Discriminating single-molecule binding events from diffraction-limited fluorescence.

Area of Science:

• Biophysics
• Computational Biology
• Microscopy

Background:

• Single-molecule localization microscopy (SMLM) offers high-resolution insights into molecular interactions.
• Traditional methods for classifying molecular binding types in SMLM are complex, time-consuming, and often require multiple dyes or kinetic analysis.

Purpose of the Study:

• To develop a novel method for synchronous classification of molecular binding events using SMLM videos.
• To leverage spatiotemporal information within diffraction-limited fluorescence signals for binding type recognition.
• To reduce the complexity and time required for analyzing molecular interactions in high-throughput single-molecule imaging.

Main Methods:

• Development of a Temporal-to-Context Convolutional Neural Network (T2C CNN) architecture.
• Integration of long-term spatial convolutions, shallow cross-connected blocks, and a pooling-free structure within the T2C CNN.
• Application of the T2C CNN to analyze DNA-PAINT experimental videos using a single fluorescent dye.

Main Results:

• The T2C CNN achieved up to 94.76% accuracy in classifying binding event videos.
• The proposed method significantly outperforms existing state-of-the-art video classification models by 15-25 percentage points.
• Observation time for binding-type recognition was reduced from minutes to seconds.

Conclusions:

• The T2C CNN effectively classifies molecular binding types from SMLM videos using a single dye, by analyzing embedded spatiotemporal information.
• This approach enables rapid, precise, and high-throughput single-molecule imaging without complex experimental setups.
• The findings pave the way for more efficient analysis of molecular interactions in biological systems.