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

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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

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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

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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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