Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression

Amin Abedini; Jonathan Levinsohn; Konstantin A Klötzer; Bernhard Dumoulin; Ziyuan Ma; Julia Frederick; Poonam Dhillon; Michael S Balzer; Rojesh Shrestha; Hongbo Liu; Steven Vitale; Andi M Bergeson; Kishor Devalaraja-Narashimha; Paola Grandi; Tanmoy Bhattacharyya; Erding Hu; Steven S Pullen; Carine M Boustany-Kari; Paolo Guarnieri; Anil Karihaloo; Daniel Traum; Hanying Yan; Kyle Coleman; Matthew Palmer; Lea Sarov-Blat; Lori Morton; Christopher A Hunter; Klaus H Kaestner; Mingyao Li; Katalin Susztak

doi:10.1038/s41588-024-01802-x

Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression

Nat Genet. 2024 Aug;56(8):1712-1724. doi: 10.1038/s41588-024-01802-x. Epub 2024 Jul 24.

Authors

Amin Abedini^#^{1

2

3

4}, Jonathan Levinsohn^#^{1

2

3

4}, Konstantin A Klötzer^{1

2

3

4}, Bernhard Dumoulin^{1

2

3

4}, Ziyuan Ma^{1

2

3}, Julia Frederick^{1

2

3}, Poonam Dhillon^{1

2

3}, Michael S Balzer^{1

2

3

5}, Rojesh Shrestha^{1

2

3}, Hongbo Liu^{1

2

3}, Steven Vitale^{1

2

3}, Andi M Bergeson^{1

2

3

4}, Kishor Devalaraja-Narashimha⁶, Paola Grandi⁷, Tanmoy Bhattacharyya⁸, Erding Hu⁸, Steven S Pullen⁹, Carine M Boustany-Kari⁹, Paolo Guarnieri⁹, Anil Karihaloo¹⁰, Daniel Traum^{2

4}, Hanying Yan¹¹, Kyle Coleman¹¹, Matthew Palmer¹², Lea Sarov-Blat⁸, Lori Morton⁶, Christopher A Hunter¹³, Klaus H Kaestner^{2

4}, Mingyao Li¹¹, Katalin Susztak^{14

15

16

17}

Affiliations

¹ Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
² Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
³ Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
⁴ Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
⁵ Nephrology, Charité - Universitätsmedizin, Berlin, Germany.
⁶ Cardiovascular and Renal Research, Regeneron Pharmaceuticals Inc., Tarrytown, NY, USA.
⁷ Genomic Sciences, GSK-Cellzome, Heidelberg, Germany.
⁸ Research and Development, GSK, Crescent Drive, Philadelphia, PA, USA.
⁹ Department of Cardiometabolic Diseases Research, Boehringer Ingelheim Pharmaceuticals, Ridgefield, CT, USA.
¹⁰ Novo Nordisk, Lexington, MA, USA.
¹¹ Department of Epidemiology, Biostatistics and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
¹² Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
¹³ Department of Pathobiology, University of Pennsylvania School of Veterinary Medicine, Philadelphia, PA, USA.
¹⁴ Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. [email protected].
¹⁵ Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. [email protected].
¹⁶ Penn/CHOP Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. [email protected].
¹⁷ Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. [email protected].

^# Contributed equally.

PMID: 39048792
DOI: 10.1038/s41588-024-01802-x

Abstract

Kidneys are intricate three-dimensional structures in the body, yet the spatial and molecular principles of kidney health and disease remain inadequately understood. We generated high-quality datasets for 81 samples, including single-cell, single-nuclear, spot-level (Visium) and single-cell resolution (CosMx) spatial-RNA expression and single-nuclear open chromatin, capturing cells from healthy, diabetic and hypertensive diseased human kidneys. Combining these data, we identify cell types and map them to their locations within the tissue. Unbiased deconvolution of the spatial data identifies the following four distinct microenvironments: glomerular, immune, tubule and fibrotic. We describe the complex organization of microenvironments in health and disease and find that the fibrotic microenvironment is able to molecularly classify human kidneys and offers an improved prognosis compared to traditional histopathology. We provide a comprehensive spatially resolved molecular roadmap of the human kidney and the fibrotic process, demonstrating the clinical utility of spatial transcriptomics.

MeSH terms

Cellular Microenvironment* / genetics
Disease Progression*
Fibrosis*
Gene Expression Profiling
Humans
Kidney Diseases* / genetics
Kidney Diseases* / pathology
Kidney* / pathology
Multiomics
Single-Cell Analysis*
Transcriptome

Abstract

MeSH terms

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