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Analysis and Quantification of Diagnostic Serum Markers and Protein Signatures for Gaucher Disease

2007; Elsevier BV; Volume: 6; Issue: 5 Linguagem: Inglês

10.1074/mcp.m600303-mcp200

1535-9484

Johannes P.C. Vissers, James Langridge, Johannes M. F. G. Aerts,

Studies on Chitinases and Chitosanases

Novel approaches for the qualitative and quantitative proteomics analysis by nanoscale LC-MS applied to the study of protein expression response in depleted and undepleted serum of Gaucher patients undergoing enzyme replacement therapy are presented. Particular emphasis is given to the method reproducibility of these LC-MS experiments without the use of isotopic labels. The level of chitotriosidase, an established Gaucher biomarker, was assessed by means of an absolute concentration determination technique for alternate scanning LC-MS generated data. Disease associated proteins, including fibrinogens, complement cascade proteins, and members of the high density lipoprotein serum content, were recognized by various clustering methods and sorting and intensity profile grouping of identified peptides. Condition-unique LC-MS protein signatures could be generated utilizing the measured serum protein concentrations and are presented for all investigated conditions. The clustering results of the study were also used as input for gene ontology searches to determine the correlation between the molecular functions of the identified peptides and proteins. Novel approaches for the qualitative and quantitative proteomics analysis by nanoscale LC-MS applied to the study of protein expression response in depleted and undepleted serum of Gaucher patients undergoing enzyme replacement therapy are presented. Particular emphasis is given to the method reproducibility of these LC-MS experiments without the use of isotopic labels. The level of chitotriosidase, an established Gaucher biomarker, was assessed by means of an absolute concentration determination technique for alternate scanning LC-MS generated data. Disease associated proteins, including fibrinogens, complement cascade proteins, and members of the high density lipoprotein serum content, were recognized by various clustering methods and sorting and intensity profile grouping of identified peptides. Condition-unique LC-MS protein signatures could be generated utilizing the measured serum protein concentrations and are presented for all investigated conditions. The clustering results of the study were also used as input for gene ontology searches to determine the correlation between the molecular functions of the identified peptides and proteins. The most frequently encountered inherent lysosomal storage disorder in man is glycosylceramidosis, better known as Gaucher disease. The disorder is caused by an inherited deficiency in glucocerebrosidase, the enzyme catalyzing the degradation of glucosylceramide to ceramide and glucose. Lipid accumulation is restricted to tissue macrophages, so-called Gaucher cells, that act as the starting point of pathophysiological processes resulting in clinical symptoms. The clinical presentation of Gaucher disease is heterogeneous with respect to age, nature, and progression of symptoms (1Beutler E. Grabowski G. Scriver C.R. Beaudet A.L. Sly W.S. Valle D. Gaucher disease, in The Metabolic Basis of Inherited Disease. McGraw-Hill Publishing Co., New York1995: 2641-2670Google Scholar). Clinical manifestation is accompanied by abnormalities in serum composition. The most striking abnormality is a thousandfold elevated serum level of chitotriosidase, a protein massively expressed and secreted by the pathological Gaucher cells (2Hollak C.E.M. van Weely S. van Oers M.H.J. Aerts J.M.F. G. Marked elevation of plasma chitotriosidase activity; a novel hallmark of Gaucher disease.J. Clin. Investig. 1994; 93: 1288-1292Crossref PubMed Scopus (754) Google Scholar). Although chitotriosidase is an excellent biomarker, a major drawback is the frequent genetic deficiency in this enzyme among Caucasians with approximately one in every 20 individuals not expressing any chitotriosidase (3Boot R.G. Renkema G.H. Verhoek M. Strijland A. Bliek J. de Meulemeester T.M. Mannens M.M. Aerts J.M.F. G. The human chitotriosidase gene. Nature of inherited enzyme deficiency.J. Biol. Chem. 1998; 273: 25680-25685Abstract Full Text Full Text PDF PubMed Scopus (344) Google Scholar). This limitation has prompted a search for further Gaucher cell biomarkers. The availability of biomarkers for Gaucher disease is of particular importance because the availability of effective therapeutic interventions based on supplementation with recombinant glucocerebrosidase (4Barton N.W. Furbish F.S. Murray G.J. Garfield M. Brady R.O. Therapeutic response to intravenous infusions of glucocerebrosidase in a patient with Gaucher disease.Proc. Natl. Acad. Sci. U. S. A. 1990; 87: 1913-1916Crossref PubMed Scopus (335) Google Scholar) or pharmacological reduction of glycosphingolipid biosynthesis (5Cox T. Lachmann R. Hollak C.E. Aerts J.M.F. G. van Weely S. Hrebicek M. Platt F. Butters T. Dwek R. Moyses C. Gow I. Elstein D. Zimran A. Novel oral treatment of Gaucher’s disease with N-butyldeoxynojirimycin (OGT918) to decrease substrate biosynthesis.Lancet. 2000; 355: 1481-1485Abstract Full Text Full Text PDF PubMed Scopus (676) Google Scholar) is costly. The monitoring of chitotriosidase as a biomarker of Gaucher disease is generally applied in a clinical setting for both therapy initiation and optimization of individual dose regimes (6Aerts J.M.F. G. Hollak C.E.M. van Breemen M. Maas M. Groener J.E. Boot R.G. Identification and use of biomarkers in Gaucher disease and other lysosomal storage diseases.Acta Paediatr. Suppl. 2005; 94 (37–38): 43-46Crossref PubMed Scopus (57) Google Scholar). Given the limitations concerning chitotriosidase, identification and quantification of additional biomarkers for Gaucher disease is therefore of great value (7Boot R.G. Verhoek M. de Fost M. Hollak C.E.M. Maas M. Bleijlevens B. van Breemen M.J. van Meurs M. Boven L.A. Laman J.D. Moran M.T. Cox T.M. Aerts J.M.F. G. Marked elevation of the chemokine CCL18/PARC in Gaucher disease: a novel surrogate marker for assessing therapeutic intervention.Blood. 2004; 103: 33-39Crossref PubMed Scopus (262) Google Scholar). 2D 1The abbreviations used are: 2D, two-dimensional; PCA, principal component analysis. 1The abbreviations used are: 2D, two-dimensional; PCA, principal component analysis. gel-based separation methods combined with mass spectrometry have been the standard for the separation, identification, and quantification of proteins. The method has to date the greatest potential to separate complex protein mixtures comprising up to thousands of components. It also has limitations with regard to the separation of certain protein classes and quantification in general. The quantitative limitations have been detailed elsewhere (8Rabilloud T. Two-dimensional gel electrophoresis in proteomics: old, old fashioned, but it still climbs up the mountains.Proteomics. 2002; 2: 3-10Crossref PubMed Scopus (667) Google Scholar, 9Corthals G.L. Wasinger V.C. Hochstrasser D.F. Sanchez J.C. The dynamic range of protein expression: a challenge for proteomic research.Electrophoresis. 2000; 21: 1104-1115Crossref PubMed Scopus (517) Google Scholar), but they primarily arise from ambiguity in the identification of multiple proteins present in a single spot, identification of proteins at both extremes of the pI range, small proteins, variants and modifications, in-gel degradation, and variation in extraction efficiency. As a complementary alternative, LC-MS-based relative quantification methods have emerged to identify and quantify peptides and proteins in mixtures of various complexities. The majority of these relative quantification techniques use the introduction of stable isotopes into the samples including ICAT (10Gygi 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. Biothechnol. 1999; 17: 994-999Crossref PubMed Scopus (4339) Google Scholar), isobaric tag for relative and absolute quantification (iTRAQ) (11Ross R.L. Huang Y.N. Marchese J.N. Williamson B. Parker K. Hattan S. Khainovski N. Pillai S. Dey S. Daniels S. Purkayastha S. Juhasz P. Martin S. Bartlet-Jones M. He F. Jacobson A. Pappin D.J. Multiplex protein quantification in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.Mol. Cell. Proteomics. 2004; 3: 1154-1169Abstract Full Text Full Text PDF PubMed Scopus (3680) Google Scholar), in vivo stable isotope labeling by amino acids in cell culture (SILAC) (12Ong 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: 376-386Abstract Full Text Full Text PDF PubMed Scopus (4569) Google Scholar), and 18O labeling (13Shevchenko A. Chernushevich I. Ens W. Standing K.G. Thomas B. Wilm M. Mann M. Rapid ‘de novo’ peptide sequencing by a combination of nanoelectrospray, isotopic labeling and a quadrupole ime-of-flight mass spectrometer.Rapid Commun. Mass Spectrom. 1997; 11: 1015-1024Crossref PubMed Scopus (397) Google Scholar, 14Yao X. Freas A. Ramirez J. Demirev P.A. Fensalau C. 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus.Anal. Chem. 2001; 73: 2836-2842Crossref PubMed Scopus (777) Google Scholar). They typically require multiple sample preparation steps that could result in an increase in experiment variability and a decrease in accuracy. Recent articles have reviewed stable isotope labeling approaches and contrasted their advantages and limitations with quantitative differential in-gel electrophoresis methods (15Lill J. Proteomic tools for quantification by mass spectrometry.Mass Spectrom. Rev. 2003; 22: 182-194Crossref PubMed Scopus (129) Google Scholar, 16Hamdan M. Righetti P.G. Modern strategies of protein quantification in proteome analysis: advantages and limitations.Mass Spectrom. Rev. 2002; 21: 287-302Crossref PubMed Scopus (128) Google Scholar, 17Ong S.-E. Mann M. Mass spectrometry-based proteomics turn quantitative.Nat. Chem. Biol. 2005; 5: 252-262Crossref Scopus (1317) Google Scholar). More recently, label-free LC-MS quantification methods have been described to determine relative abundances of proteins between multiple conditions (18Silva J.C. Denny R. Dorschel C.A. Gorenstein M. Kass I.J. Li G.-Z. McKenna T. Nold M.J. Richardson K. Young P. Geromanos S.J. Quantitative proteomic analysis by accurate mass retention time pairs.Anal. Chem. 2005; 77: 2187-2200Crossref PubMed Scopus (521) Google Scholar, 19Silva J.C. Denny R. Dorschel C.A. 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, 20Hughes M.A. Silva J.C. Geromanos S.J. Townsed C.A. Quantitative proteomic analysis of drug-induced changes in mycobacteria.J. Proteome Res. 2006; 5: 54-63Crossref PubMed Scopus (65) Google Scholar, 21Wang W. Zhou H. Lin H. Roy S. Shaler T.A. Hill L.R. Norton S. Kumar P. Anderle M. Becker C. Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards.Anal. Chem. 2003; 75: 4818-4826Crossref PubMed Scopus (585) Google Scholar, 22Radulovic 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, 23Wiener M.C. Sachs J.R. Deyanova E.G. Yates N.A. Differential mass spectrometry: a label-free LC-MS method for finding differences in complex peptide and protein mixtures.Anal. Chem. 2004; 76: 6085-6096Crossref PubMed Scopus (243) Google Scholar, 24America A.H.P. Cordewener J.H.G. van Geffen M.H.A. Lommen A. Vissers J.P.C. Bino R.J. Hall R.D. Alignment and statistical difference analysis of complex peptide data sets generated by multidimensional LC-MS.Proteomics. 2006; 6: 641-653Crossref PubMed Scopus (70) Google Scholar). These methods are typically based on determining peak area ratios of the same peptides between different conditions. The quantitative reproducibility of these methods depends upon the peptide cluster efficiency, which is determined by the mass measurement accuracy and precision and the chromatographic retention time reproducibility obtained during the experiment. A recent independent study from the Association of Biomolecular Resource Facilities evaluated quantitative proteomics approaches, and it was concluded that label-free methods did at least as well as stable isotope labeling methods. 2A. M. Falick, J. A. Kowalak, W. Lane, K. Lilley, B. Phinney, C. Turck, S. Weintraub, E. Witkowska, and N. Yates, The Proteomics Research Group 2006 Quantitative Proteomics Study, Association of Biomolecular Resource Facilities, unpublished data. 2A. M. Falick, J. A. Kowalak, W. Lane, K. Lilley, B. Phinney, C. Turck, S. Weintraub, E. Witkowska, and N. Yates, The Proteomics Research Group 2006 Quantitative Proteomics Study, Association of Biomolecular Resource Facilities, unpublished data. Moreover Silva et al. (25Silva J.C. Gorenstein M.V. Li G.-Z Vissers J.P.C. Geromanos S.J. Absolute quantification of proteins by LCMSE. A virtue of parallel MS acquisition.Mol. Cell. Proteomics. 2006; 5: 144-156Abstract Full Text Full Text PDF PubMed Scopus (1140) Google Scholar) discovered that a label-free approach allows for the estimation of absolute protein concentrations, which were subsequently used for stoichiometry studies. In this study, a gel-free and label-free LC-MS approach is presented to conduct qualitative and quantitative serum analysis. The Gaucher disease protein serum profile was examined as it is biochemically and quantitatively well defined. The identification and enzyme activity determination of a known Gaucher disease biomarker will be demonstrated and cross-validated with its biochemical known activity. Furthermore clustering methods are described to evaluate the data quality of quantitative label-free LC-MS data sets. Clustering was also used for trend identifications based on absolute determined concentrations. Intensity profiling by K-means clustering of identified peptides was used to identify interrelating proteins, for example proteins that are components of the same biochemical pathway. The control and patient samples studied were known either by contact with the Netherlands Gaucher Society or by referral to the Academic Medical Center. The diagnosis of Gaucher disease was based on deficient glucocerebrosidase activity in leukocytes and/or urine samples. EDTA plasma samples were obtained from freshly drawn blood and immediately stored at −20 °C. Serum samples from the patients were obtained prior to treatment by means of enzyme replacement therapy and after 6.5 years of treatment. Chitotriosidase activity was measured as described previously (2Hollak C.E.M. van Weely S. van Oers M.H.J. Aerts J.M.F. G. Marked elevation of plasma chitotriosidase activity; a novel hallmark of Gaucher disease.J. Clin. Investig. 1994; 93: 1288-1292Crossref PubMed Scopus (754) Google Scholar). Serum samples from the control, the patient pretreatment, and the patient post-treatment were either digested as received or passed through a 10-cm × 4.6-mm multiaffinity removal system column (Agilent Technologies, Palo Alto, CA) to deplete the samples. Hence targeted high abundance proteins, including albumin, IgG, antitrypsin, IgA, transferrin, and haptoglobin, were removed. 10 μl of the undepleted serum samples was diluted with 50 mm ammonium bicarbonate (Sigma-Aldrich) prior to enzymatic digestion. A 20-μl aliquot of the serum samples was used for depletion with the multiaffinity removal system according to the manufacturer's protocol. The mobile phase buffers were provided with the system and used as received. Briefly 20 μl of serum were diluted 5-fold with 80 μl of buffer A, and particulates were removed by centrifugation through a 0.22-μm spin filter (Millipore, Billerica, MA) at 13,000 rpm for 3 min. The proteins were separated with a step gradient; the first 10 min of the gradient were maintained at 100% mobile phase A at 0.5 ml/min followed by a step to 100% mobile phase B with a flow rate of 1.0 ml/min in 0.1 min where the composition was maintained for 7 min. Reconditioning of the column was conducted with mobile phase A buffer at 1.0 ml/min for 11 min. The depletion efficiency was estimated to be 50% based on UV absorption peak area ratio of the break-through and bound fraction. The flow-through fractions were collected and buffer-exchanged with 50 mm ammonium bicarbonate, and the volume was reduced to 80 μl. 10 μl of undepleted serum was diluted with 65 μl of 50 mm ammonium bicarbonate solution and denatured in the presence of 10 μl of 1% RapiGest detergent solution (Waters Corp., Milford, MA) at 80 °C for 15 min (26Yu Y.Q. Gilar M. Lee P.J. Bouvier E.S.P. Gebler J.C. Enzyme-friendly, mass spectrometry-compatible surfactant for in-solution enzymatic digestion of proteins.Anal. Chem. 2003; 75: 6023-6028Crossref PubMed Scopus (269) Google Scholar). The serum samples were reduced in the presence of 5 μl of 100 mm dithiothreitol (Sigma-Aldrich) at 60 °C for 30 min. The proteins were alkylated in the dark in the presence of 5 μl of 200 mm iodoacetamide (Sigma-Aldrich) at ambient temperature for 30 min. Proteolytic digestion was initiated by adding 15 μl of 0.5 μg/μl sequencing grade, modified trypsin (Promega, Madison WI) and incubated overnight at 37 °C. Breakdown of the acid-labile detergent was achieved in the presence of 4 μl of an aqueous 12 m HCl solution at 37 °C for 15 min. The tryptic peptide solutions were centrifuged at 13,000 rpm for 10 min, and the supernatant was collected. The enzymatic digestion and treatment of the depleted serum solutions was as described above with the exception of the addition of 20 μl of 0.5 μg/μl trypsin solution. Prior to analyses, the tryptic peptide solutions were 10-fold diluted with an aqueous 0.1% formic acid (Sigma-Aldrich) solution. A protein digest internal standard was added (1:1 dilution with 100 fmol/μl enolase from Saccharomyces cerevisiae) to perform absolute quantification. The LC-MS analyses were performed using 2 μl of the final serum protein digest mixtures. Recombinant chitotriosidase (Genzyme, Cambridge, MA) was digested as described above with minor modification. 87 μl of a 50 mm ammonium bicarbonate solution was added to 5 μl of 1 mg/ml chitotriosidase stock solution. The recombinant chitotriosidase was reduced in the presence of 1 μl of 100 mm dithiothreitol at 60 °C for 30 min. Alkylation was conducted in the dark for 30 min by adding 2 μl of 100 mm iodoacetamide. Digestion was initiated by adding 5 μl of 0.5 μg/μl modified sequencing grade trypsin and incubated overnight at 37 °C. Nanoscale LC separation of tryptic peptides was performed with a NanoAcquity system (Waters Corp., Milford, MA) equipped with a Symmetry C18 5 μm, 5-mm × 300-μm precolumn and an Atlantis C18 3 μm, 15-cm × 75-μm analytical reversed phase column (Waters Corp.). The samples were initially transferred with an aqueous 0.1% formic acid solution to the precolumn with a flow rate of 4 μl/min for 3 min. Mobile phase A was water with 0.1% formic acid, and mobile phase B was 0.1% formic acid in acetonitrile. The peptides were separated with a gradient of 3–40% mobile phase B over 90 min at a flow rate of 300 nl/min followed by a 10-min rinse with 90% of mobile phase B. The column was re-equilibrated at initial conditions for 20 min. The column temperature was maintained at 35 °C. The lock mass was delivered from the auxiliary pump of the NanoAcquity pump with a constant flow rate of 200 nl/min at a concentration of 100 fmol of [Glu1]fibrinopeptide B/μl to the reference sprayer of the NanoLockSpray source of the mass spectrometer. All samples were analyzed in triplicate. Analysis of tryptic peptides was performed using a Q-Tof Premier mass spectrometer (Waters Corp., Manchester, UK). For all measurements, the mass spectrometer was operated in the v-mode of analysis with a typical resolving power of at least 10,000 full-width half-maximum. All analyses were performed using positive nanoelectrospray ion mode. The time-of-flight analyzer of the mass spectrometer was externally calibrated with NaI from m/z 50 to 1990 with the data post acquisition lock mass corrected using the monoisotopic mass of the doubly charged precursor of [Glu1]fibrinopeptide B. The reference sprayer was sampled with a frequency of 30 s. Accurate mass LC-MS data were collected in an alternating low energy and elevated energy mode of acquisition (27Bateman, R. H., Langridge, J. I., McKenna, T., and Richardson, K. Methods and Apparatus for Mass Spectrometry. U. S. Patent 2,385,918A, September 26, 2006Google Scholar, 28Bateman R.H. Carruthers R. Hoyes J.B. Jones C. Langridge J.I. Millar A. Vissers J.P.C. A novel precursor ion discovery method on a hybrid quadrupole orthogonal acceleration time-of-flight mass spectrometer for studying protein phosphorylation.J. Am. Soc. Mass Spectrom. 2002; 13: 792-803Crossref PubMed Scopus (172) Google Scholar). The spectral acquisition time in each mode was 1.5 s with a 0.1-s interscan delay. In low energy MS mode, data were collected at a constant collision energy of 4 eV. In elevated energy MS mode, the collision energy was ramped from 15 to 40 eV during each 1.5-s data collection cycle with one complete cycle of low and elevated energy data acquired every 3.2 s. The radio frequency applied to the quadrupole mass analyzer was adjusted such that ions from m/z 300 to 2000 were efficiently transmitted, ensuring that any ions less than m/z 300 observed in the LC-MS data only arose from dissociations in the collision cell. Continuum LC-MS data were processed and searched using ProteinLynx GlobalServer version 2.2.5 (Waters Corp.). Protein identifications were obtained with the embedded ion accounting algorithm of the software and searching a human database to which data from S. cerevisiae enolase were appended. The ion detection, clustering, and normalization were performed using ProteinLynx GlobalServer. The principles of the applied data clustering and normalization have been explained in great detail in previous publications (18Silva J.C. Denny R. Dorschel C.A. Gorenstein M. Kass I.J. Li G.-Z. McKenna T. Nold M.J. Richardson K. Young P. Geromanos S.J. Quantitative proteomic analysis by accurate mass retention time pairs.Anal. Chem. 2005; 77: 2187-2200Crossref PubMed Scopus (521) Google Scholar, 20Hughes M.A. Silva J.C. Geromanos S.J. Townsed C.A. Quantitative proteomic analysis of drug-induced changes in mycobacteria.J. Proteome Res. 2006; 5: 54-63Crossref PubMed Scopus (65) Google Scholar). Intensity measurements are typically adjusted on those components, i.e. deisotoped and charge state-reduced accurate mass retention time pairs, that replicate throughout the complete experiment for analysis at the accurate mass/retention cluster level. Components are typically clustered together with a <10 ppm mass precision and a <0.25-min time tolerance. Alignment of elevated energy ions with low energy precursor peptide ions is conducted with an approximate precision of ±0.05 min. For analysis on the protein identification and quantification level the observed intensity measurements are normalized on the intensity measurement of the identified peptides of the digested internal standard. The underlying principles of the ion accounting search algorithm have been recently described by Li et al. 3Li, G.-Z., Golick, D., Gorenstein, M. V., Silva, J. C., Vissers, J. P. C., and Geromanos, S. J. (2006) A novel ion accounting algorithm for protein database searches, Poster W079 presented at the Human Proteome Organisation (HUPO) 5th Annual World Congress, Long Beach, CA (October 28–November 1, 2006). In brief, all fragment ions within a retention time window associated to 1/10 of the chromatographic peak width of a precursor ion are time-aligned or assigned to the precursor. The resulting precursor-product ion list is then queried against a database utilizing an iterative three-step process whereby the culmination of each loop increases the selectivity and sensitivity of the next. In addition, the method utilizes limited database queries whereby each query accesses different sets and subsets of peptides from the proteins present in the database. During the first step, the data are matched to only correctly cleaved proteolytic peptides whose precursor and product ion mass tolerances are within the specified tolerances, typically 10 ppm for precursor ions and 20 ppm for product ions. As a consequence of these database search tolerances, each submitted precursor provides multiple tentative peptide identifications. However, the overall strategy of the search algorithm requires that only one peptide identification is provided for each detected precursor. As a result, all other low ranking tentative peptide identifications to each securely identified precursor are not considered. In addition, the product ions used for the validation of each high ranking precursor are removed from the precursor-product list of other co-eluting precursors, thereby eliminating them for consideration when identifying coincidentally detected precursors. During the second step, precursor and product ions that have not yet been assigned are queried against a subset database of the identified proteins from the first step. This includes missed cleavages, in-source fragments, neutral losses, and variable modifications. During the last step, the remaining unidentified ions are considered against the complete database for additional protein identifications, including peptide mass fingerprint identifications. The protein identifications were based on the detection of more than two fragment ions per peptide, more then two peptides measured per protein, and identification of the protein in at least two of three injections. The false positive rate of the ion accounting identification algorithm is typically 3–4% with a randomized database 5 times the size of the original utilized database. However, by using replication as a filter, the false positive rate is minimized as false positive identifications have a random nature and as such do not tend to replicate across injections. Additional data analysis was performed with Decisionsite (Spotfire, Somerville, MA), Excel (Microsoft Corp., Redmond, WA), and Simca-P+ (Umetrics, Umeå, Sweden). The observed intensity measurements were normalized for injection volume and protein load variability before conducting quantitative comparisons between conditions by applying scaling as outlined under “Experimental Procedures” and in previously published studies (18Silva J.C. Denny R. Dorschel C.A. Gorenstein M. Kass I.J. Li G.-Z. McKenna T. Nold M.J. Richardson K. Young P. Geromanos S.J. Quantitative proteomic analysis by accurate mass retention time pairs.Anal. Chem. 2005; 77: 2187-2200Crossref PubMed Scopus (521) Google Scholar, 20Hughes M.A. Silva J.C. Geromanos S.J. Townsed C.A. Quantitative proteomic analysis of drug-induced changes in mycobacteria.J. Proteome Res. 2006; 5: 54-63Crossref PubMed Scopus (65) Google Scholar). A binary comparison of the peptide precursor intensity measurements of two injections of one of the investigated conditions is discussed. A 45° diagonal line is obtained (Fig. 1) with almost no variation throughout the detected range. Intersection through the origin would have been obtained if not for the scatter on measurements for low intensity ions, i.e. no or minimal deviation between matched components. This example demonstrates the expected distribution in the instance of no obvious change between the investigated injections or conditions. The number of detected accurate mass/retention time pairs identified in both injections was 9292 and 9145 of which 8364 were found to be common to both injections. The number of non-redundant identified peptides from these two particular injections, utilizing the high energy fragmentation spectra and the search criteria described under “Experimental Procedures,” were 1705 (18.3%) and 1725 (18.9%), respectively. This search considered normal tryptic cleavage rules with only one missed tryptic cleavage site allowed and was limited to consider only a single modification, carbamidomethylation of cysteine residues. The summed precursor intensities of these non-redundant identifications for the two injections mentioned correspond to 45.3 and 50.7% of the total ion intensity (amount) that can be detected. These fraction numbers can be considered adequate for depleted sera and are comparable with those reported previously for microbial systems (19Silva J.C. Denny R. Dorschel C.A. 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). These types of quality control measurements were performed on all injections and conditions. For the depleted samples, comprising three conditions/nine injections, the measured median and average mass precision were 1.90 and 2.52 ppm, respectively. The median and average retention time errors were 0.80 and 0.91%. This emphasizes the required stability of intensity, mass measurement, and retention time for label-free quantitative LC-MS measurements. These observations are within the typical error measurements reported in a previous study (18Silva J.C. Denny R. Dorschel C.A. Gorenstein M. Kass I.J. Li G.-Z. McKenna T. Nold M.J. Richardson K. Young P. Geromanos S.J. Quantitative proteomic analysis by accurate mass retention time pairs.