Quantitative mass spectrometry approaches are used for absolute and relative quantification in global proteome studies. To date, relative and absolute quantification techniques are available that differ in quantification accuracy, proteome coverage, complexity and robustness. This review focuses on most common relative or absolute quantification strategies exemplified by three experimental studies. A label-free relative quantification approach was performed for the investigation of the membrane proteome of sensory cilia to the depth of olfactory receptors in Mus musculus. A SILAC-based relative quantification approach was successfully applied for the identification of core components and transient interactors of the peroxisomal importomer in Saccharomyces cerevisiae. Furthermore, AQUA using stable isotopes was exemplified to unraveling the prenylome influenced by novel prenyltransferase inhibitors. Characteristic enrichment and fragmentation strategies for a robust quantification of the prenylome are also summarized.
Liquid chromatography-mass spectrometry (LC-MS)-based proteome quantification is an approach often used to study biological processes. Continuously, different quantitative techniques coupled to LC-MS are developed that can address questions including protein-protein interactions (PPI) studies, protein abundances or posttranslational modifications between two or more physiological states (e.g. treated/non-treated, healthy/disease). Common techniques can be generally summarized in relative quantification for comparing whole proteomes or the relative amount of a high number of proteins or in absolute quantification to determine the absolute concentration of distinct proteins within a sample (see Figure 1) (Bantscheff et al., 2012; Marcus, 2012). Both protein quantification approaches uses either chemical or metabolic stable isotope labeling for protein quantification (label-based) or quantification is performed without the introduction of stable isotopes (label-free).
Label-free quantification is a cost-efficient and easy handling relative quantification technique, where the number of measured spectra is determined (spectral counting) or the average MS/MS intensities are analyzed (Liu et al., 2004; Asara et al., 2008). The biggest advantage of label-free quantification in comparison to other relative quantification approaches is the possibility to measure and compare an unlimited number of samples and the analysis of untreated proteins or peptides. However, in contrast to other technical labeling techniques, variations are higher, because samples are individually prepared and comparison occurs only during data analysis (Bantscheff et al., 2012). Label-based approaches granted more accurate relative protein quantification, including metabolic, chemical and enzymatic labeling. Stable isotope labeling with amino acids in cell culture (SILAC) and heavy 15N-labeling are two of the best studied metabolic stable isotope-based relative quantification methods so far. Labeling of single amino acids like arginine and lysine or the replacement of all nitrogens for their heavy 15N variant during cell growth reduce variances and ensure a precise quantification accuracy as cells are labeled in a very early step in the workflow. However, a limited number of different samples can be compared and metabolic labeling can cause a growth defect in some organisms (Crotty et al., 2011; Filiou et al., 2012). An alternative to this is chemical-based relative quantification such as the isobaric tags for relative and absolute quantitation (iTRAQ) or the tandem mass tags (TMT) that are fused to proteins or peptides after proteolytic digestion. In comparison to metabolic labeling, chemical labeling is more cost-efficient and suitable for all sorts of proteins or peptides, however, the variances in the samples are higher, as sample mixing is performed at a later step in the sample preparation workflow (Thompson et al., 2003; Wiese et al., 2007; Bantscheff et al., 2012). A powerful method to determine the exact protein or peptide concentration is the usage of isotopic-labeled absolute quantification (AQUA) peptides, which are spiked into the sample in a known concentration. Using this costly technique, the concentration of only a few peptides of interest can be determined and it is unsuited for large interactome or global proteome studies (Bantscheff et al., 2012). The review focus on most common relative or absolute quantification strategies exemplified by three different experimental examples: label-free relative quantification, investigation of the membrane proteome of sensory cilia to the depth of olfactory receptors in Mus musculus; SILAC-based relative quantification, identification of core components and transient interactors of the peroxisomal importomer in Saccharomyces cerevisiae (S. cerevisiae); and AQUA-based absolute quantification, analysis to unravel the prenylome influenced by e.g. novel prenyltransferase inhibitors.
The subsequent parts will give background information about relative and absolute quantification techniques focused on label-free, SILAC-based and AQUA-based quantification strategies.
Label-free relative quantification presents a high-throughput method in the field of quantitative proteomics. Sample preparation is managed without the application of labeling reagents, which makes such approaches simple and cost efficient. Relative label-free methods can roughly be subdivided into counting the number of peptide-to-spectrum matches (PSMs; spectral counting) obtained for each protein, as more abundant proteins are more likely to be observed in peptide spectra (Washburn et al., 2001; Maerkens et al., 2013) or measurement of peptide signal intensities by using the extracted ion chromatogram (XIC) (Wiener et al., 2004; Megger et al., 2013a,b). Spectral counting is based on determination and comparison of the number of tandem (MS/MS) fragment-ion spectra of peptides between different samples without the consideration of physicochemical peptide properties. This approach can easily be used for comparison of a large number of datasets and therefore has gained popularity over the last years. Proteomic tools such as absolute protein expression (APEX) and normalized spectral abundance factor (NSAF) also incorporate the length of the corresponding protein for absolute quantification (Megger et al., 2013a,b). The second label-free technique is based on the measurement of MS-signal intensity of the area under the chromatographic peak of the peptide precursor ion. In this approach peptide quantification is typically accomplished by integration of ion intensities of any given peptide over its chromatographic elution profile. In differential studies the integrated signal response of individual peptides is compared between LC-MS(/MS) runs of different samples. Subsequently, estimation of differential protein abundance is performed by an aggregation of differences measured for all (unique) peptides matching the respective protein. A step forward in the field of label-free proteomics presents MS/MS-based quantification methods like selected reaction monitoring (SRM). SRM includes targeted data dependent acquisition (DDA) with a predefined parent mass list, which presents information about peptide fragmentation (transition signals) from compounds of interest. SRM is performed on triple quadrupole instruments, where the parent ions of interest are isolated in the first quadrupole, fragmented in the second and the transition signals of interest are analyzed in the third. The usage of modern high-resolution second mass analyzers significantly increases the sensitivity of SRM methods (Dillen et al., 2012). A further alternative label-free method based on data independent acquisition (DIA) is sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) which is based on the analysis of window of masses and stepping this window across the complete mass range of interest (Gillet et al., 2012). Label-free approaches do not support multiplexing of samples and every sample is handled and measured separately, which negatively affects the reproducibility of label-free approaches and requires highly reproducible workflows and an appropriate normalization. Moreover, variations of the retention time or the m/z values have to be considered. Therefore, it is really important to reduce technical variances in all steps of the sample preparation and during LC-MS measurement. Furthermore, the impact of sample heterogeneity and contaminants in samples has to be considered (Braakman et al., 2012; Liu et al., 2012). For more details about pros and cons of label-free quantification, see Marcus (2012). Recently, label-free quantitative proteomics is increasingly used for global interaction studies. One example for protein-protein interaction mapping is the label-free based quantitative analysis of the membrane proteome of sensory cilia to the depth of olfactory receptors by (Kuhlmann et al., 2014).
Label-based relative quantification (focused on SILAC)
Label-based approaches like chemical or metabolic labeling allow a multiplexing of samples resulting in high quantification accuracy and precision. Common chemical labeling regencies like the isotope-coded affinity tag (ICAT), isobaric tags for relative and absolute quantification (iTRAQ), tandem mass tag (TMT) or dimethyl labeling present postharvest labeling methods (Gygi et al., 1999; Hsu et al., 2003; Thompson et al., 2003; Wiese et al., 2007; Lindemann et al., 2013). Basically, a chemical reaction in vitro between the reagent and the peptides of interest leads to a heavy-labeled peptide mixture. After LC-MS/MS a mass shift between two different isotopic-labeled samples is used for relative quantification. Chemical labeling is possible for a wide range of different sample types, however, ideal labeling conditions are crucial and require optimization.
In contrast to chemical labeling, SILAC involve metabolic labeling of proteins during their synthesis in vivo (Ong et al., 2002). This technique minimizes variances between different labeled samples, since they are combined in an early step of the workflow after cell growth. This results in increased reproducibility and accuracy compared to chemical or label-free methods (Ong et al., 2003a,b; Lau et al., 2014). The simple and robust SILAC strategy can be applied to various biological questions. SILAC is mostly carried out by using arginine and lysine with stable isotopes of 2H, 13C and 15N. Additionally, amino acids as methionine, histidine or leucine can be used for SILAC as well (Beynon and Pratt, 2005). A typical SILAC experiment is performed with two biological populations or cell cultures growing either in medium with unlabeled arginine/lysine (light) or in medium supplemented with isotopic labeled arginine/lysine (heavy). The cultures are mixed, proteins are extracted and proteolytic digested to peptides prior to the measurement by LC-MS/MS. In bottom-up proteomics, tryptic digestion results in peptides with C-terminal arginine and lysine. This ensures a heavy-labeling of nearly all peptides. A heavy-labeled peptide produces a specific mass shift compared to its light counterpart visible as SILAC peptide pairs in the mass spectrum. The calculation of the heavy to light-ratio performed by software tools like MaxQuant, gives an evidence in changes of protein abundance (Cox and Mann, 2008). An accurate quantification requires a complete incorporation of the heavy amino acids. Incomplete labeling can be prevented by using organisms that are unable to synthesize the employed heavy amino acids. Furthermore, the conversion of 13C6-arginine to 13C5-proline has to be taken into account and can be prevented by reducing the amount of arginine and/or increasing the amount of proline (Ong et al., 2003a,b). In general, SILAC was successfully established for mammalian cell culture (Ong et al., 2002), different model organisms such as mouse (Kruger et al., 2008), fly (Sury et al., 2010) and worm (Fredens et al., 2011) as well as cultured yeast (Oeljeklaus et al., 2014), bacteria Bacillus subtilis (Soufi et al., 2010) and plants (Gruhler et al., 2005). The following SILAC-strategies have been described so far: dynamic SILAC (Amanchy et al., 2005), heavy methyl SILAC (Ong et al., 2004), pulsed-SILAC (Schwanhausser et al., 2009), super-SILAC (Geiger et al., 2010), spike-in SILAC (Geiger et al., 2011), triple-SILAC (Bose et al., 2006) and affinity purification (AP)-MS SILAC (Blagoev et al., 2003; Oeljeklaus et al., 2012). SILAC allows multiplexing from 2-plex up to 5-plex when using five distinct isotopic variants of arginine (Molina et al., 2009). A step forward was the recently developed neutron encoding (NeuCode) method, which combines SILAC and isobaric tagging methods and was applied in a study of time-resolved response of five signaling mutants in a single 18-plex experiment (Merrill et al., 2014). With the development of super-SILAC (Geiger et al., 2010) and spike-in SILAC (Geiger et al., 2011), SILAC can now be used for the quantification of samples from tissue or body fluids and therewith has the potential to be applied for clinical applications. Furthermore, false positive identification of regulated or interacting proteins in SILAC-based experiments can also be avoided by the AP-MS SILAC technique, which was successfully implemented by Oeljeklaus et al. (2012).
Absolute quantification (focused on AQUA peptides)
The most common label-based absolute quantification method introduces heavy-labeled, so-called AQUA peptides, which serve as an internal standard on peptide level. Here, synthetic peptides with a known concentration are added to the cell lysates during protein digestion. Similar to SILAC, stable isotope labeled amino acids containing 13C and 15N are used generating a predefined mass shift. The exact concentration of the peptide of interest is calculated using the intensity of the heavy-labeled AQUA standard peptide (Gerber et al., 2003). Although AQUA offers an accurate absolute quantification, some disadvantages have to be considered using this technique. Synthesizing AQUA peptides is an expensive procedure only suitable for the quantification of a few peptides of interest. Moreover, prior information about the peptide, such as elution time, m/z ratio, charge state and the best fragmentation method, is required to quantify peptides by AQUA. Peptides that are not detectable by LC-MS/MS can never be quantified. To avoid this problem absolute quantification techniques such as full-length expressed stable isotope-labeled proteins for absolute quantification (FLEXIQuant) (Brun et al., 2007) or protein standard absolute quantification (PSAQ) (Singh et al., 2009) were developed. Here, not only peptides but entire heavy-labeled proteins with known concentrations were added to the sample prior to tryptic digestion. Beside label-based absolute protein quantification, label-free absolute quantification is a considerable, software-based technique, which usually requires no standard peptides. Methods like emPAI is based on a so-called protein abundant index (PAI), calculating the observed peptides divided by the theoretically observed peptides (Ishihama et al., 2005). Another method is absolute protein expression (APEX), which requires spectral counting. Thereby, the number of observed divided by the number of expected spectra is calculated including correction factors, which are based on prior MS-measurements (Lu et al., 2007). Top3 is an additional method calculating the relation of the three most abundant peptides between two or more samples. This is especially helpful when the protein of interest is digested incompletely, however, it requires a standard protein as reference (Silva et al., 2006). All these quantification techniques have a lower accuracy compared to quantification methods like AQUA, however, they are cost and time efficient, as no heavy-labeled standards are needed and comparison with an unlimited number of different samples is ensured. AQUA was successfully used by Wotske et al. (2012).
Relative and absolute quantification strategies
Label-free relative quantification strategy for the investigation of the membrane proteome of sensory cilia to the depth of olfactory receptors in Mus musculus
The membrane of sensory cilia contains olfactory receptors (ORs) that extend from primary olfactory sensory neurons (OSNs), which covers the olfactory epithelium (OE) in the nasal cavity in vertebrates (McEwen et al., 2008). ORs belongs to G protein-coupled receptors binding odorant and trigger a signal transduction cascade (Breer, 2003). The signal transduction pathway is well established, but the molecular processes in OSN cilia are not fully understood. The study of Kuhlmann et al. focused on the analysis of the mouse olfactory membrane ciliome to discover new resident proteins and low-abundant central components (mainly focused on ORs) in the OSNs (Kuhlmann et al., 2014). The used workflow includes a selective enrichment of olfactory ciliary membrane proteins, with an established Ca2+/K+-shock method from transgenic mice expressing the gene coding for the green fluorescent protein under control of the olfactory marker protein (OMP)-promoter (Mayer et al., 2009). Next, the enriched ciliary fractions were treated with Na2CO3 under alkaline pH to enrich integral membrane proteins (Fujiki et al., 1982). After a successful verification of the enrichment of olfactory cilia membranes by immunoblot assays, a gel-based and a gel-free (including strong cation exchange (SCX)-chromatography) LC-MS/MS was performed. The usage of two different approaches (gel-free and gel-based) was carried out to ensure a comprehensive inventory of olfactory ciliary membranes to the depth of low abundant ORs (Kuhlmann et al., 2014). In total, 4403 unique protein groups (false discovery rate <0.01) were identified, from which 3750 protein groups (85.2%) were detected after the gel-based and 4257 protein groups (96.7%) after the gel-free approach. Generally, the gel-free has more advantages compared to the gel-based analysis, because more ORs (60%, 37 of 62) were identified [including low-abundant ORs; gel-based: only 6% (4 of 62)] and an overlay of more than 80% of the protein IDs for soluble and integral membrane proteins within the biological replicates (n=3) could be reached (see Figure 2). Furthermore, higher iBAQ values (estimated relative protein abundance level: sum of all peptide intensities divided by the number of theoretically observable tryptic peptides of a protein) for the 62 ORs in the gel-free approach with an abundance range more than two orders of magnitude were measured (Kuhlmann et al., 2014). All known constituents of the canonical pathway of olfactory signal transduction were identified in the proteome study (ACIII, three CNG channel subunits, heterotrimeric G protein subunits GNAL, GNB1 and GNB13) with high iBAQ values, indicating a high expression level in olfactory cilia. Numerous proteins of the Ca2+-signaling network, such as the Ca2+-binding proteins Annexin, were identified. Annexins ANAX1, ANAX2, ANAX5 and S100A5 were successfully verified by immunoassays providing these proteins as potential new functional targets in the investigation of olfactory signal transduction. The identification of proteins involved in vesicle and intraflagellar transport (BBS7, BBS8: sorting or protein to the cilia), membrane transport processes (e.g. K+- and Na+ -channels, Na+/K+-ATPase subunit) and catalytic processes (e.g. AK1 and ALDOA) indicate a communication between the extracellular milieu and the olfactory cilia. In summary, Kuhlmann et al. presented the so far largest olfactory membrane ciliome study with detailed results about the heterogeneity and relative abundance levels of native ORs in the olfactory cilia in Mus musculus using a label-free relative quantification approach. The established workflow provides a huge potential for further analysis of stimulus-induced changes in the olfactory cilia membrane proteome to investigate the adaptation of peripheral olfactory system to external stimuli over time.
SILAC-based relative quantification strategy for the identification of core components and transient interactors of the peroxisomal importomer in S. cerevisiae
The large-scale analysis of PPI networks can be realized with techniques such as the yeast two-hybrid system or AP-MS (Fields and Sternglanz, 1994; Rigaut et al., 1999). In contrast to two-hybrid systems, where interactions of only two proteins are tested at a single time point, AP-MS can be used for the investigation of multiprotein complexes. The isolation of complexes is carried out under native conditions using a tagged bait protein subjected to one or more affinity purification steps followed by LC-MS/MS (von Mering et al., 2002). Additionally, the combination of AP-MS with SILAC allows the quantification of interaction partners inside protein complexes and can be used to probe dynamic changes (Brand et al., 2004). In an AP-MS SILAC experiment the ‘tagged’ bait protein is overproduced in light SILAC medium. In a second culture the ‘control’ protein is overproduced in heavy SILAC medium. The two differently grown cultures are mixed before AP, termed as affinity purification after mixing (AP-AM) (Wang and Huang, 2008). During mixing, an on/off exchange of light- and heavy-labeled transient bait interacting proteins can occur. In contrast, specific light-labeled interactors are constantly bound to the bait protein. Furthermore, non-specific binding of light- and heavy-labeled proteins to the affinity matrices can occur. After purification and LC-MS/MS, specific interactors are identified mainly in the light-labeled state, whereas non-specific and transient interactors will appear in the light- and heavy-labeled form. Therefore, transient interacting proteins can be falsely categorized as co-purified contaminants after AP. This results in false positive or false negative identification of interactors (Gingras et al., 2007). Affinity purification prior to mixing (AP-PM) prevents an exchange of transient interactors. A combination of both AP-experiments (AP-AM and AP-PM) results in an improved discrimination of specific, non-specific and transient interactions (Wang and Huang, 2008). Data analysis of specific and non-specific interactors should reveal the same light and heavy abundances in the AP-AM as well in the AP-PM strategy. In contrast, transient interactors will show a higher abundance in their heavy form in the AP-AM approach. Notably, the characterization of large membrane protein complexes by AP-MS presents a challenging task. Due to their hydrophobicity, purification of such complexes in their native form requires novel strategies (Oeljeklaus et al., 2009). In a dual-track SILAC approach, which combines the AP-AM and AP-PM strategies, Oeljeklaus and colleagues successfully investigated the peroxisomal importomer in S. cerevisiae (Oeljeklaus et al., 2012). During the biogenesis of peroxisomes, proteins of the importomer mediate the import of matrix proteins across the organelle membrane. Pex14p represents the central component of the multiprotein importomer complex. Using the dual-track SILAC approach, Pex14p tagged with TEVcs-Protein A (PA) was overproduced and isolated from yeast cells grown in light SILAC medium (‘tagged’-culture). Yeast cells synthesizing endogenous Pex14p served as control and were metabolic labeled with the corresponding heavy SILAC form (‘control’ culture). Lysates of differentially treated cells were subjected to AP-AM or AP-PM experiments using tagged Pex14p as bait protein in equal concentrations (see Figure 3). The dual-track AP-MS SILAC strategy resulted in the identification of known components of the importomer (Pex13p, Pex14p, Pex17p), proteins closely associated with the importomer (Dyn2) or involved in peroxisome proliferation (Pex11p). Additionally, a group of transient interaction partners including peroxins, peroxisomal membrane, and matrix proteins were identified. The results allow a clear discrimination between stable and transient interaction partners of Pex14p. Thereby, Oeljeklaus et al. identified the most detailed Pex14p interactome with nine core and twelve transient components. Furthermore, the successful application on large membrane protein complexes was carried out without organelle isolation and additional purification or washing steps. Thus, the dual-track SILAC approach allows further investigations of membrane-embedded macromolecular complexes.
AQUA-like absolute quantification of prenylated Rab proteins – a unique posttranslational modification
In comparison to other Ras superfamily members, Rab GTPases are also posttranslational modified with prenyl moieties attached to the C-terminus of the protein. Differently to other Ras GTPases, which bear a CAAX-box (with C=cysteine, A=aliphatic amino acid, X=any amino acid; defining the type of attached prenyl group, either farnesyl or geranylgeranyl) as modification signal, Rab GTPases possess a CXC- or CC-motif. Both cysteines of the CXC- or CC-motif bear a geranylgeranyl moiety; therefore Rab GTPases are often doubly geranylgeranylated (Farnsworth et al., 1994; Wennerberg et al., 2005; Triola et al., 2012). Before geranylgeranylation, each newly synthesized Rab GTPase needs to be recognized by a Rab escort protein (REP). This protein presents the bound Rab GTPase to the Rab geranylgeranyltransferase (Rab GGTase), which attaches the geranylgeranyl moieties to the cysteines of the CXC- or CC-motif (Andres et al., 1993; Farnsworth et al., 1994; Stein et al., 2003; Rak et al., 2004). After geranylgeranylation, the Rab GGTase leaves the REP complex. Finally, the REP escorts the Rab protein to the membrane of its final compartment (Stein et al., 2003). During vesicular transport and protein trafficking, Rab GTPases are bound via the geranylgeranyl moieties to a lipid membrane of a vesicle, an organelle, or the plasma membrane (Stein et al., 2003). After completing vesicular transport, the Rab protein is recognized by Rab GDP dissociation inhibitor (Rab GDI) for return transport of the Rab protein to its starting membrane (Stein et al., 2003; Goody et al., 2005; Hutagalung and Novick, 2011). Therefore, Rab GDI internalizes the geranylgeranyl moieties and extracts the protein from the lipid membrane (Goody et al., 2005). The Rab protein is returned to its starting membrane for a new initialization of a vesicular transport. Dysfunction of Rab GTPases also plays crucial roles in serious human diseases (Goitre et al., 2014). Different to other members of the Ras superfamily, pathogenesis is not developed by influences of mutations in the Rab protein itself. Furthermore, interacting proteins like REP, Rab GDI or other effectors cause diseases such as choroideremia (Andres et al., 1993), X-linked mental retardation (D’Adamo et al., 1998) or Griscelli syndrome (Menasche et al., 2003a,b). Rab dysregulation or aberrant expression was observed in cancer, due to the support of tumor progression (Recchi and Seabra, 2012; Zhen and Stenmark, 2015). Therefore, Rab proteins are interesting clinical targets. Since no Rab-specific drugs were developed so far, Rab GGTase inhibitors were used to prevent geranylgeranylation (Recchi and Seabra, 2012; Triola et al., 2012). This assures non-function of the aberrant expressed Rab proteins due to non-membranous localization. As prenylated Rabs are of low abundance, strategies were developed to target this class of proteins. A strategy prior to mass spectrometric analysis was the online chromatographic separation technique multidimensional protein identification technology (MudPIT) (Washburn et al., 2001; Franzel and Wolters, 2011). In MudPIT, peptides prepared after a tryptic digestion of complex protein samples are two-dimensionally separated. Eluting peptides from the SCX phase reach an analytical reverse-phase for hydrophobic separation. Using this technique, we were able to separate farnesylated peptides in complex cell samples. We demonstrated that prenylated Rab peptides elute much later than any other non-prenylated peptide on the reverse phase of our MudPIT column, which paves the way for clean MS/MS analysis without interfering peptides (see Figure 4) (Wotske et al., 2012). Moreover, prenylated peptides elute exclusively with high salt step conditions (NH4Ac), which adds another dimension for robust identification and thus quantification even without or with crude enrichment.
Interestingly, prenylated peptides show a loss of the prenyl group during ESI and MS/MS fragmentation. This characteristic event can be used for specific quantification approaches. For the farnesylated peptides, the protonated farnesyl group (205 Da) serves as a marker ion and a neutral loss of the corresponding ions in MS/MS spectra can be observed (Wotske et al., 2012).
Alexandrov and coworkers introduced a strategy that tags Rab proteins with a biotin-geranyl moiety and a subsequently isolation via streptavidin (Nguyen et al., 2009). For this purpose they prevented geranylgeranylation of Rab proteins in COS-7 cells by using different inhibitors (compactin, BMS, etc.) and finally attached the biotin-geranyl moieties via recombinant REP-1 and Rab GGTase, which recognize the artificial substrate biotinylated geranyl pyrophosphate (BGPP). Using MudPIT, Wotske et al. identified and quantified 42 different Rab proteins in COS-7 cells. By using this strategy, farnesylated and singly geranylgeranylated proteins can also be identified when adding recombinant farnesyltransferase or geranylgeranyltransferase for studying the whole prenylome. For quantification of the prenylated Rab GTPases they used an AQUA-like approach. In contrast to synthetic stable isotope peptides in AQUA, they used 15N-labeled overproduced Rab22A, which was spiked into the compactin-treated and BMS-treated COS-7 cell lysate as an internal standard for absolute quantification. Total amount of Rabs in the BMS-treated sample was normalized against the total amount of Rabs in the compactin-treated sample. By this approach they elucidated that the abundant RabGTPases form three clusters: GTPases that control the early steps of endocytosis (Rab5A,B,C, Rab14, Rab21, and Rab35, see Figure 5 in red); GTPases that control secretion (Rab3D, Rab10, Rab11A,B, and Rab18, see Figure 5 in green); and RabGTPases that control the traffic in and to the Golgi apparatus (Rab 1A,B, and Rab2A,B, see Figure 5 in blue). Eventually, profiling the entire prenylome to follow the mechanism and potency of different prenyltransferase inhibitors, for example, becomes feasible.
Recently, different quantitative proteomics techniques were successfully established for several biological systems and scientific questions. First, a label-free MS-intensity-based quantification method was used for the investigation of the membrane proteome of sensory cilia to the depth of olfactory receptors in Mus musculus. This work presents the largest olfactory membrane ciliome study to date that includes results about heterogeneity and relative abundance levels of native ORs in the olfactory cilia in Mus musculus. The established workflow allows further analysis of stimulus-induced changes and long term adaptation of the peripheral olfactory system, including low-abundant signaling components like ORs. Second, the dual-track SILAC approach allowed the identification of highly transient interactors resulting in the most detailed interactome study of Pex14p to date. Additionally, the strategy enabled the analysis of the membrane-localized importomer without additional AP steps or organelle isolation. This opens up future in-depth investigation of membrane protein complexes. Additionally, we investigated the quantification of less prominent posttranslational modification, namely protein prenylation and exemplified an AQUA-like strategy. For this quantification approach we developed suitable enrichment, separation and MS strategies. The demonstrated high potential of the established approaches advanced the knowledge in the individual projects. In general, relative and absolute quantitative techniques and application continuously develop. SILAC quantification evolved by the construction of so-called spike-in or super-SILAC standards. Both techniques are based on internal, isotopic-labeled protein standards of one cell line (spike-in SILAC) or two or more different cell lines (super-SILAC), which are added to the samples of interest before LC-MS/MS (Geiger et al., 2010, 2011; Shenoy and Geiger, 2015). During data analysis, samples are quantified relatively against the internal standard and fold changes are calculated between an isotopic-labeled peptide of the standard and the non-labeled peptide of interest. The techniques were applied to cell culture as well as tissues or body fluids, which indicates the high potential of different SILAC approaches in the fields of clinical research in the future (Chen et al., 2015). Another alternative to common metabolic or chemical isotopic labeling strategies was introduced by the usage of so-called N-succinimidyloxycarbonylmethyl tris(2,4,6-trimethoxyphenyl) phosphonium bromide (TMPP)-labeled peptides, which reacts with the N-terminal of proteins or peptides. TMPP-labeling reaches similar quantification accuracy compared to SILAC, however the increasing hydrophobicity of peptides lead to enhanced ionization efficiency (Shen et al., 2015). Recently, a step forward in label-free quantification was the development of DIA-based SWATH-MS introducing the concept of combining a protein library and individual digital maps. Analog to other targeted MS-methods, the quantification occurs on MS/MS level with similar accuracy and consistency. Furthermore, SWATH-MS improves the identification of small and low abundant proteins showing a high potential in the discovery of biomarker and clinical diagnosis of human tissue, where chemical or metabolic labeling is often not applicable (Anjo et al., 2017).
About the authors
Claudia Lindemann studied biochemistry at the University of Bayreuth/Germany. She completed her PhD thesis in the lab of Lars Leichert, working on the thiol-based redox regulation in Escherichia coli. She is currently working as post doc in the group of Katrin Marcus at the Medizinisches Proteom-Center (MPC), Ruhr-University Bochum. Her research focusses on the MS-based analyses of structures and post-translational modifications of proteins.
Nikolas Thomanek studied biology and biotechnology at the Ruhr-University of Bochum/Germany. He completed his PhD thesis composed at the Medizinisches Proteom-Center (MPC), Ruhr-University Bochum. He is currently working as post doc in the group of Katrin Marcus. His research focusses on the regulation of lipopolysaccharide biosynthesis in Escherichia coli and quantitative mass spectrometry using SILAC and super-SILAC.
Franziska Hundt studied Biology at the Ruhr-University Bochum in a bachelor’s and master’s-program. In 2016 she successfully completed her doctorate in the research group of Dirk Wolters. She focusses on establishing new enrichment strategies and mass spectrometric quantitation methods for studying Rab GTPases in different human cell lines.
Thilo Lerari studied biology and biotechnology at the Ruhr-University of Bochum/Germany. He completed his PhD thesis composed at the Medizinisches Proteom-Center (MPC), Ruhr-University Bochum. He is currently working as research assistant in the group of Katrin Marcus. His research focusses on quantitative phosphoproteomics and the optimization and establishment of the applied strategies for enrichment and detection.
Helmut E. Meyer studied biochemistry at the University of Tübingen. He completed his PhD thesis in the biochemistry department, Prof. Pfleiderer, of the Ruhr-University of Bochum on the primary structure of porcine LDH. He is a renowned protein chemist and was working in this field for 40 years. He founded the Medizinisches Proteom-Center and is retired as full-professor in March 2014.
Dirk Wolters obtained his PhD in 1998 from the Ruhr University Bochum. He pursued his research interest in biomolecular mass spectrometry with John R. Yates at the University of Washington, Seattle, USA and different pharmaceutical companies (Novartis, San Diego, USA; Hoffmann-La Roche, Basel, Switzerland) before he accepted a position as head of the Biomolecular Mass Spectrometry group of the Analytical Chemistry department at the Ruhr-University Bochum.
Katrin Marcus is director of the Medizinisches Proteom-Center, Ruhr-University Bochum since 2014. Her expertise is in the field of proteomics and mass spectrometry with a special focus on neurodegenerative and neuromuscular diseases. Biomarkers are investigated by the application of state-of-the-art proteomics methods and the development of new high-performance technologies proteins involved in the pathogenesis of those diseases as well as diagnosis.
All work was supported by the German Research Foundation within the research project SFB642: GTP- and ATP-dependent membrane processes. We thank Prof. Dr. Bettina Warscheid, Dr. Silke Oeljeklaus and Dr. Katja Kuhlmann for their contribution to the successful work within SFB642. The prenylation work was carried out in close collaboration with Prof. Roger Goody and other members of the Max Planck Institute of Molecular Physiology in Dortmund, Germany.
Amanchy, R., Kalume, D.E., and Pandey, A. (2005). Stable isotope labeling with amino acids in cell culture (SILAC) for studying dynamics of protein abundance and posttranslational modifications. Sci. STKE 2005, pl2.10.1126/stke.2672005pl2Search in Google Scholar PubMed
Andres, D.A., Milatovich, A., Ozcelik, T., Wenzlau, J.M., Brown, M.S., Goldstein, J.L., and Francke, U. (1993). cDNA cloning of the two subunits of human CAAX farnesyltransferase and chromosomal mapping of FNTA and FNTB loci and related sequences. Genomics 18, 105–112.10.1006/geno.1993.1432Search in Google Scholar PubMed
Anjo, S.I., Santa, C., and Manadas, B. (2017). SWATH-MS as a tool for biomarker discovery: From basic research to clinical applications. Proteomics 17, 3–4.10.1002/pmic.201600278Search in Google Scholar PubMed
Asara, J.M., Christofk, H.R., Freimark, L.M., and Cantley, L.C. (2008). A label-free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen. Proteomics 8, 994–999.10.1002/pmic.200700426Search in Google Scholar PubMed
Bantscheff, M., Lemeer, S., Savitski, M.M., and Kuster, B. (2012). Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal. Bioanal. Chem. 404, 939–965.10.1007/s00216-012-6203-4Search in Google Scholar PubMed
Blagoev, B., Kratchmarova, I., Ong, S.E., Nielsen, M., Foster, L.J., and Mann, M. (2003). A proteomics strategy to elucidate functional protein-protein interactions applied to EGF signaling. Nat. Biotechnol. 21, 315–318.10.1038/nbt790Search in Google Scholar PubMed
Bose, R., Molina, H., Patterson, A.S., Bitok, J.K., Periaswamy, B., Bader, J.S., Pandey, A., and Cole, P.A. (2006). Phosphoproteomic analysis of Her2/neu signaling and inhibition. Proc. Natl. Acad. Sci. USA 103, 9773–9778.10.1073/pnas.0603948103Search in Google Scholar PubMed PubMed Central
Braakman, R.B., Tilanus-Linthorst, M.M., Liu, N.Q., Stingl, C., Dekker, L.J., Luider, T.M., Martens, J.W., Foekens, J.A., and Umar, A. (2012). Optimized nLC-MS workflow for laser capture microdissected breast cancer tissue. J. Proteomics 75, 2844–2854.10.1016/j.jprot.2012.01.022Search in Google Scholar PubMed
Brand, M., Ranish, J.A., Kummer, N.T., Hamilton, J., Igarashi, K., Francastel, C., Chi, T.H., Crabtree, G.R., Aebersold, R., and Groudine, M. (2004). Dynamic changes in transcription factor complexes during erythroid differentiation revealed by quantitative proteomics. Nat. Struct. Mol. Biol. 11, 73–80.10.1038/nsmb713Search in Google Scholar PubMed
Brun, V., Dupuis, A., Adrait, A., Marcellin, M., Thomas, D., Court, M., Vandenesch, F., and Garin, J. (2007). Isotope-labeled protein standards: toward absolute quantitative proteomics. Mol. Cell. Proteomics 6, 2139–2149.10.1074/mcp.M700163-MCP200Search in Google Scholar PubMed
Chen, X., Wei, S., Ji, Y., Guo, X., and Yang, F. (2015). Quantitative proteomics using SILAC: principles, applications, and developments. Proteomics 15, 3175–3192.10.1002/pmic.201500108Search in Google Scholar PubMed
Cox, J. and Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372.10.1038/nbt.1511Search in Google Scholar PubMed
Crotty, F.V., Blackshaw, R.P., and Murray, P.J. (2011). Differential growth of the fungus Absidia cylindrospora on 13C/15N-labelled media. Rapid Commun. Mass Spectrom. 25, 1479–1484.10.1002/rcm.4888Search in Google Scholar PubMed
D’Adamo, P., Menegon, A., Lo Nigro, C., Grasso, M., Gulisano, M., Tamanini, F., Bienvenu, T., Gedeon, A.K., Oostra, B., Wu, S.K., et al. (1998). Mutations in GDI1 are responsible for X-linked non-specific mental retardation. Nat. Genet. 19, 134–139.10.1038/487Search in Google Scholar PubMed
Dillen, L., Cools, W., Vereyken, L., Lorreyne, W., Huybrechts, T., de Vries, R., Ghobarah, H., and Cuyckens, F. (2012). Comparison of triple quadrupole and high-resolution TOF-MS for quantification of peptides. Bioanalysis 4, 565–579.10.4155/bio.12.3Search in Google Scholar PubMed
Farnsworth, C.C., Seabra, M.C., Ericsson, L.H., Gelb, M.H., and Glomset, J.A. (1994). Rab geranylgeranyl transferase catalyzes the geranylgeranylation of adjacent cysteines in the small GTPases Rab1A, Rab3A, and Rab5A. Proc. Natl. Acad. Sci. USA 91, 11963–11967.10.1073/pnas.91.25.11963Search in Google Scholar PubMed PubMed Central
Filiou, M.D., Varadarajulu, J., Teplytska, L., Reckow, S., Maccarrone, G., and Turck, C.W. (2012). The 15N isotope effect in Escherichia coli: a neutron can make the difference. Proteomics 12, 3121–3128.10.1002/pmic.201200209Search in Google Scholar PubMed
Fredens, J., Engholm-Keller, K., Giessing, A., Pultz, D., Larsen, M.R., Hojrup, P., Moller-Jensen, J., and Faergeman, N.J. (2011). Quantitative proteomics by amino acid labeling in C. elegans. Nat. Methods 8, 845–847.10.1038/nmeth.1675Search in Google Scholar PubMed
Fujiki, Y., Hubbard, A.L., Fowler, S., and Lazarow, P.B. (1982). Isolation of intracellular membranes by means of sodium carbonate treatment: application to endoplasmic reticulum. J. Cell Biol. 93, 97–102.10.1083/jcb.93.1.97Search in Google Scholar PubMed PubMed Central
Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J.R., and Mann, M. (2010). Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods 7, 383–385.10.1038/nmeth.1446Search in Google Scholar PubMed
Geiger, T., Wisniewski, J.R., Cox, J., Zanivan, S., Kruger, M., Ishihama, Y., and Mann, M. (2011). Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics. Nat. Protoc. 6, 147–157.10.1038/nprot.2010.192Search in Google Scholar PubMed
Gerber, S.A., Rush, J., Stemman, O., Kirschner, M.W., and Gygi, S.P. (2003). Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl. Acad. Sci. USA 100, 6940–6945.10.1073/pnas.0832254100Search in Google Scholar PubMed PubMed Central
Gillet, L.C., Navarro, P., Tate, S., Rost, H., Selevsek, N., Reiter, L., Bonner, R., and Aebersold, R. (2012). Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111 016717.10.1074/mcp.O111.016717Search in Google Scholar PubMed PubMed Central
Gingras, A.C., Gstaiger, M., Raught, B., and Aebersold, R. (2007). Analysis of protein complexes using mass spectrometry. Nat. Rev. Mol. Cell Biol. 8, 645–654.10.1038/nrm2208Search in Google Scholar PubMed
Goitre, L., Trapani, E., Trabalzini, L., and Retta, S.F. (2014). The Ras superfamily of small GTPases: the unlocked secrets. Methods Mol. Biol. 1120, 1–18.10.1007/978-1-62703-791-4_1Search in Google Scholar PubMed
Goody, R.S., Durek, T., Waldmann, H., Brunsveld, L., and Alexandrov, K. (2005). Application of protein semisynthesis for the construction of functionalized posttranslationally modified rab GTPases. Methods Enzymol. 403, 29–42.10.1016/S0076-6879(05)03004-1Search in Google Scholar PubMed
Gruhler, A., Schulze, W.X., Matthiesen, R., Mann, M., and Jensen, O.N. (2005). Stable isotope labeling of Arabidopsis thaliana cells and quantitative proteomics by mass spectrometry. Mol. Cell. Proteomics 4, 1697–1709.10.1074/mcp.M500190-MCP200Search in Google Scholar PubMed
Gygi, S.P., Rist, B., Gerber, S.A., Turecek, F., Gelb, M.H., and Aebersold, R. (1999). Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994–999.10.1038/13690Search in Google Scholar PubMed
Hutagalung, A.H. and Novick, P.J. (2011). Role of Rab GTPases in membrane traffic and cell physiology. Physiol. Rev. 91, 119–149.10.1152/physrev.00059.2009Search in Google Scholar PubMed PubMed Central
Ishihama, Y., Oda, Y., Tabata, T., Sato, T., Nagasu, T., Rappsilber, J., and Mann, M. (2005). Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol. Cell. Proteomics 4, 1265–1272.10.1074/mcp.M500061-MCP200Search in Google Scholar PubMed
Kruger, M., Moser, M., Ussar, S., Thievessen, I., Luber, C.A., Forner, F., Schmidt, S., Zanivan, S., Fässler, R., and Mann, M. (2008). SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 134, 353–364.10.1016/j.cell.2008.05.033Search in Google Scholar PubMed
Kuhlmann, K., Tschapek, A., Wiese, H., Eisenacher, M., Meyer, H.E., Hatt, H.H., Oeljeklaus, S., and Warscheid, B. (2014). The membrane proteome of sensory cilia to the depth of olfactory receptors. Mol. Cell. Proteomics 13, 1828–1843.10.1074/mcp.M113.035378Search in Google Scholar PubMed PubMed Central
Lau, H.T., Suh, H.W., Golkowski, M., and Ong, S.E. (2014). Comparing SILAC- and stable isotope dimethyl-labeling approaches for quantitative proteomics. J. Proteome Res. 13, 4164–4174.10.1021/pr500630aSearch in Google Scholar PubMed PubMed Central
Lindemann, C., Lupilova, N., Muller, A., Warscheid, B., Meyer, H.E., Kuhlmann, K., Eisenacher, M., and Leichert, L.I. (2013). Redox proteomics uncovers peroxynitrite-sensitive proteins that help Escherichia coli to overcome nitrosative stress. J. Biol. Chem. 288, 19698–19714.10.1074/jbc.M113.457556Search in Google Scholar PubMed PubMed Central
Liu, H., Sadygov, R.G., and Yates, J.R., 3rd. (2004). A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76, 4193–4201.10.1021/ac0498563Search in Google Scholar PubMed
Liu, N.Q., Braakman, R.B., Stingl, C., Luider, T.M., Martens, J.W., Foekens, J.A., and Umar, A. (2012). Proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue. J. Mammary Gland Biol. Neoplasia 17, 155–164.10.1007/s10911-012-9252-6Search in Google Scholar PubMed PubMed Central
Lu, P., Vogel, C., Wang, R., Yao, X., and Marcotte, E.M. (2007). Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat. Biotechnol. 25, 117–124.10.1038/nbt1270Search in Google Scholar PubMed
Maerkens, A., Kley, R.A., Olive, M., Theis, V., van der Ven, P.F., Reimann, J., Milting, H., Schreiner, A., Uszkoreit, J., Eisenacher, M., et al. (2013). Differential proteomic analysis of abnormal intramyoplasmic aggregates in desminopathy. J. Proteomics 90, 14–27.10.1016/j.jprot.2013.04.026Search in Google Scholar PubMed PubMed Central
Mayer, U., Kuller, A., Daiber, P.C., Neudorf, I., Warnken, U., Schnolzer, M., Frings, S., and Mohrlen, F. (2009). The proteome of rat olfactory sensory cilia. Proteomics 9, 322–334.10.1002/pmic.200800149Search in Google Scholar PubMed
McEwen, D.P., Jenkins, P.M., and Martens, J.R. (2008). Olfactory cilia: our direct neuronal connection to the external world. Curr. Top. Dev. Biol. 85, 333–370.10.1016/S0070-2153(08)00812-0Search in Google Scholar PubMed
Megger, D.A., Bracht, T., Kohl, M., Ahrens, M., Naboulsi, W., Weber, F., Hoffmann, A.C., Stephan, C., Kuhlmann, K., Eisenacher, M., et al. (2013a). Proteomic differences between hepatocellular carcinoma and nontumorous liver tissue investigated by a combined gel-based and label-free quantitative proteomics study. Mol. Cell. Proteomics 12, 2006–2020.10.1074/mcp.M113.028027Search in Google Scholar PubMed PubMed Central
Megger, D.A., Bracht, T., Meyer, H.E., and Sitek, B. (2013b). Label-free quantification in clinical proteomics. Biochim. Biophys. Acta 1834, 1581–1590.10.1016/j.bbapap.2013.04.001Search in Google Scholar PubMed
Menasche, G., Feldmann, J., Houdusse, A., Desaymard, C., Fischer, A., Goud, B., and de Saint Basile, G. (2003a). Biochemical and functional characterization of Rab27a mutations occurring in Griscelli syndrome patients. Blood 101, 2736–2742.10.1182/blood-2002-09-2789Search in Google Scholar PubMed
Menasche, G., Ho, C.H., Sanal, O., Feldmann, J., Tezcan, I., Ersoy, F., Houdusse, A., Fischer, A., and de Saint Basile G. (2003b). Griscelli syndrome restricted to hypopigmentation results from a melanophilin defect (GS3) or a MYO5A F-exon deletion (GS1). J. Clin. Invest. 112, 450–456.10.1172/JCI200518264C1Search in Google Scholar
Merrill, A.E., Hebert, A.S., MacGilvray, M.E., Rose, C.M., Bailey, D.J., Bradley, J.C., Wood, W.W., El Masri, M., Westphall, M.S., Gasch, A.P., et al. (2014). NeuCode labels for relative protein quantification. Mol. Cell. Proteomics 13, 2503–2512.10.1074/mcp.M114.040287Search in Google Scholar PubMed PubMed Central
Molina, H., Yang, Y., Ruch, T., Kim, J.W., Mortensen, P., Otto, T., Nalli, A., Tang, Q.Q., Lane, M.D., Chaerkady, R., et al. (2009). Temporal profiling of the adipocyte proteome during differentiation using a five-plex SILAC based strategy. J. Proteome Res. 8, 48–58.10.1021/pr800650rSearch in Google Scholar PubMed PubMed Central
Nguyen, U.T., Guo, Z., Delon, C., Wu, Y., Deraeve, C., Franzel, B., Bon, R.S., Blankenfeldt, W., Goody, R.S., Waldmann, H., et al. (2009). Analysis of the eukaryotic prenylome by isoprenoid affinity tagging. Nat. Chem. Biol. 5, 227–235.10.1038/nchembio.149Search in Google Scholar PubMed
Oeljeklaus, S., Meyer, H.E., and Warscheid, B. (2009). New dimensions in the study of protein complexes using quantitative mass spectrometry. FEBS Lett. 583, 1674–1683.10.1016/j.febslet.2009.04.018Search in Google Scholar PubMed
Oeljeklaus, S., Reinartz, B.S., Wolf, J., Wiese, S., Tonillo, J., Podwojski, K., Kuhlmann, K., Stephan, C., Meyer, H.E., Schliebs, W., et al. (2012). Identification of core components and transient interactors of the peroxisomal importomer by dual-track stable isotope labeling with amino acids in cell culture analysis. J. Proteome Res. 11, 2567–2580.10.1021/pr3000333Search in Google Scholar PubMed
Oeljeklaus, S., Schummer, A., Suppanz, I., and Warscheid, B. (2014). SILAC labeling of yeast for the study of membrane protein complexes. Methods Mol. Biol. 1188, 23–46.10.1007/978-1-4939-1142-4_3Search in Google Scholar PubMed
Ong, S.E., Blagoev, B., Kratchmarova, I., Kristensen, D.B., Steen, H., Pandey, A., and Mann, M. (2002). Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376–386.10.1074/mcp.M200025-MCP200Search in Google Scholar PubMed
Ong, S.E., Kratchmarova, I., and Mann, M. (2003b). Properties of 13C-substituted arginine in stable isotope labeling by amino acids in cell culture (SILAC). J. Proteome Res. 2, 173–181.10.1021/pr0255708Search in Google Scholar PubMed
Rak, A., Pylypenko, O., Niculae, A., Pyatkov, K., Goody, R.S., and Alexandrov, K. (2004). Structure of the Rab7:REP-1 complex: insights into the mechanism of Rab prenylation and choroideremia disease. Cell 117, 749–760.10.1016/j.cell.2004.05.017Search in Google Scholar PubMed
Recchi, C. and Seabra, M.C. (2012). Novel functions for Rab GTPases in multiple aspects of tumour progression. Biochem. Soc. Trans. 40, 1398–1403.10.1042/BST20120199Search in Google Scholar PubMed PubMed Central
Rigaut, G., Shevchenko, A., Rutz, B., Wilm, M., Mann, M., and Seraphin, B. (1999). A generic protein purification method for protein complex characterization and proteome exploration. Nat. Biotechnol. 17, 1030–1032.10.1038/13732Search in Google Scholar PubMed
Schwanhausser, B., Gossen, M., Dittmar, G., and Selbach, M. (2009). Global analysis of cellular protein translation by pulsed SILAC. Proteomics 9, 205–209.10.1002/pmic.200800275Search in Google Scholar PubMed
Shen, H., An, M., Zou, X., Zhao, X., Wang, Q., Xing, G., and Ji, J. (2015). Evaluation of the accuracy of protein quantification using isotope TMPP-labeled peptides. Proteomics 15, 2903–2909.10.1002/pmic.201400495Search in Google Scholar PubMed
Silva, J.C., Gorenstein, M.V., Li, G.Z., Vissers, J.P., and Geromanos, S.J. (2006). Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell. Proteomics 5, 144–156.10.1074/mcp.M500230-MCP200Search in Google Scholar PubMed
Singh, S., Springer, M., Steen, J., Kirschner, M.W., and Steen, H. (2009). FLEXIQuant: a novel tool for the absolute quantification of proteins, and the simultaneous identification and quantification of potentially modified peptides. J. Proteome Res. 8, 2201–2210.10.1021/pr800654sSearch in Google Scholar PubMed PubMed Central
Soufi, B., Kumar, C., Gnad, F., Mann, M., Mijakovic, I., and Macek, B. (2010). Stable isotope labeling by amino acids in cell culture (SILAC) applied to quantitative proteomics of Bacillus subtilis. J. Proteome Res. 9, 3638–3646.10.1021/pr100150wSearch in Google Scholar PubMed
Stein, M.P., Dong, J., and Wandinger-Ness, A. (2003). Rab proteins and endocytic trafficking: potential targets for therapeutic intervention. Adv. Drug Deliv. Rev. 55, 1421–1437.10.1016/j.addr.2003.07.009Search in Google Scholar PubMed
Sury, M.D., Chen, J.X., and Selbach, M. (2010). The SILAC fly allows for accurate protein quantification in vivo. Mol. Cell. Proteomics 9, 2173–2183.10.1074/mcp.M110.000323Search in Google Scholar PubMed PubMed Central
Thompson, A., Schäfer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T., Johnstone, R., Mohammed, A.K., and Hamon, C. (2003). Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem. 75, 1895–1904.10.1021/ac0262560Search in Google Scholar PubMed
von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S., and Bork, P. (2002). Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399–403.10.1038/nature750Search in Google Scholar PubMed
Wang, X. and Huang, L. (2008). Identifying dynamic interactors of protein complexes by quantitative mass spectrometry. Mol. Cell. Proteomics 7, 46–57.10.1074/mcp.M700261-MCP200Search in Google Scholar PubMed
Washburn, M.P., Wolters, D., and Yates, J.R., 3rd. (2001). Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242–247.10.1038/85686Search in Google Scholar PubMed
Wiener, M.C., Sachs, J.R., Deyanova, E.G., and Yates, N.A. (2004). Differential mass spectrometry: a label-free LC-MS method for finding significant differences in complex peptide and protein mixtures. Anal. Chem. 76, 6085–6096.10.1021/ac0493875Search in Google Scholar PubMed
Wiese, S., Reidegeld, K.A., Meyer, H.E., and Warscheid, B. (2007). Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research. Proteomics 7, 340–350.10.1002/pmic.200600422Search in Google Scholar PubMed
Wotske, M., Wu, Y., and Wolters, D.A. (2012). Liquid chromatographic analysis and mass spectrometric identification of farnesylated peptides. Anal Chem. 84, 6848–6855.10.1021/ac301437mSearch in Google Scholar PubMed
©2017, Claudia Lindemann et al., published by De Gruyter.
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.