NS
Nathan G. Skene
Imperial College London, Karolinska Institutet, University of Cambridge, University of Reading, University of Edinburgh
Research areas: Biochemistry, Genetics and Molecular Biology · Molecular Biology · Single-cell and spatial transcriptomics · Genetic Associations and Epidemiology · Genomics and Phylogenetic Studies
0000-0002-6807-3180
About
Associate Professor and UK DRI Group Leader at Imperial College London. I lead the Neurogenomics Lab, developing statistical, single-cell and epigenomic methods to identify the cell types, developmental stages and regulatory mechanisms through which genetic risk causes neurodegenerative and neuropsychiatric disease. I also work on reproducible and agentic genomics, including versioned executable research workflows.
Employment
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Imperial College London Associate Professor and UK DRI Group Leader2019 - Present
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Karolinska Institutet Postdoctoral Researcher2016 - 2019
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University of Edinburgh Postdoctoral Researcher2014 - 2016
Education
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University of Cambridge PhD Molecular Biology2009 - 2014
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University of Cambridge MPhil Computational Biology2008 - 2009
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University of Reading BSc Artificial Intelligence and Cybernetics2005 - 2008
Projects & Funding
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Simple questions about neurodegenerative disease: Where? When? What? UK Research and Innovation2026 - 2029
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Increasing the capabilities of complex epigenetics through effective multiplexing with novel SNAP-Tn5 UK Dementia Research Institute2025 - 2027
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Using coppaFISH to determine the cell types associated with alpha-synuclein pathology UK Dementia Research Institute2025 - 2026
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Manipulating microglia states with transcription factors UK Dementia Research Institute2024 - 2025
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targetTF: seeking drug targets for neurodegenerative disease with genome-wide directional evidence UK Dementia Research Institute2023 - 2030
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Understanding selective vulnerability in Parkinson's disease: a pilot study Parkinson's UK2023 - 2026
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Development of a kit for droplet-based single-cell multi-omic TIP-seq and RNA profiling UK Dementia Research Institute2023 - 2024
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Creating commercial kit solutions for single cell epigenetic profiling of histone marks and transcription factors UK Research and Innovation2022 - 2023
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Amplifying genome coverage of single cell epigenetic profiling of the human brain UK Dementia Research Institute2021 - 2022
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Simple questions about neurodegenerative disease: Where? When? What? UK Research and Innovation2020 - 2026
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Piloting Initial Data Platform Integration and Accessibility Alzheimer's Association2020 - 2022
Publications (71)
- Single-Cell Study Designs Are Systematically Underpowered for Small-Effect Genes Save
- A roadmap for enriching rare cell populations from human post-mortem brain, demonstrated by 100% microglial purity Save
- Cell type-specific contextualisation of the human phenome: towards the systematic treatment of all rare diseases Save
- Trustworthy agentic genomics through versioned skill libraries Save
- Using GPT-4 to annotate the severity of all phenotypic abnormalities within the human phenotype ontology Save
- Challenging selective vulnerability in Parkinson’s disease: a systematic review and meta-analysis Save
- Gene expression dynamics of human and mouse craniofacial development at the single-cell level Save
- orthogene: a Bioconductor package to easily map genes within and across hundreds of species Save
- MotifPeeker: benchmarking epigenomic profiling methods using motifs Save
- Single-cell mRNA-regulation analysis reveals cell type-specific mechanisms of type 2 diabetes Save
- Enzyme Activity-Based Genome-wide Screening for Modifiers of Lysosomal Glucocerebrosidase Uncovers Candidate Risk Factors for Parkinson’s Disease Save
- MotifPeeker: Benchmarking epigenomic profiling methods using motifs Save
- CUT&Tag recovers up to half of ENCODE ChIP-seq histone acetylation peaks Save
- Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states Save
- Gene expression patterns of the developing human face at single cell resolution reveal cell type contributions to normal facial variation and disease risk Save
- Predicting cell type-specific epigenomic profiles accounting for distal genetic effects Save
- Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states Save
- Predicting cell type-specific epigenomic profiles accounting for distal genetic effects Save
- rworkflows: automating reproducible practices for the R community Save
- Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer's disease dataset Save