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

Author

Listed:
  • 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
    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
    Stony Brook University)

  • Sergei Kotelnikov

    (Stony Brook University
    Stony Brook University)

  • Christopher S. Chen

    (Boston University
    Harvard University)

  • Pingzhao Hu

    (Western University)

  • Gerald V. Denis

    (Boston University, Boston University)

  • Dima Kozakov

    (Stony Brook University
    Stony Brook University)

  • Brian Raught

    (University of Toronto)

  • Trevor Siggers

    (Boston University
    Boston University)

  • Stefan Wuchty

    (University of Miami)

  • Senthil K. Muthuswamy

    (National Cancer Institute, NIH)

  • Andrew Emili

    (Boston University School of Medicine, Boston University
    Boston University, Life Science & Engineering (LSEB-602)
    Knight Cancer Institute, Oregon Health and Science University)

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.

Suggested Citation

  • 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, 2023. "Parallelized multidimensional analytic framework applied to mammary epithelial cells uncovers regulatory principles in EMT," Nature Communications, Nature, vol. 14(1), pages 1-23, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36122-x
    DOI: 10.1038/s41467-023-36122-x
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    References listed on IDEAS

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