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1 DATA ANALYSIS IN MASS SPECTROMETRY BASED METABOLOMICS Pavel Aronov Stanford Mass Spectrometry Users Meeting September 26, 2011

2 Presented at the 2011 Stanford Mass Spectrometry t Users Meeting For personal use only. Please do not reuse or reproduce without t the author s permission. i 2

3 Types of Experiments in Metabolomics targeted non targeted quantitative semi quantitative Number of analyzed metabolites is limited by the number of available standards Absolute quantitation of metabolites (nm, mg/ml) Number of analyzed Number of analyzed metabolites is limited dby metabolites is limited dby the number of available lbl capacity of analytical l lb library spectra instrumentation Relative quantitation Relative quantitation of metabolites (fold) of metabolites (fold) 3

4 Targeted LC/MS metabolomics Absolute quantitation of metabolites using triple quadrupole instruments SUMS: Quattro Premier (Waters) and TSQ Vantage (Thermo) Chemical standards of metabolites are required, internal standards d (isotopically i labeled l metabolites, 13 C or 2 H) are desired Custom methods; amino acid method is available (~30 amino acids) SUMS contact: Karolina Krasinska 4

5 Untargeted t GC/MS based metabolomics Agilent 5795 single quadrupole GC/MS Metabolomics method based on Kind T. et al 2009, Anal Chem 81(24) Identification based on Agilent MSRI library (~700 metabolites, mostly primary metabolism) For general overview: SUMS 2010 users meeting workshop khppresentation 5

6 Untargeted t GC/MS based metabolomics Service began: Summer 2011 Sample requirements: dry or non-aqueous solvent Samples tested: tissue, bacterial cells, urine, plasma Metabolites identified: ~ Metabolites detected: ~ Data output: AMDIS IDs, non-targeted and pathway analysis is under development 6

7 Untargeted LC/MS based metabolomics Sample prep p Blood Plasma: protein precipitation it ti with 4 v of Acetonitrile, il evaporation, reconst in 5 % ACN LC Reversed Phase Chromatography Aqueous Normal Phase Chromatography MS Thermo Exactive: +ESI and ESI, R =50, m/z (+APCI if necessary) Data Analysis MZmine 2.2 accurate mass database search (HMDB) Identification structure elucidation (Orbitrap Velos), data interpretation (Mass Frontier)

8 Data Processing Workflow m/z LC/MS Map Peak ID (m/z, t R ) Area Retention time Sample1 Sample2 SampleN Peak ID Area Area Area Peak annotation and statistical analysis

9 File Format Conversion Universal MS data formats: netcdf (*.cdf) All MS manufacturers provide tools for conversion into netcdf XMLs (mzxml, mzml) Less common in metabolomics

10 Data Processing Software Proprietary MarkerLynx (Waters), SIEVE (Thermo), MarkerView (Sciex), Mass Profiler Professional (Agilent) Open Source XCMS ( edu/xcms/) and MZmine ( net/)

11 MZmine: Hardware considerations Multi-processor systems Server CPU are expensive (Xeon, Opteron), Desktop processors are ok (Phenom, Core i7) 64-bit OS with Java and CPUs to address more than 4 GB memory At least 8 GB memory (high resolution profile data)

12 MZmine: main window

13 MZmine: supported file formats

14 MZmine: use of multiple CPU cores

15 MZmine: inspection of raw data

16 MZmine: raw data SIC

17 MZmine: peak picking

18 MZmine: preview of parameters

19 MZmine: estimating the noise level

20 MZmine: zooming into noise

21 MZmine: chromatogram construction

22 MZmine: peak picking results

23 MZmine: deconvolution

24 MZmine: deconvolution parameters

25 MZmine: deconvolution results

26 MZmine: deisotoping

27 MZmine: deisotoping gp parameters

28 MZmine: deisotoping results

29 MZmine: alignment

30 Data Processing Workflow m/z LC/MS Map Peak ID (m/z, t R ) Area Retention time Sample1 Sample2 SampleN Peak ID Area Area Area Peak annotation and statistical analysis

31 MZmine: alignment parameters

32 MZmine: alignment is computationally intense

33 MZmine: alignment results

34 MZmine: gap filling

35 MZmine: removal of duplicates

36 MZmine: normalization options

37 MZmine: identification options

38 MZmine: accurate mass online search

39 Mtbl Metabolomics Databases Human Metabolome Database: h / BioCyc: Metlin: edu/ KEGG: PubChem (not biology specific): nlm nih

40 MS/MS databases Metlin tli ipp / MassBank (not only metabolites) jp/?lang=en

41 MZmine: identification results

42 Monoisotopic i vs Average Mass Fall 100 Two stable isotopes important in biochemistry Carbon-12 (100 %) and Carbon-13 (~1.1 %) Sulfur-32 (100 %) and Sulfur-34 (4.44 %) 0022 DS vitd051608sample001 (0.005) Is (1.00,1.00) e12 Tryptophan statistically can contain: HN NH no carbon-13 (M): Da (100 %) 2 C 11 H 12 N 2 O 2 one carbon-13 (M+1): Da (11.9 %) two carbons-13 (M+2): Da (1.4 %) These are monoisotopic i masses % O OH Average mass = ( * * *1.4)/113.2 = (molecular weight, g/mol) g/mol) mass

43 Elemental l composition i from accurate mass 1 H u 12 C u 14 N u 16 O u What is 28 u? N 2 (2x14u) u), CO(12u+16u)orCH 16 u) C 2 4 (2 x 12 u + 4 x 1 u)? What is u? [high accuracy] C 2H 4 (2 x u + 4 x u)

44 High Resolution High Resolution: R = 561/ ~ FWHM , amu % TOF: 7,000-50,000 Obit Orbitrap: FT ICR: amu Nominal Mass Resolution FWHM (<1000) %R = 561/0.8 ~ Accurate mass measurement is possible without high resolution. 0 m/z High resolution improves precision 44 i

45 Mass of an electron becomes important at high accuracies Even Electron (EE) Ions Typically generated by chemical ionization techniques and electrospray C 11 H 12 N 2 O 2 C 11 H 13 N 2 O Da (true mass) C 11 H 13 N 2 O Da (2.6 ppm error) Orbitrap can achieve < 1 ppm accuracy

46 Identification based on accurate mass NL: C 8 H 6 O 4 NS 6.95E ppm MeyerT_100422_sampl e0062#636 RT: AV: 1 T: FTMS {1,1} - p ESI Full ms 80 [ ] ] s[70 70 ass io ma rati e m k r rate ea cur c pe acc pic g a top ing sot ch d is Matc and nce Relative Abunda Theoretical M Acquired spectrum NL: C 8 H 6 O 4 NS 8.59E ppm C 8 H 6 O 4 NS: C 8 H 6 O 4 N 1 S 1 pa Chrg m/z Theoretical spectrum Error = Da/ Da * 1000,000 = 0.6 parts per million (ppm) H N OSO 3

47 MZmine: data export options

48 Manual Revision i of Mtbl Metabolomics Dt Data Many software tools can t remove some artifacts: t unusual adducts, fragments A majority of detected features may be isotopes, adducts, fragments and/or impurities Baran R et al 2010, Anal Chem

49 MeyerT_100422_sample0088 C18 pos R5 RT: ve Abun ndance Relativ [M + H] + ESI-MS Adducts [M + NH + 4 ] 4/27/2010 7:23:42 PM NL: 1.53E6 m/z= MS MeyerT_100422_sa mple NL: 1.37E6 m/z= MS MeyerT_100422_sa mple NL: E5 6.43E5 m/z= [M + Na ] MS 50 MeyerT_100422_sa mple Time (min) MeyerT_100422_sample0088 # RT: AV: 20 NL: 7.32E5 T: FTMS {1,1} + p ESI Full ms [ ] e undance Rela ative Ab H] [M + + [M NH 4 ] m/z [M + Na ] +

50 Unusual LC-ESI-MS Adducts [2M-H+Fe] +

51 MeyerT_100422_sample0088 C18 pos R5 RT: undance tive Abu Rela Fragments in LC-MS 4/27/2010 7:23:42 PM Hyppuric acid m/z Time (min) NL: 2.23E5 m/z= MS MeyerT_ _sample0088 NL: 2.38E5 m/z= MS MeyerT_ _sample0088 MeyerT_100422_sample0088 # RT: AV: 43 NL: 3.04E6 T: FTMS {1,1} + p ESI Full ms [ ] undance tive Abu Relat Hyppuric acid O C 8 H 8 N indole? No, fragment of hyppuric acid Not confirmed by GC-MS either m/z N H O OH

52 Conclusions Open source MZmine software is capable to perform automated peak extraction, alignment and annotation from high resolution LC/MS metabolomics and proteomics data Manual curation of fthe results is requires to rule out fragments, some adducts and isotopes 52

53 Ak Acknowledgements ld - Allis Chien - Theresa McLaughlin - Karolina Krasinska - Chris Adams - Anna Okumu - Tim Meyer - Natalie Plummer - Tammy Sirich - Angela Marcobal - Justin Sonneburg - Mike Snyder 53

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