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DOI 10.1002/pmic.201300403

REVIEW

Quantitative proteomics in the field of microbiology ¨ Andreas Otto1 , Dorte Becher2 and Frank Schmidt2 1 2

Institute for Microbiology, Ernst Moritz Arndt University Greifswald, Germany ZIK-FunGene Junior Research Group “Applied Proteomics,” Ernst-Moritz-Arndt-University, Greifswald, Germany

Quantitative proteomics has become an indispensable analytical tool for microbial research. Modern microbial proteomics covers a wide range of topics in basic and applied research from in vitro characterization of single organisms to unravel the physiological implications of stress/starvation to description of the proteome content of a cell at a given time. With the techniques available, ranging from classical gel-based procedures to modern MS-based quantitative techniques, including metabolic and chemical labeling, as well as label-free techniques, quantitative proteomics is today highly successful in sophisticated settings of high complexity such as host–pathogen interactions, mixed microbial communities, and microbial metaproteomics. In this review, we will focus on the vast range of techniques practically applied in current research with an introduction of the workflows used for quantitative comparisons, a description of the advantages/disadvantages of the various methods, reference to hallmark publications and presentation of applications in current microbial research.

Received: September 14, 2013 Revised: November 15, 2013 Accepted: December 6, 2013

Keywords: Absolute quantitation / Gel-based / Gel-free / Isotopic labeling / Mass spectrometry / Microbial proteomics

1

Introduction

The field of proteomics in microbiology has often been regarded as simplistic in comparison with the proteomics of higher organisms. On the one hand, this might be true from an analytical perspective given the expected protein species due to the smaller genomes of microbes, the fewer PTMs caused, for example, by splicing events or elaborate phosphorylation cascades in information processing, and an access to a virtually unlimited sample amount in studies dealing with batch cultures of microbes. This is further reflected in the fact that it is technically feasible to achieve coverages in proteome analyses of around 80% for a number of microbial proteomes today while this is not easily attained for higher organisms [1–6]. However, this former majority opinion has changed dramatically as today’s microbial proteomics is not ¨ Correspondence: Professor Dorte Becher, Institute for Microbiology, Ernst Moritz Arndt University Greifswald, F.-L. Jahn-Strasse 15, 17487 Greifswald, Germany E-mail: [email protected] Fax: +49-3834-864202 Abbreviations: NSAF, normalized spectrum abundance factors; SIL, stable isotope labeling; SIP, stable isotope probing; TMT, isobaric mass tags

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limited to basic in vitro characterization of single organisms to unravel the physiological implications of stress/starvation events or to describe the proteome content of a cell at a given time. Microbial proteomics is also used today in sophisticated settings of high complexity such as host–pathogen interactions, mixed microbial communities and, ultimately, microbial metaproteomics [7–10]. In addition, microbial systems have proven to be perfectly suited for the development of systems biology workflows in which different levels of biological information are combined, including genomics, transcriptomics, proteomics, and metabolomics, with the goal of ultimately modeling cellular processes in silico [11,12]. In this regard, the latter two are of major importance in any attempt at modeling real physiological situations: while proteomic techniques yield information about the “players of life,” which are the effectors of any process both inside and outside the cell, metabolomics will provide insight into metabolites and, eventually, metabolite fluxes. Since the beginning of proteomics, microbial proteomics has been at the forefront of the emerging field with important analytical techniques first applied to microbial organisms. Concurrent analysis of thousands of proteins was made possible in the mid-1970s, when O’Farrel and Klose used the revolutionary method of 2DE to compare different cultures of Escherichia coli on highly resolving 2DE gels [13, 14]. At www.proteomics-journal.com

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that time, single protein-centered biochemistry techniques were state of the art and although the term proteomics was coined not earlier than 1994, the invention of 2DE in combination with MS-based PMF [15] may be regarded as the cradle of proteomics. The turn from classical to gel-free MS-based proteomics was triggered by instrumental developments in LC-MS analytics, which also represented a development from single protein-driven to complex peptide-driven bottom-up proteomics. Here, important work was done in the first studies of gel-free analysis of complex proteomes and the introduction of multidimensional protein identification technology (MudPit) to achieve a previously unknown depth of information. This led to proteomic studies revealing the complete yeast proteome [16–18]. Consequently, most of the existing techniques for identification and quantitation of proteomes have been successfully applied in microbial proteomics. Here we would like to give an idea of the vast range of techniques available in today’s research, with an introduction of the techniques practically used in microbial proteomics, and a presentation of their advantages/disadvantages, the hallmark publications, and their application in current microbial research.

2

Classical 2DE-based proteomics

2DE-based proteomics is often considered to be the classical proteomic technique. That is, although technically challenging in terms of the skills needed by the researcher, to this day still an unparalleled technique of top-down proteomics. 2DE-based, quantitative proteomics dates back to the invention of 2DE in 1975 by O’Farrel and Klose, when two orthogonal physicochemical properties of the proteins to be analyzed (pI and Mr ) were utilized to resolve thousands of protein species on a 2D gel, combining IEF in the first and SDS PAGE in the second dimension. Staining of the resolved proteins and comparison of gel images allowed for a relative quantitative comparison of protein abundances of different proteome samples [13,14]. Since then, 2DE has evolved into a robust and relatively cheap method for separation of complex protein mixtures with the inherent advantage of being able to compare directly 2DE gels via differential gel image analysis. By using modern MS and compatible, sensitive fluorescent stains, 2DE offers high sensitivity with four orders of magnitude of linearity in quantitation. In addition, with the advent of soft ionization MS it is even possible to ensure protein identification by MALDI-TOF within a reasonable time frame and with a high throughput [19,20]. Although today 2DE is in most cases not the method of choice in large-scale proteomic studies due to issues in general applicability for all types of proteomic samples (constraints for membrane proteins; extreme pH/molecular weight), 2D gel based proteomics is, nonetheless, the very powerful method for the investigation of basic physiological questions in microbiological research and it has been used extensively in the field.  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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2.1 Gel-based proteomics signatures The analytical window of a standard 2D PAGE is perfectly suited for the resolution of microbial proteomes. As was shown for the model organism Bacillus subtilis, a coverage of about 40% of all predicted soluble, cytosolic proteins allows almost all metabolic pathways to be followed, as well as the main signatures of stress and starvation responses in physiological studies [1, 21]. Thus, 2DE-based studies have been broadly used for microbial systems to answer an array of questions ranging from basic physiology under different stress and starvation conditions, to comparison of virulence factors, to functional studies. Today, for 2DE gel-based studies the application of bioinformatics processing to biological data means a shift toward elaborate stress/signature library-generating experimental studies combining quantitative information from multiple physiological settings [22]. Setting up such databases will help to decipher key enzymes in infection-related settings (oxidative stress, cell wall stress, nutrient limitation, iron limitation) and, therefore, will aid in the search for key targets for future antimicrobial therapies [23,24]. Additionally, these libraries will help to pin down the molecular effects of antimicrobial treatment by plasma or the mode of action of novel antibiotics [25, 26]. A unique feature for microbial proteomics is the opportunity to follow the fate of newly synthesized proteins by pulsechase labeling and directly compare this information to the amount of protein on the same 2D gel [27–29]. This feature has proven to be very important for quantitative proteome studies aimed at comparing the abundances of accumulated proteins in different cell states/proteome samples, while transcriptome data usually reveal only weak correlations [30]. Differences may be caused by multiple factors—for example, PTMs including directed proteolysis, differences in half-lives of proteins and the respective mRNA transcripts, or posttranscriptional regulation of protein synthesis. Therefore, studying protein turnover with the goal of elucidating protein fate, distinguishing between protein de novo synthesis, persistence and degradation, getting increased attention in microbial proteomics both with 2D gel based workflows and with gel-free stable isotope labeling (SIL) methods [29,31,32].

2.2 Protein species analysis Contrary to the early idea of molecular biology—which became a dogma—that every gene leads to the expression of only a single protein, it soon became clear that proteomics had to deal with a large variety of protein species deriving from a given gene [33]. Protein species differentiation originating from PTM comes from a variety of processes such as phosphorylation or acetylation, limited proteolysis and truncation. Here, top-down proteomic approaches are suitable for resolving single protein species according to their physicochemical properties on the protein level and, therefore, for obtaining an overview of existent and differentially changing www.proteomics-journal.com

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protein species in relative quantitative experiments [34, 35]. Protein shifts on 2D gels that can be detected without prior assumptions as to what might have result the shifts are, for example, protein phosphorylations that lead to changes in net charge of the protein, resulting in a shift toward the more acidic range. Changes in Mr may be due to truncations in proteins or limited proteolysis resulting from physiological changes or experimental artifacts. Adding to the information encoded in pI or Mr of the protein under investigation, functional dyes such as Pro-Q Diamond or Pro-Q Emerald, engineered specifically to detect protein phosphorylation or glycan residues, allow for more focused, functional gel-based analyses [36–39]. The possibility of defining changing patterns of protein species abundance on 2D gels clearly adds benefits to 2D gel based proteomic studies: in contrast with only describing changes in protein abundance for summedup intensities of “protein expression,” changes may be looked at for all events leading to post-translational modified “versions” of a protein. Hence, an often depicted disadvantage of 2DE represents in fact the possibility of changing from an “abundome”-centric view leading to a functionally more meaningful “regulome” view for the visible entirety of proteins species in top-down approaches [40].

2.3 Future role of gel-based proteomics in microbial proteomic research For most of the scientists working with 2DE, it seems clear that classical proteomics has increasingly lost the attention of the majority of proteome researchers today. Nonetheless, 2DE has distinct advantages that make the technique unique in the field [21,41]. Due to the long-term evaluation of protocols and understanding of the underlying concepts, 2DE is a robust and cost-efficient way of analyzing microbial organisms on the protein level. While 2DE does not offer the analytical depth of gel-free approaches, resolution of proteomes on the protein level offers unique insights based on the panoramic view obtained of the physiological changes in the cell resulting from stress and starvation for protein abundances differing by up to four orders of magnitude. In addition to the benefits of global analysis of protein abundance, the combination with functional dyes for analysis of PTMs, pulse-chase labeling for following protein synthesis/proteolysis, as well as sophisticated workflows in redox proteomics will ensure the importance of 2DE-based proteomics in the foreseeable future. Comparable results leading to valid conclusions require technically mature protocols in 2DE. While 2DE gels are not—and will probably never be—amenable to proteomics of truly integral membrane proteins, recent technological advances in 2D gel techniques and reliable software solutions have proven a help in obtaining reproducible and statistically reliable, valid results [42, 43]. Although all these advantages hold their promise for successful proteomic studies, there are drawbacks to 2DE-based  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

classic proteomics that have to be considered in planning a proteomic experiment. The most basic consideration is the time and practical expertise required to perform proteome analyses relying on 2DE. Although there has been improvement in the reduction of sample replication by multiplexing of samples [44] or, more recently, the use of extremely highly resolving and ready-to-use horizontal flat-top gels [43], still the method is regarded as the bottleneck in “high-throughput” analyses. Special attention has to be paid to the quantitative information derived from 2DE gels: although 2DE is unique in resolution on the protein level, for highly complex samples multiple proteins will in all probability reside at the same location on 2DE gels despite the high resolving power [40,45]. This should be taken into account for all 2D gel based proteomic studies: quantitative description of true protein abundances between samples on 2D gels is only possible for proteins that are separated down to the maximally achievable single protein species level, for example, by using narrow range IPG strips and/or narrow ranges of sizes of the proteins under investigation [40]. For analyses with the particular goal of precise quantification of single protein species in (putatively) mixed 2D gel spots, a combination of gel-based top-down separation of proteins and metabolic labeling would be a feasible workflow. Lastly, 2DE is incompatible for proteins with extreme physicochemical properties—for example, for very small or very large proteins, or for proteins with high or low hydropathy or extreme pI. 2DE almost precludes membrane proteomics from being carried out with gel-based proteome methods [46, 47].

3

MS-based proteomics

Modern proteomic methods have evolved in response to the shortcomings experienced by researchers in classical proteomics, resulting in a paradigm shift regarding the analytes under investigation. The limitations of top-down proteomic workflows, although very successfully applied more MScentered microbial proteomics [48–50] by a few groups worldwide, led to the development of the so-called bottom-up or shotgun proteomics, which shifted the focus from proteins to proteolytic peptides as the targets for analysis by MS [51]. Since then, the change in perspective to bottom-up proteomics with MS-centered proteomic methods has proven to be a huge success, as can be judged by the comprehensiveness, sensitivity, and versatility of the proteomic studies carried out since the shift [52–54]. A key consideration prior to using MS in quantitative studies is that MS is not inherently quantitative. Differences in physicochemical properties for peptide species lead to differences in ionization efficiencies and variation in signal intensities, resulting in large differences in the mass spectrometric response. Therefore, most quantitative workflows rely on the introduction of an isotopic label to the analytes at different stages of sample preparation, based on the technique of isotope dilution MS [55]. This www.proteomics-journal.com

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allows for distinguishing between peptide species (e.g. light/heavy encoding two different cell states) and, therefore, for determination of quantitative changes in the samples.

3.1 Metabolic/in vivo labeling Metabolic labeling is regarded as the gold standard in proteomics due to the accuracy of the quantification results obtained and of the robustness of the labeling method against the introduction of biases in sample preparation prior to the LC-MS measurements [56]. For metabolic labeling, the organism under investigation is fed with stable isotope labeled versions of metabolic precursors such as amino acids, carbohydrates, or organic salts in the respective growth media. This introduces stable heavy isotopes, resulting in proteins that are changed in molecular weight. Despite the resulting difference in mass, all other physicochemical properties remain practically unchanged, unless isotopes of carbon or nitrogen are used with introduced deuterium, which leads to changes in hydrophilicity [57]. For quantitative comparisons, cell growth is carried out in light and heavy media, according to the precursors fed, giving rise to two or more cell populations. Proteolytic peptides derived from both the labeled and unlabeled forms elute at the same time from the RP column in LC-MS/MS analyses and, additionally, have the same ionization efficiencies, as both forms are chemically identical, only differing in mass. Consequently, both have the same response signals in the mass spectrometer, allowing for direct quantitative comparisons within a single MS scan [58]. A clear advantage of metabolic labeling is the early mixing of labeled and unlabeled protein extracts fixing the ratio between each heavy labeled compound and its unlabeled counterpart. Even extensive prefractionation steps or elaborate sample preparation workflows—for example, for depletion of cytosolic, abundant proteins for purification of membrane proteins—have no influence on the quantification results.

3.1.1 SILAC SILAC was introduced in 2002 with an immediate success in quantitative proteomics for eukaryotic cell lines [59]. Analogous to its predecessor, SIL [60], SILAC is based on metabolic incorporation of heavy labeled amino acids during cell growth into all proteins expressed in the living cell. For relative quantification studies, protein extracts of light (unlabeled) and heavy labeled samples are mixed and can consequently be distinguished on the molecular level by their difference in mass. Most advantageous for shotgun proteomics is the use of heavy isotopomers of arginine and lysine to ensure that all peptides resulting from proteolytic cleavage by trypsin or LysC contain at least one heavy labeled amino acid. Backbone carbon atoms or nitrogen atoms found in the amino groups of the amino acids are exchanged for their heavy 13 C  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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or 15 N counterparts to give rise to mass differences compared to the light peptides of 4, 6, or 10 amu. While SILAC is amenable to most eukaryotic systems due to the possibility of using essential amino acids, introduction of SILAC amino acids in microbial proteomics is only possible if auxotrophies exist for the SILAC amino acid used [61, 62]. For microbial proteomics SILAC had an immediate impact for large-scale studies of Saccharomyces cerevisiae [18, 63], but although predecessors of SILAC were published in studies of E. coli [64, 65]—also alternatively relying on fed amino acids such as lysine [66, 67], leucine [67–69], glycine [67], and methionine [67, 70]—the prerequisite of an organism being auxotroph for SILAC amino acids prevented use of this technology in other bacterial systems until 2008 [61]. At that time, SILAC was used in a study of membrane proteome in B. subtilis to evaluate the feasibility of relative quantitative studies based on metabolic labeling in bacterial systems. Further, accurate quantification results for bacteria were published in a large-scale physiological study of B. subtilis, which even compared phosphorylation patterns relying on this technique [71]. Using SILAC for large-scale studies incorporating subcellular prefractionation techniques was also shown in a study of S. aureus. Michalik and co-workers implemented a triple labeling strategy to examine protein stability on a large scale in glucose-starved S. aureus COL cells [31]. Compared to previous studies that were exclusively based on 2DE, the authors calculated proteolysis kinetics comparing newly synthesized, stable, and proteolytically degraded proteins on a large scale by completely gel-free methods. Taking this one step further to infection-related proteome studies, in vivo proteomics combining SIL of S. aureus internalized into human bronchial epithelial cells revealed first indications of the processes that take place inside the host during the host–pathogen interaction [7]. The introduction of SILAC has been a huge success in quantitative and functional proteomics. With SILAC, all analytes that are targeted by shotgun MS exist as isotopologue peptide pairs. By introducing the label on a metabolic level, no bias on the primary sequence is introduced, as can be the case for chemical labeling strategies. With SILAC, multiplexing of samples is possible with double, triple, even up to pentuple labeling [72], reducing the time for expensive LCMS runs significantly at the cost, however, of a significant increase in sample complexity. Further, quantitative analyses using SILAC amino acids even allow for pair matching and, therefore, for quantification without any prior identification, for example, by database searching [73]. SILAC has earned its merits, first, in approaches that have sought to describe differentially the conditions of cells or mutants on a global scale, but it has also shown its strength in the differential analysis of post-translationally modified peptides [74, 75]. SILAC as metabolic labeling can be regarded as an analytically accurate method yielding results that allow the researcher to distinguish minute changes. It is very robust with respect to experimental biases as the label is introduced at the earliest possible time. www.proteomics-journal.com

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Figure 1. (A) Fractionation workflow in a 15 N metabolic labeling approach to target system-wide proteome changes in B. subtilis [19]. (B) State-of-the-art visualization of proteome and transcriptome data by Voronoi treemaps. (C) Proteome coverage reached for B. subtilis in a glucose starvation experiment.

Despite all the advantages that have led to the success of SILAC, the technique has inherent drawbacks that have to be taken into account before one begins an investigation. Besides the already mentioned auxotrophy for SILAC amino acids, an interconversion of amino acids (arginine to proline [76]) has been observed, which requires countermeasures. In addition, using stable isotope labeled amino acids is quite expensive due to the high purity needed (usually >98%) and the relatively high concentrations needed to supplement the media.

3.1.2

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N labeling

15

N labeling is the second labeling approach that relies on metabolic incorporation of heavy stable isotopes into proteins during cell growth. In contrast with SILAC, 15 N labeling exchanges all biologically available nitrogen in the growth media for heavy 15 N nitrogen. As a result, no auxotrophies are needed in the organisms under investigation. In the simplest case, this can be an ammonium salt [61,77,78], while for other bacterial systems growth in complex, completely 15 Nlabeled media is required [1]. Unlike with SILAC, the mass differences between the heavy and light peptides compared in quantitative studies are not fixed. This makes pair matching of the two isotopologues more difficult in 15 N labeling as compared with SILAC, which can be a drawback in data analysis. However, an advantage of 15 N labeling over SILAC is the robustness with respect to an incomplete labeling that allows successful relative quantitative studies anyway [62]. As a result, a widely applied concept of relative quantification against a pooled reference is essential—a concept that combines analytical robustness, lack of physiological implications of the introduced heavy label, and the possibility of combining dissimilar proteomes [79, 80]. 15 N labeling has a long-standing tradition in the labeling of proteins for NMR measurement [81]. For bacteria, it was first used by Bunai and co-workers for the labeling of newly expressed proteins  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

in the membrane proteome of B. subtilis [82]. While classical proteomics and MALDI-TOF were used for this study, the work by Dreisbach et al. employing metabolic labeling based on 15 N incorporation and SILAC was completely gelfree. Because 2DE was not used, a large number of membrane proteins were identified and quantified between the cell states examined. Additionally, this was a good example of the comparison between 15 N labeling and SILAC in the model bacterium for Gram-positive bacteria. Although SILAC is increasingly used in bacterial systems, metabolic labeling based on 15 N incorporation is more easily implemented experimentally [62]. Hence, 15 N labeling has proven to be extremely successful in bacterial proteomics. Quantitative studies dealing with B. subtilis [2], S. aureus [1], Neisseria meningitides [83], Ralstonia eutropha [77], Nitrosomonas europaea [84], Euhalothece sp. BAA001 [85], and Corynebacterium glutamicum [86] have demonstrated the universal applicability of 15 N labeling in microbial proteomics. Of particular interest is the compatibility of 15 N labeling with large-scale studies aiming at in-depth coverage of the proteome. As shown for S. aureus and B. subtilis, quantitative information may be gathered for over 90% of all identified proteins in large-scale studies with proteome coverages for the two bacteria of 65 and 52%, respectively (Fig. 1). In these studies another benefit was observed for the complete labeling by stable 15 N isotopes. For analyzing the extremely hydrophobic stretches of transmembrane proteins, other proteolytic peptides than trypsin or LysC have to be used in sample processing [87]. Since they are detrimental in SILAC-based approaches, a full labeling with heavy nitrogen still allows for accurate relative quantification even in this challenging subproteome. A new application of 15 N labeling is the accurate quantification of live cell numbers of S. aureus by labeled bacteriophage amplification coupled with a MRM proteomic workflow [88]. For relative quantitative studies, the main drawback of 15 N labeling is the limitation to duplex comparisons of different proteomes. While this difficulty www.proteomics-journal.com

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Figure 2. (A) Elementary labeling strategy of sulfur containing amino acids from Pseudomonas putida [76]. (B) Theoretical mass shift after incorporation of 36 S. (C) Example of a tryptic peptide pair measured by a MALDI-TOF instrument. (D) Intensity correlation plot of the heavy and the corresponding light protein to characterize significant changes in the proteome.

was resolved by the already-mentioned procedure of pooled references, leading to virtually unlimited possibilities of comparison of different samples, the combination of different metabolic labeling strategies is also conceivable [31].

3.1.3 Stable isotope probing (SIP) Recent studies in microbial proteomics have entered the field of functional environmental analyses, for example, following mixed microbial consortia for the degradation of noxious substances, or studying complex settings in metaproteomics or the differentiation of very closely related bacteria in mixed consortia according to anticipated autotrophic or heterotrophic lifestyle. In most of the cases where mixed consortia are being investigated, established techniques of quantitative SIL are not applicable. Therefore, other workflows relying on stable isotope incorporation called DNASIP, RNA-SIP, phospholipid-derived fatty acids (PLFA-SIP), or protein-SIP for the respective level of omics techniques have been developed [89–91]. With protein-SIP it is possible to identify key microbial factors in complex environment-drawn samples; further, it allows, for example, for identification of active metabolic pathways and the overall activity of specific phyla in the complex metaproteome/mixed consortia samples [92, 93]. Early work was done by Snijders and co-workers who have shown the feasibility of relative abundance of isotopes determination for labeled ammonia salts incorporated into the hyperthermophilic crenarchaeon Sulfolobus solfataricus [94]. Subsequently, SIP with nitrogen salts was used in a study of acid mine drainage cultures of acidophilic microbial communities [95, 96]. Besides incorporation of labeled nitrogen, feeding with stable isotopes of carbon has also proven to be useful in protein-SIP [97]. Another possibility is a technique named sulfur SIL of amino acids for quantification (SULAQ), which relies on the stable sulfur isotopes 34 S or 36 S [98, 99] (Fig. 2). This metabolic labeling allows for in vivo  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

labeling of practically all organisms in complex environmental samples, since all organisms require this building block. Therefore, this form of labeling can be used in protein-based SIP (protein-SIP) for the characterization of organic fluxes based on sulfur-containing substances [90]. 3.2 In vitro labeling The second large group of quantitative proteomics techniques is in vitro or chemical labeling. In vitro labeling does not require the metabolic incorporation of an isotopic label and may be regarded as postbiosynthetic labeling in top-down or bottom-up proteomics [56]. For in vitro labeling, chemical or enzymatic derivatization of proteins or peptides is performed on proteomic samples regardless of their origin, which is most important for samples of limited availability or for proteome samples that cannot be metabolically labeled [100]. Two types of protein chemistries are exploited: First, thiolspecific tags are used to target sulfhydryl groups of cysteine residues; second, N-hydroxysuccinimide groups or other active esters and acids bind to primary amines such as lysine at the ␧-amino group or N-terminal ends. In the present paper, two main types of chemical labeling strategies will be covered: first, ICAT and its derivatives, which are historically meaningful as the first generation of chemical tagging methods, and which are still used in a more functional way in redox-proteomics studies; second, the commercial isobaric tags including both iTRAQ and isobaric mass tags (TMT), which are by far the most important tags used in chemical/ in vitro tagging. 3.2.1 ICATs A pioneering study of in vitro labeling central to the success of modern gel-free proteomics techniques introduced ICATs to proteomics [101]. With this technique, both relative www.proteomics-journal.com

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quantitative studies in bottom-up approaches became feasible and a distinct reduction in sample complexity was achieved. The ICATs, both the heavy and the light version, consist of three functional parts: a protein-reactive group, an isotope-coded linker, and the affinity tag. Designed as nonisobaric in the first generation, the heavy version was characterized by an additional mass of 8 amu due to the exchange with deuterium of eight protons from the isotopecoded linker. For relative quantitation, the cysteine residue labeled protein samples are mixed, proteolytically digested, and the tagged peptides are affinity purified prior to LC-MS measurement by incubation with avidin/streptavidin. Post measurement, the mass difference introduced into every cysteine is used for relative quantification on the MS1 level. With the superior applicability for S. cerevisiae proteomics already being demonstrated in the original publication and beyond [101, 102], ICAT became the method of choice in chemical labeling until the introduction of tags relying on chemistries targeting primary amines [103]. In the years after introduction, a number of variations for this type of labeling were introduced related to the recovery of labeled peptides or the chromatographic properties of labeled peptides [104–107]. With these changes—including introduction of a mass shift of 9 amu based on the replacement of 12 C atoms by 13 C atoms of the isotope-coded linker backbone and a cleavable moiety between the biotin part and the labeled peptide— problems like RT shifts caused by deuterium incorporation and unwanted increase in the peptide masses by the biotin moiety were resolved. Nonetheless, the linking chemistry was based on the availability of free thiols, which markedly limited the number of peptides yielding quantitative information. Along with pioneer studies of baker’s yeast, chemical tagging was used with various microbial systems [107–110]. It was recognized very early that proteomics benefitted from gelfree techniques, especially in investigations of pathogens. Analytical challenges for low abundance and membrane proteins were at least partially met for Pseudomonas aeruginosa [111]. Further, in a study of the pathogen Mycobacterium tuberculosis, the authors compared classical top-down proteomics by 2DE and quantitative shotgun proteomics with ICAT [107]. Although the authors were interested in showing the complementarity of the two proteomic methods, in retrospect it became clear that the limitations of ICAT with regard to proteome coverage and an introduced bias with respect to certain proteins, based, for example, on acidity and size [112], clearly diminished the information attainable from gel-free methods with regard to the depth of the proteome explored. Today, ICAT and its progeny methods are only of significance in more functional studies of the redox state of a proteome under investigation. In 2008, Leichert and co-workers introduced a technique called “OxiCat,” exploiting the weakness of ICAT to turn it into a strength [113]. By the selective labeling using ICAT of thiol groups according to their redox state in different proteomes, gel-free proteomic methods were then used to make visible the redox state of proteins in E. coli, as well as of baker’s and fission yeast [113–115].  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

3.2.2 Isobaric mass tags Today, the most successful chemical tags are the commercially available isobaric mass tags called TMTs and iTRAQ [103, 116, 117]. Unlike ICAT, TMTs rely on protein chemistry targeting primary amines of proteins or peptides by N-hydroxysuccinimide esters. This robust chemistry links the tag with its three functional subunits to the analyte: the amine reactive group already mentioned, the reporter group, and the balancer group. Although TMT and iTRAQ are different in chemical structure, both provide multiplexing up to 8-plex by variation of the masses encoded in the reporter group visible in fragment ion spectra and its engineered counterpart for providing isobaricity [118]. Once the isobaric tags have reacted with the proteolytic peptides, the mass tags lead to increased masses of all N-termini and ␧-amino groups. Further, the isobaric mass tags add up together to the signal intensity of the respective peptide on the MS1 level, for up to eight isobaric tags. Upon fragmentation of the chemically identical peptide masses bearing the isobaric TMTs, the chargeless balancer group becomes invisible for mass spectrometric analysis and the reporter ions reflecting the relative abundance of peptide species of the proteome samples compared appear in the lower region of the fragment scan. A clear-cut difference to other techniques such as metabolic labeling is the high degree of multiplexing manageable with TMTs. This multiplexing may be used both for comparison of different cell states/physiological situations and for elaborate time course studies. As early as 2008, Wolff and co-workers compared 2DE with iTRAQ in a time-resolved study on heat-stressed B. subtilis [119]. It was shown that the two techniques are complementary, with a deeper coverage reached for iTRAQ. More recently, Adav et al. have implemented iTRAQ for comparing the secretome of different strains of Aspergillus niger and Trichoderma reesei under various experimental conditions [120, 121]. These biotechnologically oriented studies demonstrate the use of 4-plex iTRAQ labeling for the former bacterium and 8-plex iTRAQ labeling for the latter for elucidating the secretome levels in accordance with the experimental settings and the carbon sources, respectively. As a truly gel-free technique, iTRAQ holds the promise of being compatible with membrane proteomics. Szopinska et al. were successful in employing iTRAQ in an LC-MALDIbased study of salinity stress in S. cerevisiae [122]. Here, targeting of the subproteome of membrane proteins was successful in elucidating endocytosis-related changes due to salt stress. Williams et al. utilized iTRAQ for studying growth dependence on temperature and carbon source of the cold-adapted methanogenic archaeon Methanococcoides burtonii, focusing on membrane and surface proteins [123, 124]. Once again, MS-based proteomic workflows were used for overcoming the limitations of classical proteomics and ICAT with respect to the analysis of hydrophobic and low-abundance proteins in a model for assessing the molecular mechanisms of cold adaptation [125]. For human pathogens, both TMT and iTRAQ www.proteomics-journal.com

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have proven to be good systems for relative quantitative proteome profiling [126–128]. Compared to metabolic labeling, chemical labeling lags behind in broad application in microbial proteomics. The main reason is the cost of a relative quantitative experiment performed with commercially available TMTs. Further, from the mass spectrometrist’s perspective, modified lysine residues lead to different trypsin cleavage specificities, resulting in longer peptides during proteolytic digestion, which are more difficult to identify by MS. In addition, it is known that the derivatization chemistry underlying the labeling process of primary amines is prone to side reactions that can lead to unexpected products and the introduction of biases in sample processing. One aspect that was detrimental for certain MS equipment was the incompatibility of the TMT reporter ions with ion-trap (hybrid) instruments due to physical constraints. Although this still holds true, new instrument workflows in collision-induced fragmentation by pulsed Q collision induced dissociation (PQD) and higher-energy C-trap dissociation (HCD) have also made ion-trap instruments compatible with TMTs [129]. The application of quantitative membrane proteomics has proven to be more advantageous with metabolic labeling due to the very early entry point of the labels during sample processing, prior to elaborate membrane purification steps.

3.3 Label free Label-free quantification workflows have become exceedingly popular in MS-based proteomics. Though not as accurate as isotope labeling based strategies, label-free proteomics has proven to be reliable in quantitation, especially for the comparison of larger cohorts of samples. In label-free quantitation there are two main concepts applied for comparisons: the first is quantitation based on spectral counting; the second is quantitation based on AUC determination on the MS1 level, where the total signal of the monoisotopic peak is counted.

3.3.1 Spectrum count approaches for label-free quantification There are a variety of spectral counting approaches, from very basic workflows that correlate the number of peptide spectrum matches to the amount of a protein in a sample, to workflows that take into account the protein length or values generated by machine learning algorithms based on existing data for prediction of the response of corresponding peptides in the mass spectrometer. Spectral counting is often considered a rough estimate of protein abundance. It is frequently performed on data postexperimentally, relying on information generated in LC-MS/MS runs solely aiming for protein identification; it is often termed “semi-quantitative” [130]. The

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simplest approach in spectral counting is the use of spectral abundance factors or normalized spectral abundance factors [131]. In the latter case, the impact of protein length and run-to-run variations in the LC are used for normalizing the spectral counts of each single protein: the summed-up spectral counts of each protein are divided by its length and this value is normalized by the total number of peptide spectrum matches in a sample. Variations of the spectral counting concept are, for example, protein abundance index, exponentially modified protein abundance index (PAI), and absolute protein expression (APEX) [130, 132, 133]. Despite the shortcomings of label-free quantitation based on spectral counting, the method is broadly applied in microbial proteomics [134, 135]. Though metabolic labeling is the gold standard in quantification for microbial systems, there are experimental settings where labeling is not applicable. This is the situation in metaproteomic studies, in studies of organisms that are noncultivatable or in biofilms that exhibit no cell growth [136]. In a study of the ecological divergence of closely related bacteria in natural microbial communities, Denef et al. characterized the succession of bacterial populations in acid mine drainage [137]. In earlier studies of this particular ecosystem, the authors also used metabolic labeling for quantitative comparisons, but it was shown for a subset of biofilm patches that the metabolic activity was not enough to achieve sufficient incorporation of heavy isotopes for labeling [96, 138]. In situations where labeling is simply not possible, estimation of abundance for certain protein classes is helpful in deciphering the major phyla responsible for metabolic activity in an environmental sample, as well as the main enzyme classes responsible for anabolic/catabolic processes. A semiquantitative estimation of protein abundances in beech leaf litter decomposition was determined in studies by Riedel and co-workers [139, 140], helping to elucidate the interplay of geochemistry and microbiology in biodegradative processes. Further, the estimation of protein abundance has been performed in a large study of the substrate-controlled succession of marine bacterioplankton populations in the North Sea [141]. Assessment of spectral counts is also helpful in the determination of processes that occur during co-cultivation of different species reflecting mechanisms of competition [142,143]. Besides using spectral counting for the relative quantitative estimation of protein abundances, these values may also be used as additional information in studies aimed either at relative comparisons by metabolic labeling or protocol optimization [144]. In a study of glucose starvation in B. subtilis, normalized spectrum abundance factors (NSAF) values were calculated for membrane proteins that were induced markedly during the shift from exponential growth to the stationary phase. In addition to judgment by the relative quantitation based on metabolic labeling, it was concluded that the abundances of transporter capacities newly installed seemed to have only a sensing function [2]. NSAF can also be used to determine the current localization of proteins inside the bacterial cell. This has proven to be very

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useful for investigating the surface and membrane proteins potentially involved in host–pathogen interaction [83, 145]. It is impressive how widely accepted semiquantitative assessment by NSAF has become in microbial proteomics. This can mainly be accounted for by the ease of applicability of calculating quantitative values at no extra cost post measurement with virtually any proteome sample under investigation. Nonetheless, application of spectral counting has its disadvantages. The researcher always has to be clear that assessment of quantitative changes is only valid using strong statistical methods, which means that low-abundance proteins with a low number of peptide spectrum matches (PSMs) will not lead to reliable data [146, 147]. Differences in sample preparation and during LC-MS runs will directly introduce unknown biases in the quantitative analysis, precluding the use of multistage sample preparation procedures in this workflow. To overcome limitations based on analytical platforms, technical replicates are indispensable, in addition to the common biological replicates, to ensure a valid basis for the data generated in the experiment.

3.3.1 Ion intensity approaches 3.3.1.1 Classical AUC Approaches relying on AUC measurement of ion abundances for given mass/retention time pairs resemble the classical gel-based proteomic workflows comparing protein spots on a 2D gel. However, instead of densitometry of stained proteins in gel spots, the AUC of extracted ion chromatograms are used to relatively compare LC-MS runs for given RT/accurate mass pairs in differential label-free proteomic studies. While spectral counting is most popular for its ease of use, independent of the processing software employed for spectrum identification, AUC determination requires more sophisticated data acquisition and particularly sophisticated software. Both commercial and academic/free software packages are available, which basically perform LC-MS run alignment, peak picking, and data extraction (reviewed in [148]). Choosing between the software suites is rather difficult and depends heavily on the demand for functionality, the settings of the acquisition system used and, of course, on whether the expensive commercial software is affordable. In recent years the attempt to find the optimal software solution in microbial proteomics is best reflected in a series of studies of the same biological system by leading groups in the field [149]. Of the more recent microbial studies relying on high-resolution data, the study by Malmstr¨om and co-workers of proteomewide abundance data for the human pathogen Leptospira interrogans set the benchmark for global, absolute quantification studies of microbial systems [4]. This work has shown that the construction of Mastermaps for LC-MS data and the extraction of feature profiles with normalized intensity data on the MS1 level based on identification of 2221 proteins allows for the comparison of protein abundances for 769 proteins over different growth states. While alignment  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

555 and MS1 feature detection/extraction was performed here using the freely available software Superhirn dating back to 2007 [150], a following study in 2009 included data for 25 different growth states of the same organism with LC-MS data processed by the commercial software Progenesis [151]. Then, in 2013, the data from 2009 were used to evaluate the performance of the different software suites Progenesis, MaxQuant, and the OpenMS pipeline [152]. While the first represents one of the most widely utilized commercial applications for AUC determination, MaxQuant was chosen for evaluation due to its wide acceptance as an academic and free software used mainly for SILAC quantification. Finally, the OpenMS pipeline was selected for evaluation because it shows a high versatility toward new, data-independent acquisition methods in proteomics [153]. The development of versatile software packages that can be tailored for processing very specific questions has dominated work in this area in recent years. Besides the absolute determination of protein abundances on a global scale (which will be covered later), label-free quantification may also be used in combination with metabolic labeling for functional studies of protein turnover [154]. Although it seems that Progenesis is only used in functional studies aiming at different levels of information, more basic, relative quantitative label-free work has been successfully done on experimental work dealing with stress experiments for Photobacterium profundum and in healthcare settings of bacteria-mediated inflammation [155, 156]. Labelfree quantification is not restricted to comparisons of LCMS runs acquired using modern ESI-MS instruments. In 2008, Neubert and co-workers demonstrated that LC-MALDI is powerful for label-free detection of protein expression differences in E. coli cultures growing on three different carbon sources. Elucidator is a large software solution for all types of metabolic labeling and chemical labeling, as well as for label-free quantification, which seems to have been discontinued despite its success in a range of studies. Its power in data alignment and statistical testing, including advanced methods for data evaluation, has been shown, for example, for label-free comparison of microbial pathogens [157, 158] and cold adaption of P. putida [159] (Fig. 3). For experimental settings that preclude using metabolic labeling, Muntel et al. have shown that label-free quantification with the Elucidator software suite is an alternative: they achieved labelfree quantification of approximately 1000 S. aureus proteins [160]. MaxQuant, still the most popular package for SILAClabeled data [63], has proven its applicability for labelfree quantification studies [161]. In the field of infectionrelated proteomics, label-free quantification performed with MaxQuant has been used for comparison of the membrane proteome of a virulent strain of Mycoplasma pneumoniae and its attenuated isogenic strain [162]. Further, label-free quantification proved suitable for comparison of the secretomes of mutants of Serratia marescens. This enabled the identification of novel toxins for killing rival bacteria of this human pathogen [163]. www.proteomics-journal.com

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Figure 3. Peptide measurement, quality control, and visualization of samples from Pseudomonas cold shock adaptation [138]. (A) Tryptic peptide measurement using highly sensitive and accurate MS instruments such as FT-ICR. (B) For later analysis, the data are controlled in respect to RT shifts using the postprocessing software Rosetta Elucidator. (C) Example of a PCA protein plot from Pseudomonas cells isolated after cold shock adaptation. (D) Manual 2D image analysis of selected isotope clusters.

3.3.1.2 Data-independent AUC Ion intensity approaches have undergone a major change in keeping with the introduction of novel MS acquisition methods. While the first studies relied on mass spectrometric instruments that were run in classical data-dependent acquisition mode, novel data-independent acquisition modes are becoming increasingly popular for the determination of relative and absolute protein abundance. One example is the MSE acquisition mode, which is implemented in mass spectrometric instruments from Waters. Here, the concept of accurate mass and retention time tags [164] is combined with a technique of data-independent fragmentation of precursor ions in a Q-Tof instrument for an unbiased acquisition of mass spectrometric data [165]. Quantitative data are generated based on TOP3/Hi3 quantification, which describes a method for absolute or relative determination of protein abundances in a proteome sample. In short, protein abundances are determined by adding up the intensity of the three most abundant peptides of every distinct protein and by comparison of this summed intensity to a spiked-in reference protein. Consequently, label-free, unbiased absolute comparisons are possible, which includes relative comparisons between proteome samples. A direct advantage for the proteomics researcher is the dedication of the mass spectrometric manufacturer in support of the technique: the seamless implementation of sample measurement and data evaluation by vendor-specific software clearly facilitates implementation and application of label-free quantification. Immediately following the original publication, Silva et al. published a study of E. coli comparing three different growth regimes based on the carbon source. This was only a proof-of-principle study for the purpose of demonstrating the comprehensiveness of protein identification and quantification of a single method, as well as the range of dynamic changes from 0.1-fold to 90fold across the samples [166]. In terms of biological studies,  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

this acquisition/quantification system has had a particular impact on investigations of extracellular proteins/secretomes of microbes. Here, the label-free quantification strategy that has marked its success, especially for spectral counting in samples that cannot be labeled, has been successfully implemented. Examples for this are, studies of the necrotrophic fungus Botrytis cinerea [167] and the Gram-positive pathogen C. pseudotuberculosis [168]. For S. aureus, the power of labelfree quantification by LC-MSE was used to determine proteome changes in six strains and three growth states. Thus, in large studies, label-free techniques are superior to other quantification techniques for the assessment of changes without additional costs or losses due to increased complexity. In terms of metaproteome/complex consortia, LC-MSE should prove successful in the future, as the two following recent examples show. Bostanci et al. demonstrated its applicability in analyses of the gingival exudatome, pioneering dental studies with larger cohorts to characterize the complex metaproteome of the human oral cavity in health and the diseased state [169]. Regarding functional studies of symbiotic bacteria, analysis of the unculturable bacterial endosymbiont Blochmannia was the subject of a quantitative proteomic investigation revealing a disproportionately high coverage of biosynthetic pathways in the proteome linked to the symbiosis in ants of the tribe Camponotini [170]. A second, truly data-independent acquisition method called SWATH has been recently introduced by the Aebersold group; like LC-MSE it is also associated with a specific instrument provider (ABSciex) [153]. This technique has been implemented as a system that acquires data repositories of proteome samples that are ready for data mining in a quantitative way. Here, the data-dependent decision step is omitted by an acquisition method alternating between overview scans that are followed by collision-induced dissociation scans of 25 Da selection windows through a range of 300– 1250 m/z. In contrast with LC-MSE , SWATH is currently used www.proteomics-journal.com

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predominantly in an MRM-like manner of quantification [171]. Instead of relying on triple quadrupole instruments targeting specific transitions that have to be known prior to LCMS measurement for method development, complete, highly accurate, and high-resolution SWATH data are interrogated postacquisition. Therefore, the interrogation of SWATH data depends mainly on preexisting identification data stored in a spectral library. In contrast to the similar concept of precursor acquisition independent of ion count (PAcIFIC) [172], SWATH was engineered together with mass spectrometric equipment, and is characterized by ion guiding features on Q1 specifically needed for SWATH and a sufficient duty cycle of fragmentation experiments. In this context it has to be mentioned that current developments in the field of targeted MS to compile spectral libraries from newly generated or existing studies will also have a major impact on attempts at interrogating DIA datasets [153]. Therefore, attempts to standardize data storage/metadata description and to establish data repositories, as well as to set up mandatory standards for data deposition—for example, by research journals—will ensure that the entire scientific community will benefit from any big datasets published [173, 174]. Taken together, label-free quantification strategies based on AUC are of increasing importance in microbial research. In contrast with spectral counting, the workflow relies heavily on stable LC-MS conditions and requires sophisticated software. Despite this—which includes the necessity for highresolution and high mass accuracy MS instruments to map reliably extracted ion chromatograms to identified masses at a given RT—due to its specificity and new developments in data acquisition strategies, label-free quantification based on AUC will have a marked impact on quantitative microbial proteomics.

3.4 Absolute determination of protein abundance Since the introduction of 2DE gels in the mid-1970s, quantification in microbial proteomics has been successful in the comparison of changing biological systems. Since then, biology has moved toward becoming a discipline that seeks to integrate molecular information on different levels to ultimately simulate the processes of life in silico. This ultimate goal is reflected in the term “systems biology.” Omics data are predestined for use in systems biology approaches— at all levels the omics techniques generate information on a global scale that could be used for the modeling of processes inside biological systems such as bacterial cells. Systems biology approaches rely on absolute proteomic data rather than on relative comparisons—this means that protein numbers per cell are calculated based on the experimental data and are used for kinetic calculations or the determination of stoichiometry, for example, for a protein complex. Early approaches in absolute quantitative proteomics relied on green fluorescent protein-tagging or immunoaffinity procedures targeting specific, introduced epitope tags in  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

557 S. cerevisiae [17, 175]. Today, the determination of absolute protein abundances rely for the most part on mass spectrometric workflows using triple quadrupole instruments and the concept of SIL/isotope dilution [176]. A milestone in accurate determination of absolute protein abundance was a paper published by Gygi and co-workers [177]. Although the “Absolute Quantification” method (termed AQUA) was and still is extremely successful for small-size studies, scientific progress resulted in workflows enabling the determination of medium-scale protein quantities [178,179] and has ultimately led to protocols for determining absolute protein abundances on a global scale [180]. In microbial proteomics, three large-scale approaches for absolute determination of protein abundances in a proteome sample have been published, with two methods based on targeted MS and the other based on Top3 quantification as described above. For the type of study based on MRM, largescale proteome datasets are first generated either by an MSbased quantitative procedure (label-free AUC) or by a 2DE gel-based procedure. In the second step, the combined intensity data per protein or the combined densitometry data from the gels are calibrated by so-called “Anchor-proteins,” which can be determined by targeted MS methods. The first groundbreaking example of large-scale approaches was the study of L. interrogans [4] already mentioned. The other approach for absolute protein quantitation based on targeted MS is a combination of 2DE and MRM. In a study by Maass and co-workers, 2D gels were calibrated by “Anchor-proteins,” in a manner analogous to the gel-free approach, to obtain largescale absolute quantitative data. [181]. Both methods yield high coverage of the bacterial proteomes and allow for acquisition of system-wide absolute data. While the method based on AUC is more advantageous in terms of proteome coverage, the 2DE-based system is cheaper and, thus, affordable also for scientists who do not have access to high-resolution MS instrumentation. The absolute determination of protein abundances by Top3 quantification is possibly the most popular workflow as it is universally applicable and is independent of sophisticated software solutions, as is required for AUCbased quantification and 2DE studies. As already introduced, the concept of Top3/Hi3 quantification following Silva et al. in combination with a spiked-in reference protein of known abundance allows for calculation of absolute abundance data in a proteome sample. This method, which is based on quantitation of best-flyer peptides, is used in two workflows, Top3 [164–166] and iBAQ (intensity-based absolute quantification) [182]. It has been shown that apart from being implemented in LC-MSE workflows, the Top3/Hi3 approach can also be used with data-dependent acquisition systems of other vendors given a sufficient reproducibility of peptide fragmentation/identification [183]. Although Top3 quantification is an integral part of the vendor’s software PLGS for Waters systems, this type of quantification has succeeded in implementations in a variety of experimental work, employing different software suites [184, 185]. Successful applications for absolute quantification studies have already been mentioned www.proteomics-journal.com

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Table 1. Summary of organisms in the references cited according to the quantitative proteomics technique used

Aspergillus niger Bacillus subtilis

2DE

SILAC

15 N

x

x

x

Botrytis cinerea Chlamydia trachomatis Corynebacterium gutamicum Corynebacterium pseudotuberculosis Cryptococcus neoformans Escherichia coli

Euhalothece sp. BAA001 Halobacterium sp. Helicobacter pylori Lactobacillus rhamnosus Leptospira interrogans Mycobacterium tuberculosis Mycoplasma pneumoniae Neisseria meningitidis Nitrosomonas europaea Photobacterium profundum Pseudomonas aeruginosa Pseudomonas fluorescence Pseudomonas putida Ralstonia eutropha Saccharomyces cerevisiae

Saccharomyces pombe Serratia marescens Staphylococcus aureus

Sulfolobus sulfataricus Tannerella forsythia Trichoderma resii

SULAQ

ICAT

iTRAQ/TMT

SpC

Top3

x x

x

x

AUC

x x x x x x

x

x

x

x

x x x x x

x x x

x x x

x

x x

x x x x x

x

x

x

x

x

x

x

x

x

x

x x

x

x

x x

x x

Metaproteome/co-culturing samples

x

x

x

References [121] [2, 7, 25, 27, 61, 71, 119, 181] [167] [157] [86] [168] [158] [65–69, 78, 80, 112, 113, 144, 165, 166] [85] [109] [45] [37] [4, 151] [40, 107, 108, 162] [154] [83, 126] [84] [156] [111] [99] [146, 159] [77] [3, 12, 18, 32, 60, 63, 101, 102, 106, 114, 116, 122, 135] [115] [163] [1, 7, 22, 23, 28, 29, 31, 88, 127, 128, 145, 160, 181] [94] [37] [120] [85, 91, 95–97, 137, 139–143, 155, 169, 170]

TMT: isobaric mass tags.

above for the combination of AQUA and AUC [4, 150–152], for AQUA and 2DE [181], as well as for Top3/Hi3 based on LC-MSE [164–166, 186]. The advantages/disadvantages already mentioned for the respective singly applied techniques (2DE, AUC, Top3), also hold true for combinations of the techniques. An important point to keep in mind for absolute quantification studies is determination of the proper experimental setting and sample processing prior to the use of any suitable absolute quantification method [187, 188].

4

Concluding remarks and outlook

The advances in quantitative proteomics over the last years have led to a paradoxical situation: ever more techniques

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and suitable software suites are available, developed especially for ease of implementation in existing research and existing workflows. On the other hand, choosing the right techniques for specific scientific questions and biological environments is getting harder due to the need to select options satisfying basic scientific requirements, and—of increasing importance—economic restraints with regard to the expenditure per dataset generated. Consequently, a thorough review of existing studies employing proofs of principle is crucial for any study design, taking into account, in particular, sample type, sample availability, and the desired depth/accuracy of quantitative proteomic information required [187]. As has been shown in this review and summarized in Table 1, quantitative proteomic studies have had a large impact on the field of microbiology as a whole. For the future, there will

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be plenty of room for improvement of existing and establishment of new techniques specifically tailored to the questions raised and the results in demand. First, the study of systems of high complexity, including microbial metaproteomics, is an emerging field that is still in its infancy with respect to differential analyses due to challenges in quantitative strategies. While metabolic labeling by protein-SIP is manageable if one chooses the right precursor for in vivo labeling—as has been shown even for highly complex environmental samples—completely label-free methods mainly based on spectral counting seem to be regarded as the method of choice for most (semi)quantitative metaproteomic studies. Second, microbial research into host–pathogen interactions for virulent bacteria calls for quantitative workflows capable of dissecting the cellular reactions of the pathogen both inside and outside the host for a very limited number of cells used for identification and quantification. Third, in times of dropping costs for the sequencing of whole genomes, proteogenomics workflows will enhance functional assignments, leading to a better and more comprehensive annotation of genomes. Fourth, absolute quantification strategies will have a major impact in the future, satisfying the need for data useful in systems biology approaches. Although absolute quantification is already feasible based on targeted proteomics with reference peptides or sophisticated label-free approaches, there is still a long way to go for the field of absolute quantification of membrane proteins. The work was financially supported by the BMBF/“Unternehmen Region” as part of the ZIK-FunGene, as well as within the framework of the SFB Transregio 34. We would like to thank Peter Germain and Florian Bonn for critical reading of the manuscript. The authors have declared no conflict of interest.

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