This MOOC introduces participants to the principles of mass spectrometry and its central role in omics sciences, with a particular focus on the processing and interpretation of the complex data produced by high-resolution experiments. The course covers the theoretical and operational foundations of mass spectrometry, examining ionization and fragmentation mechanisms, the main modes of data acquisition, and techniques for coupling with separation methods such as chromatography. Special emphasis is placed on the use of spectral databases for the annotation and identification of compounds, distinguishing between strategies based on direct querying and similarity-based approaches, while considering differences among ionization sources and the varying levels of confidence in identification. The course guides participants through the challenges posed by multi-omic analysis, such as integrating large amounts of heterogeneous data, managing their structural complexity, and preserving functional relationships among molecular information. It also explores the impact of artificial intelligence on omics data analysis, illustrating the potential of machine learning techniques for knowledge extraction, along with the risks and ethical implications associated with their use.
This MOOC introduces participants to the principles of mass spectrometry and its central role in omics sciences, with a particular focus on the processing and interpretation of the complex data produced by high-resolution experiments. The course covers the theoretical and operational foundations of mass spectrometry, examining ionization and fragmentation mechanisms, the main modes of data acquisition, and techniques for coupling with separation methods such as chromatography. Special emphasis is placed on the use of spectral databases for the annotation and identification of compounds, distinguishing between strategies based on direct querying and similarity-based approaches, while considering differences among ionization sources and the varying levels of confidence in identification. The course guides participants through the challenges posed by multi-omic analysis, such as integrating large amounts of heterogeneous data, managing their structural complexity, and preserving functional relationships among molecular information. It also explores the impact of artificial intelligence on omics data analysis, illustrating the potential of machine learning techniques for knowledge extraction, along with the risks and ethical implications associated with their use.
The course is organized into 5 modules, each divided into short units that present the fundamental principles of mass spectrometry and the software required for data processing in support of omics science methods—including genomics, transcriptomics, proteomics, and metabolomics—while accounting for their differences and addressing the challenges they share: the large amount of data produced by each experiment, their cross-disciplinary nature, and the need to preserve their functional hierarchy.
1. Module 1 – INTRODUCING MASS SPECTROMETRY
- Introducing mass spectrometry
- Ion types and fragmentation
- Hyphenation with separative techniques
- From signal to data
2. Module 2 – Application of Mass Spectrometry
- Application domains of mass spectrometry
- Introduction to omics sciences
- Rising complexity in multi-omics data analysis
3. Module 3 – Databases
- The role of spectral databases in omics interpretation
- Databases for omics sciences
- The importance of HRMS and tandem MS
4. Module 4 – Spectral Matching
- Consultation vs similarity-score databases
- EI vs ESI
- Acquisition modes: DDA and DIA
- Levels of annotation and identification
5. Module 5 – AI in Data Analysis
- What AI brings
- Potential risks
- Future perspectives
At the end of the learning path, students will be able to:
Knowledge and understanding
- Understand the basic principles of how a mass spectrometer works when applied to omics analyses.
- Be familiar with the main software tools for managing spectrometric data, with particular focus on the analysis of unknown compounds.
- Know the major mass spectral databases and the search models used within them for the annotation and identification of organic compounds of biochemical relevance.
Applying knowledge and understanding
- Apply theoretical models according to the type of analysis to be performed.
- Analyse examples of digital environments for managing raw spectrometric data.
- Use database outputs to recognize molecular species that may be bioactive.
Making judgements
- Analyse an example of software-based processing of raw analytical data obtained through mass spectrometry for the characterization of biological fluids.
- Critically evaluate the use of artificial intelligence as a support for interpreting spectrometric data.
Communication skills
- Communicate the sequence and characteristics of the workflow of a mass spectrometry analysis.
- Discuss strengths and limitations of using artificial intelligence in the interpretation of omics data generated via MS.
Learning skills
- Develop autonomy in researching automated solutions for data management in the relevant analytical field.
Passing quiz
Within each module, there are several lessons composed of videos, written texts, infographics, automatically graded quizzes, and interactive activities.
There are no prerequisites
Claudio Medana
For technical issues:
edvancedeh@unito.it
For inquiries about the course:
claudio.medana@unito.it