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Myelin–axon interface vulnerability in Alzheimer’s disease revealed by subcellular proteomics and imaging of human and mouse brain

Nature Neuroscience volume 28, pages 1418–1435 (2025)Cite this article

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Abstract

Myelin ensheathment is essential for rapid axonal conduction, metabolic support and neuronal plasticity. In Alzheimer’s disease (AD), disruptions in myelin and axonal structures occur, although the underlying mechanisms remain unclear. We implemented proximity labeling subcellular proteomics of the myelin–axon interface in postmortem human brains from AD donors and 15-month-old male and female 5XFAD mice. We uncovered multiple dysregulated signaling pathways and ligand–receptor interactions, including those linked to amyloid-β processing, axonal outgrowth and lipid metabolism. Expansion microscopy confirmed the subcellular localization of top proteomic hits and revealed amyloid-β aggregation within the internodal periaxonal space and paranodal/juxtaparanodal channels. Although overall myelin coverage is preserved, we found reduced paranode density, aberrant myelination and altered paranode positioning around amyloid-plaque-associated dystrophic axons. These findings suggest that the myelin–axon interface is a critical site of protein aggregation and disrupted neuro-glial signaling in AD.

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Fig. 1: AI-guided confocal imaging analysis revealed myelin paranode pathology in AD human postmortem brains.
Fig. 2: Proximity labeling of paranodes and the myelin–axon interface in AD human brains and mice.
Fig. 3: Subcellular paranode proteomics in AD human postmortem brains and mice.
Fig. 4: Myelin–axon interface proteome reveals protein abnormalities in AD postmortem brains.
Fig. 5: CCC analysis reveals potential AD-associated ligand–receptor interactions at the myelin–axon interface.
Fig. 6: ExM demonstrates expression of proteomic hits at the myelin–axon interface.
Fig. 7: ExM reveals amyloid deposition at the paranode.
Fig. 8: Myelin paranode abnormalities associated with axonal pathology in AD humans and mice.

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Data availability

Raw proteomics data are provided in Supplementary Table 1. The mass spectrometry proteomics datasets were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD045861. For proteomics sample information see Supplementary Table 2. SwissProt database can be accessed here: https://www.uniprot.org/uniprotkb?query=*&facets=reviewed%3Atrue%2Cmodel_organism%3A9606. GeneOntology.org database can be accessed here: https://geneontology.org/. g:profiler database can be accessed here: https://biit.cs.ut.ee/gprofiler/gost. OmniPath, a comprehensive intercellular database, can be accessed here: https://omnipathdb.org/. CellChat database can be accessed here: http://www.cellchat.org/. CellPhoneDB database can be accessed here: https://www.cellphonedb.org/. CollecTRI can be accessed here: https://github.com/saezlab/CollecTRI. Axogliaosome proteins were cited from ref. 105 (PMID: 20830807). The myelin human proteome was cited from ref. 40 (PMID: 35543322). Bulk brain tissue protein expression was cited from ref. 16 (PMID: 35115731) and ref. 17 (PMID: 32284590). Single-cell RNA expression was cited from ref. 11 (PMID: 31042697), ref. 14 (PMID: 31932797) and ref. 12 (PMID: 33432193).

Code availability

Code for STED imaging data analysis is available at https://github.com/bewersdorflab/Yifei-Lukas-Collab. R codes for the analysis of paranode size distribution can be accessed at the following location: https://github.com/ShawnQin/calcium_trace.

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Acknowledgements

This project was supported by National Institutes of Health (NIH) grants no. RF1AG058257, no. R01NS115544 and no. R01NS111961 (J.G.), a Cure Alzheimer’s Fund Research Grant (J.G.), a Yale/NIDA Neuroproteomics Center Pilot Project Grant 2019 (Y.C.), a BrightFocus Foundation Postdoctoral Fellowship Program in Alzheimer’s Disease Research (grant no. A2021003F) (Y.C.), a Yale ADRC Research Scholar Award (Y.C.), an Alzheimer’s Association Research Fellowship (grant no. 23AARF-1020552) (Y.C.), an EMBL Corporate Partnership Programme Fellowship (Y.C.) and Yale ADRC grant no. P30 AG066508 (A.C.N). I.P.-d.-S. and E.P. were supported by EMBL-EBI Core funding. EMBL IT Support is acknowledged for provision of computer and data storage servers. M.S. and J.R. were supported by the Investigator Award to J.R. from Ontario Institute for Cancer Research (OICR), an NSERC Discovery Grant no. RGPIN-2023-04646 to J.R. and a CIHR Catalyst Grant no. DV1-197665 to J.R. Funding to OICR is provided by the Government of Ontario. We thank the Yale/NIDA Neuroproteomics Center (grant no. P30 DA018343) for providing experimental design advice, technical support and funding opportunities. We thank F. Collin from the Keck MS & Proteomics Resource at the Yale School of Medicine for processing the LC–MS/MS experiments. We also thank the Keck MS & Proteomics Resource at the Yale School of Medicine for providing the necessary mass spectrometers and the accompanying biotechnology tools funded in part by the Yale School of Medicine and by the Office of The Director, NIH (grants no. S10OD02365101A1, no. S10OD019967 and no. S10OD018034). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank the Yale ADRC (grant no. P30 AG066508) and D. Davis and the University of Miami Brain Endowment Bank, an NIH NeuroBioBank, for providing human postmortem AD and control brain tissues. We thank S. Qin (Flatiron Institute) for performing the quantile–quantile plot data analysis. We thank the staff at the Center for Cellular and Molecular Imaging, Electron Microscopy Facility at Yale Medical School for assistance with the electron microscopy experiments. We thank M. Rasband (Baylor College of Medicine) and R. Hill (Dartmouth College) for critical comments and suggestions on this project. Schematic figures were created with BioRender.com (Figs. 1a,p, 2a, 3a,b,d,g,h, 4e, 5a,e–g, 6a, 7a,i and 8a,m).

Author information

Author notes

  1. Peng Yuan

    Present address: Department of Rehabilitation Medicine, Huashan Hospital, State Key Laboratory of Medical Neurobiology, Institute for Translational Brain Research, MOE Frontiers Center for Brain Science, MOE Innovative Center for New Drug Development of Immune Inflammatory Diseases, Fudan University, Shanghai, China

  2. These authors contributed equally: Iguaracy Pinheiro-de-Sousa, Mykhaylo Slobodyanyuk, Fuyi Chen.

Authors and Affiliations

  1. Department of Neurology, Yale University, New Haven, CT, USA

    Yifei Cai, Fuyi Chen, Tram Huynh, Peiyang Tang, Lei Tong, Peng Yuan & Jaime Grutzendler

  2. European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK

    Iguaracy Pinheiro-de-Sousa & Evangelia Petsalaki

  3. Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada

    Mykhaylo Slobodyanyuk & Jüri Reimand

  4. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Mykhaylo Slobodyanyuk & Jüri Reimand

  5. Keck MS & Proteomics Resource, Yale University, New Haven, CT, USA

    Jean Kanyo & TuKiet T. Lam

  6. Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA

    Lukas A. Fuentes

  7. Yale College, Yale University, New Haven, CT, USA

    Amber Braker & Rachel Welch

  8. Department of Pathology, Yale University, New Haven, CT, USA

    Anita Huttner

  9. Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA

    TuKiet T. Lam

  10. Yale/NIDA Neuroproteomics Center, Yale University, New Haven, CT, USA

    TuKiet T. Lam & Angus C. Nairn

  11. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Jüri Reimand

  12. Department of Psychiatry, Yale University, New Haven, CT, USA

    Angus C. Nairn

  13. Department of Pharmacology, Yale University, New Haven, CT, USA

    Angus C. Nairn

  14. Department of Neuroscience, Yale University, New Haven, CT, USA

    Jaime Grutzendler

  15. Wu Tsai Institute, Yale University, New Haven, CT, USA

    Jaime Grutzendler

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  1. Yifei Cai
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Contributions

Y.C. and J.G. conceived and designed the study. Y.C. performed proximity labeling proteomics and western blotting and proteomics data analysis. J.K. and T.T.L. performed LC–MS/MS and data analysis. A.C.N. supervised the proteomics experiments and analysis. M.S., Y.C. and J.R. performed the integrative pathway enrichment analysis. I.P.-d.-S., Y.C. and E.P. performed the cell–cell communication analysis. A.H. performed pathological evaluation of human brain specimens and provided the tissues. Y.C., P.T., A.B. and T.H. performed immunofluorescence and confocal imaging. P.T., T.H. and P.Y. performed myelin analysis. F.C., Y.C., T.H. and L.T. performed expansion microscopy and imaging. L.A.F., Y.C. and T.H. performed STED imaging, image analysis and quantification. T.H., P.T., A.B. and R.W. performed quantifications. Y.C. performed electron microscopy sample preparation. Y.C., T.H., P.T., A.B. and R.W. performed statistical analysis. Y.C. and J.G. prepared the manuscript. J.G. supervised the study.

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Correspondence to Yifei Cai or Jaime Grutzendler.

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Nature Neuroscience thanks Junmin Peng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Expression of myelin marker proteins in AD versus controls from snRNAseq and bulk proteomics studies.

Fold changes in myelin marker protein expression in human AD frontal cortex (including prefrontal cortex and frontal gyrus) were extracted from recent snRNA-seq and bulk proteomics datasets11,14,16,17. RNA and protein expression levels in oligodendrocyte clusters are shown in the three left columns and two right columns, respectively. Abbreviation: n.s. = not significant, sig. = significant.

Extended Data Fig. 2 AI-guided myelin quantification in Cnp-EGFP-5XFAD and Cnp-EGFP-WT mice.

a. Myelin in Cnp-EGFP-5XFAD mice and Cnp-EGFP-WT mice was quantified using AI-guided segmentation of immunofluorescence confocal images. b. Representative images showing tiled confocal images of brains from Cnp-EGFP-WT and Cnp-EGFP-5XFAD mice. Myelin (grey) was labeled by anti-GFP, and lysosomes/dystrophic neurites (red) were labeled by anti-Lamp1. Brain regions used for quantification are marked by yellow boxes. Low zoom and high zoom representative images with myelin (anti-GFP), AI-generated masks and objects. Scale bars: full-tiled images = 500 μm; low zoom myelin images = 100 μm; high zoom images = 5 μm. c. Quantification of myelin volume and mean fluorescence intensity in regions of interest for Cnp-EGFP-5XFAD mice (n = 4) and Cnp-EGFP-WT mice (n = 5); (Mann Whitney test, two-tailed). Abbreviations: RSPd = Retrosplenial area, dorsal part; PTLp = Posterior parietal association areas; DG = dentate gyrus. Error bars indicate SEM.

Extended Data Fig. 3 Super-resolution STED microscopy demonstrates spatial precision of proximity labeling.

a. Bead imaging illustrates the resolution difference between confocal microscopy (250 nm) and STED microscopy (50 nm). Scale bar = 250 nm. b-c. Representative confocal and STED images of proximity labeling in human postmortem brains showing (B) paranodes (anti-CASPR, magenta) and (C) internodes (anti-MAG, magenta), with biotinylated proteins detected by streptavidin (green); scale bars: (B) 500 nm, (C) 5 μm. d. A representative line plot shows the radius measurements of signals from the secondary antibody channel (magenta) and the streptavidin channel (green). e. A dot plot showing the radius ratio between the secondary antibody (magenta) and streptavidin (green). The average radius ratio is 1.07 (SD= 0.1); the orange line indicates the median, with the lower and upper edges representing the 25th (Q1) and 75th (Q3) percentiles. Whiskers extend to the minimum and maximum values within 1.5 times the interquartile range, and outliers (pink circles) are plotted individually (and excluded from analysis). Each dot represents a myelin segment (n = 45).

Extended Data Fig. 4 Correlation analysis of proteomics samples in humans and mice.

Correlation analysis for biological replicates of (a) MAG-labeled and (b) CASPR-labeled samples, along with the no-antibody labeled proteomic controls in (A-B) human brains and (C) in mice. Pearson correlation coefficient (R2) values are provided in each comparison box.

Extended Data Fig. 5 Caspr-labeled paranode-enriched proteomes in 5XFAD mice.

A. Schematic illustrating the experimental design. B. Partial volcano plot of proteomic hits in paranode-enriched samples from 5XFAD mice. Known paranode-related hits are shown in red and other hits in green. The gene names of the top 10 proteomic hits and known paranodal proteins are indicated. C. Venn diagram showing the number of paranode-enriched proteomic hits shared between AD humans and 5XFAD mice. D. Gene Ontology cellular component (GO-CC) analysis of the shared hits between AD humans and mice, displaying the top 8 GO-CC terms. Quantifications in panels B and D were performed two-sided.

Extended Data Fig. 6 Top 100 hits in the CASPR-labeled paranode-enriched proteome and MAG-labeled myelin-axon interface proteome in unaffected human brains.

(a) The top 100 proteomic hits identified in the paranode-enriched proteome. Proteins known to be expressed in the paranode are marked with an asterisk (‘*’): SPTAN1123, SPTBN1124,125, CNTN1126,127, CNTNAP135, ANK321,128, NFASC129, and SCN2A130. Proteins known to be expressed in the node of Ranvier and juxtaparanode are marked with a hash (‘#’): TNR1, ACTN4131, SPTBN41, VCAN1, NCAM1132, CNTNAP2129, SPTB133, CNTN21, HAPLN21, NCAN1, EPB41L21. (b) The top 100 proteomic hits identified in the myelin-axon-interface proteome. Proteins known to be expressed at the myelin-axon interface are marked with an asterisk (‘*’): CNP134, CNTN1126,127, CNTNAP135, NFASC129, NCAM1132, MOG134, MAG36,37,38, SEPTIN7135, NRCAM136, CNTN236,37,38, PLP120, CNTNAP21, LGI3137, SEPTIN8138, CADM420,139, ADAM22137, SEPTIN2138,140, SEPTIN4138. Proteins known to be related to myelin or axon are marked with a hash (‘#’): DPYSL2141, INA1142, IGSF8143,144, STX1B145, BIN1146, SNAP25147, NCAM2148, NDRG1149,150, CRMP1151, BCAS1152, KCNAB2153, LGI1154, MAPT155, GPM6A156, CAP1157, PLEKHB1158.

Extended Data Fig. 7 Integrative pathway enrichment analysis of paranode and myelin-axon interface proteomes using the ActivePathways method.

(a-b) The Enrichment Map depicts a network of pathways (FDR < 0.05) where edges connect pathways sharing many genes. Node size reflects the number of genes in each pathway, and node color indicates the dataset contribution (combined AD and control). Theme labels were curated based on the main pathways represented in each subnetwork. Only subnetworks with at least four pathways connected by edges are shown. Grey nodes indicate combined evidence of pathway enrichment in which the respective pathways were detected in the integrative analysis but not detected in either the AD or Control proteomes alone.

Extended Data Fig. 8 Cell-cell communication analysis revealing ligand-receptor interaction at the myelin-axon interface.

a-b. (A) Cell clustering and (B) cell type annotation of snRNAseq data from AD human frontal cortex (Braak stage 6) and controls (Braak stage 0). c. Enrichment analysis shows that myelin-axon interface proteomics (MAG or CASPR-labeled) are highly enriched in neurons and oligodendrocytes, but not in other cell types (related to Fig. 5b). Each row depicts contingency tables for each hypergeometric test (from top to bottom: p-values: 0.0005275, 0.0007784, 0.009328, 0.0001295). In these rows, values with two decimal places indicate residuals and the size of the circles; positive residuals denote that the observed values were more frequent than expected, while negative residuals indicate lower-than-expected frequencies. Quantifications were performed two-sided. d-e. Violin plots showing RNA expression levesl of ligand-receptor pairs in (D) control human postmortem brains (Braak stage 0) and (E) AD human postmortem brains (Braak stage 6).

Extended Data Fig. 9 Paranode-enriched and myelin-axon interface Alzheimer’s disease proteomes reveal unique subcellular changes not observed by bulk proteomics or single cell RNA transcriptomics.

Heatmaps display (a) Paranode Alzheimer’s-associated proteomes (PAPs) and (b) Myelin-axon interface Alzheimer’s proteomes (MAPs). Heatmap denotes log10 (spectral counts). Comparison between PAPs or MAPs and bulk proteomics data (middle panel, Johnson et al.16), or single nuclei RNA sequencing transcriptomics (right panels, Mathys et al.11) were performed. Both bulk proteomics and snRNAseq data were obtained from their original studies. Neuronal cell types (yellow box) and oligodendrocyte/OPC (green box) were highlighted in the snRNAseq data. Abbreviations: FC = fold change; DEG = differentially expressed genes. (A and B) Quantifications of subcellular proteomic data derived from this study were performed two-sided.

Extended Data Fig. 10 Diagram of myelin-axon disruption in AD.

a. Diagram illustrating how amyloid toxicity to axons and myelin (#1 and #2) may lead to axonal spheroid formation (a), myelin paranode/juxtaparanode disruption (b), protein perturbation at the myelin-axon interface (c) and amyloid accumulation at the interface (d). Together, these events may create a vicious cycle of dysregulated myelin-axon crosstalk and degeneration (#3). b. Diagram outlining potential signaling pathways that contribute to myelin-axon disruption, based on findings from myelin-axon interface proteomics and imaging validations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–15, titles of Supplementary Tables 1–6 and titles and captions of Supplementary Videos 1 and 2.

Reporting Summary

Supplementary Table 1

Raw and analyzed proteomics results of paranode-enriched and myelin–axon interface proteomes in humans and mice.

Supplementary Table 2

Proteomic sample information (also available at ProteomeXchange).

Supplementary Table 3

Subcellular location of proteomic hits.

Supplementary Table 4

Full lists of pathway enrichment analysis.

Supplementary Table 5

Full lists of cell–cell communication analysis in neuron-oligodendrocytes and the ligand–receptor pairs identified in subcellular proteomics.

Supplementary Table 6

Full lists of predicted downstream signaling processes induced by the ligand–receptor pairs identified at the myelin–axon interface.

Supplementary Video 1

Representative large tiling and magnified images of AI-guided immunofluorescence and imaging annotation of myelin, axons and paranodes in postmortem human brains of AD and age-matched controls, as well as Cnp-EGFP mice with or without 5XFAD background. The masks generated by AI annotation are in yellow, and the objects are in blue. Scale bar is shown in real-time during the video at the lower left corner.

Supplementary Video 2

Representative amyloid fiber accumulation at the myelin–axon interface in 5XFAD mice. Amyloid fibers (4G8 labeled, red) were observed along the axon (labeled by NFH, green), and dense amyloid coils were observed at the paranode and juxtaparanode regions (CASPR and Kv7.3 labeled, gray).

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Cai, Y., Pinheiro-de-Sousa, I., Slobodyanyuk, M. et al. Myelin–axon interface vulnerability in Alzheimer’s disease revealed by subcellular proteomics and imaging of human and mouse brain. Nat Neurosci 28, 1418–1435 (2025). https://doi.org/10.1038/s41593-025-01973-8

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