Simulation-Guided Exploration of PAINT Parameter Space for Accurate Molecular Quantification

Abstract

Molecular quantification using Point Accumulation for Imaging in Nanoscale Topography (PAINT) depends critically on probe kinetics, imaging conditions, and surface molecular properties. This study presents a simulation-guided framework to systematically explore the PAINT parameter space and identify conditions that enable molecular quantification with high accuracy. Detection thresholds for key PAINT outputs, namely point spread function density, localization cloud density, and binding event density, are defined to ensure [≥] 90% accuracy in density estimates and statistical interpretability of spatial distributions. A neural network surrogate model, trained on Monte Carlo simulations, is used to perform Sobol sensitivity analysis, revealing that probe kinetics and concentration are the dominant contributors to output variability. The model also enables rapid mapping of viable parameter regimes and shows that interpretable quantification of spatial distributions in high-density, clustered systems requires either a priori knowledge of the molecular architecture or improved spatial resolution. Overall, this framework provides quantitative guidance for optimizing PAINT experiments and supports the rational design of non-DNA-based, PAINT-compatible probes, thereby expanding the applicability of PAINT to a broader range of molecular systems.