An automated method for scanning LC-MS data sets for significant peptides and proteins, including quantitative profiling and interactive confirmation.
Differential quantification of proteins and peptides by LC-MS is a promising method to acquire knowledge about biological processes, and for finding drug targets and biomarkers. However, differential protein analysis using LC-MS has been held back by the lack of suitable software tools. Large amounts of experimental data are easily generated in protein and peptide profiling experiments, but data analysis is time-consuming and labor-intensive. Here, we present a fully automated method for scanning LC-MS/MS data for biologically significant peptides and proteins, including support for interactive confirmation and further profiling. By studying peptide mixtures of known composition, we demonstrate that peptides present in different amounts in different groups of samples can be automatically screened for using statistical tests. A linear response can be obtained over almost 3 orders of magnitude, facilitating further profiling of peptides and proteins of interest. Furthermore, we apply the method to study the changes of endogenous peptide levels in mouse brain striatum after administration of reserpine, a classical model drug for inducing Parkinson disease symptoms.
Kaplan
Anders
A
GE Healthcare Bio-Sciences AB, SE-75184, Uppsala, Sweden.
Söderström
Malin
M
Fenyö
David
D
Nilsson
Anna
A
Fälth
Maria
M
Sköld
Karl
K
Svensson
Marcus
M
Pettersen
Harald
H
Lindqvist
Staffan
S
Svenningsson
Per
P
Andrén
Per E
PE
Björkesten
Lennart
L
D000595:Amino Acid Sequence; D000818:Animals; D002853:Chromatography, Liquid; D003342:Corpus Striatum / Q000737:chemistry; D013058:Mass Spectrometry; D051379:Mice; D008969:Molecular Sequence Data; D010300:Parkinson Disease / Q000378:metabolism; D010455:Peptides / Q000032:analysis*; D011506:Proteins / Q000032:analysis*; D012984:Software
D010455:Peptides; D011506:Proteins