Live-SIMBA: an ImageJ plug-in for the universal and accelerated single molecule-guided Bayesian localization super resolution microscopy (SIMBA) method

摘要

Live-cell super-resolution fluorescence microscopy techniques allow biologists to observe subcellular structures, interactions and dynamics at the nanoscale level. Among of them, single molecule-guided Bayesian localization super resolution microscopy (SIMBA) and its derivatives produce an appropriate 50 nm spatial resolution and a 0.1-2s temporal resolution in living cells with simple off-the-shelf total internal reflection fluorescence (TIRF) equipment. However, SIMBA and its derivatives are limited by the requirement for dual-channel dataset or single-channel dataset with special design, the time-consuming calculation for extended field of view and the lack of real-time visualization tool. Here, we propose a universal and accelerated SIMBA ImageJ plug-in, Live-SIMBA, for time-series analysis in living cells. Live-SIMBA circumvents the requirement of dual-channel dataset using intensity-based sampling algorithm and improves the computing speed using multi-core parallel computing technique. Live-SIMBA also better resolves the weak signals inside the specimens with adjustable background estimation and distance-threshold filter. With improved fidelity on reconstructed structures, greatly accelerated computation, and real-time visualization, Live-SIMBA demonstrates its extended capabilities in live-cell super-resolution imaging.