Description
Defining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. We further delineate cell type-specific expression of genes associated with chronic kidney disease, diabetes and hypertension, providing insight into possible targeted therapies. This includes expression of a hypertension-associated mechano-sensory ion channel in mesangial cells, and identification of proximal tubule cell populations defined by pathogenic expression signatures. Our fully optimized, quality-controlled transcriptomic profiling pipeline constitutes a tool for the generation of healthy and diseased molecular atlases applicable to clinical samples.
Overall Design
Single-nucleus (sn)Drop-seq was used to generate RNA expression estimates across two kidney regions (cortex and medulla), 15 different individuals, 7 different tissue processing methods, and from tissues acquired from two different institutions (Washington University and University of Michigan through KPMP consortium).; From the resulting ~18,000 sequenced nuclei passing QC filtering (>400 <5000 non-MT genes detected, >50 post-QC nuclei per library, >30 nuclei per cluster), we identified 30 different cell populations (see supplementary file UCSD-WU_Single_Nuclei_Cluster_Annotations.csv).
Curator
hy_li