Download Selected reaction monitoring applied to proteomics

Special Feature: Tutorial
Received: 3 November 2010
Accepted: 12 January 2011
Published online in Wiley Online Library: 2011
(wileyonlinelibrary.com) DOI 10.1002/jms.1895
Selected reaction monitoring applied
to proteomics
Sebastien Gallien, Elodie Duriez and Bruno Domon∗
Selected reaction monitoring (SRM) performed on triple quadrupole mass spectrometers has been the reference quantitative
technique to analyze small molecules for several decades. It is now emerging in proteomics as the ideal tool to complement
shotgun qualitative studies; targeted SRM quantitative analysis offers high selectivity, sensitivity and a wide dynamic range.
However, SRM applied to proteomics presents singularities that distinguish it from small molecules analysis. This review is
an overview of SRM technology and describes the specificities and the technical aspects of proteomics experiments. Ongoing
developments aiming at increasing multiplexing capabilities of SRM are discussed; they dramatically improve its throughput
c 2011 John Wiley & Sons, Ltd.
and extend its field of application to directed or supervised discovery experiments. Copyright Keywords: selected reaction monitoring; proteomics; quantification; triple quadrupole mass spectrometer; multiplexing
Introduction
298
Over the past decade, LC–MS/MS-based proteomics has emerged
as the most effective method to study complex proteomes. In
this approach, the proteins representing a proteome or a subset
thereof are enzymatically digested to generate peptides, which
in turn are analyzed by liquid chromatography coupled to mass
spectrometry. This shotgun approach is a powerful tool to identify
proteins in complex biological samples as exemplified by a wealth
of publications.[1,2] However, it is not optimal for a systematic
quantification of these proteins because of the stochastic nature
and the limited sensitivity of the approach; most often, only relative
quantification of the abundant components is performed.
Thus, alternative MS approaches based on selected reaction
monitoring (SRM) have emerged to precisely and quantitatively
analyze complex biological samples. SRM is not a new technique
per se but its application to proteomics emerged during the
past decade. The technique was introduced in the late 1970s[3,4]
along with the development of the first triple quadrupole mass
spectrometers[5] and has been for 30 years a reference quantitative
technique to analyze small molecules,[6,7] notably in clinical
applications.[8] The hypothesis-driven nature of such experiments
overcomes the bias towards most abundant components. The
analysis targets specific subsets of analytes, peptides as surrogates
for the proteins of interest, and in this instance it is performed
by isolating, within the mass spectrometer, ions corresponding
to the molecule of interest. These ions, in the case of peptides,
doubly or triply protonated (i.e. charged) molecular species, are
then fragmented and a few specific fragments are monitored
for detection and quantification purposes. This particular mode
of operation of the triple quadrupole instrument yields the high
level of selectivity and sensitivity and the wide dynamic range
of the analysis. Already realized and still ongoing developments
have allowed to multiplex the analytes and measure larger sets
of peptides, opening new avenues in terms of productivity[9] and
experimental scope.
The most common SRM application in proteomics is the precise
quantification using isotopically labeled reference peptides. The
stable isotope dilution (SID) concept has a long history in
J. Mass. Spectrom. 2011, 46, 298–312
quantitative mass spectrometry and is the reference method.[10,11]
It was applied to peptides in 1983[12] and to protein digest in
1996[13] using fast atom bombardment (FAB) mass spectrometry.
Any mass spectrometer type can in principle take advantage
of SID, but only triple quadrupole instruments capable of SRM
can fully exploit the MS/MS potential. The combination of SID
approach and SRM technique (SID–SRM) is the golden standard
for absolute quantification, and pertinent applications include
trypsinized proteins from membrane preparation,[14] whole-cell
lysates[15] and bodily fluids including serum, plasma, urine and
synovia.[16 – 19]
The targeted LC–MS analysis of proteins by SRM has singularities
that distinguish it from the method used for small molecules. The
most striking one is that in the case of proteins, the method
is indirect and requires several intermediate steps to generate
the peptides and select the appropriate surrogate analytes. The
second one is the complexity and the dynamic range of proteomic
samples, for instance in bodily fluids, incommensurate with those
of low molecular mass samples typically analyzed even in the
case of metabolite studies. These particularities have to be taken
into account when designing a proteomic SRM experiment. A
specific point is the nature of the matrix: in SRM analysis of small
molecules, the analytes of interest represent a small fraction (ppm
or ppb) of the total sample amount but analyte and matrix have
different chemical natures and the up-front separation step is
able to effectively separate the analytes from other components.
In contrast, in the case of proteomics, both the analytes and
the background have the same chemical composition (peptides),
which represents the main challenge in terms of interferences,
ion suppression effects and thus limit of detection (LOD). These
particularities have prompted numerous improvements at the
∗
Correspondence to: Bruno Domon, Luxembourg Clinical Proteomics center
(LCP), Centre de Recherche Public de la Santé, 1 B rue Thomas Edison, L-1445
Strassen, Luxembourg. E-mail: [email protected]
Luxembourg Clinical Proteomics center (LCP), Centre de Recherche Public de la
Santé, 1 B rue Thomas Edison, L-1445 Strassen, Luxembourg
c 2011 John Wiley & Sons, Ltd.
Copyright Selected reaction monitoring applied to proteomics
Source
MS-1
CID
MS-2
Fixed
Fixed
Fixed
Fixed
m/z (Q1)
CE (Q2)
time
m/z (Q3)
Figure 1. Principle of the selected reaction monitoring performed on a triple quadruple mass spectrometer. The precursor ion selected by the first mass
filter (Q1) enters the collision cell (Q2) where it undergoes collision-induced dissociation. One fragment ions is then selected by the second mass filter
(Q2). Multiple precursor/fragment ion pairs can be monitored sequentially within a measurement cycle. Adapted from Ref. [2].
instrument control level as well as at the quantification strategy
level.
This review provides an overview of the SRM technology; it describes the specificities and the technical aspects of proteomic
experiments, focusing on quantification. The last section will
discuss ongoing developments regarding the multiplexing capabilities.
Proteomic Applications – a Historical
Perspective
J. Mass. Spectrom. 2011, 46, 298–312
Specificities of SRM Applied to Proteomics
SRM analysis of small molecules differs from proteomic applications in which peptides analyzed are surrogates for the targeted
proteins. Consequently, proteomics requires a more complex
workflow, including an experimental design step to select the
peptides suited for LC–MS measurements. The specificities and
the technical aspects of a proteomic SRM assay include five main
steps, illustrated in Fig. 2: (1) considering a specific biological or
clinical question, definition of the set of proteins of interest; (2) for
c 2011 John Wiley & Sons, Ltd.
Copyright wileyonlinelibrary.com/journal/jms
299
The SRM technique was first described in the late 1970s;
initially in the context of ‘mass-analyzed ion kinetic energy’
(MIKE) experiment performed on sector instruments.[4] The
SRM technique becomes routine when implemented on triple
quadrupole mass spectrometers, first introduced by Enke and
Yost.[5] When a triple quadrupole instrument is operated in SRM
mode, the first and the third quadrupole serve as mass filters
to specifically select predefined m/z values corresponding to the
precursor ion and a specific fragment ion of that precursor, whereas
the second quadrupole is used as collision cell (Fig. 1). Initially,
this mode of operation was called single reaction monitoring
or multiple reaction monitoring depending on whether one
precursor/fragment ion pair (transition) was monitored, or a series
of transitions were measured iteratively. These two terms were
replaced by the unique term SRM to avoid the ambiguity between
the number of transitions monitored and the number of stages
used in the mass spectrometry analysis (MSn ).[85]
The SRM mode is characterized by a high sensitivity enabling
to detect low amount of the targeted analytes even in a complex
matrix, such as those of protein digests. In addition, its targeted
nature, its high selectivity achieved by two stages of mass filtering
and its wide dynamic range make SRM ideal for quantitative
proteomics, especially when combined with SID strategies for
absolute quantification purposes. The SID strategies for peptide
quantification were first introduced by Desiderio using FAB-MS
and 18 O-incorporated standard peptide in order to determine
the amount of endogenous enkephalin in thalamus extract.[12]
This first promising result represents the cornerstone of absolute
quantification of proteins, as demonstrated by the analysis of the
enzymatic digest of a purified protein by Barr and co-workers using
continuous-flow FAB-MS and synthesized stable isotope-labeled
peptides to determine the absolute amount of apolipoprotein
A-1.[13] Later on, the strategy was applied in conjunction with SRM
to membrane proteins characterization, including the G proteincoupled receptor rhodopsin.[14] This method was also used to
quantify low abundance proteins in more complex samples.[15]
More recently, SID–SRM analysis was applied to the quantification
of low-abundance proteins in clinical biological fluids such as
serum, plasma or synovial fluid, which represent an even more
challenging task due to the complexity and large dynamic range
of proteins present in such media. The first application of this
type was the quantification of C-reactive protein, a diagnostic
biomarker of rheumatoid arthritis, in serum after depletion of
three abundant proteins.[16] The measurements performed by
SRM were compared with results from immunoassays, which
appeared to be closely correlated. Similarly, prostate-specific
antigen was also directly measured in non-depleted serum.[17]
These examples demonstrated the potential of SID–SRM analysis
to quantify some known protein biomarkers in biological fluids
provided they are within the dynamic range of detection. The
method is generally effective in clinical proteomics as illustrated
by a study of Liao et al.,[18] which aimed at identifying new protein
biomarkers reflecting disease severity in rheumatoid arthritis. They
developed a two-step proteomic approach in which biomarker
discovery is performed in synovial fluid using shotgun LC–MS/MS.
Putative biomarkers were then confirmed in serum using SID–SRM
analyses. In spite of the limited scale of the study, this work paved
the way for the biomarker development pipeline, nowadays a
benchmark in clinical proteomics.[20]
Subsequent developments have focused on one hand on
exploiting the ability to quantify proteins in a multiplexed
manner[19,21] and on the other hand on increasing SRM sensitivity
by sample fractionation, including immunoaffinity depletion, multidimensional fractionation or affinity enrichment techniques.[22]
These studies have demonstrated the feasibility of analyzing
biomarkers in bodily fluids. Minimal processing of blood samples,
such as depletion of most abundant proteins, allows reaching
a good precision (coefficients of variation below 10%) and a
sensitivity down to the µg/ml.[19] More elaborated sample preparations, such as glycocapture[21] or SISCAPA[22] which dramatically
reduce the background, allow measurements as low as ng/ml.
Numerous applications of the SRM technique have recently been
reported.[21,23 – 33]
S. Gallien, E. Duriez and B. Domon
- Biological / clinical question
- Proteomic or genomic experiment, literature mining
Definition of
protein set
- Sequence uniqueness
- LC attributes (reversed phase)
- MS properties
Selection of
peptides
- Sensitivity (intense fragments)
- Selectivity (interferences)
Selection of
transitions
- Experimental measurements to assess background interferences
Validation of
transitions
- Ionization source
- Collision conditions
Optimization of
transitions
Figure 2. Workflow of a SRM-based proteomic experiment. Firstly, the set of proteins of interest is defined considering the biological or clinical question
of the study. Secondly, for each protein, a set of ‘best representing’ peptides is determined on the basis of their uniqueness and performance in LC–MS
analysis. Thirdly, transitions maximizing sensitivity and selectivity are selected. Finally, transitions are validated to assure that the detected signals
correspond truly to the targeted peptides. An additional step of transition optimization is optionally included in the workflow to increase sensitivity of
the quantification.
each protein, the determination of the set of peptides representing
each protein; (3) selection of the transitions that maximize sensitivity and selectivity of the experiment; (4) experimental validation
of the transitions, whenever possible in biological background
and (5) if precise quantification and high sensitivity are required,
additional optimization of transitions.
Definition of the proteins of interest
The first step in the design of a SRM analysis is the definition
of target proteins. A hypothesis-driven proteomic experiment
typically aims at answering a specific biological or clinical question,
e.g. the analysis of a pathway, a protein network or the evaluation
of a set of biomarkers associated with a disease. The protein
subset of immediate interest defines the actual SRM experiment,
and results from previously acquired knowledge from -omics study
and scientific literature mining represent additional resources to
design the actual experiment. Ultimately, proteomics aims at
analyzing a whole proteome in one experiment, which remains a
long shot even with this technology.
Determination of the peptides to be analyzed
300
For each protein to be included in an SRM analysis, a set of tryptic
peptides resulting from the enzymatic digestion of the sample
is selected. Most proteomic studies use trypsin as proteolytic
enzyme, which yields analytes well suited for LC–MS analysis, i.e.
typically peptides containing 8–25 amino acids, accommodating
the m/z range of the quadrupole analyzer. Typically, a few
representative tryptic peptides for each protein are targeted to
infer its presence in a sample and to quantify it. However, their
selection is not always a trivial step. The targeted peptides, often
called proteotypic, need to fulfill some very stringent criteria,
namely, having an amino acid sequence uniquely associated with
the proteins of interest, and being consistently observed in LC–MS
wileyonlinelibrary.com/journal/jms
analyses, which is often correlated to good ionization efficiency
(i.e. detectability).
The sequence uniqueness of a peptide is determined through in
silico mapping of the amino acid sequence onto the entire protein
space (i.e. the proteome under investigation).
In order to be systematically observed in a mass spectrometric
analysis, peptides require intrinsic properties: good ionization
efficiency and a mass-to-charge ratio within the practical mass
range of the instrument. Moreover, in a quantitative experimental
workflow, other factors have to be considered. The first one is the
sample preparation; in this step peptides should be fully recovered
and soluble after digestion. Peptides presenting a missed cleavage
(incomplete digestion product) or degradation will translate in
inconsistent results. The second factor is the chromatographic
behavior of the analytes: hydrophilic peptides are poorly retained
on the stationary phase, while very hydrophobic peptides present
tailing effects, will elute late, or may even stick on the column. Poor
chromatographic behavior may also contribute to an increased
chemical background.
Peptide selection based on experimental evidences
Current practice to assess consistent peptide detection is based
on observations in initial discovery experiments from individual
laboratories compiled in proteomic data repositories. Such
databases represent a tremendous resource as their volume is
growing steadily; it includes PeptideAtlas,[34] GPM Proteomics
Database,[35] PRIDE.[36] The selection of experimentally observed
peptides relies on the number of their observations, as the spectral
count that is an indicator of the abundance of proteins in a
specific data set. For instance, PeptideAtlas provides an Empirical
Proteotypic Score (EPS) reflecting the number of samples in which
a given peptide was observed.
c 2011 John Wiley & Sons, Ltd.
Copyright J. Mass. Spectrom. 2011, 46, 298–312
Selected reaction monitoring applied to proteomics
Peptide selection based on prediction
In the absence of experimental data or sparse data sets, for
instance low abundant proteins poorly represented in repositories,
computational approaches can be used. They allow predicting
the physico-chemical properties of peptides to select the best
responding peptides. Several such tools have been published,
including ESP predictor,[37] PeptideSieve,[38] PepFly,[39] STEPP[40]
and others.[41,42] These prediction tools are trained in a first
step with existing data sets to determine the most relevant
physico-chemical properties to predict the LC and MS behavior
of the peptides generated by proteolytic digestions of proteins of
interest.
Although very helpful, at present, neither approach is sufficient
to define an optimal peptide set, in particular if signature peptides
of a given candidate have not been observed in discovery
experiments. As a matter of fact, the observability of a peptide
in discovery experiments is largely related to the richness of the
fragmentation pattern and the ability of the search engine to
reliably assign the amino acid sequence. In contrast, peptides with
fewer fragments, e.g. short amino acid sequences or the presence
of proline residues often yield fewer but more intense signals.
Such peptides may not be reported in discovery experiment but
still represent good analytes for an SRM experiment. Furthermore,
peptides containing amino acids prone to chemical modifications
can bias the quantification as they might occur under different
forms; it includes cysteine alkylation, methionine oxidation,
asparagine deamidation and N-terminal cyclization of glutamic
acid. Similarly, peptides susceptible to undergo post-translational
modifications (glycosylation, phosphorylation, etc.) might lead to
bias as they can be present in various forms. Unless it is the explicit
purpose of the assay to quantify potentially modified peptides
(using adequate sample preparation), such peptides should be
selected carefully.
This in fact raises the more general question of the representativity of a peptide for a given protein. A single peptide only defines
a small portion of a protein, irrespective of any chemical modification, proteolytic event or splice variants that can occur for this
protein. If the use of a single proteotypic peptide as a surrogate of
the protein might be sufficient within the context of a screening
experiment (see Multiplexing of Analytes Section), it is certainly unsatisfactory for a reliable absolute quantification.[43] Ideally, several
peptides distributed across the full sequence should be selected
for a given protein. For proteins existing under different forms (e.g.
isoforms), the deliberate selection of the peptides should cover
both conserved and variable domains, to quantify the variants.
Selection of SRM transitions
J. Mass. Spectrom. 2011, 46, 298–312
Validation of SRM transitions
In spite of the increased specificity provided by the two-stage
mass selection of triple quadrupole instruments, each transition
selected for a specific peptide should be evaluated in the context of
the actual biological matrix to account for unspecific contributions
of the fragment ions deriving from co-eluting species with similar
properties. Figure 3 illustrates a typical case. Evaluating the profile
of the SRM traces is a simple mean to verify the selectivity of
a given transition. The cross-correlation method based on linear
regression described[47,48] can be applied to assess the co-elution
of traces. The graphical representation allows visualization of
the data, and the resulting metrics (slope, correlation coefficient)
permit the objective assessment of the co-elution (Fig. 3(A)).
When isotopically labeled reference peptides are available,
checking the co-elution of the traces of the native peptides (see
Specificities of SRM Applied to Proteomics Section) with those of their
labeled counterparts (Fig. 3(B)) represents the ultimate validation
method. In this instance, the relative intensities measured for a
native peptide and for its labeled counterpart should be identical
for each transition and represent an additional metrics enabling
to assess the purity of the signal related to each transition and
to detect potential interferences from the background impacting
its measurement. This method, also used to determine LOD, is
nevertheless limited to experiments aiming at the quantification
c 2011 John Wiley & Sons, Ltd.
Copyright wileyonlinelibrary.com/journal/jms
301
Once the set of peptides best representing the proteins of interest
determined, it is critical to select transition ions that maximize
sensitivity and specificity of the SRM experiment. While sensitivity
is related to the signal intensity of a transition, specificity is
associated with interferences from co-eluting species that fall
within the mass selection windows of Q1 and Q3 analyzers.
The current practice is to select the two or three most intense
transitions to build an SRM assay. As previously mentioned, in
the absence of physical reference peptides, the selection of the
transitions relies on MS/MS spectra from discovery experiments.
The data (MS/MS spectra) are typically obtained on ion trap
or quadrupole-time of flight instruments and stored, without
curation, in reference spectra repositories (PeptideAtlas,[34] GPM
Proteomics Database,[35] PRIDE[36] ). More recently, systematic
efforts were undertaken to develop more standardized and
curated repositories such as SRMAtlas[44] that contains optimized
SRM transitions.
In the process, primary signal, related to the precursor ions,
is critical to maximize sensitivity. The m/z value of the first
analyzer should thus be set in order to select the most intense
mass-to-charge ratio of the targeted peptide. Although charge
state distribution of analytes is not completely independent of
experimental conditions, experimental LC–MS data available for
these analytes are very helpful to determine their dominant massto-charge ratio. Ab initio prediction, required in the absence of
experimental data, will favor doubly charged precursor ions unless
the peptide sequence contains a histidine residue promoting triply
charged ions.
The m/z value set for the second analyzer cannot directly
be deduced from previous experimental data because the relative
intensities of fragment ions depend on the type of instrument used
and operating parameters. Although fragmentation patterns have
similarities between instruments and between mass spectrometer
parameters, relative ion intensities depend on the different modes
of collision-induced dissociation, e.g. ion traps versus quadrupole
collision cell. For instance, b-type fragments of higher m/z are less
extensively represented in the triple quadrupole mass spectra.[45]
The calculated m/z values of y-ions resulting from easy cleavages,
such as those resulting from fragmentation N-terminal to a proline
residue or C-terminal to glutamate or aspartate residues, can be
selected.
In order to maximize selectivity, fragment ions with m/z values
higher than those of the precursor ion are preferred. Fragment
ions not resulting from fragmentation of the peptide backbone
(i.e. water loss from precursor or side chain fragmentations) do
not provide additional information and thus lack specificity. To
overcome some of the difficulties related to the selection process
of SRM transitions, software tools have recently emerged and
enable to expedite the development of SRM methods (see Cham
Mead et al.[46] for a review).
S. Gallien, E. Duriez and B. Domon
NVNDVIAPAFVK
SRM transition 643.86 m/z
632.38 m/z
Intens.
5
x10
2.0
?
Peak 2
Peak 1
1.5
1.0
0.5
0.0
(A)
10
20
30
40
Peak1
100
90
80
70
60
50
40
30
20
10
0
Relative intensity of
transitions (%)
Relative intensity (%)
0
averaged
561.34
632.38
745.467
1073.60
50
100
90
80
70
60
50
40
30
20
10
0
0
27 27.5 28 28.5 29 29.5 30 30.5 31
Retention time (min)
10
20
30
40
50
60
70
Relative intensity of the averaged trace (%)
SRM transition (m/z)
NVNDVIAPAFVK
SRM transition
643.86 m/z
632.38 m/z
Peak 3
NVNDVIAPAFVK(heavypeptide)
SRM transition
647.86 m/z
640.39 m/z
Relative intensity (%)
Peak 1
100
Relative intensity (%)
Peak 2
(B)
Time [min]
100
Peak1
y7
(745.46)
80
60
40
20
y5
(561.34)
y6
(632.38)
y10
(1073.60)
0
y7
(753.47)
Peak 3
80
60
40
20
y5
(569.35)
y6
(640.39)
y10
(1081.61)
0
Peak1 : Full tandem mass spectrum
(C)
Relative Abundance
745.64
100
95
90
85
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
632.38
-Comparison with reference
161.04
spectra
308.14
178.31
1073.68
379.32
64.33
222.17
844.73
393.07534.47
435.22
718.42
959.51
787.95 891.89
200
(D)
-Sequence database searching
561.37
400
600
m/z
800
1186.13 1256.61
1000
1200
Peak1 : Composite tandem mass spectrum (8 transitions)
y7
100
Relative intensity (%)
90
80
Comparison with full MS/MS
70
reference spectra from:
60
-Previous experiment
50
-Data repository
40
y6
30
y10
y5
20
y8
10
y3
y9
y4
0
393.25 464.29 561.34 632.38 745.46 844.53 959.561073.60
m/z
302
Figure 3. Specificity of SRM measurements. Monitoring one transition of the peptide NVNDVIAPAFVK leads to the observation of peaks 1 and 2 during
LC–SRM analysis. In this example, different means allowed assigning peak 1 as corresponding to the peptide NVNDVIAPAFVK. These validation methods
also provide purity assessment of measured signals. (A) Co-elution of SRM traces of a given analyte. The ‘averaged’ trace was obtained from all the
transitions monitored for the given peptide. In the right panel, the intensity of each transition was correlated against the intensity of the ‘averaged’ trace
and linear regression was conducted for each transition. The deviation of data points from regression line measured by the correlation coefficient allows
assessing the co-elution of the traces. (B) In the presence of isotopically labeled reference peptides, co-elution of SRM trace pairs and comparison of
SRM transition intensity ratios. Monitoring the corresponding transitions of the isotopically labeled peptide leads to the observation of a unique peak
(peak 3) co-eluted with peak 1. When several transitions are monitored for peptide pairs (light/heavy), relative intensities for each transition pair should
be identical. (C and D) The acquisition of a full tandem mass spectrum (SRM-triggered MS/MS acquisition) or a composite tandem mass spectrum (e.g.
eight transitions monitored) provides a fingerprint of the analyte measured in the experiment that can be compared with the reference spectrum of the
targeted peptide.
wileyonlinelibrary.com/journal/jms
c 2011 John Wiley & Sons, Ltd.
Copyright J. Mass. Spectrom. 2011, 46, 298–312
Selected reaction monitoring applied to proteomics
of a limited number of analytes, mainly because of the costs related
to pure isotopically labeled peptides.
The identity of the targeted peptides is confirmed by the
acquisition of their full tandem mass spectrum in order to
c
have a complete fragmentation pattern as fingerprint (Fig. 3).
This is achieved by acquiring full MS/MS spectra automatically
triggered by one SRM transition.[49] Such measurements disrupt
the quantification process, as significant time is required to collect
a full spectrum. The problem is particularly obvious with classical
quadrupole instruments that suffer from slow acquisition speed in
scanning mode (Q3), which interrupts the typical cycle of a SRM
experiment, and results in loss of sensitivity thus compromising
quantification if large numbers of analytes are measured. The issue
is less acute with triple quadrupole-linear ion trap instrument[50]
because full spectra can be acquired in a shorter time frame. These
instruments were developed on the basis of a triple quadrupole
mass spectrometer except that the Q3 can operate either as a
conventional quadrupole mass filter or a linear ion trap. This
results in an increased sensitivity in full tandem mass mode but
remains intrinsically less sensitive than SRM acquisition.
A new method to confirm the identity of targeted peptides
has been introduced recently[51] : A composite MS/MS spectrum
is generated by measuring multiple fragment ions (eight to ten
ions) for each peptide, instead of recording a full MS/MS spectrum
(Fig. 3(D)). The composite MS/MS spectrum is reconstructed from
peak area intensities for all SRM transitions monitored for one
peptide. The similarity between the reconstructed spectrum and
the library spectra is then evaluated using a spectral matching
scoring routine. Monitoring a high number of transitions for each
peptide, typically between 8 and 10 transitions, can be performed
effectively in a data-dependent SRM mode, switching from
conventional quantification mode to acquire punctually multiple
transitions, while maintaining an acceptable cycle time.[51] This
new technique called intelligent selected reaction monitoring is
described in detail in Improved Selectivity of SRM Measurements
Section.
Optimization of SRM transitions
J. Mass. Spectrom. 2011, 46, 298–312
CE = a
m
+b
z
as shown in Fig. 4(C). The linear equations predict the collision
energies generating the most intense b-fragments and yfragments under different conditions. In a first approximation,
these parameters (slope, intercept) are generic for a specific
instrument type, but fine tuning for each specific triple quadrupole
spectrometer can provide increased sensitivity.
Peptide Quantification Using SRM
When operated to gather qualitative information, the triple
quadrupole mass spectrometer scans a wide m/z range to generate
full MS/MS spectra essential for compound identification. Overall,
only a small fraction of the total scanning time is spent on
measuring specific fragments of ions. On the other hand, the
precision required for quantification increases with the square
root of the number of ions measured,[54] and thus the time
devoted to measure one specific ion. Thus, increased precision
can be obtained in SRM as nearly 100% of the time is devoted to
measure targeted analytes.
Strategies
Ultimately, an SRM experiment needs to precisely quantify a
large set of target proteins in complex biological samples, which
c 2011 John Wiley & Sons, Ltd.
Copyright wileyonlinelibrary.com/journal/jms
303
To perform proper quantification, high sensitivity is desired, thus
each SRM transition is maximized by tuning acquisition parameters
of the mass spectrometer.
The signal intensity is determined by the combination of
peptide ionization efficiency, its transfer into the analyzer and
its dissociation into, ideally, a few intense fragments.
The parameters associated with the ionization process and ion
optics are critical; for instance, at too low extraction voltage:
peptides are not efficiently transferred, whereas at high interface
voltage, peptides may undergo fragmentation in the ion source.
Usually, optimal conditions are determined by using a set of
reference compounds spanning the m/z range of the instrument.
Tuning of the fragmentation conditions for each peptide can further increase the signal response. In contrast to the CID performed
on ion-trap instruments, fragmentation on triple quadrupole instruments is more sensitive to experimental conditions, including
parameters such as collision energy, nature and pressure of the
collision gas. Thus, the fragmentation patterns are instrument
specific; this point is illustrated in Fig. 4(A) by selecting typical
SRM peptides, i.e. doubly charged tryptic sequences comprising
10–16 amino acids. The systematic acquisition of fragmentation
patterns under various collision conditions on a triple quadrupole
instrument and the derived pseudo-breakdown curves[52] provide
a mean to rigorously determine the optimal collision energies. Basically, the global optimum corresponds to maximum intensities
observed for the high mass y-fragments, which usually dominate
the MS/MS spectra. Typically, optimal collision conditions for doubly and triply charged peptides range between 20 and 40 V; and
most of the singly charged high mass y-ions appear to have very
similar behavior. At low collision energies, the spectrum is dominated by unfragmented precursor ions, and fragments resulting
from facile cleavages such as fragmentation N-terminal to a proline
residue, while at very high collision energies, the fragment ions
(mainly y-ions) undergo secondary dissociation yielding low mass
y-ions or internal fragments. As recently documented, b-ions are
generated at collision energies lower than y-type ions of similar
m/z.[45] The prominence of b-ions in the low m/z range reflects
their lower stability and their facile decomposition.
The fragmentation patterns are also affected by the nature of
collision gas and its pressure. As illustrated in Fig. 4(B) for three
doubly charged peptides, optimal collision energy is dependent
on the gas pressure. Different nature of collision gas may also affect
the optimal collision energy as the energy resulting of collisions
between neutral gas molecules and peptide ions that can be
converted into internal energy, is directly related to the mass of
the collision gas.[53]
Consequently, these instrumental parameters need to be
carefully monitored and controlled to ensure the inter- and intralaboratory reproducibility of fragmentation patterns and thus
quantitative SRM experiments. In practice, apart from exceptional
cases like the need to measure transitions for peptides very
difficult-to-fragment under classical conditions, the nature and the
pressure of the collision gas are kept unchanged on a platform,
and the collision energy is adjusted for each targeted peptide.
As discussed above, for a defined set of instrumental parameters,
the optimal collision energy has a fair broad range, which is directly
related to the m/z value of the peptide. It can be estimated by a
linear function such as
S. Gallien, E. Duriez and B. Domon
(A) 40
Relative abundance (%)
35
30
secondary y-fragments
Low mass y-ions
high mass y-ions
25
N-ter Pro cleavage -> y-ions
b-ions
20
Precursor
15
10
5
0
0
10
20
30
40
50
60
Collision Energy (V)
(C) 50
(B) 40
GILFVGSGVSGGEEGAR[HeavyR]
y-type ions_1.5mTorr
GISNEGQNASIK[HeavyK]
Optimal collision energy (V)
Optimized collision energy (V)
y-type ions_1mtorr
y-type ions_1.2mTorr
ELGQSGVDTYLQTK[HeavyK]
30
20
10
b-type ions_1mTorr
40
y = 0.043x - 2.052; R2 = 0.95
b-type ions_1.2mTorr
b-type ions_1.5mTorr
2
y = 0.036x - 0.157; R = 0.94
y = 0.033x + 1.842; R2 = 0.90
y = 0.036x - 1.405; R2 = 0.83
y = 0.030x + 0.432; R2 = 0.87
30
y = 0.023x + 4.374; R2 = 0.80
20
0
1mTorr
1.2mTorr 1.5mTorr 1mTorr 1.2mTorr 1.5mTorr
y-type ions
10
400
500
600
b-type ions
700
800
900
1000
1100
Precursor m/z
Type of ion_argon pressure
Figure 4. Parameters affecting CID fragmentation. (A) Influence of collision energy. Typical pseudo-breakdown curves obtained on peptides showing
relative signal intensities of precursor and selected fragment ions as a function of collision energy. (B) Influence of gas pressure. Collision energy
generating the most intense y-type and b-type fragments according to the pressure of argon (1, 1.2 and 1.5 mTorr) for three doubly charged peptides
(GILFVGSGVSGGEEGAR, ELGQSGVDTYLQTK and GISNEGQNASIK). (C) Linear regression: 17 doubly charged peptides were measured to predict collision
energies generating the most intense b-fragments and y-fragments at three different pressures of argon (1, 1.2 and 1.5 mTorr).
304
requires shorter dwell times to maintain an acceptable cycle
time (see Multiplexing of Analytes Section).The determination of
relative changes in protein concentrations is often the first stage
of a proteomics study; for instance, this is the case in comparing
concentrations in healthy and disease samples to detect and
qualify potential biomarkers. Such analyses rely on measuring
peptide ions in individual samples based on their absolute signal
intensity after proper normalization, often referred to as label-free
method.[55] Based on the first principle, the quantification in mass
spectrometry relies on the linear relationship existing between
signal intensity and the analyte concentration, provided that the
instrument is operating in its linear dynamic range. The slope
of the regression line, called response factor, is analyte specific.
The determination of inter-sample relative changes of analyte
concentrations does not require the knowledge of their response
factors as the signal intensities directly reflect concentration, once
proper normalization is performed to compensate for injection
errors and ionization conditions.
Alternatively, relative quantification can be performed using
a SID approach based on labeling: one reference sample,
wileyonlinelibrary.com/journal/jms
for example, from a healthy control, is labeled with heavy
stable isotopes (either metabolically[56] or chemically[57] or
enzymatically[58] ) and spiked into the samples from a perturbed
state (labeled using the normal reagent). Relative quantification is
then achieved by comparing signal intensities of the stable-isotope
labeled peptides from the control sample and of their unlabeled
counterparts in the perturbed sample. This method overcomes
the issues of signal fluctuation associated with analyte ionization
and matrix effects leading to ion suppression/enhancement. To
be effective, the method requires the use of isotopic labels that
do not affect chromatographic properties of the peptides. Thus,
15 N, 13 C and 18 O isotope incorporation is preferred as it does
not induce significant retention time shifts of isotopically labeled
peptides in contrast to deuterium labeling, which exhibits different
hydrophobicity and thus different retention on C18 columns. Even
if to date only few studies using ICAT and mTRAQ in conjunction
with analyses by SRM have been reported,[59,60] the technique is
generally applicable to such relative quantitative analyses.
As just highlighted, quantitative MS measurements are by
nature relative. If reference peptides (e.g. synthetic isotopically
c 2011 John Wiley & Sons, Ltd.
Copyright J. Mass. Spectrom. 2011, 46, 298–312
Selected reaction monitoring applied to proteomics
labeled homologs) are added as internal standards into the test
sample in well-defined concentrations, the absolute amount
of the corresponding targeted endogenous peptides can be
determined precisely, and thus indirectly the amount of the
associated protein.[15] The SRM technique is ideally suited for
precise quantification, and under specific conditions for absolute
quantification; most commonly it is applied to a limited number
of analytes as high purity reference material is necessary.
Evaluation of the analytical performance
J. Mass. Spectrom. 2011, 46, 298–312
Implications for biological samples
The ionization efficiency of a given analyte depends on its environment during this process. The presence of co-eluted components
during a LC–MS analysis will affect the ionization process. Matrix
effects are well documented for small molecules[68,69] and are
c 2011 John Wiley & Sons, Ltd.
Copyright wileyonlinelibrary.com/journal/jms
305
Quantification combining isotopically labeled reference peptides
and LC–SRM typically exhibits a linear response over four orders
of magnitude, but factors such as the complexity of samples and
matrix effects can reduce the actual LOD of individual peptides (see
Multiplexing of Analytes Section). Because the concentrations of
the different proteins to be quantified in a given sample are usually
spanning several orders of magnitude, an equimolar mixture of all
the isotopically labeled reference peptides may lead to signal ratios
too high for certain targeted peptides and very low for others.
This would prevent an accurate quantification, and thus dilution
series are required. Clinical guidelines state that internal standards
must be added into the samples to be analyzed at concentrations
close to the one of the endogenous targeted peptides.[61] Assay
validation is necessary over a wide dynamic range to ensure that
the peptide concentrations commonly encountered lie within
the linear dynamic range to allow a single-point calibration. The
standard procedure to determine the linearity and the limits of
detection (LOD) and quantification (LOQ) of an assay consists
in generating dilution curves, for example, by adding various
amounts of the isotopically labeled reference peptides to a
series of aliquots of the sample to be analyzed. Alternatively,
multiple dilutions of the sample and subsequent addition of a
constant amount of the isotopically labeled reference peptides
were suggested,[24,31] which is less desirable due to changes
in the background. Ideally, but limited to certain significant
applications, in which a blank sample matrix devoid of all targeted
endogenous proteins is available, assay linearity and LOD/LOQ
can be determined by adding standard proteins to aliquots
of blank sample matrix and internal standards.[17,27,28] In any
case, linear regression analysis is performed on the observed
peak area ratios (endogenous/internal standard) versus peptide
concentration ratios (endogenous/internal standard) to generate
a calibration curve for each peptide and to determine its response
factor, using graphical or statistical methods. Furthermore, linearity
determines the highest measurable concentration within the
specified conditions. The lower limit of detection (LLOD, often
referred to as LOD) and the LOQ are defined, respectively, as the
concentration level at which the analyte can be reliably detected
in the sample under consideration and as the level at which the
analyte can be detected and measured with sufficient precision.
Several methods are used to determine LOD and LOQ.[62 – 64]
The simplest one consists in calculating LOD and LOQ for a
given analyte as the amount of this analyte providing a signal
corresponding to the mean value of repeated blank sample
measurements +3 and +10 standard deviations, respectively.
Another important characteristic of a quantitative assay is the
recovery of the peptides, which ensures that the experimentally
determined concentrations reflect the actual amounts of the
proteins present in the sample. The recovery is assessing the
trueness of an assay, i.e. the difference between the average
measured value of various samples and the true concentration;
trueness reflects bias, due to systematic error but does not
take into account random experimental errors reflected in the
coefficient of variation (CV). The precision of an assay is evaluated
by repeating or reproducing the experiments, i.e. replicating
multiple measurements under exactly the same or different
experimental conditions, respectively. A recent report by Addona
et al.[28] illustrates the multi-site assessment of the precision and
reproducibility of SRM measurements of proteins in plasma. In this
study, inter-laboratory variability in detecting known amounts of
ten peptides in a complex digest was in the range of 10%. Based
on target peptides generated by digestion of standard proteins
spiked into the tryptic plasma digest, CVs showed value below 15%
for peptides at a concentration near their LOQ. This study stresses
the importance of the sample preparation: performing digestions
independently at each site after spiking intact target proteins into
non-digested plasma samples resulted in significantly higher interlaboratory variability with nearly 20% CVs and a much reduced
peptide recovery.
To overcome some of the issues sometimes associated with
the addition of multiple internal standards of well-defined purity,
artificial concatemers of isotopically labeled reference peptides
(from one or several proteins) have been proposed instead
of individual isotopically labeled reference peptides as internal
standards. In addition to expanding the range of accessible
proteotypic peptides (e.g. hydrophobic peptides) and increasing
the scale of protein quantification, this method, called QconCAT,[65]
decreases the potential bias due to protein digestion as the
standards are added beforehand (Fig. 5). However, it has been
noted that QconCAT constructs are typically digested at higher
rates than native proteins.[66] Finally, spiking a stable isotopelabeled form of the full-length proteins will account for digestion
bias, and that isotopically labeled proteins may thus represent
ideal standards. Such references, called Protein Standard Absolute
Quantification (PSAQ),[67] are introduced at the first step of sample
preparation and thus take into account bias of pre-fractionation
and of the whole sample preparation process (Fig. 5). It should,
however, be noted that, in spite of decreasing potential bias
encountered when using AQUA peptides, high-quality QconCAT
and PSAQ standards, which are well soluble and highly purified,
are often obtained with more difficulties.
This panel of methods is well-suited to generate SRM assays
but it is not sufficient to ensure the reliability of the results.
Additional considerations have to be taken into account during
the SRM method development and data evaluation. Reliable
quantification requires at least two surrogate peptides that
yield consistent results for a given protein. A larger number of
peptides (three or more) spanning, if possible, the whole protein
sequence, will validate the results. Conversely, the observation of
outliers can attest the occurrence of co- and post-translational
events or incomplete digestion. In the same lines, at least
three transitions per peptide have to be monitored to ensure
sufficient selectivity and to detect possible interferences from the
background. When interferences occur in one or more transitions
to a significant extent, another set of transitions should be selected
for quantification to ensure proper LOD/LOQ.
S. Gallien, E. Duriez and B. Domon
Figure 5. Strategies used for absolute quantification. (A) Three types of internal standard can be used. Labeled proteins (PSAQ) are added before
fractionation; labeled concatenates (QconCAT) are added before digestion; labeled peptides (AQUA) are added into peptide digests. (B) Estimation of
losses and recoveries that can be expected with the three types of internal standards. The areas represented with the use of labeled concatenates
(QconCAT) and labeled peptides (AQUA), respectively, filled with horizontal black lines or vertical red lines, indicates the windows within which recoveries
are likely to lie with these types of internal standards.
306
often referred to as ion suppression if the signal is reduced or
ion enhancement if the signal is increased for the same nominal
concentration of analyte. These phenomena can dramatically affect the performance of mass spectrometry in terms of accuracy,
repeatability, linearity of the response (signal vs concentration)
and thus the LOD and LOQ. The chromatographic separation carried out upstream of the mass spectrometer is the first basic step
to limit ion suppression by reducing background interferences in
measurements. However, a single separation dimension is often
not sufficient to handle very complex biological samples.
The problem is particularly dramatic in blood plasma samples
because protein concentrations are spanning over ten orders of
magnitude. While the LOD for samples processed with minimal
fractionation lies in the µg/ml range, LODs in the low ng/ml range
can be reached if a drastic reduction of the sample complexity is
performed.
Several sample preparation methods have been developed to
deal with the complexity and large dynamic range of complex
biological samples, including depletion of the most abundant
proteins, enrichment of a subproteome (e.g. glycoproteins or
phosphoproteins) or enrichment of the proteins of interest.
The removal of the most abundant proteins is a commonly
used strategy to enhance the detection of targeted proteins
in clinical samples. For this purpose, different strategies such
as albumin precipitation,[70] size exclusion fractionation[71,72] or
immunodepletion[73,74] have been developed.
wileyonlinelibrary.com/journal/jms
The use of immunoaffinity depletion leads to an improvement
of the LOD: a LOD of a few hundreds ng/ml can be reached
on plasma proteins.[19,30] On the other hand, immunoaffinity
depletion can be more effective when combined with other fractionation/enrichment methods. For instance, the combination of
immunodepletion with strong cation exchange chromatography
prior to LC–SRM analysis can significantly improve the LOD of
low abundant plasma proteins (low ng/ml range, which constitutes at least a 100-fold increasing compared with a direct plasma
analysis).[30] However, in clinical studies, which require a large
number of samples to be analyzed, the fractionation and sample
preparation steps need to be simplified to maintain throughput.
One approach to reduce the complexity of samples is to isolate a
specific subpopulation of the proteome in order to keep only a few
peptides as surrogates of the protein in the sample. For instance,
Stahl-Zeng et al. applied a new approach to isolate and quantify Nlinked glycopeptides in plasma with a LOQ at low ng/ml.[21] Other
isolation methods target N-terminal peptides[75,76] or peptides
containing rare amino acids, e.g. tryptophan or cysteine residues,
or a post-translational modification such as a phosphorylation or
a glycosylation.[77]
Another approach to detect and quantify low abundant proteins
consists in the specific enrichment of the proteins of interest such
as in the method developed by Anderson et al. that relies on
the use of anti-peptide antibodies to enrich specifically targeted
peptides.[22,78] This method yields a 100- to 1000-fold enrichment
c 2011 John Wiley & Sons, Ltd.
Copyright J. Mass. Spectrom. 2011, 46, 298–312
Selected reaction monitoring applied to proteomics
Sensitivity
6000
Sensitivity
Scale
Selectivity
Scale
Quantification mode
Selectivity
Intensity [counts]
5000
Screening mode
4000
cycle time: 1s
3000
2000
1000
0
30.9
10
31.0
SRM
screening
mode
31.2
31.3
31.4
5000
8
6
Shotgun
proteomics
4
31.1
Retention time [min]
6000
SRM
quantification
mode
Intensity [counts]
-Log (Concentration)
12
4000
cycle time: 2s
3000
2000
1000
2
10
100
Complexity
1000
10000
[# Proteins]
Figure 6. SRM applications. The trade-off between sensitivity, selectivity,
scale and analysis speed depends on the experiment purpose. Quantification mode is dedicated to the precise quantification of a fewer analytes.
It requires high sensitivity and selectivity obtained to the detriment of
the number of peptides analyzed (scale). Screening mode is used for the
detection and the relative quantification of a large number of peptides
which leads to a decrease in sensitivity and selectivity.
of antigen peptides and can be coupled with stable isotope
standards.
0
30.9
31.0
31.1
31.2
31.3
31.4
Retention time [min]
6000
5000
Intensity [counts]
1
4000
cycle time: 5s
3000
2000
1000
Multiplexing of Analytes
0
30.9
Over the past few years, the SRM technology has rapidly developed
to broaden its field of application. While initially limited to the
quantification of a relative small number of peptides in biological
samples, the new scheduling capabilities have enabled large
screens allowing the detection and quantification of large sets of
analytes (several hundreds).
The overall aim of targeted experiments has shifted accordingly.
The current trend attempts to detect very large sets of peptides
(as surrogate probe for specific proteins) and estimate their
abundance; i.e. detection of relative changes between samples.
In this manner, SRM is being used as a directed discovery tool
to screen for putative biomarkers screening and to investigate
pathways or protein networks (Fig. 6).
The critical parameters driving a large-scale SRM experiment are:
the number of peptides to be analyzed, the number of transitions
measured for each peptide and the dwell time of each transition.
All together they define the cycle time, expressed as
cycle time = nb analytes × nb transitions × dwell time
J. Mass. Spectrom. 2011, 46, 298–312
31.1
31.2
31.3
31.4
Retention time [min]
Figure 7. Precision of quantification is directly dependent of cycle time.
With chromatographic peak width of approximately 20 s, a cycle time
around 2 s reflects precisely the elution profile. A faster cycle time (1 s)
does not bring significant improvements, whereas a long cycle time (5 s) is
no sufficient for a precise quantification.
If transition or peptide-specific dwell times are used, in particular
to monitor low abundant ions, the cycle time adjusted for variable
dwell times is expressed as
cycle time =
n
(transitioni × dwell timei )
i=1
i.e. that a summation is performed over all transitions of all
peptides.
Considering the dynamic of an LC–MS experiment, with
chromatographic peak widths typically ranging between 15 and
30 s depending on the chromatographic conditions, the cycle
time should be adjusted in a way that eight to ten data points are
collected across the elution profile (see Fig. 7). In any case, typical
c 2011 John Wiley & Sons, Ltd.
Copyright wileyonlinelibrary.com/journal/jms
307
assuming a constant dwell time for all transitions.
31.0
S. Gallien, E. Duriez and B. Domon
Conventional LC-SRM
0
4
8
12
Time-scheduled LC-SRM
16
min
0
4
8
12
16
min
10 peptides during all
the LC-MS run
Additional peptides
monitored thanks to the
«scheduling»
Time range in which 8 peptides are monitored
in the samecycle times
Figure 8. Time-scheduled LC–SRM. Comparison of conventional and time-scheduled LC–SRM analysis. In a time-scheduled LC–SRM analysis, a time
constraint is added to schedule the expected transitions within a defined time window enclosing the retention time of the peptides. Time-scheduled
LC–SRM significantly increases the total number of peptides that can be measured in a single analysis.
Table 1. Typical parameters used for a time-scheduled SRM
experiment
Parameters
LC-run time (min)
Time window (min)
nb transitions/peptide
Default dwell time (ms)
Preset cycle time (s)
nb peptides analyzed/window
nb peptides analyzed/run
Time-scheduled SRM
60
4
2
20
2
50
750
60
2
2
20
2
50
1500
60
4
3
20
2
33
500
60
2
3
20
2
33
1000
60
2
3
10
3
100
3000
cycle times will range between 1 and 3 s and a minimal dwelltime of 10–15 ms should be set to keep a satisfying sensitivity
(acceptable signal-to-noise ratio). Often, a trade-off between the
number of transitions measured for each analyte and the dwelltime is required if a large set of peptides is analyzed.
Time-scheduled SRM
308
Recent developments in data acquisition techniques have enabled
the analysis of a larger number of peptides by using the LC elution
time as an additional constraint to monitor the transitions of
a specific peptide during the corresponding time window. In
practice, it means that the instrument method is divided into
time segments (typically 2–4 min), during which only subsets of
peptides are targeted. This particular acquisition mode, called timescheduled SRM, increases the number of peptides monitored in one
LC–MS analysis[21] while keeping the same sampling rate (cycle
time) and the same degree of sensitivity without compromising
on dwell time (see Fig. 8 and Table 1). Table 1 shows some typical
parameters used in a time-scheduled LC–SRM experiment. To
fully exploit the potential of the technique, the chromatographic
gradients have also to be set accordingly. In this purpose, LC
separation does not only aim at keeping satisfying signal-to-noise
ratios of SRM measurements by separating interferences from the
compounds of interest, but also at separating, at least partially,
the targeted peptides from each other to allow their successive
analysis. Highly multiplexed LC–SRM experiments are typically
performed with 60 min gradients. The analysis of a smaller number
wileyonlinelibrary.com/journal/jms
of peptides can in principle be performed much faster (two to three
times). However, a minimal separation has to be maintained, even
when only a few peptides are targeted, to reduce background
interferences and to satisfy acceptable signal-to-noise ratio of the
measurements.
Control and reproducibility of LC runs
In large-scale SRM experiments, the control of the conditions
and the reproducibility of the HPLC separations are critical. If
elution times are properly calibrated and can be reproduced
from experiments to experiments, it allows narrowing down the
time window used to monitor the transitions of a specific peptide.
Consequently, the LC–MS run can be divided into a higher number
of time segments, which dramatically increases the number of
peptides analyzed during the entire LC–MS run. For instance,
a two-fold decrease in the retention time window theoretically
allows a two-fold increase in the number of peptides analyzed
during a LC run as illustrated in Table 1. Such experiments require
an accurate control of the chromatography conditions, and the
standardization of the elution time. It is typically performed using
a set of reference peptides covering the entire elution range.
Improved selectivity of SRM measurements
The intelligent selected reaction monitoring (i-SRM) method has
been introduced very recently to furthermore increase the
number of peptides monitored.[51] Combined with time-scheduled
SRM, this method consists in switching between two modes of
operation: one being compound-specific, where only a few primary
transitions are monitored (typically two primary transitions),
and the second being data-dependent, where both primary
and additional secondary transitions (typically six secondary
transitions) are measured. The principle of the i-SRM acquisition
method is illustrated in Fig. 9. Only the two primary transitions
are used for the quantification and are monitored continuously in
the predefined elution time window. The acquisition of secondary
transitions is performed punctually and is triggered by the primary
transitions. The composite tandem mass spectra generated by
acquiring multiple transitions in parallel are used for transition
validation instead of full-scan tandem mass spectra or continuous
acquisition of a high number of SRM transitions for each peptide,
c 2011 John Wiley & Sons, Ltd.
Copyright J. Mass. Spectrom. 2011, 46, 298–312
Selected reaction monitoring applied to proteomics
A
Composite spectrum
Dynamic exclusion
Delay
Duplicate composite
spectrum (optional)
GISNEGQNASI K
14.99
B
15.07
D
Primary events
Primary transitions
725.4 y7
y10
1055.5
15.07
C
14.92
Secondary events
E
Secondary transitions
y3
355.2
++
y10
y5
y6
540.3
528.3
14.8
15.1
668.4
725.4
y8
854.4
y9
1055.5
968.5
15.4
Figure 9. Second generation of SRM method. (A) i-SRM logic. Two primary transitions of a given peptide are monitored continuously and trigger a
data-dependent event if both signals exceed a preset threshold. A pre-trigger delay is applied to ensure that data-dependent acquisition is performed
when the peak is reaching its apex. A dynamic exclusion is also included in the method to trigger the secondary event only once or twice for each peptide.
(B) Primary i-SRM events. (C) Data-dependent (secondary)-SRM events. (D) Ion intensities of a primary i-SRM event. (E) Ion intensities of a secondary i-SRM
event. Reprinted from Ref. [51] with permission.
Figure 10. SRM experiment with increased resolution of precursor ion. Three transitions (yellow, black and blue traces) were monitored for the peptide
LTILEELR. These transitions were measured with a mass selection window of 0.7 Da in the left panel and 0.2 Da in the right panel. The decrease in
background obtained for each transition by narrowing down the mass selection window can even be further improved applying a Boolean operation
‘AND’ on the transitions. The selectivity and the signal-to-noise ratio of the resulting signal were clearly improved as illustrated by the red traces.
which require a significant acquisition time. Thus, most of the time
is preserved to analyze a higher number of peptides.
Outlook
J. Mass. Spectrom. 2011, 46, 298–312
c 2011 John Wiley & Sons, Ltd.
Copyright wileyonlinelibrary.com/journal/jms
309
As discussed in the section on quantification, sample complexity
can dramatically affect the analytical performances, because of ion
suppression effects and interferences affecting the LOD.
The complexity of proteomic samples also impacts the
selectivity of LC–SRM experiments. The frequency of the co-eluting
interferences falling within the mass windows and tolerances of
Q1 and Q3 settings increases with sample complexity leading
to a greater likelihood of false positives. There is an imminent
need for mass spectrometry methods improving the specificity of
SRM analysis. A technique has recently emerged[79,80] and showed
great potential to reduce chemical/endogenous background noise
associated with the sample matrix.[81] This technique, called FAIMS
S. Gallien, E. Duriez and B. Domon
(high-field asymmetric waveform ion mobility spectrometry),
exploits differences in ion mobility of ions at very high electric
fields, to separate them in the millisecond timescale after LC
separation and prior to their introduction into the vacuum region
of a mass spectrometer. Even if it does not prevent from matrix
effects during the ionization process, FAIMS allows separating, at
least partially, ions of interest from co-eluted interferences.[82]
The significant residence time (in the 100 ms range) for the
FAIMS system, i.e. the time required for ions to travel through
the FAIMS device, is a drawback that can limit the number of
targeted peptides in one LC–FAIMS–SRM as illustrated in the
calculation:
nb peptides =
cycle time
[nb transitions × dwell time] + residence time
However, when several peptides share the same optimal value
of compensation voltage, acquisition methods can be designed
in a way allowing adding the residence time only once in the
calculation for this set of peptides, and thus reducing this negative
impact.
In the context of SRM analysis of complex proteomic samples,
the control of the mass selection (Q1) window of the precursor
ion is a critical parameter that allows increasing the selectivity
of SRM transitions. It improves the signal-to-noise ratio and
thus the LOD (sensitivity) by reducing biochemical background
due to co-eluting substances. Figure 10 illustrates the selectivity
improvement of a SRM experiment when selecting precursors by
narrowing the mass selection window from 0.7 to 0.2 Da.
Another method termed multiple reaction monitoring cubed
(MRM3 ) has recently been proposed to improve the selectivity of
SRM experiments.[83] This method relies on the use of a signature of
multiple second-generation product ions produced by two stages
of CID fragmentation to detect and quantify the targeted analytes.
This method was proven to decrease by three- to five-fold the
LOD and quantification of five proteins spiked in a serum sample.
However, this technique is limited to hybrid triple quadrupole
linear ion trap instruments that only have the capability to
trap, fragment and analyze the ion fragments of the primary
product ions. Furthermore, large screen experiments may not be
possible with this technique because of the long duty cycle of the
acquisition (up to 350 ms).
In addition to these hardware developments, selectivity
improvement can also be achieved thanks to computational
approaches. For example, a method developed by Sherman
et al.[84] consists in determining in silico the SRM transitions that are
the most specific for the targeted peptides within the considered
proteome. This method is based on the simulation of all transitions
potentially observable when analyzing the digest of this proteome.
Such computationally assisted designing of SRM experiments
combined with the integration of the knowledge available in
proteomic spectral libraries is likely to have widespread impact in
future SRM-based studies.
Acknowledgements
310
This work was supported by a PEARL grant from the Fonds National
de la Recherche Luxembourg (FNR). We are grateful to Dr. S
Peterman for critical reading of the manuscript. We acknowledged
Dr. M. Heymann amd J. Souady for helpful discussion. E. D. is
supported by the FP7 Program DECanBio.
wileyonlinelibrary.com/journal/jms
References
[1] R. Aebersold, M. Mann. Mass spectrometry-based proteomics.
Nature 2003, 422, 198.
[2] B. Domon, R. Aebersold. Mass spectrometry and protein analysis.
Science 2006, 312, 212.
[3] J. D. Baty, P. R. Robinson. Single and multiple ion recording
techniques for the analysis of diphenylhydantoin and its major
metabolite in plasma. Biomed. Mass Spectrom. 1977, 4, 36.
[4] D. Zakett, R. G. A. Flynn, R. G. Cooks. Chlorine isotope effects in mass
spectrometry by multiple reaction monitoring. J. Phys. Chem. 1978,
82, 2359.
[5] R. A. Yost, C. G. Enke. Triple quadrupole mass spectrometry for direct
mixture analysis and structure elucidation. Anal. Chem. 1979, 51,
1251.
[6] S. H. Hoke, K. L. Morand, K. D. Greis, T. R. Baker, K. L. Harbol,
R. L. M. Dobson. Transformations in pharmaceutical research and
development, driven by innovations in multidimensional mass
spectrometry-based technologies. Int. J. Mass Spectrom. 2001, 212,
135.
[7] R. Kostiainen, T. Kotiaho, T. Kuuranne, S. Auriola. Liquid chromatography/atmospheric pressure ionization-mass spectrometry in drug
metabolism studies. J. Mass Spectrom. 2003, 38, 357.
[8] W. Roschinger, B. Olgemoller, R. Fingerhut, B. Liebl, A. A. Roscher.
Advances in analytical mass spectrometry to improve screening for
inherited metabolic diseases. Eur. J. Pediatr. 2003, 162(Suppl 1), S67.
[9] V. Lange, P. Picotti, B. Domon, R. Aebersold. Selected reaction
monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol.
2008, 4, 222.
[10] S. J. Gaskell, K. Rollins, R. W. Smith, C. E. Parker. Determination of
serum cortisol by thermospray liquid chromatography/mass
spectrometry: comparison with gas chromatography/mass
spectrometry. Biomed. Environ. Mass Spectrom. 1987, 14, 717.
[11] A. Van Langenhove, C. E. Costello, J. E. Biller, K. Biemann, T. R.
Browne. A gas chromatographic/mass spectrometric method
for the simultaneous quantitation of 5,5-diphenylhydantoin
(phenytoin), its para-hydroxylated metabolite and their stable
isotope labelled analogs. Clin. Chim. Acta 1981, 115, 263.
[12] D. M. Desiderio, M. Kai. Preparation of stable isotope-incorporated
peptide internal standards for field desorption mass spectrometry
quantification of peptides in biologic tissue. Biomed. Mass Spectrom.
1983, 10, 471.
[13] J. R. Barr,
V. L. Maggio,
D. G. Patterson
Jr,
G. R. Cooper,
L. O. Henderson,
W. E. Turner,
S. J. Smith,
W. H. Hannon,
L. L. Needham,
E. J. Sampson.
Isotope
dilution –
mass spectrometric quantification of specific proteins: model
application with apolipoprotein A-I. Clin. Chem. 1996, 42, 1676.
[14] D. R. Barnidge, E. A. Dratz, T. Martin, L. E. Bonilla, L. B. Moran,
A. Lindall. Absolute quantification of the G protein-coupled receptor
rhodopsin by LC/MS/MS using proteolysis product peptides and
synthetic peptide standards. Anal. Chem. 2003, 75, 445.
[15] S. A. Gerber, J. Rush, O. Stemman, M. W. Kirschner, S. P. Gygi.
Absolute quantification of proteins and phosphoproteins from
cell lysates by tandem MS. Proc. Natl. Acad. Sci. USA 2003, 100, 6940.
[16] E. Kuhn, J. Wu, J. Karl, H. Liao, W. Zolg, B. Guild. Quantification of
C-reactive protein in the serum of patients with rheumatoid arthritis
using multiple reaction monitoring mass spectrometry and 13 Clabeled peptide standards. Proteomics 2004, 4, 1175.
[17] D. R. Barnidge, M. K. Goodmanson, G. G. Klee, D. C. Muddiman.
Absolute quantification of the model biomarker prostate-specific
antigen in serum by LC–MS/MS using protein cleavage and isotope
dilution mass spectrometry. J. Proteome Res. 2004, 3, 644.
[18] H. Liao, J. Wu, E. Kuhn, W. Chin, B. Chang, M. D. Jones, S. O’Neil,
K. R. Clauser, J. Karl, F. Hasler, R. Roubenoff, W. Zolg, B. C. Guild. Use
of mass spectrometry to identify protein biomarkers of disease
severity in the synovial fluid and serum of patients with rheumatoid
arthritis. Arthritis Rheum. 2004, 50, 3792.
[19] L. Anderson, C. L. Hunter. Quantitative mass spectrometric multiple
reaction monitoring assays for major plasma proteins. Mol. Cell.
Proteomics 2006, 5, 573.
[20] N. Rifai, M. A. Gillette, S. A. Carr. Protein biomarker discovery and
validation: the long and uncertain path to clinical utility. Nat.
Biotechnol. 2006, 24, 971.
[21] J. Stahl-Zeng, V. Lange, R. Ossola, K. Eckhardt, W. Krek, R. Aebersold,
B. Domon. High sensitivity detection of plasma proteins by multiple
c 2011 John Wiley & Sons, Ltd.
Copyright J. Mass. Spectrom. 2011, 46, 298–312
Selected reaction monitoring applied to proteomics
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
J. Mass. Spectrom. 2011, 46, 298–312
[37] V. A. Fusaro, D. R. Mani, J. P. Mesirov, S. A. Carr. Prediction of highresponding peptides for targeted protein assays by mass
spectrometry. Nat. Biotechnol. 2009, 27, 190.
[38] P. Mallick, M. Schirle, S. S. Chen, M. R. Flory, H. Lee, D. Martin,
J. Ranish, B. Raught, R. Schmitt, T. Werner, B. Kuster, R. Aebersold.
Computational prediction of proteotypic peptides for quantitative
proteomics. Nat. Biotechnol. 2007, 25, 125.
[39] W. S. Sanders, S. M. Bridges, F. M. McCarthy, B. Nanduri, S. C. Burgess.
Prediction of peptides observable by mass spectrometry applied
at the experimental set level. BMC Bioinformatics 2007, 8(Suppl 7),
S23.
[40] B. J. Webb-Robertson, W. R. Cannon, C. S. Oehmen, A. R. Shah,
V. Gurumoorthi, M. S. Lipton, K. M. Waters. A support vector
machine model for the prediction of proteotypic peptides for
accurate mass and time proteomics. Bioinformatics 2008, 24, 1503.
[41] O. V. Krokhin, R. Craig, V. Spicer, W. Ens, K. G. Standing, R. C. Beavis,
J. A. Wilkins. An improved model for prediction of retention times
of tryptic peptides in ion pair reversed-phase HPLC: its application
to protein peptide mapping by off-line HPLC-MALDI MS. Mol. Cell.
Proteomics 2004, 3, 908.
[42] H. Tang, R. J. Arnold, P. Alves, Z. Xun, D. E. Clemmer, M. V. Novotny,
J. P. Reilly, P. Radivojac. A computational approach toward labelfree protein quantification using predicted peptide detectability.
Bioinformatics 2006, 22, e481.
[43] M. W. Duncan, A. L. Yergey, S. D. Patterson. Quantifying proteins by
mass spectrometry: the selectivity of SRM is only part of the problem.
Proteomics 2009, 9, 1124.
[44] P. Picotti, H. Lam, D. Campbell, E. W. Deutsch, H. Mirzaei, J. Ranish,
B. Domon, R. Aebersold. A database of mass spectrometric assays
for the yeast proteome. Nat. Methods 2008, 5, 913.
[45] K. W. Lau, S. R. Hart, J. A. Lynch, S. C. Wong, S. J. Hubbard, S. J.
Gaskell. Observations on the detection of b- and y-type ions in
the collisionally activated decomposition spectra of protonated
peptides. Rapid Commun. Mass Spectrom. 2009, 23, 1508.
[46] J. A. Cham Mead, L. Bianco, C. Bessant. Free computational
resources for designing selected reaction monitoring transitions.
Proteomics 2010, 10, 1106.
[47] G. C. Thorne, S. J. Gaskell, P. A. Payne. Approaches to the
improvement of quantitative precision in selected ion monitoring:
high resolution applications. Biol. Mass Spectrom. 1984, 11, 415.
[48] M. J. MacCoss, C. C. Wu, H. Liu, R. Sadygov, J. R. Yates III. A
correlation algorithm for the automated quantitative analysis of
shotgun proteomics data. Anal. Chem. 2003, 75, 6912.
[49] R. D. Unwin, J. R. Griffiths, M. K. Leverentz, A. Grallert, I. M. Hagan,
A. D. Whetton. Multiple reaction monitoring to identify sites of
protein phosphorylation with high sensitivity. Mol. Cell. Proteomics
2005, 4, 1134.
[50] J. W. Hager, J. C. Yves Le Blanc. Product ion scanning using a Q-q-Q
linear ion trap (Q TRAP) mass spectrometer. Rapid Commun. Mass
Spectrom. 2003, 17, 1056.
[51] R. Kiyonami, A. Schoen, A. Prakash, S. Peterman, V. Zabrouskov,
P. Picotti, R. Aebersold, A. Huhmer, B. Domon. Increased selectivity,
analytical precision, and throughput in targeted proteomics. Mol.
Cell. Proteomics 2010, 10, M110 002931.
[52] F. W. McLafferty. Tandem Mass Spectrometry. John Wiley: New York,
1983.
[53] P. M. Mayer, C. Poon. The mechanisms of collisional activation of
ions in mass spectrometry. Mass Spectrom. Rev. 2009, 28, 608.
[54] M. J. MacCoss, M. J. Toth, D. E. Matthews. Evaluation and
optimization of ion-current ratio measurements by selected-ionmonitoring mass spectrometry. Anal. Chem. 2001, 73, 2976.
[55] S. Choi, J. Kim, K. Yea, P. G. Suh, S. H. Ryu. Targeted label-free
quantitative analysis of secretory proteins from adipocytes in
response to oxidative stress. Anal. Biochem. 2010, 401, 196.
[56] S. E. Ong, B. Blagoev, I. Kratchmarova, D. B. Kristensen, H. Steen,
A. Pandey, M. Mann. Stable isotope labeling by amino acids in
cell culture, SILAC, as a simple and accurate approach to expression
proteomics. Mol. Cell. Proteomics 2002, 1, 376.
[57] S. P. Gygi, B. Rist, S. A. Gerber, F. Turecek, M. H. Gelb, R. Aebersold.
Quantitative analysis of complex protein mixtures using isotopecoded affinity tags. Nat. Biotechnol. 1999, 17, 994.
[58] O. A. Mirgorodskaya, Y. P. Kozmin, M. I. Titov, R. Korner, C. P. Sonksen,
P. Roepstorff. Quantitation of peptides and proteins by matrixassisted laser desorption/ionization mass spectrometry using (18)Olabeled internal standards. Rapid Commun. Mass Spectrom. 2000,
14, 1226.
c 2011 John Wiley & Sons, Ltd.
Copyright wileyonlinelibrary.com/journal/jms
311
reaction monitoring of N-glycosites. Mol. Cell. Proteomics 2007, 6,
1809.
N. L. Anderson, N. G. Anderson, L. R. Haines, D. B. Hardie, R. W.
Olafson, T. W. Pearson. Mass spectrometric quantitation of peptides
and proteins using stable isotope standards and capture by antipeptide antibodies (SISCAPA). J. Proteome Res. 2004, 3, 235.
P. Picotti, B. Bodenmiller, L. N. Mueller, B. Domon, R. Aebersold. Full
dynamic range proteome analysis of S. cerevisiae by targeted
proteomics. Cell 2009, 138, 795.
M. A. Kuzyk, D. Smith, J. Yang, T. J. Cross, A. M. Jackson, D. B. Hardie,
N. L. Anderson, C. H. Borchers. Multiple reaction monitoring-based,
multiplexed, absolute quantitation of 45 proteins in human plasma.
Mol. Cell. Proteomics 2009, 8, 1860.
E. Kuhn, T. Addona, H. Keshishian, M. Burgess, D. R. Mani, R. T. Lee,
M. S. Sabatine, R. E. Gerszten, S. A. Carr. Developing multiplexed
assays for troponin I and interleukin-33 in plasma by peptide
immunoaffinity enrichment and targeted mass spectrometry. Clin.
Chem. 2009, 55, 1108.
H. Keshishian, T. Addona, M. Burgess, D. R. Mani, X. Shi, E. Kuhn,
M. S. Sabatine, R. E. Gerszten, S. A. Carr. Quantification of
cardiovascular biomarkers in patient plasma by targeted mass
spectrometry and stable isotope dilution. Mol. Cell. Proteomics
2009, 8, 2339.
T. Fortin, A. Salvador, J. P. Charrier, C. Lenz, X. Lacoux, A. Morla,
G. Choquet-Kastylevsky, J. Lemoine. Clinical quantitation of
prostate-specific antigen biomarker in the low nanogram/milliliter
range by conventional bore liquid chromatography–tandem
mass spectrometry (multiple reaction monitoring) coupling and
correlation with ELISA tests. Mol. Cell. Proteomics 2009, 8, 1006.
T. A. Addona, S. E. Abbatiello, B. Schilling, S. J. Skates, D. R. Mani,
D. M. Bunk,
C. H. Spiegelman,
L. J. Zimmerman,
A. J. Ham,
H. Keshishian, S. C. Hall, S. Allen, R. K. Blackman, C. H. Borchers,
C. Buck, H. L. Cardasis, M. P. Cusack, N. G. Dodder, B. W. Gibson,
J. M. Held, T. Hiltke, A. Jackson, E. B. Johansen, C. R. Kinsinger, J. Li,
M. Mesri, T. A. Neubert, R. K. Niles, T. C. Pulsipher, D. Ransohoff,
H. Rodriguez, P. A. Rudnick, D. Smith, D. L. Tabb, T. J. Tegeler,
A. M. Variyath, L. J. Vega-Montoto, A. Wahlander, S. Waldemarson,
M. Wang, J. R. Whiteaker, L. Zhao, N. L. Anderson, S.J. Fisher,
D. C. Liebler, A. G. Paulovich, F. E. Regnier, P. Tempst, S. A. Carr.
Multi-site assessment of the precision and reproducibility of
multiple reaction monitoring-based measurements of proteins in
plasma. Nat. Biotechnol. 2009, 27, 633.
V. Lange, J. A. Malmstrom, J. Didion, N. L. King, B. P. Johansson,
J. Schafer, J. Rameseder, C. H. Wong, E. W. Deutsch, M. Y. Brusniak,
P. Buhlmann, L. Bjorck, B. Domon, R. Aebersold. Targeted
quantitative analysis of Streptococcus pyogenes virulence factors
by multiple reaction monitoring. Mol. Cell. Proteomics 2008, 7, 1489.
H. Keshishian, T. Addona, M. Burgess, E. Kuhn, S. A. Carr. Quantitative,
multiplexed assays for low abundance proteins in plasma by
targeted mass spectrometry and stable isotope dilution. Mol. Cell.
Proteomics 2007, 6, 2212.
D. J. Janecki, K. G. Bemis, T. J. Tegeler, P. C. Sanghani, L. Zhai,
T. D. Hurley, W. F. Bosron, M. Wang. A multiple reaction monitoring
method for absolute quantification of the human liver alcohol
dehydrogenase ADH1C1 isoenzyme. Anal. Biochem. 2007, 369, 18.
I. van den Broek, R. W. Sparidans, J. H. Schellens, J. H. Beijnen.
Validation of a quantitative assay for human neutrophil peptide-1,
-2, and -3 in human plasma and serum by liquid chromatography
coupled to tandem mass spectrometry. J. Chromatogr. B Analyt.
Technol. Biomed. Life Sci. 2010, 878, 1085.
I. van den Broek, R. W. Sparidans, J. H. Schellens, J. H. Beijnen.
Quantitative assay for six potential breast cancer biomarker
peptides in human serum by liquid chromatography coupled
to tandem mass spectrometry. J. Chromatogr. B Analyt. Technol.
Biomed. Life Sci. 2010, 878, 590.
E. W. Deutsch, H. Lam, R. Aebersold. PeptideAtlas: a resource for
target selection for emerging targeted proteomics workflows. EMBO
Rep. 2008, 9, 429.
R. Craig, J. P. Cortens, R. C. Beavis. Open source system for analyzing,
validating, and storing protein identification data. J. Proteome Res.
2004, 3, 1234.
P. Jones, R. G. Cote, S. Y. Cho, S. Klie, L. Martens, A. F. Quinn,
D. Thorneycroft, H. Hermjakob. PRIDE: new developments and new
datasets. Nucleic Acids Res. 2008, 36, D878.
S. Gallien, E. Duriez and B. Domon
[59] L. V. DeSouza, A. M. Taylor, W. Li, M. S. Minkoff, A. D. Romaschin,
T. J. Colgan, K. W. Siu. Multiple reaction monitoring of mTRAQlabeled peptides enables absolute quantification of endogenous
levels of a potential cancer marker in cancerous and normal
endometrial tissues. J. Proteome Res. 2008, 7, 3525.
[60] R. E. Jenkins, N. R. Kitteringham, C. L. Hunter, S. Webb, T. J. Hunt,
R. Elsby, R. B. Watson, D. Williams, S. R. Pennington, B. K. Park.
Relative and absolute quantitative expression profiling of
cytochromes P450 using isotope-coded affinity tags. Proteomics
2006, 6, 1934.
[61] D. H. Chace, J. R. Barr, M. W. Duncan, D. Matern, M. R. Morris,
D. E. Palmer-Toy, A. L. Rockwood, G. Siuzdak, A. Urbani, A. L. Yergey,
Y. M. Chan. Mass Spectrometry in the Clinical Laboratory. General
Principles and Guidance; Approved Guideline (C50-A). Clinical and
Laboratory Standards Institute: Wayne, PA, USA, 2006.
[62] D. A. Armbruster, M. D. Tillman, L. M. Hubbs. Limit of detection
(LQD)/limit of quantitation (LOQ): comparison of the empirical
and the statistical methods exemplified with GC–MS assays of
abused drugs. Clin. Chem. 1994, 40, 1233.
[63] J. Vial, K. Le Mapihan, A. Jardy. What is the best means of estimating
the detection and quantification limits of a chromatographic
method? Chromatographia 2003, 57, S303.
[64] M. E. Zorn, R. D. Gibbons, W. C. Sonzogni. Weighted least-squares
approach to calculating limits of detection and quantification by
modeling variability as a function of concentration. Anal. Chem.
1997, 69, 3069.
[65] J. M. Pratt, D. M. Simpson, M. K. Doherty, J. Rivers, S. J. Gaskell, R. J.
Beynon. Multiplexed absolute quantification for proteomics using
concatenated signature peptides encoded by QconCAT genes. Nat.
Protoc. 2006, 1, 1029.
[66] K. Kito, K. Ota, T. Fujita, T. Ito. A synthetic protein approach
toward accurate mass spectrometric quantification of component
stoichiometry of multiprotein complexes. J. Proteome Res. 2007, 6,
792.
[67] V. Brun, A. Dupuis, A. Adrait, M. Marcellin, D. Thomas, M. Court,
F. Vandenesch, J. Garin. Isotope-labeled protein standards: toward
absolute quantitative proteomics. Mol. Cell. Proteomics 2007, 6,
2139.
[68] O. A. Ismaiel, M. S. Halquist, M. Y. Elmamly, A. Shalaby, H. Thomas
Karnes. Monitoring phospholipids for assessment of ion
enhancement and ion suppression in ESI and APCI LC/MS/MS for
chlorpheniramine in human plasma and the importance of multiple
source matrix effect evaluations. J. Chromatogr. B Analyt. Technol.
Biomed. Life Sci. 2008, 875, 333.
[69] D. L. Buhrman, P. I. Price, P. J. Rudewicz. Quantitation of SR 27417 in
human plasma using electrospray liquid chromatography–tandem
mass spectrometry: a study of ion suppression. J. Am. Soc. Mass
Spectrom. 1996, 7, 1099.
[70] Y. Y. Chen, S. Y. Lin, Y. Y. Yeh, H. H. Hsiao, C. Y. Wu, S. T. Chen,
A. H. Wang. A modified protein precipitation procedure for efficient
removal of albumin from serum. Electrophoresis 2005, 26, 2117.
[71] R. S. Tirumalai, K. C. Chan, D. A. Prieto, H. J. Issaq, T. P. Conrads,
T. D. Veenstra. Characterization of the low molecular weight human
serum proteome. Mol. Cell. Proteomics 2003, 2, 1096.
[72] R. Terracciano, M. Gaspari, F. Testa, L. Pasqua, P. Tagliaferri,
M. M. Cheng, A. J. Nijdam, E. F. Petricoin, L. A. Liotta, G. Cuda,
[73]
[74]
[75]
[76]
[77]
[78]
[79]
[80]
[81]
[82]
[83]
[84]
[85]
M. Ferrari, S. Venuta. Selective binding and enrichment for lowmolecular weight biomarker molecules in human plasma after
exposure to nanoporous silica particles. Proteomics 2006, 6, 3243.
T. Liu, W. J. Qian, H. M. Mottaz, M. A. Gritsenko, A. D. Norbeck,
R. J. Moore, S. O. Purvine, D. G. Camp 2nd, R. D. Smith. Evaluation
of multiprotein immunoaffinity subtraction for plasma proteomics
and candidate biomarker discovery using mass spectrometry. Mol.
Cell. Proteomics 2006, 5, 2167.
J. R. Whiteaker, H. Zhang, J. K. Eng, Fang, B. D. Piening, L. C. Feng,
T. D. Lorentzen,
R. M. Schoenherr,
J. F. Keane,
T. Holzman,
M. Fitzgibbon, C. Lin, K. Cooke, T. Liu, D. G. Camp 2nd, L. Anderson,
J. Watts, R. D. Smith, M. W. McIntosh, A. G. Paulovich. Head-to-head
comparison of serum fractionation techniques. J. Proteome Res.
2007, 6, 828.
S. Gallien, E. Perrodou, C. Carapito, C. Deshayes, J. M. Reyrat,
A. Van Dorsselaer, O. Poch, C. Schaeffer, O. Lecompte. Orthoproteogenomics: multiple proteomes investigation through
orthology and a new MS-based protocol. Genome Res. 2009, 19,
128.
A. Staes, P. Van Damme, K. Helsens, H. Demol, J. Vandekerckhove,
K. Gevaert. Improved recovery of proteome-informative,
protein N-terminal peptides by combined fractional diagonal
chromatography (COFRADIC). Proteomics 2008, 8, 1362.
H. Zhang, W. Yan, R. Aebersold. Chemical probes and tandem mass
spectrometry: a strategy for the quantitative analysis of proteomes
and subproteomes. Curr. Opin. Chem. Biol. 2004, 8, 66.
J. R. Whiteaker, L. Zhao, L. Anderson, A. G. Paulovich. An automated
and multiplexed method for high throughput peptide
immunoaffinity enrichment and multiple reaction monitoring mass
spectrometry-based quantification of protein biomarkers. Mol. Cell.
Proteomics 2010, 9, 184.
R. Guevremont, R. W. Purves. Atmospheric pressure ion focusing
in a high-field asymmetric waveform ion mobility spectrometer.
Review of Scientific Instruments 1999, 70, 1370–1383.
R. W. Purves, R. Guevremont, S. Day, C. W. Pipich, M. S. Matyjaszczyk.
Mass spectrometric characterization of a high-field asymmetric
waveform ion mobility spectrometer. Rev. Sci. Instrum. 1998, 69,
4094.
R. W. Purves, R. Guevremont. Electrospray ionization highfield asymmetric waveform ion mobility spectrometry-mass
spectrometry. Anal. Chem. 1999, 71, 2346.
T. Klaassen, S. Szwandt, J. T. Kapron, A. Roemer. Validated
quantitation method for a peptide in rat serum using liquid
chromatography/high-field asymmetric waveform ion mobility
spectrometry. Rapid Commun. Mass Spectrom. 2009, 23, 2301.
T. Fortin, A. Salvador, J. P. Charrier, C. Lenz, F. Bettsworth, X. Lacoux,
G. Choquet-Kastylevsky, J. Lemoine. Multiple reaction monitoring
cubed for protein quantification at the low nanogram/milliliter level
in nondepleted human serum. Anal. Chem. 2009, 81, 9343.
J. Sherman, M. J. McKay, K. Ashman, M. P. Molloy. Unique ion
signature mass spectrometry, a deterministic method to assign
peptide identity. Mol. Cell. Proteomics 2009, 8, 2051.
K. K. Murray, R. K. Boyd, M. N. Eberlin, G. J. Langley, L. Li, Y. Naito.
Standard definitions of terms relating to mass spectrometry. IUPAC
Recommendations, Public Draft 2006.
312
wileyonlinelibrary.com/journal/jms
c 2011 John Wiley & Sons, Ltd.
Copyright J. Mass. Spectrom. 2011, 46, 298–312