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Chronic Kidney Disease and Progression

Single-cell RNA sequencing in diabetic kidney disease: a literature review

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Article: 2387428 | Received 13 Nov 2023, Accepted 29 Jul 2024, Published online: 04 Aug 2024

References

  • Bonner R, Albajrami O, Hudspeth J, et al. Diabetic kidney disease. Prim Care. 2020;47(4):645–659. doi:10.1016/j.pop.2020.08.004.
  • Johansen KL, Chertow GM, Foley RN, et al. US Renal Data System 2020 Annual Data Report: epidemiology of Kidney Disease in the United States. Am J Kidney Dis. 2021;77(4 Suppl 1):A7–A8. doi:10.1053/j.ajkd.2021.01.002.
  • Marzolla V, Infante M, Armani A, et al. Efficacy and safety of finerenone for treatment of diabetic kidney disease: current knowledge and future perspective. Expert Opin Drug Saf. 2022;21(9):1161–1170. doi:10.1080/14740338.2022.2130889.
  • Yamazaki T, Mimura I, Tanaka T, et al. Treatment of Diabetic Kidney Disease: current and Future. Diabetes Metab J. 2021;45(1):11–26. doi:10.4093/dmj.2020.0217.
  • Yu J, Liu Y, Li H, et al. Pathophysiology of diabetic kidney disease and autophagy: a review. Medicine. 2023;102(30):e33965. doi:10.1097/MD.0000000000033965.
  • Hu S, Hang X, Wei Y, et al. Crosstalk among podocytes, glomerular endothelial cells and mesangial cells in diabetic kidney disease: an updated review. Cell Commun Signal. 2024;22(1):136. doi:10.1186/s12964-024-01502-3.
  • Chen SJ, Lv LL, Liu BC, et al. Crosstalk between tubular epithelial cells and glomerular endothelial cells in diabetic kidney disease. Cell Prolif. 2020;53(3):e12763. doi:10.1111/cpr.12763.
  • Du C, Ren Y, Li G, et al. Single cell transcriptome helps better understanding crosstalk in diabetic kidney disease. Front Med. 2021;8:657614. doi:10.3389/fmed.2021.657614.
  • Thomas MC. Targeting the pathobiology of diabetic kidney disease. Adv Chronic Kidney Dis. 2021;28(4):282–289. doi:10.1053/j.ackd.2021.07.001.
  • Jiang S, Luo M, Bai X, et al. Cellular crosstalk of glomerular endothelial cells and podocytes in diabetic kidney disease. J Cell Commun Signal. 2022;16(3):313–331. doi:10.1007/s12079-021-00664-w.
  • Zhang X, Chao P, Zhang L, et al. Single-cell RNA and transcriptome sequencing profiles identify immune-associated key genes in the development of diabetic kidney disease. Front Immunol. 2023;14:1030198. doi:10.3389/fimmu.2023.1030198.
  • Casalena GA, Yu L, Gil R, et al. The diabetic microenvironment causes mitochondrial oxidative stress in glomerular endothelial cells and pathological crosstalk with podocytes. Cell Commun Signal. 2020;18(1):105. doi:10.1186/s12964-020-00605-x.
  • Kaur H, Advani A. The study of single cells in diabetic kidney disease. J Nephrol. 2021;34(6):1925–1939. doi:10.1007/s40620-020-00964-1.
  • Jung CY, Yoo TH. Pathophysiologic mechanisms and potential biomarkers in diabetic kidney disease. Diabetes Metab J. 2022;46(2):181–197. doi:10.4093/dmj.2021.0329.
  • Wilson PC, Muto Y, Wu H, et al. Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression. Nat Commun. 2022;13(1):5253. doi:10.1038/s41467-022-32972-z.
  • Huang G, Li M, Li Y, et al. Metabolomics: a new tool to reveal the nature of diabetic kidney disease. Lab Med. 2022;53(6):545–551. doi:10.1093/labmed/lmac041.
  • Mulder S, Hamidi H, Kretzler M, et al. An integrative systems biology approach for precision medicine in diabetic kidney disease. Diabetes Obes Metab. 2018;20(Suppl 3):6–13. doi:10.1111/dom.13416.
  • Komorowsky CV, Brosius FC, 3rd, Pennathur S, et al. Perspectives on systems biology applications in diabetic kidney disease. J Cardiovasc Transl Res. 2012;5(4):491–508. doi:10.1007/s12265-012-9382-7.
  • Brosius FC, Ju W. The promise of systems biology for diabetic kidney disease. Adv Chronic Kidney Dis. 2018;25(2):202–213. doi:10.1053/j.ackd.2017.10.012.
  • Barreiro K, Dwivedi OP, Leparc G, et al. Comparison of urinary extracellular vesicle isolation methods for transcriptomic biomarker research in diabetic kidney disease. J Extracell Vesicles. 2020;10(2):e12038. doi:10.1002/jev2.12038.
  • Sembach FE, Ægidius HM, Fink LN, et al. Integrative transcriptomic profiling of a mouse model of hypertension-accelerated diabetic kidney disease. Dis Model Mech. 2021;14(10):86. doi:10.1242/dmm.049086.
  • Zheng W, Guo J, Liu ZS. Effects of metabolic memory on inflammation and fibrosis associated with diabetic kidney disease: an epigenetic perspective. Clin Epigenetics. 2021;13(1):87. doi:10.1186/s13148-021-01079-5.
  • Fan X, Xu M, Chen X, et al. Proteomic profiling and ­correlations with clinical features reveal biomarkers indicative of diabetic retinopathy with diabetic kidney disease. Front Endocrinol. 2022;13:1001391. doi:10.3389/fendo.2022.1001391.
  • Zhang S, Li X, Lin J, et al. Review of single-cell RNA-seq data clustering for cell-type identification and characterization. RNA. 2023;29(5):517–530. doi:10.1261/rna.078965.121.
  • Wu H, Gonzalez Villalobos R, Yao X, et al. Mapping the single-cell transcriptomic response of murine diabetic kidney disease to therapies. Cell Metab. 2022;34(7):1064–1078.e1066. doi:10.1016/j.cmet.2022.05.010.
  • Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6(5):377–382. doi:10.1038/nmeth.1315.
  • Lao KQ, Tang F, Barbacioru C, et al. mRNA-sequencing whole transcriptome analysis of a single cell on the SOLiD system. J Biomol Tech. 2009;20(5):266–271.
  • Flynn E, Almonte-Loya A, Fragiadakis GK. Single-cell multiomics. Annu Rev Biomed Data Sci. 2023;6(1):313–337. doi:10.1146/annurev-biodatasci-020422-050645.
  • Dai X, Cai L, He F. Single-cell sequencing: expansion, integration and translation. Brief Funct Genomics. 2022;21(4):280–295. doi:10.1093/bfgp/elac011.
  • Shalek AK, Satija R, Adiconis X, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature. 2013;498(7453):236–240. doi:10.1038/nature12172.
  • Kumar RM, Cahan P, Shalek AK, et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature. 2014;516(7529):56–61. doi:10.1038/nature13920.
  • Ding S, Chen X, Shen K. Single-cell RNA sequencing in breast cancer: understanding tumor heterogeneity and paving roads to individualized therapy. Cancer Commun. 2020;40(8):329–344. doi:10.1002/cac2.12078.
  • Huang S, Shi W, Li S, et al. Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis. Lab Chip. 2024;24(10):2601–2621. doi:10.1039/d4lc00105b.
  • Cheng J, Liao J, Shao X, et al. Multiplexing methods for simultaneous large-scale transcriptomic profiling of samples at single-cell resolution. Adv Sci. 2021;8(17):e2101229. doi:10.1002/advs.202101229.
  • Zhang P, Li X, Pan C, et al. Single-cell RNA sequencing to track novel perspectives in HSC heterogeneity. Stem Cell Res Ther. 2022;13(1):39. doi:10.1186/s13287-022-02718-1.
  • Wang S, Sun ST, Zhang XY, et al. The evolution of single-cell RNA sequencing technology and application: progress and perspectives. Int J Mol Sci. 2023;24(3):943. doi:10.3390/ijms24032943.
  • Grabski IN, Street K, Irizarry RA. Significance analysis for clustering with single-cell RNA-sequencing data. Nat Methods. 2023;20(8):1196–1202. doi:10.1038/s41592-023-01933-9.
  • Shao X, Lu X, Liao J, et al. New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data. Protein Cell. 2020;11(12):866–880. doi:10.1007/s13238-020-00727-5.
  • Farrell JA, Wang Y, Riesenfeld SJ, et al. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science. 2018;360(6392):31. doi:10.1126/science.aar3131.
  • Zhong S, Zhang S, Fan X, et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature. 2018;555(7697):524–528. doi:10.1038/nature25980.
  • Choi JR, Yong KW, Choi JY, et al. Single-cell RNA sequencing and its combination with protein and DNA analyses. Cells. 2020;9(5):1130. doi:10.3390/cells9051130.
  • Lindström NO, Tran T, Guo J, et al. Conserved and divergent molecular and anatomic features of human and mouse nephron patterning. J Am Soc Nephrol. 2018;29(3):825–840. doi:10.1681/ASN.2017091036.
  • Ryan D, Sutherland MR, Flores TJ, et al. Development of the human fetal kidney from mid to late gestation in male and female infants. EBioMedicine. 2018;27:275–283. doi:10.1016/j.ebiom.2017.12.016.
  • Schumacher A, Rookmaaker MB, Joles JA, et al. Defining the variety of cell types in developing and adult human kidneys by single-cell RNA sequencing. NPJ Regen Med. 2021;6(1):45. doi:10.1038/s41536-021-00156-w.
  • Park J, Shrestha R, Qiu C, et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science. 2018;360(6390):758–763. doi:10.1126/science.aar2131.
  • Balzer MS, Rohacs T, Susztak K. How many cell types are in the kidney and what do they do? Annu Rev Physiol. 2022;84(1):507–531. doi:10.1146/annurev-physiol-052521-121841.
  • Kretzler M, Menon R. Single-cell sequencing the glomerulus, unraveling the molecular programs of glomerular filtration, one cell at a time. J Am Soc Nephrol. 2018;29(8):2036–2038. doi:10.1681/ASN.2018060626.
  • Wu H, Kirita Y, Donnelly EL, et al. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J Am Soc Nephrol. 2019;30(1):23–32. doi:10.1681/ASN.2018090912.
  • Chung JJ, Goldstein L, Chen YJ, et al. Single-cell transcriptome profiling of the kidney glomerulus identifies key cell types and reactions to injury. J Am Soc Nephrol. 2020;31(10):2341–2354. doi:10.1681/ASN.2020020220.
  • Fu J, Akat KM, Sun Z, et al. Single-cell RNA profiling of glomerular cells shows dynamic changes in experimental diabetic kidney disease. J Am Soc Nephrol. 2019;30(4):533–545. doi:10.1681/ASN.2018090896.
  • Karaiskos N, Rahmatollahi M, Boltengagen A, et al. A single-cell transcriptome atlas of the mouse glomerulus. J Am Soc Nephrol. 2018;29(8):2060–2068. doi:10.1681/ASN.2018030238.
  • Wu J, Sun Z, Yang S, et al. Kidney single-cell transcriptome profile reveals distinct response of proximal tubule cells to SGLT2i and ARB treatment in diabetic mice. Mol Ther. 2022;30(4):1741–1753. doi:10.1016/j.ymthe.2021.10.013.
  • Wu C, Tao Y, Li N, et al. Prediction of cellular targets in diabetic kidney diseases with single-cell transcriptomic analysis of db/db mouse kidneys. J Cell Commun Signal. 2022;17(1):169–188. doi:10.1007/s12079-022-00685-z.
  • Fu J, Sun Z, Wang X, et al. The single-cell landscape of kidney immune cells reveals transcriptional heterogeneity in early diabetic kidney disease. Kidney Int. 2022;102(6):1291–1304. doi:10.1016/j.kint.2022.08.026.
  • Lake BB, Chen S, Hoshi M, et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun. 2019;10(1):2832. doi:10.1038/s41467-019-10861-2.
  • Arazi A, Rao DA, Berthier CC, et al. The immune cell landscape in kidneys of patients with lupus nephritis. Nat Immunol. 2019;20(7):902–914. doi:10.1038/s41590-019-0398-x.
  • Lin H, Ma X, Xiao F, et al. Identification of a special cell type as a determinant of the kidney tropism of SARS-CoV-2. Febs J. 2021;288(17):5163–5178. doi:10.1111/febs.16114.
  • Muto Y, Wilson PC, Ledru N, et al. Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney. Nat Commun. 2021;12(1):2190. doi:10.1038/s41467-021-22368-w.
  • Dhillon P, Park J, Hurtado Del Pozo C, et al. The nuclear receptor ESRRA protects from kidney disease by coupling metabolism and differentiation. Cell Metab. 2021;33(2):379–394.e378. doi:10.1016/j.cmet.2020.11.011.
  • Wei Y, Gao X, Li A, et al. Single-nucleus transcriptomic analysis reveals important cell cross-talk in diabetic kidney disease. Front Med. 2021;8:657956. doi:10.3389/fmed.2021.657956.
  • Price GW, Potter JA, Williams BM, et al. Connexin-mediated cell communication in the kidney: a potential therapeutic target for future intervention of diabetic kidney disease?: Joan Mott Prize Lecture. Exp Physiol. 2020;105(2):219–229. doi:10.1113/EP087770.
  • Huang Y, Li R, Zhang L, et al. Extracellular vesicles from high glucose-treated podocytes induce apoptosis of proximal tubular epithelial cells. Front Physiol. 2020;11:579296. doi:10.3389/fphys.2020.579296.
  • Chen L, Lee JW, Chou CL, et al. Transcriptomes of major renal collecting duct cell types in mouse identified by single-cell RNA-seq. Proc Natl Acad Sci USA. 2017;114(46):E9989–E9998. doi:10.1073/pnas.1710964114.
  • Stewart BJ, Ferdinand JR, Young MD, et al. Spatiotemporal immune zonation of the human kidney. Science. 2019;365(6460):1461–1466. doi:10.1126/science.aat5031.
  • Papadopoulou-Marketou N, Chrousos GP, Kanaka-Gantenbein C. Diabetic nephropathy in type 1 diabetes: a review of early natural history, pathogenesis, and diagnosis. Diabetes Metab Res Rev. 2017;33(2):41. doi:10.1002/dmrr.2841.
  • Fu J, Lee K, Chuang PY, et al. Glomerular endothelial cell injury and cross talk in diabetic kidney disease. Am J Physiol Renal Physiol. 2015;308(4):F287–297. doi:10.1152/ajprenal.00533.2014.
  • Wilson PC, Wu H, Kirita Y, et al. The single-cell transcriptomic landscape of early human diabetic nephropathy. Proc Natl Acad Sci USA. 2019;116(39):19619–19625. doi:10.1073/pnas.1908706116.
  • Lu X, Li L, Suo L, et al. Single-cell RNA sequencing profiles identify important pathophysiologic factors in the progression of diabetic nephropathy. Front Cell Dev Biol. 2022;10:798316. doi:10.3389/fcell.2022.798316.
  • Zhu M, Zhang Z, Chen Z, et al. Single-cell RNA landscape of cell fate decision of renal proximal tubular epithelial cells and immune-microenvironment in kidney fibrosis. Clin Transl Med. 2022;12(9):e1010. doi:10.1002/ctm2.1010.
  • Balzer MS, Doke T, Yang YW, et al. Single-cell analysis highlights differences in druggable pathways underlying adaptive or fibrotic kidney regeneration. Nat Commun. 2022;13(1):4018. doi:10.1038/s41467-022-31772-9.
  • Ix JH, Shlipak MG. The promise of tubule biomarkers in kidney disease: a review. Am J Kidney Dis. 2021;78(5):719–727. doi:10.1053/j.ajkd.2021.03.026.
  • Menon R, Otto EA, Hoover P, et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight. 2020;5(6):44. doi:10.1172/jci.insight.133267.
  • Menon R, Otto EA, Kokoruda A, et al. Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney. Development. 2018;145(16):38. doi:10.1242/dev.164038.
  • Czerniecki SM, Cruz NM, Harder JL, et al. High-throughput screening enhances kidney organoid differentiation from human pluripotent stem cells and enables automated multidimensional phenotyping. Cell Stem Cell. 2018;22(6):929–940 e924. doi:10.1016/j.stem.2018.04.022.
  • Harder JL, Menon R, Otto EA, et al. Organoid single cell profiling identifies a transcriptional signature of glomerular disease. JCI Insight. 2019;4(1):97. doi:10.1172/jci.insight.122697.
  • Wu H, Uchimura K, Donnelly EL, et al. Comparative analysis and refinement of human PSC-derived kidney organoid differentiation with single-cell transcriptomics. Cell Stem Cell. 2018;23(6):869–881 e868. doi:10.1016/j.stem.2018.10.010.
  • Robson KJ, Ooi JD, Holdsworth SR, et al. HLA and kidney disease: from associations to mechanisms. Nat Rev Nephrol. 2018;14(10):636–655. doi:10.1038/s41581-018-0057-8.
  • Xu X, Eales JM, Akbarov A, et al. Molecular insights into genome-wide association studies of chronic kidney disease-defining traits. Nat Commun. 2018;9(1):4800. doi:10.1038/s41467-018-07260-4.
  • Hirohama D, Abedini A, Moon S, et al. Unbiased human kidney tissue proteomics identifies matrix metalloproteinase 7 as a kidney disease biomarker. J Am Soc Nephrol. 2023;34(7):1279–1291. doi:10.1681/ASN.0000000000000141.
  • Ye Q, Xu G, Yuan H, et al. Urinary PART1 and PLA2R1 could potentially serve as diagnostic markers for diabetic kidney disease patients. Diabetes Metab Syndr Obes. 2023;16:4215–4231. doi:10.2147/DMSO.S445341.
  • Zhang Y, Li W, Zhou Y. Identification of hub genes in diabetic kidney disease via multiple-microarray analysis. Ann Transl Med. 2020;8(16):997–997. doi:10.21037/atm-20-5171.
  • Tsai YC, Kuo MC, Huang JC, et al. Single-cell transcriptomic profiles in the pathophysiology within the microenvironment of early diabetic kidney disease. Cell Death Dis. 2023;14(7):442. doi:10.1038/s41419-023-05947-1.
  • Li Y, Haug S, Schlosser P, et al. Integration of GWAS summary statistics and gene expression reveals target cell types underlying kidney function traits. J Am Soc Nephrol. 2020;31(10):2326–2340. doi:10.1681/ASN.2020010051.
  • Bai M, Chen H, Ding D, et al. MicroRNA-214 promotes chronic kidney disease by disrupting mitochondrial oxidative phosphorylation. Kidney Int. 2019;95(6):1389–1404. doi:10.1016/j.kint.2018.12.028.
  • Fu J, Wei C, Zhang W, et al. Gene expression profiles of glomerular endothelial cells support their role in the glomerulopathy of diabetic mice. Kidney Int. 2018;94(2):326–345. doi:10.1016/j.kint.2018.02.028.
  • Lombardo JA, Aliaghaei M, Nguyen QH, et al. Microfluidic platform accelerates tissue processing into single cells for molecular analysis and primary culture models. Nat Commun. 2021;12(1):2858. doi:10.1038/s41467-021-23238-1.
  • Anders HJ, Huber TB, Isermann B, et al. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nat Rev Nephrol. 2018;14(6):361–377. doi:10.1038/s41581-018-0001-y.
  • Barrera-Chimal J, Lima-Posada I, Bakris GL, et al. Mineralocorticoid receptor antagonists in diabetic kidney disease – mechanistic and therapeutic effects. Nat Rev Nephrol. 2022;18(1):56–70. doi:10.1038/s41581-021-00490-8.
  • Beermann J, Piccoli MT, Viereck J, et al. Non-coding RNAs in development and disease: background, mechanisms, and therapeutic approaches. Physiol Rev. 2016;96(4):1297–1325. doi:10.1152/physrev.00041.2015.
  • Bhatti GK, Khullar N, Sidhu IS, et al. Emerging role of non-coding RNA in health and disease. Metab Brain Dis. 2021;36(6):1119–1134. doi:10.1007/s11011-021-00739-y.
  • Guo J, Liu Z, Gong R. Long noncoding RNA: an emerging player in diabetes and diabetic kidney disease. Clin Sci. 2019;133(12):1321–1339. doi:10.1042/CS20190372.
  • Zhao Y, Yan G, Mi J, et al. The impact of lncRNA on diabetic kidney disease: systematic review and in silico analyses. Comput Intell Neurosci. 2022;2022:8400106. doi:10.1155/2022/8400106.
  • Wang X, He Y, Zhang Q, et al. Direct comparative analyses of 10X genomics chromium and smart-seq2. Genomics Proteomics Bioinformatics. 2021;19(2):253–266. doi:10.1016/j.gpb.2020.02.005.
  • Katzenelenbogen Y, Sheban F, Yalin A, et al. Coupled scRNA-Seq and intracellular protein activity reveal an immunosuppressive role of TREM2 in cancer. Cell. 2020;182(4):872–885 e819. doi:10.1016/j.cell.2020.06.032.
  • Longo SK, Guo MG, Ji AL, et al. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet. 2021;22(10):627–644. doi:10.1038/s41576-021-00370-8.
  • Lv G, Zhang L, Gao L, et al. The application of single-cell sequencing in pancreatic neoplasm: analysis, diagnosis and treatment. Br J Cancer. 2022;128(2):206–218. doi:10.1038/s41416-022-02023-x.
  • Wang M, Gu M, Liu L, et al. Single-Cell RNA Sequencing (scRNA-seq) in cardiac tissue: applications and limitations. Vasc Health Risk Manag. 2021;17:641–657. doi:10.2147/VHRM.S288090.
  • Huang K, Xu Y, Feng T, et al. The advancement and application of the single-cell transcriptome in biological and medical research. Biology. 2024;13(6):451. doi:10.3390/biology13060451.
  • Wagner DE, Klein AM. Lineage tracing meets single-cell omics: opportunities and challenges. Nat Rev Genet. 2020;21(7):410–427. doi:10.1038/s41576-020-0223-2.
  • Cai M, Bompada P, Atac D, et al. Epigenetic regulation of glucose-stimulated osteopontin (OPN) expression in diabetic kidney. Biochem Biophys Res Commun. 2016;469(1):108–113. doi:10.1016/j.bbrc.2015.11.079.
  • Deleersnijder D, Callemeyn J, Arijs I, et al. Current methodological challenges of single-cell and single-nucleus RNA-sequencing in glomerular diseases. J Am Soc Nephrol. 2021;32(8):1838–1852. doi:10.1681/ASN.2021020157.