The ANTsX ecosystem for mapping the mouse brain

Large-scale efforts by the BRAIN Initiative Cell Census Network (BICCN) are generating a comprehensive reference atlas of cell types in the mouse brain. A key challenge in this effort is mapping diverse datasets, acquired with varied imaging, tissue processing, and profiling methods, into shared coordinate frameworks. Here, we present mouse brain mapping pipelines developed using the Advanced Normalization Tools Ecosystem (ANTsX) to align MERFISH spatial transcriptomics and high-resolution fMOST morphology data to the Allen Common Coordinate Framework (CCFv3), and developmental MRI and LSFM data to the Developmental CCF (DevCCF). Simultaneously, we introduce two novel methods: 1) a velocity field-based approach for continuous interpolation across developmental timepoints, and 2) a deep learning framework for automated brain parcellation using minimally annotated and publicly available data. All workflows are open-source and reproducible. We also provide general guidance for selecting appropriate strategies across modalities, enabling researchers to adapt these tools to new data.

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Publication Details

Journal
Nature Communications
Published
2025-11-22
DOI
https://doi.org/10.1038/s41467-025-66741-5
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

The ANTsX ecosystem for mapping the mouse brain

Michael Kunst, James C. Gee, Clare Gamlin, Jeffrey Duda et al.
Nature Communications
Single-cell and spatial transcriptomics
article

The ANTsX ecosystem for mapping the mouse brain

Michael Kunst, James C. Gee, Clare Gamlin, Jeffrey Duda, Yongsoo Kim, Quanxin Wang, Staci A. Sorensen, Nicholas J. Tustison, Rachel Dalley, Mia G. Tustison, Min Chen, Lydia Ng, Fae N. Kronman
article en

Abstract

Large-scale efforts by the BRAIN Initiative Cell Census Network (BICCN) are generating a comprehensive reference atlas of cell types in the mouse brain. A key challenge in this effort is mapping diverse datasets, acquired with varied imaging, tissue processing, and profiling methods, into shared coordinate frameworks. Here, we present mouse brain mapping pipelines developed using the Advanced Normalization Tools Ecosystem (ANTsX) to align MERFISH spatial transcriptomics and high-resolution fMOST morphology data to the Allen Common Coordinate Framework (CCFv3), and developmental MRI and LSFM data to the Developmental CCF (DevCCF). Simultaneously, we introduce two novel methods: 1) a velocity field-based approach for continuous interpolation across developmental timepoints, and 2) a deep learning framework for automated brain parcellation using minimally annotated and publicly available data. All workflows are open-source and reproducible. We also provide general guidance for selecting appropriate strategies across modalities, enabling researchers to adapt these tools to new data.

Nature CommunicationsVol. 16(1)
Allen Institute for Brain Science (US), University of Virginia Medical Center (US), Allen Institute (US), University of Virginia (US), University of Pennsylvania (US), Penn State Milton S. Hershey Medical Center (US)
University of Arizona, National Institutes of Health, National Institute of Mental Health
Openalex Percentile: Top 68%
Single-cell and spatial transcriptomics
DOIOpen Access PDFOpenAlex
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