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.

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.

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.

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.

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.

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.

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.

Optical Imaging of Magnetic Particle Cluster Oscillation and Rotation in Glycerol.

Magnetic particles have been evaluated for their biomedical applications as a drug delivery system to treat asthma and other lung diseases. In this study, ferromagnetic barium hexaferrite (BaFe12O19) and iron oxide (Fe3O4) particles were suspended in water or glycerol, as glycerol can be 1000 times more viscous than water. The particle concentration was 2.50 mg/mL for BaFe12O19 particle clusters and 1.00 mg/mL for Fe3O4 particle clusters. The magnetic particle cluster cross-sectional area ranged from 15 to 1000 µµm2, and the particle cluster diameter ranged from 5 to 45 µµm. The magnetic particle clusters were exposed to oscillating or rotating magnetic fields and imaged with an optical microscope. The oscillation frequency of the applied magnetic fields, which was created by homemade wire spools inserted into an optical microscope, ranged from 10 to 180 Hz. The magnetic field magnitudes varied from 0.25 to 9 mT. The minimum magnetic field required for particle cluster rotation or oscillation in glycerol was experimentally measured at different frequencies. The results are in qualitative agreement with a simplified model for single-domain magnetic particles, with an average deviation from the model of 1.7 ± 1.3. The observed difference may be accounted for by the fact that our simplified model does not include effects on particle cluster motion caused by randomly oriented domains in multi-domain magnetic particle clusters, irregular particle cluster size, or magnetic anisotropy, among other effects.

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.

Fast Switching Dual-Frequency Nematic Liquid Crystal Tunable Filters.

We develop tunable optical filters with dual-frequency nematic liquid crystal optical retarders to enable fast switching between the passed wavelengths. The filters are composed of a series of two liquid crystal optical retarders. We select the specific thicknesses of the liquid crystal retarders and use individual biasing schemes to continuously tune the wavelength and bandwidth of the filter. This enables fine-tuned filter switching speeds of filter operation in the ms regime. We present theoretical predictions and experimental results for the electro-optical filter characterization as well as an example application for our filter in total internal reflection fluorescence microscopy. We find that our filter switching speeds can be as short as a few ms, an order of magnitude improvement over typical mechanical filter wheel switching speeds. The quality of our fluorescence images is similar to those obtained by conventional filters.

Fluorescence microscopy datasets for training deep neural networks.

BACKGROUND: Fluorescence microscopy is an important technique in many areas of biological research. Two factors that limit the usefulness and performance of fluorescence microscopy are photobleaching of fluorescent probes during imaging and, when imaging live cells, phototoxicity caused by light exposure. Recently developed methods in machine learning are able to greatly improve the signal-to-noise ratio of acquired images. This allows researchers to record images with much shorter exposure times, which in turn minimizes photobleaching and phototoxicity by reducing the dose of light reaching the sample. FINDINGS: To use deep learning methods, a large amount of data is needed to train the underlying convolutional neural network. One way to do this involves use of pairs of fluorescence microscopy images acquired with long and short exposure times. We provide high-quality datasets that can be used to train and evaluate deep learning methods under development. CONCLUSION: The availability of high-quality data is vital for training convolutional neural networks that are used in current machine learning approaches.

Artifact-free whole-slide imaging with structured illumination microscopy and Bayesian image reconstruction.

BACKGROUND: Structured illumination microscopy (SIM) is a method that can be used to image biological samples and can achieve both optical sectioning and super-resolution effects. Optimization of the imaging set-up and data-processing methods results in high-quality images without artifacts due to mosaicking or due to the use of SIM methods. Reconstruction methods based on Bayesian estimation can be used to produce images with a resolution beyond that dictated by the optical system. FINDINGS: Five complete datasets are presented including large panoramic SIM images of human tissues in pathophysiological conditions. Cancers of the prostate, skin, ovary, and breast, as well as tuberculosis of the lung, were imaged using SIM. The samples are available commercially and are standard histological preparations stained with hematoxylin-eosin. CONCLUSION: The use of fluorescence microscopy is increasing in histopathology. There is a need for methods that reduce artifacts caused by the use of image-stitching methods or optical sectioning methods such as SIM. Stitched SIM images produce results that may be useful for intraoperative histology. Releasing high-quality, full-slide images and related data will aid researchers in furthering the field of fluorescent histopathology.

Imaging Membrane Curvature inside a FcεRI-Centric Synapse in RBL-2H3 Cells Using TIRF Microscopy with Polarized Excitation.

Total internal reflection fluorescence microscopy with polarized excitation (P-TIRF) can be used to image nanoscale curvature phenomena in live cells. We used P-TIRF to visualize rat basophilic leukemia cells (RBL-2H3 cells) primed with fluorescent anti-dinitrophenyl (anti-DNP) immunoglobulin E (IgE) coming into contact with a supported lipid bilayer containing mobile, monovalent DNP, modeling an immunological synapse. The spatial relationship of the IgE-bound high affinity IgE receptor (FcεRI) to the ratio image of P-polarized excitation and S-polarized excitation was analyzed. These studies help correlate the dynamics of cell surface molecules with the mechanical properties of the plasma membrane during synapse formation.

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.

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.

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

Background: Structured 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). Findings: Five complete, 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 that we are developing. Conclusions: Various methods for SIM data acquisition and processing are actively being developed, but complete raw data from SIM experiments are not typically published. Publically 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 were processed with SIMToolbox, an open-source and freely available software solution for SIM.

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

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.

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.

Absence of a conventional spindle mitotic checkpoint in the binucleated single-celled parasite Giardia intestinalis.

The spindle assembly checkpoint (SAC) joins the machinery of chromosome-to-spindle microtubule attachment with that of the cell cycle to prevent missegregation of chromosomes during mitosis. Although a functioning SAC has been verified in a limited number of organisms, it is regarded as an evolutionarily conserved safeguard mechanism. In this report, we focus on the existence of the SAC in a single-celled parasitic eukaryote, Giardia intestinalis. Giardia belongs to Excavata, a large and diverse supergroup of unicellular eukaryotes in which SAC control has been nearly unexplored. We show that Giardia cells with absent or defective mitotic spindles due to the inhibitory effects of microtubule poisons do not arrest in mitosis; instead, they divide without any delay, enter the subsequent cell cycle and even reduplicate DNA before dying. We identified a limited repertoire of kinetochore and SAC components in the Giardia genome, indicating that this parasite is ill equipped to halt mitosis before the onset of anaphase via SAC control of chromosome-spindle microtubule attachment. Finally, based on overexpression, we show that Giardia Mad2, a core SAC protein in other eukaryotes, localizes along intracytoplasmic portions of caudal flagellar axonemes, but never within nuclei, even in mitotic cells with blocked spindles, where the SAC should be active. These findings are consistent with the absence of a conventional SAC, known from yeast and metazoans, in the parasitic protist Giardia.