MPAPASS software enables stitched multiplex, multidimensional EV repertoire analysis and a standard framework for reporting bead-based assays

  • Joshua A Welsh
  • Bryce Killingsworth
  • Julia Kepley
  • Tim Traynor
  • Sean Cook
  • Jason Savage
  • Jenn Marte
  • Min-Jung Lee
  • Hoyoung M Maeng
  • Michelle L Pleet
  • Setty Magana
  • André Gorgens
  • Cecile L Maire
  • Katrin Lamszus
  • Franz L Ricklefs
  • Maria J Merino
  • W Marston Linehan
  • Tim Greten
  • Tomer Cooks
  • Curtis C Harris
  • Andrea Apolo
  • Asim Abdel-Mageed
  • Alexander R Ivanov
  • Jane B Trepel
  • Matthew Roth
  • Mercedes Tkach
  • Aleksandar Milosavljevic
  • Clotilde Théry
  • Amy LeBlanc
  • Jay A Berzofsky
  • Eytan Ruppin
  • Kenneth Aldape
  • Kevin Camphausen
  • James L Gulley
  • Ionita Ghiran
  • Steve Jacobson
  • Jennifer C Jones

Beteiligte Einrichtungen

Abstract

Extracellular vesicles (EVs) of various types are released or shed from all cells. EVs carry proteins and contain additional protein and nucleic acid cargo that relates to their biogenesis and cell of origin. EV cargo in liquid biopsies is of widespread interest owing to its ability to provide a retrospective snapshot of cell state at the time of EV release. For the purposes of EV cargo analysis and repertoire profiling, multiplex assays are an essential tool in multiparametric analyte studies but are still being developed for high-parameter EV protein detection. Although bead-based EV multiplex analyses offer EV profiling capabilities with conventional flow cytometers, the utilization of EV multiplex assays has been limited by the lack of software analysis tools for such assays. To facilitate robust EV repertoire studies, we developed multiplex analysis post-acquisition analysis (MPAPASS) open-source software for stitched multiplex analysis, EV database-compatible reporting, and visualization of EV repertoires.

Bibliografische Daten

OriginalspracheEnglisch
Aufsatznummer100136
ISSN2667-2375
DOIs
StatusVeröffentlicht - 24.01.2022

Anmerkungen des Dekanats

© 2022.

PubMed 35474866