Highlights d SARS-CoV-2 infection in induced lung cells is characterized by phosphoproteomics d Analysis of response reveals host cell signaling and protein expression profile d Comparison to studies in undifferentiated cell lines shows unique pathology in iAT2s d Systems-level predictions find druggable pathways that can impede viral life cycle

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A proper understanding of disease etiology will require longitudinal systems-scale reconstruction of the multitiered architecture of eukaryotic signaling. Here 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. 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; –topological coupling between omics, –four distinct cell states during EMT, –omics-specific kinetic paths, –stage-specific multi-omics characteristics, –distinct regulatory classes of genes, –ligand–receptor mediated intercellular crosstalk by integrating scRNAseq and subcellular proteomics, and –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, this study provides a resource on TGFβ signaling and EMT.

TGFβ mediated epithelial to mesenchymal transition (EMT) proceeds through hybrid "E/M" states. A deeper understanding of these states and events which regulate entry to and exit from the E/M states is needed for therapeutic exploitation. We quantified >60,000 molecules across ten time points and twelve omic layers in mammary epithelial cells. Proteomes of whole cells, phosphoproteins, nucleus, extracellular vesicles, secretome and membrane resolved major shifts, E→E/M and E/M→M during EMT, and defined state-specific signatures. Metabolomics identified early activation of arachidonic acid pathway and an enzyme-mediated switch from Cytochrome P450 to Cyclooxygenase / Lipoxygenase branches during E→E/M. Single-cell transcriptomics identified GLIS2 as an early modulator of EMT. Integrative modeling-predicted combinatorial inhibition of AURKB, PP2A and SRC exposed vulnerabilities at E→E/M juncture. Covariance analysis revealed remarkable discordance between proteins and transcripts, and between proteomic layers, implying insufficiency of current approaches. Overall, this dataset provides an unprecedented resource on TGFβ signaling, EMT and cancer.