Precision, Proteome Coverage, and Dynamic Range of Arabidopsis Proteome Profiling Using 15N Metabolic Labeling and Label-free Approaches

Abstract

This study reports the comprehensive comparison of 15 N metabolic labeling and label free proteomic strategies for quantitation, with particular focus on plant proteomics. Our investigation of proteome coverage, dynamic range and quantitative precision for a wide range of mixing ratios and protein loadings aim to aid the investigators in the decision making process during experimental design. One of the main characteristics of the label free strategy is the applicability to all starting material, which is a limitation to the metabolic labeling. However, particularly at mixing ratios up to 10-fold the 15 N metabolic labeling proved to be more precise. Contrary to usual practice based on the results from this study, we suggest that nonequal mixing ratios in metabolic labeling could further increase the proteome coverage for quantitation. On the other hand, the label free strategy, in combination with low protein loading allows the extension of the dynamic range for quantitation and it is more precise at very high ratios, which could be important for certain types of experiments. Molecular & Cellular Proteomics 11: 10.1074/mcp.M112.017178, 619 -628, 2012.Quantitative comparative analyses of proteomes and their dynamic changes under various growth conditions and stimuli has become a widely used approach in experimental and systems biology. Quantitative proteome analysis is particularly important when the functional roles of proteins in biological contexts are being addressed. This has been greatly aided by the development of soft ionization methods for macromolecules (1, 2) and with ongoing developments in high accuracy mass spectrometer instrument technology (3, 4). Thus, analysis of thousands of proteins in a high-throughput manner is now almost a routine task. In parallel, completion of annotated genome sequences for a range of model organisms has opened these for large-scale proteome studies (5). In plant science, the expansion of sequencing efforts to key crop plant species provides a solid basis for efficient interpretation of acquired peptide mass spectra for proteome wide studies also in commercially important crop plants. Various methods and workflows for large-scale quantitative proteome profiling have been developed in the past years (reviewed in (6 -8)). Among them, metabolic labeling using stable isotope labeled amino acids in mammalian cell cultures (9) or full 15 N-labeling for autotrophic organisms (5, 10) are now widely used. In addition, more recently, label-free methods involving computational alignment of ion chromatograms based on accurate mass and retention time of detected ions have been developed and are increasingly used either in studies on tissue not accessible to metabolic labeling or because of their simple and cheap experimental design (6,(11)(12)(13)(14)(15)(16). In addition, chemical labeling using differential mass tags today allows quantitative comparisons in multiplexed samples (17).Previous studies comparing methods for relative quantitation of proteins in a mixture with known pro...

Identification of Cargo for Adaptor Protein (AP) Complexes 3 and 4 by Sucrose Gradient Profiling

“…To minimize putative artificial effects of differential centrifugation and protein extraction efficiencies, wild type material was mixed with labeled mutant material at the level of purified microsomal protein. The mixing ratio of labeled to unlabeled material was 1:3 leading to enhanced protein identification rates with more unlabeled protein being present in the combined complex sample (34). Although 14 N forms of the peptides can readily be quantified using MaxQuant, the software is unable to directly assign pairs of 15 N-labeled peptides.…”

“…However, using the "matched features" file containing unassigned peaks with their retention times and ion intensities, the respective 15 N-peptide can be assigned with the identified corresponding 14 N-peptide by matching ion features based on retention time alignment and the expected mass of the 15 N-labeled peptide. This procedure was validated using existing raw files of different protein mixtures ranging from 1:5 to 5:1 of 15 N protein mixed with 14 N protein that were previously used in an extensive evaluation of quantitation in 15 N metabolic labeling (34). For the matching of unidentified features to identified peptide sequences, a retention time alignment window of 1 min was used and a mass tolerance window of expected 15 N-labeled peptide masses ranging from 97.5atm% to 100atm%…”

“…Experimental design and 15 N ratio quantitation-To compare differences in protein distribution across different membrane compartments in ap-3␤ and ap-4␤ mutants with wild type plants we used a reciprocal metabolic labeling experimental design (34). We used pairwise experimental setups of wild type compared with ap-3␤ and wild type compared with ap-4␤, each repeated twice in label swap experimental design (Fig.…”

Evolution of the Phosphoenolpyruvate Carboxylase Protein Kinase Family in C3 and C4 Flaveria spp.

“…Proteins were precipitated overnight at 220°C using a 53 volume of ice-cold acetone (Sigma-Aldrich). The protein pellet was dissolved in 6 M urea and 2 M thiourea (Merck and SigmaAldrich, respectively), and 10 mg of protein was digested and desalted as described by Arsova et al (2012). Prior to desalting, the synthetic peptides for PEPC (listed in Supplemental File S6) were added to the samples in speciesspecific amounts.…”

Quantitative Proteomics Using 15N SILAC Mouse

“…Although label-based protein quantification methods are superior for quantifying biological relevant changes, label-free protein quantification methods have been widely adapted in many laboratories due to lower starting costs and improved bioinformatics software for peptide peak quantification [13][14][15][16][17][18][19]. With the recent advances in mass spectrometers and informatics, highresolution LC-MS/MS technology has become the most powerful tool to identify and quantify thousands of proteins from complex tissues.…”