Comprehensive Analysis of a Multidimensional Liquid Chromatography Mass Spectrometry Dataset Acquired on a Quadrupole Selecting, Quadrupole Collision Cell, Time-of-flight Mass Spectrometer

An in-depth analysis of a multidimensional chromatography-mass spectrometry dataset acquired on a quadrupole selecting, quadrupole collision cell, time-of-flight (QqTOF) geometry instrument was carried out. A total of 3269 CID spectra were acquired. Through manual verification of database search results and de novo interpretation of spectra 2368 spectra could be confidently determined as predicted tryptic peptides. A detailed analysis of the non-matching spectra was also carried out, highlighting what the non-matching spectra in a database search typically are composed of. The results of this comprehensive dataset study demonstrate that QqTOF instruments produce information-rich data of which a high percentage of the data is readily interpretable. An in-depth analysis of a multidimensional chromatography-mass spectrometry dataset acquired on a quadrupole selecting, quadrupole collision cell, time-of-flight (QqTOF) geometry instrument was carried out. A total of 3269 CID spectra were acquired. Through manual verification of database search results and de novo interpretation of spectra 2368 spectra could be confidently determined as predicted tryptic peptides. A detailed analysis of the non-matching spectra was also carried out, highlighting what the non-matching spectra in a database search typically are composed of. The results of this comprehensive dataset study demonstrate that QqTOF instruments produce information-rich data of which a high percentage of the data is readily interpretable. Mass spectrometers interfaced to chromatographic separation allow the acquisition of large amounts of data in a relatively short period of time. New high throughput technologies have thus been developed to utilize this ability (1Link A.J. Eng J. Schieltz D.M. Carmack E. Mize G.J. Morris D.R. Garvik B.M. Yates III, J.R. Direct analysis of protein complexes using mass spectrometry.Nat. Biotechnol. 1999; 17: 676-682Google Scholar, 2Washburn M.P. Wolters D. Yates III, J.R. Large-scale analysis of the yeast proteome by multidimensional protein identification technology.Nat. Biotechnol. 2001; 19: 242-247Google Scholar, 3Gygi S.P. Rist B. Gerber S.A. Turecek F. Gelb M.H. Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.Nat. Biotechnol. 1999; 17: 994-999Google Scholar, 4Ong S.E. Blagoev B. Kratchmarova I. Kristensen D.B. Steen H. Pandey A. Mann M. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.Mol. Cell. Proteomics. 2002; 1: 376-386Google Scholar, 5Blagoev B. Kratchmarova I. Ong S.E. Nielsen M. Foster L.J. Mann M. A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling.Nat. Biotechnol. 2003; 21: 315-318Google Scholar, 6Lasonder E. Ishihama Y. Andersen J.S. Vermunt A.M. Pain A. Sauerwein R.W. Eling W.M. Hall N. Waters A.P. Stunnenberg H.G. Mann M. Analysis of the Plasmodium falciparum proteome by high-accuracy mass spectrometry.Nature. 2002; 419: 537-542Google Scholar). The quantity of data produced renders manual analysis of a significant amount of the data impractical. Scientists are therefore dependent on automated database search engines to summarize their results, the most popular being Mascot (www.matrixscience.com) and Sequest (8Eng J.K. McCormack A.L. Yates J.R. J. Am. Soc. Mass Spectrom. 1994; 5: 976-989Google Scholar). In database searches of large datasets there is always a long list of spectra that have not been matched to anything by the search engine. There are a number of reasons why these may not match, including poor quality spectra, spectra of peptides containing modifications that were not considered in the search, or peptides that were formed by non-specific cleavages when a certain enzyme cleavage specificity was defined in the search engine. Also the data analyzed by search engines are not the raw data but rather centroided peak list data, which are not always completely representative of the raw data. These unmatched spectra are typically ignored despite the possibility they could contain important information. A summary of the complications in automated peptide and protein identification has been published recently (9Baldwin M.A. Protein identification by mass spectrometry: issues to be considered.Mol. Cell. Proteomics. 2004; 3: 1-9Google Scholar). Hence a number of groups have developed statistical analysis programs of search results to better define the reliability of the reported matches (10Keller A. Nesvizhskii A.I. Kolker E. Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.Anal. Chem. 2002; 74: 5383-5392Google Scholar, 11Anderson D.C. Li W. Payan D.G. Noble W.S. A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores.J. Proteome Res. 2003; 2: 137-146Google Scholar, 12MacCoss M.J. Wu C.C. Yates III, J.R. Probability-based validation of protein identifications using a modified SEQUEST algorithm.Anal. Chem. 2002; 74: 5593-5599Google Scholar, 13Moore R.E. Young M.K. Lee T.D. Qscore: an algorithm for evaluating SEQUEST database search results.J. Am. Soc. Mass Spectrom. 2002; 13: 378-386Google Scholar). There are many groups publishing results from large scale mass spectrometric analyses using different combinations of mass spectrometers and search engines. Unfortunately if a researcher uses one particular combination of tools it can be difficult to assess the quality of the data in studies using different instrument and search engine combinations. Hence there is a drive toward making the raw data itself available so that one can independently assess results and, if desired, reanalyze the results using an alternative searching strategy (14Burlingame A.L. Toward deciphering the knowledge encrypted in large datasets.Mol. Cell. Proteomics. 2003; 2: 425Google Scholar). In this study we present data from a multidimensional LC-MSMS experiment where we analyzed all acquired spectra manually. From this we are able to report exactly what these unmatched spectra actually constitute. We think this information is important for understanding where there are currently problems with these automated search strategies and to indicate areas where with further refinement this list of unmatched spectra could be reduced. The dataset submitted here was acquired on a QqTOF 1The abbreviation used is: QqTOF, quadrupole selecting, quadrupole collision cell, time-of-flight. geometry instrument, a QSTAR Pulsar (MDS Sciex/Applied Biosystems). A dataset of a multidimensional LC-MSMS experiment created on an ion trap, LCQ-DECA (Thermo), has already been published in this journal (15Von Haller P.D. Yi E. Donohoe S. Vaughn K. Keller A. Nesvizhskii A.I. Eng J. Li X.J. Goodlett D.R. Aebersold R. Watts J.D. The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: I. Statistically annotated datasets for peptide sequences and proteins identified via the application of ICAT and tandem mass spectrometry to proteins copurifying with T cell lipid rafts.Mol. Cell. Proteomic. 2003; 2: 426-427Google Scholar). Here we present a QSTAR dataset for comparison. Second to ion traps, QqTOF geometry instruments are the major type of instrument used for large scale proteomic analyses. This dataset submission will allow comparisons of the relative merits of data acquired on each instrument type. His-tagged Gsp1p was expressed and purified from Escherichia coli as published previously (16Allen N.P. Huang L. Burlingame A. Rexach M. Proteomic analysis of nucleoporin interacting proteins.J. Biol. Chem. 2001; 276: 29268-29274Google Scholar). Yeast cells were arrested at the G1 stage of the cell cycle using 2.5 μg/ml α-factor exposure for 3 h or at M phase using 20 μg/ml nocodazole for 3 h, and then interacting proteins were isolated as published previously (17Allen N.P. Patel S.S. Huang L. Chalkley R.J. Burlingame A. Lutzmann M. Hurt E.C. Rexach M. Deciphering networks of protein interactions at the nuclear pore complex.Mol. Cell. Proteomics. 2002; 1: 930-946Google Scholar). Proteins from each cell state (about 5–10 μg/cell state) were labeled with the cleavable ICAT reagent (Applied Biosystems, Foster City, CA) and analyzed essentially following our published protocol for ICAT of low level samples (18Hansen K.C. Schmitt-Ulms G. Chalkley R.J. Hirsch J. Baldwin M.A. Burlingame A.L. Mass spectrometric analysis of protein mixtures at low levels using cleavable 13C-isotope-coded affinity tag and multidimensional chromatography.Mol. Cell. Proteomics. 2003; 2: 299-314Google Scholar). Briefly proteins were denatured in 9 m urea and reduced with trichloroethylphosphine, and then cysteines of G1 phase-arrested proteins were alkylated with light ICAT reagent, while M phase proteins were alkylated with isotopically heavy reagent. After tryptic digestion peptides were separated by strong cation exchange using a Beckman Gold HPLC system equipped with an analytical flow upgrade. Separation was achieved using a 2.1 × 10-mm polysulfoethyl A column (PolyLC) where Buffer A was 30% ACN, 0.05% formic acid and Buffer B was buffer A containing 400 mm NH4Cl. Six fractions were collected, and each of these was successively passed through the biotin affinity cartridge (Applied Biosystems ICAT kit). Each flow-through was collected separately, and then all ICAT peptides were eluted into one fraction using 30%ACN, 0.4% trifluoroacetic acid. ICAT tags were cleaved in 95% trifluoroacetic acid. Each fraction was reverse phase cleaned up (Zip Tips, Millipore) to desalt the samples and then analyzed by reverse phase LC-MSMS. Reverse phase chromatography was performed using an Ultimate HPLC system and a Famos autosampler (both LC-Packings). Separation was achieved using a 75-μm × 150-mm Pepmap column (LC-Packings) at a flow rate of 300 nl/min. Buffer A was 0.1% formic acid, while Buffer B was acetonitrile, 0.1% formic acid. The gradient separation was 5–40% B over 105 min. As peptides eluted off the column they were introduced on line into an ESI-QqTOF instrument (QSTAR) and were analyzed using data-dependent switching between MS and MSMS modes; after a 1-s MS spectrum up to three multiply charged precursor ions could be selected for 2-s CID spectra acquisition. After a given precursor was selected, dynamic exclusion was used for the next 60 s to prevent its subsequent reselection. Peak lists of MSMS spectra from each LC-MS run were created using the Mascot.dll script (version 1.4) within Analyst. These were searched using “Batch Tag,” a new piece of software in the latest in-house developmental version of Protein Prospector (for further details see Ref. 19Chalkley R.J. Baker P.R. Huang L. Hansen K.C. Allen N.P. Rexach M. Burlingame A.L. Comprehensive analysis of a multidimensional liquid chromatography mass spectrometry dataset acquired on a QqTOF mass spectrometer: II. New developments in Protein Prospector allow for reliable and comprehensive automatic analysis of large datasets.Mol. Cell. Proteomics. 2005; 4: 1194-1204Google Scholar). Those spectra that did not return a high confidence result were manually analyzed by looking at the raw spectra in the Analyst software by interpreting amino acid sequence tags and searching in MS-Homology (Protein Prospector) or by closer examination of the results from the Batch Tag search and assessment of whether the ions observed are those one would predict to be most intense on the basis of the sites of amino acid cleavages (e.g. cleavage N-terminal to a proline or C-terminal to an aspartic acid). During analysis of the six cation exchange fractions of the non-ICAT-labeled peptides (i.e. non-cysteine-containing) a total of 3269 MSMS spectra were acquired. These spectra were initially searched using Batch Tag, a new program in Protein Prospector designed for searching of LC-MSMS data against the Swiss-Prot Database (April 3, 2004), allowing only yeast proteins plus a couple of expected non-yeast proteins (GST and human keratins) (for details of Batch Tag see Ref. 19Chalkley R.J. Baker P.R. Huang L. Hansen K.C. Allen N.P. Rexach M. Burlingame A.L. Comprehensive analysis of a multidimensional liquid chromatography mass spectrometry dataset acquired on a QqTOF mass spectrometer: II. New developments in Protein Prospector allow for reliable and comprehensive automatic analysis of large datasets.Mol. Cell. Proteomics. 2005; 4: 1194-1204Google Scholar). The database search results were used to assist in the manual analysis of each spectrum, i.e. if a sequence tag of three or four amino acids was manually interpreted and this matched to the result Batch Tag returned and this result also explained the assignment of all the major peaks in the spectrum then the assignment was accepted. Approximately 2000 of these spectra gave confident results, and these were verified only by a cursory look at plots of the ions observed and what they were matched to. The majority of these matches were on the basis of an extensive “y” ion series. The other ∼1300 spectra were manually analyzed in more extensive detail to determine whether the peptides could be de novo interpreted and, if not, why a peptide could not be confidently assigned. Following this comprehensive analysis of the dataset we could confidently assign 2368 spectra to predicted tryptic peptides that we felt a search engine should be able to identify when allowing for the modifications of oxidized methionines, protein N-terminal acetylation, and pyroglutamate formation from N-terminal glutamine residues. This left 901 spectra that for various reasons one would not expect the search engine to make a confident match. The reasons for this are summarized in Table I and reported graphically in Fig. 1.Table IThe 901 spectra that will not be identified by a database search for tryptic peptides22 peptides too short to be confident of assignment (m/z < 620)43 mixture of precursor ions226 spectra not of a peptide (ICAT, PEG,aPolyethylene glycol.…)313 spectra contain not enough ions to manually assign24 spectra of methylated trypsin25 deamidation of Asn4 peptide sequences not in databases42 non-tryptic peptides11 MSMS of peptide that has lost water in-source8 peptides formed from in-source fragmentation of abundant co-eluting peak1 peptide contains an internal disulfide bond1 spectrum contains a methylated lysine83 wrong precursor charge assignment1 wrong precursor charge assignment and multiple peptides fragmented2 wrong precursor charge assignment and not peptides78 wrong monoisotopic peak assignment14 wrong precursor charge assignment and monoisotopic peak assignment3 wrong monoisotopic peak assignment and multiple peptides fragmenteda Polyethylene glycol. Open table in a new tab 226 of the spectra were not fragmentation spectra of peptides but were rather fragments of chemical contaminants, most commonly ICAT-related products (presumably either chemical side product impurities during synthesis of the reagent or produced by side reactions during the reagent cleavage step in 95% trifluoroacetic acid). An example of one of these spectra is shown in Fig. 2. These spectra do not produce any immonium ion masses and nearly always contain characteristic fragment ions at m/z 481.28, 515.28, and 556.29. Some fragmentation spectra were of peptides less than 620 Da in mass, generally corresponding to a peptide only five amino acids in length. Many of the spectra of these short peptides do not contain enough ions to make a confident assignment, and even if the sequence could be determined, then a five-amino acid string is not sufficient to uniquely identify a protein. The selection window for the precursor ion corresponded to roughly −1.5 and +2 Da from the selected monoisotopic mass. For 43 spectra multiple precursor ions co-eluted within this mass range and were simultaneously fragmented. In some cases both of the peptides could be identified by manual analysis and in many cases at least one could be determined, but unless one component was present at a significantly higher level than the other it would be difficult for the search engine to produce a confident match. 42 spectra were of peptides that would not be formed by tryptic cleavage of proteins in the database so are either formed by non-specific tryptic cleavage or other protease cleavage during sample isolation, and a further eight were formed by in-source fragmentation of an abundant co-eluting peak to produce a peptide with no enzyme specificity. Hence searching the dataset specifying tryptic cleavage would not match these spectra, although searching with no enzyme specificity could potentially identify these. A total of 51 spectra were of modified peptides. The majority of these were either peptides where an asparagine had become deamidated to an aspartic acid or were from the trypsin, which is methylated to reduce chymotryptic activity and minimize autolysis (20Kostka V. Carpenter F.H. Inhibition of chymotrypsin activity in crystalline trypsin preparations.J. Biol. Chem. 1964; 239: 1799-1803Google Scholar). However, there was also a peptide that had an internal disulfide intact, thus having a molecular mass 2 Da less than the peptide with free sulfhydryl groups. A peptide from elongation factor 1 α was identified that had a methylated lysine. This lysine 30 is a known site of modification (7Cavallius J. Zoll W. Chakraburtty K. Merrick W.C. Characterization of yeast of Scholar). A number of spectra could not be of problems in the of the peak list used for The data are acquired as data but become to data for database in the assignment of the peak charge state and of the monoisotopic peak after this to information the ion mass, and thus the peptide will not be of these problems were most in spectra of of relatively high mass Da or and were by poor ion on monoisotopic peak to labeling of multiple on one to the software interpreting this as a of a charged ion and not of the isotope as the and peptides corresponded to sequences that were not present in either Swiss-Prot or for spectra did not contain enough information for a confident manual assignment of a peptide they were spectra with In some cases a more intense MSMS spectrum of the precursor was acquired at a in the or a ion exchange this assignment of the The list of all the spectra and their or for of assignment is in Table From reverse phase LC-MSMS analysis of six cation exchange fractions a total of 3269 CID spectra were acquired. these 2368 spectra can be confidently interpreted as tryptic peptides by a combination of database searching with manual verification or manual de novo There were in assignment of ion mass of spectra through charge state monoisotopic mass peak and charge state are not However, new software is at this For recently new Mascot that made in ion on this dataset not Also many peak including the Mascot.dll in the Analyst if they are not certain of the charge state will spectra with different charge and the MSMS that results from the multiple charge is monoisotopic peak and charge state determined more spectra could be assigned. of CID spectra were not fragmentation spectra of peptides. This is to be higher in datasets acquired on ion of the higher of data acquired by charge state of precursor ions one to to only fragment multiply charged precursor state of precursor ions on an ion can be performed using a m/z range However, many not to this as it significantly the cycle of the the number of precursor ions that are fragmented. are generally peptides are multiply charged through of on the and the C-terminal or Hence QqTOF MSMS datasets will contain significantly fragmentation spectra of This study is not the results of a database search but a manual analysis of what we think a search engine could on this For analysis of search engines on this see the study R.J. Baker P.R. Huang L. Hansen K.C. Allen N.P. Rexach M. Burlingame A.L. Comprehensive analysis of a multidimensional liquid chromatography mass spectrometry dataset acquired on a QqTOF mass spectrometer: II. New developments in Protein Prospector allow for reliable and comprehensive automatic analysis of large datasets.Mol. Cell. Proteomics. 2005; 4: 1194-1204Google Scholar). As these results are on the basis of manual there is a to the For spectra were as being fragmentation spectra of peptides. of assignment is to an to determine with confidence an for the This was in to there being ions in the spectrum, although some spectra fragment ions of which many were not from a i.e. the spectrum was a mixture of fragmentation of a peptide and a chemical Through the manual analysis of all the data we have been able to assess the quality of data acquired on a QSTAR mass This analysis has also some of the problems with the data this dataset be as completely representative of all data acquired on this type of instrument, it that the data are typically information-rich and that a high percentage of the data should be We the of and other of the in the acquisition of these data. with