HPLC−NMR Revisited: Using Time-Slice High-Performance LiquidChromatography−Solid-Phase Extraction−Nuclear MagneticResonance with Database-Assisted DereplicationKenneth T. Johansen, Sileshi G. Wubshet, and Nils T. Nyberg*

Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen,Denmark

*S Supporting Information

ABSTRACT: Time-based trapping of chromatographicallyseparated compounds onto solid-phase extraction (SPE)cartridges and subsequent elution to NMR tubes was doneto emulate the function of HPLC−NMR for dereplicationpurposes. Sufficient mass sensitivity was obtained by use of astate-of-the-art HPLC−SPE−NMR system with a cryogeni-cally cooled probe head, designed for 1.7 mm NMR tubes. Theresulting 1H NMR spectra (600 MHz) were evaluated againsta database of previously acquired and prepared spectra. The in-house-developed matching algorithm, based on partitioning ofthe spectra and allowing for changes in the chemical shifts, is described. Two mixtures of natural products were used to test theapproach: an extract of Carthamus oxyacantha (wild safflower), containing an array of spiro compounds, and an extract of theendophytic fungus Penicillum namyslowski, containing griseofulvin and analogues. The database matching of the resulting spectrapositively identified expected compounds, while the number of false positives was few and easily recognized.

I t has been well established that the hyphenated techniquehigh-performance liquid chromatography−solid-phase ex-

traction−nuclear magnetic resonance (HPLC−SPE−NMR)can provide comprehensive information about plant metabo-lites present in crude extracts.1−7 The analysis of the results canbe used to make early stop-or-go decisions in an isolationprocedure, thus avoiding spending time and resources onalready known compounds (dereplication).1−13 In HPLC−SPE−NMR, analytes are concentrated postcolumn on solid-phase extraction (SPE) cartridges prior to elution and NMRanalysis. The technique was developed from the shortcomingsof coupling HPLC separations directly to the NMR instrumentby means of a flow probe. Incompatibility of the amounts ofanalytes, solvent, and solvent modifiers between the separationsystem and the NMR analysis could thus be circumvented.6,14

Recent developments in probe head technology, leading to anincrease in sensitivity and reduction of the sample size, havemade it more efficient to elute the analytes from the SPEcartridges directly into NMR tubes.15−17 The direct hyphen-ation of the flow to the NMR instrument is hence interrupted,though this has a minor impact on the usefulness of themethod. With the current level of automation, manual handlingof the tubes is limited to transferring racks from a solventhandling robot to an automatic sample changer serving theNMR instrument. From this, it is apparent that the original ideaabout using NMR as a universal detector for compoundsseparated by HPLC18 could be reinvestigated. The aim of thiswork is to demonstrate that 1H NMR spectra, obtained throughHPLC separation of extracts with time-sliced trapping of

eluting compounds, can be used for dereplication purposes(Figure 1).Spectral databases, often in combination with alternative

methods such as HPLC−mass spectrometry (MS) and HPLC−diode array detection (DAD), have been shown to provideefficient dereplication of natural product extracts.8−11,13,19 One-

Received: November 28, 2012Accepted: February 23, 2013

Figure 1. Conceptual design of the experiments: HPLC separationwith time-sliced trapping of eluate on SPE cartridges. The cartridgesare eluted to capillary tubes and 1H NMR spectra are acquired. Thespectral profiles are matched against a database of spectra and thesuggested structures are presented. Spectra with good signal-to-noiseratio that did not give high match factors are marked with questionmarks. The data for this figure are the same as those in Figure 3, withdata in the retention time range 11−20 min and chemical shifts in therange 4−6 ppm. Axes and labels are removed for clarity.

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dimensional 1H NMR spectra provide complementarystructural information, notably about stereochemistry. 1Hchemical shifts of easily accessible signals have been used tosearch databases,12,20,21 but spectral profiles have notextensively been used as direct input in database searches.Sensitivity to changes in the solvent composition,22 concen-tration,23 and the prevalent magnetic field on the displayedspectral pattern makes the matching challenging. To take fulladvantage of the HPLC−SPE−NMR scheme presented here,we developed a robust matching algorithm that takes changesof chemical shifts into account. Each spectrum from the time-sliced trapping is matched against an in-house created database,and the suggested identity and reference spectrum is presentedfor visual inspection.Two extracts from different sources were used to test the

quality of the spectra, fidelity of the database, and the matchingscheme in this context. The first was a prefractionated extract ofCarthamus oxyacantha (wild safflower) expected to contain thelignan tracheloside, several isomers of glycosylated spiranes,and unsaturated fatty acids (compounds 1−11, Figure 2). Froma previous study, reference spectra of pure constituents havebeen obtained.1 A reanalysis of the extract using the presentedscheme could possibly indicate overlooked compounds. Thesecond extract was obtained from a culture of an endophyticfungus, Penicillum namyslowskii, which was recovered fromRhododendron tomentosum.24 The extract contained the well-known chlorinated fungistatic griseofulvin (14) together withthree analogues (12, 13, and 15)25 and would thus constitute agood example that efficient dereplication saves time andincreases the information yield.

■ EXPERIMENTAL SECTION

Chemicals. Methanol-d4, acetonitrile-d3 (99.8 atom % D),HPLC-grade ethyl acetate, and griseofulvin were obtained fromSigma−Aldrich (Stenheim, Germany). HPLC-grade methanoland acetonitrile were from VWR (Fontenay-sous-Bois, France)and formic acid, p.a., was from Merck (Darmstadt, Germany).Deionized and filtered (0.22 μm) water was prepared in-houseby use of a Milli-Q plus system (Millipore, Billerica, MA).Samples and Sample Preparation. Sampling and

preparation of the extract of Carthamus oxyacantha M. Bieb.(Asteraceae) has previously been described as VLC fraction B.1

The endophytic fungus Penicillium namyslowskii K.M. Zalessky,isolated from Rhododendron tomentosum Harmaja and grown inmalt extract broth, was prepared as previously described.24,25

Prior to the analysis, the two extracts were dissolved inmethanol at 24 and 5 mg/mL, respectively.Instrument. The HPLC−SPE−NMR system consisted of

an Agilent 1100 chromatograph composed of a quaternarypump, a photodiode array detector (DAD), an autosampler, aBruker micrOTOF-Q II mass spectrometer (MS) equippedwith an electrospray ionization (ESI) interface (operated via a1:99 flow splitter), a Knauer Smartline 120 pump forpostcolumn flow dilution, a Spark Holland Prospekt 2 SPEunit, a Gilson 215 liquid handler for automated filling of 1.7mm NMR tubes, and a Bruker Avance III 600 MHz NMRspectrometer equipped with a SampleJet sample changer.Spectra were acquired by use of either a 5 mm TXI probe or acryogenically cooled 1.7 mm TCI probe, both equipped with z-gradients. Chromatographic separations were performed on aPhenomenex Luna C18(2) column (4.6 mm × 150 mm, 3 μm)operated at 40 °C.

HPLC−SPE−NMR Analysis. Samples were separated byHPLC with a mobile phase composed of acetonitrile−water(5:95) + 0.1% formic acid (eluent A) and acetonitrile−water(95:5) + 0.1% formic acid (eluent B), with a linear gradientprofile from 20% to 100% B in 40 min at 0.5 mL/min. Eluate inthe retention time range 8.3−31.3 min was trapped on 47 GP-phase SPE cartridges (30 s/trapping) by postcolumn addition

Figure 2. Compounds identified in extract of Carthamus oxyacantha(1−11) and in fermentation broth of Penicillium namyslowskii (12−15). Compounds (number in the database is given in parentheses): 1,tracheloside (1); 2, azaelic acid (11), 3−8, glycosylated spirocompounds (2−7); 9−11, fatty acids (8−10); 14, griseofulvin (11).

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of water at 1.5 mL/min. Cartridges were dried overnight undera blanket of dry nitrogen and with pressurized nitrogen for 5min before elution with 30 μL of acetonitrile-d3 into 1.7 mmNMR tubes. The injection volumes were 25 μL (0.6 mg) and10 μL (0.05 mg), respectively. 1H NMR spectra were recordedat 300 K by use of a 30° flip angle with presaturation of theresidual water signal by excitation sculpting and presaturationduring the relaxation delay (4.0 s). One hundred twenty-eighttransients were recorded with 64k data points covering aspectral width of 12 kHz (20 ppm). Data were multiplied withan exponential function with a line broadening factor of 0.3 Hz,Fourier-transformed to 128k data points, phase-corrected, andreferenced to the residual solvent signal (CHD2CN at δ 1.94).Matching of Spectra. The comparison of reference and

analyte spectra was done using in-house-developed functionsrunning under Matlab (v. R2011a, Mathworks Inc.) with theStatistics Toolbox (v. 7.4) and Parallel Computing Toolbox (v.5.0). The function is included as Supporting Information(Table S-1) and was executed with the default parametersspecified in the file. The most important parameter values werea maximum shift of ±0.02 ppm and a threshold area of 105

(arbitrarily set to filter out spurious hits in noise regions). Priorto the matching, spectra were resampled by cubic interpolationto a common axis in the range 0−12 ppm by use of 16k datapoints.Reference Spectra for the Database. Fifty 1H NMR

spectra of known compounds were included in the database.Compound names and a selection of acquisition parameters(solvent, sweep width, relaxation delay, number of data points,and probe head) are included as Supporting Information(Table S-2). They were all acquired on the same instrument(600 MHz) and processed with a line broadening factor of 0.3Hz before Fourier transformation to 128k data points. Rangeswith solvent signals, impurity signals, artifacts, and noise weremanually set to 0 by in-house-developed functions running inMatlab (v. R2011a, Mathworks Inc.).

■ RESULTS AND DISCUSSION

The two extracts in this study, originating from a plant andliquid fermentation broth of a fungus, were separated by thesame reversed-phase HPLC method with gradient elution. Thechromatography was followed by postcolumn dilution withwater, and the analytes were trapped on resin-based SPEcartridges. The trapping was based on 30 s time slices andincluded most of the compounds eluted during the analysis asdetermined from UV and MS detection. The cartridges weredried overnight under a blanket of dry nitrogen gas and elutedwith acetonitrile-d3 directly to capillary NMR tubes in 96-position racks. The racks were manually transferred to anautosampler and analyzed by NMR spectroscopy by use of a 1.7mm cryogenically cooled probe (Figure 1).Each of the two mixtures was analyzed by single injection of

approximately 600 and 50 μg, respectively. This is significantlyhigher than the amounts usually injected in traditional HPLCanalyses and was dictated by the objective to balance theseparation efficiency with the sensitivity of the NMRspectrometer. It would be possible to increase the signal-to-noise ratios in the NMR spectra by multiple injections withaccumulative trappings.5,6,15 For this study we wanted to avoidthe risk that retention time shifts would lead to trapping ofdifferent suites of compounds.The analysis of the extract of C. oxyacantha, presented as a

pseudo-2D spectrum with chromatography traces as projectionalong the ordinate, showed the separation of a complex mixtureand spectra with good signal-to-noise ratios (Figures 1 and 3).Since the composition of the solvent is practically constant forall samples, there are no excessive fluctuations on the positionof residual solvent signals (acetonitrile at δ 1.94 and water at δ2.15−2.23). The deuterium signal of acetonitrile-d3 was used asfrequency lock, but the chemical shift of the water signal isusually sensitive toward concentration.26 The limited range ofthe water signal position is an indication that the drying of thecartridges was consistent throughout the procedure. Othersignals present throughout the samples were commonly

Figure 3. HPLC-SPE-NMR analysis of an extract from Carthamus oxyacantha. (A) Contour plot of 47 1H NMR spectra (600 MHz, 300 K) acquiredon compounds eluted from SPE cartridges with acetonitrile-d3. (B) Normalized HPLC chromatograms from UV detection (blue and green traces for210 and 254 nm, respectively) and MS detection (red trace for the base peak chromatogram). The retention time axis is aligned with thecorresponding spectra. The dashed box indicates the range of data presented in Figure 1. Chromatographic peaks identified from the database areindicated by compound numbers, and asterisks indicate compounds that have features in common with the identified glycosylated spiranes.

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observed impurity signals from methylene and methylresonances at around δ 1.3 and 0.9. These were, however, ofconsiderably smaller intensity than most of the signals fromtrapped and eluted analytes. Analysis of the endophyte extract(Figure 4) showed similar results with regard to residual solventsignals and quality of analyte signals. The chromatogramshowed two main components and several smaller peaks. Thefirst large chromatographic peak was a mixture (approximately10:1) of one major component (12) and a minor component(13) as shown in the expanded section. The temporalresolution of the pseudo-2D spectrum is also indicated in theexpanded section. Different compounds, indicated by differentspectra, are clearly separated along the retention time axis withminor contributions from mixed components.In practical terms, the analyses were considerably longer than

HPLC analyses with real-time detection, due to the cartridgedrying and elution processes. Most of the preparation, however,was done in full automation, meaning that once thechromatography was finished, the drying and elution processeswere started and the rack with filled tubes was ready for NMRanalysis the following day. For this study we primarily analyzedall samples with standard 1D 1H pulse sequences with solventsuppression to reduce the signal from water and residualsolvent signals.Each acquired analyte spectrum was matched against spectra

in the database by an algorithm based on partitioning andshifting the spectra followed by linear regression. The program,written for Matlab, is included as Supporting Information(Table S-1), together with Figures S-1 and S-2 to illustrate theprocedure. The reference spectra in the database were preparedby manually setting irrelevant data points (i.e., solvent signals,impurity signals, artifacts, and noise) to 0. The reference spectrawere hence made up of well-defined peak groups flanked by 0s.Each peak group was compared to the corresponding points inthe analyte spectrum, which were stepwise shifted to allowchanges in the absolute position of the signals. The average

value of all peak groups’ regression coefficients was returned asa match factor. A value of 100% would therefore indicate aperfect match of the spectrum to a specific reference spectrum,while the lowest possible match factor would be 0. Negativecorrelation coefficients were avoided since the phase parame-ters were set to give positive signals in both the spectra to bematched and for the reference spectra in the database. Thisapproach has three key advantages. First, it is based on relativeintensity values. It is therefore not dependent on standardizedacquisition parameters or elaborate baseline corrections.However, this also means that even the smallest signal in theanalyte spectra will be matched, and an intensity or areathreshold should be applied to filter out randomly highmatching factors in noise regions. Second, there is no need toassign or deconvolute signals in the reference spectra. Hence, itis easy to add previously acquired spectra in a continuousprocess during a study. The requirement for the operator isonly to recognize and exclude solvent and impurity signals andto split actual analyte signals into groups by setting a thresholdvalue. Third, practical use of the scheme shows that the matchfactors are fairly insensitive toward fine splitting of signals. Lessresolved small couplings in the spectral database or in theanalyte spectra still give a high match factor as long as theoverall shapes of the signals are the same. This adds to therobustness of the matching scheme, as spectra with less thanoptimal line shapes can successfully be used. One obviousdrawback is that the scheme cannot easily be used to identifycomponents in a complex mixture with many overlappingsignals, but for the current application where most of thecomponents are well separated by HPLC−SPE, this does notpose a problem.The spectral database contained 50 1H NMR spectra that

had been acquired earlier with the same instrument but not allwith the same probe head (Table S-2, Supporting Information).Eleven of the spectra were obtained during a previous study ofC. oxyacantha.1 The spectrum of an authentic sample of

Figure 4. HPLC-SPE-NMR analysis of an extract of liquid fermentation broth of Penicillium namyslowski. (A) Contour plot of 47 1H NMR spectra(600 MHz, 300 K) acquired on compounds eluted from SPE cartridges with acetonitrile-d3. The box indicates the expanded range in the foreground,with dashed lines to indicate the different spectra along the retention time axis with a subset of assigned signals. (B) Normalized HPLCchromatograms from UV detection (blue and green traces for 210 and 254 nm, respectively) and MS detection (red trace for the base peakchromatogram). The retention time axis is aligned with the corresponding spectra. Numbers indicate the structures assigned to compounds in therespective chromatographic peaks.

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griseofulvin (14) was added to the database as the compoundhad been structurally characterized as one of the maincomponents in the endophyte extract.25 The remaining 38spectra were arbitrarily selected spectra of pure natural productsacquired during a little more than a year. A complete query ofone of the HPLC−SPE−NMR analyses with 47 spectra tookabout 5 s to compute once the data were read into memory.The results are presented as colored matrices with the databaseand tested spectra along the two different axes (Figure 5).Depending on the focus of the study, these matrices can beused either to find a specific compound among a series ofspectra or to get an idea of the identity of the compound(s)represented in a particular spectrum. A red patch indicates ahigh match factor, and the suggested identity can be read onthe vertical axis.Analysis of the plant extract showed that one particular

compound in the database, azaelic acid (2), gave high matchfactors (interquartile range 62−81%) for many of the HPLC−SPE−NMR spectra in the set. The consistently high matchfactors were an effect of the ubiquitous fatty substances thatappeared in all spectra and the fact that the reference spectrumof azaelic acid contained only three peak groups, and two ofthese appeared at δ 1.30 and 1.55. However, the largest matchfactor was 97% for the spectrum of the fraction collected atretention time 10.3 min (analyte spectrum 5), which also wasconfirmed by high-resolution mass spectrometry (HRMS).The other compounds previously found in a C. oxyacantha

extract (1, 3−9), representing most of the major chromato-graphic peaks, could also be positively identified with the helpof the database match factors (78−93%) and HRMS data.When the match factors indicated that several compoundscould be possible for a particular fraction, it was in all cases thecorrect structures that had the highest rank, as verified frommanual comparison of the 1H NMR spectra and HRMS data.As an example, fraction 17 contained a mixture of the partlycoeluting compounds 4 and 5 with match factors of 78% and79%, respectively. Compound 3 gave in the same spectrum amatch factor of 74% but could be excluded as some of thedatabase peak groups were not represented in the analytespectrum. This value was also considerably lower than thehighest match factor for the row of compound 3 (90%) thatwas found in fraction 11. Corresponding rows in Figure 5A are

also illustrated in Figure S-3 (Supporting Information), wherethe distributions of all peak groups’ regression coefficients areindicated by box-and-whisker plots. Good hits are characterizedby consistently high regression coefficients for peak groups asdefined in the database spectra. Other spectra of unrelatedcompounds in the database (entries 12−50) did not give anysignificantly high match factors as can be seen in the mostlydark blue lower area of the matrix (Figure 5A, 95th percentile at30%).The fatty acid (10E,12Z)-9-hydroxyoctadeca-10,12-dienoic

acid (α-dimorphecolic acid, 9) was confirmed in fraction 45 (atretention time 30.3 min) by the database with a match factor of85%. Even though the intensity of this compound was belowthe lowest contour plot level of Figure 3, the signal-to-noise wassufficient for the visual comparison guided by the high matchfactor and database spectrum (Figure 6). Two more similar

compounds, (9Z,11E)- and (9E,11E)-13-oxooctadeca-9,11-dienoic acid (10 and 11, respectively), were included in thedatabase but did not appear among the captured fractions. Thiswas either because they were eluted outside the analyzedretention time range or because the compounds were excludedin the VLC preparation of the extract.Interestingly, there were also two fractions in the analysis of

the plant extract (marked with asterisks in Figure 3) thatappeared similar to the six glycosylated spiranes (3−8), butthey did not produce high match factors to any of the databasespectra. HRMS data combined with information obtained fromthe 1H NMR experiment suggested C-7 epimers of the spiro

Figure 5. Match factors for the comparison of database spectra (1−50, with compound categories shown in the middle) with experimental spectra,acquired in the HPLC−SPE−NMR mode, of (A) the extract of Carthamus oxyacantha and (B) the endophyte extract. The entries in the database arepresented in Table S-2 (Supporting Information).

Figure 6. Details from HPLC−SPE−NMR analysis of C. oxyacanthaextract: (A) spectrum of fraction 45 at retention time 30.3 min and(B) prepared database spectrum of α-dimorphecolic acid.

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alcohol (4) and the spiro hydroxyperoxide (7), respectively.The fractions containing compound 4, fractions 16 and 17, hada match factor to the corresponding database spectrum of 84%and 78%, respectively. The corresponding C-7 epimer infraction 14 had a match factor of 41% to compound 4 (FigureS-3, Supporting Information). The match factor between thespectrum of fraction 20 to the database spectrum of compound7 was 93%, while the fraction containing the C-7 epimerreturned the corresponding match factor 60%. These constitutegood examples that the 1D 1H NMR spectra containsstereochemical information that is not possible to obtainfrom MS data alone. Previously assigned signals for the isomers(4 and 7)1 and several two-dimensional NMR spectra (HSQC,HMBC, and COSY) were used to make complete assignments(Tables S-3 and S-4, Supporting Information). Even though thecompounds were in small amounts, it was possible to acquirethese spectra directly on the obtained fractions. The proposedstructures were confirmed by NOESY spectra obtained in aseparate experiment in which the mixture was chromato-graphically separated and the four isomers were accumulativelytrapped (n = 3).The database matching of the spectra from analysis of the

fermentation broth of P. namyslowskii gave similar results(Figure 5B). One of the major components, griseofulvin (14),gave high match factors in three consecutive fractions (86%,97%, and 89%, respectively) with retention times in the range19.3−20.3 min. Neither the analogue compounds (12, 13, and15) nor any other entries of the database gave high matchfactors. The major components of this extract had in a previousstudy been structurally characterized by use of selectivelycaptured chromatographic peaks.25 A preliminary HPLC−NMR analysis with expected chemotaxonomic components inthe database would hence be an efficient dereplication step. Apositive result (high match factors) means that the databasespectrum should be visually compared with the experimentalspectra. Conversely, negative results can be interpreted that thecompound giving rise to the spectral profile is not representedin the database and thus might be interesting for further studies.

■ CONCLUSIONSIn this work we have demonstrated that mixtures of naturalproducts can be chromatographically separated and detected byNMR spectroscopy as in the originally envisioned HPLC−NMR technique. It was accomplished by time-based trapping ofcompounds by SPE, followed by an exchange of mobile phaseto fully deuterated solvent and elution into capillary NMRtubes (HPLC−SPE−NMR). As the obtained NMR spectra ofthe fractions were acquired in a fixed solvent, it was alsopossible to directly compare them with a database of referencespectra by use of an in-house-developed algorithm. Positivelyidentified spectra could subsequently be confirmed by visualinspection, while further analyses could be limited to fractionswith unknown spectra. With the relatively small database wetested, the number of false positives was low. The method tomatch analyte with reference spectra, based on partitioning ofthe reference spectra into peak groups and allowing for smalldifferences of the chemical shifts, proved to be robust andsimple to use. The high mass sensitivity of the instrumentationallowed analysis on single injection of the mixtures in amountssuitable for separation on standard 4.6 mm i.d. analytical-scalereverse-phase HPLC columns.The described approach to trap a high number of consecutive

fractions, with subsequent elution into NMR tubes, features an

integral verification of the whole system. Incomplete drying orany other malfunctioning during the elution process is revealedby comparison with other spectra in the series and thechromatograms recorded with real-time detection (DAD andMS). A related issue is the general applicability of the HPLC−SPE−NMR method. The characteristics of the chromatographystep are different from those of the trapping step in terms ofmobile and stationary phases. This suggests that there is noguarantee that separated compounds are trapped and trans-ferred to the NMR tubes in good yields. This issue has beeninvestigated for alkaloids in a recent article by Johansen et al.27

Using the current mode of HPLC−SPE−NMR gives anindication about the success of the trapping and elutionprocesses. As NMR is a near-universal detector for organiccompounds, chromatographic peaks detected by the DAD orMS should also appear in the corresponding 1H NMR spectra.Lack of NMR signals, or spectra with lower than expectedsignal-to-noise ratios, would indicate that the trapping andelution methods should be adapted for these compounds.A preliminary analysis of a mixture by the described method

would hence reveal if the instrument and automation is in goodorder, if the compounds are suitable for the method, and if theacquired 1H NMR spectra match previously acquired spectra.From this information it is possible to guide further researchactivities such as focusing on structural characterization ofunknown (not in the database) compounds, optimizing theabsorption and desorption processes, or improving therobustness of the instrumentation and automation. Furtherpossibilities for the presented method involve multiple trappingto increase the sensitivity, including NMR-based absolutequantification of known compounds, and improving thespectral databases in terms of number of spectra, metadata,and accessibility over the net. We expect that spectral databaseswill be able to be used at different laboratories as long as thesolvent and magnetic field strengths of the instruments are thesame.

■ ASSOCIATED CONTENT

*S Supporting InformationFour tables and three figures with additional information asnoted in the text. This material is available free of charge via theInternet at http://pubs.acs.org.

■ AUTHOR INFORMATION

Corresponding Author*E-mail nn@sund.ku.dk.

NotesThe authors declare no competing financial interest.

■ ACKNOWLEDGMENTS

Dr. Mysore V. Tejesvi (Department of Biology, University ofOulu, Finland) is gratefully acknowledged for the sample ofendophytic fungus. Financial support from Drug ResearchAcademy, University of Copenhagen (K.T.J. and S.G.W.) andBruker BioSpin GmbH (K.T.J.) is gratefully acknowledged. Partof the work was funded by the project FungalFight, The DanishCouncil for Strategic Research (Grant 10-093473). The grantfor upgrade of the hyphenation equipment used in this workwas provided from the Danish Agency for Science, Technologyand Innovation via National Research Infrastructure funds.

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