This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: A KAUST-led team has developed the AI tool Unify to help scientists compare similar cell types across distant species. This comparison can help researchers assess which findings from animal studies may be most relevant to human health.
Their study is published in the journal Nature Communications. Scientists often compare cells using single-cell RNA sequencing, which shows which genes are active in each cell. When carried out throughout an organism, this information can be used to build a "cell tree," mapping different cell types and their activity.
However, most comparison tools rely on direct one-to-one gene matches. These matches become harder to find as species diverge over time, meaning important biological similarities (for example, between immune cells in humans and mice, or neurons in fish and flies) may be missed. To overcome these limitations, Unify compares the jobs that genes perform.
Where conventional methods resemble dictionaries seeking word-for-word translations, this tool looks for shared biological meaning. "Unify works with AI models that analyze protein sequences and scientific descriptions of gene functions," explains Huawen Zhong, lead author and computational biologist at KAUST. "Genes with certain similarities are grouped into units called 'macrogenes.' This allows Unify to recognize cells performing similar jobs, even when their individual genes no longer match." Unify reconstructed relationships among 125 cell types from seven species separated by more than 700 million years.
It distinguished between identical genes, similar genes that had evolved new jobs, and different genes that had evolved independently to do the same job. By analyzing immune cells across multiple species, Unify identified shared defense tactics that would be difficult to detect using methods restricted to direct gene comparison. In another experiment, it predicted the response of human blood cells to a certain immune-signaling protein, based on the response of mouse lymph node immune cells.
Across all genes, Unify's predictions were more accurate than existing methods. The KAUST team is now extending Unify to include information about gene regulation and cell positions within tissues. These additions will give researchers a fuller picture of the biological principles shared across life, informing further advances in human health research.
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