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A mass spectrometry workflow for measuring protein turnover rates in vivo

Nature Protocols volume 14, pages 3333–3365 (2019)Cite this article

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Abstract

Proteins are continually produced and degraded, to avoid the accumulation of old or damaged molecules and to maintain the efficiency of physiological processes. Despite its importance, protein turnover has been difficult to measure in vivo. Previous approaches to evaluating turnover in vivo have required custom labeling approaches, involved complex mass spectrometry (MS) analyses, or used comparative strategies that do not allow direct quantitative measurements. Here, we describe a robust protocol for quantitative proteome turnover analysis in mice that is based on a commercially available diet for stable isotope labeling of amino acids in mammals (SILAM). We start by discussing fundamental concepts of protein turnover, including different methodological approaches. We then cover in detail the practical aspects of metabolic labeling and explain both the experimental and computational steps that must be taken to obtain accurate in vivo results. Finally, we present a simple experimental workflow that enables measurement of precise turnover rates in a time frame of ~4–5 weeks, including the labeling time. We also provide all the scripts needed for the interpretation of the MS results and for comparing turnover across different conditions. Overall, the workflow presented here comprises several improvements in the determination of protein lifetimes with respect to other available methods, including a minimally invasive labeling strategy and a robust interpretation of MS results, thus enhancing reproducibility across laboratories.

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Fig. 1: Flowchart of the protocol.
Fig. 2: Mouse metabolic labeling setup and SILAC food estimates for a minimal experimental design.
Fig. 3: Mathematical modeling and fitting of the data.
Fig. 4: Custom-made gel cutter and 96-well washing setup.
Fig. 5
Fig. 6
Fig. 7: Exemplary screenshot of the graphical interface from the ‘template’ tab of the turnover script.
Fig. 8: Sample screenshot of the graphical interface from the ‘import’ tab of the turnover script.
Fig. 9: Sample screenshot of the graphical interface from the ‘pool fit’ tab of the turnover script.
Fig. 10: Sample screenshot of the graphical interface from the ‘dataset fit’ tab of the turnover script.
Fig. 11: Sample screenshot of the graphical interface from the ‘statistics’ tab of the turnover script.
Fig. 12: Variation in the determination of protein lifetimes depending on labeling intervals, biological samples and technical replicates.
Fig. 13: Evaluation of lifetime determination precision in a subset of the data that is not used to calculate the lysine pool.

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Data and code availability

The dataset presented in this protocol was originally generated in ref. 4. All data are available from the corresponding authors on reasonable request. All code from Supplementary Data 1 is also publicly available at GitHub: https://github.com/malevra/protein-turnover.

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Acknowledgements

We thank S. Truckenbrodt (IST, Austria) for initial suggestions on manuscript preparation. We thank S. Reshetniak (University Medical Center Göttingen), T. Dankovich (University Medical Center Göttingen) and I. Atanassov (Max Planck Institute for Biology of Ageing, Germany) for carefully reading and commenting on the final version of the manuscript. We thank U. Plessmann (University Medical Center Göttingen) for setting up and maintaining the LC–MS configuration. The work was supported by grants to S. O. R. from the European Research Council (ERC-2013-CoG NeuroMolAnatomy) and the Deutsche Forschungsgemeinschaft (DFG 1967/7-1, SFB1286/A3, ExC 2067/Multiscale Bioimaging) and to HU from the SFB-1286/A10.

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Author notes

  1. These authors contributed equally: Mihai Alevra, Sunit Mandad.

Authors and Affiliations

  1. Department of Neuro- and Sensory Physiology & Center for Biostructural Imaging of Neurodegeneration (BIN), University Medical Center Göttingen, Göttingen, Germany

    Mihai Alevra, Sunit Mandad, Silvio O. Rizzoli & Eugenio F. Fornasiero

  2. Department of Clinical Chemistry, University Medical Center Göttingen, Göttingen, Germany

    Sunit Mandad & Henning Urlaub

  3. Bioanalytical Mass Spectrometry Group, Max Planck Institute of Biophysical Chemistry, Göttingen, Germany

    Sunit Mandad & Henning Urlaub

  4. Department of Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences and Göttingen Center for Molecular Biosciences (GZMB), University of Göttingen, Göttingen, Germany

    Till Ischebeck

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Contributions

Conceptualization: E.F.F. and S.O.R. Methodology: E.F.F., S.M., T.I. and H.U. Software: M.A. Formal analysis: M.A., E.F.F. and S.O.R. Resources and funding acquisition: S.O.R. Writing—initial draft: E.F.F. and S.O.R. Writing—final draft, review and editing: M.A., S.M., T.I., H.U., S.O.R. and E.F.F. Supervision: E.F.F.

Corresponding authors

Correspondence to Silvio O. Rizzoli or Eugenio F. Fornasiero.

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Key references using this protocol

Fornasiero, E. F. et al. Nat. Commun. 9, 4230 (2018): https://www.nature.com/articles/s41467-018-06519-0

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2

Reporting Summary

Supplementary Data 1

MATLAB scripts, also available online at GitHub

Supplementary Data 2

Testing dataset for evaluation of the MATLAB scripts

Supplementary Data 3

Simulated dataset for evaluation of the MATLAB scripts (pools)

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Alevra, M., Mandad, S., Ischebeck, T. et al. A mass spectrometry workflow for measuring protein turnover rates in vivo. Nat Protoc 14, 3333–3365 (2019). https://doi.org/10.1038/s41596-019-0222-y

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