Quantitative shotgun proteome analysis by direct infusion


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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.

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.


  1. 1.

    Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).

    CAS  PubMed  Google Scholar 

  2. 2.

    Meyer, J. G. & Schilling, B. Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques. Expert Rev. Proteom. 14, 419–429 (2017).

    CAS  Google Scholar 

  3. 3.

    de Godoy, L. M. F. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008).

    PubMed  Google Scholar 

  4. 4.

    Hebert, A. S. et al. The one hour yeast proteome. Mol. Cell. Proteom. 13, 339–347 (2014).

    CAS  Google Scholar 

  5. 5.

    Bache, N. et al. A novel LC system embeds analytes in pre-formed gradients for rapid, ultra-robust proteomics. Mol. Cell. Proteom. 17, 2284–2296 (2018).

    CAS  Google Scholar 

  6. 6.

    Kelstrup, C. D. et al. Performance evaluation of the Q Exactive HF-X for shotgun proteomics. J. Proteome Res. 17, 727–738 (2018).

    CAS  PubMed  Google Scholar 

  7. 7.

    Gachumi, G., Purves, R. W., Hopf, C. & El-Aneed, A. Fast quantification without conventional chromatography, the growing power of mass spectrometry. Anal. Chem. 92, 8628–8637 (2020).

    CAS  PubMed  Google Scholar 

  8. 8.

    Chekmeneva, E. et al. Optimization and application of direct infusion nanoelectrospray HRMS method for large-scale urinary metabolic phenotyping in molecular epidemiology. J. Proteome Res. 16, 1646–1658 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Koulman, A. et al. High-throughput direct-infusion ion trap mass spectrometry: a new method for metabolomics. Rapid Commun. Mass Spectrom. 21, 421–428 (2007).

    CAS  PubMed  Google Scholar 

  10. 10.

    Wilm, M. et al. Femtomole sequencing of proteins from polyacrylamide gels by nano-electrospray mass spectrometry. Nature 379, 466–469 (1996).

    CAS  PubMed  Google Scholar 

  11. 11.

    Chen, S. Rapid protein identification using direct infusion nanoelectrospray ionization mass spectrometry. Proteomics 6, 16–25 (2006).

    CAS  PubMed  Google Scholar 

  12. 12.

    Pereira-Medrano, A. G., Sterling, A., Snijders, A. P. L., Reardon, K. F. & Wright, P. C. A systematic evaluation of chip-based nanoelectrospray parameters for rapid identification of proteins from a complex mixture. J. Am. Soc. Mass Spectrom. 18, 1714–1725 (2007).

    CAS  PubMed  Google Scholar 

  13. 13.

    Chen, J., Canales, L. & Neal, R. E. Multi-segment direct inject nano-ESI-LTQ-FT-ICR-MS/MS for protein identification. Proteome Sci. 9, 38 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Xiang, Y. & Koomen, J. M. Evaluation of direct infusion-multiple reaction monitoring mass spectrometry for quantification of heat shock proteins. Anal. Chem. 84, 1981–1986 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Kretschy, D. et al. High-throughput flow injection analysis of labeled peptides in cellular samples—ICP-MS analysis versus fluorescence based detection. Int. J. Mass Spectrom. 307, 105–111 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Sidoli, S. et al. One minute analysis of 200 histone posttranslational modifications by direct injection mass spectrometry. Genome Res. 29, 978–987 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Michalski, A., Cox, J. & Mann, M. More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC−MS/MS. J. Proteome Res. 10, 1785–1793 (2011).

    CAS  PubMed  Google Scholar 

  18. 18.

    Cech, N. B. & Enke, C. G. Relating electrospray ionization response to nonpolar character of small peptides. Anal. Chem. 72, 2717–2723 (2000).

    CAS  PubMed  Google Scholar 

  19. 19.

    Meyer, J. G. & A. Komives, E. Charge state coalescence during electrospray ionization improves peptide identification by tandem mass spectrometry. J. Am. Soc. Mass Spectrom. 23, 1390–1399 (2012).

  20. 20.

    Ogorzalek Loo, R. R., Lakshmanan, R. & Loo, J. A. What protein charging (and supercharging) reveal about the mechanism of electrospray ionization. J. Am. Soc. Mass Spectrom. 25, 1675–1693 (2014).

    CAS  PubMed  Google Scholar 

  21. 21.

    Annesley, T. M. Ion suppression in mass spectrometry. Clin. Chem. 49, 1041 (2003).

    CAS  PubMed  Google Scholar 

  22. 22.

    Sarvin, B. et al. Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample-specific ion distributions. Nat. Commun. 11, 3186 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Webb, I. K. et al. Experimental evaluation and optimization of structures for lossless ion manipulations for ion mobility spectrometry with time-of-flight mass spectrometry. Anal. Chem. 86, 9169–9176 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Giles, K. et al. A cyclic ion mobility-mass spectrometry system. Anal. Chem. 91, 8564–8573 (2019).

    CAS  PubMed  Google Scholar 

  25. 25.

    Meier, F. et al. Parallel accumulation–serial fragmentation (PASEF): multiplying sequencing speed and sensitivity by synchronized scans in a trapped ion mobility device. J. Proteome Res. 14, 5378–5387 (2015).

    CAS  PubMed  Google Scholar 

  26. 26.

    Swearingen, K. E. et al. Nanospray FAIMS fractionation provides significant increases in proteome coverage of unfractionated complex protein digests. Mol. Cell. Proteom. 11, M111.014985 (2012).

    Google Scholar 

  27. 27.

    Hebert, A. S. et al. Comprehensive single-shot proteomics with FAIMS on a hybrid orbitrap mass spectrometer. Anal. Chem. 90, 9529–9537 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Nagy, G. et al. Separation of β-amyloid tryptic peptide species with isomerized and racemized l-aspartic residues with ion mobility in structures for lossless ion manipulations. Anal. Chem. 91, 4374–4380 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Melani, R. D. et al. Direct measurement of light and heavy antibody chains using differential ion mobility spectrometry and middle-down mass spectrometry. MAbs 11, 1351–1357 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Purves, R. W., Prasad, S., Belford, M., Vandenberg, A. & Dunyach, J.-J. Optimization of a new aerodynamic cylindrical FAIMS device for small molecule analysis. J. Am. Soc. Mass Spectrom. 28, 525–538 (2017).

    CAS  PubMed  Google Scholar 

  31. 31.

    Hengel, S. M. et al. Data-independent proteomic screen identifies novel tamoxifen agonist that mediates drug resistance. J. Proteome Res. 10, 4567–4578 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Yi, E. C. et al. Approaching complete peroxisome characterization by gas-phase fractionation. Electrophoresis 23, 3205–3216 (2002).

    CAS  PubMed  Google Scholar 

  33. 33.

    Wang, J. et al. MSPLIT-DIA: sensitive peptide identification for data-independent acquisition. Nat. Methods 12, 1106 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Wang, J., Pérez-Santiago, J., Katz, J. E., Mallick, P. & Bandeira, N. Peptide identification from mixture tandem mass spectra. Mol. Cell. Proteom. 9, 1476–1485 (2010).

    CAS  Google Scholar 

  35. 35.

    Horn, D. M., Zubarev, R. A. & McLafferty, F. W. Automated reduction and interpretation of high resolution electrospray mass spectra of large molecules. J. Am. Soc. Mass Spectrom. 11, 320–332 (2000).

    CAS  PubMed  Google Scholar 

  36. 36.

    Jaitly, N. et al. Decon2LS: an open-source software package for automated processing and visualization of high resolution mass spectrometry data. BMC Bioinform. 10, 87 (2009).

    Google Scholar 

  37. 37.

    Tabb, D. L. et al. Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. J. Proteome Res. 9, 761–776 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Geyer, P. E., Holdt, L. M., Teupser, D. & Mann, M. Revisiting biomarker discovery by plasma proteomics. Mol. Syst. Biol. 13, 942 (2017).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Cantor, J. R. et al. Physiologic medium rewires cellular metabolism and reveals uric acid as an endogenous inhibitor of UMP synthase. Cell 169, 258–272.e17 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Oexle, H., Gnaiger, E. & Weiss, G. Iron-dependent changes in cellular energy metabolism: influence on citric acid cycle and oxidative phosphorylation. Biochim. Biophys. Acta 1413, 99–107 (1999).

    CAS  PubMed  Google Scholar 

  41. 41.

    McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  42. 42.

    Niemi, N. M. et al. Pptc7 is an essential phosphatase for promoting mammalian mitochondrial metabolism and biogenesis. Nat. Commun. 10, 3197 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Meyer, J. G. et al. Quantification of lysine acetylation and succinylation stoichiometry in proteins using mass spectrometric data-independent acquisitions (SWATH). J. Am. Soc. Mass Spectrom. 27, 1758–1771 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Ivanov, M. V. et al. DirectMS1: MS/MS-free identification of 1000 proteins of cellular proteomes in 5 minutes. Anal. Chem. 92, 4326–4333 (2020).

    CAS  PubMed  Google Scholar 

  45. 45.

    Bekker-Jensen, D. B. et al. A compact quadrupole-orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients. Mol. Cell Proteom. 19, 716–729 (2020).

    CAS  Google Scholar 

  46. 46.

    Bian, Y. et al. Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS. Nat. Commun. 11, 157 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Shishkova, E., Hebert, A. S. & Coon, J. J. Now, more than ever, proteomics needs better chromatography. Cell Syst. 3, 321–324 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics. Nat. Methods 14, 513–520 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Ong, S.-E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteom. 1, 376–386 (2002).

    CAS  Google Scholar 

  50. 50.

    Wang, M. et al. Assembling the community-scale discoverable human proteome. Cell Syst. 7, 412–421.e5 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Gupta, N. & Pevzner, P. A. False discovery rates of protein identifications: a strike against the two-peptide rule. J. Proteome Res. 8, 4173–4181 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Shanmugam, A. K., Yocum, A. K. & Nesvizhskii, A. I. Utility of RNA-seq and GPMDB protein observation frequency for improving the sensitivity of protein identification by tandem MS. J. Proteome Res. 13, 4113–4119 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Forsman, U., Sjöberg, M., Turunen, M. & Sindelar, P. J. 4-Nitrobenzoate inhibits coenzyme Q biosynthesis in mammalian cell cultures. Nat. Chem. Biol. 6, 515–517 (2010).

    CAS  PubMed  Google Scholar 

  56. 56.

    Kueng, W., Silber, E. & Eppenberger, U. Quantification of cells cultured on 96-well plates. Anal. Biochem. 182, 16–19 (1989).

    CAS  Google Scholar 

  57. 57.

    Levitsky, L. I., Klein, J. A., Ivanov, M. V. & Gorshkov, M. V. Pyteomics 4.0: five years of development of a Python proteomics framework. J. Proteome Res. 18, 709–714 (2019).

    CAS  PubMed  Google Scholar 

  58. 58.

    Meyer, J. Code for analysis of data from direct infusion shotgun proteome analysis (DI-SPA) (Zenodo, 2020); https://doi.org/10.5281/ZENODO.4115930

Download references


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




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.

Ethics declarations

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.

Additional information

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing