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Translational proteomics: the importance of mass
spectrometry-based approaches
Interest in using mass spectrometry (MS) for clinical
analyses has grown significantly in the past few years due
to its success in studies of human specimens, such as its
recent applications for single cell analysis of bone marrow
[1], direct blood sampling in multiple disease states, such
as cardiac injury [2] and breast cancer [3], and the
identification of Gram-negative bacilli in multiple clinical
samples, including blood, tissue and urine [4,5]. MS
analyses are utilized to obtain highly accurate mass
measure ments of molecules in a sample, and can sensi-
tively detect and identify molecules and subtle changes in
their composition and abundance. In parti cu lar, MS-
based proteomic applications have received considerable
attention. Proteomics is the study of the entire comple-
ment of proteins in an organism, tissue or cell and their
changes under different conditions, from disease states to
environmental variations. It has been estimated that the
human proteome contains more than 2million different
protein products or ‘proteoforms’ [6-8].
Since human proteins perform cellular functions essen-
tial to health and/or disease, obtaining knowledge of their
presence and variance is of great importance in under-
standing disease states and for advancing translational
studies, especially those related to personalized medicine
[9,10]. Human blood contains combinations of potentially
detectable proteins from different parts of the body, and
may be the single most informative sample for character-
izing an individual’s health [11]. From a clinical perspec-
tive, finding specific disease markers or biomarkers in
such fluids represents an attractive alternative to tissue
samples, due to the relative ease and less invasive nature
of collection, and the large volumes that are normally
obtainable. Proteomic studies promise to provide insights
into the dynamic nature of biological systems through
analysis of the proteins in biofluid and tissue samples,
thereby defining the state of the organism at the
molecular level. is approach not only incorporates the
complexity of gene expression, but importantly also
allows characterization of proteoforms generated by
post-translational processes. Proteome measurements
Abstract
The utility of mass spectrometry (MS)-based proteomic
analyses and their clinical applications have been
increasingly recognized over the past decade due
to their high sensitivity, specicity and throughput.
MS-based proteomic measurements have been
used in a wide range of biological and biomedical
investigations, including analysis of cellular responses
and disease-specic post-translational modications.
These studies greatly enhance our understanding of
the complex and dynamic nature of the proteome
in biology and disease. Some MS techniques, such
as those for targeted analysis, are being successfully
applied for biomarker verication, whereas others,
including global quantitative analysis (for example,
for biomarker discovery), are more challenging
and require further development. However, recent
technological improvements in sample processing,
instrumental platforms, data acquisition approaches
and informatics capabilities continue to advance
MS-based applications. Improving the detection of
signicant changes in proteins through these advances
shows great promise for the discovery of improved
biomarker candidates that can be veried pre-clinically
using targeted measurements, and ultimately used in
clinical studies- for example, for early disease diagnosis
or as targets for drug development and therapeutic
intervention. Here, we review the current state of MS-
based proteomics with regard to its advantages and
current limitations, and we highlight its translational
applications in studies of protein biomarkers.
Keywords biomarker, clinical proteomics, ion mobility
separations, mass spectrometry, multiple reaction
monitoring, selected reaction monitoring, shotgun
proteomics, targeted proteomics, translational proteomics
© 2010 BioMed Central Ltd
Mass spectrometry for translational proteomics:
progress and clinical implications
Erin Shammel Baker, Tao Liu, Vladislav A Petyuk, Kristin E Burnum-Johnson, Yehia M Ibrahim, Gordon A Anderson
andRichard D Smith*
R EVI E W
*Correspondence: rds@pnnl.gov
Biological Sciences Division, Pacic Northwest National Laboratory, Richland,
WA99352, USA
Baker et al. Genome Medicine 2012, 4:63
http://genomemedicine.com/content/4/8/63
© 2012 BioMed Central Ltd
therefore have great potential for translational applica-
tion, since both normal and altered cellular functions of
the human body are ultimately dependent on the expres-
sion and regulation of proteins. Moreover, disruptions in
protein expression are likely to serve as early indicators of
disease (that is, biomarkers) or targets for drug develop-
ment and therapeutic intervention. ese promising
clinical applications have driven the development of MS-
based approaches for proteomics, as well as other -omic
level analyses, for studying human biofluid and tissue
samples (Figure1).
Over the past decade, studies of protein biomarkers
have allowed advances in early, non-invasive diagnosis of
significant diseases, such as the identification of C-
reactive protein and troponin I as biomarkers for myo-
cardial infarction, and prostate specific antigen for prostate
cancer [12-14]. Despite these successes, efforts to identify
biomarkers have not been nearly as successful as
originally anticipated since proteomic analyses of blood
and other biological fluids have proven to be immensely
challenging because of the enormous complexity of the
samples, the vast dynamic range of protein concen tra-
tions of potential interest (for example, greater than ten
orders of magnitude in blood plasma), and the fact that
analytes of clinical interest are often present at the low
end of this concentration range [15-17]. To further
exacerbate these challenges, verification and population-
scale validation of biomarkers requires the analysis of
hundreds or even thousands of high-quality clinical
samples. e collection and storage of these samples
must be done carefully and monitored using standardized
protocols to reduce variations due to endogenous enzyme
activities or sample contamination. ese studies also
require multiple control groups and diagnostic sub-
categories of patients that are ideally gathered longi tu-
dinally over the course of disease progression. e
analysis of many patient samples is required to charac-
terize normal human genetic heterogeneity and disease
heterogeneity [18,19]. High throughput measurements
are therefore essential to achieve biostatistical signifi-
cance (Figure2).
While current MS-based proteomic measurements are
capable of providing great depth of coverage through the
use of extensive fractionation and analysis, this generally
precludes the throughput required and the levels of
sensitivity and specificity necessary for the rapid identi-
fication of clinically useful biomarkers. However, recent
technological advances in automated parallel sample
processing methods [20], multidimensional separations
prior to MS [21,22], instrumentation components and
approaches [23-27], and high-performance informatics
tools [28-30] have facilitated measurements with both
increased sensitivity and higher throughput for trans-
lational applications. In this review, we discuss the
current state of MS-based proteomics with regard to its
advantages and current limitations, and we highlight
translational applications that are being enabled by these
recent technological advances.
Advances in MS-based translational proteomics
e primary translational application of MS-based pro-
teo mics is biomarker development. However, as already
mentioned, its success has so far been quite modest and
has been mainly limited to preclinical studies. Biomarker
development is a multi-stage process that consists of
discovery, verification, validation and commercialization
[15]. For MS, the measurements fall into two categories,
where the first utilizes a discovery approach to identify
potential protein biomarkers and the second involves
verification to further assess and initially validate these
biomarkers using a larger population. Performing high-
quality measurements and rigorous statistical analyses
are essential in both steps as valuable patient samples are
used. Currently, both MS-based proteomic discovery and
verification approaches use bottom-up methods (Figure3)
in which proteins are digested into smaller peptides
before analysis [31]. However, the two approaches aim to
obtain different types of information.
Discovery approaches
In the discovery phase, broad quantitative MS measure-
ments often aim to identify peptides and proteins that
differ significantly in abundances between patient and
control groups. e main advantage of this approach is
its largely unbiased ability to characterize a whole
proteome or enriched sub-proteome in a single measure-
ment, so that the protein alterations corresponding to a
pathological or biochemical condition at a given time can
be investigated. However, performing discovery-based
proteomic analysis has proven to be quite difficult using
plasma and serum samples. In plasma, proteins have
concen trations ranging from approximately 3×10
10
pg/ml
for albumin to the low pg/ml range for some cytokines
and proteins, such as those potentially secreted or leaking
into blood, for example from tumors (Figure4a). Because
of this huge dynamic range and the fact that the proteome
in human biofluid samples is mainly represented by only
a few high abundance proteins - the 22 most abundant
proteins represent approximately 99% of the total protein
mass (Figure 4b) - analyzing all plasma proteins simul-
taneously is enormously challenging [11,32], even after
depletion of the most abundant proteins, as this exceeds
the dynamic range of mass spectrometers that are
typically used for discovery efforts (often approximately
1×10
3
to 1×10
4
for a single spectrum). To provide an
extended dynamic range for increased protein coverage it
is necessary to couple front-end separations such as
liquid chromatography (LC), multi-stage immunoaffinity
Baker et al. Genome Medicine 2012, 4:63
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depletion [33-35], fractionation [36], or a combination of
all three with MS analyses. While advanced LC
separations have already provided improvement in the
depth of coverage for proteins detected in MS studies
[37], a major problem is their concomitant reduction in
throughput, as bottom-up LC-MS analyses typically
require in the order of 1h. e detection of more proteins
(for example, thousands) from plasma is possible with
extensive off-line fractionation prior to on-line LC-MS
analyses [38], but days or weeks of LC-MS measurements
are then necessary for analysis of the multiple fractions.
While this approach is highly attractive for the detection
and discovery of potential biomarkers, the inherently low
throughput largely precludes population studies to enable
investigation of human and disease heterogeneity, and
also limits the possibility of performing personal profil-
ing. us, technological advances that greatly decrease
LC separation times or eliminate them entirely while still
maintaining a high depth of coverage are crucial for
future clinical applications.
To attain further information and identify unknown
peptides with high accuracy in bottom-up MS studies,
tandem MS (MS/MS) measurements, involving multiple
steps of MS analysis and peptide fragmentation, are
essential. Currently, many immunoassays used in trans-
lational studies measure analytes indirectly by detecting
them through their interaction with other molecules,
such as antibodies. MS provides an advantageous alter-
native to immunoassays as it involves direct measure-
ments and allows the acquisition of exact peptide
sequence information through high mass accuracy MS/
MS measurements, thereby allowing unknown peptides
to be identified with a great degree of confidence. e
simultaneous collection of MS and MS/MS measure-
ments involves the acquisition of a preliminary mass
spectrum of intact peptides, followed by disso cia tion or
fragmentation of a peptide(s) of interest, and acquisition
of the fragmentation mass spectrum. is process is
repeated for the duration of the entire LC separation,
resulting in thousands of MS and MS/MS spectra. To
Figure 1. Simultaneous MS analyses for understanding complex systems. Simultaneous study of the genome, transcriptome, proteome,
glycome, lipidome and metabolome by MS provides a systems approach to understanding dierent conditions and disease states through analysis
of variations in DNA, RNA, peptides/proteins, lipids and metabolites, respectively, in an organ, tissue, blood or other sample, or organism. MS is
one of the only analytical tools that can perform measurements at each -omic level, and thus can provide a better understanding of molecular
mechanisms and how they aect each other. PTM, post-translational modication.
600
800
1000
1200
1400
1600
1800
2000
m/z
0
10
20
30
40
50
70
80
90
60
Relative abundance
Transcriptome
Proteins
mRNA
Protein
complexes
Small molecule PTMs
(Phosphorylation, acetylation)
Proteome
Genome
DNA
Glycome
Metabolome
Lipidome
Large molecule PTMs
(Glycosylation, ubiquitination)
100
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identify the peptides with MS/MS, genomic data are
frequently used to generate theoretical sequences for
bioinformatics tools such as Mascot [39], Sequest [40]
and X! Tandem [41]. By evaluating all of the matched
MS/MS spectra, the false discovery rate of the peptide
identifications can be estimated [42-44], and improved
informatics tools are increasingly allowing identifications
from spectra that were previously unattributed due to
unexpected sequences or modification states.
Another important step in bottom-up measurements is
quantification of observed peptides to determine if any
significant changes are occurring between samples.
Quantitative measurements of peptide abundance can be
performed with or without stable isotope labeling (SIL)
of peptides (or proteins) using peptide ion peak inten-
sities or spectral counting (that is, ‘label-free’ quantifi-
cation) [45]. Several in vitro and in vivo labeling tech-
niques, such as stable isotope labeling of amino acids by
cell culture (SILAC) [46,47], isobaric tags for relative and
absolute quantification (iTRAQ) [48,49] and
18
O-labeling
[17] have been developed for MS-based quantification,
and have been shown to provide lower standard
deviations for peptide ion peak intensity measurements
compared with the label-free methods [50]. When com-
bined with off-line fractionation, these SIL methods
provide broad coverage for comprehensive proteome
charac terization. However, label-free measurements using
normalization of LC-MS analyses can also be quite effec-
tive and can avoid complications introduced by labeling
approaches [51].
At present, data-dependent MS/MS analysis of selected
peptides relies on an initial MS scan, and although it is
widely used in proteomic discovery studies, it has inherent
limitations that are associated with MS/MS under-
sampling in complex samples. To overcome these limita-
tions and improve quantification, the accurate mass and
time (AMT) tag strategy was developed for use on either
labeled or label-free samples [52]. In a typical AMT tag
study, a database is created and populated with peptide
masses and LC elution times from many LC-MS/MS
Figure 2. Biodiversity in population proteomic studies. Population proteomics allows the analysis of protein biodiversity within a population.
Because it is known that individual variation, such as the presence of point mutations and varying protein abundances, will be present in all human
studies (as depicted by the dierent chromatograms), it has become essential to develop high throughput, sensitive analytical applications to
enable measurements necessary for personalized medicine.
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measurements using representative samples from experi-
mental and control groups. High throughput LC-MS
analyses are then performed for a large number of bio-
logical replicates and the acquired datasets are compared
with the database to identify the peptides that are actually
present. is approach allows the comparison of large
numbers of peptide species that may not be identified in
normal data-dependent MS/MS studies for reasons that
include poor peptide fragmentation, co-elution of highly
abundant species and/or informatics limitations -
presumably similar factors that leave a significant number
of detected species in LC-MS/MS analyses unidentified.
Other approaches, such as data-independent MS/MS
strategies [25-27] (discussed later), have also been
developed recently to significantly enhance unbiased
discovery studies.
While these new approaches promise improved dis-
covery of biomarkers, analysis of plasma samples still
remains challenging for MS-based approaches. Discovery
efforts increasingly use proximal fluids or tissues that are
expected to be rich in biomarker candidates and present
less of a challenge in terms of the dynamic range of
proteins [15]. Various methods, such as optimal cutting
temperature compound-embedded tissues, and formalin-
fixation and paraffin-embedded tissues (with or without
laser capture microdissection), have been developed for
preparing clinical tissue samples for proteomic studies
[53]. e results from these advanced preparation
methods have been promising [54,55], and serve as a
prelude to targeted discovery or verification efforts for
measurements of candidate biomarker proteins at
presumably much lower levels in blood samples.
Verication approaches
e verification phase typically uses a much larger
number of samples, and focuses on a limited set of
candidate peptides or proteins identified in the discovery
approach. is approach can provide highly sensitive
quantification of protein abundances and aims to identify
a set of biomarker candidates with greater confidence.
ere have been significant developments in MS-based
methods for the verification approach, providing much
greater sensitivity, specificity and throughput, and more
accurate quantification than broad discovery-based
measurements.
Targeted quantitative MS-based measurements typically
employ selected reaction monitoring (SRM), using triple
quadrupole mass spectrometers. In SRM measurements,
the triple quadrupole MS allows rapid detection of a
series of targeted peptide ions and their corresponding
fragments (that is, transitions) with multiplexing and
‘scheduling’ capabilities (to perform pre-defined analyses
during specific LC elution times) along with SIL internal
peptide standards [56,57] to provide highly accurate
quantification for up to hundreds of peptides during a
single LC separation. e two-stage mass filtering in
SRM (that is, for both peptide ions and their
corresponding fragments) provides great sensitivity and
specificity for detection of the targeted peptides. is
capability often leads to observed limits of detection and
limits of quantification (LOQ) of about 10 to 100ng/ml
in plasma - several orders of magnitude lower than
presently feasible with discovery-based platforms. More-
over, recent advances such as the use of protein depletion,
limited fractionation, and targeted peptide enrichment
methodologies, such as peptide isolation with stable
isotope standard capture with anti-peptide antibodies
(SISCAPA) [58], extend practical LOQ values to low
ng/ml (or even low pg/ml) levels in blood samples
[33,59]. e implementation of other instrumental
modifications such as multi-inlet capillaries and dual-
stage ion funnels has led to further enhanced sensitivity
[60]. While selection of the correct proteotypic or
targeted peptides with good digestion and ionization
efficiency requires some effort, this has increasingly been
addressed using public repositories, including SRMAtlas
Figure 3. Bottom-up MS approach. The most common MS-based proteomics approach is bottom-up analysis. In the bottom-up approach
the proteins are rst extracted from biouids, cells or tissue. Enzymatic digestion of the proteins is then performed to fragment them into
their corresponding peptide subunits, and the peptides are separated using LC and detected with MS. LC, liquid chromatography; MS, mass
spectrometry; m/z, mass-to-charge ratio.
Extract
proteins
Digest LC
Elution time
MS
m/z
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[61,62], PeptideAtlas [63] and the Global Proteome
Machine [64]. Recent computational developments have
also allowed the creation of programs that effectively
predict proteotypic peptides given a protein amino acid
sequence [65,56], allowing the list of targeted peptides to
be derived without the need to rely on discovery-based
Figure 4. Protein dynamic range and percentage in blood plasma. (a) The normal range of protein abundances in plasma is illustrated
for a subset of 34 proteins representing the most to least abundant. The gure was assembled using data from Anderson and Anderson [11].
Because the dynamic range of protein concentrations covers over ten orders of magnitude, with the proteins of interest present at the lower
concentrations, analyzing plasma samples has proven to be very dicult. (b) The approximate percentages of each protein in plasma are further
illustrated using pie charts for the most abundant 22 proteins representing approximately 99% of the plasma protein mass. The top 10 proteins
that make up approximately 90% of all plasma proteins are shown on the left. The remaining 10% is further divided on the right with the least
abundant remaining 1% group representing thousands of proteins, which are of most interest for biomarker studies. IgA, immunoglobulin A;
IgG,immunoglobulin G; IgM, immunoglobulin M.
Remaining 1%
Albumin
IgGs
Remaining 10%
(b) Percentage of each protein in plasma
Albumin
IgGs
Remaining 10%
Remaining 1%
Transferrin
IgMs
α-1- Antitrypsin
α-2- Macroglobulin
Complement C3
Haptoglobin
Fibrinogen
IgAs
α-1-Acid glycoprotein
Apolipoprotein A−1
Apolipoprotein B
Lipoprotein (a)
Factor H
Complement C4
Complement factor B
Ceruloplasmin
Complement C1q
Complement C9
Complement C8
Prealbumin
(a) Dynamic range of proteins in plasma
Classic plasma proteins Tissue leakage Cytokines
0
2
4
6
8
10
12
Concentration (log 10 pg/ml)
Albumin
IgGs
Transferrin
Fibrinogen
IgAs
IgMs
α-1- Antitrypsin
α-2- Macroglobulin
α-1-Acid glycoprotein
Ferritin
Troponin I
Interleukin−6
Interleukin−2
Interleukin−4
TNF alpha
Tissue factor
Interleukin−8
Complement C3
Complement C4
Complement C1q
Complement C3a
Haptoglobin
Apolipoprotein A−1
Apolipoprotein B
Ceruloplasmin
Lipoprotein (a)
Myoglobin
Carcinoembryonic
C−Peptide
Thyroglobulin
TNF binding protein
Myelin basic protein
Prostate specific antigen
Prealbumin
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proteomics data. Moreover, MS targeted measurements
have also proved reproducible in assays across many
differ ent proteomics laboratories [66]. ese various
features have made SRM the current method of choice
for ultra-sensitive MS-based biomarker verification (or
pre-clinical validation).
Addressing the challenges of translational
proteomics
Despite significant advances in MS-based targeted
analyses, several performance metrics, including measure-
ment throughput and detection sensitivity, still require
compromises to biomarker discovery and verification
approaches for the translational application of MS-based
proteomic analyses. In particular, these deficiencies
result in low sampling numbers and measurement quality
that prevents detection of proteins present at low
concentrations. To achieve further progress in trans la-
tional proteomics, technological developments in MS
such as faster separations, more effective ion sources,
higher instrumental resolution/mass accuracy, detectors
with greater dynamic range, and advanced data acqui-
sition approaches are expected to increasingly allow
broad non-targeted measurements that retain the
benefits of targeted approaches.
Data-independent MS/MS acquisition has shown
promise for improving the consistency of peptide identi-
fi cations, as well as for increasing protein sequence
coverage in complex samples and creating broad un-
targeted measurements that are more similar to possible
targeted measurements [25-27]. Data-independent acqui-
si tion is a strategy that systematically queries sample sets
for the presence and quantity of essentially any protein of
interest, using the information available in fragment ion
spectral libraries to mine complete fragment ion maps.
One way of performing data-independent acquisitions is
by using sequential window acquisition of all theoretical
fragment-ion spectra (SWATH™) MS, in which repeated
cycling of a 25Da precursor isolation window is used in a
single analysis to obtain time-resolved fragment ion
spectra for all analytes detectable within a user defined
mass-to-charge ratio (m/z) precursor range. Initial results
have been very promising, with queried peptides quanti-
fied with a consistency and accuracy apparently
approaching that for SRM [25].Another approach to
exploit data-independent acquisitions involves using an
additional separation technique prior to fragmentation to
increase measurement sensitivity and the ability to
associate simultaneously fragmented precursor ions with
their corresponding fragment ions. Fast gas-phase ion-
mobility spectrometry (IMS), taking place in a timescale
of tens of milliseconds, offers an attractive ion separation
approach for data-independent acquisitions. IMS was
introduced in the 1970s [67] and utilizes the fact that ions
subject to an electric field in a buffer gas quickly reach a
steady velocity dependent on the ion shape: compact
species drift faster than those with extended structures
[68,69]. IMS can be easily coupled to quadrupole time-
of-flight MS, allowing placement of IMS between the LC
and MS stages. e resulting IMS-MS instrument pro-
duces high-resolution spectra containing both the m/z
and IMS drift time information concurrently. To perform
data-independent acquisitions, all precursor ions are
fragmented after the IMS separation but prior to MS
detection to completely eliminate MS/MS under samp-
ling. Because fragmentation occurs after the IMS separa-
tion, all fragment ions have the same drift time as the
precursors [70-72], allowing simplification of spectral de-
convolution, which adds a great benefit to this technique.
e increased sensitivity and reduced spectral conges-
tion in the IMS separation also has another advantage of
reducing or completely avoiding the LC time in complex
samples [73]. When IMS is coupled with MS, ions are
separated prior to detection, reducing detector suppression
while supplying an additional dimension for peptide
identification. Practical use of IMS-MS was initially
impeded by low sensitivity due to significant ion losses at
the IMS terminus and during transfer to MS. However,
this problem was solved by re-focusing ions with an ion
funnel at the IMS-MS interface [74], essentially prevent-
ing ion losses during the operation. e introduction of
ion funnels in 1997 [75] provided a huge improvement in
sensitivity of MS instruments as it allowed ions to be
focused through the small interface orifices necessary for
ultralow MS vacuum pressures (1×10
-7
to 1×10
-8
Torr),
as shown in Figure 5. e ion funnel is most often
implemented in the source region of mass spectrometers
to greatly increase the sensitivity of measurements, and
has gained importance with its recent inclusion in
commercially developed instruments. While these
develop ments are just a first step in the convergence of
discovery and verification platforms, further progress
will be facilitated by emerging approaches for faster and
higher resolution separations, improved MS resolution
and extended detector dynamic range.
Clinical implications
e potential to use MS-based proteomics in clinical
settings is largely judged by their ability to make robust,
sensitive, quantitative, specific and high-throughput
measure ments for highly complex biospecimens. Clinical
questions and the corresponding requirements for bio-
specimen detection determine the ability of MS to find
and routinely measure high-quality biomarkers that have
sufficient sensitivity and specificity to be clinically useful
in screening large populations - for example, for diag-
nostic tests or early disease detection. For instance, SRM
has already been widely used for measuring metabolites
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for newborn screening in clinical laboratories [76].
However, the detection of low abundance proteins in
complex samples requires measurements of high
sensitivity and large dynamic range, aspects of MS
performance that are currently achievable and presently
being greatly improved due to developments in MS-
based instruments and new separation technologies, as
mentioned earlier. Narrowing large lists of differentially
abundant proteins into defined patterns of biologically
important variations, to reveal a much smaller set of
candidate proteins that can be detected with the high
sensitivity and specificity that is needed for clinical utility,
requires verification studies typically involving many
hundreds of samples at a minimum. Currently, the low
throughput of conventional MS platforms severely inhibits
biomarker verification. However, recent improvements in
MS-based proteomic approaches, ranging from sample
processing to data acquisition as outlined earlier, are
resulting in rapid and highly sensitive MS analyses that
are now providing a viable future for MS-based measure-
ments in clinical laboratories.
When comparing MS-based proteomics with immuno-
assays, which are the current gold standard in clinical
detection of protein biomarkers, MS-based proteomics
offer a significantly shorter lead-time and cost for assay
development, high capability for multiplexed analysis,
and the ability to be highly configurable or flexible for
measuring different clinical analytes. With these
advances and unique features, MS-based translational
proteomics have the potential to become powerful tools
for decision making in the clinic, alongside other
approaches such as physical examination, in vivo
imaging, histology, biochemical assays and assessment of
demographic risk factors. eir potential applications for
discovering and measuring protein biomarkers could
include routine screening, staging of disease progression,
prediction of the course of disease, assessment of disease
outcome, monitoring disease recurrence, and
personalized assessment of drug response and toxicity, to
name a few.
Outlook and perspectives
e future of MS-based translational proteomics can be
categorized by what is currently practical, and what is
being enabled by recent technological developments. In
the short term, proteomic measurements using targeted
approaches are effective for high sensitivity and high
throughput analysis of a limited set of biomarker candi-
dates, whereas unbiased broad measurements are effec-
tive for the detection of a much larger universe of bio-
marker candidates but with less sensitivity [27]. Improve-
ments to the sensitivity of broad measurements and the
scope of targeted measurements are ultimately driving a
convergence of these platforms and are expected to
increase the ability to gain a predictive under standing of
molecular processes in complex biological systems [77].
While MS-based proteomics offers valuable information
for understanding complex biological systems, systems-
level quantitative analyses using a combination of broad
proteomic, metabolomic, lipidomic and glycomic MS
analyses (termed pan-omics) will be increasingly important
(Figure 1). ese approaches largely benefit from the
same MS-based platform developments that are allowing
advances in translational proteomics. e importance of
each -omic measurement technology has already become
evident - for example, through the success of targeted
measurements [76,78]- and their combination into trans-
formative pan-omics measurement capabilities would
likely be crucial for understanding the complexity of
biological systems. us, broad pan-omic discovery
methods, if sufficiently sensitive and effective, would be
expected to provide a much more informative clinical
toolset of biomarkers for accurate prediction of disease
onset, and for disease monitoring and prognosis.
Figure 5. Increased MS sensitivity with ion funnel focusing. Technology developments such as the ion funnel greatly increase MS instrument
sensitivity by re-focusing all ions through narrow interfaces necessary to maintain the high vacuum required for MS measurements. Two ion funnels
are depicted here. The rst funnel on the left is a conventional ion funnel that focuses the continuous beam from two capillaries through a narrow
inlet. The second funnel (right) is a trapping ion funnel used to trap and pulse ions for ion mobility spectrometry (IMS) experiments. ESI, electrospray
ionization.
ESI
source
Conventional ion funnel
Trapping ion funnel
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Acknowledgements
The authors thank Nathan Johnson for assistance in preparing the gures.
Parts of this work were supported by grants from the National Institutes of
Health National Center for Research Resources (5 P41 RR018522-10), National
Institute of General Medical Sciences (8 P41 GM103493-10), National Cancer
Institute (R21-CA12619-01, U24-CA-160019-01, and Interagency Agreement
Y01-CN-05013-29), the Washington State Life Sciences Discovery Fund, the
Entertainment Industry Foundation and its Women’s Cancer Research Fund,
and the Laboratory Directed Research and Development Program at Pacic
Northwest National Laboratory. Research was performed at the Environmental
Molecular Sciences Laboratory, a national scientic user facility sponsored by
the Department of Energy’s Oce of Biological and Environmental Research
and located at Pacic Northwest National Laboratory.
Abbreviations
AMT, accurate mass and time; IMS, ion mobility spectrometry; LC, liquid
chromatography; LOQ, limits of quantication; m/z, mass-to-charge ratio; MS,
mass spectrometry; MS/MS, tandem mass spectrometry; SIL, stable isotope
labeling; SRM, selected reaction monitoring.
Competing interests
The authors declare that they have no competing interests.
Published: 31 August 2012
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Cite this article as: Baker ES, et al.: Mass spectrometry for translational
proteomics: progress and clinical implications. Genome Medicine 2012, 4:65.
Baker et al. Genome Medicine 2012, 4:63
http://genomemedicine.com/content/4/8/63
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