Relative Proteomic Quantification and Protein Turnover Using Stable Isotopic Labeling. An other aspect of my work centers on implementation of stable-isotope labeling strategies for quantitative differential proteomics. Stable isotopic labeling in combination with mass spectrometry allows one to describe changes in protein abundance or structural modification resulting from experimental treatments. Fundamentally, existing quantitative proteomics approaches rely on the incorporation of a stable isotopic label into peptides so that one can observe differences in a control versus test samples by comparing the intensities of matched pairs of mass spectral peaks. These peaks correspond to chemically identical species that co-elute in all chromatographic steps (in the case of 15N and 13C labeled samples where the isotope effects are minimal), and share physical properties pertaining to ionization and detection in the spectrometer, yet are distinguishable by mass. This allows one to compare relative quantities for all labeled species in a sample with distinguishable masses. We have had success with several isotope-coding strategies for comparative proteomics studies, including in vitro labeling strategies using [18O]H2O (Nelson et al., 2006), and Fischer esterification with d3-methanol (Hegeman et al., 2004), and metabolic labeling of whole plants with 15N from nitrate and 13C from 13CO2 gas or [13C6]glucose. Using inexpensive 98%-[15N] KNO3, Arabidopsis plants can be grown routinely to near 98% incorporation 15N, with no observable deleterious growth effects.
Beyond quantifying relative species abundance, metabolic labeling provides additional advantages with regard to sample preparation over in vitro labeling strategies. Because the heavy and light labeled tissues are mixed immediately following collection, the samples have the perfect internal control for protein extraction and fractionation as well as for ionization and detection steps in the spectrometer. As a pilot metabolic labeling experiment we used Arabidopsis thaliana grown on [15N]KNO3 to examine proteomic changes in response to the plant hormone auxin. Implementing the relative quantification scheme based on the work of MacCoss et al. (MacCoss, M. J.,et al., (2003)Anal. Chem. 75(24), 6912-21.) we used light and/or heavy labeled peptides identified from MS/MS data with Mascot (Matrix Science) to identify over 1200 proteins in 174 soluble protein fractions and quantified relative abundance of each species. We found several important consequences of metabolic labeling for data analysis regarding the probabilistic protein identification strategies used for assignment of tandem mass spectral data (Huttlin et al., 2007, J. Prot. Res. and Nelson et al., 2007). Additional insights from this study, combined with a modified labeling strategy reported by Whitelegge and coworkers (Whitelegge, J. P. et al., (2004) Phytochemistry. 65, 1507-15.) lead to our successful implementation of a partial metabolic labeling scheme, which we have compared to the original full metabolic labeling approach in a manuscript published last spring (Huttlin et al., Mol. Cell Proteomics, 2007).
Since 2008 I have been involved as a Co-PI with developing algorithms and labeling methods for measuring protein turnover in collaboration with Jerry Cohen (PI) and Bill gray (Co-PI). These efforts have been very fruitful and several papers will be published within the next month or so. These include: 1) a description of a 13C-carbon dioxide plant growth environmental chamber (Chen et al., 2010a); 2) a method for measuring turnover in free amino acid pools from small amounts of plant material by GC-MS (Chen et al., 2010b); and an approach for using deuterium labeling of plants to examine turnover of components in SCF ubiquitin ligase complex in Arabidopsis (Yang et al., 2010).
The website for the Protein Turnover project is currently down.