Journal Article

Discriminating single-molecule binding events from diffraction-limited fluorescence

Wang, Lipo, Loh, Iong Ying, Sharma, Kamal Kant, Ang, Yan Shan, Yin, Yueming, Pathoor, Nithin, Sim, Shao Ren, Zhu, Shiwen, Wohland, Thorsten, Yung, Lin Yue Lanry

2025

Overview

Single-molecule localization microscopy enables high-resolution imaging of molecular interactions, but discriminating molecular binding types has traditionally relied on complex strategies, such as multiple dyes, time-division techniques, or kinetic analysis, that are asynchronous, invasive, or time-consuming. Here, we uncover previously overlooked spatiotemporal information embedded within diffraction-limited fluorescence, enabling synchronous classification of individual binding event videos using only a single fluorescent dye. Building on this insight, we propose a Temporal-to-Context Convolutional Neural Network (T2C CNN), which integrates long-term spatial convolutions, shallow cross-connected blocks, and a pooling-free structure to enhance contextual representation while preserving fine-grained temporal features. Applied to DNA-PAINT experiments, T2C CNN achieves up to 94.76% classification accuracy and outperforms state-of-the-art video classification models by 15-25 percentage points. Our approach enables rapid and precise binding-type recognition from fluorescence video data, reducing observation time from minutes to seconds and facilitating high-throughput single-molecule imaging without requiring multiple dye channels or extended kinetic measurements. Yin and colleagues propose that diffraction-limited fluorescence videos contain hidden binding-type information. The authors present a deep learning model, T2C CNN, which exploits that hidden information to classify molecular interactions with high accuracy using a single dye in seconds.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114/1305 / 631/1647/1888/1889 / 631/1647/245/2225 / 631/1647/328/2238 / 631/337/2265 / Accuracy / Artificial neural networks / Binding / Classification / Deep learning / Diffraction / DNA - chemistry / DNA - metabolism / Dyes / Experiments / Fluorescence / Fluorescent dyes / Fluorescent Dyes - chemistry / Fluorescent indicators / Humanities and Social Sciences / Image resolution / Kinetics / Localization / Machine learning / Microscopy / Microscopy, Fluorescence - methods / Molecular interactions / multidisciplinary / Neural networks / Neural Networks, Computer / Science / Science (multidisciplinary) / Single Molecule Imaging - methods / Temporal variations / Video data