Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/33490
Title: | Unsupervised Machine Learning-Based Clustering of Nanosized Fluorescent Extracellular Vesicles | Authors: | KUYPERS, Soren SMISDOM, Nick AMELOOT, Marcel MICHIELS, Luc HENDRIX, Jelle HOSSEINKHANI, Baharak Pintelon, Isabel Timmermans, Jean-Pierre |
Issue Date: | 2021 | Publisher: | WILEY-V C H VERLAG GMBH | Source: | Small (Weinheim. Print), 17 (5) (Art N° 2006786) | Abstract: | 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. | Notes: | Hendrix, J; Hosseinkhani, B (corresponding author), Hasselt Univ, Biomed Res Inst BIOMED, Martelarenlaan 42, B-3500 Hasselt, Belgium.; Hendrix, J (corresponding author), Hasselt Univ, Adv Opt Microscopy Ctr, Dynam Bioimaging Lab, B-3500 Hasselt, Belgium. Jelle.Hendrix@uhasselt.be; Baharak.Hosseinkhani@uhasselt.be |
Other: | Hendrix, J; Hosseinkhani, B (corresponding author), Hasselt Univ, Biomed Res Inst BIOMED, Martelarenlaan 42, B-3500 Hasselt, Belgium ; Hasselt Univ, Adv Opt Microscopy Ctr, Dynam Bioimaging Lab, B-3500 Hasselt, Belgium. Jelle.Hendrix@uhasselt.be; Baharak.Hosseinkhani@uhasselt.be | Keywords: | burst analysis spectroscopy;extracellular vesicles;machine learning;multidimensional phenotyping | Document URI: | http://hdl.handle.net/1942/33490 | ISSN: | 1613-6810 | e-ISSN: | 1613-6829 | DOI: | 10.1002/smll.202006786 | ISI #: | WOS:000607604800001 | Rights: | 2021 Wiley-VCH GmbH | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
2020.11.27.374728.full.pdf | Non Peer-reviewed author version | 1.38 MB | Adobe PDF | View/Open |
smll.202006786.pdf Restricted Access | Published version | 3.65 MB | Adobe PDF | View/Open Request a copy |
WEB OF SCIENCETM
Citations
11
checked on Oct 13, 2024
Page view(s)
60
checked on May 31, 2022
Download(s)
8
checked on May 31, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.