比利时VIB脑与疾病研究中心Stein Aerts团队近期取得重要工作进展。他们研究提出了SCENIC+策略，可以用来进行增强子和基因调控网络的单细胞多组学推断。相关研究成果2023年7月13日在线发表于《自然—方法学》杂志上。

据介绍，染色质可及性和单个细胞中基因表达的联合分析为破译增强子驱动的基因调控网络（GRN）提供了机会。

研究人员提出了一种推理增强子驱动的GRN的方法，称为SCENIC+。SCENIC+预测基因组增强子和候选上游转录因子（TF），并将这些增强子与候选靶基因联系起来。为了提高TF识别的查全率和准确性，研究人员收集了超过30000多个基序，并进行了聚类分析。研究人员在来自不同物种的不同数据集上对SCENIC+进行了基准测试，包括人类外周血单核细胞、ENCODE细胞系、黑色素瘤细胞状态和果蝇视网膜发育。随后，研究人员利用SCENIC+预测来研究大脑皮层中人类和小鼠细胞类型之间的保守TF、增强子和GRN。

最后，研究人员使用SCENIC+来研究基因沿着分化轨迹的调控动力学以及TF扰动对细胞状态的影响。SCENIC+在scenicplus.readthedocs.io上提供。

附：英文原文

Title: SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

Author: Bravo Gonzlez-Blas, Carmen, De Winter, Seppe, Hulselmans, Gert, Hecker, Nikolai, Matetovici, Irina, Christiaens, Valerie, Poovathingal, Suresh, Wouters, Jasper, Aibar, Sara, Aerts, Stein

Issue&Volume: 2023-07-13

Abstract: Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) and links these enhancers to candidate target genes. To improve both recall and precision of TF identification, we curated and clustered a motif collection with more than 30,000 motifs. We benchmarked SCENIC+ on diverse datasets from different species, including human peripheral blood mononuclear cells, ENCODE cell lines, 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 use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state. SCENIC+ is available at scenicplus.readthedocs.io.

DOI: 10.1038/s41592-023-01938-4

Source: https://www.nature.com/articles/s41592-023-01938-4