Abstract
Age-related decline in brain endothelial cell (BEC) function contributes critically to neurological disease. Comprehensive atlases of the BEC transcriptome have become available, but results from proteomic profiling are lacking. To gain insights into endothelial pathways affected by aging, we developed a magnetic-activated cell sorting-based mouse BEC enrichment protocol compatible with proteomics and resolved the profiles of protein abundance changes during aging. Unsupervised cluster analysis revealed a segregation of age-related protein dynamics with biological functions, including a downregulation of vesicle-mediated transport. We found a dysregulation of key regulators of endocytosis and receptor recycling (most prominently Arf6), macropinocytosis and lysosomal degradation. In gene deletion and overexpression experiments, Arf6 affected endocytosis pathways in endothelial cells. Our approach uncovered changes not picked up by transcriptomic studies, such as accumulation of vesicle cargo and receptor ligands, including Apoe. Proteomic analysis of BECs from Apoe-deficient mice revealed a signature of accelerated aging. Our findings provide a resource for analysing BEC function during aging.
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Data availability
The datasets generated through this work are available in a publicly accessible repository. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE123 partner repository with the dataset identifiers PXD045026, PXD045006, PXD045004, PXD044996 and PXD044993. All data supporting the findings described in this article are available in the article itself and in the supplementary materials and from the corresponding author upon reasonable request. Source data are provided with this article.
References
Sweeney, M. D., Zhao, Z., Montagne, A., Nelson, A. R. & Zlokovic, B. V. Blood–brain barrier: from physiology to disease and back. Physiol. Rev. 99, 21–78 (2019).
Siegenthaler, J. A., Sohet, F. & Daneman, R. ‘Sealing off the CNS’: cellular and molecular regulation of blood–brain barriergenesis. Curr. Opin. Neurobiol. 23, 1057–1064 (2013).
Iadecola, C. The neurovascular unit coming of age: a journey through neurovascular coupling in health and disease. Neuron 96, 17–42 (2017).
Dunn, K. M. & Nelson, M. T. Neurovascular signaling in the brain and the pathological consequences of hypertension. Am. J. Physiol. Heart. Circ. Physiol. 306, H1–H14 (2014).
Mattson, M. P. & Arumugam, T. V. Hallmarks of brain aging: adaptive and pathological modification by metabolic states. Cell Metab. 27, 1176–1199 (2018).
Lucin, K. M. & Wyss-Coray, T. Immune activation in brain aging and neurodegeneration: too much or too little? Neuron 64, 110–122 (2009).
Sweeney, M. D., Sagare, A. P. & Zlokovic, B. V. Blood–brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat. Rev. Neurol. 14, 133–150 (2018).
Jia, G., Aroor, A. R., Jia, C. & Sowers, J. R. Endothelial cell senescence in aging-related vascular dysfunction. Biochim. Biophys. Acta Mol. Basis Dis. 1865, 1802–1809 (2019).
Ungvari, Z. et al. Endothelial dysfunction and angiogenesis impairment in the ageing vasculature. Nat. Rev. Cardiol. 15, 555–565 (2018).
Donato, A. J., Machin, D. R. & Lesniewski, L. A. Mechanisms of dysfunction in the aging vasculature and role in age-related disease. Circ. Res. 123, 825–848 (2018).
Graves, S. I. & Baker, D. J. Implicating endothelial cell senescence to dysfunction in the ageing and diseased brain. Basic Clin. Pharmacol. Toxicol. 127, 102–110 (2020).
Costea, L. et al. The blood–brain barrier and its intercellular junctions in age-related brain disorders. Int. J. Mol. Sci. 20, 5472 (2019).
Goodall, E. F. et al. Age-associated changes in the blood–brain barrier: comparative studies in human and mouse. Neuropathol. Appl. Neurobiol. 44, 328–340 (2018).
Banks, W. A., Reed, M. J., Logsdon, A. F., Rhea, E. M. & Erickson, M. A. Healthy aging and the blood–brain barrier. Nat. Aging 1, 243–254 (2021).
Tarantini, S., Tran, C. H. T., Gordon, G. R., Ungvari, Z. & Csiszar, A. Impaired neurovascular coupling in aging and Alzheimer’s disease: contribution of astrocyte dysfunction and endothelial impairment to cognitive decline. Exp. Gerontol. 94, 52–58 (2017).
Tarumi, T. & Zhang, R. Cerebral blood flow in normal aging adults: cardiovascular determinants, clinical implications, and aerobic fitness. J. Neurochem. 144, 595–608 (2018).
Brandes, R. P., Fleming, I. & Busse, R. Endothelial aging. Cardiovasc. Res. 66, 286–294 (2005).
Donato, A. J., Morgan, R. G., Walker, A. E. & Lesniewski, L. A. Cellular and molecular biology of aging endothelial cells. J. Mol. Cell. Cardiol. 89, 122–135 (2015).
Di Micco, R., Krizhanovsky, V., Baker, D. & d’Adda di Fagagna, F. Cellular senescence in ageing: from mechanisms to therapeutic opportunities. Nat. Rev. Mol. Cell Biol. 22, 75–95 (2021).
Slack, R. J., Macdonald, S. J. F., Roper, J. A., Jenkins, R. G. & Hatley, R. J. D. Emerging therapeutic opportunities for integrin inhibitors. Nat. Rev. Drug Discov. 21, 60–78 (2021).
Shin, E. Y. et al. Integrin-mediated adhesions in regulation of cellular senescence. Sci. Adv. 6, eaay3909 (2020).
Hafezi-Moghadam, A., Thomas, K. L. & Wagner, D. D. ApoE deficiency leads to a progressive age-dependent blood-brain barrier leakage. Am. J. Physiol. Cell Physiol. 292, C1256–C1262 (2007).
Bell, R. D. et al. Apolipoprotein E controls cerebrovascular integrity via cyclophilin A. Nature 485, 512–516 (2012).
Migrino, R. Q. et al. Amyloidogenic medin induces endothelial dysfunction and vascular inflammation through the receptor for advanced glycation endproducts. Cardiovasc. Res. 113, 1389–1402 (2017).
Degenhardt, K. et al. Medin aggregation causes cerebrovascular dysfunction in aging wild-type mice. Proc. Natl Acad. Sci. USA 117, 23925–23931 (2020).
Migrino, R. Q. et al. Cerebrovascular medin is associated with Alzheimer’s disease and vascular dementia. Alzheimers Dement. (Amst.) 12, e12072 (2020).
Tabula Muris, C. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).
Ximerakis, M. et al. Single-cell transcriptomic profiling of the aging mouse brain. Nat. Neurosci. 22, 1696–1708 (2019).
Angelidis, I. et al. An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics. Nat. Commun. 10, 963 (2019).
Yang, A. C. et al. Physiological blood–brain transport is impaired with age by a shift in transcytosis. Nature 583, 425–430 (2020).
Chen, M. B. et al. Brain endothelial cells are exquisite sensors of age-related circulatory cues. Cell Rep. 30, 4418–4432 e4414 (2020).
Zhao, L. et al. Pharmacologically reversible zonation-dependent endothelial cell transcriptomic changes with neurodegenerative disease associations in the aged brain. Nat. Commun. 11, 4413 (2020).
Aldridge, S. & Teichmann, S. A. Single cell transcriptomics comes of age. Nat. Commun. 11, 4307 (2020).
Carlyle, B. C. et al. A multiregional proteomic survey of the postnatal human brain. Nat. Neurosci. 20, 1787–1795 (2017).
Maier, T., Guell, M. & Serrano, L. Correlation of mRNA and protein in complex biological samples. FEBS Lett. 583, 3966–3973 (2009).
de Sousa Abreu, R., Penalva, L. O., Marcotte, E. M. & Vogel, C. Global signatures of protein and mRNA expression levels. Mol. Biosyst. 5, 1512–1526 (2009).
Vogel, C. & Marcotte, E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13, 227–232 (2012).
Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019).
McDowell, I. C. et al. Clustering gene expression time series data using an infinite Gaussian process mixture model. PLoS Comput. Biol. 14, e1005896 (2018).
Rayon, T. et al. Species-specific pace of development is associated with differences in protein stability. Science 369, eaba7667 (2020).
Kim, D. S. et al. The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation. Nat. Genet. 53, 1564–1576 (2021).
Shcherbina, A. et al. Dissecting murine muscle stem cell aging through regeneration using integrative genomic analysis. Cell Rep. 32, 107964 (2020).
Garcia-Cabezas, M. A., John, Y. J., Barbas, H. & Zikopoulos, B. Distinction of neurons, glia and endothelial cells in the cerebral cortex: an algorithm based on cytological features. Front. Neuroanat. 10, 107 (2016).
Bjørnholm, K. D. et al. A robust and efficient microvascular isolation method for multimodal characterization of the mouse brain vasculature. Cell Rep. Methods 3, 100431 (2023).
Yousef, H. et al. Aged blood impairs hippocampal neural precursor activity and activates microglia via brain endothelial cell VCAM1. Nat. Med. 25, 988–1000 (2019).
Schaum, N. et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020).
Zhou, T. et al. Microvascular endothelial cells engulf myelin debris and promote macrophage recruitment and fibrosis after neural injury. Nat. Neurosci. 22, 421–435 (2019).
Safaiyan, S. et al. Age-related myelin degradation burdens the clearance function of microglia during aging. Nat. Neurosci. 19, 995–998 (2016).
Khalil, M. et al. Serum neurofilament light levels in normal aging and their association with morphologic brain changes. Nat. Commun. 11, 812 (2020).
Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).
Chen, Z. et al. Reciprocal regulation of eNOS and caveolin-1 functions in endothelial cells. Mol. Biol. Cell 29, 1190–1202 (2018).
D’Souza-Schorey, C. & Chavrier, P. ARF proteins: roles in membrane traffic and beyond. Nat. Rev. Mol. Cell Biol. 7, 347–358 (2006).
Donaldson, J. G. Multiple roles for Arf6: sorting, structuring, and signaling at the plasma membrane. J. Biol. Chem. 278, 41573–41576 (2003).
Schweitzer, J. K. & D’Souza-Schorey, C. A requirement for ARF6 during the completion of cytokinesis. Exp. Cell. Res. 311, 74–83 (2005).
Korbelin, J. et al. A brain microvasculature endothelial cell-specific viral vector with the potential to treat neurovascular and neurological diseases. EMBO Mol. Med. 8, 609–625 (2016).
Govindpani, K. et al. Vascular dysfunction in Alzheimer’s disease: a prelude to the pathological process or a consequence of it? J. Clin. Med. 8, 651 (2019).
Malek, N. et al. Vascular disease and vascular risk factors in relation to motor features and cognition in early Parkinson’s disease. Mov. Disord. 31, 1518–1526 (2016).
Iadecola, C. The pathobiology of vascular dementia. Neuron 80, 844–866 (2013).
Cohen, R. M., Small, C., Lalonde, F., Friz, J. & Sunderland, T. Effect of apolipoprotein E genotype on hippocampal volume loss in aging healthy women. Neurology 57, 2223–2228 (2001).
Espeseth, T. et al. Accelerated age-related cortical thinning in healthy carriers of apolipoprotein E ε4. Neurobiol. Aging 29, 329–340 (2008).
Love, S. et al. Premorbid effects of APOE on synaptic proteins in human temporal neocortex. Neurobiol. Aging 27, 797–803 (2006).
den Heijer, T. et al. Hippocampal, amygdalar, and global brain atrophy in different apolipoprotein E genotypes. Neurology 59, 746–748 (2002).
Laursen, J. B. et al. Endothelial regulation of vasomotion in apoE-deficient mice: implications for interactions between peroxynitrite and tetrahydrobiopterin. Circulation 103, 1282–1288 (2001).
d’Uscio, L. V. et al. Mechanism of endothelial dysfunction in apolipoprotein E-deficient mice. Arterioscler. Thromb. Vasc. Biol. 21, 1017–1022 (2001).
Ohashi, M., Runge, M. S., Faraci, F. M. & Heistad, D. D. MnSOD deficiency increases endothelial dysfunction in ApoE-deficient mice. Arterioscler. Thromb. Vasc. Biol. 26, 2331–2336 (2006).
Van Acker, T., Tavernier, J. & Peelman, F. The small GTPase Arf6: an overview of its mechanisms of action and of its role in host–pathogen interactions and innate immunity. Int. J. Mol. Sci. 20, 2209 (2019).
Palacios, F., Price, L., Schweitzer, J., Collard, J. G. & D’Souza-Schorey, C. An essential role for ARF6-regulated membrane traffic in adherens junction turnover and epithelial cell migration. EMBO J. 20, 4973–4986 (2001).
D’Souza-Schorey, C., Li, G., Colombo, M. I. & Stahl, P. D. A regulatory role for ARF6 in receptor-mediated endocytosis. Science 267, 1175–1178 (1995).
Song, L. & Pachter, J. S. Monocyte chemoattractant protein-1 alters expression of tight junction-associated proteins in brain microvascular endothelial cells. Microvasc. Res. 67, 78–89 (2004).
Crewe, C. et al. An endothelial-to-adipocyte extracellular vesicle axis governed by metabolic state. Cell 175, 695–708 (2018).
Mathiesen, A. et al. Endothelial extracellular vesicles: from keepers of health to messengers of disease. Int. J. Mol. Sci. 22, 4640 (2021).
Blackburn, J. B., D’Souza, Z. & Lupashin, V. V. Maintaining order: COG complex controls Golgi trafficking, processing, and sorting. FEBS Lett. 593, 2466–2487 (2019).
Wagner, J. et al. Overexpression of the novel senescence marker β-galactosidase (GLB1) in prostate cancer predicts reduced PSA recurrence. PLoS ONE 10, e0124366 (2015).
Debacq-Chainiaux, F., Erusalimsky, J. D., Campisi, J. & Toussaint, O. Protocols to detect senescence-associated beta-galactosidase (SA-βgal) activity, a biomarker of senescent cells in culture and in vivo. Nat. Protoc. 4, 1798–1806 (2009).
Hipp, M. S., Kasturi, P. & Hartl, F. U. The proteostasis network and its decline in ageing. Nat. Rev. Mol. Cell Biol. 20, 421–435 (2019).
Wan, B. et al. GIT1 protects traumatically injured spinal cord by prompting microvascular endothelial cells to clear myelin debris. Aging (Albany NY) 13, 7067–7083 (2021).
Johnsen, K. B., Burkhart, A., Thomsen, L. B., Andresen, T. L. & Moos, T. Targeting the transferrin receptor for brain drug delivery. Prog. Neurobiol. 181, 101665 (2019).
Terstappen, G. C., Meyer, A. H., Bell, R. D. & Zhang, W. Strategies for delivering therapeutics across the blood-brain barrier. Nat. Rev. Drug Discov. 20, 362–383 (2021).
Okuyama, T. et al. Iduronate-2-sulfatase with anti-human transferrin receptor antibody for neuropathic mucopolysaccharidosis II: a phase 1/2 trial. Mol. Ther. 27, 456–464 (2019).
Kariolis, M. S. et al. Brain delivery of therapeutic proteins using an Fc fragment blood–brain barrier transport vehicle in mice and monkeys. Sci. Transl. Med. 12, eaay1359 (2020).
Humphries, J. D., Byron, A. & Humphries, M. J. Integrin ligands at a glance. J. Cell Sci. 119, 3901–3903 (2006).
Smith, J. W., Ruggeri, Z. M., Kunicki, T. J. & Cheresh, D. A. Interaction of integrins αvβ3 and glycoprotein IIb-IIIa with fibrinogen. Differential peptide recognition accounts for distinct binding sites. J. Biol. Chem. 265, 12267–12271 (1990).
Silvestre, J. S. et al. Lactadherin promotes VEGF-dependent neovascularization. Nat. Med. 11, 499–506 (2005).
Marazuela, P. et al. MFG-E8 (LACTADHERIN): a novel marker associated with cerebral amyloid angiopathy. Acta Neuropathol. Commun. 9, 154 (2021).
Vanlandewijck, M. et al. Author Correction: A molecular atlas of cell types and zonation in the brain vasculature. Nature 560, E3 (2018).
Hanayama, R. et al. Identification of a factor that links apoptotic cells to phagocytes. Nature 417, 182–187 (2002).
Tai, L. M. et al. The role of APOE in cerebrovascular dysfunction. Acta Neuropathol. 131, 709–723 (2016).
Yang, A. C. et al. A human brain vascular atlas reveals diverse cell mediators of Alzheimeras disease risk. Nature 603, 885–892 (2022).
Halliday, M. R. et al. Accelerated pericyte degeneration and blood–brain barrier breakdown in apolipoprotein E4 carriers with Alzheimer’s disease. J. Cereb. Blood Flow Metab. 36, 216–227 (2016).
Carvalho, C. & Moreira, P. I. Oxidative stress: a major player in cerebrovascular alterations associated to neurodegenerative events. Front. Physiol. 9, 806 (2018).
Finkel, T. & Holbrook, N. J. Oxidants, oxidative stress and the biology of ageing. Nature 408, 239–247 (2000).
Johnson, A. A. & Stolzing, A. The role of lipid metabolism in aging, lifespan regulation, and age-related disease. Aging Cell 18, e13048 (2019).
Bazzoni, G. & Dejana, E. Endothelial cell-to-cell junctions: molecular organization and role in vascular homeostasis. Physiol. Rev. 84, 869–901 (2004).
Xiao, K. et al. p120-catenin regulates clathrin-dependent endocytosis of VE-cadherin. Mol. Biol. Cell 16, 5141–5151 (2005).
Iyer, S., Ferreri, D. M., DeCocco, N. C., Minnear, F. L. & Vincent, P. A. VE-cadherin-p120 interaction is required for maintenance of endothelial barrier function. Am. J. Physiol. Lung Cell. Mol. Physiol. 286, L1143–L1153 (2004).
Armulik, A. et al. Pericytes regulate the blood–brain barrier. Nature 468, 557–561 (2010).
Daneman, R. et al. The mouse blood–brain barrier transcriptome: a new resource for understanding the development and function of brain endothelial cells. PLoS ONE 5, e13741 (2010).
Diz, A. P., Truebano, M. & Skibinski, D. O. F. The consequences of sample pooling in proteomics: an empirical study. Electrophoresis 30, 2967–2975 (2009).
Jang, S., Collin de l’Hortet, A. & Soto-Gutierrez, A. Induced pluripotent stem cell-derived endothelial cells: overview, current advances, applications, and future directions. Am. J. Pathol. 189, 502–512 (2019).
Marquer, C. et al. Arf6 controls retromer traffic and intracellular cholesterol distribution via a phosphoinositide-based mechanism. Nat. Commun. 7, 11919 (2016).
Sorensen, I., Adams, R. H. & Gossler, A. DLL1-mediated Notch activation regulates endothelial identity in mouse fetal arteries. Blood 113, 5680–5688 (2009).
Monet-Lepretre, M. et al. Abnormal recruitment of extracellular matrix proteins by excess Notch3 ECD: a new pathomechanism in CADASIL. Brain 136, 1830–1845 (2013).
Zellner, A. et al. CADASIL brain vessels show a HTRA1 loss-of-function profile. Acta Neuropathol. 136, 111–125 (2018).
Hughes, C. S. et al. Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat. Protoc. 14, 68–85 (2019).
Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 13, 2513–2526 (2014).
Sinitcyn, P. et al. MaxDIA enables library-based and library-free data-independent acquisition proteomics. Nat. Biotechnol. 39, 1563–1573 (2021).
Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 17, 41–44 (2020).
Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116–5121 (2001).
Jiao, X. et al. DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics 28, 1805–1806 (2012).
Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z. & Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 47, W199–W205 (2019).
Jourquin, J., Duncan, D., Shi, Z. & Zhang, B. GLAD4U: deriving and prioritizing gene lists from PubMed literature. BMC Genomics 13, S20 (2012).
Schmid, B. et al. Generation of a set of isogenic, gene-edited iPSC lines homozygous for all main APOE variants and an APOE knock-out line. Stem Cell Res. 34, 101349 (2019).
Paquet, D. et al. Efficient introduction of specific homozygous and heterozygous mutations using CRISPR/Cas9. Nature 533, 125–129 (2016).
Kwart, D., Paquet, D., Teo, S. & Tessier-Lavigne, M. Precise and efficient scarless genome editing in stem cells using CORRECT. Nat. Protoc. 12, 329–354 (2017).
Steyer, B., Cory, E. & Saha, K. Developing precision medicine using scarless genome editing of human pluripotent stem cells. Drug Discov. Today Technol. 28, 3–12 (2018).
Skarnes, W. C., Pellegrino, E. & McDonough, J. A. Improving homology-directed repair efficiency in human stem cells. Methods 164-165, 18–28 (2019).
Concordet, J. P. & Haeussler, M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46, W242–W245 (2018).
Weisheit, I. et al. Simple and reliable detection of CRISPR-induced on-target effects by qgPCR and SNP genotyping. Nat. Protoc. 16, 1714–1739 (2021).
Moorhead, A. M., Jung, J. Y., Smirnov, A., Kaufer, S. & Scidmore, M. A. Multiple host proteins that function in phosphatidylinositol-4-phosphate metabolism are recruited to the chlamydial inclusion. Infect. Immun. 78, 1990–2007 (2010).
Challis, R. C. et al. Systemic AAV vectors for widespread and targeted gene delivery in rodents. Nat. Protoc. 14, 379–414 (2019).
Xiao, X., Li, J. & Samulski, R. J. Production of high-titer recombinant adeno-associated virus vectors in the absence of helper adenovirus. J. Virol. 72, 2224–2232 (1998).
Kislinger, G. et al. Multiscale ATUM-FIB microscopy enables targeted ultrastructural analysis at isotropic resolution. iScience 23, 101290 (2020).
Perez-Riverol, Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543–D552 (2022).
Acknowledgements
We thank T. Webb for technical advice for the endothelial cell differentiation protocol; Y. Asare and U. Schillinger for help with the AAV injection; and B. Lindner, A. Berghofer, L. Peischer, M. Schneider and A. Nottebrock for technical assistance. This study received funding from the European Union’s Horizon 2020 Research and Innovation Program SVDs@target (no. 666881, to M.D.); the European Innovation Council program (grant agreement no. 101115381, to M.D.); the Deutsche Forschungsgemeinschaft (DFG), as part of the Munich Cluster for Systems Neurology (SyNergy; EXC 2145 SyNergy – ID 390857198, to M.D.), and individual project grants (DI 722/13-1; DI 722/16-1 and BE 6169/1-1, to M.D.); the Vascular Dementia Research Foundation, through the Federal Ministry for Education and Research (BMBF, CLINSPECT-M, to M.D.); a grant from the Leducq Foundation (grant agreement N022CVD01, to M.D.); ERA-NET Neuron (MatriSVDs, to M.D.); and the LMUexcellent fund (to M.D.). Graphics from Figs. 1a, 3a,b and 4a,e were created with Biorender.
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Contributions
K.T.V., J.G.G. and M.D. designed the project. S.A.M. and A.S. performed mass spectrometry. K.T.V., R.M. and N.B. analyzed proteomics experiments. K.T.V. and J.G.G. performed and analyzed biochemical and immunocytochemistry experiments. J.G.G., D.C. and S.R. performed gene editing and analyzed cell culture experiments. M.S. performed electron microscopy. M.I.T. designed and established the publicly available database. J.K. provided AAV-BR1 and F.B. produced Arf6-AAV for the in vivo experiments. M.D., S.L., D.P., M.S., C.H. and A.E. supervised the experiments. K.T.V., J.G.G. and M.D. wrote the manuscript. All authors read and revised the manuscript.
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Extended data
Extended Data Fig. 1 Unsupervised clustering of age-related protein dynamics of BECs (all clusters).
Clusters illustrating variations in the pattern of age-related protein abundance changes. (Blue line: cluster mean, light blue band: confidence interval, yellow line: all quantified, red line: significant protein abundance in each cluster).
Extended Data Fig. 2 Mass spectrometry-based analysis of region-specific changes in Tfrc levels.
Following proteomic analysis of BECs from 18- vs 3-month-old mice, the signal intensity of Tfrc-related tryptic peptides was mapped onto the protein sequence. The mean intensity in samples from 3-month-old mice was set to 1. Significance was tested by Student´s t-test. The intracellular (aa 1–67), transmembrane (aa 68–88) and extracellular (aa 89–763) regions of Tfrc are labeled.
Extended Data Fig. 3 CRISPR/Cas9-mediated genome editing of human iPSCs to ARF6-KO.
a. ARF6 knockout strategy: Exon 2 of ARF6 was targeted by an sgRNA (target and PAM sequence shown). After editing, one base pair insertion in each allele was generated, producing a premature stop codon. b. Immunofluorescence analysis of pluripotency markers, SSEA4, NANOG, TRA160, and OCT 4 with DAPI in ARF6 KO iPSC line. Scale bar 50 µm. Experiment was repeated 4 times. c. List of top five most similar off-target sites ranked by the CFD and MIT prediction scores. Sanger sequencing detected no off-target editing.
Extended Data Fig. 4 Karyotyping of ARF6-KO edited line.
Each chromosome is presented with B allele frequencies (BAF) and Log R rations in the ARF6-KO iPSC line. Blue dots indicate all measured SNPs. For all chromosomes, BAF values indicate normal zygosities and Log R rations the absence of detectable insertions or deletions.
Extended Data Fig. 5 CRISPR/Cas9-mediated genome editing of human iPSCs to APOE-KO.
a. APOE knockout strategy: Exon 2 of APOE was targeted by an sgRNA (target and PAM sequence shown), generating a fourteen base pair deletion on one allele and a 13 base pair deletion on the other allele in the APOE KO line. The resulting frameshift exposes a nearby stop codon. b. Immunofluorescence analysis of pluripotency markers, SSEA4, NANOG, TRA160, and OCT 4 with DAPI in APOE KO iPSC line. Scale bar 50 µm. Experiment was repeated 4 times. c. Investigating CRISPR-mediated on-target effects using Sanger sequencing of SNPs near the edited locus in WT and APOE KO iPSC lines showing maintenance of both alleles after editing. d. List of top five most similar off-target sites ranked by the CFD and MIT prediction scores, respectively. Sanger sequencing detected no off-target editing.
Extended Data Fig. 6 Karyotyping of APOE-KO edited line.
Analysis of B allele frequencies (BAF) and Log R ratios for all chromosomes in the APOE KO iPSC line. Blue dots indicate all measured SNPs. All chromosomes show the absence of detectable insertions or deletions, showed by Log R ratios, while BAF values indicate for all chromosomes normal zygosities.
Supplementary information
Supplementary Table 1
All quantified proteins of BECs versus FT from 3-month-old WT mice. Yellow highlight: significant protein differences between BEC and FT preparation (FDR P < 0.05). Gray highlight: proteins are identified only in BEC preparation (comparison by two-tailed unpaired t-test, P < 0.05).
Supplementary Table 2
All quantified proteins of BECs from 3-, 6-, 12- and 18-month-old WT mice based on clusters. Red highlight: significant protein alterations during aging (comparison by ANOVA followed by Tukey’s multiple comparisons test, P < 0.05, or t-test, P < 0.05).
Supplementary Table 3
All quantified proteins of BVs from 3-month-old and 12-month-old WT mice. Yellow highlight: significant protein alterations between 12-month-old and 3-month-old WT mice (comparison by two-tailed unpaired t-test, P < 0.05).
Supplementary Table 4
All quantified proteins of BECs from Arf6-KO and WT mice. Red highlight: significant protein alterations between Arf6-KO and WT mice (comparison by two-tailed unpaired t-test, P < 0.05).
Supplementary Table 5
All quantified proteins of BECs from Arf6-GFP-AAV-treated and GFP-AAV-treated WT mice. Red highlight: significant protein alterations between Arf6-GFP-AAV-treated and GFP-AAV-treated mice (comparison by two-tailed unpaired t-test, P < 0.05).
Supplementary Table 6
All quantified proteins from GFP-AAV-treated ARF6-KO, GFP-AAV-treated WT and Arf6-GFP-AAV-treated WT iECs. Red highlight: significant protein alterations between GFP-AAV-treated ARF6-KO and WT iECs as well as between Arf6-GFP-AAV-treated and GFP-AAV-treated WT iECs (comparison by two-tailed unpaired t-test, P < 0.05).
Supplementary Table 7
All quantified proteins of BECs from 3-month-old Apoe-KO and WT mice. Red highlight: significantly altered proteins in Apoe-KO compared to WT mice (comparison by two-tailed unpaired t-test, P < 0.05).
Supplementary Table 8
All quantified proteins from APOE-KO and WT iECs. Red highlight: significant protein alterations between APOE-KO and WT iECs (comparison by two-tailed unpaired t-test, P < 0.05).
Supplementary Table 9
Details of primary and secondary antibodies for all experiments in the present study.
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Unprocessed western blots
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Todorov-Völgyi, K., González-Gallego, J., Müller, S.A. et al. Proteomics of mouse brain endothelium uncovers dysregulation of vesicular transport pathways during aging. Nat Aging 4, 595–612 (2024). https://doi.org/10.1038/s43587-024-00598-z
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DOI: https://doi.org/10.1038/s43587-024-00598-z