Cerebrospinal fluid (CSF) proteins and their structures have been implicated in aging and neurodegenerative diseases. In the present study, we used limited proteolysis–mass spectrometry (LiP–MS) to screen for new aging-associated changes in the CSF proteome using a modified analysis. We found 38 protein groups that change in abundance with aging, predominantly immunoglobulins of the IgM subclass. We discovered six high-confidence candidates that underwent structural changes with aging, of which Kng1, Itih2, Lp-PLA2 and 14-3-3 proteins have binding partners or chemical forms known previously to change in the brains of patients with Alzheimer’s disease. Orthogonal validation by western blotting identified that the LiP–MS hit Cd5l forms a covalent complex with IgM in mouse and human CSF, the abundance of which increases with aging. In human CSF, SOMAmer probe signals for all six LiP–MS hits were associated with cognitive function and/or biomarkers of neurodegeneration, especially 14-3-3 proteins YWHAB and YWHAZ. Together, our findings show that LiP–MS can uncover age-related structural changes in CSF with relevance to neurodegeneration.
This is a preview of subscription content
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $9.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Reitz, C., Brayne, C. & Mayeux, R. Epidemiology of Alzheimer disease. Nat. Rev. Neurol. 7, 137–152 (2011).
Smith, L. M. & Kelleher, N. L. Proteoform: a single term describing protein complexity. Nat. Methods 10, 186–187 (2013).
Kelleher, N. L. et al. How many human proteoforms are there? Nat. Chem. Biol. 14, 206–214 (2018).
Selkoe, D. J. & Hardy, J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 8, 595–608 (2016).
Sebastián-Serrano, Á., de Diego-García, L. & Díaz-Hernández, M. The neurotoxic role of extracellular tau protein. Int. J. Mol. Sci. 19, 998 (2018).
Bergamaschini, L. et al. Activation of the contact system in cerebrospinal fluid of patients with Alzheimer aisease. Alzheimer Dis. Assoc. Disord. 12, 102–108 (1998).
Nielsen, H., Palmqvist, S., Minthon, L., Londos, E. & Wennström, M. Gender-dependent levels of hyaluronic acid in cerebrospinal fluid of patients with neurodegenerative dementia. Curr. Alzheimer Res. 9, 257–266 (2012).
Nägga, K., Hansson, O., van Westen, D., Minthon, L. & Wennström, M. Increased levels of hyaluronic acid in cerebrospinal fluid in patients with vascular dementia. J. Alzheimer’s Dis. 42, 1435–1441 (2014).
Fonteh, A. N. et al. Alterations in cerebrospinal fluid glycerophospholipids and phospholipase A2 activity in Alzheimer’s disease. J. Lipid Res. 54, 2884–2897 (2013).
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. The hallmarks of aging. Cell 153, 1194–1217 (2013).
Feng, Y. et al. Global analysis of protein structural changes in complex proteomes. Nat. Biotech. 10, 1036–1044 (2014).
Piazza, I. et al. A map of protein–metabolite interactions reveals principles of chemical communication. Cell 172, 358–372 (2018).
Cappelletti, V. et al. Dynamic 3D proteomes reveal protein functional alterations at high resolution in situ. Cell 184, 545–559 (2021).
Geiger, R. et al. l-Arginine modulates T cell metabolismm and enhances survival and anti-tumor activity. Cell 167, 829–842 (2016).
Zampieri, M. et al. High-throughput metabolomic analysis predicts mode of action of uncharacterized antimicrobial compounds. Sci. Transl. Med. 10, eaal3973 (2018).
Wyss-Coray, T. Ageing, neurodegeneration and brain rejuvenation. Nature 539, 180–186 (2016).
Castellano, J. M. et al. Human umbilical cord plasma proteins revitalize hippocampal function in aged mice. Nature 544, 488–492 (2017).
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).
Pluvinage, J. V. et al. CD22 blockade restores homeostatic microglial phagocytosis in ageing brains. Nature 568, 187–192 (2019).
Schopper, S. et al. Measuring protein structural changes on a proteome-wide scale using limited proteolysis-coupled mass spectrometry. Nat. Protoc. 12, 2391–2410 (2017).
Ludwig, C. et al. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol. Systems Biol. 14, e8126 (2018).
Smith, J. S. et al. Characterization of individual mouse cerebrospinal fluid proteomes. Proteomics 14, 1102–1106 (2014).
Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Statist. Appl. Genet. Mol. Biol. 4, 17 (2005).
Morawski, M. et al. ECM in brain aging and dementia. Prog. Brain Res. 214, 207–227 (2014).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotech. 26, 1367–1372 (2008).
Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry–based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).
Zhang, B., Chambers, M. C. & Tabb, D. L. Proteomic parsimony through bipartite graph analysis improves accuracy and transparency. J. Proteome Res. 6, 3549–3557 (2007).
Borenstein, M., Hedges, L. V.;, Higgins, J. P. T. & Rothstein, H. R. Introduction to Meta-Analysis (John Wiley & Sons, 2009).
Brademan, D. R., Riley, N. M., Kwiecien, N. W. & Coon, J. J. Interactive peptide spectral annotator: a versatile web-based tool for proteomic applications. Mol. Cell. Proteom. 18, S193–S201 (2019).
Arai, S. et al. Obesity-associated autoantibody production requires AIM to retain the immunoglobulin M immune complex on follicular dendritic cells. Cell Rep. 3, 1187–1198 (2013).
Schmaier, A. H. The contact activation and kallikrein/kinin systems: pathophysiologic and physiologic activities. J. Thromb. Haemost. 14, 28–39 (2016).
Sathe, G. et al. Multiplexed phosphoproteomic study of brain in patients with Alzheimer’s disease and age-matched cognitively healthy controls. OMICS 24, 216–227 (2020).
Thijssen, E. H. et al. Diagnostic value of plasma phosphorylated tau181 in Alzheimer’s disease and frontotemporal lobar degeneration. Nat. Med. 26, 387–397 (2020).
Janelidze, S. et al. Plasma P-tau181 in Alzheimer’s disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia. Nat. Med. 26, 379–386 (2020).
Cunningham, R., Jany, P., Messing, A. & Li, L. Protein changes in immunodepleted cerebrospinal fluid from a transgenic mouse model of Alexander disease detected using mass apectrometry. J. Proteome Res. 12, 719–728 (2013).
Dislich, B. et al. Label-free quantitative proteomics of mouse cerebrospinal fluid detects β-site APP cleaving enzyme (BACE1) protease substrates in vivo. Mol. Cell. Proteom. 14, 2550–2563 (2015).
Schaum, N. et al. Ageing hallmarks exhibit organ-specific temporal signatures. Nature 583, 596–602 (2020).
Mrdjen, D. et al. High-himensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity 48, 380–395 (2018).
Ratliff, M. & Riley, R. L. In senescence, age-associated B cells secrete TNFa and inhibit survival of B-cell precursors. Aging Cell 12, 303–311 (2013).
Pfuhl, C. et al. Intrathecal IgM production is a strong risk factor for early conversion to multiple sclerosis. Neurology 93, e1440–e1451 (2019).
Negi, N. & Das, B. K. Decoding intrathecal immunoglobulins and B cells in the CNS: their synthesis, function, and regulation. Int. Rev. Immunol. 39, 67–79 (2020).
Nesvizhskii, A. I. & Aebersold, R. Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell. Proteom. 4, 1419–1440 (2005).
Jin, S., Daly, D. S., Springer, D. L. & Miller, J. H. The effects of shared peptides on protein quantitation in label-free proteomics by LC/MS/MS. J. Proteome Res. 7, 164–169 (2008).
Forshed, J. et al. Enhanced information output from shotgun proteomics data by protein quantification and peptide quality control (PQPQ). Mol. Cell. Proteom. 10, M111.010264 (2011).
Miyazaki, T., Yamazaki, T., Sugisawa, R., Gershwin, M. E. & Arai, S. AIM associated with the IgM pentamer: attackers on stand-by at aircraft carrier. Cell. Mol. Immunol. 15, 563–574 (2018).
Aasebø, E. et al. Effects of blood contamination and the rostro-caudal gradient on the human cerebrospinal fluid proteome. PLoS ONE 9, e90429 (2014).
Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE 5, e15004 (2010).
Kim, C. H. et al. Stability and reproducibility of proteomic profiles measured with an aptamer-based platform. Sci. Rep. 8, 8382 (2018).
Wilson, E. N. et al. Soluble TREM2 is elevated in Parkinson’s disease subgroups with elevated CSF tau. Brain 143, 932–943 (2020).
Lee, S., Sun, W., Wright, F. A. & Zou, F. An improved and explicit surrogate variable analysis procedure by coefficient adjustment. Biometrika 104, 303–316 (2017).
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).
Shuken, S. R.& Wyss-Coray, T. Aging-associated changes in CSF protein abundances and structures revealed by a modified LiP-MS screen. Zenodo https://doi.org/10.5281/zenodo.5884992 (2022).
We thank N. Olsson, J. Elias, D. Itzhak and S. Shi for help with LC–MS/MS setup and maintenance, P. Picotti and M. Therese-Mackmull for training in LiP–MS methodology, advice on data analysis and input on the manuscript, L. Gillet and P. Boersema for help with DIA instrument methods, U. Khan for literature searches, L. Eshun-Wilson for help with protein modeling, A. Owen, D. Kluger and A. Beyer for advice and feedback on statistics and meta-analysis, V. Henderson and the Stanford Alzheimer’s Disease Research Center team for clinical samples and data, and members of the Wyss-Coray Lab, Elias Lab and Picotti Lab for feedback and support. All raw data for MS experiments discussed in the main text were acquired at Stanford University Mass Spectrometry by R.D.L., K. Singhal, F. Liu and R. Matney. Other data necessary for the completion of the project and presented in Supplementary Information were acquired by S.R.S. in the Picotti Lab or Elias Lab on instruments owned by the Picotti Lab, Elias Lab or Wyss-Coray Lab. This work was funded by the NOMIS Foundation (T.W.-C.), the Paul F. Glenn Center for the Biology of Aging (T.W.-C.), the NIA-funded SADRC (P50 AG047366), the BioX Stanford Interdisciplinary Graduate Fellowship (S.R.S.), the Stanford Center for Molecular Analysis and Design Graduate Fellowship (S.R.S.) and the Stanford Graduate Fellowship (J.R.).
The authors declare no competing interests.
Peer review information
Nature Aging thanks the anonymous reviewers for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Theoretical and Computational Development of the LiP-MS Test-Control Screen (LiP-TeCS) Platform.
a, Derivation of an expression for the LiP ratio fold change (FC) in terms of concentrations and rate constants. Assumptions and details of the model are discussed in the Supplementary Information. For tryptic peptides, coefficients kcleave X are replaced by coefficients f(kcleave X) = (1 – kcleave X[PK]t) in Equations 4 and 5. b, Illustrations of protein grouping algorithms. In the indicated softwares, gray objects are omitted from the outputs to reduce redundancy and protein ID overestimation. The metapeptide method ensures that the LiP ratio numerator peptide and all peptides in the denominator PG match the same set of proteins and that all information is (matches are) reported. c, In transferrin (Tf), a single structural change (Fe binding) results in significant LiP ratio FCs at multiple sites on the same protein, shown here with yellow stars. Structural change occurs at the protein level. d, Example peptide-level LiP-MS analysis. p-value from two-sided T test, q-value from Benjamini-Hochberg correction. A PG containing multiple significant peptides by p value but not q value (blue) would not be considered a hit in the same dataset as a PG with one significant q value and numerous non-significant peptides (pink). e, Formulation of the Fisher method in this experiment. Assumptions are discussed in the Supplementary Information. f, Flow chart showing the implementation of these concepts, as well as the analysis described in Ref. 20, in a single workflow called LiP Test-Control Screen (LiP-TeCS) (black and green objects). Blue objects represent downstream interpretation. g, Screenshot of the LiP-TeCS desktop app.
Supplementary Figs. 1–18, discussions of models and assumptions, and detailed protocols.
Differential abundance analysis of protein groups in aging. 2. Differential abundance analysis of peptides in aging (PK− samples). 3. Quantitative data for all peptides. 4. Differential LiP ratio analysis in aging. 5. Protein-level LiP–MS statistics. 6. Human cohort characteristics.
R scripts for performing protein–peptide matching, Spectronaut-to-MaxQuant output formatting and LiP–TeCS analysis. C# source code for LiP–TeCS desktop application.
About this article
Cite this article
Shuken, S.R., Rutledge, J., Iram, T. et al. Limited proteolysis–mass spectrometry reveals aging-associated changes in cerebrospinal fluid protein abundances and structures. Nat Aging 2, 379–388 (2022). https://doi.org/10.1038/s43587-022-00196-x