3D structured illumination microscope using a spinning disk [Invited].

Three-dimensional (3D) structured illumination microscopy (SIM) improves spatial resolution by a factor of two in both lateral and axial directions. However, the adoption of 3D SIM is limited by low imaging speed, susceptibility to out-of-focus light, and likelihood of reconstruction errors. Here we present a novel approach for 3D SIM using a spinning disk. The disk generates a 3D lattice illumination pattern on the sample and optically reconstructs super-resolved images in real time. This technique achieves a 2-times resolution improvement with a speed up to 100 frames per second while physically rejecting 90% of the background signal.

Cardiomyocyte ryanodine receptor clusters expand and coalesce after application of isoproterenol.

Earlier work has shown that ventricular ryanodine receptors (RyR2) within a cluster rearrange on phosphorylation as well as with a number of other stimuli. Using dSTORM, we investigated the effects of 300 nmol/liter isoproterenol on RyR2 clusters. In rat ventricular cardiomyocytes, there was a symmetrical enlargement of RyR2 cluster areas, a decrease in the edge-to-edge nearest neighbor distance, and distribution changes that suggested movement to increase the cluster areas by coalescence. The surface area covered by the phosphorylated clusters was significantly greater than in the control cells, as was the cluster density. This latter change was accompanied by a decreased cluster fragmentation, implying that new tetramers were brought into the sarcoplasmic reticulum. We propose a possible mechanism to explain these changes. We also visualized individual RyR2 tetramers and confirmed our earlier electron-tomographic finding that the tetramers are in a disorganized but non-random array occupying about half of the cluster area. Multiclusters, cluster groups defined by the maximum distance between their members, were analyzed for various distances. At 100 nm, the areas occupied by the multiclusters just exceeded those of the single clusters, and more than half of the multiclusters had only a single subcluster that could initiate a spark. Phosphorylation increased the size of the multiclusters, markedly so for distances >100 nm. There was no relationship between the number of subclusters in a group and the area covered by it. We conclude that isoproterenol induces rapid, significant, changes in the molecular architecture of excitation-contraction coupling.

Structured illumination microscopy with a phase-modulated spinning disk for optical sectioning.

Among various super-resolution microscopic techniques, structured illumination microscopy (SIM) stands out for live-cell imaging because of its higher imaging speed. However, conventional SIM lacks optical sectioning capability. Here we demonstrate a new, to the best of our knowledge, approach using a phase-modulated spinning disk (PMSD) that enhances the optical sectioning capability of SIM. The PMSD consists of a pinhole array for confocal imaging and a transparent polymer layer for light phase modulation. The light phase modulation was designed to cancel the zeroth-order diffracted beam and create a sharp lattice illumination pattern using the interference of four first-order diffracted beams. In the detection optical path, the PMSD serves as a spatial filter to physically reject about 80% of the out-of-focus signals, an approach that allows for real-time optical reconstruction of super-resolved images with enhanced contrast. Furthermore, the simplicity of the design makes it easy to upgrade a conventional fluorescence microscope to a PMSD SIM system.

Human-vision-inspired cluster identification for single-molecule localization microscopy.

Single-molecule localization microscopy has enabled scientists to visualize cellular structures at the nanometer scale. However, researchers are facing great challenges in analyzing images presented by point clouds. Existing algorithms for cluster identification are coordinate-based analyses requiring users to input cutoff thresholds based on the distance or density of the point cloud. These thresholds are often one's best guess with repeated visual inspections, making the cluster assignment user-dependent. Here, we present a cluster identification algorithm mimicking the modulation transfer function of human vision. This approach does not require any input parameters and produces visually satisfactory cluster assignments. We tested this algorithm by identifying the clusters of the fusion proteins of the Nipah virus on its host cells. This algorithm was further extended to analyze three-dimensional point clouds using virus-like particles as an example.

The nanoscale organization of Nipah virus matrix protein revealed by super-resolution microscopy.

The matrix proteins (M) of many enveloped RNA viruses mediate virus assembly and budding. However, it remains poorly understood how M are involved in virus budding and how they interact with envelope proteins. Here, we show that the expression level of Nipah (NiV) M in particles produced by the host cells deviates from a gamma distribution and does not reflect that of the host cells, indicating assembly of the NiV-M in the process. Our data reveal that NiV-M affects the circularity of the particles while the NiV envelope proteins do not. The organization of NiV envelope proteins on the membrane of the particles is similar to those that do not express NiV-M, suggesting that NiV-M does not directly interact with the envelope proteins during assembly and budding.

Conditional Generative Adversarial Network for Spectral Recovery to Accelerate Single-Cell Raman Spectroscopic Analysis.

Raman spectroscopy is a powerful tool to investigate cellular heterogeneity. However, Raman spectra for single-cell analysis are hindered by a low signal-to-noise ratio (SNR). Here, we demonstrate a simple and reliable spectral recovery conditional generative adversarial network (SRGAN). SRGAN reduced the data acquisition time by 1 order of magnitude (i.e., 30 vs 3 s) by improving the SNR by a factor of ∼6. We classified five major foodborne bacteria based on single-cell Raman spectra to further evaluate the performance of SRGAN. Spectra processed using SRGAN achieved an identification accuracy of 94.9%, compared to 60.5% using unprocessed Raman spectra. SRGAN can accelerate spectral collection to improve the throughput of Raman spectroscopy and enable real-time monitoring of single living cells.

Next-Generation Antimicrobial Resistance Surveillance System Based on the Internet-of-Things and Microfluidic Technique.

Antimicrobial resistance (AMR) of foodborne pathogens is a global crisis in public health and economic growth. A real-time surveillance system is key to track the emergence of AMR bacteria and provides a comprehensive AMR trend from farm to fork. However, current AMR surveillance systems, which integrate results from multiple laboratories using the conventional broth microdilution method, are labor-intensive and time-consuming. To address these challenges, we present the internet of things (IoT), including colorimetric-based microfluidic sensors, a custom-built portable incubator, and machine learning algorithms, to monitor AMR trends in real time. As a top priority microbe that poses risks to human health, Campylobacter was selected as a bacterial model to demonstrate and validate the IoT-assisted AMR surveillance. Image classification with convolution neural network ResNet50 on the colorimetric sensors achieved an accuracy of 99.5% in classifying bacterial growth/inhibition patterns. The IoT was used to carry out a small-scale survey study, identifying eight Campylobacter isolates out of 35 chicken samples. A 96% agreement on Campylobacter AMR profiles was achieved between the results from the IoT and the conventional broth microdilution method. The data collected from the intelligent sensors were transmitted from local computers to a cloud server, facilitating real-time data collection and integration. A web browser was developed to demonstrate the spatial and temporal AMR trends to end-users. This rapid, cost-effective, and portable approach is able to monitor, assess, and mitigate the burden of bacterial AMR in the agri-food chain.

Predicted Structure of Fully Activated Tas1R3/1R3' Homodimer Bound to G Protein and Natural Sugars: Structural Insights into G Protein Activation by a Class C Sweet Taste Homodimer with Natural Sugars.

The Tas1R3 G protein-coupled receptor constitutes the main component of sweet taste sensory response in humans via forming a heterodimer with Tas1R2 or a homodimer with Tas1R3. The Tas1R3/1R3' homodimer serves as a low-affinity sweet taste receptor, stimulating gustducin G protein (GGust) signaling in the presence of a high concentration of natural sugars. This provides an additional means to detect the taste of natural sugars, thereby differentiating the flavors between natural sugars and artificial sweeteners. We report here the predicted 3D structure of active state Tas1R3/1R3' homodimer complexed with heterotrimeric GGust and sucrose. We discovered that the GGust makes ionic anchors to intracellular loops 1 and 2 of Tas1R3 while the Gα-α5 helix engages the cytoplasmic region extensively through salt bridge and hydrophobic interactions. We show that in the activation of this complex the Venus flytrap domains of the homodimer undergo a remarkable twist up to ∼100° rotation around the vertical axis to adopt a closed-closed conformation while the intracellular region relaxes to an open-open conformation. We find that binding of sucrose to the homodimer stabilizes a preactivated conformation with a largely open intracellular region that recruits and activates the GGust. Upon activation, the Gα subunit spontaneously opens up the nucleotide-binding site, making nucleotide exchange facile for signaling. This activation of GGust promotes the interdomain twist of the Venus flytrap domains. These structures and transformations could potentially be a basis for the design of new sweeteners with higher activity and less unpleasant flavors.

Campylobacter jejuni Antimicrobial Resistance Profiles and Mechanisms Determined Using a Raman Spectroscopy-Based Metabolomic Approach.

Rapid identification of antimicrobial resistance (AMR) profiles and mechanisms is critical for clinical management and drug development. However, the current AMR detection approaches take up to 48 h to obtain a result. Here, we demonstrate a Raman spectroscopy-based metabolomic approach to rapidly determine the AMR profile of Campylobacter jejuni, a major cause of foodborne gastroenteritis worldwide. C. jejuni isolates with susceptible and resistant traits to ampicillin and tetracycline were subjected to different antibiotic concentrations for 5 h, followed by Raman spectral collection and chemometric analysis (i.e., second-derivative transformation analysis, hierarchical clustering analysis [HCA], and principal-component analysis [PCA]). The MICs obtained by Raman-2nd derivative transformation agreed with the reference agar dilution method for all isolates. The AMR profile of C. jejuni was accurately classified by Raman-HCA after treating bacteria with antibiotics at clinical susceptible and resistant breakpoints. According to PCA loading plots, susceptible and resistant strains showed different Raman metabolomic patterns for antibiotics. Ampicillin-resistant isolates had distinctive Raman signatures of peptidoglycan, which is related to cell wall synthesis. The ratio of saturated to unsaturated fatty acids in the lipid membrane layer of ampicillin-resistant isolates was higher than in susceptible ones, indicating more rigid envelope structure under ampicillin treatment. In comparison, tetracycline-resistant isolates exhibited prominent Raman spectral features associated with proteins and nucleic acids, demonstrating more active protein synthesis than susceptible strains with the presence of tetracycline. Taken together, Raman spectroscopy is a powerful metabolic fingerprinting technique for simultaneously revealing the AMR profiles and mechanisms of foodborne pathogens. IMPORTANCE Metabolism plays the central role in bacteria to mediate the early response against antibiotics and demonstrate antimicrobial resistance (AMR). Understanding the whole-cell metabolite profiles gives rise to a more complete AMR mechanism insight. In this study, we have applied Raman spectroscopy and chemometrics to achieve a rapid, accurate, and easy-to-operate investigation of bacterial AMR profiles and mechanisms. Raman spectroscopy reduced the analysis time by an order of magnitude to obtain the same results achieved through traditional culture-based antimicrobial susceptibility approaches. It offers great benefits as a high-throughput screening method in food chain surveillance and clinical diagnostics. Meanwhile, the AMR mechanisms toward two representative antibiotic classes, namely, ampicillin and tetracycline, were revealed by Raman spectroscopy at the metabolome level. This approach is based on bacterial phenotypic responses to antibiotics, providing information complementary to that obtained by conventional genetic methods such as genome sequencing. The knowledge obtained from Raman metabolomic data can be used in drug discovery and pathogen intervention.

Arcobacter Identification and Species Determination Using Raman Spectroscopy Combined with Neural Networks.

Rapid and accurate identification of Arcobacter is of great importance because it is considered an emerging food- and waterborne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. We aimed to detect and discriminate Arcobacter bacteria at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with neural networks. A total of 82 reference and field isolates of 18 Arcobacter species from clinical, environmental, and agri-food sources were included. We determined that the bacterial cultivation time and growth temperature did not significantly influence the Raman spectral reproducibility and discrimination capability. The genus Arcobacter could be successfully differentiated from the closely related genera Campylobacter and Helicobacter using principal-component analysis. For the identification of Arcobacter to the species level, an accuracy of 97.2% was achieved for all 18 Arcobacter species using Raman spectroscopy combined with a convolutional neural network (CNN). The predictive capability of Raman-CNN was further validated using an independent data set of 12 Arcobacter strains. Furthermore, a Raman spectroscopy-based fully connected artificial neural network (ANN) was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass (regression coefficient >0.99). The application of both CNN and fully connected ANN improved the accuracy of Raman spectroscopy for bacterial species determination compared to the conventional chemometrics. This newly developed approach enables rapid identification and species determination of Arcobacter within an hour following cultivation.IMPORTANCE Rapid identification of bacterial pathogens is critical for developing an early warning system and performing epidemiological investigation. Arcobacter is an emerging foodborne pathogen and has become more important in recent decades. The incidence of Arcobacter species in the agro-ecosystem is probably underestimated mainly due to the limitation in the available detection and characterization techniques. Raman spectroscopy combined with machine learning can accurately identify Arcobacter at the species level in a rapid and reliable manner, providing a promising tool for epidemiological surveillance of this microbe in the agri-food chain. The knowledge elicited from this study has the potential to be used for routine bacterial screening and diagnostics by the government, food industry, and clinics.

Cardiac ryanodine receptor distribution is dynamic and changed by auxiliary proteins and post-translational modification.

The effects of the immunophilins, FKBP12 and FKBP12.6, and phosphorylation on type II ryanodine receptor (RyR2) arrangement and function were examined using correlation microscopy (line scan confocal imaging of Ca2+ sparks and dual-tilt electron tomography) and dSTORM imaging of permeabilized Wistar rat ventricular myocytes. Saturating concentrations (10 µmol/L) of either FKBP12 or 12.6 significantly reduced the frequency, spread, amplitude and Ca2+ spark mass relative to control, while the tomograms revealed both proteins shifted the tetramers into a largely side-by-side configuration. Phosphorylation of immunophilin-saturated RyR2 resulted in structural and functional changes largely comparable to phosphorylation alone. dSTORM images of myocyte surfaces demonstrated that both FKBP12 and 12.6 significantly reduced RyR2 cluster sizes, while phosphorylation, even of immunophilin-saturated RyR2, increased them. We conclude that both RyR2 cluster size and the arrangement of tetramers within clusters is dynamic and respond to changes in the cellular environment. Further, these changes affect Ca2+ spark formation.

Quinoline-containing diarylethenes: bridging between turn-on fluorescence, RGB switching and room temperature phosphorescence.

Simple structural modifications using oxidation and methylation of a quinoline-containing diarylethene result in dramatic variation of photophysical properties. Turn-on fluorescence, room temperature phosphorescence (RTP) and red-green-blue (RGB) switching were achieved in three different related compounds. Photoswitchable diarylethenes (DAEs) that exhibit turn-on fluorescence are in high demand for super-resolution microscopy, and the development of purely organic phosphorescent materials in the amorphous state is attractive but challenging. The findings reported here provide a novel toolkit for designing turn-on fluorescence DAEs for super-resolution microscopy and extending the scope of amorphous RTP materials. More importantly, we bridge between these two fundamentally significant photochemical and photophysical phenomena, and reveal structure-property relationships between DAE photochromism and RTP.

ERGO: Efficient Recurrent Graph Optimized Emitter Density Estimation in Single Molecule Localization Microscopy.

Single molecule localization microscopy (SMLM) allows unprecedented insight into the three-dimensional organization of proteins at the nanometer scale. The combination of minimal invasive cell imaging with high resolution positions SMLM at the forefront of scientific discovery in cancer, infectious, and degenerative diseases. By stochastic temporal and spatial separation of light emissions from fluorescent labelled proteins, SMLM is capable of nanometer scale reconstruction of cellular structures. Precise localization of proteins in 3D astigmatic SMLM is dependent on parameter sensitive preprocessing steps to select regions of interest. With SMLM acquisition highly variable over time, it is non-trivial to find an optimal static parameter configuration. The high emitter density required for reconstruction of complex protein structures can compromise accuracy and introduce artifacts. To address these problems, we introduce two modular auto-tuning pre-processing methods: adaptive signal detection and learned recurrent signal density estimation that can leverage the information stored in the sequence of frames that compose the SMLM acquisition process. We show empirically that our contributions improve accuracy, precision and recall with respect to the state of the art. Both modules auto-tune their hyper-parameters to reduce the parameter space for practitioners, improve robustness and reproducibility, and are validated on a reference in silico dataset. Adaptive signal detection and density prediction can offer a practitioner, in addition to informed localization, a tool to tune acquisition parameters ensuring improved reconstruction of the underlying protein complex. We illustrate the challenges faced by practitioners in applying SMLM algorithms on real world data markedly different from the data used in development and show how ERGO can be run on new datasets without retraining while motivating the need for robust transfer learning in SMLM.

Phase separation and clustering of an ABC transporter in Mycobacterium tuberculosis.

Phase separation drives numerous cellular processes, ranging from the formation of membrane-less organelles to the cooperative assembly of signaling proteins. Features such as multivalency and intrinsic disorder that enable condensate formation are found not only in cytosolic and nuclear proteins, but also in membrane-associated proteins. The ABC transporter Rv1747, which is important for Mycobacterium tuberculosis (Mtb) growth in infected hosts, has a cytoplasmic regulatory module consisting of 2 phosphothreonine-binding Forkhead-associated domains joined by an intrinsically disordered linker with multiple phospho-acceptor threonines. Here we demonstrate that the regulatory modules of Rv1747 and its homolog in Mycobacterium smegmatis form liquid-like condensates as a function of concentration and phosphorylation. The serine/threonine kinases and sole phosphatase of Mtb tune phosphorylation-enhanced phase separation and differentially colocalize with the resulting condensates. The Rv1747 regulatory module also phase-separates on supported lipid bilayers and forms dynamic foci when expressed heterologously in live yeast and M. smegmatis cells. Consistent with these observations, single-molecule localization microscopy reveals that the endogenous Mtb transporter forms higher-order clusters within the Mycobacterium membrane. Collectively, these data suggest a key role for phase separation in the function of these mycobacterial ABC transporters and their regulation via intracellular signaling.

Super-resolution modularity analysis shows polyhedral caveolin-1 oligomers combine to form scaffolds and caveolae.

Caveolin-1 (Cav1), the coat protein for caveolae, also forms non-caveolar Cav1 scaffolds. Single molecule Cav1 super-resolution microscopy analysis previously identified caveolae and three distinct scaffold domains: smaller S1A and S2B scaffolds and larger hemispherical S2 scaffolds. Application here of network modularity analysis of SMLM data for endogenous Cav1 labeling in HeLa cells shows that small scaffolds combine to form larger scaffolds and caveolae. We find modules within Cav1 blobs by maximizing the intra-connectivity between Cav1 molecules within a module and minimizing the inter-connectivity between Cav1 molecules across modules, which is achieved via spectral decomposition of the localizations adjacency matrix. Features of modules are then matched with intact blobs to find the similarity between the module-blob pairs of group centers. Our results show that smaller S1A and S1B scaffolds are made up of small polygons, that S1B scaffolds correspond to S1A scaffold dimers and that caveolae and hemispherical S2 scaffolds are complex, modular structures formed from S1B and S1A scaffolds, respectively. Polyhedral interactions of Cav1 oligomers, therefore, leads progressively to the formation of larger and more complex scaffold domains and the biogenesis of caveolae.

A Novel Mathematical Model for Studying Antimicrobial Interactions Against Campylobacter jejuni.

The aim of this study is to investigate the antimicrobial synergistic effect against Campylobacter jejuni, a leading foodborne pathogen that causes human gastroenteritis, by cinnamon oil, encapsulated curcumin, and zinc oxide nanoparticles (ZnO NPs). We compared three approaches to study the antimicrobial interactions, including the time-killing method, the fractional inhibitory concentration index (FICI) method, and a mathematical concentration-effect model. Isobologram analysis was performed to evaluate the synergy in different combinations, and a median-effect equation was applied to identify the combinations of synergistic effects at median, bacteriostatic, and bactericidal reduction levels. The time-killing method overestimated the synergistic interaction between antimicrobials, while the FICI method failed to detect an existing synergistic phenomenon. This lack of accuracy and sensitivity was mainly due to combining antimicrobials without a deep understanding of their concentration-effect relationships. Our results showed that each antimicrobial had a unique concentration-effect curve. Specifically, encapsulated curcumin showed a sharp sigmoidal curve unlike cinnamon oil and ZnO NPs. A mathematical model was applied to study the interaction between antimicrobials with a different shape of concentration-effect curve. We observed an additive effect of cinnamon oil/ZnO NPs and synergistic interactions of other binary combinations (cinnamon oil/encapsulated curcumin and ZnO NPs/encapsulated curcumin). The tertiary combination of cinnamon oil/ZnO NPs/encapsulated curcumin at IC25 (additive line <1-log CFU/mL) presented the greatest synergistic effect by reducing the bacterial population over 8-log CFU/mL. This mathematical model provided an alternative strategy to develop a new antimicrobial strategy.

Chirality discrimination at the carvone air/liquid interfaces detected by heterodyne-detected sum frequency generation.

The chiral signal of the carvone air/liquid interface is probed by heterodyne-detected sum frequency generation (HD-SFG) without the electronic resonance. The chiral SFG spectra exhibit two distinguishable spectral signatures. Four chiral vibrational peaks of the R- and S-carvone molecules are with opposite signs, which can directly determine the surface molecular chirality. Two achiral vibrational peaks are also observed with the same sign. The different spectral signatures can provide a detailed chirality characterization at the molecular interface.

Molecular Coupling between Organic Molecules and Metal.

Molecular couplings at interfaces play important roles in determining the performance of nanophotonics and molecular electronics. In this Letter, using femtosecond sum frequency generation to trace free-induction decay of vibrationally excited aromatic thiol molecules immobilized on metal with and without the bridged methylene group(s), metal surface free electron-coupled and uncoupled phenyl C-H stretching vibrational modes were identified, with dephasing times of ∼0.28 and ∼0.60 ps, respectively. For thiols on Au with the bridged methylene group(s) (benzyl mercaptan and phenylethanethiol), both the coupled and uncoupled modes were observed; for thiol on Au without the bridged methylene group (thiophenol), only the coupled mode was observed. This indicates that the bridged methylene group(s) serving as a spacer can be used to adjust the molecular coupling between the phenyl vibration and surface free electrons. The experimental approach can be used to tune molecular couplings in advanced nanophotonics and molecular electronics.

High-resolution broadband sum frequency generation vibrational spectroscopy using intrapulse interference.

We theoretically propose a new approach to obtain high-spectral-resolution sum frequency generation (SFG) vibrational spectra using intrapulse interference. By introducing a π-step phase modulation to the broadband 800 nm pulse, the broadband 800 nm laser pulse splits into two distinguishable pulses in the time domain with a fixed time delay. The resolution of the intrapulse interference SFG can be better than 1 cm-1 and is limited only by the spectral resolution of the spectrometer. This approach can accurately retrieve the amplitude and the relative phase of vibrational peaks. Additionally, the sensitivity of SFG is enhanced by adopting femtosecond IR and femtosecond visible pulses.

A stochastic assembly model for Nipah virus revealed by super-resolution microscopy.

Understanding virus assembly mechanisms is important for developing therapeutic interventions. Nipah virus (NiV) is of interest because of its high mortality rate and efficient human-human transmissions. The current model for most enveloped viruses suggests that matrix proteins (M) recruit attachment glycoproteins (G) and fusion glycoproteins (F) to the assembly site at the plasma membrane. Here we report an assembly model that differs in many aspects from the current one. Examining NiV proteins on the cell plasma membrane using super-resolution microscopy reveals that clusters of F and G are randomly distributed on the plasma membrane regardless of the presence or absence of M. Our data suggests a model in which the M molecules assemble at the plasma membrane to form virus-like particles (VLPs), while the incorporation of F and G into the nascent VLPs is stochastic.