About

Our research focuses on simple questions about complex brain traits: where do they occur (in which cell types)? When do they occur (at which developmental stages)? How do they occur (which quantitative traits of cells are affected)? We aim to develop the methods and datasets required to enable these questions to be answered systematically for any trait for which well-powered genetic data is available.

The most complete and unbiased data source currently available for the brain comes from single cell RNA-sequencing: an atlas of almost every cell type found in the central, peripheral and enteric nervous system is available. Using this dataset and other’s like it we have investigated the cell types underlying Schizophrenia, Alzheimer’s disease, Parkinsons, Stroke and cognitive ability. To get greater accuracy in interpreting genetic data we need to go beyond mouse single cell RNA-seq data though: we plan to obtain human cell atlases using ATAC-seq and other genomic techniques.

Using transcriptomic datasets mapping expression changes across the lifespan we have investigated why the onset of psychiatric diseases occurs in early adulthood (rather than in infancy, as would be expected for heavily genetic disorders). Currently this kind of data is only available for a small number of brain regions; we are looking to generate new datasets spanning the human lifespan for a range of disease relevant cell types.

Identifying the causal cell types and developmental stages will not in itself lead to cures though. To get closer to that we need to understand what changes occur on the affected cells. Our approach is based on the notion that the causal changes associated with disease will be quantitative traits, much like height or BMI, but the important changes will be on the cellular level for specific subtypes of cells (i.e. increased spine density in Reln/Ndnf+ cortical interneurons). To identify these changes and link them to genetics we need higher throughput biological methods. Towards this end we are building on the work of collaborators to develop spatial transcriptomic techniques to enable simultaneous automated identification of all cell types found in tissue sections.

Looking for a postdoc, internship, masters or PhD? Would be great to hear from you!

Applications would be welcomed from both experimentalists and analysts. If you’re background is not in biology but you’re great at programming or mathematics then get in touch. Descriptions of potential projects will be put online soon: in the meanwhile, if this is what you want to work on then just get in touch.

Recent Posts

Cell type mapping for 28 traits against whole nervous system

Our latest preprint on cell type mapping is now available on bioRxiv. This work was a collaboration with the Hjerling-Leffler and Sullivan labs. Some of the most exciting findings relate to Parkinsons disease: we discover genetic associations with most the celltypes known to degenerate in the disease (suggesting this occurs cell autonomously) but also discovered a new link to Oligodendrocytes…

Meet the Team

Principal Investigators

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Nathan Skene

Lecturer and UKDRI Group Leader

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Sarah Marzi

Edmond and Lily Safra Research Fellow and UK DRI Career Development Fellow

Post-doctoral Researchers

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Di Hu

DRI Funded Postdoc

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Zijing Liu

DRI Funded Postdoc

PhD Students

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Brian Schilder

UKDRI at Imperial Distinguished PhD Studentship

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Kitty Murphy

MRC DTC PhD

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Maria Tsalenchuk

DRI Funded PhD Student

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Roxy Zhang

PhD Student

Masters Students

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Shashank Tiwari

Visting Masters Student

Open Positions

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Open Positions

PhDs, Postdocs, Bioinformaticians and Technicians

Alumni

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Kristina Salontaji

Erasmus funded visting masters Student

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Liyueyue Liu

MRes Experimental Neuroscience

Recent Publications

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Synaptic combinatorial molecular mechanisms generate repertoires of innate and learned behavior

Integrative analysis of rare variants and pathway information shows convergent results between immune pathways, drug targets and epilepsy genes

Conditional GWAS analysis identifies putative disorder-specific SNPs for psychiatric disorders

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