A multi-tier metabolite identification framework was employed using IsoMS Pro (Nova Medical Testing Inc.) in conjunction with NovaMT Metabolite Databases 2.0. Identification was based on accurate mass and retention time information derived from chemical isotope labeling LC–MS data.
Tier 1: Positive identification was achieved by matching isotopically labeled peak pairs to a curated labeled metabolite library (CIL Library) using accurate mass and retention time.
Tier 2: Putative identification was assigned by searching remaining features against a linked identity library containing pathway-related metabolites, using accurate mass and predicted retention time matching.
Tier 3: Additional putative annotations were obtained by accurate mass matching against the MyCompoundID (MCID) database, including known endogenous metabolites and predicted metabolic products derived from one or more metabolic reactions.
"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Liquid Chromatography MS - negative - reverse-phase"],"chromatography_protocol":["Labeled metabolite samples were analyzed using an Agilent 1290 liquid chromatography system coupled to a Bruker Impact II QTOF mass spectrometer. Chromatographic separation was performed on an Agilent Eclipse Plus reversed-phase C18 column (150 × 2.1 mm, 1.8 µm particle size) maintained at 40 °C, using a water–acetonitrile mobile phase system containing 0.1% formic acid.
A gradient elution program was employed with a flow rate of 400 µL/min to achieve broad metabolome coverage. Mass spectral data were acquired across an m/z range of 220–1000 at a scan rate of 1 Hz. Quality control samples were injected at regular intervals throughout the analytical sequence to monitor signal stability, instrument performance, and reproducibility.
"],"publication":["Two-Channel Chemical Isotope Labeling Metabolomics Analysis of Human Fecal Samples."],"submitter_affiliation":["University of Alberta"],"submitter_name":["Li Node TMIC"],"organism_part":["feces"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["Sample preparation included pre-treatment steps such as protein precipitation and metabolite extraction, followed by normalization based on total metabolite concentration measured using a validated metabolome quantification kit according to the manufacturer’s SOP. Sample volumes were adjusted and dried to achieve uniform metabolite concentrations prior to analysis, and normalized samples were stored at −80 °C until further processing.
Chemical isotope labeling was performed on equal aliquots of each sample using standardized protocols. Individual samples were labeled with light isotopic reagents, while a pooled reference sample—generated by combining aliquots from all samples—was labeled with the corresponding heavy isotopic reagents and used as an internal reference. Light-labeled samples were mixed in equal volume with the heavy-labeled pooled reference prior to LC–MS analysis.
Quality control samples were prepared from pooled material and analyzed at regular intervals throughout the analytical sequence to monitor technical variability and system performance. Blank-derived signals were identified and excluded during data processing.
"],"organism":["Homo sapiens"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS13531"],"author":["Brendan Whitman. University of Alberta. bwhitman@ualberta.ca.","Dina Kao. University of Alberta. dkao@ualberta.ca."],"data_transformation_protocol":["Raw LC–MS data were first converted to .csv format using DataAnalysis 4.4 (Bruker Daltonics) and subsequently processed using IsoMS Pro 1.2.12 (Nova Medical Testing Inc.). The software performed isotopic peak pair extraction, peak ratio calculation, and feature alignment across all samples.
Redundant signals and background features were removed, and data cleaning was applied to exclude blank-derived features and those with insufficient detection frequency. Missing values were filled by the software, and post-acquisition normalization was carried out using a ratio-based total signal correction prior to metabolite identification and downstream statistical analysis.
"],"study_factor":["Treatment"],"submitter_email":["tmicli@ualberta.ca"],"sample_collection_protocol":["16 human fecal samples were collected for metabolomics analysis.
"],"omics_type":["Metabolomics"],"study_design":["ultra-performance liquid chromatography-mass spectrometry","Homo sapiens","untargeted metabolites","Feces"],"curator_keywords":["ultra-performance liquid chromatography-mass spectrometry","Homo sapiens","untargeted metabolites","Feces"],"mass_spectrometry_protocol":["Metabolite samples were analyzed using an Agilent 1290 liquid chromatography system coupled to a Bruker Impact II QTOF mass spectrometer. Data were acquired at a scan rate of 1 Hz over an m/z range of 220–1000.
Quality control samples were injected at regular intervals throughout the analytical sequence to assess instrument stability, signal drift, and reproducibility.
"],"metabolite_name":["Ethanolamine","Hydroxylamine","Ethanol","Formic acid","Propionic acid","beta-Alanine","Hydrazine","Acetic acid","Glycine","Methanol"],"additional_accession":[]},"is_claimable":false,"name":"Two-Channel Chemical Isotope Labeling Metabolomics Analysis of Human Fecal Samples","description":"Global metabolomics study of the amine and carboxyl submetabolomes of 16 human fecal samples.
","dates":{"publication":"2026-09-23","submission":"2025-12-18"},"accession":"MTBLS13531","cross_references":{}}