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Molecular & Cellular Proteomics 6:S14-S17, 2007.
© 2007 by The American Society for Biochemistry and Molecular Biology, Inc.

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Abstracts

Mini Symposium

Quantitative Proteomics: an Overview

R. Aebersold

Institute for Molecular Systems Biology, ETH Zurich; Faculty of Science, University of Zurich, Switzerland; Institute for Systems Biology, Seattle, WA

Accurate quantification of the proteins that are detected or identified in proteomics experiments has become a primary goal of proteomics research. Quantitative information is essential in many areas of biological and clinical research, including for the detection of dynamic change in biological systems, for comparative analysis between samples and for generating boundary conditions in mathematical models of biological processes. Over the last few years a multitude of methods for quantitative proteomics have been described. In this presentation we will provide an overview of the currently available methods and their performance. Specifically, we will discuss the following methods: Quantitative proteomics based on stable isotope labeling or tagging and tandem mass spectrometry; quantification based on LC-MS pattern matching with or without isotope tagging; absolute quantification via isotope labeled external standards and targeted (multiple reaction monitoring) mass spectrometry. We will also discuss issues common to all methods, including error analysis and quantitative accuracy and current methods for the generation of isotope labeled reference peptides.

MS.2

Profiling Protein Expression by Label-Free Quantitative Mass Spectrometry

P. R. Cutillas and B. Vanhaesebroeck

Bart's Institute of Cancer, Queen Mary University of London, United Kingdom

The role of mass spectrometry (MS) in proteomics has evolved from being a technique used to identify proteins present in 2D gel spots to be the preferred method for quantitative analyses. Indeed, several strategies now exist that use MS to quantify proteins; it may be argued that the most influential of all them was based on isotope labelling using ICAT reagents, an approach that made the analytical biochemist appreciate the potential of MS for large-scale protein quantification. This early work inspired the development of other, perhaps more robust, methods for global protein quantification, most of which were also based on isotope labelling (prominent and popular examples include SILAC, iTRAQ and ¹⁸O labelling). More recently, MS-based quantitative methods that do not require protein labelling are also becoming popular. These label-free methods are based on spectral counts or on using peptide ion intensities as a read-out of protein abundance. The increasing popularity of the spectral count approach may be due to its simplicity, but the accuracy of such approach is low. In contrast, label-free approaches based on measuring peptide ion intensities show levels of accuracy close to those afforded with labeling techniques, and allow researches to compare an unlimited number of samples with simple workflows that do not require chemical reaction steps, or custom reagents or media. In addition, label-free LC-MS may also allow obtaining estimates of protein abundance in absolute units. However, although in principle very powerful, implementation of this approach to quantify proteins on a genomic scale (the ultimate aim of expression proteomics) requires the use of (i) appropriate strategies in order to compare the same peptides across the samples to be evaluated, (ii) of normalization procedures to correct for experimental sources of variability, and (iii) of informatics tools to automate data analysis. In this presentation we will discuss how we have approached these problems by creating a computer program, termed PESCAL, which extracts and compares ion intensity values of peptides that are selected for MS/MS at least once across the samples to be compared. We also implemented normalization procedures that allowed us to quantify proteins on a genomic scale with a precision close to those obtained using isotopic labeling strategies. In order to investigate the limits of this approach, we tested it in a challenging sample set that consisted of 5 murine proteomes. Using a first generation Q-Tof mass spectrometer we were able to derive 44,000 independent data points to quantify the expression of 1000 proteins in 5 organs. Thus, with appropriate normalization procedures and informatics tools, label-free LC-MS is a powerful alternative to isotope labelling for quantitative proteomics.

MS.3

Profiling Unlabeled Peptide Ions: A Versatile Approach to Quantitative Proteomics and to Mapping of Post-translational Modifications

G. Jaitly¹, E. Bonneil¹, N. Jaitly², C. Pomies¹, M. Marcantonio⁴, P. Drogaris¹, and P. Thibault^1,3

¹Institute for Research in Immunology and Cancer, ³Department of Chemistry, and ⁴Department of Biochemistry, Université de Montréal, Montréal, Canada; ²Biological Science Division, Pacific Northwest National Laboratory, Richland, WA

The ability to monitor the subtle changes of protein abundances in response to specific perturbations of a biological system (e.g. cell signalling and differentiation, chemical stimulation, etc... ) plays an important in the identification of potential lead candidates as part of biomarker discovery programs. However, this task presents sizable difficulties in view of the overwhelming sample complexity and variability associated with cell extracts. In order to profile low abundance expression changes across cell extracts, we have developed MassSense, a software that provide comprehensive peptide detection and segmentation analyses from data files of different MS platforms. The detection efficiency was determined by manual examination of a dense region of a representative peptide map with ion intensities distributed over 3 orders of magnitude. We evaluated the performance, reproducibility, statistical significance and dynamic range of peptide detection on a nanoLC-MS LTQ-Orbitrap mass spectrometer using scaled mixes of protein standard digests spiked in complex cell extracts. A linear response for all spiked proteins was observed over more than 2 orders of magnitude with detection limits of 1 fmole. We also evaluated the application of quantitative proteomics in two-dimensional nanoLC-MS/MS experiments for combined expression and identification analyses of human monoblastic U937 cells stimulated with phorbol ester. Several proteins including stathmin, hnRNPQ1–3, and ribophorin that showed differential expression and phosphorylation were correlated by western blot experiments. The capability to identify subtle abundance changes across different sample sets also provided unique advantages to monitor sites of modifications in complex cell extracts. This latter aspect will be exemplified for differential phosphoproteome analyses of J774 macrophages cell exposed to interferon- and for functional assays on histone acetyl transferases to locate precise sites of acetylation.

MS.4

Quantitative Approaches for Analysis of Regulatory Post-Translational Modifications

J. J. Gorman¹, T. P. Wallis¹, K. A. Dave¹, B. R. Hamilton¹, M. J. Headlam¹, S. Linke², and D. J. Peet²

¹Protein Discovery Centre, Queensland Institute of Medical Research. Brisbane, Queensland, Australia; ²School of Molecular and Biomedical Science, The University of Adelaide, Adelaide, Australia.

Post-translational modifications provide functional switches and docking points within cellular protein networks. Functionally important post-translational modifications may be dynamic or transient in nature to respond to signals requiring pathways to be up- or down-regulated. It is essential to be able to detect such modifications and to monitor them quantitatively in a dynamic fashion in order to be able to assess their regulatory and functional significance to protein networks.

Accordingly, we have assessed and/or developed quantitative approaches for analysing post-translational hydroxylation of transcription factors involved in response to hypoxic stimuli. A comparison has been made between label-free and stable isotope labeling methods in conjunction with MALDI-TOF/TOF-MS/MS and ESI-LTQ-Orbitrap analysis of modification of Notch by the asparagine hydroxylase (FIH) that is involved in hypoxia inducible factor regulation. Quantitative analysis was an essential component of the study of this system in order to differentiate between asparagine hydroxylation and methionine oxidation of specific regions of Notch. As a consequence we have defined two specific sites of asparagine hydroxylation in Notch catalysed by FIH.

We have also investigated a stable isotope labelling approach for quantitative analysis of the post-translational phosphorylation in relation to regulation of the transcriptional activity of the Dioxin Receptor (DR).

This presentation will describe these approaches as well as the discovery of other post-translational modifications of DR and phosphorylation sites on Newcastle disease virus proteins.

MS.5

Quantification by Spectral Counting in Large Datasets

E. Deutsch

Institute for Systems Biology, Seattle, WA

Several recent works have used spectral counting as a method of label-free protein quantification in complex samples. The method is effective in shotgun proteomics experiments because higher abundance proteins will bring more distinct peptides into the detectable range and more abundant peptides will be sequenced multiple times despite efforts to minimize this. Most examples of this technique compare protein abundance within a small set of samples within one experiment. We apply spectral counting methods to large groups of datasets within the PeptideAtlas to set an approximate scale of protein abundance within specific sample types such as human plasma. We present statistical issues related to quantification by spectral counting in large groups of datasets and its utility for planning targeted proteomics experiments.

MS.6

Protein Abundance Ratios for Microbial Proteomes

M. Hackett, Q. Xia, T. Wang, G. Bosch, and F. Taub

Department of Chemical Engineering, University of Washington, Seattle, WA

The use of multidimensional capillary HPLC combined with tandem mass spectrometry has allowed high qualitative and quantitative proteome coverage of prokaryotic organisms. The determination of protein abundance change between two or more conditions has matured to the point that false discovery rates (FDRs) can be very low and for smaller proteomes coverage is sufficiently high to explicitly consider false negative error. Selected aspects of using these methods for global protein abundance assessments will be discussed. These include instrumental issues that influence the reliability of abundance ratios; a comparison of sources of non-linearity, errors, and data compression in proteomics and spotted cDNA arrays; strengths and weaknesses of spectral counting and other non-label approaches versus stable isotope metabolic labeling; and a discussion of four microbiological applications of global abundance analysis at the protein level. The applications will include examples from ongoing studies of Porphyromonas gingivalis, Methanococcus maripaludis, Agrobacterium tumefaciens and Methylobacter extorquens AM1. The oral pathogen Porphyromonas gingivalis will be discussed as an example of an organism where a large percentage of the proteome differs in relative abundance between the intracellular and extracellular phenotype. Such a global analysis presents special challenges for existing approaches to normalization and multiple hypothesis testing, that typically assume only a small percentage of the proteome is changing with respect to relative abundance. Whole-cell quantitative proteomic analyses were conducted to investigate the change from an extracellular to intracellular lifestyle for P. gingivalis, a Gram-negative intracellular pathogen associated with periodontal disease. Global protein abundance data for P. gingivalis strain ATCC 33277 internalized for 18 hours within human gingival epithelial cells and controls exposed to gingival cell culture medium were obtained at sufficient coverage to provide strong evidence that these changes are profound. A total of 385 proteins were over-expressed in internalized P. gingivalis relative to controls; 240 proteins were shown to be under-expressed. Production of several proteases, including the classical virulence factors RgpA, RgpB, and Kgp, was decreased. A separate validation study was carried out in which a 16-fold dilution of the P. gingivalis proteome was compared to the undiluted sample in order to assess the quantitative false negative rate (all ratios truly alternative), or FNR. Truly null (no change) abundance ratios from technical replicates were used to assess the rate of quantitative false positives (FPR) over the entire proteome. Similar studies of the methanogenic Archaeon M. maripaludis were carried out in which known dilutions of the entire proteome were treated as unknowns, and calculated abundance ratios were compared with the known true values. Spectral counting has poor statistical power characteristics (1-FNR) but favorable FPRs and FDRs. A non-label approach based on summed signal intensity for each protein has better power but higher FPRs and FDRs. The best overall balance between FNR and FPR was achieved using traditional stable isotope metabolic labeling. While FNRs may not be as important for other applications, for studies of microbial gene expression complete quantitative proteome coverage is desired and FNRs become a convenient metric for both coverage and the degree of confidence that we can detect abundance change at a given level of power for any particular protein-encoding ORF.

MS.7

Quantitative Proteomics in the Context Ofsystems Biology

S. J. Gaskell

Michael Barber Centre for Mass Spectrometry and Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK

The primary focus on relative quantification that has characterised the vast majority of the literature on quantitative proteomics now needs to be extended to allow absolute quantification to meet the emerging needs of systems biology approaches. No new analytical concepts are required—absolute quantification merely represents relative quantification when one of the comparators is known—but attention is directed to the need for multiple defined internal standards. Extending the principle of surrogacy that is successfully applied in most qualitative proteomics experiments, standards for quantification are typically proteolytic peptides that serve as signatures for the protein sequences from which they are derived. In conjunction with the Beynon laboratory in Liverpool, we have developed and exploited the simultaneous production of multiple isotopically labelled peptide internal standards by expression of artificial genes that correspond to concatenations of tryptic peptide sequences (1–3). Selection of appropriate signature peptides may be facilitated by the application machine learning approaches to predict mass spectrometric detectability—work conducted by the Hubbard group in Manchester (4).

In order to achieve accurate and precise quantification of proteins, analytical lessons familiar from the determination of small molecules need to be applied. Thus there are clear advantages to the implementation of high-duty cycle mass spectrometric modes such as selected ion monitoring and selected reaction monitoring during LC-MS and LC-MS/MS analyses, respectively. Moreover, optimisation of the properties of peptides to match the analytical mode can lead to substantial enhancement of the sensitivity (and consequently quantitative precision) of analyses. We have explored the use of N-terminal thiocarbamoyl derivatives (Edman derivatives and analogues) in order to promote N-terminal peptide bond cleavage and thereby achieve a concentration of product ion current in one or two fragment ions following collisional activation of peptide ions. Substantial enhancements in the sensitivity of detection have been achieved (5). Moreover, the implicit characterisation of tryptic peptide analytes in terms of molecular mass, N-terminal residue and chromatographic retention time has been shown to be adequate for highly selective detection.

These analytical approaches have been applied in several areas of absolute quantification, including the determination of enzymes involved in the yeast glycolysis pathway (6).

References

1. Beynon, R. J., Doherty, M. K., Pratt. J. M., and Gaskell, S. J. (2005) Nat. Methods 2, 587–589.

2. Pratt, J. M., Simpson, D. M., Doherty, M. K., Rivers, J., Gaskell, S. J., and Beynon, R. J. (2006) Nat. Protocols 1, 1029–1043.

3. Rivers, J., Simpson, D. M., Robertson, D. H., Gaskell, S. J., and Beynon. R. J. (May 17, 2007) Mol Cell Proteomics 10.1074/mcp.M600456-MCP200.

4. Kin Wai Lau, Siepen, J. A., Wedge, D., Eyers, C., Gaskell, S. J., and Hubbard S.J. (2007) Annual Conference of the American Society for Mass Spectrometry, Indianapolis.

5. Riba-Garcia, I., Hart, S., Beynon, R. J., and Gaskell, S. J., in preparation.

6. Carroll, K., Kell, D. B., Simpson, D. M., Beynon, R. J., and Gaskell, S. J., unpublished.

MS.8

Analysis of Protein Levels and Phosphorylation Stoichiometry from Complex Samples Using the iTRAQ Reagent

J. C. Trinidad¹, A. Thalhammer², C. G. Specht², P. R. Baker¹, A. J. Lynn¹, R. Schoepfer², and A. L. Burlingame¹

¹Mass Spectrometry Facility, Department of Pharmaceutical Chemistry, University of California, San Francisco, CA; ²Department of Pharmacology, University College London, United Kingdom

This talk will discuss practical issues involved in the quantitative analysis of protein levels and phosphorylation stoichiometry, focusing on: the use of the iTRAQ reagent for isotopic quantitation; and on the development of software tools to aid in making sense of the results.

Acquiring knowledge of protein and post-translational dynamics is a crucial step in understanding the functioning of complex cellular environments. Towards this end, we have been focused on developing methods to acquire, process, and derive insight from large-scale studies of protein levels and phosphorylation from biological samples under a range of physiological conditions. The experiments that will be discussed involve the application of the iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) reagent.

There are two main advantages of the iTRAQ reagent for our purposes. Firstly, the reagent reacts with free alpha and epsilon amines, leading to labeling of essentially all potential peptides resulting from a tryptic digest (a key consideration when doing quantitation of phosphorylation using isotopic labeling). Secondly, the multiplexed nature of the reagent allows up to four samples to be processed simultaneously. To obtain statistically reliable quantitative information regarding cellular states, larger numbers of replicates need to be analyzed than is currently prevalent in the literature. Multiplexed sample analysis will help to alleviate this bottleneck in the sample processing pipeline.

In terms of developing tools to aid in the analysis of large-scale quantitative proteomic data, much remains to be done. We have invested considerable efforts into quantitative aspects of data analysis using the Protein Prospector software package. The software now supports a range of isotopic quantitation techniques. We have begun to integrate Protein Prospector with downstream processing software that calculates such things as protein expression ratios from multiple peptides as well as relative phosphorylation stoichiometry (given protein and phosphorylation quantitation information).

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