Parallelized multidimensional analytic framework applied to mammary epithelial cells uncovers regulatory principles in EMT

Indranil Paul, Dante Bolzan, Ahmed Youssef, Keith A. Gagnon, Heather Hook, Gopal Karemore, Michael U. J. Oliphant, Weiwei Lin, Qian Liu, Sadhna Phanse, Carl White, Dzmitry Padhorny, Sergei Kotelnikov, Christopher S. Chen, Pingzhao Hu, Gerald V. Denis, Dima Kozakov, Brian Raught, Trevor Siggers, Stefan Wuchty, Senthil K. Muthuswamy and Andrew Emili ()
Additional contact information
Indranil Paul: Boston University School of Medicine, Boston University
Dante Bolzan: University of Miami
Ahmed Youssef: Boston University
Keith A. Gagnon: Boston University
Heather Hook: Boston University
Gopal Karemore: Novo Nordisk A/S
Michael U. J. Oliphant: Beth Israel Deaconess Medical Center
Weiwei Lin: Boston University School of Medicine, Boston University
Qian Liu: University of Manitoba
Sadhna Phanse: Boston University School of Medicine, Boston University
Carl White: Boston University School of Medicine, Boston University
Dzmitry Padhorny: Stony Brook University
Sergei Kotelnikov: Stony Brook University
Christopher S. Chen: Boston University
Pingzhao Hu: Western University
Gerald V. Denis: Boston University, Boston University
Dima Kozakov: Stony Brook University
Brian Raught: University of Toronto
Trevor Siggers: Boston University
Stefan Wuchty: University of Miami
Senthil K. Muthuswamy: National Cancer Institute, NIH
Andrew Emili: Boston University School of Medicine, Boston University

Nature Communications, 2023, vol. 14, issue 1, 1-23

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-36122-x

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