September 08, 2026
Artificial intelligence is making headlines for writing emails, generating images and answering questions. But one of its most promising applications may be happening behind the scenes in hospitals and research labs.
Genome BC is supporting two projects led by researchers at the University of British Columbia (UBC), including one in partnership with SnapCyte Solutions Inc., that are using AI to address time-consuming lab diagnostic bottlenecks. Overcoming these challenges could help speed diagnoses, improve research and bring us closer to more personalized healthcare.
Turning hours of manual work into minutes
Modern genomic technologies can reveal where genes are active within tissues, providing critical insights into diseases such as cancer. But generating that data is only part of the challenge. Researchers and pathologists often spend countless hours manually processing and analyzing complex datasets before they can draw meaningful conclusions.
One project, led by Dr. Xin Tang and Dr. Michael Underhill at UBC, is developing an AI-powered platform that automatically aligns spatial transcriptomics data (information that shows which genes are active and exactly where they are active within a piece of tissue), with standard pathology images. Currently, this process requires extensive manual work, making it difficult to integrate genomic information into routine clinical care. By automating the workflow, the platform could help pathologists analyze tissue more quickly, reduce diagnostic turnaround times and support the wider adoption of precision medicine in BC.
Making advanced diagnostics more accessible
A second project addresses another critical challenge. To better understand diseases like cancer, scientists are using single-nuclei transcriptomics, a technology that studies gene activity one cell at a time. This detailed view can reveal important differences between healthy and diseased cells, helping researchers develop more targeted treatments. Before the analysis can begin, however, scientists must accurately count up to thousands of cell nuclei under a microscope, a slow and repetitive task that is often done manually.
Led by Dr. Colin Collins at UBC in partnership with SnapCyte Solutions, and with samples provided by the Vancouver Prostate Centre’s Laboratory for Advanced Genome Analysis, this project is developing an AI-powered tool that automatically identifies and counts intact nuclei from microscope images. The technology is designed to reduce human error, improve data quality and minimize failed sequencing experiments, making advanced genomic technologies more efficient, reliable and accessible for research labs working with valuable or limited tissue samples.
A smarter path to precision medicine
Together, these projects are tapping into the ability of AI and machine learning to automate repetitive, time-consuming tasks that require consistency and precision. Rather than replacing scientific expertise, these tools free researchers to spend less time on manual processes and more time interpreting results, advancing diagnostics and developing personalized treatments.
