Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results

• DOI: 10.1074/mcp.ra117.000314
• Publication Date: 2017-10-26T00:30:20Z
• Authors: Roland Bruderer; Oliver M. Bernhardt; Tejas Gandhi; Yue Xuan; Julia Sondermann; Manuela Schmidt; David Gomez-Varela; Lukas Reiter
• Keywords: Proteomics; 0301 basic medicine; Technological Innovation and Resources; Reproducibility of Results; Cell Line; Mice; 03 medical and health sciences; HEK293 Cells; Sequence Analysis, Protein; Tandem Mass Spectrometry; Animals; Humans; Peptides; Chromatography, Liquid; HeLa Cells

Comprehensive, reproducible and precise analysis of large sample cohorts is one the key objectives quantitative proteomics. Here, we present an implementation data-independent acquisition using its parallel nature that surpasses limitation serial MS2 data-dependent on a quadrupole ultra-high field Orbitrap mass spectrometer. In deep single shot acquisition, identified quantified 6,383 proteins in human cell lines 2-or-more peptides/protein over 7100 when including 717 were basis peptide sequence. 7739 mouse tissues 8121 382 based Missing values for within 0.3 to 2.1% median coefficients variation 4.7 6.2% among technical triplicates. very complex mixtures, could quantify 10,780 12,192 1412 Using this optimized DIA, investigated large-protein networks before after critical period whisker experience-induced synaptic strength murine somatosensory cortex 1-barrel field. This work shows spectrometry enables proteome profiling discovery with high coverage, reproducibility, precision scalability. Mass proteomics (1.Aebersold R. Mann M. spectrometry-based proteomics.Nature. 2003; 422: 198-207Crossref PubMed Scopus (5585) Google Scholar) powerful technology profile proteomes (2.Sharma K. Schmitt S. Bergner C.G. Tyanova Kannaiyan N. Manrique-Hoyos Kongi Cantuti L. Hanisch U.-K. Philips M.-A. Rossner M.J. Simons Cell type– brain region–resolved proteome.Nat. Neurosci. 2015; 18: 1819-1830Crossref (445) Scholar, 3.Liu Y. Buil A. Collins B.C. Gillet L.C.J. Blum L.C. Cheng Vitek O. Mouritsen J. Lachance G. Spector T.D. Dermitzakis E.T. Quantitative variability 342 plasma twin population.Mol. Syst. Biol. 11: 786Crossref (218) 4.Paulo J.A. O'Connell J.D. Everley R.A. O'Brien Gygi M.A. S.P. multiplexing compares abundance 5000 cerevisiae across 10 carbon sources.J. Proteomics. 2016; 148: 85-93Crossref (102) 5.Williams E.G. Wu Jha P. Dubuis Blattmann Argmann C.A. Houten S.M. Amariuta T. Wolski W. Zamboni Aebersold Auwerx Systems liver mitochondria function.Science. 352: aad0189Crossref (206) Scholar), discover biomarkers (6.Jan Muntel Yue Xuan Sebastian Berger Lukas Reiter Richard Bachur Alex Kentsis H. Advancing Urinary Protein Biomarker Discovery by Data-independent Acquisition Quadrupole-Orbitrap Spectrometer.J. Proteome Res. 14: 4752-4762Crossref (80) 7.Keshishian Burgess M.W. Gillette Mertins Clauser K.R. Mani D.R. Kuhn E.W. Farrell L.A. Gerszten R.E. Carr S.A. Multiplexed, Workflow Sensitive Plasma Yields Novel Candidates Early Myocardial Injury.Mol. Cell. 44: 2375-2393Abstract Full Text PDF (145) investigate biological regulation through post-translational modifications (8.Hendriks I.A. Vertegaal A.C.O. A comprehensive compilation SUMO proteomics.Nat. Rev. Mol. 17: 581-595Crossref (287) 9.Zhao Jensen O.N. Modification-specific proteomics: Strategies characterization enrichment techniques.Proteomics. 2009; 9: 4632-4641Crossref (277) 10.Chen Q. Muller J.S. Pang P.-C. Laval S.H. Haslam Lochmuller Dell Global N-linked glycosylation not significantly impaired myoblasts congenital myasthenic syndromes caused defective glutamine-fructose-6-phosphate transaminase 1 (GFPT1).Biomolecules. 5: 2758-2781Crossref (9) 11.Fiskin E. Bionda Dikic I. Behrends C. host bacterial ubiquitinome response Salmonella typhimurium infection.Mol. 62: 967-981Abstract (73) study protein degradation (12.Jovanovic Rooney M.S. Przybylski D. Chevrier Satija Rodriguez E.H. Fields A.P. Schwartz Raychowdhury Mumbach M.R. Eisenhaure Rabani Gennert Lu Delorey Weissman Hacohen Regev Immunogenetics. Dynamic life cycle pathogens.Science. 347: 1259038Crossref (295) protein-protein interaction (13.Collins B. Ludovic Rosenberger Röst Vichalkovski Gstaiger Quantifying dynamics SWATH spectrometry: application 14–3-3 system.Nat. Methods. 2013; 10: 1246-1253Crossref (243) 14.Breitkreutz Choi Sharom J.R. Boucher Neduva V. Larsen Lin Z.-Y.Y. Breitkreutz B.-J.J. Stark Liu Ahn Dewar-Darch Reguly Tang X. Almeida Qin Z.S. Pawson Gingras A.-C.C. Nesvizhskii A.I. Tyers global kinase phosphatase network yeast.Science. 2010; 328: 1043-1046Crossref (524) 15.Huttlin E.L. Ting Bruckner R.J. Gebreab F. M.P. Szpyt Tam Zarraga Colby Baltier Dong Guarani Vaites L.P. Ordureau Rad Erickson B.K. Wühr Chick Zhai Kolippakkam Mintseris Obar Harris Artavanis-Tsakonas Sowa M.E. De Camilli Paulo Harper J.W. The BioPlex Network: systematic exploration interactome.Cell. 162: 425-440Abstract (862) 16.Rinner Seebacher Walzthoeni Mueller L.N. Beck Schmidt Identification cross-linked peptides from sequence databases.Nat. 2008; 315-318Crossref (5) protein-ligand or target deconvolution (17.Frei Jeon O.-Y. Kilcher Moest Henning L.M. Jost Plückthun Mercer Carreira E.M. Wollscheid Direct identification ligand-receptor interactions living cells tissues.Nat. Biotechnol. 2012; 30: 997-1001Crossref (128) 18.Savitski M.M. Reinhard F.B.M. Franken Werner Savitski M.F. Eberhard Martinez Molina Jafari Dovega R.B. Klaeger Kuster Nordlund Bantscheff Drewes Tracking cancer drugs thermal proteome.Science. 2014; 346: 1255784Crossref (579) 19.Feng Franceschi Kahraman Soste Melnik Boersema P.J. de Laureto P.P. Nikolaev Oliveira Picotti structural changes proteomes.Nat. 32: 1036-1044Crossref (212) 20.Beck Michalski Raether Lubeck Kaspar Goedecke Baessmann Hornburg Meier Paron Kulak N.a Cox impact II, resolution time-of-flight instrument shotgun proteomics.Mol. 2014-2029Abstract (121) Scholar). For all these approaches reproducibility are gain accurate picture biology. Technologically, coverage has improved recent years 21.Geiger Wehner Schaab Comparative Proteomic Analysis Eleven Common Lines Reveals Ubiquitous but Varying Expression Most Proteins.Mol. 11Abstract (577) 22.Hebert A.S. Richards A.L. Bailey D.J. Ulbrich Coughlin E.E. Westphall Coon J.J. One Hour Yeast Proteome.Mol. 13: 339-347Abstract (411) 23.Azimifar S.B. Nagaraj Cell-type-resolved liver.Cell Metab. 20: 1076-1087Abstract (111) 24.Savitski Wilhelm Hahne scalable approach false rate estimation proteomic data sets.Mol. 2394-2404Abstract (234) 25.Christoforou Mulvey C.M. Breckels Geladaki Hurrell Hayward P.C. Naake Gatto Viner Arias A.M. Lilley K.S. draft map pluripotent stem spatial Commun. 7: 9992Crossref (135) 26.Kim M.-S. Pinto Getnet Nirujogi R.S. Manda S.S. Chaerkady Madugundu A.K. Kelkar D.S. Isserlin Jain Thomas J.K. Muthusamy Leal-Rojas Kumar Sahasrabuddhe Balakrishnan Advani George Renuse Selvan L.D.N. Patil A.H. Nanjappa Radhakrishnan Prasad Subbannayya Raju Sreenivasamurthy S.K. Marimuthu Sathe G.J. Chavan Datta K.K. Sahu Yelamanchi S.D. Jayaram Rajagopalan Sharma Murthy Syed Goel Khan A.a Ahmad Dey Mudgal Chatterjee Huang T.-C. Zhong Shaw P.G. Freed Zahari Mukherjee Shankar Mahadevan Lam Mitchell C.J. Satishchandra Schroeder J.T. Sirdeshmukh Maitra Leach Drake Halushka M.K. T.S.K. Hruban R.H. Kerr C.L. Bader G.D. Iacobuzio-Donahue C.a Gowda Pandey proteome.Nature. 509: 575-581Crossref (1494) 27.Wilhelm Schlegl Gholami Lieberenz Ziegler Butzmann Gessulat Marx Mathieson Lemeer Schnatbaum Reimer U. Wenschuh Mollenhauer Slotta-Huspenina Boese J.-H. Gerstmair Faerber Mass-spectrometry-based 582-587Crossref (1312) established workflows today rely "bottom-up" where first proteolytically cleaved into resulting mixture then analyzed spectrometric (28.Bantscheff Critical review update 2007 present.Anal. Bioanal. Chem. 404: 939-965Crossref (581) (DDA), spectrometer alternates between performing survey scan (MS1) spectrum fragment ion scans (MS2). During interrogates each MS1 precursor signals. These usually selected fragmentation relative signal intensity, giving rise spectra. analysis, compared theoretically derived spectra database search engine, identifications (29.Nesvizhskii validation generated tandem spectrometry.Nat. 2007; 4: 787-797Crossref (514) Inherently, process limited (30.Tabb D.D.L. Vega-Montoto Rudnick P.A. Variyath Ham A.J.L. Bunk D.M. Kilpatrick L.E. Billheimer D.D. Blackman R.K. Cardasis H.L. Jaffe Kowalski K.A. Neubert T.A. Regnier F.E. Schilling Tegeler T.J. Wang Whiteaker Zimmerman L.J. Fisher S.J. Gibson B.W. Kinsinger C.R. Mesri Stein S.E. Tempst Paulovich A.G. Liebler D.C. Spiegelman Repeatability liquid chromatography- spectrometry.J. 761-776Crossref (389) 31.Geromanos Vissers J.P.C. Silva J.C. Dorschel Li G.Z. Gorenstein M.V. Bateman Langridge J.I. detection, correlation, comparison product ions independent LC-MS dependant LC-MS/MS.Proteomics. 1683-1695Crossref (394) sensitivity speed which can sequentially acquire (32.Michalski More than 100,000 detectable species elute runs majority inaccessible LC-MS/MS.J. 2011; 1785-1793Crossref (476) 33.Scheltema Hauschild Lange Denisov Kuehn Makarov Q Exactive HF, benchtop pre-filter, performance ultra- orbitrap analyzer.Mol. 3698-3708Abstract (231) To overcome those limitations alternative methods developed. typically slice space segments measurement counterbalance complexity samples. quickly cycles such peaks resolved along chromatographic retention time. Slicing be achieved instance (34.Purvine Eppel J.-T. Yi E.C. Goodlett Shotgun collision-induced dissociation time flight analyzer.Proteomics. 3: 847-850Crossref (130) trap (35.Venable M.-Q. Wohlschlegel Dillin Yates Automated mixtures spectra.Nat 2004; 1: 39-45Crossref (509) mobility (36.Geromanos Hughes Ciavarini purity scores enhancing accuracy samples.Anal. 1127-1139Crossref (42) Today many exist (37.Plumb Johnson Rainville Smith Wilson I.D. Castro-Perez J.M. Nicholson UPLC/MSE; new generating molecular information biomarker structure elucidation.Rapid Spectrom. 2006; 1989-1994Crossref 38.Geiger Proteomics all-ion fragmentation.Mol. 2252-2261Abstract (189) 39.Panchaud Jung Shaffer S.a Aitchison Faster, quantitative, count.Anal. 83: 2250-2257Crossref (65) 40.Gillet Navarro Tate Rost Selevsek Bonner Targeted extraction MS/MS acquisition: concept consistent analysis.Mol. (1779) 41.Egertson Merrihew G.E. N.W. MacLean B.X. Y.S. Canterbury Marsh Kellmann Zabrouskov C.C. MacCoss Multiplexed acquisition.Nat. 744-746Crossref (207) 42.Bruderer Bernhardt O.M. Gandhi Miladinović L.-Y. Messner Ehrenberger Zanotelli Butscheid Escher Rinner Extending limits acetaminophen-treated three-dimensional microtissues.Mol. 1400-1410Abstract (521) 43.Distler Kuharev Levin Schild Tenzer Suppl Material: Drift time-specific collision energies enable deep-coverage 167-170Crossref (304) 44.Weisbrod Eng Hoopmann Baker Bruce J.E. Accurate analysis: multiplexed quantification.J. 1621-1632Crossref (72) often termed (DIA) 1The abbreviations used are: DIA; acquisition; CV, Coefficient variation; DDA, Data-dependent FDR, rate; MS1, Peptide scan; MS2, Fragment S1BF, Somatosensory barrel field.. DIA originally like DDA engines optionally preprocessing (45.Silva Denny G.-Z. Richardson Wall Geromanos Simultaneous qualitative Escherichia coli proteome: sweet tale.Mol. 589-607Abstract (244) 46.Tsou Avtonomov Tucholska DIA-Umpire: computational framework 12: 258-264Crossref (385) 47.Li Golick Database searching accounting simple mixtures.Proteomics. 1696-1719Crossref (436) 48.Pak Nikitin Gluck Lisacek Scherl Clustering Filtering Tandem Spectra Acquired Data-Independent Mode.J. Am. Soc. 24: 1862-1871Crossref (13) recently, peptide-centric (49.Ting Egertson Payne Kim Maclean R.D. Noble W.S. Maccoss Peptide-centric strategy data.Mol. 2301-2307Abstract (98) was introduced spectral libraries (SWATH) (40.Gillet prerequisite generation libraries. Existing tools multiple reaction monitoring (MRM) processing applied type (50.Reiter Hüttenhain Brusniak M.-Y. Hengartner M.O. mProphet: automated statistical large-scale SRM experiments.Nat. 8: 430-435Crossref (365) 51.Röst Schubert O.T. Malmström OpenSWATH automated, targeted MS data.Nat. 219-223Crossref (500) 52.Navarro Tsou C.-C. Distler Perez-Riverol multicenter benchmarks software label-free quantification.Nat. 34: 1130-1136Crossref (202) been shown provide 53.Selevsek Chang C.-Y. Reproducible quantification Saccharomyces SWATH-MS. (2015) SWATH-mass spectrometry.Mol. 739-749Abstract (134) 54.Bruderer High iRT prediction quantitation.Proteomics. 16: 2246-2256Crossref (81) 55.Vowinckel Capuano Campbell Deery Ralser beauty being (label)-free: preparation SWATH-MS next-generation proteomics.F1000Research. 2: 272Crossref as well DDA. Consequently, increasingly (3.Liu 6.Jan 56.Parker B.L. Yang Humphrey Chaudhuri Ma Peterman James D.E. phosphoproteomics insulin signaling spectrometry.Sci. Signal. 1-9Crossref 57.Loke Ng Vilashni Lim Ho Understanding dimorphic lifestyles gastric pathogen Helicobacter pylori SWATH-based approach.Sci. Rep. 6: 26784Crossref (33) 58.Ortea Rodríguez-Ariza Chicano-Gálvez Arenas Vacas Jurado Gámez potential lung adenocarcinoma bronchoalveolar lavage fluid extraction.J. 138: 106-114Crossref (64) interested achievable state art chromatography, (33.Scheltema indexed (iRT) (54.Bruderer processing. We workflow levels Spectronaut (42.Bruderer Significant improvements dynamic range increase, increased loading, iRT, library (see Suppl. Information Table I). His version manuscript submitted FDR refined decoy model. After improvements, more acqu