Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ

Abstract

Super-resolved structured illumination microscopy (SR-SIM) is an important tool for fluorescence microscopy. SR-SIM microscopes perform multiple image acquisitions with varying illumination patterns, and reconstruct them to a super-resolved image. In its most frequent, linear implementation, SR-SIM doubles the spatial resolution. The reconstruction is performed numerically on the acquired wide-field image data, and thus relies on a software implementation of specific SR-SIM image reconstruction algorithms. We present fairSIM, an easy-to-use plugin that provides SR-SIM reconstructions for a wide range of SR-SIM platforms directly within ImageJ. For research groups developing their own implementations of super-resolution structured illumination microscopy, fairSIM takes away the hurdle of generating yet another implementation of the reconstruction algorithm. For users of commercial microscopes, it offers an additional, in-depth analysis option for their data independent of specific operating systems. As a modular, open-source solution, fairSIM can easily be adapted, automated and extended as the field of SR-SIM progresses.

Figure 1. Examples for intermediate SR-SIM results displayed as power spectra in frequency space.

(a) Visualization of the cross-correlation used for parameter estimation, with circles marking the low-frequency region excluded from the fit and the detected modulation frequency. The three insets on the upper left visualize the iterative sub-pixel fits and provide a quick feedback if the fit was successful. (b) Power spectrum of the complete, reassembled SR-SIM reconstruction of fluorescent beads (Fig. 3). The circular structure visible in the spectrum is located at ∼4.5 μm⁻¹, which coincides with the beads' size of 200 nm, and is thus expected. Scale bar, 2.5 μm⁻¹.

Figure 2. FairSIM reconstruction of data sets obtained on a simple SLM-based SR-SIM setup.

A glass surface with 200 nm Tetraspeck beads was used as test sample, excitated at 642 nm wavelength. In contrast to the (Wiener filtered) wide-field image (a), the SR-SIM reconstruction by fairSIM (b) yields clearly distinct beads. This can be found quantitatively from the cross-section plots, given for a single bead in c and two close-by beads (indistinguishable in wide-field mode) in d. A simplified sketch of the set-up used is given in e. Scale bar, 5 μm, inset 1.2 μm.

Figure 3. FairSIM reconstruction of data sets obtained on the GE Healthcare DeltaVision|OMX.

A glass surface with 200 nm Tetraspeck beads was used as a test sample, excitated at 642 nm wavelength. In contrast to the wide-field image (a) and its Wiener-filtered version (b), the 2D reconstruction by fairSIM (c) yields clearly distinct beads. The 3D SR-SIM reconstruction by SoftWORX (manufacturer's software) is provided in d for comparison. Please note that the 3D reconstruction is based on a larger amount of input data (complete z-stack), thus resulting in an improved signal-to-noise ratio. A quantitative comparison between all four images can be found as cross-section plots for a single bead in e and for two adjacent beads (indistinguishable in wide-field mode) in f. Scale bar, 5 μm, inset 1.2 μm.

Figure 4. SR-SIM measurement of an LSEC membrane stain obtained on a GE Healthcare DeltaVision|OMX.

Wide-field image (a), Wiener-filtered wide-field (b), single-slice/2D SR-SIM reconstruction by fairSIM (c) and full 3D SR-SIM reconstruction by SoftWORX (manufacturer's software) (d) are shown for comparison. Both SR-SIM reconstructions allow to clearly identify the cell's fenestrations (tiny membrane pores), which is not possible in the wide-field images. The single-slice reconstructions by fairSIM can be performed with a much lower number of input images, as no z-stack has to be acquired. Scale bar, 5 μm, inset 1.6 μm, cells stained with CellMask Deep Red.