MSPROTA

The Mass Spectrometry PROteomics daTa Analytics workshop will take place at the School of Medicine, structured as six sessions of two hours each with theoretical and practical components.

Topics

Session 1: General Overview of Mass Spectrometry (MS) applied to Proteomics

24/10/2025 - 10-12pm

This session introduces the principles of Mass Spectrometry (MS) and its application in proteomics. Participants will explore common workflows, tools, and databases for MS data interpretation, and gain a basic understanding of using High-Performance Computing (HPC) and SLURM.

Learning Outcomes

By the end of this session, participants will be able to:

• Explain the core principles of MS and its role in proteomics.
  
• Describe key steps in MS-based proteomic workflows.
  
• Identify main tools and databases for data interpretation.
  
• Perform basic interactions with HPC and SLURM.

Session 2: Data-Dependent Acquisition (DDA)

29/10/2025 - 10-12pm

This session explores strategies for analysing tandem mass spectrometry (MS/MS) data using Data-Dependent Acquisition (DDA) mode. Participants will learn how to optimise workflows for protein identification and quantification in complex biological samples, and gain practical insights into label-free quantification and key steps in data analysis.

Learning Outcomes

By the end of this session, participants will be able to:

• Explain the principles of DDA, including peak selection, dynamic exclusion, and ion filtering.
  
• Apply standardised workflows for protein identification and quantification.
  
• Perform basic quality control, normalisation, imputation, and dimensionality reduction.
  
• Conduct differential expression and pathway enrichment analyses.

Session 3: Data-Independent Acquisition (DIA)

28/11/2025 - 10-12pm

This session introduces strategies for analysing tandem mass spectrometry (MS/MS) data using Data-Independent Acquisition (DIA). Participants will learn how to optimise workflows for protein identification and quantification in complex biological samples, with a focus on label-free quantification and essential data analysis steps.

Learning Outcomes

By the end of this session, participants will be able to:

• Explain the principles of DIA and how it differs from DDA approaches.
  
• Apply standardised workflows for protein identification and quantification.
  
• Perform basic quality control, normalisation, imputation, and dimensionality reduction.
  
• Conduct differential expression and pathway enrichment analyses.

Session 4: Labelled Quantification

05/12/2025 - 10-12pm

This session focuses on labelled quantification approaches in proteomics, with an emphasis on Tandem Mass Tag (TMT) workflows using Data-Dependent Acquisition (DDA) mode. Participants will learn how to analyse TMT-based experiments for accurate protein quantification.

Learning Outcomes

By the end of this session, participants will be able to:

• Describe the principles of labelled quantification and the TMT workflow.
  
• Explain how DDA is applied in TMT-based proteomics experiments.
  
• Apply standard methods for data processing, normalisation, and batch correction.
  
• Interpret quantitative results and assess experimental variability and reproducibility.

Session 5: Immunopeptidomics

16/01/2025 - 10-12pm

This session explores advanced techniques for analysing immuno-peptidomics data, with a focus on MHC peptide elution assays, spectral library generation, and peptide identification and quantification. It is particularly suited to researchers working in personalised immunotherapy and neoantigen discovery.

Learning Outcomes

By the end of this session, participants will be able to:

• Explain the principles and applications of immuno-peptidomics.
  
• Perform and interpret MHC peptide elution assays.
  
• Generate and apply spectral libraries for peptide identification and quantification.
  
• Recognise how immuno-peptidomics informs personalised immunotherapy and neoantigen discovery.

Session 6: Post-Translational Modifications (PTMs)

23/01/2025 - 10-12pm

This session introduces enrichment strategies and computational tools for the characterisation of post-translational modifications (PTMs), including phosphorylation, glycosylation, and ubiquitination. Participants will learn how to derive precise biological insights using robust statistical models and integrative visualisation approaches.

Learning Outcomes

By the end of this session, participants will be able to:

• Describe key enrichment protocols for PTM analysis.
  
• Apply computational tools for the identification and quantification of PTMs.
  
• Use statistical and visualisation methods to interpret PTM data.
  
• Integrate PTM information to enhance understanding of protein function and regulation.