Human iPSC-RPE with the PSEN1H163R pathogenic variant recapitulates Alzheimer’s disease features and reveals melanosome defects

Human iPSC-RPE with the PSEN1H163R pathogenic variant recapitulates Alzheimer’s disease features and reveals melanosome defects | 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 Human iPSC-RPE with the PSEN1 H163R pathogenic variant recapitulates Alzheimer’s disease features and reveals melanosome defects Grace E Lidgerwood, Mehdi Mirzaei, Jenna C Hall, Damián Hernández, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6556064/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Alzheimer’s disease (AD) is characterised by progressive cognitive decline and accumulation of pathological markers such as β-amyloid (Aβ) plaques and Tau tangles. Emerging evidence suggests these markers can also be detected in the retina, positioning it as a potential surrogate for investigating AD pathophysiology. The retinal pigment epithelium (RPE) shares features with the brain and is critical for retinal health, yet its role in AD pathology remains underexplored. Methods We generated RPE cells from human induced pluripotent stem cells carrying the PSEN1 H163R pathogenic variant for AD, alongside its CRISPR-corrected isogenic control. AD-associated phenotypes were assessed. Results RPE cell cultures from the two cohorts displayed expression of Aβ and Tau, with notable differences in levels and organisation. Total Aβ 1−42 and Aβ 1−42:1−40 ratio in PSEN1 H163R RPE cell lysates were significantly elevated compared to the CRISPR isogenic controls and volume of Aβ + deposits was significantly larger in PSEN1 H163R RPE cells. Total and phosphorylated Tau proteins were also detected in both cohorts, with altered spatial organisation and localisation of pTau in PSEN1 H163R . Proteomic profiling identified more than 1,800 significantly dysregulated proteins in PSEN1 H163R RPE cells, including key AD-related proteins such as MAPT, APP, APBB1 and NRBF2. Upregulated pathways involved autophagy, intracellular trafficking and neurodegeneration, while downregulated pathways implicated mitochondrial respiration, RNA metabolism, and protein folding. Proteomics analysis of conditioned media further revealed altered secretion of matrix-associated proteins as well as increased APOE and APP in PSEN1 H163R RPE samples. PSEN1 H163R RPE cells demonstrated dysregulation in melanosome biogenesis, marked by decreased expression of core melanogenic proteins (PMEL, TYR, DCT) by proteomics analysis; and altered melanosome morphology and pigmentation by electron microscopy. Conclusion In conclusion, these findings support the RPE as a relevant and accessible in vitro model for AD research, offering insights into the role of PSEN1 in Aβ and Tau dysregulation, disease mechanisms and melanosome biogenesis, providing a promising approach to understand PSEN1 biology in the context of disease and potential biomarker discovery. It is also the first to describe a relationship between PSEN1 H163R and melanosomes in a human cellular model. retinal pigment epithelium induced pluripotent stem cells Alzheimer’s disease presenilin proteomics melanosome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterised by a decline in cognitive and neural function. While the exact mechanisms driving the disease remain elusive, several key pathological processes have been identified, including abnormal β-amyloid (Aβ) metabolism and plaque accumulation, tau hyperphosphorylation leading to neuronal damage, oxidative stress, reactive glial and microglial changes, and the resulting neuroinflammatory environment, which exacerbates damage to the sensitive cells of the nervous system. The late-onset sporadic form (sAD) typically features a prolonged, asymptomatic prodromal stage, with symptoms manifesting later in life. In contrast, early-onset familial AD (fAD) is characterised by earlier onset and is often associated with mutations in genes critical to Aβ metabolism, such as APP, PSEN1 , and PSEN2 . Despite the development of therapies targeting Aβ accumulation, such as Donanemab and Lecanemab, a definitive cure for AD remains elusive. This highlights the urgent need for preclinical biomarkers and therapeutic targets to enhance early diagnostics and treatment outcomes. A central focus of these efforts is the development of biologically relevant models that accurately recapitulate disease pathology (Zhang et al., 2024 ). Human-induced pluripotent stem cells (iPSCs) enable the development of patient-specific in vitro models that can recapitulate some of the features of AD, including Aβ plaque formation, tau hyperphosphorylation, mitochondrial dysfunction, and neuroinflammation, under controlled laboratory conditions (Penney et al., 2020 ). These models offer the possibility of studying human-specific genetic backgrounds, providing a complementary platform to animal models that bridges the gap between in vivo studies and clinical applications. Together, iPSC-based models and animal models offer synergistic insights into AD pathogenesis and therapeutic development, advancing our understanding of this complex neurodegenerative disorder. Emerging evidence suggests that hallmark AD biomarkers—such as Aβ plaques, phosphorylated tau, and activated microglia—are not confined to the brain but can also be found in peripheral or adjunct tissues, including fibroblasts (Pani et al., 2009 ), blood (Armentero et al., 2011 ); (Armentero et al., 2011 ; Borroni et al., 2010 ); (Li et al., 2024 ), blood vessels (Cortes-Canteli et al., 2010 ) and the intestines (Joachim et al., 1989 ). More recently, the eye has gained increasing attention as a potential non-invasive model to study AD and other neurodegenerative diseases (Gupta et al., 2016 ). Visual symptoms have been reported to precede cognitive decline in some cases (Brewer & Barton, 2014 ; Lim et al., 2016 ), and retinal hyperspectral imaging may predict brain Aβ load (Hadoux et al., 2019 ), suggesting that the retina could serve as a ‘mirror’ for neurodegenerative changes occurring in the brain. This connection is not surprising given the retina is a neural extension of the central nervous system. Key pathological markers of AD—Aβ plaques, phosphorylated tau, and activated microglia—are also common features of retinal diseases such as age-related macular degeneration (AMD) and glaucoma (Ohno-Matsui, 2011 ). For instance, studies have identified increased deposition of Aβ and hyperphosphorylated tau in the retinas of AD patients and transgenic AD mice (Nuñez-Diaz et al., 2024 ). Additionally, activated microglia have been implicated in the progression of both AD and retinal neurodegenerative diseases(Nuñez-Diaz et al., 2024 ; Ramirez et al., 2017 ). These overlapping pathologies potentially point to shared mechanisms across age-related neurodegenerative conditions. The retinal pigment epithelium (RPE), the outermost layer of the retina, plays essential roles in maintaining homeostasis and supporting the visual cycle. It absorbs light, processes retinoids to initiate the visual cycle, and serves as a critical interface between the neural retina and the underlying choroidal vasculature. The RPE sustains photoreceptor integrity by supplying nutrients and clearing toxic byproducts of the visual cycle, ensuring retinal health is preserved despite ongoing metabolic stress. Age-related macular degeneration (AMD), a disease characterised loss of central vision due to loss of RPE and photoreceptor cell function, is characterised by the accumulation of pathogenic extracellular deposits known as drusen. The composition of drusen has similarities to that of AD senile plaques, the most notable being Aβ deposits, but also including lipids and proteins such as APOE and Vitronectin (common proteins found in plaques in the brains of AD patients) (Luibl et al., 2006 ) Given the shared biomarkers and overlapping pathology between AMD and AD, coupled with the anatomical and physiological link between the retina and brain, it is hypothesised that the retina could serve as a non-invasive proxy for detecting the early changes associated with AD. Indeed, there is growing momentum in ophthalmology to utilise retinal imaging and screening to monitor neurological health, with the potential for eye exams to track the onset and progression of AD (Ashraf et al., 2023 ; Christinaki et al., 2022 ; Hadoux et al., 2019 ; Saeed et al., 2024 ) This study aims to evaluate the feasibility of using retinal cells as a surrogate model for AD. Specifically, we aimed to investigate whether the presence of AD biomarkers could be modelled in an RPE cell model derived from human induced pluripotent stem cells (iPSCs) carrying the PSEN1 H163R mutation. By leveraging the potential of an iPSC-derived retinal model of AD, we seek to further validate the retina as a valuable tool for mirroring AD pathology in the brain, potentially providing a proxy model that could pave the way for retinal detection and monitoring of disease. Methods iPSC culture. iPSCs ( PSEN1 H163R pathogenic variant (VAR), 41yo APOE 3/4 (Karch et al., 2018 ), and the CRISPR-edited isogenic line (COR) (Hernández et al., 2024 ) were maintained under serum-free and feeder-free conditions as previously described (Daniszewski et al., 2018 ). Briefly, cells were cultured on pre-coated with 10 µg/mL Vitronectin XF substrate (Stem Cell Technologies) prepared in CellAdhere Dilution Buffer (Stem Cell Technologies) and maintained in StemFlex basal medium (ThermoFisher) supplemented with 10% StemFlex Supplement (ThermoFisher). Medium was refreshed every second day. Cells were passaged weekly upon reaching approximately 80% confluency using ReLeSR (Stem Cell Technologies). To ensure the absence of contamination, all cell lines were routinely verified to be mycoplasma-free using the MycoAlert Mycoplasma Detection Kit (Lonza). For long-term storage, iPSCs were cryopreserved in StemFlex medium supplemented with 10% dimethyl sulfoxide (DMSO). iPSC-RPE differentiation. RPE cells were generated from iPSCs as previously described (Senabouth et al., 2022 ). Briefly, iPSCs at 70–80% confluency were transitioned to TeSR™-E6 (Stem Cell Technologies) supplemented with 1X N2 (Life Technologies) (D0) for 30 days, at which point RPE differentiation was commenced using guided differentiation media RPEM (α-MEM, 5% bovine serum albumin, MEM NEAA, L-Glutamine–Penicillin–Streptomycin, N1 and taurine–hydrocortisone–triiodothyronine. Medium was changed every second day, for 30 days, by which time cells had acquired an RPE-like polygonal morphology and visible pigmentation. Cells were passaged with 0.25% Trypsin-EDTA onto Matrigel-coated Transwells polyester inserts (0.4µm pore size) to enrich RPE cell cultures and promote proper cell polarisation. Cultures were maintained for at least 90 days to ensure the derived RPE cells were mature both physically and functionally, and to allow time for plaques and drusen-like deposits to develop, which we and others have shown to be a minimum of 90 days (Galloway et al., 2017 ). Immunocytochemistry. Immunocytochemistry was performed on cells fixed in 4% paraformaldehyde for 8 minutes at 4°C, permeabilized and blocked with 0.2% v/v Triton X-100 and 5% normal goat serum (NGS) for 60 minutes at 4°C. Primary antibodies were prepared in 5% NGS: ZO-1 (10 µg/mL, Life Technologies); Aβ 1−42 specific (D9A3A Cell Signalling Technologies, 1:1600); PMEL (5µg/mL, Abcam) and RPE65 (10µg/mL, Abcam). Cells were immunostained with isotype-specific secondary antibodies (Alexa 568 and Alexa 488, Life Technologies). Nuclei were counterstained using Hoechst (50µg/mL, Sigma-Aldrich) and mounted in ProLong Gold Antifade (Life Technologies). Specificity of the staining was verified by staining with an isotype control. Microscopy data acquisition. Confocal imaging was performed as previously described in (Hall et al., 2024 ) using a Zeiss LSM 900 confocal microscope (Biological Optical Microscopy Platform (BOMP) Facility, The University of Melbourne), equipped with 2 fluorescence GaAsP, 1 Airyscan detector and transmitted light ESID detector along with 4 diode lasers (405, 488, 561 and 640 nm). Images were acquired from samples fixed in a 96 well Cell Carrier Ultra Plate (Perkin Elmer, 6055300) in immersion media PBS -/- (Life Technologies, 14190–144) using a 20x/0.8 NA air objective. Upon instrument initialization, the plate was calibrated in the Zen 3.2 software and three random points for unbiased imaging were distributed across each well. The final Z-stack range was set to accommodate the thickest sample. Automated Z-stack centring was deployed using autofocus during the scan. ZO-1 (excited with 488 nm), Aβ (excited with 561 nm), Hoechst (excited with 405 nm) and brightfield (transmitted light) channels were imaged with a Z-stack interval of 0.54 µm over a range of 50 µm (∼100 sections). The output of the semi-automated scan was three 50 µm Z-stacks (.czi files) per well. Imaris batch analysis, statistical analysis, and figure preparation. Batch analysis was performed as previously described in (Hall et al., 2024 ) using Imaris (v9.9, Oxford Instruments) for processing the Z-stacks acquired for Aβ 1−42 specific deposits and ZO-1 + apical tight junctions. 3D version of the Z-stack and volume-fills of each dye were performed as described. This allowed for quantification of the amount and volume of the deposits. All images were taken in triplicate per well across each of the six iPSC-RPE cell cultures generated. Results are presented as mean ± SEM and were graphed using Graph Pad PRISM v9. Statistical significance was established using two-way ANOVA tests followed by Šídák's multiple comparisons test. Figure schematics were made with www.biorender.com . A p-value of less than 0.05 was considered statistically significant. For clarity, the following notations were used: *p < 0.05, **p < 0.01, ***p < 0.001. Electron microscopy sample preparation, imaging and quantification. RPE cells grown for 177 days ± 13.9 days from the PSEN1 H163R disease variant and the isogenic CRISPR-edited isogenic line on polystyrene transwell inserts were fixed in a solution of 10% paraformaldehyde, 3% (w/v) sucrose and 2.5% glutaraldehyde overnight at 4°C. Samples were washed with 0.1M Cacodylate Buffer, and incubated with a 0.1M Cacodylate Buffer to 0.5% Osmium solution for 60 minutes, and washed thoroughly with 0.1M Cacodylate Buffer. Samples were dehydrated with sequential ethanol solutions, 50%, 70%, 90%, 100%) for 5 mins at 4°C before incubating overnight in a solution of ​​75% resin (containing DDSA, Medcast / Epon resin and DMP – 30 accelerator) + 25% ethanol. The solution is replaced with 100% resin and incubated for 5 hours. Pre-labelled resin moulds were prepared, filled with fresh resin, and checked for bubbles. Samples were transferred into moulds and oriented carefully under a microscope, before being placed in a 55°C incubator overnight to allow the resin to solidify. Sections were made at 1 µm thickness, and de-plasticised by immersing in sodium ethoxide solution for 2 minutes before being sequentially in 100%, 60%, and 30% methanol for 2 minutes each. Sections were incubated in toluidine blue solution on a heat plate for 2 minutes, rinsed with H2O and then dipped in xylene, mounted and coverslipped for electron microscopy on the FEI Tecnai Spirit TEM (Thermo Fisher Scientific). Images were taken in at least three random locations at. Relative grey value of all melanosomes in the image was measured using Image J software and analysed using GraphPad Prism v10.2.3 one-way ANOVA with Tukey’s multiple comparison testing. A p-value of less than 0.05 was considered statistically significant. For clarity, the following notations were used: *p < 0.05, **p < 0.01, ***p < 0.001. Transepithelial electrical resistance (TEER). TEER measurements were taken under sterile conditions using a EVOM voltohmmeter (World Precision Instruments) on a heated platform set to 37°C. TEER measurements were taken from 11 independent iPSC-derived RPE wells for both PSEN1-pathogenic line and its CRISPR-edited isogenic line. Net TEER measurements were calculated by subtracting the value of a blank, Matrigel-coated filter without cells from the experimental value. Final resistance-area products (Ω cm2) were obtained by multiplying by the growth area of the permeable Transwell insert. Results were analysed for statistical significance using a two-tailed Student t-test and graphed using Graph Pad PRISM 9. Conditioned media for proteomics and ELISA analysis. Cultures were serum-depleted for three days prior to collection of conditioned media in serum-free RPEM (25mM HEPES replacing 5% FBS). Between 250 µL and 1.5 mL conditioned media was collected and immediately stored at -80°C until analysis (ELISA and LC-MS/MS). ELISA. To quantitatively analyse the production of Aβ 1−40 and Aβ 1−42 in iPSC-RPE cells with PSEN1 H163R and its isogenic CRISPR-corrected control, cell lysates and serum-free supernatants from the apical and basal chambers of RPE monolayers grown for 90 days. Levels of Aβ 1−40 and Aβ 1−42 in serum-free supernatants were detected using the Wako ELISA kits (296-64601, 298-64401 respectively) and cell lysates were analysed using ThermoFisher kits (KHB3481 for Aβ 1−40 and KHB3441 for Aβ 1−42 ), following manufacturer’s instructions. Absorbance at 450nm was measured using a microplate reader (Omega FLUOstar), and analyte concentrations were determined using a standard curve generated from serial dilutions of recombinant Aβ 40 or Aβ 41 peptide standards. Each sample was analysed in duplicate to ensure reproducibility. All statistical analyses and graphical illustrations were executed using GraphPad Prism 9. The data obtained from the experiments were presented as mean ± SEM. For the conditioned media ELISAs, an unpaired t-test was performed on both Aβ 1−40 and Aβ 1−42 to determine statistical significance from at least nine independent experiments. For both Aβ 1−40 and Aβ 1−42 cell lysate ELISAs, an unpaired t-test was performed with a Mann Whitney correction for non-normally distributed data to determine statistical significance from at least six independent experiments. One Aβ 1−40 reading had a failed signal and was omitted from the results, meaning there was one less datapoint for the Aβ 1−40 for the PSEN1 sample. For the ratio of Aβ 1−42 : Aβ 1−40 ratio test, an unpaired t-test was performed. A p-value of less than 0.05 was considered statistically significant. For clarity, the following notations were used: *p < 0.05, **p < 0.01, ***p < 0.001. Protein preparation for liquid chromatography-electrospray ionisation tandem mass spectrometry (LC-ESI-MS/MS). RPE cell cultures were lysed in RIPA buffer supplemented with phosphatase and protease inhibitors, sonicated with a probe sonicator (40 HZ × 2 pulses × 15 s), and insoluble debris were removed by centrifugation at 14,000 rpm for 15 min at 4°C, prior to measurement of protein contents by standard bicinchoninic acid assay (MicroBCA protein assay kit, Thermo Scientific). For the conditioned media analysis, samples were serum-deprived for two days prior to harvesting of the conditioned media, ensuring proteins associated with serum in the media did not interfere with the interpretation of the results (Lidgerwood et al., 2016 ). Proteins were lyophilised using Savant SPD131DDA speedvac (ThermoFisher). Solubilised proteins were reduced using 5 mM dithiothreitol and alkylated using 10 mM iodoacetamide. Proteins (150 µg) were initially digested at room temperature overnight using a 1:100 enzyme-to‐protein ratio using Lys‐C (Wako, Japan), followed by digestion with Trypsin (Promega, Madison, WI) for at least 4 hours at 37°C also at a 1:100 enzyme‐to‐protein ratio. Resultant peptides were acidified with 1% trifluoroacetic acid and purified using styrene divinylbenzene‐reverse-phase sulfonate (Empore) stage tips. The proteome was identified on a Tandem Mass Tag (TMT) platform (Progenetech, Sydney, Australia). Tandem Mass Tag (TMT) labelling. Three independent 10 plex TMT experiments were carried out. Ten independent PSEN1- pathogenic variant RPE and ten CRISPR edited isogenic controls were analysed for cell lysates; whilst five of each were analysed for upper chamber conditioned media to examine secreted protein. Briefly, dried peptides from each sample were resuspended in 100 mM HEPES (pH 8.2) buffer and peptide concentration measured using the MicroBCA protein assay kit. Sixty micrograms of peptide from each sample was subjected to TMT labelling with 0.8 mg of reagent per tube. Labelling was carried out at room temperature for 1 h with continuous vortexing. To quench any remaining TMT reagent and reverse the tyrosine labelling, 8 µl of 5% hydroxylamine was added to each tube, followed by vortexing and incubation for 15 min at room temperature. For each of the respective ten plex experiments, the ten labelled samples were combined, and then dried down by vacuum centrifugation. Prior to High-pH reversed-phase fractionation, the digested and TMT-labelled peptide samples were cleaned using a reverse-phase C18 clean-up column (Sep-pak, Waters) and dried in vacuum centrifuge. The peptide mixture was resuspended in loading buffer (5 mM ammonia solution (pH 10.5), separated into a total of 96 fractions using an Agilent 1260 HPLC system equipped with a quaternary pump, a degasser and a Multi-Wavelength Detector (MWD) (set at 210-, 214-, and 280-nm wavelength). Peptides were separated on a 55-min linear gradient from 3 to 30% acetonitrile in 5 mM ammonia solution pH 10.5 at a flow rate of 0.3 mL/min on an Agilent 300 Extend C18 column (3.5-µm particles, 2.1 mm ID and 150 mm in length). The 96 fractions were finally consolidated into eight fractions. Each peptide fraction was dried by vacuum centrifugation, resuspended in 1% formic acid, and desalted again using SDB-RPS (3M-Empore) stage tips. LC-ESI-MS/MS data acquisition. Mass spectrometric data were collected on an Orbitrap Lumos mass spectrometer coupled to a Proxeon NanoLC-1200 UHPLC. The 100-µm capillary column was packed with 35 cm of Accucore 150 resin (2.6 µm, 150 Å; Thermo Fisher Scientific). The scan sequence began with an MS1 spectrum (Orbitrap analysis, resolution 60,000, 400–1600 Th, automatic gain control (AGC) target 4 × 105, maximum injection time 50 ms). Data were acquired for 90 min per fraction. Analysis at the MS2 stage consisted of higher energy collision-induced dissociation (HCD), Orbitrap analysis with the resolution of 50,000, automatic gain control (AGC) 1.25 ×105, NCE (normalized collision energy) 37, maximum injection time 120 ms, and an isolation window at 0.5 Th. For data acquisition including FAIMS, the dispersion voltage (DV) was set at 5000 V, the compensation voltages (CVs) were set at − 40 V, − 60 V, and − 80 V, and TopSpeed parameter was set at 1.5 s per CV. Proteomic data analysis. Spectra were converted to mzXML via MSconvert v3.0. Database searching included all entries from the Human UniProt Database (downloaded: August 2022). The database was concatenated with one composed of all protein sequences for that database in the reversed order. Searches were performed using a 50-ppm precursor ion tolerance for total protein-level profiling. The product ion tolerance was set to 0.2 Da. These wide mass tolerance windows were chosen to maximise sensitivity in conjunction with Comet searches and linear discriminant analysis. TMT tags on lysine residues and peptide N-termini (+ 229.163 Da for TMT) and carbamidomethylation of cysteine residues (+ 57.021 Da) were set as static modifications, while oxidation of methionine residues (+ 15.995 Da) was set as a variable modification. Peptide-spectrum matches (PSMs) were adjusted to a 1% false discovery rate (FDR). PSM filtering was performed using a linear discriminant analysis, as described previously and then assembled further to a final protein-level FDR of 1%, using the Picked FDR method. An isolation purity of at least 0.7 (70%) in the MS1 isolation window was used for samples. For each protein, the filtered peptide TMT SN values were summed to create protein quantifications. To control for different total protein loading within a TMT experiment, the summed protein quantities of each channel were adjusted to be equal within the experiment. Proteins were quantified by summing reporter ion counts across all matching PSMs, also as described previously. Reporter ion intensities were adjusted to correct for the isotopic impurities of the different TMT reagents according to manufacturer specifications. Finally, each protein abundance measurement was scaled, such that the summed signal-to-noise for that protein across all channels equaled 100, thereby generating a relative abundance (RA) measurement. Investigation of protein–protein interactions and functional enrichment GO analysis of DE proteins were performed with the online STRING database version 11.0. STRING analysis on the entire proteomics dataset, generating a network of interactions (based on both evidence of functional and physical interactions). The interaction score of 0.7 was considered for the visualisation of the networks. Network lines represent the protein interaction score, which was set at a minimum medium confidence (0.4). Active interaction sources were based on text mining, experiments, databases, co-expression, neighbourhood, gene fusion, and co-occurrence data. Two criteria were applied to determine significantly regulated proteins: fold change over 1.1 and P value lower than 0.05. Results PSEN1 pathogenic variant does not impact RPE differentiation. Each line was successfully differentiated to RPE cells as previously described (Lidgerwood et al., 2016 , 2018 ) (Fig. 1 A). All lines differentiated into pigmented and polygonal monolayer cultures, and expressed canonical RPE markers RPE65, MITF, PMEL and ZO-1 in typical organisation of mature RPE (Fig. 1 B,C). To confirm the strength and integrity of the epithelial monolayer, TEER were performed. Results of both the PSEN1 H163R pathogenic variant and the isogenic CRISPR-corrected samples indicated there was no statistical difference in the average strength of the resistance of the cultures, which was above 500 Ω/cm2, consistent with highly polarised RPE monolayers (Fig. 1 D). This suggests that the PSEN1 H163R mutation bears no impact on the ability of iPSCs to differentiate into mature and functional RPE cells in vitro . PSEN1 H163R pathogenic variant RPE cells produce more Aβ 1−42 than the isogenic CRISPR-corrected isogenic controls, and express cytoplasmic Tau. Given that the PSEN1 H163R mutation is a known genetic determinant of fAD, we assessed if an AD-like phenotype could be recapitulated and rescued in the iPSC-RPE culture derived from both the diseased ( PSEN1 H163R pathogenic variant) and controls (CRISPR-corrected PSEN1 H163R isogenic) lines respectively. Immunostaining of the RPE cultures for the tight-junction marker ZO-1 was utilised to demonstrate a continuous epithelial monolayer, and provide a baseline to assess if deposits were located apically or basally to the ZO-1 plane. By immunofluorescence, the positive Aβ 1−42 staining below the ZO-1 demonstrated that basal deposits were detectable in both the PSEN 1 H163R and isogenic CRISPR control RPE (Fig. 2 A). To quantify this, we utilised our algorithm that detects deposits and provides a volume-fill calculation score (Hall et al., 2024 ), allowing for comparison of the number and volume of Aβ + deposits in PSEN 1 H163R and CRISPR control RPE. The total number of deposits was not statistically significant between the PSEN1 H163R pathogenic variant and its CRISPR edited isogenic control. However the volume (µm 3 ) of detectable deposits was significantly larger in the PSEN 1 H163R pathogenic RPE compared to the isogenic control cells (Fig. 2 C). We next analysed the raw levels of secreted Aβ 1−42 and Aβ 1−40 , and the ratio of Aβ 1−42 : Aβ 1−40 - a well-established diagnostic biomarkers of amyloid pathology using ELISA on conditioned media and lysates from RPE cultures. No statistically significant differences in pathogenic Aβ 1−42 and Aβ 1−40 in either the apical or basal conditioned media were observed (Fig. 2 D). However, Aβ 1−42 levels were statistically significantly elevated in PSEN1 H163R RPE lysates compared to CRISPR controls (Fig. 2 E), indicating intracellular Aβ accumulation. Additionally, the Aβ 1−42 :Aβ 1−40 ratio in lysates was significantly increased in the PSEN1 H163R RPE. Immunostaining of RPE monolayers was performed to assess the presence of Tau (Fig. 2 F). Both PSEN1 H163R and CRISPR RPE cells expressed non-nuclear Tau (MAPT) protein, with larger foci consistently observed in the PSEN1 H163R variant, relative to the CRISPR RPE cells. Notably, phosphorylated Tau exhibited a more spindle-like organisation in CRISPR-corrected RPE cells, suggesting a more structured association with cytoskeletal proteins (Fig. 2 F). This observation implies that Tau phosphorylation participates in cytoskeletal dynamics in RPE cells, potentially affecting their structural integrity. While Tau expression in RPE cells has not been extensively characterised, these results corroborate the findings from an earlier study on human retinal sections showing Tau immunoreactivity in the RPE (Löffler et al., 1995 ). Together, this data indicates that the PSEN1 H163R pathogenic variant RPE cells generate more Aβ than their isogenic CRISPR-corrected controls, as demonstrated by larger volume of detectable deposits ; higher levels of Aβ 1–42 in cell lysates, leading to a significant increase in the Aβ 1–42: 1–40 ratio in PSEN1 H163R . There was no significant difference in Aβ levels detected in conditioned media, which may be explained by the loss of protein due to the lack of ‘sandwiching’ of the deposits between the apical surface and a ‘capturing’ matrix. This is rectified in the cell lysate analysis, where deposits that are located basally to the RPE are captured by the Transwell matrix and therefore captured during cell lysate harvesting. We also confirmed Tau protein expression in all RPE cell cultures, and reported an apparent focal concentration of the phosphorylated protein in PSEN1 H163 . Together, these results indicate that PSEN1 H163R RPE model key pathogenic features of AD. Proteomics analysis finds 1,818 differentially expressed proteins between PSEN1 H163R pathogenic variant RPE cells and the CRISPR edited isogenic controls, relating to pathways involving cellular metabolism, cellular transport, and protein folding. We next measured protein quantification in both lines by mass spectrometry. TMT proteomics array confirmed the similarity in overall protein abundance distributions of individual biological replicates, demonstrating reproducibility in expression profiles between biological replicates and within the two sample groups. Hierarchical clustering analysis of proteins with differential abundance illustrated the overall consistency of the up and down regulation within respective control and the PSEN1 H163R cohorts (Fig. 3 A). A total of 7,605 proteins were identified across both datasets (see Supplementary Data 1), which were quantified by multiple peptides at an initial protein FDR or less than 1%. To identify significantly differentially expressed proteins, we used a combination of statistical and empirical thresholds to ensure a high confidence in the reported protein abundance differences. Differentially expressed proteins were considered significant if relative abundances in the PSEN1 H163R RPE versus CRISPR-isogenic controls were statistically significant (p-value ≤ 0.05) using a student t-test and differed by at least ± 20%. This two-step differential analysis of the PSEN1 H163R RPE versus isogenic controls resulted in 815 proteins significantly upregulated (p-value ≤ 0.05 and ≥ 1.2-fold change) and 1,020 proteins significantly downregulated (p-value ≤ 0.05 and ≤ 0.833-fold change) in the PSEN1 H163R , relative to the isogenic CRISPR controls (see Supplementary Data 1). To obtain a more detailed understanding of the molecular mechanisms and biological processes altered in RPE with a PSEN1 H163R disease harbouring variant, we conducted cellular pathway enrichment and functional protein network analyses on the differentially expressed proteins. The 1818 most significantly differentially expressed proteins between the PSEN1 H163R and CRISPR-isogenic control RPE (FC ≥ 1.2 and ≤ 0.83, p-value < 0.05) were analysed using Metascape (which incorporates data from KEGG, GO, Reactome, Canonical, CORUM, WikiPathways and PANTHER). The analysis identified enrichment in pathways processes involved in metabolism of RNA, including mRNA and RNA splicing (GO: 0000375, 0000398, 0008380, 0048024), mRNA and rRNA processing (0006396, 0006397); cellular respiration / mitochondrial functions such as organization (GO: 0007005) aerobic respiration (GO: 0009060), and cellular respiration (GO: 0045333); protein folding (GO: 0006457); protein processing in the endoplasmic reticulum (hsa04141), and vesicle-mediated transport (R-HSA-5653656, GO:0016050) (Fig. 3 C). Some pathways were also enriched in KEGG pathway analysis of the dataset (Fig. 3 D). Proteins involved in AD pathways were significantly represented in multi-pathway analysis (hsa05010), with 65 of the annotated 354 proteins involved in the pathways represented in our dataset (Fig. 3 D- 3 E), including 29 upregulated and 36 downregulated in PSEN1 H163R RPE. These could be further segmented into AD subsets based on attributes relating to their biological function (GO), including oxidative phosphorylation (GO: 0006119) and ATP synthesis (GO: 0042775); mitochondrial organisation (GO: 0007005); autophagy (GO: 0010506); regulation of cell death (GO: 0010942); tubulins; and proteins involved in amyloid β metabolic process (GO: 1902993). These three proteins – MAPT, APP, and PSEN2– are annotated proteins that are constituents of β-amyloid deposits (BTO: 0002774). Proteins significantly upregulated in PSEN1 H163R RPE belonged to pathways involved in AD, including endocytosis, beta amyloid-related processes, membrane trafficking and autophagy. To understand biological processes that were upregulated in the PSEN1 H163R RPE, STRING and Metascape software were utilised to categorise proteins into pathways. Examining Biological Processes alone (GO), sorted by weighted means between the observed/expected ratio and -log(FDR), the PSEN1 H163R pathogenic variant RPE were enriched for proteins involved in cellular transport (vesicle, protein, cytoskeletal), localisation and regulation of autophagy (Fig. 4 A). Expanding the analysis criteria across KEGG, GO, Reactome, Canonical, CORUM, WikiPathways and PANTHER using Metascape found that PSEN1 H163R RPE were significantly enriched for proteins belonging to membrane trafficking and intracellular transport pathways (Fig. 4 B). These included vesicle-mediated transport (R-HSA-5653656, including proteins DST, DYNC1I1, BLOC1S1, MAPT, PRKCZ, SOD1, BAG3, AP3S2, KIF1C, IFT27, KIF3A, CEP131, BICD2, SNAPIN, KIFBP, IFT52, DYNC2LI1, IFT25, FYCO1, TTC21B, IFT74, SPG11, SSX2IP, BLOC1S2, BLOC1S3), endosome transport (GO:0016197, including proteins LYST, MTM1, SNAPIN, CHMP4A, HOOK2, SNX16, AKTIP, PLEKHF2, HOOK3, CHMP7, CHMP4B) and endocytosis (GO: 0006897, hsa04144, R-HSA-8856828, including proteins GRK2, AMPH, ARRB1, CLTA, CLTB, DAB2, HLA-E, HSPA8, PRKCZ, SH3GL1, EEA1, ASAP2, WASL, CYTH1, ZFYVE9, ZFYVE16, IQSEC1, ARPC5, STAM2, SPART, RAB11FIP5, ARFGAP3, CHMP4A, SNX12, EHD3, SH3GLB1, SH3GLB2, SNX6,RBSN, ARFGAP2, CHMP7, CHMP4B). Protein-containing complex assembly pathways (GO:0043254), including processes involving cytoskeletal organization, regulation of protein polymerization, and intracellular protein transport were also significantly represented. In PSEN1 H163R RPE cells, data revealed significant enrichment of pathways related to membrane dynamics and protein trafficking, consistent with the known multifunctional role of PSEN1 in regulating these processes. Among the upregulated pathways, those associated with neurodegeneration were prominently represented, with Alzheimer’s disease (hsa05010) identified as the most significantly enriched disease-related pathway. Within the Alzheimer’s disease pathway, several proteins were markedly upregulated in PSEN1 H163R RPE cells. MAPT was the sixth most differentially expressed protein, showing a 2.4-fold increase. NRBF2, a regulator of autophagic degradation of APP C-terminal fragments, was the fifth most upregulated protein, also with a 2.4-fold increase. Additional upregulated proteins included S100 family members (S100A6, S100A13, S100A16), GAP43—associated with AD diagnosis and neuropathology and APBB1 (FE65), which interacts with APP and contributes to Aβ production (Fig. 3 B). Other proteins relating to the AD term that were upregulated in the PSEN1 H163R RPE included FAS, BID, CALM3, CAPN2, CASP3, ND6, NDUFB1, NDUFB9, PIK3C3, PIK3R2, PPP3CB, PPP3R1, MAP2K1, RELA, TRAF2, TUBA4A, UQCRH, IKBKG, BECN1, ATG13, RB1CC1, TUBB4B, ADRM1, ATG2A, SLC39A14, NRBF2, NDUFB11, ATG101, TUBA1C, KLC4. Interestingly, we also observed a significant increase in the expression of PSEN2 in the PSEN1 H163R RPE, which may suggest a compensatory mechanism is involved. Pathways related to RNA splicing, metabolism and processing were significantly downregulated in PSEN1 H163R RPE. Pathways significantly downregulated in the PSEN1 H163R RPE cells were predominantly related to RNA processing, mitochondrial organization and assembly, respiration, and protein trafficking (Fig. 5 A, 5 B). Among the top 47 most significantly down regulated proteins in PSEN1 H163R RPE (based on a minimum FC ≥ 2.0) were a number of histone proteins related to epigenetic and transcriptional gene regulation (including through RNA localisation) (H4C1, H3C13, H2AX, H3-2, H2BC9, H2AC4, H3C1, H2AC21); several myosin and myofibril proteins related to myofibril assembly, cytoskeletal transport and protein localisation to organelle (MYH7, MYL1, MYH2, MYL4, MYH3, MYH8, TNNT1, TNNT3, TNNI2, MYOM2), a range of solute carrying proteins, including SLC25A11, SLC25A12, SLC25A19, SLC25A1, SLC25A15, SLC5A3, SLC5B4, SLC39A7, SCL25A40, SLC30A6, which have known roles in mitochondrial transport; numerous mitochondrial proteins ND1, ND2, ND4, NDUFA4, NDUFA8, NDUFA9, NDUFB5, NDUFB6, NDUFS4, with widespread roles in aerobic respiration and respiratory electron transport (Fig. 3 B). Significantly, Alzheimer’s-related downregulated proteins in PSEN1 H163R RPE included the amyloid precursor protein APP (1.3 fold) and clusterin CLU (2.2 fold), which at low levels can alter aggregation, toxicity, and transport of Aβ (Wojtas et al., 2017 ). Together, these results indicate that pathways related to AD phenotypes, including proteins involved in Aβ and Tau pathways, were differentially expressed between the PSEN1 H163R and CRISPR-corrected RPE cells, indicating that the PSEN1 H163R pathogenic variant could augment typical AD related proteins and pathways in an RPE model. Key secreted proteins involved in AD and cellular matrisome proteins were altered in the PSEN1 H163R RPE cells. Finally, the conditioned media of the samples were analysed to examine differences in the secreted proteins, and the pathways they were associated with. A total of 656 secreted proteins were detected across both samples, with 80 proteins found to be significantly differentially expressed between the PSEN1 H163R and CRISPR-control samples (see Supplementary Data 2). These include 32 proteins that were significantly downregulated and 48 that were significantly upregulated in PSEN1 H163R RPE (Fig. 6 A-B). Notable AD-related proteins that were significantly upregulated in PSEN1 H163R RPE were apolipoprotein E APOE (8th most significant, 1.71 FC increase); pigment-epithelium-derived factor (SERPINF1) (10th most significant, 2.14 FC increase) and the amyloid-beta precursor protein APP (1.9 FC increase). Serum amyloid A-4 protein (SAA4) was significantly downregulated in PSEN1 H163R RPE (1.43 FC decrease) (Fig. 6 C). Pathway and process enrichment analysis through Metascape found that disease pathways significantly enriched in the dataset related almost exclusively to diseases involving amyloid dysregulation, including Alzheimer’s disease, amyloidosis, dementia, and retinal drusen (a feature of age-related macular degeneration) (Fig. 6 D). All proteins implicated in these pathways were upregulated in PSEN1 H163R RPE, with the exception of two (ACHE, SAA4), which were downregulated (Fig. 6 D-E). Interestingly, almost half of the significantly differentially expressed proteins (38 in total) belonged to the ‘matrisome’, a collection of more than 300 proteins that have known roles in cellular matrices (Hynes & Naba, 2012 ). These include COL4A2, DCN, FBN1, FBN2, EFEMP1, FN1, HSPG2, TNC, LAMA5, LAMB1, LAMC1, LTBP1, MATN2, SPARC, THBS1, COL14A1, SPON1, NID2, EFEMP2, OIT3, AGRN, PLOD1, TIMP1, TIMP2, F13A1, RAP1B, TLN1, VASP, C1S, CFH, SLC2A1, IGF2, JUP, NRP2, SERPINF1, F2, CORO1A, ANGPTL7. PSEN1 H163R mutations alter the expression of canonical RPE proteins involved in melanosome biogenesis and melanosome formation. Melanosomes are key organelles in RPE responsible for visual function through photo absorption and reducing oxidative stress. The biogenesis of melanosomes in RPE cells is a tightly regulated, multistep process that follows four distinct stages (Fig. 6 A). The initial processes governing melanosome formation are not well understood, however it is known to be related to secretory, endosomal, cytoskeletal, and trafficking pathways, which were significantly implicated in the differential expression between PSEN1 H163R and its isogenic CRISPR control. Results of the proteomics screen indicated that the PSEN1 H163R mutation had significant and reproducible impacts on the expression of key melanosome proteins. These included significant downregulation of melanosome protein (PMEL, 1.45-fold), tyrosinase (TYR, 1.45-fold) and L-dopachrome tautomerase (DCT/TYRP2, 1.9-fold), which are fundamental to the sequential formation of mature melanosomes (Fig. 6 B). To explore the melanosome in greater detail, samples were prepared for transmission electron microscopy, which enables visualisation of minute details such as organelle structure, localisation, and organisation. Independent differentiations of PSEN1 H163R RPE and CRISPR-isogenic control were analysed. Qualitatively, the melanosomes from the PSEN1 H163R were irregularly shaped, as opposed to the typical ovoid morphology of melanosomes (Fig. 6 C). PMEL fibrils were organised in a sheet manner, resembling ‘fingerprints’ in the CRISPR-control RPE, while organisation was irregular in the PSEN1 H163R RPE, resembling more disordered or random arrangements (Fig. 6 C). The melanosomes also appeared to be less pigmented, which could indicate defects in the transition from Stage II-IV melanosomes. Quantification of the relative mean grey levels was performed using Fiji, a proxy measure of the ‘darkness’ or stage IV maturation of the melanosomes. Results indicated that PSEN1 H163R pathogenic variant RPE exhibited significantly less pigmented melanosomes compared to the CRISPR-isogenic control (Fig. 6 D). Similarly, the size of the melanosomes was also quantified, which found that on average, the PSEN1 H163R RPE had significantly smaller diameters relative to the CRISPR-isogenic controls (Fig. 6 E), suggesting the effects on melanosome maturation and assembly is unique to the PSEN1 H163R RPE. Together this data suggests that PSEN1 plays a novel role in maintaining the homeostasis of melanosome biogenesis and maturation in RPE cells, and that when perturbed with the PSEN1 H163R pathogenic variant, leads to alterations in the expression of key RPE proteins involved in melanogenesis. Discussion This study demonstrates that RPE cells derived from iPSCs carrying the PSEN1 H163R pathogenic variant exhibit augmented pathological features associated with AD, including increased intracellular Aβ 1−42 , elevated Aβ 1−42:1−40 ratios, Tau expression, and proteomic signatures associated with neurodegeneration. These findings validate the utility of iPSC-RPE as a non-neuronal, retinal model of AD pathology and reinforce the potential of the retina as a surrogate tissue for AD research and biomarker discovery. Despite not being a neural cell type, the RPE is known to express MAPT( Löffler et al., 1995 ) and APP ( Seth et al., 2008 ), allowing us to examine the impact of PSEN1 H163R on both Aβ and Tau pathology in this retinal model. While Aβ is classically associated with extracellular deposition in the brain, its accumulation in the retina, including in the RPE, has been well documented in both post-mortem AD retinal tissue (den Haan et al., 2018 ; Koronyo et al., 2017 ; Yoshida et al., 2005 ); (Koronyo-Hamaoui et al., 2011 ; L. Wang & Mao, 2021 )), cultured primary cells ((den Haan et al., 2018 ; Koronyo et al., 2017 ; Yoshida et al., 2005 ), living patients (Doustar et al., 2017 ; Hadoux et al., 2019 ), transgenic animals (Dong et al., 2018 ; Ning et al., 2008 ), and iPSC-derived retinal organoid models (James et al., 2024 ; Lavekar et al., 2023 ). In fact, Aβ is a major constituent in naturally occurring extracellular deposits of the RPE, known as drusen, which occurs naturally during aging and in macular degeneration(Ohno-Matsui, 2011 ). Furthermore, Tau aggregates have been identified in the aging human retina (Leger et al., 2011 ), while phosphorylated Tau has been detected in the retinas of Octodon degus, a rodent model exhibiting Alzheimer’s-like pathology (Du et al., 2015 ), highlighting the potential involvement of Tau in retinal neurodegeneration and a link between Tau accumulation and age-related retinal changes. Our data from the immunocytochemistry, ELISA, and proteomics assays, indicate that pathways relating to Aβ are significantly implicated in PSEN1 H163R RPE cells, as evidenced by significant increases in Aβ deposit volume, significantly increased levels in cell lysates, and a dysregulation of many pathways relating to an increase in Aβ levels in the PSEN1 H163R RPE cells. Indeed, cell lysate analysis clearly showed elevated Aβ 1−42 and Aβ 1−42:1−40 ratios, which are recognised diagnostic markers of AD (Dubois et al., 2021 ). Although conditioned media ELISA showed no significant difference in secreted Aβ levels, this could be explained by limitations of the culture model, which provided no matrix for the secreted Aβ to imbed. Tau expression in RPE cells was confirmed by immunostaining, consistent with prior reports (Löffler et al., 1995 ) and our own study showing expression of MAPT increasing with RPE maturity (Lidgerwood et al., 2021 ). In PSEN1 H163R RPE cells, Tau protein appeared more aggregated, while phosphorylated Tau in CRISPR controls exhibited a more filamentous, spindle-like organisation, consistent with its association with cytoskeletal proteins. Tau’s cytoskeletal interactions may impact cellular architecture in RPE cells, warranting further investigation, especially given the evidence of Tau accumulation in retinal neurodegeneration that may precede brain pathology (Chiasseu et al., 2017 ). Proteomics revealed widespread dysregulation of pathways relevant to Alzheimer’s disease. Among the most significantly upregulated proteins were MAPT, NRBF2, a regulator of autophagic APP degradation (Zeng et al., 2021 )(Yang et al., 2017 )(Zeng et al., 2021 ), GAP43, which has been linked to AD pathology (Ariaei et al., 2024 ; Franzmeier et al., 2024 )(Sandelius et al., 2019 )(Ariaei et al., 2024 ; Franzmeier et al., 2024 ), and APBB1 (FE65), a protein that binds to the amyloid precursor protein (APP) and modulates the production of Aβ(Bruni et al., 2002 ; Donato et al., 2013 ), (King & Scott Turner, 2004 ; Trommsdorff et al., 1998 ). Multiple S100 proteins, previously implicated in AD (Cristóvão et al., 2018 )(Donato et al., 2013 ), were also elevated. Downregulated proteins were predominantly linked to mitochondrial respiration, protein folding, and RNA processing—reflecting energy metabolism deficits frequently observed in AD (Xu et al., 2023 ; Yuan et al., 2024 ). Notably, several mitochondrial complex proteins and solute carriers were suppressed, indicating impaired mitochondrial biogenesis and function. The altered matrisome composition in conditioned media, including differential secretion of APOE, APP, and ECM proteins, further indicates that PSEN1 influences extracellular matrix regulation, a pathway shared with both AMD and AD. Together, these findings suggest that the PSEN1 H163R mutation alters critical pathways involved in cytoskeletal organisation, autophagy, and APP metabolism, with downstream effects on protein trafficking, vesicular transport, and mitochondrial function. This likely has broader implications for neural retinal health, as the RPE is one of the most critical gatekeepers of outer retina homeostasis and visual processing. A significant finding was the disruption of melanosome biogenesis in PSEN1 H163R . Melanosome biogenesis is governed by a complex, multi-step process, requiring morphological and functional modification of endosomal compartments. PMEL protein - a non-pathogenic amyloid fibril - is first nucleated and cleaved by BACE2-containing enzymes in multivesicular endosomes, initiating the first stage of melanogenesis (Stage I). At this stage, melanosomes are unpigmented, and classed as ‘immature’ premelanosomes. In Stage II, PMEL fibrils assemble into organised sheets, serving as the matrix for subsequent melanin synthesis and deposition in Stage III by enzymes such as Tyrosinase (TYR), tyrosinase-related protein (TYRP1) and dopachrome tautomerase (DCT), ultimately leading to complete masking of the PMEL fibrils by Stage IV, forming fully mature melanosomes. The link between PSEN and melanosome biogenesis has not be extensively explored, however a previous study found that Tyr, Tyrp1, and DCT/Tyrp2 (commonly known as dopachrome tautomerase or tyrosinase-related protein 2) are physiological substrates for presenilins in mice, and that Psen1 mutant mice had defective pigmentation caused by tyrosinase mislocalization (R. Wang et al., 2006 ). A study of zebrafish found that Psen2 is required for normal skin pigmentation (Jiang et al., 2018 ). To the best of our knowledge, this research is the first to demonstrate a PSEN1 -mediated defect in melanosome maturity and size in a human model. Our results found that PSEN1 H163R RPE showed reduced expression of core melanogenic proteins (PMEL, TYR, DCT), and TEM revealed abnormal melanosome morphology and pigmentation, represented by melanosomes with smaller diameters and less reduced grey value (a measure of ‘darkness’) respectively. As melanosomes share trafficking and maturation pathways with lysosomes, this finding supports a broader role for PSEN1 in organelle maturation and homeostasis within the RPE. Furthermore, the RPE provides an excellent model to understand the processes that govern non-toxic amyloidogenic pathways, such a melanosome biogenesis, and the intersection of this with presenilins, which have a clear but still-unknown role in the process. Conclusion In summary, this study demonstrates that the PSEN1 H163R variant drives AD-like molecular and cellular changes in human iPSC-derived RPE. These findings support the RPE as a relevant and accessible model to study AD pathology, offering insights into disease mechanisms beyond the CNS, and opening new avenues for retinal biomarker development. Abbreviations RPE, iPSC, AD, TEM, LC-ESI-MS/MS, Aβ, TEER. Declarations Ethics approval and consent to participate : Not applicable. Consent for publication: Not applicable. Availability of data and materials: proteomics data files will be uploaded to publicly available ProteomeXchange consortium through the PRIDE database, with a dataset ID to be provided. Competing interests: The authors declare that they have no competing interests. Funding: this research was supported by a DHB Foundation grant (GEL, AP), a Dementia Australia Norma Beaconsfield grant (GEL), an MJ Gething Award (GEL), a National Health and Medical Research Council Senior Research Fellowship (AP, 1154389), a Dame Kate Campbell Fellowship (AP), and a Momentum Fellowship (GEL). Authors’ contributions: G.E.L. and A.P conceptualised the original project and created methodologies for undertaking the project.Investigation, including all physical experiments, were undertaken by G.E.L., M.M., J.C.H., D.H, U.G., A.v.d.M., J.Y.W.M.Resources were provided by D.H., C.M.K., A.M.G., A.P.Data analysis was performed by G.E.L., M.M., J.C.H., A.P. Writing - original draft: G.E.L., A.P.Writing - review & editing: all authors.Supervision and project administration: A.P.; Funding Acquisition, G.E.L., A.P. Acknowledgements: We thank João Paulo for his technical support with LC-MS/MS; and the DIAN participants and their families for their dedication and altruism and the research and support staff at each of the DIAN sites for their contributions to the study. We gratefully acknowledge the altruism of the participants and their families and the contributions of the DIAN research and support staff at each of the participating sites for their contributions to this study. The DIAN Expanded Registry welcomes contact from any families or treating clinicians interested in research about autosomal dominant familial Alzheimer’s disease. Data collection and sharing for this project were supported by The Dominantly Inherited Alzheimer’s Network (DIAN; the German Center for Neurodegenerative Diseases (DZNE), and Raul Carrea Institute for Neurological Research (FLENI). Partial support was provided by the Research and Development Grants for Dementia from Japan Agency for Medical Research and Development (AMED) and by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI). We also acknowledge the Biological Optical Microscopy Platform and the Melbourne Cytometry Platform (Melbourne Brain Centre Node) at the University of Melbourne for technical assistance. DIAN Consortium Full Name and Credentials: Sarah Adams, MS; Ricardo Allegri, PhD; Aki Araki; Nicolas Barthelemy, PhD; Randall Bateman, MD; Jacob Bechara, BS; Tammie Benzinger, MD, PhD; Sarah Berman, MD, PhD; Courtney Bodge, PhD; Susan Brandon, BS; William (Bill) Brooks, MBBS, MPH; Jared Brosch, MD, PhD; Jill Buck, BSN; Virginia Buckles, PhD; Kathleen Carter, PhD; Lisa Cash, BFA; Charlie Chen, BA; Jasmeer Chhatwal, MD, PhD; Patricio Chrem Mendez, MD; Jasmin Chua, BS; Helena Chui, MD; Laura Courtney, BS; Carlos Cruchaga, PhD; Gregory S Day, MD; Chrismary DeLaCruz, BA; Darcy Denner, PhD; Anna Diffenbacher, MS; Aylin Dincer, BS; Tamara Donahue, MS; Jane Douglas, MPh; Duc Duong, BS; Noelia Egido, BS; Bianca Esposito, BS; Anne Fagan, PhD; Marty Farlow, MD; Becca Feldman, BS, BA; Colleen Fitzpatrick, MS; Shaney Flores, BS; Nick Fox, MD; Erin Franklin, MS; Nelly Joseph-Mathurin, PhD; Hisako Fujii, PhD; Samantha Gardener, PhD; Bernardino Ghetti, MD; Alison Goate, PhD; Sarah Goldberg, MS, LPC, NCC; Jill Goldman, MS, MPhil, CGC; Alyssa Gonzalez, BS; Brian Gordon, PhD; Susanne Gr¨aber-Sultan, PhD; Neill Graff-Radford, MD; Morgan Graham, BA; Julia Gray, MS; Emily Gremminger, BA; Miguel Grilo, MD; Alex Groves; Christian Haass, PhD; Lisa H¨asler, MSc; Jason Hassenstab, PhD; Cortaiga Hellm, BA; Elizabeth Herries, BA; Laura Hoechst-Swisher, MS; Anna Hofmann, MD; Anna Hofmann; David Holtzman, MD; Russ Hornbeck, MSCS, MPM; Yakushev Igor, MD; Ryoko Ihara, MD; Takeshi Ikeuchi, MD; Snezana Ikonomovic, MD; Kenji Ishii, MD; Clifford Jack, MD; Gina Jerome, MS; Erik Johnson, MD, PHD; Mathias Jucker, PhD; Celeste Karch, PhD; Stephan K¨aser, PHD; Kensaku Kasuga, MD; Sarah Keefe, BS; William (Klunk, MD, PHD; Robert Koeppe, PHD; Deb Koudelis, MHS, RN; Elke Kuder-Buletta, RN; Christoph Laske, PhD; Allan Levey, MD, PHD; Johannes Levin, MD; Yan Li, PHD; Oscar Lopez MD, MD; Jacob Marsh, BA; Ralph Martins, PhD; Neal Scott Mason, PhD; Colin Masters, MD; Kwasi Mawuenyega, PhD; Austin McCullough, PhD Candidate; Eric McDade, DO; Arlene Mejia, MD; Estrella Morenas-Rodriguez, MD, PhD; John Morris, MD; James Mountz, MD; Cath Mummery, PhD; Neelesh Nadkarni, MD, PhD; Akemi Nagamatsu, RN; Katie Neimeyer, MS; Yoshiki Niimi, MD; James Noble, MD; Joanne Norton, MSN, RN, PMHCNS-BC; Brigitte Nuscher; Ulricke Obermüller; Antoinette O’Connor, MRCPI; Riddhi Patira MD; Richard Perrin, MD, PhD; Lingyan Ping, PhD; Oliver Preische, MD; Alan Renton, PhD; John Ringman, MD; Stephen Salloway, MD; Peter Schofield, PhD; Michio Senda, MD, PhD; Nicholas T Seyfried, D. 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Autophagy protein NRBF2 attenuates endoplasmic reticulum stress-associated neuroinflammation and oxidative stress via promoting autophagosome maturation by interacting with Rab7 after SAH. Journal of Neuroinflammation , 18 (1), 210. Zhang, J., Zhang, Y., Wang, J., Xia, Y., Zhang, J., & Chen, L. (2024). Recent advances in Alzheimer’s disease: Mechanisms, clinical trials and new drug development strategies. Signal Transduction and Targeted Therapy , 9 (1), 211. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx Supplementary Table 1: Excel File of all data from cell lysate LC-MS/MS TMT proteomics analysis, including parameters, significantly differentially expressed proteins and STRING pathway analysis of gene ontology (GO), KEGG and DISEASE pathways. SupplementaryTable2.xlsx Supplementary Table 2: Excel File of all data from conditioned media LC-MS/MS TMT proteomics analysis. 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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-6556064","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454740710,"identity":"5407e98e-e19a-44af-af3d-7fa0c93868d4","order_by":0,"name":"Grace E Lidgerwood","email":"data:image/png;base64,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","orcid":"","institution":"University of Melbourne","correspondingAuthor":true,"prefix":"","firstName":"Grace","middleName":"E","lastName":"Lidgerwood","suffix":""},{"id":454740711,"identity":"7e2a8a97-6390-4da1-bb4f-87bbf6799152","order_by":1,"name":"Mehdi Mirzaei","email":"","orcid":"","institution":"Macquarie University","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Mirzaei","suffix":""},{"id":454740712,"identity":"ee69e551-253f-482c-a636-23c93778ab91","order_by":2,"name":"Jenna C Hall","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Jenna","middleName":"C","lastName":"Hall","suffix":""},{"id":454740713,"identity":"7a7c5c43-5ec7-42db-8b57-d789e6728298","order_by":3,"name":"Damián Hernández","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Damián","middleName":"","lastName":"Hernández","suffix":""},{"id":454740714,"identity":"c72e2611-1c25-4403-a6ff-33d371b201d6","order_by":4,"name":"Una Greferath","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Una","middleName":"","lastName":"Greferath","suffix":""},{"id":454740715,"identity":"32922d75-d92f-49fc-bf25-ed3b6afcf29b","order_by":5,"name":"Alison van de Meene","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Alison","middleName":"van","lastName":"de Meene","suffix":""},{"id":454740716,"identity":"1df76ef7-964f-4bad-9ba6-838584f94b1a","order_by":6,"name":"Jessica YW Ma","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"YW","lastName":"Ma","suffix":""},{"id":454740717,"identity":"3850cfc2-bb88-4254-b3e2-83cc3bffc9eb","order_by":7,"name":"Celeste M Karch","email":"","orcid":"","institution":"Washington University in St. Louis","correspondingAuthor":false,"prefix":"","firstName":"Celeste","middleName":"M","lastName":"Karch","suffix":""},{"id":454740718,"identity":"c47fc528-b8b7-4875-bb4f-3449f97ebd1e","order_by":8,"name":"Alison M Goate","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Alison","middleName":"M","lastName":"Goate","suffix":""},{"id":454740719,"identity":"f6b6d7ed-af51-4b1f-94f6-c5a0e7512146","order_by":9,"name":"Alice Pébay","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Alice","middleName":"","lastName":"Pébay","suffix":""}],"badges":[],"createdAt":"2025-04-29 11:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6556064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6556064/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82538814,"identity":"bced421f-80ca-4486-b416-c9df23ee006c","added_by":"auto","created_at":"2025-05-12 16:17:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":218385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) Representative images of the iPSC-RPE cultures after 60 days of differentiation, prior to passaging (top) and 90 days after passaging and enrichment for RPE\u0026nbsp; (150 days total culture, bottom) \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRepresentative immunofluorescence staining of 90 day old RPE cultures for canonical RPE markers RPE65, MITF, PMEL, ZO-1, n=8 for both the PSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e\u003cem\u003e pathogenic variant and CRISPR-edited isogenic control (scale bars 20 µm) \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(C) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eTEER measures indicating no statistical significant difference in epithelial cell strength between the PSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e\u003cem\u003e and CRISPR control (n=11 each). Error bars: SEM, statistical analysis: unpaired t-test, NS = not statistically significant\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(p\u0026gt;0.05).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6556064/v1/a0b78b27693d3b7838dc4860.jpg"},{"id":82538813,"identity":"0d71c779-b8dd-4985-a298-0f6c51396516","added_by":"auto","created_at":"2025-05-12 16:17:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164924,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(A) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRepresentative immunostaining of PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e RPE and isogenic CRISPR isogenic controls for ZO-1 (green) and Aβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1-42\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e (red, arrows) (n=12 CRISPR, n=8 PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e). Scale bars = 30 µm. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Representative Imaris rendering of immunostained RPE against \u003c/em\u003eAβ\u003csub\u003e1-42\u003c/sub\u003e specific \u003cem\u003eplaques (yellow) and ZO-1 (purple) using volume-fill algorithm (n=4 PSEN1 \u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e; n=10 CRISPR, aged for 122±5 days (n= 10 CRISPR isogenic controls, n = 4 PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e).\u0026nbsp; Scale bars = 30 µm.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e (C) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eGraphical data of the number of Aβ\u003c/em\u003e \u003cem\u003edeposits of analysed PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e and isogenic CRISPR isogenic control RPE; and tallied.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eError bars: SEM; statistical analysis: unpaired t-test, * p\u0026lt;0.05.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e (D)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e ELISA assay of secreted levels of Aβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1-42\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e and Aβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1-40\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e (pg/µg protein) in serum-free conditioned media (n=10 independent biological replicates for each CRISPR and PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e RPE for apical CM; n=10 independent biological replicates for CRISPR and n=9 for PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e RPE for basal CM. Error bars: SEM, statistical analysis: unpaired t-test, NS = not significant, * = p\u0026lt;0.05.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eMean age of culture (days): 118.1±6 PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R \u003c/em\u003e\u003c/sup\u003e\u003cem\u003eand 117.6±7 CRISPR.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e (E) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eELISA assay of Aβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1-42\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e, Aβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1-40\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e and the ratio of Aβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1-42 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003e: Aβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1-40\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e (pMol/L/100µg protein) (n=6 CRISPR and PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e for Aβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1-42\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e ELISA; n=6 CRISPR and n=5 PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e for Aβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1-40\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e ELISA and Aβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1-42 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003e: Aβ\u003c/em\u003e\u003csub\u003e\u003cem\u003e1-40\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e ratio). Error bars: SEM; statistical analysis: unpaired t-test; ** p\u0026lt;0.01, * p\u0026lt;0.05, NS p\u0026gt;0.05. Mean age of culture (days): 183±29 PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R \u003c/em\u003e\u003c/sup\u003e\u003cem\u003eand 149±1.5 CRISPR. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(F) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRepresentative immunostaining of PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e RPE and isogenic CRISPR isogenic controls for Tau (MAPT, red) and phospho-Tau (pMAPT, red) (n=3 CRISPR, n=3 PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e). Scale bars = 20 µm.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6556064/v1/81e736a35895f074d34148c2.jpg"},{"id":82538817,"identity":"0a301a6a-643d-4d0a-9a50-6d52939ea259","added_by":"auto","created_at":"2025-05-12 16:17:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":582791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Heatmaps (hierarchical clustering) of the log-transformed ratios of differentially expressed proteins (PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e vs. CRISPR-edited isogenic control). Column colours (cyan, red) indicate sample type; red and green color-coding indicate relative increase or decrease in protein abundance, respectively. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eA volcano plot showing the most significantly differentially expressed proteins (log\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e FC \u0026gt;2.0) between PSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e\u003cem\u003e and CRISPR, with Alzheimer’s-related proteins shaded in grey ovals. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(C) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eNetwork of enriched terms (similarity \u0026gt; 0.3 connected by edges, p at least \u0026lt;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e-3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e), coloured by cluster ID, where nodes that share the same cluster ID are typically close to each other (generated in Metascape v3.5, modified in Cytoscape v3.10.3). (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eD) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eTop 15 pathways enriched in the dataset, generated using ShinyGO 0.82. False Discovery Rates (FDR) measures statistical significance (Benjamini-Hochberg method to correct for multiple testing) and correlates to node size, while Fold Enrichment (FE) indicates the effect size (proteins in this dataset / corresponding percentage in the background genes), where red indicates a stronger FE than blue. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(E)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Proteins identified in the dataset from the Alzheimer’s disease STRING shown in table format, with corresponding mean expression levels for PSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e\u003cem\u003e and CRISPR control groups, Fold Change (FC) in PSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e\u003cem\u003e relative to CRISPR controls, and p-value (student unpaired t-test). (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eF)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Alzhiemer’s disease STRING (hsa05010),\u003c/em\u003e with upregulated proteins in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e\u003cem\u003e RPE\u003c/em\u003e \u003cem\u003eindicated by green spheres; downregulated by red spheres; interaction scores calculated based on high confidence (0.7); thicker connecting lines corresponding to higher confidence. Clusters are shaded by pathways that are overrepresented.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6556064/v1/0b2e0a098f4e6a7178b2bbcb.png"},{"id":82539769,"identity":"02358b86-33da-46a9-8889-c8aa6f630876","added_by":"auto","created_at":"2025-05-12 16:25:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70163,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Lollipop plot showing enrichment of biological processes in PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e RPE, sorted by weighted means between the observed/expected ratio and -log(FDR). Point size represents the gene count for that category, FDR measures the significance (p-values corrected for multiple testing within each category using the Benjamini–Hochberg correction). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eNetwork of enriched terms in PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e RPE (similarity \u0026gt; 0.3 connected by edges, p at least \u0026lt;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e-3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e), coloured by cluster ID, where nodes that share the same cluster ID are typically close to each other (generated in Metascape v3.5, modified in Cytoscape v3.10.3).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6556064/v1/a33d3fe7616f75f8effd2866.jpg"},{"id":82538818,"identity":"08bab303-372d-4dca-821c-48be92f0edb1","added_by":"auto","created_at":"2025-05-12 16:17:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78813,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Lollipop plot showing enrichment of biological processes in the downregulated proteins in PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e RPE, sorted by weighted means between the observed/expected ratio and -log(FDR). Point size represents the gene count for that category, FDR measures the significance (p-values corrected for multiple testing within each category using the Benjamini–Hochberg correction). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eNetwork of enriched terms in the downregulated proteins in PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e RPE (similarity \u0026gt; 0.3 connected by edges, p at least \u0026lt;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e-3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e), coloured by cluster ID, where nodes that share the same cluster ID are typically close to each other (generated in Metascape v3.5, modified in Cytoscape v3.10.3).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6556064/v1/c8a2a3636720b526b1dd90d6.jpg"},{"id":82540170,"identity":"35c02312-fbc7-4037-841b-262d0b42fbcc","added_by":"auto","created_at":"2025-05-12 16:33:53","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":153084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e(A) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eHeatmaps (hierarchical clustering) of the log-transformed ratios of differentially expressed proteins (PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e vs. CRISPR-edited isogenic control). Red and blue color-coding indicate relative increase or decrease in protein abundance, respectively. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e A volcano plot showing the most significantly differentially expressed proteins (log\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e FC \u0026gt;1.2) between PSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e\u003cem\u003e and CRISPR. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(C) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eDisease pathways significantly represented in the differentially expressed proteins, based on Log\u003c/em\u003e\u003csup\u003e\u003cem\u003e10\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e (observed proteins / expected proteins). * Italicised proteins = downregulated in PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R \u003c/em\u003e\u003c/sup\u003e\u003cem\u003eRPE. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(D) \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eSTRING representation of proteins related to amyloidosis. Green spheres = upregulated protein in PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R \u003c/em\u003e\u003c/sup\u003e\u003cem\u003eRPE, red spheres = downregulated in PSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R \u003c/em\u003e\u003c/sup\u003e\u003cem\u003eRPE. STRING based on high confidence (0.7); thicker connecting lines corresponding to higher confidence.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6556064/v1/eb7e1fffbc4a1d1489256c55.jpg"},{"id":90600628,"identity":"95ae04db-0e22-4902-95e2-039841a7ea32","added_by":"auto","created_at":"2025-09-04 14:31:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2776729,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6556064/v1/fa4eccf2-bd0d-42b8-b288-130e36289415.pdf"},{"id":82538836,"identity":"01637851-6022-4c3c-bd2a-c1ae44c6c0f4","added_by":"auto","created_at":"2025-05-12 16:17:53","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6629647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExcel File of all data from cell lysate LC-MS/MS TMT proteomics analysis, including parameters, significantly differentially expressed proteins and STRING pathway analysis of gene ontology (GO), KEGG and DISEASE pathways.\u003c/p\u003e","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6556064/v1/bf3c6b920376bb5ae9156fef.xlsx"},{"id":82540851,"identity":"c0229306-adb4-44fe-b2d1-9176089f8b88","added_by":"auto","created_at":"2025-05-12 16:41:53","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":158107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExcel File of all data from conditioned media LC-MS/MS TMT proteomics analysis.\u003c/p\u003e","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6556064/v1/dca3d402cfbdb6e1445f129b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eHuman iPSC-RPE with the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePSEN1\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eH163R\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e pathogenic variant recapitulates Alzheimer’s disease features and reveals melanosome defects\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a progressive neurodegenerative disorder characterised by a decline in cognitive and neural function. While the exact mechanisms driving the disease remain elusive, several key pathological processes have been identified, including abnormal β-amyloid (Aβ) metabolism and plaque accumulation, tau hyperphosphorylation leading to neuronal damage, oxidative stress, reactive glial and microglial changes, and the resulting neuroinflammatory environment, which exacerbates damage to the sensitive cells of the nervous system. The late-onset sporadic form (sAD) typically features a prolonged, asymptomatic prodromal stage, with symptoms manifesting later in life. In contrast, early-onset familial AD (fAD) is characterised by earlier onset and is often associated with mutations in genes critical to Aβ metabolism, such as \u003cem\u003eAPP, PSEN1\u003c/em\u003e, and \u003cem\u003ePSEN2\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eDespite the development of therapies targeting Aβ accumulation, such as Donanemab and Lecanemab, a definitive cure for AD remains elusive. This highlights the urgent need for preclinical biomarkers and therapeutic targets to enhance early diagnostics and treatment outcomes. A central focus of these efforts is the development of biologically relevant models that accurately recapitulate disease pathology (Zhang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHuman-induced pluripotent stem cells (iPSCs) enable the development of patient-specific \u003cem\u003ein vitro\u003c/em\u003e models that can recapitulate some of the features of AD, including Aβ plaque formation, tau hyperphosphorylation, mitochondrial dysfunction, and neuroinflammation, under controlled laboratory conditions (Penney et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These models offer the possibility of studying human-specific genetic backgrounds, providing a complementary platform to animal models that bridges the gap between \u003cem\u003ein vivo\u003c/em\u003e studies and clinical applications. Together, iPSC-based models and animal models offer synergistic insights into AD pathogenesis and therapeutic development, advancing our understanding of this complex neurodegenerative disorder.\u003c/p\u003e \u003cp\u003eEmerging evidence suggests that hallmark AD biomarkers\u0026mdash;such as Aβ plaques, phosphorylated tau, and activated microglia\u0026mdash;are not confined to the brain but can also be found in peripheral or adjunct tissues, including fibroblasts (Pani et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), blood (Armentero et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e); (Armentero et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Borroni et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); (Li et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), blood vessels (Cortes-Canteli et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and the intestines (Joachim et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). More recently, the eye has gained increasing attention as a potential non-invasive model to study AD and other neurodegenerative diseases (Gupta et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Visual symptoms have been reported to precede cognitive decline in some cases (Brewer \u0026amp; Barton, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and retinal hyperspectral imaging may predict brain Aβ load (Hadoux et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), suggesting that the retina could serve as a \u0026lsquo;mirror\u0026rsquo; for neurodegenerative changes occurring in the brain.\u003c/p\u003e \u003cp\u003eThis connection is not surprising given the retina is a neural extension of the central nervous system. Key pathological markers of AD\u0026mdash;Aβ plaques, phosphorylated tau, and activated microglia\u0026mdash;are also common features of retinal diseases such as age-related macular degeneration (AMD) and glaucoma (Ohno-Matsui, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For instance, studies have identified increased deposition of Aβ and hyperphosphorylated tau in the retinas of AD patients and transgenic AD mice (Nu\u0026ntilde;ez-Diaz et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, activated microglia have been implicated in the progression of both AD and retinal neurodegenerative diseases(Nu\u0026ntilde;ez-Diaz et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ramirez et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These overlapping pathologies potentially point to shared mechanisms across age-related neurodegenerative conditions.\u003c/p\u003e \u003cp\u003eThe retinal pigment epithelium (RPE), the outermost layer of the retina, plays essential roles in maintaining homeostasis and supporting the visual cycle. It absorbs light, processes retinoids to initiate the visual cycle, and serves as a critical interface between the neural retina and the underlying choroidal vasculature. The RPE sustains photoreceptor integrity by supplying nutrients and clearing toxic byproducts of the visual cycle, ensuring retinal health is preserved despite ongoing metabolic stress. Age-related macular degeneration (AMD), a disease characterised loss of central vision due to loss of RPE and photoreceptor cell function, is characterised by the accumulation of pathogenic extracellular deposits known as drusen. The composition of drusen has similarities to that of AD senile plaques, the most notable being Aβ deposits, but also including lipids and proteins such as APOE and Vitronectin (common proteins found in plaques in the brains of AD patients) (Luibl et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eGiven the shared biomarkers and overlapping pathology between AMD and AD, coupled with the anatomical and physiological link between the retina and brain, it is hypothesised that the retina could serve as a non-invasive proxy for detecting the early changes associated with AD. Indeed, there is growing momentum in ophthalmology to utilise retinal imaging and screening to monitor neurological health, with the potential for eye exams to track the onset and progression of AD (Ashraf et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Christinaki et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hadoux et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Saeed et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis study aims to evaluate the feasibility of using retinal cells as a surrogate model for AD. Specifically, we aimed to investigate whether the presence of AD biomarkers could be modelled in an RPE cell model derived from human induced pluripotent stem cells (iPSCs) carrying the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e mutation. By leveraging the potential of an iPSC-derived retinal model of AD, we seek to further validate the retina as a valuable tool for mirroring AD pathology in the brain, potentially providing a proxy model that could pave the way for retinal detection and monitoring of disease.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eiPSC culture.\u003c/b\u003e iPSCs (\u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e pathogenic variant (VAR), 41yo APOE 3/4 (Karch et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the CRISPR-edited isogenic line (COR) (Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) were maintained under serum-free and feeder-free conditions as previously described (Daniszewski et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Briefly, cells were cultured on pre-coated with 10 \u0026micro;g/mL Vitronectin XF substrate (Stem Cell Technologies) prepared in CellAdhere Dilution Buffer (Stem Cell Technologies) and maintained in StemFlex basal medium (ThermoFisher) supplemented with 10% StemFlex Supplement (ThermoFisher). Medium was refreshed every second day. Cells were passaged weekly upon reaching approximately 80% confluency using ReLeSR (Stem Cell Technologies). To ensure the absence of contamination, all cell lines were routinely verified to be mycoplasma-free using the MycoAlert Mycoplasma Detection Kit (Lonza). For long-term storage, iPSCs were cryopreserved in StemFlex medium supplemented with 10% dimethyl sulfoxide (DMSO).\u003c/p\u003e \u003cp\u003e \u003cb\u003eiPSC-RPE differentiation.\u003c/b\u003e RPE cells were generated from iPSCs as previously described (Senabouth et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Briefly, iPSCs at 70\u0026ndash;80% confluency were transitioned to TeSR\u0026trade;-E6 (Stem Cell Technologies) supplemented with 1X N2 (Life Technologies) (D0) for 30 days, at which point RPE differentiation was commenced using guided differentiation media RPEM (α-MEM, 5% bovine serum albumin, MEM NEAA, L-Glutamine\u0026ndash;Penicillin\u0026ndash;Streptomycin, N1 and taurine\u0026ndash;hydrocortisone\u0026ndash;triiodothyronine. Medium was changed every second day, for 30 days, by which time cells had acquired an RPE-like polygonal morphology and visible pigmentation. Cells were passaged with 0.25% Trypsin-EDTA onto Matrigel-coated Transwells polyester inserts (0.4\u0026micro;m pore size) to enrich RPE cell cultures and promote proper cell polarisation. Cultures were maintained for at least 90 days to ensure the derived RPE cells were mature both physically and functionally, and to allow time for plaques and drusen-like deposits to develop, which we and others have shown to be a minimum of 90 days (Galloway et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunocytochemistry.\u003c/b\u003e Immunocytochemistry was performed on cells fixed in 4% paraformaldehyde for 8 minutes at 4\u0026deg;C, permeabilized and blocked with 0.2% v/v Triton X-100 and 5% normal goat serum (NGS) for 60 minutes at 4\u0026deg;C. Primary antibodies were prepared in 5% NGS: ZO-1 (10 \u0026micro;g/mL, Life Technologies); Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e specific (D9A3A Cell Signalling Technologies, 1:1600); PMEL (5\u0026micro;g/mL, Abcam) and RPE65 (10\u0026micro;g/mL, Abcam). Cells were immunostained with isotype-specific secondary antibodies (Alexa 568 and Alexa 488, Life Technologies). Nuclei were counterstained using Hoechst (50\u0026micro;g/mL, Sigma-Aldrich) and mounted in ProLong Gold Antifade (Life Technologies). Specificity of the staining was verified by staining with an isotype control.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicroscopy data acquisition.\u003c/b\u003e Confocal imaging was performed as previously described in (Hall et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) using a Zeiss LSM 900 confocal microscope (Biological Optical Microscopy Platform (BOMP) Facility, The University of Melbourne), equipped with 2 fluorescence GaAsP, 1 Airyscan detector and transmitted light ESID detector along with 4 diode lasers (405, 488, 561 and 640 nm). Images were acquired from samples fixed in a 96 well Cell Carrier Ultra Plate (Perkin Elmer, 6055300) in immersion media PBS -/- (Life Technologies, 14190\u0026ndash;144) using a 20x/0.8 NA air objective. Upon instrument initialization, the plate was calibrated in the Zen 3.2 software and three random points for unbiased imaging were distributed across each well. The final Z-stack range was set to accommodate the thickest sample. Automated Z-stack centring was deployed using autofocus during the scan. ZO-1 (excited with 488 nm), Aβ (excited with 561 nm), Hoechst (excited with 405 nm) and brightfield (transmitted light) channels were imaged with a Z-stack interval of 0.54 \u0026micro;m over a range of 50 \u0026micro;m (\u0026sim;100 sections). The output of the semi-automated scan was three 50 \u0026micro;m Z-stacks (.czi files) per well.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImaris batch analysis, statistical analysis, and figure preparation.\u003c/b\u003e Batch analysis was performed as previously described in (Hall et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) using Imaris (v9.9, Oxford Instruments) for processing the Z-stacks acquired for Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e specific deposits and ZO-1\u003csup\u003e+\u003c/sup\u003e apical tight junctions. 3D version of the Z-stack and volume-fills of each dye were performed as described. This allowed for quantification of the amount and volume of the deposits. All images were taken in triplicate per well across each of the six iPSC-RPE cell cultures generated. Results are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM and were graphed using Graph Pad PRISM v9. Statistical significance was established using two-way ANOVA tests followed by Š\u0026iacute;d\u0026aacute;k's multiple comparisons test. Figure schematics were made with \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.biorender.com\" target=\"_blank\"\u003ewww.biorender.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.biorender.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. A p-value of less than 0.05 was considered statistically significant. For clarity, the following notations were used: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e \u003cb\u003eElectron microscopy sample preparation, imaging and quantification.\u003c/b\u003e RPE cells grown for 177 days\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9 days from the \u003cem\u003ePSEN1\u003c/em\u003e \u003csup\u003eH163R\u003c/sup\u003e disease variant and the isogenic CRISPR-edited isogenic line on polystyrene transwell inserts were fixed in a solution of 10% paraformaldehyde, 3% (w/v) sucrose and 2.5% glutaraldehyde overnight at 4\u0026deg;C. Samples were washed with 0.1M Cacodylate Buffer, and incubated with a 0.1M Cacodylate Buffer to 0.5% Osmium solution for 60 minutes, and washed thoroughly with 0.1M Cacodylate Buffer. Samples were dehydrated with sequential ethanol solutions, 50%, 70%, 90%, 100%) for 5 mins at 4\u0026deg;C before incubating overnight in a solution of ​​75% resin (containing DDSA, Medcast / Epon resin and DMP \u0026ndash; 30 accelerator)\u0026thinsp;+\u0026thinsp;25% ethanol. The solution is replaced with 100% resin and incubated for 5 hours. Pre-labelled resin moulds were prepared, filled with fresh resin, and checked for bubbles. Samples were transferred into moulds and oriented carefully under a microscope, before being placed in a 55\u0026deg;C incubator overnight to allow the resin to solidify. Sections were made at 1 \u0026micro;m thickness, and de-plasticised by immersing in sodium ethoxide solution for 2 minutes before being sequentially in 100%, 60%, and 30% methanol for 2 minutes each. Sections were incubated in toluidine blue solution on a heat plate for 2 minutes, rinsed with H2O and then dipped in xylene, mounted and coverslipped for electron microscopy on the FEI Tecnai Spirit TEM (Thermo Fisher Scientific). Images were taken in at least three random locations at. Relative grey value of all melanosomes in the image was measured using Image J software and analysed using GraphPad Prism v10.2.3 one-way ANOVA with Tukey\u0026rsquo;s multiple comparison testing. A p-value of less than 0.05 was considered statistically significant. For clarity, the following notations were used: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTransepithelial electrical resistance (TEER).\u003c/b\u003e TEER measurements were taken under sterile conditions using a EVOM voltohmmeter (World Precision Instruments) on a heated platform set to 37\u0026deg;C. TEER measurements were taken from 11 independent iPSC-derived RPE wells for both PSEN1-pathogenic line and its CRISPR-edited isogenic line. Net TEER measurements were calculated by subtracting the value of a blank, Matrigel-coated filter without cells from the experimental value. Final resistance-area products (Ω cm2) were obtained by multiplying by the growth area of the permeable Transwell insert. Results were analysed for statistical significance using a two-tailed Student t-test and graphed using Graph Pad PRISM 9.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConditioned media for proteomics and ELISA analysis.\u003c/b\u003e Cultures were serum-depleted for three days prior to collection of conditioned media in serum-free RPEM (25mM HEPES replacing 5% FBS). Between 250 \u0026micro;L and 1.5 mL conditioned media was collected and immediately stored at -80\u0026deg;C until analysis (ELISA and LC-MS/MS).\u003c/p\u003e \u003cp\u003e \u003cb\u003eELISA.\u003c/b\u003e To quantitatively analyse the production of Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e and Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e in iPSC-RPE cells with \u003cem\u003ePSEN1\u003c/em\u003e \u003csup\u003eH163R\u003c/sup\u003e and its isogenic CRISPR-corrected control, cell lysates and serum-free supernatants from the apical and basal chambers of RPE monolayers grown for 90 days. Levels of Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e and Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e in serum-free supernatants were detected using the Wako ELISA kits (296-64601, 298-64401 respectively) and cell lysates were analysed using ThermoFisher kits (KHB3481 for Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e and KHB3441 for Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e), following manufacturer\u0026rsquo;s instructions. Absorbance at 450nm was measured using a microplate reader (Omega FLUOstar), and analyte concentrations were determined using a standard curve generated from serial dilutions of recombinant Aβ\u003csub\u003e40\u003c/sub\u003e or Aβ\u003csub\u003e41\u003c/sub\u003e peptide standards. Each sample was analysed in duplicate to ensure reproducibility. All statistical analyses and graphical illustrations were executed using GraphPad Prism 9. The data obtained from the experiments were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. For the conditioned media ELISAs, an unpaired t-test was performed on both Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e and Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e to determine statistical significance from at least nine independent experiments. For both Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e and Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e cell lysate ELISAs, an unpaired t-test was performed with a Mann Whitney correction for non-normally distributed data to determine statistical significance from at least six independent experiments. One Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e reading had a failed signal and was omitted from the results, meaning there was one less datapoint for the Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e for the \u003cem\u003ePSEN1\u003c/em\u003e sample. For the ratio of Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e : Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e ratio test, an unpaired t-test was performed. A p-value of less than 0.05 was considered statistically significant. For clarity, the following notations were used: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e \u003cp\u003e \u003cb\u003eProtein preparation for liquid chromatography-electrospray ionisation tandem mass spectrometry (LC-ESI-MS/MS).\u003c/b\u003e RPE cell cultures were lysed in RIPA buffer supplemented with phosphatase and protease inhibitors, sonicated with a probe sonicator (40 HZ \u0026times; 2 pulses \u0026times; 15 s), and insoluble debris were removed by centrifugation at 14,000 rpm for 15 min at 4\u0026deg;C, prior to measurement of protein contents by standard bicinchoninic acid assay (MicroBCA protein assay kit, Thermo Scientific). For the conditioned media analysis, samples were serum-deprived for two days prior to harvesting of the conditioned media, ensuring proteins associated with serum in the media did not interfere with the interpretation of the results (Lidgerwood et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Proteins were lyophilised using Savant SPD131DDA speedvac (ThermoFisher). Solubilised proteins were reduced using 5 mM dithiothreitol and alkylated using 10 mM iodoacetamide. Proteins (150 \u0026micro;g) were initially digested at room temperature overnight using a 1:100 enzyme-to‐protein ratio using Lys‐C (Wako, Japan), followed by digestion with Trypsin (Promega, Madison, WI) for at least 4 hours at 37\u0026deg;C also at a 1:100 enzyme‐to‐protein ratio. Resultant peptides were acidified with 1% trifluoroacetic acid and purified using styrene divinylbenzene‐reverse-phase sulfonate (Empore) stage tips. The proteome was identified on a Tandem Mass Tag (TMT) platform (Progenetech, Sydney, Australia).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTandem Mass Tag (TMT) labelling.\u003c/b\u003e Three independent 10 plex TMT experiments were carried out. Ten independent PSEN1- pathogenic variant RPE and ten CRISPR edited isogenic controls were analysed for cell lysates; whilst five of each were analysed for upper chamber conditioned media to examine secreted protein. Briefly, dried peptides from each sample were resuspended in 100 mM HEPES (pH 8.2) buffer and peptide concentration measured using the MicroBCA protein assay kit. Sixty micrograms of peptide from each sample was subjected to TMT labelling with 0.8 mg of reagent per tube. Labelling was carried out at room temperature for 1 h with continuous vortexing. To quench any remaining TMT reagent and reverse the tyrosine labelling, 8 \u0026micro;l of 5% hydroxylamine was added to each tube, followed by vortexing and incubation for 15 min at room temperature. For each of the respective ten plex experiments, the ten labelled samples were combined, and then dried down by vacuum centrifugation. Prior to High-pH reversed-phase fractionation, the digested and TMT-labelled peptide samples were cleaned using a reverse-phase C18 clean-up column (Sep-pak, Waters) and dried in vacuum centrifuge. The peptide mixture was resuspended in loading buffer (5 mM ammonia solution (pH 10.5), separated into a total of 96 fractions using an Agilent 1260 HPLC system equipped with a quaternary pump, a degasser and a Multi-Wavelength Detector (MWD) (set at 210-, 214-, and 280-nm wavelength). Peptides were separated on a 55-min linear gradient from 3 to 30% acetonitrile in 5 mM ammonia solution pH 10.5 at a flow rate of 0.3 mL/min on an Agilent 300 Extend C18 column (3.5-\u0026micro;m particles, 2.1 mm ID and 150 mm in length). The 96 fractions were finally consolidated into eight fractions. Each peptide fraction was dried by vacuum centrifugation, resuspended in 1% formic acid, and desalted again using SDB-RPS (3M-Empore) stage tips.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLC-ESI-MS/MS data acquisition.\u003c/b\u003e Mass spectrometric data were collected on an Orbitrap Lumos mass spectrometer coupled to a Proxeon NanoLC-1200 UHPLC. The 100-\u0026micro;m capillary column was packed with 35 cm of Accucore 150 resin (2.6 \u0026micro;m, 150 \u0026Aring;; Thermo Fisher Scientific). The scan sequence began with an MS1 spectrum (Orbitrap analysis, resolution 60,000, 400\u0026ndash;1600 Th, automatic gain control (AGC) target 4 \u0026times; 105, maximum injection time 50 ms). Data were acquired for 90 min per fraction. Analysis at the MS2 stage consisted of higher energy collision-induced dissociation (HCD), Orbitrap analysis with the resolution of 50,000, automatic gain control (AGC) 1.25 \u0026times;105, NCE (normalized collision energy) 37, maximum injection time 120 ms, and an isolation window at 0.5 Th. For data acquisition including FAIMS, the dispersion voltage (DV) was set at 5000 V, the compensation voltages (CVs) were set at \u0026minus;\u0026thinsp;40 V, \u0026minus;\u0026thinsp;60 V, and \u0026minus;\u0026thinsp;80 V, and TopSpeed parameter was set at 1.5 s per CV.\u003c/p\u003e \u003cp\u003e \u003cb\u003eProteomic data analysis.\u003c/b\u003e Spectra were converted to mzXML via MSconvert v3.0. Database searching included all entries from the Human UniProt Database (downloaded: August 2022). The database was concatenated with one composed of all protein sequences for that database in the reversed order. Searches were performed using a 50-ppm precursor ion tolerance for total protein-level profiling. The product ion tolerance was set to 0.2 Da. These wide mass tolerance windows were chosen to maximise sensitivity in conjunction with Comet searches and linear discriminant analysis. TMT tags on lysine residues and peptide N-termini (+\u0026thinsp;229.163 Da for TMT) and carbamidomethylation of cysteine residues (+\u0026thinsp;57.021 Da) were set as static modifications, while oxidation of methionine residues (+\u0026thinsp;15.995 Da) was set as a variable modification. Peptide-spectrum matches (PSMs) were adjusted to a 1% false discovery rate (FDR). PSM filtering was performed using a linear discriminant analysis, as described previously and then assembled further to a final protein-level FDR of 1%, using the Picked FDR method. An isolation purity of at least 0.7 (70%) in the MS1 isolation window was used for samples. For each protein, the filtered peptide TMT SN values were summed to create protein quantifications. To control for different total protein loading within a TMT experiment, the summed protein quantities of each channel were adjusted to be equal within the experiment. Proteins were quantified by summing reporter ion counts across all matching PSMs, also as described previously. Reporter ion intensities were adjusted to correct for the isotopic impurities of the different TMT reagents according to manufacturer specifications. Finally, each protein abundance measurement was scaled, such that the summed signal-to-noise for that protein across all channels equaled 100, thereby generating a relative abundance (RA) measurement. Investigation of protein\u0026ndash;protein interactions and functional enrichment GO analysis of DE proteins were performed with the online STRING database version 11.0. STRING analysis on the entire proteomics dataset, generating a network of interactions (based on both evidence of functional and physical interactions). The interaction score of 0.7 was considered for the visualisation of the networks. Network lines represent the protein interaction score, which was set at a minimum medium confidence (0.4). Active interaction sources were based on text mining, experiments, databases, co-expression, neighbourhood, gene fusion, and co-occurrence data. Two criteria were applied to determine significantly regulated proteins: fold change over 1.1 and P value lower than 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003ePSEN1 pathogenic variant does not impact RPE differentiation.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEach line was successfully differentiated to RPE cells as previously described (Lidgerwood et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). All lines differentiated into pigmented and polygonal monolayer cultures, and expressed canonical RPE markers RPE65, MITF, PMEL and ZO-1 in typical organisation of mature RPE (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB,C). To confirm the strength and integrity of the epithelial monolayer, TEER were performed. Results of both the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e pathogenic variant and the isogenic CRISPR-corrected samples indicated there was no statistical difference in the average strength of the resistance of the cultures, which was above 500 Ω/cm2, consistent with highly polarised RPE monolayers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). This suggests that the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e mutation bears no impact on the ability of iPSCs to differentiate into mature and functional RPE cells \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePSEN1\u003c/b\u003e \u003csup\u003e \u003cb\u003eH163R\u003c/b\u003e \u003c/sup\u003e \u003cb\u003epathogenic variant RPE cells produce more Aβ\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u0026minus;42\u003c/b\u003e\u003c/sub\u003e \u003cb\u003ethan the isogenic CRISPR-corrected isogenic controls, and express cytoplasmic Tau.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGiven that the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e mutation is a known genetic determinant of fAD, we assessed if an AD-like phenotype could be recapitulated and rescued in the iPSC-RPE culture derived from both the diseased (\u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e pathogenic variant) and controls (CRISPR-corrected \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e isogenic) lines respectively. Immunostaining of the RPE cultures for the tight-junction marker ZO-1 was utilised to demonstrate a continuous epithelial monolayer, and provide a baseline to assess if deposits were located apically or basally to the ZO-1 plane. By immunofluorescence, the positive Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e staining below the ZO-1 demonstrated that basal deposits were detectable in both the \u003cem\u003ePSEN\u003c/em\u003e1\u003csup\u003eH163R\u003c/sup\u003e and isogenic CRISPR control RPE (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). To quantify this, we utilised our algorithm that detects deposits and provides a volume-fill calculation score (Hall et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), allowing for comparison of the number and volume of Aβ\u003csup\u003e+\u003c/sup\u003e deposits in \u003cem\u003ePSEN\u003c/em\u003e1\u003csup\u003eH163R\u003c/sup\u003e and CRISPR control RPE. The total number of deposits was not statistically significant between the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e pathogenic variant and its CRISPR edited isogenic control. However the volume (\u0026micro;m\u003csup\u003e3\u003c/sup\u003e) of detectable deposits was significantly larger in the \u003cem\u003ePSEN\u003c/em\u003e1\u003csup\u003eH163R\u003c/sup\u003e pathogenic RPE compared to the isogenic control cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eWe next analysed the raw levels of secreted Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e and Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e, and the ratio of Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e : Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e - a well-established diagnostic biomarkers of amyloid pathology using ELISA on conditioned media and lysates from RPE cultures. No statistically significant differences in pathogenic Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e and Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e in either the apical or basal conditioned media were observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). However, Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e levels were statistically significantly elevated in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE lysates compared to CRISPR controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), indicating intracellular Aβ accumulation. Additionally, the Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e:Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e ratio in lysates was significantly increased in the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE.\u003c/p\u003e \u003cp\u003eImmunostaining of RPE monolayers was performed to assess the presence of Tau (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Both \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e and CRISPR RPE cells expressed non-nuclear Tau (MAPT) protein, with larger foci consistently observed in the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e variant, relative to the CRISPR RPE cells. Notably, phosphorylated Tau exhibited a more spindle-like organisation in CRISPR-corrected RPE cells, suggesting a more structured association with cytoskeletal proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). This observation implies that Tau phosphorylation participates in cytoskeletal dynamics in RPE cells, potentially affecting their structural integrity. While Tau expression in RPE cells has not been extensively characterised, these results corroborate the findings from an earlier study on human retinal sections showing Tau immunoreactivity in the RPE (L\u0026ouml;ffler et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTogether, this data indicates that the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e pathogenic variant RPE cells generate more Aβ than their isogenic CRISPR-corrected controls, as demonstrated by larger volume of detectable deposits ; higher levels of Aβ\u003csup\u003e1\u0026ndash;42\u003c/sup\u003e in cell lysates, leading to a significant increase in the Aβ\u003csup\u003e1\u0026ndash;42: 1\u0026ndash;40\u003c/sup\u003e ratio in \u003cem\u003ePSEN1\u003c/em\u003e \u003csup\u003eH163R\u003c/sup\u003e. There was no significant difference in Aβ levels detected in conditioned media, which may be explained by the loss of protein due to the lack of \u0026lsquo;sandwiching\u0026rsquo; of the deposits between the apical surface and a \u0026lsquo;capturing\u0026rsquo; matrix. This is rectified in the cell lysate analysis, where deposits that are located basally to the RPE are captured by the Transwell matrix and therefore captured during cell lysate harvesting. We also confirmed Tau protein expression in all RPE cell cultures, and reported an apparent focal concentration of the phosphorylated protein in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163\u003c/sup\u003e. Together, these results indicate that \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE model key pathogenic features of AD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eProteomics analysis finds 1,818 differentially expressed proteins between PSEN1\u003c/b\u003e \u003csup\u003e \u003cb\u003eH163R\u003c/b\u003e \u003c/sup\u003e \u003cb\u003epathogenic variant RPE cells and the CRISPR edited isogenic controls, relating to pathways involving cellular metabolism, cellular transport, and protein folding.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe next measured protein quantification in both lines by mass spectrometry. TMT proteomics array confirmed the similarity in overall protein abundance distributions of individual biological replicates, demonstrating reproducibility in expression profiles between biological replicates and within the two sample groups. Hierarchical clustering analysis of proteins with differential abundance illustrated the overall consistency of the up and down regulation within respective control and the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A total of 7,605 proteins were identified across both datasets (see Supplementary Data 1), which were quantified by multiple peptides at an initial protein FDR or less than 1%.\u003c/p\u003e \u003cp\u003eTo identify significantly differentially expressed proteins, we used a combination of statistical and empirical thresholds to ensure a high confidence in the reported protein abundance differences. Differentially expressed proteins were considered significant if relative abundances in \u003cem\u003ethe PSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE versus CRISPR-isogenic controls were statistically significant (p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05) using a student t-test and differed by at least\u0026thinsp;\u0026plusmn;\u0026thinsp;20%. This two-step differential analysis of \u003cem\u003ethe PSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE versus isogenic controls resulted in 815 proteins significantly upregulated (p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and \u0026ge;\u0026thinsp;1.2-fold change) and 1,020 proteins significantly downregulated (p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 and \u0026le;\u0026thinsp;0.833-fold change) in \u003cem\u003ethe PSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e, relative to the isogenic CRISPR controls (see Supplementary Data 1).\u003c/p\u003e \u003cp\u003eTo obtain a more detailed understanding of the molecular mechanisms and biological processes altered in RPE with a \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e disease harbouring variant, we conducted cellular pathway enrichment and functional protein network analyses on the differentially expressed proteins. The 1818 most significantly differentially expressed proteins between the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e and CRISPR-isogenic control RPE (FC\u0026thinsp;\u0026ge;\u0026thinsp;1.2 and \u0026le;\u0026thinsp;0.83, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were analysed using Metascape (which incorporates data from KEGG, GO, Reactome, Canonical, CORUM, WikiPathways and PANTHER). The analysis identified enrichment in pathways processes involved in metabolism of RNA, including mRNA and RNA splicing (GO: 0000375, 0000398, 0008380, 0048024), mRNA and rRNA processing (0006396, 0006397); cellular respiration / mitochondrial functions such as organization (GO: 0007005) aerobic respiration (GO: 0009060), and cellular respiration (GO: 0045333); protein folding (GO: 0006457); protein processing in the endoplasmic reticulum (hsa04141), and vesicle-mediated transport (R-HSA-5653656, GO:0016050) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Some pathways were also enriched in KEGG pathway analysis of the dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Proteins involved in AD pathways were significantly represented in multi-pathway analysis (hsa05010), with 65 of the annotated 354 proteins involved in the pathways represented in our dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), including 29 upregulated and 36 downregulated in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE. These could be further segmented into AD subsets based on attributes relating to their biological function (GO), including oxidative phosphorylation (GO: 0006119) and ATP synthesis (GO: 0042775); mitochondrial organisation (GO: 0007005); autophagy (GO: 0010506); regulation of cell death (GO: 0010942); tubulins; and proteins involved in amyloid β metabolic process (GO: 1902993). These three proteins \u0026ndash; MAPT, APP, and PSEN2\u0026ndash; are annotated proteins that are constituents of β-amyloid deposits (BTO: 0002774).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eProteins significantly upregulated in PSEN1\u003c/b\u003e \u003csup\u003e \u003cb\u003eH163R\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eRPE belonged to pathways involved in AD, including endocytosis, beta amyloid-related processes, membrane trafficking and autophagy.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo understand biological processes that were upregulated in the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE, STRING and Metascape software were utilised to categorise proteins into pathways. Examining Biological Processes alone (GO), sorted by weighted means between the observed/expected ratio and -log(FDR), the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e pathogenic variant RPE were enriched for proteins involved in cellular transport (vesicle, protein, cytoskeletal), localisation and regulation of autophagy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Expanding the analysis criteria across KEGG, GO, Reactome, Canonical, CORUM, WikiPathways and PANTHER using Metascape found that \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE were significantly enriched for proteins belonging to membrane trafficking and intracellular transport pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These included vesicle-mediated transport (R-HSA-5653656, including proteins DST, DYNC1I1, BLOC1S1, MAPT, PRKCZ, SOD1, BAG3, AP3S2, KIF1C, IFT27, KIF3A, CEP131, BICD2, SNAPIN, KIFBP, IFT52, DYNC2LI1, IFT25, FYCO1, TTC21B, IFT74, SPG11, SSX2IP, BLOC1S2, BLOC1S3), endosome transport (GO:0016197, including proteins LYST, MTM1, SNAPIN, CHMP4A, HOOK2, SNX16, AKTIP, PLEKHF2, HOOK3, CHMP7, CHMP4B) and endocytosis (GO: 0006897, hsa04144, R-HSA-8856828, including proteins GRK2, AMPH, ARRB1, CLTA, CLTB, DAB2, HLA-E, HSPA8, PRKCZ, SH3GL1, EEA1, ASAP2, WASL, CYTH1, ZFYVE9, ZFYVE16, IQSEC1, ARPC5, STAM2, SPART, RAB11FIP5, ARFGAP3, CHMP4A, SNX12, EHD3, SH3GLB1, SH3GLB2, SNX6,RBSN, ARFGAP2, CHMP7, CHMP4B). Protein-containing complex assembly pathways (GO:0043254), including processes involving cytoskeletal organization, regulation of protein polymerization, and intracellular protein transport were also significantly represented.\u003c/p\u003e \u003cp\u003eIn \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE cells, data revealed significant enrichment of pathways related to membrane dynamics and protein trafficking, consistent with the known multifunctional role of PSEN1 in regulating these processes. Among the upregulated pathways, those associated with neurodegeneration were prominently represented, with Alzheimer\u0026rsquo;s disease (hsa05010) identified as the most significantly enriched disease-related pathway. Within the Alzheimer\u0026rsquo;s disease pathway, several proteins were markedly upregulated in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE cells. MAPT was the sixth most differentially expressed protein, showing a 2.4-fold increase. NRBF2, a regulator of autophagic degradation of APP C-terminal fragments, was the fifth most upregulated protein, also with a 2.4-fold increase. Additional upregulated proteins included S100 family members (S100A6, S100A13, S100A16), GAP43\u0026mdash;associated with AD diagnosis and neuropathology and APBB1 (FE65), which interacts with APP and contributes to Aβ production (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eOther proteins relating to the AD term that were upregulated in the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE included FAS, BID, CALM3, CAPN2, CASP3, ND6, NDUFB1, NDUFB9, PIK3C3, PIK3R2, PPP3CB, PPP3R1, MAP2K1, RELA, TRAF2, TUBA4A, UQCRH, IKBKG, BECN1, ATG13, RB1CC1, TUBB4B, ADRM1, ATG2A, SLC39A14, NRBF2, NDUFB11, ATG101, TUBA1C, KLC4. Interestingly, we also observed a significant increase in the expression of PSEN2 in the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE, which may suggest a compensatory mechanism is involved.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePathways related to RNA splicing, metabolism and processing were significantly downregulated in PSEN1\u003c/b\u003e \u003csup\u003e \u003cb\u003eH163R\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eRPE.\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePathways significantly downregulated in the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e RPE cells were predominantly related to RNA processing, mitochondrial organization and assembly, respiration, and protein trafficking (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Among the top 47 most significantly down regulated proteins in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e RPE (based on a minimum FC\u0026thinsp;\u0026ge;\u0026thinsp;2.0) were a number of histone proteins related to epigenetic and transcriptional gene regulation (including through RNA localisation) (H4C1, H3C13, H2AX, H3-2, H2BC9, H2AC4, H3C1, H2AC21); several myosin and myofibril proteins related to myofibril assembly, cytoskeletal transport and protein localisation to organelle (MYH7, MYL1, MYH2, MYL4, MYH3, MYH8, TNNT1, TNNT3, TNNI2, MYOM2), a range of solute carrying proteins, including SLC25A11, SLC25A12, SLC25A19, SLC25A1, SLC25A15, SLC5A3, SLC5B4, SLC39A7, SCL25A40, SLC30A6, which have known roles in mitochondrial transport; numerous mitochondrial proteins ND1, ND2, ND4, NDUFA4, NDUFA8, NDUFA9, NDUFB5, NDUFB6, NDUFS4, with widespread roles in aerobic respiration and respiratory electron transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Significantly, Alzheimer\u0026rsquo;s-related downregulated proteins in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE included the amyloid precursor protein APP (1.3 fold) and clusterin CLU (2.2 fold), which at low levels can alter aggregation, toxicity, and transport of Aβ (Wojtas et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTogether, these results indicate that pathways related to AD phenotypes, including proteins involved in Aβ and Tau pathways, were differentially expressed between the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e and CRISPR-corrected RPE cells, indicating that the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003e\u003cem\u003eH163R\u003c/em\u003e\u003c/sup\u003e pathogenic variant could augment typical AD related proteins and pathways in an RPE model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eKey secreted proteins involved in AD and cellular matrisome proteins were altered in the PSEN1\u003c/b\u003e \u003csup\u003e \u003cb\u003eH163R\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eRPE cells.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFinally, the conditioned media of the samples were analysed to examine differences in the secreted proteins, and the pathways they were associated with. A total of 656 secreted proteins were detected across both samples, with 80 proteins found to be significantly differentially expressed between the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e and CRISPR-control samples (see Supplementary Data 2). These include 32 proteins that were significantly downregulated and 48 that were significantly upregulated in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Notable AD-related proteins that were significantly upregulated in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE were apolipoprotein E APOE (8th most significant, 1.71 FC increase); pigment-epithelium-derived factor (SERPINF1) (10th most significant, 2.14 FC increase) and the amyloid-beta precursor protein APP (1.9 FC increase). Serum amyloid A-4 protein (SAA4) was significantly downregulated in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE (1.43 FC decrease) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Pathway and process enrichment analysis through Metascape found that disease pathways significantly enriched in the dataset related almost exclusively to diseases involving amyloid dysregulation, including Alzheimer\u0026rsquo;s disease, amyloidosis, dementia, and retinal drusen (a feature of age-related macular degeneration) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). All proteins implicated in these pathways were upregulated in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE, with the exception of two (ACHE, SAA4), which were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E). Interestingly, almost half of the significantly differentially expressed proteins (38 in total) belonged to the \u0026lsquo;matrisome\u0026rsquo;, a collection of more than 300 proteins that have known roles in cellular matrices (Hynes \u0026amp; Naba, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These include COL4A2, DCN, FBN1, FBN2, EFEMP1, FN1, HSPG2, TNC, LAMA5, LAMB1, LAMC1, LTBP1, MATN2, SPARC, THBS1, COL14A1, SPON1, NID2, EFEMP2, OIT3, AGRN, PLOD1, TIMP1, TIMP2, F13A1, RAP1B, TLN1, VASP, C1S, CFH, SLC2A1, IGF2, JUP, NRP2, SERPINF1, F2, CORO1A, ANGPTL7.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePSEN1\u003c/b\u003e \u003csup\u003e \u003cb\u003eH163R\u003c/b\u003e \u003c/sup\u003e \u003cb\u003emutations alter the expression of canonical RPE proteins involved in melanosome biogenesis and melanosome formation.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMelanosomes are key organelles in RPE responsible for visual function through photo absorption and reducing oxidative stress. The biogenesis of melanosomes in RPE cells is a tightly regulated, multistep process that follows four distinct stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The initial processes governing melanosome formation are not well understood, however it is known to be related to secretory, endosomal, cytoskeletal, and trafficking pathways, which were significantly implicated in the differential expression between \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e and its isogenic CRISPR control.\u003c/p\u003e \u003cp\u003eResults of the proteomics screen indicated that the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e mutation had significant and reproducible impacts on the expression of key melanosome proteins. These included significant downregulation of melanosome protein (PMEL, 1.45-fold), tyrosinase (TYR, 1.45-fold) and L-dopachrome tautomerase (DCT/TYRP2, 1.9-fold), which are fundamental to the sequential formation of mature melanosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo explore the melanosome in greater detail, samples were prepared for transmission electron microscopy, which enables visualisation of minute details such as organelle structure, localisation, and organisation. Independent differentiations of \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE and CRISPR-isogenic control were analysed. Qualitatively, the melanosomes from the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e were irregularly shaped, as opposed to the typical ovoid morphology of melanosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). PMEL fibrils were organised in a sheet manner, resembling \u0026lsquo;fingerprints\u0026rsquo; in the CRISPR-control RPE, while organisation was irregular in the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE, resembling more disordered or random arrangements (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The melanosomes also appeared to be less pigmented, which could indicate defects in the transition from Stage II-IV melanosomes. Quantification of the relative mean grey levels was performed using Fiji, a proxy measure of the \u0026lsquo;darkness\u0026rsquo; or stage IV maturation of the melanosomes. Results indicated that \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e pathogenic variant RPE exhibited significantly less pigmented melanosomes compared to the CRISPR-isogenic control (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Similarly, the size of the melanosomes was also quantified, which found that on average, the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE had significantly smaller diameters relative to the CRISPR-isogenic controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eE), suggesting the effects on melanosome maturation and assembly is unique to the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE.\u003c/p\u003e \u003cp\u003eTogether this data suggests that \u003cem\u003ePSEN1\u003c/em\u003e plays a novel role in maintaining the homeostasis of melanosome biogenesis and maturation in RPE cells, and that when perturbed with the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e pathogenic variant, leads to alterations in the expression of key RPE proteins involved in melanogenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that RPE cells derived from iPSCs carrying the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e pathogenic variant exhibit augmented pathological features associated with AD, including increased intracellular Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e, elevated Aβ\u003csub\u003e1\u0026minus;42:1\u0026minus;40\u003c/sub\u003e ratios, Tau expression, and proteomic signatures associated with neurodegeneration. These findings validate the utility of iPSC-RPE as a non-neuronal, retinal model of AD pathology and reinforce the potential of the retina as a surrogate tissue for AD research and biomarker discovery.\u003c/p\u003e \u003cp\u003eDespite not being a neural cell type, the RPE is known to express \u003cem\u003eMAPT(\u003c/em\u003eL\u0026ouml;ffler et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and \u003cem\u003eAPP (\u003c/em\u003eSeth et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), allowing us to examine the impact of \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e on both Aβ and Tau pathology in this retinal model. While Aβ is classically associated with extracellular deposition in the brain, its accumulation in the retina, including in the RPE, has been well documented in both post-mortem AD retinal tissue (den Haan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Koronyo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yoshida et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); (Koronyo-Hamaoui et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; L. Wang \u0026amp; Mao, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)), cultured primary cells ((den Haan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Koronyo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yoshida et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), living patients (Doustar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hadoux et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), transgenic animals (Dong et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ning et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and iPSC-derived retinal organoid models (James et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lavekar et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In fact, Aβ is a major constituent in naturally occurring extracellular deposits of the RPE, known as drusen, which occurs naturally during aging and in macular degeneration(Ohno-Matsui, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Furthermore, Tau aggregates have been identified in the aging human retina (Leger et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), while phosphorylated Tau has been detected in the retinas of Octodon degus, a rodent model exhibiting Alzheimer\u0026rsquo;s-like pathology (Du et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), highlighting the potential involvement of Tau in retinal neurodegeneration and a link between Tau accumulation and age-related retinal changes.\u003c/p\u003e \u003cp\u003eOur data from the immunocytochemistry, ELISA, and proteomics assays, indicate that pathways relating to Aβ are significantly implicated in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE cells, as evidenced by significant increases in Aβ deposit volume, significantly increased levels in cell lysates, and a dysregulation of many pathways relating to an increase in Aβ levels in the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE cells. Indeed, cell lysate analysis clearly showed elevated Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e and Aβ\u003csub\u003e1\u0026minus;42:1\u0026minus;40\u003c/sub\u003e ratios, which are recognised diagnostic markers of AD (Dubois et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although conditioned media ELISA showed no significant difference in secreted Aβ levels, this could be explained by limitations of the culture model, which provided no matrix for the secreted Aβ to imbed.\u003c/p\u003e \u003cp\u003eTau expression in RPE cells was confirmed by immunostaining, consistent with prior reports (L\u0026ouml;ffler et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and our own study showing expression of \u003cem\u003eMAPT\u003c/em\u003e increasing with RPE maturity (Lidgerwood et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE cells, Tau protein appeared more aggregated, while phosphorylated Tau in CRISPR controls exhibited a more filamentous, spindle-like organisation, consistent with its association with cytoskeletal proteins. Tau\u0026rsquo;s cytoskeletal interactions may impact cellular architecture in RPE cells, warranting further investigation, especially given the evidence of Tau accumulation in retinal neurodegeneration that may precede brain pathology (Chiasseu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProteomics revealed widespread dysregulation of pathways relevant to Alzheimer\u0026rsquo;s disease. Among the most significantly upregulated proteins were MAPT, NRBF2, a regulator of autophagic APP degradation (Zeng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)(Yang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)(Zeng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), GAP43, which has been linked to AD pathology (Ariaei et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Franzmeier et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)(Sandelius et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)(Ariaei et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Franzmeier et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and APBB1 (FE65), a protein that binds to the amyloid precursor protein (APP) and modulates the production of Aβ(Bruni et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Donato et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), (King \u0026amp; Scott Turner, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Trommsdorff et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Multiple S100 proteins, previously implicated in AD (Crist\u0026oacute;v\u0026atilde;o et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)(Donato et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), were also elevated. Downregulated proteins were predominantly linked to mitochondrial respiration, protein folding, and RNA processing\u0026mdash;reflecting energy metabolism deficits frequently observed in AD (Xu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, several mitochondrial complex proteins and solute carriers were suppressed, indicating impaired mitochondrial biogenesis and function. The altered matrisome composition in conditioned media, including differential secretion of APOE, APP, and ECM proteins, further indicates that \u003cem\u003ePSEN1\u003c/em\u003e influences extracellular matrix regulation, a pathway shared with both AMD and AD. Together, these findings suggest that the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e mutation alters critical pathways involved in cytoskeletal organisation, autophagy, and APP metabolism, with downstream effects on protein trafficking, vesicular transport, and mitochondrial function. This likely has broader implications for neural retinal health, as the RPE is one of the most critical gatekeepers of outer retina homeostasis and visual processing.\u003c/p\u003e \u003cp\u003eA significant finding was the disruption of melanosome biogenesis in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e. Melanosome biogenesis is governed by a complex, multi-step process, requiring morphological and functional modification of endosomal compartments. PMEL protein - a non-pathogenic amyloid fibril - is first nucleated and cleaved by BACE2-containing enzymes in multivesicular endosomes, initiating the first stage of melanogenesis (Stage I). At this stage, melanosomes are unpigmented, and classed as \u0026lsquo;immature\u0026rsquo; premelanosomes. In Stage II, PMEL fibrils assemble into organised sheets, serving as the matrix for subsequent melanin synthesis and deposition in Stage III by enzymes such as Tyrosinase (TYR), tyrosinase-related protein (TYRP1) and dopachrome tautomerase (DCT), ultimately leading to complete masking of the PMEL fibrils by Stage IV, forming fully mature melanosomes.\u003c/p\u003e \u003cp\u003eThe link between PSEN and melanosome biogenesis has not be extensively explored, however a previous study found that Tyr, Tyrp1, and DCT/Tyrp2 (commonly known as dopachrome tautomerase or tyrosinase-related protein 2) are physiological substrates for presenilins in mice, and that \u003cem\u003ePsen1\u003c/em\u003e mutant mice had defective pigmentation caused by tyrosinase mislocalization (R. Wang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). A study of zebrafish found that \u003cem\u003ePsen2\u003c/em\u003e is required for normal skin pigmentation (Jiang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To the best of our knowledge, this research is the first to demonstrate a \u003cem\u003ePSEN1\u003c/em\u003e-mediated defect in melanosome maturity and size in a human model. Our results found that \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE showed reduced expression of core melanogenic proteins (PMEL, TYR, DCT), and TEM revealed abnormal melanosome morphology and pigmentation, represented by melanosomes with smaller diameters and less reduced grey value (a measure of \u0026lsquo;darkness\u0026rsquo;) respectively. As melanosomes share trafficking and maturation pathways with lysosomes, this finding supports a broader role for \u003cem\u003ePSEN1\u003c/em\u003e in organelle maturation and homeostasis within the RPE. Furthermore, the RPE provides an excellent model to understand the processes that govern non-toxic amyloidogenic pathways, such a melanosome biogenesis, and the intersection of this with presenilins, which have a clear but still-unknown role in the process.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study demonstrates that the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e variant drives AD-like molecular and cellular changes in human iPSC-derived RPE. These findings support the RPE as a relevant and accessible model to study AD pathology, offering insights into disease mechanisms beyond the CNS, and opening new avenues for retinal biomarker development.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRPE, iPSC, AD, TEM, LC-ESI-MS/MS, Aβ, TEER.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e proteomics data files will be uploaded to publicly available ProteomeXchange consortium through the PRIDE database, with a dataset ID to be provided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003ethis research was supported by a DHB Foundation grant (GEL, AP), a Dementia Australia Norma Beaconsfield grant (GEL), an MJ Gething Award (GEL), a National Health and Medical Research Council Senior Research Fellowship (AP, 1154389), a Dame Kate Campbell Fellowship (AP), and a Momentum Fellowship (GEL).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions:\u0026nbsp;\u003c/strong\u003eG.E.L. and A.P conceptualised the original project and created methodologies for undertaking the project.Investigation, including all physical experiments, were undertaken by G.E.L., M.M., J.C.H., D.H, U.G., A.v.d.M., J.Y.W.M.Resources were provided by D.H., C.M.K., A.M.G., A.P.Data analysis was performed by G.E.L., M.M., J.C.H., A.P. Writing - original draft: G.E.L., A.P.Writing - review \u0026amp; editing: all authors.Supervision and project administration: \u0026nbsp;A.P.; Funding Acquisition, G.E.L., A.P.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe thank João Paulo for his technical support with LC-MS/MS; and the DIAN participants and their families for their dedication and altruism and the research and support staff at each of the DIAN sites for their contributions to the study. We gratefully acknowledge the altruism of the participants and their families and the contributions of the DIAN research and support staff at each of the participating sites for their contributions to this study. The DIAN Expanded Registry welcomes contact from any families or treating clinicians interested in research about autosomal dominant familial Alzheimer’s disease. Data collection and sharing for this project were supported by The Dominantly Inherited Alzheimer’s Network (DIAN; the German Center for Neurodegenerative Diseases (DZNE), and Raul Carrea Institute for Neurological Research (FLENI). Partial support was provided by the Research and Development Grants for Dementia from Japan Agency for Medical Research and Development (AMED) and by the Korea Health Technology R\u0026amp;D Project through the Korea Health Industry Development Institute (KHIDI). We also acknowledge the Biological Optical Microscopy Platform and the Melbourne Cytometry Platform (Melbourne Brain Centre Node) at the University of Melbourne for technical assistance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDIAN Consortium Full Name and Credentials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSarah Adams, MS; Ricardo Allegri, PhD; Aki Araki; Nicolas Barthelemy, PhD; Randall Bateman, MD; Jacob Bechara, BS; Tammie Benzinger, MD, PhD; Sarah Berman, MD, PhD; Courtney Bodge, PhD; Susan Brandon, BS; William (Bill) Brooks, MBBS, MPH; Jared Brosch, MD, PhD; Jill Buck, BSN; Virginia Buckles, PhD; Kathleen Carter, PhD; Lisa Cash, BFA; Charlie Chen, BA; Jasmeer Chhatwal, MD, PhD; Patricio Chrem Mendez, MD; Jasmin Chua, BS; Helena Chui, MD; Laura Courtney, BS; Carlos Cruchaga, PhD; Gregory S Day, MD; Chrismary DeLaCruz, BA; Darcy Denner, PhD; Anna Diffenbacher, MS; Aylin Dincer, BS; Tamara Donahue, MS; Jane Douglas, MPh; Duc Duong, BS; Noelia Egido, BS; Bianca Esposito, BS; Anne Fagan, PhD; Marty Farlow, MD; Becca Feldman, BS, BA; Colleen Fitzpatrick, MS; Shaney Flores, BS; Nick Fox, MD; Erin Franklin, MS; Nelly Joseph-Mathurin, PhD; Hisako Fujii, PhD; Samantha Gardener, PhD; Bernardino Ghetti, MD; Alison Goate, PhD; Sarah Goldberg, MS, LPC, NCC; Jill Goldman, MS, MPhil, CGC; Alyssa Gonzalez, BS; Brian Gordon, PhD; Susanne Gr¨aber-Sultan, PhD; Neill Graff-Radford, MD; Morgan Graham, BA; Julia Gray, MS; Emily Gremminger, BA; Miguel Grilo, MD; Alex Groves; Christian Haass, PhD; Lisa H¨asler, MSc; Jason Hassenstab, PhD; Cortaiga Hellm, BA; Elizabeth Herries, BA; Laura Hoechst-Swisher, MS; Anna Hofmann, MD; Anna Hofmann; David Holtzman, MD; Russ Hornbeck, MSCS, MPM; Yakushev Igor, MD; Ryoko Ihara, MD; Takeshi Ikeuchi, MD; Snezana Ikonomovic, MD; Kenji Ishii, MD; Clifford Jack, MD; Gina Jerome, MS; Erik Johnson, MD, PHD; Mathias Jucker, PhD; Celeste Karch, PhD; Stephan K¨aser, PHD; Kensaku Kasuga, MD; Sarah Keefe, BS; William (Klunk, MD, PHD; Robert Koeppe, PHD; Deb Koudelis, MHS, RN; Elke Kuder-Buletta, RN; Christoph Laske, PhD; Allan Levey, MD, PHD; Johannes Levin, MD; Yan Li, PHD; Oscar Lopez MD, MD; Jacob Marsh, BA; Ralph Martins, PhD; Neal Scott Mason, PhD; Colin Masters, MD; Kwasi Mawuenyega, PhD; Austin McCullough, PhD Candidate; Eric McDade, DO; Arlene Mejia, MD; Estrella Morenas-Rodriguez, MD, PhD; John Morris, MD; James Mountz, MD; Cath Mummery, PhD; Neelesh Nadkarni, MD, PhD; Akemi Nagamatsu, RN; Katie Neimeyer, MS; Yoshiki Niimi, MD; James Noble, MD; Joanne Norton, MSN, RN, PMHCNS-BC; Brigitte Nuscher; Ulricke Obermüller; Antoinette O’Connor, MRCPI; Riddhi Patira MD; Richard Perrin, MD, PhD; Lingyan Ping, PhD; Oliver Preische, MD; Alan Renton, PhD; John Ringman, MD; Stephen Salloway, MD; Peter Schofield, PhD; Michio Senda, MD, PhD; Nicholas T Seyfried, D. Phil; Kristine Shady, BA, BS; Hiroyuki Shimada, MD, PhD; Wendy Sigurdson, RN; Jennifer Smith, PhD; Lori Smith, PA-C; Beth Snitz, PhD; Hamid Sohrabi, PhD; Sochenda Stephens, BS, CCRP; Kevin Taddei, BS; Sarah Thompson, PA-C; Jonathan V¨oglein, MD; Peter Wang, PhD; Qing Wang, PhD; Elise Weamer, MPH; Chengjie Xiong, PhD; Jinbin Xu, PhD; Xiong Xu, BS, MS.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAriaei, A., Ghorbani, A., Habibzadeh, E., Moghaddam, N., Chegeni Nezhad, N., Abdoli, A., Mazinanian, S., Sadeghi, M., \u0026amp; Mayeli, M. (2024). Investigating the association between the GAP-43 concentration with diffusion tensor imaging indices in Alzheimer\u0026rsquo;s dementia continuum. \u003cem\u003eBMC Neurology\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 397.\u003c/li\u003e\n\u003cli\u003eArmentero, M. 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Recent advances in Alzheimer\u0026rsquo;s disease: Mechanisms, clinical trials and new drug development strategies. \u003cem\u003eSignal Transduction and Targeted Therapy\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1), 211.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"retinal pigment epithelium, induced pluripotent stem cells, Alzheimer’s disease, presenilin, proteomics, melanosome","lastPublishedDoi":"10.21203/rs.3.rs-6556064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6556064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is characterised by progressive cognitive decline and accumulation of pathological markers such as β-amyloid (Aβ) plaques and Tau tangles. Emerging evidence suggests these markers can also be detected in the retina, positioning it as a potential surrogate for investigating AD pathophysiology. The retinal pigment epithelium (RPE) shares features with the brain and is critical for retinal health, yet its role in AD pathology remains underexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe generated RPE cells from human induced pluripotent stem cells carrying the \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e pathogenic variant for AD, alongside its CRISPR-corrected isogenic control. AD-associated phenotypes were assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eRPE cell cultures from the two cohorts displayed expression of Aβ and Tau, with notable differences in levels and organisation. Total Aβ\u003csub\u003e1\u0026minus;42\u003c/sub\u003e and Aβ\u003csub\u003e1\u0026minus;42:1\u0026minus;40\u003c/sub\u003e ratio in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE cell lysates were significantly elevated compared to the CRISPR isogenic controls and volume of Aβ\u003csup\u003e+\u003c/sup\u003e deposits was significantly larger in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE cells. Total and phosphorylated Tau proteins were also detected in both cohorts, with altered spatial organisation and localisation of pTau in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e. Proteomic profiling identified more than 1,800 significantly dysregulated proteins in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE cells, including key AD-related proteins such as MAPT, APP, APBB1 and NRBF2. Upregulated pathways involved autophagy, intracellular trafficking and neurodegeneration, while downregulated pathways implicated mitochondrial respiration, RNA metabolism, and protein folding. Proteomics analysis of conditioned media further revealed altered secretion of matrix-associated proteins as well as increased APOE and APP in \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE samples.\u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e RPE cells demonstrated dysregulation in melanosome biogenesis, marked by decreased expression of core melanogenic proteins (PMEL, TYR, DCT) by proteomics analysis; and altered melanosome morphology and pigmentation by electron microscopy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, these findings support the RPE as a relevant and accessible \u003cem\u003ein vitro\u003c/em\u003e model for AD research, offering insights into the role of \u003cem\u003ePSEN1\u003c/em\u003e in Aβ and Tau dysregulation, disease mechanisms and melanosome biogenesis, providing a promising approach to understand \u003cem\u003ePSEN1\u003c/em\u003e biology in the context of disease and potential biomarker discovery. It is also the first to describe a relationship between \u003cem\u003ePSEN1\u003c/em\u003e\u003csup\u003eH163R\u003c/sup\u003e and melanosomes in a human cellular model.\u003c/p\u003e","manuscriptTitle":"Human iPSC-RPE with the PSEN1H163R pathogenic variant recapitulates Alzheimer’s disease features and reveals melanosome defects","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 16:17:48","doi":"10.21203/rs.3.rs-6556064/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07ce3598-0f86-4847-8b28-1566bca86beb","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-04T14:23:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-12 16:17:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6556064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6556064","identity":"rs-6556064","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}