Nathan Skene

Nathan Skene's interests lie in using human genetics to gain insight into the neurobiology of brain disorders and cognitive traits. A core part of his work has am focused on identifying the causal cell types underlying complex genetic traits. His research groups aim to tackle basic questions about brain disorders: where they occur, at which developmental stage genetic insults take place, and the biological processes acted on within each cell type involved.

His postdoctoral research was done at the Karolinska Institutet (KI) where he worked with Jens Hjerling-Leffler as part of the Functional Neuromics project. At KI he was involved in developing large scale single cell RNA-seq atlases of brain cell types, and using these datasets to gain insight into genetic disorders. Using this approach he was able to show that multiple cell types play a role in the etiology of schizophrenia, while only microglia appear to be influenced by the common genetic factors influencing Alzheimers. He developed the EWCE and MAGMA_Celltyping R packages to facilitate these analyses.

He gained an undergraduate degree in Artificial Intelligence and Cybernetics from the University of Reading in 2008, followed by an MPhil at the University of Cambridge in Computational Biology in 2009. He went on to do a PhD in Molecular Biology at the Wellcome Trust Sanger Institute working with Prof Seth Grant. During this time he worked on the Genes to Cognition programme, analysing the transcriptomic changes seen in a mice carrying a wide range of synaptic mutations. Later, while working between the University of Edinburgh and UCL, he studied how postnatal gene expression changes influence the onset of psychiatric disorders.

Publications

Journals

• Choi S, Schilder BM, Abbasova L, et al., 2023, EpiCompare: R package for the comparison and quality control of epigenomic peak files, Bioinformatics Advances, Vol:3, ISSN:2635-0041
• Mucha M, Skrzypiec AE, Kolenchery JB, et al., 2023, miR-483-5p offsets functional and behavioural effects of stress in male mice through synapse-targeted repression of Pgap2 in the basolateral amygdala., Nat Commun, Vol:14
• Ranson JM, Bucholc M, Lyall D, et al., 2023, Harnessing the potential of machine learning and artificial intelligence for dementia research, Brain Informatics, Vol:10, ISSN:2198-4018
• Murphy AE, Skene NG, 2022, A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis., Nat Commun, Vol:13