Article | Published:

BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes

Nature Methodsvolume 15pages440448 (2018) | Download Citation


Great advances have been made in sensitivity and acquisition speed on the Orbitrap mass analyzer, enabling increasingly deep proteome coverage. However, these advances have been mainly limited to the MS2 level, whereas ion beam sampling for the MS1 scans remains extremely inefficient. Here we report a data-acquisition method, termed BoxCar, in which filling multiple narrow mass-to-charge segments increases the mean ion injection time more than tenfold as compared to that of a standard full scan. In 1-h analyses, the method provided MS1-level evidence for more than 90% of the proteome of a human cancer cell line that had previously been identified in 24 fractions, and it quantified more than 6,200 proteins in ten of ten replicates. In mouse brain tissue, we detected more than 10,000 proteins in only 100 min, and sensitivity extended into the low-attomolar range.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

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


  1. 1.

    Harper, J. W. & Bennett, E. J. Proteome complexity and the forces that drive proteome imbalance. Nature 537, 328–338 (2016).

  2. 2.

    Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

  3. 3.

    Schwenk, J. M. et al. The Human Plasma Proteome draft of 2017: building on the Human Plasma PeptideAtlas from mass spectrometry and complementary assays. J. Proteome Res. 16, 4299–4310 (2017).

  4. 4.

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

  5. 5.

    Mann, M., Kulak, N. A., Nagaraj, N. & Cox, J. The coming age of complete, accurate and ubiquitous proteomes. Mol. Cell 49, 583–590 (2013).

  6. 6.

    Link, A. J. et al. Direct analysis of protein complexes using mass spectrometry. Nat. Biotechnol. 17, 676–682 (1999).

  7. 7.

    Altelaar, A. F. M., Munoz, J. & Heck, A. J. R. Next-generation proteomics: toward an integrative view of proteome dynamics. Nat. Rev. Genet. 14, 35–48 (2013).

  8. 8.

    Richards, A. L., Merrill, A. E. & Coon, J. J. Proteome sequencing goes deep. Curr. Opin. Chem. Biol. 24, 11–17 (2015).

  9. 9.

    Venable, J. D., Dong, M.-Q., Wohlschlegel, J., Dillin, A. & Yates, J. R. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39–45 (2004).

  10. 10.

    Chapman, J. D., Goodlett, D. R. & Masselon, C. D. Multiplexed and data-independent tandem mass spectrometry for global proteome profiling. Mass Spectrom. Rev. 33, 452–470 (2014).

  11. 11.

    Ting, Y. S. et al. Peptide-centric proteome analysis: an alternative strategy for the analysis of tandem mass spectrometry data. Mol. Cell. Proteomics 14, 2301–2307 (2015).

  12. 12.

    Eliuk, S. & Makarov, A. Evolution of Orbitrap mass spectrometry instrumentation. Annu. Rev. Anal. Chem. 8, 61–80 (2015).

  13. 13.

    Zubarev, R. A. & Makarov, A. Orbitrap mass spectrometry. Anal. Chem. 85, 5288–5296 (2013).

  14. 14.

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

  15. 15.

    Bruderer, R. et al. Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results. Mol. Cell. Proteomics 16, 2296–2309 (2017).

  16. 16.

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

  17. 17.

    Michalski, A. et al. Mass-spectrometry-based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer. Mol. Cell. Proteomics 10, M111.011015 (2011).

  18. 18.

    Egertson, J. D. et al. Multiplexed MS/MS for improved data-independent acquisition. Nat. Methods 10, 744–746 (2013).

  19. 19.

    Cox, J. & Mann, M. MaxQuant enables high peptide-identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

  20. 20.

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

  21. 21.

    Geyer, P. E. et al. Plasma proteome profiling to assess human health and disease. Cell Syst. 2, 185–195 (2016).

  22. 22.

    Liu, Y. et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol. Syst. Biol. 11, 786 (2015).

  23. 23.

    Bekker-Jensen, D. B. et al. An optimized shotgun strategy for the rapid generation of comprehensive human proteomes. Cell Syst. 4, 587–599 (2017).

  24. 24.

    Pasa-Tolić, L., Masselon, C., Barry, R. C., Shen, Y. & Smith, R. D. Proteomic analyses using an accurate mass and time tag strategy. Biotechniques 37, 621–624 (2004).

  25. 25.

    Zhang, B., Käll, L. & Zubarev, R. A. DeMix-Q: quantification-centered data processing workflow. Mol. Cell. Proteomics 15, 1467–1478 (2016).

  26. 26.

    Geiger, T., Wehner, A., Schaab, C., Cox, J. & Mann, M. Comparative proteomic analysis of 11 common cell lines reveals ubiquitous, but varying, expression of most proteins. Mol. Cell. Proteomics 11, M111.014050 (2012).

  27. 27.

    Kulak, N. A., Geyer, P. E. & Mann, M. Loss-less nano-fractionator for high-sensitivity, high-coverage proteomics. Mol. Cell. Proteomics 16, 694–705 (2017).

  28. 28.

    Tye, B. K. MCM proteins in DNA replication. Annu. Rev. Biochem. 68, 649–686 (1999).

  29. 29.

    Moyer, S. E., Lewis, P. W. & Botchan, M. R. Isolation of the Cdc45–Mcm2–7–GINS (CMG) complex, a candidate for the eukaryotic DNA replication fork helicase. Proc. Natl. Acad. Sci. USA 103, 10236–10241 (2006).

  30. 30.

    Kulak, N. A., Pichler, G., Paron, I., Nagaraj, N. & Mann, M. Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat. Methods 11, 319–324 (2014).

  31. 31.

    Bruderer, R. et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol. Cell. Proteomics 14, 1400–1410 (2015).

  32. 32.

    Röst, H. L., Malmström, L. & Aebersold, R. Reproducible quantitative proteotype data matrices for systems biology. Mol. Biol. Cell 26, 3926–3931 (2015).

  33. 33.

    Röst, H. L. et al. TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics. Nat. Methods 13, 777–783 (2016).

  34. 34.

    Navarro, P. et al. A multicenter study benchmarks software tools for label-free proteome quantification. Nat. Biotechnol. 34, 1130–1136 (2016).

  35. 35.

    Collins, B. C. et al. Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH–mass spectrometry. Nat. Commun. 8, 291 (2017).

  36. 36.

    Sharma, K. et al. Cell-type- and brain-region-resolved mouse brain proteome. Nat. Neurosci. 18, 1819–1831 (2015).

  37. 37.

    Grinfeld, D., Aizikov, K., Kreutzmann, A., Damoc, E. & Makarov, A. Phase-constrained spectrum deconvolution for Fourier transform mass spectrometry. Anal. Chem. 89, 1202–1211 (2017).

  38. 38.

    Gillet, L. C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717 (2012).

  39. 39.

    Gallien, S., Kim, S. Y. & Domon, B. Large-scale targeted proteomics using internal standard triggered–parallel reaction monitoring (IS-PRM). Mol. Cell. Proteomics 14, 1630–1644 (2015).

  40. 40.

    Erickson, B. K. et al. A strategy to combine sample multiplexing with targeted proteomics assays for high-throughput protein signature characterization. Mol. Cell 65, 361–370 (2017).

  41. 41.

    Ong, S.-E. & Mann, M. A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat. Protoc. 1, 2650–2660 (2006).

  42. 42.

    Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics 13, 2513–2526 (2014).

  43. 43.

    Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

  44. 44.

    R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2008).

  45. 45.

    Wiśniewski, J. R., Hein, M. Y., Cox, J. & Mann, M. A “proteomic ruler” for protein copy number and concentration estimation without spike-in standards. Mol. Cell. Proteomics 13, 3497–3506 (2014).

  46. 46.

    Vizcaíno, J. A. et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 44(D1), D447–D456 (2016).

Download references


We acknowledge all members of the Department of Proteomics and Signal Transduction for help and fruitful discussions; in particular, we thank G. Borner, S. Doll, D. Hornburg, N. Kulak, M. Murgia, M. Raeschle and K. Sharma. We thank G. Sowa, I. Paron and K. Mayr for technical support and P. Treit and N. Skotte for comments on the manuscript. This research was partially supported by funding from the German Research Foundation (DFG–Gottfried Wilhelm Leibniz Prize) (F.M., M.M.), the European Union's Horizon 2020 research and innovation program under grant agreement 686547 (MSmed project) (F.M., P.E.G., S.V.W., J.C., M.M.) and the Max Planck Society for the Advancement of Sciences (F.M., P.E.G., S.V.W., J.C., M.M.).

Author information


  1. Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany

    • Florian Meier
    • , Philipp E. Geyer
    • , Sebastian Virreira Winter
    •  & Matthias Mann
  2. NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark

    • Philipp E. Geyer
    •  & Matthias Mann
  3. Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Martinsried, Germany

    • Juergen Cox


  1. Search for Florian Meier in:

  2. Search for Philipp E. Geyer in:

  3. Search for Sebastian Virreira Winter in:

  4. Search for Juergen Cox in:

  5. Search for Matthias Mann in:


F.M. and M.M. conceptualized the method; P.E.G. contributed to the development of the post-processing algorithms; J.C. designed and implemented the post-processing algorithms; F.M. and P.E.G. performed the experiments; F.M., P.E.G., S.V.W., J.C. and M.M. analyzed the data; and F.M. and M.M. wrote the manuscript with input from all of the authors.

Competing interests

F.M., J.C. and M.M. are inventors on a patent application covering the method described herein (applicant: Max-Planck-Gesellschaft zur Förderung der Wissenschaften; application number: PCT/EP2018/051290).

Corresponding author

Correspondence to Matthias Mann.

Integrated supplementary information

  1. Supplementary Figure 1 Investigation of key BoxCar acquisition parameters using a D-optimal Design of Experiment to model linear and quadratic effects.

    The objective of the DoE was to maximize the number of detected peptide features (m/z 400–1,200, z > 1) in 45-min runs of 1 μg HeLa digest. a,b, The 4D response contour plot illustrates the effect of the following factors: the number of BoxCar scans per cycle (x axis), the number of boxes per scan (y axis) as well as the effect of the maximum ion injection time in percent of the transient time (Fill) for a resolution of 60,000 (a) and 120,000 (b) at m/z 200. The results indicate that the number of features increases with the maximum fill time, which is in accordance with the expected increase in dynamic range and improved signal-to-noise ratios. Furthermore, the benefits of increasing the resolving power overcompensate for the downside of lengthening the cycle time. The effects of varying the number of BoxCar scans and boxes were less prominent; however, the results imply that a combination of three scans with about 12 boxes each yields best performance. Stars indicate the settings used for data acquisition in the present study for 45-min (white) and 100-min (green) gradients.

  2. Supplementary Figure 2 Feature detection in BoxCar and standard full scans.

    a, Number of detected multiply charged features from 1 μg of HeLa digest over a 45-min gradient with the standard and BoxCar methods. b, Dynamic range of the detected features as a function of retention time.

  3. Supplementary Figure 3 Stable-isotope-label-based quantification with BoxCar.

    a,b, Quantification of peptide (N = 24,487) and protein (N = 1,647) (b) ratios from a human cancer cell line in a two-channel SILAC experiment, acquired in triplicate single runs with the BoxCar method and applying the intensity correction as described in the main text. The heavy and light channels were mixed in a 1:3 ratio, which is accurately reflected in the density plots.

  4. Supplementary Figure 4 Peptide label-free quantification in ten replicate 45-min runs with the BoxCar method and our standard shotgun method.

    a, Pearson correlation analysis of the non-normalized peptide intensities. The median pairwise correlation coefficients were 0.96 and 0.97 for the BoxCar and shotgun replicates, and 0.95 for cross-correlated pairs (total N = 36,736 peptides). b, Pairwise peptide feature intensity ratios of a representative BoxCar–shotgun pair as a function of m/z (N = 30,924).

  5. Supplementary Figure 5 Label-free quantification benchmark.

    E. coli lysate was mixed with a human cancer cell line (HeLa) lysate in 1:2 and 1:12 ratios (peptide w/w, E. coli:HeLa). a,b, The scatterplot indicates median MaxLFQ ratios of human (red) and E. coli (blue) proteins that were fully quantified in triplicate single runs (N = 3) of each sample with the shotgun (N = 5,214 proteins) (a) and BoxCar (N = 5,699 proteins) (b) acquisition method. One-sided Student's t-tests return in total 962 significantly changing E. coli proteins at a permutation-based FDR below 0.05 for BoxCar, which is 35% more than with the standard method.

  6. Supplementary Figure 6 Dynamic range in clinical samples.

    Comparison of isotope patterns (features) from human plasma samples that were commonly identified and quantified in triplicate 45-min shotgun and BoxCar runs (m/z 400–1,200, N = 11,918 for each). a, Detection time per feature as a measure of sensitivity. b, Number of detected isotope peaks as a measure of intra-scan dynamic range.

  7. Supplementary Figure 7 Single-run protein quantification with peptide libraries.

    Comparison of the number and dynamic range of identified features by matching from a deep library into single shotgun (gray) and BoxCar (red) runs. ac, Analysis of a human cancer cell line digest in a 45-min gradient. df, Analysis of a mouse cerebellum digest in a 100-min single run.

  8. Supplementary Figure 8 Absolute abundance range of mouse cerebellum proteins quantified in 100-min BoxCar single runs.

    a, Estimated protein amount on the analytical column per 1-μg peptide injection (N = 10,569). b, Estimated copy numbers per cell for proteins annotated with the GOCC term ‘transcription factor complexes’ (N = 185).

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–8, Supplementary Tables 1–3 and Supplementary Note 1

  2. Reporting Summary

  3. Supplementary Protocol

    BoxCar acquisition and data analysis

  4. Supplementary Data

    Peptide and protein identifications and quantitative information for all proteomics experiments

  5. Source Data, Figure 1

  6. Source Data, Figure 2

  7. Source Data, Figure 3

  8. Source Data, Figure 4

  9. Source Data, Figure 5

  10. Source Data, Figure 6

About this article

Publication history




Issue Date


Further reading