An Integrated, Directed Mass Spectrometric Approach for In-depth Characterization of Complex Peptide Mixtures *S

LC-MS/MS has emerged as the method of choice for the identification and quantification of protein sample mixtures. For very complex samples such as complete proteomes, the most commonly used LC-MS/MS method, data-dependent acquisition (DDA) precursor selection, is of limited utility. The limited scan speed of current mass spectrometers along with the highly redundant selection of the most intense precursor ions generates a bias in the pool of identified proteins toward those of higher abundance. A directed LC-MS/MS approach that alleviates the limitations of DDA precursor ion selection by decoupling peak detection and sequencing of selected precursor ions is presented. In the first stage of the strategy, all detectable peptide ion signals are extracted from high resolution LC-MS feature maps or aligned sets of feature maps. The selected features or a subset thereof are subsequently sequenced in sequential, non-redundant directed LC-MS/MS experiments, and the MS/MS data are mapped back to the original LC-MS feature map in a fully automated manner. The strategy, implemented on an LTQ-FT MS platform, allowed the specific sequencing of 2,000 features per analysis and enabled the identification of more than 1,600 phosphorylation sites using a single reversed phase separation dimension without the need for time-consuming prefractionation steps. Compared with conventional DDA LC-MS/MS experiments, a substantially higher number of peptides could be identified from a sample, and this increase was more pronounced for low intensity precursor ions.

[1]  S. Gygi,et al.  Quantitative analysis of complex protein mixtures using isotope-coded affinity tags , 1999, Nature Biotechnology.

[2]  R. Aebersold,et al.  Protein identification with a single accurate mass of a cysteine-containing peptide and constrained database searching. , 2000, Analytical chemistry.

[3]  R. Aebersold,et al.  Approaching complete peroxisome characterization by gas‐phase fractionation , 2002, Electrophoresis.

[4]  Alexey I Nesvizhskii,et al.  Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. , 2002, Analytical chemistry.

[5]  Richard D. Smith,et al.  Mass measurement errors caused by “local” frequency perturbations in FTICR mass spectrometry , 2002, Journal of the American Society for Mass Spectrometry.

[6]  M. Mann,et al.  Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC). , 2003, Journal of proteome research.

[7]  R. Aebersold,et al.  A statistical model for identifying proteins by tandem mass spectrometry. , 2003, Analytical chemistry.

[8]  R. Aebersold,et al.  Mass spectrometry-based proteomics , 2003, Nature.

[9]  R. Aebersold,et al.  Abundance ratio-dependent proteomic analysis by mass spectrometry. , 2003, Analytical chemistry.

[10]  M. Campbell,et al.  PANTHER: a library of protein families and subfamilies indexed by function. , 2003, Genome research.

[11]  Chris F. Taylor,et al.  A common open representation of mass spectrometry data and its application to proteomics research , 2004, Nature Biotechnology.

[12]  J. Yates,et al.  A model for random sampling and estimation of relative protein abundance in shotgun proteomics. , 2004, Analytical chemistry.

[13]  J. Shabanowitz,et al.  A neutral loss activation method for improved phosphopeptide sequence analysis by quadrupole ion trap mass spectrometry. , 2004, Analytical chemistry.

[14]  Bruno Domon,et al.  Implications of new proteomics strategies for biology and medicine. , 2004, Journal of proteome research.

[15]  A. Schmidt,et al.  A novel strategy for quantitative proteomics using isotope‐coded protein labels , 2005, Proteomics.

[16]  K. Resing,et al.  Comparison of Label-free Methods for Quantifying Human Proteins by Shotgun Proteomics*S , 2005, Molecular & Cellular Proteomics.

[17]  M. Mann,et al.  Status of complete proteome analysis by mass spectrometry: SILAC labeled yeast as a model system , 2006, Genome Biology.

[18]  H. Clevers,et al.  Wnt signalling in stem cells and cancer , 2005, Nature.

[19]  P. Roepstorff,et al.  Highly Selective Enrichment of Phosphorylated Peptides from Peptide Mixtures Using Titanium Dioxide Microcolumns* , 2005, Molecular & Cellular Proteomics.

[20]  R. Aebersold,et al.  Scoring proteomes with proteotypic peptide probes , 2005, Nature Reviews Molecular Cell Biology.

[21]  Sarah Calvo,et al.  Systematic identification of human mitochondrial disease genes through integrative genomics , 2006, Nature Genetics.

[22]  R. Aebersold,et al.  Mass Spectrometry and Protein Analysis , 2006, Science.

[23]  Friedrich Lottspeich,et al.  Quantitative Profiling of the Membrane Proteome in a Halophilic Archaeon*S , 2006, Molecular & Cellular Proteomics.

[24]  M. Mann,et al.  The human urinary proteome contains more than 1500 proteins, including a large proportion of membrane proteins , 2006, Genome Biology.

[25]  Michelle S. Scott,et al.  Global Survey of Organ and Organelle Protein Expression in Mouse: Combined Proteomic and Transcriptomic Profiling , 2006, Cell.

[26]  M. Mann,et al.  Global, In Vivo, and Site-Specific Phosphorylation Dynamics in Signaling Networks , 2006, Cell.

[27]  I. Tomlinson,et al.  Colorectal cancer and genetic alterations in the Wnt pathway , 2006, Oncogene.

[28]  Brendan K Faherty,et al.  Optimization and Use of Peptide Mass Measurement Accuracy in Shotgun Proteomics*S , 2006, Molecular & Cellular Proteomics.

[29]  Ruedi Aebersold,et al.  The Implications of Proteolytic Background for Shotgun Proteomics*S , 2007, Molecular & Cellular Proteomics.

[30]  Steven P Gygi,et al.  Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry , 2007, Nature Methods.

[31]  Nichole L. King,et al.  Development and validation of a spectral library searching method for peptide identification from MS/MS , 2007, Proteomics.

[32]  Patrick G. A. Pedrioli,et al.  A high-quality catalog of the Drosophila melanogaster proteome , 2007, Nature Biotechnology.

[33]  Ruedi Aebersold,et al.  An integrated chemical, mass spectrometric and computational strategy for (quantitative) phosphoproteomics: application to Drosophila melanogaster Kc167 cells. , 2007, Molecular bioSystems.

[34]  Lukas N. Mueller,et al.  SuperHirn – a novel tool for high resolution LC‐MS‐based peptide/protein profiling , 2007, Proteomics.

[35]  D. Muddiman,et al.  Parts-per-billion mass measurement accuracy achieved through the combination of multiple linear regression and automatic gain control in a Fourier transform ion cyclotron resonance mass spectrometer. , 2007, Analytical chemistry.

[36]  Scott A Gerber,et al.  Large-scale phosphorylation analysis of alpha-factor-arrested Saccharomyces cerevisiae. , 2007, Journal of proteome research.

[37]  S. Carr,et al.  64 Systematic identification of human mitochondrial disease genes through integrative genomics , 2007 .

[38]  Ruedi Aebersold,et al.  Reproducible isolation of distinct, overlapping segments of the phosphoproteome , 2007, Nature Methods.

[39]  Lukas N. Mueller,et al.  An integrated mass spectrometric and computational framework for the analysis of protein interaction networks , 2007, Nature Biotechnology.

[40]  Ruedi Aebersold,et al.  Identification of cross-linked peptides from large sequence databases , 2008, Nature Methods.

[41]  Joshua E. Elias,et al.  Large-Scale Phosphorylation Analysis of -Factor-Arrested Saccharomyces cerevisiae , 2009 .