Spatially resolved human kidney multi-omics single cell atlas highlights the key role of the fibrotic microenvironment in kidney disease progression

Amin Abedini(University of Pennsylvania), Jonathan Levinsohn, Konstantin A. Klötzer, Bernhard Dumoulin, Ziyuan Ma(University of Pennsylvania), Julia Frederick(University of Pennsylvania), Poonam Dhillon(University of Pennsylvania), Michael S. Balzer(University of Pennsylvania), Rojesh Shrestha(University of Pennsylvania), Hongbo Liu(University of Pennsylvania), Steven Vitale(University of Pennsylvania), Kishor Devalaraja‐Narashimha(Regeneron (United States)), Paola Grandi, Tanmoy Bhattacharyya(GlaxoSmithKline (United States)), Erding Hu(GlaxoSmithKline (United States)), Steven S. Pullen(Boehringer Ingelheim (United States)), Carine M. Boustany‐Kari(Boehringer Ingelheim (United States)), Paolo Guarnieri(Boehringer Ingelheim (United States)), Anil Karihaloo(Novo Nordisk (United States)), Daniel Traum, Hanying Yan(University of Pennsylvania), Kyle Coleman(University of Pennsylvania), Matthew Palmer(University of Pennsylvania), Lea Sarov‐Blat(GlaxoSmithKline (United States)), Lori Morton(Regeneron (United States)), Christopher A. Hunter(University of Pennsylvania), Klaus H. Kaestner, Mingyao Li(University of Pennsylvania), Katalin Suszták(University of Pennsylvania)
bioRxiv (Cold Spring Harbor Laboratory)
October 26, 2022
Cited by 42Open Access
Full Text

Abstract

Abstract Kidneys possess one of the most intricate three-dimensional cellular structures in the body, yet the spatial and molecular principles of kidney health and disease remain inadequately understood. Here, we have generated high-quality datasets for 81 samples, including single cell (sc), single nuclear (sn), spot level (Visium) and single cell resolution (CosMx) spatial (sp)-RNA expression, and sn open chromatin, capturing cells from healthy, diabetic, and hypertensive diseased human kidneys. By combining the snRNA, snATAC and scRNA sequencing we identify cell types and map these cell types to their locations within the tissue. Unbiased deconvolution of the spatial data identifies 4 distinct spatial microenvironments: glomerular, immune, tubule and fibrotic. We describe the complex, heterogenous cellular and spatial organization of human microenvironments in health and disease. Further, we find that the fibrotic microenvironment spatial gene signature is not only able to molecularly classify human kidneys, but it also offers an improved prognosis prediction compared to traditional histopathological analysis. We provide a comprehensive spatially resolved molecular roadmap of the human kidney and the fibrotic process, demonstrating the clinical utility of spatial transcriptomics.


Related Papers