Translational Discovery Omics II – Proteomics
Course content
The aim of the course is to provide the participants with:
i) An overview of the major high-end quantitative proteomics technologies with a focus on mass spectrometry-based proteomics;
ii) An overview of key applications of quantitative proteomics exemplified with studies on cellular signalling, analysis of patient samples and single-cell proteomics;
iii) An introduction to clinical sample handling for proteome analysis, hands-on plasma proteomics workflow and processing of proteomics data at different levels.
The main themes covered in the course include:
- Principles of mass spectrometry technologies, protein sequencing and quantitation.
- Introduction to proteomics and applications in biology including examples with translational impact between industry/clinics and academia.
- Best practices for clinical sample handling.
- Hands-on exercise demonstrating a typical plasma proteomics workflow.
- Data analysis and visualisation of proteomics data using standard-in-the-field software.
BRIDGE – Translational Excellence Programme
Upon completing the course, participants should be able to:
Knowledge
- Demonstrate a basic understanding of different mass spectrometry-based proteomics workflows, experimental design, pre-analytical variability and methods including hands-on experience with plasma proteomics.
- Have a general understanding of the main statistical concepts required for proteomics data analysis.
- Understand the role of the proteome in protein signalling pathways in health and disease.
Skills and Competences
- Perform mass spectrometry-based proteome analysis of biological and clinical samples.
- Process raw mass spectrometry data and obtain peptide and protein identification from proteome analysis using available software tools (Spectronaut or DIA-NN).
- Use data analysis and visualisation programmes such as Perseus to explore and perform basic bioinformatic analysis of mass spectrometry data.
- Understand the important aspects of translational omics with a focus on clinical sample handling, the importance of pre-analytical variability and be able to discuss and communicate these to other scientists, clinicians, and the public.
- Critically evaluate proteomic results and interpretation of downstream data analysis to answer biological questions and hypotheses.
The course is organised as a mix of lectures, practical
exercises, and laboratory work.
Lectures will introduce state-of-the-art proteomics, applications,
and data analysis tools and include guest lecturers from
academia/industry presenting proteomics work with translational
impact. Practical exercises will allow course participants to learn
by doing, using concrete examples related to the course content.
Moreover, a significant part of the course will include a group
work-based lab exercise, with hands-on training in clinical sample
handling and proteome analysis, supervised by specialists in the
field.
The course will end with an evaluation, where participants must
reflect on the course learning outcomes and provide feedback for
course development.
The course literature will be listed on Absalon.
Participants must meet the admission criteria of the BRIDGE – Translational Excellence Programme (Fellowship or BRIDGE Omics Programme).
The BRIDGE – Translational Excellence Programme offers a small number of selected PhD graduates:
- a two-year postdoctoral fellowship in translational medicine (BRIDGE Programme)
- a package of three courses in Translational Discovery Omics (BRIDGE Omics Programme)
This course is only available to PhD graduates enrolled in either of the programmes. Fellows and participants are automatically enrolled upon admission to one of the programmes.
For further information about the BRIDGE Programme, please visit the website: www.bridge.ku.dk
For further information about the Omics Programme, please visit the website: www.bridge.ku.dk/about/academic-curriculum/bridgeomics/
- ECTS
- 0 ECTS
- Type of assessment
-
Continuous assessmentRequirement to attend classes
- Type of assessment details
- Attendance and active participation are required. By active
participation the participant must demonstrate:
i) An overview of the major high-end quantitative proteomics technologies (label-free and tandem mass tag labelling) used to analyse patient and biological samples via high-resolution mass spectrometry.
ii) Knowledge of standard go-to software solutions for raw mass spectrometry data analysis, including downstream software packages for data interpretation and basic visualisation.
iii) An understanding of important factors influencing pre-analytical sample variability and optimal experimental design for the analysis of clinical samples.
iv) Knowledge of biological applications using quantitative proteomics, including proteome quantitation and functional protein interaction networks. - Examination prerequisites
-
Participants are automatically registered for the examination upon admission to the BRIDGE – Translational Excellence Programme (Fellowship or BRIDGE Omics Programme).
- Aid
- All aids allowed
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
Criteria for exam assessment
Active contribution and course participation according to the BRIDGE Guidelines and Practicalities.
Part time Master and Diploma courses
- Category
- Hours
- Lectures
- 9,5
- Class Instruction
- 1
- Preparation
- 4
- Practical exercises
- 4,5
- Laboratory
- 4
- English
- 23,0
Kursusinformation
- Language
- English
- Course number
- SBRI19002U
- ECTS
- 0 ECTS
- Programme level
- Part Time Master
Ph.D.
- Placement
- Autumn
- Schedulegroup
-
See course dates and programme in Absalon.
- Capacity
- 20 participants
- Studyboard
- Study Board for the Professionel Master´s Degree Programmes at The Faculty og Health and Medical Science
Contracting department
- Center for Protein Research
Contracting faculty
- Faculty of Health and Medical Sciences
Course Coordinators
- Kristina Bennet Emdal (14-6d746b75766b706330676f66636e42657274306d7730666d)
- Jesper Velgaard Olsen (12-6c677572677430716e75677042657274306d7730666d)
- Ole Østergaard (14-716e67307175766774696363746642657274306d7730666d)
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