scholarly journals Extending resolution of structured illumination microscopy with sparse deconvolution

2021 ◽  
Author(s):  
Weisong Zhao ◽  
Shiqun Zhao ◽  
Liuju Li ◽  
Xiaoshuai Huang ◽  
Shijia Xing ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Super Resolution ◽  
Live Cell ◽  
Frame Rate ◽  
Live Cells ◽  
Photon Flux ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Spatiotemporal Resolution

Abstract The spatial resolutions of live-cell super-resolution microscopes are limited by the maximum collected photon flux. Taking advantage of a priori knowledge of the sparsity and continuity of biological structures, we develop a deconvolution algorithm that further extends the resolution of super-resolution microscopes under the same photon budgets by nearly twofold. As a result, sparse structured illumination microscopy (Sparse-SIM) achieves ~60 nm resolution at a 564 Hz frame rate, allowing it to resolve intricate structural intermediates, including small vesicular fusion pores, ring-shaped nuclear pores formed by different nucleoporins, and relative movements between the inner and outer membranes of mitochondria in live cells. Likewise, sparse deconvolution can be used to increase the three-dimensional resolution and contrast of spinning-disc confocal-based SIM (SD-SIM), and operates under conditions with the insufficient signal-to-noise-ratio, all of which allows routine four-color, three-dimensional, ~90 nm resolution live-cell super-resolution imaging. Overall, sparse deconvolution may be a general tool to push the spatiotemporal resolution limits of live-cell fluorescence microscopy.

scholarly journals Super-Resolution Imaging by Dual Iterative Structured Illumination Microscopy

2021 ◽  
Author(s):  
Anna Loeschberger ◽  
Yauheni Novikau ◽  
Ralf Netz ◽  
Marie-Christin Spindler ◽  
Ricardo Benavente ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Brain Slices ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Living Cells ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Spatiotemporal Resolution ◽  
Coated Pits ◽  
Resolution Imaging

Three-dimensional (3D) multicolor super-resolution imaging in the 50-100 nm range in fixed and living cells remains challenging. We extend the resolution of structured illumination microscopy (SIM) by an improved nonlinear iterative reconstruction algorithm that enables 3D multicolor imaging with improved spatiotemporal resolution at low illumination intensities. We demonstrate the performance of dual iterative SIM (diSIM) imaging cellular structures in fixed cells including synaptonemal complexes, clathrin coated pits and the actin cytoskeleton with lateral resolutions of 60-100 nm with standard fluorophores. Furthermore, we visualize dendritic spines in 70 micrometer thick brain slices with an axial resolution < 200 nm. Finally, we image dynamics of the endoplasmatic reticulum and microtubules in living cells with up to 255 frames/s.

scholarly journals Live Cell Imaging of Single RNA Molecules with Fluorogenic Mango II Arrays

2019 ◽  
Author(s):  
Adam D. Cawte ◽  
Peter J. Unrau ◽  
David S. Rueda
Keyword(s):  
Single Molecule ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Live Cell ◽  
Rna Aptamers ◽  
Live Cells ◽  
Structured Illumination ◽  
Rna Molecules ◽  
Cellular Processes ◽  
Rna Imaging

AbstractRNA molecules play vital roles in many cellular processes. Visualising their dynamics in live cells at single-molecule resolution is essential to elucidate their role in RNA metabolism. RNA aptamers, such as Spinach and Mango, have recently emerged as a powerful background-free technology for live-cell RNA imaging due to their fluorogenic properties upon ligand binding. Here, we report a novel array of Mango II aptamers for RNA imaging in live and fixed cells with high contrast and single-molecule sensitivity. Direct comparison of Mango II and MS2-tdMCP-mCherry dual-labelled mRNAs show marked improvements in signal to noise ratio using the fluorogenic Mango aptamers. Using both coding (β-actin mRNA) and long non-coding (NEAT1) RNAs, we show that the Mango array does not affect cellular localisation. Additionally, we can track single mRNAs for extended time periods, likely due to fluorophore exchange. This property makes the arrays readily compatible with structured illumination super-resolution microscopy.

scholarly journals Fringe optimization for structured illumination super-resolution microscope with digital micromirror device

2019 ◽  
Vol 12 (03) ◽  
pp. 1950014 ◽  
Author(s):  
Xibin Yang ◽  
Qian Zhu ◽  
Zhenglong Sun ◽  
Gang Wen ◽  
Xin Jin ◽  
...  
Keyword(s):  
Modulation Depth ◽  
Low Cost ◽  
Super Resolution ◽  
Fluorescent Labeling ◽  
Live Cells ◽  
Digital Micromirror Device ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Wide Range ◽  
Fringe Patterns

Structured illumination microscopy (SIM) is a promising super-resolution technique for imaging subcellular structures and dynamics due to its compatibility with most commonly used fluorescent labeling methods. Structured illumination can be obtained by either laser interference or projection of fringe patterns. Here, we proposed a fringe projector composed of a compact multi-wavelength LEDs module and a digital micromirror device (DMD) which can be directly attached to most commercial inverted fluorescent microscopes and update it into a SIM system. The effects of the period and duty cycle of fringe patterns on the modulation depth of the structured light field were studied. With the optimized fringe pattern, [Formula: see text] resolution improvement could be obtained with high-end oil objectives. Multicolor imaging and dynamics of subcellular organelles in live cells were also demonstrated. Our method provides a low-cost solution for SIM setup to expand its wide range of applications to most research labs in the field of life science and medicine.

scholarly journals High-speed multiplane structured illumination microscopy of living cells using an image-splitting prism

Nanophotonics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 143-148
Author(s):  
Adrien Descloux ◽  
Marcel Müller ◽  
Vytautas Navikas ◽  
Andreas Markwirth ◽  
Robin van den Eynde ◽  
...  
Keyword(s):  
High Speed ◽  
Three Dimensional ◽  
Super Resolution ◽  
Optical Element ◽  
Spatial Light Modulators ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Image Stack ◽  
Light Modulators ◽  
3D Information

AbstractSuper-resolution structured illumination microscopy (SR-SIM) can be conducted at video-rate acquisition speeds when combined with high-speed spatial light modulators and sCMOS cameras, rendering it particularly suitable for live-cell imaging. If, however, three-dimensional (3D) information is desired, the sequential acquisition of vertical image stacks employed by current setups significantly slows down the acquisition process. In this work, we present a multiplane approach to SR-SIM that overcomes this slowdown via the simultaneous acquisition of multiple object planes, employing a recently introduced multiplane image splitting prism combined with high-speed SIM illumination. This strategy requires only the introduction of a single optical element and the addition of a second camera to acquire a laterally highly resolved 3D image stack. We demonstrate the performance of multiplane SIM by applying this instrument to imaging the dynamics of mitochondria in living COS-7 cells.

scholarly journals Obtaining 3D Super-resolution Information from 2D Super-resolution Images through a 2D-to-3D Transformation Algorithm

2017 ◽  
Author(s):  
Andrew Ruba ◽  
Wangxi Luo ◽  
Joseph Kelich ◽  
Weidong Yang
Keyword(s):  
Single Molecule ◽  
Rotational Symmetry ◽  
Three Dimensional ◽  
Super Resolution ◽  
Live Cells ◽  
Limited Information ◽  
Spatiotemporal Resolution ◽  
Superresolution Microscopy ◽  
Localization Analysis ◽  
Probability Information

AbstractCurrently, it is highly desirable but still challenging to obtain three-dimensional (3D) superresolution information of structures in fixed specimens as well as dynamic processes in live cells with a high spatiotemporal resolution. Here we introduce an approach, without using 3D superresolution microscopy or real-time 3D particle tracking, to achieve 3D sub-diffraction-limited information with a spatial resolution of ≤ 1 nm. This is a post-localization analysis that transforms 2D super-resolution images or 2D single-molecule localization distributions into their corresponding 3D spatial probability information. The method has been successfully applied to obtain structural and functional information for 25-300 nm sub-cellular organelles that have rotational symmetry. In this article, we will provide a comprehensive analysis of this method by using experimental data and computational simulations.

scholarly journals GPU-accelerated real-time reconstruction in Python of three-dimensional datasets from structured illumination microscopy with hexagonal patterns

2021 ◽  
Vol 379 (2199) ◽  
Author(s):  
Hai Gong ◽  
Wenjun Guo ◽  
Mark A. A. Neil
Keyword(s):  
Moving Objects ◽  
Three Dimensional ◽  
Simulated Data ◽  
Super Resolution ◽  
Light Sheet ◽  
Processing Unit ◽  
Structured Illumination ◽  
Reconstruction Algorithms ◽  
Structured Illumination Microscopy ◽  
Axially Moving

We present a structured illumination microscopy system that projects a hexagonal pattern by the interference among three coherent beams, suitable for implementation in a light-sheet geometry. Seven images acquired as the illumination pattern is shifted laterally can be processed to produce a super-resolved image that surpasses the diffraction-limited resolution by a factor of over 2 in an exemplar light-sheet arrangement. Three methods of processing data are discussed depending on whether the raw images are available in groups of seven, individually in a stream or as a larger batch representing a three-dimensional stack. We show that imaging axially moving samples can introduce artefacts, visible as fine structures in the processed images. However, these artefacts are easily removed by a filtering operation carried out as part of the batch processing algorithm for three-dimensional stacks. The reconstruction algorithms implemented in Python include specific optimizations for calculation on a graphics processing unit and we demonstrate its operation on experimental data of static objects and on simulated data of moving objects. We show that the software can process over 239 input raw frames per second at 512 × 512 pixels, generating over 34 super-resolved frames per second at 1024 × 1024 pixels. This article is part of the Theo Murphy meeting issue ‘Super-resolution structured illumination microscopy (part 1)’.

scholarly journals Advances in High-Speed Structured Illumination Microscopy

2021 ◽  
Vol 9 ◽  
Author(s):  
Tianyu Zhao ◽  
Zhaojun Wang ◽  
Tongsheng Chen ◽  
Ming Lei ◽  
Baoli Yao ◽  
...  
Keyword(s):  
Live Cell Imaging ◽  
High Speed ◽  
Cell Imaging ◽  
Super Resolution ◽  
Live Cell ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Light Power ◽  
Recent Developments ◽  
Super Resolution Microscopy

Super-resolution microscopy surpasses the diffraction limit to enable the observation of the fine details in sub-cellular structures and their dynamics in diverse biological processes within living cells. Structured illumination microscopy (SIM) uses a relatively low illumination light power compared with other super-resolution microscopies and has great potential to meet the demands of live-cell imaging. However, the imaging acquisition and reconstruction speeds limit its further applications. In this article, recent developments all targeted at improving the overall speed of SIM are reviewed. These comprise both hardware and software improvements, which include a reduction in the number of raw images, GPU acceleration, deep learning and the spatial domain reconstruction. We also discuss the application of these developments in live-cell imaging.

scholarly journals Imaging tissues and cells beyond the diffraction limit with structured illumination microscopy and Bayesian image reconstruction

2018 ◽  
Author(s):  
Jakub Pospíšil ◽  
Tomáš Lukeš ◽  
Justin Bendesky ◽  
Karel Fliegel ◽  
Kathrin Spendier ◽  
...  
Keyword(s):  
High Resolution ◽  
Open Source ◽  
Fluorescent Protein ◽  
Super Resolution ◽  
Live Cells ◽  
Structured Illumination ◽  
Optical Sectioning ◽  
Structured Illumination Microscopy ◽  
Raw Data ◽  
Bayesian Image Reconstruction

AbstractBackgroundStructured illumination microscopy (SIM) is a family of methods in optical fluorescence microscopy that can achieve both optical sectioning and super-resolution effects. SIM is a valuable method for high resolution imaging of fixed cells or tissues labeled with conventional fluorophores, as well as for imaging the dynamics of live cells expressing fluorescent protein constructs. In SIM, one acquires a set of images with shifting illumination patterns. This set of images is subsequently treated with image analysis algorithms to produce an image with reduced out-of-focus light (optical sectioning) and/or with improved resolution (super-resolution).FindingsFive complete and freely available SIM datasets are presented including raw and analyzed data. We report methods for image acquisition and analysis using open source software along with examples of the resulting images when processed with different methods. We processed the data using established optical sectioning SIM and super-resolution SIM methods, and with newer Bayesian restoration approaches which we are developing.ConclusionVarious methods for SIM data acquisition and processing are actively being developed, but complete raw data from SIM experiments is not typically published. Publicly available, high quality raw data with examples of processed results will aid researchers when developing new methods in SIM. Biologists will also find interest in the high-resolution images of animal tissues and cells we acquired. All of the data was processed with SIMToolbox, an open source and freely available software solution for SIM.

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