Hill Research New Preprint: AI-Powered Framework for Cross-Species Single-Cell Insight

Hill Research announces a new preprint on BioRxiv, introducing a transformative approach to cross-species single-cell analysis through artificial intelligence. 

Translational biology is subject to a major challenge: leveraging abundant animal model data to gain insights into human biology, particularly in settings where human data are scarce or hard to collect. 

The study presents CSLAN (Cross-Species Latent Alignment Network), a transfer learning framework that leverages mouse scRNA-seq data to classify human trauma-related immune cells with 96.67% accuracy. By fine-tuning only the encoder in an encoder-decoder architecture and using strategic gene feature selection, CSLAN preserves latent biological signals while ensuring computational efficiency. 

Key advantages of CSLAN include: 

· Bridging species gaps with species-invariant genomic features 

· Reducing data needs in resource-limited human studies 

· Enabling scalable translational research via model-informed latent alignment 

This framework reveals deep evolutionary conservation of core transcriptional programs and offers a powerful, data-efficient tool for biomedical research beyond trauma, including disease modeling and drug development. CSLAN could accelerate immune profiling in early-phase trials, rare disease studies, or pediatric cohorts where human sample acquisition is limited. 

This work was developed in collaboration with leading experts across pharmaceutical, academic, biotech, and technology sectors. We thank our coauthors for their invaluable contributions. 

At Hill Research, we combine deep AI expertise with a problem-solving mindset to deliver innovative, scalable solutions in biomedical research. We welcome collaboration and partnerships to advance AI-driven science discovery and help translate complex biological data into meaningful insights. 

The preprint is publicly available under a CC-BY-NC 4.0 license: 

https://doi.org/10.1101/2025.06.09.655966 

For inquiries or collaboration opportunities, please contact: info@hillresearch.ai