Statistical identification of differentially labeled peptides from liquid chromatography tandem mass spectrometry.
LC-MS/MS with certain labeling techniques such as isotope-coded affinity tag (ICAT) enables quantitative analysis of paired protein samples. However, current identification and quantification of differentially expressed peptides (and proteins) are not reliable for large proteomics screening of complex biological samples. The number of replicates is often limited because of the high cost of experiments and the limited supply of samples. Traditionally, a simple fold change cutoff is used, which results in a high rate of false positives. Standard statistical methods such as the two-sample t-test are unreliable and severely underpowered due to high variability in LC-MS/MS data, especially when only a small number of replicates are available. Using an advanced error pooling technique, we propose a novel statistical method that can reliably identify differentially expressed proteins while maintaining a high sensitivity, particularly with a small number of replicates. The proposed method was applied both to an extensive simulation study and a proteomics comparison between microparticles (MPs) generated from platelet (platelet MPs) and MPs isolated from plasma (plasma MPs). In these studies, we show a significant improvement of our statistical analysis in the identification of proteins that are differentially expressed but not detected by other statistical methods. In particular, several important proteins - two peptides for beta-globin and three peptides for von Willebrand Factor (vWF) - were identified with very small false discovery rates (FDRs) by our method, while none was significant when other conventional methods were used. These proteins have been reported with their important roles in microparticles in human blood cells: vWF is a platelet and endothelial cell product that binds to P-selectin, GP1b, and GP IIb/IIIa, and beta-globin is one of the peptides of hemoglobin involved in the transportation of oxygen by red blood cells.
Cho
Hyungjun
H
Department of Statistics, Korea University, Seoul, Korea.
Smalley
David M
DM
Theodorescu
Dan
D
Ley
Klaus
K
Lee
Jae K
JK
D002853:Chromatography, Liquid / Q000379:methods / Q000706:statistics & numerical data*; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D007553:Isotope Labeling / Q000379:methods / Q000706:statistics & numerical data; D010455:Peptides / Q000032:analysis* / Q000145:classification*; D040901:Proteomics / Q000379:methods / Q000706:statistics & numerical data; D013194:Staining and Labeling / Q000379:methods / Q000706:statistics & numerical data*; D053719:Tandem Mass Spectrometry / Q000379:methods / Q000706:statistics & numerical data*
D010455:Peptides