FriendlyClearMap: an optimized toolkit for mouse brain mapping and analysis

Abstract

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.

The ANTsX Ecosystem for Mapping the Mouse Brain

“…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.…”

Virtual reality-empowered deep-learning analysis of brain cells

“…Segmentation masks were saved as tiff stacks by toggling the 'save' option in the last segmentation step. ClearMap was ported to Python (v.3.5) before use, but functioned identically 46 . We only used the cell segmentation portions, no pre-processing (for example ClearMap2's flat-field correction) or post-processing, such as atlas alignment, were performed.…”

“…For visualizing the cells and regions in atlas space, we used BrainRender 20 (v.2) with a modified density plot function 46 . To visualize the segmented cells in the original image space, we combined the area-wise color code from the Allen Brain Atlas with the 3D segment mask output by the connected component analysis.…”

“…The result is a cell mask file with each cell being color coded according to the brain area that it belongs to, which makes overlaying with the original image data in for example Fiji easy and allows for direct visual inspection of the segmentation results. Finally, we used the Allen Institute for Brain Science's cortical flat-map code (https://github.com/int-brain-lab/atlas) with adaptions 46 to include our heat maps.…”

ClearFinder: a Python GUI for annotating cells in cleared mouse brain

“…In addition, neither tool offers statistical analysis and data visualization functionality. Despite some attempts at making these pipelines more accessible to biologists and microscopists [6], the installation and usage still requires intensive support from skilled bioinformaticians or computer scientists. Here, we developed ClearFinder, a Graphical User Interface (GUI) unifying the functionality of both ClearMap and CellFinder, while at the same time simplifying the setup process and offering fundamental statistical analysis of results.…”