PhD: Single-Cell Multi-omics of Cardio-Blood Development

University of East Anglia; Norwich; On-site; GBP 20,000 - 24,000; Full time

Job summary

The University of East Anglia invites applications for a four-year Norwich Research Park Biosciences Doctoral Training Partnership PhD project. It investigates how enhancer activity shapes early cardiovascular development in the chick embryo.

You will apply single-cell multi-omics, integrating gene-expression and chromatin-accessibility data, learning computational approaches, reconstructing gene-regulatory networks, and identifying candidate enhancers linked to cell states and trajectories.

Qualifications

• Experience with single-cell multi-omics and integration of gene expression and chromatin accessibility data.
• Experience cloning enhancers into reporter constructs and in vivo imaging.
• Familiarity with CRISPR-based perturbation and motif mutagenesis.

Responsibilities

• Investigate how enhancer activity changes as blood, endothelial and cardiac lineages emerge in the chick embryo.
• Learn computational approaches to integrate single-cell RNA-sequencing and ATAC-sequencing data.
• Reconstruct gene-regulatory networks and identify candidate enhancers linked to specific cell states and developmental trajectories.
• Clone candidate enhancers into fluorescent reporter constructs and visualise their activity in living embryos.
• Test how selected enhancers control gene expression, cell behaviour and fate decisions in vivo using CRISPR-based perturbation.

Skills

Single-cell multi-omics

Gene expression analysis

ATAC-seq data analysis

CRISPR-based perturbation

In vivo imaging

Computational approaches

Tools

Fluorescent reporter constructs

CRISPR-based perturbation

Job description

The University of East Anglia invites applications for a four-year Norwich Research Park Biosciences Doctoral Training Partnership PhD project. It investigates how enhancer activity shapes early cardiovascular development in the chick embryo.

You will apply single-cell multi-omics, integrating gene-expression and chromatin-accessibility data, learning computational approaches, reconstructing gene-regulatory networks, and identifying candidate enhancers linked to cell states and trajectories.