High-throughput, imaging based mechanical phenotyping of prostate cancer cells

Yuri Belotti, Tianjun Huang, Stephen McKenna, Ghulam Nabi, David McGloin

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

Abstract

Prostate cancer (PCa) is the second most common type of cancer in the world and the fifth highest cause of cancer-related deaths in men [1], with the highest prevalence in the United States and Western Europe. The current diagnostic gold standards are controversial and typically lead to over-diagnosis [2]. Blood tests are commonly used to check the level of the prostate specific antigen (PSA), when in fact this antigen is organ-specific but not cancer-specific. Lack of clarity over diagnosis as well as prognosis leads to large numbers of unnecessary treatments, which are highly invasive and with a range of unpleasant side effects. New methods are therefore required to improve the existing clinical outcomes at both the diagnostic and the prognostic level.Here we present a hydrodynamic stretcher [3], with time-resolved capabilities, in which single cells are deformed hydrodynamically in a microfluidic device. We report a dynamic mechanical phenotyping analysis of conventional prostate cell lines: DU145, characterised by moderate metastatic potential [4] and PNT2, commonly used as healthy controls. We focus on their mechanical characterisation using a novel microfluidic hydrodynamic stretching device. Each cell is imaged using high-speed microscopy (300,000 frames per second) during its interaction with a pinching flow over successive frames. An ad hoc automatic tracking algorithm enables us to quantify and record the cellular Roundness [5] temporal profiles, that is a measure of how each cell changes its shape over time as a response to the applied stress. These profiles are then used as a biomarker to identify difference between the two samples. Finally, we classify the two cell lines based on their time-resolved Roundness using machine learning approaches.
Original languageEnglish
Title of host publicationEuropean Quantum Electronics Conference, EQEC 2017
PublisherOptica Publishing Group (formerly OSA)
ISBN (Electronic)9781509067367
ISBN (Print)9781509067367
DOIs
Publication statusPublished - 2017
EventEuropean Quantum Electronics Conference, EQEC 2017 - Munich, Germany
Duration: 25 Jun 201729 Jun 2017

Publication series

NameOptics InfoBase Conference Papers
VolumePart F81-EQEC 2017

Conference

ConferenceEuropean Quantum Electronics Conference, EQEC 2017
Country/TerritoryGermany
CityMunich
Period25/06/1729/06/17

Fingerprint

Dive into the research topics of 'High-throughput, imaging based mechanical phenotyping of prostate cancer cells'. Together they form a unique fingerprint.

Cite this