Q&A of Untargeted Metabolomics Mass Spectrometry Analysis
- The metabolite concentration is below the detection limit of the instrument.
- The sample preparation or extraction methods are not suitable for that metabolite.
- The metabolite is not present in current databases.
- Ion suppression or matrix interference affects the mass spectrometry signal.
Q1: How does repeated freeze-thawing affect untargeted metabolomics results?
A1: Repeated freeze-thaw cycles can lead to the degradation or transformation of metabolites, altering their original abundance and potentially introducing false positives or false negatives. This compromises both data accuracy and reproducibility. To avoid this, samples should be aliquoted and stored at -80°C to prevent multiple freeze-thaw events.
Q2: What’s the difference between positive (POS) and negative (NEG) ion modes?
A2: The choice between POS and NEG ion modes depends on the chemical properties of the metabolites. Basic or neutral compounds—such as amines, lipids, and sugars—generally ionize better in positive mode. In contrast, acidic compounds containing carboxyl or sulfonic acid groups are more suited to negative mode. Since acidic mobile phases are commonly used, positive mode tends to offer higher sensitivity and broader applicability. In practice, POS and NEG data are acquired and processed separately, then integrated during pathway analysis. Using both modes strategically helps expand metabolite coverage and enhances analytical depth.
Q3: What’s the difference between untargeted and targeted metabolomics?
A3: Untargeted metabolomics aims to detect as many metabolites as possible without prior assumptions, making it ideal for early-stage research such as mechanism exploration and differential metabolite screening. Targeted metabolomics focuses on quantifying specific, known metabolites with high sensitivity and accuracy—often used for validating findings and confirming biological mechanisms. A common strategy is to first use untargeted metabolomics to identify potential differential metabolites, followed by targeted analysis to precisely quantify and functionally validate those with biological relevance.
Q4: Why are some metabolites not detected or identified?
A4: Possible reasons include:
Q5: What’s the difference between internal and external standards?
A5: External standards generate calibration curves using known concentrations for absolute quantification. Internal standards, on the other hand, help correct for systematic errors and matrix effects during sample extraction, injection, and ionization. It’s important to note that adding an internal standard only aids in data correction, but true internal standard quantification requires combining it with a proper calibration curve.
Q6: How can the function of a differential metabolite be validated?
A6: Functional validation typically involves three steps:
1. Qualitative and Quantitative Validation: Use targeted analysis with triple quadrupole mass spectrometry to measure the absolute concentration of the metabolite across sample groups, confirming whether the observed differences are reliable and reproducible.
2. Metabolic Pathway Mapping: Map the metabolite to databases such as KEGG to explore its role within metabolic networks, including potential upstream regulators and downstream biological effects.
3. Functional Studies and Multi-omics Integration: Use cell or animal models to modulate the metabolite level and observe changes in phenotype, signaling pathways, or gene/protein expression. Integrating transcriptomics and proteomics data can provide additional layers of evidence.
Q7: What types of metabolites are best analyzed by GC-MS versus LC-MS?
A7: GC-MS is ideal for small, volatile, and thermally stable metabolites such as organic acids, fatty acids, and monosaccharides—often requiring derivatization. LC-MS is better suited for polar or thermally labile metabolites like amino acids, nucleotides, peptides, and lipids, and is widely used for complex biological samples.
Q8: For untargeted metabolomics, is it better to submit pooled samples or individual samples?
A8: Submitting individual samples is strongly recommended. In untargeted metabolomics, analyzing individual samples preserves biological variability and enables comprehensive downstream bioinformatics analyses such as statistical testing, differential metabolite screening, principal component analysis (PCA), OPLS-DA modeling, and pathway enrichment. This approach aligns with current academic research standards and publication requirements.
While analyzing pooled samples can reduce costs, they mask individual differences, prevent statistical analysis, and limit the ability to identify key metabolites. Pooled samples are only appropriate for specific purposes such as platform development, quality control (QC) sample construction, or preliminary exploratory studies—not for formal research or publication.
Q9: In untargeted metabolomics, how can we distinguish whether a metabolite is endogenous or exogenous?
A9: To differentiate metabolite origin in untargeted studies, consider the following:
1. Database reference: Use HMDB, KEGG, METLIN, LipidMaps, or Exposome-Explorer to assess whether a compound is endogenous or linked to external exposures.
2. Review literature and metabolic pathways: Metabolites in core metabolic pathways (e.g., TCA cycle) are usually endogenous; those related to drugs, food, or pollutants are likely exogenous.
3. Use of controls: When designing the experiment, consider including control groups that lack specific exogenous compounds. This can help determine whether certain metabolites originate from those external sources.
4. Isotope labeling: By administering exogenous compounds labeled with specific isotopes, researchers can trace their metabolic fate within the organism and identify which downstream metabolites originate from these compounds.
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