Multi-omic analysis of human kidney tissue to identify biomarkers and therapies targeting pathogenic cell phenotypes

Background

Chronic kidney disease (CKD) is a major public health problem, affecting ~11% of the UK population and conferring an increased risk of cardiovascular disease and end-stage kidney disease (ESRD), which necessitates dialysis or transplantation¹. Hence, novel therapies are required to prevent progression of CKD or promote kidney repair. Furthermore, we need to determine which therapies work best for specific kidney diseases or stages of disease progression. 

The kidney is a complex organ, comprised of multiple cell types, therefore to identify the molecular pathways that are activated/deactivated in each cell type we are employing state-of-the-art single-cell multimodal molecular techniques. As part of a previous Precision Medicine PhD studentship, we employed single-nuclear RNA/ATAC-seq on tissue from the non-tumerous portion of human kidneys removed due to tumour, including a subset of patients where the tumour obstructed the ureter causing kidney injury, inflammation and scarring³. We identified a discrete subset of tubular cells that adopts a pro-inflammatory phenotype and employed high-plex spatial single-cell molecular analysis to localize pro-inflammatory tubular cells to the fibrotic niche. Importantly, we identified the AP-1 transcription factor as a key master regulator of the inflammatory tubular cell phenotype and administration of an AP-1 inhibitor ameliorated inflammation and fibrosis in a murine model of kidney disease. This provides proof-of-principle that single-cell, multi-omic interrogation of kidney tissue can identify novel therapies to treat kidney disease. 

To identify disease-specific pathways of renal injury, we are currently performing snRNA-seq on archived frozen kidney biopsy cores from patients with a diverse range of kidney diseases. Uniquely, we have access to biopsies from patients with vasculitis, both before and after immunosuppressive treatment, enabling discrimination between mechanisms of injury and repair. Our preliminary analysis has identified tubular cell, inflammatory and fibroblast subsets that are disease-specific or present specifically during disease progression or repair. This raises the exciting possibility that we can develop disease-specific therapies to slow progression of kidney disease or indeed enhance kidney repair. This is vitally important, as kidney disease is often silent until the late stages, meaning patients often present with advanced disease.

Aims

The current project will aim to interrogate our existing snRNA-seq datasets to identify cellular mechanisms of kidney disease, develop tools to identify pathogenic/reparative cells in human kidney biopsies and screen drugs to inhibit development of pathogenic cell types. Specific aims for the project are to:

1. integrate snRNA-seq data from diverse kidney diseases (diabetes/IgA nephropathy/obstructive nephropathy/vasculitis/minimal change disease) to identify generic and disease-specific injurious and reparative cell phenotypes and correlate these with clinical outcomes
2. use the snRNA-seq data to generate antibody panels that could be applied to human kidney biopsies to identify patients with a high burden of deleterious cell subsets in order to more precisely target therapies to those most likely to benefit
3. to transfect a kidney tubular cell line⁴ with a fluorescent reporter of the inflammatory tubular phenotype, thereby generating an in vitro model for high-throughput screening of drugs that inhibit development of the inflammatory tubular cell phenotype or signaling to immune cells/fibroblasts

Training outcomes

The student will work with clinicians, experimental biologists and bioinformaticians to address key research questions in kidney disease. They will gain generic and transferable skills including: handling of large datasets in the R environment, visualization of complex snRNA-seq and spatial transcriptomic data, statistics, presenting data to peers, writing of manuscripts, etc. They will be trained in development of multiplex immunofluorescence antibody panels for imaging kidney tissue on Phenoimager and data analysis in QPath. In addition, they will learn renal cell biology, and be trained in core wet bench skills including tissue culture, viral transfection and generation of reporter assays.

References

¹Kidney Research UK. Chronic Kidney Disease: An Action Plan for Scotland. 2024.

²GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020 Feb 29;395(10225):709-733

³Reck M, Baird DP, Veizades S, Sutherland C, Bell RMB, Hur H, Cairns C, Janas PP, Campbell R, Nam A, Yang W, Schurman N, Williams C, O'Sullivan E, Beniazza M, Corsinotti A, Bellamy C, Hughes J, Laird A, Denby L, Chandra T, Ferenbach DA, Conway BR. Multiomic analysis of human kidney disease identifies a tractable inflammatory and pro-fibrotic tubular cell phenotype.  Nat Commun. 2025 May 22;16(1):4745 

⁴Teenan O, Sahni V, Henderson RB, Conway BR, Moran CM, Hughes J, Denby L. Sonoporation of Human Renal Proximal Tubular Epithelial Cells In Vitro to Enhance the Liberation of Intracellular miRNA Biomarkers. Ultrasound Med Biol. 2022 Jun;48(6):1019-1032.