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Quantifying single-cell secretion in real time using resonant hyperspectral imaging

Cell communication is primarily regulated by secreted proteins, whose inhomogeneous secretion often indicates physiological disorder. Parallel monitoring of innate protein-secretion kinetics from individual cells is thus crucial to unravel systemic malfunctions. Here, we report a label-free, high-th...

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Detalles Bibliográficos
Autores principales: Juan-Colás, José, Hitchcock, Ian S., Coles, Mark, Johnson, Steven, Krauss, Thomas F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310807/
https://www.ncbi.nlm.nih.gov/pubmed/30530663
http://dx.doi.org/10.1073/pnas.1814977115
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author Juan-Colás, José
Hitchcock, Ian S.
Coles, Mark
Johnson, Steven
Krauss, Thomas F.
author_facet Juan-Colás, José
Hitchcock, Ian S.
Coles, Mark
Johnson, Steven
Krauss, Thomas F.
author_sort Juan-Colás, José
collection PubMed
description Cell communication is primarily regulated by secreted proteins, whose inhomogeneous secretion often indicates physiological disorder. Parallel monitoring of innate protein-secretion kinetics from individual cells is thus crucial to unravel systemic malfunctions. Here, we report a label-free, high-throughput method for parallel, in vitro, and real-time analysis of specific single-cell signaling using hyperspectral photonic crystal resonant technology. Heterogeneity in physiological thrombopoietin expression from individual HepG2 liver cells in response to platelet desialylation was quantified demonstrating how mapping real-time protein secretion can provide a simple, yet powerful approach for studying complex physiological systems regulating protein production at single-cell resolution.
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spelling pubmed-63108072019-01-04 Quantifying single-cell secretion in real time using resonant hyperspectral imaging Juan-Colás, José Hitchcock, Ian S. Coles, Mark Johnson, Steven Krauss, Thomas F. Proc Natl Acad Sci U S A Physical Sciences Cell communication is primarily regulated by secreted proteins, whose inhomogeneous secretion often indicates physiological disorder. Parallel monitoring of innate protein-secretion kinetics from individual cells is thus crucial to unravel systemic malfunctions. Here, we report a label-free, high-throughput method for parallel, in vitro, and real-time analysis of specific single-cell signaling using hyperspectral photonic crystal resonant technology. Heterogeneity in physiological thrombopoietin expression from individual HepG2 liver cells in response to platelet desialylation was quantified demonstrating how mapping real-time protein secretion can provide a simple, yet powerful approach for studying complex physiological systems regulating protein production at single-cell resolution. National Academy of Sciences 2018-12-26 2018-12-10 /pmc/articles/PMC6310807/ /pubmed/30530663 http://dx.doi.org/10.1073/pnas.1814977115 Text en Copyright © 2018 the Author(s). Published by PNAS. http://creativecommons.org/licenses/by/4.0/ This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (http://creativecommons.org/licenses/by/4.0/) .
spellingShingle Physical Sciences
Juan-Colás, José
Hitchcock, Ian S.
Coles, Mark
Johnson, Steven
Krauss, Thomas F.
Quantifying single-cell secretion in real time using resonant hyperspectral imaging
title Quantifying single-cell secretion in real time using resonant hyperspectral imaging
title_full Quantifying single-cell secretion in real time using resonant hyperspectral imaging
title_fullStr Quantifying single-cell secretion in real time using resonant hyperspectral imaging
title_full_unstemmed Quantifying single-cell secretion in real time using resonant hyperspectral imaging
title_short Quantifying single-cell secretion in real time using resonant hyperspectral imaging
title_sort quantifying single-cell secretion in real time using resonant hyperspectral imaging
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310807/
https://www.ncbi.nlm.nih.gov/pubmed/30530663
http://dx.doi.org/10.1073/pnas.1814977115
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