Prof Alain van Gool, PhD
Head Radboud Proteomics Center
UMC St Radboud
Radboud Proteomics Center
- exciting times !
September 3rd 2013

Contents
• Radboud Proteomics Center
• Setting
• People
• Proteomics approaches
• Bottom-up proteomics
• Targeted proteomics
• Top-down proteomics
• Center for Proteomics, Glycomics and Metabolomics
• Fit in Radboud Personalized Healthcare

Proteomics in Nijmegen
• Started 2004
• Initially combination of technologies
• Department of Laboratory Medicine (Prof Fred Sweep)
Laboratory of Genetic, Endocrine and Metabolic Disease (Prof Ron Wevers)
• Expertise shift from 2D-gel electrophoresis to LC-MS
• Various internal/external projects and collaborators
• Set-up:
o Core of 5 permanent staff
o Associated lab co-workers from other UMC
departments

Prof. Alain van Gool PhD
Molecular and cellular biology
Background:
• Pharmaceutical biomarkers
• Molecular profiling
• Translational and personalized medicine
Jolein Gloerich PhD
Biochemistry
Background:
• Proteomics
• Fatty acid oxidation disorders
Maurice van Dael BSc
Food & Toxicology
Background:
• Proteomics
• Metabolite analysis
Hans Wessels BSc
Biochemistry
Background:
• Proteomics
• Microbiology
• Mitochondrial biochemistry
+ guest co-users of hardware/software:
Technicians from various
Post-docs research institutes
PhD students in UMC St Radboud
Jenneke Keizer BSc
Clinical chemistry
Background:
• HPLC
• Mass spectrometric
metabolite analysis
Radboud Proteomics Centre
Radboud University Medical Centre – 774
Geert Grooteplein Zuid 10
6525 GA Nijmegen, Netherlands
RadboudProteomicsCentre@umcn.nl Route 774
RPC lab

• Proteome profiling
- Differential protein expression
- Protein complex composition
- Labelfree
- Labeled (SILAC, SPITC/PIC)
- Protein correlation profiling
• Protein identification
- Purified proteins
- Complex mixtures
• Protein characterization
- Phosphorylation
- Ubiquitinylation
- Acetylation/Methylation
- Glycosylation
• Peptide/protein quantitation
- Relative quantitation
- Absolute quantitation
Whole proteome analysis De novo protein identification
Protein complex isolation and characterization
Proteomics Expertise

Shared platforms in Radboud Proteomics Center
Proteome profilingProtein characterizationProtein identification
Peptide quantitation
(with Prof. Ron Wevers)
Clinical proteomics
(with Dr. Waander van Heerde)
Glycoproteomics
(with Dr. Dirk Lefeber)
QTOFMS
SELDI-TOFMS
Protein quantitation
(with Dr. Dorine Swinkels)
MALDI-TOFMS
• Shared expertise
• Shared resources
• Maximal use
QqQMSIonTrapMS
HybridLTQ-FTMS
QTOFETDMS
Q-ExactiveMS
Proteome profiling
(with Prof. Ulrich Brandt)

Radboud Proteomics Centre
The growing availability of genomic sequence information, together with improvements of protein
characterization by mass spectrometry, facilitates protein research enormously. To exploit these
opportunities the Radboud Proteomics Centre (RPC) was established in 2003.
Our aim is to initiate, coordinate and facilitate proteomics research activities. The RPC offers fundamental
technological tools and knowledge transfer for proteomics research by making them available for
academic and industrial researchers, both within and outside the Radboud University Nijmegen.
Since its establishment, the RPC has played a crucial role in numerous research projects within the
Radboud University and in many fruitful collaborations with other (international) universities and life-
sciences companies.
Route 774

Radboud Proteomics Center: portfolio
Research
• Projects
• Service
External
• Projects
• Service
• Consultancy
Patient care
• Health care focus
• Biomarkers
• Consortia (NL, EU)
The RPC is Radboud’s proteomics expertise center:
• Provide consultancy for potential proteomics applications
• Implement proper experimental design for each proteomics project
• Continue to do good proteomics science through collaboration

Proteomics applications
• Bottom-up proteomics (shotgun)
• Protein identification
• Differential protein expression profiling
Established (>300 projects done)
• Targeted proteomics
• Absolute/relative quantitation
Emerging (5 projects ongoing)
• Top-down proteomics
• Intact protein characterization
• Differential PTM analysis
New

Bottom-up proteomics

Workflow bottom-up proteomics

Bottom-up proteomics principle

Applications of bottom-up proteomics
• Determine differential protein expression in:
• Health/disease
• Time
• Before/after treatment
• Identification of protein-protein interaction partners:
• Protein correlation profiling
• (Tandem) affinity purification
Information is obtained on peptide level, deduce protein effects

Example of cellular proteome profiling project
Results
Samples
Up
regulated
Down
regulated
Differential analysis
-10
-5
0
5
10 ∞
∞
178 Differentially
expressed proteins
Results
Gene ontology: cellular localization
• In total 3,824 proteins were identified in either sample
(98.7% cell specific)
• A total of 2,550 proteins was quantified and used for
differential analysis
• 178 proteins were differentially expressed due to
treatment:
• 138 proteins upregulated
• 40 proteins downregulated
Conclusions
Project with TNO
Q: how does proteome
cell line x look like?
Q: First look at effect
treatment on proteome
(feasibility)
→ GeLC-MS approach

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RelativeAbundance
NL:
1.21E7
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RelativeAbundance
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RelativeAbundance
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RelativeAbundance
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RelativeAbundance
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RelativeAbundance
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Distinct chromatograms between fractions with excellent signal-to-noise and resolution
GeLC-MS proteomics

Ion map for untreated cells gel slice 6

Ion map for untreated cells gel slice 6
Green square: identified MS2 spectrum Blue square: unidentified MS2 spectrum

Data set statistics
Parameter HF HF TGFß Total
MS spectra 26626 29426 56052
MS/MS spectra 59038 66577 125615
Isotope patterns
detected
(real ions)
251664 284994 536658
Sequenced isotope
patterns (Average for
z≥1)
52918 (26.7%) 60679 (26.2%) 113597 (26.4%)
MS/MS identification
rate
52.7% 53.3% 54.5%
Identified peptides
(redundant)
34972 39117 74089
Precursor mass error 0.29 ± 0.39 ppm 0.29 ± 0.39 ppm 0.29 ± 0.39
3800 identified proteins in one sample

Differential protein expression profiling: results
Proteome coverage
Origin of contaminants
Proteome coverage

A publically available data set of blue native migration patterns for more than 950 proteins from a
mitochondrial HEK293 fraction using complementary acryl amide gradients
Hans Wessels et al, PloS One 8, 2013
Hierarchical clustering
Cluster: 28S mt-Ribosome
Cluster: 39S mt-Ribosome
Cluster: F1F0 ATP synthase
Cluster: cytochrome b-c1 complex
Cluster: NADH dehydrogenase & TCP1
Cluster: trifunctional enzyme & isocitrate dehydrogenase
Cluster: cytochrome C oxidase & mt-Ribosomal subcomplex
Wessels HJ
Vogel RO
Rodenburg R
Gloerich J
Van Gool A
Van den Heuvel, L
Smeitink JAM
Nijtmans L
Lightowlers R
Example of complexome
analysis project
What subcomplexes in mitochondrial proteome?
• HEK293 cells
• Isolation native mitochondrial protein complexes
• GeLC-MS using blue native gel electrophoresis
and nLC-LTQ-FT MS
• Mascot protein identification
• IDEAL-Q protein quantitation
• Hierarchical clustering based on co-migration

mt-Ribosome complexes
200 kDa subcomplex
Conclusions
• Publically available BN LC-MS/MS data set of >950 proteins
(equivalent of 1900 2D BN SDS-PAGE westerblots!!!)
• Applications in de novo protein complex identification,
prioritization of interaction candidate proteins from other analyses
• New insights into mt-Ribosomal proteome interactions

Compare LTQ-FT-ICR MS versus MaXis 4G QTOF MS
UPS2 spiked in 500ng E.coli background
» Sample 1: 250fmol – 25amol
» Sample 2: 500fmol – 50amol
Digest, analyse all peptides, convert to protein data
→ Theoretical value (Sample 1/ sample 2) for each protein: 0.5
UPS2 standard from Sigma:
• 48 proteins
• 5 orders of magnitude
• Protein Mw for each level within range of 10-70 kDa (approximately)
Benchmarking: technical performance test
Ongoing

Identification =
Sensitivity ↑↑
Accuracy ↑↑

Targeted Proteomics

Method of the year 2012

Workflow bottom-up proteomics
Workflow targeted proteomics

Targeted Proteomics: focus on peptides of interest
Protein A
Protein A isoform
Protein B

Targeted proteomics: SRM assay development
Pro’s
• Selective
• Quantitative
• Reproducible
• Quite sensitive
Con’s
• Assay development
• Low resolution MS
Etc …

Applications of targeted proteomics
(Absolute) quantitation of targets for:
• Biomarkers
• Diagnostic test
• Specific for specific protein variants (splice, PTM, etc)
• Quantitative analysis of specific pathways
• Metabolic pathways
• Signalling cascades
• Quality control
• Large scale targeted proteomics
• Comparable approach as DNA/RNA microarrays
• Complete proteome SRM assays for different organisms
Schubert OT, et al. Cell Host Microbe. 2013: 13(5):602-12
The Mtb proteome library: a resource of assays to quantify the complete proteome of Mycobacteriumtuberculosis
Research
Diagnostics

How do SRM data look like?
Measurement of HSA peptide (LVNEVTEFAK)
Linear over high dynamic range

How do SRM data look like?
Measurement of a peptide in complex matrix
(tissue homogenate)
Which peak?
Heavy labeled standard
• Confirmation of peak
• Used for accurate (absolute) quantitation

Top-down Proteomics

Workflow bottom-up proteomics
Workflow targeted proteomics
Workflow top-down proteomics
Protein
Protein
ions

Top-down or bottom-up?
• Bottom-up proteomics
(peptides)
+ Most mature
+ Widely used, sufficient tools
+ Can handle complex samples
- PTM analysis
- Protein processing
• Top-down proteomics
(intact proteins)
+ PTM analysis on protein level
+ Protein processing
- Relatively new approach
- Complexity of the sample
QTOF QTOF MALDI

cytochromeC_100903091148 #206 RT: 16.653 AV: 1 NL: 1.34E6
T: FTMS + p NSI u SIM ms [649.40-653.40]
651.2 651.3 651.4 651.5 651.6 651.7 651.8 651.9
m/z
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
1000000
1100000
1200000
1300000
651.3954
651.4481
651.5009
651.3427
651.6062
651.2375
651.6592
651.2900
651.7120
651.7638
651.1844 651.8701
Top-down proteomics: intact protein MS
ETD/ECD fragmentation
Cytochrome C (12.4 kDa)

Contents
• Radboud Proteomics Center
• Setting
• People
• Proteomics approaches
• Bottom-up proteomics
• Targeted proteomics
• Top-down proteomics
• Center for Proteomics, Glycomics and Metabolomics
• Fit in Radboud Personalized Healthcare

Center for Proteomics, Glycomics and Metabolomics
Radboud
Glycomics
Facility
Radboud
Proteomics
Center
Radboud
Metabolomics
Group
Part of Department of
Laboratory Medicine
Integrated focus:
Research
Biomarkers
Diagnostics

Example Glycomics:
From clinical Omics to personalized treatment in NCDG:
• 12 families with liver disease and dilated cardiomyopathy (5-20 years)
• Initial clinical assessment didn’t yield clear cause of symptoms
• Specific sugar loss of serum transferrin identified via glycoproteomics
• Genetic defect in glycosylation enzyme identified via exome sequencing
• Outcome: Explanation of disease
• Outcome: Dietary intervention as succesful personalized therapy
• Outcome: Glycoprofile being developed as diagnostic test by mass spectrometry
Dietary
intervention
{Dirk Lefeber et al,
NEJM 2013}Incomplete glycosylation Complete glycosylation

Radboud Personalized Healthcare
Center for Proteomics, Glycomics & Metabolomics

Personalized Healthcare @ UMC St Radboud
Genetics
Molecular
diagnostic
test
Patient
Molecular and functional analysis
Incomplete
clinical
diagnosis
Personalized
treatment
Interactive technology platforms
Biomarker
assay
Proteomics
Glyco(proteo)micsMetabolomics
Bioinformatics
Imaging
Prof. Alain van Gool, COST conference Personalized Medicine, 21 June 2012
40

Radboud Proteomics Center
Research
• Projects
• Service
External
• Projects
• Service
• Consultancy
Patient care
• Health care focus
• Biomarkers
• Consortia (NL, EU)
RadboudProteomicsCentre@umcn.nl

Diagnostics Innovation
Urinary glycan
profiling
Serum glycan
profiling
O-glycan profiling
PNGaseF chip
Chemical biology
Glycopeptide
profiling
glycolipid
profiling
Whole protein
glycoprofiling
Nucleotide-
sugars
Radboud Glycomics Facility
Monique van Scherpenzeel, Dirk Lefeber

Application areas
• Mechanisms of glycosylation disorders
Linking genes to glycomics profiles
Understanding neuromuscular pathophysiology
• Glycomics Tech Platform
Services
Functional foods
Glycan tracers
Biomarkers
Radboud Glycomics Facility
Monique van Scherpenzeel, Dirk Lefeber

Radboud Metabolomics Group
Standard targeted analyses
• Organic acids
• Amino acids
• Purines&Pyrimidines
• Monosaccharides/Polyols
• Carnitine(-esters)
• Sterols
Innovation
• Assay development for specific
metabolites or metabolite classes
• Untargeted metabolite profiling
• Metabolite biomarker identification
Leo Kluijtmans, Ron Wevers

Finding metabolomics differences
in individual patients
Human plasma
20 controls vs 1 patient
Agilent QTOF MS-data
- Reverse phase liquid chromatography
- Positive mode
- Features
•Accurate mass (165.07898)
• Retention time
• Intensity
XCMS
Alignment
Peak comparison
> 10000 Features
Chemometric pipeline
• T-test
• PCA
• P95
Metabolite identification
Online database HMDB
phenylalanine
DIAGNOSIS OF INBORN ERROR OF METABOLISM

Example Metabolomics: A blind study
Plasma sample choice : Dr. C.D.G Huigen
Analytical chemistry : E. van der Heeft
Chemometrics : Dr. U.F.H. Engelke
Diagnosis : Prof. dr. R.A. Wevers;
Dr. L.A.J. Kluijtmans
 Test 10 samples from 10 patients with 5 different
Inborn Error of Metabolism’s
 21 controls

The blind study
 MSUD (2) → leucine, isoleucine, valine, 3-methyl-2-oxovaleric acid
 Aminoacylase I deficiency (2) → N-acetylglutamine, N-acetylglutamic acid,
N-acetylalanine, N-acetylserine, N-acetylasparagine, N-acetylglycine
 Prolinemia type II (2) → proline, 1-pyrroline-5-carboxylic acid
 Hyperlysinemia (2) → pipecolic acid, lysine, homoarginine, homocitrulline
 3-Hydroxy-3-methylglutaryl-CoA lyase deficiency (2) → 3-methylglutaryl-
carnitine, 3-methylglutaconic acid, 3-hydroxy-2-methylbutanoic acid, 3-hydroxy-3-
methylglutaric acid
Diagnostic metabolites found in blood plasma
 Correct diagnosis in all 10 patients
 Five different IEM’s identified by
differential metabolites
 The approach works!!!
 Validated method  diagnostic SOP

Contents
• Radboud Proteomics Center
• Setting
• People
• Proteomics approaches
• Bottom-up proteomics
• Targeted proteomics
• Top-down proteomics
• Center for Proteomics, Glycomics and Metabolomics
• Fit in Radboud Personalized Healthcare