FriendlyClearMap: An optimized toolkit for mouse brain mapping and analysis

Negwer, Moritz; Bosch, Bram; Bormann, Maren; Hesen, Rick; Lütje, Lukas; Aarts, Lynn; Rossing, Carleen; Kasri, Nael Nadif; Schubert, Dirk W.

doi:10.1101/2023.02.16.528882

Cited by 4 publications

(6 citation statements)

References 80 publications

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“…Segmentation masks were saved as tiff stacks by toggling the "save" option in the last segmentation step. ClearMap 1 was ported to Python 3 before use, but functioned identically 32 . We only used the cell segmentation portions, no pre-processing (e.g.…”

Section: Clearmapmentioning

confidence: 99%

Virtual reality empowered deep learning analysis of brain activity

Kaltenecker,

Al-Maskari,

Negwer

et al. 2023

Preprint

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Tissue clearing and fluorescent microscopy are powerful tools for unbiased organ-scale protein expression studies. Critical for interpreting expression patterns of large imaged volumes are reliable quantification methods. Here, we present DELiVR a deep learning pipeline that uses virtual reality (VR)-generated training data to train deep neural networks, and quantify c-Fos as marker for neuronal activity in cleared mouse brains and map its expression at cellular resolution. VR annotation significantly accelerated the speed of generating training data compared to conventional 2D slice based annotation. DELiVR detects cells with much higher precision than current threshold-based pipelines, and provides an extensive toolbox for data visualization, inspection and comparison. We applied DELiVR to profile cancer-related mouse brain activity, and discovered a novel activation pattern that distinguishes between weight-stable cancer and cancer-associated weight loss. Thus, DELiVR provides a robust mouse brain analysis pipeline at cellular scale that can be used to study brain activity patterns in health and disease.

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Section: Clearmapmentioning

confidence: 99%

Virtual reality empowered deep learning analysis of brain activity

Kaltenecker,

Al-Maskari,

Negwer

et al. 2023

Preprint

Self Cite

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“…The data supporting the results of this article are available in the GigaScience repository, GigaDB [ 75 ].…”

Section: Data Availabilitymentioning

confidence: 99%

FriendlyClearMap: an optimized toolkit for mouse brain mapping and analysis

et al. 2022

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Background Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratory’s use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2’s CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline. Findings For more precise alignment, we add a landmark-based atlas registration to ClearMap’s functions as well as include young mouse reference atlases for developmental studies. We provide an alternative cell segmentation method besides ClearMap’s threshold-based approach: Ilastik’s Pixel Classification, importing segmentations from commercial image analysis packages and even manual annotations. Finally, we integrate BrainRender, a recently released visualization tool for advanced 3-dimensional visualization of the annotated cells. Conclusions As a proof of principle, we use FriendlyClearMap to quantify the distribution of the 3 main GABAergic interneuron subclasses (parvalbumin+ [PV+], somatostatin+, and vasoactive intestinal peptide+) in the mouse forebrain and midbrain. For PV+ neurons, we provide an additional dataset with adolescent vs. adult PV+ neuron density, showcasing the use for developmental studies. When combined with the analysis pipeline outlined above, our toolkit improves on the state-of-the-art packages by extending their function and making them easier to deploy at scale.

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“…Other packages use landmark-based approaches to image registration including SMART— 33 an R package for semi-automated landmark-based registration and segmentation of mouse brain based on WholeBrain. 34 Relatedly, FriendlyClearMap 35 and mBrainAligner 36 are both landmark-based approaches to mapping of the mouse brain. Whereas the former employs Elastix 37 functionality, the latter is based on developed methodology referred to as coherent landmark mapping .…”

Section: Introductionmentioning

confidence: 99%

The ANTsX Ecosystem for Mapping the Mouse Brain

Tustison,

Chen,

Kronman

et al. 2024

Preprint

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Precision mapping techniques coupled with high resolution image acquisition of the mouse brain permit the study of the spatial organization of gene expression and their mutual interaction for a comprehensive view of salient structural/functional relationships. Such research is facilitated by standardized anatomical coordinate systems, such as the well-known Allen Common Coordinate Framework (AllenCCFv3), and the ability to spatially map to such standardized spaces. The Advanced Normalization Tools Ecosystem is a comprehensive open-source software toolkit for generalized quantitative imaging with applicability to multiple organ systems, modalities, and animal species. Herein, we illustrate the utility of ANTsX for generating precision spatial mappings of the mouse brain and potential subsequent quantitation. We describe ANTsX-based workflows for mapping domain-specific image data to AllenCCFv3 accounting for common artefacts and other confounds. Novel contributions include ANTsX functionality for velocity flow-based mapping spanning the spatiotemporal domain of a longitudinal trajectory which we apply to the Developmental Common Coordinate Framework. Additionally, we present an automated structural morphological pipeline for determining volumetric and cortical thickness measurements analogous to the well-utilized ANTsX pipeline for human neuroanatomical structural morphology which illustrates a general open-source framework for tailored brain parcellations.

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FriendlyClearMap: An optimized toolkit for mouse brain mapping and analysis

Cited by 4 publications

References 80 publications

Virtual reality empowered deep learning analysis of brain activity

Virtual reality empowered deep learning analysis of brain activity

FriendlyClearMap: an optimized toolkit for mouse brain mapping and analysis

The ANTsX Ecosystem for Mapping the Mouse Brain

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