QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain

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.

Interactive machine learning for soybean seed and seedling quality classification

“…Among these tools, Ilastik is open-source software that allows the development of models based on interactive machine learning with images; it is easy to use and ideal for users without substantial computational knowledge 4 . This software has been used in recent studies to measure the confluence of Hep G2 cell culture in phase-contrast micrographs 11 , high-throughput screening for quantifying thrips damage 12 , quantification and spatial analysis of features in histological images of rodent brains 13 , and others. However, there is still no information on application of interactive machine learning via Ilastik in plant science and, in particular, in studies on seeds and seedlings.…”

Navigating the Murine Brain: Toward Best Practices for Determining and Documenting Neuroanatomical Locations in Experimental Studies

“…The advantage of this approach is that atlas coordinates are directly exported, but it can be tedious to apply to larger data sets. New tools and workflows are currently being developed in the Human Brain Project that will allow (semi-)automated extraction of labeled features from serial images ( Kreshuk et al, 2014 ; Papp et al, 2016 ; Yates et al, 2017 ).…”

Nutil: A Pre- and Post-processing Toolbox for Histological Rodent Brain Section Images

“…Quantifications may be performed on the entire image or in regions defined by masks. Nutil quantifies labeling in each parcellated brain region, extracts spatial coordinates for visualization in 3D reference atlas space, and generates output including: reports (in CSV or HTML format), coordinates (in JSON format), and reference atlas map images superimposed with the extracted features that are color-coded according to their assigned anatomical location (Yates et al, 2019). Quantifier starts by applying a breadth-first search (BFS) method to identify areas of interest in a series of images as specified by colors defined in the Nutil GUI.…”

“…With image pre-processing required for most analytic pipelines ( Yates et al, 2019 ), there is a need for access to user-friendly tools that can perform the most commonly required transformations. Furthermore, for brain microscopy data, researchers typically endeavor to spatially analyze features in the images by sorting outputs according to anatomical brain region.…”

“…In light of published 3D reference atlases for mouse and rat brains ( Lein et al, 2007 ; Oh et al, 2014 ; Papp et al, 2014 ; Kjonigsen et al, 2015 ), and software for generating brain atlas maps that are customized to match the proportions and cutting plane of the sections ( Puchades et al, 2019 ), we have created a pre- and post-processing toolbox for histological brain section images that aims to meet both these needs. An early beta version of the tool was an integral part of the QUINT workflow for quantification and spatial analysis of labeling in rodent brain sections ( Yates et al, 2019 ), see https://www.youtube.com/watch?v=yPkAbSfla_c . Nutil has since been expanded and improved considerably with the present manuscript describing the full range of Nutil functionalities.…”