Contact-Inhibited Chemotaxis in De Novo and Sprouting Blood-Vessel Growth

Abstract

Blood vessels form either when dispersed endothelial cells (the cells lining the inner walls of fully formed blood vessels) organize into a vessel network (vasculogenesis), or by sprouting or splitting of existing blood vessels (angiogenesis). Although they are closely related biologically, no current model explains both phenomena with a single biophysical mechanism. Most computational models describe sprouting at the level of the blood vessel, ignoring how cell behavior drives branch splitting during sprouting. We present a cell-based, Glazier–Graner–Hogeweg model (also called Cellular Potts Model) simulation of the initial patterning before the vascular cords form lumens, based on plausible behaviors of endothelial cells. The endothelial cells secrete a chemoattractant, which attracts other endothelial cells. As in the classic Keller–Segel model, chemotaxis by itself causes cells to aggregate into isolated clusters. However, including experimentally observed VE-cadherin–mediated contact inhibition of chemotaxis in the simulation causes randomly distributed cells to organize into networks and cell aggregates to sprout, reproducing aspects of both de novo and sprouting blood-vessel growth. We discuss two branching instabilities responsible for our results. Cells at the surfaces of cell clusters attempting to migrate to the centers of the clusters produce a buckling instability. In a model variant that eliminates the surface–normal force, a dissipative mechanism drives sprouting, with the secreted chemical acting both as a chemoattractant and as an inhibitor of pseudopod extension. Both mechanisms would also apply if force transmission through the extracellular matrix rather than chemical signaling mediated cell–cell interactions. The branching instabilities responsible for our results, which result from contact inhibition of chemotaxis, are both generic developmental mechanisms and interesting examples of unusual patterning instabilities.

Modeling Morphogenesisin silicoandin vitro: Towards Quantitative, Predictive, Cell-based Modeling

“…These models feature spatially extended representations of endothelial cells, and assume that (as, e.g., [33] observed) the response of the cell to the chemoattractant is local along the membrane rather than occurring at the cell center. When placed together in an in silico Petri dish these simulated ECs organize into disconnected, round "blobs" of endothelial cells instead of vascularlike networks.…”

“…Alternative additional mechanisms, including cell adhesion [30] and contact-inhibited chemotaxis [33] (Figure 3D) also suffice for network formation. Interestingly, the set of cell behaviors described in these cell-based models also suffice for reproducing endothelial sprouting from clusters of ECs, suggesting that vasculogenesis and aspects of angiogenesis are-at least partly-two sides of the same coin [32,33].…”

Individual cell-based models of cell scatter of ARO and MLP-29 cells in response to hepatocyte growth factor

“…Increased Met levels have been observed in primary thyroid cells [12,19], suggesting that Met may mediate some of the phenotypic alterations observed in these cells: these data allowed to precisely define the role for HGF and its receptor in the evolution of thyroid tumors and to find which of the many cellular events that are known to follow Met activation effectively take place in thyroid cells.…”

“…Obviously, if λ in = 0 cells undergo uncorrelated Brownian motion, while if λ in is large the motion is almost ballistic. In this case, it is also needed to add a PDE, to model the diffusion and absorption of HGF in the culture from a macroscopical point of view [19,20]: the equation regulating the evolution in time of the growth factor is …”

“…We here model the just described behavior of both cell lines at a mesoscopic level using the Cellular Potts Model (a lattice-based, Monte Carlo approach developed for instance in [11,17,18,19,20]), which allows to describe cellcell adhesion and cell migration. An energy minimization philosophy, a set of constraints and auxiliary conditions determine how the cells move.…”