Keynote talk via Livestream: Understanding disease states and evolution using single cell data Event

Abstract

The wealth of single-cell datasets and the associated resources available at present allow to explore disease heterogeneity and evolution at an unprecedented resolution and at scale.

In this talk I will give an overview of the use of high throughput single-cell sequencing technologies in three different directions.

First, I will show the use of single-cell RNA-Seq datasets to deconvolve the cell-type composition and abundance of bulk transcriptomic data.

Second, I will focus on the challenges of integrating single-cell multi-omic assays performed simultaneously in the same sample, with the aim of preserving most of the biologically relevant information.

Finally, I will show an example where de novo cell-type annotation, a time-consuming process, can be avoided in favour of a semi-automatic approach that leverages the information offered by single-cell atlases.

For each scenario I will show relevant applications to a disease condition, such as liver fibrosis, breast cancer, and Crohn's disease.

About the speaker

Francesca Nadalin, PhD, is a computational biologist that completed her Bachelor's and Master's degrees in Mathematics at the University di Udine, Italy, and later earned her PhD in Computer Science in 2014 from the same institution. Francesca Nadalin gained extensive research experience as a postdoctoral fellow at Université Pierre et Marie Curie (now Sorbonne Université) and then as a research engineer at Institut Curie, both in Paris.

She joined EMBL in 2020 first as an ETPOD fellow, with a joint affiliation at IIT in Milan and EMBL-EBI in Hinxton, and then as part of the Gut Cell Atlas Project at EMBL-EBI, which is supported by the HELMSLEY Charitable Trust.

She is currently focusing on understanding disease heterogeneity and evolution at the single cell level, notably by developing computational methods to analyse and integrate gene expression and multi-omic data.