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High-throughput and high-sensitivity phosphoproteomics with the EasyPhos platform

Nature Protocols volume 13, pages 1897–1916 (2018)Cite this article

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Abstract

Mass spectrometry has transformed the field of cell signaling by enabling global studies of dynamic protein phosphorylation (‘phosphoproteomics’). Recent developments are enabling increasingly sophisticated phosphoproteomics studies, but practical challenges remain. The EasyPhos workflow addresses these and is sufficiently streamlined to enable the analysis of hundreds of phosphoproteomes at a depth of >10,000 quantified phosphorylation sites. Here we present a detailed and updated workflow that further ensures high performance in sample-limited conditions while also reducing sample preparation time. By eliminating protein precipitation steps and performing the entire protocol, including digestion, in a single 96-well plate, we now greatly minimize opportunities for sample loss and variability. This results in very high reproducibility and a small sample size requirement (≤200 μg of protein starting material). After cell culture or tissue collection, the protocol takes 1 d, whereas mass spectrometry measurements require ~1 h per sample. Applied to glioblastoma cells acutely treated with epidermal growth factor (EGF), EasyPhos quantified 20,132 distinct phosphopeptides from 200 μg of protein in less than 1 d of measurement time, revealing thousands of EGF-regulated phosphorylation events.

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Fig. 1: Schematic of the EasyPhos platform for high-throughput phosphoproteomics.
Fig. 2: Performance evaluation of the EasyPhos method using limited starting material.
Fig. 3: Evaluation of the performance of the updated EasyPhos workflow with respect to starting material, MS instruments, and LC gradient duration.
Fig. 4: Demonstration of the phosphoproteomics workflow.

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Acknowledgements

We thank members of the Proteomics and Signal Transduction Group; the Metabolic Systems Biology Group; and the staff of SydneyMS for support and discussions; and, in particular, I. Paron, K. Mayr, and G. Sowa for MS technical assistance; J. Cox for bioinformatic tools; and N. Nagaraj, E. Humphrey, B. Parker, and M. Larance for technical discussions. This work was funded in part by the Max Planck Society for the Advancement of Science, the Novo Nordisk Foundation (grant NNF15CC0001) (M.M.), and the National Health and Medical Research Council (NHMRC) (grants GNT1061122 and GNT1086850) (D.E.J.). Thanks to J. Cobcroft for her generous funding support (S.J.H.).

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Authors and Affiliations

  1. School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia

    Sean J. Humphrey & David E. James

  2. The Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia

    Sean J. Humphrey & David E. James

  3. Max Planck Institute of Biochemistry, Martinsried, Germany

    Ozge Karayel & Matthias Mann

  4. Sydney Medical School, The University of Sydney, Sydney, New South Wales, Australia

    David E. James

  5. NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

    Matthias Mann

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Contributions

S.J.H. designed the studies and developed methods. S.J.H. and O.K. performed the phosphoproteomic experiments and analyzed the data. M.M. and D.E.J. supervised the project and wrote the manuscript with S.J.H. and O.K.

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Correspondence to Sean J. Humphrey or Matthias Mann.

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M.M., O.K., and D.E.J. declare that they have no competing interests. S.J.H. is an inventor on a provisional patent (no. 2018901179) relating to parts of the described protocol.

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Key references using this protocol

1. Humphrey, S. J., Azimifar, S. B. & Mann, M. Nat. Biotechnol. 33, 990–995 (2015) https://doi.org/10.1038/nbt.3327

2. Robles, S., Humphrey, S. J. & Mann, M. Cell Metab. 25, 118–127 (2017) https://doi.org/10.1016/j.cmet.2016.10.004

3. Sacco, F. et al. Nat. Commun. 7, 13250 (2016) https://doi.org/10.1038/ncomms13250

4. Steger, M. et al. eLife 5, e12813 (2016) https://doi.org/10.7554/eLife.12813

Integrated supplementary information

Supplementary Figure 1 Comparison of the original EasyPhos workflow and the updated protocol described here.

(a) Distribution of peptide length for phosphopeptides identified by both methods, or those uniquely identified by the updated workflow. (b) Fraction of phosphopeptides containing missed cleavages in the updated and original workflows, as well as three published deep phosphoproteome studies employing different strategies (SCX fractionation27, high pH reversed-phase fractionation29, and single-run31). (c) Phosphopeptide quantification as a proportion of the total quantifiable data points, i.e. total number of phosphopeptides quantified in any n sample ÷ (total number of phosphopeptides identified × n replicate samples measured).

Supplementary Figure 2 Evaluation of the sensitivity of the updated EasyPhos workflow with different starting materials and MS instruments.

(a) Phosphopeptides (left) and Class 1 phosphorylation sites (right) quantified from a dose-titration experiment spanning 25–750 µg of total protein material (determined by BCA assay) from untreated U87 cells using the protocol described here. Protein was pooled immediately after cell lysis and divided into four workflow replicates (n = 4) for each starting material condition. Only the amount of protein for a single replicate was carried forward for each independent workflow replicate. Samples were measured on a Q Exactive HF mass spectrometer using 90-min gradients. (b) Quantification of Class 1 phosphorylation sites from the experiment depicted in Figure 3a. Phosphopeptides from a dose-titration experiment spanning 12.5–400 µg of total protein starting material (determined by BCA assay) from untreated U87 cells using the protocol described here. Protein was pooled immediately after cell lysis and divided into four workflow replicates (n = 4) for each starting material condition. Only the amount of protein for a single replicate was carried forward for each independent workflow replicate. Samples were measured on either Q Exactive HF (“Instrument: HF”) or Q Exactive HF-X (“Instrument: HF-X”) mass spectrometers as indicated, using 60-min LC gradients. Data were processed with or without the “Match between runs” algorithm in MaxQuant enabled as indicated, to transfer MS2 identifications between samples. Error bars denote mean and standard deviation of the workflow replicates.

Supplementary Figure 3 Investigation of optimal MS instrument settings for phosphopeptide quantification on the Q Exactive HF-X mass spectrometer.

In these experiments data were acquired with replicate injections of the same sample, equivalent to enrichment from 100 µg of cell lysate per injection (a-d) or as otherwise indicated (e-f). Ranges were chosen based on instrument manufacturer recommendations. The effect of Ion funnel RF level on (a) number of MS/MS scans performed during a 60-min LC gradient, and (b) the number of phosphorylation sites quantified (all) and localized accurately to a single residue (MaxQuant phosphorylation site localization probability >0.75, Class 1). Effect of Normalized Collision Energy (NCE) on (c) MS2 scan identification, and (d) the number of phosphorylation sites quantified (all) and localized accurately to a single residue (MaxQuant phosphorylation site localization probability >0.75, Class 1). Effect of MS2 resolution and MS2 maximum ion injection time on the quantification of Class 1 phosphorylation sites with either (e) low phosphopeptide load (equiv. to enrichment from 50 µg of cell lysate) or (f) higher phosphopeptide load (equiv. to enrichment from 400 µg of cell lysate). The number of phosphorylation sites quantified are reported for each combined MS2 resolution and ion injection time setting.

Supplementary Figure 4 Schematic of the 96-well deep-well plate used for sample digestion and phosphopeptide enrichment.

Cut-away view shows characteristic features of the plate design that enable rapid and efficient sample aspiration. A disposable glass pipette attached to a vacuum line can be used to aspirate supernatants without disturbing the centrifuge-pelleted beads by sliding the pipette down the square corner of each well and stopping when reaching the “aspiration stop point” depicted above. Only a small amount of supernatant should remain in the well (~ 10 µl), making wash steps highly efficient without loss of beads.

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Humphrey, S.J., Karayel, O., James, D.E. et al. High-throughput and high-sensitivity phosphoproteomics with the EasyPhos platform. Nat Protoc 13, 1897–1916 (2018). https://doi.org/10.1038/s41596-018-0014-9

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