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Data-analysis strategies for image-based cell profiling
Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involv...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6871000/ https://www.ncbi.nlm.nih.gov/pubmed/28858338 http://dx.doi.org/10.1038/nmeth.4397 |
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author | Caicedo, Juan C Cooper, Sam Heigwer, Florian Warchal, Scott Qiu, Peng Molnar, Csaba Vasilevich, Aliaksei S Barry, Joseph D Bansal, Harmanjit Singh Kraus, Oren Wawer, Mathias Paavolainen, Lassi Herrmann, Markus D Rohban, Mohammad Hung, Jane Hennig, Holger Concannon, John Smith, Ian Clemons, Paul A Singh, Shantanu Rees, Paul Horvath, Peter Linington, Roger G Carpenter, Anne E |
author_facet | Caicedo, Juan C Cooper, Sam Heigwer, Florian Warchal, Scott Qiu, Peng Molnar, Csaba Vasilevich, Aliaksei S Barry, Joseph D Bansal, Harmanjit Singh Kraus, Oren Wawer, Mathias Paavolainen, Lassi Herrmann, Markus D Rohban, Mohammad Hung, Jane Hennig, Holger Concannon, John Smith, Ian Clemons, Paul A Singh, Shantanu Rees, Paul Horvath, Peter Linington, Roger G Carpenter, Anne E |
author_sort | Caicedo, Juan C |
collection | PubMed |
description | Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences. |
format | Online Article Text |
id | pubmed-6871000 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-68710002019-11-25 Data-analysis strategies for image-based cell profiling Caicedo, Juan C Cooper, Sam Heigwer, Florian Warchal, Scott Qiu, Peng Molnar, Csaba Vasilevich, Aliaksei S Barry, Joseph D Bansal, Harmanjit Singh Kraus, Oren Wawer, Mathias Paavolainen, Lassi Herrmann, Markus D Rohban, Mohammad Hung, Jane Hennig, Holger Concannon, John Smith, Ian Clemons, Paul A Singh, Shantanu Rees, Paul Horvath, Peter Linington, Roger G Carpenter, Anne E Nat Methods Article Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences. Nature Publishing Group US 2017-09-01 2017 /pmc/articles/PMC6871000/ /pubmed/28858338 http://dx.doi.org/10.1038/nmeth.4397 Text en © The Author(s) 2017 This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Caicedo, Juan C Cooper, Sam Heigwer, Florian Warchal, Scott Qiu, Peng Molnar, Csaba Vasilevich, Aliaksei S Barry, Joseph D Bansal, Harmanjit Singh Kraus, Oren Wawer, Mathias Paavolainen, Lassi Herrmann, Markus D Rohban, Mohammad Hung, Jane Hennig, Holger Concannon, John Smith, Ian Clemons, Paul A Singh, Shantanu Rees, Paul Horvath, Peter Linington, Roger G Carpenter, Anne E Data-analysis strategies for image-based cell profiling |
title | Data-analysis strategies for image-based cell profiling |
title_full | Data-analysis strategies for image-based cell profiling |
title_fullStr | Data-analysis strategies for image-based cell profiling |
title_full_unstemmed | Data-analysis strategies for image-based cell profiling |
title_short | Data-analysis strategies for image-based cell profiling |
title_sort | data-analysis strategies for image-based cell profiling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6871000/ https://www.ncbi.nlm.nih.gov/pubmed/28858338 http://dx.doi.org/10.1038/nmeth.4397 |
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