ArticleNature methods2024
Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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76 citing papers in PubMed.
- Latent causal diffusions for single-cell perturbation modeling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Systematic benchmarking and optimal strategy selection of cross-species integration methods.Briefings in bioinformatics · 2026Article
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- Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.Advanced drug delivery reviews · 2026Review
- Single-cell insights into plant growth, adaptation, and evolution.Journal of integrative plant biology · 2026Review
- Unify learns cellular evolution with universal multimodal embeddings.Nature communications · 2026Article
- Single-cell foundation modeling with species-native protein tokens links regenerative competence across frog and mouse.bioRxiv : the preprint server for biology · 2026Article
- Distinct transcriptional programs define cranial motor neuron subtypes during vertebrate development.bioRxiv : the preprint server for biology · 2026Article
- CroCoNet: a framework for the quantitative comparison of gene regulatory networks across species.Genome biology · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- scDifformer: diffusion-based post-training for virtual cell modeling across large-scale single-cell data.Nucleic acids research · 2026Article
- The genetic foundations of convergent traits.Nature reviews. Genetics · 2026Review
- Computational modelling of cell identity.The Biochemical journal · 2026Review
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- PlasmidGPT: A generative framework for plasmid analysis and generation.Science advances · 2026Article
- Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.Briefings in bioinformatics · 2026Review
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A multi-omic single-cell landscape of perinatal mouse skin maps lineage specification and reveals shared dynamics in human fetal skin.Experimental & molecular medicine · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
16 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, interspecies genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes' biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN can detect functionally related genes coexpressed across species, redefining differential expression for cross-species analysis. Applying SATURN to three species whole-organism atlases and frog and zebrafish embryogenesis datasets, we show that SATURN can effectively transfer annotations across species, even when they are evolutionarily remote. We also demonstrate that SATURN can be used to find potentially divergent gene functions between glaucoma-associated genes in humans and four other species.
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