A method to estimate the cellular composition of the mouse brain from heterogeneous datasets

Abstract

The mouse brain contains a rich diversity of inhibitory neuron types that have been characterized by their patterns of gene expression. However, it is still unclear how these cell types are distributed across the mouse brain. We developed a computational method to estimate the densities of different inhibitory neuron types across the mouse brain. Our method allows the unbiased integration of diverse and disparate datasets into one framework to predict inhibitory neuron densities for uncharted brain regions. We constrained our estimates based on previously computed brain-wide neuron densities, gene expression data from in situ hybridization image stacks together with a wide range of values reported in the literature. Using constrained optimization, we derived coherent estimates of cell densities for the different inhibitory neuron types. We estimate that 20.3% of all neurons in the mouse brain are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. We find that our density estimations improve as more literature values are integrated. Our pipeline is extensible, allowing new cell types or data to be integrated as they become available. The data, algorithms, software, and results of our pipeline are publicly available and update the Blue Brain Cell Atlas. This work therefore leverages the research community to collectively converge on the numbers of each cell type in each brain region.

Variations on the theme: focus on cerebellum and emotional processing

“…For example, the extensive datasets from the Allen Brain Institute ( ISH Data., 2023 : Allen Brain Atlas: Mouse Brain.) and Blue Brain Cell Atlas ( Rodarie et al, 2022 ) provides exhaustive information on cell types and densities that might be used for detailed model reconstructions ( Figure 4 ). This information must now be crossed with that on cerebellar regional connectivity and behavioral localization of functions addressing those regions and pathways that are critical for emotional processing.…”

“… Neuron density and thickness in the mouse cerebellum based on the Blue Brain Cell Atlas. (A) Estimations of the thickness of the molecular layer in mm for each subregion of the cerebellar cortex, using the algorithm proposed by Rodarie et al (2022) . (B) Estimations of the density of excitatory and inhibitory neurons in the molecular and granular layer for each region of the cerebellar cortex.…”

A neuroscientist’s guide to using murine brain atlases for efficient analysis and transparent reporting

“…To facilitate correct citation, Kleven et al (2023a) proposed an Atlas ontology model and an overview of the versioning of the two most commonly used volumetric murine brain atlases, the WHS rat brain atlas and the Allen Mouse Brain CCF. Beyond consistent and correct citation of atlases, any customizations (see “Customizing brain atlases for analysis and visualization”) should be clearly documented ( Rodarie et al, 2021 ).…”

giRAff: an automated atlas segmentation tool adapted to single histological slices

“…Indeed, 3D histology protocols are time-consuming, expensive, and neurobiologists often acquire only a limited number of slices. Therefore, the delineation of anatomical regions is mostly performed manually on the experimental data and/or the identification of their corresponding atlas slice is based on prior anatomical knowledge (Lebenberg et al, 2010 ; Ye et al, 2016 ; Iglesias et al, 2018 ; Pichat et al, 2018 ; Balakrishnan et al, 2019 ; Chen et al, 2019 ; Chon et al, 2019 ; Henderson et al, 2019 ; Pallast et al, 2019 ; Wu et al, 2019 ; Yates et al, 2019 ; Bayraktar et al, 2020 ; Hérard et al, 2020 ; Lam et al, 2022 ; Rodarie et al, 2022 ).…”