Neurogenomics and Brain Disease Modelling

A.Y. 2021/2022
Overall hours
Learning objectives
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.
Expected learning outcomes
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
Course syllabus and organization

Single session

Lesson period
First semester
Course syllabus
The frontal and practical lessons will be centered on the following topics:
1. Molecular basis of human brain development, down to single-cell omic resolution
2. Molecular architectures of neuropsychiatric and neurological disorder and integrated approaches combining the following layers:
2.1. Genomics
2.2. Epigenomics
2.3. Transcriptomics
2.4. Proteomics
2.5. Exposomics
2.6. Image analysis
3. Epidemiological cohorts and datasets of human brain development in health and disease; single-cell atlases of nervous systems
4. Advanced in vitro models to recapitulate human brain development
4.1. Brain organoids and overview of analytical approaches for in vivo/in vitro benchmarking (dimensionality reduction, correlation, differential expression, WGCNA, bulk deconvolution, single cell transcriptomics).
4.2. Population-scale and multiplexing of neurodevelopmental physiopathology
5. Single cell brain multi-omics
5.1. Single cell RNASeq analytical pipelines (Scanpy, Seurat) for the study of populations composing the developing brain
5.2. Pseudotime approaches to infer neurodevelopmental trajectories
5.3. Emerging single cell omics approaches and multi-modal data integration: sc-ATACSeq, CITESeq, spatial transcriptomics;
5.4. Editing-based lineage and environmental recording at single cell resolution
5.5. Network Based Methods for the analysis of Omics Data and network inference
Prerequisites for admission
Courses of either of the "Alignment plans" of the 1st semester.

Furthermore, we expect the students to already own the following set of skills:
· Concrete background of Next Generation Sequencing (available platforms , basic concepts of NGS techniques, read preprocessing and alignment)
· Transcriptomics/Epigenomics fundamentals (gene quantification and differential expression analysis, peak calling, differential peak occupancy, etc.)
· Basic concepts of single cell transcriptomics
· Unix/BASH Command line
· Base knowledge of R OR Python Programming
· Statistics, Linear algebra (SUPER Optional, you tell me)
Teaching methods
Class lectures and practices (4CFUs for frontal lessons, 2 CFUs for practical classes);

Frontal lessons will be focused on all theoretical aspects of the course, including through the in depth analysis of key papers and forum discussions in the field.

During practical classes, students will have the opportunity to use their laptop to apply methods for bulk RNASeq and ChiPSeq Data Analysis, single cell RNASeq data analysis and visualization, and apply Network Based methods for the analysis of public databases.
Teaching Resources
All the slides, along with the code used during practical lectures will be available on GitHub/GitLab, along with the channels, along with a OneDrive password-protected directory
Assessment methods and Criteria
Students will be assigned projects, to be developed in small groups. At the exam, students will present and discuss with the instructors the results obtained.
BIO/11 - MOLECULAR BIOLOGY - University credits: 6
Lectures: 48 hours