Inference of Intercellular Communications and Multilayer Gene-Regulations of Epithelial–Mesenchymal Transition From Single-Cell Transcriptomic Data

Abstract

Epithelial-to-mesenchymal transition (EMT) plays an important role in many biological processes during development and cancer. The advent of single-cell transcriptome sequencing techniques allows the dissection of dynamical details underlying EMT with unprecedented resolution. Despite several single-cell data analysis on EMT, how cell communicates and regulates dynamics along the EMT trajectory remains elusive. Using single-cell transcriptomic datasets, here we infer the cell–cell communications and the multilayer gene–gene regulation networks to analyze and visualize the complex cellular crosstalk and the underlying gene regulatory dynamics along EMT. Combining with trajectory analysis, our approach reveals the existence of multiple intermediate cell states (ICSs) with hybrid epithelial and mesenchymal features. Analyses on the time-series datasets from cancer cell lines with different inducing factors show that the induced EMTs are context-specific: the EMT induced by transforming growth factor B1 (TGFB1) is synchronous, whereas the EMTs induced by epidermal growth factor and tumor necrosis factor are asynchronous, and the responses of TGF-β pathway in terms of gene expression regulations are heterogeneous under different treatments or among various cell states. Meanwhile, network topology analysis suggests that the ICSs during EMT serve as the signaling in cellular communication under different conditions. Interestingly, our analysis of a mouse skin squamous cell carcinoma dataset also suggests regardless of the significant discrepancy in concrete genes between in vitro and in vivo EMT systems, the ICSs play dominant role in the TGF-β signaling crosstalk. Overall, our approach reveals the multiscale mechanisms coupling cell–cell communications and gene–gene regulations responsible for complex cell-state transitions.

A Coagulation-Related Gene-Based Prognostic Model for Invasive Ductal Carcinoma

“…Previous studies have revealed the correlations between these signaling pathways. Epithelial-to-mesenchymal transition (EMT) and hedgehog signaling pathways can lead to angiogenesis, loss of normal organ function, or cancer by modulating TGF-β signaling pathway in cancer progression ( Wang et al, 2013 ; Bausch et al, 2020 ; Sha et al, 2020 ). In skin diseases, the promoted angiogenesis and lymphatic vessel enlargement were a response to ultraviolet (UV; Okazaki et al, 2012 ).…”

Investigating epithelial‐mesenchymal heterogeneity of tumors and circulating tumor cells with transcriptomic analysis and biophysical modeling

“…In silico modeling of EMT and Notch underlying circuitry dynamics recently predicted that lateral induction between hybrid E/M cells can facilitate the formation of hybrid E/M multicellular clusters [59,60]. Similarly, reconstruction of TGF‐ cell‐cell communication networks recently suggested that hybrid E/M phenotypes can act as both senders and receivers of the signaling, thus facilitating EMT transitions in other cells [8]. Therefore, modeling the effect of cell‐cell communication on EM plasticity and CTC cluster distributions could be another interesting future direction.…”

“…During EMT, epithelial cancer cells can partially or completely lose their E-cadherin-mediated cell-cell adhesion and apicobasal polarity, instead of becoming motile, mesenchymal cells [2]. Recent experimental and computational findings suggest that EMT is not necessarily a binary process; instead cells can manifest one or more hybrid E/M phenotype(s) with mixed traits of epithelial and mesenchymal cells along the EMT (or EMP) spectrum [3][4][5][6][7][8]. Indeed, cellular heterogeneity is emerging as a hallmark of cancer, with cells with distinct EMT phenotypes often localized in distinct tumor regions [9,10].…”

Single-Cell RNA-Seq Analysis Reveals the Acquisition of Cancer Stem Cell Traits and Increase of Cell–Cell Signaling during EMT Progression

“…The optimal number of clusters is established based on the maximal gap between consecutive eigenvalues. Since this study especially focuses on intermediate EMT states, we followed previous applications of the method and constraint to a minimum of three clusters to identify at least one potential intermediate state [ 10 ]. The starting cluster for trajectory inference was identified based on epithelial and mesenchymal scores of the clusters.…”

“…Single-cell transcriptomics now offers unprecedented resolution on gene expression regulation during EMT, proving that many EMT-associated molecular programs are context specific, thus making a general definition even more challenging [ 8 ]. Recently, intermediate EMT states and the transitions between them have been identified from single-cell RNA-sequencing (scRNA-seq) data in various human and mouse cancers [ 9 , 10 ]. Many questions remain, however, on how to characterize these intermediate states and their connection with other axes of cancer progression, such as tumor-initiating ability and cell–cell signaling.…”