Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data

Abstract

Recent advances in single-cell techniques catalyze an emerging field of studying how cells convert from one phenotype to another, in a step-by-step process. Two grand technical challenges, however, impede further development of the field. Fixed cell–based approaches can provide snapshots of high-dimensional expression profiles but have fundamental limits on revealing temporal information, and fluorescence-based live-cell imaging approaches provide temporal information but are technically challenging for multiplex long-term imaging. We first developed a live-cell imaging platform that tracks cellular status change through combining endogenous fluorescent labeling that minimizes perturbation to cell physiology and/or live-cell imaging of high-dimensional cell morphological and texture features. With our platform and an A549 VIM-RFP epithelial-to-mesenchymal transition (EMT) reporter cell line, live-cell trajectories reveal parallel paths of EMT missing from snapshot data due to cell-cell dynamic heterogeneity. Our results emphasize the necessity of extracting dynamical information of phenotypic transitions from multiplex live-cell imaging.

Transcriptomic-based quantification of the epithelial-hybrid-mesenchymal spectrum across biological contexts

“…Quantifying the spectrum of epithelial-hybrid-mesenchymal cell states in cancer has garnered recent interest due to a surge in the availability of in vitro and in vivo spatial and/or temporal dynamic and high-throughput data at multiple levels -transcriptomic, proteomic, epigenetic, metabolic, and morphological (Bocci et al, 2019;Cook and Vanderhyden, 2020;Deshmukh et al, 2021;Devaraj and Bose, 2019;Jia et al, 2019;Johnson et al, 2021;Karacosta et al, 2019;McFaline-Figueroa et al, 2019;Serresi et al, 2021;Stylianou et al, 2019;Wang et al, 2020). Phenotypic plasticity and heterogeneity along the EMP spectrum has been postulated to be a more important criteria for defining the survival fitness of a cancer cell population than the predominance of a specific phenotype (Brown et al, 2021;Chakraborty et al, 2021), suggesting possible benefits to a more heterogeneous population through cooperation among cancer cells with varying EMP phenotypes (Neelakantan et al, 2017;Tsuji et al, 2008).…”

Multi-Stability and Consequent Phenotypic Plasticity in AMPK-Akt Double Negative Feedback Loop in Cancer Cells

“…Besides these specific examples, EMP and CSCs are archetypal examples of phenotypic switching reported in many carcinomas [42,43] and non-epithelial tumors [44][45][46][47]. Recent progress in collecting high-throughput spatiotemporal data and mapping the regulatory networks underlying these axes of plasticity has led to developing complex mechanism-based and population-based mathematical models to decode dynamical traits of phenotypic switching such as dose-dependence, reversibility, hysteresis and transition rates among the cell states [48][49][50][51][52][53][54][55][56].…”

Biophysical and Biochemical Attributes of Hybrid Epithelial/Mesenchymal Phenotypes

“…These investigations that integrate quantitative image analysis with population-level mathematical models offer not only a way to augment the molecular marker-based investigations of EMT, but also a pipeline to understanding the dynamical principles that govern the underlying cellular plasticity landscape. For instance, in A549 cells with endogenous RFP (red fluorescent protein) label for Vimentin [41], live-cell imaging-based analysis of individual cell trajectories (in a high-dimensional morphological feature space) identified two distinct celltransition paths with varying Vimentin dynamics, thereby amplifying heterogeneity in hybrid E/M states.…”