Computational neuroanatomy: mapping cell-type densities in the mouse brain, simulations from the Allen Brain Atlas

4th International Conference on Mathematical Modeling in Physical Sciences

物理学;数学

Grange, Pascal^1

Xi'An Jiaotong-Liverpool University, Department of Mathematical Sciences, Science Building SD557, 111 Ren'ai Rd, Jiangsu Province, Suzhou

215123, China^1

关键词: Electrophysiological activity;    Expression profile;    Genetic classification;    In-situ hybridization;    Optimization problems;    Phenotypic diversity;    Quadratic optimization problems;    Transcriptome profiles;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/633/1/012070/pdf
DOI  :  10.1088/1742-6596/633/1/012070

来源: IOP

【 摘 要 】

The Allen Brain Atlas of the adult mouse (ABA) consists of digitized expression profiles of thousands of genes in the mouse brain, co-registered to a common three-dimensional template (the Allen Reference Atlas).This brain-wide, genome-wide data set has triggered a renaissance in neuroanatomy. Its voxelized version (with cubic voxels of side 200 microns) is available for desktop computation in MATLAB. On the other hand, brain cells exhibit a great phenotypic diversity (in terms of size, shape and electrophysiological activity), which has inspired the names of some well-studied cell types, such as granule cells and medium spiny neurons. However, no exhaustive taxonomy of brain cell is available. A genetic classification of brain cells is being undertaken, and some cell types have been chraracterized by their transcriptome profiles. However, given a cell type characterized by its transcriptome, it is not clear where else in the brain similar cells can be found. The ABA can been used to solve this region-specificity problem in a data-driven way: rewriting the brain-wide expression profiles of all genes in the atlas as a sum of cell-type-specific transcriptome profiles is equivalent to solving a quadratic optimization problem at each voxel in the brain. However, the estimated brain-wide densities of 64 cell types published recently were based on one series of co-registered coronal in situ hybridization (ISH) images per gene, whereas the online ABA contains several image series per gene, including sagittal ones. In the presented work, we simulate the variability of cell-type densities in a Monte Carlo way by repeatedly drawing a random image series for each gene and solving the optimization problem. This yields error bars on the region-specificity of cell types.