Neurogenomics and Brain Disease Modelling

The aim of this course is to introduce Neurogenomics, covering the foundations, state of the art and outlook of the field. The perspective of the course is the multi-scale digitization that is transforming our understanding of neural structure and function, with a special focus on the bioinformatic and computational challenges of human neurobiology in health and disease. The course will thus cover the foundations of human neurodevelopmental biology all the way to the multilayered architecture of neuropsychiatric and neurological disorders, covering the range of bioinformatic and computational approaches that illuminate them and spanning the full range from single-cell multi omics to digital cognitive and behavioral phenotyping.

A unifying theme will be the reflection on innovative models in human neurobiology, intersecting the digital representation of neural function with the digitization of neural tissue enabled by the advances in programming and reprogramming that ushered in the paradigm shift of 3D in vitro models such as brain organoids. Throughout, the perspective on models will be cast through the relevant repertoire of computational analyses that make such models intelligible and integrated, including genome and epigenome sequencing, genetic association studies and eQTL, transcriptomic, proteomics, exposomics and image analysis, along with the presentation of the most relevant datasets and resources. The single cell multi-omic resolution of these layers will be a strong focus, through its revealing impact on the molecular architecture of human brain development, function and disease.

At the end, students are expected to have matured breadth and depth of knowledge in the following topics:
1. Molecular basis of human brain development, including through a single-cell omic view of how epigenetic and transcriptional landscapes are regulated during physiological and pathological neurodevelopment
2. Genomic and epigenomic architecture of neuropsychiatric and neurological disorders
3. Advanced in vitro models to recapitulate human brain development
4. Epidemiological cohorts and datasets of human brain development in health and disease
5. High-throughput assays to analyse in vitro models of human brain structure and function, alongside the respective computational methodologies
6. Challenges and computational algorithms related to data integration and modelling of batch effects
7. Multi-omics data integration to recapitulate salient features of brain structure and function in vitro models
8. Single-cell atlases of nervous systems
9. FAIR (Findable, Accessible, Interoperable, Reusable) principles for data analysis and reproducible research