Voltar
Artigo Acesso aberto Revisado por pares
BIBTEX RIS

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

2017; Elsevier BV; Volume: 16; Issue: 12 Linguagem: Inglês

10.1074/mcp.ra117.000314

ISSN

1535-9484

Autores

Roland Bruderer, Oliver M. Bernhardt, Tejas Gandhi, Yue Xuan, Julia Regina Sondermann, Manuela Schmidt, David Gómez‐Varela, Lukas Reiter,

Tópico(s)

Metabolomics and Mass Spectrometry Studies

Resumo

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

Referência(s)