ECTS credits: 5

Course parameters:
Language: English

Level of course: PhD and early career researcher course

Time of year: 21-25 August 2023.

No. of contact hours/hours in total incl. preparation, assignment(s) or the like:

5 days of lectures, exercises and workshops (48 h)

Preparation by reading book chapters, articles and preparing data for analysis (50 h)

Capacity limits: max. 30 participants. PhD students and early career researchers have highest priority.

Course fee: Academic 400 €, Industry 700 €, which covers materials and food.

The course has been held before at:

2021 — Aarhus University, Denmark

2022 — University of Tübingen, Germany

2023 — Statens Serum Institut, Denmark

Objectives of the course:
The summer school will introduce non-targeted mass spectrometry-based metabolomics with a strong focus on biomedical research and hands-on training in metabolomics data mining tools. The summer school is offered through a collaboration between Aarhus University, the University of Tübingen, and Statens Serum Institut.

Non-targeted metabolomics is the comprehensive characterization of metabolites and metabolism in biological systems. In the human body, these metabolites include for example lipids, amino acids, carbohydrates, vitamins, food additives, drugs, cosmetics, contaminants, as well as pollutants.  Thus, it is a direct readout of key factors that influence health and provides unique opportunities for personalized health care and precision medicine. As a consequence, non-targeted metabolomics methods are increasingly being used in biomedical research to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes.

The aim of the summer school is to introduce state-of-the-art non-targeted mass spectrometry-based metabolomics applications in biomedical research, including their possibilities and shortcomings. The course will briefly introduce analytical hardware, acquisition strategies, study design, sample preparation as well as quality control. The main focus (day 2-5) will be on working with state-of-the-art metabolomics data mining tools for (pre-)processing (MZmine), metabolite annotation (GNPS, MS2LDA, Sirius+CSI:FingerID), and statistical analysis. Participants can bring their own non-targeted metabolomic data for use in the workshops.

Instructors
Madeleine Ernst¹, Markus Fleischauer², Martin Hansen³, Steffen Heuckeroth⁴, Florian Huber⁵, Alan Jarmusch⁶, Scott Jarmusch⁷, Efi Kontou⁷, Fleming Kretschmer², Filip Ljung¹, Filip Ottosson¹, Daniel Petras⁸, Robin Schmid⁹, Abzer K. Pakkir Shah⁸, Justin J.J. van der Hooft¹⁰, Ming Wang¹¹ (online), and more…

¹Statens Serum Institut, Denmark, ²Friedrich-Schiller University Jena, Germany
³Aarhus University, Denmark, ⁴University of Münster, Germany, ⁵Düsseldorf University of Applied Sciences, Germany,⁶National Institute of Environmental Health Sciences, United States,⁷Technical University of Denmark, ⁸University of Tübingen, Germany, ⁹Institute of Organic Chemistry and Biochemistry Prague, Czech Republic, ¹⁰Wageningen University & Research, the Netherlands, ¹¹University of California - Riverside, United States.
 

Learning outcomes and competences:
At the end of the course, the student should be able to:

• Understand basic principles of high-resolution tandem mass spectrometry 
• Understand typical mass spectrometry data formats 
• Carry out data pre-processing using MZmine 
• Understand and apply metabolite identification confidence levels
• Use and understand tools for advanced metabolite identification (e.g. GNPS, MS2LDA, Matchms and Sirius+CSI:FingerID)
• Understand and use basic multi- and univariate statistical methods, e.g. PCA, and differential analysis

Perform a full workflow for basic analysis of a non-targeted metabolomics experiment, including data preprocessing, metabolite annotation, statistical analysis, and biological interpretation            

Compulsory programme:
The summer school takes place as a five days intensive course. Prior to the course, the participants are expected to have familiarized themselves with the listed literature. It is expected that all participants are highly active in the workshops. On the final day, the participants will present their (workshop) results to have their performance assessed.              

Course contents:
The teaching will be carried out as lectures, exercises, workshops and participant presentations. During the workshops, participants will form small groups and carry out various data analyses with their own data set or provided study data sets. On the final day each participant will present their analyses and findings. Furthermore, a number of visiting expert researchers and companies are intensively involved in the course to communicate the most recent advances in the field of metabolomics.

Prerequisites:
The participants need to have a background in natural science at MSc level. MSc in natural sciences or related field. Experience in R, Python, or any metabolomics preprocessing software (e.g. MZmine, XCMS, OpenMS) or compound annotation tool (e.g. GNPS, MS2LDA, Sirius+CSI:FingerID, Matchms) is of advantage.

Type of course/teaching methods:
Lecture, seminars, conference talks, exercises, workshops, and participant presentations.

Literature:
A total of ca. 15 scientific papers and book chapters will be distributed to the participants no later than June 30, 2023. A tentative list is provided below.

• Bundy, J. G., Davey, M. P. & Viant, M. R. Environmental metabolomics: A critical review and future perspectives. Metabolomics 5, 3–21 (2009).
• Viant, M. R. et al. Use cases, best practice and reporting standards for metabolomics in regulatory toxicology. Nat. Commun. 10, (2019).
• Sorokina, M. & Steinbeck, C. Review on natural products databases: Where to find data in 2020. J. Cheminform. 12, 1–51 (2020).
• Domingo-Almenara X., Siuzdak G. (2020) Metabolomics Data Processing Using XCMS. In: Computational Methods and Data Analysis for Metabolomics. doi.org/10.1007/978-1-0716-0239-3_2
• Hodgson, J. Mass spectrometry searches using MASST. Nat. Biotechnol. 38, 19–22 (2020).
• Nothias, L. F. et al. Feature-based molecular networking in the GNPS analysis environment. Nat. Methods 17, 905–908 (2020).
• Koelmel, J. P. et al. Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation. J. Am. Soc. Mass Spectrom. 28, 908–917 (2017).
• Nash, W. J. & Dunn, W. B. From mass to metabolite in human untargeted metabolomics: Recent advances in annotation of metabolites applying liquid chromatography-mass spectrometry data. TrAC - Trends Anal. Chem. 120, 115324 (2019).
• Suggested book: Alvarez-Munoz, D and Farre, M. Environmental Metabolomics, 1st ed. Elsevier (2020).

Course assessment:
The participants will present their (workshop) generated data to have their performance assessed. The participants will receive a diploma, after active participation, completing the PhD and early career researcher summer school.

Provider:
Department of Environmental Science, Aarhus University

Time:
21-25 August 2023

Place:
Statens Serum Institut, Artillerivej 5, 2300 Copenhagen, Denmark 

Registration:
To enquire about registration and any questions, please contact Associate Professor Martin Hansen, martin.hansen@envs.au.dk