Exosome Structures Supported by Machine Learning Can Be Used as a Promising Diagnostic Tool.

Cansever Mutlu, E; Kaya, M; Küçük, I; Ben-Nissan, B; Stamboulis, A

Exosome Structures Supported by Machine Learning Can Be Used as a Promising Diagnostic Tool.

Cansever Mutlu, E Kaya, M Küçük, I Ben-Nissan, B Stamboulis, A

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Materials (Basel), 2022, 15, (22), pp. 7967
Issue Date:: 2022-11-11

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Field	Value	Language
dc.contributor.author	Cansever Mutlu, E
dc.contributor.author	Kaya, M
dc.contributor.author	Küçük, I
dc.contributor.author	Ben-Nissan, B
dc.contributor.author	Stamboulis, A
dc.date.accessioned	2023-02-14T03:48:12Z
dc.date.available	2022-11-02
dc.date.available	2023-02-14T03:48:12Z
dc.date.issued	2022-11-11
dc.identifier.citation	Materials (Basel), 2022, 15, (22), pp. 7967
dc.identifier.issn	1996-1944
dc.identifier.issn	1996-1944
dc.identifier.uri	http://hdl.handle.net/10453/166107
dc.description.abstract	Principal component analysis (PCA) as a machine-learning technique could serve in disease diagnosis and prognosis by evaluating the dynamic morphological features of exosomes via Cryo-TEM-imaging. This hypothesis was investigated after the crude isolation of similarly featured exosomes derived from the extracellular vehicles (EVs) of immature dendritic cells (IDCs) JAWSII. It is possible to identify functional molecular groups by FTIR, but the unique physical and morphological characteristics of exosomes can only be revealed by specialized imaging techniques such as cryo-TEM. On the other hand, PCA has the ability to examine the morphological features of each of these IDC-derived exosomes by considering software parameters such as various membrane projections and differences in Gaussians, Hessian, hue, and class to assess the 3D orientation, shape, size, and brightness of the isolated IDC-derived exosome structures. In addition, Brownian motions from nanoparticle tracking analysis of EV IDC-derived exosomes were also compared with EV IDC-derived exosome images collected by scanning electron microscopy and confocal microscopy. Sodium-Dodecyl-Sulphate-Polyacrylamide-Gel-Electrophoresis (SDS-PAGE) was performed to separate the protein content of the crude isolates showing that no considerable protein contamination occurred during the crude isolation technique of IDC-derived-exosomes. This is an important finding because no additional purification of these exosomes is required, making PCA analysis both valuable and novel.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Materials (Basel)
dc.relation.isbasedon	10.3390/ma15227967
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	03 Chemical Sciences, 09 Engineering
dc.title	Exosome Structures Supported by Machine Learning Can Be Used as a Promising Diagnostic Tool.
dc.type	Journal Article
utslib.citation.volume	15
utslib.location.activity	Switzerland
utslib.for	03 Chemical Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2023-02-14T03:48:07Z
pubs.issue	22
pubs.publication-status	Published online
pubs.volume	15
utslib.citation.issue	22

Abstract:

Principal component analysis (PCA) as a machine-learning technique could serve in disease diagnosis and prognosis by evaluating the dynamic morphological features of exosomes via Cryo-TEM-imaging. This hypothesis was investigated after the crude isolation of similarly featured exosomes derived from the extracellular vehicles (EVs) of immature dendritic cells (IDCs) JAWSII. It is possible to identify functional molecular groups by FTIR, but the unique physical and morphological characteristics of exosomes can only be revealed by specialized imaging techniques such as cryo-TEM. On the other hand, PCA has the ability to examine the morphological features of each of these IDC-derived exosomes by considering software parameters such as various membrane projections and differences in Gaussians, Hessian, hue, and class to assess the 3D orientation, shape, size, and brightness of the isolated IDC-derived exosome structures. In addition, Brownian motions from nanoparticle tracking analysis of EV IDC-derived exosomes were also compared with EV IDC-derived exosome images collected by scanning electron microscopy and confocal microscopy. Sodium-Dodecyl-Sulphate-Polyacrylamide-Gel-Electrophoresis (SDS-PAGE) was performed to separate the protein content of the crude isolates showing that no considerable protein contamination occurred during the crude isolation technique of IDC-derived-exosomes. This is an important finding because no additional purification of these exosomes is required, making PCA analysis both valuable and novel.

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http://hdl.handle.net/10453/166107