Mapping in vivo signal transduction defects by phosphoproteomics

Review

Mapping in vivo signal transduction defects by phosphoproteomics Taras Stasyk and Lukas A. Huber Biocenter, Division of Cell Biology, Innsbruck Medical University, Fritz-Pregl Strasse 3, A-6020 Innsbruck, Austria

Abnormal protein phosphorylation is implicated in a variety of diseases, but until recently the complexity of tissue material, technical limitations, and the substantial volume of required data processing did not allow large-scale phosphoproteomic analysis of patient material, despite tremendous progress in developing mass spectrometry technologies. Phosphoproteomic approaches were primarily developed using model systems such as transformed cell lines, but technological advances in proteomics now make it feasible to analyze thousands of phosphorylation sites in a quantitative manner in patient materials or complex animal and cellular model systems to identify signaling abnormalities. This review summarizes very recent phosphoproteomic studies on complex tissue material, including tissue samples in biobanks, to complement recent reviews that focus primarily on technical advances in instrumentation and methods. Several successful examples reviewed here suggest it is now possible to apply phosphoproteomic techniques to address more challenging medical questions such as mapping within patient samples signal transduction defects that are relevant for diagnosis and individualized treatment development. Recent advances in the phosphoproteomic analysis of transformed cell lines Phosphorylation is a collection of nine post-translational protein modifications (PTMs), out of approximately 500 that are known (563 entries in the RESID Database, September 2011 release; http://www.ebi.ac.uk/RESID/), which regulates many crucial functions of living organisms. Phosphorylation is one of the most abundant PTMs (more than 200 000 phosphosites are estimated to be present in mammalian cells [1]) and is clearly the most studied. Among 87 702 experimentally identified PTMs currently present in the high-quality, manually curated Swiss-Prot database phosphorylation is the largest group (57 403), dominating by an order of magnitude any other PTM [e.g. acetylation (6663) or N-linked glycosylation (5446)], according to the PTMCuration resource (http:// selene.princeton.edu/PTMCuration) [2]. It is a small PTM that makes the local charges of amino acid residues more acidic, most commonly the amino acids serine, threonine and tyrosine, but at least six other residues have been shown to be phosphorylated (histidine, cysteine, arginine, lysine, aspartate, and glutamate [3]). The activity

Glossary Collision-induced dissociation (CID): is fragmentation of peptides during tandem MS analysis via collisions with inert gas molecules. Phosphopeptide MS/MS spectra frequently lack sufficient information to allow for assignment of phosphorylation sites within the peptide sequence due to the loss of labile phosphogroups from these events. Dimethyl labeling: is a triplex stable isotope chemical labeling strategy, which labels the N terminus and amino groups of lysines, producing a 4 Da difference for each derivatized isotopic pair. Electron transfer dissociation (ETD): is fragmentation based on the transfer of an electron. ETD fragmentation produces reporter ions of lower intensity that are not ideal for accurate quantitation. Electrostatic repulsion-hydrophilic interaction chromatography (ERLIC): anionic phosphopeptides can be selectively retained on a weak anion exchange (WAX) column while at the same time other peptides are electrostatically repulsed by the column. Filter aided sample preparation (FASP): a method which utilizes a high concentration of sodium dodecyl sulfate (SDS) for efficient protein extraction. The FASP technique efficiently exchanges SDS for urea in a centrifugal ultrafiltration unit, followed by protein digestion and elution of high purity MScompatible peptides. High-energy collisional dissociation (HCD): the MS/MS spectrum is analyzed with high mass accuracy in the Orbitrap mass analyzer. Hydrophilic interaction liquid chromatography (HILIC): phosphopeptides can be selectively retained using this method due to their relatively high hydrophilicity. Immobilized metal affinity chromatography (IMAC): negatively charged phosphate groups interact with positively charged metal ions, such as Fe(III), chelated onto a solid resin. Isobaric tag for relative and absolute quantification (iTRAQ): is a method that attaches a chemical group to primary amino groups (the N terminus and lysine side chains) in a sample after culturing or collection and allows for comparison of two to eight different samples. The tags used are all the same mass (isobaric), and the differential labeling is detected in the MS/MS fragmentation spectra. Isotope coded protein labeling (ICPL): is based on stable isotope tagging at the free amino groups of intact proteins of up to four different samples. Label-free quantitation: is a method that relies on the integration of the total MS signal of each eluting peptide and compares the values between different LC-MS/MS runs. Liquid chromatography (LC): is used in proteomics to separate peptides in complex mixtures before mass spectrometry analysis. Mass spectrometry (MS): is an analytical technique for the identification of the composition of compounds based on the mass-to-charge ratios of charged particles. Metal oxide affinity chromatography (MOAC): similar to IMAC, a metal oxide that interacts with phosphate groups is used to preferentially isolate phosphorylated peptides. Currently, titanium dioxide (TiO2) is the most popular metal oxide resin used to enrich phosphopeptides. Sequential elution from IMAC (SIMAC): a method that combines the complementary strengths of IMAC with TiO2 chromatography for separation of monophosphorylated from multiply phosphorylated peptides. Strong anion exchange chromatography (SAX): negatively charged phosphopeptides are isolated based on their attraction to a positively charged solid support. Strong cation exchange chromatography (SCX): at low pH phosphopeptides are enriched in the earlier eluted fractions due to the negatively charged phosphogroups, which have lower affinity for the negatively charged solid support. Stable isotope labeling by amino acids in cell culture (SILAC): is a sample preparation method that allows a quantitative comparison of peptide composition between two conditions by MS. Tandem mass spectrometry (MS/MS): combines a first round of analysis (MS1) for the detection and selection of precursor ions, which are then further

Corresponding author: Huber, L.A. ([email protected]) 1471-4914/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.molmed.2011.11.001 Trends in Molecular Medicine, January 2012, Vol. 18, No. 1

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Review fragmented and analyzed by a second round of analysis (MS2). LC-MS/MS is a common analytical technique where liquid chromatography is coupled to tandem mass spectrometry. Tandem mass tags (TMTs): are similar to iTRAQ tags. Using the TMT tags one can compare two to six different samples.

state of proteins often depends on the phosphorylation status of specific amino acid sites. Owing to these changes in local environment, phosphorylation modulates protein complex formation, protein stability and enzyme activity, as well as controlling signal transduction in response to extracellular and intracellular stimuli. Crucial cellular functions utilizing signal transduction pathways, such as progression through the cell cycle, are dependent on cycles of phosphorylation and dephosphorylation. Because abnormal protein phosphorylation has been implicated in many human diseases [4], including cancer [5], diabetes [6], autoimmune diseases [7,8], cardiovascular diseases [9] and neurodegenerative disorders such as Alzheimer’s disease [10], a majority of current drugs are designed to be kinase specific (for a review see [11]). It is probable that a large number of unknown phosphorylation motifs are involved in many more diseases. Unraveling the identity of these sites is therefore a main aim of most phosphoproteomic studies because they might provide clues to signaling defects when differentially regulated in patients. By reviewing key publications of the past few years in the field of in vivo phosphoproteomics, we would like to draw the attention of the scientific community to new approaches for use in studying human diseases that will increase the impact of phosphoproteomics on medical research. Detailed analysis of different, but in principle equally important, steps of phosphoproteomics analysis are out of the scope of this review. Technological advances in sample preparation, phosphopeptide enrichment methods, chromatographic sample fractionations, introduction of the latest generation of high-resolution mass spectrometers, as well as accompanying computational tools have been extensively reviewed elsewhere [12–17]. Recent advances in mass spectrometry (MS)-based phosphoproteomics techniques allow analysis of phosphorylation-driven signal transduction in a reproducible and quantitative manner with high-confidence of phosphosite identification [18]. Although mass spectrometers can detect peptides present in low attomolar concentrations, they are limited in the dynamic range of detection (e.g. only approximately 16% of 100 000 features, probably representing peptides according to stringent filtering criteria, had been targeted by MS/ MS (see Glossary) in a recent technical report [19]). Most phosphosites are present at substoichiometric levels; therefore, to be detectable, phosphopeptides must be enriched before MS analysis. Several affinity purification techniques have been developed and successfully applied to isolate phosphopeptides from complex mixtures, including those based on antiphosphotyrosine antibodies, immobilized metal affinity chromatography (IMAC) and metal oxide affinity chromatography (MOAC) [20–22]. Titanium dioxide (TiO2)-based enrichment emerged recently as an excellent option, being more selective for phosphopeptides versus non-phosphorylated peptides. The selectivity of 44

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phosphopeptide enrichment is not absolute for all of the above-mentioned techniques, therefore non-phosphorylated peptides of similar properties, mainly containing acidic residues (glutamic and aspartic acids), histidine residues, or sialic acid containing glycopeptides, interfere with enrichment strategies [20,23]. Although IMAC and TiO2-based enrichment are proven efficient methods for purification of phosphopeptides, prefractionation of complex protein samples is still an indispensible requirement for phosphoproteome analysis. Several chromatographic methods such as strong cation (SCX) [24] and anion exchange chromatography (SAX) [25], hydrophilic interaction liquid chromatography (HILIC) [26] and electrostatic repulsion-hydrophilic interaction chromatography (ERLIC) [27] have been used for fractionation of phosphopeptides. For instance, a commonly used strategy is the combination SCX for fractionation and TiO2 enrichment. A comparison of different chromatographic strategies by Zarei et al. [28] coupled to a phosphopeptide enrichment prior to MS analysis revealed that the SCX-TiO2 approach identified a higher number of phosphopeptides, whereas the ERLIC-TiO2 approach was better suited for the identification of multi-phosphorylated peptides. Having been established for large-scale in vitro phosphoproteomics experiments, the above-mentioned fractionation approaches have to be downscaled for phosphopeptide enrichment from tissue patient samples. Analyzing the phosphoproteome of a limited amount of tissue samples is currently a challenging task. Recently, Di Palma et al. described [29] a HILIC-based approach in combination with reversed phase (RP) in an off-line twodimensional liquid chromatography (2D-LC) strategy. Interestingly, although no phosphopeptide enrichment was used here, nearly 900 phosphopeptides (of more than 20 000 unique peptides from approximately 3500 proteins) were identified in low amounts of starting material (1.5 mg of lysate), making this fractionation approach a major step towards more sensitive analysis. Technological developments in MS with a special focus on phosphoproteomics have been recently reviewed elsewhere [12,14,30–33]. The most common quantification strategies are based on stable isotope labeling by amino acids in cell culture (SILAC) [34]. Alternatives to this form of metabolic incorporation of labels in vitro can also be applied, such as the use of isobaric tags for relative and absolute quantitation (iTRAQ) [35], tandem mass tags (TMTs) [36], isobaric peptide termini labeling (IPTL) [37], non-isobaric isotope-coded protein label (ICPL) [38] or dimethyl labeling [39]. The main advantages and limitations of current methods for quantitative proteomics are summarized in Table 1. In addition, recent remarkable analytical advances are making phosphoproteomics a widely accepted and more robust approach than previously. Interestingly, in the vast majority of quantitative phosphoproteomics studies cell lines were the samples being analyzed. There are several reasons: (i) it is relatively easy to generate a large amount of starting material; (ii) isotopically labeled amino acids can easily be incorporated during cell culture; and (iii) a variety of well-characterized as well as novel ligands, such as growth factors, cytokines,

Review

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Table 1. Quantification techniques for in vivo phosphoproteomics Quantification method SILAC

Advantages

Limitations

- Minimal sample preparation error due to sample pooling at the level of cells - Accurate quantification - High throughput

Super-SILAC

- Applicable to human tissue material - Minimal experimental error due to early sample mixing - The depth and quantitative accuracy are similar to SILAC - High throughput - Low cost - The same advantages as for superSILAC - Signaling-specific phosphopeptide standards can be generated

- Limited to in vivo metabolically labeled animal models, such as mice [45], flies [46] and newts [47], and to primary cells, which can survive 2–3 weeks of in vitro culturing - An increase in sample complexity - Only two to three conditions may be directly compared - Cost-prohibitive - Different tissue-specific superSILAC mixtures are needed - Quantification is limited to proteins present in super-SILAC mixtures

Spike-in SILAC

iTRAQ/TMT

Dimethyl labeling

Label-free quantitation

2DE-based approaches (multiplexed proteomics technology, DIGE)

- Up to eight conditions without increasing sample complexity - Broad application (labeling samples after protein digestion) - Accurate quantification - Peptide identification rates are superior for iTRAQ 4-plex, followed by TMT 6-plex and then iTRAQ 8-plex [61] - Cost-effective alternative to more expensive chemical labeling approaches - Can be incorporated prior to phosphopeptide enrichment - Broad applicability - Can be used to compare the large number of primary samples needed for clinical studies - No chemical or metabolic labeling - Broad applicability

Original publication Ong et al. (2002) [34]

In vivo application

Geiger et al. (2010) [54]

Breast tumor tissues Geiger et al. (2010) [54]

- Quantification is limited to proteins present in standard - For heterogeneous tissues a superSILAC strategy might be more appropriate - Additional variations from the labeling procedure - Labeling reduces the number of identified phosphopeptides, which was improved recently [62] - May be cost-prohibitive

Geiger et al. (2011) [49]

Insulin signaling in mouse liver Monetti et al. (2011) [50]

Ross et al. (2004) [35] Thompson et al. (2003) [36]

Primary murine T cells Iwai et al. (2010) [6]

- Allows the comparison of only three samples in parallel - Differences in chromatographic elution can occur, deuterated species are slightly more hydrophilic - Highly reproducible and highresolution chromatography and MS are absolutely required

Hsu et al. (2003) [77], Boersema et al. (2008) [39]

Hepatocellular carcinoma Song et al. (2011) [78]

Spectral TIC method Asara et al. (2008) [79]

Postmortem human brain tissue Herskowitz et al. (2010) [67]

Steinberg et al. (2003) [80]

Postmortem human hippocampus of Alzheimer’s disease patients Di Domenico et al. (2011) [81]

- Limited to detection of a few hundred most abundant proteins - Low throughput - Poor recovery of hydrophobic membrane proteins - Low dynamic range

specific inhibitors and other drugs are available and easily applied to study cellular signaling in great detail (e.g. during time course experiments). SILAC-based quantitative phosphoproteomic analyses using high-resolution MS have been performed to study several signaling pathways, including the interleukin 2 (IL-2) signaling cascade involved in T cell proliferation, differentiation and apoptosis [40], b arrestin-mediated signaling downstream of seven transmembrane receptors [41], transforming growth factor b (TGF-b) signaling [42], epidermal growth factor (EGF) and heregulin (HRG) signaling [43], as well as insulin signaling [44]. Current methods were developed using model systems such as transformed cell lines and the key publications in the history of quantitative phosphoproteomics have been recently reviewed elsewhere [30]. These methods have matured and are now ready to be

Primary macrophages Weintz et al. (2010) [48]

applied in more advanced approaches to address specific medical questions such as the identification of signaling defects in the limited and complex material of patient samples. The leading publications on in vivo phosphoproteomics are discussed below. Analysis of primary cells and human materials Direct SILAC-labeling cannot be applied to patient tissue samples and is therefore limited to in vivo metabolically labeled animal models (Figure 1), such as mice [45], flies [46] and newts [47]. Additionally, classical SILAC is applicable to primary cells, which can survive the 2–3 weeks of in vitro culturing to incorporate isotopically labeled metabolic components into the proteome. A recent publication by Weintz et al. [48] demonstrates the feasibility of using SILAC-based approaches followed by TiO2-based 45

Review

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In vivo phosphoproteomics Primary cells

Human tissues, b biobanks

Model organisms

Representative cell lines SILAC labeled heavy isotope

One cell line SILAC labeled heavy isotope

SILAC (limited)

Control Stimulated

Super-mix as internal standard

Mix as internal standard

Mix 1:1 SuperSILAC

Mix 1:1 Spike-in SILAC

P

R

O

T

E

I

N

S

Digestion (trypsin)

P

E

P

T

I

Chemical labeling (iTRAQ, TMT)

D

E

S

Fractionation (SCX, SAX, HILIC, ERLIC)

P

E

P

T

I

D

E

S Phosphopeptide enrichment (TiO2/ IMAC/ SIMAC)

Label-free quantitation

P

H

O

S

P

H

O

-

P

E

P

T

I

D

E

S

High-resolution mass spectrometry TRENDS in Molecular Medicine

Figure 1. Methods for quantitative in vivo phosphoproteomic analyses. The most advanced quantification strategies are based on stable isotope labeling by amino acids in cell culture (SILAC). Classical SILAC is limited to primary cells, such as primary macrophages, which can survive a few weeks in culture to incorporate stable isotopes [48]. A spike-in SILAC methodology relies on quantitation of in vivo protein samples in comparison to a SILAC-labeled internal standard and is applicable to any type of signaling analysis and to any organism or tissue. For instance, SILAC-labeled phosphorylated standard mixtures of liver cell lines stimulated or not with insulin can be used to study in vivo insulin signaling in liver [49]. In more complex tissue materials such as human tumor biopsies, a super-SILAC mix, which combines phosphoproteomes isolated from several cell lines most accurately representing the tissue under investigation, can be applied (e.g. a mixture of different SILAC-labeled breast cancer cell lines to study breast cancer) [54]. Alternately, in vitro stable isotope labeling methods, such as isobaric tags for relative and absolute quantitation (iTRAQ) and label-free quantitation strategies can be applied. Before performing quantitative high-resolution MS analysis phosphopeptides have to be enriched using affinity methods such as metal oxide chromatography. The blue line in the upper part of the figure represents the samples and the green line represents the addition of SILAC-labeled standards.

phosphopeptide enrichment and high-resolution MS to study changes in phosphorylation within primary macrophages. This systems level quantitative analysis of the phosphoproteome of lipopolysaccharide (LPS)-activated bone marrow-derived macrophages identified more than 1800 phosphoproteins comprising nearly 7000 phosphorylation sites, two-thirds of which were previously unknown. A combination of bioinformatic analyses identified canonical pathways associated with toll-like receptor (TLR) signaling as well as novel signaling pathways, including those involving the cytoskeleton (e.g. actin binding proteins coronin, filamin and palladin) and the kinases 46

mammalian target of rapamycin (mTOR), PI3K/AKT and ATM/ATR. This phosphoproteomics study provides a global outlook of innate immune activation by TLR signaling in primary macrophages. The limitation of SILAC-labeling to cultured cells prompted the development of new SILACbased in vivo approaches. Spike-in SILAC method to study in vivo signaling The Mann group recently reported a spike-in SILAC methodology that relies on SILAC-based quantitation but uses internal standards for comparison of in vivo protein samples [49,50]. As standards for the spike-in SILAC approach,

Review heavy isotope-labeled phosphopeptides are derived from cell line(s) similar to the most prominent cell types from the tissue of interest (e.g. hepatoma cell lines to study in vivo signaling in liver) and equimolar amounts are added to all of the samples to be analyzed, thereby generating heavy-to-light ratios during MS analysis. Normalized ratios between different samples (e.g. untreated versus treated or patient samples versus healthy control samples) allow accurate comparison of phosphopeptide abundance. As a proof-of-principle, this technique was used to study insulin signaling in mouse liver tissue by utilizing metabolically labeled mouse hepatoma Hepa1–6 cells with heavy forms of lysine and arginine as the standard [49]. A representative set of insulin-dependent and insulin-independent phosphosites in the standard was achieved by mixing equal parts of total protein lysates prepared from cells untreated and cells stimulated with insulin. Importantly, to detect in vivo signaling defects, an internal standard for in vivo measurements has to be a mixture of cell lines, unstimulated and stimulated (using, for example, the growth factor of interest), and as similar in composition as possible to the in vivo situation for a tissue under analysis. One has to emphasize another important step in sample preparation that significantly contributed to the general success of this approach. The authors used the filter aided sample preparation (FASP) method [51], based on a method reported by Manza et al. [52], which utilizes a high concentration of sodium dodecyl sulfate (SDS) for efficient protein extraction while still producing MS-compatible pure peptides. The FASP technique efficiently exchanges SDS for urea in a centrifugal ultrafiltration unit, followed by protein digestion and elution of high purity peptides (Box 1). Peptides were fractionated by strong cation exchange chromatography (six fractions), and the phosphopeptides were enriched using TiO2 beads and separated by reverse phase chromatography coupled to a linear ion trap Orbitrap Velos mass spectrometer with very high sequencing speed. By acquiring both MS and MS/ MS scans measurements in the Orbitrap analyzer, partsper-million range mass accuracy for both precursor and MS/MS fragments was obtained, enabling identification of 15 000 phosphosites in mouse liver and quantitative comparison of 10 000 sites regulated by insulin treatment. Analysis of the MS data using the freely available MaxQuant software [53] generated the largest quantified in vivo dataset to date [50]. Importantly, the depth and quantitative accuracy of this particular in vivo experiment were similar to SILAC-based cell culture experiments. The spike-in SILAC method for in vivo phosphoproteome analyses has several advantages over other quantification strategies listed in Table 1: (i) mixing the standard with the sample at the beginning of processing largely avoids technical errors originating from a lack of reproducibility during parallel phosphopeptide enrichment of different samples; (ii) the standard sample, when carefully chosen, represents tens of thousands of phosphopeptides present in close to in vivo abundance, which is the prerequisite for accurate quantification; (iii) theoretically, peptides carrying any PTM can be enriched from spiked-in SILAC digests for subsequent MS-based analysis; and (iv) the technology is applicable to patient material.

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Box 1. Points to consider before in vivo phosphoproteomic analysis  Sample preparation is crucial for successful phosphoproteomics. Snap freezing tissues preserves phosphorylation. Phosphatase and protease inhibitors are mandatory.  A high concentration of sodium dodecyl sulfate (SDS) might be beneficial for protein extraction from complex tissue material as part of the filter aided sample preparation (FASP) method. For phosphoproteomics of formalin-fixed and paraffin-embedded (FFPE) material, the typical tissue samples in biobanks, boiling in SDS combined with FASP sample preparation method is necessary (FFPE–FASP approach).  Several quantitative methods are available (spike-in SILAC, iTRAQ, label-free quantitation). Please see Table 1 for a comparison of advantages and limitations of existing techniques.  The phosphopeptide enrichment step must be optimized depending on sample complexity and the amount of starting material.  The reproducibility of chromatography runs (especially for labelfree quantitation) is important for reliable data collection.

For very complex tissue material, a standard comprising a mixture of several different metabolically labeled cell lines might be more appropriate. Such an approach, named super-SILAC, was also recently described by Mann and colleagues [54]. This approach has evolved from previous studies where SILAC-labeled cells were used to quantify proteins from total mouse brain [55] or primary mouse hepatocytes [56]. To cover a broad range of a particular tissue type they selected different cell lines, representing the tissue under investigation as close as possible and mixed equal amounts of the SILAC-labeled proteomes. This super-SILAC mixture is then added in a 1:1 ratio to extracted tissue proteins and used as the internal quantitative standard. In this report, the authors described different super-SILAC mixes used to study breast or brain tumor tissues. These super-SILAC mixtures are superior to a single cell linebased internal standard and can be used to robustly and accurately quantify several thousand proteins, including low abundant regulatory and transformation-related proteins. Although there are no super-SILAC-based phosphoproteomics studies published yet, high sensitivity, quantitative accuracy of the method and especially the fact that tissue samples are mixed with standard very early in the procedure, suggest that this method will set new standards in the quantitative analysis of human tissue phosphoproteomes. Theoretically, tissue-specific super-SILAC mixtures can be created to study different human tumor tissues as a tool for biomarker discovery. The same standard mixture centrally created could also be used as a common internal standard to study large numbers of patient samples worldwide to identify, for instance, biomarker candidates, signaling abnormalities underlying certain pathology or patient response to therapy. Chemical labeling by iTRAQ Recently, an integrated iTRAQ-based strategy for largescale quantitative profiling of phosphopeptides in complex samples was reported by He and coauthors [57]. In this approach, phosphopeptides were first enriched using TiO2 affinity chromatography, labeled with iTRAQ reagents, 47

Review and further fractionated by SCX. The phosphopeptides were analyzed in an Orbitrap mass spectrometer, where conditions were optimized for high-energy collisional dissociation (HCD) to balance the overall peptide identification and quantitation using the relative abundance of iTRAQ reporter ions. This approach allowed identification of 3557 phosphopeptides from HeLa cell lysates, of which 2709 were quantified. Although developed for transformed cell lines, the method is fully compatible with any complex biological mixture, such as primary cells, tissues and biological fluids. Another example of an iTRAQ-based phosphoproteomics study demonstrates the power of the approach to discover new, potentially drug-relevant targets in cellular signaling. Recently, the Sabatini group conducted a systematic investigation of the mTOR kinase-regulated phosphoproteome in two different model cell lines upon insulin stimulation in the presence or absence of rapamycin or Torin1, both specific mTOR kinase inhibitors [58]. From HEK293 cells, 4256 phosphopeptides corresponding to 1661 distinct proteins were identified; 127 phosphopeptides from 93 proteins were mTOR regulated. From mouse embryonic fibroblasts 7299 unique phosphopeptides corresponding to 2406 proteins were identified, of which 231 phosphopeptides from 174 proteins were regulated by mTOR. From this study the adaptor protein Grb10 was identified as a novel mTOR complex 1 substrate that mediates inhibition of the PI3K–AKT pathway. mTOR complex 1 rapamycin-derived inhibitors have been in medical trials as anticancer drugs with limited success; however, the feedback activation of PI3 kinase discovered in this study [58] could be a reason for the unexpectedly low clinical efficacy of these inhibitors and suggests possible future directions in combinatorial application of anticancer drugs to improve their efficacy. The first application of quantitative tyrosine phosphoproteomics to primary murine T cells was recently reported [6]. Tyrosine-phosphorylated peptides were immunoprecipitated from iTRAQ-labeled samples and additionally purified by IMAC, which enabled identification and quantification of 77 phosphosites. Several proteins were differentially phosphorylated upon T cell receptor (TCR) stimulation. Additionally, the level of phosphorylation of these proteins in activated CD4+ T cells isolated from the non-obese diabetic (NOD) mouse strain (animal model to study type 1 diabetes) and from control diabetes-resistant mice was compared. Detected differences in the dynamics of the phosphorylation of several proteins suggested a contribution of the signaling network in NOD T cells to their over-reactivity. A recent study by Pandey and colleagues demonstrates that iTRAQ-based quantitative MS can be applied to patient material [59]. The protein expression in esophageal squamous cell carcinoma (ESCC) tumor tissues was compared with the corresponding adjacent normal tissue from ten patients. LC-MS/MS analysis of strong cation exchange chromatography fractions led to the identification of 257 proteins differentially expressed in ESCC compared with normal. Several previously known protein biomarkers as well as several novel proteins were identified in this screen and further validated by immunohistochemistry. Although 48

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phosphorylation status of the regulated proteins was not reported, this would, in principle, be possible. Isobaric tags (iTRAQ and TMT) HCD-based quantification on the LTQ Orbitrap was shown to be a highly precise approach in quantitative proteomics [60]. One has to admit here that although up to eight different biological samples can be simultaneously and quantitatively compared using 8-plex iTRAQ labels, a recent report by the Mechtler group showed that three commercially available isobaric tags significantly differ with regard to peptide identification rates, generating superior results for iTRAQ 4-plex, followed by TMT 6-plex and then iTRAQ 8-plex [61]. Thingholm et al. [62] observed that iTRAQ and TMT labeling considerably reduces the number of identified phosphopeptides by MS in comparison to the same samples without labeling and suggest the usage of NH4OH to increase the number of phosphopeptide identifications. Label-free quantitation Label-free quantitation strategies, developed as an alternative to stable isotope-labeling approaches, require reproducibility of chromatographic separations for automated alignment of multiple LC-MS separations. This can be obtained by system recalibration before each chromatographic separation or by using spiked standard mixtures of peptides with known elution times covering a broad range of the chromatogram [63,64]. For label-free quantitative phosphoproteomics the crucial step is effective and reproducible separation of phosphorylated peptides from the more abundant non-phosphorylated peptides in total digests, as each sample must be enriched separately. Two recently published reports addressed the reproducibility of phosphopeptide enrichment prior to label-free LC-MS analysis [65,66]. Soderblom et al. [65] optimized a TiO2 enrichment protocol by comparing 20 different conditions, including various concentrations of binding modifiers and peptide-to-resin capacity ratios. The authors described a general label-free quantitative phosphoproteomics strategy for the analysis of any biological sample, including human tissues. Applying the strategy to as little as 200 mg of protein from total zebrafish (Danio rerio) embryo lysates allowed the quantitation of 719 phosphopeptides. Montoya et al. [66] optimized TiO2 chromatography to isolate phosphopeptides and demonstrated a correlation between the signal generated by a phosphopeptide ion and its concentration in samples, making label-free LC-MS/MS suitable for in-depth relative quantification of phosphorylation without the need for chemical or metabolic labeling. Label-free techniques can be used to compare the large number of primary samples needed for clinical studies. The two above-mentioned reports demonstrated robust isolation of hundreds of phosphopeptides from small amounts of biological material for subsequent quantification in relatively short time with good accuracy and linearity. Levey and colleagues recently applied phosphoproteomics to characterize postmortem human brain tissue [67]. IMAC followed by LC-MS/MS and label-free quantification was employed to identify differentially regulated phosphoproteins in human brain tissues with frontotemporal lobar degeneration (FTLD) in comparison with unaffected control tissues. Samples of 800 mg protein were sequentially

Review digested in-solution with the endopeptidases LysC and trypsin and phosphopeptides were enriched by an optimized IMAC protocol using FeCl3 followed by LC-MS/MS. Analyses revealed 786 phosphopeptides representing approximately 50% of the total peptides analyzed. This phosphoproteome analysis revealed six proteins differentially regulated in FTLD: NDRG2 and GFAP had an increased number of phosphospectra, whereas MAP1A, Nogo, PKCg, and HSP90AA1 had significantly fewer spectra when compared with control brain. Although this was a successful application of phosphoproteomics to medical samples, this study was biased towards the most abundant phosphoproteins because of the limited amount of sample material and because the quantification was performed based on the total number of all MS/MS spectra that identify a given phosphoprotein, irrespective of the phosphorylation sites. Encouraging results were recently published by Ostasiewicz et al. [68] demonstrating the feasibility of qualitative and quantitative analysis of phosphoproteomes in formalinfixed and paraffin-embedded (FFPE) material, typical tissue samples available in biobanks. The authors developed a protocol that combines boiling in SDS with a FASP sample preparation method. They show that the FFPE–FASP approach allows quantitative investigation of fixed tissues and is suitable for comparative analysis of thousands of phosphorylation sites. Analysis of FFPE tissue from SILAC mice revealed that phosphorylation was quantitatively preserved and that phosphorylation sites can be analyzed by LC-MS/ MS to the same extent as fresh samples. During this study, phosphopeptides were enriched on TiO2 beads, separated into four fractions using the strong anion exchange (SAX) approach and analyzed in 230 min gradients on the LTQ Orbitrap Velos using HCD fragmentation. In duplicate experiments, 7718 different phosphopeptides in the fresh and 6870 different phosphopeptides in the FFPE tissue material were identified. Thus, large banks of human samples with associated patient information, currently available from many pathology laboratories, can be used for the identification of disease-related signaling defects and potential drug targets. Concluding remarks Several successful examples of quantitative phosphoproteomic investigations of complex tissues reviewed above strongly suggest a high potential of this approach towards much broader clinical application in the future. However, several basic problems in phosphoproteomics remain unsolved despite tremendous advances in MS-based analyses over the past few years. An important issue in phosphoproteomics is phosphosite localization, which is much more difficult than the sole identification of phosphorylated peptide sequences. High-resolution MS-based phosphoproteomics, as shown in several publications discussed above, is able to identify and quantify thousands of phosphopeptides, but clear identification of the phosphorylation site(s) is more challenging. Difficulties in identification of phosphorylated amino acid residue(s) in the phosphopeptide are often caused by the limited MS/MS fragmentation efficiency, which can vary depending on amino acid sequence and fragmentation method used. Phosphosite assignment in most commonly used database-searching

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algorithms such as Mascot [69] and Sequest [70] are highly dependent on the mass accuracy. Therefore, the introduction of the latest generation of high-resolution mass spectrometers such as LTQ Orbitrap Velos greatly improved the peptide identification rate [71]. By contrast, none of the currently available MS/MS fragmentation methods, including collision-induced dissociation (CID), electron transfer dissociation (ETD) or HCD, is perfectly suited for phosphopeptide sequencing. Recently improved sensitivity of HCD measurements made large-scale phosphoproteomics analyses feasible by combining high mass accuracy in MS scans and MS/MS fragmentations with a high sequencing speed [72]. Differences in interpretation methods for analysis of raw MS data often cause the lack of reproducibility between different MS laboratories even when the same datasets are analyzed [73]. Using different software, one can obtain different numbers of identified peptides as well as different phosphosite annotations. However, false discovery rates of peptide identifications (FDRs) can be efficiently controlled using target/decoy database strategies [74]. Assigned phosphorylation sites often have to be validated manually, which is hardly possible in large-scale phosphoproteomic studies. To assist in this process, several bioinformatic tools have been developed to evaluate phosphosite assignment (reviewed in [30]). Very recently Olsen and colleagues identified a novel fragmentation mechanism on an LTQ Orbitrap Velos instrument during HCD, which significantly improves phosphosite localization confidence [18]. This finding might significantly improve determination of phosphorylation sites. The phosphorylation statuses of a specific protein as well as the phosphoproteome in general are generated by two opposite reactions: phosphorylation by kinases and dephosphorylation by phosphatases. Therefore, an increase in monophosphorylated peptides as detected by MS does not necessarily correlate with increased phosphorylation by the relevant kinase(s). The observed increase might be the result of dephosphorylation of a double phosphorylated peptide by specific phosphatases. Additionally, differentially phosphorylated peptides might be cleaved by trypsin with different efficiency. Therefore, increased coverage of phosphoproteomics using complementary approaches (e.g. sequential elution from IMAC, SIMAC [75]) is desirable to compensate for the bias towards mono (TiO2) or multiple-phosphorylated (IMAC) peptides, inherent in current enrichment techniques. Transcriptional regulation is not expected to play a significant role during short signaling experiments (lasting only minutes), but does play a major role in longer time course stimulations (hours or days). A potential solution to this problem is to perform phosphoproteomics and a complementary transcriptome analysis in parallel, as was done recently in a functional phosphoproteomics screen of TLRactivated macrophages [48]. These authors used a recently described method for metabolic tagging and purification of nascent RNA [76], which allowed a sensitive detection of early changes in transcription. Such complementary approaches suggest that only a minor fraction of ligandinduced phosphorylation results from induced protein expression and the majority of phosphorylations detected 49

Review Box 2. Future challenges  Increase the coverage of independent runs in LC-MS/MS with limited overlap of peptide identification due to ‘undersampling’ (many low abundance proteins remain undetected in current large-scale proteomic analyses due to the limits in the rate at which MS/MS spectra can be acquired).  High-resolution mass spectrometry based phosphoproteomics is able to identify and quantify thousands of phosphopeptides, but clear identification of the phosphorylation site(s) within this peptide is much more difficult. Novel fragmentation mechanisms might significantly improve phosphosite identification confidence in the future.  Parallel analysis of the phosphoproteome and total proteome from the same sample.  Future advances in computational biology with a capacity to analyze increasing proteomic data and integrate proteomic, genomic and transcriptomic data for system level interpretation, and to fill the existing gap between data and knowledge.  Validation of a tremendous amount of data will be needed.

precede transcriptional responses. By contrast, even nascent transcriptome analysis will not allow detection of fast phosphorylation signal protein degradation. Therefore, parallel to phosphoproteomics total proteome analysis of the same sample is needed to determine an influence of protein expression on phosphorylation levels. Further progress in quantitative MS should allow such large-scale proteomics analyses in the near future. Tremendous advances over the past few years in highresolution MS, phosphopeptide enrichment approaches and bioinformatic computational analyses allowed significant development of in vivo phosphoproteomics. Quantitative phosphoproteomic approaches are becoming routine in many laboratories, but the field remains challenging (Box 2). We anticipate a rapid increase in quantitative phosphoproteomics data obtained on patient material. In addition, further technological developments needed to analyze limited amounts of tissue material from individual patients as well as systematic analyses of the large biobanks of well annotated patient tissues available worldwide will dramatically improve our understanding of the phosphoproteome in disease. Acknowledgments We apologize to all colleagues whose work could not be discussed and cited here owing to space restrictions. The Austrian Proteomics Platform (APP) within the Austrian Genome Program (GEN-AU), Vienna, Austria and the Special research Program ‘Cell Proliferation and Cell Death in Tumors’ (SFB021, Austrian Science Fund, FWF) and the COMET Center ONCOTYROL (Federal Ministry for Transport Innovation and Technology, BMVIT), the Federal Ministry of Economics and Labor the Federal Ministry of Economy, Family and Youth (BMWA/BMWFJ) and the Tiroler Zukunftsstiftung (TZS) support work in the Huber laboratory.

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