Crossley, R. M. | Search bioRxiv

bioRxiv2024

Electrotaxis disrupts patterns of cell-cell interactions of human corneal epithelial cells in vitro

Electrotaxis, the process by which eukaryotic cells establish a polarity and move directionally along an electric field, is a well-studied mechanism to steer the migration of cells in vitro and in vivo. While the influence of an electric field on single cells in culture is well-documented, the influence of the electric field on cell-cell interactions has not been well studied. In this work, we quantify the length, duration and number of cell-cell interactions during electrotaxis of human corneal epithelial cells and compare the properties of these interactions with those arising in the absence of an electric field. We find that contact inhibition of locomotion and velocity alignment, two key behaviours observed during dynamic physical interactions between cells in vitro, are strongly affected by an electric field. Further-more, we establish a link between the location of a cell-cell contact on the cell surface and the resulting cell interaction behaviours. By mapping the regions of the cell surface with a characteristic response to contact with another cell, we find that the spatial distribution of possible responses upon cell-cell contact is altered upon induction of an electric field. Altogether, this work shows how the electric field not only influences individual cell motility and directionality, but also affects cell-cell interactions.

cell biology↗

bioRxiv2024

Identifiability of heterogeneous phenotype adaptation from low-cell-count experiments and a stochastic model

Phenotypic plasticity contributes significantly to treatment failure in many cancers. Despite the increased prevalence of experimental studies that interrogate this phenomenon, there remains a lack of applicable quantitative tools to characterise data, and importantly to distinguish between resistance as a discrete phenotype and a continuous distribution of phenotypes. To address this, we develop a stochastic individual-based model of plastic phenotype adaptation through a continuously-structured phenotype space in low-cell-count proliferation assays. That our model corresponds probabilistically to common partial differential equation models of resistance allows us to formulate a likelihood that captures the intrinsic noise ubiquitous to such experiments. We apply our framework to assess the identifiability of key model parameters in several population-level data collection regimes; in particular, parameters relating to the adaptation velocity and cell-to-cell heterogeneity. Significantly, we find that cell-to-cell heterogeneity is practically non-identifiable from both cell count and proliferation marker data, implying that population-level behaviours may be well characterised by homogeneous ordinary differential equation models. Additionally, we demonstrate that population-level data are insufficient to distinguish resistance as a discrete phenotype from a continuous distribution of phenotypes. Our results inform the design of both future experiments and future quantitative analyses that probe phenotypic plasticity in cancer. Author SummaryMany cancers adaptively and reversibly develop resistance to treatment, adding complexity to predictive model development and, by extension, treatment design. While so-called drug challenge experiments are now commonly employed to interrogate phenotypic plasticity, there are very few quantitative tools available to interpret the biological data that arises. In particular, it remains unclear what is needed from drug challenge experiments in order to identify the phenotypic structure of a population that responds adaptively to treatment. In this work, we develop a new individual-level mathematical model of phenotypic plasticity in parallel with a structured model calibration process. Applying our framework to various existing and potential experimental designs reveals that experiments that yield only population-level data cannot distinguish between drug resistance as a distinct cell state, or drug resistance as a continuum of cell states. Consequentially, at the population-level, we demonstrate that common mathematical models that assume a set of distinct cell states can characterise the behaviour of cell populations that, in actuality, respond through a continuum of states. Importantly, our results shed light on both the mathematical models and experiments required to capture phenotypic plasticity in cancer.

cancer biology↗