Non-Parametric Spatial Analysis Approaches in Continuous and Discrete Space to Maximize the Investigative Power of Super-Resolution Microscopy

On March 7th, Professor Rengasayee (Sai) Veeraraghavan from Ohio State's Department of Biomedical Engineering will share insights into innovative spatial analysis techniques for super-resolution microscopy. 

Cardiac biology and physiology are governed by proteins organized within nanodomains, whose function cannot be fully understood without accounting for their ultrastructural properties. Using super-resolution microscopy and quantitative image analysis, iOS research explores these structures and their impact on cardiac physiology. A major challenge in analyzing single-molecule localization microscopy (SMLM) data is its computational complexity and overcounting artifacts. To address this, iOS developed SPACE-VorTeCS, a hyperparameter-free, Voronoi tessellation-based method that enables fast, unbiased cluster analysis. This approach streamlines spatial pattern analysis, making it significantly more efficient for large SMLM datasets.