RNA Splicing in Immune Cells: Mechanisms, Regulation, and Predictive Modelling

PROJECT OUTLINE:

This DPhil project aims to elucidate the mechanisms of RNA splicing within immune cells, focusing on the computational analysis of alternative splicing events and their impact on immune homeostasis and inflammatory responses. By leveraging pre-existing long-read single-cell sequencing data, this research will develop and apply computational methods to analyse splicing patterns, identify key regulatory elements, and integrate multi-omics data to understand the functional consequences of alternative splicing in immune regulation.

OBJECTIVES:

1. Map RNA Splicing in Immune Cells:

• Data Utilisation: Utilise pre-existing long-read single-cell RNA sequencing (scCOLOR-seq^{1, 2}) data from healthy immune cells to capture the full transcriptome and elucidate splicing regulation mechanisms.
• Computational Analysis: Develop and employ bioinformatics pipelines to process sequencing data, focusing on isoform diversity and splicing events. Conduct Differential Isoform Usage (DIU) tests to identify significant splicing changes between conditions.

• Data Integration: Integrate RNA-seq data with epigenetic and transcriptomic profiles to identify regulatory elements influencing splicing. Examine the roles of RNA-binding proteins (RBPs) and non-coding RNAs (ncRNAs) in modulating splicing events.
• Machine Learning Models: Implement machine learning approaches, such as random forests and autoencoders, to predict regulatory elements and their impact on splicing. Employ feature selection techniques to highlight key regulators.

• Causal Inference: Use Structural Causal Models (SCMs) to infer causal relationships between splicing regulators and observed splicing patterns. Identify key elements driving splicing modulation under inflammatory conditions.
• Predictive Modelling: Develop ensemble machine learning models to predict splicing outcomes based on identified regulatory features. Use Graph Neural Networks (GNNs) to integrate gene interaction data and enhance predictive accuracy. 

METHODOLOGIES:

• Bioinformatics Pipelines: Design and implement custom bioinformatics pipelines for processing long-read single-cell RNA sequencing data. This includes alignment, isoform reconstruction, and differential splicing analysis.
• Data Integration: Utilise multi-omics data integration techniques to combine additional public bulk transcriptomic, epigenetic, and proteomic data.
• Machine Learning: Apply machine learning algorithms to identify patterns and predict regulatory elements involved in RNA splicing. Key components include feature selection, model training, and validation.
• Causal Inference Models: Implement SCMs to determine causal relationships between splicing events and regulatory elements. Construct and validate models to ensure robust causal inference.

EXPECTED OUTCOMES:

• Comprehensive Splicing Map: Generate a detailed map of RNA splicing events in immune cells under various activation states, providing insights into the role of splicing in immune regulation.
• Regulatory Element Identification: Identify key regulatory elements and pathways involved in splicing modulation, contributing to the understanding of splicing regulation mechanisms.
• Predictive Models: Develop predictive models for splicing outcomes based on identified regulatory features, facilitating the prediction of splicing changes in response to inflammatory stimuli.
• Publications and Tools: Produce high-impact publications and develop computational tools and pipelines to be made available to the scientific community, fostering further research in the field.

RESEARCH ENVIRONMENT: 

The project will be conducted in a collaborative and interdisciplinary environment, leveraging expertise in computational biology, immunology, and bioinformatics. Access to state-of-the-art sequencing technologies and high-performance computing resources will support the research. The supervisory team, led by experts in RNA biology and computational analysis, will provide guidance and mentorship throughout the project, ensuring successful completion and impactful contributions to the field.

ESSENTIAL CRITERIA: 

• Hold or be about to obtain a first or upper second-class BSc degree or a Master’s degree (or equivalent) in subjects relevant to 1) biology or 2) computer science, engineering, statistics, maths, or data science.
• Proficient in or have a desire to learn R and/or Python programming.
• The desire to learn state-of-the-art wet-lab molecular biology techniques.

REFERENCES:

1. Philpott et al. 2021, Nanopore sequencing of single-cell transcriptomes with scCOLOR-seq. Nature Biotechnology. Link
2. Sun et al. 2024, Correcting PCR amplification errors in unique molecular identifiers to generate accurate numbers of sequencing molecules. Nature methods. Link
3. Cribbs et al. 2020, Dissecting the role of BET bromodomain proteins BRD2 and BRD4 in Human NK cell function. Frontiers in Immunology. Link
4. Cribbs et al. 2020. Histone H3K27me3 demethylases regulate human Th17 cell development and effector functions by impacting on metabolism. PNAS. Link 

RESEARCH GROUP: 

Led by Assoc. Prof. Adam Cribbs at the Botnar Research Centre, University of Oxford, the multi-disciplinary team includes experts in computational biology, immunology, and bioinformatics. The group focuses on developing novel long-read applications and computational analysis frameworks to enhance disease treatment.

TRAINING:

The Botnar Research Centre offers training in molecular and cell biology, single-cell sequencing, computational techniques, and data analysis. Students will attend lectures, seminars, and conferences, with opportunities to collaborate with researchers from various institutes and the international Human Cell Atlas network.

A core curriculum of lectures will be taken in the first term to provide a solid foundation in a broad range of subjects including musculoskeletal biology, inflammation, epigenetics, translational immunology, data analysis and the microbiome.  Students will also be required to attend regular seminars within the Department and those relevant in the wider University.

Students will have access to various courses run by the Medical Sciences Division Skills Training Team and other Departments. All students are required to attend a 2-day Statistical and Experimental Design course at NDORMS and run by the IT department (information will be provided once accepted to the programme).

HOW TO APPLY:

Please contact the relevant supervisor(s), to register your interest in the project, and the departmental Education Team (graduate.studies@ndorms.ox.ac.uk), who will be able to advise you of the essential requirements for the programme and provide further information on how to make an official application.

Interested applicants should have, or expect to obtain, a first or upper second-class BSc degree or equivalent in a relevant subject and will also need to provide evidence of English language competence (where applicable). The application guide and form is found online and the DPhil will commence in October 2025.

Applications should be made to the following programme, using the specified course code. 

D.Phil in Molecular and Cellular Medicine (course code: RD_MP1)

For further information, please visit http://www.ox.ac.uk/admissions/graduate/applying-to-oxford.

The Botnar Institute is a proud supporter of the Academic Futures scholarship programme, designed to address under-representation and help improve equality, diversity and inclusion in our graduate student body. The Botnar and the wider University rely on bringing the very best minds from across the world together, whatever their race, gender, religion or background to create new ideas, insights and innovations to change the world for the better. Up to 50 full awards are available across the three programme streams, and you can find further information on each stream on their individual tabs (Academic futures | Graduate access | University of Oxford).