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Journal of The American Society for Mass Spectrometry

, Volume 21, Issue 9, pp 1534–1546 | Cite as

Relative, label-free protein quantitation: Spectral counting error statistics from nine replicate MudPIT samples

  • Bret CooperEmail author
  • Jian Feng
  • Wesley M. Garrett
Article

Abstract

Nine replicate samples of peptides from soybean leaves, each spiked with a different concentration of bovine apotransferrin peptides, were analyzed on a mass spectrometer using multidimensional protein identification technology (MudPIT). Proteins were detected from the peptide tandem mass spectra, and the numbers of spectra were statistically evaluated for variation between samples. The results corroborate prior knowledge that combining spectra from replicate samples increases the number of identifiable proteins and that a summed spectral count for a protein increases linearly with increasing molar amounts of protein. Furthermore, statistical analysis of spectral counts for proteins in two- and three-way comparisons between replicates and combined replicates revealed little significant variation arising from run-to-run differences or data-dependent instrument ion sampling that might falsely suggest differential protein accumulation. In these experiments, spectral counting was enabled by PANORAMICS, probability-based software that predicts proteins detected by sets of observed peptides. Three alternative approaches to counting spectra were also evaluated by comparison. As the counting thresholds were changed from weaker to more stringent, the accuracy of ratio determination also changed. These results suggest that thresholds for counting can be empirically set to improve relative quantitation. All together, the data confirm the accuracy and reliability of label-free spectral counting in the relative, quantitative analysis of proteins between samples.

Keywords

False Discovery Rate Tandem Mass Spectrum Spectral Count Common Protein Quantitative Proteomic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© American Society for Mass Spectrometry 2010

Authors and Affiliations

  1. 1.Soybean Genomics and Improvement LaboratoryUSDA-ARSBeltsvilleUSA
  2. 2.SnoqualmieUSA
  3. 3.Animal Biosciences and Biotechnology LaboratoryUSDA-ARSBeltsvilleUSA

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