Deependra Raj Acharya (Institude of Digital Anti-Aging Healthcare, Inje University, Brain Korea 21 (BK21) FOUR Project)

Young Beom Kwak (Institude of Digital Anti-Aging Healthcare, Inje University, Brain Korea 21 (BK21) FOUR Project)

Deependra Raj Acharya (Institude of Digital Anti-Aging Healthcare, Inje University, Brain Korea 21 (BK21) FOUR Project)

Jang Mi Han (Institude of Digital Anti-Aging Healthcare, Inje University, Brain Korea 21 (BK21) FOUR Project)

Young Beom Kwak (Institude of Digital Anti-Aging Healthcare, Inje University, Brain Korea 21 (BK21) FOUR Project)

Caenorhabditis elegans is a widely used model organism for LC-MS-based metabolomics, but methodological variability limits reproducibility across studies. A systematic review of LC-MS-based C. elegans metabolomics studies published between 2020 and 2026 was conducted using PubMed and ScienceDirect. Experimental design, sample preparation, LC-MS platforms, chromatographic separation, data processing, statistical analysis, and pathway analysis were evaluated. Most studies employed the N2 strain, synchronized L4 larvae, methanol-based extraction, and UHPLC coupled with high-resolution QTOF or Orbitrap MS. Monophasic methanol extraction was the predominant approach, whereas biphasic methanol-chloroform-water and MTBE-based extraction methods were used to achieve broader coverage of both polar metabolites and lipids. Bead beating and sonication were the predominant tissue disruption methods, followed by homogenization using bead mills, vortex mixing, or rotor-stator homogenizers. Raw LC-MS data were primarily processed using Compound Discoverer, XCMS, Mzmine, or MS-DIAL, with PCA and PLS-DA and MetaboAnalyst-KEGG being the most widely used tools for statistical and pathway analysis. Standardized LC-MS workflows and data analysis are essential to improve the reliability and biological interpretation of C. elegans metabolomics studies.

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