α1-Antitrypsin Binds to the Glucocorticoid Receptor with Anti-Inflammatory and Antimycobacterial Significance in Macrophages.

α1-Antitrypsin (AAT), a serine protease inhibitor, is the third most abundant protein in plasma. Although the best-known function of AAT is irreversible inhibition of elastase, AAT is an acute-phase reactant and is increasingly recognized to have a panoply of other functions, including as an anti-inflammatory mediator and a host-protective molecule against various pathogens. Although a canonical receptor for AAT has not been identified, AAT can be internalized into the cytoplasm and is known to affect gene regulation. Because AAT has anti-inflammatory properties, we examined whether AAT binds the cytoplasmic glucocorticoid receptor (GR) in human macrophages. We report the finding that AAT binds to GR using several approaches, including coimmunoprecipitation, mass spectrometry, and microscale thermophoresis. We also performed in silico molecular modeling and found that binding between AAT and GR has a plausible stereochemical basis. The significance of this interaction in macrophages is evinced by AAT inhibition of LPS-induced NF-κB activation and IL-8 production as well as AAT induction of angiopoietin-like 4 protein, which are, in part, dependent on GR. Furthermore, this AAT-GR interaction contributes to a host-protective role against mycobacteria in macrophages. In summary, this study identifies a new mechanism for the gene regulation, anti-inflammatory, and host-defense properties of AAT.

Improving optical sectioning with spinning disk structured illumination microscopy.

A new fluorescence microscopy technique for optical sectioning was investigated. This technique combined Spinning Disk microscopy (SD) with Structured Illumination Microscopy (SIM), resulting in more background removal than either method. Spinning Disk Structured Illumination Microscopy (SD-SIM) resulted in higher signal-to-background ratios. The method detected and quantified a dendritic spine neck that was impossible to detect with either SIM or SD alone.

Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach.

Raman spectroscopy, combined with machine learning techniques, holds great promise for many applications as a rapid, sensitive, and label-free identification method. Such approaches perform well when classifying spectra of chemical species that were encountered during the training phase. That is, species that are known to the neural network. However, in real-world settings, such as in clinical applications, there will always be substances whose spectra have not yet been taken. When the neural network encounters these new species during the testing phase, the number of false positives becomes uncontrollable, limiting the usefulness of these techniques, especially in public safety applications. To overcome these barriers, we implemented the recently introduced Entropic Open Set and Objectosphere loss functions. To demonstrate the efficacy and efficiency of this approach, we compiled a database of hyperspectral Raman images of 40 chemical species separating them into three class categorizations. The known class consisted of 20 biologically relevant species comprising amino acids, the ignored class was 10 "irrelevant" species comprising bio-related chemicals, and the never seen before class was 10 various chemical species that the neural network had not seen before. We show that this approach not only enables the network to effectively separate the unknown species while preserving high accuracy on the known ones and reducing false positives but also performs better than the current gold standards in machine learning techniques. This opens the door to using Raman spectroscopy, combined with our novel machine learning algorithm, in a variety of practical applications. Availability and implementation: freely available on the web at https://github.com/BalytskyiJaroslaw/RamanOpenSet.git.

Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software.

With the widespread uptake of two-dimensional (2D) and three-dimensional (3D) single-molecule localization microscopy (SMLM), a large set of different data analysis packages have been developed to generate super-resolution images. In a large community effort, we designed a competition to extensively characterize and rank the performance of 2D and 3D SMLM software packages. 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 SMLM software and provides a holistic view of how the latest 2D and 3D SMLM packages perform in realistic conditions. This resource allows researchers to identify optimal analytical software for their experiments, allows 3D SMLM software developers to benchmark new software against the current state of the art, and provides insight into the current limits of the field.

Publisher Correction: Super-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy software.

In the version of this paper originally published, Figure 4a contained errors that were introduced during typesetting. The bottom 11° ThunderSTORM image is an xz view but was incorrectly labeled as xy, and the low x-axis value in the four line profiles was incorrectly set as -60 instead of -50. These errors have been corrected in the PDF and HTML versions of the paper.

Optimization of laser deposited silver nanoparticle substrates for surface-enhanced raman spectroscopy.

Creating sensitive and reproducible substrates for surface-enhanced Raman spectroscopy (SERS) has been a challenge in recent years. While SERS offers significant benefits over traditional Raman spectroscopy, certain hindrances have limited their commercial use, especially in settings where low limits of detection are necessary. We studied a variety of laser-deposited silver microstructured SERS substrates with different morphology as a means to optimize analyte detection. We found that using a 405 nm laser to deposit lines of silver nanoparticles (AgNPS) from a 2 mM silver nitrate and sodium citrate solution offered not only the best enhancement, but also the most consistent and reproducible substrates. We also found that the probability of deposition by laser was wavelength dependent and that longer wavelengths were less likely to deposit than shorter wavelengths. This work offers a better understanding of the laser deposition process as well as how substrate shape and structure effect SERS signals.

Giardia intestinalis mitosomes undergo synchronized fission but not fusion and are constitutively associated with the endoplasmic reticulum.

BACKGROUND: Mitochondria of opisthokonts undergo permanent fission and fusion throughout the cell cycle. Here, we investigated the dynamics of the mitosomes, the simplest forms of mitochondria, in the anaerobic protist parasite Giardia intestinalis, a member of the Excavata supergroup of eukaryotes. The mitosomes have abandoned typical mitochondrial traits such as the mitochondrial genome and aerobic respiration and their single role known to date is the formation of iron-sulfur clusters. RESULTS: In live experiments, no fusion events were observed between the mitosomes in G. intestinalis. Moreover, the organelles were highly prone to becoming heterogeneous. This suggests that fusion is either much less frequent or even absent in mitosome dynamics. Unlike in mitochondria, division of the mitosomes was absolutely synchronized and limited to mitosis. The association of the nuclear and the mitosomal division persisted during the encystation of the parasite. During the segregation of the divided mitosomes, the subset of the organelles between two G. intestinalis nuclei had a prominent role. Surprisingly, the sole dynamin-related protein of the parasite seemed not to be involved in mitosomal division. However, throughout the cell cycle, mitosomes associated with the endoplasmic reticulum (ER), although none of the known ER-tethering complexes was present. Instead, the ER-mitosome interface was occupied by the lipid metabolism enzyme long-chain acyl-CoA synthetase 4. CONCLUSIONS: This study provides the first report on the dynamics of mitosomes. We show that together with the loss of metabolic complexity of mitochondria, mitosomes of G. intestinalis have uniquely streamlined their dynamics by harmonizing their division with mitosis. We propose that this might be a strategy of G. intestinalis to maintain a stable number of organelles during cell propagation. The lack of mitosomal fusion may also be related to the secondary reduction of the organelles. However, as there are currently no reports on mitochondrial fusion in the whole Excavata supergroup, it is possible that the absence of mitochondrial fusion is an ancestral trait common to all excavates.

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.

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.

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.

Open-source microscope add-on for structured illumination microscopy.

Super-resolution techniques expand the abilities of researchers who have the knowledge and resources to either build or purchase a system. This excludes the part of the research community without these capabilities. Here we introduce the openSIM add-on to upgrade existing optical microscopes to Structured Illumination super-resolution Microscopes (SIM). The openSIM is an open-hardware system, designed and documented to be easily duplicated by other laboratories, making super-resolution modality accessible to facilitate innovative research. The add-on approach gives a performance improvement for pre-existing lab equipment without the need to build a completely new system.

Super-Resolution Imaging of Neuronal Structures with Structured Illumination Microscopy.

Super-resolution structured illumination microscopy (SR-SIM) is an optical fluorescence microscopy method which is suitable for imaging a wide variety of cells and tissues in biological and biomedical research. Typically, SIM methods use high spatial frequency illumination patterns generated by laser interference. This approach provides high resolution but is limited to thin samples such as cultured cells. Using a different strategy for processing raw data and coarser illumination patterns, we imaged through a 150-micrometer-thick coronal section of a mouse brain expressing GFP in a subset of neurons. The resolution reached 144 nm, an improvement of 1.7-fold beyond conventional widefield imaging.

Super-resolution imaging of neuronal structure with structured illumination microscopy.

Super-resolution structured illumination microscopy (SR-SIM) is a method in optical fluorescence microscopy which is suitable for imaging a wide variety of cells and tissues in biological and biomedical research. Typically, SIM methods use high spatial frequency illumination patterns generated by laser interference. This approach provides high resolution but is limited to thin samples such as cultured cells. Using a different strategy for processing the raw data and coarser illumination patterns, we imaged through a 150 µm thick coronal section of a mouse brain expressing GFP in a subset of neurons. The resolution reached 144 nm, an improvement of 1.7 fold beyond conventional widefield imaging.

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.

Flexible Multiplane Structured Illumination Microscope with a Four-Camera Detector.

Fluorescence microscopy provides an unparalleled tool for imaging biological samples. However, producing high-quality volumetric images quickly and without excessive complexity remains a challenge. Here, we demonstrate a four-camera structured illumination microscope (SIM) capable of simultaneously imaging multiple focal planes, allowing for the capture of 3D fluorescent images without any axial movement of the sample. This setup allows for the acquisition of many different 3D imaging modes, including 3D time lapses, high-axial-resolution 3D images, and large 3D mosaics. We imaged mitochondrial motions in live cells, neuronal structure in Drosophila larvae, and imaged up to 130 µm deep into mouse brain tissue. After SIM processing, the resolution measured using one of the four cameras improved from 357 nm to 253 nm when using a 30×/1.05 NA objective.

Replication timing of pseudo-NORs.

In mammalian cells, transcriptionally active ribosomal genes are replicated in the early S phase, and the silent ribosomal genes in the late S phase, though mechanisms of this timing remain unknown. UBF (Upstream Binding Factor), a DNA binding protein and component of the pol I transcription machinery, is considered to be responsible for the loose chromatin structure of the active rDNA. Here we question whether such structure alone can ensure early replication of DNA. We investigate this problem on the model of pseudo-NORs, the tandem arrays of heterologous DNA sequence with high affinity for UBF, introduced into human chromosomes. Such arrays are not transcribed, yet efficiently bind UBF and mimic the chromatin structure of active rDNA. In our study, a human derived stable cell line containing one pseudo-NOR on the chromosome 10 was transiently transfected with UBF-GFP and PCNA-RFP, which allowed us to observe in vivo the growth of pseudo-NORs resulted from their replication. We found that replication of pseudo-NORs is not restricted to the early S phase, but continues in the late S phase at a significant level. These results were confirmed in the experiments with incorporation of thymidin analog EdU and BrdU ChIP assay. Similar results were obtained with another cell line containing pseudo-NOR on the chromosome 7. Our data indicate that the specific loose structure of chromatin, produced by the architect protein UBF, is not sufficient for the early replication.

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.

Self-Supervised Poisson-Gaussian Denoising.

We extend the blindspot model for self-supervised denoising to handle Poisson-Gaussian noise and introduce an improved training scheme that avoids hyperparameters and adapts the denoiser to the test data. Self-supervised models for denoising learn to denoise from only noisy data and do not require corresponding clean images, which are difficult or impossible to acquire in some application areas of interest such as low-light microscopy. We introduce a new training strategy to handle Poisson-Gaussian noise which is the standard noise model for microscope images. Our new strategy eliminates hyperparameters from the loss function, which is important in a self-supervised regime where no ground truth data is available to guide hyperparameter tuning. We show how our denoiser can be adapted to the test data to improve performance. Our evaluations on microscope image denoising benchmarks validate our approach.