Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning

Abstract Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid...

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Autores principales:	Diane N. H. Kim, Alexander A. Lim, Michael A. Teitell
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7752a033f33d43dea83c773f3e54baa9
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spelling	oai:doaj.org-article:7752a033f33d43dea83c773f3e54baa92021-12-02T19:17:04ZRapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning10.1038/s41598-021-98567-82045-2322https://doaj.org/article/7752a033f33d43dea83c773f3e54baa92021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98567-8https://doaj.org/toc/2045-2322Abstract Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow systems or time-lapse images that provide high throughput data for cells at single time points, or of time-lapse images that require delayed post-experiment analyses, respectively. To date, QPM studies have not imaged specific cells over time with rapid, concurrent analyses during image acquisition. In order to study biological phenomena or cellular interactions over time, efficient time-dependent methods that automatically and rapidly identify events of interest are desirable. Here, we present an approach that combines QPM and machine learning to identify tumor-reactive T cell killing of adherent cancer cells rapidly, which could be used for identifying and isolating novel T cells and/or their T cell receptors for studies in cancer immunotherapy. We demonstrate the utility of this method by machine learning model training and validation studies using one melanoma-cognate T cell receptor model system, followed by high classification accuracy in identifying T cell killing in an additional, independent melanoma-cognate T cell receptor model system. This general approach could be useful for studying additional biological systems under label-free conditions over extended periods of examination.Diane N. H. KimAlexander A. LimMichael A. TeitellNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Diane N. H. Kim Alexander A. Lim Michael A. Teitell Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning
description	Abstract Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow systems or time-lapse images that provide high throughput data for cells at single time points, or of time-lapse images that require delayed post-experiment analyses, respectively. To date, QPM studies have not imaged specific cells over time with rapid, concurrent analyses during image acquisition. In order to study biological phenomena or cellular interactions over time, efficient time-dependent methods that automatically and rapidly identify events of interest are desirable. Here, we present an approach that combines QPM and machine learning to identify tumor-reactive T cell killing of adherent cancer cells rapidly, which could be used for identifying and isolating novel T cells and/or their T cell receptors for studies in cancer immunotherapy. We demonstrate the utility of this method by machine learning model training and validation studies using one melanoma-cognate T cell receptor model system, followed by high classification accuracy in identifying T cell killing in an additional, independent melanoma-cognate T cell receptor model system. This general approach could be useful for studying additional biological systems under label-free conditions over extended periods of examination.
format	article
author	Diane N. H. Kim Alexander A. Lim Michael A. Teitell
author_facet	Diane N. H. Kim Alexander A. Lim Michael A. Teitell
author_sort	Diane N. H. Kim
title	Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning
title_short	Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning
title_full	Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning
title_fullStr	Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning
title_full_unstemmed	Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning
title_sort	rapid, label-free classification of tumor-reactive t cell killing with quantitative phase microscopy and machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7752a033f33d43dea83c773f3e54baa9
work_keys_str_mv	AT dianenhkim rapidlabelfreeclassificationoftumorreactivetcellkillingwithquantitativephasemicroscopyandmachinelearning AT alexanderalim rapidlabelfreeclassificationoftumorreactivetcellkillingwithquantitativephasemicroscopyandmachinelearning AT michaelateitell rapidlabelfreeclassificationoftumorreactivetcellkillingwithquantitativephasemicroscopyandmachinelearning
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Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning

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