Automatically digital extracellular vesicles analyzer for size‐dependent subpopulation analysis in surface plasmon resonance microscopy

Author:

Feng Xie1,Zhai Chunhui1,Xu Jiaying1,Yang Yuting2,Yu Hui1

Affiliation:

1. School of Biomedical Engineering Shanghai Jiao Tong University Shanghai People's Republic of China

2. School of Sensing Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai People's Republic of China

Abstract

AbstractThe heterogeneity in extracellular vesicles (EVs) introduces an extra level of complexity in small EV‐based liquid biopsy for cancer diagnosis. Surface plasmon resonance microscopy (SPRM) offers sensitive and multifunctional analysis for single tiny EVs. However, the EV SPRM image analysis has been extremely laborious and time‐consuming due to the low contrast and bad signal‐to‐noise ratio. Herein, we present a digital EV analyzer (DEA), a software package, for the automatic analysis of EV data in the SPRM platform. This package enabled fully automated single EV identification, digital counting, sizing EV, and quantification of dwell time. Integrated with a classification algorithm and intelligent searching algorithm for subpopulation analysis, DEA was able to accurately group EVs with five different origins. We hope this software tool would largely promote the application of SPRM technology into cancer diagnosis with EVs.

Publisher

Wiley

Subject

Biomedical Engineering,Biomaterials

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