Epithelial-mesenchymal transition (EMT) is a cellular process during which epithelial cells acquire mesen chymal phenotypes and behaviour following the down regulation of epithelial features. EMT is triggered in response to signals that cells receive from their micro environment. The epithelial state of the cells in which EMT is initiated is characterized by stable epithelial cell-cell junctions, apical-basal polarity and interac tions with basement membrane. During EMT, changes in gene expression and posttranslational regulation mechanisms lead to the repression of these epithelial characteristics and the acquisition of mesenchymal char acteristics. Cells then display fibroblastlike morphol ogy and cytoarchitecture, as well as increased migratory capacity. Furthermore, these now migratory cells often acquire invasive properties (Fig. 1). EMT was first described by researchers studying early embryogenesis as a programme with welldefined cellular features 1,2. It is now widely accepted that EMT occurs normally during early embryonic development, to enable a variety of morphogenetic events, as well as later in development and during wound healing in adults.

The process of epithelial-to-mesenchymal transition (EMT) is an essential type of cellular plasticity associated with a change from epithelial cells that function as a barrier consisting of a sheet of tightly connected cells to cells with properties of mesenchyme that are not attached to their neighbors and are highly motile. This phenotypic change occurs during development and also contributes to pathological processes, such as cancer progression. The molecular mechanisms controlling the switch between the fully epithelial and fully mesenchymal phenotypes and cells that have characteristics of both (partial EMT) are controversial, and multiple theoretical models have been proposed. To test these theoretical models, we systematically measured the changes in the abundance of proteins, mRNAs, and microRNAs (miRNAs) that represent the core regulators of EMT induced by transforming growth factor-β1 (TGF-β1) in the human breast epithelial cell line MCF10A at the population and single-cell levels. We provide experimental confirmation for a model of cascading switches in phenotypes associated with TGF-β1-induced EMT of MCF10A cells that involves two double-negative feedback loops: one between the transcription factor SNAIL1 and the miR-34 family and another between the transcription factor ZEB1 and the miR-200 family. Furthermore, our data showed that whereas the transition from epithelial to partial EMT was reversible for MCF10A cells, the transition from partial EMT to mesenchymal was mostly irreversible at high concentrations of TGF-β1.

Epithelial to mesenchymal transition (EMT) plays an important role in embryonic development, tissue regeneration, and cancer metastasis. Whereas several feedback loops have been shown to regulate EMT, it remains elusive how they coordinately modulate EMT response to TGF-β treatment. We construct a mathematical model for the core regulatory network controlling TGF-β-induced EMT. Through deterministic analyses and stochastic simulations, we show that EMT is a sequential two-step program in which an epithelial cell first is converted to partial EMT then to the mesenchymal state, depending on the strength and duration of TGF-β stimulation. Mechanistically the system is governed by coupled reversible and irreversible bistable switches. The SNAIL1/miR-34 double-negative feedback loop is responsible for the reversible switch and regulates the initiation of EMT, whereas the ZEB/miR-200 feedback loop is accountable for the irreversible switch and controls the establishment of the mesenchymal state. Furthermore, an autocrine TGF-β/miR-200 feedback loop makes the second switch irreversible, modulating the maintenance of EMT. Such coupled bistable switches are robust to parameter variation and molecular noise. We provide a mechanistic explanation on multiple experimental observations. The model makes several explicit predictions on hysteretic dynamic behaviors, system response to pulsed stimulation, and various perturbations, which can be straightforwardly tested.

Highlights d Modeling time-resolved scRNA-seq data avoids pitfalls of splicing RNA velocities d Dynamo reconstructs analytical vector fields from discrete velocity vectors d Vector fields reveal the timing and mechanisms of human hematopoiesis d Dynamo allows cell-state transition path and in silico perturbation predictions