Unsupervised Machine Learning‐Based Clustering of Nanosized Fluorescent Extracellular Vesicles

• Published in: Small (Weinheim an der Bergstrasse, Germany) Germany), 2021-02, Vol.17 (5), p.e2006786-n/a
• Main Authors: Kuypers, Sören, Smisdom, Nick, Pintelon, Isabel, Timmermans, Jean‐Pierre, Ameloot, Marcel, Michiels, Luc, Hendrix, Jelle, Hosseinkhani, Baharak
• Format: Article
• Language: English
• Subjects: Algorithms; Biomarkers; burst analysis spectroscopy; Clustering; Diagnostic systems; Endothelial cells; Extracellular vesicles; Fluorescence; Machine learning; Microscopes; Multidimensional data; multidimensional phenotyping; Nanoparticles; Nanotechnology; Quality assessment; Signal classification; Subgroups; Unsupervised learning; Vesicles
• ISSN: 1613-6810; 1613-6829
• DOI: 10.1002/smll.202006786

Extracellular vesicles (EV) are biological nanoparticles that play an important role in cell‐to‐cell communication. The phenotypic profile of EV populations is a promising reporter of disease, with direct clinical diagnostic relevance. Yet, robust methods for quantifying the biomarker content of EV have been critically lacking, and require a single‐particle approach due to their inherent heterogeneous nature. Here, multicolor single‐molecule burst analysis microscopy is used to detect multiple biomarkers present on single EV. The authors classify the recorded signals and apply the machine learning‐based t‐distributed stochastic neighbor embedding algorithm to cluster the resulting multidimensional data. As a proof of principle, the authors use the method to assess both the purity and the inflammatory status of EV, and compare cell culture and plasma‐derived EV isolated via different purification methods. This methodology is then applied to identify intercellular adhesion molecule‐1 specific EV subgroups released by inflamed endothelial cells, and to prove that apolipoprotein‐a1 is an excellent marker to identify the typical lipoprotein contamination in plasma. This methodology can be widely applied on standard confocal microscopes, thereby allowing both standardized quality assessment of patient plasma EV preparations, and diagnostic profiling of multiple EV biomarkers in health and disease. Herein, single burst analysis (SBA) is applied to profile multiple markers on single fluorescently‐labeled extracellular vesicles (EV). SBA can be applied to detect multiple fluorescent markers on single EV using any confocal microscope and requires a small sample volume. The current study demonstrates that machine learning combined with SBA can determine different EV subpopulations in a sample.