PhD Studentship: One read, one cell: Decoding cell identity with long-read sequencing

Background What if we could resolve the epigenome of individual cell types from complex human tissues without ever having to physically separate the cells? The majority of disease-associated genetic variants exert their effects by modulating gene expression, driving considerable interest in profiling epigenomic variation at scale. Epigenomic patterns, chemical modifications to DNA and chromatin that controls which genes are active, are highly cell-type specific. When studying epigenetics, the tissue choice stron...

Opportunity details

Background What if we could resolve the epigenome of individual cell types from complex human tissues without ever having to physically separate the cells? The majority of disease-associated genetic variants exert their effects by modulating gene expression, driving considerable interest in profiling epigenomic variation at scale. Epigenomic patterns, chemical modifications to DNA and chromatin that controls which genes are active, are highly cell-type specific. When studying epigenetics, the tissue choice strongly influences the findings. A critical limitation of existing studies is that bulk tissues (such as whole blood) comprise a mixture of cell types, potentially masking true biological signals. Historically, resolving this has required the physical isolation of individual cell populations, costly, low-throughput and impractical for population-level studies. Long-read sequencing changes this paradigm by detecting epigenetic modifications alongside genetic sequencing, and by capturing continuous stretches of a single DNA molecule so that each read provides a snapshot of its cell type of origin. We have developed a framework that classifies cell type for individual reads by comparing methylation patterns to reference profiles for different cell types. This enables the generation of cell type-specific epigenetic profiles directly from long-read sequencing data, bypassing the need for experimental cell separation. The project will exploit ~50,000 Oxford Nanopore long-read genomes available in UK Biobank, providing a unique opportunity to resolve cell-type-specific epigenetic variation at population scale. Objectives Evaluate the predictive accuracy and optimal conditions for computational cell-type-of-origin prediction from long-read sequencing data. Curate a compre...

Eligibility

They will need strong mathematical and programming skills, a genuine interest in cutting-edge biotechnologies such as long-read sequencing, and willingness to engage with cell biology to interpret their findings.

Requirements

They will need strong mathematical and programming skills, a genuine interest in cutting-edge biotechnologies such as long-read sequencing, and willingness to engage with cell biology to interpret their findings.

Funding and benefits

Funding eligibility: UK Students, International Students. Funding amount: £21,805 The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7..

Required documents

reference

How to apply

Apply the framework to large population biobank datasets to generate cellular composition estimates and cell-type-specific epigenetic profiles at scale. Methodology The student will apply our suite of computational tools to blood-based long-read sequencing datasets, benchmarking cell-type-of-origin predictions against reference purified datasets to determine optimal conditions for the approach.

Open the official application information

Verify the deadline and final conditions on the official provider page before submitting an application.