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Quantitative shotgun proteome analysis by direct infusion

Nature Methods volume 17, pages 1222–1228 (2020)Cite this article

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Abstract

Liquid chromatography–mass spectrometry (LC–MS) delivers sensitive peptide analysis for proteomics but requires extensive analysis time, reducing throughput. Here, we demonstrate that gas-phase peptide separation instead of LC enables fast proteome analysis. Using direct infusion–shotgun proteome analysis (DI-SPA) by data-independent acquisition mass spectrometry (DIA-MS), we demonstrate the targeted quantification of over 500 proteins within minutes of MS data collection (~3.5 proteins per second). We show the utility of this technology in performing a complex multifactorial proteomic study of interactions between nutrients, genotype and mitochondrial toxins in a collection of cultured human cells. More than 45,000 quantitative protein measurements from 132 samples were achieved in only ~4.4 h of MS data collection. Enabling fast, unbiased proteome quantification without LC, DI-SPA offers an approach to boost throughput, critical to drug and biomarker discovery studies that require analysis of thousands of proteomes.

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Fig. 1: Overview of DI-SPA by DIA-MS strategy for peptide identification.
Fig. 2: Peptide and protein identification by DI-SPA.
Fig. 3: Peptide and protein quantification with DI-SPA.
Fig. 4: Application of DI-SPA for rapid screening of human cellular responses to toxins.

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Data availability

All raw data (along with the Excel sheet giving details of each file), filtered and unfiltered search results and quantification files are available on MassIVE under the dataset identifier MSV000085156 (https://doi.org/10.25345/C5M686). The MassIVE repository also includes the relevant human FASTA database ‘2019-03-14-td-UP000005640.fasta’. Detailed descriptions of the RAW data files are on MassIVE under the folder ‘other’ in the Excel file ‘Raw data files descriptions v3.xlsx’. The massive repository includes the human spectral libraries for use with MSPLIT-DIA and the files used to create libraries. Source data are provided with this paper.

Code availability

All data-analysis code is written in Python and R and is available on GitHub from https://github.com/jgmeyerucsd/DI2A or from Zenodo (https://doi.org/10.5281/ZENODO.4115930)58.

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Acknowledgements

We thank D. Hwang for help with preparing figures, A. Williams for help with writing, J. Mabry for assistance in the generation of the PPTC7 knockout cell line, A. Hebert for helpful discussions and the Cantor Lab for their generous gift of HPLM. This work was supported by the following NIH grants: T15 LM007359 (J.G.M.), P41 GM108538 (J.J.C.) and R01 DK098672 (D.J.P.).

Author information

Authors and Affiliations

  1. Department of Biomolecular Chemistry, University of Wisconsin—Madison, Madison, WI, USA

    Jesse G. Meyer & Joshua J. Coon

  2. Department of Chemistry, University of Wisconsin—Madison, Madison, WI, USA

    Jesse G. Meyer & Joshua J. Coon

  3. National Center for Quantitative Biology of Complex Systems, University of Wisconsin—Madison, Madison, WI, USA

    Jesse G. Meyer & Joshua J. Coon

  4. Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, USA

    Jesse G. Meyer

  5. Morgridge Institute for Research, Madison, WI, USA

    Natalie M. Niemi, David J. Pagliarini & Joshua J. Coon

  6. Department of Biochemistry, University of Wisconsin—Madison, Madison, WI, USA

    Natalie M. Niemi & David J. Pagliarini

  7. Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA

    Natalie M. Niemi & David J. Pagliarini

  8. Departments of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO, USA

    David J. Pagliarini

  9. Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

    David J. Pagliarini

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  1. Jesse G. Meyer
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Contributions

Conceptualization, J.G.M., N.M.N. and D.J.P. Data curation, J.G.M. Formal analysis, J.G.M. Funding acquisition, J.G.M., D.J.P. and J.J.C. Investigation, J.G.M. and N.M.N. Methodology, J.G.M. and N.M.N. Project administration, J.G.M., D.J.P. and J.J.C. Resources, J.G.M., D.J.P. and J.J.C. Software, J.G.M. Supervision, J.G.M., D.J.P. and J.J.C. Validation, J.G.M. and N.M.N. Visualization, J.G.M. Writing—original draft, J.G.M. Writing—review and editing, J.G.M., N.M.N., D.J.P. and J.J.C.

Corresponding authors

Correspondence to Jesse G. Meyer or Joshua J. Coon.

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Competing interests

J.J.C. is a consultant for Thermo Fisher Scientific. J.G.M., N.M.M. and D.J.P. have no competing interests.

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Peer review information Arunima Singh was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Theoretical Analysis of Peptide Complexity Reduction by Gas-phase Fractionation quadrupole isolation width and FAIMS compensation voltage (CV).

This analysis uses the maximum CV signal for all peptide precursor masses identified from stepped CV analysis of the human proteome. Stacked barplots show the number of peptide precursor masses per bin split by the contribution from each FAIMS CV fraction. The top panel shows precursor masses per 4 m/z isolation bin. The middle panel shows a roughly linear decrease in the maximum number of peptide precursor masses when the isolation width is decreased to 2 m/z. The bottom panel shows a nonlinear decrease in the number of peptide precursor masses due to selection by FAIMS gas phase fractionation at constant quadruople isolation width.

Extended Data Fig. 2 Examples of infusion data traces.

Tryptic peptides from the MCF7 proteome (1 mg/mL) were infused as described for DI-SPA analysis, but precursor ions (MS1) were measured. a, Description of general flowgram parameters over time. b, MS1 trace of the no FAIMS experiment (top) and extracted ion chromatograms of various randomly chosen multiply charged m/z values (±10ppm) show a consistent pattern of elution for all masses. This suggests that peptides are not retained or separated in our setup. c, Comparison of the signal from without FAIMS versus FAIMS using each CV setting from −30 V to −80 V. d,.Example flow-gram from DI-SPA-PRM-MS of 100 fmol/μL angiotensin II showing the typical smooth trace of mass- and FAIMS-selected peptide precursor.

Extended Data Fig. 3 DI-SPA scouting experiments for untargeted peptide identification.

a, Fixed and varied parameters for each of the parameter scouting experiments in Fig. 2a–c. Values highlighted in yellow were varied with the other values in that row fixed. b, Schematic of scouting experiment with actual data. Peptides were directly infused into the mass spectrometer over the duration of a scouting experiment. The first selection is performed by FAIMS according to compensation voltage (CV). FAIMS CV is fixed at a value between −30 volts and −80 volts while cycling through the second selection by m/z with the first quadrupole isolation window. is stepped across the m/z range of interest (400–1,000 here) to isolate specific subfractions of the peptide population. The FAIMS and quadrupole-selected peptides are fragmented by HCD, and finally the fragment ions are detected in the orbitrap to produce a tandem mass spectra. No precursor ion scans (MS1) are collected. MS/MS spectra from DI-SPA are identified by spectral library search.

Extended Data Fig. 4 Enriched KEGG pathways including all protein members of those pathways identified by DI-SPA (matching Fig. 2e).

Pathway enrichment analysis was done in Cytoscape with the plugin clueGO. Larger circles correspond to lower corrected p-value of term enrichment, and the colored portion of the circle gives the proportion of proteins in that pathway that were identified.

Extended Data Fig. 5 Robustness and reproducibility of DI-SPA.

Tryptic peptides from the MCF7 proteome (1 mg/mL) were analyzed 100 times with a shortened version of the parameter scouting method (Extended Data Fig. 3). a, TIC traces of the infusion data from injection #1, #25, #50, #75, #100, and those five overlaid. b, The number of peptide identifications from MSPLIT-DIA per analysis (FDR < 0.01) and (c) the distribution of peptide identifications summarized as a boxplot. The boxplot shows the median (percentile 50%) with an orange line, and the box represents the inner quartile range (IQR) Q1 and Q3 (percentiles 25 and 75). Whiskers show Q1 - 1.5*IQR and Q3 + 1.5*IQR.

Extended Data Fig. 6 Application of DI-SPA to human plasma.

Two different purchased human plasma samples were analyzed by by DI-SPA-MS using the parameter scouting method strategy shown in Extended Data Fig. 3. The number of identifications for the two sources of human plasma were compared with and without depletion.

Extended Data Fig. 7 Workflow for preparation of standard samples to assess quantitative DI-SPA.

A549 cells were grown in DMEM media containing either light lysine and arginine (LIGHT) or 13C6, 15N2-lysine and 13C6,15N4 L-arginine (HEAVY) and then combined at various ratios including: 1:8, 1:4, 1:2, 1:1, 2:1, 4:1, and 8:1 (HEAVY:LIGHT). Samples were then lysed proteins were reduced and alkylated, and proteolysis was initiated with trypsin. Peptides from trypsin digestion were desalted and then data was collected in parallel with either traditional nanoLC-MS/MS to verify SILAC ratios and provide a benchmark, or with DI-SPA to determine quantitative quality. Data from nanoLC-MS/MS was analyzed using MaxQuant to identify and quantify peptides, and data from DI-SPA was analyzed with MSPLIT-DIA and custom code in python and R.

Extended Data Fig. 8 Examples of relationships between DI-SPA data collection settings for different peptides and their corresponding proteins.

Peptides that uniquely identify proteins are found with a combination of gas-phase fractionation by FAIMS and precursor mass isolation with the first quadrupole (Q1). In this example, two unique peptides from different proteins are co-isolated with FAIMS compensation voltage (CV) of −50 V and Q1 set to 437 m/z. A single peptide is isolated with CV of −70 V and the same Q1 setting of 437 m/z, and two more peptides are isolated with a CV of −80 V at Q1 set to 438.5 m/z. Library spectra are shown for each peptide. The three most abundant singly charged y-ions in the library spectra are used for peptide quantification unless otherwise noted.

Extended Data Fig. 9 Comparison of Quantification from peptides shared between LC-MS (MaxQuant) and DI-SPA analysis.

Data are from peptides quantified with both methods from enriched mitochondria. Bands around the regression line show the 95% confidence interval.

Extended Data Fig. 10 DI-SPA quantification of proteins from mitochondria subcellular fractions.

a, Overlap of 37 mitochondrial proteins quantified: 149 from the purified mitochondria experiment and 56 from the whole cell experiment. b, Heatmap showing trend of general decrease in the 149 proteins annotated mitochondrial from DI-SPA analysis of the purified mitochondria. Light is the signal from the experimental condition and heavy is the signal from the SILAC standard protein. *Benjamini-Hochberg adjusted p-value <0.05, exact corrected p-values: PHB=0.023, CISY=0.023, THIL=0.036, CH10 = 0.036. n = 3 independent biological replicates of mitochondria preparations from 293 T cells from one independent experiment. p-values are from a two-tailed T-test assuming equal variance, and corrected p-value is from Benjamini-Hochberg multiple hypothesis testing correction. Source Data is available as Supplementary Table 6.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4.

Reporting Summary

Supplementary Data 1

Zip archive containing summary results for each of the quantified proteins across all the treatment conditions: (1) tables with the three-way ANOVA results and (2) images of the protein quantity across conditions.

Supplementary Table 1

Protein identifications from the best MCF7 scouting experiment conditions.

Supplementary Table 2

Protein identifications from the various human plasma sample analyses.

Supplementary Table 3

Peptide identification summary from A549 cell analysis.

Supplementary Table 4

Proteins targeted for quantification in Fig. 3c.

Supplementary Table 5

Quantitative results from the MitoTox experiment.

Supplementary Table 6

Quantitative results from DI-SPA of isolated mitochondria.

Source data

Source Data Fig. 4

Statistical source data

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Meyer, J.G., Niemi, N.M., Pagliarini, D.J. et al. Quantitative shotgun proteome analysis by direct infusion. Nat Methods 17, 1222–1228 (2020). https://doi.org/10.1038/s41592-020-00999-z

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