Mechanistic and experimental models of cell migration reveal the importance of cell-to-cell pushing in cell invasion

Abstract

Moving fronts of cells are essential for development, repair and disease progression. Therefore, understanding and quantifying the details of the mechanisms that drive the movement of cell fronts is of wide interest. Quantitatively identifying the role of intercellular interactions, and in particular the role of cell pushing, remains an open question. In this work, we report a combined experimental-modelling approach showing that intercellular interactions contribute significantly to the spatial spreading of a population of cells. We use a novel experimental data set with PC-3 prostate cancer cells that have been pretreated with Mitomycin-C to suppress proliferation. This allows us to experimentally separate the effects of cell migration from cell proliferation, thereby enabling us to focus on the migration process in detail as the population of cells recolonizes an initially-vacant region in a series of two-dimensional experiments. We quantitatively model the experiments using a stochastic modelling framework, based on Langevin dynamics, which explicitly incorporates random motility and various intercellular forces including: (i) long range attraction (adhesion); and (ii) finite size effects that drive short range repulsion (pushing). Quantitatively comparing the ability of this model to describe the experimentally observed population-level behaviour provides us with quantitative insight into the roles of random motility and intercellular interactions. To quantify the mechanisms at play, we calibrate the stochastic model to match experimental cell density profiles to obtain estimates of cell diffusivity, D, and the amplitude of intercellular forces, f 0. Our analysis shows that taking a standard modelling approach which ignores intercellular forces provides a poor match to the experimental data whereas incorporating intercellular forces, including short-range pushing and longer range attraction, leads to a faithful representation of the experimental observations. These results demonstrate a significant role of cell pushing during cell front movement and invasion.

Identifiability analysis for stochastic differential equation models in systems biology

“…Based on the structural identifiability results, we expect the likelihood (and for a uniform prior, the posterior density) to be constant along the identifiable parameter combinationũ 1 ¼ u 1 À u 2 , and we see this in figure 4d. These results also suggest that, should one of θ 1 or θ 2 be known (for example, if the cells are treated with an anti-proliferative drug that enforces θ 1 = 0 [149]) the other be identifiable. However, lower and upper bounds for θ 1 and θ 2 , respectively, are able to be established as a direct consequence of the prior assumption that all parameters are strictly positive.…”

3D printed inserts for reproducible high throughput screening of cell migration

“…The cells respond to various chemical and biophysical cues that originate internally or externally and migrate in single cell or collective fashion ( SenGupta et al, 2021 ; Lusby et al, 2022 ; Merino-Casallo et al, 2022 ; Pawluchin and Galic, 2022 ). Over the past decade many successful attempts have been made to unravel cell migration phenomenon with respect to cell behavior during migration and molecular players involved in it using experimental and simulation techniques ( Grada et al, 2017 ; Haridas et al, 2018 ; Matsiaka et al, 2019 ; Melo et al, 2021 ). Especially in cancer, understanding cell migration is very important because of its principal role in metastasis.…”

“…Apart from few exceptions such as epithelial cell migration involved in skin wound closure and cell migration on bones, all cell migration phenomena occur in 3 dimensions (3D) in normal physiological setup ( Yamada and Sixt, 2019 ). Surrounding extracellular matrix (ECM) and neighboring cells have direct impact on the mode and rate of cell migration ( Stuelten et al, 2018 ; Matsiaka et al, 2019 ; Yamada and Sixt, 2019 ; SenGupta et al, 2021 ; Lusby et al, 2022 ; Merino-Casallo et al, 2022 ; Pawluchin and Galic, 2022 ). In recent years, the cell migration studies have seen few technical or experimental advances such as study of cell migration in 3D environment (e.g.,: in Matrigel™ support) ( Audoin et al, 2022 ) or in soft or hard biocompatible hydrogels ( Trappmann et al, 2017 ), cell migration in spheroids ( Zhang et al, 2022 ), and cell migration in microfluidic devices ( Schwarz et al, 2016 ; Shih et al, 2019 ).…”