Constraints and trade-offs shape the evolution of T cell search strategies

Abstract

AbstractTwo decades of in vivo imaging have revealed how diverse the shapes and motion patterns of migrating T cells can be. This finding has sparked the notion of “search strategies”: T cells may have evolved ways to search for antigen efficiently and might even adapt their motion to the task at hand. Mathematical models have indeed confirmed that observed T-cell migration patterns resemble a theoretical optimum in several contexts; for example, frequent turning, stop-and-go motion, or alternating short and long motile runs have all been interpreted as deliberately tuned behaviours, optimising the cell’s chance of finding antigen. But the same behaviours could also arise simply because T cells can’t follow a straight, regular path through the tight spaces they navigate. Even if T cells can be shown to follow a theoretically optimal pattern, the question remains: has that pattern truly been evolved for this particular searching task, or does it merely reflect how the cell’s migration machinery and surroundings constrain motion paths?We here examine to what extent cells can evolve search strategies when faced with realistic constraints. Using a cellular Potts model (CPM), where motion arises from interactions between intracellular dynamics, cell shape, and a constraining environment, we simulate an evolutionary process in which cells “optimise” a simple task: explore as much area as possible. We find that cells evolve several motility characteristics previously attributed to search optimisation, even though these features were not beneficial for the task given here. Our results stress that “optimal” search strategies do not always stem from evolutionary adaptation: instead, they may be the inevitable side effects of interactions between cell shape, intracellular actin dynamics, and the diverse environments T cells face in vivo.