High-Throughput, Time-Resolved Mechanical Phenotyping of Prostate Cancer Cells

Belotti, Y; Tolomeo, S; Conneely, MJ; Huang, T; McKenna, SJ; Nabi, G; McGloin, D

High-Throughput, Time-Resolved Mechanical Phenotyping of Prostate Cancer Cells

Belotti, Y Tolomeo, S Conneely, MJ Huang, T McKenna, SJ Nabi, G McGloin, D

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Publication Type:: Journal Article
Citation:: Scientific Reports, 2019, 9 (1)
Issue Date:: 2019-12-01

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Field	Value	Language
dc.contributor.author	Belotti, Y	en_US
dc.contributor.author	Tolomeo, S	en_US
dc.contributor.author	Conneely, MJ	en_US
dc.contributor.author	Huang, T	en_US
dc.contributor.author	McKenna, SJ	en_US
dc.contributor.author	Nabi, G	en_US
dc.contributor.author	McGloin, D https://orcid.org/0000-0002-0075-4481	en_US
dc.date.available	2019-03-08	en_US
dc.date.issued	2019-12-01	en_US
dc.identifier.citation	Scientific Reports, 2019, 9 (1)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132169
dc.description.abstract	© 2019, The Author(s). Worldwide, prostate cancer sits only behind lung cancer as the most commonly diagnosed form of the disease in men. Even the best diagnostic standards lack precision, presenting issues with false positives and unneeded surgical intervention for patients. This lack of clear cut early diagnostic tools is a significant problem. We present a microfluidic platform, the Time-Resolved Hydrodynamic Stretcher (TR-HS), which allows the investigation of the dynamic mechanical response of thousands of cells per second to a non-destructive stress. The TR-HS integrates high-speed imaging and computer vision to automatically detect and track single cells suspended in a fluid and enables cell classification based on their mechanical properties. We demonstrate the discrimination of healthy and cancerous prostate cell lines based on the whole-cell, time-resolved mechanical response to a hydrodynamic load. Additionally, we implement a finite element method (FEM) model to characterise the forces responsible for the cell deformation in our device. Finally, we report the classification of the two different cell groups based on their time-resolved roundness using a decision tree classifier. This approach introduces a modality for high-throughput assessments of cellular suspensions and may represent a viable application for the development of innovative diagnostic devices.	en_US
dc.relation.ispartof	Scientific Reports	en_US
dc.relation.isbasedon	10.1038/s41598-019-42008-0	en_US
dc.title	High-Throughput, Time-Resolved Mechanical Phenotyping of Prostate Cancer Cells	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	9	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

© 2019, The Author(s). Worldwide, prostate cancer sits only behind lung cancer as the most commonly diagnosed form of the disease in men. Even the best diagnostic standards lack precision, presenting issues with false positives and unneeded surgical intervention for patients. This lack of clear cut early diagnostic tools is a significant problem. We present a microfluidic platform, the Time-Resolved Hydrodynamic Stretcher (TR-HS), which allows the investigation of the dynamic mechanical response of thousands of cells per second to a non-destructive stress. The TR-HS integrates high-speed imaging and computer vision to automatically detect and track single cells suspended in a fluid and enables cell classification based on their mechanical properties. We demonstrate the discrimination of healthy and cancerous prostate cell lines based on the whole-cell, time-resolved mechanical response to a hydrodynamic load. Additionally, we implement a finite element method (FEM) model to characterise the forces responsible for the cell deformation in our device. Finally, we report the classification of the two different cell groups based on their time-resolved roundness using a decision tree classifier. This approach introduces a modality for high-throughput assessments of cellular suspensions and may represent a viable application for the development of innovative diagnostic devices.

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http://hdl.handle.net/10453/132169