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Maximizing Peptide Identification Events in Proteomic Workflows Using Data-Dependent Acquisition (DDA)

2013; Elsevier BV; Volume: 13; Issue: 1 Linguagem: Inglês

10.1074/mcp.m112.026500

1535-9484

Nicholas W. Bateman, Scott P. Goulding, Nicholas Shulman, Avinash K. Gadok, Karen K. Szumlinski, Michael J. MacCoss, Christine C. Wu,

Metabolomics and Mass Spectrometry Studies

Current analytical strategies for collecting proteomic data using data-dependent acquisition (DDA) are limited by the low analytical reproducibility of the method. Proteomic discovery efforts that exploit the benefits of DDA, such as providing peptide sequence information, but that enable improved analytical reproducibility, represent an ideal scenario for maximizing measureable peptide identifications in "shotgun"-type proteomic studies. Therefore, we propose an analytical workflow combining DDA with retention time aligned extracted ion chromatogram (XIC) areas obtained from high mass accuracy MS1 data acquired in parallel. We applied this workflow to the analyses of sample matrixes prepared from mouse blood plasma and brain tissues and observed increases in peptide detection of up to 30.5% due to the comparison of peptide MS1 XIC areas following retention time alignment of co-identified peptides. Furthermore, we show that the approach is quantitative using peptide standards diluted into a complex matrix. These data revealed that peptide MS1 XIC areas provide linear response of over three orders of magnitude down to low femtomole (fmol) levels. These findings argue that augmenting "shotgun" proteomic workflows with retention time alignment of peptide identifications and comparative analyses of corresponding peptide MS1 XIC areas improve the analytical performance of global proteomic discovery methods using DDA. Current analytical strategies for collecting proteomic data using data-dependent acquisition (DDA) are limited by the low analytical reproducibility of the method. Proteomic discovery efforts that exploit the benefits of DDA, such as providing peptide sequence information, but that enable improved analytical reproducibility, represent an ideal scenario for maximizing measureable peptide identifications in "shotgun"-type proteomic studies. Therefore, we propose an analytical workflow combining DDA with retention time aligned extracted ion chromatogram (XIC) areas obtained from high mass accuracy MS1 data acquired in parallel. We applied this workflow to the analyses of sample matrixes prepared from mouse blood plasma and brain tissues and observed increases in peptide detection of up to 30.5% due to the comparison of peptide MS1 XIC areas following retention time alignment of co-identified peptides. Furthermore, we show that the approach is quantitative using peptide standards diluted into a complex matrix. These data revealed that peptide MS1 XIC areas provide linear response of over three orders of magnitude down to low femtomole (fmol) levels. These findings argue that augmenting "shotgun" proteomic workflows with retention time alignment of peptide identifications and comparative analyses of corresponding peptide MS1 XIC areas improve the analytical performance of global proteomic discovery methods using DDA. Label-free methods in mass spectrometry-based proteomics, such as those used in common "shotgun" proteomic studies, provide peptide sequence information as well as relative measurements of peptide abundance (1Wu C.C. MacCoss M.J. Shotgun proteomics: tools for the analysis of complex biological systems.Curr. Opin. Mol. Ther. 2002; 4: 242-250PubMed Google Scholar, 2Neilson K.A. Ali N.A. Muralidharan S. Mirzaei M. Mariani M. Assadourian G. Lee A. van Sluyter S.C. Haynes P.A. Less label, more free: approaches in label-free quantitative mass spectrometry.Proteomics. 2011; 11: 535-553Crossref PubMed Scopus (537) Google Scholar, 3Nilsson T. Mann M. Aebersold R. Yates 3rd, J.R. Bairoch A. Bergeron J.J. Mass spectrometry in high-throughput proteomics: ready for the big time.Nat. Methods. 2010; 7: 681-685Crossref PubMed Scopus (387) Google Scholar). A common data acquisition strategy is based on data-dependent acquisition (DDA) 1The abbreviations used are:DDAdata-dependent acquisitionXICextracted ion chromatogramMS1precursor mass spectraMS/MStandem mass spectrometryAMTaccurate mass and time tagPTMposttranslationally modifiedv/vvolume/volumeIAA2-iodoacetamideFAformic acidHgbhemoglobinnLC-MS/MSnano-liquid chromatography tandem mass spectrometryi.d.inner diametero.d.outer diameterRresolutionHCDhigher-energy collisional dissociationppmparts per millionKlysineRarginineCVcoefficient of variationPSD95postsynaptic density protein-95NMDAN-methyl-D-aspartatemGluR1Ametabotropic glutamate receptor 1 ADGL-alphaSn1-specific diacylglycerol lipase alphaCpceruloplasminCfdcomplement factor DApoElipoprotein scavenger apolipoprotein EKOknockoutWTwild-typeHLhemolysis. 1The abbreviations used are:DDAdata-dependent acquisitionXICextracted ion chromatogramMS1precursor mass spectraMS/MStandem mass spectrometryAMTaccurate mass and time tagPTMposttranslationally modifiedv/vvolume/volumeIAA2-iodoacetamideFAformic acidHgbhemoglobinnLC-MS/MSnano-liquid chromatography tandem mass spectrometryi.d.inner diametero.d.outer diameterRresolutionHCDhigher-energy collisional dissociationppmparts per millionKlysineRarginineCVcoefficient of variationPSD95postsynaptic density protein-95NMDAN-methyl-D-aspartatemGluR1Ametabotropic glutamate receptor 1 ADGL-alphaSn1-specific diacylglycerol lipase alphaCpceruloplasminCfdcomplement factor DApoElipoprotein scavenger apolipoprotein EKOknockoutWTwild-typeHLhemolysis. where the most abundant precursor ions are selected for tandem mass spectrometry (MS/MS) analysis (1Wu C.C. MacCoss M.J. Shotgun proteomics: tools for the analysis of complex biological systems.Curr. Opin. Mol. Ther. 2002; 4: 242-250PubMed Google Scholar, 2Neilson K.A. Ali N.A. Muralidharan S. Mirzaei M. Mariani M. Assadourian G. Lee A. van Sluyter S.C. Haynes P.A. Less label, more free: approaches in label-free quantitative mass spectrometry.Proteomics. 2011; 11: 535-553Crossref PubMed Scopus (537) Google Scholar). DDA attempts to minimize redundant peptide precursor selection and maximize the depth of proteome coverage (2Neilson K.A. Ali N.A. Muralidharan S. Mirzaei M. Mariani M. Assadourian G. Lee A. van Sluyter S.C. Haynes P.A. Less label, more free: approaches in label-free quantitative mass spectrometry.Proteomics. 2011; 11: 535-553Crossref PubMed Scopus (537) Google Scholar). However, the analytical reproducibility of peptide identifications obtained using DDA-based methods result in <75% overlap between technical replicates (3Nilsson T. Mann M. Aebersold R. Yates 3rd, J.R. Bairoch A. Bergeron J.J. Mass spectrometry in high-throughput proteomics: ready for the big time.Nat. Methods. 2010; 7: 681-685Crossref PubMed Scopus (387) Google Scholar, 4Tabb D.L. Vega-Montoto L. Rudnick P.A. Variyath A.M. Ham A.J. 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. 2010; 9: 761-776Crossref PubMed Scopus (389) Google Scholar). Comparisons of peptide identifications between replicate analyses have shown that the rate of new peptide identifications increases sharply following two replicate sample injections and gradually tapers off after approximately five replicate injections (4Tabb D.L. Vega-Montoto L. Rudnick P.A. Variyath A.M. Ham A.J. 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. 2010; 9: 761-776Crossref PubMed Scopus (389) Google Scholar). This phenomenon is due, in part, to the semirandom sampling of peptides in a DDA experiment (5Liu H. Sadygov R.G. Yates 3rd, J.R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics.Anal. Chem. 2004; 76: 4193-4201Crossref PubMed Scopus (2066) Google Scholar). data-dependent acquisition extracted ion chromatogram precursor mass spectra tandem mass spectrometry accurate mass and time tag posttranslationally modified volume/volume 2-iodoacetamide formic acid hemoglobin nano-liquid chromatography tandem mass spectrometry inner diameter outer diameter resolution higher-energy collisional dissociation parts per million lysine arginine coefficient of variation postsynaptic density protein-95 N-methyl-D-aspartate metabotropic glutamate receptor 1 A Sn1-specific diacylglycerol lipase alpha ceruloplasmin complement factor D lipoprotein scavenger apolipoprotein E knockout wild-type hemolysis. data-dependent acquisition extracted ion chromatogram precursor mass spectra tandem mass spectrometry accurate mass and time tag posttranslationally modified volume/volume 2-iodoacetamide formic acid hemoglobin nano-liquid chromatography tandem mass spectrometry inner diameter outer diameter resolution higher-energy collisional dissociation parts per million lysine arginine coefficient of variation postsynaptic density protein-95 N-methyl-D-aspartate metabotropic glutamate receptor 1 A Sn1-specific diacylglycerol lipase alpha ceruloplasmin complement factor D lipoprotein scavenger apolipoprotein E knockout wild-type hemolysis. Alternate label-free methods focused on measuring the abundance of intact peptide ions, such as the accurate mass and time tag (AMT) approach (6Conrads T.P. Anderson G.A. Veenstra T.D. Pasa-Tolic L. Smith R.D. Utility of accurate mass tags for proteome-wide protein identification.Anal. Chem. 2000; 72: 3349-3354Crossref PubMed Scopus (257) Google Scholar, 7Prakash A. Mallick P. Whiteaker J. Zhang H. Paulovich A. Flory M. Lee H. Aebersold R. Schwikowski B. Signal maps for mass spectrometry-based comparative proteomics.Mol. Cell. Proteomics. 2006; 5: 423-432Abstract Full Text Full Text PDF PubMed Scopus (102) Google Scholar, 8Radulovic D. Jelveh S. Ryu S. Hamilton T.G. Foss E. Mao Y. Emili A. Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry.Mol. Cell. Proteomics. 2004; 3: 984-997Abstract Full Text Full Text PDF PubMed Scopus (199) Google Scholar, 42Cutillas P.R. Vanhaesebroeck B. Quantitative profile of five murine core proteomes using label-free functional proteomics.Mol. Cell. Proteomics. 2007; 6: 1560-1573Abstract Full Text Full Text PDF PubMed Scopus (100) Google Scholar), are aimed at differential analyses of extracted ion chromatogram (XIC) areas integrated from high mass accuracy peptide precursor mass spectra (MS1 spectra) exhibiting discrete chromatographic elution times. This method is particularly powerful for the analysis of post-translationally modified (PTM) peptides as pairing the low abundance of PTM candidates with the variable nature of DDA complicates comparisons between samples (9Schilling B. Rardin M.J. Maclean B.X. Zawadzka A.M. Frewen B.E. Cusack M.P. Sorensen D.J. Bereman M.S. Jing E. Wu C.C. Verdin E. Kahn C.R. Maccoss M.J. Gibson B.W. Platform-independent and Label-free Quantitation of Proteomic Data Using MS1 Extracted Ion Chromatograms in Skyline: Application to protein acetylation and phosphorylation.Mol. Cell. Proteomics. 2012; 11: 202-214Abstract Full Text Full Text PDF PubMed Scopus (334) Google Scholar, 43Alcolea M.P. Casado P. Rodriguez-Prados J.-C. Vanhaesebroeck B. Cutillas P.R. Phosphoproteomic analysis of leukemia cells under basal and drug-treated conditions identifies markers of kinase pathway activation and mechanisms of resistance.Mol. Cell. Proteomics. 2012; 11: 453-466Abstract Full Text Full Text PDF PubMed Scopus (57) Google Scholar). However, label-free strategies focused on the analysis of peptide MS1 XIC areas are dependent on a priori knowledge of peptide ions and retention times (2Neilson K.A. Ali N.A. Muralidharan S. Mirzaei M. Mariani M. Assadourian G. Lee A. van Sluyter S.C. Haynes P.A. Less label, more free: approaches in label-free quantitative mass spectrometry.Proteomics. 2011; 11: 535-553Crossref PubMed Scopus (537) Google Scholar, 3Nilsson T. Mann M. Aebersold R. Yates 3rd, J.R. Bairoch A. Bergeron J.J. Mass spectrometry in high-throughput proteomics: ready for the big time.Nat. Methods. 2010; 7: 681-685Crossref PubMed Scopus (387) Google Scholar, 4Tabb D.L. Vega-Montoto L. Rudnick P.A. Variyath A.M. Ham A.J. 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. 2010; 9: 761-776Crossref PubMed Scopus (389) Google Scholar, 5Liu H. Sadygov R.G. Yates 3rd, J.R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics.Anal. Chem. 2004; 76: 4193-4201Crossref PubMed Scopus (2066) Google Scholar, 6Conrads T.P. Anderson G.A. Veenstra T.D. Pasa-Tolic L. Smith R.D. Utility of accurate mass tags for proteome-wide protein identification.Anal. Chem. 2000; 72: 3349-3354Crossref PubMed Scopus (257) Google Scholar, 7Prakash A. Mallick P. Whiteaker J. Zhang H. Paulovich A. Flory M. Lee H. Aebersold R. Schwikowski B. Signal maps for mass spectrometry-based comparative proteomics.Mol. Cell. Proteomics. 2006; 5: 423-432Abstract Full Text Full Text PDF PubMed Scopus (102) Google Scholar, 8Radulovic D. Jelveh S. Ryu S. Hamilton T.G. Foss E. Mao Y. Emili A. Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry.Mol. Cell. Proteomics. 2004; 3: 984-997Abstract Full Text Full Text PDF PubMed Scopus (199) Google Scholar, 9Schilling B. Rardin M.J. Maclean B.X. Zawadzka A.M. Frewen B.E. Cusack M.P. Sorensen D.J. Bereman M.S. Jing E. Wu C.C. Verdin E. Kahn C.R. Maccoss M.J. Gibson B.W. Platform-independent and Label-free Quantitation of Proteomic Data Using MS1 Extracted Ion Chromatograms in Skyline: Application to protein acetylation and phosphorylation.Mol. Cell. Proteomics. 2012; 11: 202-214Abstract Full Text Full Text PDF PubMed Scopus (334) Google Scholar, 10Smith R.D. Anderson G.A. Lipton M.S. Pasa-Tolic L. Shen Y. Conrads T.P. Veenstra T.D. Udseth H.R. An accurate mass tag strategy for quantitative and high-throughput proteome measurements.Proteomics. 2002; 2: 513-523Crossref PubMed Scopus (404) Google Scholar). Thus, prospective analyses of samples are needed to assess peptides and their respective retention times. This prospective analysis may not be possible for reagent-limited samples. Further, the usage of previously established peptide features in the analysis of different sample types can be confounded by unknown matrix effects that can produce variable retention time characteristics and peptide ion suppression (2Neilson K.A. Ali N.A. Muralidharan S. Mirzaei M. Mariani M. Assadourian G. Lee A. van Sluyter S.C. Haynes P.A. Less label, more free: approaches in label-free quantitative mass spectrometry.Proteomics. 2011; 11: 535-553Crossref PubMed Scopus (537) Google Scholar). Therefore, proteomic strategies that make use of DDA, to provide peptide sequence information and identify features within the sample, but that also use MS1 data for comparisons between samples, represent an ideal combination for maximizing measureable peptide identification events in "shotgun" proteomic discovery analyses. Here we describe an analytical workflow that combines traditional DDA methods with the analysis of retention time aligned XIC areas extracted from high mass accuracy peptide precursor MS1 spectra. This method resulted in a 25.1% (±6.6%) increase in measureable peptide identification events across samples of diverse composition because of the inferential extraction of peptide MS1 XIC areas in sample sets lacking corresponding MS/MS events. These findings were observed in measurements of peptide MS1 XIC abundances using sample types ranging from tryptic digests of olfactory bulb tissues dissected from Homer2 knockout and wild-type mice to mouse blood plasma exhibiting differential levels of hemolysis. We further establish that this method is quantitative using a dilution series of known bovine standard peptide concentrations spiked into mouse blood plasma. These data show that comparative analysis between samples should be performed using peptide MS1 data as opposed to semirandomly sampled peptide MS/MS data. This approach maximizes the number of peptides that can be compared between samples. The concentration of protein in commercial mouse blood plasma (D408–04-0050, Rockland Immunochemicals, Inc. Gilbertsville, PA) was determined using the DC Protein Assay (Bio-Rad Laboratories, Hercules, CA) and equivalent amounts of protein were immunodepleted using a MARS-mouse 3 column (Agilent Technologies, Santa Clara, CA). Briefly, 1.0 mg of total plasma protein was diluted in depletion Buffer A to 5.0 μg/μl, centrifuged for 1 min at 14,000 rpm/4 °C in a 5417R refrigerated microcentrifuge (Eppendorf, Hauppauge, NY), filtered through a 0.22 μm spin filter by centrifugation at 5000 rpm/4 °C for 5 min, and immunodepleted as per manufacturers recommendations. Depleted plasma proteins were extracted by methanol/chloroform precipitation (11Wessel D. Flugge U.I. A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids.Anal. Biochem. 1984; 138: 141-143Crossref PubMed Scopus (3163) Google Scholar), solubilized in 0.2% RapiGest SF Surfactant (Waters Corporation, Milford, MA) in 25 mm ammonium bicarbonate before being heated for 5 min at 100 °C and diluted to 0.1% RapiGest. Reactive cysteines were reduced by addition of 5.0 mm dithiothreitol (DTT) and incubating at 60 °C for 30 min followed by alkylation in 15 mm 2-iodoacetamide (IAA) at room temperature for 30 min under darkness. Porcine sequencing grade modified trypsin endoproteinase (Promega Corporation, Madison, WI) was added at a 1:100 enzyme to protein ratio and samples were incubated at 37 °C overnight. RapiGest was hydrolyzed in 120 mm hydrochloric acid and samples were centrifuged 3 × 45 min at 14,000 rpm/4 °C to remove insoluble precipitates. Resulting peptide digests were desalted using an offline Pierce C-18 spin column (Thermo Scientific, Rockford, IL) as per manufacturers recommendations and lyophilized peptides were resuspended at a concentration of 0.2 μg/μl in 0.1% (v/v) formic acid (FA). A commercial tryptic digest of six bovine proteins mixed at equal molar concentrations (Bruker-Michrome, Auburn, CA) was diluted to 0.2 μg/μl in 0.1% (v/v) FA and a dilution series was produced in digested mouse plasma. A six point dilution curve corresponding to (1) 0.2 μg/μl, (2) 0.15 μg/μl, (3) 0.1 μg/μl, (4) 0.05 μg/μl, (5) 0.025 μg/μl, and (6) 0.002 μg/μl of bovine peptides in a constant matrix of 0.2 μg/μl mouse plasma digest was generated. Olfactory bulb tissue was dissected from three Homer2 knockout (129/Sv X C57BL/6) (12Shin D.M. Homer 2 tunes G protein-coupled receptors stimulus intensity by regulating RGS proteins and PLC GAP activities.J. Cell Biol. 2003; 162: 293-303Crossref PubMed Scopus (78) Google Scholar) and three wild-type male mice obtained from Dr. Karen K. Szumlinski (University of California, Santa Barbara) and subjected to a modified postsynaptic density enrichment strategy previously described by Phillips et al. (13Phillips G.R. Huang J.K. Wang Y. Tanaka H. Shapiro L. Zhang W. Shan W.S. Arndt K. Frank M. Gordon R.E. Gawinowicz M.A. Zhao Y. Colman D.R. The presynaptic particle web: ultrastructure, composition, dissolution, and reconstitution.Neuron. 2001; 32: 63-77Abstract Full Text Full Text PDF PubMed Scopus (379) Google Scholar, 14Phillips G.R. Florens L. Tanaka H. Khaing Z.Z. Fidler L. Yates J.R. Colman D.R. Proteomic comparison of two fractions derived from the transsynaptic scaffold.J. Neurosci. Res. 2005; 81: 762-775Crossref PubMed Scopus (66) Google Scholar). Briefly, olfactory bulbs were pooled according to genotype and homogenized using a 15 ml Potter-Elvehjem tissue grinder with PTFE pestle (Wheaton Science Products, Millville, NJ) over wet ice in 3 ml of a 0.32 m sucrose solution containing 0.1 mm CaCl2, 1 mm MgCl2, and 1X phosphatase inhibitor mixture (Halt Phosphatase Inhibitor Mixture, Thermo Scientific, Rockford, IL). The homogenate was brought to a working concentration of 1.25 m sucrose via the addition of 17 ml of a sucrose solution containing 0.1 mm CaCl2 and 1X phosphatase inhibitor mixture, overlaid with 10 ml of a 1.0 m sucrose solution containing 0.1 mm CaCl2, and centrifuged at 100,000 × g/4 °C for 3 h in a Sorvall WX90 Ultracentrifuge (Thermo Scientific, Rockford, IL). Synaptosomes (∼1 ml) were collected from the 1.0/1.25 M interface and incubated for 30 min in 10 ml of 20 mm Tris-buffered saline containing 1% Triton X-100 at pH 6. Samples were then centrifuged at 40,000 × g/4 °C, supernatants were decanted, and pellets were incubated in 1 ml of a second buffer containing 20 mm Tris-buffered saline and 1% Triton X-100 at pH 8. Samples were centrifuged at 40,000 × g/4 °C in a Sorvall MTX 150 Micro-Ultracentrifuge (Thermo Scientific, Rockford, IL) to obtain enriched synaptic density pellets. Tryptic digests of synaptic density proteins were prepared identically to methods described above except samples were solubilized in 0.2% RapiGest containing 50 mm ammonium bicarbonate and subjected to a cleanup step using Agilent cleanup C-18 pipette tips (Agilent Technologies, Santa Clara, CA) according to manufacturer's recommendations. Lyophilized peptides were resuspended at a final concentration of 0.25 μg/μl in 0.1% (v/v) FA. Further, peptide identifications (method detailed below) corresponding to cutaneous keratins were removed from this sample set resulting in a loss of 1.31% of total peptide identifications. Hemolysates were prepared from a 1:4 mixture of whole blood obtained by cardiac puncture of a C57BL/6 mouse and commercial mouse plasma (Rockland Immunochemicals, Inc. Gilbertsville, PA). Blood plasma mixtures were subjected to (1) gentle shaking by hand for 30 s to emulate excessive mixing of blood samples post-collection (15Lippi G. Blanckaert N. Bonini P. Green S. Kitchen S. Palicka V. Vassault A.J. Plebani M. Haemolysis: an overview of the leading cause of unsuitable specimens in clinical laboratories.Clin. Chem. Lab. Med. 2008; 46: 764-772Crossref PubMed Scopus (310) Google Scholar, 16Sowemimo-Coker S.O. Red blood cell hemolysis during processing.Transfus. Med. Rev. 2002; 16: 46-60Crossref PubMed Scopus (282) Google Scholar) (low hemolysis condition) or (2) sampling of the supernatant and a fraction of the blood cell pellet emulating poor separator barrier integrity (15Lippi G. Blanckaert N. Bonini P. Green S. Kitchen S. Palicka V. Vassault A.J. Plebani M. Haemolysis: an overview of the leading cause of unsuitable specimens in clinical laboratories.Clin. Chem. Lab. Med. 2008; 46: 764-772Crossref PubMed Scopus (310) Google Scholar) following centrifugation (high hemolysis condition). Blood plasma was prepared by centrifuging whole blood mixtures for 10 min at 5000 rpm/4 °C in a 5417R refrigerated microcentrifuge (Eppendorf, Hauppauge, NY) and 0.1 mm phenylmethylsulfonyl fluoride (PMSF, Thermo Scientific, Rockford, IL) was then added to plasma supernatants before storage at −80 °C. The extent of hemolysis was determined by direct spectrophotometric measurement of hemoglobin (Hgb) concentration as per the AII method detailed by Fairbanks et al. (17Fairbanks V.F. Ziesmer S.C. O'Brien P.C. Methods for measuring plasma hemoglobin in micromolar concentration compared.Clin. Chem. 1992; 38: 132-140Crossref PubMed Scopus (115) Google Scholar) using an Epoch Microplate Spectrophotometer (BioTek, Winooski, VT). Plasma samples were immunodepleted on a MARS-mouse 3 column and quantitated by DC protein assay as described above. Twenty micrograms of depleted protein was run ∼2.0 cm into a 5.0% bis-acrylamide stacking gel and processed for in-gel digestion as previously described(18Bateman N.W. Sun M. Hood B.L. Flint M.S. Conrads T.P. Defining central themes in breast cancer biology by differential proteomics: conserved regulation of cell spreading and focal adhesion kinase.J. Proteome Res. 2010; 9: 5311-5324Crossref PubMed Scopus (31) Google Scholar) using 5 mm DTT in 25 mm ammonium bicarbonate and incubation at 60 °C and 15 mm IAA in 25 mm ammonium bicarbonate for reduction and alkylation of reactive cysteine residues, respectively. Lyophilized peptides were resuspended at a final concentration of 0.2 μg/μl in 0.1% (v/v) FA. Peptide digests corresponding to the bovine/mouse plasma dilution series or Homer2 knockout and wild-type olfactory bulb tissues were resolved by nLC-MS/MS using an EASY-nLC II (Thermo Scientific, San Jose, CA) coupled online via electrospray ionization (ESI) to an Orbitrap Elite mass spectrometer (Thermo Scientific, San Jose, CA). Randomized, triplicate injections of 1.0 μg of peptide extracts were resolved on a 75 μm i.d. by 360 μm o.d. by 300 mm long fused silica capillary column (Polymicro Technologies, Phoenix, AZ) slurry-packed in-house with 5 μm particle size, 125 Å pore size C-18 silica-bonded stationary phase (phenomenex, Torrance, CA). After sample injection, peptides were eluted from the column using a gradient of 2% mobile phase B (99.9% (v/v) acetonitrile (ACN)/0.1% (v/v) FA) to 12% mobile phase B over 60 min stepping up to 32% mobile phase B for an additional 60 min at a constant flow rate of 200 nL/min followed by a column wash consisting of 80% mobile phase B for an additional 6 min. The column used for olfactory bulb tissues was heated to 40 °C. The Orbitrap Elite mass spectrometer was configured to collect high resolution (R = 60,000 at m/z 400) broadband mass spectra (m/z 400–1400) from which the ten most abundant peptide molecular ions, dynamically determined from the MS1 scan, were selected for MS/MS using a relative collision-induced dissociation (CID) energy of 35%. Peptide digests from differentially hemolyzed blood plasma were resolved by nLC on a 75 μm i.d. by 360 μm o.d. by 250 mm long fused silica capillary column as described above, but coupled online via ESI to a Q Exactive mass spectrometer (Thermo Scientific, San Jose, CA). After sample injection, peptides were eluted from the column using a linear gradient of 2% to 42% mobile phase B over 120 min at a constant flow rate of 250 nL/min followed by a column wash consisting of 80% mobile phase B for an additional 10 min. The Q Exactive mass spectrometer was configured to collect high resolution (R = 70,000 at m/z 200) broadband mass spectra (m/z 400–1400) and MS/MS events (R = 17,500 at an automatic gain control target of 2.0E+5 and an underfill ratio of 20%) on the five most abundant peptide molecular ions dynamically determined from the MS1 scan using a relative higher-energy collisional dissociation (HCD) energy of 35%. A default dynamic exclusion setting was used to minimize redundant selection of peptides. Peptide identifications were obtained by searching nLC-MS/MS RAW file data using a pipeline that consisted of extraction of MS1 and MS2 data (MakeMS2, version 2.28) (19McDonald W.H. Tabb D.L. Sadygov R.G. MacCoss M.J. Venable J. Graumann J. Johnson J.R. Cociorva D. Yates 3rd, J.R. MS1, MS2, and SQT-three unified, compact, and easily parsed file formats for the storage of shotgun proteomic spectra and identifications.Rapid Commun. Mass Spectrom. 2004; 18: 2162-2168Crossref PubMed Scopus (294) Google Scholar), Bullseye/Hardklör (version 1.25) (20Hsieh E.J. Hoopmann M.R. MacLean B. MacCoss M.J. Comparison of database search strategies for high precursor mass accuracy MS/MS data.J. Proteome Res. 2010; 9: 1138-1143Crossref PubMed Scopus (99) Google Scholar) at default values, and searching with SEQUEST (version 27) (21Eng J. McCormack A. Yates J. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database.J. Am. Soc. Mass Spectrom. 1994; 5: 976-989Crossref PubMed Scopus (5420) Google Scholar) against a UniProt-derived mouse proteome database obtained from the European Bioinformatics Institute (version 12/2011; 23,412 protein entries) or a modified mouse proteome database including the six bovine standard proteins (sequences derived directly from bovine standard specification sheet, Bruker-Michrome, Auburn, CA) using the following parameters: trypsin (KR); two missed cleavages sites; 10 ppm precursor mass tolerance, 0.36 amu fragment ion tolerance, and static modifications for cysteine carboxyamidomethylation (m/z of 57.02146). Resulting peptide identifications were processed by Percolator which used decoy search results obtained from a randomized database (22Kall L. Canterbury J.D. Weston J. Noble W.S. MacCoss M.J. Semi-supervised learning for peptide identification from shotgun proteomics datasets.Nat. Methods. 2007; 4: 923-925Crossref PubMed Scopus (1368) Google Scholar) Filtered search results with a peptide spectrum match, q-value ≤ 0.01, equivalent to a false discovery rate of ≤ 1.0%, were used in downstream analyses (supplemental Tables S1A–S1C). Retention time alignment and extraction of MS1 spectra was performed using a Percolator output file (perc.xml), SEQUEST search result files (SQT), and RAW data files in a prototype version of Topograph (version 0.0.0.259) (23Hsieh E.J. Shulman N.J. Dai D.F. Vincow E.S. Karunadharma P.P. Pallanck L.