Abstract
Recent advances in mass spectrometry and separation technologies created the opportunities for deep proteome characterization using shotgun proteomics approaches. The “real world” sample complexity and high concentration range limit the sensitivity of this characterization. The common strategy for increasing the sensitivity is sample fractionation prior to analysis either at the protein or the peptide level. Typically, fractionation at the peptide level is performed using linear gradient high-performance liquid chromatography followed by uniform fraction collection. However, this way of peptide fractionation results in significantly suboptimal operation of the mass spectrometer due to the non-uniform distribution of peptides between the fractions. In this work, we propose an approach based on peptide retention time prediction allowing optimization of chromatographic conditions and fraction collection procedures. An open-source software implementing the approach called FractionOptimizer was developed and is available at http://hg.theorchromo.ru/FractionOptimizer. The performance of the developed tool was demonstrated for human embryonic kidney (HEK293) cell line lysate. In these experiments, we improved the uniformity of the peptides distribution between fractions. Moreover, in addition to 13,492 peptides, we found 6787 new peptides not identified in the experiments without fractionation and up to 800 new proteins (or 25%).
Similar content being viewed by others
References
Ferguson PL, Smith RD. Proteome analysis by mass spectrometry. Annu Rev Biophys Biomol Struct. 2003;32:399–424. https://doi.org/10.1146/annurev.biophys.32.110601.141854.
Pirmoradian M, Budamgunta H, Chingin K, Zhang B, Astorga-Wells J, Zubarev RA. Rapid and deep human proteome analysis by single-dimension shotgun proteomics. Mol Cell Proteomics. 2013;12:3330–8. https://doi.org/10.1074/mcp.O113.028787.
Zhang Y, Fonslow BR, Shan B, Baek M, Yates JR. Protein analysis by shotgun/bottom-up proteomics. Chem Rev. 2013;113:2343–94. https://doi.org/10.1021/cr3003533.
Harper JW, Bennett EJ. Proteome complexity and the forces that drive proteome imbalance. Nature. 2016;537:328–38. https://doi.org/10.1038/nature19947.
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. 2011;10:1785–93. https://doi.org/10.1021/pr101060v.
Kelstrup CD, Jersie-Christensen RR, Batth TS, Arrey TN, Kuehn A, Kellmann M, et al. Rapid and deep proteomes by faster sequencing on a benchtop quadrupole ultra-high-field Orbitrap mass spectrometer. J Proteome Res. 2014;13:6187–95. https://doi.org/10.1021/pr500985w.
Nagaraj N, Alexander Kulak N, Cox J, Neuhauser N, Mayr K, Hoerning O, et al. System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap. Mol Cell Proteomics. 2012;11:M111.013722. https://doi.org/10.1074/mcp.M111.013722.
Hebert AS, Richards AL, Bailey DJ, Ulbrich A, Coughlin EE, Westphall MS, et al. The one hour yeast proteome. Mol Cell Proteomics. 2014;13:339–47. https://doi.org/10.1074/mcp.M113.034769.
Chick JM, Kolippakkam D, Nusinow DP, Zhai B, Rad R, Huttlin EL, et al. A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides. Nat Biotechnol. 2015;33:743–9. https://doi.org/10.1038/nbt.3267.
Gholami AM, Hahne H, Wu Z, Auer FJ, Meng C, Wilhelm M, et al. Global proteome analysis of the NCI-60 cell line panel. Cell Rep. 2013;4:609–20. https://doi.org/10.1016/j.celrep.2013.07.018.
Aguilar M. Reversed-phase high-performance liquid chromatography. In: Aguilar MI, editors. HPLC of Peptides and Proteins. New Jersey: Humana Press; 2004. pp. 9–22. https://doi.org/10.1385/1-59259-742-4:9.
Mant CT, Hodges RS. Analysis of peptides by high-performance liquid chromatography. In: Eckenhoff RG, Dmochowski IJ, editors. Methods in enzymology. New York: Academic Press; 1996. pp. 3–50. https://doi.org/10.1016/S0076-6879(96)71003-0.
Mitulović G. New HPLC techniques for proteomics analysis: a short overview of latest developments. J Liq Chromatogr Relat Technol. 2015;38:390–403. https://doi.org/10.1080/10826076.2014.941266.
Gokce E, Andrews GL, Dean RA, Muddiman DC. Increasing proteome coverage with offline RP HPLC coupled to online RP nanoLC-MS. J Chromatogr B Anal Technol Biomed Life Sci. 2011;879:610–4. https://doi.org/10.1016/j.jchromb.2011.01.032.
Moruz L, Pichler P, Stranzl T, Mechtler K, Käll L. Optimized nonlinear gradients for reversed-phase liquid chromatography in shotgun proteomics. Anal Chem. 2013;85:7777–85. https://doi.org/10.1021/ac401145q.
Wang Y, Yang F, Gritsenko MA, Wang Y, Clauss T, Liu T, et al. Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells. Proteomics. 2011;11:2019–26. https://doi.org/10.1002/pmic.201000722.
Dwivedi RC, Spicer V, Harder M, Antonovici M, Ens W, Standing KG, et al. Practical implementation of 2D HPLC scheme with accurate peptide retention prediction in both dimensions for high-throughput bottom-up proteomics. Anal Chem. 2008;80:7036–42. https://doi.org/10.1021/ac800984n.
Gorshkov AV, Tarasova IA, Evreinov VV, Savitski MM, Nielsen ML, Zubarev RA, et al. Liquid chromatography at critical conditions: comprehensive approach to sequence-dependent retention time prediction. Anal Chem. 2006;78:7770–7. https://doi.org/10.1021/ac060913x.
Goloborodko AA, Levitsky LI, Ivanov MV, Gorshkov MV. Pyteomics—a Python framework for exploratory data analysis and rapid software prototyping in proteomics. J Am Soc Mass Spectrom. 2013;24:301–4. https://doi.org/10.1007/s13361-012-0516-6.
Lobas AA, Karpov DS, Kopylov AT, Solovyeva EM, Ivanov MV, Ilina IY, et al. Exome-based proteogenomics of HEK-293 human cell line: coding genomic variants identified at the level of shotgun proteome. Proteomics. 2016;16:1980–91. https://doi.org/10.1002/pmic.201500349.
Craig R, Beavis RC. TANDEM: matching proteins with tandem mass spectra. Bioinformatics. 2004;20:1466–7. https://doi.org/10.1093/bioinformatics/bth092.
Ivanov MV, Levitsky LI, Lobas AA, Panic T, Laskay ÜA, Mitulovic G, et al. Empirical multidimensional space for scoring peptide spectrum matches in shotgun proteomics. J Proteome Res. 2014;13:1911–20. https://doi.org/10.1021/pr401026y.
Choi M, Eren-Dogu ZF, Colangelo C, Cottrell J, Hoopmann MR, Kapp EA, et al. ABRF proteome informatics research group (iPRG) 2015 study: detection of differentially abundant proteins in label-free quantitative LC-MS/MS experiments. J Proteome Res. 2017;16:945–57. https://doi.org/10.1021/acs.jproteome.6b00881.
Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics. 2014;13:2513–26. https://doi.org/10.1074/mcp.M113.031591.
Zhou Y, Gao J, Zhu H, Xu J, He H, Gu L, et al. Enhancing membrane protein identification using a simplified centrifugation and detergent-based membrane extraction approach. Anal Chem. 2018. https://doi.org/10.1021/acs.analchem.7b03710.
Laskay ÜA, Lobas AA, Srzentić K, Gorshkov MV, Tsybin YO. Proteome digestion specificity analysis for rational design of extended bottom-up and middle-down proteomics experiments. J Proteome Res. 2013;12:5558–69. https://doi.org/10.1021/pr400522h.
Griffin NM, Yu J, Long F, Oh P, Shore S, Li Y, et al. Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat Biotechnol. 2010;28:83–9. https://doi.org/10.1038/nbt.1592.
Bubis JA, Levitsky LI, Ivanov MV, Tarasova IA, Gorshkov MV. Comparative evaluation of label-free quantification methods for shotgun proteomics. Rapid Commun Mass Spectrom. 2017;31:606–12. https://doi.org/10.1002/rcm.7829.
Ishihama Y, Oda Y, Tabata T, Sato T, Nagasu T, Rappsilber J, et al. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol Cell Proteomics. 2005;4:1265–72. https://doi.org/10.1074/mcp.M500061-MCP200.
Zybailov B, Mosley AL, Sardiu ME, Coleman MK, Florens L, Washburn MP. Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J Proteome Res. 2006;5:2339–47. https://doi.org/10.1021/pr060161n.
Acknowledgments
This work was supported by the Russian Science Foundation, project no. 14-14-00971. The authors thank Dr. Irina A. Tarasova and Mark V. Ivanov for helpful discussions.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Electronic supplementary material
ESM 1
(PDF 1577 kb)
Rights and permissions
About this article
Cite this article
Solovyeva, E.M., Lobas, A.A., Kopylov, A.T. et al. FractionOptimizer: a method for optimal peptide fractionation in bottom-up proteomics. Anal Bioanal Chem 410, 3827–3833 (2018). https://doi.org/10.1007/s00216-018-1054-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00216-018-1054-2