Liquid Chromatography-Mass Spectrometry-bad Quantitative Proteomics*
Published,JBC Papers in Press,June1,2011,DOI10.1074/jbc.R110.199703
Fang Xie,Tao Liu,Wei-Jun Qian,Vladislav A.Petyuk,
and Richard D.Smith1
From the Biological Sciences Division,Pacific Northwest National Laboratory,Richland,Washington99352
LC-MS-bad quantitative proteomics has become increas-ingly applied to a wide range of biological applications due to growing capabilities for broad proteome coverage and good accuracy and precision in quantification.Herein,we review the current LC-MS-bad quantification methods with respect to their advantages and limitations and highlight their potential applications.
LC-MS-bad quantitative proteomic approaches have become increasingly popular over the past decade(1–7).In general,discovery-bad proteomic efforts lend themlves to global analys whereby a broad survey of the proteome is per-formed across various samples,and the quantitative differences among them are estimated.In discovery-bad efforts,the breadth of analysis is emphasized more than
the precision and accuracy of quantification.For studies in which the qualities are crucial such as verification efforts,the tactic switches to nsitive,preci,and accurate analysis of a few targeted pro-teins in relatively large t of samples and internal standards are often ud.
Fig.1illustrates an LC-MS-bad global proteomic workflow in which proteins are converted into peptides for identification and “bottom-up”proteomics).Typically, methods are applied in conjunction with enzymatic digestion of proteins and subquent measurement of one or more peptides from each protein that rve as effective measurement surro-gates.We note that direct measurement of intact “top-down”proteomics)is another analytical option but is beyond the scope of this minireview and therefore not dis-cusd herein.In global analys,relative quantification of pep-tides usually involves either label-free or stable isotope labeling techniques(1)to discern differences in protein abundances among different biological conditions,and results are often expresd as“-fold changes.”Overall,label-free approaches have wider dynamic range and broader proteome coverage, whereas stable isotope labeling approaches offer higher quan-tification precision and accuracy(8).Another common ap-proach is absolute quantification,which determines the exact amount or concentration of a peptide/protein in a given sample and requires the u of an appropriate“internal”standard.All of the approaches have considerably different pros and cons that must be weighed before deciding which one is best for a specific cour of study.
Challenges affecting quantification in a bottom-up pro-teomic workflow stem from the wide range of peptide and protein physicochemical properties that give ri to large differences in MS respons(8).Sample handling,digestion efficiency,and paration also can have an impact on results.As such,relative peptide intensities may not directly reflect the relative abundances of different proteins.A major factor that influences LC-MS-bad quantification via electrospray ioniza-tion is ion suppression(9).Peptide intensity depends on the quantity of the peptide being ionized as well as on ionization efficiency and,under some conditions,on the properties of co-eluting peptides.The u of lower flow Ͻ100nl/min) (9,10)or internal standards(11,12)can help alleviate ion sup-pression.Other issues in LC-MS-bad quantification include the paration peak capacity and reproducibility of the chroma-tography and the mass measurement accuracy and resolving power of the mass spectrometer.Significant technological advances such as the development and commercialization of ultra-performance LC and high-mass accuracy/resolution mass spectrometers have substantially overcome the issues, making LC-MS-bad quantification more reliable and acces-sible to biologists.
Basically,there is no recognized“one-size-fits-all”method that fulfills every quantitative need,and available options for quantification can make it difficult for an investigator to choo the most appropr
iate approach to answer particular biological questions.This minireview prents the advantages and limita-tions of commonly ud LC-MS-bad protein quantification approaches and provides guidelines for rearchers who may not be familiar with but would like to benefit from quantitative proteomic measurements.
Label-free Quantification
Straightforward and inexpensive,label-free quantification is being increasingly applied to proteomic measurements.With-out the need to modify peptides/proteins with stable isotope-containing compounds or to add heavy isotope-labeled internal standards to the sample,label-free approaches require minimal manipulation of the sample and can be ud on any type of biological materials.Conceptually,label-free quantification allows for the comparison of an unlimited number of samples; however,each sample has to be analyzed individually(no sam-ple multiplexing).This type of approach usually offers wide dynamic range,which is especially advantageous when rela-
*This work was supported,in whole or in part,by National Institutes of Health Grant RR018522from the National Center for Rearch Resources for Inte-grative Biology(to R.D.S.).This work was also supported by a United States Department of Energy early career rearch award(to W.-J.Q.).Experi
men-tal work was performed in the Environmental Molecular Sciences Labora-tory,a Department of Energy/Biological and Environmental Rearch national scientific ur facility on the Pacific Northwest National Labora-tory campus in Richland,WA.Pacific Northwest National Laboratory is a multiprogram national laboratory operated by Battelle for the Department of Energy under Contract DE-AC05-76RLO1830.This is the fourth article in the Thematic Minireview Series on Biological Applications of Mass Spec-trometry.This minireview will be reprinted in the2011Minireview Com-pendium,which will be available in January,2012.
1To whom correspondence should be addresd.E-mail:v.THE JOURNAL OF BIOLOGICAL CHEMISTRY VOL.286,NO.29,pp.25443–25449,July22,2011
Printed in the U.S.A.
MINIREVIEW This paper is available online at
tively large-abundance changes are expected.The two major label-free quantification approaches are spectral counting and MS ion intensity (or peak area)measurement.
Spectral unting the number of MS/MS spec-tra identifying a given peptide or protein,re
prents a simple approach for relative quantification without stable isotope labeling (13,14).The rationale behind this method,which has been experimentally validated (13),is that the frequency for which a peptide is lected for MS/MS fragmentation is posi-tively correlated to its quantity in the data-dependent acquisi-tion operation mode.In this mode,a survey MS scan is acquired,followed by lection of typically Յ10of the most abundant ions in the survey MS scan for subquent MS/MS analysis (i.e.undersampling).Spectral counts for different pep-tides from a given protein can be summed up for relative quan-tification of the protein;however,the linearity and the number of quantified proteins will depend on the data-dependent acquisition tting details (15).Although spectral counting is straightforward,small numbers of spectral counts for proteins prent in low abundance provide less robust quantitative measurements of the proteins due to statistical limitations and other factors such as the need to limit the fal discovery rate so as not to “count”lower quality spectra (16).
Label-free quantification can also be performed bad on MS ion intensity (or peak area)for the peptides in a given sample.The LC-MS-bad approaches alleviate the undersampling issue inherent in typical LC-MS/MS analysis (17).An example of this type of approach is the accurate mass and time tag strat-egy (18–20),which utilizes high-resolution high-mass accuracy LC-MS to an
alyze individual samples.Peptides are identified by matching accurate mass and normalized elution time features to tho stored in a previously established reference databa of peptides.Following accurate alignment of detected LC-MS fea-tures (i.e.LC retention time and m /z values)across different analys,the areas under chromatographic elution profiles of the identified peptides can be compared among different sam-ples for relative quantification.Similar quantitative approaches that rely on direct LC-MS measurements,feature alignment,and peak identifications have also been reported (21,22).
In label-free intensity-bad quantification,any variations in sample preparation,LC-MS reproducibility,ionization effi-ciency,and other sources of “instrument drift”can lead to incread measurement error.Therefore,it is important to nor-malize the data to correct (as much as possible)for systematic variations (23).Software tools for performing LC-MS feature alignments,peak matching,data normalization,and statistical analys of label-free quantification data are available (20,22,24).Although label-free intensity-bad quantification has been broadly applied,its accuracy and reliability for quantifica-tion are inherently limited by the reproducibility related
to
FIGURE 1.General workflow for LC-MS-bad global proteomics.Proteins in complex biological samples are first converted into peptides by proteolytic digestion (ptic digestion).The resulting peptide mixture is then parated by LC and ionized by electrospray before entering the mass spectrometer.In a typical data-dependent acquisition operation mode,a full MS spectrum is acquired
for the peptides that are eluting from the LC column at any given time;one of the most intensive ion species (i.e.peptides)is then isolated and fragmented to obtain the MS pattern of its fragments (i.e.MS/MS spectrum).Becau peptide bonds are prone to fragmentation under collision-induced dissociation conditions in the MS/MS analysis and produce predominantly b -or y -type ions (N-or C-terminal fragments carrying charge,respectively),the peptide quence can be readily deduced from the MS/MS spectrum.This process is fully automated by arching the MS/MS spectra against protein quence databas.Possible post-translational modifications can also be identified by including dynamic modification on certain amino acid residues (e.g.Ser,Thr,or Tyr for phosphorylation)in the databa arch.Quantification of each peptide is typically performed at the extracted ion chromatogram level;however,for isobaric tagging approaches (e.g.iTRAQ),quantification is carried out at the MS/MS spectrum level.PTMs ,post-translational modifications.
MINIREVIEW:LC-MS-bad Quantitative Proteomics
sample processing,LC-MS platform,etc.The factors should be considered and minimized when applying label-free quanti-fication approaches.
Stable Isotope Labeling Approaches for Quantification Stable isotope labeling approaches enable accurate quantifi-cation bad on a stable isotope dilution concept(1).Becau a stable isotope-labeled peptide has the same chemical properties as its native equivalent,the two peptides within a mixture should exhibit identical behaviors during LC and electrospray ionization process and be parated by their differences in mass,thus enabling accurate peptide(but not necessarily pro-tein)quantification.The relative abundance differences mea-sured by MS between the two peptide forms are taken to quan-titatively reflect true differences in abundance within the mixture.13C,15N,and18O are commonly ud stable isotopes (25)that can be incorporated into proteins or peptides meta-bolically,enzymatically,or chemically.Although sometimes ud,2H is less desirable becau it changes the physicochemi-cal properties of peptides such that the heavy form elutes slightly earlier than the light form in rever-pha LC(26).In stable isotope labeling methods,samples are combined after labeling and analyzed by LC-MS or LC-MS/MS,which esn-tially avoids the uncertainty induced by variations in instru-ment performance between measurements.As a result,quan-tification precision and accuracy are markedly improved compared with label-free approaches.
The most popular metabolic labeling approach is stable iso-tope labeling with amino acids in cell cult
ure(SILAC)2(27). Briefly,one or veral amino acids(typically Arg or Lys)are labeled with“heavy”13C and/or15N atoms)and added to the growth medium.Then,the heavy isotope-labeled amino acids are incorporated into all the proteins after veral cell doublings.Equal amounts of the heavy isotope-labeled pro-teome and the normal-labeled proteome can be mixed at the level of either intact qual numbers of heavy and nor-mal isotope-labeled cells)or cell qual amounts of heavy and normal isotope-labeled protein contents)and ana-lyzed.Peptide abundance ratios are determined in the MS mode by comparing the intensities of the labeled and unlabeled peptides within a survey mass spectrum.SILAC can be ud to quantify in vivo protein turnover)as the isotopes are introduced during the natural biosynthesis process. Another advantage is that multiplexing is possible due to
the availability of veral 12C
614N
4
-Arg,13C
6
14N
4
-
Arg,and13C
615N
4
-Arg)(1,12).A shortcoming is that SILAC is
limited to cell cultures and cannot easily be applied to tissues or biofluids.
With enzymatic labeling,18O atoms are incorporated into the C terminus of every proteolytic peptide during or after pro-tein digestion catalyzed by trypsin(28),Glu-C(29),or some other proteas(30).Previously,two main issues,incomplete labeling and18O back-exchange to16O,limited the application of18O labeling for global quantitative proteomics.Typically, 18O labeling is performed when a sample is digested in H
2
18O solution with trypsin;however,different peptides often have different labeling efficiencies.We and others demonstrated more effective labeling by directly incubating peptides in18O water with trypsin as a catalyst(31,32),and a recently improved protocol that incorporates a boiling step after labeling now pre-vents oxygen back-exchange(33).18O labeling coupled with the accurate mass and time tag approach has been applied in pair-wi global quantitative proteomics(31,34),and more large-scale applications have also been reported(35,36).The main advantages of18O labeling are its simplicity in labeling,rela-tively low cost,and applicability to all types of tissues,cells,and biological fluids).Additionally,many different software tools are available to facilitate the16O/18O-bad quantification(31,37,38).
Another commonly applied stable isotope labeling strategy is bad on incorporating isotope-containing tags into proteins or peptides via chemical reactions,a strategy that can be applied to any type of biological materials.The first reported chemical labeling proteomic approach was isotope-coded affinity tag (ICAT),which reacts specifically to the sulfhydryl group of Cys residues(39).Relative protein abundance is determined in the MS mode,and the mass shift is9Da per ICAT-labeled Cys (provided13C is ud in the heavy form)(40).More recently, novel chemically reacti
ve tags have been developed for labeling peptides that facilitate quantitative analysis of multiple samples ultiplexing),which is particularly uful for following a biological system over multiple time points(8, 41).The most commonly ud tags for peptide labeling are iso-baric tags for relative and absolute quantification(iTRAQ)(42–44)and tandem mass tags(TMT)(45,46).iTRAQ and TMT labels react specifically with primary amine groups of tryptic N-termini and the side chains of Lys residues,and the tagging reactions are largely complete without major side reactions.The same peptides labeled with different isobaric tags have exactly the same mass and co-elute precily in LC parations.Although tags remain indistinguishable in the MS scan,they fragment into reporter ions of different mass in the MS/MS scan.The intensities of the different reporter ions are then ud to determine the relative abundance of the corre-sponding peptides and proteins in different samples.It is important to note that both iTRAQ-and TMT-bad quantifi-cations require the ability to obrve low m/z fragment the reporter ions),which limits the type of mass spectrometers that can be ud.The iTRAQ reagent can be utilized to analyze up to four(4-plex iTRAQ)or eight(8-plex iTRAQ)samples simultaneously,whereas TMT can address two to six samples. Becau iTRAQ and TMT label-bad quantifications are measured at the MS/MS level,potentially higher signal/noi ratios may be obtained for quantification compared with tho obtained at the MS level.Additionally,the accuracy of MS/MS level quantification depends on the isol
ation window for lected precursors in the first stage MS(8,47),which is typi-cally3Thomson,as all ions within that window will fragment, and potential interferences could skew the quantification results.
2The abbreviations ud are:SILAC,stable isotope labeling with amino acids
in cell culture;ICAT,isotope-coded affinity tag;iTRAQ,isobaric tag for rel-
ative and absolute quantification;TMT,tandem mass tag;SRM,lected
reaction monitoring.
MINIREVIEW:LC-MS-bad Quantitative Proteomics
“Universal”Reference for Relative Quantification
A challenge for large-scale quantitative applications involv-ing label-free and/or labeling-bad proteomics is maintaining platform reproducibility for large studies that may extend over veral months or years.The u of a stable isotope-labeled whole proteome as a universal internal standard offers a solu-tion to this challenge.Briefly,a pooled reference sample can be generated,and
the digested peptides can be labeled with18O(as an example)to rve as the universal reference.The labeled reference sample can be added to each unlabeled biological sample so that each unlabeled peptide will have a correspond-ing18O-labeled version of the peptide from the universal refer-ence.Relative peptide and protein abundances in many differ-ent samples can be compared bad on their ratios to the universal reference in each analysis.The concept is similar to the u of synthetic isotope-labeled peptides as internal stan-dards(48)that are added to each biological sample.The18O-labeled universal reference can be generated from any type of biological sample and requires only a single step to label a digest of the pooled sample.This strategy has been applied to two-dimensional proteome mapping of mou brain(49)and in human plasma proteome studies to discover biomarkers(36, 50).
A“super-SILAC”approach has been applied to quantify pro-teins in human tumor tissues(51).In this approach,equal amounts of SILAC-labeled proteins from veral previously established cancer-derived cell lines were a super-SILAC mixture).This mixture rved as a“global inter-nal standard”to quantify relative protein abundance from mul-tiple tissue samples of the same tumor type.Unlike the18O-labeled universal reference,the super-SILAC strategy is limited by the availability of appropriate cell lines for generating SILAC-labeled samples and cannot be applied to biofluids. Alt
hough the universal reference or global internal standard approach offers great flexibility for large-scale relative quanti-fication studies,it is difficult to produce a“true”universal ref-erence that contains all the proteins and protein forms of inter-est and that can be reproducibly generated.The nature of adding a reference/standard makes it inevitable to dilute every sample1:1with the standard and thus makes it more difficult to detect and quantify lower abundance proteins.
Targeted Quantification
Global quantitative proteomics inherently suffers some lim-itations as a result of missing data in individual analys,fal identifications,reproducibility issues,and computational chal-lenges.Targeted quantification approaches using lected reac-tion monitoring(SRM)or multiple reaction monitoring for accurate quantification of lected analytes are gaining in pop-ularity as a means of overcoming the limitations.SRM is typ-ically performed using a“triple quadrupole”tandem mass spec-trometer that consists of a lection quadruple for the precursor ion,a collision quadrupole,and another lection quadrupole for the fragments.Specifically,an SRM assay defines a list of precursor m/z values associated with specific retention times.The critical rules for precursor lection are defined in a comprehensive review(52).For each m/z value,the assay defines one or veral fragment ions that are predicted to have good MS respon and are readily distinguished fro
m interference.By tting the MS platform to exclusively monitor predefined precursor-to-fragment ion transitions in rapid suc-cession,a specific ion can be detected and quantified when it has the expected m/z value and produces fragments of the expected m/z.Recently,an interesting variant,intelligent SRM, was developed to confirm the precursor identity without signif-icantly perturbing the SRM quantification(53).In this method, additional transitions are acquired in a data-dependent fashion (triggered when all the primary transitions exceed a predefined threshold),which increas the specificity of the analysis. Among the benefits of SRM-bad quantification is the excellent reproducibility attained by using labeled synthetic peptides as internal standards(54).Additionally,the approach is less affected by sample complexity,as noi signals are fil-tered out at both the precursor and fragment levels(55).As a result,SRM has the highest nsitivity and a wide dynamic range that extends4–5orders of magnitude,which makes SRM well suited for targeted quantification experiments such as bio-marker verification.SRM can also be applied to quantify par-ticular peptide ubiquitination and phosphor-ylation)(41,56).
An important application of SRM is absolute quantification, which is achieved by using known concentrations of synthetic isotope-labeled peptides as internal standards(48).In this method,a target peptide is synthesized in an isotope-labeled form and added to the protein digest at a known concen
tration. The concentration of the native peptide is determined by com-paring the ion intensities between the labeled and unlabeled forms.The internal standard can correct for ion suppression and matrix effects(11).For an experiment with multiple sam-ples,the u of an internal standard ensures a fair comparison across all samples.However,the protein amount determined by absolute quantification may not reflect the true expression lev-els in the original sample becau the internal standard is added after digestion and thus cannot correct for the variable loss or enrichments that occur during sample preparation(57), true“absolute quantification.”Using an isotope-labeled protein as the internal standard may alleviate this problem becau the surrogate protein can be combined with the target protein at the very beginning of the analytical process.The standard can be either a full-length isotope-labeled equivalent of a target pro-tein(58)or an isotope-labeled artificial concatamer of proteo-typic peptides from veral proteins of interest,which allows multiplexing(59).Regardless,identifying and synthesizing suit-able internal standards are not trivial tasks,which limits the absolute quantification approach to a restricted t of pre-screened proteins(1),making it suitable for applications such as validating potential protein candidates of interest or quantify-ing particular post-translational modifications such as ubiquiti-nation(60).
Factors to Consider When Choosing a Quantification Strategy
When it comes to quantitative proteomics,many options are available,each with their own t of advantages and disadvan-tages.Some factors to be considered when making a choice
MINIREVIEW:LC-MS-bad Quantitative Proteomics
include the types of samples,the number of samples to be com-pared,the biological sources and complexity of the samples,the analytical needs (e.g.quantification precision,accuracy and whether an absolute concentration is necessary)of the biolog-ical problem,and the cost of the experiment (12).
Table 1summarizes the performance characteristics for each of the common LC-MS-bad proteomic quantification strate-gies.Among the stable isotope labeling techniques,SILAC offers the best accuracy for quantification becau it labels at the cell culture level.Both 18O and isobaric labeling (e.g.iTRAQ or TMT),which label at the peptide level,offer similar levels of accuracy.Fig.2further exemplifies application of the distinc-tive strategies for different types of studies.Note that advances in the field continue to broaden the spectrum of applications.Deciding which is the best quantitative approach to u is made more difficult by the fact that the modification stoichi-ometry may also change within a protein.For instance,many identified phosphoproteins have more than one phosphorylation site that is differentially regulated with individual functions (61).There
fore,it is crucial to distinguish whether the abundance change comes from site-specific phosphorylation or from the whole protein.Several quantitative phosphoproteomic studies have employed SILAC or iTRAQ-labeled SRM techniques to map the phosphorylation signaling network upon epidermal growth factor stimulation in different samples (41,62,63).
Conclusion and Perspective
In conclusion,proteomic quantification is a multifaceted term encompassing global and targeted measurements that can involve relative and/or absolute abundance determinations across large ts of proteins.No single “gold standard method”can resolve all of the analytical problems associated with
pro-FIGURE 2.Schematic diagrams of three major strategies in quantitative proteomics using stable isotope labeling.A ,pairwi comparison is ud to compare two samples;18O labeling,SILAC,and ICAT fall into this category.B ,multiple comparison is ud to compare up to four,six,or eight samples depending the isobaric tags ud (i.e.4-plex iTRAQ,6-plex TMT,or 8-plex iTRAQ).C ,large-scale comparison employs an internal standard:18O-labeled universal reference,super-SILAC mixture,or synthetic isotope-labeled peptides.AQUA ,absolute quantification.
TABLE 1
Overview of the characteristics of different LC-MS-bad quantification approaches
Proteome coverage
Sample preparation workflow complexity Quantification precision Quantification dynamic
range (log 10)Quantification
level No.of samples to compare Cost per sample Spectral counting High Low Low (Ͼ30%RSD a )
2–3MS/MS Unlimited Low AUC/ion intensity High Low Medium (10–30%RSD)2–3MS Unlimited Low SILAC High High High (Ͻ10%RSD)
1–2MS 2–3High 18O Medium Medium Medium (10–20%RSD)1–2MS 2Low ICAT
Low
Medium High (Ͻ10%RSD)1–2MS 2Medium iTRAQ/TMT Medium to high b Medium High (Ͻ10%RSD)
1–2MS/MS 2–8
High 18
O universal reference Medium Medium Medium (10–20%RSD)1–2MS Unlimited Low Super-SILAC Medium High High (Ͻ10%RSD)1–2MS Unlimited High SRM
Low
High
High (Ͻ10%RSD)
4–5
MS/MS
Unlimited
High c
a RSD,relative standard deviation;AUC,area under the curve.
b
Prefractionation can lead to high proteome coverage in the iTRAQ or TMT approach.c
Synthesis of standard peptides or proteins is complex and can be very expensive.
MINIREVIEW:LC-MS-bad Quantitative Proteomics
teomic quantification;biologists must choo the most appro-priate method for particular biological applications. Although LC-MS-bad quantitative proteomics has made and is continuing to make large strides toward better under-standing biological systems,its potential has not been fully real-ized.Bottom-up proteomic approaches rely on the assumption that proteins are completely digested i
nto peptides that are all reproducibly detectable in MS analysis(12);this is rarely true in practice.Moreover,preci and accurate quantification of a specific protein is only achievable when the peptides are exclu-sively derived from a particular protein,so-called proteotypic peptides(64).“Missing values”still prent a formidable chal-lenge in proteomic data analysis,and effective quantification of most post-translational modifications is still in its infancy.
To realize true proteome-wide quantification,higher per-formance platforms providing better parations and linear respons over a wider dynamic range are required.Higher mass resolution and measurement accuracy can help differen-tiate target peptides from co-eluting molecules with similar mass;higher nsitivity will facilitate quantification on low-abundant proteins;and faster scanning rates will assist in quan-tifying proteins on the basis of more peptides(1).In addition, automated and more complete/uniform protein digestion, more efficient and higher throughput labeling,and more intel-ligent platform control software are also required to promote the wide application of quantitative proteomics. REFERENCES
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MINIREVIEW:LC-MS-bad Quantitative Proteomics
