title: A data processing pipeline for mammalian proteome dynamics studies using stable isotope metabolic labeling.
abstract: In a recent study, in vivo metabolic labeling using (15)N traced the rate of label incorporation among more than 1700 proteins simultaneously and enabled the determination of individual protein turnover rate constants over a dynamic range of three orders of magnitude (Price, J. C., Guan, S., Burlingame, A., Prusiner, S. B., and Ghaemmaghami, S. (2010) Analysis of proteome dynamics in the mouse brain. Proc. Natl. Acad. Sci. U. S. A. 107, 14508-14513). These studies of protein dynamics provide a deeper understanding of healthy development and well-being of complex organisms, as well as the possible causes and progression of disease. In addition to a fully labeled food source and appropriate mass spectrometry platform, an essential and enabling component of such large scale investigations is a robust data processing and analysis pipeline, which is capable of the reduction of large sets of liquid chromatography tandem MS raw data files into the desired protein turnover rate constants. The data processing pipeline described in this contribution is comprised of a suite of software modules required for the workflow that fulfills such requirements. This software platform includes established software tools such as a mass spectrometry database search engine together with several additional, novel data processing modules specifically developed for (15)N metabolic labeling. These fulfill the following functions: (1) cross-extraction of (15)N-containing ion intensities from raw data files at varying biosynthetic incorporation times, (2) computation of peptide (15)N isotopic incorporation distributions, and (3) aggregation of relative isotope abundance curves for multiple peptides into single protein curves. In addition, processing parameter optimization and noise reduction procedures were found to be necessary in the processing modules in order to reduce propagation of errors in the long chain of the processing steps of the entire workflow.
authors.affiliation: Department of Pharmaceutical Chemistry and Mass Spectrometry Facility, University of California, San Francisco, CA 94158-2517, USA. sguan@cgl.ucsf.edu
authors.forename: Shenheng
authors.initials: S
authors.lastname: Guan
authors.forename: John C
authors.initials: JC
authors.lastname: Price
authors.forename: Stanley B
authors.initials: SB
authors.lastname: Prusiner
authors.forename: Sina
authors.initials: S
authors.lastname: Ghaemmaghami
authors.forename: Alma L
authors.initials: AL
authors.lastname: Burlingame
mesh_terms: D000818:Animals; D001798:Blood Proteins / Q000737:chemistry / Q000378:metabolism; D001921:Brain / Q000378:metabolism; D002853:Chromatography, Liquid; D003627:Data Interpretation, Statistical; D007553:Isotope Labeling; D008099:Liver / Q000378:metabolism; D051379:Mice; D008970:Molecular Weight; D010446:Peptide Fragments / Q000737:chemistry; D055550:Protein Stability; D020543:Proteome / Q000737:chemistry / Q000378:metabolism*; D012984:Software; D053719:Tandem Mass Spectrometry
chemical_list: D001798:Blood Proteins; D010446:Peptide Fragments; D020543:Proteome