We report the development and application of a novel spectral barcoding approach that exploits our multiexcitation (MX) Raman spectroscopy-based methodology for improved label-free detection and classification of complex biological samples. To develop our improved MX-Raman methodology, we utilized post-mortem brain tissue from several neurodegenerative diseases (NDDs) that have considerable clinical overlap. For improving our methodology we used three sources of spectral information arising from distinct physical phenomena to assess which was most important for NDD classification. Spectral measurements utilized combinations of data from multiple, distinct excitation laser wavelengths and polarization states to differentially probe molecular vibrations and autofluorescence signals. We demonstrate that the more informative MX-Raman (532 nm-785 nm) spectra are classified with 96.7% accuracy on average, compared to conventional single-excitation Raman spectroscopy that resulted in 78.5% accuracy (532 nm) or 85.6% accuracy (785 nm) using linear discriminant analysis (LDA) on 5 NDD classes. By combining information from distinct laser polarizations we observed a nonsignificant increase in classification accuracy without the need of a second laser (785 nm-785 nm polarized), whereas combining Raman spectra with autofluorescence signals did not increase classification accuracy. Finally, by filtering out spectral features that were redundant for classification or not descriptive of disease class, we engineered spectral barcodes consisting of a minimal subset of highly disease-specific MX-Raman features that improved the unsupervised and cross-validated clustering of MX-Raman spectra. The results demonstrate that increasing spectral information content using our optical MX-Raman methodology enables enhanced identification and distinction of complex biological samples but only when that information is independent and descriptive of class. The future translation of such technology to biofluids could support diagnosis and stratification of patients living with dementia and potentially other clinical conditions such as cancer and infectious disease.

Vascular basement membranes (BMs), composed of laminins, collagen IV, fibronectin, and perlecan, are secreted by endothelial cells, pericytes, smooth muscle cells (SMCs), and astrocytes. In the brain, amyloid beta (Aβ) is eliminated along cerebrovascular BMs of capillaries and arteries as intramural periarterial drainage (IPAD). Ageing modifies vascular BMs, impairing IPAD and leading to Aβ deposition as cerebral amyloid angiopathy. To better understand the molecular determinants of IPAD in ageing, we quantified the relative abundance of BMs secreted by human-derived cerebral endothelial cells, pericytes, brain vascular SMCs, and astrocytes in vitro. We then assessed BM protein levels in SMCs under hypercapnia (8% CO) as a model of vascular ageing, with and without Aβ exposure. Of the four cell types, we found SMCs secreted the highest levels of fibronectin, laminin, and perlecan, whilst pericytes secreted the highest levels of collagen IV. Hypercapnia increased the expression of collagen IV and fibronectin in SMCs but decreased the expression of laminin. The expression of perlecan increased under hypercapnia, but only in the presence of Aβ. This work highlights the varying compositions of vascular BMs and the dynamic differential responses of SMCs to Aβ and hypercapnia, helping to elucidate the age-related changes that impair IPAD in cerebral vessels.

The role of surfactant proteins A and D (SP-A and SP-D) in lung clearance and translocation to secondary organs of inhaled nanoparticles was investigated by exposing SP-A and SP-D knockout (AKO and DKO) and wild type (WT) mice nose-only for 3 hours to an aerosol of 20 nm gold nanoparticles (AuNPs). Animals were euthanised at 0-, 1-, 7- and 28-days post-exposure. Analysis by inductively coupled plasma mass spectrometry (ICP-MS) of the liver and kidneys showed that extrapulmonary translocation was below the limits of detection. Imaging of the lungs by laser ablation ICP-MS confirmed the homogenous distribution of AuNPs. Coherent anti-Stokes Raman Scattering, Second Harmonic Generation and Two-Photon Fluorescence imaging were applied for semi-quantitative analysis of the uptake of AuNPs by alveolar macrophages and found uptake increased with time post-exposure, peaking after 7 days, and with the largest increase in uptake being in WT mice. Single particle ICP-MS allowed particle counting and sizing of AuNPs in the lungs showing that particle agglomeration following deposition within the lung was greater for the wildtype than the knockout models, indicating a role for SP-A and SP-D in agglomeration, however, any effect of this on overall lung clearance was minimal. For all groups, the Au (mass) lung burden initial clearance half-time was approximately 20-25 d, however, the AuNP (particle number) lung burden clearance half-time was shorter at approximately 10 days. In general terms, differences between the results for the three models were limited, indicating the preferential clearance of smaller particles from the lung.

Osteoarthritis (OA) is a complex disease of cartilage characterised by joint pain, functional limitation, and reduced quality of life with affected joint movement leading to pain and limited mobility. Current methods to diagnose OA are predominantly limited to X-ray, MRI and invasive joint fluid analysis, all of which lack chemical or molecular specificity and are limited to detection of the disease at later stages. A rapid minimally invasive and non-destructive approach to disease diagnosis is a critical unmet need. Label-free techniques such as Raman Spectroscopy (RS), Coherent anti-Stokes Raman scattering (CARS), Second Harmonic Generation (SHG) and Two Photon Fluorescence (TPF) are increasingly being used to characterise cartilage tissue. However, current studies are based on whole tissue analysis and do not consider the different and structurally distinct layers in cartilage. In this work, we use Raman spectroscopy to obtain signatures from the superficial (top) and deep (bottom) layer of healthy and osteoarthritic cartilage samples from 64 patients (19 control and 45 OA). Spectra were acquired both in the 'fingerprint' region from 700 to 1720 cm and high-frequency stretching region from 2500 to 3300 cm. Principal component and linear discriminant analysis was used to identify the peaks that contributed significantly to classification accuracy of the different samples. The most pronounced differences were observed at the proline (855 cm and 921 cm) and hydroxyproline (877 cm and 938 cm), sulphated glycosaminoglycan (sGAG) (1064 cm and 1380 cm) frequencies for both control and OA as well as the 1245 cm and 1272 cm, 1320 cm and 1345 cm, 1451 cm collagen modes were altered in OA samples, consistent with expected collagen structural changes. Classification accuracy based on Raman fingerprint spectral analysis of superficial and deep layer cartilage for controls was found to be 97% and 93% on using individual/all spectra and, 100% and 95% on using mean spectra per patient, respectively. OA diseased cartilage was classified with an accuracy of 88% and 84% for individual/all spectra, and 96% and 95% for mean spectra per patient based on analysis of the superficial and the deep layers, respectively. Raman spectra from the C-H stretching region (2500-3300 cm) resulted in high classification accuracy for identification of different layers and OA diseased cartilage but low accuracy for controls. Differential changes in superficial and deep layer cartilage signatures were observed with age (under 60 and over 60 years), in contrast, less significant differences were observed with gender. Prominent chemical changes in the different layers of cartilage were preliminarily imaged using CARS, SHG and TPF. Cell clustering was observed in OA together with differences in pericellular matrix and collagen structure in the superficial and the deep layers correlating with the Raman spectral analysis. The current study demonstrates the potential of Raman Spectroscopy and multimodal imaging to interrogate cartilage tissue and provides insight into the chemical and structural composition of its different layers with significant implications for OA diagnosis for an increasing aging demographic.

The parasagittal dura, a tissue that lines the walls of the superior sagittal sinus, acts as an active site for immune-surveillance, promotes the reabsorption of cerebrospinal fluid, and facilitates the removal of metabolic waste products from the brain. Cerebrospinal fluid is important for the distribution of growth factors that signal immature neurons to proliferate and migrate. Autism spectrum disorder is characterized by altered cerebrospinal fluid dynamics.

One of the hallmarks of Alzheimer's disease (AD) is the deposition of amyloid-β (Aβ) within the extracellular spaces of the brain as plaques and along the blood vessels in the brain, a condition also known as cerebral amyloid angiopathy (CAA). Clusterin (CLU), or apolipoprotein J (APOJ), is a multifunctional glycoprotein that has a role in many physiological and neurological conditions, including AD. The apolipoprotein E (APOE) is a significant genetic factor in AD, and while the primary physiological role of APOE in the brain and peripheral tissues is to regulate lipid transport, it also participates in various other biological processes, having three basic human forms: APOE2, APOE3, and APOE4. Notably, the APOE4 allele substantially increases the risk of developing late-onset AD. The main purpose of this review is to examine the roles of CLU and APOE in AD pathogenesis in order to acquire a better understanding of AD pathogenesis from which to develop targeted therapeutic approaches.

Around the world, individuals are living longer, but an increased average lifespan does not always equate to an increased healthspan. With advancing age, the increased prevalence of ageing-related diseases can have a significant impact on health status, functional capacity, and quality of life. It is therefore vital to develop comprehensive classification and staging systems for ageing-related pathologies, diseases and syndromes. This will allow societies to better identify, quantify, understand, and meet the healthcare, workforce, wellbeing, and socioeconomic needs of ageing populations, while supporting the development and utilisation of interventions to prevent or to slow, halt or reverse the progression of ageing-related pathologies.

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.

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.

There is growing evidence that inflammation impairs erythrocyte structure and function. We assessed the impact of mild systemic inflammation on erythrocyte fragility in three different settings. In order to investigate causation, erythrocyte osmotic fragility was measured in mice challenged with a live attenuated bacterial strain to induce low-grade systemic inflammation; a significant increase in erythrocyte osmotic fragility was observed. To gather evidence that systemic inflammation is associated with erythrocyte fragility in humans, two observational studies were conducted. First, using a retrospective study design, the relationship between reticulocyte-based surrogate markers of haemolysis and high-sensitivity C-reactive protein was investigated in 9292 healthy participants of the UK Biobank project. Secondly, we prospectively assessed the relationship between systemic inflammation (measured by the urinary neopterin/creatinine ratio) and erythrocyte osmotic fragility in a mixed population (n = 54) of healthy volunteers and individuals with long-term medical conditions. Both human studies were in keeping with a relationship between inflammation and erythrocyte fragility. Taken together, we conclude that mild systemic inflammation increases erythrocyte fragility and may contribute to haemolysis. Further research is needed to assess the molecular underpinnings of this pathway and the clinical implications in inflammatory conditions.

The relationship between amyloidosis and vasculature in cognitive impairment and Alzheimer's disease (AD) pathogenesis is increasingly acknowledged. We conducted a quantitative and topographic assessment of retinal perivascular amyloid plaque (AP) distribution in individuals with both normal and impaired cognition. Using a retrospective dataset of scanning laser ophthalmoscopy fluorescence images from twenty-eight subjects with varying cognitive states, we developed a novel image processing method to examine retinal peri-arteriolar and peri-venular curcumin-positive AP burden. We further correlated retinal perivascular amyloidosis with neuroimaging measures and neurocognitive scores. Our study unveiled that peri-arteriolar AP counts surpassed peri-venular counts throughout the entire cohort (P < 0.0001), irrespective of the primary, secondary, or tertiary vascular branch location, with a notable increase among cognitively impaired individuals. Moreover, secondary branch peri-venular AP count was elevated in the cognitively impaired (P < 0.01). Significantly, peri-venular AP count, particularly in secondary and tertiary venules, exhibited a strong correlation with clinical dementia rating, Montreal cognitive assessment score, hippocampal volume, and white matter hyperintensity count. In conclusion, our exploratory analysis detected greater peri-arteriolar versus peri-venular amyloidosis and a marked elevation of amyloid deposition in secondary branch peri-venular regions among cognitively impaired subjects. These findings underscore the potential feasibility of retinal perivascular amyloid imaging in predicting cognitive decline and AD progression. Larger longitudinal studies encompassing diverse populations and AD-biomarker confirmation are warranted to delineate the temporal-spatial dynamics of retinal perivascular amyloid deposition in cognitive impairment and the AD continuum.

Low-grade chronic inflammation is associated with many age-related conditions. Non-invasive methods to monitor low-grade chronic inflammation may improve the management of older people at risk of poorer outcomes. This longitudinal cohort study has determined baseline inflammation using neopterin volatility in monthly urine samples of 45 independent older adults (aged 65-75 years). Measurement of neopterin, an inflammatory metabolite, enabled stratification of individuals into risk categories based on how often in a 12-month period their neopterin level was raised. Hearing was measured (pure-tone audiometry) at baseline, 1 year and 3 years of the study. Results show that those in the highest risk category (neopterin raised greater than 50% of the time) saw greater deterioration, particularly in high-frequency, hearing. A one-way Welch's ANOVA showed a significant difference between the risk categories for change in high-frequency hearing (W (3, 19.6) = 9.164, p = 0.0005). Despite the study size and duration individuals in the highest risk category were more than twice as likely to have an additional age-related morbidity than those in the lowest risk category. We conclude that volatility of neopterin in urine may enable stratification of those at greatest risk of progression of hearing loss.

ALL_EARS@UoS is a patient and public involvement and engagement (PPIE) group for people with lived experience of hearing loss. The purpose of the group is to share experiences of hearing loss and hearing healthcare, inform research and improve services for patients at University of Southampton Auditory Implant Service. A year after inception, we wanted to critically reflect on the value and challenges of the group. Four members of ALL_EARS@UoS were recruited to an evaluation steering group. This paper reports the evaluation of the group using the UK Standards for Public Involvement.

A small but persistent proportion of individuals do not gain the expected benefit from cochlear implants(CI). A step-change in the understanding of factors affecting outcomes could come through data science. This study evaluates clinical data capture to assess the quality and utility of CI user's health records for data science, by assessing the recording of otitis media. Otitis media was selected as it is associated with the development of sensorineural hearing loss and may affect cochlear implant outcomes.

There are no disease-modifying treatments available for geographic atrophy (GA), the advanced form of dry age-related macular degeneration. Current murine models fail to fully recapitulate the features of GA and thus hinder drug discovery. Here we describe a novel mouse model of retinal degeneration with hallmark features of GA. We used an 810 nm laser to create a retinal lesion with central sparing (RLCS), simulating parafoveal atrophy observed in patients with progressive GA. Laser-induced RLCS resulted in progressive GA-like pathology with the development of a confluent atrophic lesion. We demonstrate significant changes to the retinal structure and thickness in the central unaffected retina over a 24-week post-laser period, confirmed by longitudinal optical coherence tomography scans. We further show characteristic features of progressive GA, including a gradual reduction in the thickness of the central, unaffected retina and of total retinal thickness. Histological changes observed in the RLCS correspond to GA pathology, which includes the collapse of the outer nuclear layer, increased numbers of GFAP + , CD11b + and FcγRI + cells, and damage to cone and rod photoreceptors. We demonstrate a laser-induced mouse model of parafoveal GA progression, starting at 2 weeks post-laser and reaching confluence at 24 weeks post-laser. This 24-week time-frame in which GA pathology develops, provides an extended window of opportunity for proof-of-concept evaluation of drugs targeting GA. This time period is an added advantage compared to several existing models of geographic atrophy.

Tauopathy is characterized by neuronal dysfunction and degeneration occurring as a result of changes to the microtubule-associated protein tau. The neuronal changes evident in tauopathy bear striking morphological resemblance to those reported in models of Wallerian degeneration. The mechanisms underpinning Wallerian degeneration are not fully understood although it can be delayed by the expression of the slow Wallerian degeneration (Wld) protein, which has also been demonstrated to delay axonal degeneration in some models of neurodegenerative disease. Given the morphological similarities between tauopathy and Wallerian degeneration, this study investigated whether tau-mediated phenotypes can be modulated by co-expression of Wld. In a model of tauopathy in which expression of human 0N3R tau protein leads to progressive age-dependent phenotypes, Wld was expressed with and without activation of the downstream pathway. The olfactory receptor neuron circuit was used for these studies in adults, and the larval motor neuron system was employed in larvae. Tau phenotypes studied included neurodegeneration, axonal transport, synaptic deficits and locomotor behaviour. Impact on total tau was ascertained by assessing total, phosphorylated and misfolded tau levels by immunohistochemistry. Activation of the pathway downstream of Wld completely suppressed tau-mediated degeneration. This protective effect was evident even if the pathway downstream of Wld was activated several weeks after tau-mediated degeneration had become established. Though total tau levels were not altered, the protected neurons displayed significantly reduced MC1 immunoreactivity suggestive of clearance of misfolded tau, as well as a trend for a decline in tau species phosphorylated at the AT8 and PHF1 epitopes. In contrast, Wld expression without activation of the downstream protective pathway did not rescue tau-mediated degeneration in adults or improve tau-mediated neuronal dysfunction including deficits in axonal transport, synaptic alterations and locomotor behaviour in tau-expressing larvae. This collectively implies that the pathway mediating the protective effect of Wld intersects with the mechanism(s) of degeneration initiated by tau and can effectively halt tau-mediated degeneration at both early and late stages. Understanding the mechanisms underpinning this protection could identify much-needed disease-modifying targets for tauopathies.

Evidence suggests that TNF inhibitors (TNFi) used to treat rheumatoid arthritis (RA) may protect against Alzheimer's disease progression by reducing inflammation.

Single photon emission tomography (SPECT) can detect early changes in brain perfusion to support the diagnosis of dementia. Inflammation is a driver for dementia progression and measures of inflammation may further support dementia diagnosis.

Neuroinflammation is an integral part of Alzheimer's disease (AD) pathology. Inflammatory mediators can exacerbate the production of amyloid-β (Aβ), the propagation of tau pathology and neuronal loss.