This document describes a new method called multiple products monitoring (MpM) for quantifying target peptides using liquid chromatography-mass spectrometry (LC-MS). MpM monitors multiple product ions from the MS2 spectra of target peptides, rather than selecting a single product ion as in selected/multiple reaction monitoring. It uses a scoring system based on the intensities of matching product ions between the target and reference MS2 spectra. The authors demonstrate that MpM improves sensitivity and selectivity for peptide quantification compared to conventional approaches.

1. Multiple Products Monitoring as a Robust Approach for Peptide
Quantification
Je-Hyun Baek, Hokeun Kim, Byunghee Shin, and Myeong-Hee Yu*
Functional Proteomics Center, Korea Institute of Science and Technology, Hawolgok-dong, Seongbuk-gu,
Seoul 136-791, Korea
Received October 11, 2008
Quantification of target peptides and proteins is crucial for biomarker discovery. Approaches such as
selected reaction monitoring (SRM) and multiple reaction monitoring (MRM) rely on liquid chroma-
tography and mass spectrometric analysis of defined peptide product ions. These methods are not
very widespread because the determination of quantifiable product ion using either SRM or MRM is a
very time-consuming process. We developed a novel approach for quantifying target peptides without
such an arduous process of ion selection. This method is based on monitoring multiple product ions
(multiple products monitoring: MpM) from full-range MS2 spectra of a target precursor. The MpM
method uses a scoring system that considers both the absolute intensities of product ions and the
similarities between the query MS2 spectrum and the reference MS2 spectrum of the target peptide.
Compared with conventional approaches, MpM greatly improves sensitivity and selectivity of peptide
quantification using an ion-trap mass spectrometer.
Keywords: peptide quantification • product ion monitoring • multiple products monitoring, target
verification
Introduction
Verification of biomarker candidates requires the accurate
quantification of target proteins in human body fluids, such
as saliva, urine, and serum. Antibodies against these newly
discovered candidates are frequently unavailable, and substi-
tutes for antibody-based detection assays (i.e., Western blotting
or ELISA) have been sought in clinical proteomics. Liquid
chromatography-mass spectrometry (LC-MS)-based proteom-
ics provides new opportunities for high-throughput analyses
in biological and clinical research.1
Most current advancements
in protein identification and quantification have adopted
MS intensity-based quantification with isotope labeling, such
as SILAC (stable isotope labeling by amino acids in cell culture)2
and ICAT (isotope-coded affinity tags),3
or label-free quanti-
fication.4
Concerns about MS intensity-based quantification
include the following: (i) specificity issues arise when a MS2
spectrum for the target MS peak pair is lacking; (ii) MS scans
with wide ranges tend to lose sensitivity; and (iii) reproducibility
in label-free quantification is difficult to maintain between LC-
MS runs.5
Selected reaction monitoring (SRM), multiple reac-
tion monitoring (MRM), and isobaric tags for relative and
absolute quantification (iTRAQ) often overcome such limits
using MS2 intensity-based quantification through the continu-
ous monitoring of a selected precursor and its product ion.6-9
These methods have been successfully applied to a variety of
biological applications, including elucidation of cellular signal-
ing networks,10
analysis of virulence factors,11
and detection
of potential biomarkers in human plasma.12
Nevertheless, the
practical use of SRM and MRM in proteomics is less widespread
than expected, since SRM and MRM require trial and error in
selecting product ions of target peptides.8
Candidate peptides
for target proteins and their representative MRM transitions
must be selected first, and then the uniqueness (selectivity) and
sensitivity of the selected MRM transitions must be validated
by quantification analysis. Moreover, additional MS2 data are
required for target confirmation because the SRM and MRM
data do not contain sequence information.
Several MS2-based quantification approaches have been
developed. With the use of data-independent acquisition in
shotgun proteomic analysis, Yates’ group performed automated
quantitative analysis of complex peptide mixtures from tandem
mass spectra using Census and RelEX software.13,14
The chro-
matograms reconstituted from their MS2 scans showed good
signal-to-noise ratios and were more accurate in quantitative
analysis than MS-based quantification. Additionally, Arnott et
al. and Vathany et al. showed that quantification using tandem
mass spectra enabled the selective detection of target pro-
teins.15,16
However, these methods are not sufficiently robust
in target peptide verification with respect to selectivity and
sensitivity. In the current study, we introduce a new, robust
approach for peptide quantification, termed multiple products
monitoring (MpM), which is based on monitoring the majority
of product ions obtained in the MS2 scan. MpM enhances the
quantitative analysis of target peptides by avoiding the arduous
step of selecting transitions to be monitored. In addition, MpM
improves the sensitivity and selectivity of peptide quantification.
* Corresponding author: Dr. Myeong-Hee Yu, Functional Proteomics
Center, Korea Institute of Science and Technology, 39-1 Hawolgok-dong,
Seongbuk-gu, Seoul 136-791, Korea. E-mail: mhyu@kist.re.kr. Fax: +82-2-
958-6919.
10.1021/pr800853k CCC: $40.75  2009 American Chemical Society Journal of Proteome Research 2009, 8, 3625–3632 3625
Published on Web 05/26/2009

2. Experimental Procedure
Sample Preparation. The 48-protein mixture (Sigma S5697,
proteomic dynamic range standard set), yeast lysate, and
human plasma proteins were denatured in labeling buffer (6
M urea, 0.05% SDS, 5 mM EDTA, and 50 mM ammonium
bicarbonate, pH 8.0) for 30 min, reduced with 3 mM Tris (2-
carboxyethyl) phosphine hydrochloride for 30 min, and alky-
lated with 5 mM iodoacetamide for 30 min while shaking at
50 °C. The protein concentration was determined using the
Bradford assay. Protein samples were digested with trypsin
(Promega, Madison, WI) at a protein-to-trypsin ratio of 50:1
(w/w). SDS and other reagents were removed from the digested
protein sample using a mixed strong cation exchange cartridge
(OASIS, Waters). Peptides were eluted by adding 5% ammonia
in methanol, dried in a speed-vac, and dissolved in 0.4% acetic
acid prior to analysis. The 48-protein mixture and isotopically
heavy cytochrome c demo peptides (Cat. No. 300100) were
donated by Sigma and Thermo Electron, respectively. The
heavy peptide (MW ) 1174.62 Da) is 7 Da heavier than the
light peptide, which has the sequence TGPNLHGLFGR (un-
derlined leucine residue is composed of seven 13
C atoms). The
standard peptides were serially diluted in the 0.01-200 fmol
range.
mTRAQ Labeling of Enolase Peptides. Lyophilized enolase
(100 µg) was dissolved in 20 µL of 0.5 M TEAB buffer (pH 8.5).
The protein sample was digested with 2 µg of trypsin at 37 °C
overnight and then divided into two tubes (each 50 µg) and
dried in a speed-vac. Light and heavy mTRAQ reagents were
dissolved in 50 µL of isopropanol, transferred into each tube,
and mixed. The samples were adjusted to a pH greater than
8.0 with 0.5 M TEAB buffer (pH 8.5) and incubated for 1 h at
room temparature. After labeling, excess reagents were re-
moved by a mixed strong cation exchange cartridge (OASIS,
Waters). Peptides were eluted by the addition of 5% ammonia
in 45% H2O and 50% acetonitrile. The light and heavy mTRAQ-
labeled enolase peptides were mixed in 4:1, 2:1, 1:1, and 0.5:1
ratios and then spiked into a digested plasma sample. The
samples were dried in a speed-vac and dissolved in 0.4% acetic
acid prior to analysis.
Liquid Chromatography and Mass Spectrometric Analysis.
The standard peptides (0.01-200 fmol), a tryptic digest of the
48-protein mixture (1/50 amount), yeast lysate (0.75 µg), a
tryptic digest of plasma (2 µg), and mTRAQ-labeled light
(25-200 fmol) and heavy (50 fmol) enolase peptides in a tryptic
digest of plasma (2 µg) were subjected to LC-MS/MS analysis
for MpM peptide quantification. Each peptide sample was
loaded onto a C18 (Magic C18aq, Michrom BioResources,
Auburn, CA)-packed trap column and separated using a
capillary C18 column (20 cm × 75 µm) coupled with a
nanospray tip. Peptides were eluted using a 30-min linear
gradient of 5-35% solution B in a 60-min run (Solution A, 0.1%
formic acid in H2O; Solution B, 0.1% formic acid in 100%
acetonitrile). Elution was performed at a flow rate of 300 nL/
min using either the Eksigent MDLC or Agilent 1100 Nanopump
system. Peptides were identified using the LTQ XL linear ion
trap mass spectrometer (Thermo Finnigan, San Jose, CA). To
identify peptides, we used an MS survey scan with values
ranging between 300 and 2000 m/z with one microscan being
followed by a data-dependent scan with a dynamic exclusion
for the MS2 scans (isolation width, 3 m/z; normalized collision
energy, 28-35%; exclusion duration, 5 min). To quantify the
peptides using the SRM and MpM methods, we used an MS
survey scan with values ranging between 300 and 2000 m/z.
This scan was followed by 2-3 consecutive, full-range MS2
scans and 2-3 SRM MS2 scans (one or three microscans) with
several fixed precursor m/z values (target peptide isolation
width, 3 m/z; normalized collision energy, 35%; modified
automatic gain control for MS2, 1.0 × 105
). For absolute
quantification using the MpM method, a known amount of
isotopically labeled peptide (heavy peptide) was spiked into a
plasma sample that was already digested, and MpM peak areas
for heavy and light peptides were calculated. To quantify
mTRAQ-labeled peptides using the MpM method, we used a
narrower isolation width of the target precursor (1 m/z),
considering the mass difference between the light and heavy
pair. All MS and MS2 scans for the MpM and SRM methods
were obtained using an LTQ XL linear ion trap in centroid
mode. To increase the number of targets for quantification,
we divided the LC-MS/MS run into several segments (each
segment had a 5-min interval and included a maximum of six
precursor m/z values for the target peptides in the duty cycle)
or used the turboscan mode for one LC-MS/MS run.
Data Searching and Search Parameters. The data files for
the tandem mass spectra were generated with the extract-msn
program (v.3) using Bioworks software (v3.2) and a minimum
ion-count threshold of 15 and a minimum intensity of 100. The
SEQUEST searches (TurboSequest v.27, rev 12) were individu-
ally performed without enzymatic restriction against protein
sequence databases, including 180 contaminants: 261 878
entries in the Uniprot sequence database containing 48 pro-
teins, 6939 entries in yeast, and 57 564 entries in IPI.hu-
man.v3.14. Mass tolerance for precursor ions with an average
mass type was set to 3.0 amu, and that for product ions with
a monoisotopic mass type was set to 1 amu. Search criteria
included a variable modification of 16 Da for methionine
oxidation. For mTRAQ-labeled samples, search criteria included
variable modifications of 16 Da for methionine oxidation and
140 Da (light) or 144 Da (heavy) for each mTRAQ adduct. Trans
Proteomic Pipeline (v.3.5) software from the Institute for
Systems Biology (Seattle, WA), which includes the peptide
probability score (P) programs, PeptideProphet and Protein-
Prophet,17
was used to validate peptide/protein identification
(P g 0.9). Protein validation has a 86.6% sensitivity and a 0.7%
error at P g 0.9.
Results
Overall Scheme of Targeted LC-MS/MS for MpM. We
performed a series of “discovery” runs on several protein
samples, including yeast enolase, the 48-protein mixture, and
human plasma, to generate a library of high-confidence MS2
spectra using an LTQ ion-trap mass instrument and searched
the spectra using the SEQUEST algorithm. We then performed
a targeted LC-MS/MS analysis on the LTQ ion-trap mass
instrument in a data-independent manner by specifying a set
of precursor m/z values for target peptides. In this analysis,
we deliberately chose to acquire MS2 data on only the m/z
values that would ultimately produce CID (collision induced
dissociation) spectra on our target peptides. The searched
spectral data in discovery runs for this inclusion list was used
as the matching source (master spectra) for our subsequent
analysis using the MpM algorithm as described below. The
MpM program requires two types of experimental data (Figure
1). One type is the master MS2 spectra of the target peptide
and its peptide sequences (dta, out, and xls formats), which
have already been acquired and searched during the discovery
research articles Baek et al.
3626 Journal of Proteome Research • Vol. 8, No. 7, 2009

3. step. The other type is the targeted LC-MS/MS spectra for the
target precursors (mzXML format). The combined results of the
product ion intensity in the targeted MS2 spectra and spectral
comparisons between the master and targeted MS2 spectra
generate MpM scores. The MpM chromatogram that is recon-
stituted from the MpM scores was used for target peptide
quantification. MpM is a quantitative method that uses the
product ion intensity as the quantitative metric rather than
precursor ion intensity (as is done in both SRM and MRM),
but uses multiple product ions instead of a single product ion.
MpM Score Algorithm. The MpM score is the product of
the matched, fragment ion-intensity sum and a scale that
represents the similarity of the query spectrum to the master
spectrum. User-friendly executable software for both MpM
scoring and quantification was implemented in Java language
(Supporting Information, Figure S1).
a. Selecting Product Ions from the Master MS2
Spectrum. During the prescoring step (Figure 2), a mass table
was generated for the product ions which were previously
identified as a-, b-, and y-ions in the master MS2 spectrum.
This table includes a list of m/z values, their intensities, and
their ranks (“Top N”) in terms of intensities of the product ions.
“Top 1” is the most intense ion of all identified product ions
in the master spectrum. The number of top-ranked ions is
determined as (n × 2 - 1), where n is the number of peptide
backbone cleavage sites. The number corresponding to the b1
ion is not included in Top N because, theoretically, it is not
observed after dissociation. Charge states are not considered
because the number of the observed product ions of charges
greater than +2 was not significant. The intensities of the top-
ranked ions in the master spectrum were normalized with
respect to the intensity of “Top 1”.
b. Matching Step. Targeted MS2 spectra within the defined
precursor mass window (default ) (0.1 m/z) were isolated
from the targeted LC-MS/MS run. Full-range targeted MS2
spectra were compared with the master MS2 spectrum one by
one (Figure 2, Matching step). In each comparison, the ions
(m/z) of the master mass table were matched with their
counterparts in the targeted MS2 spectrum. Note that while
the “Top 1” in the master spectrum is generally the base peak,
the “Top 1” in a target MS2 spectrum is not necessarily the
base peak of the target spectrum. After matching, the target
peak intensities of the matched ions are normalized with
respect to the “Top 1” in the target MS2 spectrum. To avoid
useless matching with nonspecific spectra, a target spectrum
is excluded if the intensity of the “Top 1” peak in the target
spectrum is less than 1% of the intensity of the base peak in
the target MS2 spectrum (the default mass window for seeking
the “Top 1” is (0.6 m/z).
c. Scoring Step (Intensity-Based Scoring). Scoring of each
targeted MS2 spectrum is performed using a score function that
considers both the absolute intensity and number of matched
product ions in the target MS2 spectrum (Figure 2). The
equation,
where Ic,t is the absolute intensity of a cross-matched product
ion (c) in a target MS2 spectrum t, ωr is a weight function that
relates the similarities of the normalized intensities of the
matched ions, and ωnmi is a second weight function that relates
the number of matched ions between the query spectrum and
the master spectrum. All weights for MpM scoring range
between 0 and 1. The first weight is defined as ωr ) 1 - |ip,m -
ic,t|, where ip,m is the relative intensity of a product ion (p) in
the master mass table and ic,t is the relative intensity of the
cross-matched ion (c) in the targeted MS2 spectrum. The
second weight function (ωnmi) considers the number of matched
ions between the master and target MS2 spectra and assigns
any unmatched target spectra an extremely low weight factor,
reducing the MpM score substantially. The second weight
function is defined as a sigmoidal function, ωnmi ) 1/[1 +
exp{-(xnmi - x0)/R}], where xnmi (0-1) is the fraction of matched
ions divided by theoretically maximum number of ions (de-
scribed above as 2n - 1; n is the number of peptide backbone-
cleavage sites), x0 is a constant related to the center point of
the sigmoidal curve, and R is a second constant related to the
curve shape (Figure 3). As shown in Figure 3B and 3C, the
optimal values of the two constants were x0 ) 0.7 and R ) 0.6,
which were used as default values in the MpM algorithm. If
xnmi is greater than 0.7 (i.e., if the cross-matched value is >70%
between the master and target MS2 spectra), the initial MpM
score is almost entirely preserved (ωnmi approaches 1). If the
xnmi is less than 0.7, the MpM score diminishes exponentially.
Figure 1. Overall scheme of MpM. Quantification of a target
peptide is performed by comparing a master MS2 spectrum
acquired during the discovery step with targeted MS2 spectra
acquired from an inclusion list for the target precursor. The MpM
chromatogram reconstituted from the MpM scores is used for
quantification of the target peptide.
Figure 2. Scoring process using the MpM approach. Numbers
in the black arrows indicate the normalized intensities of the
product ions in the master MS2 spectrum with respect to the
base peak (i.e., y5
+
). Numbers in the gray arrows indicate
the normalized intensities of the product ions in the targeted MS2
spectra with respect to the “Top 1” in each targeted MS2
spectrum. Lined arrows at the bottom indicate the matched
product ions (marked with asterisks in the targeted MS2 spec-
trum), while dashed arrows with question marks indicate un-
matched ions. Scoring considers both the absolute intensity and
the number of matched product ions in each target MS2
spectrum.
MpM score ) ωnmi ∑(Ic,t × ωr)
Multiple Products Monitoring Approach for Peptide Quantification research articles
Journal of Proteome Research • Vol. 8, No. 7, 2009 3627

4. A sigmoidal function was adopted instead of a linear function
(Figure S2) because the increased similarity in matching
between the master and targeted MS2 spectra resulted in a
significant increase in the number of matched product ions
(Figure S3).
d. Parameters. All parameters can be adjusted in the option
window of the MpM program. It is also possible to import and
export files with user-defined parameters. The parameter file
contains tolerance of precursor and product ions, the number
of top-ranked product ions (“Top N”), sigmoidal weight
constants, noise thresholds, and a method for calculating the
area under the peak. The default parameters present in the
current MpM program are optimized for low-resolution, ion-
trap mass spectrometry. Depending on the mass accuracy of
the mass spectrometer, sample complexity, and the amount
of target peptide (<100 amol), some adjustment of the MpM
parameters is needed for better results (e.g., mass windows for
both matching product ions and searching for the “Top 1”
peak).
Quantitative Analysis of MpM. The linearity, sensitivity, and
selectivity of peptide quantification using the MpM method
were analyzed. To confirm the linearity and sensitivity of this
method, we performed targeted LC-MS/MS analysis with
varying amounts (more than 4 orders of magnitude) of a
standard cytochrome c peptide (TGPNLHGLFGR). Linearity
analysis in three replicates revealed that both MpM and SRM
methods had excellent linearity (R2
values were 0.9963 and
0.9962 for MpM and SRM, respectively) in the range between
0.01 and 200 fmol (Figure 4). The coefficient of variation (CV)
of MpM was 7.7 ( 4.3% and that of SRM was 6.9 ( 4.7%. MpM
showed, on average, a 4.4-fold higher sensitivity than SRM
(calculated from the value of the peak area shown in Figure
4).
As shown in Figure 5, the MpM method yielded more
selective chromatograms than single product ion monitoring
(termed SpM, a method similar to SRM). Several tryptic
peptides showed more than one peak during the SpM quan-
titative analysis of the 48-protein mixture, whereas the MpM
chromatogram produced a unique peak at the expected reten-
tion time (indicated by asterisks in SpM in Figure 5A).
Moreover, the average MS2 spectrum showed excellent speci-
ficity at the MpM peak region (Figure 5B, ii), whereas those
spectra in the two outside regions had no specificity for the
peptide (Figure 5B, i and iii). The MS2 spectrum of the top-
scored scan in the MpM chromatogram was the spectrum with
the highest Xcorr (Figure 5B, bottom).
To apply the MpM method for absolute quantification, we
spiked an isotopically heavy standard cytochrome c peptide
(5 fmol) into a human plasma sample digested with trypsin.
Target peptide quantification was performed using targeted LC-
MS/MS analysis with m/z values of precursors that cor-
responded to light and heavy peptides in the same duty cycle.
A comparison of light and heavy peak areas in the recon-
structed MpM chromatogram (Figure 6) yielded approximately
6.6 fmol cytochrome c in the human plasma sample.
To convince that MpM can be applied to the quantification
of multiple peptides, we performed MpM analysis with a
dilution series of mTRAQ-labeled enolase tryptic peptides.
Since the mTRAQ reagents are duplex amine-specific and stable
isotope-tagged reagents, samples mixed with varying ratios of
light and heavy mTRAQ-labeled peptides are useful to evaluate
the use of the MpM approach for quantifying multiple peptides.
Four different ratios (4:1, 2:1, 1:1, and 0.5:1) of light versus heavy
mTRAQ-labeled tryptic digests of enolase were spiked into a
Figure 3. Properties of the sigmoidal weight function in similarity
matching between the master and target MS2 spectra. (A)
Sigmoidal curve showing the relationship between Xnmi (fraction
of the matched ions) and ωnmi. The dashed line indicates the
center point (ωnmi ) 0.5) of the curve, which yielded a default
value of x0 ) 0.7. (B) The optimal x0 constant (0.7) shows the
target specificity of the high weights of the target peptide peak
(RT ) ∼16.6 min), while the lower value (0.3) of x0 yields high
weights of the nontarget peptides. (C) A magnified view of a
peptide elution profile (the dashed line) is shown along with MpM
chromatograms generated by three different constant values of
R (0.2 for 4, 0.6 for b, and 0.9 for 3). On the basis of close
similarities between the shape of the MpM chromatogram and
the elution profile of the target peptide, the optimal value of 0.6
was determined for constant R.
Figure 4. The linearity and sensitivity of SRM and MpM. Three
LC-MS/MS replicates were performed with varying amounts of
heavy cytochrome c peptide (0.01-200 fmol). The peptide
sequence (MW ) 1174.62 Da) is TGPNLHGLFGR (underlined
leucine residue is composed of seven 13
C atoms). A triply charged
precursor ion of the standard peptide (393.01 m/z) was used for
linearity analysis. The base peak (y9
2+
, 509.48 m/z) was used in
SRM analysis (isolation width of 2 m/z) and the product ions from
Top 1 to Top 19 were used for MpM analysis.
research articles Baek et al.
3628 Journal of Proteome Research • Vol. 8, No. 7, 2009

5. human plasma sample, and the MpM analysis was performed
using the heavy mTRAQ-labeled enolase peptides (50 fmol) as
an internal standard. Table 1 shows the quantification results
for five mTRAQ-labeled enolase peptides (three replicates). The
ratios measured were close to the expected (∼8.3% error).
To compare the MpM and SRM methods in terms of
sensitivity at the attomolar level, we first quantified a sample
that included only target peptide (cytochrome c peptide). The
base peak (y9
2+
, 509.48 m/z) of the MS2 spectrum for SRM
analysis and the product ions from Top 1 to Top 19 for MpM
analysis were monitored. MpM was performed with modified
parameters (mass window for matching product ions, 2 m/z;
mass window for searching the Top 1 peak, 2 m/z; scoring
process filter, 0.001%). Figure 7A shows that the MpM method
allows for the quantification of the target peptide at the 10-
amol level. On the other hand, the SRM method could not
quantify peptides at the same level due to both a low intensity
and noisy background. We also compared both MpM and SRM
for the sensitive detection of two enolase peptides at 500 amol
(Figure 7B). Each base peak ion was monitored for SRM, and
20 multiple product ions were monitored for MpM. MpM of
the enolase peptides provided a much higher sensitivity than
did SRM.
Discussion
Interest over large-scale, target protein quantification using
mass spectrometry has grown remarkably.1,18,19
MS2-intensity
based SRM and MRM methods using triple quadrupole mass
spectrometry are prominent methods of current use for such
quantification.11,20
However, these methods still have inherent
disadvantages. The method described here is a robust quan-
titative approach for target peptides and can be applied to
various types of tandem mass instruments.
MpM Enhances Selectivity and Sensitivity through
Monitoring of Multiple Product Ions. The MS2 intensity-based
MRM approach has the advantage of acquiring up to 1000
transitions during one experiment, but it requires such arduous
tasks as optimizing CID energy, selecting product ions to be
monitored, and experimentally validating proper transitions.
Methods for determining proper transitions, such as MIDAS
(multiple reaction monitoring-initiated detection and sequenc-
ing),21
TIQAM (targeted identification for quantitative analysis
by MRM),11
and AIMS (accurate inclusion mass screening),22
have been recently developed. These methods aid the selection
of a proper product ion for quantification through in silico
analysis,21,22
but they do not bypass the trial and error that is
necessary for the identification of the proper product ions and
the optimal conditions for analysis. In addition, the conven-
tional approaches, which consider only a single product ion,
do not always yield high sensitivity especially when narrow
mass windows (0.7-1.0 m/z) for precursor and product ions
are set for selectivity. A broader mass window (i.e., 3 m/z) for
a precursor or product ion would improve sensitivity but tends
to decrease selectivity. Such limitations in obtaining both
Figure 5. Selectivity and specificity of MpM with a complex
sample. (A) Chromatograms for four target precursor ions (m/z
value indicated at the top) of the 48-protein mixture were
obtained from the SpM and MpM methods. A single product ion
(m/z inside the upper panels) was monitored with Xcalibur (SpM,
upper panels), and multiple product ions were monitored with
MpM from targeted MS2 spectra (MpM, lower panels). The
matched product ions used for each MpM analysis were from
Top 1 to Top N (denoted inside the lower panels). The product
ions were monitored using an isolation width of 1.5 m/z. Asterisks
indicate peaks occurring at the expected retention time. (B) MpM
chromatogram for a tryptic peptide (VNQIGTLSESIK) of yeast
enolase (top) along with MS2 spectra corresponding to the
regions designated as (i), (ii), and (iii). The MpM score at the peak
(ii) has a high Xcorr (3.65-4.05), while the other regions (i and
iii) show lower Xcorr values (average ) 1.08).
Figure 6. MpM quantification of cytochrome c in a plasma
sample. The plasma sample was digested with trypsin and spiked
with an AQUA heavy peptide (TGPNLHGLFGR). (A) Base peak
chromatogram of the plasma sample in a targeted LC-MS/MS
run. (B) Reconstituted MpM chromatograms for the cytochrome
c peptide. Absolute amount of the target peptide was calculated
from the peak area (native light and spiked heavy).
Multiple Products Monitoring Approach for Peptide Quantification research articles
Journal of Proteome Research • Vol. 8, No. 7, 2009 3629

6. sensitivity and selectivity can be overcome if a large number
of product ions are considered. The MpM approach developed
in the current study does not require the time-consuming
determination of a quantifiable single product ion, and provides
high selectivity and sensitivity simultaneously. As shown in
Figure 5, the MpM method yields better performance in both
selectivity and sensitivity compared with the methods that
consider only a single product ion. The MpM approach will
be very effective when several peptides with the same precursor
m/z value are coeluted in targeted LC-MS/MS run in a short
time gradient (<30 min) because the algorithm can still dif-
ferentiate target peptides based on their full-range MS2 spectra.
The increase in sensitivity of the MpM method over the SRM
method was also confirmed both at low concentrations (atto-
molar level) (Figure 7) and in a complex sample (Figure S4).
These results consistently support that MpM is superior to SRM
in both selectivity and sensitivity for quantification.
Application of MpM. As a modified AQUA (absolute quan-
tification of peptide) strategy using MS2 spectra, MpM is widely
applicable for target quantification with heavy standard pep-
tides. In a complex sample (Figure 6), each MpM chromato-
gram showed a unique peak corresponding to the target
peptide with little background noise, and the amount of
cytochrome c protein in the serum sample (∼19.2 ng/mL) was
successfully quantified. The plasma levels of cytochrome c have
been reported in the range of 0.1-210 ng/mL.23
We also
showed that, by using appropriate internal standards (e.g.,
mTRAQ-labeled enolase peptides), the MpM method allowed
for absolute quantification of multiple target peptides in a
complex sample, such as plasma (Table 1). These results
support strongly that the MpM approach can be applied to the
absolute quantification of target proteins using heavy standard
peptides.
MpM can also be performed using theoretically generated
MS2 spectra for target peptides, even though no previous
experimental MS2 data exist. With the use of a theoretical MS2
spectrum obtained either from a simulation library24
or from
MassAnalyzer25
as the master spectrum for the target peptide,
the MpM method can be applied to targeted LC-MS/MS
analysis (with the target precursor m/z value). After performing
MpM using the theoretical master spectrum, an experimental
MS2 spectrum with the highest MpM score can be selected as
a new master MS2 spectrum for targeted LC-MS/MS analysis.
While the MpM approach has advantages in various aspects
of peptide quantification, the number of quantifiable target
peptides is limited by the number of acquired MS2 spectra.
While either SRM or MRM analysis has a short cycle time with
narrow range filtering (or scan) of the product ion, MpM has
a relatively longer cycle time due to full-range MS2 scanning.
The minimum time for full-range MS2 scanning is generally
0.35 s in the case of a LTQ XL ion-trap mass spectrometer.
Given a peak elution time of approximately 30 s and the
minimum number of points (∼15) required for the quantifica-
tion of an MpM chromatogram peak, a cycle time of 2 s allows
target quantification of six precursors. To increase the number
of quantifiable target peptides produced by ion-trap mass
spectrometry, we introduced certain modifications for targeted
LC-MS/MS analysis. Specifically, we divided a targeted LC-MS/
MS run into several segments and used a turboscan for the
Table 1. MpM Analysis of mTRAQ-Labeled Enolase Peptides in Plasma
precursor m/z observed ratiob
(light/heavy)
peptide sequencea
z light heavy #1 #2 #3 #4
LNQLLR 2 449.05 451.03 3.03 ( 1.06 2.29 ( 1.00 1.00 ( 1.02 0.43 ( 1.01
TFAEALR 2 474.55 476.53 4.38 ( 1.01 1.79 ( 1.02 0.70 ( 1.02 0.32 ( 1.02
IGSEVYHNLK 3 480.89 483.54 5.27 ( 1.07 2.09 ( 1.02 0.93 ( 1.02 0.36 ( 1.08
AVDDFLISLDGTANK 3 620.70 623.35 4.54 ( 1.03 3.39 ( 1.03 1.57 ( 1.04 0.60 ( 1.09
VNQIGTLSESIK 2 785.41 789.38 4.35 ( 1.04 2.02 ( 1.01 0.96 ( 1.02 0.52 ( 1.02
Expected Ratio 4.00 2.00 1.00 0.50
Ratio Meanc
4.25 ( 1.15 2.26 ( 1.18 1.00 ( 1.21 0.43 ( 1.19
a
Bold fonts in the sequence indicate the sites for mTRAQ labeling. b
Geometric mean and geometric standard deviation of triple replicate runs.
c
Geometric means and geometric standard deviation of the observed ratios in five peptides. Note that all observed ratios were normalized for the
observed ratio mean of 1:1 sample.
Figure 7. Comparison of MpM and SRM. (A) Lower limit of
detection with a cytochrome c heavy peptide (TGPNLHGLFGR)
at the 10-amol level. A doubly charged precursor ion (588.53 m/z)
was used for the analysis. MpM analysis with product ions from
Top 1 to Top 19 yielded a good quantifiable chromatogram,
whereas SRM analysis for a single product ion (y9
2+
, 509.48 m/z)
produced noisy peaks. (B) Sensitivity comparison with two
enolase tryptic peptides at the 500-amol level. The solid line
indicates the MpM chromatogram and the dashed line indicates
the SRM chromatogram. Precursor m/z values and charge states
of the two peptides are presented at the right side of each peptide
sequence. Information of the single product ion monitored for
SRM analysis is also presented in each plot.
research articles Baek et al.
3630 Journal of Proteome Research • Vol. 8, No. 7, 2009

7. MS2 scan. The ability to perform the MpM approach for 30
targets in 30 min with six segments (results shown in Figure
S5) suggests that up to six targets can be quantified in each
segment of the targeted LC-MS/MS run. In addition, obtaining
targeted MS2 spectra in turboscan mode (faster than normal
scan speed) increases the number of target precursors by
approximately 3-fold. MpM chromatograms obtained using a
turboscan also yielded a quantifiable chromatogram of a target
peptide (Figure S6). If both segmentation and a turboscan are
combined, the number of target precursors is expected to
approach 300 within 90 min using the LTQ XL ion-trap mass
spectrometer (6 targets per segment × 18 segments for 90 min
gradient time × 3 times the number of scans).
The MpM approach is applicable to most types of fragmen-
tation (e.g., CID, ETD, and HCD) as it basically monitors the
multiple product ions that were annotated in previous runs.
In addition, even MRM experiments can adopt an MpM
approach if multiple transitions for a target precursor are
monitored and the sigmoidal weight of MpM scoring is
excluded. Highly accurate MS2 spectra from LTQ-Orbitrap mass
spectrometry can also be used in the MpM approach for highly
selective quantification. High-quality MS and MS2 spectra allow
for the precise isolation of target precursor scans and accurate
peak matching between the master and targeted MS2 spectra
within an extremely narrow window size for product ions
(∼0.05 m/z). As an effective way to increase MpM target
number, using inclusion lists for target precursors and defining
repeat counts for data-dependent acquisition (dynamic inclu-
sion) in targeted LC-MS/MS analysis is worth considering. This
approach may yield more than 300 targets without divided
segments in targeted LC-MS/MS run. This issue will require
discussion with software developers of mass spectrometer
manufacturers.
In conclusion, we have developed a robust approach for the
practical quantification of target peptides that is based on
sensitive and informative features of MS2 spectra. Spectral
pattern matching with useful information extracted from the
MS2 spectra has multiple advantages, including improved
selection of the monitored product ion as well as improved
sensitivity and selectivity in peptide quantification. Compared
with conventional approaches (e.g., SRM), MpM greatly im-
proves the quantification of target peptides and can be easily
applied to various types of mass spectrometry. Further method
development on repeated dynamic inclusion has the potential
to improve the MpM approach even further by increasing the
number of specific targets quantified. The MpM approach has
the potential to provide an alternative method for specific
detection assays used to study candidate biomarkers, such as
Western blotting or ELISA, when antibodies are not available
for the newly discovered candidates.
Acknowledgment. We thank Dr. S. W. Lee and Dr. E.
Paek for technical advice and useful comments on the
manuscript. This study was supported by a grant from the
Korean Ministry of Education, Science, and Technology
(FPR08A1-030 of the 21C Frontier Functional Proteomics
Program).
Supporting Information Available: . Supplementary
Figure S1-S6: image of the MpM program; comparison of the
sigmoidal weight function and linear weight function; Xnmi
values of the MS2 spectra around the target peptide elution;
low-amount (4 amol) detection of a spiked peptide in a
complex sample; quantification of 30 target peptides using six
segments in a targeted LC-MS/MS analysis; sensitivity of MpM
quantification performed with turboscan data. This material
is available free of charge via the Internet at http://pubs.acs.org.
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