The document describes the Radboud Centre for Proteomics, Glycomics & Metabolomics at the Radboud University Medical Center. The center aims to translate research into biomarkers and diagnostics using proteomics, glycomics, and metabolomics expertise. It has key experts in these areas and provides services to both internal and external partners for research projects, biomarkers identification, and diagnostic test development. An example is described where glycoprofiling was used to diagnose a rare genetic disease and identify a dietary intervention as a successful personalized therapy.

1. The Radboud Centre for
Proteomics, Glycomics & Metabolomics:
Translating Research to Biomarkers to Diagnostics
Science Meets Business event
Novio Tech Campus Nijmegen
28th Nov 2013
Prof Alain van Gool
Head Biomarkers in Personalized Healthcare
Head Radboud Center for Proteomics, Glycomics
and Metabolomics
Coordinator Radboud Technology Centers

2. Radboudumc
• Mission: “To have a significant impact on healthcare”
• Strategic focus on Personalized Healthcare
• Core activities:
• Patient care
• Research
• Education
•
•
•
•
11.000 colleagues
50 departments
3.000 students
1.000 beds (ambition to close 500 by improving
healthcare)
• First academic centre outside US to fully implement EPIC

3. Translational medicine @ Radboudumc

4. Radboudumc Technology Centres
Alain van Gool
Bioinformatics
Flow
cytometry
Preclinical
pharmacology
Proteomics
Metabolomics
Glycomics
Genetics
Otto Boerman
Preclinical
Imaging
Radboudumc
Technology
Centers
Big Data
Robotic
operations
Microscopy
Clinical
trials
Cleanrooms
Malaria lab
Neuroscience
unit
Biobank
Maximize synergy within Radboudumc and with external partners / organisations
Eg.
Next Generation Life Sciences

5. Radboud Centre for Proteomics, Glycomics & Metabolomics
Research
Radboud
Proteomics
Center
Biomarkers
Radboud
Glycomics
Facility
Diagnostics
Radboud
Metabolomics
Group
Mass spectrometry – NMR based, 20 dedicated fte, part of diagnostic laboratory (Department
Laboratory Medicine), close interaction with Radboudumc scientists and external partners

6. Radboud Centre for Proteomics, Glycomics & Metabolomics
Key experts:
Proteomics
Jolein Gloerich
Hans Wessels
Alain van Gool
Glycomics
Monique Scherpenzeel
Dirk Lefeber
Metabolomics
Leo Kluijtmans
Ron Wevers
Mass spectrometry – NMR based, 20 dedicated fte, part of diagnostic laboratory (Department
Laboratory Medicine), close interaction with Radboudumc scientists and external partners

7. Radboud Centre for Proteomics, Glycomics & Metabolomics
Research
Patient care
External
• Projects
• Service
• Health care focus
• Biomarkers, diagnostics
• Consortia (NL, EU)
• Projects
• Service
Key features:
• Expertise centre rather than service facility
• Focus to translate Research to Biomarkers to Diagnostics
• Application of many years Omics expertise to customer’s specific needs
• Ambition to grow with long-term strategic projects, collaborations, staff and impact

8. Radboud Centre for Proteomics, Glycomics & Metabolomics
• Proteomics
Key experts:
• Bottom-up (shot-gun) proteomics
• Targeted proteomics
• Top-down proteomics
• Glycomics
• Glycan profiling
• (Targeted) Glycoproteomics
• Metabolomics
• Untargeted metabolomics
• Targeted metabolite profiling
Research
Biomarkers
Jolein Gloerich
Hans Wessels
Alain van Gool
Monique Scherpenzeel
Dirk Lefeber
Leo Kluijtmans
Ron Wevers
Diagnostics

9. Proteomics
• Proteome profiling
- Differential protein expression
- Protein complex composition
- Labelfree
- Labeled (SILAC, SPITC/PIC)
- Protein correlation profiling
Whole proteome analysis
De novo protein identification
• Protein identification
- Purified proteins
- Complex mixtures
• Protein characterization
- Phosphorylation
- Ubiquitinylation
- Acetylation/Methylation
- Glycosylation
• Peptide/protein quantitation
- Relative quantitation
- Absolute quantitation
Protein complex isolation and characterization
Proteomics 2009
Nature 2010
EMBO Journal 2010
Nature 2011
Analytical Chemistry 2011 Expert Reviews Proteomics 2012

10. Proteomics approaches
• 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

11. Applications of bottom-up proteomics
• Differential protein expression in:
• Health/disease
• Time
• Before/after treatment
• Protein-protein interactions:
• Protein correlation profiling
• (Tandem) affinity purification
Information is obtained on peptide level, deduce protein effects

12. Example of cellular proteome profiling project
Project with TNO
Q: how does proteome cell
line x look like?
Q: First look at effect
treatment on proteome
(feasibility)
→ GeLC-MS approach
Down
regulated
Up
regulated
Differential analysis
Samples
Results
Results
Gene ontology: cellular localization
10
Conclusions
∞
5
0
-5
-10
178 Differentially
expressed proteins
∞
• 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

13. 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
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

14. Applications of targeted proteomics
Research
(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
Diagnostics
• 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

15. Method of the year 2012

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

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

18. Examplë: SRM output data
Measurement of a peptide in complex matrix
(tissue homogenate)
Use of heavy labeled standard
•
•
Confirmation of peak
Used for accurate (absolute) quantitation

19. Applications top-down proteomics
Analysis of intact proteins by ESI-Q-tof MS
Compound Spectra
Intens.
+MS, 0.985-10.524min, Smoothed (0.07,6,SG), Baseline subtracted(0.80), Deconvoluted (MaxEnt, 2673.57-3122.37, *1.75, 10000)
148224.0781
8000
148062.0367
6000
148387.2015
4000
148550.0889
2000
148713.2075
147916.0294
0
147250
MAB
ESI - MS
147500
147750
148000
148250
148500
148750
149000
149250
149500
Intact MAB spectrum
On protein level: • Analysis post-translational modifications / protein processing
• Protein complex composition and dynamics
• Biotech and biomedical research (and diagnostics?)
m/z

20. Analysis of intact Trastuzumab by top-down proteomics
Quantitative analysis of
intact protein isoforms
-
N/C-terminal truncations
Splice variants
Post-translational modifications
(glycosylation, phosphorylation,
etc)
Analysis:
-
Single charged ion = intact protein
148 kDa!
Single proteins
-
Multiple charged ion
Protein (sub)complexes
OK
?

21. Analysis of a 40-subunit protein complex
Mitochondrial complex I of Y. lipolytica
•
•
•
•
Established subunits: 40
Subunits encoded by mitochondrial DNA: 7
Subunits encoded by nuclear DNA: 33
Structural elucidation in progress
• Problem: 3D structures of modelled subunits do not fit within measured structure
by electron miscroscopy
• Hypothesis: Unknown N-terminal and/or C-terminal processing
• Study: Combine Top-Down and Bottom-Up characterization of all subunits

22. LC-MS ion map of 40-subunit protein complex
Survey View
m/z
2500
2000
1500
1000
500
10
20
30
40
50
60
70
Time [min]

23. ESI spectrum of 1 subunit
Survey View
Intens.
x104
m/z
+MS, 56.8-58.7min #3408-3522
6+
7+
6+
'1682.1905
7+
'1442.0208
1.682 m/z Da
5
2500
8+
4
8+
'1261.8938
5+
2000
3
9+
6+
9+
'1121.7954
1500
7+
2
8+
9+
1000
10+
10+
5+
1
10+
'1009.7168
5+
'2018.4295
500
0
1000
1200
10
1400
1600
20
1800
2000
30
2200
m/z
40
50
60
70
Time [min]

24. Fully characterized N7BM subunit
16.062 m/z Da
Characterized protein form
• N-terminus processing: Methionine truncation
• C-terminus processing: None
• Additional PTMs: Protein N-terminal acetylation (S2)
Mass error: 0.0145 Da (0.9 ppm)

25. Radboud Centre for Proteomics, Glycomics & Metabolomics
• Proteomics
Key experts:
• Bottom-up (shot-gun) proteomics
• Targeted proteomics
• Top-down proteomics
• Glycomics
• Glycan profiling
• (Targeted) Glycoproteomics
• Metabolomics
• Untargeted metabolomics
• Targeted metabolite profiling
Research
Biomarkers
Jolein Gloerich
Hans Wessels
Alain van Gool
Monique Scherpenzeel
Dirk Lefeber
Leo Kluijtmans
Ron Wevers
Diagnostics

26. Glycomics
Source: Allison Doerr, Nature Methods 9,36 (2012)

27. Glycosylation markers in human medicin
• Biomarker for disease and therapy monitoring: rheumatoid arthritis,
oncology, hepatitis
• MUC2 glycosylation in colon carinoma
• Human blood groups (A, B, O, AB)
• CDTect (Carbohydrate-Deficient transferrin)
• Infectious diseases
• IgA nephropathy
IgA
1% of genes directly involved in glycosylation
About 50% of proteins is glycosylated

28. Glycosylation types
• N-glycosylation
• Asparagin linked
• 8 - 20 saccharides
• O-glycosylation
• Serine/Threonine linked
• <10 sacchariden
• Glycosaminoglycans
• 100-200 disaccharide units
• Agrin, Perlecan, Syndecan, Glypican
• Glycolipids

29. Glycomics approaches
Diagnostics
Research
Urinary glycan
profiling
Chemical biology
Serum glycan
profiling
Glycopeptide
profiling
O-glycan profiling
glycolipid
profiling
PNGaseF chip
Nucleotidesugars
Whole protein
glycoprofiling

30. Glycomics application areas
• Mechanisms of glycosylation disorders
Linking genes to glycomics profiles
Understanding neuromuscular pathophysiology
• Glycomics Technology Platform
Services
Functional foods
Glycan tracers
Biomarkers

31. Glycan analysis by nanoChip-QTOF MS
• High-resolution glycoprofiling
• Microfluidic chip system results in simplified operating conditions, increased
reproducibility and robustness
• CHIP formats: C18, Carbograph, C8, HILIC, phosphopeptides, PNGaseF

32. Whole serum glycomics
B4GalT1
Bio-informatics :
• Coupling with public glyco-databases
• Annotation of glycan linkages

33. 33
Example: glycoproteomics in rare diseases
•
•
•
•
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
{Dirk Lefeber et al,
NEJM 2013}
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin applied as diagnostic test (MS-based)
Dietary
intervention
ChipCube-LC- Q-tof MS
Incomplete glycosylation
Complete glycosylation

34. Radboud Centre for Proteomics, Glycomics & Metabolomics
• Proteomics
Key experts:
• Bottom-up (shot-gun) proteomics
• Targeted proteomics
• Top-down proteomics
• Glycomics
• Glycan profiling
• (Targeted) Glycoproteomics
• Metabolomics
• Untargeted metabolomics
• Targeted metabolite profiling
Research
Biomarkers
Jolein Gloerich
Hans Wessels
Alain van Gool
Monique Scherpenzeel
Dirk Lefeber
Leo Kluijtmans
Ron Wevers
Diagnostics

35. Metabolomics approaches
Research
Diagnostics
Equipment
• Assay development for specific
metabolites or metabolite classes
• Untargeted metabolite profiling
• Metabolite biomarker identification
•
•
•
•
•
•
•
•
•
•
•
Organic acids
Amino acids
Purines&Pyrimidines
Monosaccharides/Polyols
Carnitine(-esters)
Sterols
GC
2 GC-MS
3 LC-MS/MS
2 amino acid analysers
HPLC

36. Example: targeted diagnostics in metabolic disease
Organic acids
GC-MS
Amino acids
Amino acid analyser
Carnitine-ester profile
LC-MS/MS
Purines & pyrimidines
- HPLC & LC-MS/MS

37. Example: untargeted metabolomics to diagnose
individual patients
Chemometric pipeline
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
• T-test
• PCA
• P95
Metabolite identification
Online database HMDB
DIAGNOSIS OF INBORN ERROR OF METABOLISM
phenylalanine

38. Integrated databases

39. 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

40. The blind study
Diagnostic metabolites found in blood plasma
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
• Correct diagnosis in all 10 patients
• Five different IEM’s identified by
differential metabolites
• The approach works!!!
• Validated method  diagnostic SOP
• Planned for execution in line with genetics

41. Radboud Centre for Proteomics, Glycomics & Metabolomics
• Proteomics
Key experts:
• Bottom-up (shot-gun) proteomics
• Targeted proteomics
• Top-down proteomics
• Glycomics
• Glycan profiling
• (Targeted) Glycoproteomics
• Metabolomics
• Untargeted metabolomics
• Targeted metabolite profiling
Research
Biomarkers
Jolein Gloerich
Hans Wessels
Alain van Gool
Monique Scherpenzeel
Dirk Lefeber
Leo Kluijtmans
Ron Wevers
Diagnostics

42. 42
A problem in biomarker land
The innovation gap in biomarker
research & development
Number of
biomarkers
Gap 1
Gap 2
Discovery
Clinical
validation/confirmation
Diagnostic
test
Imbalance between biomarker discovery and application.
• Gap 1:
• Gap 2:
Strong focus on discovery of new biomarkers, few biomarkers progress
beyond initial publication to multi-center clinical validation.
Insufficient demonstrated added value of new clinical biomarker and
limited development of a commercially viable diagnostic biomarker test.

43. 43
Some numbers
Eg Biomarkers in time: Prostate cancer
May 2011: 2,231 biomarkers
Nov 2012: 6,562 biomarkers
Oct 2013: 8,358 biomarkers
Alzheimer’s Disease
Chronic Obstructive
Pulmonary Disease
Type II Diabetes
Mellitis
EU: CE marking
USA: LDT, 510(k), PMA
Data obtained from Thomson Reuters Integrity Biomarker Module
(April 2013)

44. Shared biomarker research through open innovation
Shared knowledge,
technologies and objectives
We need to set up a open innovation network to share biomarker knowledge and
jointly develop and validate biomarkers (at level of NL and EU):
1. Assay development of (diagnostic) biomarkers
2. Clinical biomarker quantification/validation/confirmation
Funding: NL – STW; EU - Horizon2020, IMI; Fast track pharma funds

45. Contact information
• Proteomics
RadboudProteomicsCentre@umcn.nl
Jolein.Gloerich@radboudumc.nl
Alain.van Gool@radboudumc.nl
• Glycomics
Monique.vanscherpenzeel@radboudumc.nl
Dirk.Lefeber@radboudumc.nl
• Metabolomics
Leo.Kluijtmans@radboudumc.nl
Ron.Wevers@radboudumc.nl
• Biomarkers
Alain.van Gool@radboudumc.nl
Ron.Wevers@radboudumc.nl
Visiting address: Radboud umc, route 774/830

47. Back-ups

48. Personalized Healthcare @ Radboudumc
People are different
Stratification by multilevel diagnosis
+
Patient’s preference of treatment
Exchange experiences in
care communities
Select personalized therapy

49. 49
Issue 2:
The big current bottleneck in Next Generation Life Sciences:
Translation is key !
(Big) data
Knowledge
Understanding
Decision
Action

50. Experimental setup

51. ESI spectrum of 6+ charged subunit
Survey View
m/z
Intens.
x104
+MS, 56.8-58.7min #3408-3522
6+
'1682.1905
6+
1.682 m/z Da
5
2500
4
2000
3
6+
1500
2
1000
1
6+
'1686.0180
6+
'1684.8561
6+
'1679.3550
6+
'1688.5147
12+
'1690.9286
6+
'1692.6745
500
0
1677.5
1680.0
10
1682.5
1685.0
1687.5
20
1690.0
1692.5
301695.0
1697.5
40
m/z
50
60
70
Time [min]

52. Deconvoluted spectrum of 1 subunit
Survey View
m/z
Intens.
x104
+MS, 56.8-58.7min, Baseline subtracted(0.80), Deconvoluted (MaxEnt, 503.09-2244.16, *0.063125, 50000)
Mr
'10087.0920
10.088 m/z Da
2500
8
2000
6
1500
4
1000
2
Mr
'10125.0318
Mr
'10110.0557
Mr
'10141.0021
Mr
'10103.0766
Mr
'10132.0368
Mr
'10069.0770
Mr
'10149.0079
500
0
10070
10080
10090 10
10100
10110
2010120
10130
30
10140
10150
m/z
40
50
60
70
Time [min]

53. Small to large intact subunits in a single analysis
9 kDa subunit (deconvoluted)
Intens.
x105
20 kDa subunit (deconvoluted)
+MS, 51.9-52.6min, Deconvoluted (MaxEnt, 503.09-2410.26, *0.10625, 50000)
Mr
'9631.9697
Intens.
x105
+MS, 43.0-44.3min, Deconvoluted (MaxEnt, 503.09-2421.67, *0.10625, 50000)
Mr
'20725.4879
1.0
1.5
0.8
1.0
0.6
0.4
0.5
0.2
Mr
'9654.9367
Mr
Mr
'9603.9448'9617.9600
Mr
'9669.9202
Mr
'20744.4732
Mr
'9685.8928
Mr
'9644.9081
0.0
9550
9600
9650
9700
9750
m/z
+MS, 54.6-56.9min, Smoothed (0.07,3,SG), Deconvoluted (MaxEnt, 498.39-2528.81, *0.664063, 8000)
49989.6584
Mr
'20781.4432
0.0
20680
49 kDa subunit (deconvoluted)
Intens.
x104
Mr
Mr
'20763.4648
'20755.4811
Mr
'20707.5208
20700
20720
20740
20760
20780
20800
m/z
75 kDa subunit (deconvoluted)
Intens.
x104
+MS, 37.9-41.1min, Deconvoluted (MaxEnt, 503.09-2472.80, *0.664063, 8000)
75196.3196
6
8
5
4
6
3
4
2
2
1
74340.9883
76237.1362
0
49400
49600
49800
50000
50200
50400
50600
m/z
73500
74000
74500
75000
75500
76000
76500
77000
77500
m/z

54. Top down / bottom up analysis of NUMM protein (13,2 kDa)
Top-Down LC-MS/MS (ETD)
Top-Down NSI-MS/MS (ETD)
Bottom-Up LC-MS/MS (CID & ETD)
Hypothesized protein form
• N-terminus processing: Targeting sequence cleavage at S18
• C-terminus processing: None
• Additional PTMs: None
Matched peptide sequences in red, amino acids matched as ETD fragment ions are marked yellow (only for Top-Down data)

55. Deconvoluted and simulated spectra
Compound Spectra
Intens.
x105
Mr
'13107.3636
Measured spectrum
+MS, 14.5-15.6min, Deconvoluted (MaxEnt, 566.30-2196.57, *0.063125, 50000)
2.5
2.0
1.5
1.0
0.5
0.0
x105
3.0
C₆₆₃H₁₀₂₈N₁₉₂O₂₀₃S₆, , 15119.4339
1+
15128.4567
Simulated spectrum - unprocessed form
(database entry)
2.5
2.0
1.5
1.0
0.5
0.0
x105
3.0
C₅₇₄H₈₈₁N₁₆₆O₁₇₈S₅, , 13107.3587
1+
13114.3768
Simulated spectrum - hypothesized form
(according to MS/MS results)
2.5
2.0
1.5
1.0
0.5
0.0
13000
13250
13500
13750
14000
14250
14500
14750
15000
m/z

56. Overlay of deconvoluted and simulated spectra NUMM subunit
13.114 m/z Da
Mass error: 0.0049 Da (0.4 ppm)