I got my Bachelor and Master degrees in Mathematics from the University of Udine in 2006 and 2010, respectively.
In 2011 I started a PhD in Computer Science at the University of Udine where, under the supervision of Prof. Alberto Policriti, I have been developing algorithms for the alignment and de novo assembly of Illumina sequencing data.
After obtaining my PhD in 2014, I moved to Paris to start a postdoc at LCQB - Université Pierre et Marie Curie (now Sorbonne Université) in the Analytical Genomics team headed by Prof. Alessandra Carbone. At LCQB, I worked on protein-protein interactions and co-evolution of amino-acid sequences.
I subsequently moved to the Immunity and Cancer Unit at Institut Curie, Paris, where I had a research engineer position leading the bioinformatic analyses of three research teams.
Since April 2020 I am EMBL postdoctoral fellow jointly affiliated at Dr. Francesco Nicassio lab (IIT) and Dr. John Marioni and Dr. Irene Papatheodorou research groups (EMBL-EBI).
My current research line is focused on the development of computational methods for the analysis and integration of single-cell sequencing assays in the context of breast cancer.
Integrating single-cell transcriptional and epigenetic data to analyse plasticity in cancer
LAY PUBLIC SUMMARY
With single-cell sequencing technologies, a wealth of information (genomic, transcriptomic, epigenomic, etc.) can be obtained on thousands of individual cells simultaneously. These technologies permit to identify new cell populations and to define their transcriptional heterogeneity in an almost unsupervised way. However, great potential comes with great challenges: single-cell data are much sparser compared to bulk data, making the identification of differences between cell sub-populations and the integration of the information returned by complementary assays more difficult. My project aims at the development of computational methods for the analysis and integration of sequencing data from different single-cell assays while specifically focusing on the breast cancer models developed in the lab.
By finely characterizing a chemotherapy-resistant cell population in vitro, we will be able to understand the molecular mechanisms involved and thus indentify the transcriptional and epigenetic key markers required for cancer progression, in a controlled setting. From a bioinformatics perspective, these two sets of information are difficult to match at single-cell level, especially when dealing with cell-type subsets. Hence, we expect that the methodological approaches developed within the framework of this project will prove extremely valuable when addressing a wide variety of biological questions, where a complex interplay between multiple factors and subtle transcriptomic or epigenomic changes are expected to occur.
Triple-negative breast cancer (TNBC) is the most aggressive breast cancer type, due to its capacity to adapt and become resistant to chemotherapy. Such capacity, known as cell plasticity, is established at the epigenomic rather than at the genomic level. Only a small subset of cells within a TNBC population acquires a drug resistant phenotype. This sub-population is expected to show a unique transcriptional or epigenetic fingerprint, whose precise characterization still defy us. In this context, single-cell sequencing technologies come into play: recent advances in the field have made available several assays (scRNA-Seq, scATAC-Seq, scDNA-Seq, to name a few) that allow the discovery and characterization, from different angles, of new sub-populations on whose properties only very few assumptions need to be made. Moreover, the introduction of gene perturbations via the CRISPR system has allowed the study of gene knock-outs impact at large scale and at single-cell level. Non-targeting CRISPR assays can be used for cell tracing and, as such, are being successfully applied to the study of cancer cell plasticity.
scRNA-Seq studies are now being routinely employed in many labs, with the best practices for the analysis of single-cell transcriptomic data already quite well established. In comparison to scRNA-Seq, scATAC-Seq generates data that are more challenging to analyse due to a higher sparsity and a weak signal. In the ATAC-Seq technology, which stands for Assay for Transposase-Accessible Chromatin with high-throughput sequencing, a non-specific transposase (Tn5) fragments the DNA sequences lying on open chromatin regions, which can be then captured and sequenced. By coupling gene expression information with DNA accessibility, a cell’s epigenomic status could be linked with its transcriptional dynamics. Combined assays exist, but they have not been fully developed yet.
We plan to use a TNBC model and combine scRNA-Seq with scATAC-Seq to study the cells response to chemotherapy. The aim of the project is twofold. On the one hand, we will provide a finer characterization of the surviving cell sub-population. This has become possible only with the advent of the single-cell technology, given the absence of molecular markers suitable for the isolation of the sub-population of interest. On the other hand, we plan to develop ad hoc computational methods that take into consideration several single-cell assays and permit to understand the dynamics of transcriptional plasticity in the surviving clones. These two lines of enquiry are expected to provide an in-depth understanding of the molecular mechanisms of chemotherapy resistance in breast cancer cells in vitro.
REBIT-POD postdoctoral fellowship — EMBL (April 2020 – April 2023)
Short-term scientific mission — COST Action BM1006 (June 10-25, 2013)
PhD scholarship (January 2011 – December 2013)