High resolution imaging and analysis of extracellular vesicles using mass spectral imaging and machine learning

Bamford, Sarah Elizabeth, Vassileff, Natasha ORCID: 0000-0001-6554-4496, Spiers, Jereme G ORCID: 0000-0001-5872-8983, Gardner, Wil, Winkler, David A, Muir, Benjamin W, Hill, Andrew F ORCID: 0000-0001-5581-2354 and Pigram, Paul J ORCID: 0000-0002-7972-492X (2023) High resolution imaging and analysis of extracellular vesicles using mass spectral imaging and machine learning. Journal of Extracellular Biology, 2 (9). ISSN 2768-2811


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.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/47147
DOI 10.1002/jex2.110
Official URL https://onlinelibrary.wiley.com/doi/10.1002/jex2.1...
Subjects Current > FOR (2020) Classification > 3205 Medical biochemistry and metabolomics
Current > Division/Research > Institute for Health and Sport
Keywords extracellular vesicles, biomarkers, imaging, machine learning, mass spectral imaging
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