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A platform for high-throughput bioenergy production phenotype characterization in single cells

Driven by an increasing number of studies demonstrating its relevance to a broad variety of disease states, the bioenergy production phenotype has been widely characterized at the bulk sample level. Its cell-to-cell variability, a key player associated with cancer cell survival and recurrence, howev...

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Autores principales: Kelbauskas, Laimonas, Glenn, Honor, Anderson, Clifford, Messner, Jacob, Lee, Kristen B., Song, Ganquan, Houkal, Jeff, Su, Fengyu, Zhang, Liqiang, Tian, Yanqing, Wang, Hong, Bussey, Kimberly, Johnson, Roger H., Meldrum, Deirdre R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5368665/
https://www.ncbi.nlm.nih.gov/pubmed/28349963
http://dx.doi.org/10.1038/srep45399
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author Kelbauskas, Laimonas
Glenn, Honor
Anderson, Clifford
Messner, Jacob
Lee, Kristen B.
Song, Ganquan
Houkal, Jeff
Su, Fengyu
Zhang, Liqiang
Tian, Yanqing
Wang, Hong
Bussey, Kimberly
Johnson, Roger H.
Meldrum, Deirdre R.
author_facet Kelbauskas, Laimonas
Glenn, Honor
Anderson, Clifford
Messner, Jacob
Lee, Kristen B.
Song, Ganquan
Houkal, Jeff
Su, Fengyu
Zhang, Liqiang
Tian, Yanqing
Wang, Hong
Bussey, Kimberly
Johnson, Roger H.
Meldrum, Deirdre R.
author_sort Kelbauskas, Laimonas
collection PubMed
description Driven by an increasing number of studies demonstrating its relevance to a broad variety of disease states, the bioenergy production phenotype has been widely characterized at the bulk sample level. Its cell-to-cell variability, a key player associated with cancer cell survival and recurrence, however, remains poorly understood due to ensemble averaging of the current approaches. We present a technology platform for performing oxygen consumption and extracellular acidification measurements of several hundreds to 1,000 individual cells per assay, while offering simultaneous analysis of cellular communication effects on the energy production phenotype. The platform comprises two major components: a tandem optical sensor for combined oxygen and pH detection, and a microwell device for isolation and analysis of single and few cells in hermetically sealed sub-nanoliter chambers. Our approach revealed subpopulations of cells with aberrant energy production profiles and enables determination of cellular response variability to electron transfer chain inhibitors and ion uncouplers.
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spelling pubmed-53686652017-03-30 A platform for high-throughput bioenergy production phenotype characterization in single cells Kelbauskas, Laimonas Glenn, Honor Anderson, Clifford Messner, Jacob Lee, Kristen B. Song, Ganquan Houkal, Jeff Su, Fengyu Zhang, Liqiang Tian, Yanqing Wang, Hong Bussey, Kimberly Johnson, Roger H. Meldrum, Deirdre R. Sci Rep Article Driven by an increasing number of studies demonstrating its relevance to a broad variety of disease states, the bioenergy production phenotype has been widely characterized at the bulk sample level. Its cell-to-cell variability, a key player associated with cancer cell survival and recurrence, however, remains poorly understood due to ensemble averaging of the current approaches. We present a technology platform for performing oxygen consumption and extracellular acidification measurements of several hundreds to 1,000 individual cells per assay, while offering simultaneous analysis of cellular communication effects on the energy production phenotype. The platform comprises two major components: a tandem optical sensor for combined oxygen and pH detection, and a microwell device for isolation and analysis of single and few cells in hermetically sealed sub-nanoliter chambers. Our approach revealed subpopulations of cells with aberrant energy production profiles and enables determination of cellular response variability to electron transfer chain inhibitors and ion uncouplers. Nature Publishing Group 2017-03-28 /pmc/articles/PMC5368665/ /pubmed/28349963 http://dx.doi.org/10.1038/srep45399 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
Kelbauskas, Laimonas
Glenn, Honor
Anderson, Clifford
Messner, Jacob
Lee, Kristen B.
Song, Ganquan
Houkal, Jeff
Su, Fengyu
Zhang, Liqiang
Tian, Yanqing
Wang, Hong
Bussey, Kimberly
Johnson, Roger H.
Meldrum, Deirdre R.
A platform for high-throughput bioenergy production phenotype characterization in single cells
title A platform for high-throughput bioenergy production phenotype characterization in single cells
title_full A platform for high-throughput bioenergy production phenotype characterization in single cells
title_fullStr A platform for high-throughput bioenergy production phenotype characterization in single cells
title_full_unstemmed A platform for high-throughput bioenergy production phenotype characterization in single cells
title_short A platform for high-throughput bioenergy production phenotype characterization in single cells
title_sort platform for high-throughput bioenergy production phenotype characterization in single cells
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5368665/
https://www.ncbi.nlm.nih.gov/pubmed/28349963
http://dx.doi.org/10.1038/srep45399
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