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Review
. 2021 Aug 17:8:657614.
doi: 10.3389/fmed.2021.657614. eCollection 2021.

Single Cell Transcriptome Helps Better Understanding Crosstalk in Diabetic Kidney Disease

Affiliations
Review

Single Cell Transcriptome Helps Better Understanding Crosstalk in Diabetic Kidney Disease

Chunyang Du et al. Front Med (Lausanne). .

Abstract

Years of research revealed that crosstalk extensively existed among kidney cells, cell factors and metabolites and played an important role in the development of diabetic kidney disease (DKD). In the last few years, single-cell RNA sequencing (scRNA-seq) technology provided new insight into cellular heterogeneity and genetic susceptibility regarding DKD at cell-specific level. The studies based on scRNA-seq enable a much deeper understanding of cell-specific processes such as interaction between cells. In this paper, we aim to review recent progress in single cell transcriptomic analyses of DKD, particularly highlighting on intra- or extra-glomerular cell crosstalk, cellular targets and potential therapeutic strategies for DKD.

Keywords: crosstalk; diabetic kidney disease; glomerulus; single-cell RNA sequencing; tubular epithelial cell.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
scRNA-seq experimental workflow. All scRNA-seq techniques include several common steps: enzymatic dissociation of the sample into a single cell suspension; individualized RNA capture (individual transcriptome barcoding); reverse transcription and transcriptome amplification; cDNA library preparation; sequencing.
Figure 2
Figure 2
Diagrammed normal glomerular structure.
Figure 3
Figure 3
Intraglomerular crosstalk based on ligand-receptor pair analysis of scRNA-seq. Pairs in orange frames come from the analysis of kidney of human with early diabetic nephropathy. Pairs in purple frames come from the analysis of kidney of diabetic mice. SLIT3, slit guidance ligand 3; ROBO2, roundabout guidance receptor 2; NAMPT, Nicotinamide phosphoribosyltransferase; INSR, insulin receptors; CCN1, cellular communication network factor1; ITGB3/5, integrin beta3/5; ITGAV, integrin subunit alpha-V; Tgfb3, Transforming growth factor 3; Sdc4, syndecan 4; Notch3, notch reporter 3; Tmem108, transmembrane protein 108; NRG3, neuregulin-3; ERBB4, Erb-B2 receptor tyrosine kinase 4; Bmp2, bone morphogenetic protein 2; Bmp7, bone morphogenetic protein 7; Bmpr2, bone morphogenetic protein receptor 2; Epha3, erythropoietin-producing hepatocellular carcinoma A3; VEGFA, vascular endothelial growth factor A; Flt1(VEGFR1, vascular endothelial growth factor receptor 1; Kdr (VEGFR2), vascular endothelial growth factor receptor 2; Angpt, angiopoietin; Tie (Tek): angiopoietin 1 receptor; Efnb2, ephrin B2; Efna1, ephrin A1; LTBP1, latent transforming growth factor (TGF)-beta binding protein-1; Cdh5, cadherin 5; Dag1, dystroglycan 1; Sema6a, semaphorin 6a; Itm2b, Integral membrane protein 2B; Cdf5, cycling dof factors 5; EFEMP1, Epidermal Growth Factor-containing Fibulin-like Extracellular Matrix Protein 1; EGFR, Epidermal Growth Factor Receptor. [Pairs in orange are summarized from Mitu et al. (93) and pairs in purple are summarized from Fu et al. (35)].

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