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:
2.6. 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
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)
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.
Materiale di riferimento
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
Modalità di verifica dell’apprendimento e criteri di valutazione
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.