Reconstruction of the mouse brain regions from the spatial gene expression data

Understanding the structural basis of nervous system still remains one of the primary challenges in neuroscience. New perspectives for the studies of brain anatomy are emerging with the availability of large-scale spatial gene expression data. Exploration of these data promises to deliver new insights

into the understanding of relations between genes and brain structure. We performed an analysis of the gene expression for more than 4000 genes in the mouse brain with the spatial resolution of 200 micrometers. These data are freely available from the Allen Brain Atlas [1] project. After data filtering and scaling, distance matrices between all brain voxels (volumetric pixels) were calculated for a number of common distance measures. We also proposed and utilized a novel distance measure based on the locality of gene expression. Then the brain volume was hierarchically clustered with a variety of techniques [2]. In order to verify the resulting brain parcellations we compared them by means of a quantitative measure to the histologically-defined anatomical reference atlas and found significant overlap between automatically derived spatial clusters and anatomical structures.

We also compared brain regions by the number of genes expressed. Here the number of simultaneously activated genes was considered to be proportional to the amount of accumulated evolutionary changes. This allows us to identify dentate gyrus and adjoining hippocampal regions as the structures with the richest expression and, hence, less conservative in the evolutionary sense. Other regions of the cerebral cortex show intermediate levels of expression variability while other brain structures have accumulated fewer changes in the process of evolution, with olfactory bulbs and cerebellum being most conservative.

This work was supported by Russian FTP "Scientific and scientific-pedagogical personnel".

[1] Ng L., Bernard A., Lau C. et. al., "An anatomic gene expression atlas of the adult mouse brain" (2009) Nature Neuroscience, 12 (3): 356-362.

[2] Xu R. and Wunsch D., "Survey of clustering algorithms" (2005) IEEE Transactions on Neural Networks, 16 (3): 645-678.