If you’re a data scientist investigating spatial transcriptomics (ST), you’ll be interested in this new AI-driven tool called “Spotiphy” (which stands for SPOT Imager with Pseudo-single-cell-resolution HistologY).

The tool, funded in part by NCI, lets you combine the strengths of sequencing ST (for broad gene coverage across larger regions) and imaging ST (for detailed cellular-level information) to bridge the information gap between these two techniques.

With Spotiphy, you can bring the power of single-cell resolution to a larger tumor section with higher gene coverage, giving you information on the genes that are active—both within a tumor and in the surrounding regions.

In their recent study, the researchers applied Spotiphy to sequencing-based ST data from three breast tissue samples, generating single-cell RNA (i.e., inferred RNA) data for 23,200 cells. They were able to map gene expression profiles for key biomarkers, further reveal the tumor’s spatial complexity, and fill in spatial data gaps. Most importantly, using the AI-driven approach they were able to visualize gene distribution patterns across entire tissue sections, capturing details on cell interactions and gaining a more complete picture of the tumor and its microenvironment.

Senior author, Dr. Jiyang Yu, of St. Jude Children’s Research Hospital, said, “Spotiphy offers a comprehensive computational toolkit that enables researchers to characterize cells within the tumors and fully chart the tumor’s microenvironment.”

Dr. Yu noted, “Our study highlights the advantages of Spotiphy over other tools, in terms of both accuracy and effectiveness. By bringing single-cell resolution to whole-transcriptomic ST data, we’re able to preserve the unique spatial distribution patterns of genes and create new opportunities for deeper insights in cancer and other diseases.”

See the CBIIT training article for more information on the basics of ST.

NCI’s Division of Cancer Biology helped support this research.