Expression data from MCF10A cells treated with TGFβ for multiple time points [scRNA-seq]

We combine state-of-the-art data acquisition platforms and bioinformatics tools to devise PAMAF, a workflow that simultaneously examines twelve omics modalities, i.e., protein abundance from whole-cells, nucleus, exosomes, secretome and membrane; N-glycosylation, phosphorylation; metabolites; mRNA, miRNA; and, in parallel, single-cell transcriptomes. Here we apply PAMAF in an established in vitro model of TGFβ-induced epithelial to mesenchymal transition (EMT) to quantify >61,000 molecules from 12 omics and 10 timepoints over 12 days. Bioinformatics analysis of this EMT-ExMap resource allowed us to identify; –unexpected topological coupling between omics, –four distinct cell states during EMT (E, E/M-1, E/M-2, M), –omics-specific kinetic paths, –stage-specific multi-omics characteristics, –distinct regulatory classes of genes, –ligand–receptor mediated intercellular crosstalk using an innovative pipeline integrating scRNAseq and subcellular proteomics, and –novel combinatorial drug targets (e.g., Hedgehog signaling and CAMK-II) to inhibit EMT, which we validate using a 3D mammary duct-on-a-chip platform. Overall, while this study provides an unprecedented resource on TGFβ signaling and EMT, PAMAF-like workflows can be applied to generate comprehensive molecular landscapes of other multifaceted biological processes. MCF10A cells treated with TGFβ for multiple time points.