Multi-modal single-cell analysis of human kidney tissue to identify novel anti-inflammatory therapies for kidney disease

Background

Chronic kidney disease (CKD) is a major public health problem; it affects ~7% of the UK population and is a risk factor for cardiovascular disease and for end-stage kidney disease (ESRD), necessitating dialysis or transplantation. Hence, novel therapies are required to prevent progression of CKD or promote kidney repair. Furthermore, we must determine which therapies work best for specific kidney diseases or time-points during disease progression. 

The kidney is a complex organ with multiple specialist cell types. We have employed state-of-the-art single-cell multimodal molecular techniques to identify the key cell types that are present in the healthy and injured kidney and the molecular pathways that are activated/deactivated in each cell type. We performed 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 scarring. Following kidney injury, a discrete subset of tubular cells adopts a pro-inflammatory, pro-fibrotic phenotype. Furthermore, high-plex spatial single-cell molecular analysis determined that specifically this subset of inflammatory tubular cells localize to the fibrotic niche. ATAC-seq analysis highlighted the AP-1 transcription factor as promoting this inflammatory tubular cell phenotype and administration of an AP-1 inhibitor ameliorated inflammation and fibrosis in a murine model of ischaemic kidney disease. The analysis, performed by a former Precision Medicine PhD student, highlights how integrated single-cell multiomics can identify therapies targeted to novel renal cell subsets and our manuscript is now in revision for Nature Communications¹. 

We now aim to focus on characterizing the immune cell subsets as they mediate both injury and repair in the kidney². In a pre-clinical model of reversible unilateral ureteric obstruction, we identified 12 different myeloid cell clusters, some of which were specific to injury or repair³. Similarly, in the obstructed human kidney, we have identified 23 different myeloid cell phenotypes, but their role in disease/repair is unknown. We are currently performing snRNA-seq on kidney biopsy tissue from patients with different kidney diseases to identify which myeloid cell phenotypes are present in specific kidney diseases or time-points during progression. Uniquely, we will include analysis of kidneys undergoing repair: kidneys where the ureters have been de-obstructed by stenting or renal vasculitis which has been treated by immunosuppression.

Aims

Our goal is to characterise the myeloid cell subsets during injury and repair in a diverse range of human kidney diseases in order to identify therapies to prevent injury or promote repair and test these in pre-clinical models. Specific aims for the project are to:

1. integrate snRNA-seq data from diverse kidney diseases (diabetic/IgA/obstructive nephropathy, vasculitis, minimal change disease) to identify generic core and disease-specific responses to injury, focusing on myeloid cell subsets 
2. analyse spatial transcriptomic data to determine which myeloid cell subsets co-locate with injured/repairing tubular cells and myofibroblasts
3. perform ligand-receptor analysis to determine how myeloid cells are recruited to the kidney and signal to adjacent cells to promote injury/repair
4. test whether therapies targeting key signalling pathways can prevent kidney disease/promote repair using in vitro assays and murine models

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, visualization of complex data, statistics, presenting data to peers, writing of manuscripts, etc. In addition, they will gain specific skills in programming and analysis of cutting edge technologies including snRNAseq and spatial transcriptomic data using packages such as Seurat, Harmony, CellChat in the R environment. They will also have the opportunity to validate key findings in in vitro and in vivo models of kidney disease.  

References

1. https://assets-eu.researchsquare.com/files/rs-3964901/v1/a57c2770-5dbe-42b9-b1fb-02a10c320c1a.pdf?c=1709051347
2. Macrophages in the kidney in health, injury and repair Bell RMB, Conway BR. Int Rev Cell Mol Biol. 2022;367:101-147. doi: 10.1016/bs.ircmb.2022.01.005. Epub 2022 Feb 28
3. Kidney Single-Cell Atlas Reveals Myeloid Heterogeneity in Progression and Regression of Kidney Disease.  Conway BR, O'Sullivan ED, Cairns C, O'Sullivan J, Simpson DJ, Salzano A, Connor K, Ding P, Humphries D, Stewart K, Teenan O, Pius R, Henderson NC, Bénézech C, Ramachandran P, Ferenbach D, Hughes J, Chandra T, Denby L. J Am Soc Nephrol 2020;31:2833-54