Under the supervision of Dr Matthew R Lewis, my work as a Data Science Research Associate aims at developing and applying DESI-MS imaging at the MRC-NIHR Phenome Centre.
Currently, I am responsible for the optimisation of optimal protocols for the integration of quality control processes and analytical quantification in DESI-MS imaging experiments.
I have been awarded a pre-Bologna Laurea degree (eq. to MSc) in Physics (with specialisation in Theoretical Physics) at the Universita' Degli Studi di Bari, Italy. Afterwards, I have joined Prof. Roberto Bellotti group, with the task of developing machine learning methodologies for the automatic segmentation of the human hippocampus from structural MRI scans, as part of the Alzheimer's Disease research programme.
As a PhD student, under the supervision of Prof. Robert C Glen and Prof. Jeremy K. Nicholson, my main focus has been developing robust signal processing pipelines for the pre-processing of DESI-MS imaging data, and the application of unsupervised learning techniques to understand the molecular heterogeneity of cancer.
During my PhD thesis writing-up period, and after graduating, I have been a member of Prof. Zoltan Takats lab, where I have continued my work on the statistical modelling of DESI-MS imaging data. In particular, I have focussed on the application of graph theory techniques to identify signatures of local metabolic pathways, exploiting the idea of colocalization using the spatial distributions of the measured ions. During this period, I have also started designing and developing optimisation procedures aimed at integrating quality control and analytical references in DESI-MS imaging experiments, to quantify and disentangle the biological from the technical variability of the measured signals.
During 2020, I have worked at the Francis Crick Institute in Dr Paola Scaffidi lab, where I have applied unsupervised learning on scRNA-seq data to model the functional role of the epigenetic regulatory network in cancer resistance and plasticity.
I have joined the MRC-NIHR Phenome Centre in September 2020.
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et al., 2020, Metabolic fingerprinting links oncogenic PIK3CA with enhanced arachidonic acid-derived eicosanoids, Cell, Vol:181, ISSN:0092-8674, Pages:1596-1611.e27
et al., 2019, Colocalization features for classification of tumors using desorption electrospray ionization mass spectrometry imaging, Analytical Chemistry, Vol:91, ISSN:0003-2700, Pages:6530-6540
et al., 2020, A multimodal analysis in breast cancer: Revealing metabolic heterogeneity using DESI-MS imaging with Laser-microdissection coupled transcriptome approach., AACR Virtual Special Conference on Tumor Heterogeneity - From Single Cells to Clinical Impact, AMER ASSOC CANCER RESEARCH, ISSN:0008-5472
et al., 2019, Near real-time stratification of PIK3CA mutant breast cancers using the iKnife, 211th Meeting of the Pathological-Society-of-Great-Britain-and-Ireland, Wiley, Pages:S8-S8, ISSN:0022-3417