SMB 28112013 Alain van Gool - Technologiecentra Radboudumc

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

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

Translational medicine @ Radboudumc

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

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

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


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

• 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

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

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

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

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

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

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

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
Etc …

Con’s
• Assay development
• Low resolution MS

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

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

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

?

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

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]

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]

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)

Glycomics

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

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

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

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

Glycomics application areas
• Mechanisms of glycosylation disorders
Linking genes to glycomics profiles
Understanding neuromuscular pathophysiology

• Glycomics Technology Platform
Services
Functional foods
Glycan tracers
Biomarkers

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

Whole serum glycomics
B4GalT1

Bio-informatics :
• Coupling with public glyco-databases
• Annotation of glycan linkages

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

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

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

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

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
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

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

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)

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

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

SMB 28112013 Alain van Gool - Technologiecentra Radboudumc

Personalized Healthcare @ Radboudumc

People are different

Stratification by multilevel diagnosis

+
Patient’s preference of treatment

Exchange experiences in
care communities

Select personalized therapy

49

Issue 2:

The big current bottleneck in Next Generation Life Sciences:

Translation is key !

(Big) data
Knowledge

Understanding
Decision
Action

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]

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]

Small to large intact subunits in a single analysis
9 kDa subunit (deconvoluted)
Intens.
x105


+MS, 51.9-52.6min, Deconvoluted (MaxEnt, 503.09-2410.26, *0.10625, 50000)

Mr
'9631.9697

Intens.
x105


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

Intens.
x104

Mr
Mr
'20763.4648
'20755.4811

Mr
'20707.5208

20700

20720

20740

20760

20780

20800

m/z

Intens.
x104

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

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)

Deconvoluted and simulated spectra
Compound Spectra
Intens.
x105

Mr
'13107.3636

Measured spectrum


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

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

SMB 28112013 Alain van Gool - Technologiecentra Radboudumc

SMB 28112013 Alain van Gool - Technologiecentra Radboudumc

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