Time-Resolved Inspection of Ionizable Lipid-Facilitated Lipid Nanoparticle Disintegration and Cargo Release at an Early Endosomal Membrane Mimic.

Advances in lipid nanoparticle (LNP) design have contributed notably to the emergence of the current clinically approved mRNA-based vaccines and are of high relevance for delivering mRNA to combat diseases where therapeutic alternatives are sparse. LNP-assisted mRNA delivery utilizes ionizable lipid-mediated cargo translocation across the endosomal membrane driven by the acidification of the endosomal environment. However, this process occurs at a low efficiency, a few percent at the best. Utilizing surface-sensitive fluorescence microscopy with a single LNP and mRNA resolution, we have investigated pH-controlled interactions between individual LNPs and a planar anionic supported lipid bilayer (SLB) formed on nanoporous silica, mimicking the electrostatic conditions of the early endosomal membrane. For LNPs with an average diameter of 140 nm, fusion with the anionic SLB preferentially occurred when the pH was reduced from 6.6 to 6.0. Furthermore, there was a delay in the onset of LNP fusion after the pH drop, and upon fusion, a significant fraction (>70%) of mRNA was released into the acidic solution representing the endosomal lumen, while a fraction of mRNA remained bound to the SLB even after reversing the pH to neutral cytosolic conditions. Finally, a comparison of the fusion efficiency of two LNP formulations with different surface concentrations of gel-forming lipids correlated with differences in the protein translation efficiency previously observed in human primary cell transfection studies. Together, these findings emphasize the relevance of biophysical investigations of ionizable lipid-containing LNP-assisted mRNA delivery mechanisms while potentially also offering means to optimize the design of LNPs with enhanced endosomal escape capabilities.

Electrophoretic Deposition Interferometric Scattering Mass Photometry.

Interferometric scattering microscopy (iSCAT) has rapidly developed as a quantitative tool for the label-free detection of single macromolecules and nanoparticles. In practice, this measurement records the interferometric scattering signal of individual nanoparticles in solution as they land and stick on a coverslip, exhibiting an intensity that varies linearly with particle volume and an adsorption rate that reflects the solution-phase transport kinetics of the system. Together, such measurements provide a multidimensional gauge of the particle size and concentration in solution over time. However, the landing kinetics of particles in solution also manifest a measurement frequency limitation imposed by the slow long-range mobility of particle diffusion to the measurement interface. Here we introduce an effective means to overcome the inherent diffusion-controlled sampling limitation of spontaneous mass photometry. We term this methodology electrophoretic deposition interferometric scattering microscopy (EPD-iSCAT). This approach uses a coverslip supporting a conductive thin film of indium tin oxide (ITO). Charging this ITO film to a potential of around +1 V electrophoretically draws charged nanoparticles from solution and binds them in the focal plane of the microscope. Regulating this potential offers a direct means of controlling particle deposition. Thus, we find for a 0.1 nM solution of 50 nm polystyrene nanoparticles that the application of +1 V to an EPD-iSCAT coverslip assembly drives an electrophoretic deposition rate constant of 1.7 s-1 µm-2 nM-1. Removal of the potential causes deposition to cease. This user control of EPD-iSCAT affords a means to apply single-molecule mass photometry to monitor long-term changes in solution, owing to slow kinetic processes. In contrast with conventional coverslips chemically derivatized with charged thin films, EPD-iSCAT maintains a deposition rate that varies linearly with the bulk concentration.

Dual-Angle Interferometric Scattering Microscopy for Optical Multiparametric Particle Characterization.

Traditional single-nanoparticle sizing using optical microscopy techniques assesses size via the diffusion constant, which requires suspended particles to be in a medium of known viscosity. However, these assumptions are typically not fulfilled in complex natural sample environments. Here, we introduce dual-angle interferometric scattering microscopy (DAISY), enabling optical quantification of both size and polarizability of individual nanoparticles (radius <170 nm) without requiring a priori information regarding the surrounding media or super-resolution imaging. DAISY achieves this by combining the information contained in concurrently measured forward and backward scattering images through twilight off-axis holography and interferometric scattering (iSCAT). Going beyond particle size and polarizability, single-particle morphology can be deduced from the fact that the hydrodynamic radius relates to the outer particle radius, while the scattering-based size estimate depends on the internal mass distribution of the particles. We demonstrate this by differentiating biomolecular fractal aggregates from spherical particles in fetal bovine serum at the single-particle level.

Studying the Feasibility and Acceptability of an Interactive Web-based Lethal Means Safety Decision Aid for Hospitalized Adults With Suicide Risk (Lock to Live).

OBJECTIVE: Lock to Live is an interactive web-based lethal means safety decision aid that promotes temporary storage of firearms and medications. It has primarily been provided to suicidal patients in emergency department settings. The goal of this study was to evaluate the feasibility and acceptability of the Lock to Live decision aid with hospitalized adults at increased risk of suicide. METHODS: Subjects provided demographic information and completed the Columbia-Suicide Severity Rating Scale after which they completed the Lock to Live program followed by a survey. RESULTS: Twenty participants were recruited for this study, 5 of whom had access to firearms and 19 of whom had access to medications. Lock to Live was feasible to use as the mean length of time to complete the program was 10.0±5.3 minutes. It was acceptable to most participants as 75% of participants found it to be easy to use, and 65% of participants agreed that Lock to Live was helpful in making a decision about changing access to firearms/medications. CONCLUSION: Lock 2 Live decision aid appears to be a feasible and acceptable tool for hospitalized patients at risk for suicide.

Single-shot self-supervised object detection in microscopy.

Object detection is a fundamental task in digital microscopy, where machine learning has made great strides in overcoming the limitations of classical approaches. The training of state-of-the-art machine-learning methods almost universally relies on vast amounts of labeled experimental data or the ability to numerically simulate realistic datasets. However, experimental data are often challenging to label and cannot be easily reproduced numerically. Here, we propose a deep-learning method, named LodeSTAR (Localization and detection from Symmetries, Translations And Rotations), that learns to detect microscopic objects with sub-pixel accuracy from a single unlabeled experimental image by exploiting the inherent roto-translational symmetries of this task. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy, also when analyzing challenging experimental data containing densely packed cells or noisy backgrounds. Furthermore, by exploiting additional symmetries we show that LodeSTAR can measure other properties, e.g., vertical position and polarizability in holographic microscopy.

Diffusion of Lipid Nanovesicles Bound to a Lipid Membrane Is Associated with the Partial-Slip Boundary Condition.

During diffusion of nanoparticles bound to a cellular membrane by ligand-receptor pairs, the distance to the laterally mobile interface is sufficiently short for their motion to depend not only on the membrane-mediated diffusivity of the tethers but also in a not yet fully understood manner on nanoparticle size and interfacial hydrodynamics. By quantifying diffusivity, velocity, and size of individual membrane-bound liposomes subjected to a hydrodynamic shear flow, we have successfully separated the diffusivity contributions from particle size and number of tethers. The obtained diffusion-size relations for synthetic and extracellular lipid vesicles are not well-described by the conventional no-slip boundary condition, suggesting partial slip as well as a significant diffusivity dependence on the distance to the lipid bilayer. These insights, extending the understanding of diffusion of biological nanoparticles at lipid bilayers, are of relevance for processes such as cellular uptake of viruses and lipid nanoparticles or labeling of cell-membrane-residing molecules.

Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography.

Characterization of suspended nanoparticles in their native environment plays a central role in a wide range of fields, from medical diagnostics and nanoparticle-enhanced drug delivery to nanosafety and environmental nanopollution assessment. Standard optical approaches for nanoparticle sizing assess the size via the diffusion constant and, as a consequence, require long trajectories and that the medium has a known and uniform viscosity. However, in most biological applications, only short trajectories are available, while simultaneously, the medium viscosity is unknown and tends to display spatiotemporal variations. In this work, we demonstrate a label-free method to quantify not only size but also refractive index of individual subwavelength particles using 2 orders of magnitude shorter trajectories than required by standard methods and without prior knowledge about the physicochemical properties of the medium. We achieved this by developing a weighted average convolutional neural network to analyze holographic images of single particles, which was successfully applied to distinguish and quantify both size and refractive index of subwavelength silica and polystyrene particles without prior knowledge of solute viscosity or refractive index. We further demonstrate how these features make it possible to temporally resolve aggregation dynamics of 31 nm polystyrene nanoparticles, revealing previously unobserved time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates.

Characteristic heterotopic ossification of the proximal humerus after total shoulder arthroplasty.

OBJECTIVE: A bony spur in a characteristic location involving the proximal humerus is identified on post-operative radiographs in some patients with history of total shoulder arthroplasty. The spur is theorized to represent heterotopic ossification near the attachment site of the pectoralis major tendon on the proximal humerus which is partially detached and then reattached during total shoulder arthroplasty. In this study, we determine the morphology, incidence, demographic associations, and clinical impact of this finding. MATERIALS AND METHODS: This is a single-center, retrospective study of 500 patients who underwent total shoulder arthroplasty (250 standard and 250 reverse technique) between 2012 and 2017. Pre- and post-operative shoulder radiographs were reviewed to identify and measure the characteristic spur; inter-observer agreement was evaluated between the two reviewers. Incidence, demographic associations, and clinical significance were then determined. RESULTS: The study group included 268 men and 234 women with a mean age of 70 (42-89) years, and clinical follow-up of 25 (1-84) months. Characteristic heterotopic ossification was seen in 88 patients (17.6%) and was first noted radiographically at a mean (interquartile range) of 12.1 (11.5-12.8) months after surgery. Male sex (adjusted odds ratio (95% confidence interval), 3.00 (0.68-5.34), p < 0.001) was independently associated with heterotopic ossification. No significant relationships between heterotopic ossification and adverse clinical outcomes were observed. CONCLUSION: Characteristic heterotopic ossification of the proximal humerus in patients status post total shoulder arthroplasty is a common imaging finding that is not associated with adverse clinical outcomes.

FRET-Based Assay for the Quantification of Extracellular Vesicles and Other Vesicles of Complex Composition.

Research in the field of extracellular vesicles is rapidly expanding and finding footholds in many areas of medical science. However, the availability of methodologies to quantify the concentration of membrane material present in a sample remains limited. Herein, we present a novel approach for the quantification of vesicle material, specifically the quantification of the total lipid membrane surface area, found in a sample using Förster resonance energy transfer (FRET). In this assay, sonication is used to drive the fusion between vesicles in the sample to be quantified and liposomes containing a pair of FRET fluorophores. The change in emission spectrum upon vesicle fusion is directly related to the total membrane surface area of the sample added, and a calibration curve allows for the quantification of a variety of vesicle species, including enveloped viruses, bacterial outer membrane vesicles, and mammalian extracellular vesicles. Without extensive optimization of experimental parameters, we were able to quantify down to ∼109 vesicles/mL, using as little as 60 µL of the sample. The assay precision was comparable to that of a commercial nanoparticle tracking analysis system. While its limit of detection was slightly higher, the FRET assay is superior for the detection of small vesicles, as its performance is vesicle-size-independent. Taken together, the FRET assay is a simple, robust, and versatile method for the quantification of a variety of purified vesicle samples.

Constructing a library of metal and metal-oxide nanoparticle heterodimers through colloidal assembly.

Nanoparticle dimers composed of different metals or metal oxides, as well as different shapes and sizes, are of wide interest for applications ranging from nanoplasmonic sensing to nanooptics to biomedical engineering. Shaped nanoparticles, like triangles and nanorods, can be particularly useful in applications due to the strong localized plasmonic hot-spot that forms at the tips or corners. By placing catalytic, but traditionally weakly- or non-plasmonic nanoparticles, such as metal oxides and metals like palladium, in these hot-spots, an enhanced function for sensing, photocatalysis or optical use is predicted. Here, we present an electrostatic colloidal assembly strategy for nanoparticles, incorporating different sizes, shapes and metal or metal oxide compositions into heterodimers with smaller gaps than are achievable using nanofabrication techniques. This versatile method is demonstrated on 14 combinations, including a variety of shaped gold nanoparticles as well as palladium, iron oxide, and titanium oxide nanoparticles. These colloidal nanoparticles are stabilized with traditional surfactants, such as citrate, CTAB, PVP and oleic acid/oleylamines, indicating the wide applicability of our approach. Heterodimers of gold and palladium are further analyzed using cathodoluminescence to demonstrate the tunability of these "plasmonic molecules". Since systematically altering the absorption and emission of the plasmonic nanoparticles dimers is crucial to extending their functionality, and small gap sizes produce the strongest hot-spots, this method indicates that the electrostatic approach to heterodimer assembly can be useful in creating new nanoparticle dimers for many applications.

Size and Refractive Index Determination of Subwavelength Particles and Air Bubbles by Holographic Nanoparticle Tracking Analysis.

Determination of size and refractive index (RI) of dispersed unlabeled subwavelength particles is of growing interest in several fields, including biotechnology, wastewater monitoring, and nanobubble preparations. Conventionally, the size distribution of such samples is determined via the Brownian motion of the particles, but simultaneous determination of their RI remains challenging. This work demonstrates nanoparticle tracking analysis (NTA) in an off-axis digital holographic microscope (DHM) enabling determination of both particle size and RI of individual subwavelength particles from the combined information about size and optical phase shift. The potential of the method to separate particle populations is demonstrated by analyzing a mixture of three types of dielectric particles within a narrow size range, where conventional NTA methods based on Brownian motion alone would fail. Using this approach, the phase shift allowed individual populations of dielectric beads overlapping in either size or RI to be clearly distinguished and quantified with respect to these properties. The method was furthermore applied for analysis of surfactant-stabilized micro- and nanobubbles, with RI lower than that of water. Since bubbles induce a phase shift of opposite sign to that of solid particles, they were easily distinguished from similarly sized solid particles made up of undissolved surfactant. Surprisingly, the dependence of the phase shift on bubble size indicates that only those with 0.15-0.20 µm radius were individual bubbles, whereas larger bubbles were actually clusters of bubbles. This label-free means to quantify multiple parameters of suspended individual submicrometer particles offers a crucial complement to current characterization strategies, suggesting broad applicability for a wide range of nanoparticle systems.

Visualizing Spatial Variations of Plasmon-Exciton Polaritons at the Nanoscale Using Electron Microscopy.

Polaritons are compositional light-matter quasiparticles that have enabled remarkable breakthroughs in quantum and nonlinear optics, as well as in material science. Recently, plasmon-exciton polaritons (plexcitons) have been realized in hybrid material systems composed of transition metal dichalcogenide (TMDC) materials and metal nanoparticles, expanding polaritonic concepts to room temperature and nanoscale systems that also benefit from the exotic properties of TMDC materials. Despite the enormous progress in understanding TMDC-based plexcitons using optical-based methods, experimental evidence of plexcitons formation has remained indirect and mapping their nanometer-scale characteristics has remained an open challenge. Here, we demonstrate that plexcitons generated by a hybrid system composed of an individual silver nanoparticle and a few-layer WS2 flake can be spectroscopically mapped with nanometer spatial resolution using electron energy loss spectroscopy in a scanning transmission electron microscope. Experimental anticrossing measurements using the absorption-dominated extinction signal provide the ultimate evidence for plexciton hybridization in the strong coupling regime. Spatially resolved EELS maps reveal the existence of unexpected nanoscale variations in the deep-subwavelength nature of plexcitons generated by this system. These findings pioneer new possibilities for in-depth studies of the local atomic structure dependence of polariton-related phenomena in TMDC hybrid material systems with nanometer spatial resolution.

Neutron Drip Line in the Ca Region from Bayesian Model Averaging.

The region of heavy calcium isotopes forms the frontier of experimental and theoretical nuclear structure research where the basic concepts of nuclear physics are put to stringent test. The recent discovery of the extremely neutron-rich nuclei around ^{60}Ca O. B. Tarasov et al. [Phys. Rev. Lett. 121, 022501 (2018)10.1103/PhysRevLett.121.022501] and the experimental determination of masses for ^{55-57}Ca S. Michimasa et al. [Phys. Rev. Lett. 121, 022506 (2018)10.1103/PhysRevLett.121.022506] provide unique information about the binding energy surface in this region. To assess the impact of these experimental discoveries on the nuclear landscape's extent, we use global mass models and statistical machine learning to make predictions, with quantified levels of certainty, for bound nuclides between Si and Ti. Using a Bayesian model averaging analysis based on Gaussian-process-based extrapolations we introduce the posterior probability p_{ex} for each nucleus to be bound to neutron emission. We find that extrapolations for drip-line locations, at which the nuclear binding ends, are consistent across the global mass models used, in spite of significant variations between their raw predictions. In particular, considering the current experimental information and current global mass models, we predict that ^{68}Ca has an average posterior probability p_{ex}≈76% to be bound to two-neutron emission while the nucleus ^{61}Ca is likely to decay by emitting a neutron (p_{ex}≈46%).

Label-free spatio-temporal monitoring of cytosolic mass, osmolarity, and volume in living cells.

Microorganisms adapt their biophysical properties in response to changes in their local environment. However, quantifying these changes at the single-cell level has only recently become possible, largely relying on fluorescent labeling strategies. In this work, we utilize yeast (Saccharomyces cerevisiae) to demonstrate label-free quantification of changes in both intracellular osmolarity and macromolecular concentration in response to changes in the local environment. By combining a digital holographic microscope with a millifluidic chip, the temporal response of cellular water flux was successfully isolated from the rate of production of higher molecular weight compounds, in addition to identifying the produced compounds in terms of the product of their refractive index increment [Formula: see text] and molar mass. The ability to identify, quantify and temporally resolve multiple biophysical processes in living cells at the single cell level offers a crucial complement to label-based strategies, suggesting broad applicability in studies of a wide-range of cellular processes.

Depletion of regulator-of-G-protein signaling-10 in mice exaggerates high-fat diet-induced insulin resistance and inflammation, and this effect is mitigated by dietary green tea extract.

The interaction between insulin resistance and inflammation plays a central role in the development of chronic diseases, although the mechanism is not fully understood. We previously demonstrated that regulator of G-protein signaling-10 (RGS10) protein is a negative modulator of the inflammatory response in macrophages and microglia. Because inflammation is a critical component in the development of high fat diet-induced insulin resistance, in this study we investigated whether RGS10 is involved in the diet-dependent regulation of glucose tolerance and insulin sensitivity. We hypothesized that the absence of RGS10 would exaggerate high-fat diet (HFD)-induced insulin resistance and inflammation response. Our results showed that RGS10 knockout (KO) mice fed a HFD gained significantly more weight and developed severe insulin resistance compared to wild-type (WT) mice fed HFD. Furthermore, compared to WT HFD-fed mice, KO mice fed the HFD displayed inflammatory phenotypes such as decreased adipose tissue expression of the anti-inflammatory M2 markers YM1 and Fizz1 and increased expression of the proinflammatory M1 cytokine interleukin 6 in adipose and CD11b, CD68 and interleukin 1ß in liver tissues. The impact of RGS10 deficiency on the exaggeration of HFD-induced insulin resistance and inflammation was ameliorated by oral consumption of green tea extract. Our results demonstrate that RGS10 is an important part of a protective mechanism involved in in regulating metabolic homeostasis by reducing inflammatory responses, which could potentially lead to an innovative new approach targeting inflammation and insulin resistance.

Multidimensional Hybridization of Dark Surface Plasmons.

Synthetic three-dimensional (3D) nanoarchitectures are providing more control over light-matter interactions and rapidly progressing photonic-based technology. These applications often utilize the strong synergy between electromagnetic fields and surface plasmons (SPs) in metallic nanostructures. However, many of the SP interactions hosted by complex 3D nanostructures are poorly understood because they involve dark hybridized states that are typically undetectable with far-field optical spectroscopy. Here, we use experimental and theoretical electron energy loss spectroscopy to elucidate dark SPs and their interactions in layered metal-insulator-metal disc nanostructures. We go beyond the established dipole SP hybridization analysis by measuring breathing and multipolar SP hybridization. In addition, we reveal multidimensional SP hybridization that simultaneously utilizes in-plane and out-of-plane SP coupling. Near-field classic electrodynamics calculations provide excellent agreement with all experiments. These results advance the fundamental understanding of SP hybridization in 3D nanostructures and provide avenues to further tune the interaction between electromagnetic fields and matter.

Ecosystem Model Skill Assessment. Yes We Can!

NEED TO ASSESS THE SKILL OF ECOSYSTEM MODELS: Accelerated changes to global ecosystems call for holistic and integrated analyses of past, present and future states under various pressures to adequately understand current and projected future system states. Ecosystem models can inform management of human activities in a complex and changing environment, but are these models reliable? Ensuring that models are reliable for addressing management questions requires evaluating their skill in representing real-world processes and dynamics. Skill has been evaluated for just a limited set of some biophysical models. A range of skill assessment methods have been reviewed but skill assessment of full marine ecosystem models has not yet been attempted. NORTHEAST US ATLANTIS MARINE ECOSYSTEM MODEL: We assessed the skill of the Northeast U.S. (NEUS) Atlantis marine ecosystem model by comparing 10-year model forecasts with observed data. Model forecast performance was compared to that obtained from a 40-year hindcast. Multiple metrics (average absolute error, root mean squared error, modeling efficiency, and Spearman rank correlation), and a suite of time-series (species biomass, fisheries landings, and ecosystem indicators) were used to adequately measure model skill. Overall, the NEUS model performed above average and thus better than expected for the key species that had been the focus of the model tuning. Model forecast skill was comparable to the hindcast skill, showing that model performance does not degenerate in a 10-year forecast mode, an important characteristic for an end-to-end ecosystem model to be useful for strategic management purposes. SKILL ASSESSMENT IS BOTH POSSIBLE AND ADVISABLE: We identify best-practice approaches for end-to-end ecosystem model skill assessment that would improve both operational use of other ecosystem models and future model development. We show that it is possible to not only assess the skill of a complicated marine ecosystem model, but that it is necessary do so to instill confidence in model results and encourage their use for strategic management. Our methods are applicable to any type of predictive model, and should be considered for use in fields outside ecology (e.g. economics, climate change, and risk assessment).