2013-11-28 science meets business, nijmegen
DESCRIPTION
Interactive discussion with public and private collaboration partners on the capabilities and access of the Radboudumc Technology Centers.TRANSCRIPT
The Radboud Centre for Proteomics, Glycomics & Metabolomics: Translating Research to Biomarkers to Diagnostics
Prof Alain van Gool
Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers
Head Biomarkers in Personalized Healthcare
Science Meets Business event
Novio Tech Campus Nijmegen 28th Nov 2013
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
Genetics
Bioinformatics Preclinical
pharmacology
Clinical trials
Flow cytometry
Cleanrooms
Neuroscience unit
Robotic operations
Preclinical Imaging
Microscopy
Malaria lab Biobank
Big Data
Radboudumc Technology Centres
Proteomics Metabolomics
Glycomics
Radboudumc Technology
Centers
Eg. Next Generation Life Sciences Maximize synergy within Radboudumc and with external partners / organisations
Alain van Gool Otto Boerman
Radboud Proteomics Center
Radboud Metabolomics Group
Radboud Glycomics Facility
Research Biomarkers Diagnostics
Mass spectrometry – NMR based, 20 dedicated fte, part of diagnostic laboratory (Department Laboratory Medicine), close interaction with Radboudumc scientists and external partners
Radboud Centre for Proteomics, Glycomics & Metabolomics
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
Radboud Centre for Proteomics, Glycomics & Metabolomics
Research • Projects • Service
External • Projects • Service
Patient care • Health care focus • Biomarkers, diagnostics • Consortia (NL, EU)
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
Radboud Centre for Proteomics, Glycomics & Metabolomics
• Proteomics • Bottom-up (shot-gun) proteomics • Targeted proteomics • Top-down proteomics
• Glycomics
• Glycan profiling • (Targeted) Glycoproteomics
• Metabolomics
• Untargeted metabolomics • Targeted metabolite profiling
Radboud Centre for Proteomics, Glycomics & Metabolomics
Research Biomarkers Diagnostics
Key experts: Jolein Gloerich Hans Wessels Alain van Gool Monique Scherpenzeel Dirk Lefeber Leo Kluijtmans Ron Wevers
Proteomics
• 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 2009 Nature 2010 EMBO Journal 2010 Nature 2011 Analytical Chemistry 2011 Expert Reviews Proteomics 2012
• 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
Proteomics approaches
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
Conclusions
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
Project with TNO Q: how does proteome cell line x look like? Q: First look at effect treatment on proteome (feasibility) → GeLC-MS approach
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
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
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
Method of the year 2012
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 …
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
MAB ESI - MS Intact MAB spectrum
Compound Spectra
147916.0294
148062.0367
148224.0781
148387.2015
148550.0889
148713.2075
+MS, 0.985-10.524min, Smoothed (0.07,6,SG), Baseline subtracted(0.80), Deconvoluted (MaxEnt, 2673.57-3122.37, *1.75, 10000)
0
2000
4000
6000
8000
Intens.
147250 147500 147750 148000 148250 148500 148750 149000 149250 149500 m/z
Applications top-down proteomics
Analysis of intact proteins by ESI-Q-tof MS
On protein level: • Analysis post-translational modifications / protein processing • Protein complex composition and dynamics • Biotech and biomedical research (and diagnostics?)
Analysis of intact Trastuzumab by top-down proteomics
Multiple charged ion
Single charged ion = intact protein
Analysis:
- Single proteins OK
- Protein (sub)complexes ?
Quantitative analysis of intact protein isoforms - N/C-terminal truncations - Splice variants - Post-translational modifications
(glycosylation, phosphorylation, etc)
148 kDa!
Analysis of a 40-subunit protein complex
Mitochondrial complex I of Y. lipolytica
• 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
• Established subunits: 40 • Subunits encoded by mitochondrial DNA: 7 • Subunits encoded by nuclear DNA: 33 • Structural elucidation in progress
LC-MS ion map of 40-subunit protein complex Survey View
500
1000
1500
2000
2500
m/z
10 20 30 40 50 60 70 Time [min]
ESI spectrum of 1 subunit Survey View
500
1000
1500
2000
2500
m/z
10 20 30 40 50 60 70 Time [min]
'1009.716810+
'1121.79549+
'1261.89388+
'1442.02087+ '1682.1905
6+
'2018.42955+
+MS, 56.8-58.7min #3408-3522
0
1
2
3
4
5
4x10
Intens.
1000 1200 1400 1600 1800 2000 2200 m/z
5+
6+
7+
8+
9+
10+
5+
6+ 7+
8+
9+
10+
1.682 m/z Da
Fully characterized N7BM subunit
Mass error: 0.0145 Da (0.9 ppm)
Characterized protein form
• N-terminus processing: Methionine truncation • C-terminus processing: None • Additional PTMs: Protein N-terminal acetylation (S2)
16.062 m/z Da
• Proteomics • Bottom-up (shot-gun) proteomics • Targeted proteomics • Top-down proteomics
• Glycomics
• Glycan profiling • (Targeted) Glycoproteomics
• Metabolomics
• Untargeted metabolomics • Targeted metabolite profiling
Radboud Centre for Proteomics, Glycomics & Metabolomics
Research Biomarkers Diagnostics
Key experts: Jolein Gloerich Hans Wessels Alain van Gool Monique Scherpenzeel Dirk Lefeber Leo Kluijtmans Ron Wevers
Source: Allison Doerr, Nature Methods 9,36 (2012)
Glycomics
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
1% of genes directly involved in glycosylation About 50% of proteins is glycosylated
IgA
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
Diagnostics Research
Urinary glycan profiling
Serum glycan profiling
O-glycan profiling
PNGaseF chip
Chemical biology
Glycopeptide profiling
glycolipid profiling
Whole protein glycoprofiling
Nucleotide-sugars
Glycomics approaches
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
Bio-informatics : • Coupling with public glyco-databases • Annotation of glycan linkages
Whole serum glycomics
B4GalT1
Example: glycoproteomics in rare diseases
33
• 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
{Dirk Lefeber et al,
NEJM 2013}
Dietary intervention
Incomplete glycosylation Complete glycosylation
ChipCube-LC- Q-tof MS
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin applied as diagnostic test (MS-based)
• Genetic defect in glycosylation enzyme identified via exome sequencing
• Proteomics • Bottom-up (shot-gun) proteomics • Targeted proteomics • Top-down proteomics
• Glycomics
• Glycan profiling • (Targeted) Glycoproteomics
• Metabolomics
• Untargeted metabolomics • Targeted metabolite profiling
Radboud Centre for Proteomics, Glycomics & Metabolomics
Research Biomarkers Diagnostics
Key experts: Jolein Gloerich Hans Wessels Alain van Gool Monique Scherpenzeel Dirk Lefeber Leo Kluijtmans Ron Wevers
Metabolomics approaches
Diagnostics • Organic acids • Amino acids • Purines&Pyrimidines • Monosaccharides/Polyols • Carnitine(-esters) • Sterols
Research • Assay development for specific
metabolites or metabolite classes • Untargeted metabolite profiling • Metabolite biomarker identification
Equipment • GC • 2 GC-MS • 3 LC-MS/MS • 2 amino acid analysers • HPLC
Example: targeted diagnostics in metabolic disease
Amino acids Amino acid analyser
Carnitine-ester profile LC-MS/MS
Purines & pyrimidines - HPLC & LC-MS/MS
Organic acids GC-MS
DIAGNOSIS OF INBORN ERROR OF METABOLISM
Example: untargeted metabolomics to diagnose 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
Integrated databases
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
• Planned for execution in line with genetics
• Proteomics • Bottom-up (shot-gun) proteomics • Targeted proteomics • Top-down proteomics
• Glycomics
• Glycan profiling • (Targeted) Glycoproteomics
• Metabolomics
• Untargeted metabolomics • Targeted metabolite profiling
Radboud Centre for Proteomics, Glycomics & Metabolomics
Research Biomarkers Diagnostics
Key experts: Jolein Gloerich Hans Wessels Alain van Gool Monique Scherpenzeel Dirk Lefeber Leo Kluijtmans Ron Wevers
A problem in biomarker land
Imbalance between biomarker discovery and application.
• Gap 1: Strong focus on discovery of new biomarkers, few biomarkers progress beyond initial publication to multi-center clinical validation.
• Gap 2: Insufficient demonstrated added value of new clinical biomarker and limited development of a commercially viable diagnostic biomarker test.
Discovery Clinical validation/confirmation
Diagnostic test
Number of biomarkers
Gap 1
Gap 2
42
The innovation gap in biomarker research & development
Some numbers
Data obtained from Thomson Reuters Integrity Biomarker Module (April 2013)
Alzheimer’s Disease
Chronic Obstructive Pulmonary Disease
Type II Diabetes Mellitis
Eg Biomarkers in time: Prostate cancer May 2011: 2,231 biomarkers Nov 2012: 6,562 biomarkers Oct 2013: 8,358 biomarkers
EU: CE marking
USA: LDT, 510(k), PMA
43
Shared biomarker research through open innovation
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
Shared knowledge,
technologies and objectives
Funding: NL – STW; EU - Horizon2020, IMI; Fast track pharma funds
Contact information
• Proteomics
• Glycomics
• Metabolomics
• Biomarkers
Visiting address: Radboud umc, route 774/830
[email protected] [email protected] Alain.van [email protected] [email protected] [email protected] [email protected] [email protected] Alain.van [email protected] [email protected]
Back-ups
Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+ Patient’s preference of treatment
Exchange experiences in care communities
Select personalized therapy
Issue 2:
The big current bottleneck in Next Generation Life Sciences:
49
(Big) data
Knowledge
Understanding
Decision
Action
Translation is key !
Experimental setup
ESI spectrum of 6+ charged subunit Survey View
500
1000
1500
2000
2500
m/z
10 20 30 40 50 60 70 Time [min]
6+
'1679.35506+
'1682.19056+
'1684.85616+
'1686.01806+
'1688.51476+
'1690.928612+
'1692.67456+
+MS, 56.8-58.7min #3408-3522
0
1
2
3
4
5
4x10
Intens.
1677.5 1680.0 1682.5 1685.0 1687.5 1690.0 1692.5 1695.0 1697.5 m/z
6+
1.682 m/z Da
Deconvoluted spectrum of 1 subunit Survey View
500
1000
1500
2000
2500
m/z
10 20 30 40 50 60 70 Time [min]
'10069.0770Mr
'10087.0920Mr
'10103.0766Mr
'10110.0557Mr
'10125.0318Mr
'10132.0368Mr
'10141.0021Mr
'10149.0079Mr
+MS, 56.8-58.7min, Baseline subtracted(0.80), Deconvoluted (MaxEnt, 503.09-2244.16, *0.063125, 50000)
0
2
4
6
8
4x10
Intens.
10070 10080 10090 10100 10110 10120 10130 10140 10150 m/z
10.088 m/z Da
Small to large intact subunits in a single analysis
9 kDa subunit (deconvoluted)
75 kDa subunit (deconvoluted) 49 kDa subunit (deconvoluted)
'9603.9448Mr
'9617.9600Mr
'9631.9697Mr
'9644.9081Mr
'9654.9367Mr
'9669.9202Mr
'9685.8928Mr
+MS, 51.9-52.6min, Deconvoluted (MaxEnt, 503.09-2410.26, *0.10625, 50000)
0.0
0.5
1.0
1.5
5x10
Intens.
9550 9600 9650 9700 9750 m/z
49989.6584
+MS, 54.6-56.9min, Smoothed (0.07,3,SG), Deconvoluted (MaxEnt, 498.39-2528.81, *0.664063, 8000)
2
4
6
8
4x10
Intens.
49400 49600 49800 50000 50200 50400 50600 m/z
74340.9883
75196.3196
76237.1362
+MS, 37.9-41.1min, Deconvoluted (MaxEnt, 503.09-2472.80, *0.664063, 8000)
0
1
2
3
4
5
6
4x10
Intens.
73500 74000 74500 75000 75500 76000 76500 77000 77500 m/z
20 kDa subunit (deconvoluted)
'20707.5208Mr
'20725.4879Mr
'20744.4732Mr
'20755.4811Mr '20763.4648
Mr
'20781.4432Mr
+MS, 43.0-44.3min, Deconvoluted (MaxEnt, 503.09-2421.67, *0.10625, 50000)
0.0
0.2
0.4
0.6
0.8
1.0
5x10
Intens.
20680 20700 20720 20740 20760 20780 20800 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)
Matched peptide sequences in red, amino acids matched as ETD fragment ions are marked yellow (only for Top-Down data)
Hypothesized protein form
• N-terminus processing: Targeting sequence cleavage at S18 • C-terminus processing: None • Additional PTMs: None
Deconvoluted and simulated spectra Compound Spectra
'13107.3636Mr +MS, 14.5-15.6min, Deconvoluted (MaxEnt, 566.30-2196.57, *0.063125, 50000)
15128.45671+C₆₆₃H₁₀₂₈N₁₉₂O₂₀₃S₆, , 15119.4339
13114.37681+ C₅₇₄H₈₈₁N₁₆₆O₁₇₈S₅, , 13107.3587
0.0
0.5
1.0
1.5
2.0
2.5
5x10
Intens.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
5x10
0.0
0.5
1.0
1.5
2.0
2.5
3.0
5x10
13000 13250 13500 13750 14000 14250 14500 14750 15000 m/z
Measured spectrum
Simulated spectrum - unprocessed form (database entry)
Simulated spectrum - hypothesized form (according to MS/MS results)
Overlay of deconvoluted and simulated spectra NUMM subunit
Mass error: 0.0049 Da (0.4 ppm)
13.114 m/z Da