Evaluation of Normalization Methods on GeLC-MS/MS Label-Free Spectral Counting Data to Correct for Variation during Proteomic Workflows

Normalization of spectral counts (SpCs) in label-free shotgun proteomic approaches is important to achieve reliable relative quantification. Three different SpC normalization methods, total spectral count (TSpC) normalization, normalized spectral abundance factor (NSAF) normalization, and normalization to selected proteins (NSP) were evaluated based on their ability to correct for day-to-day variation between gel-based sample preparation and chromatographic performance. Three spectral counting data sets obtained from the same biological conidia sample of the rice blast fungus Magnaporthe oryzae were analyzed by 1D gel and liquid chromatography-tandem mass spectrometry (GeLC-MS/MS). Equine myoglobin and chicken ovalbumin were spiked into the protein extracts prior to 1D-SDS- PAGE as internal protein standards for NSP. The correlation between SpCs of the same proteins across the different data sets was investigated. We report that TSpC normalization and NSAF normalization yielded almost ideal slopes of unity for normalized SpC versus average normalized SpC plots, while NSP did not afford effective corrections of the unnormalized data. Furthermore, when utilizing TSpC normalization prior to relative protein quantification, t-testing and fold-change revealed the cutoff limits for determining real biological change to be a function of the absolute number of SpCs. For instance, we observed the variance decreased as the number of SpCs increased, which resulted in a higher propensity for detecting statistically significant, yet artificial, change for highly abundant proteins. Thus, we suggest applying higher confidence level and lower fold-change cutoffs for proteins with higher SpCs, rather than using a single criterion for the entire data set. By choosing appropriate cutoff values to maintain a constant false positive rate across different protein levels (i.e., SpC levels), it is expected this will reduce the overall false negative rate, particularly for proteins with higher SpCs.

[1]  A. Shevchenko,et al.  Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels. , 1996, Analytical chemistry.

[2]  István Nagy,et al.  Quantitative proteome and transcriptome analysis of the archaeon Thermoplasma acidophilum cultured under aerobic and anaerobic conditions. , 2010, Journal of proteome research.

[3]  R. Dean,et al.  Improving Proteome Coverage on a LTQ-Orbitrap Using Design of Experiments , 2011, Journal of the American Society for Mass Spectrometry.

[4]  D. Chelius,et al.  Identification and relative quantitation of protein mixtures by enzymatic digestion followed by capillary reversed-phase liquid chromatography-tandem mass spectrometry. , 2002, Analytical chemistry.

[5]  Michael K. Coleman,et al.  Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors. , 2006, Methods.

[6]  T. Shaler,et al.  Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. , 2003, Analytical chemistry.

[7]  Mihaela E. Sardiu,et al.  Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics , 2008, Proceedings of the National Academy of Sciences.

[8]  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.

[9]  D. Chelius,et al.  Quantitative profiling of proteins in complex mixtures using liquid chromatography and mass spectrometry. , 2002, Journal of proteome research.

[10]  Tao Xu,et al.  Quantitative Mass Spectrometry Identifies Insulin Signaling Targets in C. elegans , 2007, Science.

[11]  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.

[12]  R. Dean,et al.  Direct comparison of stable isotope labeling by amino acids in cell culture and spectral counting for quantitative proteomics. , 2010, Analytical chemistry.

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

[14]  D. B. Weatherly,et al.  A Heuristic Method for Assigning a False-discovery Rate for Protein Identifications from Mascot Database Search Results * , 2005, Molecular & Cellular Proteomics.

[15]  Michael K. Coleman,et al.  Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. , 2006, Journal of proteome research.

[16]  N. Samatova,et al.  Detecting differential and correlated protein expression in label-free shotgun proteomics. , 2006, Journal of proteome research.

[17]  John R. Yates,et al.  Analysis of the myosinII-responsive focal adhesion proteome reveals a role for β-Pix in negative regulation of focal adhesion maturation , 2011, Nature Cell Biology.

[18]  G. Khush.  What it will take to Feed 5.0 Billion Rice consumers in 2030 , 2005, Plant Molecular Biology.

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

[20]  B. Cooper,et al.  Relative, label-free protein quantitation: Spectral counting error statistics from nine replicate MudPIT samples , 2010, Journal of the American Society for Mass Spectrometry.

[21]  Valmir Carneiro Barbosa,et al.  PatternLab for proteomics: a tool for differential shotgun proteomics , 2008, BMC Bioinformatics.

[22]  Cathryn J. Rehmeyer,et al.  The genome sequence of the rice blast fungus Magnaporthe grisea , 2005, Nature.

[23]  E. Feener,et al.  Label-free Quantitative Analysis of One-dimensional PAGE LC/MS/MS Proteome , 2008, Molecular & Cellular Proteomics.

[24]  M. Wiener,et al.  Differential mass spectrometry: a label-free LC-MS method for finding significant differences in complex peptide and protein mixtures. , 2004, Analytical chemistry.

[25]  David C Muddiman,et al.  Analysis of the low molecular weight fraction of serum by LC-dual ESI-FT-ICR mass spectrometry: precision of retention time, mass, and ion abundance. , 2004, Analytical chemistry.

[26]  R. Dean,et al.  Comparison of stable-isotope labeling with amino acids in cell culture and spectral counting for relative quantification of protein expression. , 2011, Rapid communications in mass spectrometry : RCM.

[27]  S. Ou.  Pathogen Variability and Host Resistance in Rice Blast Disease , 1980 .

[28]  Linfeng Wu,et al.  Role of spectral counting in quantitative proteomics , 2010, Expert review of proteomics.

[29]  Adam M. Hawkridge,et al.  Coupling of a vented column with splitless nanoRPLC-ESI-MS for the improved separation and detection of brain natriuretic peptide-32 and its proteolytic peptides. , 2009, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[30]  M. Mann,et al.  Parts per Million Mass Accuracy on an Orbitrap Mass Spectrometer via Lock Mass Injection into a C-trap*S , 2005, Molecular & Cellular Proteomics.

[31]  John R Yates,et al.  Quantitative mass spectrometry identifies insulin signaling targets in C. elegans. , 2007, Nature Reviews Molecular Cell Biology.

[32]  Dana Pascovici,et al.  Differential metabolic response of cultured rice (Oryza sativa) cells exposed to high‐ and low‐temperature stress , 2010, Proteomics.

[33]  Elias Bareinboim,et al.  Bioinformatics Applications Note Analyzing Marginal Cases in Differential Shotgun Proteomics , 2022 .