Frontiers in Molecular Biosciences (Oct 2022)

Photometric method for dual targeting of surface and surface-associated proteins on extracellular vesicles in the multiparametric test

  • Lee-Ann Marie Clegg,
  • Jenni Kathrine Sloth,
  • Rikke Bæk,
  • Malene Møller Jørgensen,
  • Malene Møller Jørgensen

DOI
https://doi.org/10.3389/fmolb.2022.917487
Journal volume & issue
Vol. 9

Abstract

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Extracellular vesicles (EVs) have become a topic of interest within the field of diagnostic biomarkers; however, recent developments in the study of EVs have increased the need for simpler but still comprehensive methods for characterization. Here, we describe how to simultaneously measure several surface or surface-associated proteins on EVs using a multiparametric microarray-based analysis termed Extracellular Vesicle Array (EV Array), which is developed to catch and phenotypically characterize small EVs. Previously, this analysis has been limited to measuring only one fluorescent signal per analysis. The analysis relies on antibodies printed onto a solid surface, for catching the EVs carrying the specific surface or surface-associated proteins, and on the subsequent fluorescent detection. For the optimization of detection, two antibodies with attached Cy3 or Cy5 were added to various combinations of the EV surface or surface-associated proteins: CD9, CD63, CD81, flotillin-1, and HSP90. In this study, the EV surface or surface-associated proteins were analyzed in human plasma from six healthy subjects. Changes observed in signal intensities from Cy3 and Cy5 related specifically to these combinations and allowed for a comparison of the two different fluorescent signals. When comparing the results, it was observed that it is possible to measure the EV surface or surface-associated proteins at both 532 nm (Cy3) and 635 nm (Cy5) simultaneously without a significant change in signals from the detection molecules. This allows us to measure multiple EV marker proteins in a single analysis, thereby more quickly finding complex biomarker patterns in a sample.

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