Journal of Extracellular Vesicles (Feb 2023)

An in vitro approach to understand contribution of kidney cells to human urinary extracellular vesicles

  • Karina Barreiro,
  • Abigail C. Lay,
  • German Leparc,
  • Van Du T. Tran,
  • Marcel Rosler,
  • Lusyan Dayalan,
  • Frederic Burdet,
  • Mark Ibberson,
  • Richard J. M. Coward,
  • Tobias B. Huber,
  • Bernhard K. Krämer,
  • Denis Delic,
  • Harry Holthofer

DOI
https://doi.org/10.1002/jev2.12304
Journal volume & issue
Vol. 12, no. 2
pp. n/a – n/a

Abstract

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Abstract Extracellular vesicles (EV) are membranous particles secreted by all cells and found in body fluids. Established EV contents include a variety of RNA species, proteins, lipids and metabolites that are considered to reflect the physiological status of their parental cells. However, to date, little is known about cell‐type enriched EV cargo in complex EV mixtures, especially in urine. To test whether EV secretion from distinct human kidney cells in culture differ and can recapitulate findings in normal urine, we comprehensively analysed EV components, (particularly miRNAs, long RNAs and protein) from conditionally immortalised human kidney cell lines (podocyte, glomerular endothelial, mesangial and proximal tubular cells) and compared to EV secreted in human urine. EV from cell culture media derived from immortalised kidney cells were isolated by hydrostatic filtration dialysis (HFD) and characterised by electron microscopy (EM), nanoparticle tracking analysis (NTA) and Western blotting (WB). RNA was isolated from EV and subjected to miRNA and RNA sequencing and proteins were profiled by tandem mass tag proteomics. Representative sets of EV miRNAs, RNAs and proteins were detected in each cell type and compared to human urinary EV isolates (uEV), EV cargo database, kidney biopsy bulk RNA sequencing and proteomics, and single‐cell transcriptomics. This revealed that a high proportion of the in vitro EV signatures were also found in in vivo datasets. Thus, highlighting the robustness of our in vitro model and showing that this approach enables the dissection of cell type specific EV cargo in biofluids and the potential identification of cell‐type specific EV biomarkers of kidney disease.

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