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Updated: Feb 23, 2026

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Multi-species integration, alignment and annotation of single-cell RNA-seq data with CAMEX.

Zhen-Hao Guo^1,2,3, De-Shuang Huang^4,5, Shihua Zhang^6,7,8

¹College of Electronics and Information Engineering, Tongji University, Shanghai, China.

Nature Communications

|February 21, 2026

Summary

This summary is machine-generated.

We developed CAMEX, a novel tool for integrating and analyzing single-cell RNA sequencing (scRNA-seq) data across multiple species. CAMEX enhances cross-species cell type annotation and evolutionary studies.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Evolutionary Biology
Genomics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) offers insights into cellular origins and evolution across species.
Integrating and annotating cross-species scRNA-seq data is challenging due to technical and biological variability.

Purpose of the Study:

To introduce CAMEX, a Graph Neural Network (GNN) tool for multi-species scRNA-seq data integration, alignment, and annotation.
To leverage many-to-many homologous relationships for improved cross-species analysis.

Main Methods:

Developed CAMEX, a heterogeneous Graph Neural Network (GNN) model.
Utilized many-to-many homologous relationships for data integration.
Applied CAMEX to cross-species benchmarking datasets.

Main Results:

CAMEX outperforms existing methods in multi-species scRNA-seq integration.
Successfully aligned diverse species across different developmental stages.
Enabled detection of species-specific cell types and marker genes.

Conclusions:

CAMEX provides a powerful framework for cross-species single-cell data analysis.
Facilitates deeper understanding of evolutionary processes at single-cell resolution.
Offers insights into organ and organism origins through comparative genomics.