Proteomic and Metabolomic Approaches to Biomarker Discovery || Proteomic and Mass Spectrometry Technologies for Biomarker Discovery

C H A
P T E R
2

Proteomic and Mass SpectrometryTechnologies for Biomarker DiscoveryAndrei P. Drabovich1, Maria P. Pavlou2, Ihor Batruch1,

Eleftherios P. Diamandis1e4

1Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON, Canada,2Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada,

3Department of Clinical Biochemistry, University Health Network, Toronto, ON, Canada,4Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, ON, Canada

Ph

O U T L I N E

Introduction 1
8
Protein Biomarker Discovery andDevelopment Pipeline 18

Proteomic Samples 20

Protein Identification by Mass Spectrometry 22

roteomttp://

Protein Digestion
23 Protein and Peptide Separation Techniques 23 Protein and Peptide Ionization Techniques 23 Mass Spectrometry Instrumentation 24 Deconvolution and Database Search of
Tandem Mass Spectra
25
Post-Translational Modifications as DiseaseBiomarkers 25

Protein Quantification by Mass Spectrometry 27

ic and Metabolomic Approaches to Biomarker Discoverydx.doi.org/10.1016/B978-0-12-394446-7.00002-9 17

Label-Free Quantification
27 Metabolic and Enzymatic Labeling 28 Chemical Labeling 29 Selected Reaction Monitoring Assays 29 Separation and Enrichment Strategies for
Quantification of Low-AbundanceProteins
30
Biomarker Verification 30

Biomarker Validation 31

Limitations of Mass Spectrometry for ProteinBiomarker Discovery 32

Conclusions and Future Outlook: IntegratedBiomarker Discovery Platform 32

References 33

Copyright � 2013 Elsevier Inc. All rights reserved.

http://dx.doi.org/10.1016/B978-0-12-394446-7.00002-9

2. PROTEOMIC AND MASS SPECTROMETRY TECHNOLOGIES FOR BIOMARKER DISCOVERY18

NONSTANDARD ABBREVIATIONS

Da DaltonsELISA enzyme-linked immunosorbent assayESI electrospray ionizationFDA the U.S. Food and Drug AdministrationFWHM full width at half maximumLC liquid chromatographym/z mass-to-charge-ratioMALDI matrix-assisted laser desorption/

ionizationMS mass spectrometry/spectrometerMS1 mass spectrum collected for all precursor

ions in sample prior to fragmentationMS/MS tandem mass spectrometry, or mass

spectrum collected for fragment ionsPTM post-translational modificationSILAC stable isotope labeling by amino acids

in cell cultureSRM selected reaction monitoringTOF time-of-flight mass spectrometryXIC extracted ion chromatogram

INTRODUCTION

Proteomics is defined as a large-scale study ofprotein expression, structure, and function intime and space. Relative to genome, transcrip-tome, or metabolome analysis, large diversityof protein sequences and multiple post-translational modifications make proteome anal-ysis an even more challenging undertaking.Unlike the genome, the proteome is dynamic;a static set of genes may result in differentproteomic phenotypes depending on the devel-opmental stage of an organism and environ-mental factors. The dynamic nature of theproteome results in a wide range of protein refer-ence values in healthy individuals, thus compli-cating the clinical applications of proteomics.

The last two decades have seen an impressiveprogress in proteomics, mainly due to significantadvances in mass spectrometry, high-throughputantibody production, and bioinformatics and

biostatistics algorithms. The Human ProteomeProject was launched in September 2010 witha goal to identify and characterize at least oneprotein product for each of the 20,300 protein-coding genes.1 Disease-driven initiatives of theHuman Proteome Project lay the foundation forclinical and diagnostic applications of proteins,such as development of disease biomarkers.

PROTEIN BIOMARKER DISCOVERYAND DEVELOPMENT PIPELINE

Development of protein biomarkers isa multiple phase procedure, analogous to thedrug development process. The biomarker devel-opment pipeline includes the formulation ofa specific clinical question, identification ofproteins, selectionof biomarker candidates, verifi-cation of candidates in an independent cohort ofsamples, rigorous validation of candidates, devel-opment and validation of a clinical assay, andfinally assay approval by regulatory healthagencies, such as the U.S. Food and DrugAdministration (FDA) or the EuropeanMedicinesAgency (Figure 1). The cost of a biomarker devel-opment study is estimated in the range of one-tenth of a drug development study; thediscovery-to-clinical assay timeline may exceedmany years. For example, the latest cancerbiomarker cleared by the FDA, the HE4 protein,was discovered in 2000,2 but its clinical assaywas only approved in 2008.3

Prior to the launch of a biomarker discoverystudy, one should first consider unmet clinicalneeds, decide whether a diagnostic molecule hasa potential to answer a specific clinical questionwith a certain confidence, and predict whetherthe answer would aid in physicians’ decisionmaking. It should always be acknowledged thatthe clinical decision will be made based ona biomarker performance in combination withnoninvasive medical imaging techniques, such asmagnetic resonance imaging (MRI) and ultra-sound. Performance of a marker with high area

FIGURE 1 The proteomic biomarkerdevelopment pipeline. As biomarkercandidates proceed through the pipeline,the number of clinical samples increases,while analytical technologies changefrom complex and low-throughputmassspectrometry methods to straightfor-ward and high-throughput immu-noaffinity assays.

PROTEIN BIOMARKER DISCOVERY AND DEVELOPMENT PIPELINE 19

under the receiver operating characteristic (ROC)curvemaynot be theonly requirement for success-ful use of biomarkers in clinics. Instead, based ondisease character and the cost of the follow-upexamination, biomarkers with either higher sensi-tivity or higher specificity may be preferable.4

Different types of genetic sequence features orbiomolecules, such as gene mutations, SNP vari-ants, mRNA transcripts, and metabolites can beused as disease biomarkers. There is a clearadvantage to using proteins as biomarkers, andthis advantage stems from the diversity ofproteins. There is an estimated number of20,300 genes,1 7,900 uniquemetabolites,5 approx-imately 100,000 mRNA transcripts, and up to 1.8million different protein species, if we considerpost-translational modifications.6 Being the ulti-mate products of gene expression, proteinsreflect multiple genomic and transcriptomicalterations in their sequences, post-translationalmodifications, and cellular abundance level.A fraction of proteins is secreted into blood andbiological fluids and can thus be detected bynoninvasive diagnostic tests. The immense diver-sity of protein species increases the chance toidentify a marker, or a panel of markers, foreach disease state. The diversity of protein vari-ants, however, significantly increases the analyt-ical challenge of correct detection andmeasurement of a specific variant in biologicalsamples. For example, detection of a particularnucleotide in the genome of a cell should meetthe analytical challenge of searching through3.2�109 nucleotides, while the detection ofa specific amino acid in interleukin 6 sequence

in blood plasma has the challenge of searchingthrough 1013 aminoacids.7 Use of altered post-translational modifications and protein isoformsas biomarkers would be an even more chal-lenging undertaking due to the even highercomplexity and dynamic turnover of post-translational modifications (PTMs). For thisreason, the majority of protein biomarker studiesare still focused on the search for altered proteinconcentrations in biological samples.

Identification of proteins in cells, tissues, andbiological fluids is dominated by mass spectro-metryebased techniques, even though proteinand antibody arrays found their own niches.8e10

At the protein identification phase of a biomarkerdevelopment pipeline, several thousandprotein species are detected in a limited numberof biological samples. Relative quantificationapproaches are then used to compile a shortlist of candidates for verification in the indepen-dent set of clinical samples.

Biomarker verification is an important step toexclude false positive discoveries made due tothe biological and technological bias introducedat the identification phase. Assays used for veri-fication, such as enzyme-linked immunosorbentassays (ELISA) and selected reaction monitoring(SRM),11 provide accurate and reliable compar-ison of protein levels in dozens to hundreds ofclinical samples.

Validation of protein biomarkers includestesting their performance in very large cohortsof clinical samples. Such studies employ stan-dardized preclinical protein assays, rigorousblinded analysis, and multicenter collaborative


trials. Finally, a clinical assay is developed fora biomarker and subjected to approval by regu-latory health agencies. In vitro diagnostic assaysfor more than 200 unique proteins are currentlyapproved by the FDA,12 and the majority ofthem are based on ELISA. There is no a singlemass spectrometryebased protein assay usedin the clinics yet,13 but a lot of efforts arecurrently aimed toward the introduction ofsuch assays into clinical practice.14e16

PROTEOMIC SAMPLES

The choice of the proteomic sample suitable forbiomarker discovery study depends on a specificclinical question addressed, sample availability,and limitations of a biological model (Figure 2).An array of proteomic samples can be used, butblood plasma or serum are the most relevant bio-logical fluids for biomarkers intended forscreening, diagnostic, or surveillance applica-tions. Blood is the most abundant body fluidand is easily collected by venipuncture, a proce-dure with minimal invasiveness. Given that allorgans are perfused by it, blood reflects the phys-iologic state of the body at any time.17 However,the proteomic analysis of blood plasma or serumis very challenging due to the wide dynamicrange of protein concentrations exceeding tenorders of magnitude and the presence of lipidsand salts. The range of protein concentrations inblood exceeds the dynamic range of mass spec-trometry analyses by five or six orders of magni-tude.18,19 Low-abundant proteins present in theblood are usually masked by high-abundanceproteins, 22 of which constitute 99% of the totalprotein mass.18 In addition, physiologicalconcentrations of salts and lipids interferewith mass spectrometryebased analysis.20

Depletion of high-abundance proteins andextensive fractionation may improve detectionof low-abundance proteins but at the cost ofdecreased throughput and reproducibility ofanalysis.

In the quest for noninvasive diagnosticprotein markers, urine is an attractive biologicalfluid, given that it can be collected noninvasivelyand in large quantities. Although urine proteo-mics has been widely explored for identificationof biomarkers related to renal or urogenitaldisorders, other health conditions such as cancerand inflammation in distant organs may alsoresult in changes of the urine proteome.21,22

Even though urine contains fewer proteinscompared to plasma, the urine proteome is stillcomplex, with more than 1,500 proteins identi-fied in healthy individuals.23 Another challengeof urine is the need for normalization and stan-dardization of protein levels across differentsamples. Protein concentrations in urine dependon the glomerular filtration rate and thus shouldbe normalized against reference molecules suchas creatinine.24 The Human Kidney and UrineProteome Project (HKUPP),25 a HumanProteome Organization (HUPO)esponsoredscientific initiative, provides guidelines for stan-dardized collection and storage of urine samplesalong with protocols for urine sample prepara-tion and aims to construct a reference databaseof normal human urine.

Due to the challenges of biomarker discoveryin blood and urine, the potential of other biolog-ical specimens is being widely explored. Primarysites of disease such as tissues and proximalfluids are attractive alternatives for biomarkercandidate identification and verification.Commonly used proximal fluids include ascites,cerebrospinal fluid, seminal plasma, expressedprostatic secretion, nipple aspirate fluid, saliva,tears, pancreatic juice, and others. Proximal fluidssuch as ascites fluid in pancreatic and ovariancancers26 often enclose the site of the diseaseand accumulate disease-specific proteins result-ing in their higher concentration relative to blood.For example, median levels of CA-125, anovarian cancer biomarker used in the clinics,were found as 696 and 18,563 U/mL in serumand ascites fluid, respectively.27 Proximal diseasefluids, however, are usually collected through the

FIGURE 2 Proteomic samples used for biomarker discovery, along with their advantages and limitations. Tissue samplesand proximal fluids are usually obtained through the highly invasive procedures such as surgery or biopsy, require strictethical approval by institutional review boards, and are thus the least available specimens for proteomic experiments. Celllines, on the contrary, are readily available through commercial suppliers.

PROTEOMIC SAMPLES 21


invasive procedures that limit their clinicalpotential.28

Diseased tissue is the specimen of choice todiscover tissue-based prognostic and predictivebiomarkers because tissues have high levels ofprotein biomarkers.29 However, biomarker candi-dates identified by tissue proteomics may not bedetectable in the systemic bloodstream due toinsufficient leakage from the tissue to blood,increased degradation by endogenous proteases,or enhanced clearance by the kidneys.30 A majorobstacle in proteomic analysis of tissues is theheterogeneity of cellular and extracellular compo-sition. Laser capture microdissection (LCM) hasbeen proposed as a tool for isolating pure cellpopulations from tissues and thus reducingsuch heterogeneity.31 However, LCM yieldssmall sample sizes, is labor intensive, andrequires fresh frozen tissues and a high level ofexpertise.32 An advantage of tissues over otherspecimens is the ability to obtain adjuvant nonaf-fected tissues from the same individual to serve asa control, thusminimizing the effects of biologicalheterogeneity. Nevertheless, an adjacent tissuemay also be transformed at the molecular leveland thus may not represent the healthy tissue.33

Given that formalin-fixed paraffin-embedded(FFPE) tissues were widely collected andpreserved for more than a century, the exploita-tion of FFPE tissues for biomarker discoverywarrants a detailed investigation. Recent findingsshow that FFPE tissues, a source of potentialbiomarkers that has yet to be mined, may becompatible with mass spectrometryebased pro-teomic analysis.34

Ex vivo systems, such as cell lines and animalmodels, are also utilized for biomarkerdiscovery. Cell lines are readily available, allowfor identification of low-abundance proteinsdue to the reduced sample complexity, andfacilitate studies with minimized biological andexperimental variability due to cell growthunder well-defined conditions. However, nosingle cell line can recapitulate diseaseheterogeneity and account for the disease

microenvironment.35 Animal models, in contrastto cell lines, incorporate the effect of the hostmicroenvironment. In addition, animal modelsoffer minimum intraindividual variability interms of genetic variation and environmentalconditions. Furthermore, animal-derived biolog-ical samples can be collected at any stage of thedisease development.36 Nevertheless, whetheranimal disease models can be accurately trans-lated into human disease models remains anopen-ended question.

Regardless of the biological material of choice,clinical samples should be collected in a stan-dardized way following predefined standardoperating procedures (SOPs) to minimize varia-tions due to sample collection, handling, andstorage.37 Samples should have detailed clinicalannotations, such as gender, race, age, andconcurrent use of medications. Given the limitedavailability of clinical samples, it has beenproposed that high-quality samples should beused at the late stages of biomarker develop-ment.38 However, analysis of specimens ofunknown quality at the identification phaseincreases the risk of generating false positivemarkers that will drain financial and clinicalresources at the verification and validationphases. The issue of sample collection and pres-ervation for prospective studies along with theneed for storage of very large specimen collec-tion has driven the development of multiple bio-banking initiatives. Biobanking incorporates theproper clinical annotation of specimens alongwith managing ethical, legal, and social issuesthat may vary in different states and regions.39

International networking of biobanks facilitatesthe use of high-quality biological specimens fortranslational and clinical research.

PROTEIN IDENTIFICATIONBY MASS SPECTROMETRY

Mass spectrometryebased approaches toprotein identification involve either detection of

PROTEIN IDENTIFICATION BY MASS SPECTROMETRY 23

intact proteins, referred to as top-down proteo-mics, or identification of protein cleavage prod-ucts, referred to as bottom-up or shotgunproteomics. Top-down strategies retain a lot ofinformation about protein sequence, protein iso-forms, as well as their PTMs. Recent advances intop-down proteomics allow for identification ofhundreds of intact proteins in yeast andmammalian cells40,41; however, clinical applica-tions of top-down proteomics are still limited.

Bottom-up proteomic approaches suffer froma loss of information about protein isoforms andPTMs, especially for low-abundance proteins.On the contrary, bottom-up proteomics greatlybenefits from superior liquid chromatography(LC) separation of peptides prior to mass spec-trometry, requires lower amounts of material,and provides better peptide fragmentation andhigher sensitivity. Due to the very high numberof routine protein identifications in biologicalsamples, bottom-up proteomics remains theplatform of choice for biomarker discoverypipelines. Process of protein identification bybottom-up proteomic methods involves a set ofconsecutive steps, such as protein digestion,peptide separation by LC, peptide ionization,gas-phase peptide separation, peptide fragmen-tation, and detection of mass-to-charge ratios(m/z) and intensities of peptide ions and theirtandem mass spectrometry (MS/MS) fragments.The variety of mass spectrometry platforms usedfor protein identification is described in thefollowing subsections.

Protein Digestion

Bottom-up proteomic approaches involveproteolytic cleavage of proteins into short peptidefragments by proteases. The most widely usedenzyme is a chemically modified trypsin thatselectively cleaves peptide bonds C-terminal tolysine and arginine residues.42 A distinct advan-tage of the use of trypsin is generation ofshort doubly or triply charged peptides that arewater soluble, well separated by both strong

cation-exchange and reversed-phase chromatog-raphy, andwell ionized by electrospray ionization(ESI). To increase thenumberofpeptide identifica-tions and protein sequence coverage, proteindigestion protocols may be complemented byproteases with different sequence specificities,such as LysC, ArgC, AspN, and GluC.43

Protein and Peptide SeparationTechniques

In the last two decades, two-dimensional poly-acrylamide gel electrophoresis was a method ofchoice for protein separation in both top-downand bottom-up protein identification work-flows.44,45 Lately, with advances of bottom-upproteomics, separation techniques focused onfractionation of short tryptic peptides. Work-flows with two-dimensional separation ofpeptides by strong cation-exchange chroma-tography or isoelectric focusing followed byreversed-phase liquid-chromatography allowedfor identification of thousands of proteins andsignificant increase in proteome coverage.46,47

Protein and Peptide IonizationTechniques

One of the mass spectrometry advancementsthat facilitated routine identification of proteinsand peptides in biological samples was thediscovery of soft ionization techniques.48,49 Softionization such as matrix-assisted laser desorp-tion ionization (MALDI)48 and ESI49 improvedthe transfer of large biological molecules intothe gas phase without significant structuraldecomposition.

MALDI-MS (mass spectrometry) is applied tothe analysis of a variety of molecules that rangefrom small organic compounds to large biomole-cules such as immunoglobulins.50 MALDI is initi-ated by the absorption of a UV laser beam bya matrix material mixed with a biologicalsample.48 As the laser strikes the matrix, it causesablation of the surface material and the


consequent transfer of singly charged analyte ionsinto the gas phase. Because MALDI is moretolerant to background contaminants thatsuppress ionization in ESI, such as detergents,extensive sample cleanup is not mandatory.Proteins or peptides can be separated offline intomultiple fractions and spotted onto a MALDImatrix plate prior to MS analysis. Recently,MALDI-TOF (time-of-flight mass spectrometry)emerged as a technique for imaging mass spec-trometry (IMS) intended for the analysis of smallmolecules and intact proteins in cells and humantissues.51 IMS requires pretreatment of thin tissueslices with the MALDI matrix followed by scan-ning of the tissue by laser beam, therebyproviding a two- and even three-dimensionalspatial distribution of intensities of protein,peptide, and small-molecule ions.52 IMS holdsa promise to replace immunohistochemical stain-ing (IHC) of tissues and facilitate high-throughputapproaches to verification of tissue biomarkers.52

Unlike MALDI, ESI involves an online intro-duction of samples into the mass spectrometerin a solvated state and is currently the mostwidely used technique for the proteomicbiomarker discovery. Application of voltage,typically 2,000 to 5,000 V to the sample emittertip, leads to formation of highly charged drop-lets that eventually evaporate, allowing ions toenter the mass spectrometer.49 Presence ofhigh-abundance peptides, organic molecules,solvent additives, and detergents can signifi-cantly reduce ionization efficiency of low-abundance peptides, an effect referred to asionization suppression. A variety of proteinand peptide depletion or fractionationapproaches is frequently used to reduce compe-tition of low-abundance analytes for charge,diminish ion suppression, and thus increasethe number of peptide and protein identifica-tions.18 To alleviate the effect of contaminants,differential proteomic biomarker profilingshould employ identical sample preparationprotocols, LC-MS instrumentation, and bio-informatics algorithms.

Mass Spectrometry Instrumentation

The past decade has seen substantial improve-ments in tandem MS instrumentation, effici-ency of ion transfer by ion optics, and datainterpretation algorithms. Within a decade,protein identification increased from a few dozenproteins to the routine identification of more than10,000 proteins in mammalian cells.53,54

MALDI-MS is widely used in combinationwith the time-of-flight (TOF) instruments, asboth ionization mode and mass measurementoccur in a pulsed fashion. TOF analyzers derivethe mass of an analyte by measuring the flighttime of each ion in a vacuum tube. BecauseTOF instruments have been one of the earlierinstruments capable of high mass accuracy,they were very frequently used for top-downproteomics and studies of protein post-translational modifications.

As opposed to TOF instruments with pulsedmode of ion separation, ion-trapping (IT) instru-ments accumulate ions prior to their massmeasurement. Since ions are given sufficienttime to fill the trap, IT instruments have reason-ably high sensitivity. In addition, IT instrumentsemploy ESI, have fast scanning speeds, and offerthe ability to perform multiple levels of fragmen-tation of the same analyte, but at the expense ofpoor mass accuracy (100 to 200 ppm) and resolu-tion (2,000 full width at half maximum[FWHM]).

Quadrupole instruments use the principle offiltering peptide ions in the oscillating electricfields, transmitting only ions within the narrowand predefined m/z range. Advantages of quad-rupoles include fast scan times and high sensi-tivity; resolution of quadrupoles, however,remains relatively low (1,000 FWHM). Triplequadrupole mass spectrometers employ consec-utive filtering of precursor peptide ions and frag-mentation and filtering of fragments, thusincreasing selectivity of analysis.

The introduction of hybrid instruments thatcombined different modes of ion selection,

POST-TRANSLATIONAL MODIFICATIONS AS DISEASE BIOMARKERS 25

fragmentation, and separation has revolution-ized the field of proteomics. For example, thecombination of an ion-trap with an Orbitrap,an analyzer that traps ions in an orbit and usesFourier transform algorithm to derive m/z,allows for a two-stage identification of peptides,solely in the ion-trap, or in the Orbitrap,55,56 orconcurrently in both ion-trap and Orbitrap.Such a setup provides high-mass accuracy (1 to5 ppm) in MS and MS/MS modes, resolutionup to 240,000, and relatively fast scan speeds.LTQ-FTICR, an instrument based on Fouriertransform ion cyclotron resonance, offers capa-bilities of an Orbitrap with resolutions up to750,000.57 The newest hybrid TOF analyzersalso provide high sensitivity, high mass accuracy(2 to 5 ppm) and resolution (10,000 to 40,000) inMS1 and MS/MS modes, and fast scan time.58

Such instruments are equipped with two quad-rupoles in front of the TOF analyzer and enableanalysis of either whole proteins or trypticpeptides in complex samples. High mass-accuracy and resolution allow for filtering outexact ion masses, thereby reducing backgroundnoise and eliminating co-eluting contaminants.

Deconvolution and Database Searchof Tandem Mass Spectra

Regardless of proteomic platform and choiceof the MS instrument, the general method ofprotein or peptide sequencing remains thesame. In all cases, measurement of m/z ofprecursor ion is followed by its fragmentationby collision-induced dissociation (CID), electron-capture dissociation (ECD)59 or electron-transferdissociation (ETD).60 The resulting raw spectrumfiles contain an m/z ratio of precursor ions andits MS/MS fragments. In the bottom-up proteo-mic approach, peptides are identified via match-ing of experimental MS/MS spectra totheoretical spectra derived from an in silicodigest of a database containing all knownprotein sequences.61 Another search approachuses the vast number of publicly available

experimentally derived mass spectra to compilespectral libraries. This process is often referredto as peptide-spectrum matching; it offersfaster data analysis and fewer false-positiveidentifications.62 Probability of the correctpeptide matching at the MS and MS/MS levelsis based on deviation of experimental parentand fragment m/z from theoretical m/z and isassessed using various scoring algorithms, suchas Sequest, Mascot, Tandem, SpectumMill, Phe-nyx, OMSSA, and others.61,63e66 As a result,peptide sequences are derived with certainstatistical probabilities and false discovery rates.The use of high-resolution-accuracy instrumentsreduces the number of peptides that fall withinthe theoretical m/z range in database, therebyreducing the number of false-positive peptide-spectrum matches.57 Not all spectra match thetheoretical database, as some spectra originatefrom peptides with PTMs that are not definedin the search algorithm, from peptides withSNPs, miscleaved peptides, solvent ions,contaminant small molecules, lipids, or evenairborne molecules of building materials.67

An approach to circumvent the issue ofnonspecific or naturally occurring cleavageproducts is to perform de novo sequencing inwhich peptide sequences are derived directlyfrom MS1 precursor and MS/MS fragmentions, without matching to the theoretical data-base. This challenging task, however, requiresclean MS/MS spectra and no interference fromfragment ions originating from co-elutingpeptides and contaminants. The advantage ofthis approach is the identification of PTMs andunexpected proteolytic peptide fragments.

POST-TRANSLATIONALMODIFICATIONS AS DISEASE

BIOMARKERS

Possible disease-specific post-translationalmodifications include phosphorylation, glycosyl-ation, methylation, acetylation, ubiquitination,


lipidation, and proteolysis.68 Disease state in thiscase is reflected at the level of post-translationalmodifications rather than concentration of thescaffold protein.

Glycosylation and phosphorylation are themost widely studied PTMs. Because manysecreted and extracellular proteins are glyco-sylated, disturbed glycosylation patterns of

=

PROTEIN QUANTIFICATION BY MASS SPECTROMETRY 27

proteins in blood may indicate an ongoing path-ological process in a distant organ.69,70 Further-more, disturbed glycosylation patterns may betissue-specific even in the case if the protein itselfis expressed in multiple tissues. A differentialphosphorylation pattern has been noted inseveral neurodegenerative diseases.71

Disease-specific PTMs are often missed in thebottom-up proteomics studies because peptideswith PTMs are often poorly ionized by ESI ormissed in the consequent bioinformatics analysisthat does not search for all possible PTMs.Further advances in bottom-up proteomics willeventually lead to the more detailed investiga-tion of PTMs in disease. To enable efficientPTM analysis, multiple approaches to enrichPTM peptides, such as lectin72 or titaniumoxide73 chromatography, can be used. Analysisof highly branched and heterogeneous oligosac-charide chains would require efficient de novosequencing methods. High-resolution massspectrometry has a lot of potential to enablerobust top-down analysis of PTM variations inpathological states.

PROTEIN QUANTIFICATIONBY MASS SPECTROMETRY

Protein identification workflows allow forcataloging proteomes of biological samples butcannot provide accurate and reproducible

FIGURE 3 Quantitative mass spectrometry approaches. (A) Macids in cell culture). Control and treated cells are grown in the mto allow for five or six cell divisions, then lysed, mixed in equimanalyzed by LC-MS/MS. Heavy-isotope labeled peptides showICAT (isotope-coded affinity tags). Cysteine residues are labeledigested, peptides are purified by affinity methods, and analyzlabeled tags show a mass shift in the MS1 spectrum. (C) Chemiquantification) or TMT (tandem mass tags). Equimolar amountslabeled with isobaric amine-reactive tags, mixed and analyzed byshow a mass shift in the MS/MS spectrum. (D) Label-free apspectral counting. Following protein digestion, each sample typetry. XIC measures integrated MS1 intensity of a precursor ion; sion was fragmented by MS/MS.

quantification of proteins in large numbers ofbiological samples. In some biological processes,a small change in protein levels may be crucialand lead to substantial changes in cell signalingoutcome or cellular phenotype.74 Quantitativeproteomic methods that are accurate and repro-ducible enough to reveal relatively smallchanges in protein levels (�20%) are essential.Multiple strategies available for protein quantifi-cation (Figure 3) can be categorized as eitherlabel-free methods or methods involving proteinand peptide labeling with chemical tags or heavyisotopes of carbon (C13) or nitrogen (N15). Themajor advantage of label-assisted over label-free methods is the ability of former methodsto derive differential protein ratios withina single MS analysis, as well as higher quanti-tative accuracy and precision.75 Label-freeapproaches generally have a wide dynamicrange of quantification of four or five orders ofmagnitude and allow for quantitative compar-ison of large numbers of samples.

Label-Free Quantification

Label-free quantification, such as spectralcounting and extracted ion chromatograms(XIC), offered low sample preparation costsand was greatly improved lately with the useof high-resolution instruments, reproduciblechromatography, and powerful data analysissoftware.76e78 Spectral counting relies on

etabolic labeling, or SILAC (stable isotope labeling by aminoedia with light- or heavy-isotope labeled lysine and arginine

olar amounts based on total protein, digested by trypsin, andan MS1 mass shift of 6 to 10 Da. (B) Chemical labeling byd with light or heavy tags, proteins are mixed and trypsined by LC-MS/MS. As a result, peptides with heavy isotope-cal labeling by iTRAQ (isobaric tags for relative and absoluteof total protein extracts are digested by trypsin, peptides areLC-MS/MS. Following peptide fragmentation, reporter ionsproaches including XIC (extracted ion chromatogram) ande is analyzed separately by high-resolution mass spectrom-pectral counting measures the number of times the precursor


counting the number of times that all peptidescorresponding to a protein were sequenced.The more abundant the protein, the highernumber of tryptic peptides is available forsequencing, resulting in more MS/MS events,referred to as spectral counts. Spectral countingis applied to relative and absolute protein quan-tification between different MS runs. Absoluteprotein quantification requires normalization ofspectral counts by correcting for protein length(normalized spectral abundance factor, orNSAF)79 or the possible number of trypticpeptides (exponentially modified protein abun-dance index, emPAI).80 This method hasa dynamic range of about two to three ordersof magnitude but suffers from low precision,accuracy, and reproducibility, especially forlow abundance proteins that are identified byfew spectral counts.81

XIC-based quantification methods rely onmeasuring the three-dimensional space ofpeptide ion intensity, m/z, and chromato-graphic elution time. Because XIC quantifica-tion is more accurate and suitable formeasuring relative abundances of medium-abundance proteins, even a single MS/MSspectral count event will have a correspondingMS1 chromatographic peak that can be inte-grated.81 MS/MS fragmentation is still per-formed to determine identity of each peak butis not used for quantification. Another variantof XIC quantification, intensity-based absolutequantification (iBAQ), involves dividing thesum of XIC peptide intensities by the numberof theoretically observable peptides.82 XICquantification requires reproducible chroma-tography to enable alignment of peptide peaksand achieves a dynamic range of four orders ofmagnitude.

Metabolic and Enzymatic Labeling

A common metabolic labeling strategy,SILAC (stable isotope labeling with amino acidsin cell culture), involves addition of heavy

isotope-labeled (13C and 15N) amino acids intothe cell culture media and consecutive incorpo-ration of these amino acids into protein sequenceupon its translation in the cell.83 In SILAC exper-iments, treated and control cells are cultured inthe media with heavy (13C and 15N) or light(12C and 14N) isotope-labeled lysine and argi-nine, respectively. Upon five or more cell divi-sions, an equimolar mixture of both cell lysatesis subjected to the sample preparation protocol.Heavy peptides in such a mixture have physicaland chemical properties identical to those oflight peptides but show an MS1 mass shift. Ratioof heavy-to-light peptide intensities correspondsto relative protein abundances between treatedand control cells. SILAC experiments have excel-lent precision as any run-to-run variation inLC-MS does not affect the peptide ratio;however, performing SILAC on complexsamples using slow scanning instruments anddynamic exclusion settings results in missedprotein identifications due to the doubledsample complexity. Only actively dividing cells,such as established cancer cell lines, areamenable to SILAC. Some primary and slowdividing cells can hardly be cultured for fivedivisions and, as a result, cannot be fully labeled.Labeling of proteins of whole organisms, such asbacteria, yeast, fruit flies, and even mice, is alsopossible by feeding them a diet containingheavy-labeled amino acids.84e89 A heavy SILACprotein mixture can also be used as a referencestandard when spiked into nonlabeled normaland disease biological fluids.90 On the negativeside, SILAC experiments are relatively expensiveand have a quite narrow differential quantifica-tion range of approximately twentyfold.91

Another approach to incorporate heavyisotopes into peptides involves exchange of two16O atoms for two 18O atoms on C-terminalpeptides during enzymatic digestion of proteinsin deuterated water (H2

18O).92 As a result, anMS1 shift of 4 Da between 16O- and 18O-labeledpeptides is observed. The major caveat of thismethodology, however, is a nonhomogenous

PROTEIN QUANTIFICATION BY MASS SPECTROMETRY 29

labeling, which results in mixed labels 16O18O,thereby affecting O16/O18 ratios.

Chemical Labeling

Approaches to chemical labeling of proteomicsamples use heavy or light isotope-labeled andchemically reactive tags. For instance, isotope-coded affinity tags (ICAT) allow for labeling ofcysteine residues in proteins.93 Once labeled,proteins from both groups are combined,affinity-purified through biotin tags, andpeptides with heavy and light labels are quanti-fied based on their differential MS1 signals.Exclusive labeling of cysteines is the main limita-tion of this approach, as it reduces proteinsequence coverage. On the other hand, due tothe affinity capture of these peptides, samplecomplexity is significantly simplified, whichfacilitates quantification of low-abundanceproteins.

Isobaric tags for relative and absolute quanti-fication (iTRAQ)94 or tandem mass tags (TMT)95

are amine-reactive tags that produce reporterions upon MS/MS peptide fragmentation.Following protein digestion, iTRAQ allows forpeptide labeling in up to eight different biolog-ical conditions. Following labeling, peptidesfrom all conditions are pooled together andanalyzed by LC-MS/MS (Figure 3). Unlike otherlabeling approaches, iTRAQ utilizes MS/MSspectra for relative quantification.75

Selected Reaction Monitoring Assays

Selected reaction monitoring (SRM) isa quantitative analytical assay performed ona triple-quadrupole, quadrupole-iontrap, orquadrupole-TOF mass spectrometer. Althoughprotein identification approaches are tuned toidentify thousands of proteins in a limitednumber of samples, SRM assays are intendedto measure a very limited number of proteinsin a large set of samples. This makes SRM an

attractive technique for biomarker verificationand possibly even validation.

In general, SRM assay includes the followingsteps: digestion of proteins, LC separation ofpeptides, ionization of peptides with ESI,filtering of peptides in the first quadrupole, frag-mentation of peptides in the second quadrupole,filtering of peptide fragments in the third quad-rupole, and measurement of intensities of threeselected fragment ions.96,97 A known amountof a heavy-isotope labeled peptide is oftenspiked into the digest and used to calculate theabsolute amount of the endogenous lightpeptide. Addition of stable-isotope labeledpeptide standards increases specificity andreproducibility of quantification due to thecorrect identification of analyte peak in the pres-ence of multiple contaminant peptides andaccurate relative quantification. It is sometimesaccepted that trypsin digestion proceeds to fullconversion and that the amount of proteotypicpeptide reflects the absolute amount of the cor-responding protein. Such an assumption is notalways correct but is acceptable when the rela-tive abundance of proteins is measured. Moreaccurate measurement of absolute proteinamounts is achieved with heavy isotope-labeled proteins98 or concatenated peptide stan-dards,99 which account for variation of proteindigestion.

With state-of-the-art SRM assay, up to 100peptides representing 100 medium-to-high-abundance proteins in the range 0.1 mg/mL to1 mg/mL can be measured simultaneously inthe unfractionated digest of biological fluidwhile achieving coefficients of variation under20%.47,100 There are several concerns withSRM-based assays, and these mostly stem fromsample complexity and limitations in instrumentsensitivity and selectivity. Ideally, the number ofsample preparation steps prior to LC-SRMmeasurement should be minimal in order toallow for high-throughput analysis and mini-mize variability, although this benefit comes atthe cost of decreased assay sensitivity.


SRM assays are developed using either exper-imental proteome identification data or publiclyavailable databases such as Peptide Atlas101 orGPM proteome database.102 Advantages ofthese databases include integration of hundredsof experiments and unique algorithms to rankproteotypic peptides by their performance inLC-MS/MS experiments and to predict peptidessuitable for SRM assay development. Syntheticpeptides can also be used at this point to facili-tate assay development. Software tools designedto aid in SRM assay development includecommercial software provided by instrumentvendors, such as Pinpoint� (Thermo FisherInc.) and MRMPilot� (AB Sciex Inc.), aswell as license-free Skyline,103 MRMaid,104

mProphet,105 and SRMCollider.106 Among allMS techniques, SRM assays remain the methodsof choice for protein quantification andbiomarker verification due to their sensitivity,high-throughput capabilities, and multiplexingpotential.

Separation and Enrichment Strategiesfor Quantification of Low-AbundanceProteins

Relatively low sensitivity and moderatethroughput of mass spectrometry-based proteinassays (w100 ng/mL) remain two major limita-tions of their use for biomarker validationstudies and clinical analysis. Because bloodserum levels of many established clinicalbiomarkers are in the 10 pg/mL to 10 ng/mLrange,18 high-abundance proteins mask low-abundance biomarkers and significantlycompromise their quantification by mass spec-trometry. Thus, LC-SRM measurement of low-abundance proteins can be achieved onlythrough additional separation and enrichment.

A set of strategies, such as strong anion- orcation-exchange chromatography and isoelectricfocusing, are used to remove high-abundanceor enrich low-abundance proteins.107e109

Major high-abundance proteins can also be

removed by immunodepletion using the affinitycolumns.107,110 Alternatively, low-abundanceproteins can be enriched by affinity purificationusing antibodies or aptamers111e113; however,this approach has a reduced multiplexing poten-tial. Similar approaches, such as SISCAPA,employ antibodies developed against proteo-typic peptides.114,115 Because antibody develop-ment against synthetic peptides is morestraightforward relative to intact proteins, useof such approaches is increasing. Improved sensi-tivity (down to 1 ng/mL) and increased multi-plexing and throughput capabilities ofSISCAPA assays enable accurate verification ofbiomarker candidates in blood plasma.115,116 Inaddition, as many known protein biomarkersin clinical use are post-translationally modi-fied with N-glycosylation,117 lectin affinitychromatography is sometimes used to enrichN-glycoproteins and N-glycopeptides prior toLC-SRM analysis.96,117

BIOMARKER VERIFICATION

Upon completion of the protein identificationphase, anywhere from dozens to hundreds ofproteins are usually selected as potentialbiomarkers. Large variation of analysis andpoor reproducibility of commonly used label-free approaches constitute serious technologicallimitations of the identification phase. Biologicalfactors such as intraindividual variations ofprotein levels during the day as well as wideinterindividual distribution of physiologicallevels of proteins in healthy individuals alsoresult in a significant bias. The potential ofa certain protein biomarker should be confirmedfirst by verification in the independent set ofsamples. Even though there is a rapidlyincreasing number of publications reporting iden-tification of potential biomarkers, the rate ofnewly approved protein biomarkers is steadilydecreasing in the last decade.18,118 This decreasecan be partially explained by a high number of

BIOMARKER VALIDATION 31

false-positive candidates generated at the identifi-cation phase, difficulties of proceeding tobiomarker verification and validation phases,and shortage of academic grants that fund trans-lation of discovery data into clinics.

Regardless of the specimen analyzed duringthe protein identification phase, verificationshould be performed with specimens that areintended for clinical use and accurately reflectthe target population.119 Proper controlsubjects, as defined by the inclusion or exclu-sion criteria, are essential for meaningful datainterpretation and should be matched for phys-iologic factors such as age and gender to controlpotential confounding factors. Preanalyticalsources of variation such as biases in samplecollection and storage should be also carefullyevaluated, especially given that verificationstudies are performed with retrospectivelycollected samples. Finally, the size of study pop-ulation should be calculated to ensure adequatestatistical power,119 and the results of the studymust undergo a rigorous statistical analysis.

Importance of an appropriate statistical anal-ysis is sometimes overlooked in the biomarkerdiscovery field. At the initial steps of proteinbiomarker discovery, thousands of proteins aretypically identified and selected based on theirrelative abundance in disease versus controlgroups. Proper selection of candidates, however,should include robust statistical analysis basedon statistical probability (p-values) of differenti-ating groups of samples. Furthermore, becausethousands of proteins are tested simultaneously,p-values should be corrected for multiple testinghypothesis.120,121 Such correction should also beperformed when a set of biomarker candidatesis verified by a multiplex SRM assay. Develop-ment of multimarker diagnostic signatureswould require even more advanced statisticalalgorithms.122,123

By the end of the verification phase, manybiomarker candidates are eliminated, resultingin a small and manageable list of candidates thatwill proceed to the biomarker validation phase.

BIOMARKER VALIDATION

Biomarker validation is a multifaceted proce-dure that requires collaboration of multiple clin-ical centers and carries a significant financialburden. Ideally, only the most promising candi-dates that have proven their potential at the veri-fication phase and for which robust quantitativeassays have been developed will enter the vali-dation phase. The importance of high-qualityquantitative assays was demonstrated by theProstate Lung Colorectal Ovarian (PLCO)Cancer Screening Trial in which multipleovarian cancer biomarker candidates weretested.124 As a result, it was shown that onlymarkerswith analytical assays achieving a coeffi-cient of variation less than 30% performed withadequate diagnostic sensitivity.

Validation studies should be performed inboth a retrospective and prospective mannerusing independent sample cohorts ideallycollected by multiple hospitals.119 Unbiasedpresentation of the results of validation studiesholds the key for the final assessment ofa biomarker performance.125 Study populationshould recapitulate the general population bothin terms of disease prevalence and disease stageto allow for correct data interpretation and evalu-ation of biomarker performance. Power calcula-tions are necessary to define the appropriatestudy size and ensure statistical significance. Vali-dation phase requires large numbers of high-quality specimens, availability of which may bethe bottleneck of biomarker development. Inter-national multicenter collaborations and central-ized registries of clinical samples are founded toalleviate this limitation.125 To minimize preana-lytical biases, all samples should be collected,stored, and processed using predefined standardoperating procedures. Influence of preanalyticalparameters such as sample handling and samplepreparation, protein stability, intra- and interindi-vidual variations need to be addressed prior tothe large-scale validation studies. The ultimate


question of the validation phase is whether thebiomarker candidate addresses the unmet clinicalneed that prompted its discovery. However, thetrue clinical utility of a biomarker cannot beassessed without the introduction of the markerin the clinic and continuous monitoring of itsperformance for extended periods of time.

LIMITATIONS OF MASSSPECTROMETRY FOR PROTEIN

BIOMARKER DISCOVERY

Limitations of protein biomarker develop-ment studies stem from biological factors, suchas intra- and interindividual variation of proteinconcentrations, preanalytical variations, such asprotein stability, and technological limitationsof proteomic sample preparation and massspectrometry.

Major limitations of proteomics and massspectrometry, in general, and as a technique forbiomarker discovery studies, include:

• Lack of the general quantitative relationshipbetween ion intensity and the amount ofanalyte, which makes all MS-basedmeasurements relative

• Significant effect of matrix resulting in the ionsuppression and deviation from linearcorrelation between protein amount andspectral intensity of the same analyte

• Multiple steps of protein fractionation,derivatization, and trypsin digestion inbottom-up proteomic approaches that lead tohigh day-to-day variability and lowreproducibility of protein assays

Biological biases and poor quality of clinicalsamples, amplified by technological limitationsofmass spectrometry, often lead to a large numberof false positive discoveries. Taking into accountthe high cost of mass spectrometry instrumentsand their maintenance, complex data analysis,and the need for highly experienced personnel,

a large number of false discoveries makesbiomarker discovery a quite expensive and notvery effective exercise and may lead to generalfrustration in proteomics. Awareness of the meth-odological limitations of proteomics and massspectrometry and careful design of biomarkerdevelopment pipelines should decrease thenumber of potential biomarkers that never endup in the clinic and hopefully alleviate disappoint-ment in proteomics.126

CONCLUSIONS AND FUTUREOUTLOOK: INTEGRATEDBIOMARKER DISCOVERY

PLATFORM

A set of biological concepts and analytical tech-niques can be incorporated into an integratedprotein biomarker development platform.Current biomarker discovery strategies often relyon identification of differentially expressedproteins and their association with a certaindisease. The exact mechanism of differentialexpression and the functional role of proteinbiomarkers in disease are often not known andnot studied. An integrated biomarker discoveryplatform needs to be complemented withgenomic, transcriptomic, and metabolomic data.The main purpose of an integrated platform isnot only to make the use of data accumulated byall -omics technologies but also plan all steps andphases down the long road that would lead tothe clinical assay approved by health agencies. Itshould be always acknowledged that the ultimategoal of biomarker development is not merelyseparate groups of clinical samples, but to providereliable guidance for correct decision making inclinics, such as performing relevant diagnosticbiopsy or surgery or providing relevant therapy.Thus, the discovery, verification, and validationsteps of the biomarker discovery pipeline shouldbe tuned for a specific purpose d a priori thebiomarker development study.

REFERENCES 33

Overall, the protein biomarker discovery anddevelopment field is projected to grow signifi-cantly and become an important part of bio-medical research aimed at detecting diseases atearly stages, reducing the financial burden onhealthcare and allowing for personalized medi-cine approaches.

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Proteomic and Metabolomic Approaches to Biomarker Discovery || Proteomic and Mass Spectrometry Technologies for Biomarker Discovery