Decoding epithelial-mesenchymal transitions with multi-omics

Epithelial-mesenchymal transition (EMT) is a highly dynamic and plastic cellular programme essential for embryonic development, tissue repair and ageing, and it is co-opted in pathological conditions such as cancer and fibrosis. The continuum of intermediate states and pronounced cellular heterogeneity that characterize EMT have historically limited systematic investigation across regulatory layers, including the transcriptome, epigenome, proteome and metabolome. Recent advances in high-throughput omics technologies, particularly single-cell and spatially resolved platforms, have enabled comprehensive profiling of cellular states at unprecedented resolution. These approaches, in parallel with computational methodologies such as machine learning frameworks, network-based modelling and integrative multi-omics, have been instrumental in extracting biological insight from intrinsically complex datasets. In this Review, we discuss how the convergence of emerging omics technologies and computational frameworks has advanced EMT research, outline key analytical considerations and discuss current challenges and future opportunities for biomarker discovery and translational applications.