SBRI19002U Translational Discovery Omics II - Proteomics

Volume 2022/2023
Education

BRIDGE - Translational Excellence Programme

 

Content

The main themes covered in the course include:

  • Mass spectrometry technologies and principles for protein sequencing.
  • Introduction to proteomics and applications in biology with examples from industry and academia
  • Best practice for clinical sample handling, plasma proteomics hands-on workflow and data analysis

 

Learning Outcome

The aim of the course is to provide the participants with: i) an overview of the major high-end quantitative proteomics technologies with focus on mass spectrometry; ii) an overview of key biological applications that quantitative proteomics screens can be applied in, including proteome quantitation, post-translational modification (PTM) analysis and functional protein interaction networks iii) introduction to clinical sample handling for proteome analysis and processing of proteomics data at different levels.
 

Upon completing the course, participants should be able to:

 

Knowledge

  • Demonstrate a basic understanding of different mass spectrometry-based proteomics workflows, experimental design and methods including hands-on experience with plasma proteomics.
  • Gain a general understanding of the main statistical concepts required for proteomics.
  • Understand the role of protein signaling pathways and functional protein interaction networks in health and disease.


Skills and Competences

  • Ablility to 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 different software tools (MaxQuant, Spectronaut).
  • Use data analysis and visualization programs such as Perseus, StringApp, and Cytoscape including ability to integrate proteomics data with curated datasets such as signaling pathways and protein-protein interactions.
  • Understand the important aspects of translational omics and be able to discuss and communicate these to other scientists, clinicians, and the public.
  • Critically evaluate the results and interpretation of downstream data analysis to answer biological questions and hypotheses.

 

Course literature is published on Absalon.

Participants must meet the admission criteria in BRIDGE - Translational Excellence Programme
The course is organized as a mixture of lectures and scientific seminars by invited speakers including technical lectures about state-of-the-art proteomics technologies used to study patient samples, cellular signaling pathways, functional protein interaction networks and global proteome changes. In addition, the course will include group work, case studies/journal clubs, demonstrations, and practical exercises provided and supervised by the specialists in the field.
  • Category
  • Hours
  • Lectures
  • 10
  • Class Instruction
  • 5
  • Preparation
  • 5
  • Practical exercises
  • 10
  • Total
  • 30
Oral
Continuous feedback during the course of the semester
Credit
0 ECTS
Type of assessment
Continuous assessment
Course participation
Type of assessment details
Attendance and active participation
Exam registration requirements

Participants are automatically registered for the Examination upon course registration.

Aid
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
Criteria for exam assesment

Active contribution and course participation according to the BRIDGE Guidelines.

By active participation the student must demonstrate:

 i) An overview of the major high-end quantitative proteomics technologies (label-free and tandem-mass-tag  labeling) used for analyzing patient and biological samples using high-resolution mass spectrometry,

ii) Knowledge on standard go-to software solutions for raw mass spectrometry data analysis including downstream software packages for data interpretation and basic visualization 

iii) Knowledge of important factors influencing pre-analytical sample variability and optimal experimental design for analysis of clinical samples.

iv) Knowledge of biological applications using quantitative proteomics including proteome quantitation, analysis of post-translational modifications, and functional protein interaction networks.