Imperial College London


Faculty of MedicineDepartment of Brain Sciences

Lecturer in Dementia Research, UK DRI Group Leader



n.skene Website




515Burlington DanesHammersmith Campus





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.



Hu D, Abbasova L, Schilder BM, et al., 2022, CUT&Tag recovers up to half of ENCODE ChIP-seq peaks

Murphy AE, Skene NG, 2022, A balanced measure shows superior performance of pseudobulk methods over mixed models and pseudoreplication approaches in single-cell RNA-sequencing analysis

Andrews B, Murphy AE, Stofella M, et al., 2022, Multidimensional Dynamics of the Proteome in the Neurodegenerative and Aging Mammalian Brain, Molecular & Cellular Proteomics, Vol:21

Murphy A, Schilder BM, Skene N, 2021, MungeSumstats: a Bioconductor package for the standardisation and quality control of many GWAS summary statistics, Bioinformatics, Vol:37, ISSN:1367-4803, Pages:4593-4596

Khozoie C, Fancy N, Marjaneh MM, et al., 2021, scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

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