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Metabolomics Workshop I Orbi 4 - 2012 The world leader in serving science 1 Metabolomics Profiling: Current Practice and Challenges • Goals • Challenges 1) Qualitative & Quantitative assessment of the biochemical composition of the samples • Complexity of biological samples 2) Differential analysis between sample groups • Wide range of concentration 3) Identify compounds responsible for changes • Lack of standards • Diversity of small molecule metabolites (LC challenge) • Multiple sources of variability • Incomplete information – majority of components in LC/MS are unknowns • Structure elucidation of unknowns is expensive 2 Metabolomics Profiling: Current Practice and Challenges • Challenges • Complexity of biological samples • Diversity of small molecule metabolites (LC challenge) • Wide range of concentration • Multiple sources of variability • Lack of standards • Incomplete information – majority … Not typical for metabolomics of components in LC/MS are unknowns • Structure elucidation of unknowns …. more realistic situation (without is expensive proper tools) 3 Cornerstones of Metabolomics Workflow 1. Sample preparation 2. HPLC separation 3. Mass Spectrometry detection 4. Data processing and reporting 4 Cornerstones of Metabolomics Workflow 1. Sample preparation 2. HPLC separation 3. Mass Spectrometry detection 4. Data processing and reporting 5 Chromatography for Metabolomics • Lipids • Typically not a big problem • RPLC works pretty well (C18, C8, C30 for isomer) • Polar metabolites • Miscellaneous types of compounds • Some are “difficult” compounds – some amino acids, nucleotides • Various and good LC methods are available – but usually not for all the compounds together • GOAL – a single LC method for all polar metabolites challenge … 6 Chromatography for Metabolomics - Polar Metabolites • RPLC (C18) • Good reproducibility • No or limited retention for very polar compounds • Ion pairing • Significant ion suppression • Possible decrease of dynamic range of Orbitrap (filling trap with ion pairing ions) • Ion Chromatography • Good for specific group of compounds (e.g. organic acids, nucleotides, …) • Not a broad (“universal”) method for al metabolites 7 Profiling of Organic Acid Metabolites by Capillary IC Glycolate Oxalate 75 73 89 61 Glycerate Fumarate 115 71 105 75 Lactate Malate 89 43 2-Hydroxyisobutyrate 133 115 2-Hydroxybutyrate Tartrate 149 87 103 57 Pyruvate 2-Oxoglutarate 87 43 145 101 Glutarate-d6 cis-Aconitate 173 85 137 93 Citrate Succinate 191 111 117 73 2 8 4 6 8 10 12 14 10 12 14 16 18 20 trans-Aconitate isoCitrate 22 24 Chromatography for Metabolomics - Polar Metabolites • RPLC (C18) • Good reproducibility • No or limited retention for very polar compounds • Ion pairing • Significant ion suppression • Possible decrease of dynamic range of Orbitrap (filling trap with ion pairing agent) • Poor retention time reproducibility • Ion Chromatography • Good for specific group of compounds (e.g. organic acids, nucleotides, …) • Not a broad (“universal”) method for al metabolites • HILIC • Good retention for polar compounds • Compounds eluting in organic solvent • Good potential to be a “universal” for polar metabolites • Historically, methods have been tricky to develop and reproduce 9 HILIC LC-MS for Metabolomics • Luna NH2 100A, 3um, 150 x 2 mm • Column temp.: 15`C • Biological matrix samples • reconstituted in 50% ACN • Inj vol. 1 uL • A = 5 mM AcONH4 pH 9.9 • B = ACN • Q Exactive • Pos/Neg switching • R = 70k 10 HILIC for Metabolomics: Amino Acids (most [M+H]+) RT: 0.00 - 20.00 SM: 5G RT: 6.02 AA: 5248539477 Leu/Ile separated RT: 7.76 AA: 650551637 Tyr RT: 8.01 AA: 187502422 Gly RT: 8.34 AA: 1128162804 Gln RT: 8.61 AA: 57259176 Ser RT: 8.76 AA: 844873242 Arg RT: 12.89 AA: 774302105 Glu RT: 12.90 AA: 8223136 pSer [M-H]0 11 2 5.47 4 15.17 8.25 6 8 10 Time (min) 12 14 16 18 20 HILIC for Metabolomics: Nucleotides RT: 4.00 - 28.00 SM: 5G RT: 9.29 AA: 3915504 dAMP RT: 15.23 AA: 20152930 AMP 18.86 RT: 16.66 AA: 365001 GMP RT: 18.57 AA: 8162921 ADP RT: 20.92 AA: 885386 CTP [M-H]- RT: 21.32 AA: 2203457 UTP [M-H]- RT: 21.62 AA: 3344803 ATP [M-H]- RT: 23.02 AA: 78634 GTP [M-H]4 12 6 8 10 12 14 16 Time (min) 18 20 22 24 26 28 HILIC for Metabolomics: Sugar Phosphates & Organic Acids (all [M-H]-) RT: 0.00 - 28.02 SM: 5G RT: 5.24 AA: 1910441 Pyruvate 11.38 12.19 RT: 14.86 AA: 300254421 Malate RT: 14.88 AA: 3970448 Glucose-6-phosphate RT: 15.01 AA: 66545477 α-Ketoglutarate RT: 15.11 AA: 242102 Oxaloacetate RT: 17.57 AA: 178250854 Citrate RT: 17.68 AA: 224903 3-Phosphoglycerate RT: 17.97 AA: 166714 Glyceraldehyde-3-phosphate 0 13 2 4 6 8 10 12 14 16 Time (min) 18 20 22 24 26 28 Cornerstones of Metabolomics Workflow 1. Sample preparation 2. HPLC separation 3. Mass Spectrometry detection 4. Data processing and reporting 14 Good HR/AM Mass Spectrometer for Metabolomics? • Accurate mass stability • Robust accuracy over extended periods – (set it and forget it) • Ability to do pos/neg switching within a run and maintain accuracy • Can save 50% of analysis time • Speed • Compatibility with the most demanding UHPLC separations • Resolution • Primary discriminator for the analytes of interest - (more is better) • Want as much as we can get without compromising sensitivity • Sensitivity • As good if not better than a triple quad 15 Alternating Polarity Switching – Cycle time (R = 70k) RT: 7.64 - 8.42 7.70 7.72 100 80 7.76 NL: 8.45E8 TIC MS Metabolomics_sample_ 141 8.13 8.32 8.34 8.11 8.20 8.01 8.03 7.78 7.86 60 TIC 40 20 7.97 7.95 60 40 8.05 8.07 8.09 8.11 8.13 8.15 8.18 8.26 8.28 7.93 7.91 7.88 7.86 8.00 80 7.96 8.08 8.10 7.94 8.12 40 7.92 20 7.87 7.90 [Gly+H]+ NL: 6.06E6 m/z= 74.0244-74.0252 F: FTMS - p ESI Full ms [70.00-1000.00] MS Metabolomics_sample_ 141 8.04 8.06 60 NL: 1.85E7 m/z= 76.0389-76.0397 F: FTMS + p ESI Full ms [70.00-1000.00] MS Metabolomics_sample_ 141 Relative Abundance 7.99 8.03 80 0 100 100 80 12.55 8.14 8.19 8.21 8.27 [Gly-H]- 13.13 13.37 60 TIC 20 0 100 60 8.0 Time (min) 8.2 12.80 12.78 12.76 40 12.72 12.66 0 100 12.85 12.83 12.81 12.79 60 12.91 12.93 12.95 12.97 12.99 13.01 13.03 [Glu+H]+ 13.09 80 13.18 13.37 NL: 7.63E7 m/z= 146.0452-146.0466 F: FTMS - p ESI Full ms [70.00-1000.00] MS Metabolomics_sample_ 141 12.90 12.92 12.94 12.96 12.98 12.77 12.75 8.35 20 7.8 12.84 80 20 NL: 6.77E7 m/z= 148.0597-148.0611 F: FTMS + p ESI Full ms [70.00-1000.00] MS Metabolomics_sample_ 141 12.86 12.89 40 0 8.4 12.71 12.65 13.00 [Glu-H]- 13.02 13.06 13.15 13.27 0 12.6 16 NL: 9.33E8 TIC MS Metabolomics_sample_ 141 12.86 12.89 12.95 12.82 12.99 12.74 13.07 40 0 100 20 RT: 12.43 - 13.52 12.8 13.0 Time (min) 13.2 13.4 SIM Increase sensitivity of your Q Exactive for targeted metabolomics …. significantly (if not dramatically) Animation of SIM on Q Exactive *3:30 min 17 Full-MS vs. SIM in Metabolomics T: 0.00 - 28.01 SM: 5G RT: 12.72 AA: 6955648 100 Full-scan (identical sample) SIM RT: 0.00 - 28.00 SM: 3G NL: 7.50E5 m/z= 664.11308-664.11972 F: FTMS + p ESI Full ms [70.00-1000.00] MS ICIS rpmi_468_1004 50 RT: 12.33 AA: 40294435 100 NL: 4.27E6 m/z= 664.11308-664.11972 F: FTMS + p ESI SIM msx ms MS ICIS RPMI_468_SIM_3001 50 NAD+ NAD+ 0 NL: 0 m/z= 666.12872-666.13538 F: FTMS + p ESI Full ms [70.00-1000.00] MS rpmi_468_1004 100 ! 50 NL: 1.06E4 m/z= 742.06447-742.07189 F: FTMS - p ESI Full ms [70.00-1000.00] MS ICIS rpmi_468_1004 RT: 17.52 AA: 55919 100 50 0 NL: 1.38E4 m/z= 371.53641-371.54013 F: FTMS - p ESI Full ms [70.00-1000.00] MS ICIS rpmi_468_1004 RT: 20.21 AA: 87582 100 50 NADH 0 0 RT: 23.76 AA: 224530 100 Diphosphoglycerate NL: 2.48E4 m/z= 264.95067-264.95331 F: FTMS - p ESI Full ms [70.00-1000.00] MS ICIS rpmi_468_1004 100 NADP+ 0 NL: 1.25E4 m/z= 371.53641-371.54013 F: FTMS - p ESI SIM msx ms MS ICIS RPMI_468_SIM_3001 RT: 20.15 AA: 119607 100 NADPH 0 RT: 23.82 AA: 686997 100 Diphosphoglycerate 50 0 NL: 1.27E5 m/z= 742.06447-742.07189 F: FTMS - p ESI SIM msx ms MS ICIS RPMI_468_SIM_3001 RT: 17.43 AA: 867351 50 NADPH 50 100 50 NADP+ NL: 1.66E5 m/z= 666.12872-666.13538 F: FTMS + p ESI SIM msx ms MS ICIS RPMI_468_SIM_3001 RT: 15.24 AA: 716959 50 NADH 0 0 0 0 18 5 10 15 Time (min) 20 25 0 5 10 15 Time (min) NADH is observed when using multiplexing SIM 20 25 NL: 4.25E4 m/z= 264.95067-264.95331 F: FTMS - p ESI SIM msx ms MS ICIS RPMI_468_SIM_3001 Q Exactive (Exactive Plus) settings for Metabolomics LC-MS assays The world leader in serving science 19 Q Exactive Settings for Pos-Neg switching Full-MS negative (-) 20 (+) QE Settings for Pos-Neg switching SIM dd-MS2 (-) 21 (+) QE Settings for Pos-Neg switching SIM dd-MS2 (-) Inclusion mass list 22 (+) Thinks to Consider When Setting Your Orbi for Metabolomics • Q Exactive, Exactive Plus, Orbi Velos/Elite • Tune parameters: • S-lens: Lower the setting ~ 30 - 40 % (default = 50%) • To avoid in-source fragmentation of fragile analytes • Lower Tube lens values for LTQ Orbi (XL), Exactive • LTQ Orbitraps • Very small ions and larger ions together 2 scan events: 1) 70 - 200 Da 2) 150 - 1000 Da • HESI 2 ion probe settings • Aux gas heat (vaporizer) can be set high (depending on LC flow 500`C) • Capillary temperature set mid-low - ~ 275`C for Q Exactive, Exactive Plus, Orbi Velos/Elite) ~ 250`C for LTQ Orbi (XL), Exactive 23 Targeted Screening and Quantitation for Metabolomics TraceFinder The world leader in serving science 24 TraceFinder 2.1 • Quantitation and targeted screening software platform • 1) Method setup, 2) acquisition, 3) data processing, 4) reporting • All-in-one package • Fast method development for integrated multi-residue analysis • All Thermo MS platforms supported – Orbitraps, IT, TSQ, GC-MS • Automated report generation • In the next version (very close future) • HR/AM product-ion database • HR/AM MS/MS library (spectral matching) • Isotopic pattern matching (… of precursor ions in MS) 25 TraceFinder - Compound Datastore 26 TraceFinder: Method Development - Compound Identification and Detection XIC Target Compounds 27 Integration Parameters TraceFinder Method Development: Calibration Setup 28 TraceFinder Batch View: Sample Sequence Sequence List Select compounds to be reported Reports • Built in • Possibility for custom made 29 TraceFinder Data Review: Quan Results Compound List Sequence List XIC 30 Cal. curve SIEVE Differential Analysis Software The world leader in serving science 31 SIEVE 2.0: The Differential Analysis Software • For label free, semi-quantitative differential analysis • Aids discovery of molecular changes between states • Relative quant and trend plots over multiple sample groups • Identification using Chemspider or local database search • PCA and other statistical analysis tools New algorithm for small molecules Proteomics, metabolomics and lipodomics workflows 32 SIEVE 2.0 - Background Subtraction (New) Sample - Solvent blank = Analyte signals ~98% of lower intensity signals are eliminated 33 Data Processing Anatomy of a UHPLC/Orbitrap Data Set 5 100 852.9720 m/z window 853.4727 [M+H]+ = ± 1 Da 853.9745 z =+2 • >1,000,000 data points 854.4817 0 0 853.0 853.5 854.0 854.5 855.0 m/z [M+H]+ [M+Na]+ [M-H2O+H]+ [2M+H]+ [M+NH4]+ [2M+Na]+ [M+K]+ [M-2(H2O)+H]+ [M+CH3CN+H]+ +3 +2 Z=2 12% ~100,000 extracted ion peaks. • Peak area ranges ~ 7 orders 100 200 300 400 500 600 700 800 900 1000 Adduct +4 • % Assignments 100 12.1 8.3 4.7 3.8 3.1 2.7 2.5 • Much irrelevant data • Much redundant data • High quality data from the Orbitrap mass analyzer allows for more precise automated data processing 2.1 Z=3 (10%) Other (5%) 34 Data Processing Z=1 (73%) +1 Need to be able to reduce the data to chemical entities SIEVE 2.0 - Component Detection – Declustering (New) Adducts, fragments and multimers 524.3703, z=1, I=4.2E+08, 100% [M+H]+ 21.9816 546.3517, z=1, I=1.0E+08, 24.6% 562.3232, z=1, I=1.1E+06, 0.3% [M+K]+ [M+Na]+ 37.9554 Isotopic peaks A+1 525.3730, I=1.2E+08, 28.9% A+2 526.3756, I=2.3E+07, 5.5% A+3 527.3784, I=3.0E+06, 0.7% A+4 528.3811, I=3.9E+05, 0.1% Isotopic peaks A+1 547.3535, I=2.9E+07, 27.8% A+2 548.3577, I=5.6E+06, 5.4% A+3 Constituents are represented by base component 35 549.3595, I=9.0E+05, 0.9% Component Detection – D4-Succinic Acid One Compound: 31 ions 18 Adducts 13 Isotopomers 36 Example of a Component Rat O blank m/z 232.1541, RT = 3.54 min 37 Rat L Accurate Mass Identification Component MW chemspider web service 38 MolWt 290.079 306.074 314.01 380.1254 382.1047 426.0945 436.1153 450.0793 468.1051 472.1 477.1266 478.0742 486.1157 494.0691 Local database (.csv) Expression Name L-Epicatechin Epigallocatechin D-glycoside of vanillin Vellokaempferol 3-5-dimethyl ether Velloquercetin 4 -methyl ether Epigallocatechin 3-O-(4-hydroxybenzoate) Epigallocatechin 3-O-cinnamate Quercetin 4 -galactoside Epigallocatechin 3-O-caffeate Epigallocatechin 3-O-(3-O-methylgallate) Isorhamnetin 7-alpha-D-Glucosamine;Quercetin 3 -methyl ether 7-alpha-D-Glucosamine;7-[(2-Amino-2-deoxy-alpha-D-glucopyranosyl)oxy]-3 Quercetin 7-glucuronide Epigallocatechin 3-O-(3-5-di-O-methylgallate) Myricetin 3-glucuronide List of candidates SIEVE 2.1 Features • Improved peak detection & integration • Complement native algorithm with PPD • Elemental composition • ChemSpider, DBLookup search • RT & Formula in DBLookup • Local database searches can now be limited by retention time and elemental composition • Enhanced filtering capability • Pathway mapping • KEGG visualization of full experimental results 39 Pathway Annotation in Sieve 2.1 40 Case Study The world leader in serving science 41 Metabolomics Application - ZDF Rat Serum • ZDF Lean rat serum (n=3) • ZDF Obese rat serum (n=3) • Water blanks (n=3) • ESI Positive Ion full scan LC-MS • Q Exactive 70K resolution • Goals: • Find the exact monoisotopic MW of components that are statistically significantly different between the Lean and Obese groups. • Determine putative ID’s by an exact mass/formula search of Human Metabolome DB using ChemSpider 42 ZDF Rat Serum – SIEVE 2.0 Analysis UHPLC Conditions: Accela 1250 • • • • • Samples: 50µL serum precipitated with 150µL cold methanol, 0.1% formic acid Internal Standard: 5µg/mL of d5-Hippuric acid (200µL) Column: Hypersil GOLD aQ 2.1x150mm, 1.9µm, 50°C Mobile Phase: A: 0.1% formic acid in Water, B: 0.1% formic acid in Acetonitrile Injection: 3µL Time (min) % A % B 0.00 100 0 600 6.00 80 20 600 8.00 60 40 600 12.00 5 95 600 14.00 5 95 600 14.10 100 0 600 17.50 100 0 600 Q Exactive Conditions: • Full scan MS: 70K resolution, ESI+ and ESI-, m/z 65-850, 0-14 min • AGC Target = 1.0 E+6, Max IT = 120 ms • Source: Vaporizer temp = 400°C, S-Lens = 35 43 Flow (µL/min) m/z 232.1541, RT = 3.54 min, p = 2.7 E-6, 3.0-fold change OBESE LEAN 44 ChemSpider ID of C4 Carnitine, m/z 232.1541 45 Examples of Significant Changes in ZDF Rats 46 C4 Carnitine p = 2.73 E-6 Leucine Uric Acid p = 1.35 E-6 Uracil p = 1.40 E-4 p = 2.49 E-6 Lean vs. Obese Rat Serum by SIEVE 2.0 Obese Lean PCA Results 47 Significant Changes from Obese vs. Lean ZDF Rats Chem Spider ID’s Ratios in red are downregulated in Obese rats Ratios in green were upregulated in Obese rats Branched amino acids & acylcarnitines are known hallmarks of Type II Diabetes in the literature1 1. Muoio, D.M.; Newgard, C.B. Nature Rev. Mol. Cell Biol. 2008, 9, 193-205. 48 Sieve Parameters The world leader in serving science 49 What Do Those Parameters Mean? 50 SIEVE Parameters Mass range RT range Change to false and rerun alignment if aligned data does not look good I don’t change these values Multiplier for background removal In this case a peak would have to be 10X more in sample than in blank to be observed Minimum intensity threshold 10ppm is fine I do not change 51 Minimum number of scans across the peak SIEVE Parameters Be sure to change this when moving from pos to neg mode Used in background subtraction routine I do not change Exclusion list set for your own setup. No need to change. This is part of the algorithm. Not used Electronic noise removal – do not change How tightly the adducts/dimer apex needs to be to be considered part of the component. Can be changed but 5 scans seems to work These parameters are not used for component detection Database search parameters 52 Things to Remember for Sieve • Sieve is performing an in depth analysis of large data sets. It will take a little time for it to process. • A faster computer will help. Sieve is 64-Bit compatible and does multi-threading. • Remember to look the alignment. • Make sure you are using the correct adduct table. • Run blanks: all samples labeled blanks will be used in background subtraction 53