Metabolomic and
Proteomics Solutions
for Integrated Biology
Steve Fischer
Christine Miller
Agilent Technologies
ASMS 2014
Integrating Biological Analysis Using Pathways
R
R
HO
Protein B
Protein A
Protein X
• Identifies why the pathway is active
• Suggests follow-on experiments
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What’s New for Integrated Biology Workflows?
Correlation Analysis in MPP
– Find connections in omics data
– Easily compare results to metadata, against samples or across
experiments
KEGG Pathways in Pathway Architect
– Most widely used database for biological pathways
– Data from omics studies is displayed on KEGG pathways
– Integration simplifies the customers’ workflow to interpret their results
using biological pathways
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Correlation Analysis: Sample to Sample
pH
9
2
7 Sample correlation heat map
indicates sample relationship
within a pH and samples of
different pH
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Correlate Analytical Results to Meta Data
Metadata can be displayed as
• Heat maps
• Colored strips
• Graphical plots
• Discrete plots
• Text
Visualize the significant relationships!
• Maps observable sample information to analytical results
• Provides flexible visualization of meta data (e.g. time points, growth
conditions, patient data etc.) to facilitate interpretation
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Entity to Entity Correlations
Supported comparisons:
• Within an experiment
• Between experiments
(genes and proteins,
proteins and metabolites
etc.)
Clicking on any square brings
up the regression analysis
Which Proteins, Genes or Metabolites Are
Connected?
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Pathway Architect Renders Accurate KEGG
Graphics and Annotates Experimental Results
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Rich biological information
available in KEGG
Heat-strips and color
coding displaying results
What’s New for Integrated Biology Workflows?
New Profinder batch feature extractor
– New recursive analysis ensures fewer false positives and removal of
missing values
– Process a large number of LC/MS data files, quickly.
– Rapid review and editing of peaks (re-integration or deletion)
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Profinder: Batch Recursive Feature Extraction
Untargeted Data Mining Workflow
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Find compounds using Molecular Feature Extractor
• rMFE – use batch information to improve the feature quality
• Filter – remove unreliable or marginal features
Recursive analysis using Find by Ion
• Target data mine - Use filtered, rMFE feature list to on data files
• Filter – remove unreliable or marginal features
Find by Ion MFE rMFE Filter Filter
LC/MS Data Mining MassHunter Profinder Batch Processor
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Single software for untargeted
and targeted feature extraction
Designed specifically for the
needs of the metabolomics
user
Processes many samples!
Minimizes false positive and
negative results
Compound Centric: review and
manual editing functionalities
Group 1
Group 2
Group 3
Group 4
Compound group level
information
Details for a single
compound
Stacked/overlaid
EICs
MS
spectra
ASMS 2014 Omics VIP
Profinder: Manual Feature Integration
Data Quality Enhancement
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Before manual curation After manual curation
What’s New for Integrated Biology Workflows?
Skyline
– Improved Agilent default CE optimization values (February 2014)
– Agilent Automation Tool for Skyline for automating method development
(February 2014)
– New (coming this summer!) MPP export
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New Automation Link in Skyline
ASMS 2014 Omics VIP
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New Agilent Automation Tool For Skyline and the
QQQ Acquisition Software
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New! Export Skyline Results to MPP+ Pathway
Architect for Chemometric Analysis/Visulaization
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What’s New for Integrated Biology Workflows?
Spectrum Mill B.04.01
• Protein discovery (identification from data-dependent experiments)
• Translating from discovery to target analysis (converting protein id results
to MRM)
• Integration with MPPs workflows
ASMS 2014 Omics VIP
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Spectrum Mill: Label-free Calculation of Protein
Abundance
Spectrum Mill provides this unique
capability as part of protein database
searching:
• Precursor area is extracted and
stored
• MS/MS spectrum is matched to a
peptide
• Peptide precursor area is
assigned to that peptide
• Protein area = sum of areas for
peptides matched to the protein
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Clear separation of yeast treatments
Quickly Map Discovery Proteomics Results to
Pathways
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Spectrum Mill protein-protein
comparison mode groups
proteins across the entire
data set
Export results to MPP
Pathway visualization in Pathway Architect
Complete Metabolomics Workflow A
gile
nt
LC
MS
an
d G
CM
S
Separate &
Detect
Feature
Finding
Quantitate
Alignment &
Statistics Identify
Pathway
Analysis
Statistics
and
visualization
ID Browser using
Agilent Fiehn
Library, Agilent
METLIN Library,
Pathway
Architect
LCMS and GCMS Data can be analyzed
together in the same project
Mass Profiler Professional
Q-TOF, TOF*
Triple Quad, MSD
MassHunter Qual
Profinder
MassHunter Quant
ASMS 2014 Omics VIP
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Discovery ProteomicsWorkflow A
gile
nt
LC
MS
Separate &
Detect
Identify or
Feature Find
Statistics &
Visualization
Pathway
Analysis
Statistics
and
visualization
Pathway
Architect
Omics data can be analyzed
together in the same project
Mass Profiler Professional
and Pathway Architect
Q-TOF
Spectrum Mill
Skyline
Profinder
ASMS 2014 Omics VIP
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Targeted Proteomics Workflow A
gile
nt
LC
MS
Separate &
Detect
Target and
quantitate
Statistics &
Visualization
Pathway
Analysis
Statistics
and
visualization
Pathway
Architect
Omics data can be analyzed
together in the same project
Mass Profiler Professional
and Pathway Architect
Q-TOF, QQQ
MassHunter
Quant
Spectrum Mill
Skyline
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Yeast Multi-omics: Metabolomics and Proteomics
Treatments:
• Wild type (WT) - no treatment
• Calcium control (CC) - CaCl2
• FK - FK506 and CaCl2
• CA - Cyclosporin A and CaCl2
Metabolomics: Wet mill with 5:3:3 CHCl3:CH3OH:H2O. Only the aqueous is analyzed
Proteomics: Samples were lysed using glass beads with vortexing and digested using the modified FASP protocol
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Spin down and PBS wash after 15min of Ca2+
exposure
OD600 0.8
WT Calcium
Control
FK506 Cyclosporin A
WT CC FK CA
4 0.8L
cultures
1hr later add fraction to bring culture to
200mM CaCl2
Add 4mL of 1mg/mL
FK506 or CylA in 90:10
EtOH:Tween 20
4mL of 90:10
EtOH:Tween 20
Multi-omic Analysis: Purine Metabolism
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Multi-omic Analysis: Alanine, Aspartate and
Glutamate Metabolism
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Finding Relationships Using Entity-Entity
Correlation Analysis of Protein Data
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1 = positively correlated
0 = uncorrelated
-1 = negative correlated
Find New Biology Faster: Select Positively
Correlated Proteins for Pathway Analysis
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Biosynthesis of Amino Acids
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Pathway-based Multi-omic Discovery-to-Validation
to Enable Integrated Biology
Pathway
Architect
Skyline
Spectrum
Mill
Pathway to
PCDL
MPP
Q-TOF
Acquisition
QQQ
Acquisition
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Integrated Biology Workflow Enhancements From
Agilent to Accelerate Your Research
Comprehensive -omics workflows for metabolomics, proteomics, and genomics
Pathway Architect now supports KEGG pathway database
• Multi-omics support
• Data visualization directly on pathway
• True rendering of KEGG pathways
Correlation analysis for discovery new biology/relationships
• Valuable when pathways are unavailable or not relevant
• Connects biology to meta data
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Thank you!
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