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Noise Genetics: Inferring Protein Function by Correlating Phenotype with Protein Levels and Localization in Individual Human Cells
To understand gene function, genetic analysis uses large perturbations such as gene deletion, knockdown or over-expression. Large perturbations have drawbacks: they move the cell far from its normal working point, and can thus be masked by off-target effects or compensation by other genes. Here, we...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3945223/ https://www.ncbi.nlm.nih.gov/pubmed/24603725 http://dx.doi.org/10.1371/journal.pgen.1004176 |
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author | Farkash-Amar, Shlomit Zimmer, Anat Eden, Eran Cohen, Ariel Geva-Zatorsky, Naama Cohen, Lydia Milo, Ron Sigal, Alex Danon, Tamar Alon, Uri |
author_facet | Farkash-Amar, Shlomit Zimmer, Anat Eden, Eran Cohen, Ariel Geva-Zatorsky, Naama Cohen, Lydia Milo, Ron Sigal, Alex Danon, Tamar Alon, Uri |
author_sort | Farkash-Amar, Shlomit |
collection | PubMed |
description | To understand gene function, genetic analysis uses large perturbations such as gene deletion, knockdown or over-expression. Large perturbations have drawbacks: they move the cell far from its normal working point, and can thus be masked by off-target effects or compensation by other genes. Here, we offer a complementary approach, called noise genetics. We use natural cell-cell variations in protein level and localization, and correlate them to the natural variations of the phenotype of the same cells. Observing these variations is made possible by recent advances in dynamic proteomics that allow measuring proteins over time in individual living cells. Using motility of human cancer cells as a model system, and time-lapse microscopy on 566 fluorescently tagged proteins, we found 74 candidate motility genes whose level or localization strongly correlate with motility in individual cells. We recovered 30 known motility genes, and validated several novel ones by mild knockdown experiments. Noise genetics can complement standard genetics for a variety of phenotypes. |
format | Online Article Text |
id | pubmed-3945223 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39452232014-03-12 Noise Genetics: Inferring Protein Function by Correlating Phenotype with Protein Levels and Localization in Individual Human Cells Farkash-Amar, Shlomit Zimmer, Anat Eden, Eran Cohen, Ariel Geva-Zatorsky, Naama Cohen, Lydia Milo, Ron Sigal, Alex Danon, Tamar Alon, Uri PLoS Genet Research Article To understand gene function, genetic analysis uses large perturbations such as gene deletion, knockdown or over-expression. Large perturbations have drawbacks: they move the cell far from its normal working point, and can thus be masked by off-target effects or compensation by other genes. Here, we offer a complementary approach, called noise genetics. We use natural cell-cell variations in protein level and localization, and correlate them to the natural variations of the phenotype of the same cells. Observing these variations is made possible by recent advances in dynamic proteomics that allow measuring proteins over time in individual living cells. Using motility of human cancer cells as a model system, and time-lapse microscopy on 566 fluorescently tagged proteins, we found 74 candidate motility genes whose level or localization strongly correlate with motility in individual cells. We recovered 30 known motility genes, and validated several novel ones by mild knockdown experiments. Noise genetics can complement standard genetics for a variety of phenotypes. Public Library of Science 2014-03-06 /pmc/articles/PMC3945223/ /pubmed/24603725 http://dx.doi.org/10.1371/journal.pgen.1004176 Text en © 2014 Farkash-Amar et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Farkash-Amar, Shlomit Zimmer, Anat Eden, Eran Cohen, Ariel Geva-Zatorsky, Naama Cohen, Lydia Milo, Ron Sigal, Alex Danon, Tamar Alon, Uri Noise Genetics: Inferring Protein Function by Correlating Phenotype with Protein Levels and Localization in Individual Human Cells |
title | Noise Genetics: Inferring Protein Function by Correlating Phenotype with Protein Levels and Localization in Individual Human Cells |
title_full | Noise Genetics: Inferring Protein Function by Correlating Phenotype with Protein Levels and Localization in Individual Human Cells |
title_fullStr | Noise Genetics: Inferring Protein Function by Correlating Phenotype with Protein Levels and Localization in Individual Human Cells |
title_full_unstemmed | Noise Genetics: Inferring Protein Function by Correlating Phenotype with Protein Levels and Localization in Individual Human Cells |
title_short | Noise Genetics: Inferring Protein Function by Correlating Phenotype with Protein Levels and Localization in Individual Human Cells |
title_sort | noise genetics: inferring protein function by correlating phenotype with protein levels and localization in individual human cells |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3945223/ https://www.ncbi.nlm.nih.gov/pubmed/24603725 http://dx.doi.org/10.1371/journal.pgen.1004176 |
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