Journal of Experimental Botany, Vol. 57, No. 7, pp. 1493–1499, 2006
Plant Proteomics Special Issue
doi:10.1093/jxb/erj141
Methods of quantitative proteomics and their application to
plant organelle characterization
Kathryn S. Lilley1,* and Paul Dupree1
1
Department of Biochemistry, University of Cambridge, Building 0, Downing Site, Cambridge CB2 1QW, UK
Received 10 April 2005; Accepted 26 January 2006
Abstract
Many cell biologists wish to know the subcellular localization of proteins of interest. Proteomics methods
have the potential to describe the entire protein
content of organelles. However, practical limitations
in organelle isolation and analysis of low abundance
proteins have meant that organelle proteomics has
had, until recently, only limited success. Some examples of quantitative proteomic methods and their use in
the study of plant organelle proteomes are discussed
here. It is concluded that 2D-difference gel electrophoresis (2D-DIGE) as well as differential isotope tagging
strategies coupled to non-gel-based LC-MS are proving
useful in this area of research.
Key words: 2D-DIGE, 2D-PAGE, endoplasmic reticulum, Golgi
apparatus, isotope labelling, LOPIT, organelle fractionation,
quantitative proteomics, PCA, PLS-DA.
Introduction
Assigning a subcellular location to a protein is very
desirable for biologists for two reasons. First, it can help
elucidate their role in the cell, as proteins are spatially
organized according to their function (Dreger, 2003).
Second, it refines present knowledge of cellular processes
by pinpointing certain activities to specific organelles.
However, only a small percentage of Arabidopsis proteins
have been localized by microscopy to specific organelles.
Since a substantial proportion of the predicted Arabidopsis
proteome has no ascribed potential functions, defining the
localization of uncharacterized proteins is of particular
importance to plant biologists. In order to be fully useful, it
is essential to determine the localization of a protein to
a specific organelle with great confidence. If this is done by
biochemical analyses, in general, the organelle preparation
must be free from contamination from other organelle
types. In plants, however, as in every other eukaryotic
system, some organelles such as the mitochondria, and
chloroplasts are relatively easy to obtain in a pure form
(van Wijk, 2004; Millar et al., 2005), whereas many endomembrane organelles are impossible to purify without considerable contamination from other organelles with similar
densities (Carter et al., 2004; Szponarski et al., 2004;
Wu et al., 2004). The consequence of this is that the
novel proteins identified in organelle proteomic studies
cannot be confidently assigned to the organelle unless subsequently confirmed by microscopy.
Enrichment of organelles is possible, and one method to
overcome the purification issues associated with many
organelle types is to look at the distribution of known organelle markers through different protein separation procedures. Assuming that an organelle has a unique distribution
pattern it is possible to identify novel organelle components
by comparing enrichment through these procedures with
known markers in conjunction with high throughput proteomics technologies (de Duve, 1971; Dunphy and Rothman,
1983). In order to do this effectively accurate profiling is required, which, in turn, necessitates the application of robust
quantitation methodology (Andersen et al., 2003). What
follows is a review of current methods of quantitative proteomics, their strengths and weaknesses, and application to
the study of plant organelles.
Gel-based methods to study the proteome
quantitatively
The breadth of techniques available to carry out quantitative proteomics continues to expand at a considerable rate.
* To whom correspondence should be addressed. E-mail: [email protected]
Abbreviations: BVA, biological variance analysis; 2D-DIGE, 2D-difference gel electrophoresis; DRM, detergent-resistant membrane domains; ICAT, isotope
coded affinity tagging; LOPIT, localization of organelle proteins by isotope tagging; MS, mass spectrometry; 2D-PAGE, two-dimensional-polyacrylamide gel
electrophoresis; LC-MS/MS, liquid chromatography coupled to tandem mass spectrometry.
ª The Author [2006]. Published by Oxford University Press [on behalf of the Society for Experimental Biology]. All rights reserved.
For Permissions, please e-mail: [email protected]
1494 Lilley and Dupree
Traditionally, two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) has been the protein separation
technique most associated with proteomics (Klose, 1975).
Despite the development of alternative non-gel based
proteomic strategies which will be discussed later in this
paper, 2D-PAGE remains one of the key methodologies in
proteomics studies. In this technique, proteins first undergo
isoelectric focusing separating species based on their net
charge. The orthogonal second dimension is then used to
separate further based on molecular weight in the presence
of denaturing conditions. The excellent resolving power of
this technique can result in the separation of thousands
of the major proteins in a tissue or subcellular fraction.
Visualization of protein spots is achievable by the use of
visible stains such as silver and Coomassie and fluorescent
stains such as Sypro Ruby (Molecular Probes) and Deep
Purple (GE Healthcare). Proteins within spots of interest are
then identified by firstly digesting to peptides, typically
with trypsin and subsequent analysis using mass spectrometric methods. Employing 2D-PAGE to compare the
relative abundance of proteins across samples sets has been
hampered by several factors. These include limited sensitivity especially when used in conjunction with visible
dyes, irreproducibility of 2D gels resulting from considerable gel-to-gel variation and normalization of spot intensities across gel sets. Difference gel electrophoresis
(2D-DIGE) circumvents many of these issues, and allows
for more accurate and sensitive quantitative proteomics
studies. The 2D-DIGE technique was first described by
Jon Minden’s laboratory (Ünlü et al., 1997) and has subsequently been refined and marketed by Amersham Biosciences (now part of GE Healthcare). This technique relies
on pre-electrophoretic labelling of samples with one of
three spectrally-resolvable fluorescent CyDyes (Cy2, Cy3,
and Cy5) allowing multiplexing of samples into the same
gel. Figure 1 shows the scanned images from a typical 2DDIGE gel. There are currently two forms of CyDye
labelling chemistries available: minimal labelling which
results in low-stoichiometry labelling of the e-amine groups
of lysine side-chains, and saturation labelling leading to
the stoichiometric labelling of cysteine residues (Shaw
et al., 2003; Wheeler et al., 2003).
The ‘minimal labelling’ DIGE chemistry is the most
established (Tonge et al., 2001; Alban et al., 2003). In this
chemistry CyDye DIGE Fluors react with primary amino
groups, typically the terminal amino group of lysine side
chains. The detection limit is in the order of 150–500 pg
of a single protein depending on the experiment, with a
linear response in protein concentration over five orders of
magnitude. By comparison, silver staining has a detection
limit of 1 ng of protein with a dynamic range of less than
two orders of magnitude (Tonge et al., 2001). The labelling
system is compatible with the downstream processing
commonly used to identify proteins via mass spectrometry
and database interrogation, which involves the generation
of tryptic peptides within excised gel plugs as peptide
generation is mostly unhindered given that so few lysine
residues are modified by dye labelling. Minimal labelling
ensures that quantification is performed using protein
molecules that have been labelled only once. The labelled
portion of the protein may migrate at a higher apparent
molecular mass than the majority of the unlabelled protein
especially in the case of lower Mr species. It is therefore
usual to post-stain 2D-DIGE gels with a total protein stain
to ensure that the maximum amount of protein is excised
for subsequent in-gel digestion and mass spectrometry.
The saturation labelling chemistry is a much newer
addition to the GE Healthcare DIGE portfolio, and, as yet,
there are only a few instances of their successful use in the
literature (Shaw et al., 2003; Greengauz-Roberts et al.,
2005). In this chemistry the CyDyes are supplied with
a thiol reactive maleimide group and carry no intrinsic
charge and results in the labelling of every cysteine residue
within a protein. The saturation labelling is therefore much
more sensitive than minimal labelling, as more fluorophor
is incorporated into each protein species. Shaw et al. (2003)
reported an order of magnitude increase in sensitivity over
the original minimal dyes. Whilst the added sensitivity that
these dyes provide is attractive, particularly when used in
conjunction with scarce samples, their use is technically
more challenging and excludes proteins that contain no
cysteine residues from the analysis.
The 2D-DIGE methodology is at its most useful when
interrogating the protein expression profiles over multiple
sets of samples. In the case of the minimal labelling
chemistry, samples to be compared are labelled with either
Cy3 or Cy5 CyDye DIGE Fluors, whereas the Cy2 CyDye
DIGE Fluor is used to label a pooled sample comprising
equal amounts of each of the samples within the study, and
acts as an internal standard. The use of this internal standard ensures that all proteins present in the samples
are represented, assisting both inter- and intra-gel matching. Variation in spot volumes due to gel-specific variation, such as sample entry and electrophoresis in the first
dimension and second dimension SDS-PAGE gel, will be
the same for each sample within a single gel. Thus, the
relative amount of a protein in a gel in one sample
compared with another will be unaffected. The spot
volumes are normalized using a method based on the
assumption that the majority of protein spots have not
changed in expression level, and accounts for dye related
discrepancies arising from differences in laser intensities,
intrinsic fluorescent properties of the gel matrix and filter
transmittance (Alban et al., 2003). Direct comparisons of
spot volumes are made between the Cy3- or Cy5-labelled
samples and the Cy2-labelled internal standard for that gel,
and these ratios are normalized and compared with the
ratios generated for that particular protein from the other
gels. This approach is referred to as Biological Variance
Analysis (BVA). The presence of the same-pooled standard
Quantitative organelle proteomics
1495
Fig. 1. Typical 2D-DIGE gel images. Soluble proteins were extracted from Arabidopsis callus liquid-grown cultures. (A) Cells grown in low nitrogen
Murashige and Skoog medium are labelled with Cy3 (green). (B) Soluble proteins extracted from a similar culture grown in standard Murashige and
Skoog medium are labelled with Cy5 (red). (C) The merged image reveals few obvious differences between the cell cultures. Quantitative analysis using
software is needed to determine significant differences between the two samples (see text).
present on each gel, therefore, allows for the application
of Student’s t test and ANOVA statistical analyses to replicate single- and multi-variable samples despite having samples, separated on different DIGE gels. For the analysis,
software developed for the DIGE system (DeCyder by
GE Healthcare, Sweden) is typically used, although other
software programs are now available.
The design of a protein profiling analysis experiment
using DIGE is paramount to the amount of statistical
significance that can be placed on the data. Consideration
must be given to methods employed to assess both
biological and experimental noise within the system. In
DIGE experiments, the biological variance is often greater
than the technical variance associated with this technique
(Karp et al., 2005a). The number of gel replicates that
are needed for the experiment to have sufficient sensitivity
to detect expression changes have been the subject of a
recent publication (Karp and Lilley, 2005). In this study,
a power analysis of the minimal labelling 2D-DIGE approach was executed for a set of technical replicates from
a single bacterial growth where no difference in expression was anticipated. Power can be defined as 1–b where
b is the false negative rate as determined by univariate
statistical tests. The power of a technique depends on the
noise of the variance (noise), effect size (change in expression), number of replicates, and nominal significance set
by the researcher. It was demonstrated within this study
that the type of sample used in a 2D-DIGE experiment had
no impact on the power of the technique and that technical variance was fairly reproducible. It further demonstrated the role of replicates in detecting statistically robust
changes in protein expression and showed the potential of
adding a few extra replicates in terms of increasing not only
the sensitivity of the size of changes measured, but also the
significant increase in the cost of the experiment. In the
author’s laboratory, for instance, to achieve the generally
accepted target power of 0.8 (20% false negative rate),
required four replicate gels to detect a 2-fold change in
expression, six gels for a 1.5-fold change and 16 gels for
a 1.25-fold change. These data were achieved using an
approach incorporating the Cy2 labelled internal standard.
A simpler experimental design where pairwise comparisons
between samples labelled with Cy3 and Cy5, in other words
leaving out the Cy2 internal standard in the experimental
design, had significantly less power.
Typically, univariate statistical analysis has been used in
the analysis of data from multi-gel experiments to identify
differential expression between sample types by looking for
significant changes in spot volume. Multivariate statistical
tests, which look for correlated changes between sample
types, provide an alternative approach for identifying spots
with differential expression. Karp et al. (2005b) utilized
partial least squares-discriminant analysis (PLS-DA), a
multivariate statistical approach, combined with an iterative threshold process to identify which protein spots had
the greatest contribution to the model, and compared this
to univariate tests for three data sets. This included one
data set where no biological difference was expected.
This study demonstrated the roles of both multivariate
PLS-DA and univariate tests for identifying spots with
expression changes. As multivariate approaches identify
correlated changes, it is particularly useful for recognizing
pathway changes and other correlated events. The multivariate models produced also have potential as diagnostic
tools, and as the diagnoses will be based on a global picture
it will be less affected by individual protein fluctuations.
Univariate methods, however, are more appropriate for
identifying a protein change that is significant, but whose
expression changes occurred in isolation with respect to the
other proteins detected in the gel. The study also indicated
that both univariate and multivariate approaches should be
used to maximize the data analysis procedure of such data
sets. In turn, this combined statistical analysis improves the
detection rate of global proteomics changes.
1496 Lilley and Dupree
Applications of 2D-DIGE in plant organelle
studies
To date there have been several studies published using
2D-DIGE in conjunction with plant material (Borner
et al., 2003, 2005; Kubis et al., 2003, 2004; Ndimba
et al., 2003; Maeda et al., 2004). Several of these have
involved the study of proteins within subcellular structures.
A study from the authors’ laboratories used a comparative proteomics approach using 2D-DIGE and LC-MS/MS
to investigate the protein content of detergent-resistant
sphingolipid- and sterol-rich membrane domains (DRMs)
from plant membranes (Borner et al., 2005). It is impossible
to purify these DRMs from other membrane contamination,
but the proteins in the DRMs could be identified because
of their differential solubility in Triton X100. It was
observed in a pairwise comparison using DIGE that DRMs
are highly enriched in specific proteins. These included
glycosylphosphatidylinositol-anchored proteins, and proteins of the stomatin/prohibitin/hypersensitive response
family, suggesting that the DRMs were derived from plasma
membrane domains. Integral membrane proteins were not
revealed in the DIGE analysis. The results indicate that the
preparation of DRMs can yield a very specific set of
membrane proteins, and suggest that the plasma membrane
contains phytosterol and sphingolipid-rich lipid domains
with a specialized protein composition. The proteins
associated with these domains have provided important
new experimental avenues for understanding plant cell
polarity and cell surface processes (Fig. 2).
Jarvis and co-workers have used DIGE extensively in
the elucidation of the mechanism of preprotein import
into chloroplasts which is facilitated by multimeric translocon complexes in the outer and inner envelopes of the
chloroplast (Kubis et al., 2003, 2004). In this study
relatively pure chloroplast preparations could be made as
this organelle has physical parameters sufficiently unique
to allow its purification. This DIGE work was carried out
before Cy2 was available, and the studies involved quantitative pairwise comparisons. By comparing the complement of choroplast proteins from wild-type Arabidopsis
thaliana with that from mutants in various subunits of the
TOC translocon, the authors were able to interrogate the
specificity of various translocon complexes for the types
of protein imported.
Non-gel-based methods to study the proteome
quantitatively
2D-DIGE is the most powerful 2D-PAGE based approach
for protein profiling studies by virtue of its ability to
multiplex and link samples across numerous different gels
and samples by the use of the internal standard. The 2DPAGE technology is not without considerable limitations.
The following groups of proteins are poorly represented
on a 2D gel: those with extreme pIs or molecular weight,
lower abundance proteins, and hydrophobic membrane
proteins—which make up a mechanistically important
subset of proteins and are likely to play crucial roles within
organelles. The dynamic range of protein concentration in
a cell is much greater than can be handled by any current
proteomics technology, and low abundance proteins are
less tractable in 2D gels owing to total protein load
limitations. Membrane proteins are also problematic even
when detergents such as amidosulphobetaine 14 (ASB14)
(Santoni et al., 2000) are used. Integral membrane proteins
have a tendency to precipitate during isoelectric focusing
and their study is best carried out using alternative
quantitative methodologies.
Quantitative non-2D gel-based technologies are becoming more and more routine and have the potential to give
information about subsets of proteins missing from a the
2D-DIGE approach. These technologies fall into two
categories, one employing the use of stable isotopes, the
second requiring no prelabelling of proteins.
Quantitation using stable isotope labelling
Fig. 2. Highly schematic diagram of a lipid raft in the plant plasma
membrane. Certain proteins and lipids have an affinity for each other that is
revealed by their detergent-insolubility. The study of detergent resistant
membranes (DRMs) suggests these domains contain sterols and sphingolipids. Proteomic analysis of the DRMs indicates GPI-anchored proteins
and a specific set of other plasma membrane proteins are present in the
domains. The size and organization of the domains in vivo is unknown.
This involves quantitation using differential incorporation
of stable isotopes either in vivo or in vitro. There are several
ways in which this can be achieved. One method involves
the growth of cultures in the presence of a defined medium
containing a heavy isotope, typically 15N (Conrads et al.,
2001; Ong et al., 2002; Krijgsveld et al., 2003), and use in
plants has only recently (Gruhler et al., 2005) been
reported. Samples grown in the presence of the natural
isotope and the heavy isotope can be pooled, reduced to
peptides, and the peptides separated by multidimensional
liquid chromatography before application to mass spectrometry. The relative abundance of a peptide generated
from a protein within cultures being compared is then
Quantitative organelle proteomics
1497
Fig. 3. Quantitative proteomic analysis allows proteins in different organelles to be distinguished based on their differential fractionation in density
gradients. A LOPIT experiment was carried out using the isotopic label ICAT. This PCA scores plot shows clustering of proteins according to their
subcellular localization.
calculated by measuring ion intensities of the ‘light’ and
‘heavy’ versions of the same peptide within the mass
spectrometer. The use of tandem mass spectrometry in
this approach leads to the relative quantification of the peptides, as well as generating data for protein identification
via database interrogation.
A more widely applicable variation of this method is
to label extracted protein with tags which can be produced
in more than one isotopic form, such as the isotope coded
affinity tagging (ICAT) reagent which was pioneered by
Aebersold in 1999 and which has been further developed
and marketed by Applied Biosystems (Gygi et al., 1999; Li
et al., 2003). This method involves the labelling of cysteine residues within a protein with a biotinylated tag
which can be purchased in a ‘light’ and ‘heavy’ format. The
‘heavy’ tag has nine 13C atoms and hence after labelling,
pooling of the ‘light’ and ‘heavy’ labelled samples and
trypsinolysis, the same peptide present in both starting
samples will be present in two forms, one of which will
be nine Daltons heavier than the other. Simplification
of the peptide mixture is brought about by applying the
peptides to a monomeric avidin column which will bind
only those peptides containing the biotin tag.
Label-free quantitation
The second quantitation approach relies on peak intensity
measurements of peptides detected by mass spectrometry
(Andersen et al., 2003, Chelius et al., 2003) or on the
number of msms spectra per protein detected in a mass
spectrometric experiment (Liu et al., 2004). These two
sub-methods are refereed to as label-free proteomics. An
advantage of these two approaches is the reduction in
the cost of experiments when compared with the expense
of employing stable isotopes. There are, however, several
disadvantages of these approaches when used in conjunction with complex peptide mixtures. Changes in peptide
chromatography conditions and ionization efficiency may
affect quantitation. This problem is compounded when
using multi-dimensional chromatography separations of
complex mixtures since slight variance in chromatography
will lead to irreproducible peptide separations.
The application of quantitative proteomics in
plant organelle studies
The study of DRMs using DIGE highlighted the fact that
the DRM proteins appear to be derived predominantly from
the PM (Borner et al., 2005). However, integral membrane
proteins could not be quantified, and so it could not be
proved that these proteins were specifically enriched. To
address this problem, ICAT labelling has recently been
used to compare DRM proteins with the control mixed
organelle membranes. In addition, comparing DRM
proteins with PM proteins prepared by Dextran-PEG two
phase partitioning has also begun (T Weimar, KS Lilley,
P Dupree, unpublished results). The results so far indicate
that DRM proteins are largely but not uniquely derived
from the plasma membrane, and secondly that some plasma
membrane proteins are not enriched in the DRMs. These
results have implications for models of the role of lipid
rafts in plant organelle membranes.
To discover novel proteins in endomembrane organelles, a proteomics technique has been developed for the
1498 Lilley and Dupree
subcellular localization of integral membrane proteins in
Arabidopsis thaliana by employing the use of differential
isotope labelling (Dunkley et al., 2004a, b). This method,
the Localization of Organelle Proteins by Isotope Tagging
(LOPIT) is based upon analytical centrifugation and hence
is not dependent on the production of pure organelles. The
technique involves partial separation of organelles by
density gradient centrifugation followed by the analysis
of protein distributions in the gradient by ICAT labelling
and mass spectrometry (MS). Multivariate data analysis
techniques can then be used to group proteins according to
their distributions and hence localizations. Upon carrying
out the LOPIT technique on membrane fractions isolated
from Arabidopsis thaliana liquid callus cultures, it was
observed that proteins that already had experimentally
derived locations within subcellular structures clustered
together within PCA analysis consistent with their location
(Fig. 3). In total, 170 proteins were identified and their
density gradient distributions determined. A subset of 28 of
these proteins with known or predicted locations was used
to validate this technique. The clear separation of the ER
and Golgi, despite having very similar densities, demonstrates that LOPIT can be used to discriminate between the
residents of these two organelles. It has been shown that
the LOPIT technique can be used to discriminate Golgi,
ER, plasma membrane, and mitochondrial/plastid proteins.
To prove the ability of LOPIT to determine the subcellular
localization of previously uncharacterized proteins, a PLSDA model was built using the proteins with known
localizations. A test set of proteins with predicted ER or
Golgi localizations were correctly assigned to the ER or
Golgi categories by the model. It is important to note that
proteins that were not classified may belong to organelles
not included in the PLS-DA training set, such as the
prevacuolar compartment. The non-classified proteins may
also represent proteins localized to multiple compartments.
The density gradient distributions, and therefore PCA
scores, of these proteins are combinations of the density
gradient profiles of the different organelles in which they
reside.
As far as is known, this is the first example of a proteomic
method that can, with confidence, discriminate between
proteins resident in the ER and the Golgi. A significant
drawback of using ICAT labelling is the low peptide
coverage, as only cysteine-containing peptides are analysed
which will impact not only the coverage of the organellome
but also the statistical power of the approach. Recent
advances in non-gel-based technologies such as iTRAQ
(Ross et al., 2004), will circumvent these shortcomings. In
the iTRAQ approach, proteins are differentially labelled
with up to four different isotope tags at the peptide level,
thus every peptide produced from the digest of a complex
sample is labelled. The added complexity of the peptide
mixtures produced necessitates the application of multidimensional peptide separation, but recent publications
(Zhang et al., 2005) have demonstrated the great potential
of this approach, particularly with respect to the large
number of proteins that can be co-quantitated and the
statistical robustness of the data.
The future of plant organelle studies
The LOPIT approach, especially in conjunction with
a differential isotope tagging strategy allowing co-analysis
of all peptides, such as iTRAQ, promises to yield a raft
of localization information for proteins associated with
organelles. A drawback of this methodology is that posttranslational information is largely lost during this analysis
as modified peptides are not likely to be detected unless
they are specifically enriched. 2D-DIGE still offers an
attractive alternative for many organelle studies as posttranslational modification associated with a pI shift will
be detected on 2D-PAGE gels. For many studies, both
approaches may be used in a complementary manner to
maximize the amount of information about the location
of proteins and their modification state.
Label-free quantitative proteomics has yet to be applied
to the plant organelle proteome. It is still unclear, however,
how powerful this technique is, given that each sample in
a comparative study will be processed separately during all
steps of protein extraction, digestion to peptides, peptide
separation over multiple dimensions, and MS analysis. The
variance associated with these steps may be too great to
allow the subtle discrimination required to assign proteins
confidently to such organelles as the ER and Golgi.
In all the methods used to date to study the proteomes of
organelles, one is justified in saying that only the most
abundant proteins present have been characterized because
of the sensitivity and measurable dynamic range limitations
of LC-MS/MS. One of the main challenges for the future is
the development of robust techniques which allow further
mining of the organellome.
Acknowledgements
We thank Jason Wong and Godfrey Miles for Fig. 1. This work was
supported by the Biotechnology and Biological Sciences Research
Council and by a European Community Framework V Research
Training Network Contract (HPRN-CT-2002-00262) Biointeractions.
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