Liquid chromatography quadrupole time-of
Contents
1. Trauger S Manchester M amp Siuzdak G Response and recovery in the plasma metabolome tracks the acute LCMV induced immune response J Proteome Res 8 3578 3587 2009 Panopoulos A D et al The metabolome of induced pluripotent stem cells reveals metabolic changes occurring in somatic cell reprogramming Cell Res 22 168 177 2012 Yanes 0 et al Metabolic oxidation regulates embryonic stem cell differentiation Nat Chem Biol 6 411 417 2010 Marshall A G amp Hendrickson C L High resolution mass spectrometers Annu Rev Anal Chem 1 579 599 2008 NATURE PROTOCOLS VOL 8 NO 3 2013 459 npg 2013 Nature America Inc All rights reserved PROTOCOL 31 32 33 34 35 36 37 38 39 40 41 42 Verhoeven H A Ric de Vos C H Bino R J amp Hall R D Plant metabolomics strategies based upon quadrupole time of flight mass spectrometry QTOF MS Plant Metabolomics 57 33 48 2006 Kamleh A et al Metabolomic profiling using Orbitrap Fourier transform mass spectrometry with hydrophilic interaction chromatography a method with wide applicability to analysis of biomolecules Rapid Commun Mass Spectrom 22 1912 1918 2008 Breitling R Pitt A R amp Barrett M P Precision mapping of the metabolome Trends Biotechnol 24 543 548 2006 Brown S C Kruppa G amp Dasseux J L Metabolomics applications of FT ICR mass spectrometry Mass Spect2. e Spectral files from the original profiling experiment e Software for mass spectral analysis provided by instrument vendor e g Agilent MassHunter AB Sciex PeakView Bruker Compass Waters MassLynx PROCEDURE PROTOCOL NMR for example has the benefit of structural identification and accurate characterization furthermore when coupled to LC it can be highly effective for metabolite elucidation Metabolites lacking commercial standards should be chemically synthesized and compared as above gt gt For some experiments this level of rigor may be unnecessary depending on the scope of the biologi cal question EQUIPMENT SETUP LC MS instrument setup This protocol is mainly based on using an Agilent 1200 series HPLC system coupled to an Agilent 6538 Q TOF MS with Agilent MassHunter Version B 04 00 and XCMSOnline software version 1 21 1 There are many other hardware and software combinations that can be used with METLIN check the instrumentation and software documentation for assistance To ensure a high level of mass accuracy the instrument should be calibrated before running the samples according to the manufacturer s guidelines Ensure that sam ples are properly mixed and thawed before placing them in an autosampler tray Install mobile phases prime system pump and tubing Install the column and ensure that it is properly equilibrated before injecting the samples XCMS output spreadsheet For the analysis of untarge
3. 2007 Kind T et al FiehnLib mass spectral and retention index libraries for metabolomics based on quadrupole and time of flight gas chromatography mass spectrometry Anal Chem 81 10038 10048 2009 Xu F Zou L amp Ong C N Multiorigination of chromatographic peaks in derivatized GC MS metabolomics a confounder that influences metabolic pathway interpretation J Proteome Res 8 5657 5665 2009 Nordstrom A Want E Northen T Lehtio J amp Siuzdak G Multiple ionization mass spectrometry strategy used to reveal the complexity of metabolomics Anal Chem 80 421 429 2007 Wishart D S et al The human cerebrospinal fluid metabolome J Chromatogr B 871 164 173 2008 Lu W Bennett B D amp Rabinowitz J D Analytical strategies for LC MS based targeted metabolomics J Chromatogr B 871 236 242 2008 Kaddurah Daouk R et al Lipidomic analysis of variation in response to simvastatin in the Cholesterol and Pharmacogenetics Study Metabolomics 6 191 201 2010 Vinayavekhin N amp Saghatelian A Untargeted metabolomics Curr Protoc Mol Biol 90 30 1 1 30 1 24 2001 Johnson C H et al Radiation metabolomics 4 UPLC ESI QTOFMS based metabolomics for urinary biomarker discovery in y irradiated rats Radiat Res 175 473 484 2011 Trupp M et al Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment PLoS ONE 7 38386 2012 Wikoff W R Kalisak E
4. amp Vorholt J A Nanoscale ion pair reversed phase HPLC MS for sensitive metabolome analysis Anal Chem 83 850 855 2010 Castro Perez J et al Localization of fatty acyl and double bond positions in phosphatidylcholines using a dual stage CID fragmentation coupled with ion mobility mass spectrometry J Am Soc Mass Spectrom 22 1552 1567 2011 Hsu F F amp Turk J Elucidation of the double bond position of long chain unsaturated fatty acids by multiple stage linear ion trap mass spectrometry with electrospray ionization J Am Soc Mass Spectrom 19 1673 1680 2008 Thomas M C et al Ozone induced dissociation elucidation of double bond position within mass selected lipid ions Anal Chem 80 303 311 2007 Gian Luigi R Dietary n 6 and n 3 polyunsaturated fatty acids From biochemistry to clinical implications in cardiovascular prevention Biochem Pharmacol 77 937 946 2009 Ding J et al Capillary LC coupled with high mass measurement accuracy mass spectrometry for metabolic profiling Anal Chem 79 6081 6093 2007 Lindon J C amp Nicholson J K Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics Annu Rev Anal Chem 1 45 69 2008 Cravatt B et al Chemical characterization of a family of brain lipids that induce sleep Science 268 1506 1509 1995 Yanes 0 Tautenhahn R Patti G J amp Siuzdak G Expanding coverage of the metabolome for g
5. include examples of metabolites that have no exact match in METLIN and metabolites that co elute with other metabolic peaks of similar m z For our first example the metabolic peak of interest has an m z of 496 3409 and an RT of 24 5 The ion spectrum is extracted Fig 1c from the TIC and upon inspection of the spectrum at m z 496 3409 another peak is observed at m z 518 3219 which is 21 981 amu larger This is characteristic of the M Na peak and supports the fact that m z 496 3409 is the M H peak Na H 21 9820 As also noted Fig 1d two isotope peaks for the m z 496 3409 peak can be seen m z 497 3440 and m z 498 3455 As these peaks are approximately 1 and 2 from the M H peak it adds vali dation that this is a singly charged ion and that m z 496 3409 is indeed the protonated monoisotopic mass of the molecule To determine the structure of the species at 480 3082 in Figure 2 caution must be taken to avoid potential contami nation from the species at m z 479 7786 M 2H m z 480 2805 and m z 482 2569 M 2H Indeed when m z 480 3082 is isolated and fragmented the spectrum in Figure 2b is obtained which contains both m z 480 2805 isotope of m z 479 7786 and m z 482 2567 species In this situation m z 480 3082 cannot be identified as the MS MS spectrum is suppressed and contaminated If chromatography is used to separate these species as shown in Figure 2c a pure MS MS Spectrum can be obtained for m z 480
6. intensity is required to ensure that the MS MS spectra have sufficient signal to noise ratios S N If the peak is not pure i e with co eluting species within 1 2 m z or intense enough it will be difficult to obtain good MS MS spectra and thus a meaningful characterization All examined features with good chromatographic resolution and peak intensities can be grouped for the MS MS experiments in Stage 3 TROUBLESHOOTING Stage 3 Perform targeted MS MS A CRITICAL The purpose of this section is to perform targeted MS MS for the list of features with acceptable chromato graphic resolution and peak intensity as discussed in Stage 2 Various instruments have different ways to perform targeted MS MS experiments Here we used the Agilent Q TOF as an example 11 In MassHunter software open the instrument method used to collect the original MS profiling data b 482 2569 482 2567 482 7582 479 7786 80 2805 480 3082 Figure 2 Insufficient chromatographic resolution of a species can lead to overlapping peaks that produce convoluted MS MS spectra a Insufficent resolution of the species m z 480 3082 from other components in a d 339 2892 sample provides several overlapping peaks b Fragmentation of the unresolved species m z 480 3082 from panel a results in a convoluted spectrum containing at least two species m z 480 3084 and m z 482 2567 c Chromatographic resolution of the species m z 480 3084 d Fragme
7. structural determination This protocol describes an NATURE PROTOCOLS VOL 8 NO 3 2013 451 npg 2013 Nature America Inc All rights reserved PROTOCOL approach to provide rigorous characterization of metabolites from LC MS based metabolomic data Q TOF based characterization of metabolites In this protocol metabolites are characterized using an LC Q TOF instrument in combination with the METLIN database http metlin scripps edu The Q TOF provides the ability to collect both high resolution precursor and fragmentation data facilitating the characterization of metabolites When used in conjunction with the METLIN database which provides the user with the ability to search for the precursor ion its fragments and neutral losses the characterization of metabolites is highly augmented METLIN is the largest curated database of high resolution tandem mass spec tra covering over 10 000 metabolites The fragmentation spectra are essential for the elucidation and confirmation of metabolites Matching the retention time and fragmentation of a metabolite with those of an authentic standard can confirm its identity One of the advantages of tandem MS in the Q TOF is that collision energies can be adjusted to enhance or decrease the degree of frag mentation thereby revealing more information about the metabo lite Some metabolites however do not fragment well or fragment poorly when an adduct e g Na is present The ad
8. with the same parameters 865 M H 5 allopurinol TROUBLESHOOTING pres ae Stage 1 If it is determined that your Mew L Ny 136 0385 metabolic peak of interest is an iso NH tope peak one must be cautious that s this may be a false positive If your OH peak is an adduct other than M H ee ae or M H one should look back at the 137 0458 CAS 68 94 0 _ Nims pl S original profiling experiment to see ai 4 whether the monoisotopic peak or UN s M H or M H is also dysregulated If this is the case complete this pro tocol with the M H or M H ion If it is not dysregulated do another simple search in METLIN with the correct ad duct selected As we discussed above Total 7 Metabolites Figure 4 Screenshot of the returned metabolites from the search for 137 045 in METLIN with structural and mass spectral information Supplementary Fig 1 in source fragments should also be checked These in source fragments always co elute with their parent ions If the in source fragment ion is identified one should look for the parent ion at the same retention time If the parent ion is also dysregulated complete this protocol with the parent ion 456 VOL 8 NO 3 2013 NATURE PROTOCOLS npg 2013 Nature America Inc All rights reserved Figure 5 Screenshot of the spectrum of hypoxanthine The fragme
9. 3084 Fig 2d which is characterized as lysoPE 18 1 0 0 The use of a narrow isola tion window may also be useful to prevent contamination by other species if the mass difference of two species is sufficient The characterization of three metabolites phenylalanine arachidonic acid and hypoxanthine is depicted in Figure 6 The simple fragmentation of the experimental phenylalanine Fig 6a and the more complex arachidonic acid Fig 6b match the standard METLIN spectra in both intensity ratio and accurate mass of the fragments supporting their identification The experimental spectrum for hypoxanthine in negative mode Fig 6d matches well with the METLIN spectrum although there is substantially more contamination in the experimental sample than observed in positive mode Fig 6c The observation that hypoxanthine is dysregulated in both positive and negative modes also supports the characterization of this peak In addition to the MS MS pattern the retention time is another key parameter to consider As seen in Figure 4 a search for m z 137 0450 returns seven hits The first five hits such as threonate are organic acids and the remaining two hits allopurinol and hypoxanthine are more basic metabolites The two types of metabolites could be differentiated by their retention time and ionization efficiency using positive mode ESI This helps narrow down the candidates before comparing MS MS spectra However to further differentiate allopurinol
10. accept able match you can go to Stage 7 OOOO ONE aes STRUCTURE If several high intensity ions are miss ECETES iasa basins ing or the ratios are markedly different 4244 M H 4 Threonate O OH as seen in Fig 7 in which the inten s CAS 70753616 awa sity ratios between the experimental M View 116 spectra in black are different from the Tt standard spectra in red you have not OH found a match TROUBLESHOOTING 35473 M H 4 D threonic acid NO O sail a C4H805 wien Stage 7 Verify that the OH characterization is correct using a ue OR standard 27 If you found an exact match E OH O between your experimental spectra at PEE ai ii both the precursor and fragment lev 137 0444 CAS HO els then you have characterized the 96 0872 Aw metabolite Depending on the level of OH confidence needed in your analysis you should follow up with additional asess met 4 lerythronteacia NO OH O techniques to support your identifica mz Formula C4H805 tion Techniques such as FTICR MS or vuna iiia o A A NMR can give you an additional level 136 0372 B of confidence although metabolite OH concentrations often prevent the use of NMR to characterize metabolites 35474 mH 4 DLerythronic acid NO OH Q The highest level of confidence is mz EOE SAS s 137 0444 CAS HO obtained when standards are synthe M OH sized or purchased and compared by oe A LC MS MS to confirm retention time OH and MS MS
11. and hypoxanthine MS MS matching is necessary 458 VOL 8 NO 3 2013 NATURE PROTOCOLS npg 2013 Nature America Inc All rights reserved PROTOCOL Another example for the importance of retention time is shown in Figure 7 The precursor ion m z 300 2889 is appropriate for both sphingosine C 18 and palmitoylethanolamide which have the same formula of C 3H3zNO Fig 7a These molecules are indistinguishable by accurate mass alone If these molecules were not resolved by chromatography both species would be selected to fragment at the same time generating a convoluted spectrum that would hinder the identification of either species When resolved the individual species can be analyzed and structures can be assigned to each peak as represented by peaks 1 and 3 in Figure 7a The relative retention time can support a structural assignment In Figure 7 two additional peaks 2 and 4 can be seen which are analogs of 1 and 3 but are an additional two carbon units long In general on C18 based columns increasing chain number and increasing saturation increases the retention time for a group of molecules with the same functional group Observing a later retention time for sphingosine C 20 over sphingosine C 18 and stearoyl ethanolamide over palmitoylethanolamide is consistent with their characterization Figure 7b shows the importance of MS MS spectral matching to differentiate the N N dimethylsphingosine DMS peak 5 and its isobaric species sphi
12. ation pattern Here a seven step protocol is suggested for such a characterization by using the METLIN metabolite database The protocol starts from untargeted metabolomic LC Q TOF MS data that have been analyzed with the bioinformatics program XCMS and it describes a strategy for selecting interesting features as well as performing subsequent targeted tandem MS The seven steps described will require 2 4 h to complete per feature depending on the compound INTRODUCTION Metabolomics has emerged as a powerful technique for under standing the small molecule basis of biological processes such as those associated with disease pathogenesis interactions of micro bial communities microbial biochemistry plant physiology drug mode of action and metabolism In general there are two technological platforms used to perform metabolomics which involve either nuclear magnetic resonance NMR spectroscopy or MS 1 12 Although NMR provides unique structural informa tion about metabolites it suffers from limitations in sensitivity and chemical resolution In contrast MS provides less conclusive structural information but given its sensitivity and large dynamic range it allows for the detection of many more chemical species in a single experiment Each of these technologies has been successfully applied to systematically studying metabolites however MS meth ods are more commonly used for comprehensive investigations that are glo
13. atograms In Type select EIC 5 On the MS Chromatogram tab set the MS level to MS for m z value s type in your value 6 On the Advanced tab define the single m z expansion to a symmetric parts per million p p m value For this example 496 3409 20 ppm was used Click OK The EIC should appear as in Figure 1b and a peak with an appropriate retention time RT for your peak of interest should be visible The EIC will also display other species with very similar m z indicating isobaric species that may be present 7 With the Walk Chromatogram cursor selected click on the EIC at the retention time of your peak of interest The MS spectrum will appear NATURE PROTOCOLS VOL 8 NO 3 2013 453 npg 2013 Nature America Inc All rights reserved PROTOCOL Figure 1 Determination of monoisotopic peak a b charge state and adduct of the precursor ion a The TIC for a represenative sample b The EIC showing one peak at m z 496 3409 c The mass spectrum at 24 5 min scaled to highlight the peaks at m z 496 3409 and 518 3219 which represents the protiated M H and sodiated M Na species for the same metabolomic feature d Zooming in 14 16 18 20 22 24 26 28 30 32 34 min 14 16 18 20 22 24 26 28 30 32 34 min further on the peak at 496 3409 reveals a series of isotope peaks of M 1 and M 2 C 496 3409 d 496 3409 8 By using the Range Select cursor zoom in on the MS spectra as in Figu
14. bal in scope The strength of MS based metabolomics is best realized when coupled to a chromatographic technique such as capillary electrophoresis gas chromatography GC or LC the latter two being the most popular GC MS based metabolomics is a robust well established technique 3 gt Because of the reproduc ibility of the chromatography retention time can be paired with the electron impact derived fragmentation spectra and com pared against the National Institute of Standards and Technology NIST or Fiehn metabolomic databases to make identifica tions However the majority of metabolites must be derivatized to make them more volatile and more thermally stable which intro duces a source of error and complicates identification In the past decade LC MS based analysis has moved to the forefront because of its ability to analyze and identify underiva tized and thermally labile metabolites In contrast with electron impact EI electrospray ionization ESI and to a lesser extent atmospheric pressure chemical ionization is a soft mechanism for ionizing molecules leaving the molecular ion intact There are two major approaches to LC MS based metabolomic experi ments the targeted 23 and untargeted 4 28 analysis In untar geted metabolomics one tries to observe as many unknown and known metabolic peaks as possible comparing the ion intensity between the same peaks present in two or more groups of samples The d
15. d by reversed phase HPLC MS MS Note that the isobaric species 1 and 3 are well separated and can be identified Without separation a convoluted spectrum would be produced b Characterization of N N dimethylsphingosine DMS In a separate analysis DMS peak 5 was observed which is isobaric with sphingosine C 20 peak 2 and stearoyl ethanolamide not observed in this analysis Top the MS MS spectra of DMS black acquired at the collision energy of 20 V and 40 V respectively Bottom the MS MS spectra of sphingosine C 20 red in METLIN database with the collision energy of 20 V and 40 V respectively Comparison of the experimental spectra of DMS against sphingosine C 20 reveals a poor match because of different ratios between the higher intensity species at 20 V and a poor correlation in the lower mass species at 40 V TIMING This protocol should take 2 4 h depending on the metabolite ANTICIPATED RESULTS This protocol allows one to characterize a peak of interest in an untargeted metabolomic experiment if it is a metabolite found in METLIN or is an analog of a metabolite in METLIN Metabolites that are not in METLIN or not analogs of known metabolites are difficult to identify with this technique although this protocol will provide information that would be valuable when used in combination with other analytical techniques Some cases that have proved challenging when attempting to identify unknown metabolites are discussed below they
16. der development For better confidence standards should be acquired and run on the same instrument npg 2013 Nature America Inc All rights reserved with the same instrument parameters The retention time and fragmentation patterns must then match between the sample and the standard to extend the Q TOF based characterization to identification and if the retention time does not match it implies that the characterization is incorrect For metabolites in which a higher level of confidence is needed an orthogonal method should also be used to validate the metabolite structure MATERIALS REAGENTS e Acetonitrile with 0 1 vol vol formic acid Honeywell B amp J brand LC MS grade CAUTION Acetonitrile is highly flammable e Water with 0 1 vol vol formic acid Honeywell B amp J brand LC MS grade Extracted samples from biofluids yeast cells or animal tissues in autosam pler vials Sample extraction methods have been extensively reported in the previous literature2 3 gt 2 EQUIPMENT e LC Q TOF system ultraperformance liquid chromatography UPLC or LC system Q TOF mass spectrometer column C18 HILIC and so on used in initial profiling experiment Instrument method from MS profiling experiment A personal computer with an Internet connection and a web browser e XCMS output spreadsheet from an MS profiling experiment extracted sample analyzed using the LC Q TOF system see Equipment Setup for more detail
17. duct stabilizes the ion and can give limited fragmentation but trying different ionization strategies or solvent mixtures can ameliorate this Untargeted metabolomics begins with an initial profiling experi ment often in which two or more sample groups are profiled via LC MS and statistically compared with only the dysregulated metabolites being characterized 9 56 There are a few excep tions in which only one sample group is analyzed in studies char acterizing as many metabolites as possible in one biofluid 38 Two excellent protocols are available for LC MS profiling experiments in urine and in plasma and serum These protocols can be easily adjusted to other sample types The key to obtaining good results is to carefully design the experiments so that there are enough biologi cal replicates to make the results statistically significant i e they must not be underpowered Appropriate power calculations must be carried out first to determine the sample size that will have a statistically significant effect There are a number of factors that need to be considered such as biological variation sample prepara tion and others these are discussed in more detail by Brown et al Depending on the biological variability of the system we recom mend that the minimal numbers of each sample group be four to six for cell culture six to eight for animals and ten or more for humans After analysis of the initial profiling data by
18. ectroscopy review of a key methodology in the metabolomics toolbox Phytochem Anal 21 22 32 2010 10 Powers R NMR metabolomics and drug discovery Magn Reson Chem 47 S2 S11 2009 Ti 12 13 14 15 16 1 18 19 20 21 22 23 24 25 26 27 28 29 30 Dettmer K Aronov P A amp Hammock B D Mass spectrometry based metabolomics Mass Spectrom Rev 26 51 78 2007 Lei Z Huhman D amp Sumner L W Mass spectrometry strategies in metabolomics J Biol Chem 286 25435 25442 2011 Smart K F Aggio R B M Van Houtte J R amp Villas Boas S G Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography mass spectrometry Nat Protoc 5 1709 1729 2010 Dunn W B et al Procedures for large scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry Nat Protoc 6 1060 1083 2011 Chan E C Y Pasikanti K K amp Nicholson J K Global urinary metabolic profiling procedures using gas chromatography mass spectrometry Nat Protoc 6 1483 1499 2011 Fiehn 0 et al Metabolite profiling for plant functional genomics Nat Biotechnol 18 1157 1161 2000 Babushok V I et al Development of a database of gas chromatographic retention properties of organic compounds J Chromatogr A 1157 414 421
19. ion Ion mobility can also aid in the separation of isobaric species in gas phase which reduces contamination of MS MS spectra Fourth in source fragmentation is sometimes observed for species containing a labile group It can generate one or more abundant fragments that show a similar level of dysregulation compared with other peaks at the same retention time If two or more dysregulated peaks co elute one must ensure that the peaks are not fragments from the same molecule In Supplementary Figure 1 an example of this is shown in which two species m z of 339 2892 and m z 480 3084 with the same retention time are observed to be dysregulated The peak 480 3085 corre sponds to a lysoPE 18 1 0 0 whereas 339 2892 is a major frag ment of this lysoPE a dehydrated oleoyl 18 1 glycerol Without recognizing that the lysoPE is the dysregulated metabolite one may falsely identify the in source fragment oleoyl glycerol as a dysregulated metabolite In addition new tandem MS techniques such as MSE from Waters and SWATH from AB Sciex have recently emerged MS MS data acquired from MSE and SWATH techniques have not yet been tested with METLIN MS MS spectral comparison Finally this approach does not provide an unequivocal iden tification of a metabolite It does however provide a higher level of confidence than high resolution mass alone To quan titatively evaluate the confidence of metabolite identification a scoring system is un
20. isadvantage of this technique is that it is not optimized for a specific metabolite and is less quantitative The advantage is that it provides an opportunity to observe a large number of known and unknown metabolites which may provide novel insights into a biological system 2 Coupled to a high resolution mass spec trometer 9 such as a TOF 231 Orbitrap 23 or a Fourier trans form ion cyclotron resonance FT ICR 4 instrument high mass accuracy can be obtained This can greatly reduce the number of potential molecular formulas corresponding to one metabolic peak but there may still be several possible molecular formulas that are appropriate for the accurate mass data depending on the resolution of the instrument and numerous potential isomers for each molecular formula More structural information can be obtained by examining the fragmentation pattern Combining the high resolution precursor ion with data from a fragmenta tion mechanism obtained by MS MS reduces the number of possible metabolites to a single structure or a narrow set of struc tures see limitations below When searching against a metabolite database in the case of this protocol the METLIN database gt http metlin scripps edu it is therefore best to match both the accurate mass and the fragmentation data MS MS spec tra for each metabolite peak Retention times relative to other metabolites of known identity and similar structural class also support the
21. lobal metabolite profiling Anal Chem 83 2152 2161 2011 Tautenhahn R et al An accelerated workflow for untargeted metabolomics using the METLIN database Nat Biotechnol 30 826 828 2012
22. ngosine C 20 peak 2 Investigators who have access to pure standards of compounds that are not currently characterized in METLIN can email metlin scripps edu to arrange for these to be added to the database Note Supplementary information is available in the online version of the paper ACKNOWLEDGMENTS This work was supported by the California Institute of Regenerative Medicine no TR1 01219 G S the US National Institutes of Health nos RO1 CA170737 G S R24 EY017540 G S P30 MH062261 G S RC1 HL101034 G S P01 DA026146 G S and 1RO1 ES022181 01 G J P and the US National Institutes of Health National Institute on Aging no L30 AGO 038036 G J P Financial support was also received from the US Department of Energy grant nos FGO02 07ER64325 and DE ACO205CH11231 G S AUTHOR CONTRIBUTIONS Z J Z A W S and J W and contributed equally to the work described G J P and G S supervised the work A W S J W and G J P performed the experiments Z J Z A W S J W and C H J wrote the manuscript Z J Z S M Y G J P and G S read and revised the manuscript COMPETING FINANCIAL INTERESTS The authors declare no competing financial interests Published online at http www nature com doifinder 10 1038 nprot 2013 004 Reprints and permissions information is available online at http www nature com reprints index html 1 Wang Z et al Gut flora metabolism of phosphatidylcholine promotes cardiovasc
23. npg 2013 Nature America Inc All rights reserved PROTOCOL Liquid chromatography quadrupole time of flight mass spectrometry characterization of metabolites guided by the METLIN database Zheng Jiang Zhu Andrew W Schultz Junhua Wang Caroline H Johnson Steven M Yannone Gary J Patti gt gt amp Gary Siuzdak 1Scripps Center for Metabolomics and Mass Spectrometry The Scripps Research Institute La Jolla California USA Life Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA 3Department of Chemistry Washington University St Louis Missouri USA Department of Genetics Washington University St Louis Missouri USA gt Department of Medicine Washington University St Louis Missouri USA Correspondence should be addressed to G J P gjpattij wustl edu or G S siuzdak scripps edu Published online 7 February 2013 doi 10 1038 nprot 2013 004 Untargeted metabolomics provides a comprehensive platform for identifying metabolites whose levels are altered between two or more populations By using liquid chromatography quadrupole time of flight mass spectrometry LC Q TOF MS hundreds to thousands of peaks with a unique m z ratio and retention time are routinely detected from most biological samples in an untargeted profiling experiment Each peak termed a metabolomic feature can be characterized on the basis of its accurate mass retention time and tandem mass spectral fragment
24. ns include 3 5 fmol of dimeth ylsphingosine DMS per mg of dorsal horn gt or an upper attomolar range in the analysis of Methylobacterium extroquens AM ref 44 Second MS based analysis provides little if any information about the stereochemistry of the metabolites identified and is often insufficient to determine the positions of double bonds in acyl tails Some specialized techniques have been developed to overcome this problem and have involved the use of ion mobility the addition of Lit with multiple rounds of fragmentation and ozone induced dissociation The location of these bonds may be important for example isobaric 3 or 6 isomers of a lipid can have markedly different biological roles 8 Third isobaric species that co elute will provide a convoluted mass spectrum making it difficult to characterize either species MS is prone to ion suppression therefore co eluted species also affect the quantification of molecules and reduce the ability to observe ions that are less capable of ionization in the presence of an interfering metabolite Furthermore isobaric and other species with very similar masses could be fragmented together if not well isolated thus introducing contamination into the MS MS spec tra and hindering characterization possibly leading to false nega tives Appropriate chromatographic methods can be developed which can help resolve different species and reduce some issues with ion suppress
25. ntation of a resolved spectrum of m z 480 3084 from panel c allows 478 5 479 5 480 5 481 5 482 5 m z 50 100 150 200 250 300 350 400 450 500 mz for the characterization of lysoPE 18 1 0 0 480 3084 339 2892 is 50 100 150 200 250 300 350 400 450 500 mz 481 3106 480 7810 st 00 pe o vt 480 3084 454 VOL 8 NO 3 2013 NATURE PROTOCOLS npg 2013 Nature America Inc All rights reserved PROTOCOL METLIN Metabolite Search 12 Under the Q TOF tab click on the tab for targeted Simple MS MS Simple Advanced Batch Fragment Multiple Fragment Neutral Loss Unknowns Mass 137 045 Tolerance 30 ppm 13 Input the m z value of the feature set an RT window barge edad a of at least 1 min and set isolation to medium unless Negative M Na co eluting species dictate a narrower window More than one ee feature may be programmed as needed M K MACNA Na 14 Save this method and then inject and analyze the sam M 2Na H ple with the new method The collected data will be used in shina Stage 5 ted M 2Na Stage 4 Search precursor in METLIN wu 2P HLC Aaducts 15 In your web browser open METLIN http metlin M CH3OH H Select alnomo gt oducts scripps edu In Search select Simple Find Metabolites Reset Figure 3 Screenshot of metabolite search in METLIN The simple metabolite 16 In the mass widow input the accurate mass value of the search panel with 137 045 inpu
26. ntation spectrum is shown Clicking on the other voltages in the black bounded box displays the appropriate spectrum The act of rolling over a fragment peak such as 119 with your mouse reveals a predicted fragment structure and details about the exact mass and relative intensity of that fragment Stage 2 If a co eluting metabolic peak is within 1 2 m z of your ion of interest it may provide a convoluted spectrum If you suspect that this is the case you should refragment this species with a narrower isolation window If it is within 1 Da this may not be sufficient to isolate the species and you may need to use another approach to identify this peak If two ions are co eluting different chromato graphic conditions may allow these two Species to be separated as in Figure 2 Intensity PROTOCOL Hypoxanthine Mode m z 119 0350 Collision Energy 20 V Intensity 45 25 50 100 125 150 Mass m z Predicted Fragment Structure M Mass 119 0358 OO A Please mouse over the spectrum to view the detail information of each peak Use left mouse button to zoom in click and drag and zoom out double click Stage 4 If no metabolites are returned from the search you can increase the tolerance value or add additional adducts if appropriate For the ionic metabolites when searching the METLIN database the neutral should be chosen for the charge setting In addition the isotopic pat
27. ones with MS MS data indicated by a View button Fig 4 24 Click on View The spectrum will appear Fig 5 25 Click on individual lines in the spectral table to select a specific precursor and voltage the appropriate spectrum will appear You can right click and drag a box to zoom in Roll your cursor over a spectral peak and the exact mass for the fragment will be displayed along with a predicted structure for that fragment if available Click Reset zoom in the upper left to zoom back out Right click and hold move to move the spectral window around the page To close click on close in the upper right corner Stage 6 Compare experimental MS MS with METLIN 26 Compare your experimental spectra with the spectra in METLIN by visual inspection If the same fragment ions are present in the experimental spectra and the METLIN spectra with very similar intensity ratios you have a match as seen for phenylalanine Fig 6a arachidonic acid Fig 6b and hypoxanthine in positive and negative modes NATURE PROTOCOLS VOL 8 NO 3 2013 455 PROTOCOL METLIN Metabolites Fig 6c d Hypoxanthine in positive mode Fig 6c is a good match as the major experimental fragments are of similar intensity as the standard Mass 137 045 with 30 ppm mass accuracy Change Query npg 2013 Nature America Inc All rights reserved although there is some low intensity contamination If you find an
28. re 1c Determine the adduct of your peak In this case 496 3409 is likely M H as a peak of 22 Da 518 3219 is present which would correspond to the M Na 518 3219 497 3440 498 3455 480 485 490 495 500 505 510 515 520 525 530 m z 495 496 497 498 499 mz 9 Zoom in further on the MS spectrum Fig 1d and determine the charge for the peak As there is a series of isotope peaks 1 Da larger after the most intense peak it is singly charged Subtracting the proton provides the neutral mass for this species of 495 3336 TROUBLESHOOTING Stage 2 Inspect the MS data to determine whether the peak is real and of sufficient intensity 10 Look for co eluting ions within 1 2 m z of the peak of interest in the MS spectra as these may have convoluted the fragment spectra In Figure 2 a group of peaks is observed in which the separation is insufficient Several species such as m z 480 2805 m z 480 3082 and m z 482 2569 are not resolved and will fragment together creating convoluted MS MS spectra Fig 2b Once the species m z 480 3082 is fully resolved by chromatography Fig 2c the generated MS MS spec trum shows good spectral purity In addition to achieving high quality MS MS spectra the feature of interest should have an intensity greater than 5 000 for an Agilent Q TOF The intensity requirement is empirical Other Q TOF instruments from different vendors may have different intensity requirements The parent ion
29. rom Rev 24 223 231 2005 Smith C A et al METLIN a metabolite mass spectral database Ther Drug Monit 27 747 751 2005 Patti G J et al Metabolomics implicates altered sphingolipids in chronic pain of neuropathic origin Nature Chem Biol 8 232 234 2012 Psychogios N et al The human serum metabolome PLoS One 6 e16957 2011 Chen L Zhou L Chan E C Y Neo J amp Beuerman R W Characterization of the human tear metabolome by LC MS MS J Proteome Res 10 4876 4882 2011 Want E J et al Global metabolic profiling procedures for urine using UPLC MS Nat Protoc 5 1005 1018 2010 Nebert D W Zhang G amp Vesell E S From human genetics and genomics to pharmacogenetics and pharmacogenomics past lessons future directions Drug Metab Rev 40 187 224 2008 Brown M et al A metabolome pipeline from concept to data to knowledge Metabolomics 1 39 51 2005 Smith C A Want E J O Maille G Abagyan R amp Siuzdak G XCMS processing mass spectrometry data for metabolite profiling using nonlinear peak alignment matching and identification Anal Chem 78 779 787 2006 460 VOL 8 NO 3 2013 NATURE PROTOCOLS 43 44 45 46 47 48 49 50 51 52 53 Tautenhahn R Patti G J Rinehart D amp Siuzdak G XCMS Online a web based platform to process untargeted metabolomic data Anal Chem 84 5035 5039 2012 Kiefer P Delmotte N L
30. spectra in the METLIN database are acquired on Agilent Q TOF mass spectrometers Although we have demonstrated that other Q TOF mass spectrom eters have similar MS MS spectra to those in the METLIN database 3 the relative intensities of fragment ions in MS MS spectra may be slightly different depending on the instru ment settings In addition MS MS spectra in METLIN database are acquired with an isolation window of 1 3 Da and thus there is no iso topic peak for fragment ions When MS MS spectra in Stage 3 are acquired with a wider isolation win dow e g 4 Da one should expect that isotopic peaks will be shown in the MS MS spectra NATURE PROTOCOLS VOL 8 NO 3 2013 457 npg 2013 Nature America Inc All rights reserved PROTOCOL b Sa a AA 5 ey 1 Sphingosine C 18 C H 7NO 5 N N dimethylsphingosine C20H4 NO3 i 2 Sphingosine C 20 C 9H NO w 2 ee ee ee S 15 20 25 30 35 40 min c MH MH allied N N 3 Palmitoyl ethanolamide C4 8H37NO3 i ESI 20 V ESI 40 V Pl Apt gt ty Pag ESI 20 V ESI 40 V 4 Stearoyl ethanolamide C H NO 20 30 40 50 min LLE OLE Figure 7 The importance of retention time accurate mass and fragmentation for identification a Separation of sphingosine C 18 peak 1 sphingosine C 20 peak 2 palmitoyl ethanolamide peak 3 and stearoyl ethanolamide peak 4 from a tissue extract analyze
31. ted and M H selected as the adduct parent ion Fig 3 17 Select the charge and adducts determined in Stage 1 Steps 8 and 9 18 The default and maximum tolerance of 30 ppm is generally acceptable for Q TOF experiments adjust the parameters as appropriate for your specific mass spectrometer Generally it is best to use a slightly wider window than the theoretical tolerance for an instrument 19 Click on the Find Metabolites button TROUBLESHOOTING Stage 5 Search MS MS in METLIN 20 Open the newly created MS MS data file in Agilent MassHunter To examine the MS MS spectra select Chromatogram gt Extract Chromatograms for Type select TIC and in the MS Chromatogram tab select MS level MS MS and select the precursor ion for the peak of interest 21 Use the Walk Chromatogram cursor to click on individual scans at and near your peak of interest 22 Inspect the individual MS MS scans at and around this RT to assess spectral purity Often a portion of the precursor ion will remain intact making it easier to identify the spectrum of interest and assess spectral purity Generally if a similar frag mentation pattern is consistently seen across a few scans and the MS spectrum lacks co eluting species within a few m z then the spectra can be considered pure and sufficiently intense to identify the peak of interest TROUBLESHOOTING 23 Scroll through the metabolites returned by the METLIN search in Stage 4 to find
32. ted mass spectrometric data we recommended using XCMSOnline software https xcmsonline scripps edu which can process and analyze data from Agilent AB Sciex Bruker Thermo Fisher and Waters hardware The file formats of these platforms can be seen at https xcmsonline scripps edu docs fileformats html along with notes on how to convert the files into the appropriate formats The user manual for XCMSOn line can be found at https xcmsonline scripps edu docs usermanual pdf and related information can also be found in a recent publication Stage 1 Determine adduct and charge of a metabolite feature of interest A CRITICAL The total ion chromatogram TIC and extracted ion chromatograms EIC or XIC should be retrieved from the spectral files from the original profiling experiment This can be done through the data analysis software provided by the instrument vendor Each instrument vendor has its own software and each offers similar functions for retrieving the TIC and EICs Here we used Agilent MassHunter as an example to demonstrate this stage of the procedure 1 Pick peaks of interest from the XCMS output spreadsheet see Equipment Setup 2 By using MassHunter open the spectral file for a sample and search for the peak of interest by retention time and accurate mass 3 In MassHunter select File Open Data File to select the data to analyze The TIC should be displayed as in Figure 1a 4 Select Chromatograms Extract Chrom
33. tern distribution also helps predict the empirical formula of unknown a g b OH o ee oO NH3 o N rT L phenylalanine 4 ESI 20 V 2 a ro MH oT 2 NIMO MW WS oo Oo Ke Norg NNOO N m z NS 38 ee OOOO OH AZO gt NA WOW F O E u MH nm Oo o MH c o d lt o N ap lt 2 B sec 2 o TF Hypoxanthine ESI 20 V OH 7 y aN Do 55 030 65 038 82 040 h amp N O X oOo M Figure 6 A comparison of experimental black and METLIN standard red spectra for three metabolites a d Phenylalanine a arachidonic acid b and hypoxanthine in positive c and negative d mode Ht N 55 055 SSO SS 67 054 81 069 93 069 107 084 0Z0 18 0Z0 6 H N LA 121 100 163 147 9LLSEL LEL 6tL 8r L E9L 980 Z0 OOL LEZHE Hypoxanthine ESI 20 V 65 015 Arachidonic acid ESI 20 V 207 137 221 151 EG L 60g LGE Loe 192 026 O M M o 269 133 El 690 135 031 m z compounds Most data analysis tools provided by instrument vendors have this function Stage 5 If you cannot identify the precursor ion in Step 22 you may want to rerun the sample performing fragmentation at a lower energy in Stage 3 If the precursor is identified but there is insufficient fragmentation you may want to rerun the sample fragmenting at a higher energy in Stage 3 Stage 6 Note that MS MS
34. ular disease Nature 472 57 63 2011 2 Wikoff W R Gangoiti J A Barshop B A amp Siuzdak G Metabolomics identifies perturbations in human disorders of propionate metabolism Clin Chem 53 2169 2176 2007 3 Wikoff W R et al Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites Proc Natl Acad Sci USA 106 3698 3703 2009 4 Vinayavekhin N amp Saghatelian A Regulation of alkyl dihydrothiazole carboxylates ATCs by iron and the Pyochelin gene cluster in Pseudomonas aeruginosa ACS Chem Biol 4 617 623 2009 5 Kalisiak J et al Identification of a new endogenous metabolite and the characterization of its protein interactions through an immobilization approach J Am Chem Soc 131 378 386 2008 6 Leiss K A Maltese F Choi Y H Verpoorte R amp Klinkhamer P G L Identification of chlorogenic acid as a resistance factor for thrips in Chrysanthemum Plant Physiol 150 1567 1575 2009 7 Allen J et al Discrimination of modes of action of antifungal substances by use of metabolic footprinting Appl Environ Microbiol 70 6157 6165 2004 8 Clayton T A Baker D Lindon J C Everett J R amp Nicholson J K Pharmacometabonomic identification of a significant host microbiome metabolic interaction affecting human drug metabolism Proc Natl Acad Sci USA 106 14728 14733 2009 9 Ludwig C amp Viant M R Two dimensional J resolved NMR sp
35. using a peak alignment and statistical analysis package such as XCMS or XCMSOnline a list of dysregulated metabolic peaks with a retention time and m z will be generated The protocol reported here is for the systematic analysis of the dysregulated features on this list The stages of the procedure are as follows i determine the adduct and charge of a metabolite feature of interest ii inspect MS data to determine whether a peak is real and of sufficient intensity for MS MS iii perform targeted MS MS iv search precursor in METLIN v search MS MS in METLIN vi compare experimental MS MS with METLIN and vii verify that the characterization is correct using a standard Limitations of this approach Many of the limitations listed below can be mitigated using special ized MS techniques and thus may not impose real challenges It is 452 VOL 8 NO 3 2013 NATURE PROTOCOLS however very important to consider these points when carrying out general metabolomic approaches and before optimizing methods for specific chemical species or in response to specific problems First low abundance ions can be hard to identify if the precursor ion intensity is low generally below 5 000 counts for an Agilent Q TOF making it difficult to obtain the high quality fragment spectra needed to support a structural assignment This is not how ever a problem for many peaks and examples of high sensitivity MS based metabolite identificatio
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