Retinal Proteome Changes Mirror Brain Pathology and Reveal Synaptic and Cytoskeletal Dysfunction in Alzheimer's

Retinal Proteome Changes Mirror Brain Pathology and Reveal Synaptic and Cytoskeletal Dysfunction in Alzheimer's | 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 Retinal Proteome Changes Mirror Brain Pathology and Reveal Synaptic and Cytoskeletal Dysfunction in Alzheimer's Jessica Santiago, Dovilė Pocevičiūtė, Patrik Önnerfjord, The Netherlands Brain Bank, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8908397/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Visual dysfunction is increasingly recognized as an early feature of Alzheimer's disease (AD), yet the molecular mechanisms underlying retinal neurodegeneration and their relationship to cerebral pathology remain unclear. Here, we performed comprehensive mass spectrometry-based proteomics on paired retinal and hippocampal tissue from the same postmortem donors (8 AD, 8 non-demented controls) to identify disease-associated molecular signatures and assess their overlap between these tissues. Using a sequential dual-extraction protocol, we identified 370 differentially abundant retinal proteins in AD, including established APP-processing regulators (SORL1, APMAP) and synaptic proteins. Retinal proteomes clearly separated AD from control cases in principal component analysis. Notably, 87% of proteins were detected in both retina and hippocampus, with 68 differentially abundant proteins shared between tissues. Many retinal proteins correlated with neuropathological stages of disease, and four proteins (APMAP, CD109, NRXN1, PACSIN3) showed particularly strong retina-brain correlations. Functional enrichment analysis revealed convergent alterations in synaptic organization, cytoskeletal dynamics, mitochondrial function, cell adhesion, and APP metabolism in both tissues. Cell-type mapping using single-cell retinal reference data indicated that most proteomic changes were broadly distributed across cell types, though some showed enrichment in microglia or photoreceptors. These findings demonstrate that the AD retina undergoes substantial molecular alterations that mirror brain pathology. The identified molecular changes provide mechanistic insights into visual dysfunction in AD and support the retina as an accessible window for assessing brain pathology, with retinal proteins correlated with cerebral pathology representing promising candidates for non-invasive biomarker development. Retina Alzheimer’s disease Proteomics Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Alzheimer’s disease (AD) is the leading cause of dementia worldwide, affecting about one in nine people aged 65 and older and more than one-third of those over 85, with prevalence rising as populations age [ 1 ]. Characterized by the accumulation of amyloid-β (Aβ) and hyperphosphorylated tau [ 11 , 12 , 19 , 77 ], AD is now recognized as a multifactorial neurodegenerative disorder. Many pathological processes, including inflammation, blood–brain barrier dysfunction, and synaptic loss, begin up to two decades before cognitive symptoms begin [ 4 , 7 , 41 , 68 , 72 , 98 , 99 ]. Proteomic analyses of brain and CSF reveal that AD involves a coordinated remodeling of the proteome, where metabolic and mitochondrial disturbances are closely linked with alterations in neuronal structure and glial activity [ 43 , 64 , 89 , 96 ]. Although cognitive decline is the defining clinical feature of AD, visual deficits are increasingly recognized as a common and early symptom [ 1 ]. While findings on high-contrast visual acuity are mixed, consistent evidence points to reductions in low-contrast acuity, contrast sensitivity, depth perception, and motion-related vision [ 16 , 50 , 67 , 70 , 94 ]. Moreover, several prevalent eye conditions affecting the retina, such as age-related macular degeneration, glaucoma, and diabetic retinopathy, have been linked to a higher risk of AD and dementia [ 17 , 24 , 78 ]. Importantly, individuals with vision impairment have been shown to have more than triple the odds of cognitive impairment, even after controlling for common risk factors [ 26 ]. Whether each of these associations reflects shared vulnerability between the retina and brain, a direct contribution of reduced sensory input to neurodegeneration, or both remains unclear. Regardless of clinical symptoms, retinal neurodegeneration in AD is well documented [ 27 ]. In vivo imaging consistently demonstrates thinning of the retinal nerve fiber layer and ganglion cell layer by optical coherence tomography (OCT) [ 57 , 88 ], while experimental techniques such as curcumin-enhanced fluorescence and hyperspectral imaging have reported retinal Aβ signals correlating with brain Aβ [ 30 , 44 ]. Complementing these findings, postmortem studies showed accumulation of Aβ, tau, and other amyloidogenic proteins in AD retina, along with vascular alterations and glial activation [ 20 , 21 , 29 , 33 , 49 , 60 , 73 , 75 , 76 , 81 , 82 , 93 ]. Retinal proteomic analyses further revealed an increase in proteins associated with inflammation and neurodegeneration, along with a downregulation of proteins linked to mitochondrial function and photoreceptor integrity [ 45 ]. Despite growing evidence of retinal involvement in AD, key questions remain about the molecular alterations driving retinal neurodegeneration, how these changes mirror cerebral pathology, and the potential of retinal proteins as non-invasive biomarkers. Understanding these processes could clarify the visual deficits seen in patients and support biomarker development: given the transparency of the eye, the retina offers unique accessibility for high-resolution imaging and direct optical assessment of neurons, glia, and vasculature in vivo [ 6 , 53 ]. To contribute toward answering these questions, we designed a mass spectrometry-based proteomics study analyzing postmortem retinas and hippocampi from the same individuals. This paired design, combined with a sequential proteomic approach to capture proteins of varying solubilities and from diverse cellular compartments, provides a comprehensive view of retinal pathology and enables direct comparison of molecular changes in the retina and brain within each subject. Our study had three principal aims: (1) to identify protein expression and pathway alterations in AD versus non-demented control (NC) retinas, (2) to assess the similarities between retinal and brain molecular changes within the same individuals, and (3) to identify retinal proteins with potential utility as retinal biomarkers for AD. MATERIALS AND METHODS Tissue samples and donor information Paired retinal and hippocampal post-mortem tissue samples were obtained from The Netherlands Brain Bank (NBB), comprising neuropathologically confirmed AD (n = 8) and NC (n = 8). Individuals with a diagnosis of macular degeneration were excluded, and none of the participants had other significant ophthalmological conditions, including glaucoma or diabetic retinopathy. Detailed demographic and clinical characteristics of the study cohort are provided in Table 1 and Supplementary Table 1. Neuropathological evaluation followed Braak and Braak (1991)[ 11 ]. Neurofibrillary changes were staged using the classical six Braak stages (I–VI). Amyloid-β (Aβ) plaques were assessed according to the cortical distribution as described in the original paper: O (0, no detectable amyloid), A (1, sparse plaques restricted to association cortices), B (2, numerous plaques in association areas with occasional involvement of primary cortices), and C (3, abundant plaques throughout association, belt, and core/primary cortical fields)[ 11 ]. Although this Aβ staging method is not commonly applied today, it was the classification approach used by the biobank at the time of autopsy. Written informed consent for the use of tissue and clinical data in research was obtained from all donors or their legal representatives, in accordance with the Declaration of Helsinki and the European Code of Conduct for Brain Banking. The tissue collection protocol was approved by the Medical Ethical Committee of the VU Medical Center, Amsterdam, and all research procedures were reviewed and approved by the regional ethical review board in Lund. Table 1 Cases included in the study NC (n = 8) AD (n = 8) Age, years (mean ± SD) 79.0 ± 13.7 78.2 ± 13.0 Gender, M/F (% female) 4/4 (50%) 2/6 (75%) Post-mortem delay, minutes (mean ± SD) 362 ± 79 360 ± 56 APOE4 positive, n (%) 3 (38%) 5 (62.5%) Neurofibrillary tangle score (median, range) 1.5 (0–3) 5.0 (4–6) Amyloid-β score (median, range) 0 (0–3) 3 (2–3) Data are shown as mean ± SD for age and post-mortem delay, n (%) for gender and APOE4 status, and median (range) for NFT and Aβ scores. Processing of retinal and hippocampal samples for Mass Spectrometry At autopsy, the lenses of the right eyes were removed, filled with O.C.T. mounting medium (Vector Laboratories), and subsequently frozen for storage at -80°C. Each eyeball was cut into eight clefts, leaving a 0.5 cm margin around the optic nerve intact. Retinal tissue was collected from the nasal superior region for this study. Hippocampal tissue was snap-frozen at autopsy in 1 cm-thick sections and stored at -80°C. For analysis, the tissue was further sectioned into 0.5 cm-thick slices. A 3 mm biopsy punch (Kai Medical) was used to dissect samples from the Cornu Ammonis 1 (CA1) region adjacent to the subiculum. The CA1 was selected due to its vulnerability to AD pathology [ 11 , 47 ]. Both retinal and hippocampal samples were transferred to 1.5 mL Pink Rino Tubes with screw caps, and 100 µL of Lysis buffer (50 mM Tris-HCl pH 7.5, 50 mM NaCl, 1 mM EDTA, 5 mM NaH₂PO₄, 1 mM DTT, 0.1% phosphatase 1, 0.03% phosphatase 2, 0.05% protease inhibitors; Sigma-Aldrich) was added. Samples were homogenized in a Bullet Blender Storm Pro (BT24M, Next Advance, Inc., Troy, NY, USA) at speed 8 for 3 min, followed by centrifugation at 14,000 × g for 10 min. Supernatants were collected into new Eppendorf tubes. The residual material was washed with 50 µL Lysis buffer, centrifuged again, and the supernatant pooled with the previous fraction. The remaining tissue in the Rino tubes underwent a second extraction using RIPA buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.1% sodium deoxycholate) and supernatants were collected in new Eppendorf tubes. This two-step protocol was designed to capture proteins with different biochemical properties and solubilities. The Lysis buffer uses mild ionic conditions without detergents, extracting soluble cytoplasmic proteins and loosely membrane-associated proteins while mostly preserving cellular structures. The subsequent RIPA buffer employs both ionic and non-ionic detergents (Triton X-100 and sodium deoxycholate) under higher ionic strength, enabling the extraction of proteins from disrupted membranes, protein complexes, and cellular compartments resistant to mild lysis. This sequential approach allows detection of proteins that might exist in different biochemical states (e.g., soluble vs. complex-bound) or subcellular localizations between disease and control tissues, providing complementary views of the proteome that would not be captured by a single extraction method. For downstream processing, 50 µL of each sample was reduced with DTT (final 10 mM) at 56°C for 30 min, followed by alkylation with iodoacetamide (final 20 mM) for 30 min at RT in the dark. Proteins were precipitated with ice-cold ethanol (final 90%) overnight at − 20°C and pelleted by centrifugation at 14,000 × g for 10 min. Pellets were air-dried and resuspended in 50 µL 100 mM ammonium bicarbonate, then disrupted using a BioRuptor (Diagenode Inc., Denville, USA) for 20 cycles of 15 s on/off. Samples were centrifuged at 14,000 × g for 10 min, and the supernatant transferred to new tubes. Protein concentration was determined using a NanoDrop (DeNovix, AH Diagnostics) at A₂₈₀ nm. For digestion, 15 µg of protein per sample was incubated with trypsin (Promega, Madison, WI) at a 1:50 enzyme: protein ratio overnight at 37°C. Digestion was stopped by adding 5 µL of 10% trifluoroacetic acid (TFA). Samples were dried in a SpeedVac and resuspended in 22 µL of 2% acetonitrile/0.1% TFA for mass spectrometry analysis. Liquid Chromatography -Tandem Mass Spectrometry (LC-MS/MS) Analysis Peptide separation and mass spectrometry: For analysis, 2 µL of each sample was injected into an Exploris 480 mass spectrometer (Thermo Fisher Scientific) coupled to a Vanquish Neo UHPLC system (Thermo Fisher Scientific). Peptides were first loaded onto an Acclaim PepMap 100 C18 precolumn (75 µm × 2 cm, Thermo Scientific) and then separated on an EASY-Spray C18 column (75 µm × 25 cm, 2 µm, 100 Å, ES902) at a flow rate of 300 nL/min and a column temperature of 45°C. A 120 min nonlinear gradient was applied using Solvent A (0.1% FA in water) and Solvent B (0.1% FA in 80% ACN): 5–25% B over 100 min, 25–32% B over 12 min, and 32–45% B over 8 min. Mass spectrometry acquisition: Data were acquired in data-dependent acquisition (DDA) mode in positive polarity. Full MS1 scans were acquired at a resolution of 120,000 (m/z 200), with a normalized AGC target of 300% and a maximum injection time of 45 ms over a mass range of 350–1400 m/z. Precursors were isolated using a 1.3 m/z window and fragmented by HCD with a normalized collision energy of 30. MS2 spectra were recorded in the Orbitrap at a resolution of 15,000, with a normalized AGC target of 100% and custom maximum injection time. An intensity threshold of 10⁴ and dynamic exclusion of 60 s were applied. Raw spectra were processed using Proteome Discoverer 2.5 (Thermo Fisher Scientific) and searched against the UniProt Human canonical database (UP000005640) for protein identification and label-free quantification across retinal and hippocampal tissues. Precursor and fragment tolerances were set to 10 ppm and 0.02 Da, respectively. Trypsin was specified as the protease, with methionine oxidation, asparagine deamidation, phosphorylation (STY), oxidation (M), and protein N-terminal acetylation set as variable modifications, and cysteine carbamidomethylation as a fixed modification. Label-free quantification was performed using peptide peak intensities extracted from Proteome Discoverer. Preprocessing Label-free protein intensities (obtained via Proteome Discoverer 2.5) were first filtered to retain proteins identified at ≤ 1% FDR, supported by at least two unique peptides, and detected in a minimum of 70% of samples. After log₂ transformation and median normalization per sample (row-wise), missing values were imputed using a left-shifted, normal distribution (shifted 2 SD below the protein’s mean log-intensity, with a width of 0.3×SD, capped at the lowest observed value). This models undetected low-abundance signals and is standard for Missing-Not-At-Random (MNAR) imputation in label-free proteomics [ 36 , 48 ]. Statistical Analyses Differential abundance between AD and NC samples was assessed separately in the retina and hippocampus using linear mixed-effects modeling (via statsmodels MixedLM). The fixed-effects structure was specified as: Protein ~ C(Buffer) * C(Diagnosis) + C(Sex) + Age + (1∣Subject) where Buffer denotes the extraction method (Lysis or RIPA), Diagnosis indicates AD versus NC, Sex and Age were included as covariates to control for demographic differences, and Subject identity was modeled as a random intercept to account for paired measures (both buffers from the same donor). For both tissues, the primary focus was on the main effect of Diagnosis; the Buffer × Diagnosis interaction was evaluated as a secondary outcome to explore potential extraction-method specific differentials. Regression coefficients (β) and false discovery rate (FDR)-adjusted p-values were reported per protein. Proteins meeting FDR < 0.05 were designated differentially abundant proteins (DAPs). The intersection of DAPs between retina and hippocampus was identified for downstream comparative analyses. Partial Spearman correlation analyses (adjusted for age and sex) were conducted to examine disease-related associations. First, correlations were computed between each of the top 20 up- and down-regulated retinal proteins (based on FDR-adjusted p-values) and stages of Alzheimer’s pathology, neurofibrillary tangle (NFT) and amyloid β (Aβ) plaque burden. Second, proteins that were differentially abundant in both retina and brain and exhibited changes in the same direction were correlated between tissues to identify shared molecular alterations. Third, retinal values of those proteins showing significant retina-brain correlations were further correlated with retinal phosphorylated tau (p-tau) values at sites previously reported to be altered in the presence of AD pathology in our earlier study[ 73 ]. Enrichment Analyses Functional enrichment of differentially abundant proteins (DAPs) was performed using g:Profiler, examining Gene Ontology (BP, MF, CC), and Reactome pathways. Analyses were conducted using two complementary background sets: (i) the full proteome detected in the experiment, to account for proteins actually measured, and (ii) the Human Protein Atlas of proteins in the retina or hippocampus, to provide a broader reference of human proteins. Using both backgrounds ensures robust enrichment results while mitigating potential biases from incomplete experimental detection. Terms with FDR < 0.05 were considered significant. All normalization, statistical analyses, and data visualizations described above were performed using Python (version 3.13.1). DAPs were mapped to retinal cell types using single-cell reference data from the Human Cell Atlas Retina v1.0 and visualized via the Cell x Gene Discover platform [ 51 , 65 ]. DAPs were first plotted to display their combined expression across all cells in a UMAP embedding of annotated retinal populations. Individual highly changed DAPs were also visualized individually to show their expression patterns across specific cell types. RESULTS Retinal AD proteome shows synaptic, cytoskeletal, and mitochondrial changes After applying quality control to retain only high confidence, consistently detected proteins, 4,346 retinal proteins were included in the study. To explore patterns of variation in the retinal proteome, including potential differences associated with disease status, we performed principal component analysis (PCA). Because our dual-buffer extraction protocol was designed to capture proteins from different cellular compartments and/or solubility profiles, we analyzed the lysis and RIPA fractions separately at this step. In both cases, the PCA revealed clear clustering of AD and NC samples along the first two principal components (Figure. 1A). In order to identify proteins that differed between AD and NC retinas, we used a linear mixed model adjusted for age and sex. This analysis identified 239 differentially abundant proteins (DAPs; FDR < 0.05), independent of the extraction method (main effect diagnosis) (Fig. 1 B)(Supplementary Table 2). Using the same approach, 43 of these retinal DAPs were also detected in the hippocampus. To further examine how these proteins relate to disease progression, we correlated the top 20 most significantly up- and downregulated DAPs with neuropathological stages of amyloid-β and neurofibrillary tangle (NFT) progression. The most highly upregulated proteins, including APPL2, EYS and APMAP, showed significant positive correlations with neuropathological stages of AD, whereas downregulated proteins, such as CD109, PACSIN3, and VCAN, showed negative correlations (Fig. 1 C). To explore the functions, pathways, and cellular components affected in AD retina, we performed functional enrichment analysis of the DAPs. The enrichment analysis revealed significant alterations across multiple cellular compartments and biological processes. For cellular components (GO:CC), the most prominently enriched terms included organelle-related structures (organelle, organelle membrane), synaptic compartments (synapse, presynapse, synaptic vesicle, synaptic vesicle membrane), cell-cell communication structures (cell junction), and various vesicular transport systems (intracellular vesicle, cytoplasmic vesicle, exocytic vesicle). Notably, mitochondrial compartments (mitochondrial intermembrane space, organelle envelope lumen) showed enrichment among upregulated DAPs. For biological processes (GO:BP), the analysis identified enrichment in cell-cell junction assembly, consistent with alterations in intercellular communication and structural organization. Molecular function analysis (GO:MF) revealed enrichment in cytoskeletal protein binding, further supporting disruptions in cellular architecture (FDR < 0.05, Fig. 1 D). To gain insight into the cellular involvement of AD-associated changes in the retina, we mapped our differentially abundant proteins (DAPs) onto single-cell transcriptomic reference data from the Human Cell Atlas Retina v1.0 [ 51 ]. When considering all DAPs together, we observed a broad representation across multiple retinal cell types, with a particularly strong signal in ganglion cells, amacrine, and cones (Fig. 1 E). Examining the top DAP-associated genes individually revealed that most were not strongly cell-type-specific; however, some were more commonly expressed in microglia, with both increases and decreases observed in AD. Among these, DPYD and SORL1 stood out as more prominent, showing relatively high expression in microglia. Other genes, such as APMAP, VCAN, and TSNAX, were also more detected in microglia than in other cell types, though at lower levels and restricted to a percentage of cells. Notably, EYS, which was increased in AD, exhibited pronounced expression in rods and cones (Fig. 1 F) (Supplementary Fig. 1). Sequential extraction reveals buffer-dependent AD-associated changes in APP processing To extend our findings beyond buffer-independent effects, we next examined how extraction conditions influenced the detection of AD-related changes. Because Lysis and RIPA solubilize partly overlapping but distinct protein pools, the Diagnosis × Buffer interaction in our model allowed us to identify alterations whose magnitude differed between buffers, as well as changes detectable under one extraction condition only, potentially reflecting differences in protein solubility, subcellular localization, or incorporation into tightly bound complexes. Proteomic comparison of the two fractions identified 4,346 retinal proteins in total: 3,794 (87.3%) were shared across both buffers, while 144 (3.3%) and 408 (9.4%) were unique to Lysis and RIPA, respectively (Fig. 2 A). Among the DAPs with a main effect of diagnosis, indicating consistent AD-related changes across buffers, several also showed a significant Diagnosis × Buffer interaction, reflecting a stronger effect in one extraction method. Additional proteins exhibited interaction effects only, indicating that their AD-associated changes were exclusively captured in a single buffer (Fig. 2 B) (Supplementary Table 2). Among the total 370 DAPs identified in the retina, 239 showed consistent disease-related changes across buffers. Of these, 131 (35.4%) exhibited similar abundance in both buffers, 104 (28.1%) were more abundant in Lysis, and 4 (1.1%) were more abundant in RIPA. The remaining 131 DAPs displayed buffer-dependent effects only, with 105 (28.4%) detected exclusively in RIPA and 26 (7.0%) exclusively in Lysis. Altogether, buffer-dependent proteins, those showing stronger or exclusive extraction patterns, accounted for the majority (64.6%) of disease-associated proteins identified (Fig. 2 B). Notably, several proteins associated with critical pathological processes were only found to be different in RIPA buffer, including NCSTN, a component of the gamma-secretase complex that processes APP [ 31 ]; BAX, a central mediator of retinal ganglion cell death [ 54 ]; NEFH and SNCG, previously associated with glaucomatous damage; and OAT, deficiency of which causes gyrate atrophy of the choroid and retina [ 86 ]. Gene Ontology enrichment analysis revealed distinct cellular component profiles between the two extraction buffers. Lysis, using mild conditions to extract soluble and loosely-associated proteins, was enriched for extracellular vesicles, exosomes, and membrane-bounded organelles, whereas RIPA, using detergents to extract proteins from compartments resistant to mild lysis and those in protein complexes, preferentially captured proteins from the cytosol, postsynaptic regions, and cell junctions. Both buffers captured extracellular and organelle-associated proteins, but with different efficiencies (Fig. 2 C). Functional enrichment analysis further showed that RIPA-extracted proteins were overrepresented in biological processes related to transmembrane transport and amyloid precursor protein biosynthesis, and in molecular functions such as RNA binding and structural molecule activity (Fig. 2 D). Retina and hippocampus show strong proteomic similarity, and 68 share DAPs in AD In the hippocampal CA1 region, 4,204 proteins were identified using the same quality control criteria applied to the retina, of which 3,973 (87%) were also detected in the retina, highlighting a substantial proteomic overlap between the two tissues (Fig. 3 A). To assess overall proteomic variation, we performed principal component analysis (PCA) of the hippocampal dataset. A clustering pattern separating Alzheimer’s disease (AD) and non-demented control (NC) samples was observed along the first two principal components, particularly in the lysis fraction (Fig. 3 B). Differential abundance analysis using a linear mixed model adjusted for age and sex identified 524 proteins with significant differences between AD and NC, regardless of extraction method (FDR < 0.05; Fig. 3 C). To evaluate the relationship between protein abundance changes and AD pathology severity, we examined partial Spearman correlations between the top DAPs and stages of neuropathological severity, amyloid-β plaques and NFT. Most upregulated proteins correlated positively with neuropathological burden, while downregulated proteins correlated negatively with disease stage (Fig. 3 D). Several proteins previously linked to AD in human hippocampal proteomics were also observed here [ 39 , 71 ]. For instance, synapse-related proteins, including SYP, YWHAG, DLAT, and PDHB, were downregulated, whereas AQP4, GJA1, and HSPB1, associated with inflammatory or stress-related processes, were upregulated. Notably, APOE levels were also increased in AD in our data. To evaluate whether buffer-dependent extraction patterns observed in the retina were also present in the brain, we performed a similar analysis in the hippocampal CA1 region. A total of 763 differentially abundant proteins (DAPs) were identified in the hippocampus when considering buffer effects. Among these, 68 DAPs were also detected in the retina. Dividing the DAPs by extraction method, 374 (49.0%) exhibited similar abundance across both buffers, 142 (18.6%) were stronger in Lysis, and 8 (1.0%) were stronger in RIPA. The remaining 239 DAPs showed buffer-dependent effects only, with 164 (21.5%) detected exclusively in RIPA and 75 (9.8%) exclusively in Lysis (Supplementary Fig. 1). Retina and brain show overlapping pathway changes and correlated AD-associated proteins To assess whether AD-associated proteomic changes in the retina reflect those occurring in the brain, we compared all 370 retinal DAPs (including extraction-dependent ones) with the 763 DAPs identified in the hippocampus. By considering the complete set of disease-associated proteins regardless of buffer dependency, we aimed to capture the full spectrum of retinal proteomic changes and their relationship to brain pathology. We first examined overlap at the individual protein level to identify shared disease-associated proteins between tissues. At the individual protein level, 29 of the 68 DAPs shared by both tissues exhibited changes in the same direction. To focus on proteins with robust effect sizes for subsequent correlation analyses, we selected concordantly regulated proteins with absolute Beta values > 1.2 in both tissues. Among proteins upregulated in both tissues, 2 proteins met this criterion: APMAP, and ISOC2. Among proteins downregulated in both tissues, 5 proteins met this criterion: AAMP, CD109, LTF, NRXN1, and PACSIN3. To further investigate the relationship between retina and brain protein expression, we performed partial Spearman correlation analysis on these proteins, adjusting for age and sex. Of these, 4 proteins showed strong significant correlations between retina and brain after FDR correction (q < 0.05): APMAP (ρ = 0.800, FDR = 0.0006), CD109 (ρ = 0.815, FDR = 0.0006), NRXN1 (ρ = 0.791, FDR = 0.0006), and PACSIN3 (ρ = 0.750, FDR = 0.0006) (Fig. 4 A–D). These associations were attenuated and lost significance after adjustment for diagnosis. Given the relevance of tau pathology in AD, we next examined whether retinal levels of these four proteins were associated with retinal phosphorylated tau (p-tau) at phosphorylation sites (p202, p231, p396 + 404), previously reported to be affected in the presence of AD pathology [ 73 ]. Partial Spearman correlation analysis adjusted for age and sex revealed significant positive correlations for APMAP with the three phosphorylation sites and negative correlations of CD109 and PACSIN3 with p202 (Fig. 4 E). Within the AD group, only the association between p231 and PACSIN3 remained significant (q = 0.03), whereas CD109 and NRXN1 showed trend-level correlations with p231 (q = 0.08 for both). No correlations were observed within the NC group. Beyond individual protein overlap, we performed functional enrichment analysis on all differentially abundant proteins from both tissues. Analyses included the combined set of proteins, upregulated and downregulated proteins separately, and buffer-specific profiles for Lysis and RIPA. This approach allowed us to determine whether retina and hippocampus share convergent biological processes and pathways despite potential differences in specific protein identities. Gene Ontology enrichment analysis revealed several shared pathways between retina and brain DAPs (Fig. 4 F). Biological processes included regulation of transmembrane transport, import into cell, cell–cell junction assembly, and amyloid precursor protein biosynthetic process. Molecular function terms encompassed cell adhesion molecule binding, cadherin binding, and cytoskeletal protein binding. Cellular component analysis showed enrichment in extracellular exosome, extracellular organelle, cell junction, and synaptic compartments. Reactome pathway analysis highlighted Bassign interactions, RHO GTPases activate CIT, axon guidance, nervous system development, and metabolism of amino acids and derivatives as shared pathways. DISCUSSION Our proteomic analysis reveals molecular alterations in the AD retina that suggest involvement of multiple pathological pathways. The separation of AD and control samples in the principal component analysis indicates the disease seems to have a profound effect on the retinal (and hippocampal) proteome, supported by 370 proteins showing differential abundance between AD retinas and controls. The substantial proteomic overlap between retina and hippocampus (87% of total proteins) provides a foundation for comparison, with 68 proteins showing disease-associated changes in both tissues. Among these, APMAP, CD109, NRXN1, and PACSIN3 demonstrated strong retina-brain correlations, with APMAP also associated with retinal p-tau levels. Pathway enrichment analyses of differentially abundant proteins revealed several altered pathways that we discuss under five major categories: (1) synaptic and vesicular processes; (2) cytoskeleton and intracellular transport; (3) APP processing and membrane signaling; (4) mitochondrial function and cellular stress; and (5) cell-cell junctions, adhesion, and barrier integrity. Synaptic and vesicular processes was the most prominent category altered, with enrichment of terms related to synaptic organization (pre- and post-synapse, glutamatergic synapse) and vesicle recycling machinery in both tissues. Within this category, several synaptic organizers, including NRXN1, NRXN3, NECAB2, and CACNB2, were significantly reduced in AD retina. These changes align with robust evidence linking early synaptic protein loss in the brain to tau pathology and cognitive decline [ 85 , 90 ], where synaptic alterations are among the strongest predictors of cognitive impairment [ 3 , 61 ]. Our findings suggest that AD-related synaptic remodeling is extended to retinal neurons. Previous studies corroborate this concept: rodent models show early retinal synaptic changes associated with Aβ and tau, including synapse loss in the inner plexiform layer and altered neurotransmission [ 14 , 52 ], and demonstrate that brain-derived Aβ can reach the retina via the optic nerve, causing synaptic dysfunction [ 13 ]. Whether synaptic changes in the human retina occur independently of, or precede, local Aβ and tau deposition is an open question. Cytoskeleton and intracellular transport was another strongly altered category, suggesting widespread disruption of structural and trafficking networks in both retina and brain. Enriched terms included microtubule organization, actin-based projections, and motor protein complexes such as kinesins and dyneins [ 97 ]. In brain tissue, hyperphosphorylated tau destabilizes microtubules, impairs axonal transport, and contributes to synaptic dysfunction and neurodegeneration [ 42 , 63 , 69 ]. Similar mechanisms appear to operate in the retina as tau accumulation and phosphorylation have been observed in human AD retinas by multiple groups [ 18 , 21 , 34 , 73 , 91 ]. Experimental studies further demonstrate that amyloid-β can disrupt axonal transport and compromise cytoskeletal stability in both the brain and retina [ 22 ]. Together, these findings suggest that cytoskeletal and transport deficits represent a systemic vulnerability in AD, affecting both central and retinal circuits and potentially contributing to early visual dysfunction before overt neuronal loss. APP processing is a central pathway in Alzheimer’s disease, as its proteolytic cleavage determines whether non-amyloidogenic fragments or amyloidogenic Aβ peptides are produced. In the brain, dysregulation of this pathway drives Aβ accumulation, plaque formation, and downstream synaptic and signaling disturbances [ 9 , 87 ]. Analysis of our retinal proteomic data shows enrichment of terms including “amyloid precursor protein biosynthetic process,” “regulation of APP biosynthetic process,” and several membrane signaling complexes, indicating that APP processing could also be altered in the eye. Although some studies have reported no significant Aβ or APP differences in post-mortem AD retinas [ 21 ], others have reported Aβ deposition and APP-related changes in retinal tissue [ 44 , 49 , 75 ]. Mechanistic work supports the possibility of retinal Aβ involvement, showing that Aβ can be transported from brain to eye via the ocular glymphatic system [ 13 ]. Importantly, in our study, APP-related changes were detected only with the RIPA-extracted method, suggesting that these changes might be associated with intracellular organelles or complexes that require harsher extraction conditions. Taken together, while discrepancies exist, our results suggest the retina exhibits at least some amyloid-related molecular remodeling. Mitochondrial dysfunction and cellular stress are recognized features of Alzheimer's disease. In brain tissue, impaired oxidative phosphorylation, disrupted mitochondrial dynamics, and increased reactive oxygen species contribute to synaptic failure and neuronal degeneration [ 2 , 92 ]. Our retinal proteomic analysis revealed enrichment of terms such as 'mitochondrion,' 'mitochondrial intermembrane space,' and stress-related pathways including 'cellular responses to stress' and 'lysosome,' suggesting that similar bioenergetic challenges may occur in retinal tissue. Disruption of cell–cell junctions and adhesion complexes is increasingly recognized as another contributor to Alzheimer’s disease pathology. In the brain, the integrity of the blood–brain barrier (BBB) depends on tight junctions (claudins, occludin, ZO-1), adherens junctions (VE-cadherin), and gap junctions formed by connexins. Loss or disorganization of these junctional proteins leads to increased vascular permeability, neuroinflammation, and impaired neuronal signaling [ 84 , 95 ]. Our retinal proteomic analysis revealed enrichment of pathways such as ‘cell–cell junction organization,’ ‘apical junction complex,’ ‘focal adhesion,’ and ‘cadherin binding,’ suggesting potential effects on the blood-retina barrier (BRB). Experimental models have shown that retinal vascular amyloid deposition correlates with tight junction loss and BRB breakdown, paralleling cerebral amyloid angiopathy [ 79 , 80 ]. Whether similar mechanisms contribute to retinal pathology in human AD warrants further investigation. To date, only one previous proteomic study has examined retinal changes in AD. Koronyo et al. [ 45 ] examined six AD and six control samples from the temporal hemiretina using a single-buffer protocol and reported 886 differentially expressed proteins, correction for multiple testing was not reported. Individual proteins were categorized according to biological functions, highlighting increased apoptosis, necrosis, and inflammation as retinal features of AD, alongside mitochondrial dysfunction and loss of photoreceptor-related proteins. While our study confirms some of these observations, such as changes in inflammatory and photoreceptor-related proteins, it expands the picture by identifying broader mechanistic categories through the enrichment of pathways, including synaptic organization, cytoskeletal remodeling, and APP processing. Comparisons should, however, be made with caution, as methodological differences and the distinct retinal regions analyzed can influence protein detection and the specific changes observed. Our analysis revealed a substantial proteomic overlap between retina and hippocampus, with 87% of proteins shared across both tissues. Despite this extensive overlap, disease-associated changes were not uniform: 68 differentially abundant proteins (DAPs) were common to both tissues, yet only 29 changed in the same direction. Importantly, although individual proteins did not consistently follow the same trend, the broader pathways were altered similarly in both tissues, showing downregulation of synaptic and cytoskeletal processes and upregulation of mitochondrial compartments and stress-related pathways, for example. This divergence at the protein level, despite pathway-level similarity, could reflect differences in disease stage, local adaptive responses, or tissue-specific vulnerability. For example, proteins involved in APP processing, such as APMAP and SORL1, showed increased abundance in the retina, which may represent adaptive responses, whereas downregulation of these proteins has been associated with AD in brain tissue[ 55 , 56 ]. Such bidirectional regulation is well documented in AD, where early compensatory responses such as increased synaptic plasticity and transient neuroinflammatory activity that initially promote clearance of toxic species eventually fail, leading to progressive neurodegeneration [ 37 , 59 ]. The increased abundance of SORL1 in the AD retina deserves special attention, as this protein has been implicated in retinal pathologies beyond its well-established role in Alzheimer's disease. SORL1, also known as SORLA and LR11, has been linked to age-related retinal changes, with SORL1 knockout mice displaying abnormal retina morphology in late-adult phenotyping, and STRING analysis demonstrating that SORL1 forms a network with age-related macular degeneration (AMD) GWAS genes [ 32 ]. Moreover, elevated vitreous levels of soluble SORL1 have been found in idiopathic epiretinal membrane [ 35 ] and diabetic retinopathy[ 83 ]. These observations suggest that SORL1 dysregulation may affect retinal homeostasis across multiple disease contexts, and that its upregulation in the AD retina could reflect shared molecular mechanisms between Alzheimer's disease and other disorders affecting the retina. Four proteins, APMAP, CD109, NRXN1, and PACSIN3, showed strong correlations between retina and brain. APMAP also correlated positively with retinal phosphorylated tau at multiple sites. APMAP regulates APP processing through the lysosomal-autophagic pathway [ 56 ], and its deletion worsens Aβ deposition and memory in AD mice [ 28 , 56 ]. However, AD brains show an increase in the dysfunctional APMAP2 splice variant alongside reduced levels of APMAP-interacting proteins that suppress Aβ [ 28 ]. The elevated APMAP observed here may thus represent compensatory upregulation or accumulation of dysfunctional splice variants rather than functional protein. Another protein with strong retina-brain correlation, CD109, is a modulator of TGF-β and inflammatory signaling [ 8 , 25 ], and has been proposed as a therapeutic target in AD, where atorvastatin-mediated reduction of caveolin-1/CD109 may help prevent TGF-receptor degradation [ 25 ]. The third protein, NRXN1, is a presynaptic adhesion molecule essential for neurotransmission that is directly targeted by Aβ oligomers, which bind to neurexins and reduce their surface expression, impairing synapse formation [ 58 ]. Beyond direct Aβ binding, neurexins seem to be compromised through multiple mechanisms in AD. Neurexins are processed by the same α- and γ-secretases that cleave APP, and AD-linked presenilin mutations alter this processing [ 10 , 74 ]. Additionally, AD brains show reduced NRXN3 expression that inversely correlates with neuroinflammation, and a NRXN3 splicing variant interacts with APOE ε4 to increase AD risk [ 38 ]. Lastly, PACSIN3 regulates membrane curvature and endocytosis, playing roles in vesicle trafficking and cytoskeletal dynamics [ 23 ]. While PACSIN3's specific role in AD is unclear, related family members PACSIN1 and PACSIN2 are implicated in tau-mediated synaptic dysfunction and impaired BBB Aβ clearance, respectively [ 15 , 66 ], suggesting the PACSIN family may broadly contribute to AD pathogenesis. These proteins represent potential molecular links between retinal and cerebral pathology, though their utility as biomarkers requires validation in larger cohorts and assessment of their detectability through non-invasive methods in future studies. Mapping all DAPs to single-cell retinal data revealed a broad distribution across cell types, with prominent representation in ganglion cells, consistent with previous reports of ganglion cell loss in AD [ 18 , 46 , 57 ]. DAPs were also overrepresented in amacrine cells, which are interneurons, and intriguingly, in cones, a photoreceptor type unique to the retina. While overall enrichment patterns lacked strong cell-type specificity, some highly altered proteins showed preferential expression in particular cell types. For example, SORL1 and APMAP were primarily enriched in microglia, supporting evidence of microglial involvement in the AD retina [ 45 , 60 , 93 ], even though inflammation-related pathways were not statistically enriched in our dataset. Notably, EYS (EGF-like photoreceptor maintenance factor), predominantly expressed in photoreceptors and a major gene for rod-cone dystrophies, [ 5 , 40 ] was among the most upregulated proteins in AD retinas. While the previous proteomics study reported downregulation of some photoreceptor-related proteins [ 45 ], the increase in EYS observed here may reflect differences in the retinal regions analyzed (nasal superior versus temporal) or differential responses among photoreceptor protein subclasses, while nevertheless supporting the common observation of photoreceptor involvement. Cell-type mapping further showed enrichment of differentially abundant proteins in cones, suggesting that photoreceptor-related changes extend beyond ganglion cells and inner retinal neurons. Although bulk proteomics has inherent limitations for cell-type attribution and this enrichment may partly reflect shifts in relative retinal composition, the photoreceptor-specific expression of EYS indicates that AD-related molecular alterations may also affect outer retina. This warrants further investigation, as photoreceptors represent a retina-specific cell type that may reveal unique aspects of neurodegeneration in this tissue. LIMITATIONS This study provides valuable insight into the molecular changes occurring in the retina in AD and how they correlate with alterations in the brain of the same individuals; however, several limitations should be considered. First, the use of postmortem tissue from individuals with established dementia reflects predominantly end-stage disease and therefore may not capture earlier or more dynamic molecular changes that occur during AD initiation or progression. The sample size was small, which reduced statistical power for detecting moderate or subtle effect sizes and limited the ability to perform subgroup or stage-specific analyses. Even so, the study was sufficiently powered to identify robust disease-associated differences, as reflected by the separation of diagnostic groups in principal component analysis. Although postmortem delay was consistent between diagnostic groups, the use of postmortem tissue also introduces factors, such as tissue handling and protein degradation, that may not fully reflect in vivo conditions. In addition, analyses were restricted to a single retinal region, which may not capture spatial heterogeneity across the retina. Finally, cell-type enrichment was inferred from transcriptomic reference datasets, which do not always correspond to protein-level expression. CONCLUSION The Alzheimer's retina undergoes molecular alterations mirroring brain pathology, including synaptic dysfunction, cytoskeletal remodeling, and APP-processing changes, with some evidence suggesting retina-specific features such as altered photoreceptor proteins. These mechanisms might contribute to the visual symptoms in AD. The concordant retinal-brain changes suggest that retinal molecular signatures may reflect brain pathology, and proteins showing strong cross-tissue correlations represent interesting candidates for future biomarker studies. While this exploratory study requires validation in larger cohorts, especially for specific protein changes, the molecular signatures and their brain correspondence provide a foundation for understanding retinal AD pathology and suggest the retina's potential as an accessible window for monitoring neurodegeneration in Alzheimer's disease. Abbreviations ACN Acetonitrile AD Alzheimer’s disease AGC Automatic gain control Aβ Amyloid-beta APP Amyloid precursor protein APOE4 Apolipoprotein E ε4 allele BORC Biogenesis of lysosome-related organelles complex BP Biological Process (Gene Ontology) CC Cellular Component (Gene Ontology) CSF Cerebrospinal fluid DAPs Differentially abundant proteins DDA Data-dependent acquisition DTT Dithiothreitol EDTA Ethylenediaminetetraacetic acid FA Formic acid FDR False discovery rate GO Gene Ontology HCD Higher-energy collisional dissociation HDAC Histone deacetylase LC-MS/MS Liquid chromatography–tandem mass spectrometry MF Molecular Function (Gene Ontology) MNAR Missing-not-at-random MS1/MS2 Mass spectrometry level 1 and 2 scans NFT Neurofibrillary tangle NC Non-demented control OCT Optical coherence tomography p-tau Phosphorylated tau PCA Principal component analysis PRIDE Proteomics Identifications Database RIPA Radioimmunoprecipitation assay buffer RNA Ribonucleic acid TFA Trifluoroacetic acid UHPLC Ultra-high-performance liquid chromatography Declarations Ethics approval and consent to participate Informed consent for using retina and brain tissue, as well as clinical data for research purposes, was obtained from the patients or from their closest relatives in accordance with the International Declaration of Helsinki and the Code of Conduct for Brain Banking. The tissue collection protocols were approved by the medical ethics committee of VU Amsterdam, and the Swedish Ethical Review Authority approved the study (Dnr 2021/04270). Consent for publication Not applicable. Competing Interests MW has acquired research support (for the institution) from Eli Lilly. Funding The study was funded by the Brain Foundation (FO2023-0113), Crafoord Foundation (20230519), Dementia Foundation (2024), Greta and Johan Kockska Foundation (2024), the Åhlén Foundation (243007), the Swedish Research Council (2024–02875), Multipark (2024), Stiftelsen för gamla tjänarinnor (2025 − 337), and the Alzheimer’s Foundation (AF-1010435). Author JWV is supported by the SciLifeLab & Wallenberg Data Driven Life Science Program (grant: KAW 2020.0239) and the Swedish Research Council (2024–03642). None of the funders has been involved in the design of the study, collection, analysis, interpretation of data, and/or the writing of the manuscript. Author Contribution J.S. and M.W. contributed to the study concept and design and were the primary contributors to the discussion and interpretation of results. D.P. prepared the samples. J.S. analyzed the data with support from J.V., who assisted in planning the analytical strategy, applying statistical methods, and interpreting the results. P.O. provided support with initial processing and interpretation of mass spectrometry data. J.S. drafted the first version of the manuscript. All authors critically reviewed the manuscript for intellectual content and approved the final version. Acknowledgement The authors thank the Netherlands Brain Bank (NBB) for providing retina and brain tissue and for the neuropathological evaluations, and the Center for Translational Proteomics at Lund University's Medical Faculty for conducting mass spectrometry analyses. Data Availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [62] partner repository with the dataset identifier PXD073336. Data are pseudonymized and comply with GDPR regulations and the ethical approval granted by the Medical Ethical Committee of the VU Medical Center, Amsterdam. The processed data generated during the analysis, supporting the results presented in this study, are available within the main manuscript and the Supplementary Information files. References (2025) 2025 Alzheimer's disease facts and figures. Alzheimer's Dement 21: e70235 Doi https://doi.org/10.1002/alz.70235 Adav SS, Park JE, Sze SK (2019) Quantitative profiling brain proteomes revealed mitochondrial dysfunction in Alzheimer's disease. 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Br J Ophthalmol 107:275–282. 10.1136/bjophthalmol-2021-319508 Shi H, Koronyo Y, Fuchs DT, Sheyn J, Jallow O, Mandalia K, Graham SL, Gupta VK, Mirzaei M, Kramerov AA al (2023) Retinal arterial Abeta(40) deposition is linked with tight junction loss and cerebral amyloid angiopathy in MCI and AD patients. Alzheimers Dement 19:5185–5197. 10.1002/alz.13086 Shi H, Koronyo Y, Fuchs DT, Sheyn J, Wawrowsky K, Lahiri S, Black KL, Koronyo-Hamaoui M (2020) Retinal capillary degeneration and blood-retinal barrier disruption in murine models of Alzheimer's disease. Acta Neuropathol Commun 8:202. 10.1186/s40478-020-01076-4 Shi H, Koronyo Y, Rentsendorj A, Fuchs DT, Sheyn J, Black KL, Mirzaei N, Koronyo-Hamaoui M (2021) Retinal Vasculopathy in Alzheimer's Disease. Front Neurosci 15:731614. 10.3389/fnins.2021.731614 Shi H, Koronyo Y, Rentsendorj A, Regis GC, Sheyn J, Fuchs DT, Kramerov AA, Ljubimov AV, Dumitrascu OM Rodriguez AR (2020) Identification of early pericyte loss and vascular amyloidosis in Alzheimer's disease retina. Acta Neuropathol 139: 813–836 10.1007/s00401-020-02134-w Shiba T, Bujo H, Takahashi M, Sato Y, Jiang M, Hori Y, Maeno T, Shirai K (2013) Vitreous fluid and circulating levels of soluble lr11, a novel marker for progression of diabetic retinopathy. Graefes Arch Clin Exp Ophthalmol 251:2689–2695. 10.1007/s00417-013-2373-9 Sweeney MD, Sagare AP, Zlokovic BV (2018) Blood-brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat Rev Neurol 14:133–150. 10.1038/nrneurol.2017.188 Taddei RN, K ED (2025) Synapse vulnerability and resilience underlying Alzheimer's disease. 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Nat Aging 4: 33–47 10.1038/s43587-023-00550-7 Tzioras M, McGeachan RI, Durrant CS, Spires-Jones TL (2023) Synaptic degeneration in Alzheimer disease. Nat Rev Neurol 19:19–38. 10.1038/s41582-022-00749-z Walkiewicz G, Ronisz A, Van Ginderdeuren R, Lemmens S, Bouwman FH, Hoozemans JJM, Morrema THJ, Rozemuller AJ, De Hart de Ruyter FJ Groef L et al (2024) Primary retinal tauopathy: A tauopathy with a distinct molecular pattern. Alzheimers Dement 20: 330–340 10.1002/alz.13424 Wei Y, Du X, Guo H, Han J, Liu M (2024) Mitochondrial dysfunction and Alzheimer's disease: pathogenesis of mitochondrial transfer. Front Aging Neurosci 16:1517965. 10.3389/fnagi.2024.1517965 Xu QA, Boerkoel P, Hirsch-Reinshagen V, Mackenzie IR, Hsiung GR, Charm G, To EF, Liu AQ, Schwab K, Jiang K et al (2022) Muller cell degeneration and microglial dysfunction in the Alzheimer's retina. Acta Neuropathol Commun 10: 145 10.1186/s40478-022-01448-y Xu Y, Aung HL, Hesam-Shariati N, Keay L, Sun X, Phu J, Honson V, Tully PJ, Booth A, Lewis E al (2024) Contrast Sensitivity, Visual Field, Color Vision, Motion Perception, and Cognitive Impairment: A Systematic Review. J Am Med Dir Assoc 25:105098. 10.1016/j.jamda.2024.105098 Yamazaki Y, Shinohara M, Shinohara M, Yamazaki A, Murray ME, Liesinger AM, Heckman MG, Lesser ER, Parisi JE, Petersen RCet al et al (2019) Selective loss of cortical endothelial tight junction proteins during Alzheimer's disease progression. Brain 142:1077–1092. 10.1093/brain/awz011 Yarbro JM, Shrestha HK, Wang Z, Zhang X, Zaman M, Chu M, Wang X, Yu G, Peng J (2025) Proteomic landscape of Alzheimer's disease: emerging technologies, advances and insights (2021–2025). Mol Neurodegener 20:83. 10.1186/s13024-025-00874-5 Yildiz A (2025) Mechanism and regulation of kinesin motors. Nat Rev Mol Cell Biol 26:86–103. 10.1038/s41580-024-00780-6 Yu Y, Chen R, Mao K, Deng M, Li Z (2024) The Role of Glial Cells in Synaptic Dysfunction: Insights into Alzheimer's Disease Mechanisms. Aging Dis 15:459–479. 10.14336/AD.2023.0718 Yuan Y, Zhao G, Zhao Y (2024) Dysregulation of energy metabolism in Alzheimer's disease. J Neurol 272: 2 10.1007/s00415-024-12800-8 Additional Declarations Competing interest reported. MW has acquired research support (for the institution) from Eli Lilly. Supplementary Files floatimage1.png Graphical abstract Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 10 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers invited by journal 19 Feb, 2026 Editor assigned by journal 19 Feb, 2026 Submission checks completed at journal 19 Feb, 2026 First submitted to journal 18 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-8908397","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594541438,"identity":"a5ac8b28-5a01-4482-b987-dcbae304e7eb","order_by":0,"name":"Jessica Santiago","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Santiago","suffix":""},{"id":594541439,"identity":"ae9ce748-2190-4da1-92a0-60984af1d7c4","order_by":1,"name":"Dovilė Pocevičiūtė","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Dovilė","middleName":"","lastName":"Pocevičiūtė","suffix":""},{"id":594541440,"identity":"94a39f38-c469-44f0-8ff8-ce5ac880d7be","order_by":2,"name":"Patrik Önnerfjord","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Patrik","middleName":"","lastName":"Önnerfjord","suffix":""},{"id":594541441,"identity":"73fda8e1-57eb-4fca-84d8-9643d164cd0c","order_by":3,"name":"The Netherlands Brain Bank","email":"","orcid":"","institution":"Netherlands Institute for Neuroscience","correspondingAuthor":false,"prefix":"","firstName":"The","middleName":"Netherlands Brain","lastName":"Bank","suffix":""},{"id":594541442,"identity":"9a702127-f6ff-4aa2-adba-73852ddf47a6","order_by":4,"name":"Jacob Vogel","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Vogel","suffix":""},{"id":594541443,"identity":"ef235b71-0b0d-40a2-92bb-f3d60af8c161","order_by":5,"name":"Malin Wennström","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYFAC5oYDyFw5IDZgeIBXCyNcC2MDkDAGa0kgoAWFldhASItu+8HGwwUMh+XMpdufP66o2Ja+tr15A0NiG4M9Pw4tZmcSGw7PYDhsbDnnjGHjmTO3c7edOVYA0pI4swGHlgNALTwMtxM33MhhbGxsA2q5kWMA0pJgcACHlvMPwVrqN9xIfwjSkm52/w1Yi709Li03ILYkGNxIMARpSTC7wQPWwrgBl19ugGwx+G8IdJjhzIYztw23nUkrOJBwTiJxBk6HJR/+zFORJm9wI/3Bx4aK2/Jmxw9vfPChzMaeH4f3IcAAjQ80XwKf+lEwCkbBKBgFBAAAlVho62wiYIAAAAAASUVORK5CYII=","orcid":"","institution":"Lund University","correspondingAuthor":true,"prefix":"","firstName":"Malin","middleName":"","lastName":"Wennström","suffix":""}],"badges":[],"createdAt":"2026-02-18 10:39:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8908397/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8908397/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103347876,"identity":"05eff09b-1ae6-49e5-b5bf-d2eacc0e1ad9","added_by":"auto","created_at":"2026-02-24 16:29:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1386561,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRetinal proteome profiling reveals molecular signatures and suggests cell type–specific alterations in Alzheimer’s disease\u003c/strong\u003e (A) Principal component analysis (PCA) of the retinal proteome shows clusters of non-demented controls (NC, blue) and Alzheimer’s disease (AD, red) samples. The left panel represents the Lysis extraction and the right panel the RIPA extraction. (B) Volcano plot displaying 236 differentially abundant proteins (DAPs) in AD retina compared with NC (FDR \u0026lt; 0.05). The x-axis shows the effect size (β coefficient for diagnosis) and the y-axis the statistical significance (−log₁₀ of FDR adjusted p-value). Red dots denote proteins elevated in AD, blue dots those reduced in AD, and green dots indicate proteins differentially abundant in both retina and hippocampus. (C) The heatmap shows partial Spearman correlations between top DAPs based on FDR-adjusted p-values and neuropathological stages (Aβ = amyloid-β plaques; NFT = neurofibrillary tangles), adjusted for age and sex. Blue indicates negative and red positive correlations. Significance is indicated by asterisks (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001). (D) Top enriched biological terms for down-regulated DAPs (left), all DAPs (middle), and up-regulated DAPs (right). Dot size reflects the number of proteins per pathway, and color intensity corresponds to statistical significance (−log₁₀ of adjusted p-value). (E) Cell–type expression patterns of retinal DAPs mapped onto single-cell RNA-sequencing reference data from the Human Cell Atlas Retina v1.0. The left panel shows a UMAP projection of annotated retinal cell populations, and the right panel displays the combined expression of the DAPs across these cell types. (F) Cell-type enrichment analysis of the most significantly up- and down-regulated DAPs across retinal populations. Dot size indicates the percentage of cells expressing each plotted gene within a given cell type, while color intensity reflects normalized expression relative to all genes shown in the plot (scale bar, top right).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8908397/v1/8946b70c7605e2ea1efe9cee.png"},{"id":103347875,"identity":"792d6de0-11ab-4621-836d-4eff4fba40e0","added_by":"auto","created_at":"2026-02-24 16:29:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":795532,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative proteomic analysis of retinal tissue using Lysis versus RIPA extraction buffers.\u003c/strong\u003e (A) Venn diagram showing overlap of proteins identified by Lysis and RIPA buffers in retina. (B) Scatter plot (left) comparing effect sizes of differentially abundant proteins (DAPs) between extraction buffers, where x-axis represents the effect size in Lysis buffer and y-axis represents the effect size in RIPA buffer. Proteins are color-coded by their extraction profile. Pie chart (right) shows the proportional distribution of DAP categories (n=370 total). (C) Gene Ontology Cellular Component (GO:CC) enrichment analysis showing terms significantly enriched in proteins extracted by RIPA (orange) or Lysis (green) buffers. (D) Gene Ontology Biological Process (GO:BP) and Molecular Function (GO:MF) enrichment analysis for buffer-specific protein profiles. Statistical significance expressed as -log10 (q-value).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8908397/v1/5b6c4a966efa7a6901bd1492.png"},{"id":103506411,"identity":"67fefd76-59a2-472f-9309-e1a8beccdaa3","added_by":"auto","created_at":"2026-02-26 13:36:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":561880,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe hippocampal proteome shows extensive overlap with the retina and shared differentially abundant proteins (DAPs).\u003c/strong\u003e (A) Venn diagram illustrating the overlap between proteins identified in the retina (purple) and hippocampus (green). (B) Principal component analysis (PCA) of the hippocampal proteome reveals a clustering pattern separating non-demented controls (NC, blue) and Alzheimer’s disease (AD, red) samples, particularly in the Lysis fraction. The left and right panels represent the Lysis and RIPA extractions, respectively. (C) Volcano plot displaying 523 DAPs in the AD hippocampus compared with NC (FDR \u0026lt; 0.05). The x-axis represents the effect size (β coefficient for diagnosis) and the y-axis the statistical significance (−log₁₀ adjusted p-value). Red dots denote proteins upregulated in AD, blue dots those downregulated in AD, and green dots indicate proteins differentially abundant in both retina and hippocampus. (D) The heatmap shows partial Spearman correlations between top DAPs and neuropathological stages (Aβ = amyloid-β plaques; NFT = neurofibrillary tangles), adjusted for age and sex. Blue indicates negative and red positive correlations. Significance is indicated by asterisks (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8908397/v1/0ad9991bd5b9dd52eb76c860.png"},{"id":103347873,"identity":"b5e629dd-a804-4ea5-aa34-00485ea33833","added_by":"auto","created_at":"2026-02-24 16:29:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":654245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRetina and brain show many common enriched pathways and proteins with strong cross-tissue correlations that are also associated with retinal p-tau.\u003c/strong\u003e Retina and brain in AD show common enriched pathways. (A–D) Scatter plots showing partial Spearman correlations between retina and brain protein levels for four proteins with the strongest retina-brain correlations (APMAP, COLGB, MRFAP1, PRKCDBP), adjusted for age and sex. Each point represents an individual sample (ND in blue, AD in red). Dashed lines indicate linear regression fits. Correlation coefficients (r) and FDR-corrected q-values are shown in each panel. (E) Heatmap of Spearman correlations between these four proteins and multiple retinal tau phosphorylation sites (pThr12, pSer199, pThr231, pSer396/404). Red indicates positive and blue indicates negative correlations; r values are displayed in each cell. Significance is indicated by asterisks (*p \u0026lt; 0.05, **p \u0026lt; 0.01). (F) Gene Ontology enrichment analysis showing pathways/terms commonly enriched in retina and hippocampus (FDR \u0026lt; 0.05), based on all proteins analyzed. Bar colors indicate pathway categories: Biological Process (blue), Molecular Function (orange), Cellular Component (green), and Reactome pathways (purple).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8908397/v1/b66f08f57676dfef20756d2a.png"},{"id":103510029,"identity":"e03b622e-e60f-4d3f-9d70-466a874db45c","added_by":"auto","created_at":"2026-02-26 14:02:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4515259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8908397/v1/a62e0f89-0e41-4059-973f-434b06faaca4.pdf"},{"id":103506993,"identity":"c90da442-7cf7-4264-9ecd-889d01503d74","added_by":"auto","created_at":"2026-02-26 13:40:09","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":449265,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8908397/v1/056c7a47388a091673d02828.png"}],"financialInterests":"Competing interest reported. MW has acquired research support (for the institution) from Eli Lilly.","formattedTitle":"\u003cp\u003eRetinal Proteome Changes Mirror Brain Pathology and Reveal Synaptic and Cytoskeletal Dysfunction in Alzheimer's\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is the leading cause of dementia worldwide, affecting about one in nine people aged 65 and older and more than one-third of those over 85, with prevalence rising as populations age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Characterized by the accumulation of amyloid-β (Aβ) and hyperphosphorylated tau [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], AD is now recognized as a multifactorial neurodegenerative disorder. Many pathological processes, including inflammation, blood\u0026ndash;brain barrier dysfunction, and synaptic loss, begin up to two decades before cognitive symptoms begin [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. Proteomic analyses of brain and CSF reveal that AD involves a coordinated remodeling of the proteome, where metabolic and mitochondrial disturbances are closely linked with alterations in neuronal structure and glial activity [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough cognitive decline is the defining clinical feature of AD, visual deficits are increasingly recognized as a common and early symptom [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While findings on high-contrast visual acuity are mixed, consistent evidence points to reductions in low-contrast acuity, contrast sensitivity, depth perception, and motion-related vision [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. Moreover, several prevalent eye conditions affecting the retina, such as age-related macular degeneration, glaucoma, and diabetic retinopathy, have been linked to a higher risk of AD and dementia [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Importantly, individuals with vision impairment have been shown to have more than triple the odds of cognitive impairment, even after controlling for common risk factors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Whether each of these associations reflects shared vulnerability between the retina and brain, a direct contribution of reduced sensory input to neurodegeneration, or both remains unclear.\u003c/p\u003e \u003cp\u003eRegardless of clinical symptoms, retinal neurodegeneration in AD is well documented [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In vivo imaging consistently demonstrates thinning of the retinal nerve fiber layer and ganglion cell layer by optical coherence tomography (OCT) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], while experimental techniques such as curcumin-enhanced fluorescence and hyperspectral imaging have reported retinal Aβ signals correlating with brain Aβ [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Complementing these findings, postmortem studies showed accumulation of Aβ, tau, and other amyloidogenic proteins in AD retina, along with vascular alterations and glial activation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Retinal proteomic analyses further revealed an increase in proteins associated with inflammation and neurodegeneration, along with a downregulation of proteins linked to mitochondrial function and photoreceptor integrity [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite growing evidence of retinal involvement in AD, key questions remain about the molecular alterations driving retinal neurodegeneration, how these changes mirror cerebral pathology, and the potential of retinal proteins as non-invasive biomarkers. Understanding these processes could clarify the visual deficits seen in patients and support biomarker development: given the transparency of the eye, the retina offers unique accessibility for high-resolution imaging and direct optical assessment of neurons, glia, and vasculature in vivo [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo contribute toward answering these questions, we designed a mass spectrometry-based proteomics study analyzing postmortem retinas and hippocampi from the same individuals. This paired design, combined with a sequential proteomic approach to capture proteins of varying solubilities and from diverse cellular compartments, provides a comprehensive view of retinal pathology and enables direct comparison of molecular changes in the retina and brain within each subject. Our study had three principal aims: (1) to identify protein expression and pathway alterations in AD versus non-demented control (NC) retinas, (2) to assess the similarities between retinal and brain molecular changes within the same individuals, and (3) to identify retinal proteins with potential utility as retinal biomarkers for AD.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTissue samples and donor information\u003c/h2\u003e \u003cp\u003ePaired retinal and hippocampal post-mortem tissue samples were obtained from The Netherlands Brain Bank (NBB), comprising neuropathologically confirmed AD (n\u0026thinsp;=\u0026thinsp;8) and NC (n\u0026thinsp;=\u0026thinsp;8). Individuals with a diagnosis of macular degeneration were excluded, and none of the participants had other significant ophthalmological conditions, including glaucoma or diabetic retinopathy. Detailed demographic and clinical characteristics of the study cohort are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eNeuropathological evaluation followed Braak and Braak (1991)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Neurofibrillary changes were staged using the classical six Braak stages (I\u0026ndash;VI). Amyloid-β (Aβ) plaques were assessed according to the cortical distribution as described in the original paper: O (0, no detectable amyloid), A (1, sparse plaques restricted to association cortices), B (2, numerous plaques in association areas with occasional involvement of primary cortices), and C (3, abundant plaques throughout association, belt, and core/primary cortical fields)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Although this Aβ staging method is not commonly applied today, it was the classification approach used by the biobank at the time of autopsy.\u003c/p\u003e \u003cp\u003e Written informed consent for the use of tissue and clinical data in research was obtained from all donors or their legal representatives, in accordance with the Declaration of Helsinki and the European Code of Conduct for Brain Banking. The tissue collection protocol was approved by the Medical Ethical Committee of the VU Medical Center, Amsterdam, and all research procedures were reviewed and approved by the regional ethical review board in Lund.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCases included in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNC (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAD (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, M/F (% female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4/4 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/6 (75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-mortem delay, minutes (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e362\u0026thinsp;\u0026plusmn;\u0026thinsp;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360\u0026thinsp;\u0026plusmn;\u0026thinsp;56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPOE4 positive, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurofibrillary tangle score (median, range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmyloid-β score (median, range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for age and post-mortem delay, n (%) for gender and APOE4 status, and median (range) for NFT and Aβ scores.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcessing of retinal and hippocampal samples for Mass Spectrometry\u003c/h3\u003e\n\u003cp\u003eAt autopsy, the lenses of the right eyes were removed, filled with O.C.T. mounting medium (Vector Laboratories), and subsequently frozen for storage at -80\u0026deg;C. Each eyeball was cut into eight clefts, leaving a 0.5 cm margin around the optic nerve intact. Retinal tissue was collected from the nasal superior region for this study.\u003c/p\u003e \u003cp\u003eHippocampal tissue was snap-frozen at autopsy in 1 cm-thick sections and stored at -80\u0026deg;C. For analysis, the tissue was further sectioned into 0.5 cm-thick slices. A 3 mm biopsy punch (Kai Medical) was used to dissect samples from the Cornu Ammonis 1 (CA1) region adjacent to the subiculum. The CA1 was selected due to its vulnerability to AD pathology [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBoth retinal and hippocampal samples were transferred to 1.5 mL Pink Rino Tubes with screw caps, and 100 \u0026micro;L of Lysis buffer (50 mM Tris-HCl pH 7.5, 50 mM NaCl, 1 mM EDTA, 5 mM NaH₂PO₄, 1 mM DTT, 0.1% phosphatase 1, 0.03% phosphatase 2, 0.05% protease inhibitors; Sigma-Aldrich) was added. Samples were homogenized in a Bullet Blender Storm Pro (BT24M, Next Advance, Inc., Troy, NY, USA) at speed 8 for 3 min, followed by centrifugation at 14,000 \u0026times; g for 10 min. Supernatants were collected into new Eppendorf tubes. The residual material was washed with 50 \u0026micro;L Lysis buffer, centrifuged again, and the supernatant pooled with the previous fraction. The remaining tissue in the Rino tubes underwent a second extraction using RIPA buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.1% sodium deoxycholate) and supernatants were collected in new Eppendorf tubes.\u003c/p\u003e \u003cp\u003eThis two-step protocol was designed to capture proteins with different biochemical properties and solubilities. The Lysis buffer uses mild ionic conditions without detergents, extracting soluble cytoplasmic proteins and loosely membrane-associated proteins while mostly preserving cellular structures. The subsequent RIPA buffer employs both ionic and non-ionic detergents (Triton X-100 and sodium deoxycholate) under higher ionic strength, enabling the extraction of proteins from disrupted membranes, protein complexes, and cellular compartments resistant to mild lysis. This sequential approach allows detection of proteins that might exist in different biochemical states (e.g., soluble vs. complex-bound) or subcellular localizations between disease and control tissues, providing complementary views of the proteome that would not be captured by a single extraction method.\u003c/p\u003e \u003cp\u003eFor downstream processing, 50 \u0026micro;L of each sample was reduced with DTT (final 10 mM) at 56\u0026deg;C for 30 min, followed by alkylation with iodoacetamide (final 20 mM) for 30 min at RT in the dark. Proteins were precipitated with ice-cold ethanol (final 90%) overnight at \u0026minus;\u0026thinsp;20\u0026deg;C and pelleted by centrifugation at 14,000 \u0026times; g for 10 min. Pellets were air-dried and resuspended in 50 \u0026micro;L 100 mM ammonium bicarbonate, then disrupted using a BioRuptor (Diagenode Inc., Denville, USA) for 20 cycles of 15 s on/off. Samples were centrifuged at 14,000 \u0026times; g for 10 min, and the supernatant transferred to new tubes. Protein concentration was determined using a NanoDrop (DeNovix, AH Diagnostics) at A₂₈₀ nm.\u003c/p\u003e \u003cp\u003eFor digestion, 15 \u0026micro;g of protein per sample was incubated with trypsin (Promega, Madison, WI) at a 1:50 enzyme: protein ratio overnight at 37\u0026deg;C. Digestion was stopped by adding 5 \u0026micro;L of 10% trifluoroacetic acid (TFA). Samples were dried in a SpeedVac and resuspended in 22 \u0026micro;L of 2% acetonitrile/0.1% TFA for mass spectrometry analysis.\u003c/p\u003e\n\u003ch3\u003eLiquid Chromatography -Tandem Mass Spectrometry (LC-MS/MS) Analysis\u003c/h3\u003e\n\u003cp\u003ePeptide separation and mass spectrometry: For analysis, 2 \u0026micro;L of each sample was injected into an Exploris 480 mass spectrometer (Thermo Fisher Scientific) coupled to a Vanquish Neo UHPLC system (Thermo Fisher Scientific). Peptides were first loaded onto an Acclaim PepMap 100 C18 precolumn (75 \u0026micro;m \u0026times; 2 cm, Thermo Scientific) and then separated on an EASY-Spray C18 column (75 \u0026micro;m \u0026times; 25 cm, 2 \u0026micro;m, 100 \u0026Aring;, ES902) at a flow rate of 300 nL/min and a column temperature of 45\u0026deg;C. A 120 min nonlinear gradient was applied using Solvent A (0.1% FA in water) and Solvent B (0.1% FA in 80% ACN): 5\u0026ndash;25% B over 100 min, 25\u0026ndash;32% B over 12 min, and 32\u0026ndash;45% B over 8 min.\u003c/p\u003e \u003cp\u003eMass spectrometry acquisition: Data were acquired in data-dependent acquisition (DDA) mode in positive polarity. Full MS1 scans were acquired at a resolution of 120,000 (m/z 200), with a normalized AGC target of 300% and a maximum injection time of 45 ms over a mass range of 350\u0026ndash;1400 m/z. Precursors were isolated using a 1.3 m/z window and fragmented by HCD with a normalized collision energy of 30. MS2 spectra were recorded in the Orbitrap at a resolution of 15,000, with a normalized AGC target of 100% and custom maximum injection time. An intensity threshold of 10⁴ and dynamic exclusion of 60 s were applied.\u003c/p\u003e \u003cp\u003eRaw spectra were processed using Proteome Discoverer 2.5 (Thermo Fisher Scientific) and searched against the UniProt Human canonical database (UP000005640) for protein identification and label-free quantification across retinal and hippocampal tissues. Precursor and fragment tolerances were set to 10 ppm and 0.02 Da, respectively. Trypsin was specified as the protease, with methionine oxidation, asparagine deamidation, phosphorylation (STY), oxidation (M), and protein N-terminal acetylation set as variable modifications, and cysteine carbamidomethylation as a fixed modification. Label-free quantification was performed using peptide peak intensities extracted from Proteome Discoverer.\u003c/p\u003e\n\u003ch3\u003ePreprocessing\u003c/h3\u003e\n\u003cp\u003eLabel-free protein intensities (obtained via Proteome Discoverer 2.5) were first filtered to retain proteins identified at \u0026le;\u0026thinsp;1% FDR, supported by at least two unique peptides, and detected in a minimum of 70% of samples. After log₂ transformation and median normalization per sample (row-wise), missing values were imputed using a left-shifted, normal distribution (shifted 2 SD below the protein\u0026rsquo;s mean log-intensity, with a width of 0.3\u0026times;SD, capped at the lowest observed value). This models undetected low-abundance signals and is standard for Missing-Not-At-Random (MNAR) imputation in label-free proteomics [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eDifferential abundance between AD and NC samples was assessed separately in the retina and hippocampus using linear mixed-effects modeling (via statsmodels MixedLM). The fixed-effects structure was specified as:\u003c/p\u003e \u003cp\u003eProtein\u0026thinsp;~\u0026thinsp;C(Buffer) * C(Diagnosis)\u0026thinsp;+\u0026thinsp;C(Sex)\u0026thinsp;+\u0026thinsp;Age + (1∣Subject)\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eBuffer\u003c/em\u003e denotes the extraction method (Lysis or RIPA), \u003cem\u003eDiagnosis\u003c/em\u003e indicates AD versus NC, \u003cem\u003eSex\u003c/em\u003e and \u003cem\u003eAge\u003c/em\u003e were included as covariates to control for demographic differences, and \u003cem\u003eSubject\u003c/em\u003e identity was modeled as a random intercept to account for paired measures (both buffers from the same donor).\u003c/p\u003e \u003cp\u003eFor both tissues, the primary focus was on the main effect of Diagnosis; the Buffer \u0026times; Diagnosis interaction was evaluated as a secondary outcome to explore potential extraction-method specific differentials. Regression coefficients (β) and false discovery rate (FDR)-adjusted p-values were reported per protein. Proteins meeting FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were designated differentially abundant proteins (DAPs). The intersection of DAPs between retina and hippocampus was identified for downstream comparative analyses.\u003c/p\u003e \u003cp\u003ePartial Spearman correlation analyses (adjusted for age and sex) were conducted to examine disease-related associations. First, correlations were computed between each of the top 20 up- and down-regulated retinal proteins (based on FDR-adjusted p-values) and stages of Alzheimer\u0026rsquo;s pathology, neurofibrillary tangle (NFT) and amyloid β (Aβ) plaque burden. Second, proteins that were differentially abundant in both retina and brain and exhibited changes in the same direction were correlated between tissues to identify shared molecular alterations. Third, retinal values of those proteins showing significant retina-brain correlations were further correlated with retinal phosphorylated tau (p-tau) values at sites previously reported to be altered in the presence of AD pathology in our earlier study[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEnrichment Analyses\u003c/h2\u003e \u003cp\u003eFunctional enrichment of differentially abundant proteins (DAPs) was performed using g:Profiler, examining Gene Ontology (BP, MF, CC), and Reactome pathways. Analyses were conducted using two complementary background sets: (i) the full proteome detected in the experiment, to account for proteins actually measured, and (ii) the Human Protein Atlas of proteins in the retina or hippocampus, to provide a broader reference of human proteins. Using both backgrounds ensures robust enrichment results while mitigating potential biases from incomplete experimental detection. Terms with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant. All normalization, statistical analyses, and data visualizations described above were performed using Python (version 3.13.1).\u003c/p\u003e \u003cp\u003eDAPs were mapped to retinal cell types using single-cell reference data from the Human Cell Atlas Retina v1.0 and visualized via the Cell x Gene Discover platform [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. DAPs were first plotted to display their combined expression across all cells in a UMAP embedding of annotated retinal populations. Individual highly changed DAPs were also visualized individually to show their expression patterns across specific cell types.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRetinal AD proteome shows synaptic, cytoskeletal, and mitochondrial changes\u003c/h2\u003e \u003cp\u003eAfter applying quality control to retain only high confidence, consistently detected proteins, 4,346 retinal proteins were included in the study. To explore patterns of variation in the retinal proteome, including potential differences associated with disease status, we performed principal component analysis (PCA). Because our dual-buffer extraction protocol was designed to capture proteins from different cellular compartments and/or solubility profiles, we analyzed the lysis and RIPA fractions separately at this step. In both cases, the PCA revealed clear clustering of AD and NC samples along the first two principal components (Figure. 1A).\u003c/p\u003e \u003cp\u003eIn order to identify proteins that differed between AD and NC retinas, we used a linear mixed model adjusted for age and sex. This analysis identified 239 differentially abundant proteins (DAPs; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), independent of the extraction method (main effect diagnosis) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB)(Supplementary Table\u0026nbsp;2). Using the same approach, 43 of these retinal DAPs were also detected in the hippocampus. To further examine how these proteins relate to disease progression, we correlated the top 20 most significantly up- and downregulated DAPs with neuropathological stages of amyloid-β and neurofibrillary tangle (NFT) progression. The most highly upregulated proteins, including APPL2, EYS and APMAP, showed significant positive correlations with neuropathological stages of AD, whereas downregulated proteins, such as CD109, PACSIN3, and VCAN, showed negative correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTo explore the functions, pathways, and cellular components affected in AD retina, we performed functional enrichment analysis of the DAPs. The enrichment analysis revealed significant alterations across multiple cellular compartments and biological processes. For cellular components (GO:CC), the most prominently enriched terms included organelle-related structures (organelle, organelle membrane), synaptic compartments (synapse, presynapse, synaptic vesicle, synaptic vesicle membrane), cell-cell communication structures (cell junction), and various vesicular transport systems (intracellular vesicle, cytoplasmic vesicle, exocytic vesicle). Notably, mitochondrial compartments (mitochondrial intermembrane space, organelle envelope lumen) showed enrichment among upregulated DAPs. For biological processes (GO:BP), the analysis identified enrichment in cell-cell junction assembly, consistent with alterations in intercellular communication and structural organization. Molecular function analysis (GO:MF) revealed enrichment in cytoskeletal protein binding, further supporting disruptions in cellular architecture (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTo gain insight into the cellular involvement of AD-associated changes in the retina, we mapped our differentially abundant proteins (DAPs) onto single-cell transcriptomic reference data from the Human Cell Atlas Retina v1.0 [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. When considering all DAPs together, we observed a broad representation across multiple retinal cell types, with a particularly strong signal in ganglion cells, amacrine, and cones (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Examining the top DAP-associated genes individually revealed that most were not strongly cell-type-specific; however, some were more commonly expressed in microglia, with both increases and decreases observed in AD. Among these, DPYD and SORL1 stood out as more prominent, showing relatively high expression in microglia. Other genes, such as APMAP, VCAN, and TSNAX, were also more detected in microglia than in other cell types, though at lower levels and restricted to a percentage of cells. Notably, EYS, which was increased in AD, exhibited pronounced expression in rods and cones (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF) (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSequential extraction reveals buffer-dependent AD-associated changes in APP processing\u003c/h2\u003e \u003cp\u003eTo extend our findings beyond buffer-independent effects, we next examined how extraction conditions influenced the detection of AD-related changes. Because Lysis and RIPA solubilize partly overlapping but distinct protein pools, the Diagnosis \u0026times; Buffer interaction in our model allowed us to identify alterations whose magnitude differed between buffers, as well as changes detectable under one extraction condition only, potentially reflecting differences in protein solubility, subcellular localization, or incorporation into tightly bound complexes.\u003c/p\u003e \u003cp\u003eProteomic comparison of the two fractions identified 4,346 retinal proteins in total: 3,794 (87.3%) were shared across both buffers, while 144 (3.3%) and 408 (9.4%) were unique to Lysis and RIPA, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Among the DAPs with a main effect of diagnosis, indicating consistent AD-related changes across buffers, several also showed a significant Diagnosis \u0026times; Buffer interaction, reflecting a stronger effect in one extraction method. Additional proteins exhibited interaction effects only, indicating that their AD-associated changes were exclusively captured in a single buffer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eAmong the total 370 DAPs identified in the retina, 239 showed consistent disease-related changes across buffers. Of these, 131 (35.4%) exhibited similar abundance in both buffers, 104 (28.1%) were more abundant in Lysis, and 4 (1.1%) were more abundant in RIPA. The remaining 131 DAPs displayed buffer-dependent effects only, with 105 (28.4%) detected exclusively in RIPA and 26 (7.0%) exclusively in Lysis. Altogether, buffer-dependent proteins, those showing stronger or exclusive extraction patterns, accounted for the majority (64.6%) of disease-associated proteins identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Notably, several proteins associated with critical pathological processes were only found to be different in RIPA buffer, including NCSTN, a component of the gamma-secretase complex that processes APP [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]; BAX, a central mediator of retinal ganglion cell death [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]; NEFH and SNCG, previously associated with glaucomatous damage; and OAT, deficiency of which causes gyrate atrophy of the choroid and retina [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGene Ontology enrichment analysis revealed distinct cellular component profiles between the two extraction buffers. Lysis, using mild conditions to extract soluble and loosely-associated proteins, was enriched for extracellular vesicles, exosomes, and membrane-bounded organelles, whereas RIPA, using detergents to extract proteins from compartments resistant to mild lysis and those in protein complexes, preferentially captured proteins from the cytosol, postsynaptic regions, and cell junctions. Both buffers captured extracellular and organelle-associated proteins, but with different efficiencies (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Functional enrichment analysis further showed that RIPA-extracted proteins were overrepresented in biological processes related to transmembrane transport and amyloid precursor protein biosynthesis, and in molecular functions such as RNA binding and structural molecule activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRetina and hippocampus show strong proteomic similarity, and 68 share DAPs in AD\u003c/h2\u003e \u003cp\u003eIn the hippocampal CA1 region, 4,204 proteins were identified using the same quality control criteria applied to the retina, of which 3,973 (87%) were also detected in the retina, highlighting a substantial proteomic overlap between the two tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eTo assess overall proteomic variation, we performed principal component analysis (PCA) of the hippocampal dataset. A clustering pattern separating Alzheimer\u0026rsquo;s disease (AD) and non-demented control (NC) samples was observed along the first two principal components, particularly in the lysis fraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eDifferential abundance analysis using a linear mixed model adjusted for age and sex identified 524 proteins with significant differences between AD and NC, regardless of extraction method (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). To evaluate the relationship between protein abundance changes and AD pathology severity, we examined partial Spearman correlations between the top DAPs and stages of neuropathological severity, amyloid-β plaques and NFT. Most upregulated proteins correlated positively with neuropathological burden, while downregulated proteins correlated negatively with disease stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Several proteins previously linked to AD in human hippocampal proteomics were also observed here [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. For instance, synapse-related proteins, including SYP, YWHAG, DLAT, and PDHB, were downregulated, whereas AQP4, GJA1, and HSPB1, associated with inflammatory or stress-related processes, were upregulated. Notably, APOE levels were also increased in AD in our data.\u003c/p\u003e \u003cp\u003eTo evaluate whether buffer-dependent extraction patterns observed in the retina were also present in the brain, we performed a similar analysis in the hippocampal CA1 region. A total of 763 differentially abundant proteins (DAPs) were identified in the hippocampus when considering buffer effects. Among these, 68 DAPs were also detected in the retina. Dividing the DAPs by extraction method, 374 (49.0%) exhibited similar abundance across both buffers, 142 (18.6%) were stronger in Lysis, and 8 (1.0%) were stronger in RIPA. The remaining 239 DAPs showed buffer-dependent effects only, with 164 (21.5%) detected exclusively in RIPA and 75 (9.8%) exclusively in Lysis (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRetina and brain show overlapping pathway changes and correlated AD-associated proteins\u003c/h2\u003e \u003cp\u003eTo assess whether AD-associated proteomic changes in the retina reflect those occurring in the brain, we compared all 370 retinal DAPs (including extraction-dependent ones) with the 763 DAPs identified in the hippocampus. By considering the complete set of disease-associated proteins regardless of buffer dependency, we aimed to capture the full spectrum of retinal proteomic changes and their relationship to brain pathology. We first examined overlap at the individual protein level to identify shared disease-associated proteins between tissues.\u003c/p\u003e \u003cp\u003eAt the individual protein level, 29 of the 68 DAPs shared by both tissues exhibited changes in the same direction. To focus on proteins with robust effect sizes for subsequent correlation analyses, we selected concordantly regulated proteins with absolute Beta values\u0026thinsp;\u0026gt;\u0026thinsp;1.2 in both tissues. Among proteins upregulated in both tissues, 2 proteins met this criterion: APMAP, and ISOC2. Among proteins downregulated in both tissues, 5 proteins met this criterion: AAMP, CD109, LTF, NRXN1, and PACSIN3.\u003c/p\u003e \u003cp\u003eTo further investigate the relationship between retina and brain protein expression, we performed partial Spearman correlation analysis on these proteins, adjusting for age and sex. Of these, 4 proteins showed strong significant correlations between retina and brain after FDR correction (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05): APMAP (ρ\u0026thinsp;=\u0026thinsp;0.800, FDR\u0026thinsp;=\u0026thinsp;0.0006), CD109 (ρ\u0026thinsp;=\u0026thinsp;0.815, FDR\u0026thinsp;=\u0026thinsp;0.0006), NRXN1 (ρ\u0026thinsp;=\u0026thinsp;0.791, FDR\u0026thinsp;=\u0026thinsp;0.0006), and PACSIN3 (ρ\u0026thinsp;=\u0026thinsp;0.750, FDR\u0026thinsp;=\u0026thinsp;0.0006) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;D). These associations were attenuated and lost significance after adjustment for diagnosis.\u003c/p\u003e \u003cp\u003eGiven the relevance of tau pathology in AD, we next examined whether retinal levels of these four proteins were associated with retinal phosphorylated tau (p-tau) at phosphorylation sites (p202, p231, p396\u0026thinsp;+\u0026thinsp;404), previously reported to be affected in the presence of AD pathology [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Partial Spearman correlation analysis adjusted for age and sex revealed significant positive correlations for APMAP with the three phosphorylation sites and negative correlations of CD109 and PACSIN3 with p202 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Within the AD group, only the association between p231 and PACSIN3 remained significant (q\u0026thinsp;=\u0026thinsp;0.03), whereas CD109 and NRXN1 showed trend-level correlations with p231 (q\u0026thinsp;=\u0026thinsp;0.08 for both). No correlations were observed within the NC group.\u003c/p\u003e \u003cp\u003eBeyond individual protein overlap, we performed functional enrichment analysis on all differentially abundant proteins from both tissues. Analyses included the combined set of proteins, upregulated and downregulated proteins separately, and buffer-specific profiles for Lysis and RIPA. This approach allowed us to determine whether retina and hippocampus share convergent biological processes and pathways despite potential differences in specific protein identities. Gene Ontology enrichment analysis revealed several shared pathways between retina and brain DAPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Biological processes included regulation of transmembrane transport, import into cell, cell\u0026ndash;cell junction assembly, and amyloid precursor protein biosynthetic process. Molecular function terms encompassed cell adhesion molecule binding, cadherin binding, and cytoskeletal protein binding. Cellular component analysis showed enrichment in extracellular exosome, extracellular organelle, cell junction, and synaptic compartments. Reactome pathway analysis highlighted Bassign interactions, RHO GTPases activate CIT, axon guidance, nervous system development, and metabolism of amino acids and derivatives as shared pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur proteomic analysis reveals molecular alterations in the AD retina that suggest involvement of multiple pathological pathways. The separation of AD and control samples in the principal component analysis indicates the disease seems to have a profound effect on the retinal (and hippocampal) proteome, supported by 370 proteins showing differential abundance between AD retinas and controls. The substantial proteomic overlap between retina and hippocampus (87% of total proteins) provides a foundation for comparison, with 68 proteins showing disease-associated changes in both tissues. Among these, APMAP, CD109, NRXN1, and PACSIN3 demonstrated strong retina-brain correlations, with APMAP also associated with retinal p-tau levels. Pathway enrichment analyses of differentially abundant proteins revealed several altered pathways that we discuss under five major categories: (1) synaptic and vesicular processes; (2) cytoskeleton and intracellular transport; (3) APP processing and membrane signaling; (4) mitochondrial function and cellular stress; and (5) cell-cell junctions, adhesion, and barrier integrity.\u003c/p\u003e \u003cp\u003eSynaptic and vesicular processes was the most prominent category altered, with enrichment of terms related to synaptic organization (pre- and post-synapse, glutamatergic synapse) and vesicle recycling machinery in both tissues. Within this category, several synaptic organizers, including NRXN1, NRXN3, NECAB2, and CACNB2, were significantly reduced in AD retina. These changes align with robust evidence linking early synaptic protein loss in the brain to tau pathology and cognitive decline [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], where synaptic alterations are among the strongest predictors of cognitive impairment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Our findings suggest that AD-related synaptic remodeling is extended to retinal neurons. Previous studies corroborate this concept: rodent models show early retinal synaptic changes associated with Aβ and tau, including synapse loss in the inner plexiform layer and altered neurotransmission [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and demonstrate that brain-derived Aβ can reach the retina via the optic nerve, causing synaptic dysfunction [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Whether synaptic changes in the human retina occur independently of, or precede, local Aβ and tau deposition is an open question.\u003c/p\u003e \u003cp\u003eCytoskeleton and intracellular transport was another strongly altered category, suggesting widespread disruption of structural and trafficking networks in both retina and brain. Enriched terms included microtubule organization, actin-based projections, and motor protein complexes such as kinesins and dyneins [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. In brain tissue, hyperphosphorylated tau destabilizes microtubules, impairs axonal transport, and contributes to synaptic dysfunction and neurodegeneration [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Similar mechanisms appear to operate in the retina as tau accumulation and phosphorylation have been observed in human AD retinas by multiple groups [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. Experimental studies further demonstrate that amyloid-β can disrupt axonal transport and compromise cytoskeletal stability in both the brain and retina [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Together, these findings suggest that cytoskeletal and transport deficits represent a systemic vulnerability in AD, affecting both central and retinal circuits and potentially contributing to early visual dysfunction before overt neuronal loss.\u003c/p\u003e \u003cp\u003eAPP processing is a central pathway in Alzheimer\u0026rsquo;s disease, as its proteolytic cleavage determines whether non-amyloidogenic fragments or amyloidogenic Aβ peptides are produced. In the brain, dysregulation of this pathway drives Aβ accumulation, plaque formation, and downstream synaptic and signaling disturbances [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Analysis of our retinal proteomic data shows enrichment of terms including \u0026ldquo;amyloid precursor protein biosynthetic process,\u0026rdquo; \u0026ldquo;regulation of APP biosynthetic process,\u0026rdquo; and several membrane signaling complexes, indicating that APP processing could also be altered in the eye. Although some studies have reported no significant Aβ or APP differences in post-mortem AD retinas [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], others have reported Aβ deposition and APP-related changes in retinal tissue [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Mechanistic work supports the possibility of retinal Aβ involvement, showing that Aβ can be transported from brain to eye via the ocular glymphatic system [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Importantly, in our study, APP-related changes were detected only with the RIPA-extracted method, suggesting that these changes might be associated with intracellular organelles or complexes that require harsher extraction conditions. Taken together, while discrepancies exist, our results suggest the retina exhibits at least some amyloid-related molecular remodeling.\u003c/p\u003e \u003cp\u003eMitochondrial dysfunction and cellular stress are recognized features of Alzheimer's disease. In brain tissue, impaired oxidative phosphorylation, disrupted mitochondrial dynamics, and increased reactive oxygen species contribute to synaptic failure and neuronal degeneration [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Our retinal proteomic analysis revealed enrichment of terms such as 'mitochondrion,' 'mitochondrial intermembrane space,' and stress-related pathways including 'cellular responses to stress' and 'lysosome,' suggesting that similar bioenergetic challenges may occur in retinal tissue.\u003c/p\u003e \u003cp\u003eDisruption of cell\u0026ndash;cell junctions and adhesion complexes is increasingly recognized as another contributor to Alzheimer\u0026rsquo;s disease pathology. In the brain, the integrity of the blood\u0026ndash;brain barrier (BBB) depends on tight junctions (claudins, occludin, ZO-1), adherens junctions (VE-cadherin), and gap junctions formed by connexins. Loss or disorganization of these junctional proteins leads to increased vascular permeability, neuroinflammation, and impaired neuronal signaling [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. Our retinal proteomic analysis revealed enrichment of pathways such as \u0026lsquo;cell\u0026ndash;cell junction organization,\u0026rsquo; \u0026lsquo;apical junction complex,\u0026rsquo; \u0026lsquo;focal adhesion,\u0026rsquo; and \u0026lsquo;cadherin binding,\u0026rsquo; suggesting potential effects on the blood-retina barrier (BRB). Experimental models have shown that retinal vascular amyloid deposition correlates with tight junction loss and BRB breakdown, paralleling cerebral amyloid angiopathy [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Whether similar mechanisms contribute to retinal pathology in human AD warrants further investigation.\u003c/p\u003e \u003cp\u003eTo date, only one previous proteomic study has examined retinal changes in AD. Koronyo et al. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] examined six AD and six control samples from the temporal hemiretina using a single-buffer protocol and reported 886 differentially expressed proteins, correction for multiple testing was not reported. Individual proteins were categorized according to biological functions, highlighting increased apoptosis, necrosis, and inflammation as retinal features of AD, alongside mitochondrial dysfunction and loss of photoreceptor-related proteins. While our study confirms some of these observations, such as changes in inflammatory and photoreceptor-related proteins, it expands the picture by identifying broader mechanistic categories through the enrichment of pathways, including synaptic organization, cytoskeletal remodeling, and APP processing. Comparisons should, however, be made with caution, as methodological differences and the distinct retinal regions analyzed can influence protein detection and the specific changes observed.\u003c/p\u003e \u003cp\u003eOur analysis revealed a substantial proteomic overlap between retina and hippocampus, with 87% of proteins shared across both tissues. Despite this extensive overlap, disease-associated changes were not uniform: 68 differentially abundant proteins (DAPs) were common to both tissues, yet only 29 changed in the same direction. Importantly, although individual proteins did not consistently follow the same trend, the broader pathways were altered similarly in both tissues, showing downregulation of synaptic and cytoskeletal processes and upregulation of mitochondrial compartments and stress-related pathways, for example. This divergence at the protein level, despite pathway-level similarity, could reflect differences in disease stage, local adaptive responses, or tissue-specific vulnerability. For example, proteins involved in APP processing, such as APMAP and SORL1, showed increased abundance in the retina, which may represent adaptive responses, whereas downregulation of these proteins has been associated with AD in brain tissue[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Such bidirectional regulation is well documented in AD, where early compensatory responses such as increased synaptic plasticity and transient neuroinflammatory activity that initially promote clearance of toxic species eventually fail, leading to progressive neurodegeneration [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The increased abundance of SORL1 in the AD retina deserves special attention, as this protein has been implicated in retinal pathologies beyond its well-established role in Alzheimer's disease. SORL1, also known as SORLA and LR11, has been linked to age-related retinal changes, with SORL1 knockout mice displaying abnormal retina morphology in late-adult phenotyping, and STRING analysis demonstrating that SORL1 forms a network with age-related macular degeneration (AMD) GWAS genes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, elevated vitreous levels of soluble SORL1 have been found in idiopathic epiretinal membrane [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and diabetic retinopathy[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. These observations suggest that SORL1 dysregulation may affect retinal homeostasis across multiple disease contexts, and that its upregulation in the AD retina could reflect shared molecular mechanisms between Alzheimer's disease and other disorders affecting the retina.\u003c/p\u003e \u003cp\u003eFour proteins, APMAP, CD109, NRXN1, and PACSIN3, showed strong correlations between retina and brain. APMAP also correlated positively with retinal phosphorylated tau at multiple sites. APMAP regulates APP processing through the lysosomal-autophagic pathway [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], and its deletion worsens Aβ deposition and memory in AD mice [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. However, AD brains show an increase in the dysfunctional APMAP2 splice variant alongside reduced levels of APMAP-interacting proteins that suppress Aβ [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The elevated APMAP observed here may thus represent compensatory upregulation or accumulation of dysfunctional splice variants rather than functional protein. Another protein with strong retina-brain correlation, CD109, is a modulator of TGF-β and inflammatory signaling [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and has been proposed as a therapeutic target in AD, where atorvastatin-mediated reduction of caveolin-1/CD109 may help prevent TGF-receptor degradation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The third protein, NRXN1, is a presynaptic adhesion molecule essential for neurotransmission that is directly targeted by Aβ oligomers, which bind to neurexins and reduce their surface expression, impairing synapse formation [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Beyond direct Aβ binding, neurexins seem to be compromised through multiple mechanisms in AD. Neurexins are processed by the same α- and γ-secretases that cleave APP, and AD-linked presenilin mutations alter this processing [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Additionally, AD brains show reduced NRXN3 expression that inversely correlates with neuroinflammation, and a NRXN3 splicing variant interacts with APOE ε4 to increase AD risk [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Lastly, PACSIN3 regulates membrane curvature and endocytosis, playing roles in vesicle trafficking and cytoskeletal dynamics [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. While PACSIN3's specific role in AD is unclear, related family members PACSIN1 and PACSIN2 are implicated in tau-mediated synaptic dysfunction and impaired BBB Aβ clearance, respectively [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], suggesting the PACSIN family may broadly contribute to AD pathogenesis. These proteins represent potential molecular links between retinal and cerebral pathology, though their utility as biomarkers requires validation in larger cohorts and assessment of their detectability through non-invasive methods in future studies.\u003c/p\u003e \u003cp\u003eMapping all DAPs to single-cell retinal data revealed a broad distribution across cell types, with prominent representation in ganglion cells, consistent with previous reports of ganglion cell loss in AD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. DAPs were also overrepresented in amacrine cells, which are interneurons, and intriguingly, in cones, a photoreceptor type unique to the retina. While overall enrichment patterns lacked strong cell-type specificity, some highly altered proteins showed preferential expression in particular cell types. For example, SORL1 and APMAP were primarily enriched in microglia, supporting evidence of microglial involvement in the AD retina [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e], even though inflammation-related pathways were not statistically enriched in our dataset. Notably, EYS (EGF-like photoreceptor maintenance factor), predominantly expressed in photoreceptors and a major gene for rod-cone dystrophies, [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] was among the most upregulated proteins in AD retinas. While the previous proteomics study reported downregulation of some photoreceptor-related proteins [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], the increase in EYS observed here may reflect differences in the retinal regions analyzed (nasal superior versus temporal) or differential responses among photoreceptor protein subclasses, while nevertheless supporting the common observation of photoreceptor involvement. Cell-type mapping further showed enrichment of differentially abundant proteins in cones, suggesting that photoreceptor-related changes extend beyond ganglion cells and inner retinal neurons. Although bulk proteomics has inherent limitations for cell-type attribution and this enrichment may partly reflect shifts in relative retinal composition, the photoreceptor-specific expression of EYS indicates that AD-related molecular alterations may also affect outer retina. This warrants further investigation, as photoreceptors represent a retina-specific cell type that may reveal unique aspects of neurodegeneration in this tissue.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLIMITATIONS\u003c/h2\u003e \u003cp\u003eThis study provides valuable insight into the molecular changes occurring in the retina in AD and how they correlate with alterations in the brain of the same individuals; however, several limitations should be considered. First, the use of postmortem tissue from individuals with established dementia reflects predominantly end-stage disease and therefore may not capture earlier or more dynamic molecular changes that occur during AD initiation or progression. The sample size was small, which reduced statistical power for detecting moderate or subtle effect sizes and limited the ability to perform subgroup or stage-specific analyses. Even so, the study was sufficiently powered to identify robust disease-associated differences, as reflected by the separation of diagnostic groups in principal component analysis. Although postmortem delay was consistent between diagnostic groups, the use of postmortem tissue also introduces factors, such as tissue handling and protein degradation, that may not fully reflect in vivo conditions. In addition, analyses were restricted to a single retinal region, which may not capture spatial heterogeneity across the retina. Finally, cell-type enrichment was inferred from transcriptomic reference datasets, which do not always correspond to protein-level expression.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe Alzheimer's retina undergoes molecular alterations mirroring brain pathology, including synaptic dysfunction, cytoskeletal remodeling, and APP-processing changes, with some evidence suggesting retina-specific features such as altered photoreceptor proteins. These mechanisms might contribute to the visual symptoms in AD. The concordant retinal-brain changes suggest that retinal molecular signatures may reflect brain pathology, and proteins showing strong cross-tissue correlations represent interesting candidates for future biomarker studies. While this exploratory study requires validation in larger cohorts, especially for specific protein changes, the molecular signatures and their brain correspondence provide a foundation for understanding retinal AD pathology and suggest the retina's potential as an accessible window for monitoring neurodegeneration in Alzheimer's disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcetonitrile\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAGC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAutomatic gain control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAβ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmyloid-beta\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmyloid precursor protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPOE4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eApolipoprotein E ε4 allele\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBORC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiogenesis of lysosome-related organelles complex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBiological Process (Gene Ontology)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCellular Component (Gene Ontology)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCerebrospinal fluid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially abundant proteins\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eData-dependent acquisition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDithiothreitol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEthylenediaminetetraacetic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFormic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigher-energy collisional dissociation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHistone deacetylase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLC-MS/MS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLiquid chromatography\u0026ndash;tandem mass spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMolecular Function (Gene Ontology)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMNAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMissing-not-at-random\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMS1/MS2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMass spectrometry level 1 and 2 scans\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNFT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeurofibrillary tangle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-demented control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOptical coherence tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ep-tau\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhosphorylated tau\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRIDE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProteomics Identifications Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRIPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRadioimmunoprecipitation assay buffer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibonucleic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTrifluoroacetic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUHPLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUltra-high-performance liquid chromatography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Informed consent for using retina and brain tissue, as well as clinical data for research purposes, was obtained from the patients or from their closest relatives in accordance with the International Declaration of Helsinki and the Code of Conduct for Brain Banking. The tissue collection protocols were approved by the medical ethics committee of VU Amsterdam, and the Swedish Ethical Review Authority approved the study (Dnr 2021/04270).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eMW has acquired research support (for the institution) from Eli Lilly.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study was funded by the Brain Foundation (FO2023-0113), Crafoord Foundation (20230519), Dementia Foundation (2024), Greta and Johan Kockska Foundation (2024), the \u0026Aring;hl\u0026eacute;n Foundation (243007), the Swedish Research Council (2024\u0026ndash;02875), Multipark (2024), Stiftelsen f\u0026ouml;r gamla tj\u0026auml;narinnor (2025\u0026thinsp;\u0026minus;\u0026thinsp;337), and the Alzheimer\u0026rsquo;s Foundation (AF-1010435). Author JWV is supported by the SciLifeLab \u0026amp; Wallenberg Data Driven Life Science Program (grant: KAW 2020.0239) and the Swedish Research Council (2024\u0026ndash;03642). None of the funders has been involved in the design of the study, collection, analysis, interpretation of data, and/or the writing of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.S. and M.W. contributed to the study concept and design and were the primary contributors to the discussion and interpretation of results. D.P. prepared the samples. J.S. analyzed the data with support from J.V., who assisted in planning the analytical strategy, applying statistical methods, and interpreting the results. P.O. provided support with initial processing and interpretation of mass spectrometry data. J.S. drafted the first version of the manuscript. All authors critically reviewed the manuscript for intellectual content and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the Netherlands Brain Bank (NBB) for providing retina and brain tissue and for the neuropathological evaluations, and the Center for Translational Proteomics at Lund University's Medical Faculty for conducting mass spectrometry analyses.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [62] partner repository with the dataset identifier PXD073336. Data are pseudonymized and comply with GDPR regulations and the ethical approval granted by the Medical Ethical Committee of the VU Medical Center, Amsterdam. The processed data generated during the analysis, supporting the results presented in this study, are available within the main manuscript and the Supplementary Information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e(2025) 2025 Alzheimer's disease facts and figures. Alzheimer's Dement 21: e70235 Doi \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/alz.70235\u003c/span\u003e\u003cspan address=\"10.1002/alz.70235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdav SS, Park JE, Sze SK (2019) Quantitative profiling brain proteomes revealed mitochondrial dysfunction in Alzheimer's disease. 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J Neurol 272: 2 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00415-024-12800-8\u003c/span\u003e\u003cspan address=\"10.1007/s00415-024-12800-8\" 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":"[email protected]","identity":"acta-neuropathologica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aneu","sideBox":"Learn more about [Acta Neuropathologica](https://link.springer.com/journal/401)","snPcode":"401","submissionUrl":"https://submission.springernature.com/new-submission/401/3","title":"Acta Neuropathologica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Retina, Alzheimer’s disease, Proteomics","lastPublishedDoi":"10.21203/rs.3.rs-8908397/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8908397/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVisual dysfunction is increasingly recognized as an early feature of Alzheimer's disease (AD), yet the molecular mechanisms underlying retinal neurodegeneration and their relationship to cerebral pathology remain unclear. Here, we performed comprehensive mass spectrometry-based proteomics on paired retinal and hippocampal tissue from the same postmortem donors (8 AD, 8 non-demented controls) to identify disease-associated molecular signatures and assess their overlap between these tissues. Using a sequential dual-extraction protocol, we identified 370 differentially abundant retinal proteins in AD, including established APP-processing regulators (SORL1, APMAP) and synaptic proteins. Retinal proteomes clearly separated AD from control cases in principal component analysis. Notably, 87% of proteins were detected in both retina and hippocampus, with 68 differentially abundant proteins shared between tissues. Many retinal proteins correlated with neuropathological stages of disease, and four proteins (APMAP, CD109, NRXN1, PACSIN3) showed particularly strong retina-brain correlations. Functional enrichment analysis revealed convergent alterations in synaptic organization, cytoskeletal dynamics, mitochondrial function, cell adhesion, and APP metabolism in both tissues. Cell-type mapping using single-cell retinal reference data indicated that most proteomic changes were broadly distributed across cell types, though some showed enrichment in microglia or photoreceptors. These findings demonstrate that the AD retina undergoes substantial molecular alterations that mirror brain pathology. The identified molecular changes provide mechanistic insights into visual dysfunction in AD and support the retina as an accessible window for assessing brain pathology, with retinal proteins correlated with cerebral pathology representing promising candidates for non-invasive biomarker development.\u003c/p\u003e","manuscriptTitle":"Retinal Proteome Changes Mirror Brain Pathology and Reveal Synaptic and Cytoskeletal Dysfunction in Alzheimer's","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 16:29:44","doi":"10.21203/rs.3.rs-8908397/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-10T15:51:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T21:19:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T20:28:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255402301364346791514733458111121749810","date":"2026-02-20T15:31:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325556141539592797669406311325645359306","date":"2026-02-19T19:20:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-19T14:24:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-19T14:17:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-19T05:34:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Neuropathologica","date":"2026-02-18T10:25:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-neuropathologica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aneu","sideBox":"Learn more about [Acta Neuropathologica](https://link.springer.com/journal/401)","snPcode":"401","submissionUrl":"https://submission.springernature.com/new-submission/401/3","title":"Acta Neuropathologica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7c5568b3-bd81-4133-8b39-40f2401ec972","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T15:56:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 16:29:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8908397","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8908397","identity":"rs-8908397","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}