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Fully Automated Computational Approach for Precisely Measuring Organelle Acidification with Optical pH Sensors
[Image: see text] pH balance and regulation within organelles are fundamental to cell homeostasis and proliferation. The ability to track pH in cells becomes significantly important to understand these processes in detail. Fluorescent sensors based on micro- and nanoparticles have been applied to me...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9052195/ https://www.ncbi.nlm.nih.gov/pubmed/35404562 http://dx.doi.org/10.1021/acsami.2c00389 |
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author | Chandra, Anil Prasad, Saumya Alemanno, Francesco De Luca, Maria Rizzo, Riccardo Romano, Roberta Gigli, Giuseppe Bucci, Cecilia Barra, Adriano del Mercato, Loretta L. |
author_facet | Chandra, Anil Prasad, Saumya Alemanno, Francesco De Luca, Maria Rizzo, Riccardo Romano, Roberta Gigli, Giuseppe Bucci, Cecilia Barra, Adriano del Mercato, Loretta L. |
author_sort | Chandra, Anil |
collection | PubMed |
description | [Image: see text] pH balance and regulation within organelles are fundamental to cell homeostasis and proliferation. The ability to track pH in cells becomes significantly important to understand these processes in detail. Fluorescent sensors based on micro- and nanoparticles have been applied to measure intracellular pH; however, an accurate methodology to precisely monitor acidification kinetics of organelles in living cells has not been established, limiting the scope of this class of sensors. Here, silica-based fluorescent microparticles were utilized to probe the pH of intracellular organelles in MDA-MB-231 and MCF-7 breast cancer cells. In addition to the robust, ratiometric, trackable, and bioinert pH sensors, we developed a novel dimensionality reduction algorithm to automatically track and screen massive internalization events of pH sensors. We found that the mean acidification time is comparable among the two cell lines (ΔT(MCF-7) = 16.3 min; ΔT(MDA-MB-231) = 19.5 min); however, MCF-7 cells showed a much broader heterogeneity in comparison to MDA-MB-231 cells. The use of pH sensors and ratiometric imaging of living cells in combination with a novel computational approach allow analysis of thousands of events in a computationally inexpensive and faster way than the standard routes. The reported methodology can potentially be used to monitor pH as well as several other parameters associated with endocytosis. |
format | Online Article Text |
id | pubmed-9052195 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-90521952022-04-29 Fully Automated Computational Approach for Precisely Measuring Organelle Acidification with Optical pH Sensors Chandra, Anil Prasad, Saumya Alemanno, Francesco De Luca, Maria Rizzo, Riccardo Romano, Roberta Gigli, Giuseppe Bucci, Cecilia Barra, Adriano del Mercato, Loretta L. ACS Appl Mater Interfaces [Image: see text] pH balance and regulation within organelles are fundamental to cell homeostasis and proliferation. The ability to track pH in cells becomes significantly important to understand these processes in detail. Fluorescent sensors based on micro- and nanoparticles have been applied to measure intracellular pH; however, an accurate methodology to precisely monitor acidification kinetics of organelles in living cells has not been established, limiting the scope of this class of sensors. Here, silica-based fluorescent microparticles were utilized to probe the pH of intracellular organelles in MDA-MB-231 and MCF-7 breast cancer cells. In addition to the robust, ratiometric, trackable, and bioinert pH sensors, we developed a novel dimensionality reduction algorithm to automatically track and screen massive internalization events of pH sensors. We found that the mean acidification time is comparable among the two cell lines (ΔT(MCF-7) = 16.3 min; ΔT(MDA-MB-231) = 19.5 min); however, MCF-7 cells showed a much broader heterogeneity in comparison to MDA-MB-231 cells. The use of pH sensors and ratiometric imaging of living cells in combination with a novel computational approach allow analysis of thousands of events in a computationally inexpensive and faster way than the standard routes. The reported methodology can potentially be used to monitor pH as well as several other parameters associated with endocytosis. American Chemical Society 2022-04-11 2022-04-27 /pmc/articles/PMC9052195/ /pubmed/35404562 http://dx.doi.org/10.1021/acsami.2c00389 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Chandra, Anil Prasad, Saumya Alemanno, Francesco De Luca, Maria Rizzo, Riccardo Romano, Roberta Gigli, Giuseppe Bucci, Cecilia Barra, Adriano del Mercato, Loretta L. Fully Automated Computational Approach for Precisely Measuring Organelle Acidification with Optical pH Sensors |
title | Fully
Automated Computational Approach for Precisely
Measuring Organelle Acidification with Optical pH Sensors |
title_full | Fully
Automated Computational Approach for Precisely
Measuring Organelle Acidification with Optical pH Sensors |
title_fullStr | Fully
Automated Computational Approach for Precisely
Measuring Organelle Acidification with Optical pH Sensors |
title_full_unstemmed | Fully
Automated Computational Approach for Precisely
Measuring Organelle Acidification with Optical pH Sensors |
title_short | Fully
Automated Computational Approach for Precisely
Measuring Organelle Acidification with Optical pH Sensors |
title_sort | fully
automated computational approach for precisely
measuring organelle acidification with optical ph sensors |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9052195/ https://www.ncbi.nlm.nih.gov/pubmed/35404562 http://dx.doi.org/10.1021/acsami.2c00389 |
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