Fig. 1

Gene expression profiles across voxels in the VTA. (a) Overall scheme of the analytical approach involving (i) data acquisition from the ABA, (ii) data analysis (correlation-based gene classification and selection of marker candidates), and (iii) validation of the candidates. (b) Spatially annotated voxels encompassing the VTA. The VTA volume is split into the subregions from the center along the AP, ML, and DV axes, and the 42 voxels were assigned into the subregions after mapping the voxels into the VTA volume. Cross-section views (AP-LM, AP-DV, and LM-DV) show how voxels are divided by the AP, ML, and DV axes. Different colors are used to denote the voxels at the four quadrants in the cross-sections. (c) Gene expression intensity is estimated for each voxel. For gene i, expression intensity was estimated as the sum of expressing pixel intensities divided by the sum of expressing pixels from four ISH images covering a voxel. (d) Gene expression matrix for m genes and n voxels (m = 1143 and n = 42). Element (i, j) in the matrix indicates expression intensity for gene i and voxel j. e. Estimation of the cell count in a unit area (left) and a voxel (right). Neuronal cells were labeled with selective neuronal cell marker, NeuN (red) and imaged at 12 distinct sampling points of the VTA region. For cell counting in each voxel, 10 z-stacked images covering a cube of 50 × 200 × 200 μm³ (left) were combined to generate a 2-D projected image from which cells were counted, and the cell count was then multiplied by 4. The average numbers of neuronal cells were calculated and used to determine proportional cell populations (3 mice and 10 brain slices, M location: 10 images, L location: 10 images)