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.

ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging.

UNLABELLED: ThunderSTORM is an open-source, interactive and modular plug-in for ImageJ designed for automated processing, analysis and visualization of data acquired by single-molecule localization microscopy methods such as photo-activated localization microscopy and stochastic optical reconstruction microscopy. ThunderSTORM offers an extensive collection of processing and post-processing methods so that users can easily adapt the process of analysis to their data. ThunderSTORM also offers a set of tools for creation of simulated data and quantitative performance evaluation of localization algorithms using Monte Carlo simulations. AVAILABILITY AND IMPLEMENTATION: ThunderSTORM and the online documentation are both freely accessible at https://code.google.com/p/thunder-storm/.

A benchmark for comparison of cell tracking algorithms.

MOTIVATION: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. RESULTS: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. AVAILABILITY AND IMPLEMENTATION: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.

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.

High density 3D localization microscopy using sparse support recovery.

Single-molecule localization microscopy methods offer high spatial resolution, but they are not always suitable for live cell imaging due to limited temporal resolution. One strategy is to increase the density of photoactivated molecules present in each image, however suitable analysis algorithms for such data are still lacking. We present 3denseSTORM, a new algorithm for localization microscopy which is able to recover 2D or 3D super-resolution images from a sequence of diffraction limited images with high densities of photoactivated molecules. The algorithm is based on sparse support recovery and uses a Poisson noise model, which becomes critical in low-light conditions. For 3D data reconstruction we use the astigmatism and biplane imaging methods. We derive the theoretical resolution limits of the method and show examples of image reconstructions in simulations and in real 2D and 3D biological samples. The method is suitable for fast image acquisition in densely labeled samples and helps facilitate live cell studies with single molecule localization microscopy.

Flexible structured illumination microscope with a programmable illumination array.

Structured illumination microscopy (SIM) has grown into a family of methods which achieve optical sectioning, resolution beyond the Abbe limit, or a combination of both effects in optical microscopy. SIM techniques rely on illumination of a sample with patterns of light which must be shifted between each acquired image. The patterns are typically created with physical gratings or masks, and the final optically sectioned or high resolution image is obtained computationally after data acquisition. We used a flexible, high speed ferroelectric liquid crystal microdisplay for definition of the illumination pattern coupled with widefield detection. Focusing on optical sectioning, we developed a unique and highly accurate calibration approach which allowed us to determine a mathematical model describing the mapping of the illumination pattern from the microdisplay to the camera sensor. This is important for higher performance image processing methods such as scaled subtraction of the out of focus light, which require knowledge of the illumination pattern position in the acquired data. We evaluated the signal to noise ratio and the sectioning ability of the reconstructed images for several data processing methods and illumination patterns with a wide range of spatial frequencies. We present our results on a thin fluorescent layer sample and also on biological samples, where we achieved thinner optical sections than either confocal laser scanning or spinning disk microscopes.

Why has nature invented three stop codons of DNA and only one start codon?

We examine the standard genetic code with three stop codons. Assuming that the synchronization period of length 3 in DNA or RNA is violated during the transcription or translation processes, the probability of reading a frameshifted stop codon is higher than if the code would have only one stop codon. Consequently, the synthesis of RNA or proteins will soon terminate. In this way, cells do not produce undesirable proteins and essentially save energy. This hypothesis is tested on the AT-rich Drosophila genome, where the detection of frameshifted stop codons is even higher than the theoretical value. Using the binomial theorem, we establish the probability of reading a frameshifted stop codon within n steps. Since the genetic code is largely redundant, there is still space for some hidden secondary functions of this code. In particular, because stop codons do not contain cytosine, random C → U and C → T mutations in the third position of codons increase the number of hidden frameshifted stops and simultaneously the same amino acids are coded. This evolutionary advantage is demonstrated on the genomes of several simple species, e.g. Escherichia coli.

Minimizing detection errors in single molecule localization microscopy.

Fluorescence microscopy using single molecule imaging and localization (PALM, STORM, and similar approaches) has quickly been adopted as a convenient method for obtaining multicolor, 3D superresolution images of biological samples. Using an approach based on extensive Monte Carlo simulations, we examined the performance of various noise reducing filters required for the detection of candidate molecules. We determined a suitable noise reduction method and derived an optimal, nonlinear threshold which minimizes detection errors introduced by conventional algorithms. We also present a new technique for visualization of single molecule localization microscopy data based on adaptively jittered 2D histograms. We have used our new methods to image both Atto565-phalloidin labeled actin in fibroblast cells, and mCitrine-erbB3 expressed in A431 cells. The enhanced methods developed here were crucial in processing the data we obtained from these samples, as the overall signal to noise ratio was quite low.