Quantitative proteomic analysis of biogenesis-based classification for extracellular vesicles

Published

January 1, 2001

Author(s)

Linwen Zhang, Jeremie Parot, Vincent Hackley, Illarion Turko

Abstract

terms and is much more heterogeneous and complex. Since the cell membrane origin of EVs predetermines their biological functions, the un-derstanding of EV biogenesis is important for accurate interpretation of observed results. Combining analytical separation of EV subpopulations with proteomic-based tracking of biogenesis is an attractive approach. In the present study, we pro-pose to take advantage of selective expression of some proteins in plasma or endosomal membranes and to use these pro-teins as plasma membrane-specific or endosomal membrane-specific markers. We have demonstrated that a quantitative mass spectrometry analysis allows simultaneous measurement of plasma membrane-specific and endosomal membrane-specific proteins in microvesicles and exosomes obtained after differential ultracentrifugation. Based on the quantitative appearance of these protein markers, we concluded that there is no actual size limitation and both microvesicles and exo-somes can be represented by large and small vesicles. We also found several occasions of predominant appearance of spe-cific proteins in selected EV samples. This observation can point to the existence of multiple protein-specific subpopulations of EVs. The further application of multi-detector asymmetrical-flow field-flow fractionation proved beneficial with respect to isolation of subpopulations with simultaneous physical quantification of the EVs.

Citation

ACS Journal of Proteome Research

Pub Type

Journals

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Keywords

xtracellular vesicles, exosomes, microvesicles, classification, targeted proteomics, QconCATs, multiple reaction monitoring, Field Flow Fractionation

Citation

Zhang, L. , Parot, J. , Hackley, V. and Turko, I. (2001), Quantitative proteomic analysis of biogenesis-based classification for extracellular vesicles, ACS Journal of Proteome Research, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930408 (Accessed December 1, 2025)