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Revealing invisible cell phenotypes with conditional generative modeling
Biological sciences, drug discovery and medicine rely heavily on cell phenotype perturbation and microscope observation. However, most cellular phenotypic changes are subtle and thus hidden from us by natural cell variability: two cells in the same condition already look different. In this study, we...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10567685/ https://www.ncbi.nlm.nih.gov/pubmed/37821450 http://dx.doi.org/10.1038/s41467-023-42124-6 |
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author | Lamiable, Alexis Champetier, Tiphaine Leonardi, Francesco Cohen, Ethan Sommer, Peter Hardy, David Argy, Nicolas Massougbodji, Achille Del Nery, Elaine Cottrell, Gilles Kwon, Yong-Jun Genovesio, Auguste |
author_facet | Lamiable, Alexis Champetier, Tiphaine Leonardi, Francesco Cohen, Ethan Sommer, Peter Hardy, David Argy, Nicolas Massougbodji, Achille Del Nery, Elaine Cottrell, Gilles Kwon, Yong-Jun Genovesio, Auguste |
author_sort | Lamiable, Alexis |
collection | PubMed |
description | Biological sciences, drug discovery and medicine rely heavily on cell phenotype perturbation and microscope observation. However, most cellular phenotypic changes are subtle and thus hidden from us by natural cell variability: two cells in the same condition already look different. In this study, we show that conditional generative models can be used to transform an image of cells from any one condition to another, thus canceling cell variability. We visually and quantitatively validate that the principle of synthetic cell perturbation works on discernible cases. We then illustrate its effectiveness in displaying otherwise invisible cell phenotypes triggered by blood cells under parasite infection, or by the presence of a disease-causing pathological mutation in differentiated neurons derived from iPSCs, or by low concentration drug treatments. The proposed approach, easy to use and robust, opens the door to more accessible discovery of biological and disease biomarkers. |
format | Online Article Text |
id | pubmed-10567685 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105676852023-10-13 Revealing invisible cell phenotypes with conditional generative modeling Lamiable, Alexis Champetier, Tiphaine Leonardi, Francesco Cohen, Ethan Sommer, Peter Hardy, David Argy, Nicolas Massougbodji, Achille Del Nery, Elaine Cottrell, Gilles Kwon, Yong-Jun Genovesio, Auguste Nat Commun Article Biological sciences, drug discovery and medicine rely heavily on cell phenotype perturbation and microscope observation. However, most cellular phenotypic changes are subtle and thus hidden from us by natural cell variability: two cells in the same condition already look different. In this study, we show that conditional generative models can be used to transform an image of cells from any one condition to another, thus canceling cell variability. We visually and quantitatively validate that the principle of synthetic cell perturbation works on discernible cases. We then illustrate its effectiveness in displaying otherwise invisible cell phenotypes triggered by blood cells under parasite infection, or by the presence of a disease-causing pathological mutation in differentiated neurons derived from iPSCs, or by low concentration drug treatments. The proposed approach, easy to use and robust, opens the door to more accessible discovery of biological and disease biomarkers. Nature Publishing Group UK 2023-10-11 /pmc/articles/PMC10567685/ /pubmed/37821450 http://dx.doi.org/10.1038/s41467-023-42124-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lamiable, Alexis Champetier, Tiphaine Leonardi, Francesco Cohen, Ethan Sommer, Peter Hardy, David Argy, Nicolas Massougbodji, Achille Del Nery, Elaine Cottrell, Gilles Kwon, Yong-Jun Genovesio, Auguste Revealing invisible cell phenotypes with conditional generative modeling |
title | Revealing invisible cell phenotypes with conditional generative modeling |
title_full | Revealing invisible cell phenotypes with conditional generative modeling |
title_fullStr | Revealing invisible cell phenotypes with conditional generative modeling |
title_full_unstemmed | Revealing invisible cell phenotypes with conditional generative modeling |
title_short | Revealing invisible cell phenotypes with conditional generative modeling |
title_sort | revealing invisible cell phenotypes with conditional generative modeling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10567685/ https://www.ncbi.nlm.nih.gov/pubmed/37821450 http://dx.doi.org/10.1038/s41467-023-42124-6 |
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