Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens

Recent advancements in functional genomics have provided an unprecedented ability to measure diverse molecular modalities, but learning causal regulatory relationships from observational data remains challenging. Here, we leverage pooled genetic screens and single cell sequencing (i.e. Perturb-seq) to systematically identify the targets of signaling regulators in diverse biological contexts. We demonstrate how Perturb-seq is compatible with recent and commercially available advances in combinatorial indexing and next-generation sequencing, and perform more than 1,500 perturbations split across six cell lines and five biological signaling contexts. We introduce an improved computational framework (Mixscale) to address cellular variation in perturbation efficiency, alongside optimized statistical methods to learn differentially expressed gene lists and conserved molecular signatures. Finally, we demonstrate how our Perturb-seq derived gene lists can be used to precisely infer changes in signaling pathway activation for in-vivo and in-situ samples. Our work enhances our understanding of signaling regulators and their targets, and lays a computational framework towards the data-driven inference of an ‘atlas’ of perturbation signatures. We performed Perturb-seq experiments in six different cancer cell lines from different tissues of origin: A549 (lung), MCF7 (breast), HT29 (colon), HAP1 (bone marrow), BxPC3 (pancreas), and K562 (bone marrow). To facilitate multiplexed gene knockdown screens, we modified each of these lines to express a CRISPR interference (CRISPRi) dCas9-KRAB-MeCP2 cassette. We exposed each cell line to five distinct stimuli representing well-established pathway regulators: interferon-beta (IFNB), interferon-gamma (IFNG), transforming growth factor beta (TGFB), tumor necrosis factor-alpha (TNFA), and insulin (INS). For each pathway, we selected 44 to 61 genes based on a literature review of known regulators. For each gene, we selected three independent single guide RNAs (sgRNA) from the Dolcetto genome-wide CRISPRi library, as well as 14 non-targeting (NT) controls. For each pathway, we separately infected all six cell lines with the pathway-specific sgRNA library, and then stimulated infected cells with the corresponding cytokine for 24 hours to activate signaling.

功能基因组学领域的近期进展，赋予了我们前所未有的能力来检测多样化的分子模态，但从观测数据中学习因果调控关系仍颇具挑战。本研究利用混合遗传筛选与单细胞测序（即Perturb-seq），系统鉴定了不同生物背景下信号调控因子的靶标。我们证实Perturb-seq可兼容当前商业化的组合索引与新一代测序技术进展，并完成了覆盖6种细胞系与5种生物信号背景的1500余次基因扰动实验。我们开发了一款改良的计算框架（Mixscale）以解决扰动效率的细胞异质性问题，并辅以优化的统计方法来筛选差异表达基因集与保守分子特征。最后，本研究展示了如何利用Perturb-seq得到的基因集，精准推断体内（in-vivo）与原位（in-situ）样本的信号通路激活状态变化。本研究加深了我们对信号调控因子及其靶标的理解，并为基于数据驱动的扰动特征图谱（atlas）推断构建了计算框架。本研究在6种不同组织来源的癌细胞系中开展了Perturb-seq实验：A549（肺）、MCF7（乳腺）、HT29（结肠）、HAP1（骨髓）、BxPC3（胰腺）与K562（骨髓）。为实现多重基因敲低筛选，我们对上述细胞系均进行了工程改造，使其表达CRISPR干扰（CRISPRi）系统的dCas9-KRAB-MeCP2表达盒。我们将每种细胞系分别暴露于5种经典的通路调控刺激因子：β干扰素（IFNB）、γ干扰素（IFNG）、转化生长因子β（TGFB）、肿瘤坏死因子α（TNFA）与胰岛素（INS）。针对每条通路，我们基于已知调控因子的文献调研，筛选出44至61个靶基因。针对每个靶基因，我们从Dolcetto全基因组CRISPRi文库中选取3条独立的单向导RNA（sgRNA），同时设置14份非靶向（NT）对照。针对每条通路，我们分别用该通路特异性的sgRNA文库感染全部6种细胞系，随后用对应的细胞因子刺激感染后的细胞24小时以激活信号通路。