Parkinson’s disease risk factors are expressed at brain barriers

Parkinson’s disease risk factors are expressed at brain barriers | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Parkinson’s disease risk factors are expressed at brain barriers View ORCID Profile Floriane Bretheau , Océane Arevalo , Vincent Gélinas , Louise Reveret , Océane Martinez , Marlie Ali Yandza , Bastien Jacquet , Léa Ferreira , Morgan Bérard , Charles Joly Beauparlant , Steve Lacroix , Éric Boilard , Benoit Laurent , Frederic Calon , Martin Parent , Arnaud Droit , Aurélie de Rus Jacquet doi: https://doi.org/10.1101/2025.10.20.683561 Floriane Bretheau 1 Centre de recherche du CHU de Québec - Université Laval, Axe Neurosciences , Québec, QC, Canada , G1V 4G2 2 Département de psychiatrie & neurosciences, Faculté de médecine, Université Laval , Québec, QC, Canada , G1V 0A6 Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Floriane Bretheau Océane Arevalo 1 Centre de recherche du CHU de Québec - Université Laval, Axe Neurosciences , Québec, QC, Canada , G1V 4G2 2 Département de psychiatrie & neurosciences, Faculté de médecine, Université Laval , Québec, QC, Canada , G1V 0A6 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vincent Gélinas 3 Centre de recherche du CHU de Québec - Université Laval, Axe Endocrinologie et Néphrologie , Québec, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Louise Reveret 1 Centre de recherche du CHU de Québec - Université Laval, Axe Neurosciences , Québec, QC, Canada , G1V 4G2 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Océane Martinez 1 Centre de recherche du CHU de Québec - Université Laval, Axe Neurosciences , Québec, QC, Canada , G1V 4G2 2 Département de psychiatrie & neurosciences, Faculté de médecine, Université Laval , Québec, QC, Canada , G1V 0A6 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marlie Ali Yandza 1 Centre de recherche du CHU de Québec - Université Laval, Axe Neurosciences , Québec, QC, Canada , G1V 4G2 2 Département de psychiatrie & neurosciences, Faculté de médecine, Université Laval , Québec, QC, Canada , G1V 0A6 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bastien Jacquet 1 Centre de recherche du CHU de Québec - Université Laval, Axe Neurosciences , Québec, QC, Canada , G1V 4G2 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Léa Ferreira 4 Centre de recherche sur le vieillissement, Centre Intégré Universitaire de Santé et Services Sociaux de l’Estrie-Centre Hospitalier Universitaire de Sherbrooke , Sherbrooke, QC, Canada 5 Département de biochimie et génomique fonctionnelle, Faculté de médecine et des sciences de la santé, Université de Sherbrooke , Sherbrooke, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Morgan Bérard 6 CERVO Brain Research Center , Québec, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Charles Joly Beauparlant 3 Centre de recherche du CHU de Québec - Université Laval, Axe Endocrinologie et Néphrologie , Québec, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steve Lacroix 1 Centre de recherche du CHU de Québec - Université Laval, Axe Neurosciences , Québec, QC, Canada , G1V 4G2 7 Département de médecine moléculaire, Faculté de médecine, Université Laval , Québec, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Éric Boilard 8 Centre de recherche du CHU de Québec-Université Laval, Axe Maladies infectieuses et immunitaires , Québec, QC, Canada 9 Département de microbiologie, des maladies infectieuses et de l’immunologie, Faculté de médecine, Université Laval , Québec, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benoit Laurent 4 Centre de recherche sur le vieillissement, Centre Intégré Universitaire de Santé et Services Sociaux de l’Estrie-Centre Hospitalier Universitaire de Sherbrooke , Sherbrooke, QC, Canada 5 Département de biochimie et génomique fonctionnelle, Faculté de médecine et des sciences de la santé, Université de Sherbrooke , Sherbrooke, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Frederic Calon 1 Centre de recherche du CHU de Québec - Université Laval, Axe Neurosciences , Québec, QC, Canada , G1V 4G2 10 Faculté de pharmacie, Université Laval , Québec, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin Parent 6 CERVO Brain Research Center , Québec, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arnaud Droit 3 Centre de recherche du CHU de Québec - Université Laval, Axe Endocrinologie et Néphrologie , Québec, QC, Canada 7 Département de médecine moléculaire, Faculté de médecine, Université Laval , Québec, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aurélie de Rus Jacquet 1 Centre de recherche du CHU de Québec - Université Laval, Axe Neurosciences , Québec, QC, Canada , G1V 4G2 2 Département de psychiatrie & neurosciences, Faculté de médecine, Université Laval , Québec, QC, Canada , G1V 0A6 Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: aurelie.jacquet{at}crchudequebec.ulaval.ca Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Parkinson’s disease (PD) is characterized by the selective loss of dopaminergic neurons in the substantia nigra pars compacta , but whether its etiology is cell autonomous remains unclear. Increasing evidence implicates the blood-central nervous system (CNS) barriers in disease development, highlighting the importance of identifying genetic risk factors linked to cells forming the cerebrovasculature to advance this emerging area of research. The objective of this study is to identify PD genetic risk factors associated with blood-brain (BBB) and blood-cerebrospinal fluid (BCSFB) barriers, and to validate protein localization in human tissue and experimental models. To do so, we integrated genome-wide association studies and single nuclei RNA-sequencing datasets from the human postmortem substantia nigra (SN), midbrain, or cortical samples from control and PD donors. An in-depth bioinformatics analysis identified genes enriched in cell types that form the multicellular architecture of brain barriers, including CAVIN2 , ANXA1 , ANO2 , and LRP1B . We further validated whether corresponding proteins were present in cell types associated with the blood-CNS barriers in human and mouse post-mortem tissues, as well as in iPSC-differentiated cells and choroid plexus organoids. Results showed that quantifying the proportion of endothelial cells expressing PD-related genes was under-evaluated at the transcript level compared to immunofluorescence analyses. In addition, we observed that CAVIN2 and ANXA1 proteins were more abundant at the vasculature of the substantia nigra vs. cortex, and CAVIN2 protein levels were reduced in PD vs. control human postmortem tissues. In contrast, the investigation of mouse postmortem samples demonstrated that the CAVIN2 protein is only present in a subset of mouse blood vessels, compared to nearly all vessels in human tissue. Similarly, mouse ANXA1 protein localizes to dopaminergic neurons of the substantia nigra and not at the vasculature, as seen in human tissue. The primary outcome of this study is the identification of PD-relevant risk genes specifically expressed at brain barriers and enriched in PD-relevant brain regions. The secondary outcome is the demonstration of poor transcript-protein correlation in – at least – a subset of PD risk factors, and a low interspecies conservation of protein localization for the selected candidates. In conclusion, the BBB and BCSFB may represent understudied contributors to PD, endothelial-specific proteins appear differentially regulated compared to transcripts, and experimental models require comprehensive validation to ensure relevance to the human condition. Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease, affecting more than 11 million individuals worldwide in 2021, with a projected 50% increase in global incidence by 2035 1 . PD is a progressive disorder characterized by motor symptoms such as resting tremor, bradykinesia, and rigidity, along with non-motor manifestations including cognitive, psychiatric, and sleep disturbances 2 . Hallmark neuropathological features include the degeneration of dopaminergic neurons in the substantia nigra pars compacta 3 , 4 and neuroinflammation 5 – 7 , and emerging reports propose that dysfunctional brain barriers may contribute to disease onset or progression 8 – 15 . Although its exact etiology remains unclear, PD is now recognized as resulting from the interaction of aging, genetic, and environmental factors 16 . Genetic factors account for approximately 5 to 10% of diagnosed cases 17 and the largest meta-gene wide association study (GWAS) to date have identified more than 90 loci associated with increased PD risk, highlighting a wide range of genes and biological mechanisms involved in disease susceptibility 18 , 19 . Examples of risk factors include STK39, GAK, SNCA, LRRK2, SYT11 , and HIP1R 20 , 21 , which are implicated in functional pathways across different cell types, such as neurons and glial cells 22 . In fact, the computational integration of GWAS and single cell or single nuclei RNA-sequencing datasets underscored the contribution of multiple cell types to PD risk, thus providing biological context to genetic risk factors. Oligodendrocytes and neurons are commonly identified in these studies, along with microglia and astrocytes which play key roles regulating cerebrovascular function 23 , 24 . However, the association of PD traits with other cell types that form brain barriers remains to be characterized. The neuro-glia-vascular unit plays a key role in maintaining brain homeostasis through the integrity of the blood–brain barrier (BBB) and the blood–cerebrospinal fluid barrier (BCSFB) 25 . These barriers constitute essential physiological interfaces that maintain cerebral homeostasis. Indeed, they selectively regulate the exchanges of molecules and transmigration of immune cells between the blood and the brain parenchyma, and the BCSFB located at the choroid plexus (ChP) and meninges regulate exchanges between the blood and the cerebrospinal fluid (CSF). In PD, BBB alterations have been reported 26 – 29 and recent clinical studies suggest a correlation between morphological changes at the ChP, motor symptom progression and dopaminergic degeneration 30 , 31 . The ChP produces CSF, and previous studies have documented a shift in the CSF molecular profile over the course of the disease 32 , 33 , further suggesting a possible implication of this tissue in disease progression. Therefore, brain barriers may contribute to PD pathophysiology and represent a potential target for the development of new therapeutic strategies 34 . In this context, this study proposes an in-depth investigation of the PD-associated genes most relevant to cell types forming the BBB (e.g. endothelial cells, pericytes) and the BCSFB (e.g. epithelial and ependymal cells). We leveraged open access GWAS and single nuclei RNA-sequencing (snRNA-seq) datasets, and identified genes of interest including ANXA1 , ANO2 , CAVIN2, and genes of the low-density lipoprotein receptor family. We then confirmed protein expression and cellular localization in human postmortem brain and ChP tissue donated by people with PD or unaffected controls. Finally, to support future experimental and translational studies on these risk factors, their presence was validated in multiple relevant experimental models, including mouse and in vitro models derived from human induced pluripotent stem cells (iPSCs). Overall, we propose that PD-related genetic factors that affect brain barriers represent an underexplored opportunity to appreciate the complexity of PD etiology. However, it is cautioned that patterns of gene expression are not necessarily conserved across species, thereby warranting careful validation of experimental models to ensure future translation to patients. Material and methods Sex as a biological variable Study design related to the human postmortem characterization of proteins of interest included male and female donors. However, PD is more prevalent in males vs females and the sample cohort reflects this sex bias. Study design related to the validation of proteins of interest in mouse and human iPSCs did not include sex as a biological variable, as previously published studies did not indicate sex bias in the presence/absence of the protein of interest in these models. These observations are corroborated by human postmortem data provided in this study. However, the biological function of these proteins may vary between male and female, and follow-up studies in experimental models may benefit from a sex-based stratification. Processing snRNA-sequencing study datasets Bioinformatics analysis were performed on three publicly available datasets from Kamath et al., 2022 (GSE178265) 35 , Feleke et al., 2021 (GEO GSE178146) 36 and Smajic et al., 2022 (GSE157783) 24 . These datasets captured the same cell types, including astrocytes, oligodendrocytes, pericytes, microglia, neurons, and endothelial cells. To study the BCSFB, we leveraged the dataset published by Yang et al 37 and extracted single nuclei gene expression data for endothelial, epithelial, mesenchymal, ependymal cells and monocytes. Each unique cell type was identified using established markers ( Supplementary Table 1 ). Data processing The in-house RStudio package, SingleCell, which wraps and optimizes the main functionalities of the Seurat R package to analyze a Seurat object, was used to process the input data and form a clear analysis of the important markers per cell type. All matrices of RNA counts, features, and barcodes of the samples from the four different studies were extracted and placed into four distinct Seurat objects. For Yang et al., 2021, only the control samples for plexus choroid ( n = 6 ) were used. For the other three datasets, all samples were used. Data analysis For each dataset, the in-house function SingleCell::analyze_integrated was performed based on the Seurat v4.4.0 package. First, a filtering step was applied, requiring a minimum of 100 gene features and 100 gene counts per features. To retain a maximum number of cells, no top threshold was used. Normalization was performed using Seurat::NormalizeData with the base parameters. Then, High Variable Genes (HVG) were identified using Seurat::FindVariableFeatures with the Variance Stabilizing Transformation (vst) argument with 2,000 features to stabilize the variance in cell expression. Furthermore, Seurat::ScaleData was used to scale the data with base parameters. 30 Principal Component Analysis (PCA) dimensions were used in the Seurat::RunPCA function to reduce the dimensionality and identify important patterns within the variation of cells expression. For each dataset, clusters were found with the base parameters of Seurat::FindClusters with a resolution of 0.2 to identify the base clusters for each dataset. Based on a set of key gene markers ( Supplementary Table 1 ), clusters were assigned to a defined cell type, and PD-related genes of interest were mapped using a Uniform Manifold Approximation and Projection (UMAPs). Fold changes were calculated using the FindMarkers by Seurat with the following parameters: min.pct = 0.05, and logfc.threshold = 0.25. Identification of arterial, capillary, and venous cells To separate endothelial cells based on their zonation identity, clusters identified as endothelial cells were subdivided and analyzed using the base function of Seurat::FindSubCluster. Arterial cells were identified by the expression of VEGFC and ALPL , capillary cells by MDSD2A and SCLA5 , and venous cells by IL1R1 and NR2F2 , and the threshold set at a minimum of log1p(0.5) expression. Association of PD genetic risk factors with cell types relevant to brain barriers The gene list was extracted from the GWAS catalog v1.0.2.1 that compiled a total of 59 studies ( https://www.ebi.ac.uk/gwas/docs/file-downloads ) 38 . For each of the three snRNA-seq datasets, the GWAS gene list was reduced based on the genes found by the Average Expression base function from Seurat v.4.4.0 on the defined clusters 39 . For the Smajić et al. dataset, because the features were encoded in ENSEMBL, the GWAS gene list was translated with the base function mapIds from the AnnotationDb package version 1.64.1 with the database org.Hs.eg.db version 3.17.0 mentioning the ALIAS keytype 40 . To identify genes enriched in cell types of the neuro-glia-vascular unit and ChP under non-neurodegenerative conditions, the gene expression value for each cell type of interest was divided by the neuronal expression value for that particular gene. This analysis was carried out solely on datasets generated using control samples to avoid bias due to underlying neurodegenerative conditions. The top 100 genes enriched in endothelial cells were extracted and serve as the basis for comparing cell types of the neuro-glia-vascular unit. Genes with at least 10-fold higher expression level in cell types of interest vs. neurons were used to generate gene ontology enrichment data via Metascape 41 . Human postmortem brain tissues Tissue samples were provided by the human brain bank at CERVO Brain Research Centre (Quebec, Canada), and written informed consent was obtained before donation (ethical approval provided by the Institutional Review Board CIUSSS de la Capitale-Nationale, project 2013-3 (146-2012, IUSMQ). The Université Laval Ethics Committee approved the brain collection procedures and the storing and handling of postmortem human brain material. Ethical approval for the use of human postmortem tissue was received from the Centre de Recherche du CHU de Quebec-Université Laval’s Institutional Review Board (project #2024-6837). Analyses were performed in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki). Detailed demographics and clinical information on the postmortem brain samples is provided in Table 1 . PD diagnosis was confirmed by a detailed neuropathologic examination that considered Lewy pathology consensus criteria 42 – 44 . These PD patients had no evidence of other cognitive, psychiatric or neurological disorders. In this study, postmortem human frontal cortex and substantia nigra (SN) tissue sections were examined for 5 control (1 female and 4 males, median age 81.4 years) and 5 PD donors (1 female and 5 males, median age 74 years). Postmortem delay (PMD) ranged from 3h to 23h for control donors and from 2.5h to 17h for PD donors. For human ChP samples, we examined sections from the lateral ChP of 4 control (2 females and 2 males, median age 73 years) and 4 PD donors (1 female and 3 males, median age 73 years). Postmortem delay (PMD) ranged from 3h to 18h for controls and from 15h to 36h for PD. View this table: View inline View popup Download powerpoint Table 1. Demographics and clinical data. Culture of human iPSCs The iPSC lines used in this study were reprogrammed from two different individuals. A first control line (female, line #1) was kindly provided by Prof. Dr. Thomas Gasser (Universitätsklinikum Tübingen) and Prof. Dr. Hans R. Schöler (Max-Planck Institute). This line served as the isogenic control of a LRRK2 G2019S pair, which was not investigated in this study. A second control line (female, line #2) was kindly provided by Prof. Dr. Tilo Kunath (University of Edinburgh). This line served as the isogenic control of an SNCA triplicated pair, which was not investigated in this study. These iPSC lines were cultured and maintained in growth medium consisting in 50% mTeSR Plus and 50 % TeSR E8 medium (StemCell Technologies, Vancouver, BC, Canada), passaged as small cellular aggregates using ReLeSR (StemCell Technologies), and plated onto Geltrex-coated culture dishes for maintenance (Thermo Fisher Scientific, Waltham, MA). Differentiation of iPSC-derived astrocytes Control iPSCs lines were first differentiated into neural progenitor cells (NPCs) according to an established protocol 45 . Briefly, iPSCs were harvested with Accumax (Stemcell Technologies) and seeded at a density of 2×10 5 cell/cm 2 in 6 well plates pre-coated with Geltrex (FisherScientific, Hampton, NH) in mTeSR Plus medium supplemented with 10 µM Y-27632 (Selleck Chemicals). The iPSCs were then neuralized by dual SMAD inhibition to generate midbrain-patterned NPCs. Briefly, the cultures were sequentially exposed to LDN193189, SB431542, SHH(C25II), Purmorphamine and CHIR99021 until day 11 of the differentiation protocol, as described before 45 . On day 11, the cells were harvested using Accumax and seeded at a density of 7×10⁶ cells/well in 6-well plate pre-coated with Geltrex, in NPC medium (Neurobasal, 2% (v/v) B27, 1% (v/v) GlutaMAX, 20 ng/mL FGF2 (PeproTech, Cranbury, NJ), and 20 ng/mL EGF (Thermo Fisher) supplemented with 3 μM CHIR99021. On day 13, the medium was replaced daily with fresh NPC medium. On day 18, astrocyte differentiation was initiated by harvesting and seeding NPCs at 15,000 cells/cm 2 in NPC medium supplemented with 10 µM Y-27632. The next day, the cells were washed with 1X PBS and maintained in astrocyte medium (ScienCell, Carlsbad, CA), followed by weekly passaging at a seeding density of 15,000 cells/cm² until day 28, as previously published 45 . This procedure yields 100% GFAP-positive astrocytes that have been extensively characterized in previous studies 46 , 47 . Differentiation of iPSC-derived ChP organoids and fixation ChP organoids were generated using the STEMdiff Choroid Plexus Organoid Differentiation and Maturation kits (Stemcell Technologies, Vancouver, BC), as per the manufacturer’s instructions. Briefly, iPSCs were seeded at 9,000 cells/well in 96-well ultra-low attachment plates in seeding medium (organoid formation medium containing 10 µM Y-27632 (Selleckchem, Burlington, ON). Then, 100 µL of organoid formation medium was added on days 2 and 4. On day 5, the organoids were carefully transferred to 24-well ultra-low attachment plates containing 500 µL of induction medium. On day 7, the organoids were transferred on a parafilm sheet and embedded in 15 µL Matrigel (Corning, Glendale, AZ). After 30 minutes incubation at 37°C, Matrigel droplets containing organoids were transferred to a 6-well ultra-low adherent plate containing 3 mL of expansion medium. On days 10 and 13 medium was changed for choroid plexus differentiation medium, specific medium to induce ChP differentiation. On day 15, medium was changed for maturation medium and organoids were placed on an orbital shaker (50 RPM) located in a 37°C cell culture incubator. Medium was changed every other 2 to 3 days. After 52 days, the organoids were washed twice with phosphate buffer saline (PBS) and fixed overnight in 4% paraformaldehyde (PFA) at 4°C. Lastly, the organoids were washed twice with PBS and stored in PBS-0.02% sodium azide until cryosectionning. Animals Male C57BL/6 and Slco1c1-icre/ERT2 mice were purchased from Charles River Laboratories or The Jackson Laboratory (JAX). All mice were housed in a facility that maintained temperature control at 23°C, a 12h/12h light-dark cycle, and ad libitum access to food and water. 3 to 5 months old mice were overdosed with a mixture of ketamine (400mg/kg) and xylazine (40 mg/kg) and transcardially perfused with PBS, and two subsequent procedures were employed to ensure optimal visualization of proteins of interest. To visualize Cavin2, mice were only perfused with PBS and brains were fresh frozen until tissue processing. To visualize ANXA1 and LRP1, PBS perfusion was followed by perfusion with 4% PFA, pH 7.4, in PBS. Brains were extracted, post-fixed for an additional 24h in 4% PFA at 4°C and then transferred into PBS+30% sucrose for at least 24h before tissue sectioning. Animal handling was performed in accordance with the guidelines of the Canadian Council on Animal Care and were approved by the Comité de Protection des Animaux du CRCHUQ-UL (protocols #CHU-2020-554; #CHU-2024-1574 and #CHU-24-1635). Isolation of mouse brain capillaries The method used to generate microvessel-enriched extracts from fresh mouse brain samples has been described previously 48 – 51 . Briefly, this procedure consists in a series of centrifugation steps, including a density gradient centrifugation with dextran (Millipore sigma, Burlington, MA), after which the tissue is filtered through a 20-µm nylon filter. This generates two fractions: the material retained on the filter consists in cerebral microvessels (isolated microvessel-enriched fraction), whereas the filtrate consists in microvessel-depleted parenchymal cell populations. Isolated microvessels on the filter were resuspended in 3 ml of microvessel isolation buffer (HBSS; 15 mM HEPES, 147 mM NaCl, 4 mM KCl, 3 mM CaCl 2 , and 12 mM MgCl 2 ) supplemented with 1% BSA and protease/phosphatase inhibitors, and spun at 2000 g for 10 min at 4 °C. The supernatant was discarded, and the pellet resuspended in 100 μl PBS. These cerebrovascular extracts were then deposited on glass slides (5 μl per slide) and left at room temperature (RT) for 30 min to allow adhesion. Afterwards, they were fixed using a 4% PFA solution in PBS, for 15 min at RT, and processed for immunostaining. ChP organoid and postmortem tissue processing Organoids and mouse and human ChP tissues were embedded in Shandon™ M-1 Embedding Matrix or OCT (Thermo Fisher Scientific) with tissue sections cut at a thickness of 10 µm for organoids and mouse tissues, and at 18 µm for human tissues, using a cryostat (model CM3050S; Leica Biosystems, Concord, ON). All sections were collected directly onto Surgipath X-tra® slides which have a permanent positively charged surface (Leica Microsystems Canada) and stored at −20°C until use. Immunostaining ChP organoids and postmortem tissue ChP organoids, mouse and human tissues were labeled by immunofluorescence as follows. First, tissues were dried under vacuum at 4°C for 15 min. Human brain sections were exposed to white light for 4 days at 4 °C prior to tissue processing to reduce autofluorescence. Tissues were washed 3 times with KPBS (0.38% potassium phosphate dibasic anhydrous, 0.045% potassium phosphate monobasic anhydrous, 0.81 % NaCl in H 2 0 milliQ) for 8 min. For mouse and human tissues (except ChP), antigen retrieval was performed using a sodium citrate buffer at 85 °C for 15 min and cooled down for 30 min. Slices were then washed in KPBS (3 x 8 min) and incubated for 1 hour at RT in blocking buffer (5% donkey serum (Millipore sigma), 0.1% Triton-X (Millipore Sigma) in KPBS). Tissues were washed three times with KPBS for 8 minutes each, and then incubated overnight in the primary antibody solution at 4°C. Primary antibodies used in this study are listed in Supplementary table 2 and were used at the following dilutions: rabbit anti-Annexin 1 (1:100, Invitrogen, 71-3400), rabbit anti-Annexin 1 (1:200, Abcam, ab214486), rabbit anti-ANO2 (1:200, Abcam, 113443), mouse anti-AQP1 (1:100, Santa Cruz Biotechnology, SC-32737), goat anti-collagen IV (1:500, Millipore Sigma, AB769), mouse anti-GFAP (1:200, Novus Biologicals, NBP1-05197), rabbit anti-SDPR (CAVIN2) (1:200, Bio-Techne, NBP2-57218), rabbit anti-SDPR (CAVIN2) (1:200, Proteintech, 12339-1-AP), rabbit anti-LRP1 (1:200, Abcam, Ab92544). The next day, the slices were washed in KPBS (3 x 8 min) and incubated for 2.5 hours in Alexa-conjugated secondary antibodies (1:250 to 1:500 dilution, in blocking buffer). After incubation, the slices were washed three times in KPBS, incubated for 10 min in a DAPI nuclear stain solution (1:5,000, in KBPS) and washed again three times in KPBS. Human tissues were incubated in 0.1% Sudan Black solution prepared in 70% ethanol (Greenfield Global, Toronto, ON). Finally, slices were mounted using Fluoromount-G medium (Fisher Scientific, Waltham, MA). iPSCs-derived astrocytes iPSC-derived astrocytes were seeded in 48-well plates (40,000 cells/cm²) on coverglass pre-coated with Geltrex (Fisher Scientific). After 24 hours of incubation, cells were washed twice with Dulbecco’s PBS (DPBS) and fixed for 20 minutes in a 4% PFA solution under a chemical hood. Cells were then washed twice with DPBS and incubated for 1 hour at RT in a blocking/permeabilization solution (10% (v/v) FBS, 1% (w/v) BSA and 0.3% (v/v) Triton-X in DPBS). After two additional DPBS washes, the cells were incubated overnight at 4 °C in the primary antibody solution consisting of 1% (w/v) BSA in DPBS. The following antibodies were used: rabbit anti-Annexin 1 (1:200, Invitrogen, 71-3400), rabbit anti-Annexin 1 (1:200, Abcam, ab214486), rabbit anti-SDPR (CAVIN2) (1:200, Bio-Techne, NBP2-57218), rabbit anti-SDPR (CAVIN2) (1:200, Proteintech, 12339-1-AP), rabbit anti-ANO2 (1:200, Bio-Techne, NBP2-92888), rabbit anti-ANO2 (1:200, Abcam, 113443), mouse anti-GFAP (1:500, Novus Biologicals, NBP1-05197). The next day, cells were washed twice with DPBS and incubated for 1 hour at RT in Alexa-conjugated secondary antibodies (1:1,000 dilution, 1% BSA in PBS). Lastly, cells were washed twice in DPBS and incubated for 5 minutes at RT in a DAPI nuclear stain solution (1:5,000 dilution). Coverglasses were then mounted onto slides with a drop of ProLong™ Diamond Antifade Mountant. Slides were dried at RT protected from light for 24 hours. Coverslip edges were sealed with clear nail polish, and slides were stored at 4 °C until image acquisition. Mouse brain capillaries All samples were blocked and permeabilized for 1 h with PBS containing 10% (v/v) Normal Horse Serum (NHS) and 0.2% (v/v) Triton X-100. Incubation with primary antibodies (rabbit anti-SDPR (CAVIN2) (Proteintech, 12339-1-AP), goat anti-collagen IV (Merck Millipore, AB769) was performed overnight at 4 °C in PBS with 1% (v/v) NHS and 0.05% (v/v) Triton X-100. After three washes, the samples were incubated for 1 hour at RT in Alexa-conjugated secondary antibodies (1:1,000 dilution, 1% Normal Horse Serum in PBS). Slides were then incubated in a DAPI nuclear stain solution for 10 min at RT, and mounted with mounting medium (VectaMount AQ Mounting Medium). Confocal microscopy Tissue sections and astrocyte monolayers were imaged on a Zeiss LSM 800 confocal microscope system equipped with 405-nm, 499-nm, 561-nm and 640-nm lasers. Confocal images were acquired using the Zen Blue Edition software (version Zen Blue 3.9, Zeiss, Oberkochen, Germany). Image analysis The quantification of vascular-specific CAVIN2 signal intensity was performed using Fiji (v. 2.9.0). First a mask was created using the collagen IV channel by (i) applying a threshold (Huang method) and (ii) removing background noise using the “particle analysis” and “remove outliers” (radius=2) tools. Next, the regions of interest (ROI) created from the collagen IV mask were overlayed onto the CAVIN2 channel, and CAVIN2 signal intensity located within the ROIs was measured. For large vascular segments (e.g. arterioles), ROI selection was manually curated to ensure best fit. To ensure that the final quantifications reflect vascular signal, particles smaller than 5 µm 2 were excluded from analysis. Statistical analysis Data was analyzed using GraphPad Prism version 10 (La Jolla, CA). Data encompassing multiple groups were analyzed using one-way or two-way ANOVA with multiple comparison correction ( Fig. 1 , 3 , 4 ). Download figure Open in new tab Figure 1. snRNA-seq datasets captured cell types of the neuro-glia-vascular unit. A . UMAP illustrating cell type clusters for the dataset by Smajic et al. B-D. Graphs show the number of nuclei for each cell type included in the study and obtained from Smajić et al. (B), Kamath et al. (C), and Feleke et al. (D). E . UMAP showing endothelial expression of gene markers of arterial, capillary and venous cells (Smajić et al. dataset). F-H. Graphs represent the proportion of arterial, capillary, and venous endothelial cell nuclei in control and PD samples, for Smajić et al. (F), Kamath et al. (G), and Feleke et al. (H). I. Heatmap shows gene expression for PD risk factors (Smajić et al. dataset) for each cell type. Genes are organized from the least to the highest expressed in neurons. * Genes in the 90 percentile for each cell type. Abbreviation: Ctl, control; ECs, endothelial cells; PD, Parkinson’s disease. Data availability SnRNA-seq datasets used in this study were published in publicly available repositories, and they were obtained using accession numbers GSE178265, GSE178146, and GSE157783. Custom code generated to analyze the snRNA-seq data will be made available upon publication. Results PD risk factors are enriched in cell types forming the neuro-glia-vascular unit To identify PD risk factors enriched in cells forming the neuro-glia-vascular unit, we leveraged three publicly available snRNA-seq datasets generated from human postmortem tissue of the substantia nigra (SN), midbrain, and anterior cingulate cortex of controls and PD donors. The cell types of interest were identified by marker genes previously established and include endothelial cells, pericytes, microglia, and astrocytes ( Supplementary Table 1 ). In addition, oligodendrocytes were included in the analysis to serve as a reference glial cell type not associated with the neuro-glia-vascular unit. Neurons were included to enable the identification of PD risk factors that are not only enriched in cell types associated with brain barriers, but also display low expression in neurons. In the perspective of producing a meta-analysis, we attempted to integrate these three datasets as a single Seurat object and correct for potential batch effects that would confound data analysis. Integration was performed with an in-house package based on Seurat workflow that normalizes and scales data before finding common anchors (gene modules) between all three datasets. However, due to substantial batch effect, the datasets could not be integrated and were consequently analyzed separately. The snRNA-seq dataset published by Smajić et al. originates from midbrain samples collected from 6 control and 5 idiopathic PD donors. The Kamath et al. dataset includes the SN from 8 control and 10 PD donors, while the Feleke et al. dataset comprises anterior cingulate cortex from 7 control and 7 PD donors. Data UMAP separated cell clusters and confirmed that datasets captured cell types of the neurovascular unit ( Fig. 1A ). Here, we focused our study on cell types involved in BBB maintenance and regulation (i.e., endothelial cells, pericytes, astrocytes, and microglia), as well as oligodendrocytes and neurons. Nuclei quantification showed that oligodendrocytes represent the most abundant cell population in datasets originating from the midbrain and SN ( Fig. 1B-C ) , while neurons are the most abundant nuclei in the dataset produced using cortical tissue ( Fig. 1D ). Nonetheless, endothelial cells are represented in all three studies, and they can be further separated by zonation along the vascular arborescence to identify arterial ( VEGFC , ALPL ), capillary ( MFSD2A , SLCA5 ) and venous ( IL1R1 , NR2F2 ) endothelial cells ( Fig. 1E ). Data showed that capillary endothelial nuclei are over-represented in the midbrain and SN samples, while nuclei of venous identity are more abundant in the cortical sample ( Fig. 1F-H ). To identify PD risk factors relevant to each cell type of the neuro-glia-vascular unit, the expression level of GWAS-relevant genes was calculated for endothelial cells, pericytes, microglia, astrocytes, oligodendrocytes, and neurons originating from control donors ( Fig 1I ). We reasoned that the most relevant genes would be those with the highest fold expression when compared to neuronal expression, and we restricted this analysis to control samples to avoid biases and misrepresentation of cell populations lost or increased over the course of PD. This strategy led to the identification of Caveolae Associated Protein 2 (CAVIN2), Anoctamin 2 (ANO2) and Annexin A1 (ANXA1) as the top 3 enriched genes in endothelial cells vs. neurons. Other enriched genes include GJA5 (pericytes), the Major Histocompatibility Complex (MHC) Class II family ( HLA-DRB5 , HLA-DRB1 , HLA-DRA ; microglia), ITPKB (astrocytes), CD38 (pericytes, astrocytes), and DMRT2 (oligodendrocytes) ( Fig. 2A-E ). Of note, the pattern of GWAS-related gene expression appears closely related between oligodendrocytes and neurons, and between endothelial cells and pericytes. The analysis of gene ontology (GO)-relevant pathways specific to endothelial cells and pericytes revealed that PD risk factors associated with these cell types (i.e. 10-fold enriched vs. neuronal expression) are implicated in immune responses, transport, cell activation, and vascular function ( Fig. 2F ). Download figure Open in new tab Figure 2. Integration of GWAS and snRNA-seq reveals PD risk factors genes enriched at the neuro-glia-vascular unit. A . Heatmap shows PD risk factors enriched in each cell type (Smajić et al. dataset). The scale is fold-change expression relative to neurons. Gene names in red indicate PD risk factors further investigated in this study. B-E. Dot plots represent the percent and relative expression of PD risk factor enriched in endothelial cells (ECs) and pericytes (B), astrocytes (C), microglia (D), and oligodendrocytes (E). F . Gene ontology analysis reveals pathways most affected by PD risk factors enriched in ECs and pericytes. Altered expression of the caveolae-associated CAVIN2 protein at the PD brain vasculature Among the PD risk factors enriched at the neuro-glia-vascular unit, CAVIN2 , ANO2 and ANXA1 , are the most expressed in brain endothelial cells. To confirm expression and localization of the encoded proteins, human postmortem tissue from control (n= 5) or PD (n= 5) donors was sectioned from the frontal cortex and SN, and immunostained for fluorescence microscopy. The vascular enrichment of CAVIN2 is demonstrated by the colocalization with Collagen IV in both the cortex and SN of control and PD patients ( Fig. 3A, B , Supplementary Fig. 1A-B ). Furthermore, a quantitative analysis revealed a significant decrease in CAVIN2 intensity in the cortex and SN of PD donors compared to controls (****p < 0.0001) ( Fig. 3C ), suggesting disease-related alterations at the protein level that were not observed in snRNA-seq studies. The quantification also revealed that CAVIN2 is enriched in the SN vs. cortex, further supporting the relevance of this risk factor to the regional vulnerability observed in PD pathology. Signal specificity was confirmed using a second antibody (Supplementary Fig. 1C ). When comparing the pattern of protein vs. gene expression, data revealed that up to 96% of brain vessels were positive for CAVIN2 by immunostaining, but only 38 to 78% endothelial nuclei expressed CAVIN2 , with variability depending on the snRNA-seq dataset ( Fig. 3D, E ). Lastly, we confirmed that expression of this PD-relevant gene is conserved across species, albeit at lower levels in mouse vs. human tissue. Immunofluorescence images spanning the wild-type mouse cortex and SN, as well as isolated mouse capillaries, confirmed vascular CAVIN2 signal in some (but not all) vascular segments ( Fig. 3F, G , Supplementary Fig. 1D, E ). Download figure Open in new tab Figure 3. CAVIN2 is expressed by brain endothelial cells and protein level is altered in PD brains. A-B. Representative confocal images showing CAVIN2 (white), Collagen IV (green), TH (orange) expression in the SN (A) and frontal cortex (B) of human postmortem samples from controls and PD donors. C. Quantification of CAVIN2 signal intensity in collagen IV-positive blood vessels associated with acquisitions A and B. The violin plot shows the median (dashed line) and interquartile range (solid line). Data are from five patients and five control donors (n˃ 1,510 vessels analyzed for each condition). Statistical analysis was performed using two-way ANOVA with Tukey’s multiple comparisons correction. D. UMAPs represent CAVIN2 expression level in endothelial nuclei related to Smajić et al. and Kamath et al. E. Graph representing the proportion of CAVIN2 -expressing endothelial nuclei by snRNA-seq (blue bars), and CAVIN2-positive vascular fragments by immunofluorescence (purple bars). F-G. Representative confocal images showing the presence of CAVIN2 (red), Collagen IV (green), TH (white) in blood vessels of the mouse SN (F) , cortex and hippocampus (G ). White arrows point to CAVIN2-positive blood vessels. ( H ) Representative confocal images showing the presence of CAVIN2 (white) and COLLAGEN IV in mouse brain capillaries. Abbreviations: Ctl, control; PD, Parkinson’s disease. The cell type-specificity of ANXA1 in the cortex and SN is not conserved across species The investigation of ANXA1 in human postmortem cortical and SN tissue by immunofluorescence confirmed a strong vascular localization, along with a presence in discrete astrocyte populations ( Fig. 4, A-B, H , Supplementary Fig. 2A-B ). The quantification of ANXA1 at the vasculature did not reveal differences between control and PD donors, however fluorescent signal was higher in the SN compared to the cortex ( Fig. 4C ) . However, the pattern of gene vs. protein expression showed that the proportion of ANXA1 -expressing endothelial nuclei is lower than protein abundance in situ ( Fig. 4D-E ). To support future investigations and confirm that this PD-relevant protein has a similar cellular localization across species, we obtained wild-type mouse brain samples and performed immunofluorescence staining for ANXA1, using the same antibody. Images show that mouse ANXA1 is enriched in dopaminergic neurons of the SN, and the protein was not reliably detected in the cortex or hippocampus ( Fig. 4F-G , Supplementary Fig. 2C-D ), suggesting that the pattern of ANXA1 protein localization is not conserved across species. Furthermore, we attempted to characterize ANO2 as a potential risk factor of interest and performed immunostaining of human postmortem tissue. The resulting images revealed the presence of occasional astrocytic staining in the cortex, but there are few resources available to investigate this protein and signal specificity could not be validated by the second selected antibody (Supplementary Fig. 3A ). Furthermore, the expression and cellular localization of ANO2 could not be validated in mouse brain sections, as available antibodies yielded poor image quality. Nonetheless, we leveraged translational human cell culture models to validate the presence of proteins of interest in these platforms. The ANXA1 and ANO2 proteins were selected for this analysis, as they were identified in discrete astrocyte populations of the human postmortem brain. Primary astrocytes as well as two independent control iPSC-derived astrocyte lines were immunostained, and the images confirm ANXA1 and ANO2 expression in these cultures using two different antibodies ( Fig 4I-J ; Supplementary Fig. 2E-F; Supplementary Fig. 3 ). Lastly, ANXA1 signal specificity was confirmed using a second primary antibody (Supplementary Fig. 2G ). Overall, these results support the future investigation of CAVIN2 and ANXA1 as neurovascular PD risk factors potentially implicated in disease neuropathology, and species differences in protein localization should be considered when selecting experimental models. The development of new resources would be valuable efforts to further explore the contributions of ANO2 to PD pathology. Download figure Open in new tab Figure 4. ANXA1 is localized in human blood vessels and astrocytes, and in mouse dopaminergic neurons. A-B. Representative confocal images showing ANXA1 (white), Collagen IV (green), TH (orange) levels in blood vessels of the SN (A) and cortex (B) in controls and patients with PD. C. Quantification of ANXA1 signal intensity in blood vessels associated with acquisitions A and B. The violin plot shows the median (dashed line) and interquartile range (solid line). Data are from five patients and five control donors (n ˃ 1,070 vessels analyzed for each condition). Statistical analysis was performed using two-way ANOVA with Tukey’s multiple comparisons correction (ns: non significant). D. UMAPs represent ANXA1 gene expression level in endothelial nuclei. E. Graph represents the proportion of ANXA1 -expressing endothelial nuclei by snRNA-seq (blue bars), and ANXA1-positive vascular fragments by immunofluorescence (purple bars). F-G. Representative confocal images showing ANXA1 (green) and TH (orange) in the mouse SN (F) and in the cortex and hippocampus (G). H-J. Representative confocal images showing ANXA1 (white) and Collagen IV or GFAP (green) in postmortem human cortical astrocytes (H), primary human astrocytes (I), and iPSC-derived astrocytes (J). Abbreviations: Ctl, control; ECs, endothelial cells; PD, Parkinson’s disease; SN, substantia nigra. PD risk factors are enriched in cell types forming the BCSFB at the ChP Despite evidence demonstrating that aging and neurodegenerative diseases induce both morphological and functional changes at the ChP 31 , 52 – 55 , this brain structure has been largely overlooked in PD-focused studies. Therefore, we mirrored our bioinformatics analysis of the neuro-glia-vascular unit using a published snRN-seq dataset of the human ChP 56 ( Fig. 5A-B , Supplementary Table 1 ). We identified PD risk factors most enriched in endothelial, ependymal, epithelial, mesenchymal cells and macrophages that could inform disease etiology ( Fig. 5C ). Among these, ANO2 , ELOVL7 , and ANXA1 are genes enriched at both the neuro-glia-vascular unit and the ChP, therefore they were selected for in-depth validation. In addition, LRP1B was identified as a risk factor of interest. It is a member of the low-density lipoprotein (LDL) receptor family, which consists of 7 structurally related proteins, among which features the LRP1 receptor implicated in α-synuclein transport and propagation 57 . To validate the presence of these gene products at the ChP and ependymal regions of the brain, we prepared samples originating from the lateral ventricle of human and mouse postmortem tissues, and generated ChP organoids derived from control iPSCs ( Fig. 5D-E ). First, the morphological integrity of each ChP tissue was validated by immunostaining using established ChP markers ( Fig. 5F-H ) . Then, we assessed the anatomical localization of the proteins of interest. Among the candidates, ELOVL7 and LRP1B were not further pursued because the commercially available antibodies tested in this study yielded poor image quality. However, high quality and validated antibodies are commercially available to study LRP1, which is structurally related to LRP1B and a protein of interest for its role in α-synuclein propagation. Therefore, LRP1 immunostaining was performed on human postmortem tissue from control (n= 4) or PD (n= 4) donors ( Fig. 6A , Supplementary Fig. 4A-B) , and the images show protein expression in the epithelial and stromal layers of the ChP. The presence of LRP1 at the mouse ChP was also confirmed ( Fig. 6B , Supplementary Fig. 4C) and immunostaining of human iPSC-derived ChP organoids validated that this advanced in vitro model can be leveraged to study this protein ( Fig. 6C ) . Download figure Open in new tab Figure 5. Identification of PD risk factors expressed at the BCSFB. A. Schematic localisation and cellular organisation of the ChP in mouse and human brains. B. i. UMAP represents snRNA-seq clusters that separate ChP cell types. ii. Graph shows the number of nuclei identified in the snRNA-seq study by Yang et al. C. Heatmap shows cell type-specific gene expression of PD risk factors. * Gene in the 90 percentile for each cell type. Gene names in red indicate PD risk factors investigated in this study. D. Timeline of ChP organoid differentiation from human iPSCs. E. Representative image of a mature CSF-producing ChP organoid fixed in 4% PFA at day 30 of differentiation. F-H. Representative confocal images showing APQ1 (red) and Collagen IV (green) in human ( F ), mouse ( G ) and organoid ( H ) ChP. Download figure Open in new tab Figure 6. LRP1 is present at the ChP and cellular localization is conserved across species. A-C. Representative confocal images showing LRP1 (white), AQP1 (red) and Collagen IV (green) in ChP tissue from human control and PD postmortem tissues ( A ), mouse ( B ) and ChP organoid ( C ). The ependymal layer is enriched in ANXA1 and ependymal expression is conserved across species We visualized the ANXA1 protein by immunostaining human postmortem tissue of the lateral ventricle/ChP from control and PD donors, and observed the presence of this protein at the ependymal layer ( Fig. 7A ). This cellular lining of the brain ventricles forms a protective barrier with the brain parenchyma, and ANXA1 immunostaining was observed in both control (n= 4) and PD (n= 4) human postmortem tissues ( Supplementary Fig. 5A-B ). Then, we confirmed that experimental models recapitulate human-based observations. Notably, the mouse ependymal layer is also enriched in ANXA1 ( Fig. 7B ; Supplementary Fig. 5C ), and the protein was observed within the cellular soma and neuroepithelial cilia. However, ANXA1 gene expression in epithelial nuclei did not mirror protein signal ( Supplementary Fig. 5D ). Furthermore, ChP organoids recapitulated a cellular structure that highly resembled the ANXA1 expressing ependyma ( Fig. 7C ). Signal specificity was confirmed using a second antibody across all sample types (Supplementary Fig. 6 ). In addition to LRP1 and ANXA1, ANO2 was also explored as a PD-relevant protein potentially expressed at the ChP. The ANO2 antibodies did not produce detectable signal at the ChP, despite being identified as a top candidate in endothelial cells located at the ChP. This observation suggests that experimental resources are needed to investigate novel genes and proteins of interest such as ANO2. Download figure Open in new tab Figure 7. Annexin 1 is localized in ependymal cells located at the border of the ventricle. A. Representative confocal images showing GFAP (red), Collagen IV (green) and ANXA1 (white) localized at the ependymal layer forming the border of the lateral ventricle in control and PD postmortem tissues. B-C. Confocal images showing GFAP (red), Collagen IV (green) and ANXA1 (white) immunostaining in mouse lateral ventricle ( B ) and ChP organoids generated using two independent iPSC lines ( C ). Download figure Open in new tab Figure 8. PD risk factors enriched at the neuro-glia-vascular unit by cell type and class of biological function. Discussion This study leveraged published snRNA-seq datasets of human postmortem midbrain, SN, and cortical tissue, followed by an integration with GWAS data to identify PD risk genes associated with cell types that form and maintain functional brain barriers, namely the BBB and BCSFB. The top candidates include genes associated with BBB transcytosis (CAVIN2), BBB protection and anti-inflammation (ANXA1), and the most enriched pathways relate to innate immunity, vascular function, and regulation of cell activation. The investigation was extended to receptor-mediated transcytosis (LRP1), a transport mechanism that regulates molecular exchanges across brain barriers. Brain barriers are highly relevant to investigate PD risk factors implicated in protein transcytosis and inflammation A first key observation resulting from this study is that PD risk factors are expressed by cell types forming brain barriers, including the BCSFB. Previous reports have attempted to associate PD risk loci with specific cell types, and neurons, microglia and oligodendrocytes were the most frequently associated with enriched risk loci and differential expression between PD and control samples 58 – 60 . For example, a study leveraged mouse single-cell RNA-seq (scRNA-seq) data to propose that dopaminergic neurons, enteric neurons, and oligodendrocytes are associated with PD risk genes, and an analysis of differential gene expression in human PD vs control postmortem brain samples suggested altered GWAS-related gene expression profiles in dopaminergic neurons and oligodendrocytes 61 . In another report, gene enrichment across glial and neuronal cell types of the SN, putamen and frontal cortex included endothelial cells, but did not incorporate pericytes. However, this study compared mouse vs. human transcriptomic datasets and data support the observation of species differences in the enrichment of specific GWAS variants 22 . In another study, cell types of the neuro-glia-vascular unit, including endothelial cells and pericytes, were investigated alongside other glial and neuronal populations but the authors concluded that microglia and oligodendrocytes were the most closely linked cell types to PD 23 . However, a consistent feature of scRNA-seq and snRNA-seq datasets across published studies is the overrepresentation of oligodendrocyte, microglia, and astrocyte nuclei, often accounting for 70-80% of all cells 23 . As a result, it is reasonable to suggest that less abundant cell types, such as endothelial cells and pericytes, may yield lower statistical power in large GWAS-Multi-omic integration studies compared to other populations. Here, we proposed a distinct computational approach in which we calculated cell type enrichment as a ratio of gene expression normalized to neuronal expression. In addition, we focused the calculation of gene enrichment to control samples to avoid potential bias that could be introduced by shifts in cell populations in a disease environment. Furthermore, differential gene expression between control and PD samples was not considered a primary indicator of cell type relevance to enable a scenario where regulation of protein abundance may differ from regulation of gene expression. This strategy revealed candidate genes associated with each cell type of the neuro-glia-vascular unit and the ChP/ependyma. The genes overexpressed in endothelial cells and pericytes related, in part, to innate immunity, vascular function, and regulation of cell activation. CAVIN2 is a protein involved in the regulation of caveolae biogenesis which, in the context of brain barriers, refers to invaginations at the plasma membrane of endothelial cells that serve as one of the essential regulatory pathways mediating molecular transport at the BBB 62 . Caveolae transcytosis at the BBB is a tightly regulated mechanism, but little is known about the implications of CAVIN2 in these mechanisms. Data provided in this study suggest that patterns of CAVIN2 levels differ between human and mice, as less vascular segments presented with CAVIN2 fluorescent signal in mouse vs. human tissue. However, the human and mouse CAVIN2 are homologous and biological function is therefore likely to be conserved across species. A study proposed that mouse CAVIN2 may regulate caveolae shape, and a Cavin2 knockout mouse model induced the loss of caveolae formation in the lung and adipose tissue 63 . Interestingly, a study performed in rat brains demonstrated that the endothelial cell efflux pump P-glycoprotein (P-gp) colocalized with CAVIN2 in caveolae-containing fractions, suggesting that rats may be a more appropriate model to study brain CAVIN2, and the protein plays a potential role in P-gp regulatory mechanisms 64 . Functional P-gp is necessary to prevent the entry of potentially toxic molecules into the brain, and [ 11 C]-verapamil-based clinical imaging studies suggested reduced P-pg efflux activity in late-stage people with PD (frontal white matter regions), while P-pg activity appeared increased in patients at early stages (brainstem) 65 , 66 . Abundant literature on the role of ANXA1 as an anti-inflammatory and BBB-restorative protein has been published. However, data provided in this study demonstrate the significant discrepancy between mouse and human protein localization in the brain (except at the ependymal layer), and therefore studies that investigated ANXA1 in mouse models may not fully translate to human biology. Nonetheless, these studies support the relevance of the protein in pathophysiological processes relevant to PD and other neurodegenerative diseases. For example, ANXA1 promoted the degradation of amyloid-beta (Aβ) in a murine neuroblastoma cell line 67 , and treatment with human recombinant ANXA1 improved BBB integrity in 5xFAD and Tau-P301L mice, two Alzheimer’s disease models 68 . To the best of our knowledge, the role of ANXA1 at the ependymal layer in health and disease has yet to be explored. In the present study, we did not detect differences in ANXA1 vascular levels between control and PD tissue, but other reports have associated elevated ANXA1 in reactive astrocytes localized at regions of human postmortem brain infarcts 69 . Despite a significant enrichment of ANXA1 at the human brain vasculature, PD-focused studies have mostly investigated the role of ANXA1 in neurons, as several reports identified SOX6+/ANXA1+ dopaminergic neurons selectively vulnerable in PD mouse models 70 , 71 . In the human SN pars compacta, mRNA-based quantification estimated that approximately 8% of TH+ dopaminergic neurons were also ANXA1+ in control samples, and these populations appeared lost in PD tissue 72 . The MitoPark transgenic mouse model, characterized by the selective depletion of mitochondrial transcription factor A from dopaminergic neurons, recapitulated the selective vulnerability of this cell population 71 , and the manipulation of ANXA1+ dopaminergic neurons induced motor impairments in mouse models 70 , 71 . The present study also investigated LRP1 distribution at the control and PD ChP, and confirmed epithelial localization regardless of disease status. This protein has been identified as an α-synuclein receptor 73 , but the role of LRP1 in α-synuclein CSF-to-blood clearance at the ChP remains to be investigated. Published reports suggest that LRP1 may efflux Aβ from the CSF to the blood 74 , 75 , thereby supporting a similar function towards α-synuclein that could reduce the burden of pathological protein in people with PD. In addition, LRP1 is localized in brain endothelial cells, glia, and neurons, where is regulates α-synuclein spread and transcytosis across the BBB 73 , 76 , 77 . The bidirectionality of α-synuclein exchanges at the BBB 78 along with evidence that LRP1 is implicated in α-synuclein pathology, places brain endothelial cells and ChP epithelial cells in a strong position to serve as potential targets to restore brain homeostasis in PD. Gene expression profiles of PD risk factors benefit from protein-level analyses in human postmortem tissues A second key observation is that gene expression profiles derived from snRNA-seq analyses do not systematically translate to protein abundance in human postmortem brain tissue. This difference highlights potential limitations of single nuclei sequencing, including technical variability and varying sequencing depth that may overlook low abundance transcripts. Validation at the protein level is therefore encouraged to obtain accurate and comprehensive understanding of potential functional outcomes. For example, in this study, we selected CAVIN2 and ANXA1 as two PD risk factors of interest enriched in brain endothelial cells. Transcriptomic data suggested that only a fraction of endothelial nuclei expressed CAVIN2 , while immunostaining revealed fluorescent signal in most vascular fragments. Similarly to CAVIN2 , ANXA1 transcriptomic data poorly reflected protein abundance in human postmortem brain tissue. Gene expression quantifications suggested that ∼20-50% of brain endothelial cells expressed ANXA1 , while immunostained neurovascular fragments revealed nearly 100% fluorescence signal in the cortex and SN of both control and PD samples. To confirm these observations, immunostaining was performed using an additional antibody for each target. The second CAVIN2 (Proteintech, 12339-1-AP) and ANXA1 (Invitrogen, 71-3400) antibodies provided similar staining patterns. In line with these observations, accumulating studies have been exploring transcript-protein correlations and documented the poor predictive value of transcriptomic studies to estimate protein abundance 79 – 83 . In the brain, studies observed that correlation may increase significantly for subsets of protein categories (e.g. kinases and membrane-associated proteins 84 , and for specific signaling pathways (e.g. oxidative metabolism and protein synthesis/modification 85 . In a PD-specific context, a study by Zaccaria et al. used laser capture microdissection to extract SN-located dopaminergic neurons from postmortem tissue of control of PD donors, followed by RNA sequencing and nanoliquid chromatography–mass spectrometry to measure transcript and protein abundance, and the authors reported a single transcript-protein pair that were both significantly dysregulated in PD vs control dopaminergic neurons 86 . Furthermore, aging is the highest non-genetic PD risk factor, and a proteomics analyses of the aging cortex identified uncoupled transcript-protein alterations that further complexify the integration of GWAS and transcriptomic datasets to predict the association of cell types to PD pathology. Whether these discrepancies are technical or biological in nature remains to be elucidated 87 , but previously published reports and data presented in this study caution against relying solely on transcriptomic data to conclude on the association of specific cell types with PD risk factors, and suggest that validation of protein expression be included in future studies to offer a comprehensive overview of PD etiology. A systematic comparative analysis identified interspecies variation in cell type specificity of selected PD risk factors A third key observation relates to inter-species differences in protein expression by PD-relevant cell types, which may challenge the translation of research findings documented in mouse models. The most striking observation documented in this study is the abundance of ANXA1 at the human vasculature of the cortex and SN, while the protein is absent at the vasculature of the mouse brain. Instead, and despite 87.5% sequence homology between human and mouse, ANXA1 appeared enriched in dopaminergic neurons of the mouse SN. However, the localization of this protein at the ependymal barrier of the ventricles was conserved across species. The investigation of CAVIN2 revealed that the protein is abundant in human brain endothelial cells, but vascular enrichment was less consistent in mouse brain tissue, and a published study did not detect CAVIN2 in mouse brain by western blot 63 , thus questioning the relevance of mouse models to study these two PD risk factors. In contrast, LRP1 immunostaining confirmed conserved protein expression at both the human and mouse ChP. However, published reports documented lower LRP1 levels in human vs. mouse brain microvessels 88 .Future studies could investigate if LRP1 protein levels are different between mouse and human ChP, as such findings could guide experimental studies aimed at measuring α-synuclein transcytosis or clearance at the BCSFB. The availability of human-based in vitro platforms derived from iPSCs provides abundant and renewable material to support investigations of PD risk factors. Here, we confirmed that iPSC-derived astrocytes positively immunostain for ANXA1 and ANO2, and that the pattern of cellular localization is similar to human primary midbrain astrocytes. Furthermore, we generated iPSC-derived ChP organoids and validated LRP1 and ANXA1 protein expression localized at cellular structures reminiscent of their in vivo counterparts. Future studies could take advantage of advanced tissue engineering, organ-on-chip, and organoid technologies 89 – 92 to propose innovative tools that closely mimic protein expression and distribution observed in the human brain, and increase the translational potential of research studies dedicated to understanding the role of brain barriers in neurodegenerative diseases, including PD. Competing Interest The authors declare no conflicts of interest. Authors contributions (using CRediT taxonomy, available at https://casrai.org/credit ) F.B.: Investigation, Methodology, Formal Analysis, Validation, Visualization, Writing—original draft. O.A.: Investigation, Formal Analysis, Visualization, Writing—original draft. V.G.: Formal Analysis, Visualization. L.R.: Investigation, Methodology, Visualization. O.M.: Investigation. M.A.Y.: Investigation. B.J.: Formal Analysis, Methodology, Visualization. L.F.: Methodology. M.B.: Resources. C.B.: Methodology, Supervision. S.L.: Resources, Writing—review & editing. E.B: Writing—review & editing. B.L.: Resources, Methodology, Writing—review & editing. F.C.: Resources, Writing—review & editing. M.P.: Resources, Methodology, Writing—review & editing. A.D.: Supervision, Resources, Writing—review & editing. A.d.R.J.: Conceptualization, Investigation, Formal Analysis, Methodology, Validation, Visualization, Supervision, Funding acquisition, Writing—original draft. Acknowledgements The authors would like to express their gratitude to the families and people who donated their brains, and therefore enabled the completion of this study. We would also like to express our sincere thanks to Ms. Martine Saint-Pierre for generously sharing her expertise of postmortem immunofluorescence and tissue processing. Funding was provided by the Fondation du CHU de Québec, the Parkinson’s Foundation (Launch Award), and a FRQS Junior 1 Research Scholar Award to AdRJ. OA was supported by a Desjardins and Fondation du CHU de Québec MSc Fellowship. OM was supported by a summer fellowship from the Neuroscience Axis (CRCHUq-UL). MAY was supported by summer fellowships from the Parkinson’s Foundation, MITACS, Sorbonne Université neurosciences. EB is recipient of a Merit award from the Fonds de Recherche en Santé du Québec. Funder Information Declared Fondation du CHU de Québec Parkinson's Foundation, https://ror.org/05mx85j86 Fonds de Recherche du Québec - Santé, https://ror.org/02eqrsj93 Centre de recherche du CHU de Québec-Université Laval, https://ror.org/04rgqcd02 Mitacs, https://ror.org/00cjrc276 Sorbonne Université, https://ror.org/02en5vm52 References 1. ↵ Luo Y , Qiao L , Li M , Wen X , Zhang W , Li X . Global, regional, national epidemiology and trends of Parkinson’s disease from 1990 to 2021: findings from the Global Burden of Disease Study 2021 . Front Aging Neurosci . 2025 ; 16 : 1498756 . doi: 10.3389/FNAGI.2024.1498756 OpenUrl CrossRef PubMed 2. ↵ Poewe W , Seppi K , Tanner CM , et al. Parkinson disease . Nat Rev Dis Primers . 2017 ; 3 : 1 – 21 . doi: 10.1038/NRDP.2017.13 OpenUrl CrossRef 3. ↵ Dang X , Zhang Z , Luo XJ . Mendelian Randomization Study Using Dopaminergic Neuron-Specific eQTL Nominates Potential Causal Genes for Parkinson’s Disease . Mov Disord . 2022 ; 37 ( 12 ): 2451 – 2456 . doi: 10.1002/MDS.29239 OpenUrl CrossRef PubMed 4. ↵ Wang Q , Wang M , Choi I , et al. Molecular profiling of human substantia nigra identifies diverse neuron types associated with vulnerability in Parkinson’s disease . Sci Adv . 2024 ; 10 ( 2 ): 8287 . doi: 10.1126/SCIADV.ADI8287 OpenUrl CrossRef 5. ↵ Lavisse S , Goutal S , Wimberley C , et al. Increased microglial activation in patients with Parkinson disease using [18F]-DPA714 TSPO PET imaging . Parkinsonism Relat Disord . 2021 ; 82 : 29 – 36 . doi: 10.1016/j.parkreldis.2020.11.011 OpenUrl CrossRef PubMed 6. Chen Y , Dai J , Tang L , et al. Neuroimmune transcriptome changes in patient brains of psychiatric and neurological disorders . Mol Psychiatry . 2023 ; 28 ( 2 ): 710 – 721 . doi: 10.1038/S41380-022-01854-7 OpenUrl CrossRef PubMed 7. ↵ Castillo-Rangel C , Marin G , Hernández-Contreras KA , et al. Neuroinflammation in Parkinson’s Disease: From Gene to Clinic: A Systematic Review . International Journal of Molecular Sciences 2023, Vol 24, Page 5792 . 2023 ; 24 ( 6 ): 5792 . doi: 10.3390/IJMS24065792 OpenUrl CrossRef 8. ↵ Lemarchant S , Engelhardt B , Cicchetti F , et al. Restoring brain barriers: an innovative approach for treating neurological disorders . Fluids Barriers CNS . 2025 ; 22 ( 1 ): 1 – 31 . doi: 10.1186/S12987-025-00688-Z OpenUrl CrossRef PubMed 9. Wong YY , Wu CY , Yu D , et al. Biofluid markers of blood-brain barrier disruption and neurodegeneration in Lewy body spectrum diseases: A systematic review and meta-analysis . Parkinsonism Relat Disord . 2022 ; 101 : 119 – 128 . doi: 10.1016/j.parkreldis.2022.06.004 OpenUrl CrossRef PubMed 10. Al-Bachari S , Naish JH , Parker GJM , Emsley HCA , Parkes LM . Blood-Brain Barrier Leakage Is Increased in Parkinson’s Disease . Front Physiol . 2020 ; 11 . doi: 10.3389/FPHYS.2020.593026 OpenUrl CrossRef 11. Janelidze S , Hertze J , Nägga K , et al. Increased blood-brain barrier permeability is associated with dementia and diabetes but not amyloid pathology or APOE genotype . Neurobiol Aging . 2017 ; 51 : 104 – 112 . doi: 10.1016/j.neurobiolaging.2016.11.017 OpenUrl CrossRef PubMed 12. Kuan WL , Bennett N , He X , et al. α-Synuclein pre-formed fibrils impair tight junction protein expression without affecting cerebral endothelial cell function . Exp Neurol . 2016 ; 285 (Pt A ): 72 – 81 . doi: 10.1016/j.expneurol.2016.09.003 OpenUrl CrossRef PubMed 13. Pienaar IS , Lee CH , Elson JL , et al. Deep-brain stimulation associates with improved microvascular integrity in the subthalamic nucleus in Parkinson’s disease . Neurobiol Dis . 2015 ; 74 ( 1 ): 392 – 405 . doi: 10.1016/j.nbd.2014.12.006 OpenUrl CrossRef PubMed 14. Gray MT , Woulfe JM . Striatal blood-brain barrier permeability in Parkinson’s disease . J Cereb Blood Flow Metab . 2015 ; 35 ( 5 ): 747 – 750 . doi: 10.1038/JCBFM.2015.32 OpenUrl CrossRef PubMed 15. ↵ Pisani V , Stefani A , Pierantozzi M , et al. Increased blood-cerebrospinal fluid transfer of albumin in advanced Parkinson’s disease . J Neuroinflammation . 2012 ; 9 ( 1 ): 1 – 4 . doi: 10.1186/1742-2094-9-188 OpenUrl CrossRef PubMed 16. ↵ Kalia L V. , Lang AE. Parkinson’s disease . The Lancet . 2015 ; 386 ( 9996 ): 896 – 912 . doi: 10.1016/S0140-6736(14)61393-3 OpenUrl CrossRef PubMed 17. ↵ Jia F , Fellner A , Kumar KR . Monogenic Parkinson’s Disease: Genotype, Phenotype, Pathophysiology, and Genetic Testing . Genes (Basel) . 2022 ; 13 ( 3 ). doi: 10.3390/GENES13030471 OpenUrl CrossRef 18. ↵ Kim JJ , Vitale D , Otani DV , et al. Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease . Nat Genet . 2024 ; 56 ( 1 ): 27 – 36 . doi: 10.1038/s41588-023-01584-8 OpenUrl CrossRef 19. ↵ Nalls MA , Blauwendraat C , Vallerga CL , et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies . Lancet Neurol . 2019 ; 18 ( 12 ): 1091 – 1102 . doi: 10.1016/S1474-4422(19)30320-5 OpenUrl CrossRef PubMed 20. ↵ Sharma M , Ioannidis JPA , Aasly JO , et al. Large-scale replication and heterogeneity in Parkinson disease genetic loci . Neurology . 2012 ; 79 ( 7 ): 659 . doi: 10.1212/WNL.0B013E318264E353 OpenUrl CrossRef PubMed 21. ↵ Witoelar A , Jansen IE , Wang Y , et al. Genome-wide pleiotropy between Parkinson disease and autoimmune diseases . JAMA Neurol . 2017 ; 74 ( 7 ): 780 – 792 . doi: 10.1001/JAMANEUROL.2017.0469 OpenUrl CrossRef PubMed 22. ↵ Kia DA , Zhang D , Guelfi S , et al. Identification of Candidate Parkinson Disease Genes by Integrating Genome-Wide Association Study, Expression, and Epigenetic Data Sets . JAMA Neurol . 2021 ; 78 ( 4 ): 464 – 472 . doi: 10.1001/JAMANEUROL.2020.5257 OpenUrl CrossRef PubMed 23. ↵ Lee AJ , Kim C , Park S , et al. Characterization of altered molecular mechanisms in Parkinson’s disease through cell type–resolved multiomics analyses . Sci Adv . 2023 ; 9 ( 15 ). doi: 10.1126/SCIADV.ABO2467 OpenUrl CrossRef 24. ↵ Smajic S , Prada-Medina CA , Landoulsi Z , et al. Single-cell sequencing of human midbrain reveals glial activation and a Parkinson-specific neuronal state . Brain . 2022 ; 145 ( 3 ): 964 – 978 . doi: 10.1093/BRAIN/AWAB446 OpenUrl CrossRef PubMed 25. ↵ Reid MM , Menon S , Liu H , et al. Human brain vascular multi-omics elucidates disease-risk associations . Neuron . 2025 ; 0 ( 0 ). doi: 10.1016/J.NEURON.2025.07.001 OpenUrl CrossRef 26. ↵ Al-Bachari S , Naish JH , Parker GJM , Emsley HCA , Parkes LM . Blood–Brain Barrier Leakage Is Increased in Parkinson’s Disease . Front Physiol . 2020 ; 11 : 593026 . doi: 10.3389/FPHYS.2020.593026 OpenUrl CrossRef 27. Lu Z , Yin H , Xiang P , Yi X , Tian X , Gong Q . Multimodal Imaging of Blood–Brain Barrier Dysfunction in Parkinson’s Disease: From Pathological Mechanisms to Therapeutic Opportunities . European Journal of Neuroscience . 2025 ; 62 ( 5 ): e70237 . doi: 10.1111/EJN.70237 OpenUrl CrossRef PubMed 28. Lau K , Kotzur R , Richter F . Blood–brain barrier alterations and their impact on Parkinson’s disease pathogenesis and therapy . Transl Neurodegener . 2024 ; 13 ( 1 ): 1 – 24 . doi: 10.1186/S40035-024-00430-Z OpenUrl CrossRef PubMed 29. ↵ Abbott NJ , Rönnbäck L , Hansson E . Astrocyte-endothelial interactions at the blood-brain barrier . Nat Rev Neurosci . 2006 ; 7 ( 1 ): 41 – 53 . doi: 10.1038/NRN1824 OpenUrl CrossRef PubMed Web of Science 30. ↵ Jeong SH , Park CJ , Jeong HJ , et al. Association of choroid plexus volume with motor symptoms and dopaminergic degeneration in Parkinson’s disease . J Neurol Neurosurg Psychiatry . 2023 ; 94 ( 12 ): 1047 – 1055 . doi: 10.1136/JNNP-2023-331170 OpenUrl Abstract / FREE Full Text 31. ↵ Yang D , Zhu B , Ren J , et al. Investigating the association between choroid plexus volume and the pathogenesis of Parkinson’s disease . Parkinsonism Relat Disord . 2025 ; 139 . doi: 10.1016/J.PARKRELDIS.2025.107987 OpenUrl CrossRef 32. ↵ Tsukita K , Sakamaki-Tsukita H , Kaiser S , et al. High-Throughput CSF Proteomics and Machine Learning to Identify Proteomic Signatures for Parkinson Disease Development and Progression . Neurology . 2023 ; 101 ( 14 ): E1434 – E1447 . doi: 10.1212/WNL.0000000000207725 OpenUrl CrossRef PubMed 33. ↵ Rutledge J , Lehallier B , Zarifkar P , et al. Comprehensive proteomics of CSF, plasma, and urine identify DDC and other biomarkers of early Parkinson’s disease . Acta Neuropathol . 2024 ; 147 ( 1 ). doi: 10.1007/S00401-024-02706-0 OpenUrl CrossRef 34. ↵ Sweeney MD , Sagare AP , Zlokovic B V . Blood-brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders . Nat Rev Neurol . 2018 ; 14 ( 3 ): 133 – 150 . doi: 10.1038/NRNEUROL.2017.188 OpenUrl CrossRef PubMed 35. ↵ Kamath T , Abdulraouf A , Burris SJ , et al. Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson’s disease . Nat Neurosci . 2022 ; 25 ( 5 ): 588 – 595 . doi: 10.1038/S41593-022-01061-1 OpenUrl CrossRef PubMed 36. ↵ Feleke R , Reynolds RH , Smith AM , et al. Cross-platform transcriptional profiling identifies common and distinct molecular pathologies in Lewy body diseases . Acta Neuropathol . 2021 ; 142 ( 3 ): 449 – 474 . doi: 10.1007/S00401-021-02343-X OpenUrl CrossRef PubMed 37. ↵ Yang AC , Kern F , Losada PM , et al. Dysregulation of brain and choroid plexus cell types in severe COVID-19 . Nature . 2021 ; 595 ( 7868 ): 565 – 571 . doi: 10.1038/S41586-021-03710-0 OpenUrl CrossRef PubMed 38. ↵ Cerezo M , Sollis E , Ji Y , et al. The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity . Nucleic Acids Res . 2025 ; 53 ( D1 ): D998 – D1005 . doi: 10.1093/NAR/GKAE1070 OpenUrl CrossRef PubMed 39. ↵ Hao Y , Hao S , Andersen-Nissen E , et al. Integrated analysis of multimodal single-cell data . Cell . 2021 ; 184 ( 13 ): 3573 – 3587.e29 . doi: 10.1016/j.cell.2021.04.048 OpenUrl CrossRef PubMed 40. ↵ Bioconductor - AnnotationDbi . Accessed October 6, 2025 . https://bioconductor.org/packages/release/bioc/html/AnnotationDbi.html 41. ↵ Zhou Y , Zhou B , Pache L , et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets . Nat Commun . 2019 ; 10 ( 1 ). doi: 10.1038/S41467-019-09234-6 OpenUrl CrossRef 42. ↵ McKeith IG , Ferman TJ , Thomas AJ , et al. Research criteria for the diagnosis of prodromal dementia with Lewy bodies . Neurology . 2020 ; 94 ( 17 ): 743 – 755 . doi: 10.1212/WNL.0000000000009323 OpenUrl Abstract / FREE Full Text 43. McKeith IG , Dickson DW , Lowe J , et al. Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium . Neurology . 2005 ; 65 ( 12 ): 1863 – 1872 . doi: 10.1212/01.WNL.0000187889.17253.B1 OpenUrl Abstract / FREE Full Text 44. ↵ McKeith IG , Boeve BF , Dickson DW , et al. Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium . Neurology . 2017 ; 89 ( 1 ): 88 – 100 . doi: 10.1212/WNL.0000000000004058 OpenUrl Abstract / FREE Full Text 45. ↵ de Rus Jacquet A. Preparation and Co-Culture of iPSC-Derived Dopaminergic Neurons and Astrocytes . Curr Protoc Cell Biol . 2019 ; 85 ( 1 ): e98 . doi: 10.1002/CPCB.98 OpenUrl CrossRef PubMed 46. ↵ de Rus Jacquet A , Alpaugh M , Denis HL , et al. The contribution of inflammatory astrocytes to BBB impairments in a brain-chip model of Parkinson’s disease . Nat Commun . 2023 ; 14 ( 1 ): 1 – 21 . doi: 10.1038/S41467-023-39038-8 OpenUrl CrossRef PubMed 47. ↵ Jacquet A de R , Tancredi JL , Lemire AL , Desantis MC , Li WP , O’shea EK. The lrrk2 g2019s mutation alters astrocyte-to-neuron communication via extracellular vesicles and induces neuron atrophy in a human ipsc-derived model of parkinson’s disease . Elife . 2021 ; 10 . doi: 10.7554/ELIFE.73062 OpenUrl CrossRef 48. ↵ Leclerc M , Tremblay C , Bourassa P , Schneider JA , Bennett DA , Calon F . Lower GLUT1 and unchanged MCT1 in Alzheimer’s disease cerebrovasculature . J Cereb Blood Flow Metab . 2024 ; 44 ( 8 ): 1417 – 1432 . doi: 10.1177/0271678X241237484 OpenUrl CrossRef 49. Louise R , Manon L , Vincent E , et al. Higher angiotensin-converting enzyme 2 (ACE2) levels in the brain of individuals with Alzheimer’s disease . Acta Neuropathol Commun . 2023 ; 11 ( 1 ): 159 . doi: 10.1186/S40478-023-01647-1 OpenUrl CrossRef PubMed 50. Leclerc M , Bourassa P , Tremblay C , et al. Cerebrovascular insulin receptors are defective in Alzheimer’s disease . Brain . 2023 ; 146 ( 1 ): 75 – 90 . doi: 10.1093/BRAIN/AWAC309 OpenUrl CrossRef PubMed 51. ↵ Bourassa P , Tremblay C , Schneider JA , Bennett DA , Calon F . Beta-amyloid pathology in human brain microvessel extracts from the parietal cortex: relation with cerebral amyloid angiopathy and Alzheimer’s disease . Acta Neuropathol . 2019 ; 137 ( 5 ): 801 – 823 . doi: 10.1007/S00401-019-01967-4 OpenUrl CrossRef PubMed 52. ↵ Baruch K , Rosenzweig N , Kertser A , et al. Breaking immune tolerance by targeting Foxp3+ regulatory T cells mitigates Alzheimer’s disease pathology . Nat Commun . 2015 ; 6 . doi: 10.1038/ncomms8967 OpenUrl CrossRef PubMed 53. González-Marrero I , Giménez-Llort L , Johanson CE , et al. Choroid plexus dysfunction impairs beta-amyloid clearance in a triple transgenic mouse model of alzheimer’s disease . Front Cell Neurosci . 2015 ; 9 ( FEB ). doi: 10.3389/fncel.2015.00017 OpenUrl CrossRef PubMed 54. Sun Z , Li C , Zhang J , Wisniewski T , Ge Y. Choroid plexus aging: structural and vascular insights from the HCP-aging dataset . Fluids Barriers CNS . 2024 ; 21 ( 1 ). doi: 10.1186/s12987-024-00603-y OpenUrl CrossRef PubMed 55. ↵ Xu Z , Liu C , Wang R , Gao X , Hao C , Liu C . A combination of lycopene and human amniotic epithelial cells can ameliorate cognitive deficits and suppress neuroinflammatory signaling by choroid plexus in Alzheimer’s disease rat . Journal of Nutritional Biochemistry . 2021 ; 88 . doi: 10.1016/j.jnutbio.2020.108558 OpenUrl CrossRef PubMed 56. ↵ Yang AC , Kern F , Losada PM , et al. Dysregulation of brain and choroid plexus cell types in severe COVID-19 . Nature . 2021 ; 595 ( 7868 ): 565 – 571 . doi: 10.1038/s41586-021-03710-0 OpenUrl CrossRef PubMed 57. ↵ May P , Woldt E , Matz RL , Boucher P . The LDL receptor-related protein (LRP) family: An old family of proteins with new physiological functions . Ann Med . 2007 ; 39 ( 3 ): 219 – 228 . doi: 10.1080/07853890701214881 OpenUrl CrossRef PubMed Web of Science 58. ↵ Smajic S , Prada-Medina CA , Landoulsi Z , et al. Single-cell sequencing of human midbrain reveals glial activation and a Parkinson-specific neuronal state . Brain . 2022 ; 145 ( 3 ): 964 – 978 . doi: 10.1093/BRAIN/AWAB446 OpenUrl CrossRef PubMed 59. Dehestani M , Kozareva V , Blauwendraat C , Fraenkel E , Gasser T , Bansal V . Transcriptomic changes in oligodendrocytes and precursor cells associate with clinical outcomes of Parkinson’s disease . Mol Brain . 2024 ; 17 ( 1 ): 1 – 10 . doi: 10.1186/S13041-024-01128-Z OpenUrl CrossRef PubMed 60. ↵ Li YI , Wong G , Humphrey J , Raj T . Prioritizing Parkinson’s disease genes using population-scale transcriptomic data . Nat Commun . 2019 ; 10 ( 1 ): 1 – 10 . doi: 10.1038/S41467-019-08912-9 OpenUrl CrossRef PubMed 61. ↵ Bryois J , Skene NG , Hansen TF , et al. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease . Nat Genet . 2020 ; 52 ( 5 ): 482 – 493 . doi: 10.1038/S41588-020-0610-9 OpenUrl CrossRef PubMed 62. ↵ Ayloo S , Gu C . Transcytosis at the blood–brain barrier . Curr Opin Neurobiol . 2019 ; 57 : 32 – 38 . doi: 10.1016/J.CONB.2018.12.014 OpenUrl CrossRef PubMed 63. ↵ Hansen CG , Shvets E , Howard G , Riento K , Nichols BJ . Deletion of cavin genes reveals tissue-specific mechanisms for morphogenesis of endothelial caveolae . Nat Commun . 2013 ; 4 ( 1 ): 1 – 13 . doi: 10.1038/NCOMMS2808 OpenUrl CrossRef PubMed 64. ↵ Tome ME , Schaefer CP , Jacobs LM , et al. Identification of P-glycoprotein co-fractionating proteins and specific binding partners in rat brain microvessels . J Neurochem . 2015 ; 134 ( 2 ): 200 – 210 . doi: 10.1111/JNC.13106 OpenUrl CrossRef PubMed 65. ↵ Kortekaas R , Leenders KL , Van Oostrom JCH , et al. Blood-brain barrier dysfunction in parkinsonian midbrain in vivo . Ann Neurol . 2005 ; 57 ( 2 ): 176 – 179 . doi: 10.1002/ANA.20369 OpenUrl CrossRef PubMed Web of Science 66. ↵ Bartels AL , Willemsen ATM , Kortekaas R , et al. Decreased blood-brain barrier P-glycoprotein function in the progression of Parkinson’s disease, PSP and MSA . J Neural Transm (Vienna) . 2008 ; 115 ( 7 ): 1001 – 1009 . doi: 10.1007/S00702-008-0030-Y OpenUrl CrossRef PubMed 67. ↵ Ries M , Loiola R , Shah UN , Gentleman SM , Solito E , Sastre M . The anti-inflammatory Annexin A1 induces the clearance and degradation of the amyloid-β peptide . J Neuroinflammation . 2016 ; 13 ( 1 ): 1 – 15 . doi: 10.1186/S12974-016-0692-6 OpenUrl CrossRef PubMed 68. ↵ Ries M , Watts H , Mota BC , et al. Annexin A1 restores cerebrovascular integrity concomitant with reduced amyloid-β and tau pathology . Brain . 2021 ; 144 ( 5 ): 1526 – 1541 . doi: 10.1093/BRAIN/AWAB050 OpenUrl CrossRef PubMed 69. ↵ Shijo M , Hamasaki H , Honda H , et al. Upregulation of Annexin A1 in Reactive Astrocytes and Its Subtle Induction in Microglia at the Boundaries of Human Brain Infarcts . J Neuropathol Exp Neurol . 2019 ; 78 ( 10 ): 961 – 970 . doi: 10.1093/JNEN/NLZ079 OpenUrl CrossRef PubMed 70. ↵ Mantas I , Contestabile A , Skara V , et al. Anxa1+ dopamine neuron vulnerability defines prodromal Parkinson’s disease bradykinesia and procedural motor learning impairment . bioRxiv . Published online December 22 , 2024 : 2024.12.22.629963 . doi: 10.1101/2024.12.22.629963 OpenUrl CrossRef 71. ↵ Fushiki A , Ng D , Lewis ZR , et al. A Vulnerable Subtype of Dopaminergic Neurons Drives Early Motor Deficits in Parkinson’s Disease . bioRxiv . Published online December 21, 2024 . doi: 10.1101/2024.12.20.629776 OpenUrl Abstract / FREE Full Text 72. ↵ Hadjas LC , Kollman GJ , Linderhof L , et al. Parkinson’s Disease-vulnerable and -resilient dopamine neurons display opposite responses to excitatory input . bioRxiv . Published online June 7, 2025 : 2025.06.03.657460 . doi: 10.1101/2025.06.03.657460 OpenUrl Abstract / FREE Full Text 73. ↵ Chen K , Martens YA , Meneses A , et al. LRP1 is a neuronal receptor for α-synuclein uptake and spread . Mol Neurodegener . 2022 ; 17 ( 1 ). doi: 10.1186/S13024-022-00560-W OpenUrl CrossRef 74. ↵ Gu H , Zhong Z , Jiang W , et al. The role of choroid plexus in IVIG-induced beta-amyloid clearance . Neuroscience . 2014 ; 270 : 168 – 176 . doi: 10.1016/j.neuroscience.2014.04.011 OpenUrl CrossRef PubMed 75. ↵ Fujiyoshi M , Tachikawa M , Ohtsuki S , et al. Amyloid-β peptide(1-40) elimination from cerebrospinal fluid involves low-density lipoprotein receptor-related protein 1 at the blood-cerebrospinal fluid barrier . J Neurochem . 2011 ; 118 ( 3 ): 407 – 415 . doi: 10.1111/J.1471-4159.2011.07311.X OpenUrl CrossRef PubMed Web of Science 76. ↵ Luo H , Peng C , Wu C , et al. Low-density lipoprotein receptor-related protein 1 mediates α-synuclein transmission from the striatum to the substantia nigra in animal models of Parkinson’s disease . Neural Regen Res . 2026 ; 21 ( 4 ): 1595 – 1606 . doi: 10.4103/NRR.NRR-D-23-01965 OpenUrl CrossRef PubMed 77. ↵ Huang R , Gao Y , Duan Q , et al. Endothelial LRP1-ICD Accelerates Cognition-Associated Alpha-Synuclein Pathology and Neurodegeneration through PARP1 Activation in a Mouse Model of Parkinson’s Disease . Mol Neurobiol . 2023 ; 60 ( 2 ): 979 – 1003 . doi: 10.1007/S12035-022-03119-4 OpenUrl CrossRef PubMed 78. ↵ Sui YT , Bullock KM , Erickson MA , Zhang J , Banks WA . Alpha synuclein is transported into and out of the brain by the blood-brain barrier . Peptides (NY) . 2014 ; 62 : 197 – 202 . doi: 10.1016/J.PEPTIDES.2014.09.018 OpenUrl CrossRef 79. ↵ Lemée JM , Clavreul A , Aubry M , et al. Integration of transcriptome and proteome profiles in glioblastoma: Looking for the missing link . BMC Mol Biol . 2018 ; 19 ( 1 ): 1 – 10 . doi: 10.1186/S12867-018-0115-6 OpenUrl CrossRef PubMed 80. Bauernfeind AL , Babbitt CC . The predictive nature of transcript expression levels on protein expression in adult human brain . BMC Genomics . 2017 ; 18 ( 1 ): 1 – 11 . doi: 10.1186/S12864-017-3674-X OpenUrl CrossRef PubMed 81. Moritz CP , Mühlhaus T , Tenzer S , Schulenborg T , Friauf E . Poor transcript-protein correlation in the brain: negatively correlating gene products reveal neuronal polarity as a potential cause . J Neurochem . 2019 ; 149 ( 5 ): 582 – 604 . doi: 10.1111/JNC.14664 OpenUrl CrossRef PubMed 82. Liang Q , Jiang Y , Shieh AW , et al. The impact of common variants on gene expression in the human brain: from RNA to protein to schizophrenia risk . bioRxiv. Published online June 5 , 2023 . doi: 10.1101/2023.06.04.543603 OpenUrl Abstract / FREE Full Text 83. ↵ Khatir I , Brunet MA , Meller A , et al. Decoupling of mRNA and Protein Expression in Aging Brains Reveals the Age-Dependent Adaptation of Specific Gene Subsets . Cells . 2023 ; 12 ( 4 ): 615 . doi: 10.3390/CELLS12040615 OpenUrl CrossRef 84. ↵ Bauernfeind AL , Babbitt CC . The predictive nature of transcript expression levels on protein expression in adult human brain . BMC Genomics . 2017 ; 18 ( 1 ): 1 – 11 . doi: 10.1186/S12864-017-3674-X OpenUrl CrossRef PubMed 85. ↵ Bauernfeind AL , Soderblom EJ , Turner ME , et al. Evolutionary Divergence of Gene and Protein Expression in the Brains of Humans and Chimpanzees . Genome Biol Evol . 2015 ; 7 ( 8 ): 2276 – 2288 . doi: 10.1093/GBE/EVV132 OpenUrl CrossRef PubMed 86. ↵ Zaccaria A , Antinori P , Licker V , Kövari E , Lobrinus JA , Burkhard PR . Multiomic Analyses of Dopaminergic Neurons Isolated from Human Substantia Nigra in Parkinson’s Disease: A Descriptive and Exploratory Study . Cell Mol Neurobiol . 2022 ; 42 ( 8 ): 2805 – 2818 . doi: 10.1007/S10571-021-01146-8 OpenUrl CrossRef PubMed 87. ↵ Liu Y , Beyer A , Aebersold R . On the Dependency of Cellular Protein Levels on mRNA Abundance . Cell . 2016 ; 165 ( 3 ): 535 – 550 . doi: 10.1016/j.cell.2016.03.014 OpenUrl CrossRef PubMed 88. ↵ Zhang W , Liu QY , Haqqani AS , et al. Differential expression of receptors mediating receptor-mediated transcytosis (RMT) in brain microvessels, brain parenchyma and peripheral tissues of the mouse and the human . Fluids Barriers CNS . 2020 ; 17 ( 1 ): 1 – 17 . doi: 10.1186/S12987-020-00209-0 OpenUrl CrossRef PubMed 89. ↵ de Rus Jacquet A , Alpaugh M , Denis HL , et al. The contribution of inflammatory astrocytes to BBB impairments in a brain-chip model of Parkinson’s disease . Nat Commun . 2023 ; 14 ( 1 ): 1 – 21 . doi: 10.1038/S41467-023-39038-8 OpenUrl CrossRef PubMed 90. Vetter J , Palagi I , Waisman A , Blaeser A . Recent advances in blood-brain barrier-on-a-chip models . Acta Biomater . 2025 ; 197 : 1 – 28 . doi: 10.1016/j.actbio.2025.03.041 OpenUrl CrossRef PubMed 91. Gong L , Zhang Y , Zhu Y , et al. Rapid generation of functional vascular organoids via simultaneous transcription factor activation of endothelial and mural lineages . Cell Stem Cell . 2025 ; 32 ( 8 ): 1200 – 1217.e6 . doi: 10.1016/j.stem.2025.05.014 OpenUrl CrossRef 92. ↵ Wimmer RA , Leopoldi A , Aichinger M , et al. Human blood vessel organoids as a model of diabetic vasculopathy . Nature . 2019 ; 565 ( 7740 ): 505 – 510 . doi: 10.1038/S41586-018-0858-8 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted October 21, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Parkinson’s disease risk factors are expressed at brain barriers Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Parkinson’s disease risk factors are expressed at brain barriers Floriane Bretheau , Océane Arevalo , Vincent Gélinas , Louise Reveret , Océane Martinez , Marlie Ali Yandza , Bastien Jacquet , Léa Ferreira , Morgan Bérard , Charles Joly Beauparlant , Steve Lacroix , Éric Boilard , Benoit Laurent , Frederic Calon , Martin Parent , Arnaud Droit , Aurélie de Rus Jacquet bioRxiv 2025.10.20.683561; doi: https://doi.org/10.1101/2025.10.20.683561 Share This Article: Copy Citation Tools Parkinson’s disease risk factors are expressed at brain barriers Floriane Bretheau , Océane Arevalo , Vincent Gélinas , Louise Reveret , Océane Martinez , Marlie Ali Yandza , Bastien Jacquet , Léa Ferreira , Morgan Bérard , Charles Joly Beauparlant , Steve Lacroix , Éric Boilard , Benoit Laurent , Frederic Calon , Martin Parent , Arnaud Droit , Aurélie de Rus Jacquet bioRxiv 2025.10.20.683561; doi: https://doi.org/10.1101/2025.10.20.683561 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7629) Biochemistry (17660) Bioengineering (13881) Bioinformatics (41911) Biophysics (21436) Cancer Biology (18578) Cell Biology (25482) Clinical Trials (138) Developmental Biology (13371) Ecology (19887) Epidemiology (2067) Evolutionary Biology (24302) Genetics (15599) Genomics (22483) Immunology (17728) Microbiology (40364) Molecular Biology (17163) Neuroscience (88537) Paleontology (666) Pathology (2830) Pharmacology and Toxicology (4821) Physiology (7637) Plant Biology (15129) Scientific Communication and Education (2045) Synthetic Biology (4290) Systems Biology (9817) Zoology (2269)