Global Feature Detection
Detect thousands of molecular features across the measured mass and retention-time range.
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Discovery Workflows
Untargeted Metabolomics
Broad, discovery-driven measurement of small molecules in biological samples using high-resolution mass spectrometry.
Global molecular profiling
Capture a wider range of biological changes.
Untargeted metabolomics is designed for studies where the metabolites driving a biological effect are not fully known in advance. Rather than limiting measurement to a predefined panel, the workflow captures a broad range of detectable molecular features and evaluates how those features change in relation to experimental treatments, disease states, time points, exposures, or other study variables.
Comparative Biology
Identify features that differ across experimental groups, phenotypes, exposures, or time points.
Annotation & Interpretation
Prioritize biologically relevant features and connect supported annotations to pathways, compound classes, and literature.
End-to-end workflow
From experimental design to biological interpretation
Successful untargeted studies depend on more than instrument acquisition. We work with investigators across the full analytical process, with quality control and study design considered from the beginning.
01
Study Design
Define the study groups, covariates, sample collection handling and storage, QC strategy, and primary biological questions.
02
Sample Preparation
Analyzing hydrophilic and hydrophobic fractions separately can broaden chemical coverage. Consider the chemical classes most relevant to the study.
03
LC-MS Acquisition
Hydrophilic fractions can be analyzed by HILIC or reversed-phase LC-MS; hydrophobic fractions are analyzed by reversed-phase LC-MS.
04
Data Processing
Process molecular features through alignment, filtering, annotation, normalization, and quality assessment.
05
Interpretation
Integrate statistical results, visualization, and biological context to prioritize meaningful findings.
Before samples arrive
Study design matters
Untargeted metabolomics is highly sensitive to biological variability, sample collection, storage, preparation, analytical order, and batch effects. Early consultation helps make sure the analytical strategy is aligned with the question you want to answer.
We typically discuss
- Experimental groups, sample numbers, and biological replication
- Randomization, repeated measures, and relevant covariates
- Sample collection, storage, freeze-thaw history, and matrix considerations
- Pooled QC samples, blanks, and randomization
- The statistical comparisons and biological questions planned downstream
Common project goals
- Discover metabolic signatures associated with treatment, disease or phenotype
- Characterize treatment, exposure, nutritional, or environmental responses
- Identify candidate biomarkers for follow-up studies
- Investigate pathway- or mechanism-level changes
- Generate hypotheses for subsequent targeted validation
Analytical options
Choose the analytical coverage
Sample preparation and LC methods can be selected to optimize detection of compound classes of interest. Separate analysis of hydrophilic and hydrophobic fractions provides more comprehensive metabolome coverage. Either or both fractions can be selected. A methanol crash can also be used to analyze hydrophilic and hydrophobic molecules together as a more economical but less comprehensive option.
Biological sample
Hydrophilic fraction
HILIC LC-MS
Greater retention of highly polar metabolites.
Reversed-phase LC-MS
Complementary coverage of moderately polar and semi-polar compounds.
Hydrophobic fraction
Reversed-phase LC-MS
Profiling of lipids and other hydrophobic molecular species.
Results & deliverables
Results include a complete analysis report, not just a spreadsheet of molecular features
The report is tailored to the study design and the level of evidence available.
✓Processed abundance matrix with feature-level metadata
✓Quality-control and data-filtering assessment
✓Univariate and multivariate statistical analysis
✓Compound annotation and biological prioritization
✓Publication-ready visualization and interpretation
support
Data interpretation
Feature discovery is only the beginning
Once study-relevant features are identified, statistical context, compound annotation, biological relevance, and validation all contribute to determining which findings warrant further investigation.
↗
Statistics & Visualization
Study-design appropriate statistics, effect-size assessment, clustering, dimension reduction, and plots.
⌕
Annotation & Context
Database-supported annotation, pathway context, literature review, and prioritization for follow-up.
▥
Validation & Follow-up
Confirm key findings with targeted analysis, authentic standards, additional cohorts, or focused mechanistic studies.
Important: Untargeted metabolomics does not provide definitive identification and absolute
quantitation for every detected feature. Compound annotation confidence varies by feature and should be
considered
when interpreting results or selecting compounds for targeted confirmation.
Planning an untargeted metabolomics study?
Contact the core early so we can help align the design, analytical workflow, and data-analysis plan.