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

Area of Science:

• Systems biology
• Molecular and cellular biology
• Biochemistry

Background:

• Understanding disease etiology requires reconstructing eukaryotic signaling pathways.
• Longitudinal, systems-scale analysis is crucial for deciphering complex cellular processes.
• Epithelial to mesenchymal transition (EMT) is a key process in development and disease.

Purpose of the Study:

• To develop and apply a comprehensive multi-omics workflow (PAMAF) for longitudinal analysis of cellular signaling.
• To investigate the molecular mechanisms and cell state dynamics during TGFβ-induced EMT.
• To identify novel therapeutic targets for inhibiting EMT.

Main Methods:

• Developed PAMAF, a workflow integrating 12 omics modalities (proteomics, N-glycosylation, phosphorylation, metabolomics, mRNA, miRNA, single-cell transcriptomics).
• Applied PAMAF to an in vitro TGFβ-induced EMT model, analyzing 10 timepoints over 12 days.
• Utilized bioinformatics to analyze >61,000 molecules, identifying molecular crosstalk and cell state transitions.

Main Results:

• Discovered topological coupling between different omics layers and identified four distinct cell states during EMT.
• Characterized omics-specific kinetic paths and stage-specific multi-omics signatures.
• Identified ligand-receptor mediated intercellular crosstalk and validated combinatorial drug targets (Hedgehog signaling, CAMK-II) to inhibit EMT using a 3D mammary duct-on-a-chip model.

Conclusions:

• PAMAF provides a powerful resource for systems-scale reconstruction of cellular signaling and disease etiology.
• The study elucidates key molecular events and cell state dynamics during EMT.
• Identified actionable therapeutic strategies targeting EMT, validated in a relevant preclinical model.