David Oliveira

During medical school, the dream of helping as many people as possible led me to obesity and metabolic research. During my Ph.D. I was initially focused on the arcuate nucleus of the hypothalamus and later shifted to single-cell genomics. I found critical biases in current single-cell data analysis and addressed them by developing TopOMetry, a python toolkit for topological data that is guaranteed to find optimal representations for cellular diversity in single-cell genomics data.

My post-doctoral research with Prof. Ana Domingos aims to provide a molecular, neuroanatomical, and functional characterisation of sympathetic neural networks using single-cell genomics, whole-body tissue-clearing and 3D-imaging, and intersectional mouse models. From a molecular perspective, I created SympAtlas,  the first interspecies molecular atlas of sympathetic neurons across sympathetic ganglia, by generating single-cell data from human ganglia and integrating it to public data. To characterise the sympathetic neuroanatomy, I developed SymClear, an optimised tissue-clearing and 3D-imaging pipeline for visualising sympathetic circuits in whole mice with single-axon resolution. These imaging results allowed the creation Carta Sympathetica, the first realistic representation of sympathetic circuits, which reveals a new sympathetic neuroanatomy and novel anatomical structures.  By combining SympAtlas and SymClear, I’m now characterising novel neuronal populations in the sympathetic ganglia. Collectively, these results support a new model of sympathetic neural circuits composed of diverse populations along the rostrocaudal axis, in contrast to the current view of the sympathetic as a “monolithic effector” branch of the nervous system.

I am always happy to chat about neuroscience, metabolism, machine learning, imaging and data analysis in general. Reach me at david.oliveira@dpag.ox.ac.uk if you’d like to have a chat.