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High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells
Digital holographic cytometry (DHC) permits label-free visualization of adherent cells. Dozens of cellular features can be derived from segmentation of hologram-derived images. However, the accuracy of single cell classification by these features remains limited for most applications, and lack of st...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5607248/ https://www.ncbi.nlm.nih.gov/pubmed/28931937 http://dx.doi.org/10.1038/s41598-017-12165-1 |
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author | Hejna, Miroslav Jorapur, Aparna Song, Jun S. Judson, Robert L. |
author_facet | Hejna, Miroslav Jorapur, Aparna Song, Jun S. Judson, Robert L. |
author_sort | Hejna, Miroslav |
collection | PubMed |
description | Digital holographic cytometry (DHC) permits label-free visualization of adherent cells. Dozens of cellular features can be derived from segmentation of hologram-derived images. However, the accuracy of single cell classification by these features remains limited for most applications, and lack of standardization metrics has hindered independent experimental comparison and validation. Here we identify twenty-six DHC-derived features that provide biologically independent information across a variety of mammalian cell state transitions. When trained on these features, machine-learning algorithms achieve blind single cell classification with up to 95% accuracy. Using classification accuracy to guide platform optimization, we develop methods to standardize holograms for the purpose of kinetic single cell cytometry. Applying our approach to human melanoma cells treated with a panel of cancer therapeutics, we track dynamic changes in cellular behavior and cell state over time. We provide the methods and computational tools for optimizing DHC for kinetic single adherent cell classification. |
format | Online Article Text |
id | pubmed-5607248 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-56072482017-09-24 High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells Hejna, Miroslav Jorapur, Aparna Song, Jun S. Judson, Robert L. Sci Rep Article Digital holographic cytometry (DHC) permits label-free visualization of adherent cells. Dozens of cellular features can be derived from segmentation of hologram-derived images. However, the accuracy of single cell classification by these features remains limited for most applications, and lack of standardization metrics has hindered independent experimental comparison and validation. Here we identify twenty-six DHC-derived features that provide biologically independent information across a variety of mammalian cell state transitions. When trained on these features, machine-learning algorithms achieve blind single cell classification with up to 95% accuracy. Using classification accuracy to guide platform optimization, we develop methods to standardize holograms for the purpose of kinetic single cell cytometry. Applying our approach to human melanoma cells treated with a panel of cancer therapeutics, we track dynamic changes in cellular behavior and cell state over time. We provide the methods and computational tools for optimizing DHC for kinetic single adherent cell classification. Nature Publishing Group UK 2017-09-20 /pmc/articles/PMC5607248/ /pubmed/28931937 http://dx.doi.org/10.1038/s41598-017-12165-1 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Hejna, Miroslav Jorapur, Aparna Song, Jun S. Judson, Robert L. High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells |
title | High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells |
title_full | High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells |
title_fullStr | High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells |
title_full_unstemmed | High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells |
title_short | High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells |
title_sort | high accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5607248/ https://www.ncbi.nlm.nih.gov/pubmed/28931937 http://dx.doi.org/10.1038/s41598-017-12165-1 |
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