Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations

Abstract

Despite significant progress, high-speed live-cell super-resolution studies remain limited to specialized optical setups, generally requiring intense phototoxic illumination. Here, we describe a new analytical approach, super-resolution radial fluctuations (SRRF), provided as a fast graphics processing unit-enabled ImageJ plugin. 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. Meanwhile, for data sets similar to those obtained in PALM or STORM imaging, SRRF achieves resolutions approaching those of standard single-molecule localization analysis. 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. We demonstrate this by super-resolution live-cell imaging over timescales ranging from minutes to hours.

Introduction

Camera 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. 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). SIM, however, requires expensive additional optical components to achieve resolutions on the order of ∼150 nm. As a consequence, recent years have seen considerable focus put on adapting PALM- and STORM-like approaches to allow live-cell nanoscopy. 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. The analytical detection and localization of these individually resolvable fluorophores populates a highly accurate map of fluorophore positions1,2.

Live-cell SMLM depends on the capacity to detect enough fluorophores to super-resolve structures, in a time small enough to minimize motion-blur artefacts4,5,6. A recent analysis of >30 software packages designed for processing SMLM data7 shows that these algorithms are capable of approaching the Cramér–Rao theoretical precision limit8when the distance between emitting fluorophores is >∼1.5 μm. 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 precision7,9. Nonetheless, at smaller separations (or equivalently, higher densities), even these algorithms suffer from substantial imprecision, poor recall and artefacts7. 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. This task is difficult or impossible when dealing with dynamic processes in cells that frequently present a large heterogeneity in fluorophore densities. Three algorithms in particular have overcome this maximum density limit by circumventing the requirement for individual fluorophore localizations. These are deconSTORM10, which reconstructs super-resolution images by averaging extensively deconvolved images of sub-populations of fluorophores; 3B (ref. 11), which uses Bayesian analysis; and super-resolution optical fluctuation imaging (SOFI)12, which uses higher-order statistical analysis of temporal fluorophore intensity fluctuations.

Here, we present a novel analytical approach, super-resolution radial fluctuations (SRRF), which follows similar principles to deconSTORM, 3B and SOFI, where analysis of a sequence of images acquired in a standard widefield or total internal reflection fluorescence (TIRF) microscope directly generates a super-resolution reconstruction without fluorophore detection and localization. However, contrary to other approaches, SRRF provides a single analytical framework that enables high-fidelity reconstructions for a wide variety of data sets, with the final resolution of the SRRF image dictated by the properties of the data set analysed. For example, super-resolution reconstructions at a resolution of at least 50 nm, comparable to current benchmark localization algorithms, are obtained for SMLM data. High-resolution reconstructions are also obtained for confocal microscopy data (80–98 nm) and widefield LED-illuminated data (103–106 nm). Moreover, super-resolution information at a temporal resolution of 1 s and a spatial resolution down to 60 nm can be extracted from live samples using conventional fluorophores, low-intensity illumination and as few as 100 raw frames.

Results

SRRF algorithm

The SRRF algorithm is provided in the form of the NanoJ-SRRF software package (Supplementary Software), a freely available open-source plugin for the popular ImageJ or Fiji image analysis software13. SRRF is a fast, threshold free algorithm comprising temporal analysis of a sub-pixel geometrical measure applied to an image sequence (Fig. 1,Supplementary Note 1, Supplementary Figs 1 and 2). SRRF assumes the image is formed of point sources convolved with a point spread function (PSF) that displays a higher degree of local symmetry than the background. This assumption draws on similar geometric principles used by one of the most accurate low-density SMLM methods, radial symmetry7,14,15. Rather than detect and localize single radially symmetric points, however, for each frame in an image sequence SRRF calculates the degree of local gradient convergence (referred to here as radiality) across the entire frame, on a sub-pixel basis. Thus, information in the gradient field, which would otherwise be discarded by a localization technique, is maintained for further temporal analysis. In the case of a single fluorophore this results in a continuous conical distribution with a significantly reduced full-width-half-maximum (FWHM) that can be displayed on an up-sampled pixel grid (Fig. 1a). This radiality distribution is capable of distinguishing two Gaussian PSFs separated by ∼0.7 times the Gaussian FWHM (1.7 times the PSF s.d. σ;Supplementary Movie 1). In addition, the FWHM of this radiality distribution can be adjusted by changing the radius at which the gradient convergence is measured. The distribution is independent of PSF intensity and is robust to changes in PSF size. Furthermore, the radiality distribution preserves deformations of the PSF, for example astigmatic PSFs and polarization effects, without a displacement in the peak position. The radiality map of a full image will, however, include a number of non-fluorophore-associated radiality peaks, as transient local radial symmetries can occur in image noise. Weighting the radiality map by the fluorescence intensity of the acquired frame and local image gradient magnitudes allows the map to be de-noised without a significant increase in the FWHM of the radiality peaks. Further de-noising and contrast enhancement can be achieved by applying temporal analysis to generate a single super-resolution frame from the resulting radiality image sequence (Fig. 1b), which can also further enhance the resolution (Supplementary Note 1). This enhancement is based on two concepts. (a) Noise-induced radiality peaks are uncorrelated in time, thus a pixel-wise temporal correlation at their location will approach zero as the number of imaged frames increases. (b) The highest degree of temporal correlation is located at the centre of the radiality peaks generated by fluorophore signal. As in the case of SOFI12, higher-order temporal cumulants can be calculated on the radiality map further enhancing contrast and, in the case of low-density data sets, increasing resolution by reducing the FWHM of the radiality distribution. However, compared with SOFI, the undesirable non-linear response to brightness at higher-order cumulants16 is to a large extent alleviated in SRRF since the peak radiality of all fluorophores is similar and independent of the fluorophore brightness.

Figure 1: The SRRF algorithm.

(a) Spatial analysis. Left: 3D surface plot of a pixelated simulated widefield PSF. Middle: surface plot of the gradient magnitude (arrows indicate direction). Right: 3D surface plot of the measured radiality PSF. (b) Temporal analysis. Left: a 100 frame simulation of a pair of fluctuating fluorophores separated by the sigma (σ) of the widefield PSF (135 nm). The true fluorophore positions are indicated in red and the pixelated simulated noisy intensity distribution is shown in grey. Middle: stack of radiality maps obtained by applying radiality to each image in the simulated image sequence. Right: SRRF image acquired by higher-order temporal analysis of the stack of radiality maps. Scale bar, 500 nm.