Model-based understanding of single-cell CRISPR screening

Bin Duan, Chi Zhou, Chengyu Zhu, Yifei Yu, Gaoyang Li, Shihua Zhang, Chao Zhang, Xiangyun Ye, Hanhui Ma, Shen Qu, Zhiyuan Zhang, Ping Wang (), Shuyang Sun () and Qi Liu ()
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Bin Duan: College of Life Science, Tongji University
Chi Zhou: College of Life Science, Tongji University
Chengyu Zhu: College of Life Science, Tongji University
Yifei Yu: College of Life Science, Tongji University
Gaoyang Li: Shanghai Tenth People’s Hospital of Tongji University
Shihua Zhang: Academy of Mathematics and Systems Science
Chao Zhang: College of Life Science, Tongji University
Xiangyun Ye: Shanghai Chest Hospital Shanghai Jiaotong University
Hanhui Ma: School of Life Science and Technology ShanghaiTech University
Shen Qu: College of Life Science, Tongji University
Zhiyuan Zhang: Shanghai Jiao Tong University School of Medicine
Ping Wang: Shanghai Tenth People’s Hospital of Tongji University
Shuyang Sun: Shanghai Jiao Tong University School of Medicine
Qi Liu: College of Life Science, Tongji University

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: Abstract The recently developed single-cell CRISPR screening techniques, independently termed Perturb-Seq, CRISP-seq, or CROP-seq, combine pooled CRISPR screening with single-cell RNA-seq to investigate functional CRISPR screening in a single-cell granularity. Here, we present MUSIC, an integrated pipeline for model-based understanding of single-cell CRISPR screening data. Comprehensive tests applied to all the publicly available data revealed that MUSIC accurately quantifies and prioritizes the individual gene perturbation effect on cell phenotypes with tolerance for the substantial noise that exists in such data analysis. MUSIC facilitates the single-cell CRISPR screening from three perspectives, i.e., prioritizing the gene perturbation effect as an overall perturbation effect, in a functional topic-specific way, and quantifying the relationships between different perturbations. In summary, MUSIC provides an effective and applicable solution to elucidate perturbation function and biologic circuits by a model-based quantitative analysis of single-cell-based CRISPR screening data.

Date: 2019
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DOI: 10.1038/s41467-019-10216-x

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