Tomas Lukes

Complementarity of PALM and SOFI for super-resolution live-cell imaging of focal adhesions.

Live-cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenge for super-resolution microscopy. Here we address this important issue by combining photoactivated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed-cell focal adhesion images, we investigate the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework is used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualize the dynamics of focal adhesions, and reveal local mean velocities around 190 nm min-1. The complementarity of PALM and SOFI is assessed in detail with a methodology that integrates a resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as fluorophore densities and photoactivation or photoswitching kinetics.

SIMToolbox: a MATLAB toolbox for structured illumination fluorescence microscopy.

UNLABELLED SIMToolbox is an open-source, modular set of functions for MATLAB equipped with a user-friendly graphical interface and designed for processing two-dimensional and three-dimensional data acquired by structured illumination microscopy (SIM). Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured illumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination. AVAILABILITY AND IMPLEMENTATION SIMToolbox, example data and the online documentation are freely accessible at http://mmtg.fel.cvut.cz/SIMToolbox. CONTACT ghagen@uccs.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Three-dimensional super-resolution structured illumination microscopy with maximum a posteriori probability image estimation

We introduce and demonstrate a new high performance image reconstruction method for super-resolution structured illumination microscopy based on maximum a posteriori probability estimation (MAP-SIM). Imaging performance is demonstrated on a variety of fluorescent samples of different thickness, labeling density and noise levels. The method provides good suppression of out of focus light, improves spatial resolution, and allows reconstruction of both 2D and 3D images of cells even in the case of weak signals. The method can be used to process both optical sectioning and super-resolution structured illumination microscopy data to create high quality super-resolution images.

SOFI Simulation Tool: A Software Package for Simulating and Testing Super-Resolution Optical Fluctuation Imaging

Super-resolution optical fluctuation imaging (SOFI) allows one to perform sub-diffraction fluorescence microscopy of living cells. By analyzing the acquired image sequence with an advanced correlation method, i.e. a high-order cross-cumulant analysis, super-resolution in all three spatial dimensions can be achieved. Here we introduce a software tool for a simple qualitative comparison of SOFI images under simulated conditions considering parameters of the microscope setup and essential properties of the biological sample. This tool incorporates SOFI and STORM algorithms, displays and describes the SOFI image processing steps in a tutorial-like fashion. Fast testing of various parameters simplifies the parameter optimization prior to experimental work. The performance of the simulation tool is demonstrated by comparing simulated results with experimentally acquired data.

Comparison of image reconstruction methods for structured illumination microscopy

Structured illumination microscopy (SIM) is a recent microscopy technique that enables one to go beyond the diffraction limit using patterned illumination. The high frequency information is encoded through aliasing into the observed image. By acquiring multiple images with different illumination patterns aliased components can be separated and a highresolution image reconstructed. Here we investigate image processing methods that perform the task of high-resolution image reconstruction, namely square-law detection, scaled subtraction, super-resolution SIM (SR-SIM), and Bayesian estimation. The optical sectioning and lateral resolution improvement abilities of these algorithms were tested under various noise level conditions on simulated data and on fluorescence microscopy images of a pollen grain test sample and of a cultured cell stained for the actin cytoskeleton. In order to compare the performance of the algorithms, the following objective criteria were evaluated: Signal to Noise Ratio (SNR), Signal to Background Ratio (SBR), circular average of the power spectral density and the S3 sharpness index. The results show that SR-SIM and Bayesian estimation combine illumination patterned images more effectively and provide better lateral resolution in exchange for more complex image processing. SR-SIM requires one to precisely shift the separated spectral components to their proper positions in reciprocal space. High noise levels in the raw data can cause inaccuracies in the shifts of the spectral components which degrade the super-resolved image. Bayesian estimation has proven to be more robust to changes in noise level and illumination pattern frequency.

Performance evaluation of image quality metrics with respect to their use for super-resolution enhancement

Super-resolution (SR) methods enable to obtain high resolution image (HR) from multiple low resolution input images (LR) of the same scene. Development of SR algorithms for real applications require reliable image quality metrics for performance comparison of various SR methods. In this paper, six standard SR methods were implemented and subjective image quality assessment was performed to evaluate the visual quality of the enhanced images. Nine image quality metrics were examined with respect to their ability to characterize the observed subjective image quality of SR enhancement.

Quantitative super-resolution single molecule microscopy dataset of YFP-tagged growth factor receptors

Abstract Background Super-resolution single molecule localization microscopy (SMLM) is a method for achieving resolution beyond the classical limit in optical microscopes (approx. 200 nm laterally). Yellow fluorescent protein (YFP) has been used for super-resolution single molecule localization microscopy, but less frequently than other fluorescent probes. Working with YFP in SMLM is a challenge because a lower number of photons are emitted per molecule compared with organic dyes, which are more commonly used. Publically available experimental data can facilitate development of new data analysis algorithms. Findings Four complete, freely available single molecule super-resolution microscopy datasets on YFP-tagged growth factor receptors expressed in a human cell line are presented, including both raw and analyzed data. We report methods for sample preparation, for data acquisition, and for data analysis, as well as examples of the acquired images. We also analyzed the SMLM datasets using a different method: super-resolution optical fluctuation imaging (SOFI). The 2 modes of analysis offer complementary information about the sample. A fifth single molecule super-resolution microscopy dataset acquired with the dye Alexa 532 is included for comparison purposes. Conclusions This dataset has potential for extensive reuse. Complete raw data from SMLM experiments have typically not been published. The YFP data exhibit low signal-to-noise ratios, making data analysis a challenge. These datasets will be useful to investigators developing their own algorithms for SMLM, SOFI, and related methods. The data will also be useful for researchers investigating growth factor receptors such as ErbB3.

Combined multi-plane phase retrieval and super-resolution optical fluctuation imaging for 4D cell microscopy

Super-resolution fluorescence microscopy provides unprecedented insight into cellular and subcellular structures. However, going ‘beyond the diffraction barrier’ comes at a price, since most far-field super-resolution imaging techniques trade temporal for spatial super-resolution. We propose the combination of a novel label-free white light quantitative phase imaging with fluorescence to provide high-speed imaging and spatial super-resolution. The non-iterative phase retrieval relies on the acquisition of single images at each z-location and thus enables straightforward 3D phase imaging using a classical microscope. We realized multi-plane imaging using a customized prism for the simultaneous acquisition of eight planes. This allowed us to not only image live cells in 3D at up to 200 Hz, but also to integrate fluorescence super-resolution optical fluctuation imaging within the same optical instrument. The 4D microscope platform unifies the sensitivity and high temporal resolution of phase imaging with the specificity and high spatial resolution of fluorescence microscopy.By combining the sensitivity and high temporal resolution of phase imaging with the specificity and high spatial resolution of fluorescence microscopy, a 4D microscope is demonstrated that visualizes in three dimensions the fast cellular processes in living cells at up to 200 Hz.

Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging

Quantitative approaches for characterizing molecular organization of cell membrane molecules under physiological and pathological conditions profit from recently developed super-resolution imaging techniques. Current tools employ statistical algorithms to determine clusters of molecules based on single-molecule localization microscopy (SMLM) data. These approaches are limited by the ability of SMLM techniques to identify and localize molecules in densely populated areas and experimental conditions of sample preparation and image acquisition. We have developed a robust, model-free, quantitative clustering analysis to determine the distribution of membrane molecules that excels in densely labeled areas and is tolerant to various experimental conditions, i.e. multiple-blinking or high blinking rates. The method is based on a TIRF microscope followed by a super-resolution optical fluctuation imaging (SOFI) analysis. The effectiveness and robustness of the method is validated using simulated and experimental data investigating nanoscale distribution of CD4 glycoprotein mutants in the plasma membrane of T cells.The ability to quantify the organization of cell membrane molecules is limited by the density of labeling and experimental conditions. Here, the authors use super-resolution optical fluctuation (SOFI) for molecular density and clustering analyses, and investigate nanoscale distribution of CD4 glycoprotein.

Super-resolution fight club: A broad assessment of 2D & 3D single-molecule localization microscopy software

With the widespread uptake of 2D and 3D single molecule localization microscopy, a large set of different data analysis packages have been developed to generate super-resolution images. To guide researchers on the optimal analytical software for their experiments, we have designed, in a large community effort, a competition to extensively characterise and rank these options. We generated realistic simulated datasets for popular imaging modalities – 2D, astigmatic 3D, biplane 3D, and double helix 3D – and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D single molecule localization microscopy software, provides a holistic view of how the latest 2D and 3D single molecule localization software perform in realistic conditions, and ultimately provides insight into the current limits of the field.