Instrumental Solutions for Metabolomics Dr. Desislav Donchev US14702384-16012196010132-S01-A01.tif 300 400 500 600 700 800 3100 3200 3300 3400 3500 3600 3700 3800 Color Scale R = [52 1484] G = [26 514]
Systems Biology: Integrative multi-omics approach Glycomics Interactomics Metallomics Cellomics Genomics Transcriptomics Proteomics Metabolomics Genes mrna Proteins Metabolites Constant level Levels are context-dependent: changes with physiological, developmental and pathological state of living cells
What is Metabolomics? Metabolomics is the comparative analysis of endogenous metabolites found in biological samples: biomarker for a disease or drug effect production process change Metabolites are the by-products of metabolism Range of physico-chemical properties Classes: Amino acids, Sugars, organic acids, fatty acids, lipids Molecular weight ~ 50-1000 Number of metabolites: 2500-3000 Concentrations span a wide dynamic range
Metabolite ID Is Not Metabolomics Metabolomics Is the study of endogenous metabolites Analysis is done using mass profiling of many samples to find statistically relevant features Features represent endogenous metabolites Features can be identified using database searches or by MS/MS spectra Metabolite ID Is the study of pharmaceutical drug biotransformation products Analysis is done using mass profiling comparing a treated vs. control Features represent metabolism products of a drug candidate Many metabolism products can be predicted based on drug structure Identification requires MS/MS analysis and is not based on database searching Page 5
Metabolomics Workflow - Overview GC/MS Analysis 1) Mass Hunter MFE 2) AMDIS (GC/MS) 1) Mass Profiler 2) GeneSpring MS 1) METLIN Personal DB 2) Fiehn library Pathway Architect LC/MS Analysis Peak Finding Statistical Analysis Metabolite Identification System Biology Analysis Purpose: Separate and detect metabolites by the appropriate tool Purpose: Find and quantitate all metabolites Purpose: Find meaningful differences in sample sets Purpose: Identify the interesting metabolites Crucial Purpose: Understand the biological meaning of the data Page 6
LCMS GCMS Agilent Metabolomics Workflow Separate & Detect Feature Finding Quantitate Alignment & Statistics Identify Pathways Mass Profiler (MP) GC/MSD GC-QQQ MassHunter Qual AMDIS or Find by chromatographic deconvolution Mass Profiler Professional (MPP) ID Browser METLIN Pathway module Cytoscape LC-TOF/QTOF LC-QQQ MassHunter Qual MFE, Find by Formula, Find by Ion Page 7
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MS Type Summary 6200 TOF & 6500 QTOF, 6400 Triple Only Agilent has complete MS portfolio for metabolomics 5975C GC/MS D 2009 ASMS Metabolomics Survey Results http://metabolomics.us/2009/asmsmetabolomicsworkshop/surveyre sults/ Page 9
Instrumentation for Metabolomics 5975C GC/MS 7000B GC/QQQ Hi-DEF Q-TOF Q-TOF 6500 series 6500 series Infiniti QQQ TOF 1290 LC 6400 Series 6200 series Page 10
Metabolomics Untargetted or Targetted Analysis Untargeted - Profiling of unknowns Accurate Mass instrument (LC-TOF, Q-TOF) Track metabolites using retention time and mass Find differential metabolites (features) using statistical analysis programs Identify differential metabolites Interpret results in context of biological pathway analysis software tools Integrate into Systems Biology context Targeted - Known metabolites only Absolute quantitation - Need external and internal standards Unit mass instruments (LC-QQQ or GC-QQQ) Data is acquired in SIM or MRM mode Flux analysis Pathway analysis and model building software for biological interpretation Page 11
Page 12 Agilent 6550 ifunnel QTOF
ifunnel-enabled 6550 QTOF 10X Sensitivity Gain Drives New Applications Huge Gains in Sensitivity Dramatically improved quantitative capabilities New Qual/Quan Workflows Superior metabolite and protein detection Non-targeted compound screening Extraordinary Performance >40,000 resolution 50 spectra /sec MS; 33 spectra/sec MS/MS 5 orders of linear dynamic range <0.6 ppm MS mass accuracy Unrivalled sensitivity 6550 ifunnel Q-TOF LC/MS System Page 13
Customer Survey Bottleneck Summary 81% of respondents say - Challenges due to software NOT hardware 1.Compound Identification 35% 2.Biological Significance 27% 3.Data Processing 14% 4.Statistical Analysis 5% Agilent has software tools to fill these gaps 2009 ASMS Metabolomics Survey Results http://metabolomics.us/2009/asmsmetabolomicsworkshop/surveyre sults/ Page 14
Deconvolution Reporting Software Peak detected with Trace Ion Detection TIC TIC & Spectrum Component 1 Component 2 Component 3 Deconvoluted comps and spectra Mathematical Separation matrix interference Deconvolution Some m/z values pure Some m/z values mixed target m/z Page 15
Molecular feature extraction Raw data Extracted Features Page 16
Molecular Formula Generation (MFG) Algorithm Use All Available Information Scoring based on Monoisotopic mass (varies in ppm) Mass Match + Isotope distribution (varies in %) Isotope spacing (varies in ppm) Abund. Match + Spacing Match = Overall Score Without use of isotope information, the first hit would have been incorrect! Page 17
Finding Differences : Statistics analysis Statistics Differential Analysis & Visualization Mass Profiler Performs pair wise differential analysis Designed for TOF data only Simple t-test METLIN Personal database is integrated Mass Profiler Professional Simple or complex data sets Performs many types of statistical analyses Numerous visualizations Import and process data from Agilent: GC/MS, LC/TOF, LC/Q-TOF, LC/QQQ data) Identify metabolites using integrated ID browser Page 18
Metabolite Identification Schemes ID compounds of interest Agilent Fiehn GC/MS Metabolomics Library METLIN AMRT Database (LC/MS) Molecular Formula Generation (MFG) Interpret MS/MS library search ID of true unknown Acquire targeted MS/MS on organic synthesized compounds (based on hypothesized structure) Isolate compound and do NMR (needs time, cost and money) Page 20
Agilent Fiehn Metabolomics GC/MS RTL Library Dedicated GC/MS Analysis Method for Metabolomics Developed By Dr. Oliver Fiehn Anal. Chem. 2000, 72, 3573-3580 Chemical derivatization of active groups Oximation of alpha-keto groups first Silylation of active protons MSTFA GC/MS retention time locked method Reproducible retention times EI library with retention indexing (RI) Library supports modified methods Contains 800 compounds Page 21
Identification: METLIN Personal Database Overview Metabolite-specific database for metabolomics research Database installed on a PC Contains ~22,000 compounds ~8000 lipids added from LipidMaps Manual and batch searches Query based on monoisotopic mass Customizable Add compounds Assign chromatographic retention times to metabolites Create subset databases Works with other Agilent software Page 22
Methanol % AMRT database generation in METLIN: TIC example of a standards mixture 2.1 x 30 mm, 3.5 µm SB-C8 Guard 2.1 x 50 mm, 3.5 µm SB-aq C18 100 80 60 40 20 Over 700 Standards for most common metabolite classes analyzed in bins of 25-50 Simple linear gradient: 2% methanol to 98% methanol in 13 minutes, 6 minute hold at 98% methanol; Flow rate 0.6 ml/min Compatible with ESI/APCI and positive/negative ionization modes EIC of each standard is evaluated: Retention Time and Accurate Mass are added to the METLIN Personal Metabolite Database Page 23
The Complete Metabolomics Solution Discover Validate Identify Validate Page 24
Online Metabolomics Lab www.metabolomics-lab.com Page 25