TY - GEN
T1 - High-throughput, imaging based mechanical phenotyping of prostate cancer cells
AU - Belotti, Yuri
AU - Huang, Tianjun
AU - McKenna, Stephen
AU - Nabi, Ghulam
AU - McGloin, David
N1 - Published in: 2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85039793772&partnerID=8YFLogxK
U2 - 10.1109/CLEOE-EQEC.2017.8087785
DO - 10.1109/CLEOE-EQEC.2017.8087785
M3 - Published conference contribution
AN - SCOPUS:85039793772
SN - 9781509067367
T3 - Optics InfoBase Conference Papers
BT - European Quantum Electronics Conference, EQEC 2017
PB - Optica Publishing Group (formerly OSA)
T2 - European Quantum Electronics Conference, EQEC 2017
Y2 - 25 June 2017 through 29 June 2017
ER -