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Mapping the human kidney using single-cell genomics

Abstract

The field of single-cell genomics and spatial technologies is rapidly evolving and has already provided unprecedented insights into complex tissues. Major advances have been made in dissecting the cellular composition and spatiotemporal interactions that mediate developmental processes in the fetal kidney. Single-cell technologies have also provided detailed insights into the heterogeneity of cell types within the healthy adult and shed light on the complex cellular mechanisms that contribute to kidney disease. The in-depth characterization of specific cell types associated with acute kidney injury and glomerular diseases has potential for the development of prognostic biomarkers and new therapeutics. Analyses of pathway activity in clear-cell renal cell carcinoma can predict the sensitivity of tumour cells to specific inhibitors. The identification of the cell of origin of renal cell carcinoma and of new cell types within the tumour microenvironment also has implications for the development of targeted therapeutics. Similarly, single-cell sequencing has provided new insights into the mechanisms underlying kidney fibrosis, specifically our understanding of myofibroblast origins and the contribution of cell crosstalk within the fibrotic niche to disease progression. These and future studies will enable the creation of a map to aid our understanding of the cellular processes and interactions in the developing, healthy and diseased kidney.

Key points

  • Single-cell RNA sequencing has enabled dissection of the cellular heterogeneity of complex tissues, as well as the characterization of rare cell populations.

  • Differential gene expression analyses and pseudotemporal predictions have yielded insights into the mechanisms of mesenchymal-to-epithelial transition, nephron progenitor self-renewal and podocyte development.

  • Inference of pathway activity for druggable pathways in clear-cell renal cell carcinoma can predict the sensitivity of tumour cells to pathway inhibitors, which could facilitate the identification of optimal combinational therapy.

  • Investigation of fibrosis-related gene expression in single-cell RNA sequencing data has enabled precise understanding of fibroblast and pericyte-to-myofibroblast differentiation trajectories.

  • Single-cell RNA sequencing has facilitated characterization of a dedifferentiated VCAM1+ population of proximal tubule cells and revealed its broad relevance in renal cell carcinoma, kidney injury and kidney fibrosis.

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Fig. 1: Insights into the mechanisms driving kidney development.
Fig. 2: Cellular heterogeneity among different sections of the nephron.
Fig. 3: Clear-cell renal cell carcinoma at a single-cell resolution.
Fig. 4: Pathogenesis of kidney fibrosis.

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Both authors researched data for the article, contributed substantially to discussion of the content and wrote the article. R.K. reviewed and/or edited the manuscript before submission.

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Glossary

RNA-velocity

A computational method that predicts future states of cells by comparing the quantitative abundance of spliced and unspliced mRNA of genes.

Pseudotime analysis

A computational method that orders single cells along a developmental trajectory by identifying similarities and continuous changes in the transcriptome of these cells.

Alternative splicing

A process, in which different mRNA molecules are generated from the same gene by removing different parts of the gene during transcription. This process enables different proteins to be encoded by the same gene.

Isoform switching

The process by which a cell switches from producing one isoform of a transcript to another isoform.

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Schreibing, F., Kramann, R. Mapping the human kidney using single-cell genomics. Nat Rev Nephrol 18, 347–360 (2022). https://doi.org/10.1038/s41581-022-00553-4

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