2013-09-03 Radboudumc NCMLS Technical Forum

Prof Alain van Gool, PhD

Head Radboud Proteomics Center

UMC St Radboud

Radboud Proteomics Center

- exciting times !

September 3rd 2013

Contents

• Radboud Proteomics Center

• Setting

• People

• Proteomics approaches

• Bottom-up proteomics

• Targeted proteomics

• Top-down proteomics

• Center for Proteomics, Glycomics and Metabolomics

• Fit in Radboud Personalized Healthcare

Proteomics in Nijmegen

• Started 2004

• Initially combination of technologies

• Department of Laboratory Medicine (Prof Fred Sweep)

Laboratory of Genetic, Endocrine and Metabolic Disease (Prof Ron Wevers)

• Expertise shift from 2D-gel electrophoresis to LC-MS

• Various internal/external projects and collaborators

• Set-up:

o Core of 5 permanent staff

o Associated lab co-workers from other UMC

departments

Prof. Alain van Gool PhD Molecular and cellular biology Background: • Pharmaceutical biomarkers • Molecular profiling • Translational and personalized medicine

Jolein Gloerich PhD Biochemistry Background: • Proteomics • Fatty acid oxidation disorders

Maurice van Dael BSc Food & Toxicology Background: • Proteomics • Metabolite analysis

Hans Wessels BSc Biochemistry Background: • Proteomics • Microbiology • Mitochondrial biochemistry

+ guest co-users of hardware/software:

Technicians from various

Post-docs research institutes

PhD students in UMC St Radboud

Jenneke Keizer BSc Clinical chemistry Background: • HPLC • Mass spectrometric metabolite analysis

Radboud Proteomics Centre Radboud University Medical Centre – 774 Geert Grooteplein Zuid 10 6525 GA Nijmegen, Netherlands [email protected] Route 774

RPC lab

• 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 Expertise

Shared platforms in Radboud Proteomics Center

Proteome profiling Protein characterization Protein identification

Peptide quantitation (with Prof. Ron Wevers)

Clinical proteomics (with Dr. Waander van Heerde)

Glycoproteomics (with Dr. Dirk Lefeber)

QT

OF

MS

SE

LD

I-T

OF

MS

Protein quantitation (with Dr. Dorine Swinkels)

MA

LD

I-T

OF

MS

• Shared expertise

• Shared resources

• Maximal use

QqQ

MS

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rap

MS

Hyb

rid

LT

Q-F

T M

S

QT

OF

ET

D M

S

Q-E

xa

ctive

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Proteome profiling (with Prof. Ulrich Brandt)

Radboud Proteomics Centre

The growing availability of genomic sequence information, together with improvements of protein characterization by mass spectrometry, facilitates protein research enormously. To exploit these opportunities the Radboud Proteomics Centre (RPC) was established in 2003. Our aim is to initiate, coordinate and facilitate proteomics research activities. The RPC offers fundamental technological tools and knowledge transfer for proteomics research by making them available for academic and industrial researchers, both within and outside the Radboud University Nijmegen. Since its establishment, the RPC has played a crucial role in numerous research projects within the Radboud University and in many fruitful collaborations with other (international) universities and life-sciences companies.

Route 774

Radboud Proteomics Center: portfolio

Research • Projects

• Service

External • Projects

• Service

• Consultancy

Patient care • Health care focus

• Biomarkers

• Consortia (NL, EU)

The RPC is Radboud’s proteomics expertise center:

• Provide consultancy for potential proteomics applications

• Implement proper experimental design for each proteomics project

• Continue to do good proteomics science through collaboration

Proteomics applications

• 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

Bottom-up proteomics

Workflow bottom-up proteomics

11

Bottom-up proteomics principle

Applications of bottom-up proteomics

• Determine differential protein expression in:

• Health/disease

• Time

• Before/after treatment

• Identification of protein-protein interaction partners:

• Protein correlation profiling

• (Tandem) affinity purification

Information is obtained on peptide level, deduce protein effects

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

Conclusions

Project with TNO

Q: how does proteome

cell line x look like?

Q: First look at effect

treatment on proteome

(feasibility)

→ GeLC-MS approach

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Distinct chromatograms between fractions with excellent signal-to-noise and resolution

GeLC-MS proteomics

Ion map for untreated cells gel slice 6

Ion map for untreated cells gel slice 6

Green square: identified MS2 spectrum Blue square: unidentified MS2 spectrum

Data set statistics

Parameter HF HF TGFß Total

MS spectra 26626 29426 56052

MS/MS spectra 59038 66577 125615

Isotope patterns detected (real ions)

251664 284994 536658

Sequenced isotope patterns (Average for z≥1)

52918 (26.7%) 60679 (26.2%) 113597 (26.4%)

MS/MS identification rate

52.7% 53.3% 54.5%

Identified peptides (redundant)

34972 39117 74089

Precursor mass error 0.29 ± 0.39 ppm 0.29 ± 0.39 ppm 0.29 ± 0.39

3800 identified proteins in one sample

Differential protein expression profiling: results

Proteome coverage

Origin of contaminants

Proteome coverage

A publically available data set of blue native migration patterns for more than 950 proteins from a

mitochondrial HEK293 fraction using complementary acryl amide gradients Hans Wessels et al, PloS One 8, 2013

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

Wessels HJ

Vogel RO

Rodenburg R

Gloerich J

Van Gool A

Van den Heuvel, L

Smeitink JAM

Nijtmans L

Lightowlers R

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

mt-Ribosome complexes

200 kDa subcomplex

Conclusions • Publically available BN LC-MS/MS data set of >950 proteins

(equivalent of 1900 2D BN SDS-PAGE westerblots!!!)

• Applications in de novo protein complex identification,

prioritization of interaction candidate proteins from other analyses

• New insights into mt-Ribosomal proteome interactions

Compare LTQ-FT-ICR MS versus MaXis 4G QTOF MS

UPS2 spiked in 500ng E.coli background

» Sample 1: 250fmol – 25amol

» Sample 2: 500fmol – 50amol

Digest, analyse all peptides, convert to protein data

→ Theoretical value (Sample 1/ sample 2) for each protein: 0.5

UPS2 standard from Sigma:

• 48 proteins

• 5 orders of magnitude

• Protein Mw for each level within range of 10-70 kDa (approximately)

Benchmarking: technical performance test

Ongoing

Identification =

Sensitivity ↑↑

Accuracy ↑↑

Targeted Proteomics

Method of the year 2012


26

Workflow targeted proteomics

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 …

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

http://www.ncbi.nlm.nih.gov/pubmed/23684311





How do SRM data look like?

Measurement of HSA peptide (LVNEVTEFAK)

Linear over high dynamic range

How do SRM data look like?

Measurement of a peptide in complex matrix

(tissue homogenate) Which peak?

Heavy labeled standard

• Confirmation of peak

• Used for accurate (absolute) quantitation

Top-down Proteomics


33

Workflow targeted proteomics

Workflow top-down proteomics

Protein

Protein

ions

Top-down or bottom-up?


(peptides)

+ Most mature

+ Widely used, sufficient tools

+ Can handle complex samples

- PTM analysis

- Protein processing


(intact proteins)

+ PTM analysis on protein level

+ Protein processing

- Relatively new approach

- Complexity of the sample

QTOF QTOF MALDI

cytochromeC_100903091148 #206 RT: 16.653 AV: 1 NL: 1.34E6T: FTMS + p NSI u SIM ms [649.40-653.40]

651.2 651.3 651.4 651.5 651.6 651.7 651.8 651.9

m/z

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

1100000

1200000

1300000

Inte

nsity

651.3954

651.4481

651.5009

651.3427

651.6062

651.2375

651.6592

651.2900651.7120

651.7638

651.1844 651.8701

Top-down proteomics: intact protein MS

ETD/ECD fragmentation

Cytochrome C (12.4 kDa)

Contents


• Setting

• People







Center for Proteomics, Glycomics and Metabolomics

Radboud

Glycomics

Facility

Radboud

Proteomics

Center

Radboud

Metabolomics

Group

Part of Department of

Laboratory Medicine

Integrated focus:

Research

Biomarkers

Diagnostics

Example Glycomics: From clinical Omics to personalized treatment in NCDG:

• 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

• Outcome: Explanation of disease

• Outcome: Dietary intervention as succesful personalized therapy

• Outcome: Glycoprofile being developed as diagnostic test by mass spectrometry

Dietary

intervention

{Dirk Lefeber et al,

NEJM 2013} Incomplete glycosylation Complete glycosylation

Radboud Personalized Healthcare

Center for Proteomics, Glycomics & Metabolomics

Personalized Healthcare @ UMC St Radboud

Genetics

Molecular

diagnostic

test

Patient

Molecular and functional analysis

Incomplete

clinical

diagnosis

Personalized

treatment

Interactive technology platforms

Biomarker

assay

Proteomics

Glyco(proteo)mics Metabolomics

Bioinformatics

Imaging

Prof. Alain van Gool, COST conference Personalized Medicine, 21 June 2012 40

Radboud Proteomics Center

Research • Projects

• Service

External • Projects

• Service

• Consultancy

Patient care • Health care focus

• Biomarkers

• Consortia (NL, EU)

[email protected]

Diagnostics Innovation

Urinary glycan profiling

Serum glycan profiling

O-glycan profiling

PNGaseF chip

Chemical biology

Glycopeptide profiling

glycolipid profiling

Whole protein glycoprofiling

Nucleotide-sugars

Radboud Glycomics Facility

Monique van Scherpenzeel, Dirk Lefeber

Application areas

• Mechanisms of glycosylation disorders

Linking genes to glycomics profiles

Understanding neuromuscular pathophysiology

• Glycomics Tech Platform Services

Functional foods

Glycan tracers

Biomarkers

Radboud Glycomics Facility

Monique van Scherpenzeel, Dirk Lefeber

Radboud Metabolomics Group

Standard targeted analyses • Organic acids • Amino acids • Purines&Pyrimidines • Monosaccharides/Polyols • Carnitine(-esters) • Sterols

Innovation • Assay development for specific

metabolites or metabolite classes • Untargeted metabolite profiling • Metabolite biomarker identification

Leo Kluijtmans, Ron Wevers

Finding metabolomics differences

in 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

DIAGNOSIS OF INBORN ERROR OF METABOLISM

Example Metabolomics: 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

Contents


• Setting

• People







2013-09-03 Radboudumc NCMLS Technical Forum

Education

Transcript of 2013-09-03 Radboudumc NCMLS Technical Forum