A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.
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Gross SM, Dane MA, Smith RL, Devlin KL, McLean IC, Derrick DS, Mills CE, Subramanian K, London AB, Torre D, Evangelista JE, Clarke DJB, Xie Z, Erdem C, Lyons N, Natoli T, Pessa S, Lu X, Mullahoo J, Li J, Adam M, Wassie B, Liu M, Kilburn DF, Liby TA, Bucher E, Sanchez-Aguila C, Daily K, Omberg L, Wang Y, Jacobson C, Yapp C, Chung M, Vidovic D, Lu Y, Schurer S, Lee A, Pillai A, Subramanian A, Papanastasiou M, Fraenkel E, Feiler HS, Mills GB, Jaffe JD, Ma'ayan A, Birtwistle MR, Sorger PK, Korkola JE, Gray JW, Heiser LM
Commun Biol 5 (1) 1066 [2022-10-07; online 2022-10-07]
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.
PubMed 36207580
DOI 10.1038/s42003-022-03975-9
Crossref 10.1038/s42003-022-03975-9
pmc: PMC9546880
pii: 10.1038/s42003-022-03975-9