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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, specificity 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-specific post-translational modifications. 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 verification, 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 significant changes in proteins through these advances shows great promise for the discovery of improved biomarker candidates that can be verified 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.
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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 2million 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
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, specicity 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-specic post-translational modications.
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 verication, 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
signicant changes in proteins through these advances
shows great promise for the discovery of improved
biomarker candidates that can be veried 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
andRichard D Smith*
Biological Sciences Division, Pacic Northwest National Laboratory, Richland,
WA99352, USA
Baker et al. Genome Medicine 2012, 4:63
© 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 (Figure1).
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 (Figure2).
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 (Figure3)
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
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 (Figure4a). 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
to 1×10
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
Page 2 of 11
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 1h. 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 dierent 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 aect each other. PTM, post-translational modication.
Relative abundance
Small molecule PTMs
(Phosphorylation, acetylation)
Large molecule PTMs
(Glycosylation, ubiquitination)
Baker et al. Genome Medicine 2012, 4:63
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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
[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 dierent chromatograms), it has become essential to develop high throughput, sensitive analytical applications to
enable measurements necessary for personalized medicine.
Baker et al. Genome Medicine 2012, 4:63
Page 4 of 11
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.
Verication 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
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
schedulingcapabilities (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 100ng/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 biouids, 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.
Digest LC
Elution time
Baker et al. Genome Medicine 2012, 4:63
Page 5 of 11
[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 dicult. (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%
Remaining 10%
(b) Percentage of each protein in plasma
Remaining 10%
Remaining 1%
α-1- Antitrypsin
α-2- Macroglobulin
Complement C3
α-1-Acid glycoprotein
Apolipoprotein A1
Apolipoprotein B
Lipoprotein (a)
Factor H
Complement C4
Complement factor B
Complement C1q
Complement C9
Complement C8
(a) Dynamic range of proteins in plasma
Classic plasma proteins Tissue leakage Cytokines
Concentration (log 10 pg/ml)
α-1- Antitrypsin
α-2- Macroglobulin
α-1-Acid glycoprotein
Troponin I
TNF alpha
Tissue factor
Complement C3
Complement C4
Complement C1q
Complement C3a
Apolipoprotein A1
Apolipoprotein B
Lipoprotein (a)
TNF binding protein
Myelin basic protein
Prostate specific antigen
Baker et al. Genome Medicine 2012, 4:63
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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
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-
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 25Da 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
to 1×10
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
Baker et al. Genome Medicine 2012, 4:63
Page 7 of 11
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
Conventional ion funnel
Trapping ion funnel
Baker et al. Genome Medicine 2012, 4:63
Page 8 of 11
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 Womens Cancer Research Fund,
and the Laboratory Directed Research and Development Program at Pacic
Northwest National Laboratory. Research was performed at the Environmental
Molecular Sciences Laboratory, a national scientic user facility sponsored by
the Department of Energys Oce of Biological and Environmental Research
and located at Pacic Northwest National Laboratory.
AMT, accurate mass and time; IMS, ion mobility spectrometry; LC, liquid
chromatography; LOQ, limits of quantication; 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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... Furthermore, some proteins are present in large quantities while others are present only in trace amounts [16]. In fact, the most common 22 proteins represent over 99% of the total proteome by mass but it is the remaining tens of thousands of proteins that are potentially interesting as biomarkers [17][18][19][20]. There is a practical challenge to resolve the interesting part of the proteome in the presence of an overwhelming amount of what, in data analytic terms, is background noise. ...
... Although a few proteins in a cell are present in high abundance, it is the remaining tens of thousands of proteins that are potentially interesting as biomarkers [17][18][19][20]. To determine a biomarker of a disease, one has to be able to quantitatively compare samples from patients with different disease states. ...
Full-text available
Multiomic analysis comprises genomics, proteomics, and metabolomics leads to meaningful insights but necessitates sifting through voluminous amounts of complex data. Proteomics in particular focuses on the end product of gene expression – i.e., proteins. The mass spectrometric approach has proven to be a workhorse for the qualitative and quantitative study of protein interactions as well as post-translational modifications (PTMs). A key component of mass spectrometry (MS) is spectral data analysis, which is complex and has many challenges as it involves identifying patterns across a multitude of spectra in combination with the meta-data related to the origin of the spectrum. Artificial Intelligence (AI) along with Machine Learning (ML), and Deep Learning (DL) algorithms have gained more attention lately for analyzing the complex spectral data to identify patterns and to create networks of value for biomarker discovery. In this chapter, we discuss the nature of MS proteomic data, the relevant AI methods, and demonstrate their applicability. We also show that AI can successfully identify biomarkers and aid in the diagnosis, prognosis, and treatment of specific diseases.
... Owing to the number of dif-ferent analytes and their range of concentrations, plasma has one of the most complex peptidomes, proteomes and metabolomes in the human body. Because the dynamic range of compound concentration can vary >10 orders of magnitude, attaining in-depth profiling of plasma peptidome/proteome/metabolome has proven difficult (Baker et al., 2012). In contrast to enzymatic and antibody-based methods, mass spectrometry (MS) methods measure the highly accurate mass and fragmentation of small molecules such as peptide and metabolites (Baker et al., 2012;Geyer et al., 2017). ...
... Because the dynamic range of compound concentration can vary >10 orders of magnitude, attaining in-depth profiling of plasma peptidome/proteome/metabolome has proven difficult (Baker et al., 2012). In contrast to enzymatic and antibody-based methods, mass spectrometry (MS) methods measure the highly accurate mass and fragmentation of small molecules such as peptide and metabolites (Baker et al., 2012;Geyer et al., 2017). In this regard, MS-based analyses offer an attractive avenue to comprehensively profile human plasma both at proteomic (Geyer et al., 2017;Ignjatovic et al., 2019;Schwenk et al., 2017) and metabolomic-levels (Anesi et al., 2019;Telu et al., 2016). ...
The spread of coronavirus disease 2019 (COVID-19) viral pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a worldwide pandemic claiming several thousands of lives worldwide. During this pandemic, several studies reported the use of COVID-19 convalescent plasma (CCP) from recovered patients to treat severely or critically ill patients. Although this historical and empirical treatment holds immense potential as a first line of response against eventual future unforeseen viral epidemics, there are several concerns regarding the efficacy and safety of this approach. This critical review aims to pinpoint the possible role of mass spectrometry-based analysis in the identification of unique molecular component proteins, peptides, and metabolites of CCP that explains the therapeutic mechanism of action against COVID-19. Additionally, the text critically reviews the potential application of mass spectrometry approaches in the search for novel plasma biomarkers that may enable a rapid and accurate assessment of the safety and efficacy of CCP. Considering the relative low-cost value involved in the CCP therapy, this proposed line of research represents a tangible scientific challenge that will be translated into clinical practice and help save several thousand lives around the world, specifically in low- and middle-income countries.
... Current developments in sample preparation methods, protein quantitation strategies, MS configurations and data analysis have all been essential to address the clinical questions that advance the discovery and validation of clinically-relevant diseases biomarkers ( Fig. 1) [17,28,44]. These progresses in MS related technologies, sample preparation methods, labeling reagents, stable isotope labeling reagents and peptide synthesis technologies, and bioinformatics have led to identification and quantification of several thousand proteins in one experiment with steadily improved sensitivity, resolution and specificity propelled proteomics into the clinic [12,15,[45][46][47]. ...
Full-text available
Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit. This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time. Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.
... Examples of commonly used clinical plasma biomarkers include the analysis of liver-specific enzymes such as alkaline phosphatase (ALP), alanine transaminase (ALT), aspartate transaminase (AST), or gamma-glutamyl transferase (GGT) for liver function and dysfunction [2][3][4][5][6][7], or the detection of troponin I as a biomarker for myocardial damage [8][9][10]. Nonetheless, protein concentrations in plasma span many orders of magnitude, and many proteins that are released from tissues and organs into the circulation are only present in low concentrations [11,12]. In addition, the tissue or cell type of origin for these "tissue leakage proteins" is often difficult to ascertain, challenging the usefulness of these proteins as biomarkers for early disease or tissue damage caused by disease progression. ...
Full-text available
The proteomic analysis of plasma holds great promise to advance precision medicine and identify biomarkers of disease. However, it is likely that many potential biomarkers circulating in plasma originate from other tissues and are only present in low abundances in the plasma. Accurate detection and quantification of low abundance proteins by standard mass spectrometry approaches remain challenging. In addition, it is difficult to link low abundance plasma proteins back to their specific tissues or organs of origin with confidence. To address these challenges, we developed a mass spectrometry approach based on the use of tandem mass tags (TMT) and a tissue reference sample. By applying this approach to nonhuman primate plasma samples, we were able to identify and quantify 820 proteins by using a kidney tissue homogenate as reference. On average, 643 ± 16 proteins were identified per plasma sample. About 58% of proteins identified in replicate experiments were identified both times. A ratio of 50 ug kidney protein to 10 ug plasma protein, and the use of the TMT label with the highest molecular weight (131) for the kidney reference yielded the largest number of proteins in the analysis, and identified low abundance proteins in plasma that are prominently found in the kidney. Overall, this methodology promises efficient quantification of plasma proteins potentially released from specific tissues, thereby increasing the number of putative disease biomarkers for future study.
... However, a drawback associated with this approach is that it is presently employed in clinical utility solely for pathogens included in the database (Sandalakis et al., 2017). For the discovery of biomarker(s), the primary method encompasses bottom-up MS, generally accompanied by nanoflow LC for separating samples into fractions and addressing the significant variations in proportions of varying proteins, termed the high dynamic range (Baker et al., 2012). A primary approach includes peptide labeling, permitting for incorporation of multiple samples each having varying labels during a sample MS run, enhancing the comparison of relative protein proportion between samples. ...
Full-text available
Dental caries is a multifactorial chronic disease resulting from the intricate interplay among acid‐generating bacteria, fermentable carbohydrates, and several host factors such as saliva. Saliva comprises several proteins which could be utilized as biomarkers for caries prevention, diagnosis, and prognosis. Mass spectrometry‐based salivary proteomics approaches, owing to their sensitivity, provide the opportunity to investigate and unveil crucial cariogenic pathogen activity and host indicators and may demonstrate clinically relevant biomarkers to improve caries diagnosis and management. The present review outlines the published literature of human clinical proteomics investigations on caries and extensively elucidates frequently reported salivary proteins as biomarkers. This review also discusses important aspects while designing an experimental proteomics workflow. The protein–protein interactions and the clinical relevance of salivary proteins as biomarkers for caries, together with uninvestigated domains of the discipline are also discussed critically.
... Indeed, typical time required for the proteome-wide analysis of cell lysates by the state-of-the-art HPLC-MS/MS systems is in the range of several hours (ten and more hours for a sample analyzed in several technical and/or biological replicates). First of all, these large instrumentation time expenses are due to the prolonged separation of the analyzed proteolytic mixturenecessary for performing tandem mass spectrometry measurement of as many as possible of its components [10,11]. In particular, for a single "drug-to-proteome" model, realization of the TPP method requires about 100 proteome-wide analyses, which is translated to the extended instrumentation time limiting wide acceptance of the method in practice. ...
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Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.
... The main issue with LC-MS applied to plasma proteome analysis is that it is not possible with this method to measure across the entire dynamic range of plasma, and it, therefore, requires additional analytical steps [10]. The large majority of mass spectrometry-based analyses on plasma is performed with what is known as a bottom-up proteomic approach [46]. In bottom-up proteomics (sometime referred to as shotgun proteomics), the proteins are proteolytically digested and are then chromatographically separated (in one or several dimensions) and the analysis is performed on the resulting peptides. ...
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The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.
Blood in the circulatory system carries information of physiological and pathological status of the human body, so blood proteins are often used as biomarkers for diagnosis, prognosis, and therapy. Human blood proteome can be explored by the latest technologies in mass spectrometry (MS), creating an opportunity of discovering new disease biomarkers. The extreme dynamic range of protein concentrations in blood, however, poses a challenge to detect proteins of low abundance, namely, tissue leakage proteins. Here, we describe a strategy to directly analyze undepleted blood samples by extensive liquid chromatography (LC) fractionation and 18-plex tandem-mass-tag (TMT) mass spectrometry. The proteins in blood specimens (e.g., plasma or serum) are isolated by acetone precipitation and digested into peptides. The resulting peptides are TMT-labeled, separated by basic pH reverse-phase (RP) LC into at least 40 fractions, and analyzed by acidic pH RPLC and high-resolution MS/MS, leading to the quantification of ~3000 unique proteins. Further increase of basic pH RPLC fractions and adjustment of the fraction concatenation strategy can enhance the proteomic coverage (up to ~5000 proteins). Finally, the combination of multiple batches of TMT experiments allows the profiling of hundreds of blood samples. This TMT-MS-based method provides a powerful platform for deep proteome profiling of human blood samples.
Spatially targeted proteomics analyzes the proteome of specific cell types and functional regions within tissue. While spatial context is often essential to understanding biological processes, interpreting sub-region-specific protein profiles can pose a challenge due to the high-dimensional nature of the data. Here, we develop a multivariate approach for rapid exploration of differential protein profiles acquired from distinct tissue regions and apply it to analyze a published spatially targeted proteomics data set collected from Staphylococcus aureus-infected murine kidney, 4 and 10 days postinfection. The data analysis process rapidly filters high-dimensional proteomic data to reveal relevant differentiating species among hundreds to thousands of measured molecules. We employ principal component analysis (PCA) for dimensionality reduction of protein profiles measured by microliquid extraction surface analysis mass spectrometry. Subsequently, k-means clustering of the PCA-processed data groups samples by chemical similarity. Cluster center interpretation revealed a subset of proteins that differentiate between spatial regions of infection over two time points. These proteins appear involved in tricarboxylic acid metabolomic pathways, calcium-dependent processes, and cytoskeletal organization. Gene ontology analysis further uncovered relationships to tissue damage/repair and calcium-related defense mechanisms. Applying our analysis in infectious disease highlighted differential proteomic changes across abscess regions over time, reflecting the dynamic nature of host-pathogen interactions.
A high sensitivity, a high-resolution platform using a structures for lossless ion manipulation (SLIM) ion mobility (IM) filter in conjunction with a triple quadrupole mass spectrometer (QQQ-MS) was developed and implemented as a means to enhance performance for targeted quantitative analysis using ion mobility mass spectrometry (IMMS). Differential mobility spectrometry (DMS) and field asymmetric ion mobility spectrometry (FAIMS) have previously been integrated with QQQ-MS but both typically suffer from limited resolution and ion transmission efficiency (i.e., sensitivity). This new approach uses SLIM to combine mobility pre-filtering, ion enrichment functionality with high-resolution parallelized mobility filtering on multiple ion paths to improve the analytical duty cycle. Multiple samples including aldosterone and cortisone as the demonstration of value on a relevant clinical application were analyzed on a standalone SLIM mobility filter as well as a SLIM-QQQ system employed in various operating modes to evaluate the duty cycle (i.e., sensitivity) improvement. 100% duty cycle SLIM mobility filtering for Agilent tuning mix ions was achieved with two identical ion paths implemented to allow for parallelization of the analysis. A ∼30–80% practical ion utilization efficiency was measured on the SLIM-QQQ system in “mobility” and “m/z” two-dimensional filtering mode for various analytes from standard mixtures, improving selectivity while also realizing a modest increase in detection/quantification limits (1.5-3x improvement). We believe this new SLIM-QQQ approach will provide a fast, high sensitivity, and high specificity targeted quantitative analysis tool which dramatically improves the ion utilization efficiency as compared to previous IMS-enabled mass spectrometry approaches.
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Context: The percentage of free prostate-specific antigen (PSA) in serum has been shown to enhance the specificity of PSA testing for prostate cancer detection, but earlier studies provided only preliminary cutoffs for clinical use. Objective: To develop risk assessment guidelines and a cutoff value for defining abnormal percentage of free PSA in a population of men to whom the test would be applied. Design: Prospective blinded study using the Tandem PSA and free PSA assays (Hybritech Inc, San Diego, Calif). Setting: Seven nationwide university medical centers. Participants: A total of 773 men (379 with prostate cancer, 394 with benign prostatic disease) 50 to 75 years of age with a palpably benign prostate gland, PSA level of 4.0 to 10.0 ng/mL, and histologically confirmed diagnosis. Main outcome measures: A percentage of free PSA cutoff that maintained 95% sensitivity for prostate cancer detection, and probability of cancer for individual patients. Results: The percentage of free PSA may be used in 2 ways: as a single cut-off (ie, perform a biopsy for all patients at or below a cutoff of 25% free PSA) or as an individual patient risk assessment (ie, base biopsy decisions on each patient's risk of cancer). The 25% free PSA cutoff detected 95% of cancers while avoiding 20% of unnecessary biopsies. The cancers associated with greater than 25% free PSA were more prevalent in older patients, and generally were less threatening in terms of tumor grade and volume. For individual patients, a lower percentage of free PSA was associated with a higher risk of cancer (range, 8%-56%). In the multivariate model used, the percentage of free PSA was an independent predictor of prostate cancer (odds ratio [OR], 3.2; 95% confidence interval [CI], 2.5-4.1; P < .001) and contributed significantly more than age (OR, 1.2; 95% CI, 0.92-1.55) or total PSA level (OR, 1.0; 95% CI, 0.92-1.11) in this cohort of subjects with total PSA values between 4.0 and 10.0 ng/mL. Conclusions: Use of the percentage of free PSA can reduce unnecessary biopsies in patients undergoing evaluation for prostate cancer, with a minimal loss in sensitivity in detecting cancer. A cutoff of 25% or less free PSA is recommended for patients with PSA values between 4.0 and 10.0 ng/mL and a palpably benign gland, regardless of patient age or prostate size. To our knowledge, this study is the largest series to date evaluating the percentage of free PSA in a population representative of patients in whom the test would be used in clinical practice.
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Identifi cation of disease biomarkers has signifi cantly increased the interest for study of the human plasma proteome. Unfortunately, disease biomarkers often appear at low concentrations. The plasma proteome has a large dynamic range of individual protein concentrations (10 orders of magnitude); therefore, identifi cation of low copy number proteins of interest is diffi cult due to the confounding presence of higher abundance proteins. The ProteoPrep ® 20 antibody-based resin has been developed to help address these problems by depleting 97-98% of total protein from plasma. This technology removes more proteins than any other comparable product currently available. Depletion of these high abundance proteins allows for visualization of proteins comigrating with, and masked by, the high abundance proteins and peptides, using methods such as LC and 2DE gels. Secondarily, depletion allows for individual proteins to be loaded at higher levels for improved visualization/ detection of lower abundance proteins. Removing 20 of the most abundant proteins from serum and/or plasma leads to the unmasking of more low copy number proteins and enables loading and detection of more proteins of interest. These combined effects will potentially fuel the discovery of proteins of biological or medical signifi cance.  Identifi cation of potential biomarkers is especially diffi cult due to the presence of higher abundance proteins. Depletion of these abundant proteins allows for visualization of proteins that comigrate with, and are masked by, the high abundance proteins on 1DE or 2DE gels. Plasma proteins can then be loaded onto the gels or IPG strips at higher levels for improved visualization and detection of low copy number proteins.  An affi nity resin has been developed for removal of 20 high abundance proteins from 8 mL of plasma. Depletion of these 20 high abundance proteins removes greater than 97% of the proteins in plasma and permits loading of 20- to 50-fold more of each individual protein for improved visualization of lower copy number proteins.
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Selected reaction monitoring (SRM), also called multiple reaction monitoring, has become an invaluable tool for targeted quantitative proteomic analyses, but its application can be compromised by nonoptimal selection of transitions. In particular, complex backgrounds may cause ambiguities in SRM measurement results because peptides with interfering transitions similar to those of the target peptide may be present in the sample. Here, we developed a computer program, the SRMCollider, that calculates nonredundant theoretical SRM assays, also known as unique ion signatures (UIS), for a given proteomic background. We show theoretically that UIS of three transitions suffice to conclusively identify 90% of all yeast peptides and 85% of all human peptides. Using predicted retention times, the SRMCollider also simulates time-scheduled SRM acquisition, which reduces the number of interferences to consider and leads to fewer transitions necessary to construct an assay. By integrating experimental fragment ion intensities from large scale proteome synthesis efforts (SRMAtlas) with the information content-based UIS, we combine two orthogonal approaches to create high quality SRM assays ready to be deployed. We provide a user friendly, open source implementation of an algorithm to calculate UIS of any order that can be accessed online at to find interfering transitions. Finally, our tool can also simulate the specificity of novel data-independent MS acquisition methods in Q1-Q3 space. This allows us to predict parameters for these methods that deliver a specificity comparable with that of SRM. Using SRM interference information in addition to other sources of information can increase the confidence in an SRM measurement. We expect that the consideration of information content will become a standard step in SRM assay design and analysis, facilitated by the SRMCollider.
Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be com pared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.
Summary The Reference Sequence (RefSeq) database provides a biologically non-redundant collection of DNA, RNA, and protein sequences. Each RefSeq represents a single, naturally occurring molecule from a particular organism. RefSeqs are frequently based on GenBank records but differ in that each RefSeq is a synthesis of information, not a piece of a primary research data in itself. Similar to a review article in the literature, a RefSeq is an interpretation by a particular group at a particular time. RefSeqs can be retrieved in several different ways: by searching the Entrez Nucleotide or Protein database, by BLAST searching, by FTP, or through links from other NCBI resources.
Ion transport in gases is characterized, with an emphasis on recent theoretical analyses. Chapters are devoted to the measurement of drift velocities and longitudinal diffusion coefficients, the measurement of transverse diffusion coefficients, stationary afterglow techniques, the kinetic theory of mobility and diffusion, the accuracy of recent theoretical results, interaction potentials and transport coefficients, and special topics and applications of the theory. Diagrams, drawings, graphs, and extensive tables of numerical data are included.
The ability to effectively focus and transmit ions from relatively high pressure ion sources is a key factor that affects sensitivity and dynamic range in mass spectrometry. To improve upon the mass spectrometric sensitivity achievable with electrospray ionization sources a novel ion funnel interface has been developed and implemented with a triple quadrupole mass spectrometer. The ion funnel effectively consists of a series of ring electrodes of progressively smaller internal diamter to which RF and DC electric fields are co-applied. The electric fields create a pseudo-potential causing the collisionally damped ions to be more effectively focused and transmitted as a collimated ion beam. The ion funnel concept we describe is supported by results of SIMION simulations, ion current measurements and implementation with a mass spectrometer. Electrospray ionization mass spectra for an initial ion funnel configuration demonstrated over an order of magnitude increase in signal relative to that of the instrument operated in its standard (capillary inlet-skimmer) configuration under similar conditions. © 1997 John Wiley & Sons, Ltd.
Epithelial ovarian carcinoma has in general a poor prognosis since the vast majority of tumors are genomically unstable and clinically highly aggressive. This results in rapid progression of malignancy potential while still asymptomatic and thus in late diagnosis. It is therefore of critical importance to develop methods to diagnose epithelial ovarian carcinoma at its earliest developmental stage, that is, to differentiate between benign tissue and its early malignant transformed counterparts. Here we present a shotgun quantitative proteomic screen of benign and malignant epithelial ovarian tumors using iTRAQ technology with LC-MALDI-TOF/TOF and LC-ESI-QTOF MS/MS. Pathway analysis of the shotgun data pointed to the PI3K/Akt signaling pathway as a significant discriminatory pathway. Selected candidate proteins from the shotgun screen were further confirmed in 51 individual tissue samples of normal, benign, borderline or malignant origin using LC-MRM analysis. The MRM profile demonstrated significant differences between the four groups separating the normal tissue samples from all tumor groups as well as perfectly separating the benign and malignant tumors with a ROC-area of 1. This work demonstrates the utility of using a shotgun approach to filter out a signature of a few proteins only that discriminates between the different sample groups.
Ion mobility coupled with mass spectrometry has evolved into a powerful analytical technique for investigating the gas-phase structures of bio-molecules. Here we present the mobility separation of some peptide and protein ions using a new hybrid quadrupole/travelling wave ion mobility separator/orthogonal acceleration time-of-flight instrument. Comparison of the mobility data obtained from the relatively new travelling wave separation device with data obtained using various other mobility separators demonstrate that whilst the mobility characteristics are similar, the new hybrid instrument geometry provides mobility separation without compromising the base sensitivity of the mass spectrometer. This capability facilitates mobility studies of samples at analytically significant levels.