he increasing utilization of mouse models in human neuroscience research places higher demands on computational methods to translate findings from the mouse brain to the human one. In this study, we develop BrainAlign, a self-supervised learning approach, for the whole brain alignment of spatial transcriptomics (ST) between humans and mice. BrainAlign encodes spots and genes simultaneously in two separated shared embedding spaces by a heterogeneous graph neural network. We demonstrate that BrainAlign could integrate cross-species spots into the embedding space and reveal the conserved brain regions supported by ST information, which facilitates the detection of homologous regions between humans and mice. Genomic analysis further presents gene expression connections between humans and mice and reveals similar expression patterns for marker genes. Moreover, BrainAlign can accurately map spatially similar homologous regions or clusters onto a unified spatial structural domain while preserving their relative positions. Comparative transcriptomics of whole brains across species is vital in neuroscience. Here, authors develop a deep learning method, BrainAlign, to align spatial transcriptomics across human and mouse brains. BrainAlign identifies conserved brain regions and uncovers similar patterns for marker genes.
Publication:
Nature Communications volume 15, Article number: 6302 (30 July 2024)
https://doi.org/10.1038/s41467-024-50608-2
Author:
Biao Zhang
School of Mathematical Sciences, Fudan University, Shanghai, China
Shuqin Zhang
School of Mathematical Sciences, Fudan University, Shanghai, China
Key Laboratory of Mathematics for Nonlinear Science, Fudan University, Ministry of Education, Shanghai, China
Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, Shanghai, China
Email：zhangs@fudan.edu.cn
Shihua Zhang
NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
Email：zsh@amss.ac.cn
地址 北京市海淀区中关村东路55号 思源楼6-7层 南楼5-6、8层 邮编：100190 电子邮箱：iam@amss.ac.cn
