Introduction to Nutritional Metabolomics

About

The course will provide a general overview of LC-MS based untargeted metabolomics from study design to results and will be exemplified with its specific application in nutrition. It will be delivered using a mixture of lectures, hands-on data preparation and analysis, computer-based practical sessions, and discussions. Visits to wet labs and instructions on human sample preparation procedures is included but there is no practical lab work.

The students will go through common steps in a typical metabolomics study using a real-life case. This case study includes plasma (or urine) samples from a nutritional intervention. The sample preparation and analysis on UPLC-QTOF have been conducted and the students will further process and analyze the acquired data with various freeware tools (e.g. R, XCMS, MZmine etc). They will finally work on identification of relevant metabolites using manual analysis assisted by several web-based databases and structure elucidation tools. The course will be concluded by presentations of reports generated by the students based on the case study.

The students should expect a fairly technical course with a strong focus on the hands-on data analysis abilities and data interpretation skills. Programming skills are not a prerequisite for entering the course and students are guided through the exercises. However, for students that are not familiar with R we expect them to explore the self-study curriculum based on short videos and texts that cover essential programming concepts.
The project work has a high workload, and hence evening work can be expected during the course week.

TEACHING AND LEARNING METHODS
Basic principles and an overview are given by mostly frontal lectures that include small tasks and quizzes.
Each practical step is then explained, and the students work in groups to perform the tasks on the dataset provided.
The course culminates in a presentation where the students describe the steps they took and the conclusions they reached.

LECTURERS
Jan Stanstrup
Henrik Munch Roager
Giorgia La Barbera
Carl Brunius

REMARKS
Breakfast (light), snacks, lunch and dinner will be provided during the course

Course leader

Jan Stanstrup

Target group

All PhD students that have metabolomics as part of their projects. That is from medical applications to nutrition, food or microbiome

Course aim

The aim of this course is to introduce the student to all phases in a nutritional metabolomics study, to instruct the students on sample handling, and to train the student in data analysis and in the use of freely available tools for the metabolomics data flow.

Learning outcomes
Intended learning outcome for the students who complete the course:

Knowledge:
• Analyze different types of study designs commonly used in metabolomics (e.g., interventional study, case-control, cohort, cross-sectional) and evaluate their strengths and weaknesses in terms of validity, bias, and applicability.
• Explain each step of the metabolomics pipeline, including sample collection, data acquisition, preprocessing, statistical analysis, and interpretation, and identify appropriate tools and methods to perform each step effectively.

Skills:
• Carry out data preprocessing using freely available tools (R/XCMS)
• Perform basic univariate and multivariate analysis (e.g. R)
• Interpret the MS/MS spectra by manual interpretation and by utilizing available tools (e.g. MetFrag, SIRIUS) and databases (e.g. HMDB, METLIN and MassBank)

Competences:
• Describe the handling of urine, plasma and other samples collected from humans for metabolomics analysis
• Understand the basic principles of UPLC-QTOF technology
• Suggest which sample type to analyze for a specific research question and propose the relevant sample collection and preparation procedure

Fee info