RDDS

Overview

Human diseases such as cancer are intrinsically entangled with complexity. The discovery of effective cures requires dealing with this complexity and greatly benefits from the development of high-resolution methods of investigation: genome-wide, multi-modal, single cell, spatially resolved. Understanding human diseases at single-cell resolution within their architectural context is a scientific challenge requiring dedicated computational analysis dealing with the volume and heterogeneity of the data. The research mission of the lab is to understand the RNA molecular mechanisms underlying dysregulation in human diseases, by combining high-throughput and high-resolution analyses, with a pan-disciplinary approach.

Research lines

Study human disease by single-cell and spatial omics

Thanks to the revolution of single cell sequencing, today we can obtain sequencing data from single cells. By looking at thousands of cells one at a time, we can see which set of genes each individual cell is transcribing, and we can capture the cellular diversity of tissues with unprecedented resolution. Single cell and spatially resolved data analysis requires the parallel development of appropriate methods for the identification of cell types, gene regulatory networks, spatial expression patterns and dependencies. The research of the lab is focused on multi-modal analysis of single cell and spatially resolved data to understand the molecular architecture of gene expression in human diseases.

Chart RNA modifications, structures and interactions in health and disease

Coding and non-coding RNA molecules are post-transcriptionally modified and dynamically interact with RNA-binding proteins and other RNAs to form ribonucleoprotein complexes (RNPs) and membrane-less organelles. This complex network of modifications and interactions ultimately regulate the existence and function of RNAs within cells. Physiological RNA modifications and interactions are frequently altered in human pathologies such as neurodegenerative diseases and tumors. To understand how mutations affect RNA-protein complexes, the lab integrates next-generation sequencing techniques (e.g. CLIP-Seq) and computational biology to obtain comprehensive maps of protein-RNA, RNA-RNA and RNA-DNA interactions with high resolution.

Develop computational strategies for RNA data science, multi-omics integration and visualization

The lab develops computational methods for the high-resolution analysis, integration and visualization of multi-omics data: RNA and protein levels, RNA splicing, structure and modifications, RNA-protein interactions, translation. The availability of public big data sets such as the Expression Atlas, ENCODE and the Human Cell Atlas also provides unprecedented opportunities and challenges for data mining, deep analysis and knowledge extraction from various omics layers concerning RNA biology.

Follow translation dynamics

Cells regulate protein synthesis by tuning the translation of specific genes. Translation affects protein levels as much as transcription but with mechanisms that are less studied. The last few years have witnessed a rapid adoption of ribosome profiling as a powerful technique to study translation at the genome-wide level and providing unique information on ribosome positions along RNAs. The lab uses polysome profiling, ribosome profiling and proteomics data to monitor translation regulation dynamics in health and disease.