ArticleResearch square2025
Modular strategies for spatial mapping of diverse cell type data of the mouse brain.
Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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13 authors.
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Abstract
Large-scale, international collaborative efforts by members of the BRAIN Initiative Cell Census Network (BICCN) consortium are aggregating the most comprehensive reference database to date for diverse cell type profiling of the mouse brain, which encompasses over 40 different multi-modal profiling techniques from more than 30 research groups. One central challenge for this integrative effort has been the need to map these unique datasets into common reference spaces such that the spatial, structural, and functional information from different cell types can be jointly analyzed. However, significant variation in the acquisition, tissue processing, and imaging techniques across data types makes mapping such diverse data a multifarious problem. Different data types exhibit unique tissue distortion and signal characteristics that precludes a single mapping strategy from being generally applicable across all cell type data. Tailored mapping approaches are often needed to address the unique barriers present in each modality. This work highlights modular atlas mapping strategies developed across separate BICCN studies using the Advanced Normalization Tools Ecosystem (ANTsX) to map spatial transcriptomic (MERFISH) and high-resolution morphology (fMOST) mouse brain data into the Allen Common Coordinate Framework (AllenCCFv3), and developmental (MRI and LSFM) data into the Developmental Common Coordinate Framework (DevCCF). We discuss common mapping strategies that can be shared across modalities and driven by specific challenges from each data type. These mapping strategies include novel open-source contributions that are made publicly available through ANTSX. These include 1) a velocity flow-based approach for continuously mapping developmental trajectories such as that characterizing the DevCCF and 2) an automated framework for determining structural morphology solely through the leveraging of publicly resources. Finally, we provide general guidance to aid investigators to tailor these strategies to address unique data challenges without the need to develop additional specialized software.
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