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Limited proteolysis–mass spectrometry reveals aging-associated changes in cerebrospinal fluid protein abundances and structures

A Publisher Correction to this article was published on 28 April 2022

This article has been updated


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.

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Fig. 1: LiP–MS experimental and analytical workflow.
Fig. 2: Mouse CSF protein abundance in aging.
Fig. 3: Aging-associated changes in structure revealed by LiP–TeCS.
Fig. 4: Studies on LiP–TeCS hits in human CSF.

Data availability

MS data have been accepted by the ProteomeXchange Consortium via the PRIDE51 partner repository with the dataset accession no. PXD031174. All other data are available from the corresponding author upon request.

Code availability

All code relevant to this study are included as supplementary files. In addition, the code can be accessed on Zenodo at (ref. 52).

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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.).

Author information

Authors and Affiliations



S.R.S. and T.W.-C. conceptualized and initiated the project and interpreted data. S.R.S. performed LC–MS/MS maintenance. S.R.S. optimized DDA and DIA instrument methods. S.R.S. and T.I. developed the CSF collection protocol. S.R.S performed CSF collection surgeries and LiP–MS sample prep. R.D.L. performed data acquisition. S.R.S. developed the LiP–TeCS theory and concepts, implemented LiP–TeCS in R, wrote the desktop app in C# and analyzed the data. S.R.S. performed and analyzed western blots and enzyme activity assays. The SomaLogic data were processed by J.R. and P.M.L. and analyzed by J.R. E.N.W. and K.I.A. analyzed the core CSF AD biomarkers. S.R.S. wrote the manuscript. S.R.S and J.R. created the figures. S.R.S., J.R., T.I. and T.W.-C. edited and commented on the manuscript.

Corresponding author

Correspondence to Tony Wyss-Coray.

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The authors declare no competing interests.

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Nature Aging thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

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 information

Supplementary Information

Supplementary Figs. 1–18, discussions of models and assumptions, and detailed protocols.

Reporting Summary

Supplementary Tables 1

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.

Supplementary Code 1

R scripts for performing protein–peptide matching, Spectronaut-to-MaxQuant output formatting and LiP–TeCS analysis. C# source code for LiP–TeCS desktop application.

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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).

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