Enhancer grammar of liver cell types and hepatocyte zonation states [FACS_ATAC]

Cell type identity is encoded by gene regulatory networks (GRN), in which transcription factors (TFs) bind to enhancers to regulate target gene expression. In the mammalian liver, lineage TFs have been characterized for the main cell types, including hepatocytes. Hepatocytes cover a relatively broad cellular state space, as they differ significantly in their metabolic state, and function, depending on their position with respect to the central or portal vein in a liver lobule. It is unclear whether this spatially defined cellular state space, called zonation, is also governed by a well-defined gene regulatory code. To address this challenge, we have mapped enhancer-GRNs (eGRNs) across liver cell types at high resolution, using a combination of single-cell multi-omics, spatial omics, GRN inference, and deep learning. We found that zonated variation in gene expression in hepatocytes, liver sinusoidal endothelial cells and hepatocellular stellate cells corroborate cell state changes in transcription and chromatin accessibility with spatial transcriptomics. eGRN mapping suggests that zonation states in hepatocytes are driven by the repressors Tcf7l1 and Tbx3, that modulate the core hepatocyte GRN, controlled by Hnf4a, Cebpa, Hnf1a, Onecut1 and Foxa1, among others. To investigate how these TFs cooperate with cell type TFs, we performed an in vivo Massively Parallel Reporter Assay (MPRA) on 12,000 hepatocyte enhancers and used these data to train a hierarchical deep learning model (called DeepLiver) that exploits both enhancer accessibility and activity. DeepLiver confirms Cebpa, Onecut, Foxa1, Hnf1a and Hnf4a as drivers of enhancer specificity in hepatocytes; Tcf7l1/2 and Tbx3 as regulators of the zonation state; and Hnf4a, Hnf1a, AP-1 and Ets as activators. Finally, taking advantage of in silico mutagenesis predictions from DeepLiver and MPRA, we confirmed that the destruction of Tcf7l1/2 or Tbx3 motifs in zonated enhancers abrogates their zonation bias. Our study provides a multi-modal explanation of the regulatory code underlying hepatocyte identity and their zonation state, that can be exploited to engineer enhancers with desired activity levels and zonation patterns. Overall design: 10x single cell multiome on the mouse liver.

细胞类型身份由基因调控网络（Gene Regulatory Networks, GRN）编码，其中转录因子（Transcription Factors, TFs）结合增强子以调控靶基因的表达。在哺乳动物肝脏中，已针对包括肝细胞在内的主要细胞类型鉴定出谱系特异性转录因子。肝细胞具有相对宽泛的细胞状态谱，其代谢状态与功能会因所处肝小叶内相对于中央静脉或门静脉的位置而存在显著差异。目前尚不清楚这种被称为分区（zonation）的空间特异性细胞状态谱，是否同样受一套明确的基因调控代码所支配。为解决这一难题，本研究结合单细胞多组学、空间组学、基因调控网络推断与深度学习技术，以高分辨率绘制了肝脏各类细胞的增强子-基因调控网络（Enhancer-GRN, eGRN）图谱。研究发现，肝细胞、肝窦内皮细胞与肝星状细胞中的基因表达分区变化，可通过空间转录组学验证转录与染色质可及性层面的细胞状态改变。增强子-基因调控网络图谱分析显示，肝细胞的分区状态由抑制因子Tcf7l1与Tbx3驱动，二者可调控由Hnf4a、Cebpa、Hnf1a、Onecut1及Foxa1等因子掌控的核心肝细胞基因调控网络。为探究这些转录因子与细胞类型特异性转录因子的协同机制，本研究针对12000个肝细胞增强子开展了体内大规模平行报告基因检测（Massively Parallel Reporter Assay, MPRA），并以此数据训练了一款同时利用增强子可及性与活性信息的分层深度学习模型（命名为DeepLiver）。DeepLiver验证了Cebpa、Onecut、Foxa1、Hnf1a及Hnf4a为肝细胞增强子特异性的驱动因子，Tcf7l1/2与Tbx3为分区状态的调控因子，而Hnf4a、Hnf1a、AP-1及Ets则为激活因子。最后，本研究借助DeepLiver与大规模平行报告基因检测得到的计算机诱变预测结果，证实破坏分区特异性增强子中的Tcf7l1/2或Tbx3结合基序，可消除其分区表达偏好性。本研究为肝细胞身份及其分区状态背后的基因调控代码提供了多模态解释，该机制可用于工程化改造具备预期活性水平与分区模式的增强子。实验设计：对小鼠肝脏开展10x单细胞多组学检测。