SCENIC+: identification of enhancers and gene regulatory networks using single-cell multiomics (cell lines)

Joint profiling of chromatin accessibility and gene expression of individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (eGRN). Here we present a new method for the inference of eGRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TF) and links these enhancers to candidate target genes. Specific TFs for each cell type or cell state are predicted based on the concordance of TF binding site accessibility, TF expression, and target gene expression. To improve both recall and precision of TF identification, we curated and clustered more than 40,000 position weight matrices that we could associate with ~1,500 human TFs. We validated and benchmarked each of the SCENIC+ components on diverse data sets from different species, including human peripheral blood mononuclear cell types, human ENCODE cell lines, human melanoma cell states, and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers, and GRNs between human and mouse cell types in the cerebral cortex. Finally, we provide new capabilities that exploit the inferred eGRNs to study the dynamics of gene regulation along differentiation trajectories; to map regulatory activities onto tissues using spatial omics data; and to predict the effect of TF perturbations on cell state. SCENIC+ provides critical insight into gene regulation, starting from multi-ome atlases of scATAC-seq and scRNA-seq. The SCENIC+ suite is available as a set of Python modules at scenicplus.readthedocs.io. Overall design: A mix of 11 wild type melanoma cell lines (MM050, MM099, MM116, MM001, MM011, MM057, MM087, MM031, MM047 and MM029) were analysed using 10x scATAC-seq experiments

对单个细胞的染色质开放性与基因表达进行联合谱型分析，为解析增强子驱动的基因调控网络（enhancer-driven gene regulatory network, eGRN）提供了可行路径。本研究提出一种全新的eGRN推断方法，命名为SCENIC+。SCENIC+可预测基因组增强子与候选上游转录因子（transcription factor, TF），并将这些增强子与其候选靶基因建立关联。该方法可基于转录因子结合位点开放性、转录因子表达水平与靶基因表达水平的一致性，预测对应细胞类型或细胞状态的特异性转录因子。为提升转录因子识别的召回率与精确率，本研究整理并聚类了超过40000个可与约1500个人类转录因子关联的位置权重矩阵（position weight matrix, PWM）。我们针对来自不同物种的多组数据集，对SCENIC+的各个组件进行了验证与基准测试，涵盖人类外周血单核细胞亚型、人类ENCODE细胞系、人类黑色素瘤细胞状态以及果蝇视网膜发育相关数据集。随后，我们利用SCENIC+的预测结果，研究了人类与小鼠大脑皮层细胞类型间保守的转录因子、增强子及基因调控网络。最后，本研究新增了多项功能，可借助推断得到的eGRN实现三类研究：沿分化轨迹探究基因调控的动态变化、利用空间组学数据将调控活性映射至组织空间，以及预测转录因子扰动对细胞状态的影响。SCENIC+可基于scATAC-seq（单细胞转座酶可及性测序）与scRNA-seq（单细胞RNA测序）多组学图谱，为基因调控研究提供关键见解。SCENIC+工具集以Python模块套件的形式发布，可通过scenicplus.readthedocs.io获取。实验整体设计：本研究采用10x scATAC-seq技术，对11株野生型黑色素瘤细胞系（MM050、MM099、MM116、MM001、MM011、MM057、MM087、MM031、MM047与MM029）的混合样本开展了分析。