Research
In our lab we address fundamental questions in chromatin organisation, epigenetics and brain biology. We develop machine learning approaches for large-scale data integration and reconstruct cis-regulatory networks to understand how a cell “chooses” its fate. In particular, we focus on the role of chromatin organisation in neuronal cell fate specification and neurodegenerative disease, aiming to bring results of fundamental research to the clinic.
Chromatin organisation
The mammalian brain is a system of exquisite complexity: composed of hundreds of distinct cell-types and cell subtypes, the brain has its own immune system, unique neural stem cell behaviours and extensive inter-cell-type crosstalk. Transcriptional and epigenetic regulation has been shown to be at the heart of the brain’s unique regulatory complexity during development, disease and homeostasis. Chromatin organisation, allowing for the packing and unpacking of specific parts of our DNA, has been shown to be key in the regulation of gene expression programs.
Causal inference
With the advent of single-cell genomics technologies, unique opportunities for studying intra- and inter-cellular regulatory interactions in complex cell populations have emerged. Where previously, the expression and chromatin landscapes of cells could only be studied at the level of a population average, often encompassing many distinct cell types, single-cell experiments allow one to measure how coordinated changes occur simultaneously within and between cell populations in complex mixtures. Teasing apart which changes simply represent correlations, and which constitute causal interactions is a major challenge in statistics and machine learning, requiring the application of modern causal inference approaches.
Neurodegenerative disease
The advent of single-cell technologies and multi-modal genomics techniques, has caused a revolution in our understanding of the brain and has ushered in a new era of therapeutic potential in the targeting of complex neurological diseases. However, our ability to generate new data now outpaces our ability to make sense of it, and an explosion of data availability means repositories contain petabytes of data which have not been used to their full potential. There is a growing demand for new approaches for integrating this data across modalities and extracting insight into fundamental biological questions in the brain and to bring new targets, drugs and strategies to the clinic.
We use machine learning approaches, offering an unprecedented opportunity to exploit the abundance of rich public data, to decipher the role of chromatin organisation in the crosstalk between the immune system and neuronal cells in the development of Alzheimer’s disease.