PhD Studentship: Decoding cellular diversity in the brain: functional annotation of neuronal subtypes across development and disease

The human brain contains an extraordinary diversity of cell types, each with distinct functions, molecular programmes and roles in disease. Even within neurons, numerous excitatory and inhibitory subtypes differ across brain regions and change across development and ageing. Understanding this diversity is essential for determining how the brain functions and why particular neuronal populations are selectively vulnerable in different brain diseases. Single-cell RNA sequencing has transformed our understanding of neuronal transcriptional diversity, but the regulatory mechanisms that establish and maintain these identities remain much less well characterised, particularly for rarer neuronal populations. We have developed methods for isolating defined cell populations from human post-mortem brain and generated multi-omic regulatory maps of major cell classes.

This PhD will extend these approaches to resolve the functional genomics of specific neuronal subtypes. The central objective is to isolate distinct neuronal populations, including excitatory and inhibitory subtypes and regionally specialised populations such as motor neurons, and determine how their regulatory landscapes vary across brain regions, development and ageing. The student will use existing single-cell and multiomic datasets to identify nuclear markers that distinguish neuronal populations, then develop fluorescence-activated nuclei sorting strategies to isolate them from human brain tissue. Samples will span cortex, hippocampus, amygdala, hypothalamus and other disease-relevant regions, with opportunities to examine development and ageing.

The project will use functional genomic technologies to characterise gene regulation within these populations. Approaches will include Oxford Nanopore long-read sequencing to map DNA modifications, ATAC-seq to identify accessible regulatory elements, and CUT&Tag profiling of H3K27ac to identify active enhancers and promoters.

A particular focus will be direct measurement of DNA hydroxymethylation, an abundant neuronal DNA modification likely to have important regulatory functions. Long-read RNA sequencing will define full-length transcripts and cell-specific isoform usage, with opportunities to explore direct RNA sequencing and the neuronal epitranscriptome. Together these datasets will provide a functional annotation of neuronal diversity, identifying regulatory elements, transcripts and epigenetic features that distinguish populations across regions and stages of life.

They will also be used to interpret human genetic studies of neurological and psychiatric disease, including whole-genome sequencing and association datasets from large national cohorts. A further objective will be to use these high-resolution reference maps to develop computational methods for estimating the proportions and molecular states of specific neuronal populations in bulk brain datasets. These deconvolution approaches can then be applied to extensive in-house DNA methylation datasets from disorders such as Alzheimer’s disease, enabling disease-associated molecular changes to be assigned to specific neuronal populations.

This is a highly interdisciplinary project spanning epigenomics, genomics, molecular biology, neuroscience and computational biology. The student will be based in Exeter (Mill, Dempster, Bamford) with collaborators at Imperial (Nott), KCL (Marzi), Cambridge (Ryten) and EBI-EMBL (Birney). The project is supported by exceptional resources, including existing multiomic human brain datasets, post-mortem tissue (multiple brain regions and developmental stages), cell-sorting platforms, state of-the-art sequencing facilities, and extensive high-performance computing. The resulting datasets and computational tools will provide a framework for understanding neuronal function and identifying the cell populations and regulatory mechanisms through which genetic and epigenetic variation contributes to brain disease.

Funded by: EPSRC Engineering and Physical Sciences Research Council 

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