Self-organization in brain tumors: how cell morphology and cell density influence glioma pattern formation

Abstract

<jats:title>Abstract</jats:title><jats:p>Modeling cancer cells is essential to better understand the dynamic nature of brain tumors and glioma cells, including their invasion of normal brain. Our goal is to study how the morphology of the glioma cell influences the formation of patterns of collective behavior such as flocks (cells moving in the same direction) or streams (cells moving in opposite direction) referred to as <jats:italic>oncostream</jats:italic>. We have observed experimentally that the presence of oncostreams correlates with tumor progression. We propose an original agent-based model that considers each cell as an ellipsoid. We show that stretching cells from round to ellipsoid increases stream formation.</jats:p><jats:p>A systematic numerical investigation of the model was implemented in ℝ<jats:sup>2</jats:sup>. We deduce a phase diagram identifying key regimes for the dynamics (e.g. formation of flocks, streams, scattering). Moreover, we study the effect of cellular density and show that, in contrast to classical models of flocking, increasing cellular density reduces the formation of flocks. We observe similar patterns in ℝ<jats:sup>3</jats:sup> with the noticeable difference that stream formation is more ubiquitous compared to flock formation.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>Self-organization is the formation of large-scale multicellular patterns that result exclusively from the interactions amongst constituent single cells. To establish the existence of self-organization in brain tumors we used agent-based modeling based on data extracted from static and dynamic genetically engineered mouse glioma models. Implementation of our model in ℝ<jats:sup>2</jats:sup> identifies the dynamics that lead to formation of flocks (cells moving in a single direction), streams (cells moving in two directions), and cells moving as swarms or scattering. Increasing cellular density reduced formation of flocks and increased the formation of streams both in ℝ<jats:sup>2</jats:sup> and in ℝ<jats:sup>3</jats:sup>. These results demonstrate the detailed mechanism leading to self-organization in brain tumors. As increasing density of oncostreams correlates with tumor malignancy, we establish a pathophysiological link between self-organization of glioma tumors and glioma malignancy. We propose the dismantling of oncostreams as a new therapeutic approach to the treatment of brain tumors.</jats:p></jats:sec>

Spatiotemporal analysis of glioma heterogeneity reveals Col1A1 as an actionable target to disrupt tumor mesenchymal differentiation, invasion and malignancy

“…2, B and C). As predicted by our in silico model (5) these results suggest that cell shape, or eccentricity, is involved in the organization of collective motion patterns.…”

“…This group studied ex-vivo explant slices of spontaneous intestinal carcinoma, and showed that cells within the tumor core were highly dynamic and display directionally correlated cell motion 50 , similar to our results described herein. Recent in silico based mathematical modelling of glioma cell dynamics by our group, showed that only elongated cells, but not spherical cells, are able to form organized aligned cellular structures in a cell-density dependent manner 47 . Our modeling studies strongly support our in-vivo and ex-vivo data described in this manuscript.…”

Analysis of U87 glioma cells by dielectrophoresis

“…Understanding characteristic features of glioma cells emerges as an important path to reveal the heterogeneity of GBM microenvironment to provide therapeutic approaches that successfully target the right subsets of glioma cells in GBM and eventually maximize efficacy and minimize the side effects of the applied therapy [5][6][7][8]. Glioma cells display striking cellular heterogeneity by changing their morphology [9][10][11], nuclear volume [12][13][14], cell-cell adhesion [15,16], cellular interaction [17], cytoskeleton organization [18][19][20][21], and by performing epithelial-to-mesenchymal transitions [22][23][24][25][26]. To characterize dynamic variations of glioma cells in the GBM microenvironment without altering genetic and phenotypic properties of the cells, we need to develop adequate tools [10].…”

“…Glioma cells display striking cellular heterogeneity by changing their morphology [9][10][11], nuclear volume [12][13][14], cell-cell adhesion [15,16], cellular interaction [17], cytoskeleton organization [18][19][20][21], and by performing epithelial-to-mesenchymal transitions [22][23][24][25][26]. To characterize dynamic variations of glioma cells in the GBM microenvironment without altering genetic and phenotypic properties of the cells, we need to develop adequate tools [10]. Precisely quantifying heterogeneity of glioma cells might contribute to reveal distinct invasive and aggressive subpopulations of glioma cells in the population [10].…”

Cell-instructive surface gradients of photo-responsive amyloid-like fibrils

“…Therefore, amyloid-like peptides featuring these favorable nanomechanical properties as well as an intrinsic bioactivity could be appropriate scaffolds for mimicking concentration gradients of the ECM. 23 Functionalization of the amyloid-scaffold e.g. with certain ECM protein-derived epitopes such as the laminin-derived peptide sequence RGD can further increase their bioactivity.…”