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Defining Exosome Profiles for Personalized Medicine with Mt-SEA Multiplex-to-Single EV Analysis Pipeline

Wednesday, September 13, 2017 — Poster Session I

12:00 p.m. – 1:30 p.m.
FAES Terrace
NCI
SYSBIO-1

Authors

  • JA Welsh
  • J Kepley
  • A Rosner
  • A Barfield
  • K McKinnon
  • V Galli
  • J Savage
  • J Marte
  • K Conlon
  • T Waldmann
  • JA Berzofsky
  • G Franchini
  • K Camphausen
  • JC Jones

Abstract

Extracellular Vesicles (EVs) carry surface receptors and cargo that are characteristic of their cells of origin. A robust pipeline for EV analysis is needed before EVs can be used as reliable clinical biomarkers. We developed a pipeline to analyze biofluid EVs before, during, and after treatment that combines mutiplex EV analysis with high-resolution single EV nano-flow cytometry. By combining multiplex analysis and high-resolution methods together into one coordinated Mutiplex-to-Single EV (Mt-SEA) pipeline, we are able to characterize the breadth and details of relevant EV subsets. Our current studies demonstrate strong correlations of "liquid biopsy" exosome repertoires with tumor burden, disease biology, and responses to treatment. EVs were isolated with size exclusion chromatography and characterized with nanoparticle tracking analysis (NanoSight LM-10). Multiplex EV capture beads were used with APC-conjugated detection antibodies to identify major EV subsets. Next, high-resolution characterization of selected individual EV epitopes was performed with a prototype nanoFCM analyzer. With this Mt-SEA approach, we are able to combine the breadth of high dimensional EV multiplex protein analysis with direct, high sensitivity quantification of epitopes on individual EVs. The use of EVs as clinical biomarkers, to evaluate tumors (extent of disease, molecular characteristics, etc) and responses to treatment, requires a combination of methods to broadly characterize EVs along and rigorously characterize certain EV subsets and their attributes. The Mt-SEA pipeline provides a broad survey of EV populations, followed by individual EV analysis. With this approach, we are able to detect tumor-associated EVs and monitor changes in their cargo during and after treatment.

Category: Systems Biology