Predicting drivers of proximal tubule cell state through regularized regression analysis of single cell multiomic sequencing

In this study, we generated a single nucleus multiomic (snRNA-seq and snATAC-seq) dataset of adult human kidney. We developed a bioinformatic tool to analyze this dataset by identifying key cis-regulatory elements and transcription factors associated with specific cell types and states. We applied this tool to identify transcription factors implicated in proximal tubule cell injury and failed repair states. We demonstrate this tool can be applied to single cell multiomic datasets to derive insight into cell type- and state-specific gene regulatory networks. CUT&RUN on primary RPTECs

本研究构建了成人肾脏的单细胞核多组学（single nucleus multiomic）数据集，包含单细胞核RNA测序（snRNA-seq）与单细胞核转座酶可及性测序（snATAC-seq）两类数据。我们开发了一款生物信息学工具用于该数据集的分析，可通过识别与特定细胞类型及细胞状态相关的关键顺式调控元件与转录因子。我们将该工具应用于识别与近端肾小管上皮细胞损伤及修复失败状态相关的转录因子。本研究证实，该工具可应用于单细胞多组学数据集，以深入解析细胞类型与细胞状态特异性的基因调控网络。针对原代RPTECs的CUT&RUN实验