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

item Cooper, Bret
item FENG, JIAN - Johns Hopkins University
item Garrett, Wesley

Submitted to: Journal of American Society for Mass Spectrometry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/30/2010
Publication Date: 5/3/2010
Citation: Cooper, B., Feng, J., Garrett, W.M. 2010. Relative, label-free protein quantitation: spectral counting error statistics from nine replicate MudPIT samples. Journal of American Society for Mass Spectrometry. 21:1534-1546.

Interpretive Summary: Proteins inside a plant cell affect many biological processes such as metabolism or development. These proteins can be detected with instruments called mass spectrometers. Recently, it has been proposed to use mass spectrometers to detect the amounts of these proteins in a cell. To be an accurate measuring tool, the instrument must be able to measure the same thing reproducibly, over and over again. If the measurements are reproducible, then the instrument will be able to accurately measure change. We tested this accuracy by using a mass spectrometer to evaluate the same sample of soybean leaf proteins nine times. Statistical evaluation of the results revealed very little variability between replicates. This means that the measurements are reproducible and that the mass spectrometer can be reliably used to measure amounts of proteins in cells. Thus, these findings are important to scientists at universities, institutes, government agencies and companies who want to measure how much proteins change during biological processes important to plant biology.

Technical 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 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 2 and 3-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 quantitation. All together, the data confirm the accuracy and reliability of label-free spectral counting in the quantitative analysis of proteins between samples.