scholarly journals Two‐Dimensional Light Scattering Anisotropy Cytometry for Label‐Free Classification of Ovarian Cancer Cells via Machine Learning

2019 ◽  
Vol 97 (1) ◽  
pp. 24-30
Author(s):  
Xuantao Su ◽  
Tao Yuan ◽  
Zhiwen Wang ◽  
Kun Song ◽  
Rongrong Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Light Scattering ◽  
Cancer Cells ◽  
Ovarian Cancer Cells ◽  
Two Dimensional ◽  
Label Free ◽  
Scattering Anisotropy ◽  
Free Classification

scholarly journals Label-Free Classification of Apoptosis, Ferroptosis and Necroptosis Using Digital Holographic Cytometry

2020 ◽  
Vol 10 (13) ◽  
pp. 4439
Author(s):  
Kendra L. Barker ◽  
Kenneth M. Boucher ◽  
Robert L. Judson-Torres
Keyword(s):  
Cell Death ◽  
Programmed Cell Death ◽  
Cancer Cells ◽  
Cancer Progression ◽  
High Throughput ◽  
Label Free ◽  
Cell Death Pathways ◽  
Test Sets ◽  
Free Classification

Apoptosis, ferroptosis and necroptosis are three distinct forms of programmed cell death. Each of these pathways can be exploited to terminate cancer cells. One promising therapeutic strategy is to activate alternative programmed cell death pathways subsequent to cancer cells evolving mechanisms to evade apoptosis. However, the interplay between distinct programmed cell death pathways and cancer progression is complex and can paradoxically promote the disease. There is a need for high-throughput assays for real-time classification of programmed cell death, both to further investigate these important biologic processes and to assess the case-by-case efficacy of targeting each pathway in patient-derived tumor cells. Here, we sought to develop a label-free, live-imaging-based assay for classifying forms of programmed cell death with single cell resolution. We used digital holographic cytometry (DHC) to monitor human melanoma cells undergoing apoptosis, ferroptosis, and necroptosis. We developed and validated models that used DHC-derived features to classify each form of cell death with 91–93% accuracy in the test sets. We conclude that high-accuracy, high-throughput, label-free classification of apoptosis, ferroptosis and necroptosis can be achieved with DHC.

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