Learning Edge Rewiring in EMT From Single Cell Data

Abstract

Cellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in gene expression and protein confirmations. However, typical computational approaches treat them as static interaction networks derived from a single experimental time point. Here, we provide a method for learning the dynamic modulation, or rewiring of pairwise relationships (edges) from a static single-cell data.We use the epithelial-to-mesenchymal transition (EMT) in murine breast cancer cells as a model system, and measure mass cytometry data three days after induction of the transition by TGFβ. We take advantage of transitional rate variability between cells in the data by deriving a pseudo-time EMT trajectory. Then we propose methods for visualizing and quantifying time-varying edge behavior over the trajectory and use these methods: TIDES (Trajectory Imputed DREMI scores), and measure of edge dynamism (3D-DREMI) to predict and validate the effect of drug perturbations on EMT.

A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition

“…Understanding the regulation of EMT is a fundamental goal in developmental and cancer biology and has the potential to yield new therapeutic opportunities for intervention in cancer. In contrast to numerous reports of "partial", "hybrid", or "intermediate" EMT stages, both our analysis and recent scRNA-seq and mass cytometry studies of a cancer line 10,11 indicate that cells are organized along a continuum during EMT.…”

“…Several studies identified discrete intermediate "stages" of EMT based on expression of a handful of marker genes [7][8][9] . However, recent single-cell mass cytometry and RNA-seq analyses of breast cancer cells suggest that they fall along a continuum 10,11 . As such, it remains unclear whether or not cells exist in functionally discrete states during EMT, and the genetic circuitry that controls the transition remains incompletely defined.…”

Using single‐cell multiple omics approaches to resolve tumor heterogeneity

“…Also, new studies propose different methods to construct dynamic protein signalling networks using single-cell protein measurement [94,95]. In particular, one new approach created a dynamic regulation network from CyTOF measurements to model the drug perturbation of the epithelial-to-mesenchymal transition [96]. This type of approach can then facilitate the discovery of critical events correlated with a cell state transition.…”

Single-Cell Multi-omics: An Engine for New Quantitative Models of Gene Regulation

“…the abundance of phosphorylated epitopes) can give key insights into the biochemical mechanisms that underlie the statistical properties of a gene regulatory network. For example, mass cytometry [53], a system that combines antibody labeling with mass spectrometry to enable protein epitope quantification in single cells, has been leveraged to perform de novo statistical inference of signaling pathway architecture [54,55]. Systems based on mass spectrometry however require a different experimental apparatus and technical skill-set from those based on DNA sequencing, which has impeded the integration of genomic and proteomic methods.…”