Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, Bioinformatics Department, College of Life Science, Tongji University, Shanghai, China.
Department of Ophthalmology, Ninghai First Hospital, Ninghai, Zhejiang, China.
Nat Commun. 2019 May 20;10(1):2233. doi: 10.1038/s41467-019-10216-x.
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.
最近开发的单细胞 CRISPR 筛选技术，分别称为 Perturb-Seq、CRISP-seq 或 CROP-seq，将汇集的 CRISPR 筛选与单细胞 RNA-seq 相结合，以在单细胞粒度上研究功能 CRISPR 筛选。在这里，我们提出了 MUSIC，这是一个用于基于模型理解单细胞 CRISPR 筛选数据的集成管道。应用于所有公开可用数据的综合测试表明，MUSIC 可以准确地量化和优先考虑单个基因扰动对细胞表型的影响，同时容忍此类数据分析中存在的大量噪声。MUSIC 从三个角度促进了单细胞 CRISPR 筛选，即优先考虑基因扰动效应作为整体扰动效应，以功能主题特异性的方式，以及量化不同扰动之间的关系。总之，MUSIC 通过对基于单细胞的 CRISPR 筛选数据进行基于模型的定量分析，为阐明扰动功能和生物回路提供了一种有效且适用的解决方案。
