Spatial Metabolomics: Molecular Imaging, Metabolic Networks & Disease Mechanisms

To provide a comprehensive understanding of spatial metabolomics technologies, computational workflows, and AI-driven approaches for analyzing molecular landscapes and uncovering metabolic mechanisms associated with health and disease.

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

• Understand the fundamentals and applications of spatial metabolomics in biomedical research.
• Explore molecular imaging technologies such as MALDI-MSI and DESI-MSI.
• Learn approaches for spatial metabolite visualization, annotation, and analysis.
• Understand metabolic pathway reconstruction and network-based interpretation.
• Apply AI and machine learning approaches for spatial metabolomics data analysis.
• Explore integration of spatial metabolomics with transcriptomics, proteomics, and other multi-omics platforms.
• Understand research applications in cancer metabolism, neuroscience, biomarker discovery, and precision medicine.

📅 Day 1: Fundamentals of Spatial Metabolomics & Molecular Imaging Technologies

• Focus: Understanding spatial metabolomics principles, molecular imaging technologies, and their role in modern biomedical research.
• Introduction to Spatial Metabolomics and its role in modern biomedical research.
• Understanding the transition from traditional metabolomics to spatially resolved molecular analysis.
• Principles of mass spectrometry imaging (MSI) and spatial molecular mapping.
• Key technologies:
  • MALDI-MSI
  • DESI-MSI
  • Imaging Mass Cytometry
  • Spatially resolved metabolite profiling
• Sample preparation, experimental design, and data acquisition strategies.
• Applications in cancer biology, neuroscience, immunology, and precision medicine.

🛠️ Hands-on:

• Explore and visualize spatial metabolomics datasets using open-source bioinformatics tools.
• Understand spatial metabolite distribution patterns through computational visualization workflows.

🧰 Tools Covered: Python, Google Colab, Jupyter Notebook, Scanpy, Pandas, Matplotlib, METASPACE

📅 Day 2: Spatial Metabolic Networks & AI-Based Data Analysis

• Focus: Applying computational and AI approaches for spatial metabolomics data processing, pathway analysis, and biomarker discovery.
• Processing and interpretation of spatial metabolomics datasets.
• Metabolite annotation, identification, and spatial distribution analysis.
• Integration of metabolomics with transcriptomics and multi-omics datasets.
• Reconstruction of metabolic pathways and regulatory networks.
• Machine Learning approaches for:
  • Metabolite classification
  • Pattern recognition
  • Disease biomarker discovery
• AI-driven spatial analysis and computational workflows for biological interpretation.

🛠️ Hands-on:

• Perform AI-based analysis of spatial metabolomics data for metabolite pattern identification and pathway visualization.
• Apply machine learning workflows for classification and spatial feature analysis.

🧰 Tools Covered: Python, Google Colab, Scikit-learn, TensorFlow/PyTorch, KEGG, Reactome, NetworkX

📅 Day 3: Spatial Metabolomics in Disease Mechanisms & Precision Medicine

• Focus: Exploring spatial metabolic landscapes, disease mechanisms, and AI-enabled precision medicine applications.
• Understanding disease biology through spatial metabolic landscapes.
• Spatial metabolomics applications in:
  • Cancer metabolism and tumor microenvironment
  • Neurodegenerative disorders
  • Inflammatory and immune diseases
  • Drug response and therapeutic monitoring
• Integration of spatial metabolomics with:
  • Single-cell technologies
  • Spatial transcriptomics
  • Proteomics
• Emerging trends:
  • AI-assisted biomarker discovery
  • Digital pathology integration
  • Predictive disease modelling

🛠️ Hands-on:

• Build a spatial metabolomics workflow for disease-associated metabolite mapping and biomarker discovery.
• Integrate spatial metabolomics datasets with biological networks and visualization platforms.

🧰 Tools Covered: Python, Google Colab, METASPACE, Cytoscape, OmicsNet, BioPython, CellProfiler

• Metabolomics, molecular biology, biotechnology & systems biology researchers
• Faculty members exploring spatial omics and computational biology
• Metabolomics & bioinformatics researchers
• Cancer biology, neuroscience & molecular medicine scholars
• Biotechnology and biomedical science students
• Pharmaceutical and biotechnology professionals
• Precision medicine and biomarker discovery researchers
• Drug development and biomedical data scientists
• AI & machine learning researchers
• Multi-omics and molecular imaging scientists
• Systems biology and data-driven biomedical researchers

Participants will gain practical knowledge of:

✅ Spatial metabolomics concepts and experimental workflows
✅ Mass spectrometry imaging and spatial molecular mapping techniques
✅ Computational analysis of spatial metabolomics datasets
✅ Metabolic pathway and network visualization approaches
✅ AI-based pattern recognition and biomarker discovery strategies
✅ Multi-omics integration for understanding disease mechanisms
✅ Application of spatial metabolomics in precision medicine research