Transcript 2009 ASMS Metabolomics Workshop

2009 ASMS Metabolomics
Workshop: Current Topics in
Metabolomics
Co-chairs: Eric Milgram and Anders Nordstrom
Philadelphia, PA
Tuesday, June 2, 2009
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Workshop Purpose
• Give practical perspectives on how
metabolomics is being used today.
• Highlight the challenges of the technique via
an interactive audience/panel discussion.
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Workshop Format
• Part 1 – Eric/Anders
– Administrative
– 2009 Metabolomics Survey Results
• Part 2 – Interactive – Discussion Leaders
1. Metabolomics Tech: hardware and software
2. Metabolite Identification
3. Assigning Biological Significance
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Mass Spectrometry Applications
to the Clinical Laboratory ‘10
February 6 - 10, 2010
Hyatt Mission Bay, San Diego CA
Two Days of Short Courses
Three Days of Presentations
Exhibits, Posters and Sponsored Lunches
Topic Areas
-Biodefense / Environmental
- Disease Marker
- Inborn Errors in Metabolism
- Metabolomics
- Molecular Diagnostics
(e.g., Infectious Disease)
- Regulations and Standards
- Small Molecule Analytes
(e.g., Vitamins, Steroids, Thyroid)
- Tissue Analysis by MALDI
- Trace Metals
- New Advances
- Proteins / Proteomics
www.msacl.org
Contact: [email protected]
Student, Postdoc and Junior Faculty Travel Fellowships Available
Request for Workshop Coordinators for
2010/2011
• Volunteers?
• Nominations?
• Use feedback form
http://MetabolomicsSurvey.com/
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Survey Results
In order to download 2009 Workshop survey results,
complete workshop feedback form before Thursday,
June 4 at 5 PM.
http://MetabolomicsSurvey.com
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Workshop Feedback Link
http://MetabolomicsSurvey.com
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Link to Workshop Survey Results
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2009 Workshop Survey Results
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Organization Type
GOV
5%
Non-Profit
2%
Academic
57%
For Profit
36%
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How long have you or your current research group been
practicing or applying metabolomics?
61
59
43
36
13
0-2 yrs
2-4 yrs
5-7 yrs >=7 yrs
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N/A
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Bottleneck Summary
8-Data acquisition/throughput; 3%
9-Other; 2%
7-Validation/Utility Studies; 5%
6-Statistical analysis; 5%
5-No opinion; 6%
1-Identification of
metabolites; 35%
4-Sample
preparation; 8%
3-Data processing/reduction; 14%
2-Assigning biological significance; 22%
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Technique Summary
173
87
25
21
9
LC/MS
GC/MS CE/MS
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NMR
Other
4
LC/NMR
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MS Type Summary
130
100
61
41
40
33
17
3
ToF
QqQ
IonTrap
Orbitrap
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1-Quad
FTICR
N/A
Other
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2
Sector
Software Category Summary
N/A
7% (24)
Proprietary
39% (124)
Open Source
25% (83)
In-house produced
29% (96)
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Software/DB Summary
KEGG
NIST
Metlin
PubChem
HMDB
Lipid MAPS
In-house developed
ChemSpider
Proprietary software-instrument vendor
Other:
Does not apply
XCMS
ChemFinder
MetaCyc
Knapsack
ChemDB
Proprietary non-instrument vendor
MDL/ACD
Binbase/SetupX
0
20
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40
60
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80
100
Software Write-In Candidates
•
•
•
•
•
•
•
Biocrates MarkerIDQ
ChromaToF
GeneData
GeneSpring
Golm DB
Lipid Search
MarkerView
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•
•
•
•
•
•
•
•
MassBank*
MassFragmenter
MassProfiler
MMCD
MZmine
Shimadzu MetID
SIDMS
SIEVE
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Survey Summary
• Reported Bottlenecks
1. Metabolite Identification
2. Assignment of Biological Significance
3. Data processing/reduction
• Instrumentation
– ToF’s dominate
– Triple-quads unexpectedly high
• Software
– Wide diversity of software packages and databases in
use.
– No widely accepted workflow solution available yet.
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Introduce Discussion Leaders
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Metabolomics Technologies –
Hardware/Software
• Steve Fischer
– Senior Applications Chemist
– Agilent Technologies
• John Shockcor
– Director of Metabolic Profiling Business Development
– Waters Corporation
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Metabolite Identification
• Annie Evans
– Senior R&D Scientist
– Metabolon
• Bill Wikoff
– Research Associate
– The Scripps Research Institute
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Assigning Biological Significance
• Chris Beecher
– Professor
– University of Michigan, Ann Arbor
 Michigan Center for Translational Pathology
• Tsutomu Masujima
– Professor
– Hiroshima University
 Molecular Medicine & Devices Lab.
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Metabolomic profiles delineate potential role for
sarcosine in prostate cancer progression
Arun Sreekumar, Laila M. Poisson, Thekkelnaycke M. Rajendiran, Amjad P. Khan, Qi Cao, Jindan Yu, Bharathi Laxman, Rohit Mehra, Robert J. Lonigro,
Yong Li, Mukesh K. Nyati, Aarif Ahsan, Shanker Kalyana-Sundaram, Bo Han, Xuhong Cao, Jaeman Byun, Gilbert S.Omenn, Debashis Ghosh,
Subramaniam Pennathur, Danny C. Alexander1, Alvin Berger2, Jeffrey R. Shuster1, John T. Wei, Sooryanarayana Varambally, Christopher Beecher2 &
Arul M. Chinnaiyan
ABSTRACT
Multiple, complex molecular events characterize cancer development and progression1,2. Deciphering the molecular networks
that distinguish organ-confined disease from metastatic disease may lead to the identification of critical biomarkers for cancer
invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human
tumours, little is known about the global metabolomic alterations that characterize neoplastic progression. Using a combination
of high-throughput liquid-and-gas-chromatography-based mass spectrometry, we profiled more than 1,126 metabolites across
262 clinical samples related to prostate cancer (42 tissues and 110 each of urine and plasma). These unbiased metabolomic
profiles were able to distinguish benign prostate, clinically localized prostate cancer and metastatic disease. Sarcosine, an Nmethyl derivative of the amino acid glycine, was identified as a differential metabolite that was highly increased during prostate
cancer progression to metastasis and can be detected non-invasively in urine. Sarcosine levels were also increased in
invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine-N-methyl transferase, the
enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion. Addition of exogenous sarcosine or
knockdown of the enzyme that leads to sarcosine degradation, sarcosine dehydrogenase, induced an invasive phenotype in
benign prostate epithelial cells. Androgen receptor and the ERG gene fusion product coordinately regulate components of the
sarcosine pathway. Here, by profiling the metabolomic alterations of prostate cancer progression, we reveal sarcosine as a
potentially important metabolic intermediary of cancer cell invasion and aggressivity.
Published February 12, 2009 in Nature, vol 457, 910-914.
Metabolomics Technologies:
hardware and software
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 Hardware
 Tandem Quads
 Tof/QTof
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 Software
 Quantitative Packages
 Statistical Packages
 Visualization Tools
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 Hardware
• Nominal Mass
 Tandem Quads
 Ion-Traps
• Accurate Mass
 Software
• Statistical packages
• Visualization tools
• Database tools
 Tof/Qtof
 OrbiTrap
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
Targeted vs Non-Targeted analysis?
 What
is your biggest
challenge/problem?
 What
improvements need to be made to
hardware?
 What
improvements need to be made to
software?
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Compound Identification in Metabolomics
?
• How to streamline routine compound identification ?
• Next generation of metabolomics databases ?
• Utility of ultrahigh mass accuracy / resolution data ?
• How useful are alternatives to CID and multi-step fragmentation ?
• Can we progress towards de novo structure determination using MS methods alone ?
• How feasible is preparative scale up for compound purification and ID by NMR ?
• Are publication standards needed for compound identification in metabolomics ?
• What new technologies or methods will emerge in the next few years ?
William Wikoff
The Scripps Research Institute
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ASMS Metabolomics Workshop
Tuesday June 2nd 2009
Annie Evans
Metabolon
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Assignment of Biological
Significance
Chris Beecher and Tsutomu Masujima
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Data Generation
• Know your system
– Understand your system’s variances
 Noise reduction & reproducibility
– Power curves
• Experiment design
– Factorial designs
• QA/QC (Standards)
– Recovery standards
– Derivatization (or chemistry) standards
– Injection standards
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Data Interpretation
• Standardization
• Normalization
– Median centered
– Z-transformation
 (Obs-MeanC) / StdDevC
• Statistics vs. Data-mining
– Statistics
 Independent observations
 False discovery
– Data-mining




Random Forest
Non-negative factorization
Partition
Singular Value Decomposition
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We can improve highway safety in the USA by
Importing More Fresh Lemons from Mexico
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Workshop Feedback Link
http://MetabolomicsSurvey.com
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Auxillary Slides
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Obstacles to Biological Relevance:
Expedience vs Relevance?
• Surrogate Model Systems
– Cell lines
 What is a proper control?
 Are mono-layer cells biochemically representative of an organ?
– Animals (non-humans)
 Interspecies differences?
• Human Subjects?
– Intersubject variability: age, race, gender, diet, …
• Sample collection constraints?
– Fresh vs banked samples?
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Obstacles to Biological Relevance:
Experimental Challenges?
• Process Artifacts
– Contaminants?
– Chemical Stability
 NADPH+ vs NADP
 Prostaglandins  isoprostanes
• Normalization strategies for urine and tissue?
• False Discovery
– How can you minimize your chances of being “Fooled by
Randomness?”
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