Keywords

Introduction to Platelet Proteomics

Proteomics is the large-scale study of proteins encoded by a genome under specific conditions and at a given time (Wilkins et al. 1996) and has also been used to study platelets and platelet-related diseases. Because of recent advances, especially in mass spectrometry (MS), proteomics now enables the measurement of multiple properties for thousands of proteins simultaneously, such as their abundance, modifications, subcellular localization, and protein–protein interactions (Larance and Lamond 2015). Platelets are estimated to contain about 5000 different proteins, spanning a wide abundance range and with different posttranslational modifications (Burkhart et al. 2012). The platelet proteome is highly dynamic as some proteins are secreted upon platelet activation and the proteome itself changes with age (Cini et al. 2015) or disease state (Macaulay et al. 2005). In addition, the capability of platelets to absorb plasma proteins further increases the proteome variability and complexity. Platelets are anuclear, having no DNA and only limited amounts of mRNA, which makes proteomic research an attractive alternative for gene expression studies. Platelets are readily available in living organisms and in relatively high amounts; therefore proteomes can also be applied to analyze platelets from children starting from small volumes of blood. As for other cell types and organisms, knowledge of the proteome is expected to be more useful than information on the transcriptome as proteins are believed to be the main effectors of gene functions. It has been estimated that the 20,000 human genes translate into one million different proteoforms due to alternatively splicing and posttranslational modifications (Brett et al. 2002). Furthermore, several studies have revealed a poor correlation between mRNA and protein profiles in lower organisms (Gygi et al. 1999a, b), and this has also been described for platelets (Burkhart et al. 2012; Londin et al. 2014). This discrepancy might be explained by a rapid decay of mRNAs in response to various stimuli and posttranslational protein ubiquitination leading to protein degradation. Moreover, a disease state may be determined by translocation of proteins to different cellular compartments rather than by changes in transcript levels. In addition, several technical issues can explain the imperfect correlation between transcriptomic and proteomic data. For example, the current transcriptomic techniques provide only limited information on alternative splicing, whereas proteomics can detect separate protein isoforms encoded by these variants. Given all these considerations, proteomics represents an efficient tool to gain deeper insights into the molecular mechanisms regulating unknown platelet functions and platelet-related diseases. We, and others, published extensive reviews on the application of proteomics in the field of platelet research, mainly focused on the commonly used methodologies and the key achievements in the elucidation of platelet-related diseases (Di Michele et al. 2012a, b; Zufferey et al. 2012; Rotilio et al. 2012). Here, we summarize recent updates for the main contemporary proteomic strategies for platelet research in humans and the most important studies that applied proteomics to unravel the molecular mechanisms underlying platelets in physiological (“Proteomic Methods Used for Platelet Research” and “Proteomic Strategies to Decipher Platelet Biology”) and pathological conditions (“Platelet Proteomics to Gain Insights in Human Diseases”).

Proteomic Methods Used for Platelet Research

Gel-Based Proteomics

Gel-based methods, including conventional 2DE (bidimensional electrophoresis) and DIGE (differential in-gel electrophoresis), are commonly used for platelet proteomics. In 2DE, proteins are first separated based on their isoelectric point along a pH gradient by isoelectric focusing and then based on their apparent molecular mass in a polyacrylamide gel by SDS-PAGE (Rogowska-Wrzesinska et al. 2013). Proteins are visualized by in-gel staining, matched in different 2DE gels, quantified, and proteins of interest are excised for further identification by MS. 2DE was first used to study the platelet proteome in the 1970s, identifying some most abundant proteins and membrane glycoproteins (Clemetson et al. 1979) and later for the identification of proteins with changed levels upon platelet storage (Snyder et al. 1987). This method also contributed to generate the proteome reference maps of quiescent platelets (Gravel et al. 1995; Marcus et al. 2000; O’Neill et al. 2002; Garcia et al. 2004a, b), activated platelets (Claeys et al. 2005; Shai et al. 2012), and platelet subproteomes including microparticles (Shai et al. 2012; Garcia et al. 2005), cytosol, and microsomes (Claeys et al. 2005). 2DE holds intrinsic limitations such as the difficulty to analyze hydrophobic, extremely charged, and very small or large proteins, as well as having a limited reproducibility among gels and the presence of multiple proteins in a single spot. Combined with restricted sample loading and the scarce sensitivity of protein stains, all of this hampers the detection of low-abundant proteins. Some of these disadvantages were overcome using DIGE, which allows improved reproducibility, higher quantification accuracy, and reduced time/costs. With DIGE, two samples and one internal standard are loaded together on the same gel after labeling with different fluorescent dyes (Cydye-2, Cydye-3, and Cydye-5), each with specific excitation/emission wavelengths (Unlu et al. 1997). The internal standard is a pool of equal amounts of all the samples used to facilitate accurate spot matching across gels, minimizing inter-gel variability, and allowing to calculate the abundance of each protein spot as a ratio to its corresponding spot in the internal standard, leading to the measurement of subtle changes in protein abundance with high statistical confidence. DIGE has been used to study platelet proteomes under basal or activated conditions (Cini et al. 2015; Baumgartner et al. 2013; Winkler et al. 2008; Veitinger et al. 2012).

Finally, 1D SDS-PAGE can be used in combination with MS in the so-called GeLC-MS approach (Shevchenko et al. 2006). The underlying workflow involves SDS-PAGE, in-gel protein staining, slicing of each lane into discrete gel bands, in-gel protein digestion, peptide purification, and LC-MS/MS analysis. GeLC-MS was successfully used to study human platelet proteomes in resting and stimulated conditions (Piersma et al. 2009; Qureshi et al. 2009; van den Bosch et al. 2014; Ambily et al. 2014), during storage (Dzieciatkowska et al. 2015a; Schubert et al. 2012a; b), or to analyze specific subproteomes including the immunoproteasome (Klockenbusch et al. 2014) and palmitoylome (Dowal et al. 2011).

Gel-Free Proteomics

Gel-based methods are progressively replaced by so-called bottom-up MS-based proteomic methods that analyze peptide mixtures derived from isolated proteomes. These techniques require lower amounts of material, which is particularly important when studying rare platelet-related diseases, platelets from young children, or subproteomes. The first step in gel-free analysis typically consists of digesting the proteome with a specific protease. This is followed by a chromatographic separation of the obtained peptides coupled to MS for analysis, subsequent data processing, and peptide identification.

The pioneering method is multidimensional protein identification technology (MudPIT) which combines orthogonal peptide separations prior to MS/MS analysis, thereby maximizing sensitivity and resolution (Washburn et al. 2001). The main advantage of MudPIT over gel-based methods is that it largely avoids protein solubilization problems as proteins are cut into peptides that are easier to solubilize. MudPIT was used to analyze the basal platelet proteomes (Finamore et al. 2010), the thrombin-induced secretome (Coppinger et al. 2004), the serine hydrolase subproteome (Holly et al. 2013), and the effects of dysregulated miRNA on thrombocythemic platelets (Xu et al. 2012).

The exponentially modified protein abundance index (emPAI) approach is widely used and assumes that abundant peptides are more often detected, but also considers that bigger proteins and proteins with many peptides in the m/z-range for efficient MS analysis will generate more observed peptides (Ishihama et al. 2005). The emPAI method was successfully employed when studying protein networks associated with platelet glycoprotein VI stimulation (Wright et al. 2011).

Combined fractional diagonal chromatography (COFRADIC) is a gel- and label-free proteomic method (Gevaert et al. 2002). Given its versatility, several COFRADIC protocols were developed, each allowing for the enrichment of a specific set of peptides (Gevaert et al. 2003; Stes et al. 2014; Staes et al. 2011). COFRADIC relies on three steps: (1) a first RP-HPLC fractionation of the digested proteome, (2) a chemical or enzymatic reaction of single or combined peptide fractions that modifies the chemical structure of selected peptides that will then obtain altered chromatographic properties, and (3) a series of RP-HPLC fractionations identical to the first one during which the altered peptides are isolated for LC-MS/MS analysis. COFRADIC was applied for studying the human platelet proteome together with GeLC-MS/MS and MudPIT, revealing that the use of complementary techniques allows for more comprehensive proteome mapping (Martens et al. 2005; Lewandrowski et al. 2009).

Labeling methods rely on the incorporation of stable isotopes, i.e., 2H, 13C, 15N, and 18O by metabolic (SILAC), chemical (ICAT, iTRAQ, or TMT), or enzymatic reactions. In stable isotope labeling by amino acids in culture (SILAC), cells are grown separately in media supplemented with heavy or light labeled essential amino acids, such as lysine and arginine (Ong et al. 2002). Although SILAC is typically only applied to cells that can be expanded in vitro, it was also successfully used for studying platelet proteomes (Kruger et al. 2008; Zeiler et al. 2014). In isobaric tags for relative and absolute quantitation (iTRAQ), quantification occurs using MS/MS spectra rather than MS spectra (Gygi et al. 1999a, b). iTRAQ makes use of an amine-reactive reagent available in different isotopic variants to label peptides at their primary amines. iTRAQ was used to analyze the proteome of human platelets focusing on inter- and intra-biological variations (Burkhart et al. 2012; Vaudel et al. 2012) and platelets stimulated with ADP, thrombin, collagen, TRAP, and/or iloprost (Beck et al. 2014; Cimmino et al. 2015). Furthermore, iTRAQ-based gel-free proteomics, combined with DIGE- and ICAT-based proteome analysis, was applied to detect changes in platelet protein levels during storage of blood products (Thon et al. 2008). Similar to iTRAQ, tandem mass tags (TMTs) employ amine-reactive chemicals to label peptides (Thompson et al. 2003), and up to ten different isobaric tags are available (Werner et al. 2014). TMT labeling was applied to study protein changes in platelets from subjects sensitive or resistant to aspirin (Floyd et al. 2014). Enzymatic labeling is based on peptide labeling with oxygen-18 isotopes (Yao et al. 2001). Stable enzymatic labeling of tryptic peptides in combination with COFRADIC was applied to analyze platelets (Staes et al. 2004).

Proteomic Strategies to Decipher Platelet Biology

General Considerations for Experimental Design of Platelet Proteomic Studies

Most proteomic studies are feasible with about 5 mL of blood allowing the isolation of 108 platelets, corresponding to 1 mg of proteins (Burkhart et al. 2012). For comparative proteomic studies, different groups should contain age- and sex-matched donors. Indeed, a DIGE study reported a quantitative variation of 18 % in platelet proteomes of a group of 56–100 years old volunteers, probably due to age differences (Winkler et al. 2008). Also another study detected important differences in the platelet proteome of children and adults using DIGE coupled to LC-MS/MS (Cini et al. 2015). Moreover, higher levels of signaling cascade proteins have been found by 2DE and MS in male compared to female donors (Eidelman et al. 2010). Significant gender differences in platelet proteins have also been detected in a recent study using QconCAT-based MRM, not only in fresh blood from donors but also in apheresis platelet concentrates following varying storage times (Dzieciatkowska et al. 2015b). Finally, inter- and intra-subject variation among the human platelet proteome was measured in four healthy donors and three different blood samples from one donor and revealed that the proteome showed about 15 % of quantitative variation among donors (Burkhart et al. 2012). These studies highlight the usefulness of including biological replicates in the experimental setup to counterbalance for the intrinsic variability of samples. In addition, also some other factors might be considered when choosing platelet donors, such as medication use (such as aspirin), lifestyle, and dietary habits. Furthermore, cigarette smoking was also found to differentially regulate the levels of several platelet proteins (Della Corte et al. 2012).

Obviously, protocols for sample preparation are also a non-negligible source of variability. For example, the analytical variation of a single sample of platelet proteins within and between four laboratories each using its own 2DE protocol was measured, and the coefficient of variation for each of the matched spots after automatic and subsequent manual matching ranged between 5 and 60 % (de Roos et al. 2008). A 2DE study also revealed changes in the platelet proteome in terms of composition rather than yield depending on the precipitation method used to purify proteins, namely, ethanol versus trichloroacetic acid (Zellner et al. 2005). The type of anticoagulant (acid citrate dextrose, heparin, EDTA, etc.) used for blood collection is of fundamental importance, as these substances can induce changes by interacting with specific platelet proteins (Capila and Linhardt 2002). Particular attention must be given to obtaining highly purified platelet fractions, due to possible contamination mainly from plasma and other blood cells. The origin of plasma proteins found in platelet proteomes, however, is still a matter of debate as it is not clear if they derive from the sample preparation method or are actually located within platelets given their surface-connected open canalicular system and its continuous exchange with plasma components. A shotgun proteome analysis of the cytoplasmic, microparticle, and secreted fractions of TRAP-activated platelets supports the platelet origin of some plasma proteins identified in platelet proteomes, thus excluding methodological artifacts (Veitinger et al. 2012). Concerning contamination with other blood cells, a protocol for isolating highly pure platelets preparations is recommended, leading to platelet preparations with less than 1 leukocyte per 106 platelets and less than 1 erythrocyte per 104 platelets, beyond <1.5 % by volume of plasma per platelet (Burkhart et al. 2012; Gambaryan et al. 2010). In this context, particularly useful are freely available protein databases of purified blood cells (including T cells, monocytes, neutrophils, erythrocytes, and platelets) obtained by both 2DE and shotgun proteomics, which may be used as references (Haudek et al. 2009).

Proteomics of Resting Platelets

Since the first attempts to characterize the human platelet proteome in the late 1970s by 2DE (Clemetson et al. 1979), a huge number of such proteome studies have followed that have identified a growing number of proteins. A combination of 2DE, immunoblotting, and N-terminal sequencing identified about 25 platelet proteins (Gravel et al. 1995). The cytosolic fraction of platelets was analyzed in a successive study using 2DE combined with MS, leading to the identification of more than 200 proteins (Marcus et al. 2000). Two 2DE studies in combination with improved first dimension separation identified more than 500 and 300 different proteins, respectively (O’Neill et al. 2002; Garcia et al. 2004a, b). COFRADIC gave a further impulse to the development of a detailed platelet proteome map of about 650 platelet proteins, including several proteins not previously detected, and hydrophobic membrane proteins (Gevaert et al. 2003). Another study based on 2DE and shotgun proteomics contributed to the compilation of the platelet proteome in basal conditions by identifying about 100 unique proteins (Finamore et al. 2010). Shotgun proteomics, based on peptide separation by OFFGEL fractionation and RP chromatography coupled to LC-MS/MS detection, was also applied to platelet proteomics and led to the identification of more than 1300 proteins (Krishnan et al. 2011). Finally, the most comprehensive proteome database of human platelets consists of more than 4000 unique (phospho)proteins and has been obtained by iTRAQ and TiO2 enrichment for the phosphorylated fraction (Burkhart et al. 2012).

Proteomics of Activated Platelets

Upon activation by endothelial cell damage or inflammatory events, platelets release a high number of proteins into the circulation, the so-called platelet secretome or releasate, which is involved in the regulation of primary hemostasis, but also in coagulation, inflammation, angiogenesis, wound healing, and some other processes (Golebiewska and Poole 2015). Numerous proteomic studies have attempted to characterize the platelet secretome using different methods. Moreover, different agonists have been used to activate platelets and induce the protein releasate, such as ADP, thrombin, TRAP, collagen, and arachidonic acid. These agonists have been used alone or in combination, influencing the composition of platelet secretome (Rogowska-Wrzesinska et al. 2013). A concentration step on reverse-phase chromatographic beads of diluted peptide mixtures following in-gel digestion of 2DE-separated proteins and MALDI-TOF-MS analysis was applied to the proteome of platelets activated by thrombin with the identification of several proteins that translocate to the cytoskeleton fraction (Gevaert et al. 2000). In another study, a combination of 2DE and MudPIT resulted in a comprehensive characterization of the secretome of thrombin-activated platelets, leading to more than 300 identified proteins, many of which were described for the first time in platelets, such as secretogranin III, cyclophilin A, and calumenin (Coppinger et al. 2004). Another MudPIT-based analysis led to the identification of 82 proteins differentially expressed in the platelet secretome upon stimulation with thrombin (McRedmond et al. 2004). In activated platelets, these proteins include chemokines, signaling molecules, and histones, as well as coagulation factors and receptors involved in thrombosis. Another study aimed at characterizing the proteome of thrombin-stimulated platelets using DIGE coupled to MALDI-TOF-MS (Della Corte et al. 2008). By comparing the secretome of unstimulated and stimulated platelets, several differentially secreted proteins were identified, including laminin A, a nuclear protein described for the first time as released by platelets. A recent study using DIGE and LC-MS/MS focused on the difference in the platelet secretome following stimulation with thrombin or collagen (Vélez et al. 2015). Thirty-eight proteins differentially present between these conditions were identified, and this relatively high number of protein differences confirms the influence of the type of agonist used for platelet stimulation on the composition of the secretome. The analysis of the platelet proteome in resting or TRAP-activated conditions by 2DE led to the identification of 31 regulated proteins (Garcia et al. 2004a, b). Among these, eight were not known to be present in platelets, including the adapter downstream of tyrosine kinase 2 (DOK-2). The number of proteins identified in the secretome of TRAP-stimulated platelets dramatically increased when using a GeLC-MS/MS approach, based on 1D SDS-PAGE and LTQ-FT MS with the identification of 716 proteins (Piersma et al. 2009). About 40 % of these proteins were identified for the first time in platelet releasates.

Platelets were also stimulated with less common agonists such as GPIV-activating collagen receptor peptide (CRP)(Wright et al. 2011). In this study, different platelet subcellular compartments were isolated using ultracentrifugation to reduce the overall complexity. The majority of differentially expressed proteins after CRP stimulation were low abundant and involved in signaling. In addition, more than half of the 663 identified proteins were not previously known to be present in platelets. An accurate quantitative proteomic strategy based on stable isotope dimethylation, SCX, and MS analysis was used to compare the platelet secretome following PAR1 or PAR4 stimulation or in resting conditions (van Holten et al. 2014). The differential release of pro- and anti-angiogenic growth factors by PAR1 and PAR4 might be important for the regulation of angiogenesis, though no large differences in protein abundance could be detected that confirmed such a hypothesis.

Some proteomic studies also investigated the effect of different agonists simultaneously on the platelet proteome. 2DE combined with LC-MS/MS was used to identify 144 platelet proteins with changing levels following activation with arachidonic acid, collagen, and thrombin (Majek et al. 2010). A more recent study analyzed the platelet secretome following thrombin and collagen treatment using a reversed releasate approach (Wijten et al. 2013). Contrary to classical studies on platelet secretomes, which may also detect proteins derived from cell lysis, in this case the protein levels in the platelets after stimulation were accurately quantified following stable isotope labeling, assuming that the released proteins are less abundant in the platelet proteome. Importantly, of the about 4500 platelet proteins quantified, only 124 were found to be secreted, spanning a concentration range of ≥5 orders. These proteins included several novel low-abundant proteins and well-known proteins such as thrombospondin and von Willebrand factor.

Analysis of Platelet Subproteomes

Platelet Granules

To reduce the complexity of analyzing total proteomes, the isolation of organelles prior to analysis is often used. Organelle isolation can be achieved via density gradient centrifugation, immunopurification, or free-flow electrophoresis (Zufferey et al. 2012). However, assessment of possible contamination from other organelles is recommended prior to analysis using organelle markers via immunoblotting, electron microscopy, or enzymatic assays. Platelets contain three types of secretory granules, alpha granules, dense granules, and lysosomes, each characterized by a different number per platelet, content, morphology, and response to stimuli (Rendu and Brohard-Bohn 2001). The proteome of alpha granules isolated by sucrose gradient ultracentrifugation was extensively characterized by GeLC-MS/MS, leading to the identification of 219 proteins (Maynard et al. 2007). Not surprisingly, the majority of these were already described in platelet secretomes, while 44 proteins were novel. The proteome of dense granules was analyzed using two proteomic methods: 2DE coupled to MALDI-TOF-MS and LC-MS/MS analysis (Hernandez-Ruiz et al. 2007). Overall, 40 proteins were identified, and most of them, such as actin-associated proteins, glycolytic enzymes, and regulatory proteins, were not previously known to reside in these organelles. More recently, a proteomic analysis carried out by subcellular fractionation on a sucrose gradient and MS analysis led to the most comprehensive characterization of the platelet granule proteome, identifying over 800 proteins (Zufferey et al. 2014).

Platelet Microparticles

In the most comprehensive analysis of platelet microparticles so far, almost 600 proteins were identified (Garcia et al. 2005). About 65 % of these proteins have been described for the first time, suggesting that these organelles have a unique protein composition. The proteome of platelet microparticles was also analyzed in a subsequent study using 2DE and LC-MS/MS, in which the microparticles were separated by gel filtration in four classes based on their size (from approximately 100 nm to greater than 500 nm), revealing major differences in protein composition (Dean et al. 2009). In particular, mitochondrial proteins were mostly present in the largest microparticles, whereas alpha granule proteins were more found in the smallest microparticle fractions. The platelet microparticles proteomes were different depending on the stimulus used to activate platelets as found by another proteomic study using 2DE (Shai et al. 2012). Twenty-six proteins were differentially expressed between shear- and thrombin-activated platelets, mainly involved in signaling pathways. The proteome of microparticles from ADP-stimulated platelets was also extensively characterized by shotgun proteomics (Capriotti et al. 2013). This approach led to the identification of more than 600 proteins, 40 % of which were described in platelet microparticles for the first time.

Platelet Plasma Membrane

Despite a major role for membrane receptors and associated proteins for platelet function, they are relatively poorly represented in proteomics studies. This is mainly due to the characteristics of these proteins, such as their low abundance, hydrophobicity, and the reduced accessibility of trypsin to transmembrane domains, which hamper both gel- and MS-based proteomics. Therefore, specific methods are required to improve plasma membrane protein identification. The first study focusing on the platelet plasma membrane proteome used pre-fractionation over a sorbitol gradient to remove high abundant cytoskeletal proteins and contaminants prior to plasma membrane isolation and GeLC-MS/MS analysis (Moebius et al. 2005). In addition, 1D SDS-PAGE separation was performed both by conventional SDS-PAGE and benzyldimethyl-n-hexadecylammonium chloride/SDS separation to improve protein resolution on gel. Almost 300 proteins could be identified, the majority of which were plasma membrane proteins. In a subsequent study, three different techniques were used for the enrichment of platelet plasma membrane proteins before LC-MS/MS analysis, namely, lectin affinity chromatography, biotin/NeutrAvidin affinity chromatography, and free-flow electrophoresis (Senis et al. 2007). A total of 136 membrane proteins were identified, many of which previously not known as platelet membrane proteins. An interesting comparative analysis of the membrane proteomes from platelets in control and thrombin-activated conditions was carried out by NeutrAvidin affinity chromatography, prior to protein separation by liquid-phase IEF and SDS-PAGE and analysis by FT-ICR MS to identify 88 differentially expressed proteins (Tucker et al. 2009). Another study identified 182 membrane proteins using a different proteomic approach (Qureshi et al. 2009). Membrane proteins were first precipitated via ultracentrifugation, resuspended in a glucopyranoside/guanidium buffer and subsequently analyzed by LC-MS/MS. The most extensive proteomic study on platelet plasma membrane proteins led to the identification of 1282 proteins, which were also relatively quantified via an emPAI-based method (Lewandrowski et al. 2009). The power of this study resides in the use of three different proteomic approaches, GeLC-MS/MS, MudPIT, and N-terminal, methionine, or cysteine COFRADIC, all in combination with aqueous two-phase partitioning.

Proteomics to Analyze Platelet Posttranslational Modifications

Platelet Phosphoproteome

Phosphoproteomics is particularly useful for studying cellular signaling events. 2DE and radioactive labeling were used to identify phosphoproteins upon thrombin activation (Immler et al. 1998). Several protein spots showing a significant increase or decrease in phosphorylation could be detected, in particular different myosin isoforms. Another study was also conducted on thrombin-stimulated platelets, focusing on tyrosine phosphorylation, which plays a central role in platelet activation (Maguire et al. 2002). Proteins containing phosphorylated tyrosine were immunoprecipitated using a monoclonal antibody, separated by 2DE, and the resulting protein spots differentially found between platelets in control and activated conditions were identified by immunoblotting and MALDI-TOF-MS. Further development of modern and more resolving techniques for phosphoprotein/peptide enrichment and MS analysis significantly contributed to shed light on signal-dependent activation responses (Zahedi et al. 2006). For example, two different approaches, treatment with 32P followed by 2DE using different pI ranges and autoradiography or immunoprecipitation coupled to GeLC-MS/MS analysis, led to the identification of 55 phosphoproteins and some in vivo phosphorylation sites (Marcus et al. 2003). In another work, using IMAC-based phosphopeptide enrichment and SCX chromatography coupled to LC-MS/MS, 564 phosphorylation sites belonging to almost 280 proteins were identified in resting platelets (Zahedi et al. 2008). An extensive analysis of the proteome and phosphoproteome of platelets in resting conditions, with particular attention to the integrin signaling pathway, was undertaken by IMAC and GeLC-MS/MS (Qureshi et al. 2009). This led to the identification of more than 1500 proteins, including 262 phosphoproteins. Another strategy for phosphoprotein identification is based on the enrichment of phosphopeptides via titanium dioxide chromatography and LC-MS/MS after a prior enrichment of plasma membrane proteins in an aqueous two phase (Premsler et al. 2011). In yet another study, protein pulldown using SH2 domains and LC-MS/MS analysis were used to investigate the phosphotyrosine state of platelets upon ADP activation (Schweigel et al. 2013). To study the phosphorylation events mediating platelet activation by collagen, a comparative phosphoproteomic study was carried on phosphotyrosine proteins in resting and CRP-activated platelets (Bleijerveld et al. 2013). This approach was based on immunoprecipitation of phosphotyrosine peptides and stable isotope labeling MS and allowed to identify more than 200 phosphotyrosine sites with an altered phosphorylation status. Also prothrombotic oxidized phospholipids in addition to thrombin were used as agonists for platelet stimulation (Zimman et al. 2014). Here, proteins from stimulated platelets were digested by trypsin, and phosphotyrosine peptides were enriched by immunoprecipitation followed by IMAC. The nonprecipitated proteins were fractionated by SCX, and the resulting fractions further enriched for phosphopeptides using TiO2 beads. Finally, phosphopeptides were identified and quantified by LC-MS/MS. Interestingly, one study focused on the characterization of the signaling pathways involved in platelet inhibition rather than platelet activation (Beck et al. 2014). Human platelets were treated with iloprost, a stable analog of prostacyclin, which is the most important physiological inhibitor of platelet activation acting on the cAMP/PKA signaling cascade. Using iTRAQ and TiO2 phosphopeptide enrichment, about 300 phosphopeptides modulated by iloprost were detected.

Platelet Glycoproteome

Studies have shown that platelets possess efficient glycosyltransferase machinery, with more than 200 glycosyltransferases and several substrates (Wandall et al. 2012). The first extensive proteomic study on platelet glycoproteome was performed using concanavalin A affinity chromatography and hydrazide chemistry for enriching glycopeptides, after which they were deglycosylated by N-glycosidase F and analyzed by LC-MS/MS (Lewandrowski et al. 2006, 2009). Concanavalin A, one of the most common lectins in glycoproteomic studies, selectively binds N-glycoproteins. This approach identified 41 glycoproteins and 70 different glycosylation sites. In a subsequent study, the same group focused on the glycosylation sites of platelet membrane proteins (Lewandrowski et al. 2007). The membrane fraction was enriched via aqueous two-phase partitioning in a polyethylene glycol/dextran polymer system, the obtained proteins were digested by trypsin, and the glycopeptides were purified by SCX chromatography prior to N-glycosidase F deglycosylation and MS analysis. By applying this method, almost 150 glycosylation sites on 79 different proteins could be identified, 75 % of which were annotated as plasma membrane proteins. The same group also introduced another strategy for glycopeptide enrichment based on electrostatic repulsion hydrophilic interaction chromatography (Lewandrowski et al. 2008). Importantly, this method might allow resolving different protein isoforms, because of the different interactions of glycopeptides with the stationary phase. By identifying 125 glycosylation sites on 66 different proteins, this work largely contributed to the compilation of an extensive glycosylation site database for human platelet proteins.

Platelet Palmitoylome

Protein palmitoylation, the covalent attachment of long chain fatty acids to cysteines, has an important role in platelet biology, being involved in the regulation of platelet activation and thrombi formation (Sim et al. 2007). A first attempt to characterize the platelet palmitoylome was performed by enriching the platelet membrane protein fraction by acyl-biotinyl exchange chemistry and LC-MS/MS analysis and led to the identification of 215 palmitoylated proteins (Dowal et al. 2011). These proteins included already known palmitoylated proteins, but also 103 new putative palmitoylated ones.

Platelet Proteomics to Gain Insights in Human Diseases

Platelet Proteomics to Study Platelet-Related Bleeding Disorders

Rare inherited platelet-related bleeding disorders are a highly heterogeneous group of disorders that can be caused by abnormal platelet numbers, morphology, and function (Freson et al. 2014). Many genes have been identified as a cause for these disorders, though several patients with an expected platelet-related bleeding disorder still do not receive a genetic diagnosis (Westbury et al. 2015). However, in recent years, next-generation sequencing has offered the potential to improve the diagnosis for such patients. However, in parallel to such genetic studies, proteomics has been used with success to gain insights in such platelet-related bleeding disorders. In 2007, changes in α-granule proteins were quantified for a patient with gray platelet syndrome (GPS) compared to a healthy control (Maynard et al. 2007). GPS is an inherited bleeding disorder caused by a reduced number of α-granules and macrothrombocytopenia. A linear sucrose gradient method was used to isolate α-granules, and proteins were separated by 1D SDS-PAGE and identified by LTQ-FT MS. About 586 proteins were identified, and it was found that soluble, biosynthetic cargo proteins were severely reduced or undetected in GPS platelets, whereas the packaging of soluble, endocytic cargo proteins was only moderately affected, supporting the defect in α-granule incorporation of proteins synthesized in megakaryocytes. Another bleeding disorder that affects α-granules is Quebec platelet disorder (QPD), characterized by high levels of urinary plasminogen activator (uPA) within platelets. The platelet proteome of four members of the same QPD family was compared to that of two healthy donors using 1D SDS-PAGE gels and LC-MS/MS analysis (Maurer-Spurej et al. 2008). Three α-granule proteins, fibrinogen, multimerin, and thrombospondin-1, were downregulated in patients’ platelets, and it was speculated that this was due to degradation by platelet-derived uPA. Combined DIGE and MALDI-ToF/ToF were used to compare the platelet releasate of eight patients with storage pool disease and bleeding symptoms to that of nine unrelated healthy controls (Di Michele et al. 2011). This more advanced technology identified 60 and 14 protein spots that varied in the technical and biological replicate studies, and most proteins are cytoskeleton-related. It was hypothesized that the dense granule defect in these storage pool disease cases might be due to an underlying cytoskeleton defect. A similar proteomic design was used to analyze platelet proteins from patients with dominant macrothrombocytopenia (Karmakar et al. 2015). Patients have altered levels of actin-binding proteins, peroxiredoxin 2, protein disulfide isomerase, and transthyretin that might be associated with the structural changes of their platelets.

Platelet Proteomics to Study Cardiovascular Disease

Arterial thrombosis is a pivotal event in the development of cardiovascular disease, and platelets play a fundamental role in this process. Platelet proteomics has been applied to study this complex disease, and we have selected some examples. The first study was performed in 2008 using 2DE and MS and focused on patients with arterial thrombosis, primarily with ischemic stroke (Arias-Salgado et al. 2008). Most of the differences detected between groups were related to cytoskeletal changes, which supported the idea of preactivated platelets. 2DE and MS analysis of platelets from patients with non-ST segment elevation acute coronary syndrome (ACS) revealed 22 differentially expressed proteins compared to matched cases with chronic ischemic cardiopathy (Parguiña et al. 2010). Most of these proteins are interconnected as part of the network related to cell assembly and morphology and are predicted to participate in platelet activation via αIIbβ3 or GPVI receptors. Platelet proteins isolated from ACS patients admitted within 24 h of chest pain were compared to these from patients with stable coronary ischemic disease (Lopez-Farre et al. 2011). Different proteins involved in cytoskeleton, glycolysis pathway, and cellular-related antioxidant system were altered in the acute phase of the coronary event as identified via 2DE and MS. The platelet proteome of ST-elevation myocardial infarction (STEMI) patients was compared to that of stable chronic ischemic cardiopathy (CAD) patients again using 2DE and MS (Parguina et al. 2011). This study found 42 differentially expressed proteins with altered major signaling pathways that include proteins related to integrin, integrin-linked kinase, and GPVI signaling. The analysis of a coronary thrombus itself by proteomics using different techniques as 2DE with MS, 1-DE with LS-MALDI MS/MS, and 1-DE with LTQ-Orbitrap identified a total of 708 proteins (Alonso-Orgaz et al. 2014). Some of these proteins were co-expressed with the platelet marker CD41 and pointed out a potential activation of a focal adhesion pathway in platelets during thrombus formation. Proteomics of microvesicles isolated from plasma of STEMI versus CAD patients was performed by DIGE and MS (Vélez et al. 2014). About 102 proteins were identified that correspond to 25 unique differentially expressed proteins that have been linked to inflammation, infarction, and thrombogenesis. The most recent proteomic study used isolated thrombi from STEMI patients during percutaneous coronary intervention at different time points after onset of pain (3 or 6 h) (Ramaiola et al. 2015). Thrombi at 3 h were platelet-rich, while at 6 h leukocyte infiltration was noticed, and proteomic differences between these time points were mainly related to changes in the cell cytoskeletal-associated proteome.

Antiplatelet therapies are widely used to prevent myocardial infarction, stroke, and other cardiovascular events. Platelet proteomics proved also to be useful to study the biological effects of antiplatelet therapy and to study mechanisms of patients’ resistance to such drugs. The platelet proteome was analyzed using 2DE for patients with stable angina undergoing percutaneous coronary intervention before angiography, 12 h after clopidogrel, and 24 h after the intervention (Volpi et al. 2012). Protein changes were detected associated with platelet activation and clopidogrel response, and most proteins belong to the cytoskeleton rearrangement, energetic metabolism, and oxidative stress functional classes. Platelet protein expression profiles from aspirin (ASA)-resistant and ASA-sensitive CAD patients were compared using 2DE and MS (Mateos-Caceres et al. 2010). Differences were found for proteins involved in energetic metabolism, cytoskeleton, oxidative stress, and cell survival, which might be due to their different ability to respond to ASA. Pre- and post-aspirin treatment platelet lysate samples (300 mg daily for 28 days) from two ASA-resistant and four ASA-sensitive healthy subjects were analyzed by GeLC-MS/MS (Floyd et al. 2014). Though the groups presented with no detectable changes in the platelet proteome at baseline, 406 differential proteins were present after aspirin treatment with a marked increase in GPIIIa for ASA resistance. Recently, a manually curated biochemical reaction network of platelet metabolism was constructed using 33 proteomic datasets and 354 literature references (Thomas et al. 2014). The effect of ASA resistance was evaluated using constraint-based modeling, providing evidence for a redirection of glycolytic, fatty acid, and nucleotide metabolism toward eicosanoid synthesis and reactive oxygen species stress as validated by novel proteomic data. The availability of such a network will stimulate data-driven system analysis of platelet metabolism in order to gain insights into pathologies.

Platelet Proteomics to Study Other Disorders

Platelet proteomics has also been used to gain insights in diseases that are linked to platelets, such as for understanding thrombosis risks for phosphomannomutase 2 (PMM2) deficiency, sepsis, deep vein thrombosis (DVT), diabetes and uremia, inflammation in cystic fibrosis, and bleeding in myelodysplastic syndrome (MDS). Some of these proteomic studies are discussed as examples. A subproteomic analysis was performed for patients with the congenital disorder of glycosylation PMM2 that have an increased risk for thrombosis (de la Morena-Barrio et al. 2014). DIGE analysis of the N-glycoproteins however showed no quantitative or qualitative differences between patients and controls. Platelet 2DE profiles of septic patients versus healthy controls resulted in five differentially expressed proteins that include GPIX and GPIIb (Liu et al. 2014). Proteins isolated form plasma-derived microparticles were tagged with iTRAQ reagents and analyzed by 2DE with LS-MALDI MS/MS for nine patients with DVT and six healthy controls (Ramacciotti et al. 2010). The differentially expressed or depleted proteins are expected to influence thrombosis via inflammation, cell shedding, inhibition of fibrinolysis, and hemostatic plug formation. Platelets from 13 diabetic patients were analyzed before and 12 weeks after pioglitazone therapy using DIGE and LS-MS/MS (Randriamboavonjy et al. 2012). More than half of the differentially expressed protein spots identified were known calpain substrates and could be classified as cytoskeletal proteins and signaling molecules. This study suggests that diabetes-induced platelet dysfunction might be due to calpain activation. Platelet proteomics was performed for uremia patients with functional versus dysfunctional platelets as tested by PFA100 (Marques et al. 2010). 2DE and MS analysis showed changes in protein levels that might have occurred at the megakaryocyte level. Shotgun nUPLC-MSE and 2DE were used to compare platelet proteins between cystic fibrosis (CF) patients and healthy controls (Pieroni et al. 2011). CF is caused by mutations in the cystic fibrosis transmembrane conductance regulator that is expressed on platelets and related to changes in inflammation for which the proteomic study provided evidence of changes in integrin signaling. The platelet proteome of myelodysplastic syndrome (MDS) patients was analyzed with DIGE and showed lower levels of proteins that are important for integrin αIIbβ3 signaling (Fröbel et al. 2013). Impaired platelet aggregation might therefore explain the bleeding complications observed for MDS patients even at sufficient platelet counts.

Less obvious are the platelet proteomic studies that have been designed to unravel disease mechanisms for neuropathologies. However, it is known that platelets do share common characteristics with neurons (Goubau et al. 2013). Again, examples of proteomic studies conducted for Alzheimer’s disease (AD), but also for monogenetic neurological diseases, are discussed. The platelet proteome was analyzed by DIGE for 34 AD cases, 13 cases with vascular dementia, 15 Parkinson cases, and 49 healthy controls (Zellner et al. 2012). The study suggested that Mao-B platelet protein levels could be a biomarker for age-related dementia in AD. In addition, the platelet membrane proteome was determined for five AD cases versus controls using 1-DE and LC-MS/MS (Donovan et al. 2013). A total of 144 proteins were altered that represent secretory granule proteins, and it was suggested that platelets may serve as a source of blood-based biomarkers in neuropathologies. DIGE with MS identification was used to analyze platelets and fibroblasts from patients with alternating lemiplegia of childhood (AHC) due to ATP1A3 mutations (Di Michele et al. 2013). A total of 93 proteins have a different expression of which seven were detected in both cell types, and this included lysosomal protein cathepsin. Functional validation studies showed that AHC might be associated with a defective regulation of apoptosis via the lysosomal cathepsin pathway.

Conclusion

The application of the proteomics and subproteomic approaches described above has provided an invaluable contribution to the elucidation of platelet physiological mechanisms. We have included a figure with the most important items discussed in this chapter (Fig. 1). Moreover, they also provided remarkable insights into the molecular basis underlying platelet-related diseases, though several challenges still remain to be addressed. However, we are confident that the integration of the most recent advances in MS-based techniques in platelet proteomic studies will allow further delineation of the mechanisms implicated in diseases and identify new biomarkers or drug targets.

Fig. 1
figure 1

Overview of proteomic technologies used in platelet research

Full size image

Take-Home Messages

  • Proteomics represents a powerful tool to study platelet proteins and the molecular mechanisms regulating unknown platelet functions and platelet-related diseases.

  • Gel-based and gel-free proteomics technologies, both with their own advantages and limits, have been efficiently used to analyze platelets in physiological and pathological conditions.

  • The experimental design of platelet proteomic studies should take into account the appropriate controls, technical/biological replicates, and sample preparation protocols.

  • Proteomics can be applied to the study of (1) whole platelet proteome (in resting or activated condition); (2) platelet subproteomes (granules, microparticles, plasma membrane); and platelet PTMs (phosphoproteome, glycoproteome, palmitoylome).

  • Platelet proteomics has been used to investigate platelet-related bleeding disorders and cardiovascular diseases, but also diseases less obviously linked to platelets.