Congratulations to Dr. Ivan Robert Nabi, Ben Cardoen, Kurt Vandevoorde, Dr. Guang Gao, Dr. Milene Ortiz Silva, and Dr. Ghassan Hamarneh on their recent discovery and paper in the Journal of Cell Biology. The interdisciplinary team, which is a collaboration with the School of Biomedical Engineering and Life Sciences Institute at UBC and the Computing Science department at Simon Fraser University, developed a novel algorithm that quantifies interaction from microscopy images using AI and biomedical computer vision. The algorithm explores the mechanisms behind complex diseases, such as metabolic and neurodegenerative disorders, that are critical in determining how the cell functions and enables faster, more precise discovery of what exactly is going on in those affected cells, leading to improved understanding and potentially novel targeted treatments.

The algorithm enables and accelerates accurate and robust discovery beyond the physical limits of the microscopes. Unlike existing approaches, it is able to handle changes in fluorescence intensity without segmentation, providing the high accuracy and resolution to enable interaction detection in cells.

Before this discovery, resolution of super-resolution microscopy for 3D voxel-based image volumes did not approach the nanometre distances that separate subcellular organelles such as mitochondria and endoplasmic reticulum. This method, MCS-DETECT, detects membrane contact sites in large superresolution microscopy volumes without requiring segmentation, demonstrating how AI software can complementenhance the performance of super-resolution microscopy hardware.

This discovery can help other life science researchers extract robust interaction statistics to quantify the effect certain genomic or pharmaceutical disruptions have on cellular health.