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High Resolution Imaging and Analysis of Single Extracellular Vesicles Using Mass Spectral Imaging and Machine Learning

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Abstract: Extracellular vesicles (EVs) are potentially useful biomarkers for disease detection and monitoring. Development of a label‐free technique for imaging and distinguishing small volumes of EVs from different cell types and cell states would be of great value. Here, we have designed a method to explore the chemical changes in EVs associated with neuroinflammation using Time‐of‐Flight Secondary Ion Mass spectrometry (ToF‐SIMS) and machine learning (ML). Mass spectral imaging was able to identify and differentiate EVs released by microglia following lipopolysaccharide (LPS) stimulation compared to a control group. This process requires a much smaller sample size (1 µL) than other molecular analysis methods (up to 50 µL). Conspicuously, we saw a reduction in free cysteine thiols (a marker of cellular oxidative stress associated with neuroinflammation) in EVs from microglial cells treated with LPS, consistent with the reduced cellular free thiol levels measured experimentally. This validates the synergistic combination of ToF‐SIMS and ML as a sensitive and valuable technique for collecting and analysing molecular data from EVs at high resolution.

Funding

Australian National Fabrication Facility; National Health and Medical Research Council, Grant/Award Number: GNT1132604; Office of National Intelligence, Australia-National Intelligence and Security Discovery Research Grant, Grant/Award Number: NI210100127

History

Publication Date

2023-09-11

Journal

Journal of Extracellular Biology

Volume

2

Issue

9

Article Number

e110

Pagination

14p.

Publisher

Wiley Periodicals, LLC on behalf of the International Society for Extracellular Vesicles

ISSN

2768-2811

Rights Statement

© 2023 The Authors. Journal of Extracellular Biology published by Wiley Periodicals, LLC on behalf of the International Society for Extracellular Vesicles. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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