Abstract
Understanding the interactions of proteins with macromolecular substrates such as DNA, microtubules, and actin filaments is critically important in molecular biology and beyond. Single-molecule experiments provide unprecedented insight into the dynamics of these interactions. However, efficient analysis of large datasets generated by such experiments remains a major challenge. Specifically, in single-molecule experiments that involve scanning of such linear substrates, the absence of a comprehensive and effective system for detecting interactions among millions of frames of high-frequency imaging data and excluding noise without bias hampers research productivity and data quality. To address these limitations, we introduce "Single-Molecule Interaction Tracker (SMITracker)", an interactive analysis platform designed to enable efficient, accurate, and scalable detection of single-molecule interaction events. The protocol begins with raw image data obtained from single-molecule experiments, which undergoes preprocessing and is transformed into a structured dataset, suitable for processing in R. Using an automatic trajectory detection algorithm and a uniform noise exclusion model, the target data are detected and organized for further analysis. Final outputs include comprehensive diffusion analysis and informative visualization, enabling side-by-side comparison of different proteins and/or experimental conditions. SMITracker is available as an R package for R users and as a Docker image for non-R users, offering a highly effective and convenient solution to the common challenges in analyzing data from single-molecule experiments.