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Advances in Instrumentation and Software For Pathway Informed Multi-Omics Analysis Agilent Technologies
1 Yeast Omics
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Biology Is Integrated
DNA RNA Protein Metabolite
2 Yeast Omics
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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
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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 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
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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 L
C/M
S an
d 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
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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
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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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7x10
0
0
0
0
1
1
1
1
1
2
2
2
2
2
+ TIC Scan CA_RP_pos_1.d
1
Counts vs. Acquisition Time (min)1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
6x10
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
2
Cpd 593: 13.373: + ECC Scan CA_ANP_pos_1.d
Counts vs. Acquisition Time (min)1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 13.5 14
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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!
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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
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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
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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
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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
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Spectrum Mill – MPP Data Exchange Supporting a Label-free Proteomics Discovery Workflow
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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
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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
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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
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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
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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
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Skyline – MPP Data Exchange Importing Targeted Results
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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
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Multi-omics Pathway Visualization of Metabolomic and Targeted Proteomics Results
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metabolites proteins
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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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Thank you!
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