Human-vision-inspired cluster identification for single-molecule localization microscopy

Single-molecule localization microscopy (SMLM) has enabled the visualization of viral and cellular structures at the nanoscale. Researchers are facing great challenges in extracting quantitative information from images represented by point clouds. Currently, researchers create various algorithms to segment the data points into nanoclusters, whose morphology and structural parameters can be quantified by various other algorithms to report the organization and behavior of single molecules in different biological contexts. However, these algorithms rely on user-defined parameters and are largely affected by the density of localizations, resulting in misleading conclusions that vary from one biological system to another and from one user to another.  We recently developed a novel cluster identification algorithm that does not require any input from users and is independent of the molecule density by mimicking a specific function of human eyes. This algorithm will enable the analysis of the nano-organization of spike proteins of SARS-CoV-2 and its VOCs to understand whether the nanoscale structure of these proteins may contribute to transmission and immune evasion of SARS-CoV-2.

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