The Radboud Centre for
Proteomics, Glycomics & Metabolomics:
Translating Research to Biomarkers to Diagnostics
Science Mee...
Radboudumc
• Mission: “To have a significant impact on healthcare”
• Strategic focus on Personalized Healthcare
• Core act...
Translational medicine @ Radboudumc
Radboudumc Technology Centres
Alain van Gool

Bioinformatics

Flow
cytometry

Preclinical
pharmacology

Proteomics
Metabol...
Radboud Centre for Proteomics, Glycomics & Metabolomics
Research
Radboud
Proteomics
Center

Biomarkers
Radboud
Glycomics
F...
Radboud Centre for Proteomics, Glycomics & Metabolomics
Key experts:
Proteomics
Jolein Gloerich
Hans Wessels
Alain van Goo...
Radboud Centre for Proteomics, Glycomics & Metabolomics

Research

Patient care

External

• Projects
• Service

• Health ...
Radboud Centre for Proteomics, Glycomics & Metabolomics
• Proteomics

Key experts:

• Bottom-up (shot-gun) proteomics
• Ta...
Proteomics
• Proteome profiling
- Differential protein expression
- Protein complex composition
- Labelfree
- Labeled (SIL...
Proteomics approaches
• Bottom-up proteomics (shotgun)
• Protein identification
• Differential protein expression profilin...
Applications of bottom-up proteomics
• Differential protein expression in:
• Health/disease
• Time
• Before/after treatmen...
Example of cellular proteome profiling project
Project with TNO
Q: how does proteome cell
line x look like?
Q: First look ...
Example of complexome analysis project
What subcomplexes in mitochondrial
proteome?
• HEK293 cells
• Isolation native mito...
Applications of targeted proteomics
Research

(Absolute) quantitation of targets for:
• Biomarkers
• Diagnostic test
• Spe...
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
Etc …

Con’s...
Examplë: SRM output data
Measurement of a peptide in complex matrix
(tissue homogenate)

Use of heavy labeled standard
•
•...
Applications top-down proteomics
Analysis of intact proteins by ESI-Q-tof MS
Compound Spectra
Intens.

+MS, 0.985-10.524mi...
Analysis of intact Trastuzumab by top-down proteomics
Quantitative analysis of
intact protein isoforms
-

N/C-terminal tru...
Analysis of a 40-subunit protein complex
Mitochondrial complex I of Y. lipolytica
•
•
•
•

Established subunits: 40
Subuni...
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 ...
ESI spectrum of 1 subunit
Survey View
Intens.
x104

m/z

+MS, 56.8-58.7min #3408-3522

6+

7+

6+
'1682.1905

7+
'1442.020...
Fully characterized N7BM subunit

16.062 m/z Da

Characterized protein form

• N-terminus processing: Methionine truncatio...
Radboud Centre for Proteomics, Glycomics & Metabolomics
• Proteomics

Key experts:

• Bottom-up (shot-gun) proteomics
• Ta...
Glycomics

Source: Allison Doerr, Nature Methods 9,36 (2012)
Glycosylation markers in human medicin
• Biomarker for disease and therapy monitoring: rheumatoid arthritis,
oncology, hep...
Glycosylation types
• N-glycosylation
• Asparagin linked
• 8 - 20 saccharides
• O-glycosylation
• Serine/Threonine linked
...
Glycomics approaches
Diagnostics

Research

Urinary glycan
profiling

Chemical biology

Serum glycan
profiling

Glycopepti...
Glycomics application areas
• Mechanisms of glycosylation disorders
Linking genes to glycomics profiles
Understanding neur...
Glycan analysis by nanoChip-QTOF MS
• High-resolution glycoprofiling
• Microfluidic chip system results in simplified oper...
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 yea...
Radboud Centre for Proteomics, Glycomics & Metabolomics
• Proteomics

Key experts:

• Bottom-up (shot-gun) proteomics
• Ta...
Metabolomics approaches
Research

Diagnostics

Equipment

• Assay development for specific
metabolites or metabolite class...
Example: targeted diagnostics in metabolic disease

Organic acids
GC-MS

Amino acids
Amino acid analyser

Carnitine-ester ...
Example: untargeted metabolomics to diagnose
individual patients

Chemometric pipeline
Human plasma
20 controls vs 1 patie...
Integrated databases
A blind study
Plasma sample choice

: Dr. C.D.G Huigen

Analytical chemistry

: E. van der Heeft

Chemometrics

: Dr. U.F....
The blind study
Diagnostic metabolites found in blood plasma
MSUD (2) → leucine, isoleucine, valine, 3-methyl-2-oxovaleric...
Radboud Centre for Proteomics, Glycomics & Metabolomics
• Proteomics

Key experts:

• Bottom-up (shot-gun) proteomics
• Ta...
42

A problem in biomarker land
The innovation gap in biomarker
research & development

Number of
biomarkers

Gap 1
Gap 2
...
43

Some numbers

Eg Biomarkers in time: Prostate cancer
May 2011: 2,231 biomarkers
Nov 2012: 6,562 biomarkers
Oct 2013: 8...
Shared biomarker research through open innovation

Shared knowledge,
technologies and objectives

We need to set up a open...
Contact information
• Proteomics

RadboudProteomicsCentre@umcn.nl
Jolein.Gloerich@radboudumc.nl
Alain.van Gool@radboudumc....
Back-ups
Personalized Healthcare @ Radboudumc

People are different

Stratification by multilevel diagnosis

+
Patient’s preference...
49

Issue 2:

The big current bottleneck in Next Generation Life Sciences:

Translation is key !

(Big) data
Knowledge

Un...
Experimental setup
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/...
Deconvoluted spectrum of 1 subunit
Survey View
m/z
Intens.
x104

+MS, 56.8-58.7min, Baseline subtracted(0.80), Deconvolute...
Small to large intact subunits in a single analysis
9 kDa subunit (deconvoluted)
Intens.
x105

20 kDa subunit (deconvolute...
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-M...
Deconvoluted and simulated spectra
Compound Spectra
Intens.
x105

Mr
'13107.3636

Measured spectrum

+MS, 14.5-15.6min, De...
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
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SMB 28112013 Alain van Gool - Technologiecentra Radboudumc

  1. 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. 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. 3. Translational medicine @ Radboudumc
  4. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 15. Method of the year 2012
  16. 16. Targeted Proteomics: focus on peptides of interest Protein A Protein A isoform Protein B
  17. 17. Targeted proteomics: SRM assay development Pro’s • Selective • Quantitative • Reproducible • Quite sensitive Etc … Con’s • Assay development • Low resolution MS
  18. 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. 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. 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. 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. 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. 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. 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. 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. 26. Glycomics Source: Allison Doerr, Nature Methods 9,36 (2012)
  27. 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. 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. 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. 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. 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. 32. Whole serum glycomics B4GalT1 Bio-informatics : • Coupling with public glyco-databases • Annotation of glycan linkages
  33. 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. 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. 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. 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. 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. 38. Integrated databases
  39. 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. 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. 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. 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. 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. 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. 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
  46. 46. Back-ups
  47. 47. Personalized Healthcare @ Radboudumc People are different Stratification by multilevel diagnosis + Patient’s preference of treatment Exchange experiences in care communities Select personalized therapy
  48. 48. 49 Issue 2: The big current bottleneck in Next Generation Life Sciences: Translation is key ! (Big) data Knowledge Understanding Decision Action
  49. 49. Experimental setup
  50. 50. 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]
  51. 51. 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]
  52. 52. 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
  53. 53. 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)
  54. 54. 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
  55. 55. Overlay of deconvoluted and simulated spectra NUMM subunit 13.114 m/z Da Mass error: 0.0049 Da (0.4 ppm)

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