Abstract
Transgenic animal models are invaluable research tools for elucidating the pathways and mechanisms involved in the development of neurodegenerative diseases. Mechanistic clues can be revealed by applying labelling techniques such as immunohistochemistry or in situ hybridisation to brain tissue sections. Precision in both assigning anatomical location to the sections and quantifying labelled features is crucial for output validity, with a stereological approach or image-based feature extraction typically used. However, both approaches are restricted by the need to manually delineate anatomical regions. To circumvent this limitation, we present the QUINT workflow for quantification and spatial analysis of labelling in series of rodent brain section images based on available 3D reference atlases. The workflow is semi-automated, combining three open source software that can be operated without scripting knowledge, making it accessible to most researchers. As an example, a brain region-specific quantification of amyloid plaques across whole transgenic Tg2576 mouse brain series, immunohistochemically labelled for three amyloid-related antigens is demonstrated. First, the whole brain image series were registered to the Allen Mouse Brain Atlas to produce customised atlas maps adapted to match the cutting plan and proportions of the sections (QuickNII software). Second, the labelling was segmented from the original images by the Random Forest Algorithm for supervised classification (ilastik software). Finally, the segmented images and atlas maps were used to generate plaque quantifications for each region in the reference atlas (Nutil software). The method yielded comparable results to manual delineations and to the output of a stereological method. While the use case demonstrates the QUINT workflow for quantification of amyloid plaques only, the workflow is suited to all mouse or rat brain series with labelling that is visually distinct from the background, for example for the quantification of cells or labelled proteins.
Highlights
Transgenic rodent models are useful tools in the study of neurodegenerative disorders, providing clues to the origins and mechanisms of the protein aggregates that accumulate and harm neurons and synapses in these conditions (Dawson et al, 2018)
We have developed the QUINT workflow based on image analysis using a series of neuroinformatic tools
We present the quantification of human amyloid precursor protein and β-amyloid deposits across a whole mouse brain series immunohistochemically labelled For the human APP N-Terminus, Aβ (4G8 mouse monoclonal antibody) and pyro-glutamate modified Aβ [pE-Aβ; J8 mouse monoclonal antibody (HartlageRübsamen et al, 2018)]
Summary
Transgenic rodent models are useful tools in the study of neurodegenerative disorders, providing clues to the origins and mechanisms of the protein aggregates that accumulate and harm neurons and synapses in these conditions (Dawson et al, 2018). The new generation of three-dimensional digital brain atlases developed for murine brains (Lein et al, 2007; Hawrylycz et al, 2011; Oh et al, 2014; Papp et al, 2014; Kjonigsen et al, 2015) serve as spatial frameworks for data sharing and integration (Boline et al, 2008; Zaslavsky et al, 2014), while providing possibilities for automation of spatial analysis To this end, we have developed the QUINT workflow based on image analysis using a series of neuroinformatic tools. A second example is shared to demonstrate the use of the workflow for quantification of another type of labelling (parvalbumin positive cells in an Allen Mouse Brain series)
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