Single-cell RNA-seq (scRNA-seq) data from multiple species present remarkable opportunities to explore cellular origins and evolution. However, integrating and annotating scRNA-seq data across different species remains challenging due to the variations in sequencing techniques, ambiguity of homologous relationships, and limited biological knowledge. To tackle the above challenges, we introduce CAMEX, a heterogeneous Graph Neural Network (GNN) tool that leverages many-to-many homologous relationships for multi-species integration, alignment, and annotation of scRNA-seq data from multiple species. Notably, CAMEX outperforms state-of-the-art methods integration on various cross-species benchmarking datasets (ranging from one to eleven species). Besides, CAMEX facilitates the alignment of diverse species across different developmental stages, significantly enhancing our understanding of organ and organism origins. Furthermore, CAMEX enables the detection of species-specific cell types and marker genes through cell and gene embedding. In short, CAMEX holds the potential to provide invaluable insights into how evolutionary forces operate across different species at single-cell resolution.

Publication:

NATURE COMMUNICATIONS

http://dx.doi.org/10.1038/s41467-026-69696-3

Author:

Zhen-Hao Guo

College of Electronics and Information Engineering, Tongji University, Shanghai, China

Ningbo Institute of Digital Twin, Ningbo Key Laboratory of MultiOmics & Multimodal Biomedical Data Mining and Computing, Eastern Institute of Technology, Ningbo, Zhejiang, China

Institute for Regenerative Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.

De-Shuang Huang

Ningbo Institute of Digital Twin, Ningbo Key Laboratory of MultiOmics & Multimodal Biomedical Data Mining and Computing, Eastern Institute of Technology, Ningbo, Zhejiang, China

Institute for Regenerative Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.

e-mail: dshuang@eitech.edu.cn

Shihua Zhang

State Key Laboratory of Mathematical Sciences, 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.

e-mail: zsh@amss.ac.cn