High-throughput 3D whole-brain quantitative histopathology in rodents

Abstract

Histology is the gold standard to unveil microscopic brain structures and pathological alterations in humans and animal models of disease. However, due to tedious manual interventions, quantification of histopathological markers is classically performed on a few tissue sections, thus restricting measurements to limited portions of the brain. Recently developed 3D microscopic imaging techniques have allowed in-depth study of neuroanatomy. However, quantitative methods are still lacking for whole-brain analysis of cellular and pathological markers. Here, we propose a ready-to-use, automated, and scalable method to thoroughly quantify histopathological markers in 3D in rodent whole brains. It relies on block-face photography, serial histology and 3D-HAPi (Three Dimensional Histology Analysis Pipeline), an open source image analysis software. We illustrate our method in studies involving mouse models of Alzheimer's disease and show that it can be broadly applied to characterize animal models of brain diseases, to evaluate therapeutic interventions, to anatomically correlate cellular and pathological markers throughout the entire brain and to validate in vivo imaging techniques.

Figure 1. 3DHAPi image analysis steps and outcomes.

First, block-face photographs are stacked to provide a spatial reference (top-middle). Histology images are stacked for each series (top-left). Then each histology image is co-registered with its corresponding block-face photograph, segmented and, converted to heat map for multimodal analysis (left). An MRI-based mouse brain atlas is registered to the block-face photographic volume (top-right). The registered atlas labels and the registered segmented histology volumes are used for the ontology-based analysis.

Figure 2. 3D histopathological volumes.

(a) Block-face photographic volume, used as a spatial reference to reconstruct histological images in 3D. (b–h) Reconstructed histological volumes. (b) Nissl volume displaying brain anatomy. (c) 6E10 immunohistochemistry (IHC) volume showing Aβ peptide deposits. (d) Anti-IgG IHC volume highlighting blood brain barrier (BBB) disruptions. (e) Segmented 6E10 IHC volume. (f) Segmented Anti-IgG IHC volume. Continuous and quantitative heat map volumes for (g) Aβ peptide deposits and (h) blood brain barrier (BBB) disruptions.

Figure 3. 3D quantification and 2D quantification comparison.

(a) Correlations of Aβ deposition quantifications using brain-wide histopathology and a routine quantification protocol in the 4 selected ROIs in APP/PS1 mice (N = 11, Spearman’s rank correlation). A linear regression line is shown in black. (b) Relative error of Aβ deposition measures when increasing the distance between analyzed equidistant sections in APP/PS1 mice (N = 11). Boxplots upper and lower hinges correspond to the first and third quartiles. The whiskers extend from the hinges to the highest or lowest values that are within 1.5 × inter-quartile-range. Bold lines represent median values. See Methods section for relative error definition. (c) A 3D rendering of 4 selected ROIs (cerebral cortex in red, striatum in light blue, hippocampal region in green and thalamus in purple). (d) Aβ deposition distribution profile along the rostro-caudal axis in an APP/PS1-13C3a mouse in the 4 selected ROIs.

Figure 4. Aβ deposition in APP/PS1dE9 transgenic mice.

(a) A representative reconstructed Aβ deposits volume in the coronal (left), axial (middle) and sagittal (right) views. (b) Segmented Aβ deposits (black) superimposed with the atlas contours (blue) computed with a Deriche filter. (c) Aβ deposition quantification in 13.5-month-old APP/PS1dE9 mice (N = 7). Boxplot representation is the same as in Fig. 3b.

Figure 5. Brain-wide anti-amyloid effect of 13C3a.

(a) Reconstructed Aβ deposition IHC volumes for one representative brain per group. (b) Reduction of Aβ deposition in APP/PS1 mice treated with 13C3a (n = 8) compared to mice treated with DM4 (n = 3). PS1 mice are shown as negative controls (n = 4) (Mann-Whitney tests, boxplot representation is the same as in Fig. 3b).

Figure 6. Multiple marker 3D histopathology.

(a) Histological coronal section images at a lateral resolution of 0.44 μm. Left: Nissl staining in blue; middle-left: Aβ deposits in brown (6E10 IHC); middle-right: microglia in black (Iba-1 IHC) and Nissl counterstaining in blue; right: phagocytic cells in brown (CD68 IHC) and light Nissl counterstaining in blue. (b) Pathological and cell markers heat maps with 150520 voxels each. Top: coronal view; middle: axial view; bottom: sagittal view.

Figure 7. Multiple marker spatial correlation.

Spearman’s rank correlations between markers are shown in the upper panels while corresponding scatter plots are shown in the lower panels. Scatter plots data points have 10% opacity to allow for better visualization.

Figure 8. In vivo – ex vivo imaging confrontation.

(a) BBB disruption heat map in coronal (top), axial (middle) and sagittal (bottom) views. (b) 3D rendering of BBB disruptions detected by 3D-HAPi in one APP/PS1-DM4 mouse presenting this particular phenotype. (c) Aβ deposition heat map in coronal (top), axial (middle) and sagittal (bottom) views. (d) 3D rendering of Aβ deposition heat map with in vivo MRI. (e) A PS1 mouse that does not display Aβ deposition neither on in vivo MRI (left) nor on Aβ deposition heat map (right) and (f) an APP/PS1 mouse displaying hypo-intense spots in the cerebral cortex (left) with a similar distribution to the Aβ deposition heat map (right). Aβ deposition heat maps have been superimposed with in vivo MRI contours to allow better visualization.