Clinical proteomics (2017/2018)

Course code
Name of lecturer
Daniela Cecconi
Daniela Cecconi
Number of ECTS credits allocated
Academic sector
Language of instruction
I sem. dal Oct 2, 2017 al Jan 31, 2018.

Lesson timetable

Go to lesson schedule

Learning outcomes

The aim of clinical proteomics is actually to find molecular signatures, to describe affected pathways and possibly identify candidate biomarkers that can help in the diagnosis, prognosis and prediction of therapeutic outcomes and elucidate pathogenic mechanisms.
Upon completing the course, students will have the knowledge necessary to recognize the strengthens and weakness of the different proteomics methodologies and of their application to current areas of clinical investigation.


• Introduction to clinical proteomics
• Strategies for protein sample preparation
• Gel-based and gel-free clinical proteomics analyses
• Protein Identification by Tandem Mass Spectrometry
• Label-Based MS clinical proteomic approaches
• Advances in clinical proteomics research for biomarkers discovery
• Clinical Proteomics to study Pancreatic Cancer Stem Cells
• Differential profiling of Breast Cancer plasma proteome for biomarkers identification
• Brain tissue proteomic analysis to identify biomarkers of Alzheimer’s Disease
• Evaluation of therapeutic effects of neural stem cells therapy in Parkinson’s disease
• Recent Advances in Cardiovascular disease
• Pharmacoproteomics for elucidating the mechanism of action of anticancer drugs

Assessment methods and criteria

The NON-attending students must contact the coordinator of the course within the first two weeks to be included in the schedule of presentations and to have the scientific paper assigned. It is suggested to attend at least 30% of lessons. It is mandatory the participation to the entire lesson in which the student exposes the article.

The final exam includes the presentation of a scientific paper and a written exam that will cover all the topics of the program.

The final vote is obtained from the following formula: Vote = Vote_exam + max of 2 points per paper presentation.