Exosome-mediated chemotaxis optimizes leader-follower cell migration

TL;DR

A theoretical and computational approach is developed to quantify the limits of exosome-mediated chemotaxis at the individual cell level and identifies molecular packaging and memory integration as key determinants of exosome-mediated information transmission.

Abstract

Cells frequently employ extracellular vesicles, or exosomes, to signal across long distances and coordinate collective actions. Exosomes diffuse slowly, can be actively degraded, and contain stochastic amounts of molecular cargo. These features raise the question of the efficacy of exosomes as a directional signal, but this question has not be systematically investigated. We develop a theoretical and computational approach to quantify the limits of exosome-mediated chemotaxis at the individual cell level. In our model, a leader cell secretes exosomes, which diffuse in the extracellular space, and a follower cell guides its migration by integrating discrete exosome detections over a finite memory window. We combine analytical calculations and stochastic simulations and show that the chemotactic velocity exhibits a non-monotonic dependence on the exosome cargo size. Small exosomes produce frequent but weak signals, whereas large exosomes produce strong but infrequent encounters. In the presence of nonlinear signal transduction, this tradeoff leads to an optimal cargo size that maximizes information throughput, as quantified by the average speed of the follower cell. Using a reduced one-dimensional model, we derive closed-form expressions coupling the optimal cargo size to follower speed as a function of secretion rate, memory time, and detection sensitivity. These results identify molecular packaging and memory integration as key determinants of exosome-mediated information transmission and highlight general design principles for optimization of migration under guidance by discrete and diffusible signaling particles.

Figures (4)

• Figure 1: Sketch of exosome-driven chemotaxis biorender. A leader cell secretes chemoattractant packaged in exosomes (yellow). A follower cell migrates towards the leader by sensing the signaling molecules within the exosomes (blue).
• Figure 2: Average velocity of the following cell as a function of the mean molecule cargo amount per secreted exosome for three sample exosome degradation times $\mathcal{T} = 0.5, 5$, and 60 minutes. The two response regimes are plotted: (a) linear response ($H = 1$) and (b) non-linear response ($H > 1$, shown as $H = 3$). Parameters used are enumerated in Table \ref{['params_table']}.
• Figure 3: Comparison of the approximate 1D model (a, b) with the 2D simulation (c, d). In the 1D model, the leader cell moves at constant speed $v_0$ and leaves behind a trail of non-diffusing ($D = 0$) exosomes. The follower cell moves to the left or right, with the probability $P_{\rm right}$ increasing with detection events. The average follower velocity (a) exhibits a maximum with cargo size for $H>1$ as in the 2D simulations (Fig \ref{['fig:figure2']}) and (b) decreases with the composite parameter $\beta = K/\nu \tau$. The 2D simulations confirm the dependence on $\beta$ through (c) secretion rate $\nu$ and (d) memory time $\tau$.
• Figure 4: Effects of exosome diffusivity on follower cell chemotaxis. (a) Simulation trajectories of the follower cell with various values of exosome diffusivity $D$ after $T = 1$ day (1440 minutes). (b) Velocity as a function of diffusivity for various total simulation times $T$. Overlaid is the physiological range of expected diffusivity using experimentally measured exosome size $a = 30 - 150$ nm. (c) Velocity as a function of mean cargo load $\bar{n}$ for constant diffusivity $D$ (blue) and a diffusivity that scales with cargo load calculated using the Stokes-Einstein equation, $D(\bar{n}) = D_0(\bar{n}_0/\bar{n})^{1/3}$ (green). Here $D_0 = 300$$\mu$m$^2$/min and $\bar{n}_0 = 50$ molecules.