Human Tau Aggregates Are Permissive to Protein Synthesis-Dependent Memory in Drosophila Tauopathy Models.

Tauopathies including Alzheimer's disease, are characterized by progressive cognitive decline, neurodegeneration, and intraneuronal aggregates comprised largely of the axonal protein Tau. It has been unclear whether cognitive deficits are a consequence of aggregate accumulation thought to compromise neuronal health and eventually lead to neurodegeneration. We use the Drosophila tauopathy model and mixed-sex populations to reveal an adult onset pan-neuronal Tau accumulation-dependent decline in learning efficacy and a specific defect in protein synthesis-dependent memory (PSD-M), but not in its protein synthesis-independent variant. We demonstrate that these neuroplasticity defects are reversible on suppression of new transgenic human Tau expression and surprisingly correlate with an increase in Tau aggregates. Inhibition of aggregate formation via acute oral administration of methylene blue results in re-emergence of deficient memory in animals with suppressed human Tau (hTau)0N4R expression. Significantly, aggregate inhibition results in PSD-M deficits in hTau0N3R-expressing animals, which present elevated aggregates and normal memory if untreated with methylene blue. Moreover, methylene blue-dependent hTau0N4R aggregate suppression within adult mushroom body neurons also resulted in emergence of memory deficits. Therefore, deficient PSD-M on human Tau expression in the Drosophila CNS is not a consequence of toxicity and neuronal loss because it is reversible. Furthermore, PSD-M deficits do not result from aggregate accumulation, which appears permissive, if not protective of processes underlying this memory variant.SIGNIFICANCE STATEMENT Intraneuronal Tau aggregate accumulation has been proposed to underlie the cognitive decline and eventual neurotoxicity that characterizes the neurodegenerative dementias known as tauopathies. However, we show in three experimental settings that Tau aggregates in the Drosophila CNS do not impair but rather appear to facilitate processes underlying protein synthesis-dependent memory within affected neurons.

Double-Clad Antiresonant Hollow-Core Fiber and Its Comparison with Other Fibers for Multiphoton Micro-Endoscopy.

Label-free and multiphoton micro-endoscopy can transform clinical histopathology by providing an in situ tool for diagnostic imaging and surgical treatment in diseases such as cancer. Key to a multiphoton imaging-based micro-endoscopic device is the optical fiber, for distortion-free and efficient delivery of ultra-short laser pulses to the sample and effective signal collection. In this work, we study a new hollow-core (air-filled) double-clad anti-resonant fiber (DC-ARF) as a high-performance candidate for multiphoton micro-endoscopy. We compare the fiber characteristics of the DC-ARF with a single-clad anti-resonant fiber (SC-ARF) and a solid core fiber (SCF). In this work, while the DC-ARF and the SC-ARF enable low-loss (<0.2 dBm-1), close to dispersion-free excitation pulse delivery (<10% pulse width increase at 900 nm per 1 m fiber) without any induced non-linearities, the SCF resulted in spectral broadening and pulse-stretching (>2000% of pulse width increase at 900 nm per 1 m fiber). An ideal optical fiber endoscope needs to be several meters long and should enable both excitation and collection through the fiber. Therefore, we performed multiphoton imaging on endoscopy-compatible 1 m and 3 m lengths of fiber in the back-scattered geometry, wherein the signals were collected either directly (non-descanned detection) or through the fiber (descanned detection). Second harmonic images were collected from barium titanate crystals as well as from biological samples (mouse tail tendon). In non-descanned detection conditions, the ARFs outperformed the SCF by up to 10 times in terms of signal-to-noise ratio of images. Significantly, only the DC-ARF, due to its high numerical aperture (NA) of 0.45 and wide-collection bandwidth (>1 µm), could provide images in the de-scanned detection configuration desirable for endoscopy. Thus, our systematic characterization and comparison of different optical fibers under different image collection configurations, confirms and establishes the utility of DC-ARFs for high-performing label-free multiphoton imaging-based micro-endoscopy.

Multimodal image and spectral feature learning for efficient analysis of water-suspended particles.

We have developed a method to combine morphological and chemical information for the accurate identification of different particle types using optical measurement techniques that require no sample preparation. A combined holographic imaging and Raman spectroscopy setup is used to gather data from six different types of marine particles suspended in a large volume of seawater. Unsupervised feature learning is performed on the images and the spectral data using convolutional and single-layer autoencoders. The learned features are combined, where we demonstrate that non-linear dimensional reduction of the combined multimodal features can achieve a high clustering macro F1 score of 0.88, compared to a maximum of 0.61 when only image or spectral features are used. The method can be applied to long-term monitoring of particles in the ocean without the need for sample collection. In addition, it can be applied to data from different types of sensor measurements without significant modifications.

Multi-Excitation Raman Spectroscopy for Label-Free, Strain-Level Characterization of Bacterial Pathogens in Artificial Sputum Media.

The current methods for diagnosis of acute and chronic infections are complex and skill-intensive. For complex clinical biofilm infections, it can take days from collecting and processing a patient's sample to achieving a result. These aspects place a significant burden on healthcare providers, delay treatment, and can lead to adverse patient outcomes. We report the development and application of a novel multi-excitation Raman spectroscopy-based methodology for the label-free and non-invasive detection of microbial pathogens that can be used with unprocessed clinical samples directly and provide rapid data to inform diagnosis by a medical professional. The method relies on the differential excitation of non-resonant and resonant molecular components in bacterial cells to enhance the molecular finger-printing capability to obtain strain-level distinction in bacterial species. Here, we use this strategy to detect and characterize the respiratory pathogens Pseudomonas aeruginosa and Staphylococcus aureus as typical infectious agents associated with cystic fibrosis. Planktonic specimens were analyzed both in isolation and in artificial sputum media. The resonance Raman components, excited at different wavelengths, were characterized as carotenoids and porphyrins. By combining the more informative multi-excitation Raman spectra with multivariate analysis (support vector machine) the accuracy was found to be 99.75% for both species (across all strains), including 100% accuracy for drug-sensitive and drug-resistant S. aureus. The results demonstrate that our methodology based on multi-excitation Raman spectroscopy can underpin the development of a powerful platform for the rapid and reagentless detection of clinical pathogens to support diagnosis by a medical expert, in this case relevant to cystic fibrosis. Such a platform could provide translatable diagnostic solutions in a variety of disease areas and also be utilized for the rapid detection of anti-microbial resistance.

Selective Imaging of Microplastic and Organic Particles in Flow by Multimodal Coherent Anti-Stokes Raman Scattering and Two-Photon Excited Autofluorescence Analysis.

Microplastic pollution is an urgent global issue. While spectroscopic techniques have been widely used for the identification of plastics collected from aquatic environments, these techniques are often labor-intensive and time-consuming due to sample collection, preparation, and long measurement times. In this study, a method for the two-dimensional detection and classification of flowing microplastic and organic biotic particles with high spatial and temporal resolutions has been proposed based on the simultaneous detection of coherent anti-Stokes Raman scattering (CARS) and two-photon excited autofluorescence (TPEAF) signals. Poly(methyl methacrylate) (PMMA), polystyrene (PS), and low-density polyethylene (LDPE) particles with sizes ranging from several tens to hundreds of micrometers were selectively detected in flow with an average velocity of 4.17 mm/s by CARS line scanning. With the same flow velocity, flowing PMMA and alga particles were measured using a multimodal system of CARS and TPEAF signals. The average intensities of both PMMA and alga particles in the CARS signals at a frequency of 2940 cm-1 were higher than the background level, while only algae emitted TPEAF signals. This allowed the classification of PMMA and alga particles to be successfully performed in flow by the simultaneous detection of CARS and TPEAF signals. With the proposed method, the monitoring of microplastics in a continuous water flow without collection or extraction is possible, which is game-changing for the current sampling-based microplastic analysis.

Identification of microplastics in a large water volume by integrated holography and Raman spectroscopy.

A noncontact method to identify sparsely distributed plastic pellets is proposed by integrating holography and Raman spectroscopy in this study. Polystyrene and poly(methyl methacrylate) resin pellets with a size of 3 mm located in a 20 cm water channel were illuminated using a collimated continuous wave laser beam with a diameter of 4 mm and wavelength of 785 nm. The same laser beam was used to take a holographic image and Raman spectrum of a pellet to identify the shape, size, and composition of material. Using the compact system, the morphological and chemical analysis of pellets in a large volume of water was performed. The reported method demonstrates the potential for noncontact continuous in situ monitoring of microplastics in water without collection and separation.

Optical fibre-tip probes for SERS: numerical study for design considerations.

Enhancement of sub-wavelength optical fields using sub-micron plasmonic probes has found many applications in chemical, material, biological and medical sciences. The enhancement is via localised surface-plasmon resonance (LSPR) which enables the highly sensitive vibrational-spectroscopy technique of surface-enhanced Raman scattering (SERS). Combining SERS with optical fibres can allow the monitoring of biochemical reactions in situ with high resolution. Here, we study the electromagnetic-field enhancement of a tapered optical fibre-tip coated with gold nanoparticles (AuNPs) using finite-element simulations. We investigate the electric-field enhancement associated with metallic NPs and study the effect of parameters such as tip-aperture radius, cone angle, nanoparticle size and gaps between them. Our study provides an understanding of the design and application of metal-nanoparticle-coated optical-fibre-tip probes for SERS. The approach of using fibre-coupled delivery adds flexibility and simplifies the system requirements in SERS, making it suitable for cellular imaging and mapping bio-interfaces.

Optimising superoscillatory spots for far-field super-resolution imaging.

Optical superoscillatory imaging, allowing unlabelled far-field super-resolution, has in recent years become reality. Instruments have been built and their super-resolution imaging capabilities demonstrated. The question is no longer whether this can be done, but how well: what resolution is practically achievable? Numerous works have optimised various particular features of superoscillatory spots, but in order to probe the limits of superoscillatory imaging we need to simultaneously optimise all the important spot features: those that define the resolution of the system. We simultaneously optimise spot size and its intensity relative to the sidebands for various fields of view, giving a set of best compromises for use in different imaging scenarios. Our technique uses the circular prolate spheroidal wave functions as a basis set on the field of view, and the optimal combination of these, representing the optimal spot, is found using a multi-objective genetic algorithm. We then introduce a less computationally demanding approach suitable for real-time use in the laboratory which, crucially, allows independent control of spot size and field of view. Imaging simulations demonstrate the resolution achievable with these spots. We show a three-order-of-magnitude improvement in the efficiency of focusing to achieve the same resolution as previously reported results, or a 26 % increase in resolution for the same efficiency of focusing.

Hepatic Steatosis Accompanies Pulmonary Alveolar Proteinosis.

Maintenance of tissue-specific organ lipid compositions characterizes mammalian lipid homeostasis. The lungs and liver synthesize mixed phosphatidylcholine (PC) molecular species that are subsequently tailored for function. The lungs progressively enrich disaturated PC directed to lamellar body surfactant stores before secretion. The liver accumulates polyunsaturated PC directed to very-low-density lipoprotein assembly and secretion, or to triglyceride stores. In each tissue, selective PC species enrichment mechanisms lie at the heart of effective homeostasis. We tested for potential coordination between these spatially separated but possibly complementary phenomena under a major derangement of lung PC metabolism, pulmonary alveolar proteinosis (PAP), which overwhelms homeostasis and leads to excessive surfactant accumulation. Using static and dynamic lipidomics techniques, we compared (1) tissue PC compositions and contents, and (2) in lungs, the absolute rates of synthesis in both control mice and the granulocyte-macrophage colony-stimulating factor knockout model of PAP. Significant disaturated PC accumulation in bronchoalveolar lavage fluid, alveolar macrophage, and lavaged lung tissue occurred alongside increased PC synthesis, consistent with reported defects in alveolar macrophage surfactant turnover. However, microscopy using oil red O staining, coherent anti-Stokes Raman scattering, second harmonic generation, and transmission electron microscopy also revealed neutral-lipid droplet accumulations in alveolar lipofibroblasts of granular macrophage colony-stimulating factor knockout animals, suggesting that lipid homeostasis deficits extend beyond alveolar macrophages. PAP plasma PC composition was significantly polyunsaturated fatty acid enriched, but the content was unchanged and hepatic polyunsaturated fatty acid-enriched PC content increased by 50% with an accompanying micro/macrovesicular steatosis and a fibrotic damage pattern consistent with nonalcoholic fatty liver disease. These data suggest a hepatopulmonary axis of PC metabolism coordination, with wider implications for understanding and managing lipid pathologies in which compromise of one organ has unexpected consequences for another.

Detection of early osteogenic commitment in primary cells using Raman spectroscopy.

Major challenges in the development of novel implant surfaces for artificial joints include osteoblast heterogeneity and the lack of a simple and sensitive in vitro assay to measure early osteogenic responses. Raman spectroscopy is a label-free, non-invasive and non-destructive vibrational fingerprinting optical technique that is increasingly being applied to detect biochemical changes in cells. In this study Raman spectroscopy has been used to obtain bone cell-specific spectral signatures and to identify any changes therein during osteoblast commitment and differentiation of primary cells in culture. Murine calvarial osteoblasts (COBs) were extracted and cultured and studied by Raman spectroscopy over a 14 day culture period. Distinct osteogenic Raman spectra were identified after 3 days of culture with strong bands detected for mineral: phosphate ν3 (1030 cm-1) and B-type carbonate (1072 cm-1), DNA (782 cm-1) and collagen matrix (CH2 deformation at 1450 cm-1) and weaker phosphate bands (948 and 970 cm-1). Early changes were detected by Raman spectroscopy compared to a standard enzymatic alkaline phosphatase (ALP) assay and gene expression analyses over this period. Proliferation of COBs was confirmed by fluorescence intensity measurements using the Picogreen dsDNA reagent. Changes in ALP levels were evident only after 14 days of culture and mRNA expression levels for ALP, Col1a1 and Sclerostin remained constant during the culture period. Sirius red staining for collagen deposition also revealed little change until day 14. In contrast Raman spectroscopy revealed the presence of amorphous calcium phosphate (945-952 cm-1) and carbonated apatite (957-962 cm-1) after only 3 days in culture and octacalcium phosphate (970 cm-1) considered a transient mineral phase, was detected after 5 days of COBs culture. PCA analysis confirmed clear separation between time-points. This study highlights the potential of Raman spectroscopy to be utilised for the early and specific detection of proliferation and differentiation changes in primary cultures of bone cells.

Nanoparticles and intracellular applications of surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectrocopy (SERS) offers ultrasensitive vibrational fingerprinting at the nanoscale. Its non-destructive nature affords an ideal tool for interrogation of the intracellular environment, detecting the localisation of biomolecules, delivery and monitoring of therapeutics and for characterisation of complex cellular processes at the molecular level. Innovations in nanotechnology have produced a wide selection of novel, purpose-built plasmonic nanostructures capable of high SERS enhancement for intracellular probing while microfluidic technologies are being utilised to reproducibly synthesise nanoparticle (NP) probes at large scale and in high throughput. Sophisticated multivariate analysis techniques unlock the wealth of previously unattainable biomolecular information contained within large and multidimensional SERS datasets. Thus, with suitable combination of experimental techniques and analytics, SERS boasts enormous potential for cell based assays and to expand our understanding of the intracellular environment. In this review we trace the pathway to utilisation of nanomaterials for intracellular SERS. Thus we review and assess nanoparticle synthesis methods, their toxicity and cell interactions before presenting significant developments in intracellular SERS methodologies and how identified challenges can be addressed.

Visualizing electromagnetic fields at the nanoscale by single molecule localization.

Coupling of light to the free electrons at metallic surfaces allows the confinement of electric fields to subwavelength dimensions, far below the optical diffraction limit. While this is routinely used to manipulate light at the nanoscale, in electro-optic devices and enhanced spectroscopic techniques, no characterization technique for imaging the underlying nanoscopic electromagnetic fields exists, which does not perturb the field or employ complex electron beam imaging. Here, we demonstrate the direct visualization of electromagnetic fields on patterned metallic substrates at nanometer resolution, exploiting a strong "autonomous" fluorescence-blinking behavior of single molecules within the confined fields allowing their localization. Use of DNA-constructs for precise positioning of fluorescence dyes on the surface induces this distance-dependent autonomous blinking thus completely obviating the need for exogenous agents or switching methods. Mapping such electromagnetic field distributions at nanometer resolution aids the rational design of nanometals for diverse photonic applications.

Gold nanoparticles explore cells: cellular uptake and their use as intracellular probes.

Understanding uptake of nanomaterials by cells and their use for intracellular sensing is important for studying their interaction and toxicology as well as for obtaining new biological insight. Here, we investigate cellular uptake and intracellular dynamics of gold nanoparticles and demonstrate their use in reporting chemical information from the endocytotic pathway and cytoplasm. The intracellular gold nanoparticles serve as probes for surface-enhanced Raman spectroscopy (SERS) allowing for biochemical characterisation of their local environment. In particular, in this work we compare intracellular SERS using non-functionalised and functionalised nanoparticles in their ability to segregate different but closely related cell phenotypes. The results indicate that functionalised gold nanoparticles are more efficient in distinguishing between different types of cells. Our studies pave the way for understanding the uptake of gold nanoparticles and their utilisation for SERS to give rise to a greater biochemical understanding in cell-based therapies.

Single nanoparticle SERS probes of ion intercalation in metal-oxide electrodes.

Probing ion-intercalating processes in electrodes is hugely important for batteries, supercapacitors, and photovoltaic devices. In this work we use single-nanoparticle (NP) probes to see real-time molecular changes correlated to electrochemically modulated ion-intercalation in metal-oxide electrodes. Using surface-enhanced Raman spectroscopy (SERS) transduced by single NP probes, we observe that the Raman frequencies and spectral intensities of the adsorbed molecules vary on cycling the electrochemical potential on a vanadium-oxide electrode. The potential-dependent frequency shifts in SERS from an electrochemically inert molecule are attributed to a Stark effect induced by chemical and structural changes as a result of ion-intercalation processes in vanadium oxide. Our study opens up a unique strategy to explore adsorbates and molecular reaction pathways on ion-intercalating materials and semiconducting interfaces.

Intracellular SERS nanoprobes for distinction of different neuronal cell types.

Distinction between closely related and morphologically similar cells is difficult by conventional methods especially without labeling. Using nuclear-targeted gold nanoparticles (AuNPs) as intracellular probes we demonstrate the ability to distinguish between progenitor and differentiated cell types in a human neuroblastoma cell line using surface-enhanced Raman spectroscopy (SERS). SERS spectra from the whole cell area as well as only the nucleus were analyzed using principal component analysis that allowed unambiguous distinction of the different cell types. SERS spectra from the nuclear region showed the developments during cellular differentiation by identifying an increase in DNA/RNA ratio and proteins transcribed. Our approach using nuclear-targeted AuNPs and SERS imaging provides label-free and noninvasive characterization that can play a vital role in identifying cell types in biomedical stem cell research.

In situ SERS monitoring of photochemistry within a nanojunction reactor.

We demonstrate a powerful SERS-nanoreactor concept composed of self-assembled gold nanoparticles (AuNP) linked by the sub-nm macrocycle cucurbit[n]uril (CB[n]). The CB[n] functions simultaneously as a nanoscale reaction vessel, sequestering and templating a photoreaction within, and also as a powerful SERS-transducer through the large field enhancements generated within the nanojunctions that CB[n]s define. Through the enhanced Raman fingerprint, the real-time SERS-monitoring of a prototypical stilbene photoreaction is demonstrated. By choosing the appropriate CB[n] nanoreactor, selective photoisomerism or photodimerization is monitored in situ from within the AuNP-CB[n] nanogap.

Spindle Cell Sarcoma of the Maxilla: A Rare Entity, Case Report, and Review of the Literature.

Spindle cell sarcoma (SCS) is a malignancy, with the most recent Surveillance, Epidemiology, and End Results (SEER) data citing a total of 250 reported cases occurring in the head and neck. Of these cases, none originated in the maxillofacial hard tissue. The diagnosis of SCS requires its differentiation from other sarcomas and spindle cell neoplasms. Therefore, a comprehensive review to reinforce its inclusion in oral and maxillofacial surgeons' differential diagnosis for osseous neoplastic pathology is desired. In the present case report, we have described a recurrent case of Maxillary Undifferentiated Spindle cell sarcoma in a 11 year old male child post chemoradiotherapy which was excised intoto successfully .

Holistic vibrational spectromics assessment of human cartilage for osteoarthritis diagnosis.

Osteoarthritis (OA) is the most common degenerative joint disease, presented as wearing down of articular cartilage and resulting in pain and limited mobility for 1 in 10 adults in the UK [Osteoarthr. Cartil.28(6), 792 (2020)10.1016/j.joca.2020.03.004]. There is an unmet need for patient friendly paradigms for clinical assessment that do not use ionizing radiation (CT), exogenous contrast enhancing dyes (MRI), and biopsy. Hence, techniques that use non-destructive, near- and shortwave infrared light (NIR, SWIR) may be ideal for providing label-free, deep tissue interrogation. This study demonstrates multimodal "spectromics", low-level abstraction data fusion of non-destructive NIR Raman scattering spectroscopy and NIR-SWIR absorption spectroscopy, providing an enhanced, interpretable "fingerprint" for diagnosis of OA in human cartilage. This is proposed as method level innovation applicable to both arthro- or endoscopic (minimally invasive) or potential exoscopic (non-invasive) optical approaches. Samples were excised from femoral heads post hip arthroplasty from OA patients (n = 13) and age-matched control (osteoporosis) patients (n = 14). Under multivariate statistical analysis and supervised machine learning, tissue was classified to high precision: 100% segregation of tissue classes (using 10 principal components), and a classification accuracy of 95% (control) and 80% (OA), using the combined vibrational data. There was a marked performance improvement (5 to 6-fold for multivariate analysis) using the spectromics fingerprint compared to results obtained from solely Raman or NIR-SWIR data. Furthermore, clinically relevant tissue components were identified through discriminatory spectral features - spectromics biomarkers - allowing interpretable feedback from the enhanced fingerprint. In summary, spectromics provides comprehensive information for early OA detection and disease stratification, imperative for effective intervention in treating the degenerative onset disease for an aging demographic. This novel and elegant approach for data fusion is compatible with various NIR-SWIR optical devices that will allow deep non-destructive penetration.

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra.

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional Neural Network to automatically learn an optimal combination of pre-processing strategies, for the classification of Raman spectra of superficial and deep layers of cartilage harvested from 45 Osteoarthritis and 19 Osteoporosis (Healthy controls) patients. Using 6-fold cross-validation, the Multi-Convolutional Neural Network achieves comparable or improved classification accuracy against the best-performing Convolutional Neural Network applied to either the raw or pre-processed spectra. We utilised Integrated Gradients to identify the contributing features (Raman signatures) in the network decision process, showing they are biologically relevant. Using these features, we compared Artificial Neural Networks, Decision Trees and Support Vector Machines for the feature selection task. Results show that training on fewer than 3 and 300 features, respectively, for the disease classification and layer assignment task provide performance comparable to the best-performing CNN-based network applied to the full dataset. Our approach, incorporating multi-channel input and Integrated Gradients, can potentially facilitate the clinical translation of Raman spectroscopy-based diagnosis without the need for laborious manual pre-processing and feature selection.