Spatial and Molecular Organization of Cells in Diseases

The purpose of this course is the training of the participants in the comprehension of how cells are spatially and hierarchically organized and communicate and the way this organization is altered in diseases.
The course is divided into two distinct modules: "High-content imaging and computational aspects of in vitro and animal model systems" is dedicated to the acquisition, processing and comprehension of data obtained from three popular techniques in the world of bioimaging, i.e., functional MRI, optical microscopy, and mass spectroscopy imaging, "Analytical challenges and opportunities from multi-omics" to the one of numerical analysis and interpretation of data obtained by means of next generation sequencing (NGS) techniques.

By the end of the course, students are expected to be able to:

1. Understand the principles and techniques of bioimaging in neuroscience.
2. Describe the functional anatomy of the human brain and its role in cognition and behavior.
3. Interpret neuroimaging data in relation to brain function.
4. Compare different imaging techniques (e.g., MRI, fMRI, EEG) and their applications.
5. Evaluate structural and functional connectivity methods.
6. Understand lesion-based approaches for studying brain function in patients.
7. Discuss the use of functional neuroimaging to investigate complex cognitive and behavioral processes.
8. Understand the principles underlying mass spectrometry techniques, in particular matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI)
10. Understand the working principle of common computational tools and methods used for analysing and interpreting data derived from mass spectrometry imaging experiments.
11. Understand the basic principle of cell mechanics and mechanobiology
12. Understand the principles underlying common methods for the imaging-based characterization of single and collective cell behavior.
12. Use selected image analysis tools to obtain quantitative information on single and collective cell behavior from time-resolved imaging data
13. Understand the fundamental principles underlying spatial transcriptomic technologies
14. Understand the key steps in the design spatial transcriptomic experiments and in the analysis of the data