Experimental design
Study structure, biological replication, batch design, randomization, controls, sample requirements, and planning for downstream statistical analysis.
Metabolomics Training Program
Practical training across the complete metabolomics workflow
The Mass Spectrometry Core offers metabolomics training for scientists at all career levels, from investigators who are new to mass spectrometry to researchers seeking more advanced experience with targeted and untargeted small-molecule workflows.
Program overview
Training built around real metabolomics workflows
Courses are designed to connect experimental design, analytical measurement, data processing, and interpretation rather than treating each step as an isolated technique. Training can cover both targeted quantitative analysis and untargeted discovery workflows, with emphasis on the decisions that affect data quality, reproducibility, and biological interpretation.
For scientists at different levels of experience
Training can be adapted for graduate students, postdoctoral fellows, research staff, faculty, and other investigators who want a stronger practical understanding of metabolomics and small-molecule mass spectrometry.
Introductory training for new users
Hands-on workflow training for active researchers
Advanced instruction in quantitative and untargeted analysis
Study-specific training tailored to research questions
Training topics
From study design to biological interpretation
The curriculum can be structured around individual topics or the complete workflow, depending on the needs and prior experience of the trainee.
Sample preparation
Preparation strategies for small-molecule analysis, extraction considerations, sample handling, internal standards, and approaches that support reproducible LC-MS data.
Quality control
Pooled QC samples, blanks, internal standards, reproducibility assessment, analytical drift, coefficient of variation, and identification of data-quality problems.
Absolute & relative quantitation
Calibration-based absolute quantitation, relative abundance measurements, internal-standard approaches, dynamic range, and interpretation of quantitative results.
Data filtering
Feature quality assessment, missing-value considerations, reproducibility filters, blank filtering, normalization concepts, and preparation of data for statistical analysis.
Statistical analysis
Univariate and multivariate analysis, multiple-testing correction, effect size, clustering, exploratory analysis, and selection of statistical approaches appropriate to the study design.
Data visualization
PCA, heatmaps, differential plots, clustering displays, and other visual approaches used to evaluate study structure and communicate metabolomics results.
Feature annotation
Accurate-mass and MS/MS-based annotation, isotope and adduct information, database searching, annotation confidence, and the distinction between a molecular feature and a confirmed compound identity.
Targeted & untargeted metabolomics
How targeted quantitative assays and untargeted discovery workflows differ in experimental goals, acquisition strategies, data processing, and interpretation.
Instrumentation
Hands-on training on high-resolution and quantitative platforms
Instrument training is performed using Thermo Scientific mass spectrometry platforms used routinely in the Core for untargeted discovery and targeted quantitative analysis.
Thermo Scientific Orbitrap systems
Training on high-resolution accurate-mass LC-MS workflows, including untargeted small-molecule analysis, data acquisition concepts, MS/MS, and feature annotation.
Triple-quadrupole LC-MS/MS systems
Training on targeted quantitative workflows, including method setup, calibration, internal standards, quantitative performance, and interpretation of targeted assay data.
Workflow
A complete view of the metabolomics process
Training emphasizes how decisions made early in a study affect later stages of analysis. Participants can follow the workflow from experimental planning through final interpretation.
1. Study designDefine the biological question, groups,
controls, replication, and analytical strategy.
2. Sample preparationPrepare samples and standards using
methods appropriate for the analytical objective.
3. LC-MS acquisitionUnderstand instrument setup, acquisition
strategy, QC placement, and analytical performance.
4. Data processingEvaluate feature quality, filtering,
normalization, and quantitative performance.
5. Statistical analysisApply appropriate statistical and
multivariate approaches to the study design.
6. VisualizationUse graphical approaches to evaluate patterns,
group differences, and analytical quality.
7. AnnotationAssess accurate mass, MS/MS, isotope information,
and database evidence.
8. InterpretationIntegrate analytical, statistical, and
annotation evidence into biologically meaningful results.
Interested in metabolomics training?
Training can be tailored to prior experience, research goals, and the specific parts of the metabolomics workflow most relevant to the participant or research group.