11), which uses Bayesian analysis and super-resolution optical fluctuation imaging (SOFI) 12, which uses higher-order statistical analysis of temporal fluorophore intensity fluctuations. These are deconSTORM 10, which reconstructs super-resolution images by averaging extensively deconvolved images of sub-populations of fluorophores 3B (ref. Three algorithms in particular have overcome this maximum density limit by circumventing the requirement for individual fluorophore localizations. This task is difficult or impossible when dealing with dynamic processes in cells that frequently present a large heterogeneity in fluorophore densities. As a consequence, it becomes critical to maintain the density of fluorophores actively emitting in each acquired frame within the boundaries of the analytical approach used. Nonetheless, at smaller separations (or equivalently, higher densities), even these algorithms suffer from substantial imprecision, poor recall and artefacts 7. Some specialized ‘high-density’ algorithms are capable of localizing fluorophores separated by distances in the range of 1.5 μm down to 0.15 μm at the cost of poorer precision 7, 9. A recent analysis of >30 software packages designed for processing SMLM data 7 shows that these algorithms are capable of approaching the Cramér–Rao theoretical precision limit 8 when the distance between emitting fluorophores is > ∼1.5 μm. Live-cell SMLM depends on the capacity to detect enough fluorophores to super-resolve structures, in a time small enough to minimize motion-blur artefacts 4, 5, 6. The analytical detection and localization of these individually resolvable fluorophores populates a highly accurate map of fluorophore positions 1, 2. These single-molecule localization microscopy (SMLM) approaches circumvent Abbe’s diffraction limit through the acquisition of a large sequence of frames (typically thousands), each containing a small population of transiently emitting non-overlapping fluorophores. As a consequence, recent years have seen considerable focus put on adapting PALM- and STORM-like approaches to allow live-cell nanoscopy. SIM, however, requires expensive additional optical components to achieve resolutions on the order of ∼150 nm. Comparably, structured illumination microscopy (SIM) 3, is an attractive alternative approach for live-cell super-resolution due to the reduced illumination requirements, fast acquisition rates and compatibility with conventional fluorophores such as green-fluorescent protein (GFP). Similar content being viewed by othersĬamera based super-resolution approaches such as photoactivated localization microscopy (PALM) 1 and Stochastic Optical Reconstruction Microscopy (STORM) 2 have become well-established methods for structural cell biology studies, achieving lateral resolutions under 30 nm. We demonstrate this by super-resolution live-cell imaging over timescales ranging from minutes to hours. The broad applicability of SRRF and its performance at low signal-to-noise ratios allows super-resolution using modern widefield, confocal or TIRF microscopes with illumination orders of magnitude lower than methods such as PALM, STORM or STED. Meanwhile, for data sets similar to those obtained in PALM or STORM imaging, SRRF achieves resolutions approaching those of standard single-molecule localization analysis. In the most challenging data sets for super-resolution, such as those obtained in low-illumination live-cell imaging with GFP, we show that SRRF is generally capable of achieving resolutions better than 150 nm. Here, we describe a new analytical approach, super-resolution radial fluctuations (SRRF), provided as a fast graphics processing unit-enabled ImageJ plugin. Despite significant progress, high-speed live-cell super-resolution studies remain limited to specialized optical setups, generally requiring intense phototoxic illumination.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |