Mapping Macaque Brainnetome Atlas to Macaque Spatial Transcriptomic Data
Poster No:
1727
Submission Type:
Abstract Submission
Authors:
Zhenwei Dong1,2, Zongchang Du1, Weiyang Shi1, Zhengyi Yang1, Tianzi Jiang1,2
Institutions:
1Institute of Automation, Chinese Academy of Sciences, Beijing, China, 2School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
First Author:
Zhenwei Dong
Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Co-Author(s):
Tianzi Jiang
Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Institute of Automation, Chinese Academy of Sciences|School of Artificial Intelligence, University of Chinese Academy of Sciences
Beijing, China|Beijing, China
Introduction:
Macaques, as one of the closest evolutionary relatives to humans, are essential model organisms in neuroscience research. The recent development of the Macaque Brainnetome Atlas (Lu et al., 2024) has enabled a more precise parcellation of the macaque brain, providing structural connectivity between macroscopic brain regions. In parallel, the Macaque Spatial Transcriptome data (Chen et al., 2023) offers a detailed view of gene expression and cell-type distribution across various regions of the macaque brain. To advance the understanding of macaque brain from a unified, multi-modal, and cross-scale perspective, this work provided the brain region mapping from the Macaque Brainnetome Atlas to the Macaque Spatial Transcriptome data.
Methods:
We used tissue Blockface images as intermediaries, with MRI scans serving as the 3D reference for reconstructing spatial transcriptome slices (Figure 1). First, brain tissue in the Blockface images was segmented using SAM (Kirillov et al., 2023) and aligned with MRI via the TIRL toolbox (Huszar et al., 2023). Next, each preprocessed spatial transcriptome slice was registered to its corresponding Blockface using affine transformation, thereby reconstructing the slices within the individual MRI space. Subsequently, the Macaque Brainnetome Atlas was nonlinearly registered to the individual MRI using ANTs. Through inverse transformation, we mapped the atlas to each spatial transcriptome slice. Based on atlas ROIs, we calculated the gene co-expression patterns between brain regions.
Results:
We completed the three-dimensional reconstruction of both the Blockface and spatial transcriptome slices. Visualization results confirmed that the reconstruction restored the original three-dimensional structure of the tissue slices, with a strong spatial correspondence to the individual MRI (Figure 2). Additionally, the parcellation information of Macaque Brainnetome Atlas was aligned to the cortical areas of the spatial transcriptome slices. Based on this, we calculated the gene co-expression patterns between brain regions and compared them with the macrostructural connections provided by the Brainnetome Atlas. This highlights the potential of multimodal and cross-scale analyses in studying brain structure and function. And the mapping results can be accessed at www.brainnetome.org.
Conclusions:
This study integrated the Macaque Brainnetome Atlas with the Macaque Spatial Transcriptome data, mapping brain region information derived from macro-scale structural connectivity onto transcriptomic slices to get the regional gene expression. This mapping provides an efficient tool for multi-modal, cross-scale structural and functional analyses of the macaque brain. On one hand, the mapping enables the use of transcriptomic analyses within finely detailed ROIs informed by macro-scale connectivity. On the other hand, structural connectivity patterns can be explored in conjunction with transcriptomic data to uncover underlying biological mechanisms. Together, this work offers a more comprehensive framework for investigating the functional and structural mechanisms of primate brains.
Modeling and Analysis Methods:
Image Registration and Computational Anatomy 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 1
Keywords:
Atlasing
Cortex
Data Registration
1|2Indicates the priority used for review
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Huszar, I. N., Pallebage-Gamarallage, M., Bangerter-Christensen, S., Brooks, H., Fitzgibbon, S., Foxley, S., ... & Jenkinson, M. (2023). Tensor image registration library: Deformable registration of stand‐alone histology images to whole‐brain post‐mortem MRI data. Neuroimage, 265, 119792.
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., ... & Girshick, R. (2023). Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4015-4026).
Lu, Y., Cui, Y., Cao, L., Dong, Z., Cheng, L., Wu, W., ... & Jiang, T. (2024). Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology. Science Bulletin.