Disease-modifying therapies for Tauopathies like Alzheimer’s disease have targeted Tau hyperphosphorylation and aggregation, as both pathological manifestations are implicated in Tau-mediated toxicity. More recently there has been a renewed interest in Tau conformation which appears to influence its toxic potential. However, the impact of Tau hyper-phosphorylation on its eventual conformation and toxic potential is less well known. Leveraging the genetic tractability of Drosophila , we generated multiple inducible human Tau transgenes with altered phosphorylation status and/or aggregation propensity. Their individual and combined impact was tested in vivo by quantifying Tau misfolding, accumulation and neurodegeneration in the aging fly nervous system. Results showed that phospho-mimicking Tau (hTau2N4R E14 ) induced profound neurodegeneration, supporting a neurotoxic role for phosphorylation. However, deletion of the aggregation-promoting 306 VQIVYK 311 motif in the microtubule-binding region altered the conformation of Tau and neurotoxicity was completely abolished. Intriguingly, overt aggregation of Tau into large filaments, detectable as high molecular weight species in biochemical preps, was not significantly reduced. Moreover, a peptide inhibitor targeting this same motif, that we have previously shown to promote off-pathway Tau aggregation, efficaciously reduced Tau-mediated behavioural deficits in aging Drosophila . Collectively we show that neurodegeneration mediated by Tau hyper-phosphorylation is gated via at least one aggregation-promoting motif of the protein, whether through a direct impact on Tau aggregation or through suppression of other mechanisms of toxicity mediated through this domain. Targeting the 306 VQIVYK 311 domain reduces the ability of hyper-phosphorylated Tau species to acquire the pathological conformation responsible for inducing neurotoxicity. This highlights the primacy of blocking the 306 VQIVYK 311 domain of Tau to alter Tau conformation, in emerging therapeutics, perhaps without the need to clear phosphorylated species.

Raman spectroscopy provides a label-free biochemical fingerprint of biofluids, but any single excitation wavelength probes only the resonance or non-resonance regime it couples to, leaving complementary information untapped. We apply multi-excitation Raman spectroscopy (MX-Raman) to human serum, combining porphyrin/heme-resonant 405 nm with carotenoid-resonant 532 nm and non-resonant 785 nm excitation to discriminate Alzheimer's disease (AD) from cognitively healthy controls in 64 individuals with cerebrospinal fluid biomarker-confirmed diagnosis. Using partial least squareslinear discriminant analysis (PLS-LDA) and five geometry-based separability metrics evaluated across latent dimensionalities with permutation testing, we show that fusion of 532 nm and 785 nm excitation is the only configuration to achieve significance across all five metrics, with separability exceeding that of either constituent wavelength alone. Critically, this advantage survives a dimensionality-matched sham (scrambled) control in which the pairing between wavelengths is broken while feature count is preserved, indicating this gain reflects genuine cross-wavelength complementarity rather than increased variable number. Supervised classification by support vector machine (SVM) independently confirms this, the 532-785 nm fusion discriminating AD from controls more accurately than either wavelength alone (area under the curve 0.80). This effect is specific to the 532-785 nm pairing, with 405 nm showing no separability above chance, and 405 nm-containing fusions exceeding their shams only at high latent dimensionality, where separability increasingly reflects model freedom rather than recoverable structure. A fluorescence-only control confirms the gains are Raman-specific, as although the 532 nm generated autofluorescence envelope carries discriminative structure in isolation, fusing it with Raman data yields no advantage over Raman alone. Complementarity in MX-Raman is therefore neither automatic nor monotonic in the number of excitations, but depends on whether a regime accesses information not already represented in the others. Given its reliance on inexpensive, portable diode lasers, MX-Raman offers a scalable route to serum-based triage, diagnosis and screening for neurodegenerative disease, pending validation in larger cohorts and predictive performance testing.

BACKGROUND:Reported accuracies of clinical Alzheimer's Disease (AD) diagnosis vary widely due to diagnostic inconsistency and patient heterogeneity. Raman spectroscopy is a label-free, laser-based method that can rapidly provide chemically rich information from biofluids. Despite this, AD classification using Raman spectroscopy has not yet been tested in a cohort representative of the clinical setting with appropriate statistical power. METHOD:Cerebrospinal fluid (CSF) samples were cross-sectionally assessed from a mixed clinical cohort of patients (N = 141) using Raman Spectroscopy. Raman spectra from AD (n = 66) and non-AD (n = 75) patients were divided into training at testing sets at a ratio of 80:20 and machine-learning (ML) models were trained, optimized and evaluated for AD classification. ML models included a support-vector machine (SVM) and a convolutional neural network (CNN). Area under the receiver operating characteristic curve (AUROC) analysis was used to assess classifier performance. To explain AD classification, key spectral features were extracted using the Mann-Whitney U test per Raman shift and spectral regions were integrated to provide univariate features that were corelated to ATN biomarker status. RESULT:Optimized ML models generalized well in the AD cohort with 93% classification accuracy observed for the SVM model (AUROC = 0.92, sensitivity = 0.92, specificity = 0.93). There was a strong correlation between the classifier score and patient ATN biomarker status. Patients with other neurodegenerative diseases were correctly classified as non-AD group including corticobasal degeneration (CBD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and Parkinson's disease. Important features for AD classification primarily included protein-derived aromatic amino acids particularly phenylalanine and tyrosine. 9 features were extracted and moderate correlations were observed for each feature with ATN biomarker status demonstrating the importance of the composite spectrum for classification. CONCLUSION:Our results demonstrate that Raman spectroscopy can accurately identify AD using CSF from a mixed clinical cohort in which other causes of dementia are prevalent. Although larger cross-sectional studies are required to assess whether accuracy is retained at a population level, establishing the utility of RS in a clinical population is an important first step towards the translation of optical biomarkers for supporting AD diagnosis in the future.

Raman spectroscopy is a powerful tool for molecular fingerprinting yet has been limited in clinical utility due to decrease in specificity when analyzing complex biological samples. Here we present MX-Raman, a multiexcitation Raman method that enhances molecular discrimination by fusing spectral data acquired with multiple laser wavelengths into a single, enhanced optical fingerprint. In this work, MX-Raman is applied to cerebrospinal fluid (CSF) samples from a heterogeneous dementia cohort, including Alzheimer's disease (AD), frontotemporal dementia (FTD) spectrum disorders, and non-neurodegenerative cognitive conditions, to achieve improved clustering and disease stratification. Furthermore, we extend the MX-Raman approach to blood plasma, demonstrating potential for a rapid, label-free, and minimally invasive diagnostic test for dementia detection and stratification. Overall, MX-Raman offers a scalable, optical technology platform that underscores how photonic advancements can address real-world clinical challenges.

There is a critical unmet need for scalable, accessible and objective diagnostic tests for stratification in dementia. Biofluid Raman spectroscopy (RS) due to its simplicity, holistic and label-free nature, is a powerful approach that has the potential to offer differential diagnosis across dementia types including Alzheimer’s disease (AD). RS is a laser-based optical method that can rapidly provide chemically rich information (‘spectral biomarkers’) from biofluids but its utility for AD diagnosis has not been established in a ‘real-world’ context, specifically from a clinically heterogenous cohort of patients. We carried out RS measurements on cerebrospinal fluid (CSF) samples of patients from a mixed clinical cohort (N = 143). All patients reported cognitive complaints and were clinically diagnosed over 2 years with conditions including AD and other neurodegenerative diseases, as well as developmental and long-term chronic conditions. Machine-learning algorithms were trained, optimised and evaluated on Raman spectra to classify AD from non-AD. AD was classified with 93

Cysteine-bound sulfane sulfur atoms in proteins have received much attention as key factors in cellular redox homeostasis. However, the role of sulfane sulfur in zinc regulation has been underinvestigated. In this study, we identified growth inhibitory factor (GIF)/metallothionein-3 (MT-3) as a sulfane sulfur-binding protein from mouse brain. We also report here that cysteine-bound sulfane sulfur atoms serve as ligands to hold and release zinc ions in GIF/MT-3 with an unexpected C–S–S–Zn structure. Oxidation of such a zinc/persulfide cluster in Zn7GIF/MT-3 results in the release of zinc ions, and intramolecular tetrasulfide bridges in apo-GIF/MT-3 efficiently undergo S–S bond cleavage by thioredoxin to regenerate Zn7GIF/MT-3. Three-dimensional molecular modeling confirmed the critical role of the persulfide group in the thermostability and Zn-binding affinity of GIF/MT-3. The present discovery raises the fascinating possibility that the function of other Zn-binding proteins is controlled by sulfane sulfur.

Reported accuracies of clinical Alzheimer's Disease (AD) diagnosis vary widely due to diagnostic inconsistency and patient heterogeneity. Raman spectroscopy is a label-free, laser-based method that can rapidly provide chemically rich information from biofluids. Despite this, AD classification using Raman spectroscopy has not yet been tested in a cohort representative of the clinical setting with appropriate statistical power. Cerebrospinal fluid (CSF) samples were cross-sectionally assessed from a mixed clinical cohort of patients ( N  = 141) using Raman Spectroscopy. Raman spectra from AD ( n  = 66) and non-AD ( n  = 75) patients were divided into training at testing sets at a ratio of 80:20 and machine-learning (ML) models were trained, optimized and evaluated for AD classification. ML models included a support-vector machine (SVM) and a convolutional neural network (CNN). Area under the receiver operating characteristic curve (AUROC) analysis was used to assess classifier performance. To explain AD classification, key spectral features were extracted using the Mann–Whitney U test per Raman shift and spectral regions were integrated to provide univariate features that were corelated to ATN biomarker status. Optimized ML models generalized well in the AD cohort with 93% classification accuracy observed for the SVM model (AUROC = 0.92, sensitivity = 0.92, specificity = 0.93). There was a strong correlation between the classifier score and patient ATN biomarker status. Patients with other neurodegenerative diseases were correctly classified as non-AD group including corticobasal degeneration (CBD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and Parkinson's disease. Important features for AD classification primarily included protein-derived aromatic amino acids particularly phenylalanine and tyrosine. 9 features were extracted and moderate correlations were observed for each feature with ATN biomarker status demonstrating the importance of the composite spectrum for classification. Our results demonstrate that Raman spectroscopy can accurately identify AD using CSF from a mixed clinical cohort in which other causes of dementia are prevalent. Although larger cross-sectional studies are required to assess whether accuracy is retained at a population level, establishing the utility of RS in a clinical population is an important first step towards the translation of optical biomarkers for supporting AD diagnosis in the future.

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.

Antimicrobial resistance (AMR) poses a global healthcare challenge, where overprescription of antibiotics contributes to its prevalence. We have developed a rapid multi-excitation Raman spectroscopy methodology (MX-Raman) that outperforms conventional Raman spectroscopy and enhances specificity. A support vector machine (SVM) model was used to identify 20 clinical isolates of Pseudomonas aeruginosa with an accuracy of 93% using MX-Raman. Antibiotic sensitivity profiles for tobramycin, ceftazidime, ciprofloxacin, and imipenem were generated for the bacterial strains and compared with their Raman spectral signatures using MX-Raman. The 20 clinical strains were distinguished according to AMR profiles. Nine models were assessed for AMR classification performance, and SVM performed best, classifying AMR profiles of each strain with 91-96% accuracy. These data provide the basis for a new rapid clinical diagnostic platform that could screen for bacterial infection and recommend effective antibiotic treatment ahead of confirmation by conventional techniques, improving clinical outcomes and reducing the spread of AMR.

Background: Disease-modifying therapies for tauopathies like Alzheimer′s disease have targeted Tau hyperphosphorylation and aggregation, as both pathological manifestations are implicated in Tau-mediated toxicity. However, the relative contributions of these pathology-linked changes to Tau neurotoxicity remain unclear. Methods: Leveraging the genetic tractability of Drosophila , we generated multiple inducible human Tau transgenes with altered phosphorylation status and/or aggregation propensity. Their individual and combined impact was tested in vivo by quantifying Tau accumulation and neurodegenerative phenotypes in the aging fly nervous system. Results: We report that phospho-mimicking Tau (hTau2N4RE14) induced profound neurodegeneration, supporting a neurotoxic role for phosphorylation. However, when we rendered hTau2N4RE14 aggregation incompetent, by deleting the 306VQIVYK311 motif in the microtubule-binding region, neurotoxicity was abolished. Moreover, a peptide inhibitor targeting this motif efficaciously reduced Tau toxicity in aging Drosophila . Conclusion: Neurodegeneration mediated by Tau hyperphosphorylation is gated via at least one aggregation-mediating motif on the protein. This highlights the primacy of blocking Tau aggregation in therapy, perhaps without the need to clear phosphorylated species. ### Competing Interest Statement The authors have declared no competing interest.

INTRODUCTION:As aggregation underpins Tau toxicity, aggregation inhibitor peptides may have disease-modifying potential. They are therefore currently being designed and target either the 306VQIVYK311 aggregation-promoting hotspot found in all Tau isoforms or the 275VQIINK280 aggregation-promoting hotspot found in 4R isoforms. However, for any Tau aggregation inhibitor to potentially be clinically relevant for other tauopathies, it should target both hotspots to suppress aggregation of Tau isoforms, be stable, cross the blood-brain barrier, and rescue aggregation-dependent Tau phenotypes in vivo. METHODS:We developed a retro-inverso, stable D-amino peptide, RI-AG03 [Ac-rrrrrrrrGpkyk(ac)iqvGr-NH2], based on the 306VQIVYK311 hotspots which exhibit these disease-relevant attributes. RESULTS:Unlike other aggregation inhibitors, RI-AG03 effectively suppresses aggregation of multiple Tau species containing both hotspots in vitro and in vivo, is non-toxic, and suppresses aggregation-dependent neurodegenerative and behavioral phenotypes. DISCUSSION:RI-AG03 therefore meets many clinically relevant requirements for an anti-aggregation Tau therapeutic and should be explored further for its disease-modifying potential for Tauopathies. HIGHLIGHTS:Our manuscript describes the development of a novel peptide inhibitor of Tau aggregation, a retro-inverso, stable D-amino peptide called RI-AG03 that displays many clinically relevant attributes. We show its efficacy in preventing Tau aggregation in both in vitro and in vivo experimental models while being non-toxic to cells. RI-AG03 also rescues a biosensor cell line that stably expresses Tau repeat domains with the P301S mutation fused to Cer/Clo and rescues aggregation-dependent phenotypes in vivo, suppressing neurodegeneration and extending lifespan. Collectively our data describe several properties and attributes of RI-AG03 that make it a promising disease-modifying candidate to explore for reducing pathogenic Tau aggregation in Tauopathies such as Alzheimer's disease. Given the real interest in reducing Tau aggregation and the potential clinical benefit of using such agents in clinical practice, RI-AG03 should be investigated further for the treatment of Tauopathies after validation in mammalian models. Tau aggregation inhibitors are the obvious first choice as Tau-based therapies as much of Tau-mediated toxicity is aggregation dependent. Indeed, there are many research efforts focusing on this therapeutic strategy with aggregation inhibitors being designed against one of the two aggregation-promoting hotspots of the Tau protein. To our knowledge, RI-AG03 is the only peptide aggregation inhibitor that inhibits aggregation of Tau by targeting both aggregation-promoting hotspot motifs simultaneously. As such, we believe that our study will have a significant impact on drug discovery efforts in this arena.

Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) has had a tremendous impact on humanity. Prevention of transmission by disinfection of surfaces and aerosols through a chemical-free method is highly desirable. Ultraviolet C (UVC) light is uniquely positioned to achieve inactivation of pathogens. We report the inactivation of SARS-CoV-2 virus by UVC radiation and explore its mechanisms. A dose of 50 mJ/cm2 using a UVC laser at 266 nm achieved an inactivation efficiency of 99.89%, while infectious virions were undetectable at 75 mJ/cm2 indicating >99.99% inactivation. Infection by SARS-CoV-2 involves viral entry mediated by the spike glycoprotein (S), and viral reproduction, reliant on translation of its genome. We demonstrate that UVC radiation damages ribonucleic acid (RNA) and provide in-depth characterization of UVC-induced damage of the S protein. We find that UVC severely impacts SARS-CoV- 2 spike protein's ability to bind human angiotensin-converting enzyme 2 (hACE2) and this correlates with loss of native protein conformation and aromatic amino acid integrity. This report has important implications for the design and development of rapid and effective disinfection systems against the SARS-CoV-2 virus and other pathogens.

Current methods for diagnosing acute and complex infections mostly rely on culture-based methods and, for biofilms, fluorescence in-situ hybridization. These techniques are labor-intensive and can take 2-4 days to return a test result, especially considering an extra culturing step required for the antibiotic susceptibility testing (AST). This places a significant burden on healthcare providers, delaying treatment and leading to adverse patient outcomes. Here, we report the complementary use of our newly developed multi-excitation Raman spectroscopy (ME-RS) method with whole-genome sequencing (WGS). Four WHO priority pathogens are AST phenotyped and their antimicrobial resistance (AMR) profile determined by WGS. On application of ME-RS method we find high correlation with the WGS characterization. Highly accurate classification based on the species (98.93%), wild-type/non-wild type (99.45%), and presence or absence of thick peptidoglycan layers in cell walls (100%), as well as at the individual strain level (99.29%). These results clearly demonstrate the potential of ME-RS as a rapid and first-stage tool for species, resistance and strain-level classification which can be followed up by WGS for confirmation. Such a workflow can facilitate efficient antimicrobial stewardship to handle and prevent the spread of AMR.

Tauopathies are a group of disorders in which the deposition of abnormally folded tau protein accompanies neurodegeneration. The development of methods for detection and classification of pathological changes in protein conformation are desirable for understanding the factors that influence the structural polymorphism of aggregates in tauopathies. We have previously demonstrated the utility of Raman spectroscopy for the characterization and discrimination of different protein aggregates, including tau, based on their unique conformational signatures. Building on this, in the present study, we assess the utility of Raman spectroscopy for characterizing and distinguishing different conformers of the same protein which in the case of tau are unique tau strains generated in vitro. We now investigate the impact of aggregation environment, cofactors, post-translational modification and primary sequence on the Raman fingerprint of tau fibrils. Using quantitative conformational fingerprinting and multivariate statistical analysis, we found that the aggregation of tau in different buffer conditions resulted in the formation of distinct fibril strains. Unique spectral markers were identified for tau fibrils generated using heparin or RNA cofactors, as well as for phosphorylated tau. We also determined that the primary sequence of the tau monomer influenced the conformational signature of the resulting tau fibril, including 2N4R, 0N3R, K18 and P301S tau variants. These results highlight the conformational polymorphism of tau fibrils, which is reflected in the wide range of associated neurological disorders. Furthermore, the analyses presented in this study provide a benchmark for the Raman spectroscopic characterization of tau strains, which may shed light on how the aggregation environment, cofactors and post-translational modifications influence tau conformation in vivo in future studies.

Specific proteins and their aggregates form toxic amyloid plaques and neurofibrillary tangles in the brains of people suffering from neurodegenerative diseases such as Alzheimer’s and Parkinson’s. It is important to study these conformational changes to identify and differentiate these diseases at an early stage so that timely medication is provided to patients. Mid-infrared spectroscopy can be used to monitor these changes by studying the line-shapes and the relative absorbances of amide bands present in proteins. This work focusses on the spectroscopy of the protein, Bovine Serum Albumin as an exemplar, and its aggregates using germanium on silicon waveguides in the 1900–1000 cm−1 (5.3–10.0 µm) spectral region.

Aggregation is a pathological hallmark of proteinopathies such as Alzheimer's disease and results in the deposition of β-sheet-rich amyloidogenic protein aggregates. Such proteinopathies can be classified by the identity of one or more aggregated proteins, with recent evidence also suggesting that distinct molecular conformers (strains) of the same protein can be observed in different diseases, as well is in subtypes of the same disease. Therefore, methods for the quantification of pathological changes in protein conformation are central to understanding and treating proteinopathies. In this work, the evolution of Raman spectroscopic molecular signatures of three conformationally distinct proteins, bovine serum albumin (α-helical-rich), β2-microglobulin (β-sheet-rich), and tau (natively disordered), was assessed during aggregation into oligomers and fibrils. The morphological evolution was tracked using atomic force microscopy and corresponding conformational changes were assessed by their Raman signatures acquired in both wet and dried conditions. A deconvolution model was developed which allowed us to quantify the conformation of the nonregular protein tau, as well as for the oligomeric and fibrillar species of each of the proteins. Principle component analysis of the fingerprint region allowed further identification of the distinguishing spectral features and unsupervised distinction. While an increase in β-sheet is seen on aggregation, crucially, however, each protein also retains a significant proportion of its native monomeric structure after aggregation. Thus, spectral analysis of each aggregated species, oligomeric, as well as fibrillar, for each protein resulted in a unique and quantitative "conformational fingerprint". This approach allowed us to provide the first differential detection of both oligomers and fibrils of the three different amyloidogenic proteins, including tau, whose aggregates have never before been interrogated using spontaneous Raman spectroscopy. Quantitative "conformational fingerprinting" by Raman spectroscopy thus demonstrates its huge potential and utility in understanding proteinopathic disease mechanisms and for providing strain-specific early diagnostic markers and targets for disease-modifying therapies.

Proteins in human samples can be used to detect the onset of a group of neurodegenerative diseases such as Alzheimer’s and Parkinson’s by studying their conformational (shape and structure) changes that can cause cognitive impairment. Proteins form aggregates from normal state (monomers) to disease state (amyloid deposition and fibril formation in central nervous system) that is associated with disease progression. These changes can be diagnosed and monitored using mid-infrared (MIR) absorption spectroscopy by studying line shapes and relative absorbance of amide bands. We have demonstrated MIR spectroscopy of proteins in three stages of aggregation: monomers, oligomers and fibrils of Bovine Serum Albumin (BSA) protein on a germanium on silicon (GOS) waveguide in the MIR wavelength region of 5.2 – 10 μm (1900 – 1000 cm-1). The protein samples were also characterised by atomic force microscopy to confirm their structure.