Studying Kidney Diseases At The Single-Cell Level

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Mengmeng Jiang et al

Abstract

Background: The kidney is a highly complex organ that performs diverse functions that are essential for health. Kidney disease occurs when the kidneys are damaged and fail to function properly. Single-cell analysis is a powerful technology that provides unprecedented insights into normal and abnormal kidney cell types and will transform our understanding of the mechanism underlying common kidney diseases.

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Summary: Our understanding of kidney disease pathogenesis is limited by the incomplete molecular characterization of cell types responsible for kidney functions. The application of single-cell technologies for the study of the kidney has revealed cellular heterogeneity, gene expression signatures, and molecular dynamics during the onset and development of kidney diseases. Single-cell analyses of kidney organoids and allograft tissues offer new insights into kidney organogenesis, disease mechanisms, and therapeutic outcomes. Collectively, a better understanding of kidney cell heterogeneity and the molecular dynamics of kidney diseases will improve diagnostic accuracy and facilitate the identification of novel treatment strategies in nephrology.

Key Message: In this review article, we summarize recent single-cell studies on kidney diseases and discuss the impact of single-cell technology on both basic and clinical nephrology research.

Introduction The kidneys are 2 bean-shaped organs that are responsible for filtering waste products, excess water, and other impurities from the blood and producing urine. The kidneys also regulate pH, salt, potassium levels, and blood pressure; control the production of red blood cells; and activate a form of vitamin D that helps the body absorb calcium [1, 2]. To date, an estimated 850 million people worldwide have kidney diseases, including chronic kidney diseases (CKD), acute kidney injury, kidney failure, and many other diseases [3]. Kidney disease occurs when the kidneys are damaged and cannot perform their function. Damage may be caused by diabetes, high blood pressure, and various other chronic (long-term) conditions. Kidney diseases can lead to other health problems, including weak bones, nerve damage, malnutrition, and heart disease. The current therapeutic strategies for patients still involve kidney transplantation or dialysis, which are costly [2, 3].

A variety of cells in the kidney, including epithelial, mesangial, endothelial, and neuronal cells, as well as a network of immune cells, interact to maintain normal kidney function. Insights into the heterogeneity of healthy kidneys and the process underlying kidney diseases will refine kidney molecular and histopathological phenotype definitions and support the development of new disease classifications. Single-cell technology has potential advantages for the determination of cell subtypes, states, and frequency changes during kidney disease onset and progression [4]. With the rapid development of high-throughput single-cell RNA sequencing (scRNA-seq), comprehensive cellular landscapes of normal kidneys have recently been constructed for precision medicine in nephrology [5–9]. The Kidney Precision Medicine Project (KPMP) was developed worldwide, aiming to obtain human kidney biopsies, create kidney tissue atlas, define disease subgroups, and eventually identify critical cells, pathways, and targets for novel therapies [10]. On the basis of the relevant single-cell transcription datasets for the kidney, researchers have also analyzed the gene expression signatures of ACE2, TMPRSS2, and SLC6A19 in kidney cell subtypes, which is critical for understanding the pathogenesis of severe acute respiratory syndrome coronavirus 2 [11]. In this review, we will focus on (1) the development and application of single-cell technologies, (2) studying the occurrence and development of kidney diseases using scRNA-seq, (3) a molecular atlas of immune cells in kidney diseases, (4) the application of scRNA-seq on kidney organoids, and (5) insight into kidney allografts by scRNA-seq (Fig. 1).