A multimodal spatial atlas of transcriptomic, morphological, and electrophysiological cell type densities in the mouse brain

Brain cells can be classified according to their transcriptomic, morphological, and electrophysiological features. Comprehensive data on the spatial density of cell types that integrate all three properties are critical for improving models of how neuronal diversity contributes to brain function, yet existing atlases lack this information. To address this gap, we created a quantitative, three-dimensional atlas of cell type density distributions in the mouse brain. We began by generating a transcriptomic cell type atlas, scaling regional density estimates from brain slices using cell counts and anatomical dimensions. For densely populated regions like the cerebellum, we further refined these estimates by applying voxel-wise corrections based on average Nissl staining intensity. To connect transcriptomic identities with functional characteristics, we leveraged patch-sequencing datasets that combine single-neuron mRNA profiles, morphological reconstructions, and electrophysiological recordings from cortical neurons. Transcriptomic types were determined from gene expression data, morphological types were assigned based on structural reconstructions, and electrophysiological types were identified using K-means clustering. The resulting whole-brain atlas (consisting of 5274 transcriptomic clusters and 458 functional morphological-electrophysiological types) and computational tools offer a high-resolution (25 μm3 voxel size), integrative resource compatible with a broad range of neuroscience applications and enable the parsing of individual cell types to reveal previously unrecognized features.