"Exponential growth of data from genomics and genetics is revolutionising our approach to neuroscience. Because of this we can now obtain definitive answers to fundamental questions about the neurodegenerative diseases. " Nathan Skene
UK DRI Group Leader
Dr Nathan Skene’s research group is working to identify the cell types and intracellular processes affected by the genetic loci which underlie neurodegenerative diseases. He began his career completing an undergraduate degree in Artificial Intelligence and Cybernetics from the University of Reading in 2008, followed by an MPhil in Computational Biology at the University of Cambridge in 2009. He went on to pursue a PhD in Molecular Biology at the Wellcome Trust Sanger Institute with Prof Seth Grant, before moving to the Karolinska Institutet in Sweden as a postdoctoral researcher with Jens Hjerling-Leffler, as part of the Functional Neuromics project. Having developed large scale single cell RNA-seq atlases of brain cell types, Dr Skene used these datasets to gain genetic insight into complex genetic diseases. He showed that at least two distinct neuronal types play a role in the aetiology of schizophrenia and that Alzheimer’s associated genetic variants are primarily active in macrophages. In 2020, he was awarded a prestigious Future Leader’s Fellowship from UK Research and Innovation. As a Group Leader at UK DRI, Dr Skene will build on his expertise, exploring some of the fundamental questions that remain in the field of neurodegeneration, using data-driven and genetics-oriented approaches.
1. At a glance
Around 850,000 people in the UK have dementia and there are currently no effective therapeutics for any of the neurodegenerative diseases that give rise to the condition. However, in order to develop treatments, we must fully understand the root causes of diseases like Alzheimer’s and Parkinson’s, and where the earliest changes in our brain are occurring.
Dr Nathan Skene is aiming to answer some of the critical questions that remain in dementia research. By investigating genetic risk, and using a range of sophisticated bioinformatic techniques, he is seeking to find the primary cell types affected in these conditions. If successful, we will gain greater understanding as to where these diseases originate, the fundamental pathways involved and crucially a panel of drug targets for developing treatments.
2. Scientific goals
Dr Nathan Skene’s work focuses on deciphering the human genetics underlying neurobiology including that of cognitive traits, brain function, and neurodegenerative disease. Having built expertise in artificial intelligence, cybergenetics and computational biology, Dr Skene became interested in single cell RNA-sequencing and the generation of brain atlases during his PhD at the Wellcome Sanger Institute. Moving to the Karolinska Institutet in Sweden, he carried out his postdoctoral studies as part of the Functional Neuromics Project – an initiative to create and utilise brain atlases to identify cellular phenotypes within the brains of transgenic animal models.
While at the Karolinska, Dr Skene used an unbiased computational approach to find 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 Alzheimer’s disease. For Parkinson’s disease, the team found that neurons that tend to degenerate in the condition, are associated with the common genetic variants which cause the disease. These neurons all express a common set of genes, suggesting that the reason they degenerate is cell autonomous. The other surprising finding from these studies was strong genetic association with oligodendrocytes in the disease.
In this UK DRI programme, Dr Skene will continue his studies probing the cellular origins of of neurodegenerative conditions, with the aim that this knowledge will form the foundations of better targeted therapeutics. He is also involved in the UK DRI Multi-omics Brain Atlas Project and works closely with the Deep Dementia Phenotyping (DEMON) Network using data science and AI for dementia research.
Main objectives and research goals:
1. Determine the precise cell types associated with neurodegenerative diseases, focusing primarily on those caused by common genetic variants (including Alzheimer’s disease, ALS, multiple sclerosis and Parkinson’s). Novel epigenetic datasets will be generated from human post-mortem tissue and statistically integrated with GWAS data. We will seek to understand for which diseases the selective vulnerability of neuronal subtypes can be explained by genetics.
2. Having identified the cell types which are implicated in disease through genetics, we will seek to identify the affected cell-type specific biological processes. Machine learning will be used to evaluate the effect of disease associated genetic variants on processes such as RNA-splicing and RNA-protein interactions.
3. Determine the time points in the lifespan at which genetic factors contribute to disease processes. To enable this to be addressed, data will be collected from disease-relevant cell types from time points spanning development through to old age.
3. Team members
Dr Di Hu (Postdoctoral Researcher)
Dr Jose Torres (Postdoctoral Researcher)
Brian Schilder (UKDRI at Imperial Distinguished PhD Student)
Alan Murphy (PhD Student)
Kitty Murphy (MRC DTC PhD Student)
Roxy Zhang (PhD Student)
Ying Tang (PhD Student)
Within UK DRI:
- Prof Bart De Strooper, UK DRI at UCL – analysis of scRNA-seq data from mouse models and post-mortem human tissue
- Prof Paul Matthews, UK DRI at Imperial – Alzheimer’s Multi-omic Atlas project
- Dr Alexi Nott, UK DRI at Imperial – Epigenetic analysis of brain cell types
- Dr Sarah Marzi, UK DRI at Imperial – Epigenetic analysis of brain cell types
- Prof Seth Grant, Associate Member at UK DRI at Edinburgh – Synaptic changes across lifespan, between regions and in disease
- Prof Caleb Webber, UK DRI at Cardiff
Beyond UK DRI:
- Dr Jens Hjerling-Leffler, Karolinska Institute – Cell type mapping of genetic traits
- Prof Patrick Sullivan, Karolinska Institute – Cell type mapping of genetic traits
- Deep Dementia Phenotyping (DEMON) network – using data science approaches to tackle dementia
Neurogenomics, Single cell, transcriptomics, epigenomics, genetics, Alzheimers, Statistics, Selective vulnerability, Genomics, Parkinsons, amyotrophic lateral sclerosis, bioinformatics, computational biology, machine learning, deep learning, cell types
GWAS, scRNA-seq, scATAC-seq, CUT&TAG, genomics, Human post-mortem tissue, LD score regression, machine learning, deep learning, statistics, computational biology
7. Key publications
Thrupp, N., Frigerio, C.S., Wolfs, L., Skene, N.G., Fattorelli, N., Poovathingal, S., Fourne, Y., Matthews, P.M., Theys, T., Mancuso, R. and de Strooper, B., 2020. Single-Nucleus RNA-Seq Is Not Suitable for Detection of Microglial Activation Genes in Humans. Cell reports, 32(13), p.108189.
Bryois, J., Skene, N.G., Hansen, T.F., Kogelman, L.J., Watson, H.J., Liu, Z., Brueggeman, L., Breen, G., Bulik, C.M., Arenas, E. and Hjerling-Leffler, J., 2020. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease. Nature Genetics, 52(5), pp.482-493.
Qian, X., Harris, K.D., Hauling, T., Nicoloutsopoulos, D., Muñoz-Manchado, A.B., Skene, N., Hjerling-Leffler, J. and Nilsson, M., 2020. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nature methods, 17(1), pp.101-106.
Skene, N.G., Bryois, J., Bakken, T.E., Breen, G., Crowley, J.J., Gaspar, H.A., Giusti-Rodriguez, P., Hodge, R.D., Miller, J.A., Muñoz-Manchado, A.B. and O’Donovan, M.C., 2018. Genetic identification of brain cell types underlying schizophrenia. Nature genetics, 50(6), pp.825-833.
Skene, N.G., Roy, M. and Grant, S.G., 2017. A genomic lifespan program that reorganises the young adult brain is targeted in schizophrenia. Elife, 6, p.e17915.
Skene, N.G. and Grant, S.G., 2016. Identification of vulnerable cell types in major brain disorders using single cell transcriptomes and expression weighted cell type enrichment. Frontiers in neuroscience, 10, p.16.