Matrix feedback enables diverse higher-order patterning of the extracellular matrix

The higher-order patterning of extra-cellular matrix in normal and pathological tissues has profound consequences on tissue function. Whilst studies have documented both how fibroblasts create and maintain individual matrix fibers and how cell migration is altered by the fibers they interact with, a model unifying these two aspects of tissue organization is lacking. Here we use computational modelling to understand the effect of this interconnectivity between fibroblasts and matrix at the mesoscale level. We created a unique adaptation to the Vicsek flocking model to include feedback from a second layer representing the matrix, and use experimentation to parameterize our model and validate model-driven hypotheses. Our two-layer model demonstrates that feedback between fibroblasts and matrix increases matrix diversity creating higher-order patterns. The model can quantitatively recapitulate matrix patterns of tissues in vivo. Cells follow matrix fibers irrespective of when the matrix fibers were deposited, resulting in feedback with the matrix acting as temporal 'memory' to collective behaviour, which creates diversity in topology. We also establish conditions under which matrix can be remodelled from one pattern to another. Our model elucidates how simple rules defining fibroblast-matrix interactions are sufficient to generate complex tissue patterns. Author summary The organization of extracellular matrix in the body is diverse and often becomes dysregulated in cancer. How such patterns emerge is poorly understood, in particular how the interplay between the fibroblasts, which produce the matrix fibers, and the fibers themselves affects emergent organization. We created a unique adaptation to the Vicsek flocking model to include feedback from a second layer representing the matrix. Fibroblasts were able to deposit, degrade and rearrange fibers and the fibers could also contribute to the vector of cell migration. We find that this feedback is sufficient to generate many complex tissue patterns, as seen in vivo. The model is able to encapsulate key fibroblast and matrix properties without compromising on scale, providing an insight into the co-evolution of two crucial components of tissues at the scale of millimeters.