QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain
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 secti...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2019-12-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fninf.2019.00075/full |
| _version_ | 1828240107758419968 |
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| author | Sharon C. Yates Nicolaas E. Groeneboom Christopher Coello Stefan F. Lichtenthaler Stefan F. Lichtenthaler Stefan F. Lichtenthaler Peer-Hendrik Kuhn Hans-Ulrich Demuth Maike Hartlage-Rübsamen Steffen Roßner Trygve Leergaard Anna Kreshuk Maja A. Puchades Jan G. Bjaalie |
| author_facet | Sharon C. Yates Nicolaas E. Groeneboom Christopher Coello Stefan F. Lichtenthaler Stefan F. Lichtenthaler Stefan F. Lichtenthaler Peer-Hendrik Kuhn Hans-Ulrich Demuth Maike Hartlage-Rübsamen Steffen Roßner Trygve Leergaard Anna Kreshuk Maja A. Puchades Jan G. Bjaalie |
| author_sort | Sharon C. Yates |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T21:35:34Z |
| format | Article |
| id | doaj.art-5dc24db4c0df4d0ba8ee4846c5996928 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-04-12T21:35:34Z |
| publishDate | 2019-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-5dc24db4c0df4d0ba8ee4846c59969282022-12-22T03:15:55ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962019-12-011310.3389/fninf.2019.00075453735QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent BrainSharon C. Yates0Nicolaas E. Groeneboom1Christopher Coello2Stefan F. Lichtenthaler3Stefan F. Lichtenthaler4Stefan F. Lichtenthaler5Peer-Hendrik Kuhn6Hans-Ulrich Demuth7Maike Hartlage-Rübsamen8Steffen Roßner9Trygve Leergaard10Anna Kreshuk11Maja A. Puchades12Jan G. Bjaalie13Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayGerman Center for Neurodegenerative Diseases (DZNE), Munich, GermanyNeuroproteomics, School of Medicine, Klinikum rechts der Isar, and Institute for Advanced Study, Technical University of Munich, Munich, GermanyMunich Cluster for Systems Neurology (SyNergy), Munich, GermanyInstitute of Pathology, Technical University of Munich, Munich, GermanyDepartment of Molecular Drug Design and Target Validation Fraunhofer Institute for Cell Therapy and Immunology, Halle (Saale), Leipzig, GermanyPaul Flechsig Institute for Brain Research, University of Leipzig, Leipzig, GermanyPaul Flechsig Institute for Brain Research, University of Leipzig, Leipzig, GermanyNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayEuropean Molecular Biology Laboratory, Heidelberg, GermanyNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayNeural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, NorwayTransgenic 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.https://www.frontiersin.org/article/10.3389/fninf.2019.00075/fullrodent brain analysisAlzheimer’s diseasequantificationworkflowAPP—amyloid precursor proteinbeta-amyloid |
| spellingShingle | Sharon C. Yates Nicolaas E. Groeneboom Christopher Coello Stefan F. Lichtenthaler Stefan F. Lichtenthaler Stefan F. Lichtenthaler Peer-Hendrik Kuhn Hans-Ulrich Demuth Maike Hartlage-Rübsamen Steffen Roßner Trygve Leergaard Anna Kreshuk Maja A. Puchades Jan G. Bjaalie QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain Frontiers in Neuroinformatics rodent brain analysis Alzheimer’s disease quantification workflow APP—amyloid precursor protein beta-amyloid |
| title | QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain |
| title_full | QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain |
| title_fullStr | QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain |
| title_full_unstemmed | QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain |
| title_short | QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain |
| title_sort | quint workflow for quantification and spatial analysis of features in histological images from rodent brain |
| topic | rodent brain analysis Alzheimer’s disease quantification workflow APP—amyloid precursor protein beta-amyloid |
| url | https://www.frontiersin.org/article/10.3389/fninf.2019.00075/full |
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