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Multi-parametric characterization of drug effects on cells

We present here a novel multi-parametric approach for the characterization of multiple cellular features, using images acquired by high-throughput and high-definition light microscopy. We specifically used this approach for deep and unbiased analysis of the effects of a drug library on five cultured...

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Autores principales: Paran, Yael, Liron, Yuvalal, Batsir, Sarit, Mabjeesh, Nicola, Geiger, Benjamin, Kam, Zvi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737707/
https://www.ncbi.nlm.nih.gov/pubmed/33363713
http://dx.doi.org/10.12688/f1000research.26254.2
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author Paran, Yael
Liron, Yuvalal
Batsir, Sarit
Mabjeesh, Nicola
Geiger, Benjamin
Kam, Zvi
author_facet Paran, Yael
Liron, Yuvalal
Batsir, Sarit
Mabjeesh, Nicola
Geiger, Benjamin
Kam, Zvi
author_sort Paran, Yael
collection PubMed
description We present here a novel multi-parametric approach for the characterization of multiple cellular features, using images acquired by high-throughput and high-definition light microscopy. We specifically used this approach for deep and unbiased analysis of the effects of a drug library on five cultured cell lines. The presented method enables the acquisition and analysis of millions of images, of treated and control cells, followed by an automated identification of drugs inducing strong responses, evaluating the median effect concentrations and those cellular properties that are most highly affected by the drug. The tools described here provide standardized quantification of multiple attributes for systems level dissection of complex functions in normal and diseased cells, using multiple perturbations. Such analysis of cells, derived from pathological samples, may help in the diagnosis and follow-up of treatment in patients.
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spelling pubmed-77377072020-12-23 Multi-parametric characterization of drug effects on cells Paran, Yael Liron, Yuvalal Batsir, Sarit Mabjeesh, Nicola Geiger, Benjamin Kam, Zvi F1000Res Method Article We present here a novel multi-parametric approach for the characterization of multiple cellular features, using images acquired by high-throughput and high-definition light microscopy. We specifically used this approach for deep and unbiased analysis of the effects of a drug library on five cultured cell lines. The presented method enables the acquisition and analysis of millions of images, of treated and control cells, followed by an automated identification of drugs inducing strong responses, evaluating the median effect concentrations and those cellular properties that are most highly affected by the drug. The tools described here provide standardized quantification of multiple attributes for systems level dissection of complex functions in normal and diseased cells, using multiple perturbations. Such analysis of cells, derived from pathological samples, may help in the diagnosis and follow-up of treatment in patients. F1000 Research Limited 2021-01-21 /pmc/articles/PMC7737707/ /pubmed/33363713 http://dx.doi.org/10.12688/f1000research.26254.2 Text en Copyright: © 2021 Paran Y et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method Article
Paran, Yael
Liron, Yuvalal
Batsir, Sarit
Mabjeesh, Nicola
Geiger, Benjamin
Kam, Zvi
Multi-parametric characterization of drug effects on cells
title Multi-parametric characterization of drug effects on cells
title_full Multi-parametric characterization of drug effects on cells
title_fullStr Multi-parametric characterization of drug effects on cells
title_full_unstemmed Multi-parametric characterization of drug effects on cells
title_short Multi-parametric characterization of drug effects on cells
title_sort multi-parametric characterization of drug effects on cells
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737707/
https://www.ncbi.nlm.nih.gov/pubmed/33363713
http://dx.doi.org/10.12688/f1000research.26254.2
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