No abstract

Natural chemical gradients to which cells respond chemotactically are often dynamic, with both spatial and temporal components. A primary example is the social amoeba Dictyostelium, which migrates to the source of traveling waves of chemoattractant as part of a self-organized aggregation process. Despite its physiological importance, little is known about how cells migrate directionally in response to traveling waves. The classic back-of-the-wave problem is how cells chemotax toward the wave source, even though the spatial gradient reverses direction in the back of the wave. Here, we address this problem by using microfluidics to expose cells to traveling waves of chemoattractant with varying periods. We find that cells exhibit memory and maintain directed motion toward the wave source in the back of the wave for the natural period of 6 min, but increasingly reverse direction for longer wave periods. Further insights into cellular memory are provided by experiments quantifying cell motion and localization of a directional-sensing marker after rapid gradient switches. The results can be explained by a model that couples adaptive directional sensing to bistable cellular memory. Our study shows how spatiotemporal cues can guide cell migration over large distances.cell motility | directional sensing | polarity | cell signaling E ukaryotic chemotaxis-the directed motion of cells along spatial gradients of chemicals-plays an essential role in a wide variety of biological processes, including embryogenesis, neuronal patterning, wound healing, and tumor dissemination (1-5), and many of its molecular components are conserved across cell types (6, 7). Much work has been devoted to understanding chemotaxis in static gradients (8, 9) and has revealed that cells are highly sensitive to spatial cues (10, 11). Natural chemical gradients, however, are often dynamic (12, 13), and chemotaxis in such environments requires an integration of spatial and temporal cues which is poorly understood. One striking example is the self-organized chemoattractant field arising during the development of the social amoeba Dictyostelium following nutrient deprivation. Here, nondissipating waves of chemoattractant travel outward from aggregation centers and provide stable long-range cues to direct the migration of cells toward the wave source. In a symmetric traveling wave, the spatial gradients in the front and back halves of the wave are equal in strength, but opposite in direction. Hence, if a cell responded simply to spatial information, it would move forward in the front of the wave and backward in the back of the wave. Thus, additional processing is needed for cells to solve the resulting back-of-the-wave problem (14) and to move efficiently toward the wave source.In principle, cells could distinguish between the front and back of the wave by the temporal gradient-the concentration increases in time in the front of the wave and decreases in time in the back of the wave. Temporal gradient sensing plays a fundamental role in bacterial chemotax...

Chemotaxis, the directed motion of a cell toward a chemical source, plays a key role in many essential biological processes. Here, we derive a statistical model that quantitatively describes the chemotactic motion of eukaryotic cells in a chemical gradient. Our model is based on observations of the chemotactic motion of the social ameba Dictyostelium discoideum, a model organism for eukaryotic chemotaxis. A large number of cell trajectories in stationary, linear chemoattractant gradients is measured, using microfluidic tools in combination with automated cell tracking. We describe the directional motion as the interplay between deterministic and stochastic contributions based on a Langevin equation. The functional form of this equation is directly extracted from experimental data by angle-resolved conditional averages. It contains quadratic deterministic damping and multiplicative noise. In the presence of an external gradient, the deterministic part shows a clear angular dependence that takes the form of a force pointing in gradient direction. With increasing gradient steepness, this force passes through a maximum that coincides with maxima in both speed and directionality of the cells. The stochastic part, on the other hand, does not depend on the orientation of the directional cue and remains independent of the gradient magnitude. Numerical simulations of our probabilistic model yield quantitative agreement with the experimental distribution functions. Thus our model captures well the dynamics of chemotactic cells and can serve to quantify differences and similarities of different chemotactic eukaryotes. Finally, on the basis of our model, we can characterize the heterogeneity within a population of chemotactic cells.