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http://hdl.handle.net/1942/33490
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DC Field | Value | Language |
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dc.contributor.author | KUYPERS, Soren | - |
dc.contributor.author | SMISDOM, Nick | - |
dc.contributor.author | AMELOOT, Marcel | - |
dc.contributor.author | MICHIELS, Luc | - |
dc.contributor.author | HENDRIX, Jelle | - |
dc.contributor.author | HOSSEINKHANI, Baharak | - |
dc.contributor.author | Pintelon, Isabel | - |
dc.contributor.author | Timmermans, Jean-Pierre | - |
dc.date.accessioned | 2021-02-17T13:40:48Z | - |
dc.date.available | 2021-02-17T13:40:48Z | - |
dc.date.issued | 2021 | - |
dc.date.submitted | 2021-02-08T09:18:00Z | - |
dc.identifier.citation | Small, 17 (5) (Art N° 2006786) | - |
dc.identifier.issn | 1613-6810 | - |
dc.identifier.uri | http://hdl.handle.net/1942/33490 | - |
dc.description.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. | - |
dc.description.sponsorship | The authors thank Veronique Vastmans and Iris Reniers for the technical assistance. S.K. was funded by Hasselt University. J.H. acknowledges funding by UH-BOF (BOF20TT06). The FWO-Hercules Foundation of Flanders (grant number R-7087), the Research Foundation Flanders (FWO, Herculesstichting) (Grant number G0H3716N) and the province of Limburg (Belgium) (tUL Impuls II) are acknowledged for funding the microscopy hardware. L.M. and B.H. acknowledge the funding by the EU/EFRO through the Interreg V Flanders-the Netherlands project Trans Tech Diagnostics (TTD) and grant number 2015N017047 of the province of Limburg. | - |
dc.language.iso | en | - |
dc.publisher | WILEY-V C H VERLAG GMBH | - |
dc.rights | 2021 Wiley-VCH GmbH | - |
dc.subject.other | burst analysis spectroscopy | - |
dc.subject.other | extracellular vesicles | - |
dc.subject.other | machine learning | - |
dc.subject.other | multidimensional phenotyping | - |
dc.title | Unsupervised Machine Learning-Based Clustering of Nanosized Fluorescent Extracellular Vesicles | - |
dc.type | Journal Contribution | - |
dc.identifier.issue | 5 | - |
dc.identifier.volume | 17 | - |
local.format.pages | 14 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.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. | - |
dc.description.notes | Jelle.Hendrix@uhasselt.be; Baharak.Hosseinkhani@uhasselt.be | - |
dc.description.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 | - |
local.publisher.place | POSTFACH 101161, 69451 WEINHEIM, GERMANY | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.bibliographicCitation.artnr | 2006786 | - |
dc.identifier.doi | 10.1002/smll.202006786 | - |
dc.identifier.pmid | 33448084 | - |
dc.identifier.isi | WOS:000607604800001 | - |
dc.identifier.eissn | 1613-6829 | - |
local.provider.type | wosris | - |
local.uhasselt.uhpub | yes | - |
local.description.affiliation | [Kuypers, Soren; Smisdom, Nick; Ameloot, Marcel; Michiels, Luc; Hendrix, Jelle; Hosseinkhani, Baharak] Hasselt Univ, Biomed Res Inst BIOMED, Martelarenlaan 42, B-3500 Hasselt, Belgium. | - |
local.description.affiliation | [Pintelon, Isabel; Timmermans, Jean-Pierre] Univ Antwerp, Antwerp Ctr Adv Microscopy ACAM, Lab Cell Biol & Histol, Univ Pl 1, B-2610 Antwerp, Belgium. | - |
local.description.affiliation | [Hendrix, Jelle] Hasselt Univ, Adv Opt Microscopy Ctr, Dynam Bioimaging Lab, B-3500 Hasselt, Belgium. | - |
local.uhasselt.international | no | - |
item.validation | ecoom 2022 | - |
item.fulltext | With Fulltext | - |
item.accessRights | Open Access | - |
item.contributor | KUYPERS, Soren | - |
item.contributor | SMISDOM, Nick | - |
item.contributor | AMELOOT, Marcel | - |
item.contributor | MICHIELS, Luc | - |
item.contributor | HENDRIX, Jelle | - |
item.contributor | HOSSEINKHANI, Baharak | - |
item.contributor | Pintelon, Isabel | - |
item.contributor | Timmermans, Jean-Pierre | - |
item.fullcitation | KUYPERS, Soren; SMISDOM, Nick; AMELOOT, Marcel; MICHIELS, Luc; HENDRIX, Jelle; HOSSEINKHANI, Baharak; Pintelon, Isabel & Timmermans, Jean-Pierre (2021) Unsupervised Machine Learning-Based Clustering of Nanosized Fluorescent Extracellular Vesicles. In: Small, 17 (5) (Art N° 2006786). | - |
crisitem.journal.issn | 1613-6810 | - |
crisitem.journal.eissn | 1613-6829 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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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 |
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