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Proteomics 2012, 12, 1591–1608
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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
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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
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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
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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
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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.
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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
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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
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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
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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].
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(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
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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
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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
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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
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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
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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,
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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,
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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
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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].
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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
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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
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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
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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.
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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].
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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.
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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
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