COURSE OVERVIEW

Single-cell omics technologies are breaking new ground in neurobiology by substantially increasing the precision and resolution with which the complex cell populations of the nervous system can be characterised. Approaches that profile several layers of information (genome, epigenome, transcriptome, proteome) allow to generate data of unprecedented depth on the molecular state of the diversity of cells composing the nervous system.

This increase in data volume and complexity generates as many opportunities as new analytical challenges.

This compact course aims at empowering participants in mastering key computational tools for the analysis of single cell multi-omics datasets, starting from individual molecular layers to then tackle their integration, alongside providing a theoretical overview of the impact of these technologies at the leading edge of neurobiology.

TARGET AUDIENCE

This course is aimed at bioinformaticians and computational biologists with (at least) a basic knowledge of omics techniques.

The core of the course will be centred around hands-on data analysis sessions. A basic understanding of Unix command line, python and/or R is required. Previous experience with single-cell analytical workflow will be considered as an added value to harness the most from the course structure.

MAIN TOPICS

Lectures: impact of multimodal approaches on neurobiology research; innovative computational approaches for multi-omics data analysis and integration.

Computational hand-on sessions: key steps and troubleshooting in the analysis of single-cell transcriptomics data; multiplexing approaches and demultiplexing algorithms; analytical workflow for single cell ATACSeq; multi-omics data integration.

Wet Lab demo session: technologies for multimodal single-cell interrogation in neural tissues.

Roundtable with core facilities: structuring integrated multi-omic/functional workflows for neural tissue.