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High-throughput physical phenotyping of cell differentiation
In this report, we present multiparameter deformability cytometry (m-DC), in which we explore a large set of parameters describing the physical phenotypes of pluripotent cells and their derivatives. m-DC utilizes microfluidic inertial focusing and hydrodynamic stretching of single cells in conjuncti...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6445007/ https://www.ncbi.nlm.nih.gov/pubmed/31057860 http://dx.doi.org/10.1038/micronano.2017.13 |
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author | Lin, Jonathan Kim, Donghyuk Tse, Henry T. Tseng, Peter Peng, Lillian Dhar, Manjima Karumbayaram, Saravanan Di Carlo, Dino |
author_facet | Lin, Jonathan Kim, Donghyuk Tse, Henry T. Tseng, Peter Peng, Lillian Dhar, Manjima Karumbayaram, Saravanan Di Carlo, Dino |
author_sort | Lin, Jonathan |
collection | PubMed |
description | In this report, we present multiparameter deformability cytometry (m-DC), in which we explore a large set of parameters describing the physical phenotypes of pluripotent cells and their derivatives. m-DC utilizes microfluidic inertial focusing and hydrodynamic stretching of single cells in conjunction with high-speed video recording to realize high-throughput characterization of over 20 different cell motion and morphology-derived parameters. Parameters extracted from videos include size, deformability, deformation kinetics, and morphology. We train support vector machines that provide evidence that these additional physical measurements improve classification of induced pluripotent stem cells, mesenchymal stem cells, neural stem cells, and their derivatives compared to size and deformability alone. In addition, we utilize visual interactive stochastic neighbor embedding to visually map the high-dimensional physical phenotypic spaces occupied by these stem cells and their progeny and the pathways traversed during differentiation. This report demonstrates the potential of m-DC for improving understanding of physical differences that arise as cells differentiate and identifying cell subpopulations in a label-free manner. Ultimately, such approaches could broaden our understanding of subtle changes in cell phenotypes and their roles in human biology. |
format | Online Article Text |
id | pubmed-6445007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-64450072019-05-03 High-throughput physical phenotyping of cell differentiation Lin, Jonathan Kim, Donghyuk Tse, Henry T. Tseng, Peter Peng, Lillian Dhar, Manjima Karumbayaram, Saravanan Di Carlo, Dino Microsyst Nanoeng Article In this report, we present multiparameter deformability cytometry (m-DC), in which we explore a large set of parameters describing the physical phenotypes of pluripotent cells and their derivatives. m-DC utilizes microfluidic inertial focusing and hydrodynamic stretching of single cells in conjunction with high-speed video recording to realize high-throughput characterization of over 20 different cell motion and morphology-derived parameters. Parameters extracted from videos include size, deformability, deformation kinetics, and morphology. We train support vector machines that provide evidence that these additional physical measurements improve classification of induced pluripotent stem cells, mesenchymal stem cells, neural stem cells, and their derivatives compared to size and deformability alone. In addition, we utilize visual interactive stochastic neighbor embedding to visually map the high-dimensional physical phenotypic spaces occupied by these stem cells and their progeny and the pathways traversed during differentiation. This report demonstrates the potential of m-DC for improving understanding of physical differences that arise as cells differentiate and identifying cell subpopulations in a label-free manner. Ultimately, such approaches could broaden our understanding of subtle changes in cell phenotypes and their roles in human biology. Nature Publishing Group 2017-05-08 /pmc/articles/PMC6445007/ /pubmed/31057860 http://dx.doi.org/10.1038/micronano.2017.13 Text en Copyright © 2017 The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Lin, Jonathan Kim, Donghyuk Tse, Henry T. Tseng, Peter Peng, Lillian Dhar, Manjima Karumbayaram, Saravanan Di Carlo, Dino High-throughput physical phenotyping of cell differentiation |
title | High-throughput physical phenotyping of cell differentiation |
title_full | High-throughput physical phenotyping of cell differentiation |
title_fullStr | High-throughput physical phenotyping of cell differentiation |
title_full_unstemmed | High-throughput physical phenotyping of cell differentiation |
title_short | High-throughput physical phenotyping of cell differentiation |
title_sort | high-throughput physical phenotyping of cell differentiation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6445007/ https://www.ncbi.nlm.nih.gov/pubmed/31057860 http://dx.doi.org/10.1038/micronano.2017.13 |
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