Quantifying spatial relationships from whole retinal images

Microscopy advances have enabled the acquisition of large-scale biological images that capture whole tissues in situ. This in turn has fostered the study of spatial relationships between cells and various biological structures, which has proved enormously beneficial toward understanding organ and organism function. However, the unique nature of biological images and tissues precludes the application of many existing spatial mining and quantification methods necessary to make inferences about the data. Especially difficult is attempting to quantify the spatial correlation between heterogeneous structures and point objects, which often occurs in many biological tissues.

We develop a method to quantify the spatial correlation between a continuous structure and point data in large (17 500 × 17 500 pixel) biological images. We use this method to study the spatial relationship between the vasculature and a type of cell in the retina called astrocytes. We use a geodesic feature space based on vascular structures and embed astrocytes into the space by spatial sampling. We then propose a quantification method in this feature space that enables us to empirically demonstrate that the spatial distribution of astrocytes is often correlated with vascular structure. Additionally, these patterns are conserved in the retina after injury. These results prove the long-assumed patterns of astrocyte spatial distribution and provide a novel methodology for conducting other spatial studies of similar tissue and structures.

The Matlab code for the method described in this article can be found at http://www.cs.ucsb.edu/∼dbl/software.php.

Supplementary data are available at Bioinformatics online.