Multiparameter cell-tracking intrinsic cytometry for single-cell characterization
An abundance of label-free microfluidic techniques for measuring cell intrinsic markers exists, yet these techniques are seldom combined because of integration complexity such as restricted physical space and incompatible modes of operation. We introduce a multiparameter intrinsic cytometry approach...
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Veröffentlicht in: | Lab on a chip 2018-01, Vol.18 (1), p.143-1439 |
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creator | Apichitsopa, N Jaffe, A Voldman, J |
description | An abundance of label-free microfluidic techniques for measuring cell intrinsic markers exists, yet these techniques are seldom combined because of integration complexity such as restricted physical space and incompatible modes of operation. We introduce a multiparameter intrinsic cytometry approach for the characterization of single cells that combines ≥2 label-free measurement techniques onto the same platform and uses cell tracking to associate the measured properties to cells. Our proof-of-concept implementation can measure up to five intrinsic properties including size, deformability, and polarizability at three frequencies. Each measurement module along with the integrated platform were validated and evaluated in the context of chemically induced changes in the actin cytoskeleton of cells. viSNE and machine learning classification were used to determine the orthogonality between and the contribution of the measured intrinsic markers for cell classification.
We introduce a multiparameter intrinsic cytometry approach for single-cell characterization that combines ≥2 label-free measurement techniques onto the same platform. |
doi_str_mv | 10.1039/c8lc00240a |
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We introduce a multiparameter intrinsic cytometry approach for single-cell characterization that combines ≥2 label-free measurement techniques onto the same platform.</description><subject>Classification</subject><subject>Cytometry</subject><subject>Deformation</subject><subject>Formability</subject><subject>Machine learning</subject><subject>Markers</subject><subject>Measurement techniques</subject><subject>Organic chemistry</subject><subject>Orthogonality</subject><subject>Tracking</subject><issn>1473-0197</issn><issn>1473-0189</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNpd0U1LxDAQBuAgiqurF-9KwYsI1UmTNMlxKX7Bigh6LmmaaNZ-rEl7WH-9rbuu4CmBeWYY3kHoBMMVBiKvtag0QEJB7aADTDmJAQu5u_1LPkGHISwAMKOp2EeTRKaCY-AH6Pmxrzq3VF7VpjM-0qaq4s4r_eGat8g1nXdNcDrSq64dhF9FtvVRGIqViUcc6fehWQ-97kt1rm2O0J5VVTDHm3eKXm9vXrL7eP5095DN5rGmPO1iy7glhSSMlsSawhaUK6rKkjEloEwEBaxIaUtaANcqFYxZy0SirSwKzbEgU3Sxnrv07WdvQpfXLowbqca0fcgTIBiSVCYjPf9HF23vm2G7UUnMgMGoLtdK-zYEb2y-9K5WfpVjyMeg80zMs5-gZwM-24zsi9qUW_qb7ABO18AHva3-XYp8A62Gg5A</recordid><startdate>20180101</startdate><enddate>20180101</enddate><creator>Apichitsopa, N</creator><creator>Jaffe, A</creator><creator>Voldman, J</creator><general>Royal Society of Chemistry</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>7U5</scope><scope>8FD</scope><scope>FR3</scope><scope>L7M</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-7705-4467</orcidid><orcidid>https://orcid.org/0000-0001-8045-5053</orcidid></search><sort><creationdate>20180101</creationdate><title>Multiparameter cell-tracking intrinsic cytometry for single-cell characterization</title><author>Apichitsopa, N ; Jaffe, A ; Voldman, J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c476t-f57f3b9354d3febfb47a4add55a80d28401a3dfd4b07ca6855ff582cf9bbc7183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Classification</topic><topic>Cytometry</topic><topic>Deformation</topic><topic>Formability</topic><topic>Machine learning</topic><topic>Markers</topic><topic>Measurement techniques</topic><topic>Organic chemistry</topic><topic>Orthogonality</topic><topic>Tracking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Apichitsopa, N</creatorcontrib><creatorcontrib>Jaffe, A</creatorcontrib><creatorcontrib>Voldman, J</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>Lab on a chip</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Apichitsopa, N</au><au>Jaffe, A</au><au>Voldman, J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multiparameter cell-tracking intrinsic cytometry for single-cell characterization</atitle><jtitle>Lab on a chip</jtitle><addtitle>Lab Chip</addtitle><date>2018-01-01</date><risdate>2018</risdate><volume>18</volume><issue>1</issue><spage>143</spage><epage>1439</epage><pages>143-1439</pages><issn>1473-0197</issn><eissn>1473-0189</eissn><abstract>An abundance of label-free microfluidic techniques for measuring cell intrinsic markers exists, yet these techniques are seldom combined because of integration complexity such as restricted physical space and incompatible modes of operation. We introduce a multiparameter intrinsic cytometry approach for the characterization of single cells that combines ≥2 label-free measurement techniques onto the same platform and uses cell tracking to associate the measured properties to cells. Our proof-of-concept implementation can measure up to five intrinsic properties including size, deformability, and polarizability at three frequencies. Each measurement module along with the integrated platform were validated and evaluated in the context of chemically induced changes in the actin cytoskeleton of cells. viSNE and machine learning classification were used to determine the orthogonality between and the contribution of the measured intrinsic markers for cell classification.
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source | Royal Society Of Chemistry Journals 2008-; Alma/SFX Local Collection |
subjects | Classification Cytometry Deformation Formability Machine learning Markers Measurement techniques Organic chemistry Orthogonality Tracking |
title | Multiparameter cell-tracking intrinsic cytometry for single-cell characterization |
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