UKRI Rutherford Fund Fellow
Novel methods for the integration of high dimensional single cell proteomic and RNA data to understand cell populations in development and disease.
Emmanouela is a UKRI Rutherford Fund Fellow working to develop a methodology for the integration of data from single cell RNA-Seq technologies with mass spectrometry (CyTOF) data in order to study cell heterogeneity and differentiation. The project’s aims include the use of machine learning techniques for the analysis along with novel visualisation tools to improve interpretability of the data.
Prior to her fellowship, Emmanouela worked for the Computational Biology Research Group providing advice and expertise on statistical analyses for a variety of projects of the WIMM, focusing on the analysis of all types of RNA Sequencing data and teaching the RNA-Seq course along with Nicki Gray. She completed her DPhil at the Ludwig Institute for Cancer Research at the University of Oxford working on the identification and analysis of single nucleotide polymorphisms (SNPs) that affect cancer in humans. She was involved in numerous projects, working with clinical and genetic data of different types of cancer including chronic lymphocytic leukemia, melanoma and pancreatic cancer. Prior to her PhD, she worked as a training fellow in Genetic Epidemiology at the University of Leicester conducting a meta-analysis of Genome Wide Association Studies (GWAS) for pulmonary function. Her first degree was in Applied Mathematics at the National Technical University of Athens before completing an MSc in Applied Statistics at the University of Oxford.
Single-cell atlas of colonic CD8+ T cells in ulcerative colitis.
Corridoni D. et al, (2020), Nat Med
Cell-intrinsic depletion of Aml1-ETO-expressing pre-leukemic hematopoietic stem cells by K-Ras activating mutation.
Di Genua C. et al, (2019), Haematologica, 104, 2215 - 2224
Reconstruction of the Global Neural Crest Gene Regulatory Network In Vivo.
Williams RM. et al, (2019), Dev Cell, 51, 255 - 276.e7
Transcriptomic profiling of the myeloma bone-lining niche reveals BMP signalling inhibition to improve bone disease.
Gooding S. et al, (2019), Nat Commun, 10
DOT1L inhibition reveals a distinct subset of enhancers dependent on H3K79 methylation.
Godfrey L. et al, (2019), Nat Commun, 10