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A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses
The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associa...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546880/ https://www.ncbi.nlm.nih.gov/pubmed/36207580 http://dx.doi.org/10.1038/s42003-022-03975-9 |
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author | Gross, Sean M. Dane, Mark A. Smith, Rebecca L. Devlin, Kaylyn L. McLean, Ian C. Derrick, Daniel S. Mills, Caitlin E. Subramanian, Kartik London, Alexandra B. Torre, Denis Evangelista, John Erol Clarke, Daniel J. B. Xie, Zhuorui Erdem, Cemal Lyons, Nicholas Natoli, Ted Pessa, Sarah Lu, Xiaodong Mullahoo, James Li, Jonathan Adam, Miriam Wassie, Brook Liu, Moqing Kilburn, David F. Liby, Tiera A. Bucher, Elmar Sanchez-Aguila, Crystal Daily, Kenneth Omberg, Larsson Wang, Yunguan Jacobson, Connor Yapp, Clarence Chung, Mirra Vidovic, Dusica Lu, Yiling Schurer, Stephan Lee, Albert Pillai, Ajay Subramanian, Aravind Papanastasiou, Malvina Fraenkel, Ernest Feiler, Heidi S. Mills, Gordon B. Jaffe, Jake D. Ma’ayan, Avi Birtwistle, Marc R. Sorger, Peter K. Korkola, James E. Gray, Joe W. Heiser, Laura M. |
author_facet | Gross, Sean M. Dane, Mark A. Smith, Rebecca L. Devlin, Kaylyn L. McLean, Ian C. Derrick, Daniel S. Mills, Caitlin E. Subramanian, Kartik London, Alexandra B. Torre, Denis Evangelista, John Erol Clarke, Daniel J. B. Xie, Zhuorui Erdem, Cemal Lyons, Nicholas Natoli, Ted Pessa, Sarah Lu, Xiaodong Mullahoo, James Li, Jonathan Adam, Miriam Wassie, Brook Liu, Moqing Kilburn, David F. Liby, Tiera A. Bucher, Elmar Sanchez-Aguila, Crystal Daily, Kenneth Omberg, Larsson Wang, Yunguan Jacobson, Connor Yapp, Clarence Chung, Mirra Vidovic, Dusica Lu, Yiling Schurer, Stephan Lee, Albert Pillai, Ajay Subramanian, Aravind Papanastasiou, Malvina Fraenkel, Ernest Feiler, Heidi S. Mills, Gordon B. Jaffe, Jake D. Ma’ayan, Avi Birtwistle, Marc R. Sorger, Peter K. Korkola, James E. Gray, Joe W. Heiser, Laura M. |
author_sort | Gross, Sean M. |
collection | PubMed |
description | The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis. |
format | Online Article Text |
id | pubmed-9546880 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95468802022-10-09 A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses Gross, Sean M. Dane, Mark A. Smith, Rebecca L. Devlin, Kaylyn L. McLean, Ian C. Derrick, Daniel S. Mills, Caitlin E. Subramanian, Kartik London, Alexandra B. Torre, Denis Evangelista, John Erol Clarke, Daniel J. B. Xie, Zhuorui Erdem, Cemal Lyons, Nicholas Natoli, Ted Pessa, Sarah Lu, Xiaodong Mullahoo, James Li, Jonathan Adam, Miriam Wassie, Brook Liu, Moqing Kilburn, David F. Liby, Tiera A. Bucher, Elmar Sanchez-Aguila, Crystal Daily, Kenneth Omberg, Larsson Wang, Yunguan Jacobson, Connor Yapp, Clarence Chung, Mirra Vidovic, Dusica Lu, Yiling Schurer, Stephan Lee, Albert Pillai, Ajay Subramanian, Aravind Papanastasiou, Malvina Fraenkel, Ernest Feiler, Heidi S. Mills, Gordon B. Jaffe, Jake D. Ma’ayan, Avi Birtwistle, Marc R. Sorger, Peter K. Korkola, James E. Gray, Joe W. Heiser, Laura M. Commun Biol Article The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis. Nature Publishing Group UK 2022-10-07 /pmc/articles/PMC9546880/ /pubmed/36207580 http://dx.doi.org/10.1038/s42003-022-03975-9 Text en © The Author(s) 2022 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Gross, Sean M. Dane, Mark A. Smith, Rebecca L. Devlin, Kaylyn L. McLean, Ian C. Derrick, Daniel S. Mills, Caitlin E. Subramanian, Kartik London, Alexandra B. Torre, Denis Evangelista, John Erol Clarke, Daniel J. B. Xie, Zhuorui Erdem, Cemal Lyons, Nicholas Natoli, Ted Pessa, Sarah Lu, Xiaodong Mullahoo, James Li, Jonathan Adam, Miriam Wassie, Brook Liu, Moqing Kilburn, David F. Liby, Tiera A. Bucher, Elmar Sanchez-Aguila, Crystal Daily, Kenneth Omberg, Larsson Wang, Yunguan Jacobson, Connor Yapp, Clarence Chung, Mirra Vidovic, Dusica Lu, Yiling Schurer, Stephan Lee, Albert Pillai, Ajay Subramanian, Aravind Papanastasiou, Malvina Fraenkel, Ernest Feiler, Heidi S. Mills, Gordon B. Jaffe, Jake D. Ma’ayan, Avi Birtwistle, Marc R. Sorger, Peter K. Korkola, James E. Gray, Joe W. Heiser, Laura M. A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses |
title | A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses |
title_full | A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses |
title_fullStr | A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses |
title_full_unstemmed | A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses |
title_short | A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses |
title_sort | multi-omic analysis of mcf10a cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546880/ https://www.ncbi.nlm.nih.gov/pubmed/36207580 http://dx.doi.org/10.1038/s42003-022-03975-9 |
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