Spatial transcriptomics allows scientists to see where genes are active within tissues, offering powerful insight into diseases such as cancer. However, it remains difficult to reliably align these molecular maps across patients or integrate them with standard pathology images used in clinical care. As a result, analysing spatial transcriptomics data often requires extensive manual work, slowing diagnosis, increasing costs and limiting its routine use in British Columbia.

Led by Dr. Xin Tang and Dr. Michael Underhill at the University of British Columbia, this project aims to develop an AI-enabled software platform to solve this problem. The platform will automatically align spatial transcriptomics data across samples and link it with standard hospital tissue slides (known as haematoxylin and eosin – or H&E – histology images). It will also provide information about specific cell-types derived from single-cell RNA sequencing to create clear, unified tissue maps that can be explored quickly by pathologists and researchers.

The project will deliver a validated research-use software prototype, tested in a British Columbia pathology lab. It aims to shorten diagnostic turnaround times, reduce labour and software costs and support BC-based precision medicine research and innovation, while strengthening the province’s health innovation ecosystem.