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Real-time three-dimensional single-particle tracking (RT-3D-SPT) allows continuous detection of individual freely diffusing objects with high spatiotemporal precision by applying closed-loop active feedback in an optical microscope. However, the current tracking speed in RT-3D-SPT is primarily limited by the response time of control actuators, impeding long-term observation of fast diffusive objects such as single molecules. Here, we present an RT-3D-SPT system with improved tracking performance by replacing the XY piezoelectric stage with a galvo scanning mirror with an approximately five-time faster response rate (~5 kHz). Based on the previously developed 3D single-molecule active real-time tracking (3D-SMART), this new implementation with a fast-responding galvo mirror eliminates the mechanical movement of the sample and allows more rapid response to particle motion. The improved tracking performance of the galvo mirror-based implementation is verified through simulation and proof-of-principle experiments. Fluorescent nanoparticles and ~ 1 kB double-stranded DNA molecules were tracked via both the original piezoelectric stage and new galvo mirror implementations. With the new galvo-based implementation, notable increases in tracking duration, localization precision, and the degree to which the objects are locked to the center of the detection volume were observed. These results suggest faster control response elements can expand RT-3D-SPT to a broader range of chemical and biological systems. 

Despite successes in tracking single molecules in vitro, the extension of active-feedback single-particle methods to tracking rapidly diffusing and unconfined proteins in live cells has not been realized. Since the existing active-feedback localization methods localize particles in real time assuming zero background, they are ill-suited to track in the inhomogeneous background environment of a live cell. Here, we develop a windowed estimation of signal and background levels using recent data to estimate the current particle brightness and background intensity. These estimates facilitate recursive Bayesian position estimation, improving upon current Kalman-based localization methods. Combined, online Bayesian and windowed estimation of background and signal (COBWEBS) surpasses existing 2D localization methods. Simulations demonstrate improved localization accuracy and responsivity in a homogeneous background for selected particle and background intensity combinations. Improved or similar performance of COBWEBS tracking extends to the majority of signal and background combinations explored. Furthermore, improved tracking durations are demonstrated in the presence of heterogeneous backgrounds for multiple particle intensities, diffusive speeds, and background patterns. COBWEBS can accurately track particles in the presence of high and nonuniform backgrounds, including intensity changes of up to three times the particle’s intensity, making it a prime candidate for advancing active-feedback single fluorophore tracking to the cellular interior.