Investigating Uncertainties in Single-Molecule Localization Microscopy Using Experimentally Informed Monte Carlo Simulation

Single-moleculelocalization microscopy (SMLM) enablesthe visualizationof cellular nanostructures in vitro with sub-20 nmresolution. While substructures can generally be imaged with SMLM,the structural understanding of the images remains elusive. To betterunderstand the link between SMLM images and the underlying structure,we developed a Monte Carlo (MC) simulation based on experimental imagingparameters and geometric information to generate synthetic SMLM images.We chose the nuclear pore complex (NPC), a nanosized channel on thenuclear membrane which gates nucleo-cytoplasmic transport of biomolecules,as a test geometry for testing our MC model. Using the MC model tosimulate SMLM images, we first optimized our clustering algorithmto separate >10(6) molecular localizations of fluorescentlylabeled NPC proteins into hundreds of individual NPCs in each cell.We then illustrated using our MC model to generate cellular substructureswith different angles of labeling to inform our structural understandingthrough the SMLM images obtained.

Automated summary

Single-molecule localization microscopy (SMLM) allows visualization of cellular nanostructures in vitro with sub-20 nm resolution. However, understanding the structural information from SMLM images has been challenging. To address this, a Monte Carlo (MC) simulation based on experimental imaging parameters was developed to generate synthetic SMLM images. This MC model was used to optimize a clustering algorithm for the separation of fluorescently labeled nuclear pore complex (NPC) proteins and to generate cellular substructures with different angles of labeling.