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

Amin AbediniiD, Jonathan L. LevinsohniD, Konstantin A. KlötzeriD, Bernhard DumouliniD, Ziyuan MaiD, Julia FrederickiD, Poonam Dhillon, Michael S. BalzeriD, Rojesh ShresthaiD, Hongbo LiuiD, Steven VitaleiD, Kishor B. Devalaraja-NarashimhaiD, Paola GrandiiD, Tanmoy BhattacharyyaiD, Erding Hu, Steven S. PulleniD, Carine BoustanyiD, Paolo GuarnieriiD, Anil K. KarihalooiD, Daniel TraumiD, Hanying YaniD, Kyle ColemaniD, Matthew PalmeriD, Lea Sarov‐Blat, Lori G. MortoniD, , Katalin SusztákiD

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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.

Abstract summary

Claude Opus 5.5 · xhigh effort · 18 Oct 2026 · from the abstract

Multi-omic and spatial profiling of 81 human kidney samples resolves four tissue microenvironments; the fibrotic one's gene signature is reported to beat histopathology at predicting outcome.

How the kidney is organised spatially and molecularly in health and disease remains poorly understood, and outcome is still predicted from traditional histopathological analysis. Combining single cell and single nucleus RNA profiling and open chromatin with two spatial platforms, across healthy, diabetic and hypertensive kidneys, lets cell types be placed back in situ and lets a fibrosis-linked spatial signature be put up against that histopathological assessment as a prognostic marker.

Key results

  1. ShownDeconvolving the spatial data without supervision splits kidney tissue into 4 recurring microenvironments - glomerular, immune, tubule and fibrotic - each with its own cellular composition in health and disease.

    Reported as 4 distinct microenvironments recovered from the spatial assays; the abstract gives no cluster statistics, cell-type proportions or stability analysis. · Abstract

  2. ClaimedThe spatial gene signature of the fibrotic microenvironment both separates kidneys molecularly and, the authors report, forecasts outcome better than conventional histopathological assessment.

    Stated as improved prognosis prediction over traditional histopathology, with no accuracy, hazard ratio, follow-up time or validation cohort reported in the abstract. · Abstract

  3. ShownThe resource couples dissociated and spatial measurements on the same disease spectrum, so cell types called from the sequencing assays can be assigned tissue locations.

    81 samples covering healthy, diabetic and hypertensive human kidneys, profiled by single cell and single nucleus RNA, single nucleus open chromatin, Visium spots and single-cell-resolution CosMx; per-group and per-assay counts are not broken out. · Abstract

Methods, data and reproducibility

Human kidneys spanning healthy, diabetic and hypertensive states were profiled with single cell RNA, single nucleus RNA and single nucleus open chromatin sequencing, plus two spatial transcriptomic platforms (spot-level Visium and CosMx at single cell resolution). Cell types from the sequencing assays were mapped onto tissue, and spatial measurements were deconvolved without supervision into microenvironments.

  • Human kidney samples from healthy, diabetic and hypertensive donors, profiled across single cell, single nucleus, open chromatin and two spatial assays · human · n = 81 samples
Methods & tools
Visium, CosMx
Limitations (2)
  • The prognostic claim arrives without any metric, follow-up description or independent validation cohort, so the margin over histopathology cannot be assessed from the text available. · noted in this summary
  • Sample numbers are given only in total (81), with no breakdown by disease group or assay, so how much of the atlas rests on diseased kidneys is unclear. · noted in this summary

Read this if you study kidney disease or spatial transcriptomics and want cell types placed in tissue context, or a fibrosis signature proposed as a prognostic readout.

Citations
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