Differential proteomics - UCMwebs.ucm.es/info/candida/master2010/monteoliva_and_albar_2004.pdf ·...

Lucia Monteoliva

is currently a visiting scientist

(postdoctoral researcher) at

the proteomics facility of the

National Center for

Biotechnology, Madrid, Spain.

Juan Pablo Albar

is a senior research scientist at

the Spanish Council for

Scientific Research (CSIC) and

Head of the proteomics facility

at the National Center for

Biotechnology, Madrid, Spain.

Keywords: Differentialproteomics, quantitativeproteomics, DIGE, SILAC,ICAT, SELDI-TOF

Juan Pablo Albar,

Proteomics Facility,

Centro Nacional de Biotecnologia

(CSIC),

UAM Campus Cantoblanco,

Darwin, 3,

Madrid, E-28049, Spain

Tel: +34 91 585 4696

Fax: +34 91 585 4506

Differential proteomics:An overview of gel and non-gel based approachesLucia Monteoliva and Juan Pablo AlbarDate received (in revised form): 18th October 2004

AbstractThe comprehensive analysis of gene expression in complex biological systems has demanded

the development of new technologies to study the cell transcriptome and the cell proteome.

Each approach has advantages and disadvantages from both the conceptual and the

methodological viewpoints. Differential proteomics, the comparison of distinct proteomes

(eg normal versus diseased cells, diseased versus treated cells etc) is of paramount importance.

Several approaches can be used and these typically involve electrophoresis and/or

chromatography combined with chemical or metabolic labelling and mass spectrometry. These

approaches aim to identify molecular targets, namely proteins, involved in different

physiopathological states. Incorporating this knowledge with knowledge from other

technologies lays the foundations of active principles at the molecular level. Here, the various

gel- and non-gel-based approaches that are used in a wide range of biological systems for the

study of differentially expressed proteins will be reviewed.

INTRODUCTIONThe science of proteomics, one of the

most important areas of research in the

post-genomic era, is not new in terms of

its experimental foundations. It has,

nonetheless, profited from unprecedented

advances in genome sequencing,

bioinformatics and the development of

robust, sensitive, reliable and reproducible

analytical techniques.

Genomics projects have produced a

large number of DNA sequences from a

wide range of organisms, including

humans and mammals. The complete

genomes for 215 organisms were available

by August 2004; of these, 28 were

eukaryotes. Together with the study of

RNA expression levels, proteomics is

associated with the analysis of global

protein expression in cells, organisms,

tissues and organelles.1,2 Proteomics is a

research field that gathers environmental

and genetic factors, and the ‘proteome’

represents the functional status of a

biological compartment.

In view of the vast amount of

information generated by genome

sequencing projects, and the need for

deciphering this information, the focus is

now on proteins, their structure, function,

interactions and modifications. Functional

genomics focuses on the characterisation

of subcellular interactions between

proteins, as well as the assessment of

macromolecular complex components.

Elucidation of protein function is

approached by characterising the

interactions that take place within cells;

this approach helps to clarify the concept

that proteins tend to form large

complexes rather than acting

independently.

Classical proteomics work involves a

separation step — usually two-

dimensional gel electrophoresis (2-DE) —

followed by an identification step, usually

mass spectrometry (MS).3 Proteins

resolved by 2-DE can be identified by in-

gel trypsin digestion via peptide mass

fingerprinting (PMF) using MS or tandem

mass spectrometry (MS/MS).4 Alternative

strategies currently involve the use of: (1)

in-solution protein extract digestion; (2)

peptide fractionation by liquid

2 2 0 & HENRY STEWART PUBLICATIONS 1473-9550. BRIEF INGS IN FUNCTIONAL GENOMICS AND PROTEOMICS . VOL 3. NO 3. 220–239. NOVEMBER 2004

chromatography or multidimensional

liquid chromatography (Mud-LC)

coupled to electrospray ionisation ion trap

tandem mass spectrometry (ESI-MS/MS);

and (3) peptide sequencing obtained

through analysis of fragmentation spectra

(MS/MS) — proteins are then identified

using computer algorithms and database

searches.5 Other recent approaches

compare different proteomes (‘differential

expression proteomics’) and involve

electrophoresis and/or chromatography

combined with metabolic or chemical

labelling.

2-DE is a relatively simple visual

method for mapping differences in

protein expression. There are certain

limitations to the universal use of this

technology, such as low detection

sensitivity and linearity, poor solubility of

membrane proteins, limited loading

capacity of gradient pH strips,

reproducibility of gels, relatively low

throughput and low linear range of

visualisation procedures.6 It is,

nevertheless, currently the most rapid

method for direct targeting of protein

expression differences. Some of the

drawbacks can be circumvented using the

differential in-gel electrophoresis (DIGE)

system developed by Amersham

Biosciences;7,8 with this technology,

fluorescent labelling of cell extracts with

one of three fluorescent dyes (Cy2, Cy3

or Cy5) is done prior to gel separation

and cellular protein levels can be

compared within a single gel.

Another approach, multidimensional

(ionic exchange, affinity and reverse-

phase) nano- or capillary chromatography

in conjunction with MS/MS, provides

unprecedented tools that enable

comparison of differentially expressed

proteins. Labelling techniques such as

isotope-coded affinity tags (ICATs),9,10

which combine differential chemical

isotope tagging of cell lysates,

multidimensional capillary

chromatography and MS/MS, have been

used extensively for differential display

analysis. Surface-enhanced laser

desorption/ionisation (SELDI) time-of-

flight mass spectrometry (TOF-MS)

enables differential protein expression

profiling of complex protein mixtures

separated by on-chip retentate

chromatography. In this paper, the use of

these and other related alternative

approaches for the differential protein

expression analysis will be discussed.11

GEL-BASED APPROACHESDifferential display via two-dimensional gel electrophoresisClassical proteomics work involves: (1) a

separation step in which proteins of

interest are separated by 2-DE (isoelectric

focusing [IEF] followed by separation as a

function of molecular mass); (2) protein

visualisation and image analysis; (3)

excising the spots to be analysed; (4) in-

gel digestion of proteins and pooling of

the released peptides; (5) analysis of the

peptide mass fingerprint (PMF) for every

digested protein through matrix-assisted

laser desorption/ionisation TOF-MS

(MALDI-TOF MS); (6) matching peptide

masses against protein databases to obtain

candidate proteins;3 and (7) validating

identification by acquisition of MS/MS

spectra of selected peptides to confirm

their sequences (Figure 1).4,12 Since

immobilised pH gradient strips (IPG)

were developed (for a review, see refs. 13

and 14), variability in experimental

conditions has decreased; this is now

undoubtedly the most widespread strategy

for comparing distinct states of two

proteomes.

Different protein visualisation methods

are available. Radiolabelling is very

sensitive, but is hazardous and expensive.

Colloidal Coomassie blue is an easy to

use, low-cost staining agent, but has poor

detectability and sensitivity and a small

linear dynamic range. Silver staining is

the most commonly used technique, as it

detects as small a quantity as a single

nanogram, but its dynamic range is also

restricted to a single order of magnitude

scale; in addition, silver staining is not

quantitative, as different proteins tend to

interact with silver ions differently.

Finally, despite of its high cost,

2-DE, LC-ESI-MS andSELDI MS as tools fordifferential proteomics

Differential proteindisplay has traditionallybeen based on 2-DEsilver-stained gels

& HENRY STEWART PUBLICATIONS 1473-9550. BRIEF INGS IN FUNCTIONAL GENOMICS AND PROTEOMICS . VOL 3. NO 3. 220–239. NOVEMBER 2004 2 2 1

Differential proteomics

fluorescent detection is gaining in

popularity because of its sensitivity and

wider linear dynamic range.15 After

protein separation and staining, further

computer-based analysis is needed to

detect differentially expressed proteins.

Computer programs are continuously

being developed and improved — eg

Progenesis (Nonlinear Dynamics), Image

Master 2D Platinum and Melanie

Software (Amersham Biosciences) or

PDQuest (Bio-Rad) — but image

Deep into visualisationmethods and imageanalysis

Figure 1: Differentialdisplay via 2-DE: classicalworkflow. Samples Aand B are resolved by 2-DE in replicate gels andsilver stained. 2-Dimages are analysed byspecific software anddifferentially expressedprotein spots areproteolytically digestedand analysed by MALDI-TOF MS. The peptidemass fingerprint (PMF) ismatched against genomicor protein databases toobtain candidateproteins. Tandem massspectrometry (MS/MS)and peptide sequencingby analysis offragmentation spectracan assist in identifyingpeptides when ambiguityremains after MALDI-TOF analysis


Monteoliva and Albar

analysis remains a time-consuming

process.

Protein identification is based on

matching peptide experimental masses of

proteolytically digested proteins (PMF)

versus the theoretical masses obtained in

the in silico digestion of all proteins in a

specific database. The result is a list of

candidate proteins with different

confidence levels. Despite its limitations,

for years this proteomic strategy has

proved to be very useful in the

identification of differentially expressed

proteins in all areas of biological research.

In cancer research, it was implemented

for the detection of tumour-associated

proteins in liver carcinoma,16 lung

adenocarcinoma,17 fibrosarcoma,18

lymphoma,19 breast cancer20 and prostate

cancer.21 Several putative tumour markers

have been reported, although validation

for clinical purposes — one step further

on — was not always fully accomplished.

This technology has also been applied to

the study of such organ-specific human

pathologies as thrombosis22 or heart

failure, sometimes studied in animal

models,23,24 and to address a variety of

new biological challenges such as the cell

biology of symbiotic, opportunistic or

pathogenic bacteria25–29 and fungi,30 or

virus–host cell interactions.31

Toxicological studies and drug-induced

differential protein expression are also

among the important application areas.32

This is just a short description of the work

published using so-called classical

expression proteomics.

Despite the potential and resolution of

2-DE, it remains a labour-intensive

technique that requires qualified personal

to obtain reproducible results. To

overcome gel to gel or intrinsic biological

sample variations, it is considered that, for

this type of expression proteomics study,

at least nine different gels are required for

each cell state (three different gels of three

different samples of the same biological

state). In any case, total proteome

coverage by 2-DE is experimentally

limited to proteins with molecular

weights in the 10�120 kilodalton (kD)

range, with neutral�acidic isoelectric

points. Basic or very basic proteins (above

pH 9.5) are rather difficult to focus. 2-D

gels also rarely display hydrophobic

proteins, and only highly abundant

proteins from total cell lysates are

currently visualised. Low abundance

proteins of physiological relevance, such

as regulators or signalling proteins, are

difficult to detect (for review, see ref. 33).

Due to these drawbacks, a further protein

fractionation step is needed prior to IEF

to reduce complexity. This pre-

fractionation can be achieved using

protocols based on differential solubility

or by established procedures such as liquid

chromatography or free flow

electrophoresis. Organelle enrichment or

membrane fraction preparations also

reduce complexity and allow assessment

of protein location in the cell. The use of

narrow, overlapping pH gradient strips

also increases the amount of protein

loaded, detection of low abundance

facilitating the proteins.6

Fluorescent two-dimensionaldifference gel electrophoresis:2-D DIGEClassical experimental design:

Cy3 versus Cy5

As mentioned above, an important

shortcoming of classical 2-DE approaches

is the intrinsic gel to gel variation that

requires several replicate gels of each

sample that are not directly overlapped.

This can be circumvented using

multiplexing methods such as fluorescent

two-dimensional difference in-gel

electrophoresis (2-D DIGE),7 which

substantially reduces variability by

displaying two or more complex protein

mixtures labelled with different

fluorescent dyes in a single 2D gel.

Fluorescent labelling also renders 2-D

DIGE much more quantitative than

colorimetric methods. It has a linear

dynamic range of four or five orders of

magnitude, by contrast with the

approximately one- or two-order range of

colloidal Coomassie and silver stains.34 As

regards sensitivity, 1 ng of standard

Use of 2-DE silver-stained gels in differentbiological systems

2-DE workflowdrawbacks

2-DE DIGE overcomesgel-to-gel variation andimproves quantitation



protein is detected with Cy3/Cy5

fluorescent labelling.35

2-D DIGE (marketed by Amersham

Biosciences) is based on the use of the

different, spectrally resolvables N-

hydroxysuccinimide derivative

fluorescent dyes Cy3 and Cy5 (for a

review, see ref. 36). These dyes label the

�-amine groups of protein lysines

specifically and covalently to form an

amide. Control and treated protein

mixtures are labelled independently with

Cy3 or Cy5 derivatives. The dyes are

designed to have the same molecular

weight and charge to ensure that proteins

common to both samples have the same

relative 2-DE mobility, regardless of the

dye used to tag them. Samples are

minimally labelled (only about 3 per cent

of the total amount of each protein is

tagged), then mixed and resolved in a

single 2-D gel. The protein spot patterns

are visualised by alternately illuminating

the gel with the excitation wavelengths

for each of the two fluorescent dyes. The

2-D images can be analysed by specific

software, such as DeCyder (Amersham

Biosciences), to detect differentially

expressed protein spots. The nature of the

minimal labelling method results in

populations of labelled and unlabelled

species for each protein. At the low

molecular weight ranges, some positional

discrepancy (shifts) between the low levels

of the labelled protein and the bulk of

unlabelled protein has been observed. In

order to maximise the amount of protein

available for MS, the total protein should

be visualised using a post-staining method

(SYPRO ruby or silver staining). Selected

spots are then robotically excised from the

gel (a preparative scale gel, if necessary)

and subjected to MS for identification.

This 2-D DIGE workflow is outlined in

Figure 2A.

The reproducibility and sensitivity of

2-D DIGE were initially established by

Unlu et al.7 using protein extracts from

two different Drosophila embryo samples.

Validation of 2-D DIGE as a tool for

toxicological applications (paracetamol

hepatotoxicity in mouse liver

homogenates, control versus treated)

determined the quantitative variation of

the process. In this study, inter-animal

response variability was about nine times

that contributed by the 2-D DIGE

process.34

Several reports have been published in

different fields using the two-dye 2-D

DIGE technology. Differential gel

electrophoresis combined with other

techniques has thus been used to analyse

insect resistance to Bacillus thuringiensis

(Bt) Cry toxins. Changes were examined

in gut proteins from the larvae of an

Indian meal moth (IMM; Plodia

interpunctella) colony showing resistance to

Bt. This study revealed a number of

changes in the levels of specific mid-gut

proteins that indicate increased

glutathione utilisation, elevation in

oxidative metabolism and differential

maintenance of energy balance within the

mid-gut epithelial cells of the resistant

larva model.37

Borner et al.38 reported proteomic (2-

D DIGE-based) and genomic analysis of

glycosylphosphatidylinositol-anchored

proteins (GAP) extracted with

phospholipase C (PLC) from Arabidopsis.

The Pi (phosphatidylinositol-specific)-

PLC-treated and control aqueous phases

were labelled with different fluorescent

Cy dyes. This revealed 30 proteins

specifically enriched in the Pi-PLC-

treated fraction and some background

proteins present in both fractions.

2-D DIGE has also been applied to

biomedical studies. In a study of renal

physiology, it was used to identify inner

medullary collecting duct proteins with

different expression in cortical or outer

cells.39 In addition, proteomic analysis of

long-term vasopressin action in the inner

medullary collecting duct of the

Brattleboro rat showed 43 proteins that

differ in abundance or in mobility; these

results were confirmed by semi-

quantitative immunoblotting and

immunohistochemical approaches.40

2-D DIGE has also been used in the

search for cancer-specific protein markers

— eg to quantify differences in protein

2-DE DIGE minimallabelling features andworkflow

Use of two fluorescentdyes in 2-DE DIGEapplications



expression between laser capture

microdissection-procured oesophageal

carcinoma cells and normal epithelial

cells.41 A preliminary study of human

infiltrating ductal breast carcinoma

demonstrated that 2-D DIGE portraits

might be a reflection of histological and

pathological status.42

Figure 2: Schematic overview of 2-D fluorescent difference in-gel electrophoresis (2-DDIGE) technology. (A) Two fluorescent dyes design. Samples A and B are fluorescently labelledwith either Cy3 or Cy5. The labelled samples are mixed and resolved in a single 2-D gel. Theprotein spot patterns are visualised by alternately illuminating the gel with Cy3 and Cy5excitation wavelengths. The 2-D images are analysed by specific software. Differentiallyexpressed protein spots, visualised with SYPRO ruby or silver post-staining, can be identifiedby mass spectrometry. (B) Three fluorescent dyes design with pooled internal standard.Samples A and B are fluorescently labelled with either Cy3 or Cy5, and a pooled internalstandard (with equal amounts of all the samples in the experiment) is labelled with Cy2.Samples are mixed and resolved in a single 2-DE gel. Protein spot patterns are visualised byalternately illuminating the gel at the excitation wavelengths of each dye. 2-D images areanalysed by specific software and the internal standard is used for normalisation. Statisticalanalysis is performed and differentially expressed protein spots can be identified by MS



Among neuroscience-related

applications, identification of proteins

with age-related expression in cat primary

visual cortex has been reported. Protein

extracts from the visual cortical area of 17

adult cats and 30-day-old kittens were

compared.43 Analysis of the brain

proteome of genetically altered mice

revealed alterations of stress-related

proteins.44

Only three protein changes were

detected in the mitochondrial proteome

from mouse heart when double knockout

mice for two isoforms of creatine kinase

and C57BL/6 mouse heart mitochondria

were compared.45 In another study, six

protein changes were implicated in

ischaemia reperfusion injury when 2-D

DIGE was used to compare normal,

ischaemic and ischaemia-reperfused rat

hearts.46

2-D DIGE has also been applied to the

study of D. melanogaster immune

responses. Comparative analysis of the

haemolymph proteome of 2,000 third-

instar Drosophila larvae showed ten

different proteins that appear very soon

after an immune challenge with

lipopolysaccharides.47

Internal standard: Cy3 versus Cy5

versus Cy2

A novel 2-D DIGE experimental design

includes the use of a special ‘internal

standard’ created by ‘pooling aliquots’ of

all samples in the experiment labelled

with a third spectrally resolvable dye,

Cy2. This pooled internal standard is run

in the same gel as control and treated

samples previously labelled with Cy3 and

Cy5. The Cy2-labelled internal standard

is composed of equal amounts of each

control and treated sample, and is

included in all gels to normalise protein

abundance measurements across multiple

gel experiments. The use of this internal

standard reduces gel to gel variation over

conventional 2-D DIGE and facilitates

the use of image analysis software

(DeCyder) for automated and accurate

spot quantitation, gel to gel matching and

statistical analysis. This improvement,

together with randomised criss-cross

experimental design in which control and

treated samples are each tagged with Cy3

or Cy5, provides statistical confidence in

the detection and quantitation of subtle

variations in protein expression.48

This three-dye internal standard

technique, outlined in Figure 2B, has

been applied in a wide range of biological

areas. Liver toxicity in model animals has

been analysed in two studies: paracetamol

toxicity49 and hydrazine toxicity as a

model of multivariate data analysis.50

Using a genetic neurokinin 1 receptor

knockout mouse model system, eight

differentially expressed proteins were

identified in cerebral cortex tissues.51 The

three-dye method has also been applied in

cancer research, using a breast cancer cell

model system, to identify differentially

expressed proteins in a growth factor

stimulation time course.35 It has also been

used to identify 52 unique proteins with

altered abundance in the proteome of

human colorectal tumour cells relative to

the adjacent normal mucosa of six

different patients.52

In microbiology, Gade et al.53

evaluated the use of 2-D DIGE, including

an internal standard, to detect and

quantify proteins specific for glucose or

N-acetylglucosamine metabolism in the

marine bacterium Pirellula sp.; 24 proteins

differing in abundance were identified

with high statistical confidence.

Saturated labelling approach

Although 2-DDIGE enables increased

confidence in the detection of protein

differences, only 3�5 per cent of a given

protein is tagged by the minimal labelling

method. To increase sensitivity,

Amersham Pharmacia developed a new set

of DIGECy3 and Cy5 fluorescent dyes.

Protein cysteine residues are fully labelled

with these newmaleimide cyanine dyes,

enhancing sensitivity for low abundance

samples. In addition, protein spots are

excised directly from the saturation-

labelled gel, eliminating the need for post-

stained preparative gels.54 Nevertheless,

only two dyes are currently commercially

The internationalstandard andappropriateexperimental designallow normalisation andstatistical analysis in2-DE DIGE experiments

Application of 2-DEDIGE using threefluorescent dyes



available, and the 2D-spot pattern is

modified. Expression differences in

motility-regulating proteins frommurine

primitive haematopoietic cell populations

have been identified using this approach.55

Other gel-based approachesFew reports have been published

describing the use of alternative

multiplexing methods for differential

expression studies. In 1983, Goldman et

al.56 described a method that made use of

metabolic labelling of proteins using

radioactive isotope-labelled amino acids,

2-DE and recording on colour negative

film by radiographic exposure. A sub-

proteome differential display method that

uses radiolabelled proteins from one

source and silver-stained proteins from a

second source, mixed in a gel in a 1:100

ratio, allowed precise discrimination

between members of each sub-proteome

(chromatographic fractions) using

commonly available software.57

Combination of radiolabelling and

SYPRO ruby staining of the same gels

allows precise quantitation of the protein

amount as well as of the 35S incorporated.

This quantitative proteome profiling was

developed by Gerner and co-workers to

determine absolute values of cell protein

amounts, as well as synthesis and turnover

rates.58 The same method was recently

used to compare quiescent human T cells,

phytohaemagglutinin-stimulated T cells

and Jurkat cells, and to study human

umbilical vein endothelial cells treated for

6 hours with vascular endothelial growth

factor.59

Because in vivo radioactive protein

labelling is not always feasible, the

fluorescence-based methods are more

widely distributed.

NON-GEL APPROACHES:MS-BASED APPROACHESQuantitative proteomics basedon stable isotope taggingand MSBecause of the limitations that arise from

classical proteomics approaches (2-DE

followed by MS), other methods have

gained popularity, such as Mud-LC on-

line with ESI-MS/MS.5,60 In these

approaches, complex mixtures of proteins

are digested in solution. The resulting

peptide mixture is fractionated by one or

several steps of capillary chromatography

and analysed in a data-dependent manner

by MS/MS. These techniques share the

limitations of 2-DE for a dynamic range

of analysis (usually 103–105) and

identification of low concentration

proteins is achieved through pre-

fractionation techniques in a similar

manner to pre-fractionation methods used

in 2-DE gel analysis. Although classically

one of the limitations of LC/MS-based

methods is the difficulty in performing

differential display analysis, several reports

have recently appeared showing the

feasibility of relative peptide

quantification with these strategies.61–64

Alternative or complementary MS-

based approaches have been developed for

differential protein expression

measurements and are currently being

improved. They are based on the

differential labelling of perturbed and

non-perturbed protein extracts with

different stable isotopes (12C/13C,14N/15N and 1H/2H). In this way, the same

peptide from two different samples will

show the same chemical behaviour, with

a difference in mass detectable by MS

techniques. Peptide peak intensities can

be used for relative quantification of these

peptides. The workflow for this

methodology (outlined in Figure 3) is as

follows: (1) differential isotopic labelling;

(2) digestion of combined protein samples

to obtain peptide mixtures; (3)

chromatographic fractionation of mixed

peptide samples; (4) analysis of the

separated peptides by MS/MS; and (5)

processing of the MS results to obtain

relative protein abundance as well as

protein identification by database

searching.

Several recent papers have reviewed in

detail the different chemical, metabolic

and enzymatic labelling techniques used

to date. The basis of these strategies, the

Less frequently usedmultiplexing methods

Isotope tagging allowsdifferential proteomicsbased on LC-MS



Figure 3: Quantitative proteomics based on stable isotope tagging and MS workflow. (A)Stable isotope labelling with amino acids in cell culture (SILAC). Cell samples are cultured inthe presence of a stable isotopically labelled amino acid (aa). Cell samples are mixed, lysed anddigested. Subsequently, they are subjected to chromatographic fractionation and analysed bytandem MS (MS/MS) to obtain relative protein abundance and protein identification bydatabase searching. (B) Isotope-coded affinity tag (ICAT). Protein samples are labelled with thelight or heavy version of the ICAT reagent, mixed and proteolytically digested. Labelledpeptides are isolated by avidin affinity chromatography and subjected to multidimensional liquidchromatography ‘online’ with MS/MS. Peptides are identified by matching the MS/MS spectraagainst databases. Protein abundance can be determined by integrating the peak areas of theextracted ion chromatograms (XIC) for each isotope-coded peptide



specific characteristics of each kind of

labelling, as well as advantages and

limitations have been discussed

extensively.65–69 Here, the focus will be

on two of the most used non-gel MS-

based quantitative proteomic approaches

and some of their recent applications will

be described.

Metabolic isotopic labelling: SILAC

Addition of stable isotope labels to culture

media during cell growth yields isotope-

labelled proteins. This approach was first

described using Saccharomyces cerevisiae

grown in either 14N minimal media or15N-enriched minimal media to

quantitate protein expression.70

The use of a specific isotopic amino

acid (eg deuterated L-leucine or 13C-

labelled arginine), rather than the entire

pool of amino acids described above, was

termed stable isotope labelling by amino

acids in cell culture (SILAC) by Mann

and colleagues (Figure 3A).65,71 In this

case, the mass difference between labelled

and unlabelled peptides is predictable, and

MS/MS spectra interpretation becomes

easier. The technique was first applied to

the relative quantitation of changes in

protein expression during the process of

muscle cell differentiation, and was

described as a simple, inexpensive and

accurate method. Pratt et al.72,73 found the

use of isotope-labelled amino acids useful

as an aid to protein identification in

peptide mass fingerprinting and for the

study of protein turnover in yeast.

SILAC has also been applied, in very

elegant approaches, to the characterisation

of protein–protein interactions in the

study of the epidermal growth factor

(EGF) pathway74 and of proteins that

participate in early stages of cell spreading

by interacting with focal adhesion

proteins.75 RNA and RNA-binding

protein involvement in cell spreading

through the spreading initiation centre

(SIC), a previously undescribed structure,

were identified. Peptide�protein

interactions in the EGF pathway were

also screened using a SILAC approach.76

Synthetic peptides in ‘active’ (tyrosine-

phosphorylated peptide of the EGF

receptor) and ‘control’ (non-

phosphorylated) forms were used as bait

in affinity pull-down experiments.

SILAC differential labelling was also

used before membrane disorganisation to

identify lipid raft proteins, to distinguish

real raft components from non-raft

contamination. A set of 241 authentic

lipid raft proteins was obtained, including

a large proportion of signalling molecules

(kinases and phosphatases or G

proteins).77 SILAC application to clinical

studies includes a recent prostate cancer

study.78 Sixty proteins were found with a

>3-fold increase in basal levels in the

highly metastatic cells, whereas levels of

22 other proteins were reduced. Western

blot experiments confirmed the results

obtained using the SILAC approach.

Chemical labelling: ICAT

Metabolic approaches based on the

biological incorporation of isotope-

labelled amino acids are not practical for

any kind of biological sample, as only

proteins extracted from growing cells can

be used. Chemical incorporation of

isotopic tags after protein extraction is

thus the best protein-tagging alternative

when metabolic labelling is not feasible.

The most commonly used method in

quantitative proteomics is based on the

use of isotope-coded affinity tag (ICAT)

reagents developed by the Aebersold

group.9

The ICAT reagent-based analysis

workflow (Figure 3B) comprises the

following steps: (1) protein extraction and

reduction of control and treated samples;

(2) chemical labelling of protein sulph-

hydryl groups with the ‘light’ and ‘heavy’

versions of the ICAT reagent; (3) mixing

of both samples and digestion by an

endoprotease; (4) isolation of labelled

peptides; (5) peptide fractionation usually

usingMud-LC— strong-cation exchange

(SCX) liquid chromatography followed by

reversed-phase (RP) microcapillary liquid

chromatography; (6) MS/MS analysis with

an ESI-tandemmass spectrometer; (7)

automated database searching to identify

SILACmetaboliclabelling has beenapplied to identifydifferentially expressedproteins as well asprotein-interactions(with proteins, peptidesor RNA)

ICAT chemical labellingis useful for all kinds ofprotein samples



peptide sequences (and, thus, the proteins

fromwhich they are derived); and (8)

determination of relative protein

abundance from theMS data. The peak

areas of the extracted ion chromatograms

(representing total ion currents for each

peptide eluted from the column at a given

time) for each isotope-coded peptide are

used to determine relative peptide (and,

hence, protein) abundance.

Strategies in which peptides eluted

from the chromatography column are

spotted directly onto a MALDI target

have also been used (see refs. 65, 66 and

68 for detailed reviews).

The first ICAT reagent, reported by

Aebersold and colleagues, was composed

of three parts: a reactive group specific for

thiol groups, a linker and a biotin moiety

for affinity chromatography purification

using immobilised avidin. The linker has

either eight deuterium atoms in the heavy

(d8) form or is undeuterated in the light

(d0) form.9 Use (or not) of deuterium

causes differential chromatographic

elution profiles of the heavy and light

forms of a given differentially labelled

peptide, yielding inaccurate

measurements of abundance. The

relatively large size of the biotin tag makes

interpretation of the MS/MS spectra

difficult; a new generation of ICAT

reagents was thus developed with nine13C or 12C atoms at the linker moiety and

a cleavable spacer to allow biotin removal

(Applied Biosystems).

Several applications have been reported

using the ICAT technology. The use of

cleavable ICAT led to development of a

method to determine the subcellular

location of membrane proteins through a

series of pairwise comparisons of gradient

fractions. This method allows assignment

of proteins to a specific compartment in

Arabidopsis thaliana without the need to

obtain pure organelles.79 Shiio and co-

workers, besides using (d0/d8) ICAT

reagents to compare the global protein

expression pattern in rat myc-null cells

versus myc-plus cells,80 developed a

method to identify and quantify

chromatin-associated proteins induced by

Myc oncoprotein expression in human B

lymphocytes.81

Two other very informative studies of

macromolecular complexes using the

ICAT reagent-based technology are the

study of Ste12 protein complexes from

yeast cells in different states82 and the

dynamic changes in transcription factor

complexes during erythroid

differentiation.83 In the former study,

(d0/d8)-ICAT was useful not only to

determine relative abundance changes in

the composition of isolated complexes,

but also to distinguish specific complex

components from co-purified proteins.

Based on ICAT strategy, Brand et al.83

reported an interesting model of

activation and repression of �-globin gene

expression during erythroid

differentiation.

Cleavable (C12/C13) ICAT has shown

its potential in other biological

applications, such as the study of the

redox state of proteins84 and identification

of metalloproteinase substrates in breast

carcinoma cells.85

ICAT reagent-based quantitative

proteomics analysis for differential

expression studies of total proteomes are

also reported. The study of whole

proteome changes in the opportunistic

bacterial pathogen, Pseudomonas aeruginosa,

cultured under conditions that induce

expression of virulence factors, identified

several conserved Gram-negative proteins

involved in that process. The comparative

ICAT analyses of membrane versus whole

cell proteins allowed the detection of

protein changes in subcellular

compartmentalisation.86 ICAT

technology was applied to the study of

changes in the global proteome of yeast as

a model eukaryotic system in two studies:

protein changes as a consequence of salt

stress87 and the comparison with an upf1

mutant.88 Eukaryotic samples of higher

complexity were analysed using ICAT

reagent and Mud-LC. Examples include

the analysis of protein expression changes

induced in murine MC3T3 osteoblast

cells,89 mouse neurones (in the analysis of

DNA damage-induced neuronal death),90

First and secondgeneration ICATreagent features

ICAT applications insubcellular proteinlocation,macromolecularcomplexes and redoxstate of proteins



cystic fibrosis,91 androgen-stimulated and

unstimulated LNcap prostate cancer

cells92 and intestinal epithelial cells in

response to an enteropathogenic

Escherichia coli strain.93

Taken together, these studies show

that, despite the enormous potential of

this technology developed for the

simultaneous analysis of complex

proteomes, we are far from being able to

generate a picture of a whole proteome.

In yeast, for example, between 56087 and

70088 proteins have been identified,

which represents about 10 per cent of the

yeast genome; 2,501 were found (about 9

per cent) in murine osteoblast cells89 and

over 2,000 in human polarised intestinal

epithelial monolayers.93 An important

disadvantage of the ICAT method is that

proteins lacking cysteines are not

detected.

A new ICAT variant based on the use

of ICAT reagent to tag samples from two

cell types, combination of the two

samples and separation by 2-DE in a

single gel, protein detection by MS-

compatible stains and identification and

quantification by MS has been reported.

The abundance ratio of the proteins in the

spot is determined by the signal intensity

ratio for the two isotopic forms of the

label peptide.94 A similar approach

requiring metabolic labelling has been

described for Plasmodium falciparum,95 rat

hepatocytes,96 the multicellular organisms

Caenorhabditis elegans and D. melanogaster97

and for the quantitation of protein

expression and site-specific

phosphorylation in yeast.98

SELDI-TOF: protein profilingSELDI-TOF MS enables analysis of

complex protein mixtures separated by

on-chip retentate chromatography.11 The

analytical procedure involves a few

common steps, beginning with

chromatographic separation. The

biological fluid of interest is pre-

fractionated or loaded directly onto

several chemically treated supports —

protein chip arrays — with specific

chromatographic features (cationic,

anionic, hydrophobic, hydrophilic, ion

metal chelating). Each surface is designed

to retain proteins according to a general

or specific physicochemical property of

the protein, yielding an on-surface

chromatographic protein separation. The

second step is MS spectra acquisition —

after washing, the immobilised proteins

are co-crystallised with a matrix on a

target surface and MS spectra are acquired

by a specific mass analyser, eg a SELDI-

TOF mass spectrometer. Low-resolution

protein patterns or retentate maps of the

proteins bound to each chromatographic

surface are thus generated. Unlike LC-

MS, which is based on elution, SELDI-

TOF MS combines retention with MS. In

most instances, it does not allow direct

identification of proteins that may be

potential disease biomarkers. Finally, in

data analysis and interpretation/

evaluation, peak comparisons are made

using multifactorial bioinformatic

software (tree classifiers, neural networks,

cluster analysis, test statistics) (Figure 4).

The technique is best for relatively small

(,20 kD) proteins and protein fragments.

This method constitutes a rapid,

reproducible analytical tool that enables

the comparative analysis of protein

expression profiles in the low fmol range,

although it has the same dynamic range

limitations as other MS technologies.

Proteins from complex biological

specimens such as serum, plasma,

intestinal fluid, urine, cell lysates and cell

secretion products have been profiled

using SELDI-TOF MS. Serum proteomic

profiling using SELDI-TOF MS is a

promising new approach for cancer

diagnostics.99 The central hypothesis is as

follows: protein or protein fragments

produced by cancer cells or their

microenvironment may eventually enter

the general circulation. The different

protein/fragment patterns could then be

analysed by MS and used for diagnostic

purposes, in combination with a

bioinformatic algorithm.100 The cancer

types that have been examined include

ovarian,101 prostate,102 breast,103

bladder104 and renal.105 Apparent

ICAT studies ofcomplex proteomes:only a small percentageof the total proteome isidentified

ICAT labellingcombined with 2-DE

SELDI-TOF combineschromatographicdifferential proteinretention and TOF MS

SELDI and detection ofcancer biomarkers



successes of this technology have recently

been extensively reviewed.100,106–108

Clinical trials are now underway and will

reveal whether these data can be

reproduced and if the platform is

sufficiently robust for clinical use. The

discriminatory peaks, if positively

identified, may represent molecules that

could be measured with simpler and

cheaper techniques for the purpose of

diagnosing cancer.

The SELDI-TOF technology is

marketed by Ciphergen Biosystems.

More than 200 papers have already been

published on the use of this method. In

general, it has been suggested that this

technique could show much higher

diagnostic sensitivity and specificity

(approaching 100 per cent) compared

with classical cancer biomarkers.109

These sensitivities/specificities are far

superior to those obtained using classical

Are SELDI-TOFpatterns useful asdiagnostic or prognasticmarkers?

Figure 4: Differentialprotein expressionprofiling by SELDI-TOFMS. Pre-fractionatedcomplex mixtures arechromatographicallyseparated using proteinchips arrays; the chipsconsist of eight or 16spots of a specificchromatographic surface(hydrophobic, cationexchange, anionexchange, metal affinity).MS spectra of boundproteins are obtained bySELDI-TOF MS. Outputdata from MS aredisplayed as trace, geland map views (top tobottom). Finally,univariate and/ormultivariate data analysisis performed with theappropriate software todetermine differentiallyexpressed proteins



techniques to detect cancer biomarkers.

Some papers110,111 have questioned the

validity of the results obtained with this

technology and propose experiments to

investigate these questions in detail

before clinical use of the technique.

Several statistical analyses have been

performed on the original ovarian cancer

work. This analysis uncovered numerous

problems with study designs and sample

collection and handling.112,113 This calls

into question the original claims of

SELDI patterns as diagnostic or

prognostic markers. Nevertheless, Zhang

et al.114 have recently reported a

coordinated multicentre study, using 503

serum samples from ovarian cancer

patients at stages I/II and III/IV of the

disease and 142 healthy women,

specifically designed to alleviate the

impact of these factors and to show the

validity of this technological platform as

well as the potential of the identified

biomarkers to improve the detection of

early-stage ovarian cancer.114 The

method must clearly be thoroughly

validated before clinical implementation

is warranted. The most important

criticism of the use of this methodology

is based on the fact that, since proteins

are not usually identified, it is difficult to

support the central hypothesis that these

differential protein features come from

cancer cells or their microenvironment.

Without identification, it cannot be

known whether a protein peak is related

to a disease state or not.

The potential of this method has been

extended to conditions such as

Alzheimer’s disease, Creutzfeldt-Jakob

disease and renal allograft rejection.115,116

It has also been used in targeted studies to

characterise protein–protein and protein–

DNA interactions,117,118 as well as to

characterise phosphorylated and

glycosylated proteins119,120 or

transcription factors.121

CONCLUDING REMARKSIn studying a biological system, both

genomics and proteomics approaches are

being implemented with increasing

frequency to obtain an integrated view of

cell physiology; genomic and proteomic

data have proven to be

complementary.122,123

The wide dynamic range of

intracellular or serum protein

concentration, the much more reduced

range detectable by bioanalytical

techniques and the lack of protein

amplification procedures are issues that

proteomics strategies must address. To

improve global proteome coverage, cell

fractionation is a mandatory step in

overcoming these important drawbacks.

Traditional approaches for differential

display protein analysis based on 2-DE

and colourimetric staining are still very

useful, and have produced many

contributions even considering their

intrinsic sensitivity- and reproducibility-

associated limitations. Nevertheless, the

uniqueness of 2-DE for easy visualisation

of protein isoforms, using two physical

parameters such as isoelectric point and

molecular weight, renders this technology

itself extremely informative.

Quantitative differential proteomics has

required the use of alternative approaches

to expand the analytical range offered by

classical 2-DE. Among them, gel- and

non-gel-based methods such as 2-D

DIGE, ICAT reagent-based methods,

SILAC and SELDI-TOF are notable. The

introduction of fluorescent 2-D DIGE

with the use of pooled internal standards,

together with sample pre-fractionation

approaches, has greatly improved the

efficiency of 2-DE methods as a truly

quantitative differential display

technology. Stable isotope tagging

strategies and MS/MS have also allowed

quantitative differential proteomics by

focusing on peptide comparison as a

‘reflection/image’ of the proteins from

which they are derived and, hence, their

proteomes. Gel- and non-gel-based

approaches provide closely related but

distinct information about proteins,

suggesting that they are complementary,

or at least supplementary, methods.

The recent introduction of SELDI-

TOF MS has allowed the acquisition of

SELDI applicationsother than cancer

Gel- and non-gel-basedapproaches arecomplementary indifferential proteomics



protein patterns from complex protein

mixtures, mainly body fluids such as

plasma or serum associated with many

human diseases. Before this technique can

be applied to clinical use, it must be

validated extensively, but it may,

nonetheless, be the most promising

approach for biomarker discovery in

clinical proteomics.

ACKNOWLEDGMENTS

We gratefully acknowledge Dr Alberto Paradela for

critical reading of the manuscript and Cathy Mark

for editorial assistance.

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