Course Advanced Flow Cytometry and Single Cell Proteomics

The course B299 - Advanced Flow Cytometry and Single Cell Proteomics is being offered by the Graduate School of Health, Aarhus University, 2027.

Criteria for participation: University degree in medicine, dentistry, nursing, or Master’s degree in other fields and/or postgraduate research fellows (PhD students and research-year medical students). In addition, some practical experience in using flow cytometry, or attending a more basic flow cytometry course, is recommended.

Aim: The course provides an in-depth understanding of modern flow cytometry and emerging cytometry technologies used in biomedical research. Participants will gain a thorough understanding of the theoretical principles underlying conventional and spectral flow cytometry, as well as practical experience with advanced experimental design and high-dimensional data analysis. Through extensive hands-on exercises, participants will learn how to design and optimize high-parameter flow cytometry experiments, implement appropriate quality controls, analyse complex datasets using state-of-the-art analytical approaches, and present results according to current best-practice standards.

Learning outcomes: 

(i) Design and optimize conventional and high-parameter flow cytometry experiments.

(ii) Select and critically evaluate appropriate flow cytometry technologies and methodologies, including spectral flow cytometry and high-dimensional approaches.

(iii) Implement and interpret essential controls, including instrument quality controls, compensation/unmixing controls, and FMO controls.

(iv) Analyse and critically evaluate flow cytometry data using both conventional gating strategies and modern high-dimensional analysis methods.

(v) Apply advanced computational tools for visualization and interpretation of complex datasets, including tSNE, UMAP and clustering-based approaches.

(vi) Identify limitations, sources of error and best-practice solutions in modern flow cytometry workflows.

(vii) Present and report flow cytometry data according to current publication standards.

Workload: The full workload of the course is expected to be 48 hours

Content: This advanced course focuses on state-of-the-art flow cytometry technologies, including spectral flow cytometry, high-parameter experimental design, and modern high-dimensional data analysis approaches increasingly used in biomedical research.

This includes

Presentation of

• different flow cytometry applications within biomedical research, including conventional and spectral flow cytometry.
• principles of high-parameter panel design and optimization.
• quality assurance, instrument performance monitoring, and validation of flow cytometry methodologies.
• compensation and spectral unmixing strategies.
• experimental design and best-practice workflows for modern flow cytometry.
• conventional and high-dimensional data analysis approaches.
• visualization, interpretation and presentation of complex flow cytometry datasets.

Practical exercises (hands-on data analysis)

• antibody titration and panel optimization.
• compensation and spectral unmixing.
• quality control and validation of instrument performance.
• manual gating strategies and identification of rare cell populations.
• high-dimensional data analysis using FlowJo, including dimensionality reduction (tSNE/UMAP) and clustering approaches.
• data visualization and presentation for publication.

Instructors:   Morten Nørgaard Andersen (AU/AUH), Anders Etzerodt (AU), Kasper Lauridsen (AUH), Timothy Bushnell (University of Rochester Medical Center, New York), Debajit Bowmick (St Jude Children’s Research Hospital, Memphis, Tennessee), and staff experienced in flow cytometry from the Dept Biomedicine (AU) and Dept Hematology (AUH).

Venue: Aarhus University, Aarhus, Denmark. Victor-Albeck building.

Participation in the course is without cost for:

• PhD students and Research Year students from Aarhus University
• PhD students enrolled at partner universities of the Nordoc collaboration
• PhD students from other institutions in the open market agreement for PhD courses

Course dates:

• 15 November 2027 09:00 - 16:00
• 16 November 2027 09:00 - 16:00
• 17 November 2027 09:00 - 16:00
• 18 November 2027 09:00 - 16:00