Mass spectrometry-based proteomics in biomedical research: emerging technologies and future strategies

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1 Mass spectrometry-based proteomics in biomedical research: emerging technologies and future strategies Geraldine M. Walsh 1,2, Jason C. Rogalski 1,2, Cordula Klockenbusch 1 and Juergen Kast 1,2,3, * In recent years, the technology and methods widely available for mass spectrometry (MS)-based proteomics have increased in power and potential, allowing the study of protein-level processes occurring in biological systems. Although these methods remain an active area of research, established techniques are already helping answer biological questions. Here, this recent evolution of MS-based proteomics and its applications are reviewed, including standard methods for protein and peptide separation, biochemical fractionation, quantitation, targeted MS approaches such as selected reaction monitoring, data analysis and bioinformatics. Recent research in many of these areas reveals that proteomics has moved beyond simply cataloguing proteins in biological systems and is finally living up to its initial potential as an essential tool to aid related disciplines, notably health research. From here, there is great potential for MS-based proteomics to move beyond basic research, into clinical research and diagnostics. When, in 2000, the draft of the sequenced human genome was introduced, many new avenues of research for exploring human health became available. One field that experienced an explosion of interest was proteomics, the study of the protein complement of a cell under certain conditions. Although these newly uncovered genome sequences revealed which protein sequences could be expressed, splicing, post-translational modifications (PTMs), tertiary structure, enzymatic activity, formation of complexes and ligand interactions combine to produce a much richer protein environment than what is simply coded for, and it is these intricate and complex processes that dictate how biological functions occur. Proteomic research is the attempt to understand all that is occurring in this complex environment, with the aim of elucidating protein-level processes involved in biological activity. 1 The Biomedical Research Centre, University of British Columbia, Vancouver, BC, Canada. 2 The Centre for Blood Research, University of British Columbia, Vancouver, BC, Canada. 3 Department of Chemistry, University of British Columbia, Vancouver, BC, Canada. *Corresponding author: Juergen Kast, The Biomedical Research Centre, 2222 Health Sciences Mall, Vancouver, BC, Canada V6T 1Z3. [email protected] 1

2 The first tentative steps towards mass spectrometry (MS)-based proteomics started in the late 1980s, well before the human genome was sequenced, when the development of softionisation techniques such as electrospray ionisation (ESI) (Ref. 1) and matrix-assisted laser desorption/ionisation (MALDI) (Ref. 2) allowed MS analysis of intact biological macromolecules for the first time. These technologies, together with the fact that peptides produced through the digestion of proteins with highly specific proteases are characteristic of their parent protein, permitted protein identification by comparison of MS data with known sequences, in silico. Progress in this field generated a great deal of fervour, and researchers began to develop new techniques, as well as incorporate established techniques, to aid proteome analysis by MS. The years that followed saw gargantuan leaps in the capabilities of MS and related technologies. So why all the excitement? It is mainly due to the ability of MS to obtain specific and sensitive information about a complex sample quickly, over a wide dynamic range. Given that the genome of a given species codes for many thousands of protein products [ for humans (National Human Genome Research Institute, which cover many orders of magnitude in abundance (ten in the case of plasma) (Ref. 3), twodimensional (2D) gel electrophoresis was initially the only technology capable of sensitive and reproducible visualisation of the proteome. MS, combined with a host of affiliated technologies, provided the first opportunity to go beyond gel-based visualisation, enabling discovery and identification of the components of a proteome on a large scale, to a depth that immunoprecipitations and 2D gels could not provide. These proteome-wide discovery experiments were the basis of the initial thrust in MS-based proteomics, inspiring a rapid rate of creation and improvement of new techniques and instrumentation in an attempt to dig deeper into the proteome, with more certainty, less sample and less time. This focus on instrumentation brought together different combinations of mass analyser and ion source, and fostered the utilisation of the strengths of different mass analysers in hybrid instruments (Ref. 4). Research and development continue to produce and improve mass spectrometers to this day (Ref. 5). Although these technique and technology improvements have resulted in the greatly increased utility and robustness of MS-based proteomics, what does this mean for tangible benefits to human health research? Essentially, it means that proteomics has moved beyond simply asking the what of a biological question, and now can routinely and robustly study the when, where, how and how much. Current popular techniques and experiment types employed in MS-based proteomics that are now being utilised in biomedical research are discussed in this review (Fig. 1). Protein and peptide separation techniques The field of MS-based proteomics can be categorised into two broad approaches. The increasingly popular top-down proteomic approach focuses on the analysis of intact proteins, whereas the more widely used bottom-up proteomic approach focuses on the analysis of peptides following proteolytic digestion of proteins, and is the main topic of this review (Ref. 6). Because bottom-up proteomic approaches require digestion of proteins into peptides prior to their analysis by MS, preanalytical sample processing plays an important role and should be carefully considered when designing and conducting these types of experiments. By far the most popular method to prepare a proteomic sample is enzymatic digestion using trypsin, which is very well suited to downstream analysis by the most common MS and tandem MS (MS/MS) techniques. However, information regarding PTMs or protein isoforms could be missed, and it is often worth considering other proteolytic enzymes or applying a panel of enzymes (Ref. 7). The digestion of proteins into peptides prior to MS analysis greatly increases the complexity of samples, and the separation of these complex samples into manageable, reproducible fractions is an issue that proteomics has battled with since its inception. Owing to several factors, including competitive ionisation of coeluting species, dynamic range limitations (the ability to analyse a weak signal in the presence of a strong signal), duty cycle constraints (how many things can be analysed per unit of time) and resolving power, it is generally known that the greater the separation before MS sequencing, the better the results 2

3 Global analysis Subcellular fractionation Lysis Unbiased MS (e.g. LC-MS/MS) Peptide/protein identification (e.g. X!tandem, SEQUEST) Cell Quantitative analysis Bioinformatic analysis Protein enrichment Lysis Quantitative analysis Enrichment of PTMs Protein elution Optional: protein separation Protein digestion (e.g. trypsin) Optional: peptide separation Biased MS SRM Workflow of typical MS-based proteomic experiments Expert Reviews in Molecular Medicine Labelling methods SILAC N15 ICAT itraq ICPL ICAT itraq ICPL Proteolytic labelling itraq ICPL AQUA Figure 1. Workflow of typical MS-based proteomic experiments. (See next page for legend.) (Ref. 8). Powerful separations are therefore a necessity for in-depth proteome analysis and there has been much research into separation technologies that are compatible with proteomic workflows. The art is now such that what was cutting-edge experimental work five years ago is now routinely performed in laboratories all over the world. For example, 3

4 Figure 1. Workflow of typical MS-based proteomic experiments. (See previous page for figure.) Whole-cell lysates can be used for a global proteome analysis, or more in-depth analysis and additional spatial information can be obtained using subcellular fractionation. Alternatively, cells can be lysed and proteins or posttranslation modifications (PTMs) of interest can be isolated by affinity enrichment methods. All methods produce protein mixtures, which can be separated further by exploiting various protein properties such as molecular weight or isoelectric points, and are digested in the next step. Separating the generated peptides is recommended and leads to deeper resolution. Peptides are then analysed by MS (e.g. LC-MS/MS) and in unbiased discovery experiments peptides and the corresponding proteins are identified using databasematching search algorithms, followed by quantitative and bioinformatic evaluation of the data. Alternatively, targeted MS for specific peptides and proteins can be performed using SRM. Quantitative information can be obtained either by label-free methods or by applying a differential isotopic labelling method at one of the stages indicated on the right: metabolic labels such as SILAC and N15 can be introduced at cell level, whereas chemical labelling methods such as itraq, ICPL or ICAT are utilised either at protein or at peptide level. Isotopic labelling can also be introduced during proteolysis, and synthetic standard isotopic peptides can be added to the peptide mixture (AQUA). Abbreviations: AQUA, absolute quantitation; ICAT, isotopecoded affinity tags; ICPL, isotope-coded protein labelling; itraq, isobaric tags for relative and absolute quantitation; LC-MS/MS, liquid chromatography tandem mass spectrometry; MS, mass spectrometry; N15, 15 N isotope; PTM, post-translational modification; SILAC, stable isotope labelling of amino acids in cell culture; SRM, selected reaction monitoring. separation of samples in at least two dimensions in the liquid phase, a strategy known as multidimensional liquid chromatography (MDLC), has many different permutations and is a large field of research on its own, leading to many of its own reviews (Refs 9, 10). Protein separation Although the final dimension of separation for MS-based proteomics is generally a reversedphase separation at the peptide level, upstream separations can use a number of different properties for fractionation of complex samples at the peptide or protein level. Many options exist for protein-level separation, each of which can utilise a different physical property of proteins to obtain varying but complementary first-dimension separations. Cation exchange (Ref. 11) and anion exchange (Ref. 12), for example, offer good separation orthogonality in fractionating proteomic samples before liquid chromatography (LC)-MS/MS. Two different technologies, however, have emerged recently and joined the mainstream: chromatofocusing (CF) (Refs 13, 14, 15), a variant of ion-exchange chromatography that separates proteins based on ph; and isoelectric focusing (IEF) (Ref. 16), which separates proteins based on their isoelectric point (pi), as is done in the first dimension of 2D gel electrophoresis. These methods are easily automatable and provide powerful protein-level separation and useful information of the physical properties of the proteins, while keeping the analytes soluble and compatible with proteomic experiments, and are therefore widely used (Ref. 17). Another commonly used method for proteinlevel sample fractionation prior to MS is the GeLC approach. It harnesses the wellestablished ability and available equipment for running gels, by separating a complex sample by molecular weight at the protein level in a single 1D SDS-PAGE gel lane, and using that lane as the first dimension in a multidimensional separation. After staining, the entire lane is excised, cut into bands and each band is treated as a fraction of the same sample. After the proteins in these bands are enzymatically digested, each band s peptide mixture can be analysed on an LC-MS/MS instrument and the results combined. The benefits of gel-based protein-level first-dimension separation are threefold: gels are often a good way of making biological samples compatible with MS analysis (e.g. by removal of detergents), method development is not needed as SDS-PAGE is a well-established technique, and the number of identifications obtained per experiment is currently second to none. In fact, of the aforementioned protein-level separation techniques, the GeLC approach has been found to provide the highest number of confident protein or peptide identifications, although the alternative approach of immobilised ph gradient (IPG)-based IEF has the benefit of slightly higher sample recovery over the GeLC separation (Ref. 17). A recently developed fractionation method termed GELFrEE (gel-eluted liquid 4

5 fraction entrapment electrophoresis) also separates proteins based on their size using a gel column; however, in contrast to the GeLC method, the proteins are eluted and collected in the liquid phase (Ref. 18). The researcher needs to be aware of these performance differences in firstdimension separations when deciding on the priorities for a given experiment. Peptide separation The development of powerful techniques and chemistries for separation at the peptide level has led many MS-based workflows to forgo the aforementioned protein-level separations entirely. Many options exist for the firstdimension separation, which have varying degrees of orthogonality: for example, reversedphase chromatography, which resolves issues with solvent compatibility; or strong ion exchange, different forms of which can provide good sample complementarity. One of the most common first-dimension separations for largescale proteomic experiments is peptide-level strong cation exchange (SCX) chromatography. There is no best answer for separation; despite each of the techniques being optimal for particular sample types, they will all provide complementary results. First-dimension separations can be performed off-line with a fraction collector, although when they are performed in-line with a reversed-phase column as the second dimension prior to MS, in a workflow called multidimensional protein information technology (MudPIT) (Refs 19, 20), the experiment is capable of significant proteome coverage (approximately 60%) in one albeit very long experiment (Ref. 21). This type of workflow is described thoroughly in a published protocol (Ref. 22). Reproducing an experiment of this type also provides 60% of the proteome, with a large number of the peptides sequenced being species that were not sequenced in the first experiment. It is estimated that it would take five MudPIT experiments performed in this way to achieve near-complete sequence coverage, a phenomenon attributed to MS/MS peptide-sampling rates. As with any MS-based analysis of complex samples, the limitations of this method are time and instrument duty cycle issues that should be considered when designing experiments and choosing which proteomic approach to use. These limitations can be attenuated by conducting biological and technical repeats and maximising separation and fractionation prior to MS analysis. Also, the development of dynamic exclusion lists to avoid run-to-run resequencing of peptides has recently increased the number of extracellular proteins identified in repeat analyses of the human embryonic stem cell secretome by an order of magnitude (Ref. 21). Expanding the MudPIT workflow to include a third dimension of separation has also been shown to work well (Ref. 23), and a study of the proteome of the serum of patients with sepsis utilised immunodepletion of abundant serum proteins followed by a 3D peptide-level separation, allowing the identification of lowabundance serum proteins while identifying ten potential serum biomarkers for sepsis (Ref. 24). Although this type of technique shifts the limitation of the method towards separation time and away from the duty cycle of the instrument, the deployment of fast, ultrahigh-pressure liquid chromatography (UPLC) (Ref. 25; waters.com/waters/nav.htm?locale=en_us&cid= ) in many laboratories is now proving that these methods are more powerful than ever. Biochemical fractionation methods The protein- or peptide-separation techniques described above allow in-depth analysis of a complex sample. However, biochemical fractionation procedures, which add an additional dimension of separation, can lead to even deeper resolution as the separation methods described previously can be performed on a less complex sample. This can be especially important in highly complex samples, such as human plasma or serum, which have a high dynamic range spanning at least ten orders of magnitude. These samples contain a small number of highly abundant proteins, whose signals can dominate MS-based analysis. Depletion of these proteins can be highly advantageous in allowing access to lower abundance species, including potential disease biomarkers, and there are many tried and tested depletion strategies available (Refs 26, 27). For cellular studies, spatial information (e.g. which proteins are found in which organelles or which proteins interact with each other) can be extremely important for understanding a complex system, and can be obtained by applying either subcellular fractionation or affinity enrichment techniques. 5

6 Subcellular fractionation For subcellular analysis, every classical biochemical fractionation procedure, whether it be membrane enrichment, nucleus precipitation or mitochondria preparation, can be used as the first enrichment step, followed by protein/ peptide separation and MS analysis. For example, plasma membrane lipid rafts were enriched to follow the effects of DMSO-induced differentiation of HL-60 cells into neutrophils by LC-MS/MS, and out of 147 identified proteins, 25 were found to be upregulated and 49 were downregulated (Ref. 28). In a different study, membrane fractionation and the hydrazide method were used to isolate 25 glycoproteins from breast cancer cell lines, which are considered putative cancer biomarkers (Ref. 29). Immunoprecipitation Affinity enrichment of a protein and its interaction partners decreases the complexity of a sample dramatically and provides information about the composition of the interaction network. Classical coimmunoprecipitation, a long-established method to isolate proteins, is the first step performed for this approach, applying either antibodies against endogenous proteins or immunoaffinity tags. Precipitated proteins are isolated afterwards and analysed as described earlier. In contrast to an immunoblot analysis, which requires a hypothesis about interaction partners and focuses on the identification of one protein, MS is an unbiased detection method and allows the discovery of several binding partners at once, including unexpected ones. However, MS is a very sensitive method and therefore stringent wash conditions, several controls and careful interpretation of the results are required to obtain correct information from this type of experiment (Ref. 30). For example, the interaction network of MYC was studied using the tandem affinity purification (TAP) approach, which allows stringent wash steps and thereby reduces false-positive identifications; 221 putative interaction partners were identified, of which only 17 were known before (Ref. 31). Another approach was applied for the study of integrin-linked kinase (ILK), where a quantitative MS approach (see below) was used to distinguish between proteins binding to the bait protein or to the tag itself and allowed the identification of several novel ILK-interacting proteins (e.g. α-tubulin) (Ref. 32). Two complementary affinity purification methods were used to identify over 40 kinases binding to dasatinib, an inhibitor with putative antitumour properties. In a second step, phosphorylated proteins were purified from cancer cells; 23 candidates identified in both pull-downs were analysed in more detail regarding their susceptibility to the inhibitor and several of these kinases were found to be inhibited by dasatinib (Ref. 33). Phosphorylation-enrichment strategies Enriching for PTMs also simplifies a complex sample, and studying the corresponding proteins can provide detailed information about signalling processes. Furthermore, even though PTMs can be identified by MS, the low stoichiometry of these modifications can lead to them being missed during analysis, a problem that can be overcome by specific affinity enrichment. One of the major modifications taking place during signal transduction is phosphorylation, the study of which termed phosphoproteomics has also pioneered technology development. Prior to MS analysis, phosphoproteins can be isolated by immunoprecipitations (e.g. by applying antibodies against phosphotyrosines) or phosphopeptides (containing modified serines, tyrosines and threonines) can be enriched by metal-supported chromatographies such as IMAC (immobilised metal affinity chromatography) (Ref. 34) or MOC (metal oxide chromatography) (Ref. 35) mostly utilising titanium dioxide. These methods have been established and optimised in recent years, and phosphoproteomics in combination with quantitative approaches such as stable isotope labelling of amino acids in cell culture (SILAC) or isobaric tags for relative and absolute quantitation (itraq) (see the next section) now has the power to study timedependent activation cascades (Ref. 36). The epidermal growth factor (EGF) signalling pathway has been studied in detail by several groups using slightly different MS approaches and can be seen as a model system for the optimisation of phosphoproteomics (Ref. 37). Mann and colleagues have applied many methods, including the application of antiphosphotyrosine antibodies (Ref. 38), the use of titanium dioxide to enrich phosphopeptides (Ref. 39) and the combination of both enrichment approaches, on the way to 6

7 developing a method termed qpace, which allows the study of very early signalling events (Ref. 40). For quantitation, they utilised SILAC. By contrast, White and colleagues used itraq to study EGF receptor signalling using a combinational enrichment approach applying antiphosphotyrosine antibodies and IMAC (Ref. 41), and extended their methodology with selected reaction monitoring (SRM) experiments (which are explained in detail later in this review), allowing a much higher reproducibility (Ref. 42). Phosphoproteomics is now used to study other and unknown signalling pathways, as for example the SYK signalling cascade, which was originally described only in haematopoietic cells but has been investigated now in human cancer cells to shed more light on the role of this kinase in cancer formation (Ref. 43). With the help of the enrichment techniques described here, MS-based proteomics can achieve high spatial and functional resolution. However, as mentioned throughout this section, a quantitative dimension is also frequently necessary to answer many of the questions currently asked by researchers. Quantitative approaches The topic of MS-based quantitation exploded in the mid-2000s (Ref. 44), and the current state of the art is reviewed thoroughly and engagingly elsewhere (Ref. 45). Essentially, despite quantitative proteomics still being an active area of research on its own, it is now also available to human health researchers who are interested in studying drug effects, biomarkers of disease and the pathways involved in disease processes. Isotopic labelling techniques Mass spectrometers are not inherently quantitative. Differences in ionisation, transmission and detection efficiency dictate that the intensity of a signal from a particular molecule is a relative measure of its abundance, but not an absolute measure. For this reason, all quantitative proteomics, even absolute quantitation is relative relative to an internal standard (Ref. 45). MS-based proteomic quantitation was therefore not thrust into the mainstream until 1999, when isotope-coded affinity tags (ICATs) were introduced (Ref. 46). These tags were the first widely available method to quantify the relative concentrations of peptides or proteins in a sample, by way of an isotope-coded chemical modifier. Briefly, each of two samples is treated with either one of a light or heavy chemical reagent that binds specifically to cysteine residues. The light and heavy tags are chemically identical, except for isotopic differences. The two samples are then mixed and digested, and the tagged peptides are enriched using avidin or streptavidin chromatography against the biotin moiety embedded in the tag. On performing MS analysis on these enriched samples, the chemically identical species from the two samples will coelute from a column and ionise with identical efficiency; however, the peptide that is modified with the light form of the reagent will appear at a known lower mass in the spectrum than the heavy tagged equivalent peptide from the other sample. One can then directly compare the peak areas of the two chemically identical coeluting peptides and thereby obtain a relative measure of their abundance. Relative quantitation, performed through isotope-coding methods similar to this, is the best way to obtain information about quantitative differences in protein expression, especially from the complex samples usual in proteomics (Fig. 2). One issue with the ICAT method described above, however, is its dependence on the modification of cysteine residues, which account for only 1.42% of the amino acids in a sample (Ref. 47). Many peptides, and even whole proteins, do not contain a cysteine, and are therefore unquantifiable by means of ICAT. This problem was resolved in 2004 with the introduction of the isobaric tags for relative and absolute quantitation (itraq) label (Ref. 48) (Applied Biosystems; systems.com). With this tag, initially four, and now up to eight, samples can be compared together, using labels with identical mass shifts. This is achieved through the differential placement of the stable isotopes onto balance and reporter pieces of the tag, which are separated by a labile bond. Each of the different tag flavours adds the same overall mass to a peptide, bound through the balance group to the primary amines on lysine side chains and the N-termini of peptides. On mixing of the samples, unlike ICAT-labelled samples, tagged peptides will appear as one signal in a normal MS scan. Only upon fragmentation does the 7

8 a Culture in media containing nonlabelled (light) amino acids Leave as control Starting culture Culture in media containing isotopically labelled (heavy) amino acids Expose to stimuli Mix samples 1:1 Analyse using protein/peptide separation and mass spectrometry Intensity SILAC m/z MS scan MS scan of light and heavy SILAC-labelled peptides: extracted ion chromatograms give quantitative information b Cultured cells or any biological material Leave as control Expose to different stimuli Mix samples evenly Analyse using protein/peptide separation and mass spectrometry Intensity m/z MS scan of all combined labelled forms of peptide Fragment and collect low-mass marker ions Intensity itraq or TMT Lyse cells, digest and label each sample with a specific itraq tag MS scan MS/MS scan m/z Intensities of marker ions give quantitative information Workflows for implementation of stable isotope labelling via metabolic (SILAC) and chemical (itraq or TMT) methods Expert Reviews in Molecular Medicine Figure 2. Workflows for implementation of stable isotope labelling via metabolic (SILAC) and chemical (itraq or TMT) methods. (See next page for legend.) 8

9 Figure 2. Workflows for implementation of stable isotope labelling via metabolic (SILAC) and chemical (itraq or TMT) methods. (See previous page for figure.) (a) SILAC incorporates isotopes early in the sample preparation procedure, maximising accuracy and reproducibility, and is generally used only for quantitation of samples from cells that can be cultured. Isotope incorporation into the amino acids themselves means peptides to be compared have different masses; therefore quantification occurs from the MS scan. (b) Chemical isotope-coded tags are applied later in the workflow, but can be applied to any biologically derived sample. Isobaric chemical tags (itraq, TMT) add equivalent masses to the peptides in the sample, but produce specific marker ions upon fragmentation, allowing quantification from the MS/MS scan. Abbreviations: itraq, isobaric tags for relative and absolute quantitation; MS, mass spectrometry; MS/MS, tandem mass spectrometry; SILAC, stable isotope labelling of amino acids in cell culture; TMT, tandem mass tag. labile bond holding the itraq modification together dissociate, forming an intense marker ion from the reporter group, each of which will be specific for one of the samples to be compared. This behaviour allows for high specificity (marker ion intensity can only be from the peptide currently being fragmented) and high sensitivity because the isobaric nature of the tag dictates that the intensities of the signals from all samples are additive for initial detection in the MS scan, and subsequent sequencing. This technique has also been successfully used in the tandem mass tag (TMT) strategy from Thermo Scientific ( piercenet.com). These types of chemical labelling strategies are a good choice when probing the proteome of human cells or tissues that cannot or should not be cultured. Unfortunately, as the combination of the samples to be compared occurs late in this workflow, there is a chance of systematic errors during sample handling (Fig. 2). Metabolic labelling techniques, in which cells are grown in isotopically labelled media and compared with those grown in normal media, have been shown to be the most accurate proteomic quantitation method mainly due to the ability to combine samples very early in the procedure (e.g. immediately after lysis), thus minimising errors involved with differential sample handling in the subsequent isolation and purification steps (Ref. 38). Unfortunately, SILAC (Ref. 49) can only be used on cells cultured in vitro, as controlling the isotopes available to a biological sample can be problematic. Although SILAC quantitation on a whole mouse has been successfully performed (Ref. 50), this type of experiment is prohibitively expensive and time consuming for most research projects and species types. Recently, however, a technique that uses a combination of five SILAC-labelled cell lines, pooled together, has been introduced as a physical proteome database, which was then compared with a nonlabelled carcinoma tissue sample, allowing the SILAC quantitation of uncultured human tissue cells (Ref. 51). This new technique allowed quantitative comparison of lobular and ductal tumours, revealing significant differences, with very low coefficients of variance, in the expression of focal adhesion and glycolytic proteins, in a clinically relevant human tissue sample. Label-free quantitation Given that the intensity of the signal in a mass spectrometer is innately a proxy of the abundance of the species in the sample, labelfree quantitation approaches have recently entered the mainstream because of their apparent ease, simplicity and cost savings. One of the numerous label-free quantitation approaches is spectral counting, in which the MS/MS spectra collected for a given species are counted and compared with those collected for the same species in a different sample. This technique uses the assumption that unbiased, intensity-based precursor ion selection leads to intense ions being selected for sequencing more frequently. The number of MS/MS spectra collected for a given analyte would therefore be a proxy of its intensity, and therefore its abundance. Like all label-free quantitation methods, systematic errors in the analysis, such as signal suppression, detector saturation and differential sample loading, occur. These errors need to be minimised and accounted for by performing many replicates, normalising the data, and statistical validation (Refs 45, 52). Although this approach has been shown to be adequate for quantitation of high-abundance components of a mixture (Ref. 53), isotopic labelling techniques, which correct for these 9

10 systematic errors and also allow for sample multiplexing, are still the method of choice for quantitative proteomics. The commonly used quantitative proteomic methods, along with their strengths and weaknesses, are shown in Table 1. Targeted analysis: selected reaction monitoring Unlike global proteomic techniques, which operate based on intensity-dependent fragmentation of precursor ions and are biased towards more abundant proteins, SRM (also known as multiple reaction monitoring, MRM) targets predetermined precursor ions for fragmentation (Fig. 3). This allows peptides from a particular protein of interest to be monitored, giving access to lower abundance species even in complex mixtures such as plasma and serum (Ref. 54). For years, SRM has been used effectively to detect and quantify drugs and drug metabolites in pharmaceutical research(ref.55).today,itisrapidlybecoming the method of choice in many fields owing to its consistency, accuracy and sensitivity. This includes basic proteome research, where advances in development and validation of these assays, as well as novel software and data repositories, are increasing the potential of the SRM approach in whole-proteome analysis. In clinical research, its potential as a biomarker verification tool is thought by some to rival the standard ELISA method and there is huge potential for the application of this approach in clinical diagnostics. TheamountofsamplerequiredforSRManalysis is small, and its sensitivity is high (attomole level) (Ref. 56); therefore, it is suitable for the analysis of samples containing small amounts of material, such as neonatal screening and therapeutic drug monitoring, meeting the throughput requirements of clinicians (Refs 55, 57). Experimental design Targeting the most appropriate peptides and fragment ions for the protein of interest is key to a successful SRM experiment; therefore some prior experimental knowledge is required. This translates into knowing the mass to charge ratio (m/z) of an abundant, consistently produced (or proteotypic ) peptide (Ref. 58) as well as the m/ z of one of its fragment ions that is generated with high intensity. These transitions (specific precursor fragment ion pairs) allow targeted analysis of a particular peptide in a complex mixture. There are many guidelines that can aid in the selection of appropriate transitions, based on prior experimentation, physicochemical parameters and in silico predictions (Ref. 59). These take into account factors that are too numerous to describe in detail here, but are outlined in several recent reviews (Refs 54, 60). There are multiple software options available to aid the design and optimisation of these transitions (Ref. 61). Many are reviewed elsewhere (Ref. 62), with a selection listed in Table 2. Although this design stage can take considerable time, once transitions are established, they can be used indefinitely for experiments studying the protein of interest. SRM and quantitation The SRM approach can be used to quantitate proteins. Relative quantitation can be conducted simply by comparing the absolute peak area of the individual samples (label-free quantitation), although it is difficult to obtain precise measurements because of differences in ionisation efficiency, analyte composition and chromatography. SRM experiments can also be combined with many of the standard isotope labels used in quantitative proteomic experiments, including ICAT, SILAC, ICPL and itraq. Additionally, several methods that aid greatly in speeding up the assay development aspect of SRM have emerged, including databases such as MRMAtlas (Ref. 63) and a method of crude synthetic peptide library production, which allow the rapid generation of validated SRM assays for whole proteomes (Ref. 56). These approaches have been pioneered using the yeast proteome, but the development of databases and resources such as this for clinically relevant tissues could help thrust SRMbased quantitation firmly into the clinical arena. Applications of SRM The advantages of SRM experiments have led to an almost exponential increase in the number of studies using this approach in recent years (Ref. 62), and SRM has now been applied to many diverse biological questions, from the quantitation of the biomarker C-reactive protein (CRP) in the serum of patients with rheumatoid arthritis (Ref. 64) to the absolute quantitation of the human liver alcohol dehydrogenase ADH1C1 isoenzyme (Ref. 65) and pyruvate kinase M2 (PKM2), a potential endometrial 10

11 Table 1. MS quantitation techniques Technique Application level Labelling method SILAC Protein Metabolic High quantitative accuracy Many comparisons per experiment Works well for cell lines Pros Cons Refs Only for in vitro culture Proline arginine conversion can cause quantitation errors N15 Protein Metabolic Good for autotrophic species Exact mass shift of peptides is unpredictable Only binary comparison ICAT Protein Chemical derivatisation itraq or TMT Protein or peptide ICPL Protein or peptide Proteolytic labelling Chemical derivatisation Chemical derivatisation Peptide During digestion Ease Cost Purification possible because of robust biotin tag; reduction of sample complexity Many comparisons per experiment Additive intensity in MS quantitation multiplexed in MS/MS scan Every tryptic peptide can in theory be quantified Many comparisons per experiment Quantifies any lysine-containing peptide Label-free Peptide None Ease Cost No extra sample handling AQUA Peptide Spike of synthesised peptide Accurate, absolute quantitation Corrects for differences in analysis Sensitive (with SRM) Targets only cysteinecontaining peptides Compression of expression ratios Variability in labelling efficiency Cost and difficulty Variability in labelling efficiency Cost and difficulty Differential rates of label incorporation Back exchange of label Only accurate for abundant species No sample multiplexing Differential signal suppression effects Expensive Need for synthesised peptide for each quantified peptide Cannot be used for discovery 49, 118, 119, 120, , 123, 124, 125, , 127, 128, , piercenet.com 131, , , 136, 137, 138, 139, 140, 141, , 144 Abbreviations: AQUA, absolute quantitation; ICAT, isotope-coded affinity tags; ICPL, isotope-coded protein labelling; itraq, isobaric tags for relative and absolute quantitation; MS/MS, tandem mass spectrometry; N15, 15 N isotope; SILAC, stable isotope labelling of amino acids in cell culture; SRM, selected reaction monitoring; TMT, tandem mass tag. 11

12 cancer marker (Ref. 66). The approach has been shown to be extremely powerful, both in confirmation of potential biomarkers and in discovery of novel biomarkers, as seen in a recent study that integrated high-throughput and SRM-based approaches to explore breast cancer in a mouse model (Ref. 67). SRM is also a promising tool to study splice variants that are putative biomarkers for cancer cells. For example, using the breast cancer mouse model mentioned above, 216 of 608 splice variants were found only in tumour cells and SRM is ideally suited to study these (Ref. 68). Using SRM coupled with stable isotope dilution MS (SIDMRM-MS), quantitative, multiplexed assays were developed for the analysis of six proteins clinically relevant to cardiac injury (Ref. 69). These widely applicable assays were conducted using plasma samples, with proteins of interest spanning four orders of magnitude. These were quantitated using a few signature peptides from each target protein, with limits of quantitation ranging between 2 and 15 ng/ml. Similarly, low ng/ml sensitivity quantitation of the prostatespecific antigen biomarker was achieved using LC- MS/MS SRM, from 100 μl of serum, demonstrating good correlation with ELISA measures (Ref. 70). Even greater sensitivity was achieved in a recent study that captured and enriched peptides with antipeptide antibodies and then used SRM-based analysis to quantitate aberrant GlcNAcylated tissue inhibitor of metalloproteinase 1 (TIMP1), a protein implicated in colorectal cancer (Ref. 71). Following enrichment and digestion of glycoproteins from patients serum, SISCAPA (stable isotope standards and capture by antipeptide antibodies) (Ref. 72) and SRM-MS permitted highly sensitive quantitation of TIMP1 at attomolar concentrations. Automation and multiplexing of this approach shows great potential for analysing large numbers of biomarkers with sufficient sensitivity, reproducibility and precision for clinical applications (Ref. 73). Still, proven reproducibility is essential for SRM assays to move beyond basic research and become a force in clinical or diagnostic assay development and application. A recent, multisite review demonstrated high reproducibility across different laboratories using different instrument platforms (Ref. 74). Another recent review questioned whether SRM-MS will replace antibody-based testing in the validation of biomarkers (Ref. 75). SRM-MS has several advantages over antibody assays for biomarker validation: SRM has exquisite sensitivity, with no crossreactivity and less specificity issues than are often associated with antibody assays; SRM is reagent independent ; SRM can be used for any MS-observable ion, making it generally cheaper than antibody assays (Ref. 76); and these assays are quantitative and easily multiplexed. This is a key point, as reality dictates that having a single biomarker for a disease is unlikely; panels of biomarkers are the more likely future of disease diagnostics, and SRM technologies are very well placed to study these. Issues still remain however, particularly regarding assay throughput and precision, which have not been thoroughly tested and currently do not meet the US Food and Drug Administration (FDA) requirements for routine clinical tests (Ref. 75). Another issue is operator familiarity, as these assays are only beginning to enter the mainstream, and it will take time for users to become comfortable with applying these new techniques. However, the hurdles facing the use of SRM in biomarker validation are slowly being overcome, and although antibody assays will still be used for biomarker validation, increasingly we can expect to see the application of SRM assays. Bioinformatic analysis With the rapid development of MS-based proteomic technologies, automated analysis of the qualitative and quantitative data resulting from large-scale proteomic studies has become increasingly important and challenging (Ref. 77). The large number of MS/MS spectra generated in a typical proteomic experiment requires several stages of analysis, including statistical validation of peptide and protein identifications, analysis of any quantitative information and interpretation of the resultant protein information. Protein identification Identification of peptides and their corresponding proteins is generally conducted using search algorithms that correlate experimental MS/MS spectra to theoretically derived spectra created from known peptide sequences. There are several different search engines available, which differ in their approaches to identifying peptide sequences. The most common search algorithms include Sequest (Ref. 78), Mascot (Ref. 79) and X!Tandem (Ref. 80). It is worth noting that these 12

13 a Ion source b Q1 Set as a mass filter Precursor ion selection Peptide: AGFAGDDAPR Intensity (cps) 6.0e4 3.5e4 3.0e4 2.0e4 1.0e4 Q2 Set as an ion guide Collision cell (peptide fragmentation) / / / / /343.2 Q3 Set as a mass filter Fragment ion selection Time (min) Mode of operation of selected reaction monitoring (SRM) Expert Reviews in Molecular Medicine (part a only) Detector Figure 3. Mode of operation of selected reaction monitoring (SRM). (a) After protein/peptide separation, peptides elute from a reversed-phase column, ionise, and enter a triple quadrupole mass spectrometer. The first quadrupole (Q1) is set as a mass filter for a specific peptide; Q2 is set as an ion guide/collision cell, where peptides selected in Q1 are fragmented; and Q3 is set as a mass filter that specifically transmits a particular fragment ion. When specific peptide fragment transitions occur, a signal is recorded by the detector, which can be plotted as a chromatogram. (b) Example chromatograms from an SRM experiment are shown. The SRM tool at the Global Proteome Machine was used to design five transitions (Q1/Q3 ion pairs) for the peptide AGFAGDDAPR from the protein β-actin (ACTB). A complex mixture, digested human platelet lysate, was used as the test sample. The resulting transitions display sufficient intensity and specificity to allow for positive identification of this peptide in the sample. The Q1/Q3 m/z ratios for each transition are displayed on the graphs in bold and the elution time (30.48) is indicated. Quantitation of the transition can be conducted using the peak area. As SRM is a fast and sensitive method, many transitions can be acquired serially in a short time, allowing quantitation of multiple transitions in a single experiment. SRM transitions (b) are reprinted from Ref. 59 ( 2009), with permission from Elsevier. Abbreviations: Q, quadrupole; m/z, mass to charge ratio; SRM, selected reaction monitoring. 13

14 Table 2. Examples of selected reaction monitoring (SRM) resources Resource Brief description Source Ref./website MRMPilot Suggests/optimises transitions Builds SRM and MIDAS workflows Tools for reviewing and archiving data SRM workflow software Builds SRM methods Processing and review of results Verify E High-throughput optimisation of SRM transitions MassHunter Optimizer Automatically optimises data acquisition parameters for SRM TIQAM (targeted identification for quantitative analysis by MRM) Optimises SRM transitions for identification and quantitation MRMer Manages MRM-based experiments Extracts precursor and product masses Calculates relative area under the curve for quantitation PeptideAtlas (incorporating MRMAtlas) Public repository useful for SRM design Tranche Public repository useful for SRM design PRIDE (protein identification database) GPMDB (Global Proteome Machine database) Public repository useful for SRM design Public repository useful for SRM design Commercial Applied Biosystems com Commercial Thermo Scientific Commercial Waters Commercial Agilent technologies Freely available from Seattle Proteomics Centre using PeptideAtlas database Freely available from Fred Hutchinson Computational Proteomics Laboratory (CPL) Proteomics Repository Freely available from Seattle Proteomics Center Ref. 145 Ref CPL/MRMer.html Ref Freely available from proteomecommons.org tranche Freely available, hosted by European Bioinformatics Institute Ref Freely available Ref Abbreviations: MIDAS, MRM-initiated detection and sequencing workflow; MRM, multiple reaction monitoring. 14

15 search engines are complementary to some extent, so it is often useful to use at least two different algorithms to analyse MS/MS data to increase confidence and sensitivity, and there are tools available to aid this (Ref. 81). Validation of the peptide and protein identifications is necessary and is often conducted by determining false discovery rates using decoy databases and other statistical methods (Ref. 82). For de novo sequencing of proteins, an approach that is useful in the analysis of PTMs or of organisms whose genome has not been sequenced, there are several software options available, including PEAKS (Ref. 83). Analysis of quantitative proteomic data Tools for the analysis of quantitative data from proteomic experiments are continuously emerging and being refined. The Trans- Proteomic Pipeline (Ref. 84) is a collection of integrated MS/MS analysis tools, including XRPESS and ASAPRatio that are used for the relative quantitation of isotopically labelled peptides and proteins. MaxQuant is a recently developed software suite for the analysis and quantitation of SILAC experiments (Ref. 85). Similarly, Mascot Distiller, from Matrix Science, determines quantitation based on the relative intensities of extracted ion chromatograms for precursors ( This approach can be used for label-free approaches, or with any chemistry that creates a precursor mass shift, for example 18 O, AQUA, ICAT, ICPL, metabolic labelling and SILAC. ProteinPilot, from Applied Biosystems ( provides protein identification and quantitation of SILAC- and itraq-based labels. For label-free approaches, there are many open-source and commercial software packages available, which are discussed in a recent review (Ref. 86). It is worth noting that for the analysis of quantitative proteomic data, no standard procedure has been developed that is broadly applicable to all experiment types. As is evident, many software tools exist, and the user still needs to understand what the software is doing in order to be able to critically analyse the results. Data-mining approaches With the rapid growth in large-scale proteomic experiments comes the generation of longer and longer lists of proteins. However, the sound biological interpretation of these data lags behind (Ref. 77). There are now several analytical strategies and tools available to extract biologically relevant information (e.g. regarding protein protein interactions, signalling pathways and biological networks) from these large proteomic datasets. These data-mining approaches have the potential to contribute to a deeper understanding of biological systems, but need to be applied and interpreted correctly. One of the most powerful tools available, and often the first tool used to conduct analysis on a large dataset, is Gene Ontology (GO) (Ref. 87). This is a controlled vocabulary that is used to standardise the way in which proteins are described across different species and databases. The consistency in terminology that this ontology provides makes it an invaluable resource for both experimentalists and bioinformaticians. GO annotation of a large MS dataset can be used to determine whether there is any enrichment or depletion for a particular GO category, or can be used to compare two different datasets. Pathway and network analysis Another useful approach is pathway analysis, which explores proteomic data in terms of biological pathways, based on known physical and functional interactions between proteins that are present. It is estimated that there are around 300 biochemical pathway analysis tools currently available (Ref. 77), with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome representing the largest databases. Many of the pathway analysis tools are freely available, but there are also some commercially available tools for example, Ingenuity Pathways Analysis from Ingenuity Systems, and GeneGo from GeneGo Inc. With so many pathway analysis options to choose from, Pathguide ( which contains information on about 317 biological pathway tools, is an invaluable resource to help guide users in selecting the most appropriate resource to use (Ref. 88). Pathguide also covers tools that model network and functional interaction information, which takes the data beyond pathway analysis and groups proteins based on participation in larger, multiprotein assemblies. For visualisation of molecular networks, Cytoscape is a useful open-source platform, which also allows integration of genetic and other information (Refs 89, 90). 15

16 There are now several meta-databases for interaction information, including STRING (Refs 91, 92), which generates interaction networks by incorporating data from many curated databases, as well as predicted interactions and pathway information. Data can be input to STRING as protein lists, and it has a user-friendly interface. MiMI, from the National Institute for Integrative Biomedical Informatics, merges data from numerous interaction databases as well as other sources and also has a Cytoscape plug-in to allow easy visualisation of networks (Ref. 93). For all interaction databases, which can have high error rates, care needs to be taken when interpreting information and the source of the interaction information should be checked manually if possible. Meta-data analysis and data integration One of the key challenges currently facing researchers is the integration of all these available data. There are several tools available for metadata analysis of proteomic data, including the database for annotation, visualisation and integrated discovery (DAVID), from the National Institute of Allergy and Infectious Diseases (NIAID), which provides a comprehensive set of functional annotation tools for investigators to understand the biological meaning behind large lists of genes. Other meta-tools include PANTHER (protein analysis through evolutionary relationships, which was designed to classify proteins (and their genes) in order to facilitate high-throughput analysis, and Babelomics, which is a suite of interconnected tools used to functionally annotate genome-scale experiments. Conceptgen, from the National Institute for Integrative Biomedical Informatics, is a web-based tool designed to explore networks of relationships between biological concepts (Ref. 94). The Global Proteome Machine ( a search engine and database for MS/MS data, links the protein identifications directly to annotation resources such as GO and KEGG within the same platform, allowing efficient examination of the GOs and pathways under- or over-represented in a particular dataset. Discussion: biological and clinical applications One of the initial goals of proteomics was the collection of inventories of whole proteomes. By applying subcellular and protein/peptide fractionation approaches, MS-based proteomics has had marked success in this endeavour, especially with the more abundant components. Many of the proteomes relevant to human health research are now well characterised, including those of human blood cells (Refs 95, 96, 97, 98), plasma (Ref. 99), cerebrospinal fluid (Ref. 100), bronchial epithelia (Ref. 101), heart muscle (Refs. 102) and a variety of cancer tissues and cells (e.g. Refs 103, 104, 105). The Normal Clinical Tissue Alliance (NCTA, wiki.thegpm.org/wiki/normal_clinical_tissue_ Alliance) provides high-quality proteomic catalogues of clinically relevant normal human tissues, such as brain and bone, and also bodily fluids, including bronchoalveolar lavage, saliva and urine. Global proteomic analysis can also be a helpful tool to investigate cell subtypes, as shown recently by applying a proteomic approach to demonstrate that the so-called endothelial progenitor cells may actually be monocytes that had taken up platelet microparticles (Ref. 106). However, the real strength behind proteomic approaches lies in the ability to compare and quantitate samples. Formerly, 2D gel analysis was one of the only ways to gain quantitative information on a set of proteins, and although there are still many current publications successfully using this approach, alternative techniques such as isotopic labelling are currently supplanting 2D gels as a preferred quantitation method. Many studies applying these approaches have successfully identified biomarkers with clinical potential. For example, a recent study used a combination of murine cancer models and itraq quantitation to discover a novel, putative biomarker for gastric cancer (Ref. 107) (Fig. 4). The biomarker was then validated in serum from cancer patients. Quantitation is especially important in the study of time-dependent processes, such as the changes that take place during storage of blood before transfusion. The platelet storage lesion has been studied by applying several complementary quantitative proteomic approaches to platelets at days 1 and 7 of storage (Ref. 108). 2D gel electrophoresis/ differential gel electrophoresis (DIGE), itraq and ICAT were used, resulting in 503 protein changes identified over the course of storage, the majority of which were identified using the itraq method. Despite this, the benefit of 16

17 a b ITIH3 Control (No tumor) In vivo cell culture itraq replicate 1 Low tumour burden Blood collection via cardiac puncture Abundant proteins depletion of plasma samples itraq replicate 2 itraq replicate 3 LC-MS/MS analysis LC-MS/MS analysis LC-MS/MS analysis Cancer Status 2-sample t-test P < n = 167 Normal MKN45 c Sensitivity Mid tumour burden 0.00 High tumour burden Sensitivity: 96% Specificity: 66% Cut-off: Specificity Discovery of the potential biomarker ITIH3 for early detection of gastric cancer Expert Reviews in Molecular Medicine 2010 Published by Cambridge University Press Figure 4. Discovery of the potential biomarker ITIH3 for early detection of gastric cancer. (See next page for legend.) using multiple quantitative proteomic approaches was evident, as less than 16% of the 503 proteins were identified by two or more proteomic approaches and only five proteins were identified by all approaches. Combining the technologies discussed in this review is a good way of utilising the power of MS-based proteomics for targeted clinical studies. For example, tandem affinity purifications, GeLC-MS/MS and itraq quantitation have been used to map the interactome of the drug target BCR ABL, a tyrosine kinase causing chronic myeloid leukaemia (Ref. 109). A tightly bound cohort of 17

18 Figure 4. Discovery of the potential biomarker ITIH3 for early detection of gastric cancer. (See previous page for figure.) (a) A mouse xenograft model was used to identify putative biomarkers for gastric cancer. Tumours were induced in mice with the human gastric cancer cell line MKN45 and mice were categorised according to tumour burden [low (length, L = 1 2 mm, volume, V = 2 3mm 3 ); mid (L = 7.5 mm, V = mm 3 ); high (L = 15 mm, V = mm 3 )]. Plasma from these mice and a control group was labelled with four different itraq labels and studied by LC-MS/MS. Triplicates were performed to obtain high-quality data. (b) Thirty-one proteins were identified as putative biomarkers, and the presence of one of these proteins, ITIH3, was analysed in serum derived from healthy humans (normal) and gastric cancer patients (cancer) by immunoblotting. ITIH3 levels were found to be elevated significantly (P-value <0.001) in cancer patients. (c) An ROC curve was generated using the data from (b) to estimate the accuracy of ITIH3 detection in gastric cancer detection. Sensitivity was determined to be very high, at 96%, whereas specificity was slightly lower, at 66%. The area under the ROC curve was found to be 0.86 (with 0.5 being a useless and 1.0 a perfect test), which implies that ITIH3 could be a valuable biomarker in early gastric cancer detection. Figure adapted with permission from Ref. 107 ( 2010 American Chemical Society). Abbreviations: itraq, isobaric tags for relative and absolute quantitation; LC-MS/MS. liquid chromatography tandem mass spectrometry; ROC, receiver operating characteristics. interconnected proteins around BCR ABL, which remodels on inhibitor treatment, was found, suggesting that the effect of the drugs is caused by a remodelling of the BCR ABL complex instead of a simple inhibition of the protein itself. In a complementary approach, novel kinase and nonkinase targets of three BCR ABL inhibitors were discovered utilising GeLC-MS/MS, itraq quantitation and IMAC phosphopeptide enrichment, showing that these MS-based experiments are a valuable tool to discover and study additional drug targets (Ref. 110). However, the clinical application of MS-based proteomics still faces several challenges (Ref. 111). Certain experiments and many putative clinical applications require the analysis of very small amounts of cells ( ). MS is a very sensitive method and in principle allows the analysis of single cells (Ref. 112). However, one major problem in the analysis of few cells lies in the standard sample preparation and digestion protocols used for MS-based proteomics, during which a high percentage of the sample can be lost (Ref. 111). An optimised lysis and digestion method was developed to address this problem, which is performed in one tube. Furthermore, by optimising the parameters of the LC-MS/MS system for the analysis of small amounts of cells it was possible to analyse as few as 500 cells, from which 167 proteins were identified (Ref. 113). Another problem still faced by the proteomic community is accessibility to low-abundance proteins, particularly in the presence of high-abundance proteins, such as in the analysis of serum. Depletion methods can be used to remove these proteins in order to investigate lower abundance proteins; however, some peptides and proteins bind to these carrier proteins and are discarded through this procedure. As an alternative approach, a differential solubilisation method was developed to enrich for low-abundance proteins in plasma. By analysing these enriched fractions with high-quality MALDI-TOF (time of flight), more than 1500 peptides from a 1 μl serum sample were identified and four new potential colon cancer biomarkers were discovered (Ref. 114). This approach has the potential to greatly contribute to the discovery of novel low-abundance biomarkers. One aspect that is especially important for the analysis of biomarkers in serum is reproducibility: it has been shown that serum proteins are degraded by endogenous proteases shortly after a blood draw, leading to varying results. However, the addition of protease inhibitors to the blood drawing tubes can counteract this effect and stabilise serum proteins (Ref. 115). As outlined in this article, MS-based proteomic approaches are now applied to many diverse aspects of clinical research, some of which are highlighted in Table 3, and the ultimate hope is for the development of diagnostic and prognostic tools that will benefit human health. As more potential biomarkers move from the discovery phase towards clinical trials, there is the need for accurate statistical and mathematical analysis of the data, in order to better determine key outcomes, for example precision and accuracy, using standardised tests such as positive predictive value (Ref. 116). 18

19 Application Table 3. Recent examples of clinical applications of proteomics Risk determination (discovery of biomarkers for disease risk) Early detection (discovery of biomarkers to aid in early diagnosis of disease) Verification/quantitation of biomarkers (validating previously discovered biomarkers, quantifying biomarkers, developing more broadly applicable assays to detect biomarkers) Proteomic classification of disease (establishing biomarker panels, determining the proteomic profile/ proteomic signature of disease) Characterisation of disease [identifying altered proteins or signalling pathways in disease, characterising disease progression, (sub)classification of disease] Cataloguing proteomes of diseased tissues/cells (creating catalogues of proteins identified in either normal or diseased tissues or cells) Examples A serological proteome (SERPA) approach was used to identify autoantibodies in melanoma patients, by identifying positive reactions between patients sera and proteins isolated from a G361 melanoma sample (Ref. 150) N-glycoproteins from enriched membranes of breast cancer cell lines were analysed to generate a set of potential biomarkers (Ref. 29) Plasma samples from gastric cancer patients were screened for a protein found to be highly expressed in a mouse model of gastric cancer; it was identified as a potential biomarker expressed in earlystage gastric cancer (Ref. 107) 2D gel separation and MS was applied to identify a protein that was significantly upregulated in hepatocellular carcinoma and elevated in plasma of patients, which could be used to detect early stages of the disease (Ref. 151) SISCAPA and SRM-MS were used to quantify TIMP1, a colorectal cancer biomarker, from patients sera at attomolar concentrations (Ref. 71) A broadly applicable multiplexed, MS-based assay was used to verify and quantify changes of biomarker proteins associated with cardiac injury in the low ng/ml range (Ref. 69) Isotope-labelled synthetic peptides and SRM was applied to screen CRP, a candidate biomarker for rheumatoid arthritis, in small volumes of human serum depleted of major plasma proteins (Ref. 64) SELDI-TOF-MS ProteinChip technology identified and tested a serum profile for distinguishing hepatocellular carcinoma and liver cirrhosis, and showed it could be a better diagnostic tool than a previously established marker (Ref. 152) An MS fingerprint based on three MALDI-TOF MS peaks was identified that specifically separated patients with rheumatoid arthritis from healthy controls (Ref. 153) Genomic and proteomic markers of mild and moderate/severe chronic allograft nephropathy in peripheral blood that could be used to predict graft loss were identified (Ref. 154) Phosphotyrosine affinity columns and SILAC were used to identify and quantitate proteins dependent on SYK signalling in human cancer cells (MCF7) in order to elucidate the role of this protein in tumour formation and progression (Ref. 43) A novel application of SILAC was used to identify significant differences in expression of focal adhesion and glycolytic proteins between lobular and ductal tumors (Ref. 51) The protein protein interaction network of the tyrosine kinase BCR- ABL, implicated in myeloid leukaemia, was charted using affinity purification and MS (Ref. 109) Differential expression analysis of human colorectal cancer cells in an in vitro model system was used to examine progression from adenoma to carcinoma (Ref. 104) 2D separation, DIGE and SERPA were applied to construct a protein expression database for human non-small-cell lung cancer (Ref. 103) Proteomic and genomic profiles of airway epithelial cells from never and current smokers were generated and correlated (Ref. 101) (continued on next page) 19

20 Table 3. Recent examples of clinical applications of proteomics (continued) Application Uncovering the effects of drugs or potential drug targets (comparing treated versus untreated samples to determine the mechanism of action of a therapeutic agent, uncovering potential drug targets) Monitoring disease progression treatment response/prognostic markers (assessing/monitoring disease progression, monitoring response of patients to treatment, determining patient prognosis) Examples Chemical proteomics together with immunoaffinity purification of tyrosine-phosphorylated peptides identified nearly 40 different kinase targets of the SRC-family kinase inhibitor dasatinib (Ref. 33) Plasma proteome changes were investigated in ALS patients before and during immunisation with glatiramer acetate in a clinical trial (Ref. 15) A chemical proteomics affinity purification approach was used for quantitative profiling of the targets of the drugs imatinib, dasatinib and bosutinib (Ref. 110) SELDI-TOF MS was used to profile and compare the serum of responding and nonresponding patients with metastatic colorectal cancer to identify biomarkers that could predict treatment response and be used for monitoring (Ref. 155) An independent prognostic factor was identified for disease-free survival and overall survival in patients with serous ovarian cancer (Ref. 156) Analysis of blood samples from patients with biopsy-confirmed acute renal allograft rejection, chronic rejection and stable graft function was used to establish serum peptidome fingerprints and aid in the early diagnosis of renal allograft rejection (Ref. 157) A novel prognostic marker for distant metastasis in non-small-cell lung cancer was identified by cancer cell secretome pleural effusion proteome analysis (Ref. 158) Abbreviations: ALS, amyotrophic lateral sclerosis; CRP, C-reactive protein; 2D, two dimensional; DIGE, difference gel electrophoresis; MALDI, matrix-assisted laser desorption/ionisation; MS, mass spectrometry; SELDI, surface-enhanced laser desorption/ionisation; SERPA, serological proteome; SILAC, isotope labelling of amino acids in cell culture; SISCAPA, stable isotope standards and capture by antipeptide antibodies; SRM, selected reaction monitoring; TIMP1, tissue inhibitor of metalloproteinase 1; TOF, time of flight. Conclusion The developments discussed above point to the future potential of the proteomic approach for the exploration of key questions in basic and clinical research as well as in establishing tools for clinical diagnostics, while emphasising the continued importance of technology and method development in pushing the boundaries of MS-based proteomics. The development of high-throughput technologies such as the recently established SRM assays for mapping the kinases and phosphatases of Saccharomyces cerevisiae makes the quantitative analysis of whole proteomes more realistic (Ref. 56). A major movement already under way is the Human Proteome Project, which will be formally launched at the HUPO 2010 congress and plans to map the human proteome in a manner analogous to the mapping of the human genome. Three different experimental approaches are proposed to achieve this: MSbased proteomics to identify and quantify proteins in tissues and cells, generation of antibodies against each protein to show cellular location, and systematic identification of interactors for every protein. This ambitious project requires established standards for proteomics-based profiling, antibody-based profiling and network-based profiling, as well as a massive bioinformatics effort to analyse, archive and make available the ensuing data (Ref. 117). With a projected ten-year time line, the Human Proteome Project includes a clear clinical focus, with its stated aim being the creation of a resource immediately available to the clinical and basic science communities in a format that assures fundamental discoveries and insight into diagnostic and treatment regimens for the patient (Ref. 117). The aspirations of the Human Proteome Project emphasise the hoped-for impact of proteomics on human health research and highlight once 20

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27 154 Kurian, S.M. et al. (2009) Biomarkers for early and late stagechronicallograftnephropathybyproteogenomic profiling of peripheral blood. PLoS One 4, e Helgason, H.H. et al. (2010) Identification of serum proteins as prognostic and predictive markers of colorectal cancer using surface enhanced laser desorption ionization-time of flight mass spectrometry. Oncology Reports 24, Li, Y.L. et al. (2010) Identification of glia maturation factor beta as an independent prognostic predictor for serous ovarian cancer. European Journal of Cancer 46, Further reading, resources and contacts 157 Sui, W. et al. (2010) Proteomic profiling of renal allograft rejection in serum using magnetic beadbased sample fractionation and MALDI-TOF MS. Clinical and Experimental Medicine Apr 8; [Epub ahead of print] 158 Wang, C.L. et al. (2009) Discovery of retinoblastoma-associated binding protein 46 as a novel prognostic marker for distant metastasis in nonsmall cell lung cancer by combined analysis of cancer cell secretome and pleural effusion proteome. Journal of Proteome Research 8, Protein and peptide separation techniques Delahunty, C. and Yates, J.R., 3rd (2005) Protein identification using 2D-LC-MS/MS. Methods 35, Describes in detail the strong cation exchange/reversed-phase method that is widely used for 2D LC-MS/MS analysis. Fang, Y., Robinson, D.P. and Foster, L.J. (2010) Quantitative analysis of proteome coverage and recovery rates for upstream fractionation methods in proteomics. Journal of Proteome Research 9, Rigorously compares the three most commonly used protein-level first-dimension separation techniques CF, IPG and GeLC. Biochemical fractionation methods Markham, K., Bai, Y. and Schmitt-Ulms, G. (2007) Co-immunoprecipitations revisited: an update on experimental concepts and their implementation for sensitive interactome investigations of endogenous proteins. Analytical and Bioanalytical Chemistry 389, Deals with immunoprecipitations and MS to identify interaction partners and mentions several valid guidelines to avoid possible pitfalls. Rogers, L.D. and Foster, L.J. (2009) Phosphoproteomics finally fulfilling the promise? Molecular Biosystems 5, An in-depth review about the development of phosphoproteomics in recent years. Dengjel, J., Kratchmarova, I. and Blagoev, B. (2009) Receptor tyrosine kinase signaling: a view from quantitative proteomics. Molecular Biosystems 5, An overview about recent approaches to study receptor tyrosine kinase signalling using MS-based proteomics. Quantitative approaches Elliott, M.H. et al. (2009) Current trends in quantitative proteomics. Journal of Mass Spectrometry 44, A thorough and well-written synopsis of the current state of quantitative proteomics, which discusses the strengths and weaknesses of the methods in more depth than they have been covered here. Targeted analysis: SRM Lange, V. et al. (2008) Selected reaction monitoring for quantitative proteomics: a tutorial. Molecular System Biology 4, 222 An informative tutorial explaining many aspects of SRM, including transition design and optimisation as well as the application of SRM for quantitative proteomics. Yocum, A.K. and Chinnaiyan, A.M. (2009) Current affairs in quantitative targeted proteomics: multiple reaction monitoring-mass spectrometry. Briefings in Functional Genomics and Proteomics 8, A useful review on SRM that includes advice on method development as well as a section on other MSbased targeted approaches. (continued on next page) 27

28 Further reading, resources and contacts (continued) Issaq, H.J. and Veenstra, T.D. (2008) Would you prefer multiple reaction monitoring or antibodies with your biomarker validation? Expert Reviews of Proteomics 5, An engaging discussion of the possible applications of SRM in clinical biomarker validation assays. Bioinformatic analysis Malik, R. et al. (2010) From proteome lists to biological impact tools and strategies for the analysis of large MS data sets. Proteomics 10, An excellent review of available tools to extract biologically relevant information from large proteomic datasets. Wang, M. et al. (2008) Label-free mass spectrometry-based protein quantification technologies in proteomic analysis. Briefings in Functional Genomics and Proteomics 7, An overview of available label-free MS-based approaches to protein quantification. Features associated with this article Figures Figure 1. Workflow of typical MS-based proteomic experiments. Figure 2. Workflows for implementation of stable isotope labelling via metabolic (SILAC) and chemical (itraq or TMT) methods. Figure 3. Mode of operation of selected reaction monitoring (SRM). Figure 4. Discovery of the potential biomarker ITIH3 for early detection of gastric cancer. Tables Table 1. MS quantitation techniques. Table 2. Examples of selected reaction monitoring (SRM) resources. Table 3. Recent examples of clinical applications of proteomics. Citation details for this article Geraldine M. Walsh, Jason C. Rogalski, Cordula Klockenbusch and Juergen Kast (2010) Mass spectrometrybased proteomics in biomedical research:. Expert Rev. Mol. Med. Vol. 12, e30, September 2010, doi: /s