Yeo, W.-H. | bioRxiv Science

bioRxiv2023

Physically informed Monte Carlo simulation of dual-wedge prism-based spectroscopic single-molecule localization microscopy

SignificanceThe dual-wedge prism (DWP)-based spectroscopic single-molecule localization microscopy (sSMLM) system offers improved localization precision and adjustable spectral or localization performance, but its nonlinear spectral dispersion presents a challenge. A systematic method can help understand the challenges and thereafter optimize the DWP systems performance by customizing system parameters to maximize spectral or localization performance for various molecular labels. AimWe developed an MC-based model which predicts the imaging output of the DWP-based sSMLM system given different system parameters. ApproachWe assessed our MC models localization and spectral precisions by comparing our simulation against theoretical equations and fluorescent microspheres. Furthermore, we simulated the DWP-based system using beamsplitters of Reflectance (R):Transmittance (T) of R50:T50 and R30:T70 and their tradeoffs. ResultsOur MC simulation showed average deviations of 2.5 nm and 2.1 nm for localization and spectral precisions against theoretical equations; and 2.3 nm and 1.0 nm against fluorescent microspheres. An R30:T70 beamsplitter improved spectral precision by 8% but worsened localization precision by 35% on average compared to an R50:T50 beamsplitter. ConclusionsThe MC model accurately predicted localization precision, spectral precision, spectral peaks, and spectral widths of fluorescent microspheres, as validated by experimental data. Our work enhances the theoretical understanding of DWP-based sSMLM for multiplexed imaging, enabling performance optimization.

bioengineering↗

bioRxiv2022

Experimental Parameters-Based Monte-Carlo Simulation of Single-Molecule Localization Microscopy of Nuclear Pore Complex to Evaluate Clustering Algorithms

Single-molecule localization microscopy (SMLM) enables the detailed visualization of nuclear pore complexes (NPC) in vitro with sub-20 nm resolution. However, it is challenging to translate the localized coordinates in SMLM images to NPC functions because different algorithms to cluster localizations as individual NPCs can be biased without ground truth for validation. We developed a Monte-Carlo simulation to generate synthetic SMLM images of NPC and used the simulated NPC images as the ground truth to evaluate the performance of six clustering algorithms. We identified HDBSCAN as the optimal clustering algorithm for NPC counting and sizing. Furthermore, we compared the clustering results between the experimental and synthetic data for NUP133, a subunit in the NPC, and found them to be in good agreement.

bioinformatics↗