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Proteomics 2012, 12, 1591–1608 1591 DOI 10.1002/pmic.201100509 REVIEW Two steps forward—one step back: Advances in affinity purification mass spectrometry of macromolecular complexes Marlene Oeffinger1,2,3 1 Institut de recherches cliniques de Montréal, Montréal, Québec, Canada Faculté de médecine, Département de biochimie, Université de Montréal, Montréal, Québec, Canada 3 Faculty of Medicine, Division of Experimental Medicine, McGill University, Montréal, Québec, Canada 2 Cellular functions are defined by the dynamic interactions of proteins within macromolecular networks. Deciphering these complex interplays is the key to getting a comprehensive picture of cellular behavior and to understanding biological systems, from a simple bacterial cell to highly regulated neuronal cells or cancerous tissue. In the last decade, affinity purification (AP) coupled to mass spectrometry has emerged as a powerful tool to comprehensively study interaction networks and their macromolecular assemblies. This review discusses recent advances in AP approaches, from cell lysis to the importance of sample preparation and the choice of AP matrix as well as the development of different epitope tags and strategies to study dynamic interactions, with an emphasis on RNA–protein interaction networks. Received: September 25, 2011 Revised: December 21, 2011 Accepted: January 12, 2012 Keywords: Affinity purification / Cell lysis / Quantitative mass spectrometry / Ribonucleoprotein complexes (RNPs) / Systems biology 1 Introduction Biological research is enabled by its available technologies. One key technological development was that of highthroughput DNA sequencing, which enabled the determination of the complete DNA sequence of several eukaryotic species. This spawned the field of functional genomics and several subsequent technologies such as microarrays and MPSS (Massively Parallel Signature Sequencing), which emerged to allow researchers to globally interrogate gene expression. Likewise, remarkable advances in the area of mass spectrometry (MS) gave birth to the field of proteomics, enabling researchers to rapidly and reliably identify proCorrespondence: Dr. Marlene H. Oeffinger, Institut de recherches cliniques de Montréal, 110 Avenue des Pins Quest, Montréal, Quebec, H2W 1R7, Canada E-mail: [email protected] Abbreviations: AP, affinity purification; CBP, calmodulin-binding protein; LAP, localization affinity purification; qMS, quantitative mass spectrometry; RAT, RNA affinity tandem tag; RBP, RNAbinding protein; RNP, ribonucleoprotein particle; ssAP, singlestep affinity purification; TAP, tandem affinity purification C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim teins isolated from biological samples. In conjunction, researchers began to use affinity-based methods to identify protein complexes on a relatively large scale in order to establish molecular-level interaction maps. Over the last decade, affinity purification mass spectrometry (AP-MS) has become a powerful method to study interactions within a variety of cellular complexes in many organisms and provides a foundation from which will one day emerge a comprehensive picture of cellular behavior. As part of the expanding proteomics field, many researchers have put considerable effort into elucidating protein–protein interaction networks in different organisms in order to better understand the interplay between proteins and to gain more insight into the potential for disease development in case of network disruptions [1–4]. Determining the interactions between macromolecules in a cell is, however, a formidable undertaking for several reasons. First, the number of interacting entities is huge. For example, in Saccharomyces cerevisiae alone there are ∼6200 open reading frames that code for proteins [5,6]. Moreover, proteins do not work in isolation but together along pathways and within organized complexes forming intricate information networks, where each protein is predicted to have between three to five direct interactions on www.proteomics-journal.com 1592 M. Oeffinger average [7–12]. Second, proteins exist in multiple states (modulated, e.g. by phosphorylation). These states are dependent on the cellular context and they confer different interaction potential and often different functions. Thus, the interactome is dynamic with interacting partners changing dependent on the context of the process or the cellular localization of the factors [13–16]. Third, the relevant specific interactions have a very wide range of affinities, and, finally, proteins are found with a broad range of abundance (101 –106 copies per cell), which changes depending on the cellular context, impacting complex formation and stoichiometry [17]. In addition, there are many proteins that interact not with proteins but with other types of macromolecules in the cell, including DNA and RNA. DNA and RNA interacting proteins include transcription factors, helicases, polyadenylation factors, ribosome biogenesis factors, to name only a few, and all of these interactions are critical for many basic cellular functions. The central role played by RNA, both as a template for protein expression as well as a regulatory molecule, has led to a growing interest to comprehensively catalog cellular ribonucleoprotein (RNP) complexes and their maturation pathways. Analysis of RNA can be performed by hybridization or sequencing-based methods; however, in the cellular environment RNA is associated, often transiently, with RNA-binding proteins (RBPs) forming functional RNP complexes. Technological advances permitting a detailed, quantitative and rapid characterization of macromolecular complexes by AP-MS, but also the development of next-generation sequencing methods for analyzing RNA populations (RNA-Seq) have provided a major step forward for the systematic charting of the dynamic, mechanistic, and structural properties of cellular protein–protein and protein–RNA networks. Here, we will review the achievements of the proteomics field over the past decade and discuss recent technological advancements in AP-coupled MS for the characterization and analysis of dynamic protein–protein and protein–RNA interaction networks with focus on the technical aspects including different approaches in sample preparation and the differences between currently used epitope tags as well as quantitative MS (qMS) approaches, RNA AP methods, and RNA analysis. 2 The isolation of macromolecules: Challenges and solutions Generally, AP-MS consists of isolating a protein of interest (bait) from any sample using affinity approaches, followed by identifying the components of the purified sample using MS (Fig. 1). If the conditions used for the AP do not disrupt protein–protein interactions, binding partners can be recovered from the sample. In contrast to techniques such as yeast two-hybrid screen, AP-MS can be performed in a near physiological context and interactions can be monitored in any selected cell type, following exposure to almost any C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Proteomics 2012, 12, 1591–1608 type of treatment. Protein interactions that depend on posttranslational modifications (PTMs) can thus be identified and the PTM itself may be mapped by MS. Under ideal circumstances, AP allows the isolation of any given macromolecule in its native state, with its adjacent native macromolecular environment completely intact. However, as mentioned above, there are many parameters that make it challenging to isolate complexes, RNP or other, with all interactions intact, and the nature of the protein interactions actually captured during AP is highly influenced by experimental conditions such as sample preparation and purification strategies [18–20]. Experimental challenges that need to be considered are efficient cell lysis, solubility, complex preservation and recovery of entire pool of proteins associated with the bait, preservation of transient interactions, fragility of nucleic acid and stability of nucleic acid–protein interactions, and, lastly, the dynamics of complexes within a network or pathway, since in many cases we are not simply looking at a static complex but a mixture of dynamic intermediates. In addition, the placement of the epitope tag and the choice of tag are points that also require some consideration for successful AP, at least in some instances. Finally, the analysis of affinity-purified complexes, including distinguishing real interactor from contaminant and determining complex composition and stoichiometry, also poses a formidable challenge. Fortunately, through the effort of many researchers, new methodologies and solutions to these challenges are being established constantly and here we will discuss a number of them in more detail. 2.1 Cell lysis – alteration of the macromolecular context Normally, a macromolecular complex and its microenvironment are surrounded by a larger cellular context. During isolation, this context is replaced by an artificial one, consisting of buffers, salts, and stabilizing agents, carefully selected to mimic a natural milieu. Even so, we cannot hope to exactly replicate the conditions found inside the cell. Disruption of the cell and dissociation of the complexes lead to intermingling of components not normally exposed to one another, and the resultant possibility of aberrant molecular interactions – a major source of “non-specific background.” Another undesired result of this unnatural intermingling is the exposure of macromolecules to the degradation enzymes that are normally kept at bay in a living cell. These include proteases, RNAses, and DNAses. In addition, disruption of the cellular membranes leads to loss of specific subcellular milieus, dispersal of energy generating gradients, and concomitant loss of the energy sources that maintain and replenish many macromolecular complexes. There are a number of different lysis methods commonly used to break cells, such as cell disruption using glass beads for yeast and bacteria, and detergent treatment, dounce homogenization, hypotonic buffers, or “freeze-thaw” protocols for mammalian cells, as www.proteomics-journal.com Proteomics 2012, 12, 1591–1608 1593 Figure 1. Single versus tandem affinity purification (TAP). (A) Tandem AP tags contain two epitope tags often separated by a protease cleavage site. Single tags are fused to the bait protein without protease cleavage site. (B) Left: In TAP approaches, protein complexes are isolated in two successive purification steps. Most often, the first purification is followed by the removal of a first AP tag through proteolytic cleavage with a site-specific protease. The released complexes are then subjected to a second round of AP and eluted by either addition of cations (CBP), low/high pH (FLAG, HA), high salt (CBP, His) or competition (FLAG, HA, streptavidin/biotin). Right: Single-step AP (ssAP) uses only a single purification tag and step. Following isolation and washed, complexes are eluted either by low/high pH (GFP, PrA, FLAG, HA) or competition (FLAG, HA). Although TAP purifications allow the purification of very clean complexes with a low background of contaminants, the stringent and often long two-step purification causes the dissociation of transient interactors, resulting in the isolation of an incomplete interactome. In both cases, eluted complexes are analyzed by MS. C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com 1594 M. Oeffinger well as more recently, cryolysis. Generally, breaking cells by employing physical shear, e.g. through high-speed mixing with glass beads (beadbeating), often results in sample heating, inefficient lysis, and high sample degradation. On the other hand, lysis of mammalian cells by harsh buffers or detergents can cause the potential disruption of cellular complexes by protein dissociation. Even milder methods such as dounce homogenization or cycles of “freeze-thawing,” which are both widely used, can result in protein degradation and postlysis rearrangements [21]. In the past, cryolysis has been mostly used by the splicing field to keep spliceosomal activity intact after cell breakage, and often this was achieved with a pestle and mortar cooled with liquid N2 or a coffee grinder, grinding frozen cell material [22]. In recent years, more sophisticated equipment such as cryomills have been used to break cells open in their frozen state. The advantages of cryolysis are many – high yield lysis (>90%), little proteolytic damage to the isolated proteins as the cell breakage step is a “solid phase” process, no proteases, RNAses, or DNAses are released, and finer particles (∼1–2 m) than most other grinding techniques from which complexes can be extracted quickly and efficiently, with less chance of aggregates; moreover, as it is applicable to any cell and tissue type, it has become more widely used in the past few years [20, 23, 24]. 2.2 Race against time: Time-dependent disintegration of macromolecular complexes Once cells are broken open, the challenge is to preserve, with high fidelity, the environment of any chosen macromolecule. Unfortunately, there are a number of factors that are potentially disruptive to this macromolecular environment: while the covalent bonds that hold individual macromolecules together are stable over time, the various noncovalent interactions are not so stable. In the absence of constant replenishment from a living cell, macromolecular complexes and their microenvironments will rapidly disperse. Typical association rates (Kass ) for binary protein complexes that are not controlled by long-range interactions have been calculated to range between 104 and 106 M−1 s−1 [25]. These association rates appear to be dominated largely by translational and rotational diffusion. In contrast, once formed, there are a wide variety of factors that contribute to holding these pairs together, including the nature of the forces involved (ionic, van der Waals, hydrogen bonding), the number of interacting atoms, and steric considerations [26–29]. This means that macromolecular associations operate under a large range of dissociation rates (Kdiss ). A standard measure that takes into account both of these factors, and so describes how readily two macromolecules form and maintain an interaction, is the equilibrium dissociation constant Kd = Kdiss /Kass . Because of the large ranges in Kass and Kdiss , Kd s for specific macromolecular interactions in a cell can extend over many orders of magnitude, from fractions of nM to over tens of C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Proteomics 2012, 12, 1591–1608 M. Upon isolation, a complex begins to dissociate depending on the above-described factors. Considering the worstcase scenario of a binary interaction between a tagged macromolecule and its partner, where the partner can dissociate but cannot rebind, under these conditions the half-life of the pair is given by 1/2 = ln2 / Kd × Kass [25]. We see that under typical conditions used for affinity isolation of tagged proteins and their partners [30], where the time taken for purification is usually >2 h, we can only expect to preserve binary interactions with Kd s of 10 nM at best and usually <1 nM. Such low Kd s represent only a small fraction of the binary interactions that occur in a cell. Although this effect is often ameliorated by the fact that many complexes have more than two components, this problem is one of the most important facing biochemical approaches to the study of macromolecular interactions. One way to preserve these interactions during isolation is fast sample processing, short clearing spins, and short incubation times, ideally under 2 h, as was shown in Cristea et al., where the loss of real complex interactors of the yeast Nup84 complex and association of contaminants were tracked over several hours [19]. A second way of increasing the speed by which tagged complexes can be isolated is by significantly increasing the surface area to which the tagged complex can bind. Widely used AP methods use large porous agarose- or sepharose-based resins, which have a varied diameter between approximately 15 and 100 m, requiring long incubation times for efficient affinity isolations, typically >2 h, which potentially causes many but the tightest complex to disintegrate [30]. Another type of resin, however, that is being used to circumvent long incubation times, is composed of magnetic beads that are smaller in diameter (1–2.8 m) than agarose or sepharose and allow for an efficient isolation of complexes in ∼10 min due to the increased surface area to volume. Moreover, using magnetic resin, complexes are isolated by placing the magnet at the side of the tube whereby beads are moved away from large particulate material that would normally cosediment during standard agarose/sepharose-based methods. Besides being faster than centrifugation, this gives the advantage that very crude cell lysates can be used as starting material for AP without precentrifugation. Several methods have been published, where magnetic resin was demonstrated to allow for minimal processing of samples and short incubation times, which permitted the isolation of more labile complexes that would have potentially dissociated after long incubation times [19, 20, 23, 31–34]. However, magnetic resins still have one drawback – their cost as well as limited suppliers, which makes them sometimes uneconomically. Nevertheless, several large-scale AP-MS studies and automated high-throughput technologies such as LUMIER have used magnetic beads successfully [23, 33–35]. An additional factor that can influence the stability of complexes to be purified and should be mentioned is the choice of extraction buffer components. Although many high-throughput studies generally use the same extraction buffer for all their baits, here the choice of extraction buffer www.proteomics-journal.com 1595 Proteomics 2012, 12, 1591–1608 components is a necessary compromise designed to be the least disruptive to the highest number of macromolecular complexes, but is therefore suboptimal for many. A number of smaller studies have shown that careful selection of an extraction buffer for each bait protein can aid the intact isolation of its associated complexes [19, 20, 32, 36, 37]. However, empirical testing and selection of an optimized buffer for each individual bait protein is not always feasible. 2.3 Isolation of macromolecules: The world of epitope tags To isolate a bait protein, one needs either specific antibodies or another molecular handle. Efficient, rapid isolation of proteins from complex cellular fractions was first accomplished using immunoprecipitation. However, this approach is severely limited, as it requires specific high-affinity antibodies and specific conditions to be established for each bait protein. Nevertheless, the principle has withstood time, and combined with affinity chromatography, this approach has led researchers to develop specific epitope or affinity tags that are created as a gene fusion, expressed in cells, and isolated using an affinity matrix specific to the tag. This approach has been remarkably successful and has been widely used over the last decade. Epitope tags also allow for a more generic purification strategy, in which a single protocol can be utilized for the purification of multiple bait proteins. Many tags have been developed for AP and include epitopes derived from Staphyloccus aureus Protein A, Influenza hemagglutinin (HA) or Myc, proteins that bind molecules with high affinity such as avidin (biotin), short peptide tags such as the widely used FLAG tag, and fluorescent protein tags such as GFP [19, 38, 39]. In addition, combination or so-called tandem AP (TAP) tags were designed to increase the purity of the isolated complexes. The most widely used tandem affinity tag to date is the TAP-tag that consists of two IgG-binding moieties of Protein A, a tobacco etch virus (TEV) protease cleavage site, and a binding site for calmodulin-binding protein (CBP) [30]. 2.4 Creation of tagged macromolecules – choosing the right tag Nowadays, there is a vast choice of epitope tags to select from. Ideally, the following criteria should be met for a tag to inform on a biological system. First, the tag must not interfere with the function of the bait protein; second, the introduction of the tag must not alter the expression or stability of the endogenous protein; third, the tag should have a high affinity and specificity for a reagent that can be immobilized on a matrix. Moreover, the tagged protein and its interacting components should be easily eluted from the matrix, both under denaturing and native conditions in case of tandem C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim tags, or when two different components of a complex are tagged to allow for a first enrichment of a pool of complexes. It is also desirable that the tag can be visualized in cells, whether by fluorescence (e.g. GFP) or immunofluorescence, and while this is not an essential requirement for successful AP, it provides the possibility to directly combine imaging with quantitative proteomics technology [40–42]. The size of the affinity tag is also important since bulky tags are more likely to interfere with the biological functions of the tagged protein, such as protein folding, recruitment into protein complexes, subcellular localization, or even its degradation. Some tags such as TAP, Protein A, and GFP are all rather large, with sizes between ∼20–30 kDa. Thus, a series of short tandem affinity tags has been designed (Table 1) [43–46]. Besides economical constraints with regard to antibody cost, especially in large genome-wide analyses, and technical considerations such as the requirement of a first-step elution under native conditions for tandem approaches and types of experiments, the choice between tags also depends on the organism to be studied. For example, higher eukaryotic cells naturally express high levels of CBPs, which are known to interfere with the binding of the CBP tag to the calmodulin resin. Therefore, in mammals and plants, a variety of more suitable alternatives, such as streptavidin-binding peptide (GS-TAP tag), localization AP (LAP) tag, or HA and FLAGHA tag have been created to replace the CBP tag (Table 1) [45–48]. 2.5 Endogenous integration versus exogenous expression Generally, overexpression of a tagged protein is undesirable as it can lead to aberrant localization, protein misfolding and aggregation, alterations in complex stoichiometry, and toxicity. Currently, however, only few organisms allow the expression of fusion-proteins at close to physiological levels. In Escherichia coli and yeast, homologous recombination allows for fast and efficient endogenous tag integration, and subsequent expression of the tagged protein at physiological levels under the control of their endogenous promoter [10,43]. In mammalian cells, entire genomic loci that include genes but also their regulatory elements and natural promoters can be cloned using bacterial artificial chromosomes (BACs) [42]. In most cases, however, tagged protein expression is driven by nonendogenous promoters from plasmids, retroviral or lentiviral vectors. In such systems, a tight control over the expression level and the tagged-protein’s localization is required [47]. A more recent system is the Flippase (Flp)-recombination system that enables the generation of isogenic human cell lines. The cloning construct contains an Flp recombination target (FRT) site that allows efficient Flp-mediated recombination at a single FRT site engineered in cells. Here, the use of the tetracycline-inducible promoter TetO allows for tight control of tagged bait protein expression [44]. www.proteomics-journal.com C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim (TEV) HB (6 kDa/11 kDa) – Human rhinovirus 3C (PreScission) TAPa (26 kDa) HA (0.75 kDa) – CHH (14 kDa) – – FLAG-HA (3 kDa) FLAG (0.75 kDa) – SH TAP (5 kDa) – TEV SPA (8 kDa) GFP (28 kDa) TEV or PreScission (LEVLFQ*GP) LAP (36 kDa) – TEV GS TAP (19 kDa) Protein A (27 kDa) TEV (ENLYFQ*G) Cleavage site TAP (20 kDa) Tag (Mw) HA peptide or low pH FLAG peptide or low/high pH High pH Low pH or high pH TEV cleavage, biotin or imidazole HR3C cleavage or imidazole TEV cleavage, biotin or imidazole TEV cleavage or EGTA Biotin or HA peptide FLAG or HA peptide TEV or PreSc/HR3C cleavage, imidazole TEV cleavage, biotin TEV cleavage or EGTA Elution Higher eukaryotes Yeast, viral systems Yeast, higher eukaryotes Small tag—less steric interference Contains four IgG-binding domains Allows protein localization Small tag—less steric interference Compatible with denaturing conditions Yeast, higher eukaryotes Yeast HR3C is active at 4⬚C Triple tag Small tag—less steric interference Small tag—less steric interference Small tag—less steric interference Allows protein localization via GFP Second high-affinity tag Most widely used tag Comments Arabidopsis Yeast Higher eukaryotes Higher eukaryotes Bacteria, yeast Higher eukaryotes Bacteria, yeast, higher eukaryotes Higher eukaryotes, Drosophila Organism(s) used in Ho et al. [77] Breitkreutz et al. [23] Breitkreutz et al. [23] Cristea et al. [19] Rout et al. [38] Tagwerker et al. [68] Rubio et al. [64] Behrends et al. [46] Honey et al. [63] Sowa et al. [45] Glatter et al. [44] Hu et al. [43] Hutchins et al. [47] Poser et al. [42] Bürckstümmer et al. [48] Rigaut et al. [30] Reference M. Oeffinger Schematic denoting different modules; cleavage site is indicated by an asterisk (*). Single-step Tandem 1st Tag – 2nd Tag – 3rd Tag Table 1. Next-generation affinity tags. A list of the most widely used tandem affinity tags 1596 Proteomics 2012, 12, 1591–1608 www.proteomics-journal.com Proteomics 2012, 12, 1591–1608 3 Commonly used AP approaches: Advantages and drawbacks Over the years, many different tags and approaches have been developed for AP. One of the most widely used methods, the TAP protocol, was developed in yeast in 1999 [30] (Fig. 1). At the time, TAP allowed the detection of protein– protein interactions with a higher signal-to-noise ratio due to its two-step purification approach compared to single-step methods. One advantage of the method in general, then as well as now, is that it is generic and allows purification of protein complexes from all subcellular compartments without prefractionation, which is particularly suitable for genomewide studies. Hence, the TAP protocol has become adopted in many high-throughput studies of protein complexes in a wide range of organisms, including E. coli, S. cerevisiae, Arabidopsis thaliana, Oryza sativa, Drosophila melanogaster, and mammalian cells [10, 43, 49–52]. This also led to the development of a growing choice of protocols and reagents because despite the fact that the TAP method has been successfully used for purification and identification of protein complexes and their components both in prokaryotes and eukaryotes and is still widely used, some inherent shortcomings of the method have been uncovered. In a systematic analysis of the yeast proteome, Gavin and colleagues found that (i) they were unable to isolate and identify interacting proteins in 22% of APs and (ii) that they were unable to purify all of the proteins they had tagged. They attributed this failure to the intrinsic quality of the TAP tag. Moreover, they reported in 18% of the cases when essential genes were TAP-tagged, viable strains were not obtained, indicating that in some cases the TAP tag may interfere with protein function, location, and complex formation [10]. In this situation, an alternative solution would be to either place the tag at the other end of the protein, or to replace it with another tag entirely. In addition, the choice of CBP as a second affinity step proved problematic for a number of reasons; first, due to its relatively low efficiency of purification, which requires consequently large amounts of starting material. Second, as previously mentioned, some endogenous mammalian proteins interact with calmodulin in a calcium-dependent manner, creating high background in the very step that was designed to eliminate such [53, 54]. Both problems have been resolved by replacing the CBP tag in some instances with other affinity tags, such as FLAG or biotin [55–58]. Another challenge facing the TAP strategy, at least in mammalian systems, comes from the competition of endogenous proteins with the exogenously expressed tagged protein in complex assembly. To alleviate the problem, RNAi was used to reduce endogenous protein expression levels, which proved to be helpful in a number of cell systems [59–61]. 3.1 A new generation of tandem affinity tags However, despite many changes and improvements, low purification yields still pose a significant problem, particularly C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1597 in cases where low-abundance interactors, transient or dynamic interactions are the target of the study. To solve this problem, varieties of affinity labels to enhance the efficiency of the method and give higher yields have been designed. In mammalian cells, one new tag, the GS-TAP, based on Protein G, which exhibit higher affinity to immunoglobulin G than Protein A, was developed. In addition, the calmodulinbinding peptide was exchanged for streptavidin-binding peptide and biotin was used for elution instead. Bürckstümmer et al. showed that using the GS-TAP tag they were able to achieve a tenfold increase in efficiency compared to the conventional TAP tag, with less initial starting material [48]. Using the Ku70-Ku80 complex as a model, the approach also allowed purification of protein complexes that were not previously observed with TAP, leading to higher success rates and identification of less-abundant protein assemblies. The technological improvements conferred by the GS-TAP tag were also confirmed by studies performed in Drosophila embryos [62]. After that the development of a number of new tandem tags followed, predominantly in mammals and plants, including the LAP, SH-TAP, the FLAG-HA tag, the SPA tag, and the CHH tag, the latter of which theoretically allows for a three consecutive purification steps and was used to isolate active Clb2-Cdc28 kinase complex from yeast [43–47, 63] (Table 1). Further modifications have been made to the protease cleavage site; in a number of tandem tags such as the TAPa tag, which is used in Arabidopsis, the TEV cleavage site has been replaced by a PreScission, which is also known as human rhinovirus 3C protease site [64]; in contrast to TEV, HRV 3C retains its enzymatic activity at 4ⴗC and thus aids the preservation of intact protein complexes during proteolytic release from the resin [65]. One drawback that remains for all tandem APs is their inability to detect transient and dynamic interactions, low stoichiometric complexes, and those interactions that occur only in specific physiological states and are underrepresented in exponentially growing cells. To stabilize transient complexes, the proteomics field has revisited an old technique – in vivo cross-linking – as a means to freeze both weak and transient interactions in place within intact cells prior to lysis [66, 67]. A recently developed tandem affinity tag, the HB tag, which consists of a 6x-His and an in vivo biotinylation signal peptide, was shown to be useful in the isolation of cross-linked complexes due to its compatibility with fully denaturing purification conditions [68]. The stringent conditions compatible with the HB tag are advantageous in the AP of cross-linked complexes as they facilitate the removal of noncross-linked, interacting proteins, which might not reflect the in vivo composition of the protein complexes but rather proteins that associated with the complex post-lysis. As reduction of background is particularly important for in vivo cross-linking approaches (since proteins cross-linked to proteins that are nonspecifically purified amplify the background), a tag that allows high-stringency extraction of cross-linked complexes significantly increases the efficiency of these purifications. The approach was used www.proteomics-journal.com 1598 M. Oeffinger successfully to purify in vivo cross-linked Skp1p, a core component of SCF-ubiquitin ligases that forms several distinct multiprotein complexes [69]. However, the identification of cross-linked peptides by MS still remains a daunting challenge. But progress is being made and the first software suites are being reported, which will make the combination of cross-linking and AP more applicable in the future [66, 70]. 3.2 Tandem APs versus single-step approaches In the last few years, a number of studies showed that a potential solution to the problem of capturing transient and low abundance interaction lies in single-step AP approaches (ssAP). Compared to a decade ago these approaches have advanced significantly, thanks to improvement in lysis methods, which help keeping complex disintegration to a minimum, and resins that allow for faster isolation of complexes, and faster sample handling overall [19, 20, 23, 71]. As previously mentioned, generally, TAP-MS methods are not designed to monitor very labile or transient interactions (typically capturing interactions with Kd lower than the mid nM range [25]). To address these limitations and capture more transient interactions, shorter protocols have been designed with a single step of purification instead of two (Fig. 1B). It was believed that ssAPs may lead to significantly higher background, however, several studies in yeast, mammalian and viral systems have shown that cryolysis, rapid sample handling, and the use of low-background resins such as magnetic beads over agarose/sepharose-based resins, can significantly reduce background to manageable levels, yielding as clean samples as any tandem approach while still preserving transient or weaker interactions [19,20,34,72]. Moreover, many of these protocols use less starting material then any commonly used tandem purification since the sample loss is greatly minimized during purification. Nevertheless, just as any AP experiment, ssAP also requires quantitative analysis methods that allow for an unbiased identification of background. Thus, many ssAP approaches are coupled to qMS measurements (either by heavy isotope labeling or spectral counting) and efficient filtering of nonbait-specific interactions [24,34,73–76]. Another requirement for clean and efficient ssAP is an epitope tag with high specificity and high Kd to its ligand. Several have been used so far, among them Protein A, GFP, FLAG, and HA [19, 20, 23, 72, 77]. Protein A has a remarkably high affinity for rabbit IgG (∼1010 M−1 ), making it ideal for the rapid isolation of complexes. Despite the relatively large size of the Protein A tag currently used in the literature (∼27 kDa, containing four IgG-binding moeties), it is innocuous to most (∼95%) proteins and so far more than 300 proteins have been tagged with Protein A in yeast [i.e. 20, 31, 32, 78–80]. Protein A is also readily removed from IgG using salts and denaturants making the elution of complexes straightforward as well as economical. A study by the Rout laboratory successfully used Protein A in an ssAP approach to copurify known com C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Proteomics 2012, 12, 1591–1608 ponents of different complexes along the yeast mRNA maturation pathway, some of which are believed to be transiently associated and had not been previously isolated by conventional TAP approach [94]. GFP, although widely applied for in vivo visualization of proteins, has until recently been used relatively little as a tool for the isolation of protein complexes. Its advantage is that tagged proteins can readily be visualized in living cells and their interactions captured via an ssAP procedure from the same culture. A number of studies have successfully used GFP as an AP tag in single-step approaches up to date, isolating complexes from a number of organisms such as yeast, mammalian cells, and viral systems [18,19,71]. Given the wide use and availability of GFP-tagged protein reagents for many organisms, this tag would be an ideal tool for AP studies. One problem, however, is the availability of reliable, high-affinity antibodies, particularly ones that have not already been preconjugated to resin potentially at too low density, thus causing increased background [18, 81]. Predominately used in mammalian systems, the FLAG tag was the first example of a fully functional epitope tag to be published in the scientific literature [82]. The size of the FLAG tag (<1 kDa) is much smaller than that of the original TAP tag, and is therefore less likely to interfere with protein–protein interactions. In the past, it has often been used as a tandem tag in conjunction with other tags such as HA, but recently some study have used it, as well as HA, successfully for ssAP approaches. Using both single FLAG and HA, Breitkreutz and colleagues characterized networks of transient interactions between yeast kinases, phosphatases, and their substrates, while Gingras and colleagues reported that by using a singlestep FLAG approach, they were able to identify specific novel interactors for the catalytic subunit of PP4, which they had not previously observed with TAP-MS [23, 72]. 4 The nature of bait – RNA versus protein 4.1 Complex isolation using RNA tags The early proteomic high-throughput studies marked a step forward for the study of RNA maturation pathways as many novel factors were identified at the time [1, 77]. While many of the components are believed to have been identified, the dynamics of proteins and complexes along RNA maturation pathways is still poorly understood. Thus, a lot of efforts are currently under way in the proteomic field to study protein– RNA networks, including ncRNAs, mRNP maturation, and ribosome biogenesis pathways. Up to now, the main effort to elucidate interactions within these networks was placed on the isolation of these complexes from the protein side. However, interesting questions can be posed when studying these complexes and networks from the viewpoint of the RNA instead. Gaining knowledge of the mechanisms of ncRNA function and RNP assembly from an RNA point of view will require generalizable methods for the purification of endogenously assembled RNPs similar to those that have www.proteomics-journal.com Proteomics 2012, 12, 1591–1608 1599 Figure 2. Gene-specific in vivo RNA affinity purification. (A) An mRNA-specific AP tag is created by adding multiple high-affinity binding sites for an RNA-binding protein (RBP) to a selected mRNA. The sites are bound with high specificity and affinity by an RBP fused to an AP tag (PrA or GFP). Often phage coat proteins/RNA hairpin systems (MS2, PP7) are used for the isolation of in vivo RNP complexes, as they do not interact with eukaryotic RNAs. The coat proteins bind to the RNA hairpin as a dimer. (B) Coexpression of mRNA and tagged RBP allows the purification of specific mRNAs from complex cellular extracts. The RNAs are purified via the bound and tagged binding protein, and after elution by low or high pH analyzed by MS for the RNP protein composition. been established for protein baits. Various strategies have been tried using RNA-based affinity chromatographic methods, most of them involving the immobilization of a selected in vitro synthesized RNA on a column to which then cellular protein fractions are added. Collectively, these methods relied upon RNA–protein interactions to take place after cellular lysate preparation, and therefore did not mirror endogenously assembled RNA–protein complexes [83]. To identify in vivo RNA-associated proteins, it would be desirable to purify a selected RNA in its native state using an AP approach (Fig. 2). To date, however, RNA affinity tags have been used with limited success for the identification of proteins of endogenously assembled RNPs despite the effort of a number of research groups to develop entirely RNA-based strategies for RNP AP. Some protocols enriched RNPs from cell extracts by prior hybridization of the RNA component with a complementary oligonucleotide [84]. This method, however, C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim has technical limitations due to the need for duplex formation of a previously single-stranded region of the RNA, which tends to destabilize RNP architecture. More recently, the addition of different RNA aptamer sequences to the RNA of interest has provided a convenient tool for RNP separation and purification. Different aptamers specific for either small molecules (e.g. streptomycin, streptavidin, or tobramycin) or for proteins and peptides (e.g. MS2 and PP7 coat proteins) bind their ligands with an affinity similar to that observed for antibodies and have been successfully used for the isolation of a variety of RNPs [83, 85–87] (Table 2; Fig. 2). While those strategies have mainly been used to recover proteins associated with RNAs transcribed and assembled into RNPs in vitro, e.g. in the characterization of spliceosomal complexes [83,88], two more recent studies also attempted the recovery of RNPs directly from lysate. One of them used an RNA Affinity in Tandem (RAT) tag, which consists of two RNA aptamers, www.proteomics-journal.com 1600 M. Oeffinger Proteomics 2012, 12, 1591–1608 Table 2. RNA affinity purification tags. A list of the most widely used RNA affinity tags for the in vitro and in vivo isolation of RNP complexes using RNA as bait Affinity tag In vitro/ in vivo Isolation mechanism Organism Reference Antisense oligo In vitro Higher eukaryotes Strepto tag In vitro Lamond et al. [130] Lingner and Cech [84] Kurth et al. [131] Bachler et al. [86] Tobramycine In vitro MS2 RNA hairpin In vitro/in vivo PP7 RNA hairpin In vivo ARibo tag In vitro RAT tag In vivo Strept S1 In vitro/in vivo RaPID In vivo Biotinylated 2’-OMe or 2’-O-alkyl- antisense oligonucleotides In vitro selected antibiotic-binding RNA is used as a tag Tobramycin-aptamer containing mRNA is bound to tobramycin column and incubated with cellular extract; eluted with tobramycin RiboTrap: specific sites for a known RBP are used to facilitate binding of a coexpressed RBP and its RNP Pseudomonas phage 7 coat protein conjugated to an epitope tag RNA is fused to an activatable ribozyme (the glmS ribozyme) and the BoxB RNA from bacteriophage (ARibo) tag via binding to a N peptide conjugate to GST, RNA is eluted by addition of GlcN6P, activating the glmS ribozyme RNA Affinity Tandem tag: PrA-PP7 CP binds to PP7 hairpins and RNA is eluted by TEV cleavage; the second step is binding to tobramycin aptamer and elution with tobramycin. Isolation of yeast in vivo complexes by binding to streptavidin, elution with biotin Bacteriophage MS2 coat protein coupled to GFP-SBP, captured by streptavidin, eluted with biotin one specific for the Pseudomonas phage 7 coat protein (PP7) and the other for tobramycin [89]. The RAT tag was used to isolate human 7SK RNPs and it was shown that 7SK RNA is part of different mixed population of RNPs with differing protein compositions and responses to cellular stress, a fact that C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Yeast Higher eukaryotes Hartmuth et al. [83] Lejeune et al. [132] Yeast Beach et al. [133] Higher eukaryotes Hogg and Goff [90] N/A Di Tomasso et al. [134] Higher eukaryotes Hogg and Collins [89] Yeast and higher eukaryotes Srisawat and Engelke [87] Butter et al. [135] Yeast and higher eukaryotes Slobodin et al. [136] could not have been demonstrated by AP using any individual 7SK RNA-associated protein. In a second study, the same authors used just the PP7 affinity tag and coat protein to isolate tagged mRNAs to examine Upf1-dependent degradation of mRNAs with long 3 UTRs [90]. www.proteomics-journal.com Proteomics 2012, 12, 1591–1608 Although these studies by Hogg et al. clearly showed that the isolation of in vivo RNPs from cell lysates and the characterization of associated proteins is possible, there is still some way to go to overcome the inherent problems of RNA AP. One problem is to find the right combination of affinity tags to lower the background of nonspecific binding. However, the main problem of RNA APs have thus far been their low copy number and the short half-life of intermediate species due to rapid processing and turnover [91,92]. Even so, the feasibility of isolating very low-abundance bait proteins such as Sac3p (∼340 molecules/cell) has recently been demonstrated by ssAP, hence, there should be no fundamental obstacle to using RNA as the handle to isolate RNPs [93, 94]. Interestingly, Hogg and colleagues note in their single-step RNA affinity approach that in the process of adapting their methodology to the purification of mRNPs, they found that the use of traditional agarose-based resins led to inefficient purification of tagged mRNP complexes. In contrast, nonporous magnetic resins allowed purification of tagged mRNAs to near homogeneity following a single step of purification. 5 Addressing dynamics of protein interactions and complex assembly over time and space When approaching the study of dynamics of networks, pathways, and protein–protein interactions, we are faced with two different challenges: (i) the experimental approach and (ii) the analysis/quantitation. Networks and pathways are not static but are made up of dynamic, changing protein–protein (and protein–nucleic acid) interactions influenced by PTMs, changing conditions and stages within a cell’s life cycle. However, most of the complexes we isolate to date represent only a static picture of a pool of complexes associated with the bait protein over a range of different states or points in time, and the spatial and temporal regulations are usually lost. What is needed are experimental approaches that allow us to isolate and study complexes that correspond to these individual stages and determine their changes in composition quantitatively. 5.1 Dynamic protein networks: Experimental approaches to dissect spatial and temporal changes in protein complex composition Early attempts to study interaction dynamics were made in S. cerevisiae where dynamics of interactions were captured by superimposing the temporal changes in gene expression during the cell cycle on static protein interaction networks [95]. A similar approach has been applied to the modeling of the cell cycle in Arabidopsis [50] and more recently, a series of tags that include GFP have been developed such as the LAP tag, which allow for parallel analyses of protein localization and native protein complexes [47] (Table 1). The LAP tag C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1601 has been successfully applied in human cells to gain first insights into the spatiotemporal assembly of cellular machineries required for mitosis [47]. A different approach to studying temporal changes and dynamics within macromolecular networks is the combination of AP with strategically blocking pathways by depletion of key proteins, a strategy that was recently used to study temporal dynamics of ribosome biogenesis in yeast [96, 97]. The idea of this approach was based on the hypothesis that preribosomes are composed of several subunits that followed specific rules of assembly either prior to or during the binding to the rRNA precursor. To obtain data on the timing and order of association of different preribosomal factors, the strategy looked at preribosomal particles isolated from mutants that block ribosome formation at different steps. This approach has been successfully used by Perez-Fernandez et al. to study the assembly of pre-90S ribosomes and enabled them to dissect the hierarchy of 90S particle assembly, identifying several protein subcomplexes that work as discrete assembly subunits as well as distinguishing two separate, and mutually independent, assembly routes [96]. A subsequent study by Lebreton and colleagues went a step further and combined this strategy with in vivo isotopic labeling and semi-qMS analysis to define different 60S ribosomal subunit maturation intermediates in yeast, comparing the composition of the purified complexes under wild-type or mutant conditions using SILAC and semi-qMS [97, 98]. Another interesting and slightly different approach to study the spatiotemporal dynamics of macromolecular complexes combined AP with electron microscopy (EM) to look at dynamics of structure instead of cellular localization or composition. Using negative stain cryo-EM, the Hurt laboratory looked at TAP-purified complexes from both pre-40S and pre-60S ribosomes to study the dynamics of structural changes during the transition between different late ribosomal maturation stages [99]. Moreover, by applying cryo-EM to HA-tagged, antibody-labeled components of Rix1p-particles purified via the Rix1-TAP bait, they were not only able to pinpoint the position of six Rix1p subcomplex components in the complex, but also to determine a mechanochemical mechanism for their removal from pre-60S ribosomes [100]. Studying network dynamics in response to different regulatory stimuli has also been an increasingly “hot” topic in other fields than RNP maturation. Rinner and colleagues identified previously unknown interactions of FoxO3A with 14–3-3 proteins, as well as FoxO3A phosphorylation sites [101]. Using a label-free ssAP-coupled-to-quantitative-MS approach, they were able to define growth state specific changes in the interaction pattern for HA-tagged FoxO3A under growth-promoting conditions and growth inhibition by serum starvation plus inhibition of PI3 kinase [101]. An even more recent study by Bisson et al., successfully combined ssAP with selected reaction monitoring (SRM) MS, a highly quantitative method to determine changes within MS samples, to identify different dynamic growth factor specific networks in stimulated cells [124]. The authors were able to demonstrate the connectivity and versatility of GRB2, an adaptor protein that participates in multiple www.proteomics-journal.com 1602 M. Oeffinger aspects of cellular function, within different growth factor signaling networks and to shed light on its involvement in the formation of stimulation-specific and time-dependent protein complexes [124]. 5.2 Quantitation and analysis of dynamic changes within networks The second challenge we face when addressing dynamics of networks is the analysis and quantitation of the, often subtle, differences between isolated complexes from different cell states or points in time. The most promising and already widely used approach consists of semiquantitative and qMSbased methods to systematically monitor changes in protein complex composition in different cells, either by using heavy-isotope labeling or label-free approaches (reviewed in [74]). qMS-based approaches combined with isotope labeling are often used to help distinguish bona fide from false positive interactions, a major challenge with the study of protein complexes and protein interaction networks, as well as to study changes within complexes in different cell backgrounds or over time. Lists of proteins binding nonspecifically to commonly used affinity resins have been recently determined and represent a useful resource [18, 102]. However, frequency filtering has its limitations as generally promiscuous proteins might actually represent genuine interactors in the context of certain baits. For this reason, several heavy-isotope labeling methods have been developed over the last decade, whereby proteins or peptides are labeled either metabolically or chemically to distinguish real from false interactor but also to compare changes within complexes of different cellular states or over time, the latter as shown by Lebreton et al. [97]. Metabolic labeling of proteins is carried out in vivo, prior to AP, by growing cells (SILAC, I-DIRT (isotopic differentiation of interactions as random or targeted)) [60,73,98]. SILAC is the most widely used metabolic labeling technique and involves the replacement of naturally occurring essential amino acids with heavy isotope labeled amino acids (4 D, 13 C, 15 N, or 18 O) during protein synthesis in the cell [98, 103]. This leads to a difference in mass for tryptically digested peptides compared with the control sample, which can be detected by MS. Labeled arginine and lysine amino acids are generally used because of the advantage that most tryptic peptides can be used for quantification [103]. While I-DIRT was developed to mainly differentiate real from nonreal interactors, SILAC is predominantly used to compare state or time-measurement series [73, 104]. Metabolic labeling has a major advantage as it incorporates stable isotopes before the purification of the protein complex thereby reducing errors due to sample handling. It is, however, very expensive. In contrast, chemical labeling methods (ICAT, ICPL, and iTRAQ) are used for labeling proteins or peptides after AP; thus, the sample is completely independent of the source and preparation [74, 103]. One advantage is that chemical labeling methods can virtu C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim Proteomics 2012, 12, 1591–1608 ally be used for any type of biological sample and at a lower cost. As an alternative to isotope labeling, label-free methods emerged due it is cost-effectiveness, relatively straightforward data analysis, and particular usefulness when used for large number of samples. The most commonly used approaches for label-free quantification are spectral counting (reviewed in [75]) and total ion current (TIC) [105]. In particular, spectral counting, where the total number of spectra that identifies a protein is used for quantitative analysis, has become an important and routine approach for analyzing protein complexes and protein interaction networks. In conjunction with spectral counting, a recently developed computational tool called “Significance Analysis of INTeractome” (SAINT) is proving particularly useful to determine bona fide protein–protein interactions [106]. SAINT converts the label-free quantification (spectral count) into confidence scores by modeling the spectral counts for each prey-bait with a mixture distribution of two components representing true and false interactions. Moreover, the program normalizes spectral counts to the length of the proteins and to the total number of spectra in the purification. SAINT has already been successfully used in two recent studies for the mapping of the kinase and phosphatase network in yeast and the interactome of the human Ser/Thr protein phosphatase 5 [23,107]. SAINT, however, is not the only analysis program that is being used to quantify labeled or label-free AP-MS data. Other platforms include MaxQuant, QTIPS (quantification by total identified peptides for SILAC), PEAKS Q, and MSQuant, to name just a few, and which are discussed in more detail elsewhere [24, 108]. Overall qMS methods, both isotope-labeled and label-free, are promising approaches to the systematically monitoring of changes in protein complex composition. Initially chemical labeling strategies have been used; e.g. ICAT was applied to assess dynamic changes in transcription factor complexes during erythroid cell differentiation [109]. Later on metabolic labeling was also used, to evaluate the dynamics of the nucleolar proteome, to map the spectrum of human 26S proteasome interacting proteins, as well as to detect dynamic members of transcription factor complexes [110–112]. Recently qMS has also been used to monitor relative affinities of different components of protein complexes. The idea is based on the quantitative measurement of interactors exchanged between protein complexes originating from a mixture of differentially isotope-labeled cell lysates based on their on/off rates. While stable associated proteins will show predominately peptides with one type of label when compared to nonspecific background proteins, transiently associated proteins or dynamic interaction partners will undergo faster exchange, thus associate with baits labeled with a different isotope and their peptide ratio will resemble a more diversely labeled mixture [111, 113]. Even though they are very promising, the measuring of relative affinities by quantitative methods has to date not been used on large-scale analyses. www.proteomics-journal.com 1603 Proteomics 2012, 12, 1591–1608 Interestingly, while quantitative proteomics have been used in genome-wide studies using not only protein but also DNA as bait, so far it has never been applied to RNA APs (reviewed in [74]) [114]. Since RNA AP in the past generally suffered from binding of a high number of unspecific RBPs to the RNA baits, the use of qMS would have two major advantages. First, the effect of differential stability of the RNA bait in crude extracts would be accounted for by normalization on the total amount of background binders in the tagged sample and control. Second, qMS can detect specific interactions even in the presence of highly abundant nonspecifically binding proteins, thus near-physiological buffer conditions for purification and washing could be used to preserve lessstable, yet specific, interactions. 5.3 RNPs: Advances in the analysis of coisolating RNAs Advances have not only been made in the quantitation and analysis of coisolating proteins though. During the past decade, microarray technologies have played an important role in shaping our understanding of transcriptome complexity and identification of RNA sets isolated by AP of specific RBPs [115]. A major drawback of this approach, however, is that profiling coverage is limited by the probe sets available for specific hybridization on the microarray. In addition, detection, measured as a fluorescent signal, is indirect and subject to a variety of noise variables, further contributing to limited sensitivity and specificity. Recently, nextgeneration RNA-Sequencing (RNA-seq) has begun to take the place of microarrays in the analysis of RNAs as part of affinity-isolated RNPs, particularly as it has become more affordable. As demonstrated recently by several studies, RNASeq provides a relatively unbiased and extremely reproducible direct and quantitative readout of cDNA sequence generated from an RNA sample [116, 117]. In the last few years, a number of variations on the theme have been developed such as CLIP-Seq (HITS-seq) or RIP-seq, all of which are used to identify coisolating RNA from affinity-purified RNPs with (CLIP-seq) or without UV cross-linking prior to cell lysis and AP [118–120]. RNA-Seq has been employed very successfully so far, e.g. to characterize yeast mRNA sequences that are bound and protected by polyribosomes, to determine sets of transcripts recognized by the splicing factor SFRS1, or to study microRNA–mRNA interaction maps [118, 119]. Although current MS approaches allow for highthroughput analysis of protein components in functional RNP complexes, this technology has had limited application to studies of the RNA component. A recent protocol, however, coupled AP with liquid chromatography-tandem MS for RNA analysis and successfully identified small RNAs in the spliceosomal RNP complex affinity purified from yeast using a Brr2-TAP [121]. C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 6 Outlook: The next frontiers The technologies to chart cellular interaction networks in different organisms have made an enormous progress in the last decade, and while the coverage is still low and a lot of questions are still open, we have already gained valuable insights into many biological processes using AP-MS approaches [122]. However, there are still many frontiers ahead in the years to come, one of which is the investigation of temporal and spatial network dynamics as the current networks provide only a static picture of all physical associations. A series of new approaches, on the basis of AP coupled to qMS, including ssAP and AP combined with SRM MS, have been designed to capture network dynamics [123]. These methods have already been successfully applied to small-scale studies, providing a stepping-stone for the first dynamic map of cellular processes and pathways [124]. Recently, crosslinking methods have been applied to capture more transient and weak interactions, however, the development of faster isolation methods may enable us in the future to isolate complexes before transient interactors dissociate, making their stabilization through chemical cross-linking unnecessary [125, 126]. One feasible direction to speed up sample isolation is through the use of smaller single-chain variable fragments (scFVs), which are fusion proteins of the variable regions of the heavy (VH ) and light (VL ) chains of immunoglobulins, as well as single-domain antibodies (nanobodies) such as VH H fragments found in camelids [81,127,129]. This combined with smaller resins (∼1 nm and below), which would allow for an even denser coverage of beads with antibodies and thus a further increase of surfaceto-volume ration, could potentially permit complex isolations within a couple of minutes. Another inherent shortcoming of AP-MS is that it does not provide information on complex topology or “nearneighborhoods” of proteins, i.e. which proteins in the complex are situated adjacent to each other and form direct contacts. Determining direct interactions within complexes is imperative for discerning their individual roles in the regulation of a pathway, as well as gaining information on the architecture of the complexes they are associated with, to, over time, build a more complete dynamic picture of different cellular networks. In some cases, the integration of available structural data has already contributed hypotheses on the modality of binding [128]. Overall, with the steady advancement of technologies, both in AP and qMS and the accessibility of these methods, more and more studies will be carried out covering so far unchartered cell biological territories including many disease-related or metabolic networks. This will over time enable us to create a comprehensive picture of cellular behavior that will integrate many different types of interactions and thus provide more accurate representations of biological processes and systems. www.proteomics-journal.com 1604 M. Oeffinger We thank D. Zenklusen for critical reading and comments on the manuscript. M.O. holds a CIHR New Investigator Award and an FRSQ Chercheur Boursier Junior I. M.O. is supported by funding from the CIHR, NSERC, FRSQ, NIH (U54 022220), and CFI. The authors have declared no conflict of interest. 7 References [1] Gavin, A. C., Bosche, M., Krause, R., Grandi, P. et al., Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002, 415, 141–147. [2] Kuhner, S., van Noort, V., Betts, M. J., Leo-Macias, A. et al., Proteome organization in a genome-reduced bacterium. Science 2009, 326, 1235–1240. [3] Goh, K. I., Cusick, M. E., Valle, D., Childs, B. et al., The human disease network. Proc. Natl. Acad. Sci. USA 2007, 104, 8685–8690. [4] Taylor, I. W., Linding, R., Warde-Farley, D., Liu, Y. et al., Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat. Biotechnol. 2009, 27, 199–204. [5] Goffeau, A., Barrell, B. G., Bussey, H., Davis, R. W. et al., Life with 6000 genes. Science 1996, 274, 546, 563–547. [6] Costanzo, M. C., Hogan, J. D., Cusick, M. E., Davis, B. P. et al., The yeast proteome database (YPD) and Caenorhabditis elegans proteome database (WormPD): comprehensive resources for the organization and comparison of model organism protein information. Nucleic Acids Res. 2000, 28, 73–76. [7] Grigoriev, A., On the number of protein-protein interactions in the yeast proteome. Nucleic Acids Res. 2003, 31, 4157– 4161. [8] Albert, I., Albert, R., Conserved network motifs allow protein-protein interaction prediction. Bioinformatics 2004, 20, 3346–3352. [9] Johnson, M. E., Hummer, G., Nonspecific binding limits the number of proteins in a cell and shapes their interaction networks. Proc. Natl. Acad. Sci. USA 2011, 108, 603–608. [10] Gavin, A., Aloy, P., Grandi, P., Krause, R. et al., Proteome survey reveals modularity of the yeast cell machinery. Nature 2006, 440, 631–636. [11] Krogan, N., Cagney, G., Yu, H., Zhong, G. et al., Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 2006, 440, 637–643. [12] Collins, S. R., Kemmeren, P., Zhao, X.-C., Greenblatt, J. F. et al., Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. Mol. Cell. Proteomics 2007, 6, 439–450. Proteomics 2012, 12, 1591–1608 [15] Liao, Y., Kariya, K., Hu, C. D., Shibatohge, M. et al., RAGEF, a novel Rap1A guanine nucleotide exchange factor containing a Ras/Rap1A-associating domain, is conserved between nematode and humans. J. Biol. Chem. 1999, 274, 37815–37820. [16] Ranganathan, R., Ross, E. M., PDZ domain proteins: scaffolds for signaling complexes. Curr. Biol. 1997, 7, R770– R773. [17] Picotti, P., Bodenmiller, B., Mueller, L. N., Domon, B. et al., Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 2009, 138, 795–806. [18] Trinkle-Mulcahy, L., Boulon, S., Lam, Y. W., Urcia, R. et al., Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes. J. Cell Biol. 2008, 183, 223–239. [19] Cristea, I., Williams, R., Chait, B., Rout, M., Fluorescent proteins as proteomic probes. Mol. Cell. Proteomics 2005, 4, 1933–1941. [20] Oeffinger, M., Wei, K. E., Rogers, R., Degrasse, J. A. et al., Comprehensive analysis of diverse ribonucleoprotein complexes. Nat. Methods 2007, 4, 951–956. [21] Abmayr, S. M. A. W., J. L., Preparation of nuclear and cytoplasmic extracts from mammalian cells. Curr. Protoc. Pharmacol. 2001, 12.3.1–12.3.13. [22] Schultz, M. C., Hockman, D. J., Harkness, T. A., Garinther, W. I. et al., Chromatin assembly in a yeast whole-cell extract. Proc. Natl. Acad. Sci. USA 1997, 94, 9034–9039. [23] Breitkreutz, A., Choi, H., Sharom, J. R., Boucher, L. et al., A global protein kinase and phosphatase interaction network in yeast. Science 2010, 328, 1043–1046. [24] Dilworth, D. J., Saleem, R. A., Rogers, R. S., Mirzaei, H. et al., QTIPS: a novel method of unsupervised determination of isotopic amino acid distribution in SILAC experiments. J. Am. Soc. Mass. Spectrom. 2010, 21, 1417– 1422. [25] Schlosshauer, M., Baker, D., Realistic protein-protein association rates from a simple diffusional model neglecting long-range interactions, free energy barriers, and landscape ruggedness. Protein Sci. 2004, 13, 1660–1669. [26] Morokuma, K., Why do molecules interact? The origin of electron donor-acceptor complexes, hydrogen bonding and proton affinity. Acc. Chem. Res. 1977, 10, 294–300. [27] Roth, C. M., Neal, B. L., Lenhoff, A. M., Van der Waals interactions involving proteins. Biophys. J. 1996, 70, 977– 987. [28] Kortemme, T., Morozov, A. V., Baker, D., An orientationdependent hydrogen bonding potential improves prediction of specificity and structure for proteins and proteinprotein complexes. J. Mol. Biol. 2003, 326, 1239–1259. [13] Hunter, N., Borts, R. H., Mlh1 is unique among mismatch repair proteins in its ability to promote crossing-over during meiosis. Genes Dev. 1997, 11, 1573–1582. [29] van Oss, C. J., Long-range and short-range mechanisms of hydrophobic attraction and hydrophilic repulsion in specific and aspecific interactions. J. Mol. Recognit. 2003, 16, 177– 190. [14] Plowman, G. D., Sudarsanam, S., Bingham, J., Whyte, D. et al., The protein kinases of Caenorhabditis elegans: a model for signal transduction in multicellular organisms. Proc. Natl. Acad. Sci. USA 1999, 96, 13603–13610. [30] Rigaut, G., Shevchenko, A., Rutz, B., Wilm, M. et al., A generic protein purification method for protein complex characterization and proteome exploration. Nat. Biotechnol. 1999, 17, 1030–1032. C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com Proteomics 2012, 12, 1591–1608 [31] Archambault, V., Chang, E., Drapkin, B., Cross, F. et al., Targeted proteomic study of the cyclin-Cdk module. Mol. Cell 2004, 14, 699–711. [32] Alber, F., Dokudovskaya, S., Veenhoff, L. M., Zhang, W. et al., The molecular architecture of the nuclear pore complex. Nature 2007, 450, 695–701. [33] Lambert, J. P., Fillingham, J., Siahbazi, M., Greenblatt, J. et al., Defining the budding yeast chromatin-associated interactome. Mol. Syst. Biol. 2010, 6, 448–464. [34] Hubner, N. C., Bird, A. W., Cox, J., Splettstoesser, B. et al., Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions. J. Cell Biol. 2010, 189, 739–754. [35] Barrios-Rodiles, M., Brown, K. R., Ozdamar, B., Bose, R. et al., High-throughput mapping of a dynamic signaling network in mammalian cells. Science 2005, 307, 1621–1625. [36] Schwenk, J., Harmel, N., Zolles, G., Bildl, W. et al., Functional proteomics identify cornichon proteins as auxiliary subunits of AMPA receptors. Science 2009, 323, 1313–1319. [37] Muller, C. S., Haupt, A., Bildl, W., Schindler, J. et al., Quantitative proteomics of the Cav2 channel nano-environments in the mammalian brain. Proc. Natl. Acad. Sci. USA 2010, 107, 14950–14957. [38] Rout, M., Aitchison, J., Suprapto, A., Hjertaas, K. et al., The yeast nuclear pore complex: composition, architecture, and transport mechanism. J. Cell. Biol. 2000, 148, 635–651. [39] Chang, I. F., Mass spectrometry-based proteomic analysis of the epitope-tag affinity purified protein complexes in eukaryotes. Proteomics 2006, 6, 6158–6166. [40] Cheeseman, I. M., Desai, A., A combined approach for the localization and tandem affinity purification of protein complexes from metazoans. Sci STKE. 2005, 266, pl1. [41] Trinkle-Mulcahy, L., Lamond, A. I., Toward a high-resolution view of nuclear dynamics. Science 2007, 318, 1402– 1407. [42] Poser, I., Sarov, M., Hutchins, J. R., Heriche, J. K. et al., BAC TransgeneOmics: a high-throughput method for exploration of protein function in mammals. Nat. Methods 2008, 5, 409–415. [43] Hu, P., Janga, S. C., Babu, M., Diaz-Mejia, J. J. et al., Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins. PLoS Biol. 2009, 7, e96. [44] Glatter, T., Wepf, A., Aebersold, R., Gstaiger, M., An integrated workflow for charting the human interaction proteome: insights into the PP2A system. Mol. Syst. Biol. 2009, 5, 237–250. [45] Sowa, M. E., Bennett, E. J., Gygi, S. P., Harper, J. W., Defining the human deubiquitinating enzyme interaction landscape. Cell 2009, 138, 389–403. [46] Behrends, C., Sowa, M. E., Gygi, S. P., Harper, J. W., Network organization of the human autophagy system. Nature 2010, 466, 68–76. [47] Hutchins, J. R., Toyoda, Y., Hegemann, B., Poser, I. et al., Systematic analysis of human protein complexes identifies chromosome segregation proteins. Science 2010, 328, 593– 599. C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1605 [48] Bürckstümmer, T., Bennett, K. L., Preradovic, A., Schütze, G. et al., An efficient tandem affinity purification procedure for interaction proteomics in mammalian cells. Nat. Methods 2006, 3, 1013–1019. [49] Ewing, R. M., Chu, P., Elisma, F., Li, H. et al., Large-scale mapping of human protein-protein interactions by mass spectrometry. Mol. Syst. Biol. 2007, 3, 89–105. [50] Van Leene, J., Hollunder, J., Eeckhout, D., Persiau, G. et al., Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana. Mol. Syst. Biol. 2010, 6, 397. [51] Rohila, J. S., Chen, M., Chen, S., Chen, J. et al., Proteinprotein interactions of tandem affinity purified protein kinases from rice. PLoS One 2009, 4, e6685. [52] Veraksa, A., Bauer, A., Artavanis-Tsakonas, S., Analyzing protein complexes in Drosophila with tandem affinity purification-mass spectrometry. Dev. Dyn. 2005, 232, 827– 834. [53] Agell, N., Bachs, O., Rocamora, N., Villalonga, P., Modulation of the Ras/Raf/MEK/ERK pathway by Ca(2+), and calmodulin. Cell Signal 2002, 14, 649–654. [54] Head, J. F., A better grip on calmodulin. Curr. Biol. 1992, 2, 609–611. [55] Knuesel, M., Wan, Y., Xiao, Z., Holinger, E. et al., Identification of novel protein-protein interactions using a versatile mammalian tandem affinity purification expression system. Mol. Cell. Proteomics 2003, 2, 1225– 1233. [56] Gloeckner, C. J., Boldt, K., Schumacher, A., Roepman, R. et al., A novel tandem affinity purification strategy for the efficient isolation and characterisation of native protein complexes. Proteomics 2007, 7, 4228–4234. [57] Schimanski, B., Nguyen, T. N., Gunzl, A., Highly efficient tandem affinity purification of trypanosome protein complexes based on a novel epitope combination. Eukaryot. Cell 2005, 4, 1942–1950. [58] Drakas, R., Prisco, M., Baserga, R., A modified tandem affinity purification tag technique for the purification of protein complexes in mammalian cells. Proteomics 2005, 5, 132– 137. [59] Forler, D., Kocher, T., Rode, M., Gentzel, M. et al., An efficient protein complex purification method for functional proteomics in higher eukaryotes. Nat. Biotechnol. 2003, 21, 89–92. [60] Selbach, M., Mann, M., Protein interaction screening by quantitative immunoprecipitation combined with knockdown (QUICK). Nat. Methods 2006, 3, 981–983. [61] Kittler, R., Pelletier, L., Ma, C., Poser, I. et al., RNA interference rescue by bacterial artificial chromosome transgenesis in mammalian tissue culture cells. Proc. Natl. Acad. Sci. USA 2005, 102, 2396–2401. [62] Kyriakakis, P., Tipping, M., Abed, L., Veraksa, A., Tandem affinity purification in Drosophila: the advantages of the GS-TAP system. Fly 2008, 2, 229–235. [63] Honey, S., Schneider, B. L., Schieltz, D. M., Yates, J. R. et al., A novel multiple affinity purification tag and its use www.proteomics-journal.com 1606 M. Oeffinger in identification of proteins associated with a cyclin-CDK complex. Nucleic Acids Res. 2001, 29, E24. [64] Rubio, V., Shen, Y., Saijo, Y., Liu, Y. et al., An alternative tandem affinity purification strategy applied to Arabidopsis protein complex isolation. Plant J. 2005, 41, 767–778. [65] Libby, R. T., Cosman, D., Cooney, M. K., Merriam, J. E. et al., Human rhinovirus 3C protease: cloning and expression of an active form in Escherichia coli. Biochemistry 1988, 27, 6262–6268. [66] Rinner, O., Seebacher, J., Walzthoeni, T., Mueller, L. N. et al., Identification of cross-linked peptides from large sequence databases. Nat. Methods 2008, 5, 315–318. [67] Leitner, A., Walzthoeni, T., Kahraman, A., Herzog, F. et al., Probing native protein structures by chemical crosslinking, mass spectrometry and bioinformatics. Mol. Cell. Proteomics 2010, 9, 1634–1649. [68] Tagwerker, C., Flick, K., Cui, M., Guerrero, C. et al., A tandem affinity tag for two-step purification under fully denaturing conditions: application in ubiquitin profiling and protein complex identification combined with in vivocross-linking. MCP 2006, 5, 737–748. [69] Petroski, M. D., Deshaies, R. J., Mechanism of lysine 48-linked ubiquitin-chain synthesis by the cullin-RING ubiquitin-ligase complex SCF-Cdc34. Cell 2005, 123, 1107– 1120. [70] Leitner, A., Walzthoeni, T., Kahraman, A., Herzog, F. et al., Probing native protein structures by chemical crosslinking, mass spectrometry, and bioinformatics. Mol. Cell. Proteomics 2010, 9, 1634–1649. [71] Moorman, N. J., Sharon-Friling, R., Shenk, T., Cristea, I. M., A targeted spatial-temporal proteomics approach implicates multiple cellular trafficking pathways in human cytomegalovirus virion maturation. Mol. Cell. Proteomics 2010, 9, 851–860. [72] Chen, G. I., Gingras, A.-C., Affinity-purification mass spectrometry (AP-MS) of serine/threonine phosphatases. Methods 2007, 42, 298–305. Proteomics 2012, 12, 1591–1608 [79] Strambio-de-Castillia, C., Blobel, G., Rout, M. P., Proteins connecting the nuclear pore complex with the nuclear interior. J. Cell Biol. 1999, 144, 839–855. [80] Devos, D., Dokudovskaya, S., Alber, F., Williams, R. et al., Components of coated vesicles and nuclear pore complexes share a common molecular architecture. PLoS Biol. 2004, 2, e380. [81] Rothbauer, U., Zolghadr, K., Muyldermans, S., Schepers, A. et al., A versatile nanotrap for biochemical and functional studies with fluorescent fusion proteins. Mol. Cell. Proteomics 2008, 7, 282–289. [82] Einhauer, A., Jungbauer, A., The FLAG peptide, a versatile fusion tag for the purification of recombinant proteins. J. Biochem. Biophys. Methods 2001, 49, 455–465. [83] Hartmuth, K., Vornlocher, H.-P., Luhrmann, R., Tobramycin affinity tag purification of spliceosomes. Method. Mol. Biol. 2004, 257, 47–64. [84] Lingner, J., Cech, T. R., Purification of telomerase from Euplotes aediculatus: requirement of a primer 3’ overhang. Proc. Natl. Acad. Sci. USA 1996, 93, 10712–10717. [85] Carey, J., Uhlenbeck, O. C., Kinetic and thermodynamic characterization of the R17 coat protein-ribonucleic acid interaction. Biochemistry 1983, 22, 2610–2615. [86] Bachler, M., Schroeder, R., von Ahsen, U., StreptoTag: a novel method for the isolation of RNA-binding proteins. RNA 1999, 5, 1509–1516. [87] Srisawat, C., Engelke, D. R., Streptavidin aptamers: affinity tags for the study of RNAs and ribonucleoproteins. RNA (New York, NY) 2001, 7, 632–641. [88] Jurica, M. S., Moore, M. J., Capturing splicing complexes to study structure and mechanism. Methods 2002, 28, 336– 345. [89] Hogg, J., Collins, K., RNA-based affinity purification reveals 7SK RNPs with distinct composition and regulation. RNA 2007, 13, 868–880. [90] Hogg, J. R., Goff, S. P., Upf1 senses 3 UTR length to potentiate mRNA decay. Cell 2010, 143, 379–389. [73] Tackett, A., DeGrasse, J., Sekedat, M., Oeffinger, M. et al., IDIRT, a general method for distinguishing between specific and nonspecific protein interactions. J. Proteome Res. 2005, 4, 1752–1756. [91] Holstege, F., Jennings, E., Wyrick, J., Lee, T. et al., Dissecting the regulatory circuitry of a eukaryotic genome. Cell 1998, 95, 717–728. [74] Ramisetty, S. R., Washburn, M. P., Unraveling the dynamics of protein interactions with quantitative mass spectrometry. Crit. Rev. Biochem. Mol. Biol. 2011, 46, 216–228. [92] Zenklusen, D., Larson, D. R., Singer, R. H., SingleRNA counting reveals alternative modes of gene expression in yeast. Nat. Struct. Mol. Biol. 2008 15, 1263– 1271. [75] Lundgren, D. H., Hwang, S. I., Wu, L., Han, D. K., Role of spectral counting in quantitative proteomics. Expert Rev. Proteomics 2010, 7, 39–53. [93] Ghaemmaghami, S., Huh, W., Bower, K., Howson, R. et al., Global analysis of protein expression in yeast. Nature 2003, 425, 737–741. [76] Gingras, A.-C., Gstaiger, M., Raught, B., Aebersold, R., Analysis of protein complexes using mass spectrometry. Nat. Rev. Mol. Cell Biol. 2007, 8, 645–654. [94] Oeffinger, M., Wei, K., Rogers, R., DeGrasse, J. et al., Comprehensive analysis of diverse ribonucleoprotein complexes. Nat. Methods 2007, 4, 951–956. [77] Ho, Y., Gruhler, A., Heilbut, A., Bader, G. et al., Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002, 415, 180–183. [95] de Lichtenberg, U., Jensen, L. J., Brunak, S., Bork, P., Dynamic complex formation during the yeast cell cycle. Science 2005, 307, 724–727. [78] Marelli, M., Smith, J. J., Jung, S., Yi, E. et al., Quantitative mass spectrometry reveals a role for the GTPase Rho1p in actin organization on the peroxisome membrane. J. Cell Biol. 2004, 167, 1099–1112. [96] Perez-Fernandez, J., Roman, A., De Las Rivas, J., Bustelo, X. R. et al., The 90S preribosome is a multimodular structure that is assembled through a hierarchical mechanism. Mol. Cell. Biol. 2007, 27, 5414–5429. C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com Proteomics 2012, 12, 1591–1608 [97] Lebreton, A., Rousselle, J.-C., Lenormand, P., Namane, A. et al., 60S ribosomal subunit assembly dynamics defined by semi-quantitative mass spectrometry of purified complexes. Nucleic Acids Res. 2008, 36, 4988–4999. [98] Ong, S. E., Blagoev, B., Kratchmarova, I., Kristensen, D. B. et al., Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 2002, 1, 376–386. [99] Schäfer, T., Maco, B., Petfalski, E., Tollervey, D. et al., Hrr25dependent phosphorylation state regulates organization of the pre-40S subunit. Nature 2006, 441, 651–655. [100] Bassler, J., Kallas, M., Pertschy, B., Ulbrich, C. et al., The AAA-ATPase Rea1 drives removal of biogenesis factors during multiple stages of 60S ribosome assembly. Mol. Cell 2010, 38, 712–721. [101] Rinner, O., Mueller, L. N., Hubálek, M., Müller, M. et al., An integrated mass spectrometric and computational framework for the analysis of protein interaction networks. Nat. Biotechnol. 2007, 25, 345–352. [102] Boulon, S., Ahmad, Y., Trinkle-Mulcahy, L., Verheggen, C. et al., Establishment of a protein frequency library and its application in the reliable identification of specific protein interaction partners. Mol. Cell. Proteomics 2010, 9, 861– 879. [103] Gouw, J. W., Krijgsveld, J., Heck, A. J., Quantitative proteomics by metabolic labeling of model organisms. Mol. Cell. Proteomics 2010, 9, 11–24. [104] Choudhary, C., Mann, M., Decoding signalling networks by mass spectrometry-based proteomics. Nat. Rev. Mol. Cell Biol. 2010, 11, 427–439. [105] Asara, J. M., Christofk, H. R., Freimark, L. M., Cantley, L. C., A label-free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen. Proteomics 2008, 8, 994–999. [106] Choi, H., Larsen, B., Lin, Z. Y., Breitkreutz, A. et al., SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat. Methods 2011, 8, 70–73. [107] Skarra, D. V., Goudreault, M., Choi, H., Mullin, M. et al., Label-free quantitative proteomics and SAINT analysis enable interactome mapping for the human Ser/Thr protein phosphatase 5. Proteomics 2011, 11, 1508–1516. [108] Cox, J., Mann, M., MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 2008, 26, 1367–1372. [109] Brand, M., Ranish, J. A., Kummer, N. T., Hamilton, J. et al., Dynamic changes in transcription factor complexes during erythroid differentiation revealed by quantitative proteomics. Nat. Struct. Mol. Biol. 2004, 11, 73–80. [110] Andersen, J., Lam, Y., Leung, A., Ong, S. et al., Nucleolar proteome dynamics. Nature 2005, 433, 77–83. [111] Wang, X., Huang, L., Identifying dynamic interactors of protein complexes by quantitative mass spectrometry. Mol. Cell. Proteomics 2008, 7, 46–57. [112] Mousson, F., Kolkman, A., Pijnappel, W. W., Timmers, H. T. et al., Quantitative proteomics reveals regulation of dynamic components within TATA-binding protein (TBP) tran- C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim 1607 scription complexes. Mol. Cell. Proteomics 2008, 7, 845– 852. [113] Fang, L., Wang, X., Yamoah, K., Chen, P. L. et al., Characterization of the human COP9 signalosome complex using affinity purification and mass spectrometry. J. Proteome Res. 2008, 7, 4914–4925. [114] Mittler, G., Butter, F., Mann, M., A SILAC-based DNA protein interaction screen that identifies candidate binding proteins to functional DNA elements. Genome Res. 2009, 19, 284– 293. [115] Jain, R., Devine, T., George, A. D., Chittur, S. V. et al., RIPchip analysis: rna-binding protein immunoprecipitationmicroarray (chip) profiling. Method. Mol. Biol. 2011, 703, 247–263. [116] Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M. et al., RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008, 18, 1509–1517. [117] Pan, Q., Shai, O., Lee, L. J., Frey, B. J. et al., Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 2008, 40, 1413–1415. [118] Sanford, J. R., Wang, X., Mort, M., Vanduyn, N. et al., Splicing factor SFRS1 recognizes a functionally diverse landscape of RNA transcripts. Genome Res. 2009, 19, 381–394. [119] Ingolia, N. T., Ghaemmaghami, S., Newman, J. R., Weissman, J. S., Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 2009, 324, 218–223. [120] Zhang, C., Darnell, R. B., Mapping in vivo protein-RNA interactions at single-nucleotide resolution from HITS-CLIP data. Nat. Biotechnol. 2011, 29, 607–614. [121] Taoka, M., Ikumi, M., Nakayama, H., Masaki, S. et al., Ingel digestion for mass spectrometric characterization of RNA from fluorescently stained polyacrylamide gels. Anal. Chem. 2010, 82, 7795–7803. [122] Yu, H., Braun, P., Yildirim, M. A., Lemmens, I. et al., Highquality binary protein interaction map of the yeast interactome network. Science 2008, 322, 104–110. [123] Picotti, P., Rinner, O., Stallmach, R., Dautel, F. et al., Highthroughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat. Methods 2010, 7, 43–46. [124] Bisson, N., James, D. A., Ivosev, G., Tate, S. A. et al., Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor. Nat. Biotechnol. 2011, 29, 653–658. [125] Puts, C. F., Lenoir, G., Krijgsveld, J., Williamson, P. et al., A P4-ATPase protein interaction network reveals a link between aminophospholipid transport and phosphoinositide metabolism. J. Proteome Res. 2010, 9, 833–842. [126] Zhang, H., Tang, X., Munske, G. R., Tolic, N. et al., Identification of protein-protein interactions and topologies in living cells with chemical cross-linking and mass spectrometry. Mol. Cell. Proteomics 2009, 8, 409–420. [127] Alvarez-Rueda, N., Behar, G., Ferre, V., Pugniere, M. et al., Generation of llama single-domain antibodies against www.proteomics-journal.com 1608 M. Oeffinger Proteomics 2012, 12, 1591–1608 methotrexate, a prototypical hapten. Mol. Immunol. 2007, 44, 1680–1690. of protein and RNA components of mRNP in mammalian cells. Methods Mol. Biol. 2004, 257, 115–124. [128] Kühner, S., van Noort, V., Betts, M. J., Leo-Macias, A. et al., Proteome organization in a genome-reduced bacterium. Science 2009, 326, 1235–1240. [133] Beach, D. L., Keene, J. D. Ribotrap: targeted purification of RNA-specific RNPs from cell lysates through immunoaffinity precipitation to identify regulatory proteins and RNAs. Methods Mol. Biol. 2008, 419, 69–91. [129] Colwill, K., Gräslund, S., A roadmap to generate renewable protein binders to the human proteome. Nat. Methods 2011, 8, 551–558. [130] Lamond, A. I., Sproat, B., Ryder, U., Hamm, J. Probing the structure and function of U2 snRNP with antisense oligonucleotides made of 2’-OMe RNA. Cell 1989, 58, 383– 390. [131] Kurth, I., Cristofari, G., Lingner, J. An affinity oligonucleotide displacement strategy to purify ribonucleoprotein complexes applied to human telomerase. Methods Mol. Biol. 2008, 488, 9–22. [132] Lejeune, F., Maquat, L. E. Immunopurification and analysis C 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim [134] Di Tomasso, G., Lampron, P., Dagenais, P., Omichinski, J. G. et al. The ARiBo tag: a reliable tool for affinity purification of RNAs under native conditions. Nucleic Acids Res. 2011, 39(3), 18–28. [135] Butter F, Scheibe M, Mörl M, Mann M. Unbiased RNAprotein interaction screen by quantitative proteomics. Proc. Natl. Acad. Sci. USA 2009, 106, 10626–10631. [136] Slobodin, B., Gerst, J. E. A novel mRNA affinity purification technique for the identification of interacting proteins and transcripts in ribonucleoprotein complexes. RNA 2010, 16, 2277–2290. www.proteomics-journal.com