Real Time Multi-modal Super-Resolution Microscopy through Super- Resolution Radial Fluctuations (SRRF-Stream)

摘要

Super-resolution radial fluctuations (SRRF) is a combination of temporal fluctuation analysis and localizationmicroscopy. One of the key differences between SRRF and other super-resolution methods is its applicability to live-celldynamics because it functions across a very wide range of fluorophore densities and excitation powers. SRRF is appliedto data from imaging modes which include widefield, TIRF and confocal, where short frame bursts (e.g. 50 frames) canbe processed to deliver spatial resolution enhancements similar to or better than structured illumination microscopy(SIM). On the other hand, with sparse data e.g. stochastic optical reconstruction microscopy (STORM), SRRF candeliver resolution similar to Gaussian fitting localization methods. Thus, SRRF could provide a route to super-resolutionwithout the need for specialized optical hardware, exotic probes or very high-power densities. We present a fast GPUbasedSRRF algorithm termed “SRRF-Stream” and apply it to imagery from an iXon EMCCD coupled to a multi-modalimaging platform, Dragonfly. The new implementation is >300 times faster than the standard CPU version running on anIntel Xeon 3.5GHz 4 core processor, and > 20 times faster than the NanoJ GPU implementation, while also beingintegrated with acquisition for real time use. In this paper we explore the image resolution and quality with EMCCD andsCMOS cameras and various fluorophores including fluorescent proteins and organic dyes.