Building predictive disease models using extracellular vesicle microscale flow cytometry and machine learning.

ABSTRACT: Extracellular vesicles (EVs) are highly abundant in human biofluids, containing a repertoire of macromolecules and biomarkers representative of the tissue of origin. EVs released by tumours can communicate key signals both locally and to distant sites to promote growth and survival or impact invasive and metastatic progression. Microscale flow cytometry of circulating EVs is an emerging technology that is a promising alternative to biopsy for disease diagnosis. However, biofluid-derived EVs are highly heterogeneous in size and composition, making their analysis complex. To address this, we developed a machine learning approach combined with EV microscale cytometry using tissue- and disease-specific biomarkers to generate predictive models. We demonstrate the utility of this novel extracell

SUBMITTER: Paproski RJ 

PROVIDER: S-EPMC9980304 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature