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Terence E. RyanScott D. Patterson*Celera Genomics Group,
45 West Gude Drive,
Rockville, MD 20850, USA.
*e-mail: scott.patterson@
celera.com
http://www.trends.com S45
Trends in Biotechnology Vol. 20 No. 12 (Suppl.), 2002 A TRENDS Guide to Proteomics | Review
0167-7799/02/$ – see front matter ©2002 Elsevier Science Ltd. All rights reserved. PII: S0167-7799(02)02089-9
Proteomics represents the systematic and broad applica-
tion of technologies that have traditionally supported the
field of protein biochemistry. In its most common appli-
cation, proteomics is used to characterize differences in
protein expression between biological specimens.
Although proteomics technologies can be used to catalog
protein differences after metabolic perturbation, its great-
est therapeutic value lies in the comparison of cells from
normal tissue with those representing a disease state
(e.g. [1]). Such comparisons could enable the identifica-
tion of disease-specific biomarkers that could be used for
diagnostic or prognostic tests, or target proteins that have
the potential for drug intervention.
Owing to the variability of natural protein expression in
the same tissue between individuals (owing to inherent
genetic, metabolic, diurnal, environmental and nutritional
differences, among others), the disease specificity of an
observed protein-expression differential needs to be rigor-
ously demonstrated.This can be achieved by characterizing
the frequency of a differential expression across a range of
samples taken from many individuals with the disease, as
well as by the relative absence of the differential expression
in other normal tissues in the same individual. These
requirements, which demonstrate the specificity for the
disease state, require an experimental design that can
encompass large numbers of experimental samples and
controls, and effective interassay comparisons between
individual samples and among sample groups.The number
of individual samples required to generate statistical confi-
dence results from a complex mixture of biological and
laboratory process considerations. For example, a single
disease can be represented by various degrees of disease
progression or characteristic phenotype. Patients with acute
myeloid leukemia (AML) can generally be classified into
one of seven French–American–British (FAB)-AML classi-
fication disease groups [2,3]. Examination of AML sam-
ples requires that they be grouped accordingly to produce
meaningful results, or that a larger number of AML
samples are examined to identify ‘pan-disease’ patterns of
differential protein expression. In addition to relevant
disease subtypes, samples need to be grouped according
to tumor staging, degree of metastasis, and known
genetic lesions. The reproducibility (variability in repli-
cate processes) and sensitivity of laboratory processes also
contributes to the number of samples needed for exami-
nation; differentials at the limits of signal-to-noise identi-
fication will require a greater number of samples to
achieve statistical importance. Statistical evaluation of the
differentially expressed proteins will establish appropriate
levels of confidence for each observation; for the processes
outlined here, we have found that ∼ 20 samples per study
point is usually sufficient. However, in general, the greater
the frequency of representation of a particular protein-
expression differential in a range of samples correlates not
only with the degree of statistical significance, but also
with the level of interest in that differential as representative
of the disease process under study. The rigor required for
these comparisons suggests that proteomics approaches
need to become standardized within each laboratory and,
in addition, the laboratory should be able to process the
requisite number of samples required to provide statistical
confidence in the results.These requirements make a ‘factory’
approach to proteomic discovery essential: a facility where
standard protocols are applied to large numbers of samples,
with the ‘product’ being the generation of information
with a high statistical confidence.
Proteomics: drug target discoveryon an industrial scaleTerence E. Ryan and Scott D. PattersonThe discovery of targets that are sufficiently robust to yield marketable therapeutics is an enormous challenge. Through theyears, several approaches have been used with varying degrees of success. These include target-independent screening oftumor-derived cell lines (disease-dependent), reductionist approaches to identifying crucial elements of disease-affectedpathways, disease-independent screening of homologs of previously drugged targets, disease-dependent ‘global’examination of gene transcript levels, and disease-dependent global examination of protein expression levels. Theseendeavors have been enabled by several major advancements in technology, most recently, the sequencing of the humangenome. This review identifies the technical issues to be addressed for industrial-scale protein-based discovery in theidentification of targets for therapeutic (or diagnostic) intervention. Such approaches aim to direct discovery in a way thatincreases the probability of robust target identification, and decreases the probability of failure owing to variable expressionin this emerging field.
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Trends in Biotechnology Vol. 20 No. 12 (Suppl.), 2002Review | A TRENDS Guide to Proteomics
Standardization and methodologyThe need for standard protocols that are reproducible in
both their execution and data output heightens the
importance of methodology in large-scale proteomics.
The complexity of biological samples, as well as the capa-
bilities of the current-generation mass spectrometers,
enables the separation of proteins or peptides into discrete,
analyzable entities. Traditionally, this high-resolution
separation step has been achieved using 2D gel elec-
trophoresis (Fig. 1). Comparisons between samples must
therefore be made on the basis of separate 2D gel experi-
ments; this requires an extraordinary level of care to
ensure that protocols for gel preparation, sample prepara-
tion, sample loading, electrophoresis conditions, and pro-
tein ‘spot’ staining and identification, precisely match [4].
Chromatographic approaches are increasingly used for
proteomic studies as they provide this precision, are rela-
tively easy to automate, and the instrument software is
robust (Fig. 1).
The complexity of protein mixtures from cellular
lysates or fractions can undergo only limited reduction
using ion exchange, molecular sieving, or affinity chro-
matography. However, mixtures of proteins from limited
chromatographic fractionation can be proteolyzed as a
group, and the resulting peptides separated by reverse-
phase chromatography with online mass spectrometric
detection [5–9]. This ‘complex-mixture’ method of gen-
erating peptides for tandem mass spectrometric identifi-
cation has been widely used in academia and industry
because of its reproducibility and ease of automation [10].
It has gained further favor over gel-based methods
because it can detect low-abundance peptides [11], and
also gives a more complete representation of cellular
proteins (particularly membrane proteins). This review
discusses issues surrounding the large-scale application of
complex-mixture proteomic analysis for drug target dis-
covery: the first step in the drug discovery and develop-
ment pipeline (Fig. 2). However, it should be noted that
the platform described here can be applied not only to the
initial stages of the pipeline, but also to all subsequent
steps (except filing and marketing).
Normal Disease
Enrichment of cell type, subcellular organelle or protein class
Image analysis
Selected spot excision
Digestion and/or MALDI-MS
MALDI-MS
Identification
Digestion of proteins
Peptide capture (e.g. ICAT-peptides on avidin)
LC–MS (quantitative analysis)
LC–MS–MS
Identification
Stable isotope labeling (e.g. ICAT™)of samples separately (combine)
d0-ICAT d8-ICAT
TRENDS in Biotechnology
Figure 1. The two most commonly used analytical approaches in proteomics
Complex mixture analysis using 2D gel electrophoresis, liquid chromatography and isotope-coded affinitytag (ICAT) reagent are the current standards for analysis of protein expression levels on a broad scale.
TRENDS in Biotechnology
Datamanagementand analysis
Data captureQuantitation IdentificationSeparation Fractionation
Preparation for analysis Sample processing Data analysis
Target discovery
Sampleacquisition
Sampleacquisition
Targetidentification
Targetvalidation
Lead IDoptimization
Preclinicaldevelopment
Clinicaldevelopment
Filing, salesand marketing
Figure 2. Workflow for large-scale proteomics approach for target discovery within a pharmaceutical setting
Although the schematic infers proteomics is applied only in target discovery, the platform can also be used for all additional parts of the traditional drug discovery pipeline (theuppermost flow-chart) except the filing and subsequent sales and marketing components. Of note is the increasing use of proteomics in the toxicology aspects of pre-clinicaldrug development.
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Trends in Biotechnology Vol. 20 No. 12 (Suppl.), 2002 A TRENDS Guide to Proteomics | Review
Biological material – a variable start to aconstant processTo begin the discovery process, samples must first be
acquired or generated in-house; this is the point at which
the variability of biological material could potentially
confound experimental analysis and therefore demands
careful experimental design.
In a large-scale proteomics factory, the process design
should account for this variability by minimizing all con-
trollable variables once the sample has entered the factory
process (Fig. 2, Table 1). When established cell lines are
used, all elements of sample preparation can be con-
trolled, from growth medium through culture conditions
to cell fractionation. Additional data can be collected from
the cell line that are useful in making the most informed
and instructive comparisons. For example, part of the
sample preparation process could include the evaluation
of cell lines for their rates of apoptosis and DNA synthe-
sis, among other measures of physiology (Table 1). Using
such applied culture conditions ensures that cell line
comparisons have minimized differences.
For human clinical material, sample-collection specifi-
cation can play only a limited role, because such materials
are usually obtained as an adjunct to a necessary medical
procedure. Biofluid collection, particularly serum, has a
considerably simpler path to sample collection control
because the collection procedure is non-invasive and rela-
tively routine. However, diverse elements ranging from
the collection vessel to the posture of the patient, and
even the rank order of sample draw in a multiple-tube
phlebotomy, can affect the quality and protein content of
serum [12,13]. In addition, the elapsed time before cen-
trifugation, the storage temperature, and serum thawing
method have all been shown to play a role in the repro-
ducibility of clinical chemistry profiles. Attention to these
‘trivial’ but known issues can be crucial in the evaluation
of proteomic data from serum.
Human (as well as animal) tissue provides an additional
series of challenges to the proteomic researcher, particu-
larly those analyzing a large number of samples. In addition
to the obvious variability in tissue harvesting procedures,
and inherent patient variability, it is important to recognize
that tissue is generally heterogeneous, comprising many
different cell types. In some cases, the cell type under study
will comprise only a minor portion of the tissue, and
would therefore be difficult to isolate in sufficient quanti-
ties without resorting to mechanical disaggregation and/or
enzymatic digestion of extracellular matrix and adhesion
molecules. Both procedures result in some degree of cell
death and damage; methods must therefore account for
these effects, and quality standards need to be set for cell
preparations derived from disaggregated tissue (Table 1).
Table 1. Summary of processes used at each stage of protein-based target discovery andQA/QC approaches used to monitor these processesa
Preparation for analysis Sample processing Data analysis
Sampleacquisition
Separation Fractionation Quantitation Identification Data capture Datamanagementand analysis
In vivo samples Dissociation Enrichment for proteins Quantitative 2D gel electrophoresis spot Sample data ProjectHuman tissues into desired of specific classes protein analysis excision and enzymatic Biological data managementHuman fluids cell type Reduction of protein by 2D gel digestion, followed by MS Fractionation data Correlation ofModel organisms (e.g. flow mixture complexity electrophoresis or online LC–MS–MS for MS data data obtained (tissues and cytometry, through separation or LC–MS using simultaneous quantitation QA and/or QC with clinical fluids) LCM) Enzymatic digestion of ICAT reagents and identification or data andXenograft tissues Subcellular protein mixture, with quantitation followed by Pipeline (data experimentalIn vitro samples fractionation or without reduction identification capture) software stateCultured cells of peptide complexity Candidates forConditioned media evaluation
Monitoring – QA/QCFlow cytometry for Flow cytometry Protein quantitation Image analysis MS instrument calibration Data integrity LIMS systems markers for markers Data and protein Chromatographic Chromatographic data LIMS systems Data reviewApoptotic rate Markers for qualitative data data analysis analysis Confirmation of
subcellular analysis resultsProliferative rate organelles Chromatographic
data analysis
aAbbreviations: ICAT, isotope-coded affinity tag; LCM, laser capture microscopy; LC–MS, liquid chromatography–mass spectrometry; LC–MS–MS,liquid chromatography–tandem mass spectrometry; LIMS, Laboratory Information Management System; MS, mass spectrometry.
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Trends in Biotechnology Vol. 20 No. 12 (Suppl.), 2002Review | A TRENDS Guide to Proteomics
Laser capture microscopy (LCM) has been used to
isolate specific cells from sections but yields only a very
small number of cells. mRNA-expression level analysis
has been successful but only limited proteomic studies
have been published [14–16]. Cell separation using fluo-
rescently or magnetically tagged antibodies to obtain
only the desired population of cells are powerful tools
that enable a focused study, and in addition, can generate
sufficient material for the proteomic analysis. However,
each enrichment step involved in isolating the desired
population begs the question: ‘What have I lost?’ In the
proteomic analysis of tumor disaggregation, sorting for
the cancer cells alone will enable a direct comparison
with normal cells of the same type from a non-malignant
site in the same organ. However, tumor-specific differ-
ences in vasculature [17,18], stromal cells or extracellu-
lar matrix will all be lost because of the restriction to
cancerous cells. Protein expression differences in these
‘lost’ tumor compartments might reveal useful biomark-
ers for diagnosis, or potential drug targets for tumor
therapy.These gains and losses from specific cell isolation
from tissues must be carefully balanced during experi-
mental design because of their profound effect on the
type and quality of data that result. Each step in tissue,
cell or protein fractionation in large-scale proteomic
processes must reconcile the depth of analysis with the
experimental aim.
Protein fractionation – divide and conquerWhether using 2D-gels or chromatographic methods, a
high-resolution separation step is essential to produce an
appropriate analyte complexity for mass spectrometry. For
‘profiling’ experiments (those aimed at identifying as
many components in a sample as possible), too high a
level of complexity will result in a less than complete sur-
vey of peptides in the mixture owing to the duty-cycle
time of the mass spectrometer (that is, not all ions will be
selected for fragmentation). By contrast, if the mixture is
too simple the mass spectrometer would operate less
efficiently (through wasted cycles), therefore resulting in
fewer identifications per unit time, which would be
unsuitable in an efficient, large-scale facility. As men-
tioned previously, limited fractionation of whole proteins
is possible using chromatographic matrices; however, it is
possible to also simplify peptide mixtures substantially to
provide appropriate levels of complexity. For example,
modification of cysteine residues with an affinity reagent
enables the capture of only those peptides containing this
amino acid [19,20]. Based upon the observed cysteinyl
content of proteins, capture of only these peptides would
substantially simplify the peptide mixture before running
mass spectrometeric analyses.
Complexity reduction using side-group modification
is an important part of the peptide quantitation method
using the isotope-coded affinity tag (ICAT ) reagent [21].
This method is a variation of the traditional stable isotope
dilution theory [22] wherein cysteinyl residues in protein
mixtures are modified by one of two isotopic forms
(d0 or d8) of a common alkylating molecule (see Fig. 1).
Proteins from the comparative standard are then tagged
with the second isotopic form of the ICAT reagent. The
two samples can then be pooled, fractionated as desired,
proteolyzed to yield peptides that are then enriched for
ICAT -tagged peptides by avidin chromatography, and
analyzed by LC–MS (Fig. 3). The quantitative ratios of a
peptide found in both samples can be established by the
relative ion current signals for each mass tag. Ions display-
ing differential relative abundance can then be identified
by targeted MS–MS. Variations on this approach that are
now being explored include: (1) the incorporation of
mass tags through solid-phase isotope transfer [23];
(2) stable-isotope metabolic protein labeling before mass
spectrometric analysis of either intact proteins (using
high-resolution mass spectrometers [24]) or proteolytic
peptides [using matrix-assisted desorption–ionization
(MALDI)-MS–MS of gel-separated proteins or chromato-
graphically separated peptides] [25–28]; and (3) incor-
poration of 16O or 18O into the C-terminus of peptides by
tryptic protein digestion of samples in either H216O or
H218O, respectively [29–31].
Combining conventional protein fractionation with
isotope tagging means that sample complexity can be
matched with the available mass spectrometric analytical
capability of the laboratory, thus promoting operation at
peak efficiency. If desired, additional reductions in sample
complexity can be achieved using affinity reagents to
enrich for specific protein classes.
Antibodies represent an affinity reagent that can selec-
tively bind proteins with a defined primary or secondary
peptide sequence. However, antibodies rarely have the
broad specificity of capture required to produce protein
mixtures of a complexity that would enable proteomic
analysis (with the exception of antibodies directed against
post-translational modifications). In addition to capture
based upon protein sequence, various methods have been
developed to enrich for proteins or peptides bearing post-
translational modifications. For example, an extensive
literature has been developed for enrichment of phospho-
proteins [32,33], lectin-based capture of glycoproteins
[34,35] and other protein classes.
Molecules that interact with the functional character-
istics of proteins are more useful as affinity-capture
reagents. Such functional (or activity-based) affinity
reagents include, for example, substrates for an enzymatic
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Trends in Biotechnology Vol. 20 No. 12 (Suppl.), 2002 A TRENDS Guide to Proteomics | Review
class, with capture based upon a high-affinity interaction
with a catalytic site. Indeed, classic ‘suicide substrates’ of
enzymes result in a covalent interaction of the substrate
and catalytic domain, enabling capture and exhaustive
washing of bound complexes. Stringent washing of com-
plexes is important for establishing the selective nature of
the affinity capture, and is effective in reducing the
amount of non-specific binders that would otherwise
complicate proteomic analysis. The availability of small-
molecule substrates for enzymes requiring activation also
promise to ‘profile’ the activation state for these enzymes
in differential analyses. Substrates that bind to activated
kinases [36,37], phosphatases [38], or proteases [39–42]
could be very useful reagents for proteomic studies because
the overall level of expression of these enzymes would
probably be relatively unchanged between the diseased
and normal cell state; instead, crucial differences might
exist in the proportion of ‘activated’ forms of these
enzymes. Such small-molecule affinity reagents might
include failed drug leads that lacked the high specificity
required for therapeutic use but that have a broad pattern
of reactivity, which is a desirable feature for generalized
capture reagents. Overall, the use of affinity capture reagents
has the potential to provide a much more coherent and
pharmaceutically useful catalog of differences between
cell states than non-selective comparisons could other-
wise enable. Furthermore, quality control of these
processes is more straightforward with the monitoring of
columns, buffers, chromatograms, and levels of enriched
or depleted proteins (Table 1)
Mass spectrometry – passing through the eyeof a needleDifferential protein expression analysis has two major
goals. The first is the relative quantitation of peptides and
the second is the identification of proteins by peptide-
mass fingerprinting of peptides, or tandem mass spec-
trometry of individual peptides. These goals are accom-
plished using mass spectrometry, and the numbers and
type of instrumentation have significant influence on the
throughput, accuracy and completeness of the protein ex-
pression comparisons that are the output of the extended
proteomic process.The depth of proteomic analysis of any
given sample is dependent on the amount and complexity
of the starting material, as well as the process and instru-
ment efficiency. Given the current sensitivity and dynamic
range of mass spectrometers, the detection of low copy
number proteins requires cellular starting material that is
590 592 594 596 598 600 602 604
596.0600.0
482 484 486 488 490 492 494
486.6
489.3
2+
3+
(a)
(b)
(c)
450
500
550
600
650
m/z
m/z
m/z
36 38
Time (min)
40
TRENDS in Biotechnology
Rel
ativ
e in
tens
ityR
elat
ive
inte
nsity
Figure 3. Liquid chromatography-mass spectrometry data from an ICAT experiment to examinedifferential protein (peptide) expression
(a) A small portion of a liquid chromatography (LC)–mass spectrometry (MS) experiment represented as a 2D map, 35–41 min on the x-axis,450–650 m/z on the y-axis and false color to represent peak intensity. (b) and (c) The mass spectra of the doubly and triply chargedisotope-coded affinity tag (ICAT) pairs respectively, circled in (a). In (b), the ICAT pair is at ~1:1 ratio and in (c) the d0:d8 ratio is ~1:2.5.The lower mass ions in each pair are alkylated with d0-ICAT and the heavy forms are alkylated with d8-ICAT . Charge state can bedetermined from both the isotope cluster of each peptide along with the mass difference between the two forms. Data from a comparison ofcytosolic extracts generated using an on-line LC–MS system. Following analysis of the differentials, only those ions that display alteredabundance are targeted for MS–MS.
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Trends in Biotechnology Vol. 20 No. 12 (Suppl.), 2002Review | A TRENDS Guide to Proteomics
roughly equivalent to the number of cells commonly
found in biological samples such as solid tumors. Because
of the unavoidable losses that are inherent in tissue, cell,
and protein fractionation, the proteomic mass spec-
trometrist commonly works at the micro-scale, reflecting
the limited amount of available material. Recent advances
in capillary LC coupled with electrospray MS (microspray
and nanospray methods) now enable high-yield MS
analyses from even smaller amounts of peptides.
Electrospray ionization (ESI) technology enables MS
analyses on chromatographically separated peptides online
(Fig. 3). This method is ideally suited to the detection of
low-abundance peptides, as well as for quantitation via
the ICAT method.
For peptide identification, mass accuracy (in the
50–100 ppm range) and isotopic resolution are essential
for accurate peptide-mass fingerprinting and important
for tandem MS methods [43,44]. Of the two methods,
tandem MS analysis gives the most definitive peptide
identification and is the preferred method for complex
mixture analysis. The high-throughput, high mass accu-
racy and resolution requirement tends to drive instru-
ment selection towards multiple electrospray-hybrid
quadrupole–time-of-flight (TOF) instruments, or instru-
ments using a source MALDI and TOF detector (although
instruments with lower mass resolution can also be
used). Recently, MALDI instruments with high-repetition
rate lasers have gained recent attention because of their
short duty-cycle, which results in the collection of thou-
sands of tandem MS spectra within several seconds [45].
This rapid acquisition rate enables the collection of
large numbers of spectra from individual peptides, im-
proving signal-to-noise ratios and subsequent peptide
identification reliability. Selection of mass spectrometry
instrumentation for the industrial proteomics laboratory
requires attention to parameters that are not optimized
in a single instrument: high-throughput capability, ion
sensitivity, and mass accuracy. For this reason, industrial
proteomic laboratories commonly use a mix of mass
spectrometers, each with their own specific monitoring
requirements, although these basically include rigorous
calibration and monitoring of column, buffer and data
output (Table 1).
Data analysis – the capillary becomes apipelineAs outlined here, a mixture of mass spectrometers and
liquid chromatographs will be the foundation of the
industrial laboratory. Data extraction from this diverse
instrument mixture must be accomplished in a way that
enables data archiving and analysis, ideally to a single
computational platform. Relevant clinical information
collected on biological specimens must be added to these
data, along with the results of other biological analyses
performed on the sample during its preparation in cell
biology and protein chemistry. All of these data (includ-
ing quality control data;Table 1) need to converge on the
computational desktop of the bioinfomatician, and be
interpretable by laboratory personnel responsible for the
laboratory process. High-speed access to a completely
assembled genome of the organism under study is also
necessary, as well as bridging software and algorithms to
compare mass spectra with a computer-generated range
of potential peptide sequences and to assign a probability
score to peptide and protein identifications.
Algorithms have been developed that can identify
from which protein a peptide MS–MS spectra was derived
(e.g. [46–51]). In an industrial proteomics laboratory,
terabyte amounts of information (chiefly, LC–MS and
MS–MS spectra) are developed that far exceed the data
storage and processing caability of desktop computers for
which some of these programs were written. For opera-
tion at this scale, algorithms and data analysis software
need to be created to run on a scaleable computing clus-
ter. A disadvantage of this requirement is that modern lab-
oratory instruments are controlled by desktop computer
systems; data created and stored on many desktop systems
will need to be extracted and transferred to the scaleable
computing cluster for subsequent analysis. At a point
downstream (preferably the desk of the bioinformatician
conducting the unified data analysis), clinical and pheno-
typic data collected for a biological specimen needs to
converge with the mass spectrometric identification of
proteins. These diverse elements must be effectively
coordinated if an industrial-scale proteomics facility is to
serve a useful role in systematic biological investigations
(Fig. 2,Table 1).
Concluding remarksIndustrial-scale proteomic analysis offers the opportunity
to conduct systematic studies into cellular and organismal
protein biochemistry for the purposes of drug target dis-
covery and academic pursuits. However, potent challenges
in sample preparation, mass spectrometric instrumenta-
tion, and data analysis need to be identified and overcome
for this process to be successful in a wider range of both
academic and industrial settings. The specific nature of
these challenges is determined by the biological study
undertaken, the availability and type of instrumentation
used, and the computational resources available to the
investigator. The successful management of these scaling
questions will advance this systematic approach to discov-
ery in the biological sciences, particularly in the discovery
of targets for therapeutics and diagnostics.
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Trends in Biotechnology Vol. 20 No. 12 (Suppl.), 2002 A TRENDS Guide to Proteomics | Review
AcknowledgementsWe would like to thank Ian McCaffery for critical reading of
the manuscript, and Aiqun Li and Bruno Domon for Fig. 3.
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