Untargeted Metabolomics Analysis of Ethanol Exposure in Liver Using LC/QTOF

Importance of the topic

Alcohol exposure causes wide-ranging biochemical effects in the liver that underlie conditions from steatosis to cirrhosis and systemic disorders. Untargeted metabolomics applied to liver tissue can reveal pathway-level perturbations and candidate biomarkers of ethanol toxicity, supporting mechanistic studies, biomarker discovery and preclinical safety assessment. Robust, high-throughput LC/QTOF workflows that combine high-resolution MS1 with comprehensive MS/MS (DIA) and accessible data-processing are therefore valuable for routine metabolomics in research and industrial laboratories.

Objectives and study overview

This work presents an end-to-end untargeted metabolomics workflow using high-resolution reversed-phase UHPLC coupled to a quadrupole time-of-flight mass spectrometer (LCMS-9030/9050). The method was demonstrated by comparing liver metabolic profiles from mice fed an ethanol-containing diet versus control animals (n=8 per group, short-term 11-day model). The goals were to: (1) acquire reproducible high-resolution MS and DIA-MS/MS data for all detectable features, (2) process and annotate features with minimal specialist MS knowledge, and (3) identify candidate metabolic markers of ethanol exposure in liver.

Methodology

Sample preparation and experimental design:

• Mouse livers harvested post-mortem; extraction in methanol:isopropanol:water (3:3:1, v/v/v) at a tissue-to-solvent ratio of 1.3 mg tissue per µL solvent.
• Supernatants evaporated to dryness and reconstituted in methanol; diluted 1:20 prior to analysis.
• Quality control (QC) samples prepared by pooling equal aliquots of all samples following mQACC recommendations.

Chromatography and mass spectrometry:

• UHPLC: Nexera X2 with a C18 reversed-phase column (2.1 × 100 mm, 1.7 µm) at 50 °C; flow 0.4 mL/min; injection 0.5 µL; autosampler at 4 °C.
• Mobile phases: A = water + 0.1% formic acid; B = acetonitrile + 0.1% formic acid. Gradient from 2% B to 90–100% B over a 31–35 min program with organic rinse sequence.
• MS: LCMS-9030/9050 QTOF with ESI in positive and negative polarities. TOF-MS full scan m/z 100–1000 plus DIA-MS/MS acquisition (45 windows covering m/z 40–1000, ~20 Da precursor isolation, collision energy spread 5–55 V). Total cycle time ~1 s.

Data processing and statistical analysis

• Raw LabSolutions files were processed directly in MS-DIAL using the lipidomics workflow for feature detection, deconvolution of DIA spectra, alignment and annotation. The MS-DIAL start-up .msp kits and external libraries (e.g., MassBank) were used to support identifications.
• Aligned peak area matrices from positive and negative modes were exported and combined for statistical analysis in MetaboAnalyst.
• Preprocessing steps included automatic imputation of missing values (replaced by 1/5 of the minimum positive value for the variable), QC-based filtering to exclude features with relative standard deviation in QCs ≥ 20%, and interquartile-range filtering to remove near-constant features (default 40%).
• Differential features were selected by volcano plot criteria: fold change >4 and p-value <0.05 between ethanol-treated and control groups.

Instrumentation

• UHPLC system: Shimadzu Nexera X2.
• Mass spectrometers: Shimadzu LCMS-9030 or LCMS-9050 QTOF (TOF-MS and DIA-MS/MS modes).
• Software: MS-DIAL for feature detection, alignment and annotation; MetaboAnalyst for statistics and visualization; LabSolutions for raw data acquisition.

Main results and discussion

• The DIA-enabled LC/QTOF workflow produced high-resolution MS1 and deconvoluted MS/MS spectra across all samples, enabling feature detection and confident spectral matching in MS-DIAL.
• Statistical comparison identified 15 annotated features as candidate markers of ethanol exposure in mouse liver (selection criteria: log2 fold change >2, equivalent to FC>4, p<0.05).
• Annotations were mostly MSI level 2 (MS/MS match to external spectra). Ethyl oleate was confirmed to MSI level 1 by comparison with an authentic standard analyzed under the same conditions (retention time and MS/MS match).
• Key classes of altered analytes included taurine conjugates (NATau species), lysophosphatidylcholines (LPC 20:5, LPC 22:5 sn-1 and sn-2), phosphatidylethanolamines (PE 36:2, PE 38:5) and multiple phosphatidylcholines (PC 37:8, PC 38:5, PC 40:5), plus L-citrulline and ethyl oleate. Several lipids increased with ethanol exposure while others decreased, indicating complex remodeling of hepatic lipid metabolism.
• The combined DIA + MS-DIAL deconvolution approach facilitated annotation of lipids and small metabolites without prior DDA setup, ensuring MS/MS coverage for low-abundance and co-eluting features.

Benefits and practical applications

• The described workflow delivers a practical, reproducible pipeline for untargeted metabolomics on Shimadzu LCMS-9030/9050 QTOFs that requires relatively low specialist MS expertise.
• DIA acquisition captures MS/MS for essentially all detectable features, improving annotation rates versus MS1-only approaches and eliminating dependence on precursor selection strategies used in DDA.
• Integration with free/open tools (MS-DIAL) and web-based statistical platforms (MetaboAnalyst) streamlines data analysis, promotes reproducibility and facilitates adoption in academic and industrial labs for biomarker discovery, toxicology and mechanistic studies.

Future trends and potential uses

• Expanding spectral and retention-time libraries, plus improved in-silico MS/MS prediction and retention-time models, will increase annotation confidence and reduce manual review time.
• Combining DIA metabolomics with orthogonal separations (HILIC, ion mobility) or targeted follow-up assays will support pathway elucidation and absolute quantitation of candidate markers.
• Application of similar end-to-end workflows to biofluids, larger cohort studies and multi-omics integration will enhance translational potential for biomarker validation and safety pharmacology.
• Advances in automated QC-aware pipelines, cloud-based processing and standardized reporting (mQACC-aligned) will improve inter-laboratory comparability and regulatory acceptance.

Conclusion

A high-resolution reversed-phase LC/QTOF workflow with DIA-MS/MS acquisition, combined with MS-DIAL and MetaboAnalyst processing, enables routine untargeted metabolomics of liver tissue with broad MS/MS coverage, robust QC practices and accessible data analysis. The method identified 15 candidate markers associated with ethanol exposure in a mouse model, including ethyl oleate confirmed by authentic standard. The workflow is adaptable to LCMS-9030 and LCMS-9050 instruments and can be extended to other tissues, biofluids and experimental designs.

Reference

1. Kirwan JA, Gika H, Beger RD, et al. Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management. Metabolomics. 2022;18(9).
2. Spicer R, Salek R, Steinbeck C. A decade after the metabolomics standards initiative, it's time for a revision. Scientific Data. 2017;4:170138.