Single-cell multi-omics analysis of mouse kidney during aging

Kidney function depends on the intricate coordination of multiple specialized cell types, each with unique transcriptional programs. Understanding the regulatory networks governing these cell types is critical for elucidating kidney development, homeostasis, and disease mechanisms. In this project, we employed a multi-omics approach integrating single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) to dissect gene regulatory programs at the resolution of individual kidney cell types. scRNA-seq data provided high-resolution gene expression profiles, while scATAC-seq enabled the mapping of chromatin accessibility across cell populations. To connect these layers, we applied the SCENIC+ pipeline, which integrates transcription factor (TF) motif enrichment, region-to-gene associations, and co-accessibility analysis to reconstruct cell-type-specific regulons. For each cell type, we computed TF activity scores (gene AUC) and cell-type specificity scores (RSS), providing a quantitative measure of regulon activity across the kidney’s diverse cell populations. This enabled the identification of key TFs driving cell-type identity. Moreover, we compared regulon activity across multiple age groups, highlighting dynamic changes in transcriptional regulation. Overall, this work demonstrates the power of integrating scRNA-seq and scATAC-seq with SCENIC+ to map cell-type-specific regulatory networks in the mouse kidney. The approach provides a foundation for future studies of kidney disease and development, offering insights into how transcriptional programs are coordinated at the single-cell level.