A Software Suite for the Generation and Comparison of Peptide Arrays from Sets of Data Collected by Liquid Chromatography-Mass Spectrometry

Abstract

There is an increasing interest in the quantitative proteomic measurement of the protein contents of substantially similar biological samples, e.g. for the analysis of cellular response to perturbations over time or for the discovery of protein biomarkers from clinical samples. Technical limitations of current proteomic platforms such as limited reproducibility and low throughput make this a challenging task. A new LC-MS-based platform is able to generate complex peptide patterns from the analysis of proteolyzed protein samples at high throughput and represents a promising approach for quantitative proteomics. A crucial component of the LC-MS approach is the accurate evaluation of the abundance of detected peptides over many samples and the identification of peptide features that can stratify samples with respect to their genetic, physiological, or environmental origins. We present here a new software suite, SpecArray, that generates a peptide versus sample array from a set of LC-MS data. A peptide array stores the relative abundance of thousands of peptide features in many samples and is in a format identical to that of a gene expression microarray. A peptide array can be subjected to an unsupervised clustering analysis to stratify samples or to a discriminant analysis to identify discriminatory peptide features. We applied the SpecArray to analyze two sets of LC-MS data: one was from four repeat LC-MS analyses of the same glycopeptide sample, and another was from LC-MS analysis of serum samples of five male and five female mice. We demonstrate through these two study cases that the SpecArray software suite can serve as an effective software platform in the LC-MS approach for quantitative proteomics. There is an increasing interest in the quantitative proteomic measurement of the protein contents of substantially similar biological samples, e.g. for the analysis of cellular response to perturbations over time or for the discovery of protein biomarkers from clinical samples. Technical limitations of current proteomic platforms such as limited reproducibility and low throughput make this a challenging task. A new LC-MS-based platform is able to generate complex peptide patterns from the analysis of proteolyzed protein samples at high throughput and represents a promising approach for quantitative proteomics. A crucial component of the LC-MS approach is the accurate evaluation of the abundance of detected peptides over many samples and the identification of peptide features that can stratify samples with respect to their genetic, physiological, or environmental origins. We present here a new software suite, SpecArray, that generates a peptide versus sample array from a set of LC-MS data. A peptide array stores the relative abundance of thousands of peptide features in many samples and is in a format identical to that of a gene expression microarray. A peptide array can be subjected to an unsupervised clustering analysis to stratify samples or to a discriminant analysis to identify discriminatory peptide features. We applied the SpecArray to analyze two sets of LC-MS data: one was from four repeat LC-MS analyses of the same glycopeptide sample, and another was from LC-MS analysis of serum samples of five male and five female mice. We demonstrate through these two study cases that the SpecArray software suite can serve as an effective software platform in the LC-MS approach for quantitative proteomics. The identification and quantification of the protein contents of biological samples plays a crucial role in biological and biomedical research (1Tyers M. Mann M. From genomics to proteomics.Nature. 2003; 422: 193-197Google Scholar, 2Aebersold R. Mann M. Mass spectrometry-based proteomics.Nature. 2003; 422: 198-207Google Scholar, 3Hanash S. Disease proteomics.Nature. 2003; 422: 226-232Google Scholar, 4Boguski M.S. McIntosh M.W. Biomedical informatics for proteomics.Nature. 2003; 422: 233-237Google Scholar). Due to the large dynamic range and the high complexity of most proteomes, it is very challenging to identify and accurately quantify the majority of proteins from such samples. LC-MS/MS-based methods are currently most efficient for the identification of a large number of proteins and have been widely applied in biological and biomedical research (5Washburn 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, 6Washburn M.P. Ulaszek R. Deciu C. Schieltz D.M. Yates III, J.R. Analysis of quantitative proteomic data generated via multidimensional protein identification technology.Anal. Chem. 2002; 74: 1650-1657Google Scholar, 7Gygi S.P. Rist B. Gerber S.A. Turecek F. Gelb M.H. Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity Biotechnol. Scholar). with such methods can accurately quantify proteins S.P. Rist B. Gerber S.A. Turecek F. Gelb M.H. Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity Biotechnol. Scholar, Aebersold R. Quantitative of proteins using isotope-coded affinity and Biotechnol. 2001; 19: Scholar, B. Mann M. by in as a and accurate approach to expression 2002; Scholar, B. M. Mann M. A to applied to Biotechnol. 2003; Scholar, D. of protein expression and S. Scholar, C. for with two of Chem. 2001; Scholar, B. S. S. S. S. S. S. M. F. protein in using Scholar). a LC-MS/MS-based quantitative proteomic samples to be are and and The peptide samples are by a multidimensional and by are by Mann M. for of large or by M. F. of proteins with Chem. and peptide are in the of for by Yates III, J.R. S. by S. Scholar, R. Mass in 2001; Scholar). are by using an such as Yates III, J.R. approach to data of peptides with in a protein Mass or D.M. protein identification by using to their a protein are by using a quantification software such as Aebersold R. Quantitative of proteins using isotope-coded affinity and Biotechnol. 2001; 19: or Aebersold R. analysis of protein abundance from data generated by and Chem. 2003; that the relative of the to the relative abundance of The identification and quantification of proteins is by the from the peptides that with the protein Aebersold R. analysis of protein abundance from data generated by and Chem. 2003; Scholar, Aebersold R. A for proteins by Chem. 2003; Scholar). The quantitative approach can identify and quantify to thousands of proteins from a biological sample is to analyze There is an increasing interest in the quantitative proteomic measurement of the protein contents of substantially similar samples. the discovery of protein biomarkers from clinical samples S. Disease proteomics.Nature. 2003; 422: 226-232Google Scholar, a serum analysis by multidimensional with 2002; Scholar, Aebersold R. throughput quantitative analysis of serum proteins using glycopeptide and and the measurement of the response of and to large of samples to be to to from proteome the the study of cellular response to and are for the identification of patterns of proteins that are by the B. Mann M. analysis of by quantitative Biotechnol. Scholar). these and similar high sample throughput and of the Quantitative is of limited for such large of of complex proteomic samples. peptide a of the peptides present in a sample is by the for D. Aebersold R. A to and data by Chem. Scholar). peptides are substantially similar samples are the of peptides that is in sample with increasing sample the peptides that are detected to be the high abundance peptides that generate many peptides are a it is very to quantitative low abundance proteins samples by the an to LC-MS/MS-based peptide by LC-MS been as a for quantitative Aebersold R. throughput quantitative analysis of serum proteins using glycopeptide and Scholar, S. S. M. of proteins and by or Chem. 2003; Scholar, D. S. S. platform for proteomic and discovery using Scholar). The LC-MS approach is the that the of peptide in a substantially similar sample identical is to the abundance of the peptide the dynamic range of the and is at to the abundance the dynamic one the relative abundance of a peptide in samples by the samples identical LC-MS and by of the same peptide in LC-MS S. S. M. of proteins and by or Chem. 2003; Scholar, D. S. 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The relative abundance is quantitative dynamic range and the The LC-MS approach for quantitative can be in the are and from samples and samples are by an using a high high or identical peptides can be with to accurate and a limited number of can be to identify a of the detected and are applied to peptide peptide relative abundance from LC-MS and identify a of peptides that stratify samples with respect to their genetic, physiological, or environmental the that the of the detected peptides is identical peptides in samples can be by their their and their and of discriminatory peptides is a to identify the of such peptides by and Aebersold R. throughput quantitative analysis of serum proteins using glycopeptide and Scholar). the relative abundance of thousands of peptides and the of discriminatory this a of discriminatory proteins the present a new software suite, SpecArray, that the of the LC-MS approach for quantitative proteomics. 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