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Semi-automated quantification of living cells with internalized nanostructures
BACKGROUND: Nanostructures fabricated by different methods have become increasingly important for various applications in biology and medicine, such as agents for medical imaging or cancer therapy. In order to understand their interaction with living cells and their internalization kinetics, several...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4714438/ https://www.ncbi.nlm.nih.gov/pubmed/26768888 http://dx.doi.org/10.1186/s12951-015-0153-x |
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author | Margineanu, Michael Bogdan Julfakyan, Khachatur Sommer, Christoph Perez, Jose Efrain Contreras, Maria Fernanda Khashab, Niveen Kosel, Jürgen Ravasi, Timothy |
author_facet | Margineanu, Michael Bogdan Julfakyan, Khachatur Sommer, Christoph Perez, Jose Efrain Contreras, Maria Fernanda Khashab, Niveen Kosel, Jürgen Ravasi, Timothy |
author_sort | Margineanu, Michael Bogdan |
collection | PubMed |
description | BACKGROUND: Nanostructures fabricated by different methods have become increasingly important for various applications in biology and medicine, such as agents for medical imaging or cancer therapy. In order to understand their interaction with living cells and their internalization kinetics, several attempts have been made in tagging them. Although methods have been developed to measure the number of nanostructures internalized by the cells, there are only few approaches aimed to measure the number of cells that internalize the nanostructures, and they are usually limited to fixed-cell studies. Flow cytometry can be used for live-cell assays on large populations of cells, however it is a single time point measurement, and does not include any information about cell morphology. To date many of the observations made on internalization events are limited to few time points and cells. RESULTS: In this study, we present a method for quantifying cells with internalized magnetic nanowires (NWs). A machine learning-based computational framework, CellCognition, is adapted and used to classify cells with internalized and no internalized NWs, labeled with the fluorogenic pH-dependent dye pHrodo™ Red, and subsequently to determine the percentage of cells with internalized NWs at different time points. In a “proof-of-concept”, we performed a study on human colon carcinoma HCT 116 cells and human epithelial cervical cancer HeLa cells interacting with iron (Fe) and nickel (Ni) NWs. CONCLUSIONS: This study reports a novel method for the quantification of cells that internalize a specific type of nanostructures. This approach is suitable for high-throughput and real-time data analysis and has the potential to be used to study the interaction of different types of nanostructures in live-cell assays. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12951-015-0153-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4714438 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47144382016-01-16 Semi-automated quantification of living cells with internalized nanostructures Margineanu, Michael Bogdan Julfakyan, Khachatur Sommer, Christoph Perez, Jose Efrain Contreras, Maria Fernanda Khashab, Niveen Kosel, Jürgen Ravasi, Timothy J Nanobiotechnology Methodology BACKGROUND: Nanostructures fabricated by different methods have become increasingly important for various applications in biology and medicine, such as agents for medical imaging or cancer therapy. In order to understand their interaction with living cells and their internalization kinetics, several attempts have been made in tagging them. Although methods have been developed to measure the number of nanostructures internalized by the cells, there are only few approaches aimed to measure the number of cells that internalize the nanostructures, and they are usually limited to fixed-cell studies. Flow cytometry can be used for live-cell assays on large populations of cells, however it is a single time point measurement, and does not include any information about cell morphology. To date many of the observations made on internalization events are limited to few time points and cells. RESULTS: In this study, we present a method for quantifying cells with internalized magnetic nanowires (NWs). A machine learning-based computational framework, CellCognition, is adapted and used to classify cells with internalized and no internalized NWs, labeled with the fluorogenic pH-dependent dye pHrodo™ Red, and subsequently to determine the percentage of cells with internalized NWs at different time points. In a “proof-of-concept”, we performed a study on human colon carcinoma HCT 116 cells and human epithelial cervical cancer HeLa cells interacting with iron (Fe) and nickel (Ni) NWs. CONCLUSIONS: This study reports a novel method for the quantification of cells that internalize a specific type of nanostructures. This approach is suitable for high-throughput and real-time data analysis and has the potential to be used to study the interaction of different types of nanostructures in live-cell assays. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12951-015-0153-x) contains supplementary material, which is available to authorized users. BioMed Central 2016-01-15 /pmc/articles/PMC4714438/ /pubmed/26768888 http://dx.doi.org/10.1186/s12951-015-0153-x Text en © Margineanu et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Margineanu, Michael Bogdan Julfakyan, Khachatur Sommer, Christoph Perez, Jose Efrain Contreras, Maria Fernanda Khashab, Niveen Kosel, Jürgen Ravasi, Timothy Semi-automated quantification of living cells with internalized nanostructures |
title | Semi-automated quantification of living cells with internalized nanostructures |
title_full | Semi-automated quantification of living cells with internalized nanostructures |
title_fullStr | Semi-automated quantification of living cells with internalized nanostructures |
title_full_unstemmed | Semi-automated quantification of living cells with internalized nanostructures |
title_short | Semi-automated quantification of living cells with internalized nanostructures |
title_sort | semi-automated quantification of living cells with internalized nanostructures |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4714438/ https://www.ncbi.nlm.nih.gov/pubmed/26768888 http://dx.doi.org/10.1186/s12951-015-0153-x |
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