Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry

Holographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to dis...

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Autores principales:	Cindy X. Chen, Han Sang Park, Hillel Price, Adam Wax
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	quantitative phase imaging (QPI) machine learning high throughput microfluidics cell biophysical properties cancer diagnostics and screening Physics QC1-999
Acceso en línea:	https://doaj.org/article/1ab226ab25a24274b50b9751052056f9
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id	oai:doaj.org-article:1ab226ab25a24274b50b9751052056f9
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spelling	oai:doaj.org-article:1ab226ab25a24274b50b9751052056f92021-12-01T15:15:08ZAutomated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry2296-424X10.3389/fphy.2021.759142https://doaj.org/article/1ab226ab25a24274b50b9751052056f92021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphy.2021.759142/fullhttps://doaj.org/toc/2296-424XHolographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to distinguishing carcinogen-exposed cells from normal cells and cancer cells. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data, we are able to build single-cell-analysis-based biophysical phenotype profiles on the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, we demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. The high classification accuracy levels show the platform’s potential in being developed into a diagnostic tool for abnormal cell screening.Cindy X. ChenHan Sang ParkHillel PriceAdam WaxFrontiers Media S.A.articlequantitative phase imaging (QPI)machine learninghigh throughput microfluidicscell biophysical propertiescancer diagnostics and screeningPhysicsQC1-999ENFrontiers in Physics, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	quantitative phase imaging (QPI) machine learning high throughput microfluidics cell biophysical properties cancer diagnostics and screening Physics QC1-999
spellingShingle	quantitative phase imaging (QPI) machine learning high throughput microfluidics cell biophysical properties cancer diagnostics and screening Physics QC1-999 Cindy X. Chen Han Sang Park Hillel Price Adam Wax Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry
description	Holographic cytometry is an ultra-high throughput quantitative phase imaging modality that is capable of extracting subcellular information from millions of cells flowing through parallel microfluidic channels. In this study, we present our findings on the application of holographic cytometry to distinguishing carcinogen-exposed cells from normal cells and cancer cells. This has potential application for environmental monitoring and cancer detection by analysis of cytology samples acquired via brushing or fine needle aspiration. By leveraging the vast amount of cell imaging data, we are able to build single-cell-analysis-based biophysical phenotype profiles on the examined cell lines. Multiple physical characteristics of these cells show observable distinct traits between the three cell types. Logistic regression analysis provides insight on which traits are more useful for classification. Additionally, we demonstrate that deep learning is a powerful tool that can potentially identify phenotypic differences from reconstructed single-cell images. The high classification accuracy levels show the platform’s potential in being developed into a diagnostic tool for abnormal cell screening.
format	article
author	Cindy X. Chen Han Sang Park Hillel Price Adam Wax
author_facet	Cindy X. Chen Han Sang Park Hillel Price Adam Wax
author_sort	Cindy X. Chen
title	Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry
title_short	Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry
title_full	Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry
title_fullStr	Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry
title_full_unstemmed	Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry
title_sort	automated classification of breast cancer cells using high-throughput holographic cytometry
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/1ab226ab25a24274b50b9751052056f9
work_keys_str_mv	AT cindyxchen automatedclassificationofbreastcancercellsusinghighthroughputholographiccytometry AT hansangpark automatedclassificationofbreastcancercellsusinghighthroughputholographiccytometry AT hillelprice automatedclassificationofbreastcancercellsusinghighthroughputholographiccytometry AT adamwax automatedclassificationofbreastcancercellsusinghighthroughputholographiccytometry
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Automated Classification of Breast Cancer Cells Using High-Throughput Holographic Cytometry

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