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Simultaneous Multiparameter Cellular Energy Metabolism Profiling of Small Populations of Cells

Functional and genomic heterogeneity of individual cells are central players in a broad spectrum of normal and disease states. Our knowledge about the role of cellular heterogeneity in tissue and organism function remains limited due to analytical challenges one encounters when performing single cel...

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Autores principales: Kelbauskas, Laimonas, Ashili, Shashaanka P., Lee, Kristen B., Zhu, Haixin, Tian, Yanqing, Meldrum, Deirdre R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5847514/
https://www.ncbi.nlm.nih.gov/pubmed/29531352
http://dx.doi.org/10.1038/s41598-018-22599-w
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author Kelbauskas, Laimonas
Ashili, Shashaanka P.
Lee, Kristen B.
Zhu, Haixin
Tian, Yanqing
Meldrum, Deirdre R.
author_facet Kelbauskas, Laimonas
Ashili, Shashaanka P.
Lee, Kristen B.
Zhu, Haixin
Tian, Yanqing
Meldrum, Deirdre R.
author_sort Kelbauskas, Laimonas
collection PubMed
description Functional and genomic heterogeneity of individual cells are central players in a broad spectrum of normal and disease states. Our knowledge about the role of cellular heterogeneity in tissue and organism function remains limited due to analytical challenges one encounters when performing single cell studies in the context of cell-cell interactions. Information based on bulk samples represents ensemble averages over populations of cells, while data generated from isolated single cells do not account for intercellular interactions. We describe a new technology and demonstrate two important advantages over existing technologies: first, it enables multiparameter energy metabolism profiling of small cell populations (<100 cells)—a sample size that is at least an order of magnitude smaller than other, commercially available technologies; second, it can perform simultaneous real-time measurements of oxygen consumption rate (OCR), extracellular acidification rate (ECAR), and mitochondrial membrane potential (MMP)—a capability not offered by any other commercially available technology. Our results revealed substantial diversity in response kinetics of the three analytes in dysplastic human epithelial esophageal cells and suggest the existence of varying cellular energy metabolism profiles and their kinetics among small populations of cells. The technology represents a powerful analytical tool for multiparameter studies of cellular function.
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spelling pubmed-58475142018-03-19 Simultaneous Multiparameter Cellular Energy Metabolism Profiling of Small Populations of Cells Kelbauskas, Laimonas Ashili, Shashaanka P. Lee, Kristen B. Zhu, Haixin Tian, Yanqing Meldrum, Deirdre R. Sci Rep Article Functional and genomic heterogeneity of individual cells are central players in a broad spectrum of normal and disease states. Our knowledge about the role of cellular heterogeneity in tissue and organism function remains limited due to analytical challenges one encounters when performing single cell studies in the context of cell-cell interactions. Information based on bulk samples represents ensemble averages over populations of cells, while data generated from isolated single cells do not account for intercellular interactions. We describe a new technology and demonstrate two important advantages over existing technologies: first, it enables multiparameter energy metabolism profiling of small cell populations (<100 cells)—a sample size that is at least an order of magnitude smaller than other, commercially available technologies; second, it can perform simultaneous real-time measurements of oxygen consumption rate (OCR), extracellular acidification rate (ECAR), and mitochondrial membrane potential (MMP)—a capability not offered by any other commercially available technology. Our results revealed substantial diversity in response kinetics of the three analytes in dysplastic human epithelial esophageal cells and suggest the existence of varying cellular energy metabolism profiles and their kinetics among small populations of cells. The technology represents a powerful analytical tool for multiparameter studies of cellular function. Nature Publishing Group UK 2018-03-12 /pmc/articles/PMC5847514/ /pubmed/29531352 http://dx.doi.org/10.1038/s41598-018-22599-w Text en © The Author(s) 2018 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
Kelbauskas, Laimonas
Ashili, Shashaanka P.
Lee, Kristen B.
Zhu, Haixin
Tian, Yanqing
Meldrum, Deirdre R.
Simultaneous Multiparameter Cellular Energy Metabolism Profiling of Small Populations of Cells
title Simultaneous Multiparameter Cellular Energy Metabolism Profiling of Small Populations of Cells
title_full Simultaneous Multiparameter Cellular Energy Metabolism Profiling of Small Populations of Cells
title_fullStr Simultaneous Multiparameter Cellular Energy Metabolism Profiling of Small Populations of Cells
title_full_unstemmed Simultaneous Multiparameter Cellular Energy Metabolism Profiling of Small Populations of Cells
title_short Simultaneous Multiparameter Cellular Energy Metabolism Profiling of Small Populations of Cells
title_sort simultaneous multiparameter cellular energy metabolism profiling of small populations of cells
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5847514/
https://www.ncbi.nlm.nih.gov/pubmed/29531352
http://dx.doi.org/10.1038/s41598-018-22599-w
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