Oxford-BMS Research Fellow
I am a computational biologist. I apply computational approaches for the analysis of sequencing data generated by single-cell multi-omics technologies to understand normal and malignant cells in hematopoiesis.
I mainly focus on the single-cell transcriptome and epigenome analyses produced by different single-cell platforms (e.g. Target-Seq, 10x genomics) for studying the complexity of hematopoietic stem and progenitor cell subpopulations. My aim is to apply single-cell multi-omics combining with a novel development of computational and statistical methods including machine learning approaches to translational medicine challenges.
My current work focuses on the integration of multiple single-cell RNA and ATAC genomics datasets from Acute Myeloid Leukemia (AML) patients undergoing clinical trials. Integrative single-cell multi-omic data sets could help to identify distinct cellular compartments, and resolve tumor heterogeneity, providing insights into deregulated pathways, and transcriptional and epigenetic signatures of mutant cells. This could potentially lead to the discovery of new targets and therapies to address the unmet medical need of AML patients.
The quiescent fraction of chronic myeloid leukemic stem cells depends on BMPR1B, Stat3 and BMP4-niche signals to persist in patients in remission.
Jeanpierre S. et al, (2020), Haematologica
Technological advances and computational approaches for alternative splicing analysis in single cells
Wen WX. et al, (2020), Computational and Structural Biotechnology Journal, 18, 332 - 343
Single-cell analysis of bone marrow-derived CD34+ cells from children with sickle cell disease and thalassemia.
Hua P. et al, (2019), Blood, 134, 2111 - 2115
Identification of two distinct pathways of human myelopoiesis.
Drissen R. et al, (2019), Sci Immunol, 4
Cell-intrinsic depletion of Aml1-ETO-expressing pre-leukemic hematopoietic stem cells by K-Ras activating mutation.
Di Genua C. et al, (2019), Haematologica