The proteomic advantage: label-free quantification of proteins expressed in bovine milk during experimentally induced coliform mastitis.

Abstract

Coliform mastitis remains a primary focus of dairy cattle disease research due in part to the lack of efficacious treatment options for the deleterious side effects of exposure to LPS, including profound intra-mammary inflammation. To facilitate new veterinary drug approvals, reliable biomarkers are needed to evaluate the efficacy of adjunctive therapies for the treatment of inflammation associated with coliform mastitis. Most attempts to characterize the host response to LPS, however, have been accomplished using ELISAs. Because a relatively limited number of bovine-specific antibodies are commercially available, reliance on antibodies can be very limiting for biomarker discovery. Conversely, proteomic approaches boast the capability to analyze an unlimited number of protein targets in a single experiment, independent of antibody availability. Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), a widely used proteomic strategy for the identification of proteins in complex mixtures, has gained popularity as a means to characterize proteins in various bovine milk fractions, both under normal physiological conditions as well as during clinical mastitis. The biological complexity of bovine milk has, however, precluded the complete annotation of the bovine milk proteome. Conventional approaches to reducing sample complexity, including fractionation and the removal of high abundance proteins, has improved proteome coverage, but the dynamic range of proteins present, and abundance of a relatively small number of proteins, continues to hinder comparative proteomic analyses of bovine milk. Nonetheless, advances in both liquid chromatography and mass spectrometry instrumentation, including nano-flow liquid chromatography (nano-LC), nano-spray ionization, and faster scanning speeds and ionization efficiency of mass spectrometers, have improved analyses of complex samples. In the current paper, we review the proteomic approaches used to conduct comparative analyses of milk from healthy cows and cows with clinical mastitis, as well as proteins related to the host response that have been identified in mastitic milk. Additionally, we present data that suggests the potential utility of LC-MS/MS label-free quantification as an alternative to costly labeling strategies for the relative quantification of individual proteins in complex mixtures. Temporal expression patterns generated using spectral counts, an LC-MS/MS label-free quantification strategy, corresponded well with ELISA data for acute phase proteins with commercially available antibodies. Combined, the capability to identify low abundance proteins, and the potential to generate temporal expression profiles, indicate the advantages of using proteomics as a screening tool in biomarker discovery analyses to assess biologically relevant proteins modulated during disease, including previously uncharacterized targets.

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Veterinary Immunology and Immunopathology 138 (2010) 252–266
Contents lists available at ScienceDirect
Veterinary Immunology and Immunopathology
journal homepage: www.elsevier.com/locate/vetimm
Research paper
The proteomic advantage: Label-free quantification of proteins
expressed in bovine milk during experimentally induced
coliform mastitis
Jamie L. Boehmera,∗, Jeffrey A. DeGrasseb, Melinda A. McFarlandb, Elizabeth A. Talla,
Kevin J. Shefcheckb, Jeffrey L. Warda, Douglas D. Bannermanc,1
aU.S. Food and Drug Administration Center for Veterinary Medicine, 8401 Muirkirk Road, Laurel, MD 20708, United States
bU.S. Food and Drug Administration Center for Food Safety and Applied Nutrition, College Park, MD 20740, United States
cBovine Functional Genomics Laboratory, USDA-Agricultural Research Service, Beltsville, MD 20705, United States
a r t i c l e i n f o
Keywords:
Bovine milk proteome
Liquid chromatography/tandem mass
spectrometry (LC–MS/MS)
Coliform mastitis
Label-free quantification
a b s t r a c t
Coliform mastitis remains a primary focus of dairy cattle disease research due in part to
the lack of efficacious treatment options for the deleterious side effects of exposure to
LPS, including profound intra-mammary inflammation. To facilitate new veterinary drug
approvals, reliable biomarkers are needed to evaluate the efficacy of adjunctive therapies
for the treatment of inflammation associated with coliform mastitis. Most attempts to
characterize the host response to LPS, however, have been accomplished using ELISAs.
Because a relatively limited number of bovine-specific antibodies are commercially avail-
able, reliance on antibodies can be very limiting for biomarker discovery. Conversely,
proteomic approaches boast the capability to analyze an unlimited number of protein tar-
gets in a single experiment, independent of antibody availability. Liquid chromatography
coupled to tandem mass spectrometry (LC–MS/MS), a widely used proteomic strategy for
the identification of proteins in complex mixtures, has gained popularity as a means to
characterize proteins in various bovine milk fractions, both under normal physiological
conditions as well as during clinical mastitis. The biological complexity of bovine milk
has, however, precluded the complete annotation of the bovine milk proteome. Conven-
tional approaches to reducing sample complexity, including fractionation and the removal
of high abundance proteins, has improved proteome coverage, but the dynamic range of
proteins present, and abundance of a relatively small number of proteins, continues to
hinder comparative proteomic analyses of bovine milk. Nonetheless, advances in both liq-
uid chromatography and mass spectrometry instrumentation, including nano-flow liquid
chromatography (nano-LC), nano-spray ionization, and faster scanning speeds and ion-
ization efficiency of mass spectrometers, have improved analyses of complex samples.
In the current paper, we review the proteomic approaches used to conduct comparative
analyses of milk from healthy cows and cows with clinical mastitis, as well as proteins
related to the host response that have been identified in mastitic milk. Additionally, we
present data that suggests the potential utility of LC–MS/MS label-free quantification
as an alternative to costly labeling strategies for the relative quantification of individ-
ual proteins in complex mixtures. Temporal expression patterns generated using spectral
counts, an LC–MS/MS label-free quantification strategy, corresponded well with ELISA data
∗Corresponding author. Tel.: +1 301 210 4281; fax: +1 301 210 4685.
E-mail address: jamie.boehmer@fda.hhs.gov (J.L. Boehmer).
1Present address: United States Department of Veterans Affairs, Office of Research Oversight, Washington, DC 20420, United States.
0165-2427/$ – see front matter. Published by Elsevier B.V.
doi:10.1016/j.vetimm.2010.10.004

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J.L. Boehmer et al. / Veterinary Immunology and Immunopathology 138 (2010) 252–266
253
for acute phase proteins with commercially available antibodies. Combined, the capability
to identify low abundance proteins, and the potential to generate temporal expression pro-
files,indicatetheadvantagesofusingproteomicsasascreeningtoolinbiomarkerdiscovery
analyses to assess biologically relevant proteins modulated during disease, including pre-
viously uncharacterized targets.
Published by Elsevier B.V.
1. Background
Mastitis caused by gram negative pathogens remains
a principal focus of veterinary research due to stag-
gering economic loss, the limited number of efficacious
treatment options, and the lack of accurate biomarkers
to evaluate the efficacy of new animal drugs proposed
as adjunctive therapies. The need to better understand
the host response to gram negative pathogens, and to
identify reliable biomarkers specific to coliform mastitis,
has led to several investigations into the soluble medi-
ators of innate immunity in the bovine mammary gland
(reviewed in Bannerman, 2009). Historically, characteri-
zation of the bovine innate immune response to LPS, and
the quantification of changes in mediators of inflamma-
tion present in bovine milk during coliform mastitis has,
however, been dominated by the use of ELISAs. While
ELISAs feature both accuracy and specificity, antibody-
based strategies are restricted by the ability to detect and
quantifyonlyoneproteinatatime,andbyarelianceonthe
availability or development of species-specific antibodies.
ELISAs, therefore, have little application to the discovery of
novel inflammatory mediators, as currently only a limited
number of bovine-specific antibodies are commercially
available.
Proteomics, defined as the identification and character-
ization of all proteins within a cell or tissue (Colantonio
andChan,2005),boastsasignificantadvantageoverELISAs
in that proteomics involves the use of analytical method-
ologies, such as liquid chromatography (LC) and mass
spectrometry (MS), to isolate, identify, and characterize
proteins, and is not reliant on the use or availability of anti-
bodies. The use of proteomics is also much less restrictive
than ELISAs in that theoretically, an unlimited number of
proteins can be analyzed simultaneously in a given exper-
iment using proteomic strategies. Furthermore, advances
in soft ionization techniques in mass spectrometry, includ-
ing electro-spray ionization (ESI), nano-spray ionization,
and matrix-assisted laser desorption/ionization (MALDI),
have broadened the applications of mass spectrometry to
include the characterization of biopolymers such as intact
proteins and peptides (reviewed in Mann et al., 2001).
Previous studies have utilized proteomic strategies
in attempts to identify protein biomarkers of the host
responsepresentinwheyfrombovinemilkduringmastitis
(Boehmeretal.,2008,2010;Smolenskietal.,2007;Hogarth
et al., 2004). In most cases, however, a limited number of
low abundance proteins have been robustly identified in
comparative proteomic analyses of the bovine milk pro-
teome.Bettercharacterizationofextremelylowabundance
proteins in bovine milk following infection with a gram
negative pathogen was of specific interest to our group,
because the assessment of the modulation in low abun-
dance proteins during disease could prove useful in the
establishment of biomarkers of coliform mastitis for use
both as diagnostic tools, and as indicators of drug efficacy.
Biomarker discovery in bovine milk has, however,
been hindered both by prominent proteomic bottlenecks,
as well as other experimental factors. The most signif-
icant factor that has precluded the identification of a
larger number of low abundance proteins related to host
response in milk, and one of the most common obsta-
cles in proteomic analyses, is the biological complexity of
the matrix. The analytical challenges associated with the
complexity of milk include protein proteolysis, the numer-
ous post-translational modifications (PTMs) that occur in
milk proteins including glycosylation, phosphorylation,
and disulfide bond formation, as well as the dynamic range
of proteins in milk (Gagnaire et al., 2009; O’Donnell et
al., 2004). The profound relative abundance of a limited
number of proteins in bovine milk is arguably the most
challenging aspect of the proteomic analysis of milk, as the
presence of abundant proteins often prohibits the robust
identification of low abundance components. Compara-
tive analyses of bovine milk are further confounded by the
dynamic range of proteins present in milk, because milk
collected from healthy cows is characterized by the abun-
dance of the caseins and whey proteins ?-lactoglobulin
and ?-lactalbumin, while milk collected from cows with
coliform mastitis is marked by the profound increase in
vascular-derived proteins, most notably serum albumin
(Fig. 1).
Conventional approaches to reducing sample complex-
ity prior to analysis, including the selective depletion of
high abundance proteins and fractionation of samples,
have not yet been effectively adapted to address the spe-
cific complexities of bovine milk. For example, attempts to
remove high abundance proteins, including casein deple-
tion by acid precipitation (Hogarth et al., 2004) and the
removal of immunoglobulins by immunoaffinity (Yamada
et al., 2002), have resulted in a rather drastic reduction
in the number of milk proteins identified when compared
to proteomic analyses of bovine milk that did not involve
removal of high abundance proteins (Boehmer et al., 2008;
Smolenski et al., 2007). An additional complication related
to the comparative analyses of bovine milk is that mul-
tiple strategies are required to reduce the complexity of
normal versus mastitic milk samples, as the dynamics of
protein abundance change during inflammation. Likewise,
it has been demonstrated that the removal of serum albu-
min,whichwouldbenecessarytoreducethecomplexityof
mastitic milk samples, can lead to the non-specific deple-
tionoflowabundanceproteins(Gundryetal.,2007),which
could interfere with biomarker discovery.

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Fig. 1. Differential protein expression in whey from normal milk (A)
and whey from mastitic milk (B) profiled using 2-dimensional gel
electrophoresis (Boehmer et al., 2008). Whey from normal milk is char-
acterized by the abundance of the casein and whey proteins, while whey
from mastitic milk, in contrast, is characterized by the increased abun-
dance of serum albumin and other vascular-derived proteins including
serotransferrin and fibrinogen.
Independent of matrix complexity, another critical
aspect when considering biomarker discovery and the uti-
lization of biomarkers to evaluate disease progression, or
theefficacyofadrugtreatment,istheevaluationofprotein
changesduringthecourseofclinicaldisease(Simpsonetal.,
2009; Mueller et al., 2008; Old et al., 2005). Regrettably,
most prior studies of protein modulation in the bovine
milk proteome during disease focused solely on normal
versus mastitic milk, rather than changes in protein abun-
dance over the course of infection (Boehmer et al., 2008;
Smolenski et al., 2007; Hogarth et al., 2004).
In biomarker discovery, however, there is still no “gold
standard” for the accurate quantification of individual
proteins in complex biological samples using proteomic
strategies,especiallyforproteinspresentinlowabundance
(Mueller et al., 2008). Several labeling strategies are avail-
able for the quantification of proteins in conjunction with
LC–MS/MS analyses, but labeling strategies can be cost
limiting, often require pair wise comparisons which can
be problematic when quantifying proteins that are only
present in a given physiological state, and labeling strate-
giesdonotallowforretrospectivequantification(Oldetal.,
2005). Consequently, label-free methods for the relative
quantification of proteins in complex biological samples
have been investigated, including the use of ion intensity,
the number of unique peptides assigned to a given protein,
and spectral counts as measures of relative abundance for
individual proteins in a complex sample (Old et al., 2005;
Zybailov et al., 2005; Liu et al., 2004). Label-free meth-
ods have gained popularity primarily because there are no
associated costs, normalization can allow for comparisons
of protein abundance across a longitudinal set of samples,
and analyses can be conducted retrospectively (Mueller
etal.,2008).Arecentstudyconductedbyourgroupfocused
on changes in the relative abundance of milk proteins over
the course of an experimentally induced coliform infection
using peptide counts, a label-free strategy, but the pro-
teins evaluated were mainly medium to high abundance
proteins (Boehmer et al., 2010).
In addition to expanding our knowledge of the bovine
milk proteome, an added objective was to further evalu-
ate the feasibility of using label-free LC–MS/MS strategies
as a screening tool to identify biologically relevant pro-
teins modulated during disease, especially low-abundance
proteins,andproteinsforwhichtherearenoavailableanti-
bodies. To assess the validity of using spectral counts to
quantify changes in proteins for which no antibody has
beendeveloped,weconductedacomparisonoftheexpres-
sion of milk proteins determined using LC–MS/MS data
with expression profiles generated using an ELISA, simi-
lar to comparisons made in our previous studies (Boehmer
et al., 2010).
A second interest was the evaluation of samples col-
lected over the course of infection from several biological
replicates. Our aim was the discovery of a reproducible
biomarker or pattern of biomarkers that presented in
several biological replicates, as consistent patterns could
suggest both a response that was indicative of coliform
mastitis, as well as the time frame following infection dur-
ing which the potential biomarker or biomarkers could
be accurately monitored. Accordingly, we sought to deter-
mine if refinements in proteomic methodology, including
investigations into utilizing more advanced approaches
such as a mass spectrometer with a faster scanning speed
and the ability to trap ions, and the use of nano-flow liq-
uid chromatography in-line with nano-spray ionization,
could enable the identification and characterization of a
greater number of low abundance proteins when com-
pared to earlier analyses of whey from mastitic bovine
milk (Boehmer et al., 2008, 2010; Smolenski et al., 2007;
Hogarth et al., 2004). Following recent analyses detailed in
the current paper, the number of low abundance proteins
identified suggests that proteomics could lead to a more
thorough annotation of the bovine milk proteome. Addi-
tionally, the close correspondence of LC–MS/MS label-free
data and ELISA data was a positive indication that pro-
teomic strategies could serve as valuable screening tools
for biomarker discovery, as well as the establishment of
biomarkers specific to coliform mastitis.
2. Proteomic tools, strategies and challenges
Protein identification through the use of mass spec-
trometry can be divided into two main categories, referred
to as top-down and bottom-up. The primary distinguish-
ing features between the two main proteomic approaches
is the isolation and fragmentation of intact proteins using
mass spectrometry in a top-down approach, versus prote-
olyticdigestionofmixturesofproteins,andthesubsequent
separation and fragmentation of peptides, in bottom-up
proteomics.Identificationofproteinsincomplexbiological

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mixtures using bottom-up proteomics is reliant upon the
measurement of the masses of the peptides that are gen-
erated following proteolytic cleavage of the proteins. The
mass of a peptide is determined using mass spectrometry,
and is based upon a mass: charge ratio (m/z). Charged pep-
tides are generated as a result of ionization, or the addition
ofaprotontothepeptide,whichresultsintheconversionof
the peptide into an ion. The two most popular forms of ion-
ization used in bottom-up proteomic analyses are ESI and
MALDI. The primary features that distinguish MALDI from
ESIarethematricesusedforionization,thechargestatesof
the ions generated, and the actual mechanisms of ion for-
mation characteristic of each method (reviewed in Mann
et al., 2001). Similar in concept and ion formation to ESI,
nanospray is another ionization method that has become
very popular in proteomic analyses in recent years. The
primary distinctions between ESI and nanospray are the
significantlylowerflowratesandsmallerneedlediameters
used for nano-spray. An advantage of nano-spray ioniza-
tion is that nano-spray sources can accommodate much
lower flow rates than ESI, down to fractions of microliters
per minute (Wilm and Mann, 1996). Additionally, droplet
formation occurs more readily using nano-spray, resulting
in increased ionization efficiency. The lower flow rates in
the nanospray technique also allow for a longer length of
analysistime,whichleadstofewermissedpeptideseluting
off the chromatographic column while the mass spectrom-
eter is engaged in MS/MS scans (Wilm and Mann, 1996).
Since the invention of soft ionization techniques,
bottom-up proteomic strategies including the use of LC
to separate peptides coupled with MS/MS for peptide
sequencing, a process commonly referred to as LC–MS/MS,
has become the most extensively applied bottom-up
proteomicapproachfortheidentificationofindividualpro-
teins in complex mixtures. LC–MS/MS involves proteolytic
digestion of complex protein mixtures followed by the
separation of peptides using one- or two-dimensional LC,
and analysis of the peptides by MS/MS (reviewed in Mann
et al., 2001). An enzyme commonly used to cut proteins
into peptides is tryspin, which digests at the amino acid
residues arginine and lysine. Peptide mixtures are sepa-
rated, prior to introduction into the mass spectrometer
for mass analysis, by passage over a chromatographic col-
umn and subsequent separation based on either charge,
called ion exchange LC, or hydrophobicity, which is termed
reverse phase (RP) LC. The number of proteins identified
using LC–MS/MS is directly dependent on the efficiency
of peptide separation (reviewed in Mann et al., 2001).
In data-dependent acquisitions, the mass spectrometer is
programmed to scan the masses of ionized peptides and
to select anywhere from 3 to 10 most abundant peptides
forfurtherfragmentationbycollision-induceddissociation
(CID). An inert gas introduced into the collision cell of the
mass spectrometer during CID induces fragmentation of
the peptides, a process which results in the production of
a tandem mass spectrum. Peak lists distilled from tandem
mass spectra are used to query against an MS/MS spectra
database to determine peptide identity, and the sequenced
peptides are assigned to proteins for protein identification.
When the chromatographic separation of peptides is
poor, the potential for selection of a peptide from a low
abundanceproteinforCIDwilldecrease,duetothefactthat
peptides from dominant proteins will be in greater num-
bers in the sample and will be preferentially selected for
further fragmentation. Additionally, poorly resolved pep-
tides tend to co-elute off the chromatographic column into
the mass spectrometer, a phenomena which leads to CID of
more than one peptide at a time. The tandem mass spec-
trum that results from co-eluting peptides thus represents
the fragmentation of a peptide mixture, and will often fail
to yield a match when searched against a protein database,
or will lead to a false positive peptide assignment.
2.1. Biological complexity and proteomic bottlenecks
Proteomic strategies, though capable of analyzing
a theoretically unlimited number of proteins in a
single experiment, are not devoid of challenges. Post-
translational modifications (PTMs) of proteins in a given
proteome, and the dynamic range of proteins present in
the sample, are direct reflections of the complexity of the
biological matrix, and can pose significant roadblocks to
protein identification. Dynamic range is one of the most
prominent bottlenecks in proteomic experiments because
many biological samples, including milk, are characterized
by the presence of a select number of highly abundant pro-
teinsthataccountforalargepercentageofthetotalprotein
concentrationinthesample,andnumerouslowabundance
proteins that comprise a very small percentage of protein
concentration(Gagnaireetal.,2009;O’Donnelletal.,2004).
Given the fact that abundant proteins are often affiliated
with a variety of biological functions and pathways, and
thus rarely meet the specificity criteria necessary to be
termed a biomarker of disease, the removal or depletion
of abundant proteins is a common first step in proteomic
analyses aimed at biomarker discovery.
In some cases, however, removal of abundant proteins
from a complex matrix can also result in the non-selective
depletionoflowabundanceproteins,aconsequencewhich
can cause the loss of potentially relevant diagnostic, clin-
ical, and biological information. Albumin, which accounts
for nearly 55% of the total protein concentration of plasma,
is often targeted for removal prior to proteomic analy-
sis. Investigation into the albuminome, or the number of
proteins that bind to, or are associated with, albumin in
plasma and are thus depleted along with the abundant
protein following the application of depletion strategies,
revealed that as many as 35 high and low abundance pro-
teins were bound to and removed along with albumin
following an affinity removal process (Gundry et al., 2007).
The increased concentration of albumin in milk during col-
iform mastitis, due to the breakdown of the blood–milk
barrier following exposure to LPS, presents a significant
analytical roadblock for the identification of low abun-
danceproteins.Abundantalbuminpeptidesoftenmaskthe
detection of low abundance proteins, and albumin deple-
tion could result in the loss of low abundance proteins that
potentially bind to albumin in milk.
There are many strategies available commercially that
are designed to remove or deplete high abundance
proteins, most notably serum albumin, from biological
samples in order to enhance the detection of low abun-

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dance proteins. Albumin removal strategies often involve
an analytical column or disc pre-packed with a form of
Cibacron Blue, a sulfonated triazine dye used for affin-
ity chromatography, immobilized onto a support matrix
(Angal and Dean, 1977). For the removal of several
high abundance proteins simultaneously, multiple affinity
removal system (MARS) affinity spin and chromatographic
columns are commercially available from Agilent Tech-
nologies. Low abundance protein enrichment strategies
are also commercially available, including a widely used
product from Bio-Rad Laboratories called ProteoMiner.
ProteoMiner columns consist of beads containing a highly
diverselibraryofhexapeptides,eachwithaspecificprotein
affinity, bound to chromatographic supports. The theory
behind ProteoMiner is that proteins in a given biological
sample, when passed over the beads, will bind to spe-
cific ligands, out-competing high abundance proteins, and
allowing excess proteins to wash off the column as flow-
through. Proteins bound to the beads on the ProteoMiner
sample column are eluted, and the resulting protein pool
is predicted to contain a more equivalent representation of
both high and low abundance proteins present in the given
sample. Nearly all of the depletion, removal, or enrich-
ment strategies that are available, however, have been
optimized, and are intended, for use with human serum
or plasma. Used according to the manufacturer’s recom-
mended protocol, the ProteoMiner enrichment strategy
was extremely effective at depleting serum albumin from
mastitic milk samples, but also resulted in the depletion of
several other milk proteins (Fig. 2). While the enrichment
forlowabundancetargetsinmastiticmilkwaseffectivefor
proteins between 10 and 15kD, most of the spots in that
range were identified as proteolysis products of the casein
proteins (data not shown). In order to make effective use of
commercially available strategies for the reduction of sam-
ple complexity, optimization would have to be performed
for milk samples specifically, which might not be econom-
ically feasible for some studies, given the high cost of many
of the kits.
In addition to serum albumin, several proteins whose
presence in blood increases during the inflammatory
response such as complement, clotting factors, adhesion
molecules, and acute phase proteins increase in concen-
tration in milk during coliform mastitis, due to the well
characterizedbreak-downoftheblood–milkbarrier.Many
of the proteins that leak into the milk from systemic cir-
culation are very large glycoproteins that become heavily
modified during the course of infection (Kjeldsen et al.,
2003; Soerensen et al., 1995), an aspect that further com-
plicates analytical challenges. Accurate identification of
modified proteins can require specialized sample prepara-
tion prior to analysis, the inclusion of numerous potential
variable modifications when querying peak lists against
protein databases, or the use of fragmentation strategies
other than CID, including electron-transfer dissociation
(ETD).
While sample complexity reduction strategies may
not be feasible approaches for enriching low abundance
targets in milk, proteomic capabilities, including both
instrumentation and fractionation options, continue to
advance. Utilizing the features of different instrument
Fig. 2. The two dimensional profiles of whey from mastitic bovine milk
following protein enrichment using ProteoMiner (BioRad Laboratories).
The abundance of the protein serum albumin, indicated in box 1 with the
white arrow, is clearly lower in the profile of proteins eluted off the beads
containing a highly diverse library of hexapeptides (A) when compared
to the profile of the proteins in the flow-through that did not bind to the
beads (B). Likewise, smaller proteins at the bottom of the gel, in box 2,
appeared to be more abundant in the protein pool eluted off the beads
(A) when compared to the column flow through (B). Also apparent is the
fact that the majority of the proteins appear in the flow through (B), as
opposedtobeingenrichedbybindingtothebeads(A).Theuseofstrategies
such as ProteoMiner, which was developed and optimized for use with
plasma and serum samples, may not be feasible or may require added
optimization steps, when used on complex biological samples such as
bovine milk.
systems, such as mass spectrometers with faster scan-
ning speeds and increased ion transmission capabilities,
in lieu of instruments with higher mass accuracy, could
lead to more protein identifications, as well as targets for
more focused analyses aimed at quantification or mass
accuracy. Nano-flow liquid chromatography in-line with a
mass spectrometer equipped with a nano-spray ionization
source could likewise result in more robust identification
of low abundance proteins, as nano-spray ionization has
demonstrated advantages over traditional ESI for protein
identification (Juraschek et al., 1998). Advances in LC and
nano-flow LC, including two-dimensional LC separation
strategies, could also drastically improve peptide separa-
tions and lead to additional protein identifications.
2.2. Quantification
An important criterion for the establishment of quality
biomarkers is reliable quantification. Accordingly, relative
and absolute quantification of changes in biomarkers in
biological matrices using proteomic strategies is a topic
that has garnered significant attention in recent years
(Simpson et al., 2009; Mueller et al., 2008; Fenselau,
2007; Roe and Griffin, 2006). Quantification methods can