DOLPHIN for exon-level single-cell RNA-seq data analysis

The researchers have developed an artificial intelligence tool that can detect previously invisible disease markers inside single cells.

In a study published in Nature Communications, the researchers demonstrate how the tool, called DOLPHIN, could one day be used by doctors to catch diseases earlier and guide treatment options.

“This tool has the potential to help doctors match patients with the therapies most likely to work for them, reducing trial-and-error in treatment,” said the senior author.

Disease markers are often subtle changes in RNA expression that can indicate when a disease is present, how severe it may become or how it might respond to treatment.

Conventional gene-level methods of analysis collapse these markers into a single count per gene, masking critical variation and capturing only the tip of the iceberg, said the researchers.

Now, advances in artificial intelligence have made it possible to capture the fine-grained complexity of single-cell data. DOLPHIN moves beyond gene-level, zooming in to see how genes are spliced together from smaller pieces called exons to provide a clearer view of cell states.

“Genes are not just one block, they’re like Lego sets made of many smaller pieces,” said the first author. “By looking at how those pieces are connected, our tool reveals important disease markers that have long been overlooked.”

In one test case, DOLPHIN analyzed single-cell data from pancreatic cancer patients and found more than 800 disease markers missed by conventional tools. It was able to distinguish patients with high-risk, aggressive cancers from those with less severe cases, information that would help doctors choose the right treatment path.

More broadly, the breakthrough lays the foundation for achieving the long-term goal of building digital models of human cells. DOLPHIN generates richer single-cell profiles than conventional methods, enabling virtual simulations of how cells behave and respond to drugs before moving to lab or clinical trials, saving time and money.

The researchers’ next step will be to expand the tool’s reach from a few datasets to millions of cells, paving the way for more accurate virtual cell models in the future.

https://www.nature.com/articles/s41467-025-61580-w

https://sciencemission.com/DOLPHIN-advances-single-cell-transcriptomics