Enrolment guidelines

This is a toolbox course where 80% of the seats are reserved for PhD students enrolled at the Faculty of SCIENCE at UCPH and 20% of the seats are reserved for PhD students from other Danish Universities/faculties (except CBS). Seats will be allocated on a first-come, first-served basis and according to the applicable rules.

Anyone can apply for the course, but if you are not a PhD student at a Danish university (except CBS), you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to applicants on the waiting list.

Aim and Content
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.

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.

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

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

Recommended Academic Qualifications
Basic knowledge of chemistry, statistics and biochemistry.

Research Area
Metabolomics, chemistry, biology, statistics

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.

Type of Assessment
Active participation in the group work and final presentation.

Course coordinator
Jan Stanstrup

Guest Lecturers
Jan Stanstrup
Henrik Munch Roager
Giorgia La Barbera
Carl Brunius

Dates
2026-06-29 – 2026-07-03

Expected frequency
Yearly in the last week of June / first week of July.

Course location
NEXS, Frederiksberg

Registration
No formal requirements. Adequate background is checked and clarified if needed. Adequate is considered having basic training in statistics, chemistry and biology or biochemistry and a well-defined interest in metabolomics.

Course fee
• Participant fee: 1800 DKK
• PhD student enrolled at SCIENCE: 0 DKK
• PhD student from Danish PhD school Open market: 0 DKK
• PhD student from Danish PhD school not Open market: 3000 DKK
• PhD student from foreign university: 3000 DKK
• Master’s student from Danish university: 0 DKK
• Master’s student from foreign university: 3000 DKK
• Non-PhD student employed at a university (e.g., postdocs): 3000 DKK
• Non-PhD student not employed at a university (e.g., from a private company): 8400 DKK

Cancellation policy
• Cancellations made up to two weeks before the course starts are free of charge.
• Cancellations made less than two weeks before the course starts will be charged a fee of DKK 3.000
• Participants with less than 80% attendance cannot pass the course and will be charged a fee of DKK 5.000
• No-show will result in a fee of DKK 5.000
• Participants who fail to hand in any mandatory exams or assignments cannot pass the course and will be charged a fee of DKK 5.000

Course fee and participant fee
PhD courses offered at the Faculty of SCIENCE have course fees corresponding to different participant types.
In addition to the course fee, there might also be a participant fee.
If the course has a participant fee, this will apply to all participants regardless of participant
type – and in addition to the course fee.

Disclaimer:
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