science. N.E.S. is supported by NYU and NYGC startup funds, NIH/NHGRI (nos. R00HG008171 and DP2HG010099), NIH/NCI (no. R01CA218668), DARPA (no. D18AP00053), the Sidney Kimmel Foundation, the Melanoma Research Alliance and the Brain and Behavior Foundation.

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Author notes

  1. These authors contributed equally: Noa Liscovitch-Brauer, Antonino Montalbano.

Authors and Affiliations

  1. New York Genome Center, New York, NY, USA

    Noa Liscovitch-Brauer, Antonino Montalbano, Jiale Deng, Alejandro Méndez-Mancilla, Hans-Hermann Wessels, Nicholas G. Moss, Chia-Yu Kung, Akash Sookdeo, Xinyi Guo, Evan Geller, Suma Jaini, Peter Smibert & Neville E. Sanjana

  2. Department of Biology, New York University, New York, NY, USA

    Noa Liscovitch-Brauer, Antonino Montalbano, Jiale Deng, Alejandro Méndez-Mancilla, Hans-Hermann Wessels, Nicholas G. Moss, Chia-Yu Kung, Akash Sookdeo, Xinyi Guo, Evan Geller & Neville E. Sanjana

  3. Technology Innovation Lab, New York Genome Center, New York, NY, USA

    Suma Jaini & Peter Smibert

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Contributions

N.E.S. conceived and supervised the project. N.E.S., A.M. and N.L.-B. designed the experiments. A.M., N.L.-B., J.D., A.M.-M., C.-Y.K. and A.S. performed the experiments. N.L.-B., A.M., J.D., N.E.S., H.-H.W. and N.G.M. analyzed the data. P.S. isolated TnY. S.J. purified PhuX7. A.M., J.D., C.-Y.K., A.S., P.S. and S.J. purified TnY. N.L.-B., A.M. and N.E.S. wrote the manuscript with input from all authors.

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Correspondence to Neville E. Sanjana.

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Competing interests

The New York Genome Center and New York University have applied for patents relating to the work in this article. N.E.S. is an adviser to Vertex.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–14 and Sequences.

Reporting Summary

Supplementary Table 1

Number of cells in each step of the CRISPR–sciATAC protocol.

Supplementary Table 2

Sequences of oligonucleotides for CRISPR–sciATAC, CRISPR libraries and RT–qPCR.

Supplementary Table 3

Gene and gRNA enrichment from essentiality screen.

Supplementary Table 4

ENCODE ChIP data sources.

Supplementary Table 5

Histone mark differential accessibility.

Supplementary Table 6

GO enrichment results for differential accessibility in EZH2-targeted cells.

Supplementary Table 7

Transcription factor binding site differential accessibility.

Supplementary Table 8

Cost comparison between CRISPR–sciATAC and Perturb–ATAC protocols.

Supplementary Table 9

Time comparison between CRISPR–sciATAC and Perturb–ATAC protocols.

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Liscovitch-Brauer, N., Montalbano, A., Deng, J. et al. Profiling the genetic determinants of chromatin accessibility with scalable single-cell CRISPR screens. Nat Biotechnol 39, 1270–1277 (2021). https://doi.org/10.1038/s41587-021-00902-x

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