Advances in

Instrumentation and

Software For Pathway

Informed Multi-Omics

Analysis

Agilent Technologies

1

Yeast Omics

Biology Is Integrated

DNA RNA Protein Metabolite

2

Yeast Omics

Integrating Biological Analysis Using Pathways

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R

R

Protein B

Protein A

Protein X

HO

Sources

• WikiPathways

• BioCyc/MetaCyc

• Generalized BioPax

• KEGG

Platforms

• GeneSpring

• Mass Profiler

Professional

• Pathway Architect

Integrating Biological Analysis Using Pathways

R

R

HO

Protein B

Protein A

Protein X

R

R

HO

Protein B

Protein A

Protein X R

R

HO

Protein B

Protein A

Protein X

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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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Baker’s Yeast is an Ideal Model Organism for

Studying Pathways

• Saccharomyces cerevisiae

is a widely used model

organism

• Biochemistry and pathways

are extensively studied

• Fully sequenced genome

• Ideal for “multi-omics”

studies with the goal of

facilitating research for

other organisms.

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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 Cyclospori

n 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

Metabolomics Workflow

Acquire Data

• Analyze metabolomics samples

• Use accurate mass LC/MS to analyze samples

Analyze Data

• Mine data using MassHunter

• Analyze in Mass Profiler Professional

Identify Compounds

• Identify metabolites using ID Browser

• Search METLIN to annotate metabolites

Pathway Analysis

• Map results to pathways using Pathway Architect

• Export protein list for next experiment

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TOF / Q-TOF For Discovery Metabolomics

Goal – Detect all metabolites

Data is acquired in spectrum mode

Metabolite tracking uses retention time and mass or mass fragments

Statistical analysis is used to find differential metabolites (features)

Feature identification is required for biological interpretation

Ideal for discovery metabolomics

Spectral quality

• Accurate mass

• Good mass resolution

• 5 orders of dynamic range

• High isotope ratio fidelity

• Maintains performance at high acquisition speeds

• Sensitive

Compound identification

• Accurate mass, isotope ratio

• MS/MS with accurate mass, isotope ratio (Q-TOF only)

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Agilent’s Complete Metabolomics Solution

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Agile

nt

LC

/MS

and G

C/M

S

Q-TOF, TOF

Feature Finding

Quantitate

MassHunter

Qual

Alignment &

Statistics

Statistics

Visualization

Identify

ID Browser using

Agilent Fiehn

Library, Agilent

METLIN Library

(including lipids)

Pathway

Analysis

Pathway

Architect

Triple Quad, MSD

LC/MS and GC/MS Data can be analyzed

together in the same project

Mass Profiler Professional

Separate &

Detect

MassHunter

Quant

or

Chromatographic Solutions for Separating

Metabolites

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http://www.chem.agilent.com/Library/primers/Public/5990-4314EN.pdf

RP ANP GC/MS

LC/MS

Less Polar More Polar

LC/MS Analysis of Metabolites in Stressed Yeast

ESI (+) by RP

Polar compounds elute close to the void volume

ESI (+) by ANP

ANP separates polar compounds that were in the void volume with RP

Using both ANP and RP yields more comprehensive coverage

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Untargeted Feature Finding MassHunter Qual:

Find by Molecular Feature

Designed for accurate mass LC/TOF and LC/QTOF data

Find compound signals

• Find co-eluting ions that are related

• Isotopes, adducts (such as Na+ / K+), and dimers

• Create a compound chromatogram and spectra

• Sum all ion signals into one value and report results as a “Feature” with retention time, neutral mass and volume

Fully automated processing

Produces a CEF file for import into Mass Profiler Professional

Compound

chromatogram

Compound

spectra

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Mass Profiler Professional Statistical Analysis and Visualization Software

Designed primarily for MS data

• Also supports NMR

Performs many types of statistical

analysis

• ANOVA, clustering, PCA, class

prediction tools

ID Browser for compound

identification

Export MS/MS target list

Pathway Architect for biological

context

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PCA Plots for Yeast Metabolites Using Different

Analytical Methodologies

RP Pos RP Neg APCI

ANP Pos ANP Neg

Culture Condition

Calcium Control

Cyclosporin A

FK506

Wild Type

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Increasing Your Confidence in Compound

Identification

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Increasing confidence in identification

Compound identification data

Accurate

Mass (AM)

AM

+

Isotope

Pattern

(IP)

AM

+

Retention

Time

(AMRT)

+

IP

MS/MS

Library

MS/MS

Library

+

AMRT

Confident compound identification is crucial for pathway visualization!

METLIN Personal Compound Database & Library Compound Identification

Q-TOF based LC-MS/MS library

Compounds in database - 64092

Compounds with MS/MS - 8040

• Collected in ESI +/- modes

• Selected monoisotopic ion

• Collected at three collision energies

• Spectra reviewed for quality

• Fragment ions confirmed

• Fragment ions mass corrected

• Noise ions removed

• Manually reviewed

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Most extensive database and MS/MS Library for metabolomics

Summary of Yeast Metabolomics Analyses Differential Features with METLIN Database Annotation

Number of Features

After

QC

METLIN

Matches

p<0.05

Cutoff

METLIN

Matches

RP-ESI pos 300 112 158 79

RP-ESI neg 523 141 418 115

RP-APCI pos 364 48 333 37

ANP-ESI pos 492 155 145 113

ANP-ESI neg 276 88 213 63

• ANP chromatography

• ESI +/-

Polar metabolites

• RP chromatography

• ESI +/-

• APCI

Non-polar metabolites

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MS/MS Identification Using the Agilent METLIN

Library

MS/MS spectra library matches:

• MS/MS spectra obtained at 10, 20 and 40eV collision energies

• Matched to METLIN PCDL library spectra

• Displayed as acquired spectra mirrored above library spectra

Hypoxanthine m/z 137.0458 (+)

10eV

20eV

40eV

83/99

56/99

63/99

Inosine m/z 267.0740 (-)

10eV

20eV

40eV

99/99

96/96

96/96

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Pathway Architect

Map and visualize data from one or

two types of –omic data on pathways

Search, browse and filter pathways

Supports biological pathways from

publicly available databases

• WikiPathways

• BioCyc

• Supported pathway formats

• BioPAX 3 – Pathway Commons,

Reactome, NCI Nature Pathway

• GPML – PathVisio –custom drawing

• Export compound list from

pathways

Metabolite Data

Overlay

List of all pathway entities, dynamically

linked to pathway selection

Pathway Architect is an optional

module in MassProfiler Professional

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Agilent-BridgeDB Resolving the Mapping Problem Between Databases

Solves the translation problem of

identification names to pathway

database names

Automatic – does not require user

intervention

• Metabolites Identifiers • KEGG, MetaCyc, PubChem. LMP, HMDB,

ChEBI, and CAS

• Proteins Identifiers: • Swiss-Prot, UniProt, and UniProt/TrEMBL

• Genes Identifiers: • Entrez Gene, GenBank, Ensembl, EC

Number, RefSeq, UniGene, HUGO, HGNC,

and EMBL

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One of the Stress Activated Pathways in Yeast

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Biology-directed Workflows to Targeted

Proteomics

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Targeted Proteomics

Transforming target proteins to target peptides

Spectrum Mill and/or Skyline

List of target proteins

Pathway Architect

MassProfiler Professional

Metabolomics

Spectrum Mill

Discovery Proteomics

GeneSpring

Genomics Literature

Visualizing Discovery Proteomics Data in

Pathways

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Q-TOF

• Acquire data-dependent proteomics data

Spectrum Mill

• Protein database search

• Produce protein summary

• Export protein abundances

Mass Profiler Professional

• Filter for quality data

• Perform statistical analysis

• Visualize differential results

Pathway Architect

• Create a multi-omics project with metabolomics results

• Visualize proteomics and metabolomics

Protein Discovery: Agilent 6550 Q-TOF with

iFunnel Technologies

Proteomics-optimized data-dependent

acquisition on Q-TOFs

Outstanding sensitivity with iFunnel

High performance nano chromatography

with Polaris-HR chip

New flexible G1992A nano source as

alternative to HPLC-Chip interface

Excellent standard flow performance

with AJS, 1290 and Advanced Bio

column

25

Yeast Omics

Spectrum Mill – MPP Data Exchange Supporting a Label-free Proteomics Discovery Workflow

26

Yeast Omics

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

Combined Pathway Visualization of Discovery

Proteomics and Metabolomics

metabolites proteins

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Biology-directed Workflows to Targeted

Proteomics

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Targeted Proteomics

Transforming target proteins to target peptides

Spectrum Mill and/or Skyline

List of target proteins

Pathway Architect

MassProfiler Professional

Metabolomics

Spectrum Mill

Discovery Proteomics

GeneSpring

Genomics Literature

Creating Targeted Methods From Protein Lists:

Metabolomics Proteomics

Metabolically interesting pathway

From discovery data

• Use Spectrum Mill MRM Selector

• Export peptide library to Skyline

From in silico prediction

• Copy protein accession number into Spectrum Mill and/or Skyline

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Targeted Proteomics: Using Pathway-Directed

Information to Inform the Next Experiment

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Pathway Architect

• Select perturbed pathway

• Export protein accession numbers

• Visualize targeted results

Spectrum Mill and Skyline

• Export results from Spectrum Mill

• Predict peptides and MRMs for new proteins

• Refine using SRM Atlas

QQQ

• Optimize best transitions

• Perform DMRM analysis

• Export areas to MPP

Mass Profiler Professional

• Filter for quality data

• Perform statistical analysis

• Visualize differential results

Targeted Proteomics: Agilent 6490 QQQ with

iFunnel Technologies

Outstanding sensitivity with iFunnel

Excellent standard flow performance

with AJS + 1290

Routine, robust nano LC with HPLC-

Chip/QQQ

tMRM mode

Skyline workflow manuals

New Skyline Automation tool

33

Yeast Omics

Skyline – MPP Data Exchange Importing Targeted Results

34

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Exporting Protein Areas From Skyline to MPP

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Review and process QQQ

results in Skyline

Export results to MPP

Pathway visualization in Pathway Architect

Multi-omics Pathway Visualization of Metabolomic

and Targeted Proteomics Results

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metabolites proteins

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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Thank you!

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