CIRCOAST: a statistical hypothesis test for cellular colocalization with network structures.

Motivation: Colocalization of structures in biomedical images can lead to insights into biological behaviors. One class of colocalization problems is examining an annular structure (disk-shaped such as a cell, vesicle or molecule) interacting with a network structure (vascular, neuronal, cytoskeletal, organellar). Examining colocalization events across conditions is often complicated by changes in density of both structure types, confounding traditional statistical approaches since colocalization cannot be normalized to the density of both structure types simultaneously. We have developed a technique to measure colocalization independent of structure density and applied it to characterizing intercellular colocation with blood vessel networks. This technique could be used to analyze colocalization of any annular structure with an arbitrarily shaped network structure. Results: We present the circular colocalization affinity with network structures test (CIRCOAST), a novel statistical hypothesis test to probe for enriched network colocalization in 2D z-projected multichannel images by using agent-based Monte Carlo modeling and image processing to generate the pseudo-null distribution of random cell placement unique to each image. This hypothesis test was validated by confirming that adipose-derived stem cells (ASCs) exhibit enriched colocalization with endothelial cells forming arborized networks in culture and then applied to show that locally delivered ASCs have enriched colocalization with murine retinal microvasculature in a model of diabetic retinopathy. We demonstrate that the CIRCOAST test provides superior power and type I error rates in characterizing intercellular colocalization compared to generic approaches that are confounded by changes in cell or vessel density. Availability and implementation: CIRCOAST source code available at: https://github.com/uva-peirce-cottler-lab/ARCAS. Supplementary information: Supplementary data are available at Bioinformatics online.

Myh11+ microvascular mural cells and derived mesenchymal stem cells promote retinal fibrosis.

Retinal diseases are frequently characterized by the accumulation of excessive scar tissue found throughout the neural retina. However, the pathophysiology of retinal fibrosis remains poorly understood, and the cell types that contribute to the fibrotic response are incompletely defined. Here, we show that myofibroblast differentiation of mural cells contributes directly to retinal fibrosis. Using lineage tracing technology, we demonstrate that after chemical ocular injury, Myh11+ mural cells detach from the retinal microvasculature and differentiate into myofibroblasts to form an epiretinal membrane. Inhibition of TGFßR attenuates Myh11+ retinal mural cell myofibroblast differentiation, and diminishes the subsequent formation of scar tissue on the surface of the retina. We demonstrate retinal fibrosis within a murine model of oxygen-induced retinopathy resulting from the intravitreal injection of adipose Myh11-derived mesenchymal stem cells, with ensuing myofibroblast differentiation. In this model, inhibiting TGFßR signaling does not significantly alter myofibroblast differentiation and collagen secretion within the retina. This work shows the complexity of retinal fibrosis, where scar formation is regulated both by TGFßR and non-TGFßR dependent processes involving mural cells and derived mesenchymal stem cells. It also offers a cautionary note on the potential deleterious, pro-fibrotic effects of exogenous MSCs once intravitreally injected into clinical patients.