IsoQuant: a software tool for stable isotope labeling by amino acids in cell culture-based mass spectrometry quantitation.

Accurate protein identification and quantitation are critical when interpreting the biological relevance of large-scale shotgun proteomics data sets. Although significant technical advances in peptide and protein identification have been made, accurate quantitation of high-throughput data sets remains a key challenge in mass spectrometry data analysis and is a labor intensive process for many proteomics laboratories. Here, we report a new SILAC-based proteomics quantitation software tool, named IsoQuant, which is used to process high mass accuracy mass spectrometry data. IsoQuant offers a convenient quantitation framework to calculate peptide/protein relative abundance ratios. At the same time, it also includes a visualization platform that permits users to validate the quality of SILAC peptide and protein ratios. The program is written in the C# programming language under the Microsoft .NET framework version 4.0 and has been tested to be compatible with both 32-bit and 64-bit Windows 7. It is freely available to noncommercial users at http://www.proteomeumb.org/MZw.html .

[1]  M. Mann,et al.  Stable Isotope Labeling by Amino Acids in Cell Culture, SILAC, as a Simple and Accurate Approach to Expression Proteomics* , 2002, Molecular & Cellular Proteomics.

[2]  John D. Venable,et al.  Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra , 2004, Nature Methods.

[3]  Stefani N. Thomas,et al.  Stable isotope labeling with amino acids in cell culture based mass spectrometry approach to detect transient protein interactions using substrate trapping. , 2011, Analytical chemistry.

[4]  Lukas N. Mueller,et al.  An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. , 2008, Journal of proteome research.

[5]  Peter R. Baker,et al.  Role of accurate mass measurement (+/- 10 ppm) in protein identification strategies employing MS or MS/MS and database searching. , 1999, Analytical chemistry.

[6]  B. Gähwiler.  Organotypic monolayer cultures of nervous tissue , 1981, Journal of Neuroscience Methods.

[7]  M. Mann,et al.  Super-SILAC mix for quantitative proteomics of human tumor tissue , 2010, Nature Methods.

[8]  R. Aebersold,et al.  Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry , 2001, Nature Biotechnology.

[9]  J. Yates,et al.  Method to correlate tandem mass spectra of modified peptides to amino acid sequences in the protein database. , 1995, Analytical chemistry.

[10]  Francis Impens,et al.  Rover: A tool to visualize and validate quantitative proteomics data from different sources , 2010, Proteomics.

[11]  Jürgen Cox,et al.  A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics , 2009, Nature Protocols.

[12]  Xin Huang,et al.  UNiquant, a program for quantitative proteomics analysis using stable isotope labeling. , 2011, Journal of proteome research.

[13]  David L Tabb,et al.  ProRata: A quantitative proteomics program for accurate protein abundance ratio estimation with confidence interval evaluation. , 2006, Analytical chemistry.

[14]  Steven P Gygi,et al.  The impact of peptide abundance and dynamic range on stable-isotope-based quantitative proteomic analyses. , 2008, Journal of proteome research.

[15]  Damon May,et al.  Software platform for rapidly creating computational tools for mass spectrometry-based proteomics. , 2009, Journal of proteome research.

[16]  Yong-Bin Kim,et al.  ProSight PTM 2.0: improved protein identification and characterization for top down mass spectrometry , 2007, Nucleic Acids Res..

[17]  R. Aebersold,et al.  ProbID: A probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data , 2002, Proteomics.

[18]  Matthias Mann,et al.  Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics , 2011, Nature Protocols.

[19]  D. N. Perkins,et al.  Probability‐based protein identification by searching sequence databases using mass spectrometry data , 1999, Electrophoresis.

[20]  M. Mann,et al.  Andromeda: a peptide search engine integrated into the MaxQuant environment. , 2011, Journal of proteome research.

[21]  S. Bryant,et al.  Open mass spectrometry search algorithm. , 2004, Journal of proteome research.

[22]  M. Mann,et al.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification , 2008, Nature Biotechnology.

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

[24]  M. Mann,et al.  MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. , 2010, Journal of proteome research.

[25]  R. Aebersold,et al.  Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry. , 2003, Analytical chemistry.

[26]  P. Pevzner,et al.  InsPecT: identification of posttranslationally modified peptides from tandem mass spectra. , 2005, Analytical chemistry.

[27]  Robertson Craig,et al.  TANDEM: matching proteins with tandem mass spectra. , 2004, Bioinformatics.

[28]  M. Mann,et al.  A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC) , 2006, Nature Protocols.

[29]  Sung Kyu Park,et al.  A quantitative analysis software tool for mass spectrometry–based proteomics , 2008, Nature Methods.