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

Abstract

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.

Expanding large-scale mechanistic models with machine learned associations and big datasets

“…The normal-like breast epithelial cell line MCF10A was recently profiled with multiple assay types under multiple ligand stimulation conditions (18). Using this newly released multi-omics dataset, our lab introduced the MOBILE pipeline for data integration and showed how ligand-specific associations can be inferred (16).…”

“…Here, we explore a combination of both methods to develop better models that will accurately represent generated biological knowledge. Connections inferred via a machine-learning pipeline (called MOBILE (16)) are inserted as new interactions into a large-scale mechanistic model (called SPARCED (17)) to better recapitulate the recently released MCF10A dataset (18). The NIH-LINCS Consortium and MCF10A Common Project recently released this dataset, consisting of multiple omics assay types on breast epithelial MCF10A cell line.…”

Morphodynamical cell-state description via live-cell imaging trajectory embedding

“…Here, we develop and apply the morphodynamical trajectory embedding method in a dataset of MCF10A mammary epithelial cells perturbed with a set of six ligands spanning major extracellular signaling pathways and inducing distinct cellular responses, including changes to cell proliferation, differentiation state, and motility 37 . The live-cell imaging data are part of a broader data collection effort through the Library of Integrated Network-Based Cellular Signatures (LINCS) consortium 38,39 MCF10A project 37 where the molecular and phenotypic responses to these ligand perturbations were explored.…”

“…Here, we develop and apply the morphodynamical trajectory embedding method in a dataset of MCF10A mammary epithelial cells perturbed with a set of six ligands spanning major extracellular signaling pathways and inducing distinct cellular responses, including changes to cell proliferation, differentiation state, and motility 37 . The live-cell imaging data are part of a broader data collection effort through the Library of Integrated Network-Based Cellular Signatures (LINCS) consortium 38,39 MCF10A project 37 where the molecular and phenotypic responses to these ligand perturbations were explored. Molecular (RNAseq, protein expression levels via reverse phase protein array (RPPA), and chromatin state ATACseq) and cellular responses (cyclic immunofluorescence, cycIF) indicate changes in canonical cell signaling pathways and initiation of unique cellular responses in each ligand condition 37 .…”

“…The live-cell imaging data are part of a broader data collection effort through the Library of Integrated Network-Based Cellular Signatures (LINCS) consortium 38,39 MCF10A project 37 where the molecular and phenotypic responses to these ligand perturbations were explored. Molecular (RNAseq, protein expression levels via reverse phase protein array (RPPA), and chromatin state ATACseq) and cellular responses (cyclic immunofluorescence, cycIF) indicate changes in canonical cell signaling pathways and initiation of unique cellular responses in each ligand condition 37 . Our live-cell imaging cell-trajectory-based analysis was developed to characterize the morphodynamical changes associated with molecular responses.…”