MTBLS1217: Stable Isotope–Assisted Plant Metabolomics: Combination of Global and Tracer-Based Labeling for Enhanced Untargeted Profiling and Compound Annotation

Abstract

Untargeted approaches and thus biological interpretation of metabolomics results are still hampered by the reliable assignment of the global metabolome as well as classification and (putative) identification of metabolites. In this work we present an liquid chromatography-mass spectrometry (LC-MS)-based stable isotope assisted approach that combines global metabolome and tracer based isotope labeling for improved characterization of (unknown) metabolites and their classification into tracer derived submetabolomes. To this end, wheat plants were cultivated in a customized growth chamber, which was kept at 400 ± 50 ppm 13CO2 to produce highly enriched uniformly 13C-labeled sample material. Additionally, native plants were grown in the greenhouse and treated with either 13C9-labeled phenylalanine (Phe) or 13C11-labeled tryptophan (Trp) to study their metabolism and biochemical pathways. After sample preparation, liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis and automated data evaluation, the results of the global metabolome- and tracer-labeling approaches were combined. A total of 1,729 plant metabolites were detected out of which 122 respective 58 metabolites account for the Phe- and Trp-derived submetabolomes. Besides m/z and retention time, also the total number of carbon atoms as well as those of the incorporated tracer moieties were obtained for the detected metabolite ions. With this information at hand characterization of unknown compounds was improved as the additional knowledge from the tracer approaches considerably reduced the number of plausible sum formulas and structures of the detected metabolites. Finally, the number of putative structure formulas was further reduced by isotope-assisted annotation tandem mass spectrometry (MS/MS) derived product ion spectra of the detected metabolites. A major innovation of this paper is the classification of the metabolites into submetabolomes which turned out to be valuable information for effective filtering of database hits based on characteristic structural subparts. This allows the generation of a final list of true plant metabolites, which can be characterized at different levels of specificity.

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 Authors: Maria Doppler, Christoph Bueschl, Bernhard Kluger, Andrea Koutnik, Marc Lemmens, Hermann Buerstmayr, Justyna Rechthaler, Rudolf Krska, Gerhard Adam, Rainer Schuhmacher

  Release date: 09-Feb-2020

 Status: Public

Organism(s)

Triticum aestivum

  Study Design

untargeted metabolites

CHEBI:phenylalanine

CHEBI:tryptophan

CHMO:ultra-performance liquid chromatography-mass spectrometry

EFO:labelling

CHMO:tandem mass spectrometry

  Experimental Factors

Treatment

Replicate

Protocol Description
Sample collection

Global Metabolome Labeling (GML)

Nutrient Solutions:

In order to prepare uniformly (U-)13C-labeled wheat, plants were grown hydroponically using nutrient solutions adapted from previously described protocols[1][2]. Nutrient composition was adapted to the growing stages by the successive use of 2 different nutrient solutions (see Table S1 in the paper associated with this study). The start solution was used until plantlets reached growth stage 11 [BBCH scale[3]] followed by the nutrient solution for the vegetative period. Iron was supplied in form of a chelate complex. To prevent ambient CO2 being dissolved in the respective nutrient solution, its preparation was started by the addition of nitric acid to achieve a pH <2.5. All other constituents (except KOH) were subsequently added while the solution was continuously homogenized with a magnetic stirrer. After autoclaving and immediately before the nutrient solution was put into the labelbox, its pH was adjusted with KOH to a value between 5.5 and 6.5.


Cultivation of Uniformly 13C Labeled Plants:

13C labeling of wheat plants was performed in a customized growth chamber (phytolabelbox) (ECH Elektrochemie Halle GmbH, Halle, Germany) that allows for the control of N2/O2 (synthetic air) and 13CO2 content in the atmosphere. Ambient air (i.e. containing 12CO2) was prevented from entering the phytolabelbox by keeping the system at a slight overpressure of 10 mbar. Prior to positioning of seedlings into the phytolabelbox, seeds of the wheat cultivar Remus were put into blocks of rock wool (Grodan Delta Anzuchtwürfel aus Steinwolle 4 × 4 × 4 cm; Grodan, Roermond, Netherlands) and watered with the start nutrient solution. Germination of seeds was carried out in closed shaded boxes to minimize light exposure. After vernalization at 4 °C for 24 h the seedlings were transferred to small containers together with perlite (Thermo-Floor, Natursand; Europerl, St. Pölten, Austria), which was covered with rock wool to prevent alga growth. The containers were transferred into the phytolabelbox and nutrient solution for the vegetative period was added. The atmospheric conditions inside the labelbox were recorded and regulated to CO2 400 ± 50 ppm under light conditions; rel. humidity ≤70%; overpressure of 8 to 10 mbar; in order to save 13CO2, carbon dioxide was allowed to enrich up to 1,000 ppm in the dark. The parameters for the climate room in which the labelbox had been placed were chosen as follows: days 0–23: 12/12 h day/night cycle with temperatures of 16 °C and 12 °C respectively. 24 days after placing seedlings into the phytolabelbox, the day/night cycle was adjusted to 14/10 h and temperature was increased to 20 °C under illumination and 16 °C in the dark. Light was provided by different types of bulbs (Iwasaki NH360FLX, MT400DL BH; Iwasaki, Tokyo, Japan) and photosynthetic photon flux density (PPFD) values between 720 and 1,100 µmol/m^2/s were obtained. Nutrient solution for the vegetative period was added regularly using an external pump. At flowering stage, samples were harvested by cutting whole ears and immediate freezing them in liquid nitrogen. Samples were stored at −80°C until further analysis. For this cultivation 80 l 13CO2 were consumed to produce 330 g fresh plant material which corresponds to roughly 4 g fresh plant material/l 13CO2.


Cultivation of Native Plants:

Native plants of cultivar Remus were cultivated in parallel to the 13C plants in a separate, identical phytolabelbox according to the protocol described above (see section Cultivation of Uniformly 13C Labeled Plants). However, 12CO2 was used instead of 13CO2.


Tracer Labeling (TL)

Plants of the cultivar Remus were grown in the greenhouse until the flowering stage as previosly described[4]. At anthesis, 10 adjoining spikelets of a single ear were treated with a total of 200 µl of an aqueous 13C9-Phe (5 g/l in water) solution. For this, for every spikelet 10 µl of the tracer-solution were applied between the palea and lemma to each of the 2 primary florets without wounding the plant, corresponding to 1 mg of 13C9-Phe per ear in total. 3 separate ears were treated to obtain replicates. The treated ears were immediately covered with a wetted plastic bag for 24 h to maintain a high humidity. The treated parts of the wheat ear were harvested 72 h after inoculation, immediately frozen in liquid nitrogen and stored at −80°C until further analysis. In parallel the same procedure was carried out for 13C9-Trp (5 g/L in water) using different wheat ears of the same flowering stage.


Ref:

[1] Hoagland DR and Arnon DI. (1950). The water-culture method for growing plants without soil. Circular. California Agricultural Experiment Station 347(2nd edit), 32 pp.

[2] Bugbee B. (2004). Nutrient management in recirculating hydroponic culture, Acta horticulturae 648:99-112. doi:10.17660/ActaHortic.2004.648.12.

[3] Zadoks JC, Chang TT, Kanzak CF. (1974). A decimal code for the growth stages of cereals. Weed Research 14(6), 415-421. doi:10.1111/j.1365-3180.1974.tb01084.x.

[4] Warth B, Parich A, Bueschl C, Schoefbeck D, Neumann NKN, Kluger B, et al. (2015). GC–MS based targeted metabolic profiling identifies changes in the wheat metabolome following deoxynivalenol treatment. Metabolomics 11(3), 722-738. doi:10.1007/s11306-014-0731-1. PMID:25972772

Extraction

Frozen wheat ears were ground to a fine powder in pre-cooled 50 ml grinding jars using a ball mill (MM400, Retsch, Haan, Germany). For each sample, 100 ± 2 mg of milled powder were transferred to 2 mL reaction tubes. 1 ml pre-cooled extraction solvent [MeOH:H2O 3:1 (v/v) + 0.1% FA; as previously described[1]] was added, vortexed for 10 s and subsequently extracted in an ultrasonic bath (Bandelin, Berlin, Germany, Sonorex Digiplus; 640 W, 4 °C) for 15 min. Extracts were centrifuged for 10 min at 14,000 rpm at 4 °C. For the tracer approaches the centrifuged extracts were diluted with acidified water (+0.1% FA) to obtain a final MeOH:H2O ratio of 1:1 [(v/v) + 0.1% FA]. For the GML approach, extracts of the native and globally labeled samples were mixed 1:1 (v/v) and subsequently diluted with acidified water to obtain a MeOH/H2O [(v/v), +0.1% FA] ratio of 1:1 (see scheme in Fig 1 in the paper associated with this study). 5 replicates were prepared for the GML- and 3 replicates for each TL-approach.


Ref:

[1] Bueschl C, Kluger B, Lemmens M, Adam G, Wiesenberger G, Maschietto V, et al. (2014). A novel stable isotope labelling assisted workflow for improved untargeted LC–HRMS based metabolomics research. Metabolomics 10(4), 754-769. doi:10.1007/s11306-013-0611-0. PMID:25057268

Chromatography

The diluted extracts were measured according to the previously published method[1]. In brief, 10 µl of extracts were injected and chromatographed via a reversed phase C18 column [XBridge C18 150 × 2.1 mm i.d., 3.5 μm (Waters, Milford, MA, USA)] using a UHPLC system (UltiMate 3000, Dionex). A linear gradient elution with increasing methanol content was utilized.


Ref:

[1] Kluger B, Bueschl C, Neumann N, Stuckler R, Doppler M, Chassy AW, et al. (2014). Untargeted Profiling of Tracer-Derived Metabolites Using Stable Isotopic Labeling and Fast Polarity-Switching LC-ESI-HRMS. Analytical Chemistry 86(23), 11533-11537. doi:10.1021/ac503290j. PMID:25372979

Mass spectrometry

The UHPLC system was coupled to an Exactive Plus Orbitrap system (Thermo Fisher Scientific) via a heated ESI interface operating in fast-polarity switching mode. Mass spectra were recorded in profile mode with a resolving power setting of 70,000 FWHM (at m/z 200) and a scan range of m/z 100 to 1,000. LC-MS/MS fragment spectra were recorded with a QExactive HF Orbitrap instrument applying the same chromatographic method as for the full scan measurements described above. MS/MS fragment spectra were acquired in data dependent MS/MS mode following a full scan with resolving power setting of 120,000 FWHM (at m/z 200). Spectra were recorded separately for positive and negative ionization mode in 2 individual measurements. 10 most intense precursor ions were automatically selected by the instrument software based on the antecedent full scan and isolated using an m/z window of ±0.75 to prevent M+1 isotopolog ions from being fragmented. Product ion spectra were recorded with normalized stepped collision energy (25, 35, 45 eV) and recorded with a resolving power setting of 30,000 FWHM (at m/z 200).

Data transformation

Data Processing:

Raw-full scan LC-HRMS data files were converted to the mzXML format with msConvert from the ProteoWizard toolbox[1] and further processed with the MetExtract II software[2].


Briefly summarized, all LC-HRMS data obtained for the GML approach were processed with the AllExtract module of MetExtract II. The software searched for pairs of co-eluting native and U-13C-labeled wheat-metabolite ions, with the aim to detect all wheat metabolites. For the Phe-tracer and Trp-tracer treated samples, LC-HRMS chromatograms were processed with the software’s TracExtract module. Here, the software searched for pairs of co-eluting native and partly 13C-labeled wheat-metabolite ions, which contained 1 or more (intact) 13C-labeled tracer-derived moieties or derivatives thereof using tracer specific parameter settings. For each of the 3 sample types, several solvent- as well as native wheat derived LC-HRMS data files were processed as native-blanks to estimate the number of false-positive results. A detailed list of all data processing parameter settings used for each experiment is provided in Table S2 of the paper associated with this study.


Combination of Global and Tracer-Based Labeling:

Data processing by MetExtract II resulted in 3 separate lists of respective wheat metabolites (i.e. derived from GML, Phe-, and Trp-labeling respectively), which were subsequently merged into 1 data table by a custom software script implemented in the Python programming language (https://www.python.org). To this end, the m/z value of the monoisotopic, native isotopologs as well as their RT, ionization mode and charge number were compared across the 3 experiments. A maximum m/z deviation of ±5 ppm and a maximum RT shift of ±0.15 min was allowed for the respective features to be merged into a single metabolite/metabolic feature of the reference list. The determined number of 13C-atoms of each experiment was not considered during the merging step as this property is dependent on the labeling approach (i.e. uniformly or partly 13C-labeled ions respectively).


The output of this strategy is a comprehensive list of detected wheat-derived ions. Moreover, ions belonging to the same metabolite such as adducts, in-source fragments, or polymers are also automatically convoluted into metabolites. For each detected metabolite ion, its total number of carbon atoms, its number of carbon atoms derived from the Phe- or Trp-tracers, m/z, RT, charge number, ionization mode, and (if available) the assigned ion species (e.g. protonated or deprotonated ions) or common in-source fragment (e.g. -H2O), as well as an identifier to which metabolite (i.e. feature group) a certain ion belongs, are reported.


Target Search, Identification, Sum Formula Generation, and Annotation of Metabolites:

After the results of all 3 approaches had been merged into a single data table, metabolites were identified based on comparison with authentic reference standards. Putative sum formulas were generated according to the Seven Golden Rules[3] and database searches were performed. A scheme giving an overview of these steps is depicted in Fig 2 of the paper associated with this study.


Ref:

[1] Kessner D, Chambers M, Burke R, Agusand D, and Mallick P. (2008). ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24(21), 2534-2536. doi:10.1093/bioinformatics/btn323. PMID:18606607

[2] Bueschl C, Kluger B, Neumann NKN, Doppler M, Maschietto V, Thallinger GG, et al. (2017). MetExtract II: A software suite for stable isotope assisted untargeted metabolomics. Anal Chem. doi: 10.1021/acs.analchem.7b02518. PMID:28787149

[3] Kind T and Fiehn O. (2007). Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics 8(1), 105. doi:10.1186/1471-2105-8-105. PMID:17389044

Metabolite identification

Metabolite identification was achieved by comparing RT, m/z, Cn from the GML experiment, and, if recorded, MS/MS fragment spectra of the detected compounds with authentic reference standards. This comparison allows level 1 identification, which is defined as successful comparison of at least two independent orthogonal parameters (e.g. MS, MS/MS, RT) of the metabolite of interest with an authentic reference standard measured under the same conditions (i.e. the same instrument and the same method)[1]. As a starting point for sum formula generation the Seven Golden Rules using C, H, N, O, S, and P were applied[2]. Only those putative sum formulas that had the same number of carbon atoms as determined in the GML approach were accepted. Subsequently, the detected metabolites were searched for in a wheat-specific in-house metabolite database collected from various sources (literature reports, PlantCyc [3], Combined Chemical Dictionary (http://ccd.chemnetbase.com/faces/chemical/ChemicalSearch.xhtml; last accessed November 2018) and Phenol Explorer[4] (currently consisting of >1100 entries) and the public repositories ChEBI[5] and ChemSpider[6]. For this, the m/z values of the detected monoisotopic ions together with their assigned total number of carbon atoms were searched against the respective database entries. For both the sum formula generation and the database queries a maximum m/z deviation of ±5 ppm was allowed. The most commonly formed adducts ([M+H]+, [M+NH4]+, [M+Na]+, [M+K]+, [M+CH3OH+H]+, [M-H]−, [M+Na-2H]−, [M+Cl]−, [M+Br]−) were considered for calculating the putative mass of the metabolite from its respective ion whenever the mass of the non-charged intact metabolites could not be derived from the MS spectra (e.g. when only 1 ion species of the metabolite was detected). Any metabolite ion that was only detected in the Phe- or the Trp-tracer experiment and for which the total number of carbon atoms was thus unknown (no match in the GML approach), the determined number of tracer-derived carbon atoms was used as the minimum number of carbon atoms for generating sum formulas and querying the compounds against the databases. Sum formula generation and data base query resulted in the identification levels 2, 3, or 4[1].


MS/MS Spectrum Evaluation:

MS/MS fragment spectra were manually inspected using the XCalibur software (Thermo Scientific, version 4.0.27.19). If both the native and the 13C-labeled forms of an ion were fragmented (e.g. [M+H]+ native form, GML form and/or tracer-labeled form) automated MS/MS fragment spectra evaluation by the FragExtract module of MetExtract II was employed. Parameter settings are listed in Table S2bof the paper associated with this study. Additionally, a search in the mzCloud database (https://www.mzcloud.org) was conducted (similarity cutoff 85; HighChem HighRes matching algorithm).


Ref:

[1] Blaženović I, Kind T, Ji J, Fiehn O. (2018). Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics. Metabolites 8(2). doi: 10.3390/metabo8020031. PMID:29748461

[2] Kind T and Fiehn O. (2007). Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics 8(1), 105. doi:10.1186/1471-2105-8-105. PMID:17389044

[3] Schläpfer P, Zhang P, Wang C, Kim T, Banf M, Chae L, et al. (2017). Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants. Plant Physiology 173(4), 2041. doi:10.1104/pp.16.01942. PMID:28228535

[4] Rothwell JA, Perez-Jimenez J, Neveu V, Medina-Remón A, M'Hiri N, García-Lobato P, et al. (2013). Phenol-Explorer 3.0: a major update of the Phenol-Explorer database to incorporate data on the effects of food processing on polyphenol content. Database 2013. doi:10.1093/database/bat070. PMID:24103452

[5] Hastings J, de Matos P, Dekker A, Ennis M, Harsha B, Kale N, et al. (2013). The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Research 41(Database issue), D456-D463. doi:10.1093/nar/gks1146. PMID:23180789

[6] Pence HE and Williams A. (2010). ChemSpider: An Online Chemical Information Resource. Journal of Chemical Education 87(11), 1123-1124. doi:10.1021/ed100697w. PMID:18428094

Source Name Organism Variant Organism part Protocol REF Sample Name Treatment Replicate
12C13CRemus_Untreated_1 Triticum aestivum Triticum aestivum cv. Remus plant ear Sample collection 12C13CRemus_Untreated_1 Global Metabolome Labeling 1
12C13CRemus_Untreated_2 Triticum aestivum Triticum aestivum cv. Remus plant ear Sample collection 12C13CRemus_Untreated_2 Global Metabolome Labeling 2
12C13CRemus_Untreated_3 Triticum aestivum Triticum aestivum cv. Remus plant ear Sample collection 12C13CRemus_Untreated_3 Global Metabolome Labeling 3
12C13CRemus_Untreated_4 Triticum aestivum Triticum aestivum cv. Remus plant ear Sample collection 12C13CRemus_Untreated_4 Global Metabolome Labeling 4
12C13CRemus_Untreated_5 Triticum aestivum Triticum aestivum cv. Remus plant ear Sample collection 12C13CRemus_Untreated_5 Global Metabolome Labeling 5
13CPhe_1 Triticum aestivum Triticum aestivum cv. Remus plant ear Sample collection 13CPhe_1 Tracer Labeling 1
13CPhe_2 Triticum aestivum Triticum aestivum cv. Remus plant ear Sample collection 13CPhe_2 Tracer Labeling 2
13CPhe_3 Triticum aestivum Triticum aestivum cv. Remus plant ear Sample collection 13CPhe_3 Tracer Labeling 3
13CTrp_1 Triticum aestivum Triticum aestivum cv. Remus plant ear Sample collection 13CTrp_1 Tracer Labeling 1
13CTrp_2 Triticum aestivum Triticum aestivum cv. Remus plant ear Sample collection 13CTrp_2 Tracer Labeling 2
13CTrp_3 Triticum aestivum Triticum aestivum cv. Remus plant ear Sample collection 13CTrp_3 Tracer Labeling 3

Assay 

Assay file name: a_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling.txt
Measurement: metabolite profiling
Technology: mass spectrometry
Platform: Liquid Chromatography MS - alternating - reverse phase

Instrumentation

Sample Name Protocol REF Post Extraction Derivatization Extract Name Protocol REF Chromatography Instrument Column model Column type Labeled Extract Name Label Protocol REF Scan polarity Scan m/z range Instrument Ion source Mass analyzer MS Assay Name Raw Spectral Data File Protocol REF Normalization Name Derived Spectral Data File Protocol REF Data Transformation Name Metabolite Assignment File
12C13CRemus_Untreated_1 Extraction MeOH:H2O 1:1 (v/v) Chromatography Thermo Scientific Dionex Ultimate 3000 UHPLC system XBridge BEH C18 (3.5 µm, 2.1 mm x 150 mm; Waters) reverse phase Mass spectrometry alternating 100-1000 Thermo Scientific Exactive Plus electrospray ionization orbitrap InternalStudyID 12C13CRemus_Untreated_1.mzXML Data transformation Metabolite identification m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
12C13CRemus_Untreated_2 Extraction MeOH:H2O 1:1 (v/v) Chromatography Thermo Scientific Dionex Ultimate 3000 UHPLC system XBridge BEH C18 (3.5 µm, 2.1 mm x 150 mm; Waters) reverse phase Mass spectrometry alternating 100-1000 Thermo Scientific Exactive Plus electrospray ionization orbitrap InternalStudyID 12C13CRemus_Untreated_2.mzXML Data transformation Metabolite identification m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
12C13CRemus_Untreated_3 Extraction MeOH:H2O 1:1 (v/v) Chromatography Thermo Scientific Dionex Ultimate 3000 UHPLC system XBridge BEH C18 (3.5 µm, 2.1 mm x 150 mm; Waters) reverse phase Mass spectrometry alternating 100-1000 Thermo Scientific Exactive Plus electrospray ionization orbitrap InternalStudyID 12C13CRemus_Untreated_3.mzXML Data transformation Metabolite identification m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
12C13CRemus_Untreated_4 Extraction MeOH:H2O 1:1 (v/v) Chromatography Thermo Scientific Dionex Ultimate 3000 UHPLC system XBridge BEH C18 (3.5 µm, 2.1 mm x 150 mm; Waters) reverse phase Mass spectrometry alternating 100-1000 Thermo Scientific Exactive Plus electrospray ionization orbitrap InternalStudyID 12C13CRemus_Untreated_4.mzXML Data transformation Metabolite identification m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
12C13CRemus_Untreated_5 Extraction MeOH:H2O 1:1 (v/v) Chromatography Thermo Scientific Dionex Ultimate 3000 UHPLC system XBridge BEH C18 (3.5 µm, 2.1 mm x 150 mm; Waters) reverse phase Mass spectrometry alternating 100-1000 Thermo Scientific Exactive Plus electrospray ionization orbitrap InternalStudyID 12C13CRemus_Untreated_5.mzXML Data transformation Metabolite identification m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
13CPhe_1 Extraction MeOH:H2O 1:1 (v/v) Chromatography Thermo Scientific Dionex Ultimate 3000 UHPLC system XBridge BEH C18 (3.5 µm, 2.1 mm x 150 mm; Waters) reverse phase Mass spectrometry alternating 100-1000 Thermo Scientific Exactive Plus electrospray ionization orbitrap InternalStudyID 13CPhe_1.mzXML Data transformation Metabolite identification m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
13CPhe_2 Extraction MeOH:H2O 1:1 (v/v) Chromatography Thermo Scientific Dionex Ultimate 3000 UHPLC system XBridge BEH C18 (3.5 µm, 2.1 mm x 150 mm; Waters) reverse phase Mass spectrometry alternating 100-1000 Thermo Scientific Exactive Plus electrospray ionization orbitrap InternalStudyID 13CPhe_2.mzXML Data transformation Metabolite identification m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
13CPhe_3 Extraction MeOH:H2O 1:1 (v/v) Chromatography Thermo Scientific Dionex Ultimate 3000 UHPLC system XBridge BEH C18 (3.5 µm, 2.1 mm x 150 mm; Waters) reverse phase Mass spectrometry alternating 100-1000 Thermo Scientific Exactive Plus electrospray ionization orbitrap InternalStudyID 13CPhe_3.mzXML Data transformation Metabolite identification m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
13CTrp_1 Extraction MeOH:H2O 1:1 (v/v) Chromatography Thermo Scientific Dionex Ultimate 3000 UHPLC system XBridge BEH C18 (3.5 µm, 2.1 mm x 150 mm; Waters) reverse phase Mass spectrometry alternating 100-1000 Thermo Scientific Exactive Plus electrospray ionization orbitrap InternalStudyID 13CTrp_1.mzXML Data transformation Metabolite identification m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
13CTrp_2 Extraction MeOH:H2O 1:1 (v/v) Chromatography Thermo Scientific Dionex Ultimate 3000 UHPLC system XBridge BEH C18 (3.5 µm, 2.1 mm x 150 mm; Waters) reverse phase Mass spectrometry alternating 100-1000 Thermo Scientific Exactive Plus electrospray ionization orbitrap InternalStudyID 13CTrp_2.mzXML Data transformation Metabolite identification m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
13CTrp_3 Extraction MeOH:H2O 1:1 (v/v) Chromatography Thermo Scientific Dionex Ultimate 3000 UHPLC system XBridge BEH C18 (3.5 µm, 2.1 mm x 150 mm; Waters) reverse phase Mass spectrometry alternating 100-1000 Thermo Scientific Exactive Plus electrospray ionization orbitrap InternalStudyID 13CTrp_3.mzXML Data transformation Metabolite identification m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
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audit
i_Investigation.txt
s_MTBLS1217.txt
MSMSSpectra.zip
a_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling.txt
m_MTBLS1217_LC-MS_alternating_reverse-phase_metabolite_profiling_v2_maf.tsv
Frontiers_SupplementaryTable3.xlsx
13CPhe_1.mzXML
13CTrp_2.mzXML
12C13CRemus_Untreated_1.mzXML
13CTrp_1.mzXML
12C13CRemus_Untreated_2.mzXML
13CPhe_2.mzXML
12C13CRemus_Untreated_4.mzXML
13CTrp_3.mzXML
13CPhe_3.mzXML
12C13CRemus_Untreated_3.mzXML
12C13CRemus_Untreated_5.mzXML
metexplore_mapping.json

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Pathways - Assay 



MetExplore Pathways Mapping

Name DB Identifier Mapped Metabolite(s)