{"paper_id":"2f8f50a5-57c8-42d6-91a5-059b0b609481","body_text":"Raman Spectroscopic Identification of Biochemical Alterations in Alzheimer’s Disease Brain Tissue | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Raman Spectroscopic Identification of Biochemical Alterations in Alzheimer’s Disease Brain Tissue Samaneh Ghazanfarpour, Rahul Kumar Das, Ivanna Ihnatovych, Norbert Sule, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8844737/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by chronic inflammation, neuronal loss, and continuous decline in memory and cognitive function. Raman Spectroscopy (RS) offers a powerful, label‑free approach for detecting early biochemical alterations in AD by generating highly sensitive molecular fingerprints. This capability is particularly valuable for identifying subtle changes associated with protein misfolding, lipid dysregulation, and oxidative stress, key processes underlying AD onset and progression. In our study, full‑spectrum RS revealed clear biochemical distinctions between control and AD brain tissues, as well as between Braak IV and Braak VI AD stages. Multivariate analytical methods, including Singular Value Decomposition (SVD) and Linear Discriminant Analysis (LDA), were applied to manage spectral complexity and highlight the principal biochemical contributors to AD pathology. Several Raman bands showed increased intensity in AD samples, such as 445 cm⁻¹ (N–C–S/C–C skeletal modes), 481 cm⁻¹ (DNA phosphate stretching), 560 cm-1 (Glycogen, tyrosine); 690 -780 cm-1 (Nucleic Acids); 748 cm⁻¹ (DNA bending), 1080 cm-1 (DNA symmetric stretching vibrations in Phosphate bonds (PO)4-2); 1554 cm⁻¹ (Amide II), and 1585 cm⁻¹ (protein‑folding–related vibrations) and 1607 cm-1 (Aromatic Amino acids- Phenylalanine/tyrosine; Cell Senescence - necrosis marker). These increases indicate enhanced protein aggregation, nucleic‑acid structural changes, and backbone reorganization. Conversely, multiple bands decreased in AD tissue, including 880 cm⁻¹ (tryptophan deformation), 951–952 cm⁻¹ (CH₃ vibrations of α‑helical proteins), 1000 cm⁻¹ (phenylalanine), 1296 cm⁻¹ (lipid CH₂ deformation), 1440 cm⁻¹ (lipid/cholesterol deformation), 1640–1680 cm⁻¹ (Amide I), and 1732 cm⁻¹ (C=O stretching). These reductions reflect loss of ordered protein secondary structure, disruption of aromatic amino‑acid environments, and extensive lipid membrane disorganization. Complementary gene‑expression analysis further demonstrated dysregulation of lipid homeostasis in AD, with altered expression of ABCA1, LIPE, CPT1A, PPARA, and SREBP‑1, indicating broad metabolic reprogramming. Together, the coordinated spectral and transcriptional shifts underscore lipid‑metabolic dysfunction as a central feature of AD. By capturing these molecular signatures, RS provides a promising tool for early detection and monitoring of AD progression. Raman spectroscopy (RS) Singular Value Decomposition (SVD) Alzheimer’s disease (AD) Neurodegeneration Cognitive decline Neuroinflammation Spectroscopic fingerprints Protein misfolding Lipid dysregulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Alzheimer’s disease (AD) is the most common cause of dementia and is defined by progressive cognitive decline accompanied by amyloid‑β plaques and neurofibrillary tau tangles [ 1 – 2 ]. Despite extensive research, early diagnosis and accurate prognosis remain difficult due to the disease’s heterogeneity, variable progression, and complex molecular and genetic influences [ 3 – 6 ]. AD pathology evolves through the spatiotemporal spread of tau, leading to synaptic dysfunction and neuronal loss [ 7 – 10 ]. Although recent therapeutic advances offer modest benefits, AD remains incurable, and early detection is essential for effective intervention. Current diagnostic frameworks rely on biomarkers of β‑amyloid and tau deposition, metabolic impairment, and structural atrophy, typically assessed through imaging and cognitive evaluation [ 1 – 2 ]. However, precisely characterizing the density, distribution, and biochemical state of these pathological proteins remains challenging. Neuropathological staging systems, such as Braak scores, provide a framework for assessing the distribution and severity of tau pathology across brain regions. Braak staging provides a neuropathologically grounded framework that reflects the anatomical spread and biochemical maturation of neurofibrillary tau pathology and correlates closely with clinical disease severity [ 7 – 10 ]. Among these stages, Braak IV and Braak VI represent distinct biological phases that illuminate key transitions in AD progression [ 11 – 14 ]. Braak IV marks a pivotal point at which tau pathology extends into association cortices and symptoms often progress from mild cognitive impairment to early dementia. This stage is defined by emerging hyperphosphorylated tau oligomers, early β‑sheet formation, subtle membrane and lipid alterations, mitochondrial stress, and rising oxidative burden, changes that precede major neuronal loss and may still be partially reversible [ 13 – 14 ]. In contrast, Braak VI reflects end‑stage disease, characterized by widespread neocortical tau deposition, extensive synaptic and neuronal degeneration, severe mitochondrial failure, chronic inflammation, and collapse of proteostatic and lipid regulatory systems [ 14 ]. Tau aggregates at this stage are dominated by insoluble, highly ordered fibrils accompanied by profound lipid depletion, protein oxidation, and activation of cell‑death pathways [ 11 – 15 ]. These features represent consolidated, largely irreversible molecular signatures corresponding to severe dementia and minimal therapeutic responsiveness. Thus, Braak IV and Braak VI represent biologically and clinically distinct phases of disease onset and progression, making their comparison particularly informative for understanding AD pathogenesis and identifying opportunities for early detection and therapeutic intervention [ 7 – 10 ]. While Braak staging correlates with disease progression, it is limited in its ability to capture the biochemical heterogeneity that influences individual trajectories of cognitive decline [ 11 – 16 ]. Similarly, genetic risk factors such as the APOE ε4 allele is strongly associated with increased susceptibility to AD and accelerated pathology [ 17 ]. However, APOE genotype alone does not fully explain variability in disease onset or prognosis, underscoring the need for complementary approaches that can resolve molecular complexity at the cellular and tissue level. Traditional diagnostic modalities, including neuroimaging and cerebrospinal fluid assays, provide valuable insights but are often invasive, costly, or insufficiently sensitive to detect subtle biochemical changes in the earliest stages of AD [ 18 – 20 ]. This gap highlights the urgent need for label-free, nonperturbative techniques capable of probing molecular alterations directly within biological specimens. AD brain tissue serves as a controlled system to identify AD‑related molecular signatures and evaluate the sensitivity of Raman spectroscopy to early biochemical changes. These foundational insights are essential for future translation to accessible biofluids such as blood, where Raman‑based assays could ultimately have diagnostic value. Raman spectroscopy (RS) has emerged as a powerful tool for studying Alzheimer’s disease (AD), offering label-free biochemical insights at the molecular level [ 22 ]. Unlike conventional methods that rely on exogenous probes, RS enables direct interrogation of brain tissue and cellular specimens while fully preserving their structural, functional, and physiological integrity [ 23 ]. As a vibrational spectroscopic technique, RS generates unique biochemical fingerprints from Raman-active biomolecules, enabling detection of subtle molecular shifts associated with protein misfolding, lipid dysregulation, and oxidative stress, which are processes intimately linked to AD onset and progression [ 23 – 25 ]. By applying multivariate calibration and classification strategies such as Singular Value Decomposition (SVD) and Linear Discriminant Analysis (LDA), RS can disentangle complex spectral datasets, revealing principal biochemical factors that drive variability across heterogeneous brain tissues [ 25 – 26 ]. Our Raman spectroscopic analysis revealed distinct biochemical differences between control and Alzheimer’s disease (AD) brain tissues across protein, lipid, carbohydrate, and nucleic‑acid vibrational regions. Integrating RS into AD research may therefore bridge critical gaps between neuropathological staging, genetic risk stratification, and molecular diagnostics. Specifically, RS holds promise for refining the interpretation of Braak scores, and improving prognostic accuracy by capturing biochemical signatures that precede overt clinical symptoms. RS holds promise for refining the interpretation of Braak scores by adding a molecular dimension to a staging system that is currently based solely on the anatomical spread of tau pathology. By revealing molecular heterogeneity within the same Braak stage, identifying subtle biochemical transitions between stages, and detecting early pathological changes before overt tangle formation, RS can provide a more nuanced understanding of disease progression. This molecular resolution has the potential to strengthen the biological meaning of Braak scores and improve their alignment with clinical outcomes. Such advances could pave the way for earlier intervention strategies, personalized therapeutic approaches, and more precise monitoring of disease progression. Improving the accuracy and timeliness of AD diagnosis is essential for effective intervention and management. Reliable biomarker detection not only facilitates early identification of the disease but also aids in monitoring progression and evaluating therapeutic outcomes. Several recent studies have demonstrated the feasibility of applying RS to blood, plasma, serum, and peripheral cells, suggesting that Raman‑derived molecular markers identified in brain tissue may ultimately be detectable in minimally invasive samples [ 27 – 28 ]. Although, our current work uses brain tissue as a discovery platform, the long‑term clinical translational potential of our work includes blood‑based or other peripheral Raman assays, which could complement existing biomarkers and address the need for label‑free, nonperturbative, and cost‑effective diagnostic tools. As the global prevalence of AD continues to rise, enhancing diagnostic methodologies will play a critical role in reducing the economic and emotional burden on patients, families, and healthcare systems. Ultimately, RS offers a noninvasive, highly sensitive diagnostic modality that holds promise for identifying early-stage Alzheimer’s disease and monitoring its progression, paving the way for improved therapeutic interventions and patient outcomes. MATERIALS AND METHODS Sample Collection and Preparation The study sample was composed of post mortem tissue samples obtained from AD patients who were diagnosed based on NINCDS-ADRDA criteria and were obtained from the archived tissue bank from Baylor. The inclusion criteria for AD samples were (1) age ≥ 50 years old, (2) access to neuropsychological and clinical data. The exclusion criteria included (1) history of psychiatric and major depressive disorder prior to the onset of AD. Inclusion and exclusion criteria for Control samples were (1) ≥ 50 years old, (2) no history of neurological or medical illness that might impact cognitive function, and (5) no diagnosis of major depressive disorder. The study was approved by the Institutional Review Board (IRB) and all protocols were approved by the Ethics Committee of the Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA. The demographic and clinical information of the samples are shown in Table I. TABLE I: Demographic and Clinical information of the samples Brain Bank Design ID Diagnosis APO_E SEX Age at death (cal) Frozen PMI Plaque score (0 = none, 1 = sparse, 2 = moderate, 3 = dense) Braak stage (0–6) Baylor 3 AD F 78 24 hours CERAD C VI Baylor 15 AD 3,4 F 76 9.5 hours CERAD C IV NYBB T-139 NC M 57 N/A NYBB T-638 NC M 78 5.5 hours Raman Spectra Acquisition of AD brain tissue Paraffin-embedded sections (4 µm) of the AD and Control samples (n = 2/ group) were obtained and were used for the Raman spectral analysis. The parafilm peaks were eliminated by software-guided subtraction of the Raman spectra of standard parafilm from the acquired spectral data of samples. No staining or chemical treatment was applied to enable preservation of the native biochemical composition of the tissue for Raman analysis. Scattered Raman light from a laser beam focused on a tissue section provided detailed information about the molecular composition of the tissue at the microscopic level. Multiple spectra were obtained per tissue section to account for heterogeneity. Raman spectra were acquired by a commercial Raman micro-spectroscope (HORIBA XploRA PLUS) equipped with a 1024×256 TE air-cooled CCD chip (pixel size 26 µm, temperature − 60°C). Spectra were acquired using a 532 nm laser operating at a power of 0.065 W, with an 1800 grooves/mm grating, a slit width of 100 µm, and a pinhole diameter of 100 µm. Each spectrum was recorded with an acquisition time of 30 seconds and three accumulations to enhance signal quality. A 40x objective was employed for focusing, and a total of 100 spectra per sample were collected for analysis. Spectra were acquired over 200–2000cm⁻¹ (fingerprint region). Raman Data Processing and Analysis HORIBA LabSpec6 software was used for the initial data processing: smoothing, baseline removal (polynomial), and normalization (unit vector), necessary to enable subsequent quantitative analysis [ 25 – 29 ]. SVD analysis of the spectra was done using Python code to obtain the critical spectral features differentiating between the tissue samples. All Raman spectra from each imaging dataset were aggregated to form an input matrix for the SVD algorithm. In particular, the Raman spectra acquired from individual points within each sample were organized to generate a matrix of size m × n, where m denotes the number of data points per spectrum, and n indicates the total number of spectra within a specific sample [ 30 – 33 ]. In our study, these dimensions were 1116 X 200. Subsequently, we applied the SVD function in Python to decompose the input matrix into matrices U, Σ, and V T . The matrix V was utilized to create SVD scatter plots, whereas the individual SVD components were stored in the matrix U [ 34 – 36 ]. Each scatter plot was based on the leading SVD components and contained two data sets, AD’s vs. controls, or between the two AD samples belonging to the Braak stage IV vs Braak stage VI, where each spectrum was represented as a single point. A separating line was constructed using Linear Discriminant Analysis (LDA), a supervised classification technique that identifies the linear combination of features which maximizes the separation between classes by maximizing the distance between their means while minimizing the variance within each class [ 36 – 37 ]. A corresponding confusion matrix, summarizing the classification performance for each sample, was also generated. Additionally, the SVD components employed in the scatter plots, each of which contained the spectral features responsible for distinguishing the datasets, were plotted. Statistical Analysis Statistical analysis was done using GraphPad Prism (v8; GraphPad Software, Boston, MA). The comparison between the AD samples vs. controls, or between the two AD samples belonging to the Braak stage IV vs Braak stage VI, was done using a non‑parametric test Mann–Whitney U test (Wilcoxon rank‑sum test). All statistical analyses were performed using non‑parametric methods due to the small sample size and the inability to assume normal distribution of the data. A two‑tailed p‑value < 0.05 was considered statistically significant. For Raman analysis, Singular Value Decomposition (SVD) was used solely for visualization purposes, not for classification or model fitting [ 37 – 38 ]. This approach inherently avoids overfitting, as SVD was not part of the predictive pipeline. We selected the first six components based on their cumulative explained variance, which captured the most informative structure in the data while minimizing noise. Linear Discriminant Analysis (LDA) was applied for classification, and we ensured robustness by performing cross-validation during model evaluation. This helped assess generalizability and mitigate overfitting risks. The following flowchart represents our steps: BODIPY™ 581/591 C11 Lipid Peroxidation Sensor: Paraffin-embedded brain tissue sections (4 µm) were obtained from clinically and neuropathologically confirmed Alzheimer’s disease (AD) cases (n = 2) and age-matched non-demented controls (n = 2). Sections were briefly equilibrated to room temperature deparaffinized and lightly fixed in 4% paraformaldehyde for 5 min to preserve morphology while maintaining lipid integrity, followed by washing in PBS. Lipid peroxidation was assessed using the BODIPY™ 581/591 C11 Lipid Peroxidation Sensor (Thermo Fisher Scientific), prepared fresh at 2 µM in PBS containing 0.05% fatty-acid–free BSA. Sections were incubated with the probe for 30 min at 37°C in a humidified, light-protected chamber, washed three times in PBS, counterstained with DAPI when required, and mounted in aqueous antifade medium. Fluorescence imaging was done using the Revolve Discover ECHO microscope using identical acquisition settings across all samples, with oxidized probe (green) detected using 488-nm excitation/500–550-nm emission and the reduced form (red) using 561-nm excitation/580–620-nm emission. For each case, multiple non-overlapping fields were imaged and analyzed in Fiji/ImageJ. Mean fluorescence intensities for oxidized (green) and reduced (red) channels were background-subtracted, and lipid peroxidation was quantified as the green-to-red fluorescence ratio for each region. Values from all fields were averaged to generate one per-case measurement, enabling descriptive comparison between AD and control groups. Gene expression analysis from human brain tissue: Brain tissue samples were obtained from clinically and neuropathologically confirmed Alzheimer’s disease (AD) patients and age‑ and sex‑matched non‑demented controls (n = 10 per group. Samples from AD patients who were diagnosed based on NINCDS-ADRDA criteria and were obtained from the archived tissue bank from Baylor. Tissue blocks (~ 50–100 mg) were dissected from the same anatomical region (Pre-frontal cortex) to minimize regional variability. Samples were snap‑frozen in liquid nitrogen immediately after collection and stored at − 80°C until RNA extraction RNA Extraction: RNA was extracted from brain tissue samples using the TRIzol® reagent (Invitrogen-Life Technologies, Carlsbad, CA). The amount of RNA was quantified using a NanoDrop ND-1000 spectrophotometer (Nano-Drop™, Wilmington, DE), and isolated RNA was stored at -80°C until it was used. Real-Time Quantitative (RT-q) PCR: Total RNA (1000 ng) that was extracted as described above was utilized for the All-in-One Universal RT cDNA Master Mix Synthesis Kit (Lamba Biotech, St. Louis, MO, Cat #G209) following the Manufacturer’s protocol. One microliter of the resultant cDNA from the RT reaction was employed as the template in PCR reactions using well-validated PCR primers for IL-6, TNF-α, GPX-1, ABCA1, LIPE, CPT1A, PPARA, and SREBP‑1 obtained from RealTimePrimers.com; and the final primer concentration used in the PCR was 0.1 µM. We used the SYBR® Green master (Bio-Rad, Hercules, CA) following the Manufacturer’s QPCR protocol, and gene expression was calculated using the comparative CT method. The threshold cycle (Ct) of each sample was determined, and β-actin was used as the endogenous reference gene. The relative level of a transcript (2ΔCt) was calculated by obtaining ΔCt (test Ct − β-actin Ct), and transcript accumulation index (TAI) was calculated as TAI = 2 −ΔΔCT [ 39 – 40 ] RESULTS Raman Spectral Analysis: Raman spectroscopic analysis revealed distinct biochemical differences between control and Alzheimer’s disease (AD) brain tissues and between Braack IV and Braack VI AD stages. The following is a detailed description of the Raman spectral variation between AD samples vs controls. Raman spectroscopic analysis revealed distinct biochemical differences between control and Alzheimer’s disease (AD) brain tissues across the 445–1732 cm⁻¹ region (Fig. 1 [c-f]) and Table 1 ). AD samples exhibited significant increases in low‑frequency vibrational modes at 445, 481, and 748 cm⁻¹, reflecting altered protein–carbohydrate environments and pronounced perturbations in nucleic‑acid backbone structure consistent with DNA fragmentation and inflammatory activity. In contrast, several protein‑ and lipid‑associated bands showed marked reductions in AD tissue. [ 41 – 44 ]. Decreases at 880, 951–952, and 1000 cm⁻¹ indicated disruption of aromatic amino‑acid environments and loss of ordered α‑helical content, while diminished intensities at 1296 and 1440 cm⁻¹ pointed to compromised lipid‑chain organization and membrane structural integrity [ 45 ]. Protein backbone alterations were further supported by increased Amide II (1554 cm⁻¹) and 1585 cm⁻¹ aromatic‑backbone modes, accompanied by a pronounced reduction in the Amide I region (1640–1680 cm⁻¹), signifying broad secondary‑structure destabilization [ 46 – 48 ]. A decrease in the 1732 cm⁻¹ carbonyl band suggested additional alterations in steroid‑ and lipid‑derived carbonyl species. Also, an enhanced signal at 560 cm⁻¹, corresponding to glycogen and tyrosine‑related skeletal modes, was observed in AD samples, suggesting altered carbohydrate storage and amino‑acid microenvironments. A broad increase in the 690–780 cm⁻¹ region, associated with nucleic‑acid vibrations, indicated elevated contributions from DNA/RNA structural components [ 47 – 49 ]. AD tissue also exhibited a marked rise in the 1080 cm⁻¹ band, representing symmetric phosphate stretching of DNA (PO₄²⁻), consistent with nucleic‑acid backbone perturbation and the 1607 cm⁻¹ band is linked to aromatic amino acids such as phenylalanine and tyrosine and often associated with protein senescence or necrotic processes, also showed pronounced elevation in AD samples [ 50 ]. Together, these spectral increases highlight distinct biochemical alterations in AD brain tissue across carbohydrate, nucleic‑acid, and protein‑related vibrational domains. Collectively, these spectral changes indicate coordinated disruptions in nucleic acids, proteins, and lipids that characterize AD‑associated molecular pathology [ 49 – 51 ]. Table 1 Raman Spectral Bands and their Biochemical Structural Assignment Raman Band (1/cm) Intensity Change (Control vs AD) Biological Relevance 445 Increase N-C-S stretch in proteins and C-C bond stretch in carbohydrates 481 Increase DNA stretching vibrations in phosphate bonds (PO)4 2- 560 Increase Glycogen, tyrosine 690–780 Increase Nucleic Acid band 748 Increase DNA bending vibration 880 Decrease Tryptophan, 𝛿 (ring) disorentation 951 Decrease 𝑣(CH 3 ) of proteins (α-helix) foldings 1000 Decrease Phenylalanine ring resonance 1080 Increase DNA symmetric stretching vibrations in Phosphate bonds (PO) 4 −2 1296 Decrease CH 2 deformation in lipid aliphatic chains 1440 Decrease CH 2 and CH 3 deformation vibrations CH deformation, Cholesterol, fatty acid band, 𝛿 (CH 2 ) (lipids) 1554 Increase Amide II band in proteins 1585 Increase Protein folding assignment 1607 Increase Aromatic Amino acids (Phenylalanine/tyrosine) Cell Senescence- necrosis marker 1640–1680 Decrease Amide I in protein band 1732 Decrease One of absorption positions for the C = O stretching vibrations of cortisone A heatmap depicting the distribution range of Raman spectral bands (560–1730 cm⁻¹) under three experimental conditions: Control, Braack IV, and Braack VI are shown in Fig. 1 f. Analysis of the Raman spectral distribution revealed condition-dependent variability across several key vibrational bands. Control samples exhibited consistently low distribution ranges across the spectrum, suggesting molecular homogeneity. In contrast, Braack IV samples showed moderate increases in variability, particularly at 720 cm⁻¹, 960 cm⁻¹, and 1440 cm⁻¹, indicating early biochemical alterations. Braack VI samples demonstrated the highest distribution ranges, with pronounced variability in bands near 720 cm⁻¹, 960 cm⁻¹, 1240 cm⁻¹, and 1440 cm⁻¹. These shifts suggest extensive molecular changes associated with advanced neurodegenerative pathology [ 52 – 53 ]. Panel I shows Clear separation between the control AD groups is observed, indicating that all SVD components effectively capture the variance in the data. Fractions of explained variance in Control sample and Braak IV are 0.636 (SVD1) and 0.035 (SVD2), and 0.623 (SVD1) and 0.055 (SVD2) for Control sample and Braak VI and 0.612 (SVD1) and 0.057 (SVD2) for Braak IV and Braak VI samples (Fig. 2 [a-l]). Separator lines generated by LDA show the boundaries between the Control region and each AD group and between Braak IV and Braak VI. Panel II shows confusion matrices, demonstrating classification performance using all SVD components for comparisons between controls and each AD group and between Braak IV and Braak VI respectively, showing, with 65 out of 100 for control samples and all 55 for Braak IV ( Fig. 2 b) and 98 out of 100 for control samples and 99 for Braak VI AD samples (Fig. 2 f) and 98 out of 100 for control samples and 99 out of 100 for Braak VI AD samples and 90 for Braak VI ( Fig. 2 j). Panel III shows Receiver operating characteristic (ROC) curve analysis confirmed the classification performance between control and each AD group and between Braak IV and Braak VI samples yielding an accuracy of 0.60, precision of 0.611, and an F1 score of 0.579 for the control vs Braak IV samples (Fig. 2 c) and an accuracy of 0.985, precision of 0.980, and an F1 score of 0.985 for the control vs Braak VI samples (Fig. 2 g) and an accuracy of 0.945, precision of 0.989, and an F1 score of 0.942 for the Braak IV vs Braak VI samples respectively (Fig. 2 k). Panel IV outlines spectral differentiation across samples showing comparative signal shifts between control vs Braak IV samples (Fig. 2 d); control vs Braak VI samples (Fig. 2 h) and Braak IV vs Braak VI samples (Fig. 2 l) respectively. Analysis of gene expression in age and sex matched post‑mortem brain tissue revealed substantial alterations in pathways related to lipid metabolism, inflammation, and oxidative stress in Alzheimer’s disease (AD). Quantitative PCR measurements demonstrated marked dysregulation of genes involved in lipid breakdown, fatty‑acid oxidation, and lipid synthesis, as well as key inflammatory cytokines and the antioxidant enzyme GPX1. Expression of LIPE, a critical lipolytic enzyme, was reduced by 91% in AD samples compared with controls (TAI = 0.09 ± 0.012; p < 0.0001). Similarly, the lipid transporter ABCA1 showed a 78% decrease (TAI = 0.22 ± 0.11; p < 0.01), and PPARA, a regulator of fatty‑acid oxidation, was reduced by 83% (TAI = 0.17 ± 0.05; p < 0.0001). Expression of GPX1, which encodes the antioxidant enzyme glutathione peroxidase‑1, was also significantly diminished, showing a 73% decrease in AD tissue (TAI = 0.27 ± 0.03; p < 0.001). Genes associated with lipid synthesis and fatty‑acid oxidation were upregulated. SREBP‑1 expression increased by 46% (TAI = 1.46 ± 0.13; p < 0.05), and CPT1A, a key enzyme in mitochondrial fatty‑acid transport, increased by 62% (TAI = 1.62 ± 0.07; p < 0.01). Markers of neuroinflammation were also elevated. TNF‑α expression was 59% higher in AD samples (TAI = 1.59 ± 0.089; p < 0.01), and IL‑6 expression increased by 61% (TAI = 1.61 ± 0.071; p < 0.01). Together, these findings indicate a coordinated disruption of lipid metabolic pathways, heightened inflammatory signaling, and reduced antioxidant capacity in AD brain tissue. Lipid peroxidation, assessed using the BODIPY™ 581/591 C11 probe, was markedly elevated in Alzheimer’s disease (AD) brain tissue compared with age‑matched controls. AD sections exhibited a pronounced shift from reduced (red) to oxidized (green) fluorescence, indicating increased peroxidation of lipid droplets, whereas control samples showed predominantly red signal (Fig. 3 m). Quantification of green‑to‑red fluorescence ratios confirmed consistently higher lipid oxidation in AD cases. Stratification by neuropathological stage revealed a clear progression of oxidative damage: Braak IV tissue showed a 22% increase (p < 0.05) in lipid peroxidation relative to control, while Braak VI tissue demonstrated a substantially greater 80% increase (p < 0.01), accompanied by more extensive punctate green aggregates throughout affected cortical regions. These findings, illustrated in Fig. 3 n, highlight a stage‑dependent escalation in lipid peroxidation and lipid aggregation with advancing tau pathology, supporting a link between oxidative lipid damage and late‑stage AD neurodegeneration. DISCUSSION Alzheimer’s disease involves a constellation of interconnected patho-mechanisms, which include amyloid‑β aggregation, tau misfolding and fibrillization, oxidative stress, lipid membrane disruption, mitochondrial dysfunction, and progressive synaptic degeneration, all of which collectively drive neurodegeneration [ 54 – 58 ]. Each of these processes produces distinct biochemical alterations in proteins, lipids, and nucleic acids, many of which manifest as changes in molecular structure, bond vibrations, and chemical composition. Raman spectroscopy (RS), with its sensitivity to protein secondary structure, β‑sheet enrichment, lipid saturation and degradation, oxidative modifications, and metabolic shifts, is uniquely positioned to detect these molecular signatures directly within tissue. In recent years, Raman spectroscopy (RS) has emerged as a promising, non-invasive analytical technique for the detection of AD-related biochemical changes [ 59 ]. In our study, Raman spectroscopic analysis revealed clear biochemical distinctions between control and Alzheimer’s disease (AD) brain tissues and between Braak IV and Braak VI AD stages, across multiple spectral regions. Several low‑frequency vibrational bands showed consistent increases in intensity in AD samples, indicating structural and molecular alterations associated with neurodegeneration. These Raman‑derived biochemical fingerprints align closely with known AD mechanisms, demonstrating that RS not only detects but also differentiates key pathological processes, thereby validating its utility as a molecularly resolved tool for probing AD progression. Raman-derived biochemical signatures at Braak IV reflect early, heterogeneous, and potentially modifiable disease processes, whereas Braak VI spectra capture the stabilized molecular fingerprint of end-stage neurodegeneration. These features make Raman spectroscopy particularly powerful for identifying early disease-associated biochemical patterns that are obscured or lost at later stages. A marked increase at 445 cm⁻¹ was observed in AD tissues, reflecting enhanced N–C–S bending and C–S stretching in proteins, along with C–C skeletal vibrations from carbohydrates. This heightened signal suggests altered environments around sulfur‑containing amino acids, changes in disulfide bonding, and increased rigidity within aggregated proteins such as amyloid‑β and hyperphosphorylated tau, both of which contain sulfur‑bearing amino acids whose local environments shift during misfolding and fibrillization [ 60 ]. The contribution from carbohydrate‑related modes also points to disrupted glucose metabolism and glycan processing, indicating broader biochemical remodeling in plaque‑ and tangle rich regions [ 60 ]. The 481 cm⁻¹ band likewise showed elevated intensity in AD samples. This feature, linked to symmetric phosphate stretching in DNA, suggests changes in nucleic acid structure or abundance, potentially due to DNA fragmentation, chromatin alterations, or extracellular DNA associated with inflammation and degeneration [ 41 – 48 ]. Together, the enhanced 445 cm⁻¹ and 481 cm⁻¹ Raman signals highlight protein, carbohydrate, and nucleic‑acid‑related molecular alterations in AD tissue, underscoring Raman spectroscopy’s sensitivity to subtle biochemical signatures of neurodegeneration. The increased intensity of specific Raman bands in AD tissue reflects widespread molecular remodeling characteristic of neurodegeneration. The elevation at 560 cm⁻¹ suggests disruptions in glycogen metabolism and altered tyrosine‑containing protein structures, both of which have been implicated in impaired neuronal energy homeostasis and oxidative stress responses in AD. Enhanced nucleic‑acid–related signals in the 690–780 cm⁻¹ region, together with the strengthened 1080 cm⁻¹ phosphate band, point toward DNA/RNA structural instability, chromatin remodeling, or increased nucleic‑acid fragmentation, which are features commonly associated with neuroinflammation, oxidative damage, and impaired genomic maintenance in AD neurons [ 41 – 48 ]. The 748 cm⁻¹ band also showed a clear increase in AD tissue, reflecting DNA bending and deformation modes. This enhancement supports the presence of nucleic‑acid structural alterations associated with AD, including genomic instability, oxidative DNA damage, and nucleic‑acid release during cell death. The elevated signal likely reflects DNA conformational stress, fragmentation, or changes in chromatin compaction [ 61 – 64 ]. Together with other spectral shifts, this increase highlights Raman spectroscopy’s sensitivity to nucleic‑acid remodeling in AD. A notable decrease was observed at 880 cm⁻¹ in AD samples. This band, linked to tryptophan vibrations, is highly sensitive to protein tertiary structure and its reduction suggests increased protein disorder, misfolding, and aggregation, which are hallmarks of amyloid‑β and tau pathology, as well as possible oxidative modification of aromatic residues [ 64 – 68 ]. Similarly, the 951–952 cm⁻¹ band decreased in AD tissue, consistent with α‑helix destabilization and the transition of proteins toward β‑sheet‑rich, aggregated states. Oxidative stress and proteolytic activity in AD may further diminish this CH₃ vibrational signature [ 61 – 67 ]. Overall, the combined increases at 445, 481, and 748 cm⁻¹ and decreases at 880 and 951–952 cm⁻¹ reflect coordinated biochemical remodeling of proteins, carbohydrates, and nucleic acids in AD brain tissue, underscoring Raman spectroscopy’s utility for detecting molecular signatures of Alzheimer’s pathology. The 1000 cm⁻¹ band, corresponding to phenylalanine ring‑breathing, showed reduced intensity in AD tissue. This decrease reflects disruptions in aromatic amino‑acid environments and is consistent with widespread protein misfolding, β‑sheet–rich aggregation of amyloid‑β and tau, and oxidative or proteolytic damage [ 61 – 68 ]. The 1296 cm⁻¹ band also decreased in AD samples. This CH₂ deformation mode reports on lipid‑chain organization, and its attenuation aligns with AD‑related membrane degradation, altered phospholipid composition, and oxidative lipid damage, indicating broader disruption of membrane structure [ 69 ]. A similar reduction occurred at 1440 cm⁻¹, a region dominated by CH₂/CH₃ deformation vibrations from lipids, cholesterol, and fatty‑acid chains. The diminished signal reflects impaired membrane integrity and lipid homeostasis, reinforcing the extensive lipid dysregulation characteristic of AD [ 69 ]. In contrast, the 1554 cm⁻¹ Amide II band increased in AD tissue. This enhancement reflects altered N–H bending and C–N stretching associated with protein backbone reorganization during amyloid‑β and tau aggregation, as well as additional structural modifications driven by inflammation and oxidative stress [ 46 – 48 ]. The 1585 cm⁻¹ band likewise increased, indicating changes in aromatic‑ring and backbone vibrations linked to protein folding and higher‑order structural transitions. This rise is consistent with β‑sheet–rich fibril formation and oxidative modification of protein assemblies [ 66 ]. Together, these spectral changes, decreases in lipid‑ and aromatic‑related bands and increases in protein‑backbone modes, highlight coordinated disruptions in protein structure, lipid organization, and overall biochemical architecture in AD brain tissue. The pronounced increase at 1607 cm⁻¹, representing aromatic amino‑acid vibrations and linked to protein senescence and necrotic pathways, further supports the presence of advanced protein damage and aggregation. This aligns with known AD hallmarks, including accumulation of oxidized proteins, misfolded aggregates, and heightened cellular stress responses [ 61 – 68 ]. The combined elevation of these Raman features underscores the multifactorial biochemical deterioration occurring in AD brain tissue, reflecting converging disruptions in energy metabolism, nucleic‑acid integrity, and protein homeostasis. The 1640–1680 cm⁻¹ region, corresponding to the Amide I band, showed reduced intensity in AD tissue. Because this band reflects C = O stretching and is highly sensitive to protein secondary structure, its attenuation indicates destabilization of α‑helical and β‑sheet content. This decrease aligns with the extensive protein misfolding and the shift of amyloid‑β and tau toward β‑sheet‑rich fibrillar aggregates, reinforcing evidence of widespread structural disruption in AD [ 68 ]. The 1732 cm⁻¹ band also decreased in AD samples. This carbonyl‑stretching region is associated with cortisone and related steroid structures. Its reduction is consistent with AD‑related metabolic dysregulation, including impaired steroidogenesis, altered glucocorticoid signaling, and oxidative modification of lipid and steroid backbones [ 61 – 68 ]. The diminished signal reflects broader disruption of carbonyl‑containing molecules and complements the protein, lipid, and nucleic‑acid alterations observed across the Raman spectrum. The comparative analysis of Braak IV and Braak VI brain tissue enabled delineation of biochemical changes that occur early in disease progression from those that represent downstream consequences of irreversible neuronal injury. As depicted in the heat map shown in Fig. 4 , the progressive increase in Raman spectral variability from Control to Braack VI reflects escalating biochemical heterogeneity, likely driven by tau-associated neurodegeneration. The bands exhibiting the most pronounced changes such as 720 cm⁻¹ (nucleic acid vibrations), 960 cm⁻¹ (phosphate backbone), and 1240–1440 cm⁻¹ (protein and lipid signatures), are consistent with known molecular disruptions in Alzheimer’s disease progression. The elevated distribution range in Braack VI samples suggests widespread molecular disorganization, potentially reflecting synaptic loss, gliosis, and altered lipid metabolism. These findings support the utility of Raman spectroscopy as a sensitive tool for detecting molecular alterations across Braack stages. The spectral bands identified may serve as candidate biomarkers for staging and monitoring disease progression, offering insights into the biochemical landscape of tauopathy. Identifying molecular features that emerge at Braak IV but intensify or become fixed at Braak VI is essential for defining early biomarkers, therapeutic targets, and mechanistic drivers of AD progression. From a translational perspective, the ability of Raman spectroscopy to distinguish Braak IV from Braak VI based on intrinsic biochemical signatures supports its potential utility as a platform for early AD detection, disease staging, and therapeutic stratification. By defining spectroscopic markers that precede extensive neuronal loss, this approach may inform the development of diagnostics and interventions aimed at intercepting AD during its most treatable phase. Alzheimer’s disease profoundly disrupts lipid homeostasis. Genes involved in lipid breakdown, fatty‑acid oxidation, and lipid synthesis show altered expression in AD brain tissue. Genes ABCA1 (ATP Binding Cassette Subfamily A Member 1), LIPE (Lipase), CPT1A (carnitine palmitoyl transferase 1A enzyme), PPARA (Peroxisome Proliferator-activated receptor-alpha), and SREBP‑1 (Sterol Regulatory Element-Binding Protein 1) represent key nodes in this metabolic network [ 69 – 75 ]. We observed modulations of these genes associated with Lipid metabolism in AD brain tissue samples. ABCA1 plays a central role in the brain’s response to disrupted lipid metabolism in Alzheimer’s disease. As AD progresses, neurons and glial cells accumulate excess free cholesterol, along with elevated levels of oxidized cholesterol derivatives that rise during oxidative stress and neuroinflammation. These lipid disturbances also drive the formation of lipid droplets, a hallmark of metabolic stress in affected brain regions. ABCA1 function is often impaired in AD, and reflects metabolic stress and defective lipid clearance [ 69 ]. LIPE, is a major lipase responsible for mobilizing stored triglycerides, showed reduced expression relative to control tissue, consistent with the lipid droplet accumulation widely reported in AD astrocytes and microglia [ 71 ]. CPT1A, is the rate‑limiting enzyme for mitochondrial fatty‑acid β‑oxidation, was found to be upregulated, in AD samples, reflecting a compensatory metabolic shift toward fatty‑acid utilization in the context of impaired glucose metabolism [ 72 – 73 ]. This pattern aligns with the broader metabolic reprogramming observed in AD. PPARA, a nuclear receptor that promotes fatty‑acid oxidation and suppresses inflammation, exhibited reduced expression in AD samples, suggesting insufficient transcriptional support for lipid clearance pathways [ 74 ]. SREBP‑1, is believed to be a master regulator of fatty‑acid and triglyceride synthesis, and its expression was increased in AD samples, reinforcing that a lipid‑anabolic state that may exist that exacerbates lipid droplet formation and metabolic stress [ 75 ]. Together, these gene‑expression changes point to a coordinated imbalance in lipid synthesis, breakdown, and oxidation, contributing to the metabolic dysfunction that characterizes the Alzheimer’s disease brain. We observed reduced GPX1 gene expression in the brains AD patients, which results in increased oxidative stress. Reduced GPX1 activity is associated with cognitive decline, and its deficiency worsens Amyloid beta-induced neurotoxicity [ 76 ]. TNF‑α and IL‑6 are two of the most consistently elevated cytokines in the Alzheimer’s disease brain, where they drive the chronic neuroinflammatory environment that accelerates neurodegeneration [ 77 – 78 ]. Activated microglia and astrocytes surrounding amyloid‑β plaques release high levels of TNF‑α, which in turn activates NF‑κB signaling and stimulates the production of inflammatory mediators, like IL‑6. This creates a self‑reinforcing inflammatory loop that impairs microglial clearance of amyloid‑β, increases amyloid precursor protein processing, and promotes tau phosphorylation. Elevated IL‑6 further amplifies microglial activation, oxidative stress, and acute‑phase responses, contributing to synaptic dysfunction and neuronal loss [ 77 – 81 ]. Both cytokines are found at increased levels in AD brain tissue but also in cerebrospinal fluid and peripheral blood, suggesting that central and systemic inflammation are interconnected in AD [ 82 – 84 ]. Our study demonstrates a clear increase in lipid peroxidation in Alzheimer’s disease (AD) brain tissue compared with age‑matched controls, as revealed by oxidation‑dependent spectral shifts of the BODIPY™ 581/591 C11 probe. The elevated green‑to‑red fluorescence ratios observed in AD samples support the concept that oxidative damage to membrane lipids is a prominent feature of AD pathology. These findings align with extensive evidence implicating oxidative stress as an early and persistent driver of neurodegeneration, particularly in regions vulnerable to amyloid and tau accumulation. Notably, the greater lipid peroxidation and more pronounced lipid aggregate formation in Braak stage VI compared with Braak stage IV suggests that oxidative membrane injury intensifies with advancing tau pathology. This progression is consistent with models proposing that mitochondrial dysfunction, impaired antioxidant defenses, and iron‑dependent lipid oxidation (including ferroptosis‑related mechanisms) become increasingly dysregulated in late‑stage AD [ 70 – 75 ]. The spatial pattern of punctate oxidized lipid aggregates observed in Braak VI tissue further supports the idea that chronic oxidative stress contributes to membrane destabilization, synaptic vulnerability, and neuronal loss. Although the small sample size limits statistical inference, the consistency of the ratiometric shift across cases underscores the robustness of lipid peroxidation as a pathological marker. Together, these results reinforce the view that lipid oxidative damage is not merely a byproduct of AD pathology but may actively contribute to disease progression, highlighting the potential value of therapeutic strategies aimed at stabilizing membrane lipids or modulating lipid‑oxidation pathways. Overall, RS provides a molecular fingerprint of biofluids and tissues by measuring vibrational modes of biomolecules, enabling the identification of subtle alterations associated with amyloid-beta and tau proteins, oxidative stress, and lipid peroxidation. Unlike conventional imaging methods, RS offers rapid, label-free analysis and can be enhanced through advanced computational approaches such as machine learning. Integrating RS into AD diagnostics may improve sensitivity and specificity, particularly in the early stages of disease, thereby supporting timely intervention and personalized care strategies. Raman spectroscopy has shown considerable promise in the context of Alzheimer’s disease by enabling early detection of subtle biochemical changes in blood and cerebrospinal fluid, often before clinical symptoms become apparent. As the disease progresses, this technique reveals increasingly pronounced signals associated with protein misfolding, lipid peroxidation, and neuroinflammation, providing valuable insights into the underlying pathology. In Alzheimer’s disease (AD), protein misfolding, lipid peroxidation, and neuroinflammation represent interconnected pathological processes that evolve across different stages of disease progression. During the preclinical phase, misfolded amyloid-β oligomers and tau seeds begin to accumulate, accompanied by subtle oxidative stress and early microglial activation [ 80 – 82 ]. As patients transition to mild cognitive impairment, amyloid plaques and tau tangles disrupt synaptic function, lipid peroxidation products increase, and microglia release pro-inflammatory cytokines [ 83 – 84 ]. In the early dementia stage, dense amyloid plaques and hyperphosphorylated tau tangles are evident, with oxidative damage to neuronal membranes and mitochondria amplifying neurotoxicity, while astrocytes and microglia sustain inflammatory cascades. Moderate dementia is marked by widespread tau pathology, high levels of lipid peroxidation biomarkers, and chronic neuroinflammation with blood–brain barrier disruption. In severe dementia, extensive neuronal death occurs due to overwhelming protein aggregation, irreversible oxidative damage, and persistent glial activation, culminating in widespread neurodegeneration and functional decline. This stage-wise interplay underscores the importance of targeting these processes collectively for early diagnosis and therapeutic intervention. Conclusion The gene‑expression profile observed in Alzheimer’s disease brain tissue points to a coordinated disruption of lipid homeostasis which reflects a maladaptive metabolic state characterized by excess lipid storage, mitochondrial dysfunction, and heightened neuroinflammation. Such a pattern underscores the central role of lipid‑metabolic dysregulation in AD pathophysiology. Overall, the modulation of the Raman spectral signatures in AD samples as compared to controls, reinforces the utility of Raman spectroscopy as a sensitive tool for detecting molecular alterations associated with AD pathology and highlight its potential for identifying early biochemical markers of neurodegeneration. Raman-based biochemical phenotyping provides a valuable bridge between neuropathological staging and molecular mechanism, offering insights into why therapeutic efficacy diminishes as AD advances to late Braak stages. Beyond its diagnostic capabilities, Raman spectroscopy holds significant clinical potential as a complementary tool to conventional imaging and laboratory tests, offering a faster, non-invasive approach for monitoring disease onset and progression. The present study serves as a mechanistic and exploratory foundation for future development of Raman‑based peripheral biomarkers rather than a direct diagnostic comparison to imaging or CSF assays. Declarations Author Contributions: Conceptualization: Alexander Khmaladze, Kinga Szigeti, Supriya D. Mahajan Investigation: Samaneh Ghazanfarpour, Rahul Kumar Das, Ivanna Ihnatovych Visualization: Samaneh Ghazanfarpour, Rahul Kumar Das Supervision: Anna Sharikova, Alexander Khmaladze, Kinga Szigeti, Supriya D. Mahajan Writing-original draft: Supriya D. Mahajan, Kinga Szigeti Writing-review and editing: Ravikumar Aalinkeel, Samaneh Ghazanfarpour, Rahul Kumar Das, Alexander Khmaladze, Kinga Szigeti, Norbert Sule, Supriya D. Mahajan All authors approved the final manuscript. Ethics approval and consent to participate The study was approved by the Institutional Review Board (IRB), and all protocols received approval from the Ethics Committee of the Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA. All procedures adhered to established ethical standards for research involving human subjects. Because the study involved analysis of post‑mortem brain tissue, the requirement for informed consent was waived by the IRB. All data were handled in compliance with the Health Insurance Portability and Accountability Act (HIPAA). Patient confidentiality was safeguarded through de‑identification of all extracted data, secure storage on password‑protected institutional servers, and restricted access limited to authorized study personnel. No identifiable personal health information was disclosed or used outside the scope of this research Consent for Publication All participants (or their legal guardians, where applicable) provided informed consent for the publication of anonymized clinical information, images, and any accompanying data included in this manuscript. The authors affirm that all identifying details have been removed or anonymized to protect patient privacy. Written consent was obtained in accordance with institutional guidelines and the ethical standards of the Declaration of Helsinki. Documentation of consent is available for review by the Editor of Acta Neurologica Communications upon request. Competing Interests The authors declare that they have no competing interests related to this work. Funding No external funding was received to support this study. All research activities were conducted without financial sponsorship from public, commercial, or nonprofit funding agencies. Acknowledgements This study was made possible in part by Alzheimer Association AARG-16–443615, Edward A. and Stephanie E. Fial Fund, Community Foundation for Greater Buffalo. The funding agencies had no role in study design, data collection, data analysis, interpretation or writing of the report. Data Availability The datasets generated and analyzed during the current study are not publicly available due to restrictions related to patient privacy, HIPAA compliance, and institutional policy governing human tissue research. De‑identified data may be made available from the corresponding author upon reasonable request and with approval from the Institutional Review Board and the Jacobs School of Medicine and Biomedical Sciences. References Knopman DS, Amieva H, Petersen RC, Chételat G, Holtzman DM, Hyman BT, Nixon RA, Jones DT (2021) Alzheimer disease. 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Antioxid (Basel) 11(11):2167. 10.3390/antiox11112167 Zheng Q, Xin, Wang (2025) Alzheimer’s disease: insights into pathology, molecular mechanisms, and therapy, Protein & Cell, Volume 16, Issue 2, February Pages 83–120. https://doi.org/10.1093/procel/pwae026 Spangenberg EE, Green KN (2017) Inflammation in Alzheimer's disease: Lessons learned from microglia-depletion models. Brain Behav Immun 61:1–11. 10.1016/j.bbi.2016.07.003 Ivanova AV, Kutuzova AD, Kuzmichev IA, Abakumov MA (2025) Alzheimer’s Disease: From Molecular Mechanisms to Promising Therapeutic Strategies. Int J Mol Sci 26(19):9444. https://doi.org/10.3390/ijms26199444 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 May, 2026 Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 28 Mar, 2026 Reviewers invited by journal 27 Mar, 2026 Editor assigned by journal 13 Feb, 2026 Submission checks completed at journal 12 Feb, 2026 First submitted to journal 10 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8844737\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":614590521,\"identity\":\"0ca68537-f583-4739-a723-355d33e9e575\",\"order_by\":0,\"name\":\"Samaneh Ghazanfarpour\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University at Albany, State University of New York\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Samaneh\",\"middleName\":\"\",\"lastName\":\"Ghazanfarpour\",\"suffix\":\"\"},{\"id\":614590522,\"identity\":\"f6d54985-f162-43ea-b24f-a0ed593994cf\",\"order_by\":1,\"name\":\"Rahul Kumar Das\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University at Buffalo, State University of New York\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rahul\",\"middleName\":\"Kumar\",\"lastName\":\"Das\",\"suffix\":\"\"},{\"id\":614590523,\"identity\":\"6c0cea47-8d56-4775-b61b-a09b928e8618\",\"order_by\":2,\"name\":\"Ivanna Ihnatovych\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University at Buffalo, State University of New York\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ivanna\",\"middleName\":\"\",\"lastName\":\"Ihnatovych\",\"suffix\":\"\"},{\"id\":614590524,\"identity\":\"7fb42fc4-550c-484e-90d7-4cf120d38291\",\"order_by\":3,\"name\":\"Norbert Sule\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Roswell Park Comprehensive Cancer Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Norbert\",\"middleName\":\"\",\"lastName\":\"Sule\",\"suffix\":\"\"},{\"id\":614590525,\"identity\":\"1e1ffca1-6ec5-4059-abff-e5ca992458cd\",\"order_by\":4,\"name\":\"Anna Sharikova\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University at Albany, State University of New York\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Anna\",\"middleName\":\"\",\"lastName\":\"Sharikova\",\"suffix\":\"\"},{\"id\":614590526,\"identity\":\"0f58e86c-ecb7-4942-bc1f-bf1edf1fa08a\",\"order_by\":5,\"name\":\"Ravikumar Aalinkeel\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University at Buffalo, State University of New York\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ravikumar\",\"middleName\":\"\",\"lastName\":\"Aalinkeel\",\"suffix\":\"\"},{\"id\":614590527,\"identity\":\"16b96b54-93a5-465c-8e9b-d8c7a1ae614d\",\"order_by\":6,\"name\":\"Alexander Khmaladze\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University at Albany, State University of New York\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Alexander\",\"middleName\":\"\",\"lastName\":\"Khmaladze\",\"suffix\":\"\"},{\"id\":614590528,\"identity\":\"ff0282ec-f4f8-40b0-8387-97cb92311f25\",\"order_by\":7,\"name\":\"Kinga Szigeti\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University at Buffalo, State University of New York\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kinga\",\"middleName\":\"\",\"lastName\":\"Szigeti\",\"suffix\":\"\"},{\"id\":614590529,\"identity\":\"fd227a39-e71f-48c0-ada9-720fac582a46\",\"order_by\":8,\"name\":\"Supriya D. Mahajan\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYHACNgYGHgYGfgkGhg8VDBYgEQPitEjOYGCccYZBglgtIGU3iNUiP+3wswc/ZOzyjG/3GDYcqJGQY2Bv3iaBT4vB7TRzwx6e5GKzO2eAWo5JGDPwHCvDr0U6wUyCh4c5cduNHPPHH9gkEhskcszwapGfnf5N8g9PfeLmGTlAW/4Btci/wa+F4XaOmTQPz+HEDRJALQfbQLbw4NdicDunTFqG53jijDvHChsO9kkYs/GkFVsQcNg2ybc91Yn9s5s3Nhz4ZiPHz3544w28DgMBxh4kDhtB5WDwgzhlo2AUjIJRMEIBAMRLSfTTzZ+EAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"University at Buffalo, State University of New York\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Supriya\",\"middleName\":\"D.\",\"lastName\":\"Mahajan\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-02-10 19:23:42\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8844737/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8844737/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":105811933,\"identity\":\"c58f2e09-e5e5-4009-8e44-c14a1a9fd3fd\",\"added_by\":\"auto\",\"created_at\":\"2026-03-31 11:33:47\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2492667,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e(a) Schematic of the Raman spectroscopy experiment design; (b) Raman Spectrum baseline subtracted, normalized average spectra from the 3 groups with 95 % confidence intervals. (b) Raman analysis showing three coordinate comparison for AD samples Blaak stage IV, Braack stage VI and control groups; (c) Raman analysis showing comparative signal shifts in Raman spectra for control samples vs Braak IV AD samples; (d) Raman analysis showing comparative signal shifts in Raman spectra for control samples vs Braak VI AD samples; (e) Raman analysis showing comparative signal shifts in Raman spectra for Braak IV vs Braak VI AD samples.\\u003cstrong\\u003e \\u003c/strong\\u003e(f)\\u003cstrong\\u003e \\u003c/strong\\u003e\\u0026nbsp;Distribution range of Raman spectral bands across Control, Braack IV, and Braack VI conditions. Heatmap depicting the distribution range of Raman spectral bands (560–1730 cm⁻¹) under three experimental conditions: Control, Braack IV, and Braack VI. The y-axis represents Raman shifts, while the x-axis categorizes the conditions. The color gradient indicates the extent of distribution, with red denoting higher variability (up to 0.13) and green indicating lower variability (down to 0.01). This visualization highlights molecular heterogeneity associated with progressive Braack staging.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig1ADproject.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8844737/v1/9fcc8781e732311f38f24aee.jpg\"},{\"id\":105811934,\"identity\":\"592ac39a-aa4e-47ed-a95b-146014e48ecf\",\"added_by\":\"auto\",\"created_at\":\"2026-03-31 11:33:47\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1983587,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSVD and LDA results demonstrate clear separation of Raman spectral clusters corresponding AD samples vs Controls and between Controls vs Braak IV and Control vs Braak VI. Fig 2 (a,e,i) shows Scatter Plots between Control vs Braak IV, Control vs Braak VI, and Braak IV vs Braak VI respectively. Fig 2 (b,f,j) shows the accompanying confusion matrices illustrate classification performance for each sample group, highlighting the accuracy of spectral differentiation across AD Stages. ROC curve analysis demonstrated clear classification performance across groups, with accuracy/precision/F1 scores of 0.60/0.611/0.579 for control vs Braak IV (Fig. 2c), 0.985/0.980/0.985 for control vs Braak VI (Fig. 2g), and 0.945/0.989/0.942 for Braak IV vs Braak VI (Fig. 2k). Corresponding spectral comparisons (Fig. 2d, h, l) illustrate the key Raman signal shifts distinguishing each pair of sample groups.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig2ADproject.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8844737/v1/9980e92b2e99d98d7e71997c.jpg\"},{\"id\":105904523,\"identity\":\"292cbd58-02ba-415e-938e-0600aa16b428\",\"added_by\":\"auto\",\"created_at\":\"2026-04-01 10:09:14\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1389570,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eIncreased lipid peroxidation and lipid aggregation in Alzheimer’s disease (AD) brain tissue. Representative confocal images of BODIPY™ 581/591 C11 staining show predominantly reduced (red) fluorescence in control sections and a marked shift toward oxidized (green) signal in AD tissue, consistent with elevated lipid peroxidation. Quantified green‑to‑red ratios demonstrate higher lipid oxidation in AD, with Braak VI samples exhibiting greater peroxidation and more abundant punctate lipid aggregates than Braak IV. Panels (a–j) show 20× images (scale bar: 400 µm); panels (j–l) show 40× images (scale bar: 100 µm). AD sections exhibited a pronounced shift from reduced (red) to oxidized (green) fluorescence, indicating increased peroxidation of lipid droplets, whereas control samples showed predominantly red signal. Quantification of the green‑to‑red fluorescence ratio (Fig. 3m) confirms significantly higher lipid oxidation in AD samples. Fig. 3n, shows a schematic highlighting increased lipid peroxidation and lipid aggregation in Braak IV (p\\u0026lt;0.05) and Braak VI AD (p\\u0026lt;0.01) brain tissue compared to the controls, supporting a link between oxidative lipid damage and late‑stage AD neurodegeneration.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig3ADproject.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8844737/v1/9685f3c07480bcbe31193eae.jpg\"},{\"id\":105811936,\"identity\":\"78863a53-66fc-4793-8ed5-795679e56f03\",\"added_by\":\"auto\",\"created_at\":\"2026-03-31 11:33:47\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2587661,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGene expression analysis in AD brain tissue samples and normal controls\\u003c/p\\u003e\\n\\u003cp\\u003e(a-e) Genes involved in Lipid metabolism LIPE, SREBP-1, CPT1A, ABCA1 and PPARA (f-g) Genes for proinflammatory Cytokines, TNF-α \\u0026amp; IL-6; (h Oxidative stress mitigator gene GPX1. (i)- Schematic of key Raman spectral bands modulated in pre-frontal cortex Brain tissue from AD patients and their specific biochemical relevance representing carbohydrates, nucleic acids, steroids, Protein \\u0026amp; Lipids and their potential relevance to AD pathophysiology namely \\u0026nbsp;neurodegeneration, Synaptic loss/Gliosis, Poor Streoidogenesis, alter Lipid peroxidation, and protein folding deformation (Amyloid beta plaques).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig4ADproject.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8844737/v1/7ec2a011f734fb3df26fe25f.jpg\"},{\"id\":105904817,\"identity\":\"30461477-a0e0-4a61-8950-70e82f33aa9c\",\"added_by\":\"auto\",\"created_at\":\"2026-04-01 10:10:38\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":219627,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eUnnumbered image in the Materials \\u0026amp; Methods section.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Uf1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8844737/v1/155f11e120239af79a9fd827.png\"},{\"id\":106093086,\"identity\":\"16d60f38-6bee-4452-a0e3-2e7145c1bf6d\",\"added_by\":\"auto\",\"created_at\":\"2026-04-03 11:33:51\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":9620679,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8844737/v1/3a2d136a-7a44-427e-9b30-99bf781dbef0.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Raman Spectroscopic Identification of Biochemical Alterations in Alzheimer’s Disease Brain Tissue\",\"fulltext\":[{\"header\":\"INTRODUCTION\",\"content\":\"\\u003cp\\u003eAlzheimer\\u0026rsquo;s disease (AD) is the most common cause of dementia and is defined by progressive cognitive decline accompanied by amyloid‑β plaques and neurofibrillary tau tangles [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Despite extensive research, early diagnosis and accurate prognosis remain difficult due to the disease\\u0026rsquo;s heterogeneity, variable progression, and complex molecular and genetic influences [\\u003cspan additionalcitationids=\\\"CR4 CR5\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. AD pathology evolves through the spatiotemporal spread of tau, leading to synaptic dysfunction and neuronal loss [\\u003cspan additionalcitationids=\\\"CR8 CR9\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Although recent therapeutic advances offer modest benefits, AD remains incurable, and early detection is essential for effective intervention. Current diagnostic frameworks rely on biomarkers of β‑amyloid and tau deposition, metabolic impairment, and structural atrophy, typically assessed through imaging and cognitive evaluation [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. However, precisely characterizing the density, distribution, and biochemical state of these pathological proteins remains challenging.\\u003c/p\\u003e \\u003cp\\u003eNeuropathological staging systems, such as Braak scores, provide a framework for assessing the distribution and severity of tau pathology across brain regions. Braak staging provides a neuropathologically grounded framework that reflects the anatomical spread and biochemical maturation of neurofibrillary tau pathology and correlates closely with clinical disease severity [\\u003cspan additionalcitationids=\\\"CR8 CR9\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Among these stages, Braak IV and Braak VI represent distinct biological phases that illuminate key transitions in AD progression [\\u003cspan additionalcitationids=\\\"CR12 CR13\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Braak IV marks a pivotal point at which tau pathology extends into association cortices and symptoms often progress from mild cognitive impairment to early dementia. This stage is defined by emerging hyperphosphorylated tau oligomers, early β‑sheet formation, subtle membrane and lipid alterations, mitochondrial stress, and rising oxidative burden, changes that precede major neuronal loss and may still be partially reversible [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. In contrast, Braak VI reflects end‑stage disease, characterized by widespread neocortical tau deposition, extensive synaptic and neuronal degeneration, severe mitochondrial failure, chronic inflammation, and collapse of proteostatic and lipid regulatory systems [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Tau aggregates at this stage are dominated by insoluble, highly ordered fibrils accompanied by profound lipid depletion, protein oxidation, and activation of cell‑death pathways [\\u003cspan additionalcitationids=\\\"CR12 CR13 CR14\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. These features represent consolidated, largely irreversible molecular signatures corresponding to severe dementia and minimal therapeutic responsiveness. Thus, Braak IV and Braak VI represent biologically and clinically distinct phases of disease onset and progression, making their comparison particularly informative for understanding AD pathogenesis and identifying opportunities for early detection and therapeutic intervention [\\u003cspan additionalcitationids=\\\"CR8 CR9\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. While Braak staging correlates with disease progression, it is limited in its ability to capture the biochemical heterogeneity that influences individual trajectories of cognitive decline [\\u003cspan additionalcitationids=\\\"CR12 CR13 CR14 CR15\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Similarly, genetic risk factors such as the APOE ε4 allele is strongly associated with increased susceptibility to AD and accelerated pathology [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. However, APOE genotype alone does not fully explain variability in disease onset or prognosis, underscoring the need for complementary approaches that can resolve molecular complexity at the cellular and tissue level. Traditional diagnostic modalities, including neuroimaging and cerebrospinal fluid assays, provide valuable insights but are often invasive, costly, or insufficiently sensitive to detect subtle biochemical changes in the earliest stages of AD [\\u003cspan additionalcitationids=\\\"CR19\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. This gap highlights the urgent need for label-free, nonperturbative techniques capable of probing molecular alterations directly within biological specimens. AD brain tissue serves as a controlled system to identify AD‑related molecular signatures and evaluate the sensitivity of Raman spectroscopy to early biochemical changes. These foundational insights are essential for future translation to accessible biofluids such as blood, where Raman‑based assays could ultimately have diagnostic value.\\u003c/p\\u003e \\u003cp\\u003eRaman spectroscopy (RS) has emerged as a powerful tool for studying Alzheimer\\u0026rsquo;s disease (AD), offering label-free biochemical insights at the molecular level [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Unlike conventional methods that rely on exogenous probes, RS enables direct interrogation of brain tissue and cellular specimens while fully preserving their structural, functional, and physiological integrity [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. As a vibrational spectroscopic technique, RS generates unique biochemical fingerprints from Raman-active biomolecules, enabling detection of subtle molecular shifts associated with protein misfolding, lipid dysregulation, and oxidative stress, which are processes intimately linked to AD onset and progression [\\u003cspan additionalcitationids=\\\"CR24\\\" citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. By applying multivariate calibration and classification strategies such as Singular Value Decomposition (SVD) and Linear Discriminant Analysis (LDA), RS can disentangle complex spectral datasets, revealing principal biochemical factors that drive variability across heterogeneous brain tissues [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. Our Raman spectroscopic analysis revealed distinct biochemical differences between control and Alzheimer\\u0026rsquo;s disease (AD) brain tissues across protein, lipid, carbohydrate, and nucleic‑acid vibrational regions.\\u003c/p\\u003e \\u003cp\\u003eIntegrating RS into AD research may therefore bridge critical gaps between neuropathological staging, genetic risk stratification, and molecular diagnostics. Specifically, RS holds promise for refining the interpretation of Braak scores, and improving prognostic accuracy by capturing biochemical signatures that precede overt clinical symptoms. RS holds promise for refining the interpretation of Braak scores by adding a molecular dimension to a staging system that is currently based solely on the anatomical spread of tau pathology. By revealing molecular heterogeneity within the same Braak stage, identifying subtle biochemical transitions between stages, and detecting early pathological changes before overt tangle formation, RS can provide a more nuanced understanding of disease progression. This molecular resolution has the potential to strengthen the biological meaning of Braak scores and improve their alignment with clinical outcomes.\\u003c/p\\u003e \\u003cp\\u003eSuch advances could pave the way for earlier intervention strategies, personalized therapeutic approaches, and more precise monitoring of disease progression. Improving the accuracy and timeliness of AD diagnosis is essential for effective intervention and management. Reliable biomarker detection not only facilitates early identification of the disease but also aids in monitoring progression and evaluating therapeutic outcomes. Several recent studies have demonstrated the feasibility of applying RS to blood, plasma, serum, and peripheral cells, suggesting that Raman‑derived molecular markers identified in brain tissue may ultimately be detectable in minimally invasive samples [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Although, our current work uses brain tissue as a discovery platform, the long‑term clinical translational potential of our work includes blood‑based or other peripheral Raman assays, which could complement existing biomarkers and address the need for label‑free, nonperturbative, and cost‑effective diagnostic tools. As the global prevalence of AD continues to rise, enhancing diagnostic methodologies will play a critical role in reducing the economic and emotional burden on patients, families, and healthcare systems. Ultimately, RS offers a noninvasive, highly sensitive diagnostic modality that holds promise for identifying early-stage Alzheimer\\u0026rsquo;s disease and monitoring its progression, paving the way for improved therapeutic interventions and patient outcomes.\\u003c/p\\u003e\"},{\"header\":\"MATERIALS AND METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSample Collection and Preparation\\u003c/h2\\u003e \\u003cp\\u003e The study sample was composed of post mortem tissue samples obtained from AD patients who were diagnosed based on NINCDS-ADRDA criteria and were obtained from the archived tissue bank from Baylor. The inclusion criteria for AD samples were (1) age\\u0026thinsp;\\u0026ge;\\u0026thinsp;50 years old, (2) access to neuropsychological and clinical data. The exclusion criteria included (1) history of psychiatric and major depressive disorder prior to the onset of AD. Inclusion and exclusion criteria for Control samples were (1)\\u0026thinsp;\\u0026ge;\\u0026thinsp;50 years old, (2) no history of neurological or medical illness that might impact cognitive function, and (5) no diagnosis of major depressive disorder. The study was approved by the Institutional Review Board (IRB) and all protocols were approved by the Ethics Committee of the Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA. The demographic and clinical information of the samples are shown in Table I.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"No\\\" id=\\\"Taba\\\" border=\\\"1\\\"\\u003e \\u003ccolgroup cols=\\\"9\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"9\\\" nameend=\\\"c9\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eTABLE I: Demographic and Clinical information of the samples\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBrain Bank\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDesign ID\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDiagnosis\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAPO_E\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSEX\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge at death (cal)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFrozen PMI\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePlaque score (0\\u0026thinsp;=\\u0026thinsp;none, 1\\u0026thinsp;=\\u0026thinsp;sparse, 2\\u0026thinsp;=\\u0026thinsp;moderate, 3\\u0026thinsp;=\\u0026thinsp;dense)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBraak stage (0\\u0026ndash;6)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBaylor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e24 hours\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eCERAD C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eVI\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBaylor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3,4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e9.5 hours\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eCERAD C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eIV\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNYBB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT-139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eN/A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNYBB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eT-638\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e5.5 hours\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eRaman Spectra Acquisition of AD brain tissue\\u003c/h3\\u003e\\n\\u003cp\\u003eParaffin-embedded sections (4 \\u0026micro;m) of the AD and Control samples (n\\u0026thinsp;=\\u0026thinsp;2/ group) were obtained and were used for the Raman spectral analysis. The parafilm peaks were eliminated by software-guided subtraction of the Raman spectra of standard parafilm from the acquired spectral data of samples. No staining or chemical treatment was applied to enable preservation of the native biochemical composition of the tissue for Raman analysis. Scattered Raman light from a laser beam focused on a tissue section provided detailed information about the molecular composition of the tissue at the microscopic level. Multiple spectra were obtained per tissue section to account for heterogeneity.\\u003c/p\\u003e \\u003cp\\u003eRaman spectra were acquired by a commercial Raman micro-spectroscope (HORIBA XploRA PLUS) equipped with a 1024\\u0026times;256 TE air-cooled CCD chip (pixel size 26 \\u0026micro;m, temperature\\u0026thinsp;\\u0026minus;\\u0026thinsp;60\\u0026deg;C). Spectra were acquired using a 532 nm laser operating at a power of 0.065 W, with an 1800 grooves/mm grating, a slit width of 100 \\u0026micro;m, and a pinhole diameter of 100 \\u0026micro;m. Each spectrum was recorded with an acquisition time of 30 seconds and three accumulations to enhance signal quality. A 40x objective was employed for focusing, and a total of 100 spectra per sample were collected for analysis. Spectra were acquired over 200\\u0026ndash;2000cm⁻\\u0026sup1; (fingerprint region).\\u003c/p\\u003e\\n\\u003ch3\\u003eRaman Data Processing and Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eHORIBA LabSpec6 software was used for the initial data processing: smoothing, baseline removal (polynomial), and normalization (unit vector), necessary to enable subsequent quantitative analysis [\\u003cspan additionalcitationids=\\\"CR26 CR27 CR28\\\" citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. SVD analysis of the spectra was done using Python code to obtain the critical spectral features differentiating between the tissue samples. All Raman spectra from each imaging dataset were aggregated to form an input matrix for the SVD algorithm. In particular, the Raman spectra acquired from individual points within each sample were organized to generate a matrix of size m \\u0026times; n, where m denotes the number of data points per spectrum, and n indicates the total number of spectra within a specific sample [\\u003cspan additionalcitationids=\\\"CR31 CR32\\\" citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. In our study, these dimensions were 1116 X 200. Subsequently, we applied the SVD function in Python to decompose the input matrix into matrices U, Σ, and V\\u003csup\\u003eT\\u003c/sup\\u003e. The matrix V was utilized to create SVD scatter plots, whereas the individual SVD components were stored in the matrix U [\\u003cspan additionalcitationids=\\\"CR35\\\" citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eEach scatter plot was based on the leading SVD components and contained two data sets, AD\\u0026rsquo;s vs. controls, or between the two AD samples belonging to the Braak stage IV vs Braak stage VI, where each spectrum was represented as a single point. A separating line was constructed using Linear Discriminant Analysis (LDA), a supervised classification technique that identifies the linear combination of features which maximizes the separation between classes by maximizing the distance between their means while minimizing the variance within each class [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. A corresponding confusion matrix, summarizing the classification performance for each sample, was also generated. Additionally, the SVD components employed in the scatter plots, each of which contained the spectral features responsible for distinguishing the datasets, were plotted.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e \\u003cp\\u003eStatistical analysis was done using GraphPad Prism (v8; GraphPad Software, Boston, MA). The comparison between the AD samples vs. controls, or between the two AD samples belonging to the Braak stage IV vs Braak stage VI, was done using a non‑parametric test Mann\\u0026ndash;Whitney U test (Wilcoxon rank‑sum test). All statistical analyses were performed using non‑parametric methods due to the small sample size and the inability to assume normal distribution of the data. A two‑tailed p‑value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant.\\u003c/p\\u003e \\u003cp\\u003eFor Raman analysis, Singular Value Decomposition (SVD) was used solely for visualization purposes, not for classification or model fitting [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. This approach inherently avoids overfitting, as SVD was not part of the predictive pipeline. We selected the first six components based on their cumulative explained variance, which captured the most informative structure in the data while minimizing noise. Linear Discriminant Analysis (LDA) was applied for classification, and we ensured robustness by performing cross-validation during model evaluation. This helped assess generalizability and mitigate overfitting risks. The following flowchart represents our steps:\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch2\\u003e\\u003cem\\u003eBODIPY\\u0026trade; 581/591 C11 Lipid Peroxidation Sensor:\\u003c/em\\u003e\\u003c/h2\\u003e\\n\\u003cp\\u003eParaffin-embedded brain tissue sections (4 \\u0026micro;m) were obtained from clinically and neuropathologically confirmed Alzheimer\\u0026rsquo;s disease (AD) cases (n\\u0026thinsp;=\\u0026thinsp;2) and age-matched non-demented controls (n\\u0026thinsp;=\\u0026thinsp;2). Sections were briefly equilibrated to room temperature deparaffinized and lightly fixed in 4% paraformaldehyde for 5 min to preserve morphology while maintaining lipid integrity, followed by washing in PBS. Lipid peroxidation was assessed using the BODIPY\\u0026trade; 581/591 C11 Lipid Peroxidation Sensor (Thermo Fisher Scientific), prepared fresh at 2 \\u0026micro;M in PBS containing 0.05% fatty-acid\\u0026ndash;free BSA. Sections were incubated with the probe for 30 min at 37\\u0026deg;C in a humidified, light-protected chamber, washed three times in PBS, counterstained with DAPI when required, and mounted in aqueous antifade medium. Fluorescence imaging was done using the Revolve Discover ECHO microscope using identical acquisition settings across all samples, with oxidized probe (green) detected using 488-nm excitation/500\\u0026ndash;550-nm emission and the reduced form (red) using 561-nm excitation/580\\u0026ndash;620-nm emission. For each case, multiple non-overlapping fields were imaged and analyzed in Fiji/ImageJ. Mean fluorescence intensities for oxidized (green) and reduced (red) channels were background-subtracted, and lipid peroxidation was quantified as the green-to-red fluorescence ratio for each region. Values from all fields were averaged to generate one per-case measurement, enabling descriptive comparison between AD and control groups.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eGene expression analysis from human brain tissue:\\u003c/h2\\u003e \\u003cp\\u003eBrain tissue samples were obtained from clinically and neuropathologically confirmed Alzheimer\\u0026rsquo;s disease (AD) patients and age‑ and sex‑matched non‑demented controls (n\\u0026thinsp;=\\u0026thinsp;10 per group. Samples from AD patients who were diagnosed based on NINCDS-ADRDA criteria and were obtained from the archived tissue bank from Baylor. Tissue blocks (~\\u0026thinsp;50\\u0026ndash;100 mg) were dissected from the same anatomical region (Pre-frontal cortex) to minimize regional variability. Samples were snap‑frozen in liquid nitrogen immediately after collection and stored at \\u0026minus;\\u0026thinsp;80\\u0026deg;C until RNA extraction\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eRNA Extraction:\\u003c/h3\\u003e\\n\\u003cp\\u003eRNA was extracted from brain tissue samples using the TRIzol\\u0026reg; reagent (Invitrogen-Life Technologies, Carlsbad, CA). The amount of RNA was quantified using a NanoDrop ND-1000 spectrophotometer (Nano-Drop\\u0026trade;, Wilmington, DE), and isolated RNA was stored at -80\\u0026deg;C until it was used.\\u003c/p\\u003e\\n\\u003ch3\\u003eReal-Time Quantitative (RT-q) PCR:\\u003c/h3\\u003e\\n\\u003cp\\u003eTotal RNA (1000 ng) that was extracted as described above was utilized for the All-in-One Universal RT cDNA Master Mix Synthesis Kit (Lamba Biotech, St. Louis, MO, Cat #G209) following the Manufacturer\\u0026rsquo;s protocol. One microliter of the resultant cDNA from the RT reaction was employed as the template in PCR reactions using well-validated PCR primers for IL-6, TNF-α, GPX-1, ABCA1, LIPE, CPT1A, PPARA, and SREBP‑1 obtained from RealTimePrimers.com; and the final primer concentration used in the PCR was 0.1 \\u0026micro;M. We used the SYBR\\u0026reg; Green master (Bio-Rad, Hercules, CA) following the Manufacturer\\u0026rsquo;s QPCR protocol, and gene expression was calculated using the comparative CT method. The threshold cycle (Ct) of each sample was determined, and β-actin was used as the endogenous reference gene. The relative level of a transcript (2ΔCt) was calculated by obtaining ΔCt (test Ct\\u0026thinsp;\\u0026minus;\\u0026thinsp;β-actin Ct), and transcript accumulation index (TAI) was calculated as TAI\\u0026thinsp;=\\u0026thinsp;2\\u003csup\\u003e\\u0026minus;ΔΔCT\\u003c/sup\\u003e [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]\\u003c/p\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eRaman Spectral Analysis:\\u003c/h2\\u003e\\n \\u003cp\\u003eRaman spectroscopic analysis revealed distinct biochemical differences between control and Alzheimer\\u0026rsquo;s disease (AD) brain tissues and between Braack IV and Braack VI AD stages. The following is a detailed description of the Raman spectral variation between AD samples vs controls. Raman spectroscopic analysis revealed distinct biochemical differences between control and Alzheimer\\u0026rsquo;s disease (AD) brain tissues across the 445\\u0026ndash;1732 cm⁻\\u0026sup1; region (Fig. \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e [c-f]) and Table \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). AD samples exhibited significant increases in low‑frequency vibrational modes at 445, 481, and 748 cm⁻\\u0026sup1;, reflecting altered protein\\u0026ndash;carbohydrate environments and pronounced perturbations in nucleic‑acid backbone structure consistent with DNA fragmentation and inflammatory activity. In contrast, several protein‑ and lipid‑associated bands showed marked reductions in AD tissue. [\\u003cspan additionalcitationids=\\\"CR42 CR43\\\" citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. Decreases at 880, 951\\u0026ndash;952, and 1000 cm⁻\\u0026sup1; indicated disruption of aromatic amino‑acid environments and loss of ordered \\u0026alpha;‑helical content, while diminished intensities at 1296 and 1440 cm⁻\\u0026sup1; pointed to compromised lipid‑chain organization and membrane structural integrity [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. Protein backbone alterations were further supported by increased Amide II (1554 cm⁻\\u0026sup1;) and 1585 cm⁻\\u0026sup1; aromatic‑backbone modes, accompanied by a pronounced reduction in the Amide I region (1640\\u0026ndash;1680 cm⁻\\u0026sup1;), signifying broad secondary‑structure destabilization [\\u003cspan additionalcitationids=\\\"CR47\\\" citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. A decrease in the 1732 cm⁻\\u0026sup1; carbonyl band suggested additional alterations in steroid‑ and lipid‑derived carbonyl species. Also, an enhanced signal at 560 cm⁻\\u0026sup1;, corresponding to glycogen and tyrosine‑related skeletal modes, was observed in AD samples, suggesting altered carbohydrate storage and amino‑acid microenvironments. A broad increase in the 690\\u0026ndash;780 cm⁻\\u0026sup1; region, associated with nucleic‑acid vibrations, indicated elevated contributions from DNA/RNA structural components [\\u003cspan additionalcitationids=\\\"CR48\\\" citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. AD tissue also exhibited a marked rise in the 1080 cm⁻\\u0026sup1; band, representing symmetric phosphate stretching of DNA (PO₄\\u0026sup2;⁻), consistent with nucleic‑acid backbone perturbation and the 1607 cm⁻\\u0026sup1; band is linked to aromatic amino acids such as phenylalanine and tyrosine and often associated with protein senescence or necrotic processes, also showed pronounced elevation in AD samples [\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. Together, these spectral increases highlight distinct biochemical alterations in AD brain tissue across carbohydrate, nucleic‑acid, and protein‑related vibrational domains. Collectively, these spectral changes indicate coordinated disruptions in nucleic acids, proteins, and lipids that characterize AD‑associated molecular pathology [\\u003cspan additionalcitationids=\\\"CR50\\\" citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e].\\u0026nbsp;\\u003c/p\\u003e\\n \\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eRaman Spectral Bands and their Biochemical Structural Assignment\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eRaman Band (1/cm)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eIntensity Change\\u003c/p\\u003e\\n \\u003cp\\u003e(Control vs AD)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003eBiological Relevance\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e445\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIncrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eN-C-S stretch in proteins and C-C bond stretch in carbohydrates\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e481\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIncrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDNA\\u0026nbsp;stretching vibrations in phosphate bonds (PO)4 2-\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e560\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIncrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eGlycogen, tyrosine\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e690\\u0026ndash;780\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIncrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNucleic Acid band\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e748\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIncrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDNA bending vibration\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e880\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDecrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eTryptophan, 𝛿 (ring) disorentation\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e951\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDecrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e𝑣(CH\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e3\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003e) of proteins (\\u0026alpha;-helix) foldings\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e1000\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDecrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ePhenylalanine ring resonance\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e1080\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIncrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDNA symmetric stretching vibrations in Phosphate bonds (PO)\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e4\\u003c/em\\u003e\\u003c/sub\\u003e\\u003csup\\u003e\\u003cem\\u003e\\u0026minus;2\\u003c/em\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e1296\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDecrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCH\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003edeformation in lipid aliphatic chains\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e1440\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDecrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCH\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003eand CH\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e3\\u003c/em\\u003e\\u003c/sub\\u003e \\u003cem\\u003edeformation vibrations\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCH deformation, Cholesterol, fatty acid band, 𝛿 (CH\\u003c/em\\u003e\\u003csub\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sub\\u003e\\u003cem\\u003e) (lipids)\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e1554\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIncrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAmide II band in proteins\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e1585\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIncrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eProtein folding assignment\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e1607\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eIncrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAromatic Amino acids (Phenylalanine/tyrosine) Cell Senescence- necrosis marker\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e1640\\u0026ndash;1680\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDecrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAmide I in protein band\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003e1732\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eDecrease\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eOne of absorption positions for the C\\u0026thinsp;=\\u0026thinsp;O stretching vibrations of cortisone\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eA heatmap depicting the distribution range of Raman spectral bands (560\\u0026ndash;1730 cm⁻\\u0026sup1;) under three experimental conditions: Control, Braack IV, and Braack VI are shown in Fig. \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ef. Analysis of the Raman spectral distribution revealed condition-dependent variability across several key vibrational bands. Control samples exhibited consistently low distribution ranges across the spectrum, suggesting molecular homogeneity. In contrast, Braack IV samples showed moderate increases in variability, particularly at 720 cm⁻\\u0026sup1;, 960 cm⁻\\u0026sup1;, and 1440 cm⁻\\u0026sup1;, indicating early biochemical alterations. Braack VI samples demonstrated the highest distribution ranges, with pronounced variability in bands near 720 cm⁻\\u0026sup1;, 960 cm⁻\\u0026sup1;, 1240 cm⁻\\u0026sup1;, and 1440 cm⁻\\u0026sup1;. These shifts suggest extensive molecular changes associated with advanced neurodegenerative pathology [\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. Panel I shows Clear separation between the control AD groups is observed, indicating that all SVD components effectively capture the variance in the data. Fractions of explained variance in Control sample and Braak IV are 0.636 (SVD1) and 0.035 (SVD2), and 0.623 (SVD1) and 0.055 (SVD2) for Control sample and Braak VI and 0.612 (SVD1) and 0.057 (SVD2) for Braak IV and Braak VI samples (Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e [a-l]). Separator lines generated by LDA show the boundaries between the Control region and each AD group and between Braak IV and Braak VI. Panel II shows confusion matrices, demonstrating classification performance using all SVD components for comparisons between controls and each AD group and between Braak IV and Braak VI respectively, showing, with 65 out of 100 for control samples and all 55 for Braak IV ( Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb) and 98 out of 100 for control samples and 99 for Braak VI AD samples (Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ef) and 98 out of 100 for control samples and 99 out of 100 for Braak VI AD samples and 90 for Braak VI ( Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ej). Panel III shows Receiver operating characteristic (ROC) curve analysis confirmed the classification performance between control and each AD group and between Braak IV and Braak VI samples yielding an accuracy of 0.60, precision of 0.611, and an F1 score of 0.579 for the control vs Braak IV samples (Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec) and an accuracy of 0.985, precision of 0.980, and an F1 score of 0.985 for the control vs Braak VI samples (Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eg) and an accuracy of 0.945, precision of 0.989, and an F1 score of 0.942 for the Braak IV vs Braak VI samples respectively (Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ek). Panel IV outlines spectral differentiation across samples showing comparative signal shifts between control vs Braak IV samples (Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed); control vs Braak VI samples (Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eh) and Braak IV vs Braak VI samples (Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003el) respectively.\\u003c/p\\u003e\\n \\u003cp\\u003eAnalysis of gene expression in age and sex matched post‑mortem brain tissue revealed substantial alterations in pathways related to lipid metabolism, inflammation, and oxidative stress in Alzheimer\\u0026rsquo;s disease (AD). Quantitative PCR measurements demonstrated marked dysregulation of genes involved in lipid breakdown, fatty‑acid oxidation, and lipid synthesis, as well as key inflammatory cytokines and the antioxidant enzyme GPX1.\\u003c/p\\u003e\\n \\u003cp\\u003eExpression of LIPE, a critical lipolytic enzyme, was reduced by 91% in AD samples compared with controls (TAI\\u0026thinsp;=\\u0026thinsp;0.09\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.012; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). Similarly, the lipid transporter ABCA1 showed a 78% decrease (TAI\\u0026thinsp;=\\u0026thinsp;0.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), and PPARA, a regulator of fatty‑acid oxidation, was reduced by 83% (TAI\\u0026thinsp;=\\u0026thinsp;0.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.05; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). Expression of GPX1, which encodes the antioxidant enzyme glutathione peroxidase‑1, was also significantly diminished, showing a 73% decrease in AD tissue (TAI\\u0026thinsp;=\\u0026thinsp;0.27\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.03; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e\\n \\u003cp\\u003eGenes associated with lipid synthesis and fatty‑acid oxidation were upregulated. SREBP‑1 expression increased by 46% (TAI\\u0026thinsp;=\\u0026thinsp;1.46\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.13; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), and CPT1A, a key enzyme in mitochondrial fatty‑acid transport, increased by 62% (TAI\\u0026thinsp;=\\u0026thinsp;1.62\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). Markers of neuroinflammation were also elevated. TNF‑\\u0026alpha; expression was 59% higher in AD samples (TAI\\u0026thinsp;=\\u0026thinsp;1.59\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.089; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), and IL‑6 expression increased by 61% (TAI\\u0026thinsp;=\\u0026thinsp;1.61\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.071; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01).\\u003c/p\\u003e\\n \\u003cp\\u003eTogether, these findings indicate a coordinated disruption of lipid metabolic pathways, heightened inflammatory signaling, and reduced antioxidant capacity in AD brain tissue.\\u003c/p\\u003e\\n \\u003cp\\u003eLipid peroxidation, assessed using the BODIPY\\u0026trade; 581/591 C11 probe, was markedly elevated in Alzheimer\\u0026rsquo;s disease (AD) brain tissue compared with age‑matched controls. AD sections exhibited a pronounced shift from reduced (red) to oxidized (green) fluorescence, indicating increased peroxidation of lipid droplets, whereas control samples showed predominantly red signal (Fig. \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003em). Quantification of green‑to‑red fluorescence ratios confirmed consistently higher lipid oxidation in AD cases. Stratification by neuropathological stage revealed a clear progression of oxidative damage: Braak IV tissue showed a 22% increase (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) in lipid peroxidation relative to control, while Braak VI tissue demonstrated a substantially greater 80% increase (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), accompanied by more extensive punctate green aggregates throughout affected cortical regions. These findings, illustrated in Fig. \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003en, highlight a stage‑dependent escalation in lipid peroxidation and lipid aggregation with advancing tau pathology, supporting a link between oxidative lipid damage and late‑stage AD neurodegeneration.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eAlzheimer\\u0026rsquo;s disease involves a constellation of interconnected patho-mechanisms, which include amyloid‑β aggregation, tau misfolding and fibrillization, oxidative stress, lipid membrane disruption, mitochondrial dysfunction, and progressive synaptic degeneration, all of which collectively drive neurodegeneration [\\u003cspan additionalcitationids=\\\"CR55 CR56 CR57\\\" citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e]. Each of these processes produces distinct biochemical alterations in proteins, lipids, and nucleic acids, many of which manifest as changes in molecular structure, bond vibrations, and chemical composition. Raman spectroscopy (RS), with its sensitivity to protein secondary structure, β‑sheet enrichment, lipid saturation and degradation, oxidative modifications, and metabolic shifts, is uniquely positioned to detect these molecular signatures directly within tissue. In recent years, Raman spectroscopy (RS) has emerged as a promising, non-invasive analytical technique for the detection of AD-related biochemical changes [\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e]. In our study, Raman spectroscopic analysis revealed clear biochemical distinctions between control and Alzheimer\\u0026rsquo;s disease (AD) brain tissues and between Braak IV and Braak VI AD stages, across multiple spectral regions. Several low‑frequency vibrational bands showed consistent increases in intensity in AD samples, indicating structural and molecular alterations associated with neurodegeneration. These Raman‑derived biochemical fingerprints align closely with known AD mechanisms, demonstrating that RS not only detects but also differentiates key pathological processes, thereby validating its utility as a molecularly resolved tool for probing AD progression. Raman-derived biochemical signatures at Braak IV reflect early, heterogeneous, and potentially modifiable disease processes, whereas Braak VI spectra capture the stabilized molecular fingerprint of end-stage neurodegeneration. These features make Raman spectroscopy particularly powerful for identifying early disease-associated biochemical patterns that are obscured or lost at later stages.\\u003c/p\\u003e \\u003cp\\u003eA marked increase at 445 cm⁻\\u0026sup1; was observed in AD tissues, reflecting enhanced N\\u0026ndash;C\\u0026ndash;S bending and C\\u0026ndash;S stretching in proteins, along with C\\u0026ndash;C skeletal vibrations from carbohydrates. This heightened signal suggests altered environments around sulfur‑containing amino acids, changes in disulfide bonding, and increased rigidity within aggregated proteins such as amyloid‑β and hyperphosphorylated tau, both of which contain sulfur‑bearing amino acids whose local environments shift during misfolding and fibrillization [\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e]. The contribution from carbohydrate‑related modes also points to disrupted glucose metabolism and glycan processing, indicating broader biochemical remodeling in plaque‑ and tangle rich regions [\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe 481 cm⁻\\u0026sup1; band likewise showed elevated intensity in AD samples. This feature, linked to symmetric phosphate stretching in DNA, suggests changes in nucleic acid structure or abundance, potentially due to DNA fragmentation, chromatin alterations, or extracellular DNA associated with inflammation and degeneration [\\u003cspan additionalcitationids=\\\"CR42 CR43 CR44 CR45 CR46 CR47\\\" citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. Together, the enhanced 445 cm⁻\\u0026sup1; and 481 cm⁻\\u0026sup1; Raman signals highlight protein, carbohydrate, and nucleic‑acid‑related molecular alterations in AD tissue, underscoring Raman spectroscopy\\u0026rsquo;s sensitivity to subtle biochemical signatures of neurodegeneration.\\u003c/p\\u003e \\u003cp\\u003eThe increased intensity of specific Raman bands in AD tissue reflects widespread molecular remodeling characteristic of neurodegeneration. The elevation at 560 cm⁻\\u0026sup1; suggests disruptions in glycogen metabolism and altered tyrosine‑containing protein structures, both of which have been implicated in impaired neuronal energy homeostasis and oxidative stress responses in AD. Enhanced nucleic‑acid\\u0026ndash;related signals in the 690\\u0026ndash;780 cm⁻\\u0026sup1; region, together with the strengthened 1080 cm⁻\\u0026sup1; phosphate band, point toward DNA/RNA structural instability, chromatin remodeling, or increased nucleic‑acid fragmentation, which are features commonly associated with neuroinflammation, oxidative damage, and impaired genomic maintenance in AD neurons [\\u003cspan additionalcitationids=\\\"CR42 CR43 CR44 CR45 CR46 CR47\\\" citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe 748 cm⁻\\u0026sup1; band also showed a clear increase in AD tissue, reflecting DNA bending and deformation modes. This enhancement supports the presence of nucleic‑acid structural alterations associated with AD, including genomic instability, oxidative DNA damage, and nucleic‑acid release during cell death. The elevated signal likely reflects DNA conformational stress, fragmentation, or changes in chromatin compaction [\\u003cspan additionalcitationids=\\\"CR62 CR63\\\" citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e]. Together with other spectral shifts, this increase highlights Raman spectroscopy\\u0026rsquo;s sensitivity to nucleic‑acid remodeling in AD.\\u003c/p\\u003e \\u003cp\\u003eA notable decrease was observed at 880 cm⁻\\u0026sup1; in AD samples. This band, linked to tryptophan vibrations, is highly sensitive to protein tertiary structure and its reduction suggests increased protein disorder, misfolding, and aggregation, which are hallmarks of amyloid‑β and tau pathology, as well as possible oxidative modification of aromatic residues [\\u003cspan additionalcitationids=\\\"CR65 CR66 CR67\\\" citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eSimilarly, the 951\\u0026ndash;952 cm⁻\\u0026sup1; band decreased in AD tissue, consistent with α‑helix destabilization and the transition of proteins toward β‑sheet‑rich, aggregated states. Oxidative stress and proteolytic activity in AD may further diminish this CH₃ vibrational signature [\\u003cspan additionalcitationids=\\\"CR62 CR63 CR64 CR65 CR66\\\" citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOverall, the combined increases at 445, 481, and 748 cm⁻\\u0026sup1; and decreases at 880 and 951\\u0026ndash;952 cm⁻\\u0026sup1; reflect coordinated biochemical remodeling of proteins, carbohydrates, and nucleic acids in AD brain tissue, underscoring Raman spectroscopy\\u0026rsquo;s utility for detecting molecular signatures of Alzheimer\\u0026rsquo;s pathology.\\u003c/p\\u003e \\u003cp\\u003eThe 1000 cm⁻\\u0026sup1; band, corresponding to phenylalanine ring‑breathing, showed reduced intensity in AD tissue. This decrease reflects disruptions in aromatic amino‑acid environments and is consistent with widespread protein misfolding, β‑sheet\\u0026ndash;rich aggregation of amyloid‑β and tau, and oxidative or proteolytic damage [\\u003cspan additionalcitationids=\\\"CR62 CR63 CR64 CR65 CR66 CR67\\\" citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe 1296 cm⁻\\u0026sup1; band also decreased in AD samples. This CH₂ deformation mode reports on lipid‑chain organization, and its attenuation aligns with AD‑related membrane degradation, altered phospholipid composition, and oxidative lipid damage, indicating broader disruption of membrane structure [\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eA similar reduction occurred at 1440 cm⁻\\u0026sup1;, a region dominated by CH₂/CH₃ deformation vibrations from lipids, cholesterol, and fatty‑acid chains. The diminished signal reflects impaired membrane integrity and lipid homeostasis, reinforcing the extensive lipid dysregulation characteristic of AD [\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn contrast, the 1554 cm⁻\\u0026sup1; Amide II band increased in AD tissue. This enhancement reflects altered N\\u0026ndash;H bending and C\\u0026ndash;N stretching associated with protein backbone reorganization during amyloid‑β and tau aggregation, as well as additional structural modifications driven by inflammation and oxidative stress [\\u003cspan additionalcitationids=\\\"CR47\\\" citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe 1585 cm⁻\\u0026sup1; band likewise increased, indicating changes in aromatic‑ring and backbone vibrations linked to protein folding and higher‑order structural transitions. This rise is consistent with β‑sheet\\u0026ndash;rich fibril formation and oxidative modification of protein assemblies [\\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e66\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTogether, these spectral changes, decreases in lipid‑ and aromatic‑related bands and increases in protein‑backbone modes, highlight coordinated disruptions in protein structure, lipid organization, and overall biochemical architecture in AD brain tissue.\\u003c/p\\u003e \\u003cp\\u003eThe pronounced increase at 1607 cm⁻\\u0026sup1;, representing aromatic amino‑acid vibrations and linked to protein senescence and necrotic pathways, further supports the presence of advanced protein damage and aggregation. This aligns with known AD hallmarks, including accumulation of oxidized proteins, misfolded aggregates, and heightened cellular stress responses [\\u003cspan additionalcitationids=\\\"CR62 CR63 CR64 CR65 CR66 CR67\\\" citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e]. The combined elevation of these Raman features underscores the multifactorial biochemical deterioration occurring in AD brain tissue, reflecting converging disruptions in energy metabolism, nucleic‑acid integrity, and protein homeostasis.\\u003c/p\\u003e \\u003cp\\u003eThe 1640\\u0026ndash;1680 cm⁻\\u0026sup1; region, corresponding to the Amide I band, showed reduced intensity in AD tissue. Because this band reflects C\\u0026thinsp;=\\u0026thinsp;O stretching and is highly sensitive to protein secondary structure, its attenuation indicates destabilization of α‑helical and β‑sheet content. This decrease aligns with the extensive protein misfolding and the shift of amyloid‑β and tau toward β‑sheet‑rich fibrillar aggregates, reinforcing evidence of widespread structural disruption in AD [\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe 1732 cm⁻\\u0026sup1; band also decreased in AD samples. This carbonyl‑stretching region is associated with cortisone and related steroid structures. Its reduction is consistent with AD‑related metabolic dysregulation, including impaired steroidogenesis, altered glucocorticoid signaling, and oxidative modification of lipid and steroid backbones [\\u003cspan additionalcitationids=\\\"CR62 CR63 CR64 CR65 CR66 CR67\\\" citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e]. The diminished signal reflects broader disruption of carbonyl‑containing molecules and complements the protein, lipid, and nucleic‑acid alterations observed across the Raman spectrum.\\u003c/p\\u003e \\u003cp\\u003eThe comparative analysis of Braak IV and Braak VI brain tissue enabled delineation of biochemical changes that occur early in disease progression from those that represent downstream consequences of irreversible neuronal injury. As depicted in the heat map shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, the progressive increase in Raman spectral variability from Control to Braack VI reflects escalating biochemical heterogeneity, likely driven by tau-associated neurodegeneration. The bands exhibiting the most pronounced changes such as 720 cm⁻\\u0026sup1; (nucleic acid vibrations), 960 cm⁻\\u0026sup1; (phosphate backbone), and 1240\\u0026ndash;1440 cm⁻\\u0026sup1; (protein and lipid signatures), are consistent with known molecular disruptions in Alzheimer\\u0026rsquo;s disease progression. The elevated distribution range in Braack VI samples suggests widespread molecular disorganization, potentially reflecting synaptic loss, gliosis, and altered lipid metabolism. These findings support the utility of Raman spectroscopy as a sensitive tool for detecting molecular alterations across Braack stages. The spectral bands identified may serve as candidate biomarkers for staging and monitoring disease progression, offering insights into the biochemical landscape of tauopathy.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIdentifying molecular features that emerge at Braak IV but intensify or become fixed at Braak VI is essential for defining early biomarkers, therapeutic targets, and mechanistic drivers of AD progression.\\u003c/p\\u003e \\u003cp\\u003eFrom a translational perspective, the ability of Raman spectroscopy to distinguish Braak IV from Braak VI based on intrinsic biochemical signatures supports its potential utility as a platform for early AD detection, disease staging, and therapeutic stratification. By defining spectroscopic markers that precede extensive neuronal loss, this approach may inform the development of diagnostics and interventions aimed at intercepting AD during its most treatable phase.\\u003c/p\\u003e \\u003cp\\u003eAlzheimer\\u0026rsquo;s disease profoundly disrupts lipid homeostasis. Genes involved in lipid breakdown, fatty‑acid oxidation, and lipid synthesis show altered expression in AD brain tissue. Genes ABCA1 (ATP Binding Cassette Subfamily A Member 1), LIPE (Lipase), CPT1A (carnitine palmitoyl transferase 1A enzyme), PPARA (Peroxisome Proliferator-activated receptor-alpha), and SREBP‑1 (Sterol Regulatory Element-Binding Protein 1) represent key nodes in this metabolic network [\\u003cspan additionalcitationids=\\\"CR70 CR71 CR72 CR73 CR74\\\" citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e]. We observed modulations of these genes associated with Lipid metabolism in AD brain tissue samples. ABCA1 plays a central role in the brain\\u0026rsquo;s response to disrupted lipid metabolism in Alzheimer\\u0026rsquo;s disease. As AD progresses, neurons and glial cells accumulate excess free cholesterol, along with elevated levels of oxidized cholesterol derivatives that rise during oxidative stress and neuroinflammation. These lipid disturbances also drive the formation of lipid droplets, a hallmark of metabolic stress in affected brain regions. ABCA1 function is often impaired in AD, and reflects metabolic stress and defective lipid clearance [\\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e]. LIPE, is a major lipase responsible for mobilizing stored triglycerides, showed reduced expression relative to control tissue, consistent with the lipid droplet accumulation widely reported in AD astrocytes and microglia [\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e]. CPT1A, is the rate‑limiting enzyme for mitochondrial fatty‑acid β‑oxidation, was found to be upregulated, in AD samples, reflecting a compensatory metabolic shift toward fatty‑acid utilization in the context of impaired glucose metabolism [\\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e73\\u003c/span\\u003e]. This pattern aligns with the broader metabolic reprogramming observed in AD. PPARA, a nuclear receptor that promotes fatty‑acid oxidation and suppresses inflammation, exhibited reduced expression in AD samples, suggesting insufficient transcriptional support for lipid clearance pathways [\\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e74\\u003c/span\\u003e]. SREBP‑1, is believed to be a master regulator of fatty‑acid and triglyceride synthesis, and its expression was increased in AD samples, reinforcing that a lipid‑anabolic state that may exist that exacerbates lipid droplet formation and metabolic stress [\\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e]. Together, these gene‑expression changes point to a coordinated imbalance in lipid synthesis, breakdown, and oxidation, contributing to the metabolic dysfunction that characterizes the Alzheimer\\u0026rsquo;s disease brain. We observed reduced GPX1 gene expression in the brains AD patients, which results in increased oxidative stress. Reduced GPX1 activity is associated with cognitive decline, and its deficiency worsens Amyloid beta-induced neurotoxicity [\\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e76\\u003c/span\\u003e]. TNF‑α and IL‑6 are two of the most consistently elevated cytokines in the Alzheimer\\u0026rsquo;s disease brain, where they drive the chronic neuroinflammatory environment that accelerates neurodegeneration [\\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e77\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e78\\u003c/span\\u003e]. Activated microglia and astrocytes surrounding amyloid‑β plaques release high levels of TNF‑α, which in turn activates NF‑κB signaling and stimulates the production of inflammatory mediators, like IL‑6. This creates a self‑reinforcing inflammatory loop that impairs microglial clearance of amyloid‑β, increases amyloid precursor protein processing, and promotes tau phosphorylation. Elevated IL‑6 further amplifies microglial activation, oxidative stress, and acute‑phase responses, contributing to synaptic dysfunction and neuronal loss [\\u003cspan additionalcitationids=\\\"CR78 CR79 CR80\\\" citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e77\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e81\\u003c/span\\u003e]. Both cytokines are found at increased levels in AD brain tissue but also in cerebrospinal fluid and peripheral blood, suggesting that central and systemic inflammation are interconnected in AD [\\u003cspan additionalcitationids=\\\"CR83\\\" citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e82\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e84\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOur study demonstrates a clear increase in lipid peroxidation in Alzheimer\\u0026rsquo;s disease (AD) brain tissue compared with age‑matched controls, as revealed by oxidation‑dependent spectral shifts of the BODIPY\\u0026trade; 581/591 C11 probe. The elevated green‑to‑red fluorescence ratios observed in AD samples support the concept that oxidative damage to membrane lipids is a prominent feature of AD pathology. These findings align with extensive evidence implicating oxidative stress as an early and persistent driver of neurodegeneration, particularly in regions vulnerable to amyloid and tau accumulation. Notably, the greater lipid peroxidation and more pronounced lipid aggregate formation in Braak stage VI compared with Braak stage IV suggests that oxidative membrane injury intensifies with advancing tau pathology. This progression is consistent with models proposing that mitochondrial dysfunction, impaired antioxidant defenses, and iron‑dependent lipid oxidation (including ferroptosis‑related mechanisms) become increasingly dysregulated in late‑stage AD [\\u003cspan additionalcitationids=\\\"CR71 CR72 CR73 CR74\\\" citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e70\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e75\\u003c/span\\u003e]. The spatial pattern of punctate oxidized lipid aggregates observed in Braak VI tissue further supports the idea that chronic oxidative stress contributes to membrane destabilization, synaptic vulnerability, and neuronal loss. Although the small sample size limits statistical inference, the consistency of the ratiometric shift across cases underscores the robustness of lipid peroxidation as a pathological marker. Together, these results reinforce the view that lipid oxidative damage is not merely a byproduct of AD pathology but may actively contribute to disease progression, highlighting the potential value of therapeutic strategies aimed at stabilizing membrane lipids or modulating lipid‑oxidation pathways.\\u003c/p\\u003e \\u003cp\\u003eOverall, RS provides a molecular fingerprint of biofluids and tissues by measuring vibrational modes of biomolecules, enabling the identification of subtle alterations associated with amyloid-beta and tau proteins, oxidative stress, and lipid peroxidation. Unlike conventional imaging methods, RS offers rapid, label-free analysis and can be enhanced through advanced computational approaches such as machine learning. Integrating RS into AD diagnostics may improve sensitivity and specificity, particularly in the early stages of disease, thereby supporting timely intervention and personalized care strategies. Raman spectroscopy has shown considerable promise in the context of Alzheimer\\u0026rsquo;s disease by enabling early detection of subtle biochemical changes in blood and cerebrospinal fluid, often before clinical symptoms become apparent. As the disease progresses, this technique reveals increasingly pronounced signals associated with protein misfolding, lipid peroxidation, and neuroinflammation, providing valuable insights into the underlying pathology. In Alzheimer\\u0026rsquo;s disease (AD), protein misfolding, lipid peroxidation, and neuroinflammation represent interconnected pathological processes that evolve across different stages of disease progression. During the preclinical phase, misfolded amyloid-β oligomers and tau seeds begin to accumulate, accompanied by subtle oxidative stress and early microglial activation [\\u003cspan additionalcitationids=\\\"CR81\\\" citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e82\\u003c/span\\u003e]. As patients transition to mild cognitive impairment, amyloid plaques and tau tangles disrupt synaptic function, lipid peroxidation products increase, and microglia release pro-inflammatory cytokines [\\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e83\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e84\\u003c/span\\u003e]. In the early dementia stage, dense amyloid plaques and hyperphosphorylated tau tangles are evident, with oxidative damage to neuronal membranes and mitochondria amplifying neurotoxicity, while astrocytes and microglia sustain inflammatory cascades. Moderate dementia is marked by widespread tau pathology, high levels of lipid peroxidation biomarkers, and chronic neuroinflammation with blood\\u0026ndash;brain barrier disruption. In severe dementia, extensive neuronal death occurs due to overwhelming protein aggregation, irreversible oxidative damage, and persistent glial activation, culminating in widespread neurodegeneration and functional decline. This stage-wise interplay underscores the importance of targeting these processes collectively for early diagnosis and therapeutic intervention.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThe gene‑expression profile observed in Alzheimer\\u0026rsquo;s disease brain tissue points to a coordinated disruption of lipid homeostasis which reflects a maladaptive metabolic state characterized by excess lipid storage, mitochondrial dysfunction, and heightened neuroinflammation. Such a pattern underscores the central role of lipid‑metabolic dysregulation in AD pathophysiology. Overall, the modulation of the Raman spectral signatures in AD samples as compared to controls, reinforces the utility of Raman spectroscopy as a sensitive tool for detecting molecular alterations associated with AD pathology and highlight its potential for identifying early biochemical markers of neurodegeneration. Raman-based biochemical phenotyping provides a valuable bridge between neuropathological staging and molecular mechanism, offering insights into why therapeutic efficacy diminishes as AD advances to late Braak stages. Beyond its diagnostic capabilities, Raman spectroscopy holds significant clinical potential as a complementary tool to conventional imaging and laboratory tests, offering a faster, non-invasive approach for monitoring disease onset and progression. The present study serves as a mechanistic and exploratory foundation for future development of Raman‑based peripheral biomarkers rather than a direct diagnostic comparison to imaging or CSF assays.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConceptualization:\\u003c/strong\\u003e Alexander Khmaladze, Kinga Szigeti, Supriya D. Mahajan\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInvestigation:\\u003c/strong\\u003e Samaneh Ghazanfarpour, Rahul Kumar Das, Ivanna Ihnatovych\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eVisualization:\\u003c/strong\\u003e Samaneh Ghazanfarpour, Rahul Kumar Das\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSupervision:\\u003c/strong\\u003e Anna Sharikova, Alexander Khmaladze, Kinga Szigeti, Supriya D. Mahajan\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eWriting-original draft:\\u003c/strong\\u003e\\u0026nbsp; Supriya D. Mahajan, Kinga Szigeti\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eWriting-review and editing:\\u003c/strong\\u003e Ravikumar Aalinkeel, Samaneh Ghazanfarpour, Rahul Kumar Das, Alexander Khmaladze, \\u0026nbsp;Kinga Szigeti, Norbert Sule, Supriya D. Mahajan\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was approved by the Institutional Review Board (IRB), and all protocols received approval from the Ethics Committee of the Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA. All procedures adhered to established ethical standards for research involving human subjects. Because the study involved analysis of post‑mortem brain tissue, the requirement for informed consent was waived by the IRB. All data were handled in compliance with the Health Insurance Portability and Accountability Act (HIPAA). Patient confidentiality was safeguarded through de‑identification of all extracted data, secure storage on password‑protected institutional servers, and restricted access limited to authorized study personnel. No identifiable personal health information was disclosed or used outside the scope of this research\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for Publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll participants (or their legal guardians, where applicable) provided informed consent for the publication of anonymized clinical information, images, and any accompanying data included in this manuscript. The authors affirm that all identifying details have been removed or anonymized to protect patient privacy. Written consent was obtained in accordance with institutional guidelines and the ethical standards of the Declaration of Helsinki. Documentation of consent is available for review by the Editor of Acta Neurologica Communications upon request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests related to this work.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNo external funding was received to support this study. All research activities were conducted without financial sponsorship from public, commercial, or nonprofit funding agencies.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was made possible in part by Alzheimer Association AARG-16\\u0026ndash;443615, Edward A. and Stephanie E. Fial Fund,\\u0026nbsp;Community Foundation for Greater Buffalo. The funding agencies had no role in study design, data collection, data analysis, interpretation or writing of the report.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and analyzed during the current study are not publicly available due to restrictions related to patient privacy, HIPAA compliance, and institutional policy governing human tissue research. De‑identified data may be made available from the corresponding author upon reasonable request and with approval from the Institutional Review Board and the Jacobs School of Medicine and Biomedical Sciences.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eKnopman DS, Amieva H, Petersen RC, Ch\\u0026eacute;telat G, Holtzman DM, Hyman BT, Nixon RA, Jones DT (2021) Alzheimer disease. 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Int J Mol Sci 26(19):9444. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/ijms26199444\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/ijms26199444\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"acta-neuropathologica-communications\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"anec\",\"sideBox\":\"Learn more about [Acta Neuropathologica Communications](https://actaneurocomms.biomedcentral.com/)\",\"snPcode\":\"40478\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/40478/3\",\"title\":\"Acta Neuropathologica Communications\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Raman spectroscopy (RS), Singular Value Decomposition (SVD) , Alzheimer’s disease (AD), Neurodegeneration, Cognitive decline, Neuroinflammation, Spectroscopic fingerprints, Protein misfolding, Lipid dysregulation\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8844737/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8844737/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\nAlzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by chronic inflammation, neuronal loss, and continuous decline in memory and cognitive function. Raman Spectroscopy (RS) offers a powerful, label‑free approach for detecting early biochemical alterations in AD by generating highly sensitive molecular fingerprints. This capability is particularly valuable for identifying subtle changes associated with protein misfolding, lipid dysregulation, and oxidative stress, key processes underlying AD onset and progression. In our study, full‑spectrum RS revealed clear biochemical distinctions between control and AD brain tissues, as well as between Braak IV and Braak VI AD stages. Multivariate analytical methods, including Singular Value Decomposition (SVD) and Linear Discriminant Analysis (LDA), were applied to manage spectral complexity and highlight the principal biochemical contributors to AD pathology. Several Raman bands showed increased intensity in AD samples, such as 445 cm⁻¹ (N–C–S/C–C skeletal modes), 481 cm⁻¹ (DNA phosphate stretching), 560 cm-1 (Glycogen, tyrosine); 690 -780 cm-1 (Nucleic Acids); 748 cm⁻¹ (DNA bending), 1080 cm-1 (DNA symmetric stretching vibrations in Phosphate bonds (PO)4-2); 1554 cm⁻¹ (Amide II), and 1585 cm⁻¹ (protein‑folding–related vibrations) and 1607 cm-1 (Aromatic Amino acids- Phenylalanine/tyrosine; Cell Senescence - necrosis marker). These increases indicate enhanced protein aggregation, nucleic‑acid structural changes, and backbone reorganization. Conversely, multiple bands decreased in AD tissue, including 880 cm⁻¹ (tryptophan deformation), 951–952 cm⁻¹ (CH₃ vibrations of α‑helical proteins), 1000 cm⁻¹ (phenylalanine), 1296 cm⁻¹ (lipid CH₂ deformation), 1440 cm⁻¹ (lipid/cholesterol deformation), 1640–1680 cm⁻¹ (Amide I), and 1732 cm⁻¹ (C=O stretching). These reductions reflect loss of ordered protein secondary structure, disruption of aromatic amino‑acid environments, and extensive lipid membrane disorganization. Complementary gene‑expression analysis further demonstrated dysregulation of lipid homeostasis in AD, with altered expression of ABCA1, LIPE, CPT1A, PPARA, and SREBP‑1, indicating broad metabolic reprogramming. Together, the coordinated spectral and transcriptional shifts underscore lipid‑metabolic dysfunction as a central feature of AD. By capturing these molecular signatures, RS provides a promising tool for early detection and monitoring of AD progression.\",\"manuscriptTitle\":\"Raman Spectroscopic Identification of Biochemical Alterations in Alzheimer’s Disease Brain Tissue\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-03-31 11:33:42\",\"doi\":\"10.21203/rs.3.rs-8844737/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-05-17T10:12:13+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-30T02:11:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"174817274660336995343710809365967146474\",\"date\":\"2026-04-22T07:59:11+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-02T07:12:49+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"95117980550803014122682193919591985143\",\"date\":\"2026-03-30T12:50:06+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"38524836928099521879193223076054567154\",\"date\":\"2026-03-28T06:41:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-03-27T14:47:58+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-02-14T00:15:55+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-02-12T13:22:21+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Acta Neuropathologica Communications\",\"date\":\"2026-02-10T19:06:22+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"acta-neuropathologica-communications\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"anec\",\"sideBox\":\"Learn more about [Acta Neuropathologica Communications](https://actaneurocomms.biomedcentral.com/)\",\"snPcode\":\"40478\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/40478/3\",\"title\":\"Acta Neuropathologica Communications\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"d0c2eb86-6ea0-41f9-a3e0-cf35212fbad8\",\"owner\":[],\"postedDate\":\"March 31st, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-05-17T10:12:13+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-30T02:11:21+00:00\",\"index\":18,\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"in-revision\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-17T10:24:03+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-03-31 11:33:42\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8844737\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8844737\",\"identity\":\"rs-8844737\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}