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Proteomics of mouse brain endothelium uncovers dysregulation of vesicular transport pathways during aging

Nature Aging volume 4, pages 595–612 (2024)Cite this article

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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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Fig. 1: Proteomic analysis of mouse BECs during aging.
Fig. 2: Unsupervised clustering of age-related protein dynamics of BECs.
Fig. 3: Age-related changes of BEC proteins implicated in vesicular transport.
Fig. 4: Effects of endothelial Arf6 on vesicular transport and mRNA processing.
Fig. 5: Apoe deficiency causes a proteomic signature of accelerated BEC 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.

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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.

Author information

Author notes

  1. These authors contributed equally: Katalin Todorov-Völgyi, Judit González-Gallego.

Authors and Affiliations

  1. Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany

    Katalin Todorov-Völgyi, Judit González-Gallego, Nathalie Beaufort, Rainer Malik, Mihail Ivilinov Todorov, Dennis Crusius, Sophie Robinson, Ali Ertürk, Dominik Paquet & Martin Dichgans

  2. Graduate School of Systemic Neuroscience (GSN), University Hospital, LMU Munich, Munich, Germany

    Judit González-Gallego & Sophie Robinson

  3. German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany

    Stephan A. Müller, Martina Schifferer, Sophie Robinson, Andree Schmidt, Christian Haass, Mikael Simons, Stefan F. Lichtenthaler & Martin Dichgans

  4. Neuroproteomics, School of Medicine and Health, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany

    Stephan A. Müller, Andree Schmidt & Stefan F. Lichtenthaler

  5. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany

    Martina Schifferer, Florence Bareyre, Ali Ertürk, Christian Haass, Mikael Simons, Dominik Paquet, Stefan F. Lichtenthaler & Martin Dichgans

  6. Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, Neuherberg, Germany

    Mihail Ivilinov Todorov & Ali Ertürk

  7. Department of Oncology, Hematology and Bone Marrow Transplantation with Section Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Jakob Körbelin

  8. Institute of Clinical Neuroimmunology, University Hospital, LMU Munich, Munich, Germany

    Florence Bareyre

  9. Biomedical Center Munich (BMC), Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany

    Florence Bareyre

  10. Division of Metabolic Biochemistry, Biomedical Center Munich (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany

    Christian Haass

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  1. Katalin Todorov-Völgyi
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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.

Corresponding authors

Correspondence to Katalin Todorov-Völgyi or Martin Dichgans.

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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

Reporting Summary

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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