Population Dynamics of EMT Elucidates the Timing and Distribution of Phenotypic Intra-tumoral Heterogeneity

Abstract

The Epithelial-to-Mesenchymal Transition (EMT) is a hallmark of cancer metastasis and morbidity. EMT is a non-binary process, and cells can be stably arrested en route to EMT in an intermediate hybrid state associated with enhanced tumor aggressiveness and worse patient outcomes. Understanding EMT progression in detail will provide fundamental insights into the mechanisms underlying metastasis. Despite increasingly available single-cell RNA sequencing data that enable in-depth analyses of EMT at the single-cell resolution, current inferential approaches are limited to bulk microarray data. There is thus a great need for computational frameworks to systematically infer and predict the timing and distribution of EMT-related states at single-cell resolution. Here, we develop a computational framework for reliable inference and prediction of EMT-related trajectories from single-cell RNA sequencing data. Our model can be utilized across a variety of applications to predict the timing and distribution of EMT from single-cell sequencing data. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/523978v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@1c56970org.highwire.dtl.DTLVardef@18d7371org.highwire.dtl.DTLVardef@19612f9org.highwire.dtl.DTLVardef@1b7cf96_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA fully stochastic model elucidates the population dynamics of EMT C_LIO_LIA data-driven pipeline is introduced to track EMT trajectories from single-cell RNA sequencing C_LIO_LICell cycle scoring reveals cell line-dependent patterns of EMT Induction C_LI