A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses

Sean M. Gross, Mark A. Dane, Rebecca L. Smith, Kaylyn L. Devlin, Ian C. McLean, Daniel S. Derrick, Caitlin E. Mills, Kartik Subramanian, Alexandra B. London, Denis Torre, John Erol Evangelista, Daniel J.B. Clarke, Zhuorui Xie, Cemal Erdem, Nicholas Lyons, Ted Natoli, Sarah Pessa, Xiaodong Lu, James Mullahoo, Jonathan LiMiriam Adam, Brook Wassie, Moqing Liu, David F. Kilburn, Tiera A. Liby, Elmar Bucher, Crystal Sanchez-Aguila, Kenneth Daily, Larsson Omberg, Yunguan Wang, Connor Jacobson, Clarence Yapp, Mirra Chung, Dusica Vidovic, Yiling Lu, Stephan Schurer, Albert Lee, Ajay Pillai, Aravind Subramanian, Malvina Papanastasiou, Ernest Fraenkel, Heidi S. Feiler, Gordon B. Mills, Jake D. Jaffe, Avi Ma’ayan, Marc R. Birtwistle, Peter K. Sorger, James E. Korkola, Joe W. Gray, Laura M. Heiser

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Article number1066
JournalCommunications Biology
Issue number1
StatePublished - Dec 2022

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)


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