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2013-11-28 Science meets Business, Nijmegen

Interactive discussion with public and private collaboration partners on the capabilities and access of the Radboudumc Technology Centers.

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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

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2013-11-28 Science meets Business, Nijmegen

  • 1. 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
  • 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
  • 4. 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
  • 5. 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
  • 6. 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
  • 7. 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
  • 8. • 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
  • 9. 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
  • 10. • 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
  • 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. 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
  • 13. 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
  • 14. 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
  • 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 Con’s • Assay development • Low resolution MS Etc …
  • 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. 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?)
  • 20. 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!
  • 21. 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
  • 22. 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]
  • 23. ESI spectrum of 1 subunit Survey View 500 1000 1500 2000 2500 m/z 10 20 30 40 50 60 70 Time [min] '1009.7168 10+ '1121.7954 9+ '1261.8938 8+ '1442.0208 7+ '1682.1905 6+ '2018.4295 5+ +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
  • 24. 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
  • 25. • 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
  • 26. Source: Allison Doerr, Nature Methods 9,36 (2012) Glycomics
  • 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 1% of genes directly involved in glycosylation About 50% of proteins is glycosylated IgA
  • 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. 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
  • 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. Bio-informatics : • Coupling with public glyco-databases • Annotation of glycan linkages Whole serum glycomics B4GalT1
  • 33. 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
  • 34. • 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
  • 35. 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
  • 36. 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
  • 37. 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
  • 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  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
  • 41. • 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
  • 42. 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
  • 43. 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
  • 44. 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
  • 45. Contact information • Proteomics • Glycomics • Metabolomics • Biomarkers Visiting address: Radboud umc, route 774/830 RadboudProteomicsCentre@umcn.nl Jolein.Gloerich@radboudumc.nl Alain.van Gool@radboudumc.nl Monique.vanscherpenzeel@radboudumc.nl Dirk.Lefeber@radboudumc.nl Leo.Kluijtmans@radboudumc.nl Ron.Wevers@radboudumc.nl Alain.van Gool@radboudumc.nl Ron.Wevers@radboudumc.nl
  • 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. Issue 2: The big current bottleneck in Next Generation Life Sciences: 49 (Big) data Knowledge Understanding Decision Action Translation is key !
  • 51. 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+ '1679.3550 6+ '1682.1905 6+ '1684.8561 6+ '1686.0180 6+ '1688.5147 6+ '1690.9286 12+ '1692.6745 6+ +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
  • 52. Deconvoluted spectrum of 1 subunit Survey View 500 1000 1500 2000 2500 m/z 10 20 30 40 50 60 70 Time [min] '10069.0770 Mr '10087.0920 Mr '10103.0766 Mr '10110.0557 Mr '10125.0318 Mr '10132.0368 Mr '10141.0021 Mr '10149.0079 Mr +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
  • 53. Small to large intact subunits in a single analysis 9 kDa subunit (deconvoluted) 75 kDa subunit (deconvoluted)49 kDa subunit (deconvoluted) '9603.9448 Mr '9617.9600 Mr '9631.9697 Mr '9644.9081 Mr '9654.9367 Mr '9669.9202 Mr '9685.8928 Mr +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) '20707.5208 Mr '20725.4879 Mr '20744.4732 Mr '20755.4811 Mr '20763.4648 Mr '20781.4432 Mr +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
  • 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) 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
  • 55. Deconvoluted and simulated spectra Compound Spectra '13107.3636 Mr +MS, 14.5-15.6min, Deconvoluted (MaxEnt, 566.30-2196.57, *0.063125, 50000) 15128.4567 1+C₆₆₃H₁₀₂₈N₁₉₂O₂₀₃S₆, , 15119.4339 13114.3768 1+ 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)
  • 56. Overlay of deconvoluted and simulated spectra NUMM subunit Mass error: 0.0049 Da (0.4 ppm) 13.114 m/z Da