An Optimized Mouse Brain Atlas for Automated Mapping and Quantification of Neuronal Activity Using iDISCO+ and Light Sheet Fluorescence Microscopy

Abstract

In recent years, the combination of whole-brain immunolabelling, light sheet fluorescence microscopy (LSFM) and subsequent registration of data with a common reference atlas, has enabled 3D visualization and quantification of fluorescent markers or tracers in the adult mouse brain. Today, the common coordinate framework version 3 developed by the Allen's Institute of Brain Science (AIBS CCFv3), is widely used as the standard brain atlas for registration of LSFM data. However, the AIBS CCFv3 is based on histological processing and imaging modalities different from those used for LSFM imaging and consequently, the data differ in both tissue contrast and morphology. To improve the accuracy and speed by which LSFM-imaged whole-brain data can be registered and quantified, we have created an optimized digital mouse brain atlas based on immunolabelled and solvent-cleared brains. Compared to the AIBS CCFv3 atlas, our atlas resulted in faster and more accurate mapping of neuronal activity as measured by c-Fos expression, especially in the hindbrain. We further demonstrated utility of the LSFM atlas by comparing whole-brain quantitative changes in c-Fos expression following acute administration of semaglutide in lean and diet-induced obese mice. In combination with an improved algorithm for c-Fos detection, the LSFM atlas enables unbiased and computationally efficient characterization of drug effects on whole-brain neuronal activity patterns. In conclusion, we established an optimized reference atlas for more precise mapping of fluorescent markers, including c-Fos, in mouse brains processed for LSFM.

Keywords: Brain atlas; C-Fos; Light sheet fluorescence microscopy; Tissue clearing; Whole brain imaging; iDISCO.

Fig. 1

LSFM-based mouse brain atlas. a) Generation of a brain template based on the LSFM autofluorescence volumes of 139 mice brains using an iterative registration and averaging algorithm. Raw light sheet scans are annotated with I_x where x stands for the animal number, and the intermediate average mouse brain volumes are annotated with A_y where y stands for the iteration step. B) Transfer of brain region segmentations from the AIBS CCFv3 to the LSFM mouse brain template. Brain regions of the AIBS CCFv3 were mapped to the LSFM template in six parts, e.g. cortex to cortex, hindbrain to hindbrain etc.

Fig. 2

Techniques for registering LSFM-imaged samples with the AIBS CCFv3. a) Illustration of one-to-one registration between a cleared LSFM-imaged sample and the AIBS CCFv3 template. b) Illustration of multi-regional registration between a cleared LSFM-imaged sample and the AIBS CCFv3 template, where the brain volumes have been divided into six larger brain areas that are mapped individually. c) Example of the registration quality in area postrema (AP) using either one-to-one or multi-regional registration. d) Illustration of the registration flow described in this manuscript. Using one-to-one registration for aligning cleared LSFM-imaged samples with the AIBS CCFv3 is fast but inaccurate in some brain regions like the AP. Multi-regional registration for aligning cleared LSFM-imaged samples with the AIBS CCFv3 template provides better accuracy but is relative slow compared to the one-to-one registration. By generating a template from cleared LSFM-imaged brains and registering the AIBS CCFv3 with it once using multi-regional registration approach we ensure good alignment accuracy between the two templates. Subsequent registrations of cleared LSFM-imaged brains with the LSFM template can then be done directly using fast one-to-one registrations. This way it is possible to achieve both fast as well as accurate registration of cleared LSFM-imaged brains. Regardless of computer performance the speed of analysis improved by a factor of six compared to the multiregional registration

Fig. 3

Improved registration of LSFM-acquired brain volumes using the LSFM atlas. a) Heatmaps illustrate the average magnitude of the deformation resulting from the registration of ten random raw LSFM brain volumes to the AIBS CCFv3 and to the LSFM atlas. b) Registration using the LSFM mouse brain atlas enables improved alignment between individual brains. Intensity variance, a measure for registration performance, was calculated per brain region for the ten random brain volumes aligned to the LSFM atlas and for the same ten brain volumes aligned to the AIBS CCFv3. Highest intensity variance was detected in both cases in ventricular and hindbrain regions (example sections, left). Statistical analysis of the intensity variance was performed using two-tailed Welch’s t-test and the resulting significant regions are visualized in the scatter plot (right) along with the mean intensity variance per major brain region for both atlases (denoted as mean IV). The results indicate that the difference in intensity variance values was small for cortical areas. However, majority of brain regions in cerebral nuclei, interbrain, midbrain, cerebellum and hindbrain exhibited higher intensity variance when the AIBS CCFv3 was used for registration compared to when the LSFM atlas was used for registration. c) Registration of the ten brain volumes was further evaluated using 27 landmarks distributed over the whole brain (overview of the landmark positions, left). The landmarks were divided between the six major brain areas in both atlases as well as in the ten brain volumes. Distances between the corresponding landmarks in the individual brains and the atlas templates were calculated after registering the ten brain volumes to the LSFM atlas and the AIBS CCFv3 (bar plot, right). For most landmarks, the calculated distances are lower when the LSFM atlas is used as template. Significant differences in distances between the two atlases was consistently observed in cerebral cortex and hindbrain. Two-tailed Welch’s t-test was applied for determining statistical significance in landmark distances between the atlases: ∗ for 0.01 ≤ p < 0.05, ∗∗ for 0.001 ≤ p < 0.01 and ∗∗∗ for p < 0.001

Fig. 4

Choice of brain atlas influences the number of c-Fos positive cells per brain region. Comparison of number of c-Fos positive cells in response to semaglutide treatment using the LSFM atlas and the AIBS CCFv3. a) Average number of detected c-Fos expressing cells in every brain region after registration to either the AIBS CCFv3 or the LSFM atlas. Regions in which the c-Fos positive cells are differentially quantified are highlighted by a circle surrounding the data points. An average cell count per group below ten is considered too low to judge. b) The bar chart lists the brain regions and the corresponding mean log₂ fold changes of quantified c-Fos positive cells in these regions according to the p value. Blue = higher with LSFM atlas, red = higher with CCFv3. NS stands for not significant, ∗ for 0.01 ≤ p < 0.05, ∗∗ for 0.001 ≤ p < 0.01 and ∗∗∗ for p < 0.001. c) Horizontally and sagittally depicted brain volumes highlight the regions in which the c-Fos cells were differentially quantified while using the LSFM atlas and the AIBS CCFv3 for the analysis (same colour code as in b and c). See Online Resource 2 for full names of the brain regions. d-e) Comparison of total number of c-Fos positive cells quantified in 3D-volumes of the nucleus of the solitary tract (NTS) and the dorsal motor nucleus of the vagus nerve (DMX) using the LSFM atlas and the AIBS CCFv3. DMX (blue) and NTS (grey) volumes of both atlases in which the signal (glow colormap) was quantified is visualized in 3D renderings. d) Quantification of c-Fos positive cells following registration of the LSFM atlas to the LSFM-acquired brain volumes showed that in average 234 ± 38 c-Fos positive cells were found in the NTS and 144 ± 14 in the DMX. e) Quantification of c-Fos positive cells following registration of the AIBS CCFv3 to the LSFM-acquired brain volumes. Here the majority of the signal is found in the DMX. Quantification revealed that on average 95 ± 16 c-Fos positive cells are counted in the NTS and 205 ± 25 in the DMX. f) Comparing the raw data to the data in alignment with atlases. DMX has a dense dark appearance compared to NTS

Fig. 5

Differentially regulated c-Fos expression in response to semaglutide administration. Up (red) and down (blue) regulation of c-Fos expression in a) semaglutide treated lean mice in comparison to vehicle treated lean mice and c) in semaglutide treated DIO mice in comparison to vehicle treated DIO mice. Differentially regulated brain regions in response to semaglutide administration in comparison to vehicle treatment and corresponding mean log₂ fold changes of c-Fos positive cells in these regions in b) lean and d) DIO mice. ∗ stands for 0.01 ≤ p < 0.05, ∗ ∗ for 0.001 ≤ p < 0.01 and ∗ ∗ ∗ for p < 0.001. P-values were adjusted for multiple comparisons using the false discovery rate. See Online Resource 2 for full names of the brain regions