Investigation of immunometabolism at the single cell level by integration of spatial and temporal multiomics

Despite the increase of genomic and transcriptomic data characterising immune cells at the single cell level, metabolomics has been left lacking in resolution and quantity of data. Continued advancements in analytical techniques (and mass spectrometry in particular) have begun to ameliorate these deficits, fuelling interest in the field of immunometabolism and how the metabolic networks of individual cells affect and are effected by their environment. Built on the expansion of multiomic datasets there are rich opportunities for new computational and mathematical methods for processing and extracting valuable insights from both metabolomic data in isolation and for the inference of metabolic states from other 'omic' data sources. However, multiple fundamental issues remain including integrating data collected at different spatial and temporal scales, strong batch effects when trying to perform meta-analysis of publicly available datasets as well as underlying inter-species differences that hamper the transfer of knowledge from non-human animal models to humans. This project will seek to exploit a highly interdisciplinary intersection of new spatial omic method development, systems immunology and new open source computational tool development. Key challenges that will be tackled include the distributed nature of regulatory networks in the body where metabolites produced in a given tissue may be utilised in another, creating networks that span multiple scales and are not completely captured by the analysis of single tissues in isolation. A related problem is the accurate integration of data from methods that measure at the whole-body vs single cell resolutions. Specifically, the project will fall under the EPSRC categories of 'Biological Informatics', as the project will seek to develop new methods for integrating different data modalities in order to build a system-level understanding of metabolic flux, and 'Mathematical Biology', as new mathematical and/or statistical methods will be needed in order to integrate these data across spatial and temporal dimensions. In addition to these primary categories, there is the possibility the project will also fall under the 'Artificial intelligence technologies' category as existing artificial intelligence methods including autoencoders and graph neural networks may need to be advanced in novel directions to make use of the intrinsic structures in the specific data used in the project. The methods developed will help to advance our understanding of how metabolic networks integrate between cells and with different aspects of cell biology. as well as the systemic effects of metabolic dysregulation in disease states - particularly those involving the immune system.

The UK's world-leading position in biomedical research is critically dependent upon training scientists with the cutting-edge research skills and technological know-how needed to drive future scientific advances. Since 2009, the EPSRC and MRC CDT in Systems Approaches to Biomedical Science (SABS) has been working with its consortium of 22 industrial and institutional partners to meet this training need.

Over this period, our partners have identified a growing training need caused by the increasing reliance on computational approaches and research software. The new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 will address this need. By embedding a sustainable approach to software and computational model development into all aspects of the existing SABS training programme, we aim to foster a culture change in how the computational tools and research software that now underpin much of biomedical research are developed, and hence how quantitative and predictive translational biomedical research is undertaken.

As with all CDT Programmes, the future impact of SABS:R^3 will be through its alumni, and by the culture change that its training engenders. By these measures, our existing SABS CDT is already proving remarkably successful. Our alumni have gone on to a wide range of successful careers, 21 in academic research, 19 in industry (including 5 in SABS partner companies) and the other 10 working in organisations from the Office of National Statistics to the EPSRC. SABS' unique Open Innovation framework has facilitated new company connections and a high level of operational freedom, facilitating 14 multi-company, pre-competitive, collaborative doctoral research projects between 11 companies, each focused on a SABS student.

The impact of sustainable and open computational approaches on biomedical research is clear from existing SABS' student projects. Examples include SAbDab which resulted from the first-ever co-sponsored doctorate in SABS, by UCB and Roche. It was released as open source software, is embedded in the pipelines of several pharmaceutical companies (including UCB, Medimmune, GSK, and Lonza) and has resulted in 13 papers. The SABS student who developed SAbDab was initially seconded to MedImmune, sponsored by EPSRC IAA funding; he went on to work at Roche, and is now at BenevolentAI. Similarly, PanDDA, multi-dataset X-ray crystallographic software to detect ligand-bound states in protein complexes is in CCP4 and is an integral part of Diamond Light Source's XChem Pipeline. The SABS student who developed PanDDA was awarded an EMBO Fellowship.

Future SABS:R^3 students will undertake research supported by both our industrial partners and academic supervisors. These supervisors have a strong track record of high impact research through the release of open source software, computational tools, and databases, and through commercialisation and licensing of their research. All of this research has been undertaken in collaboration with industrial partners, with many examples of these tools now in routine use within partner companies.

The newly focused SABS:R^3 will permit new industrial collaborations. Six new partners have joined the consortium to support this new bid, ranging from major multinationals (e.g. Unilever) to SMEs (e.g. Lhasa). SABS:R^3 will continue to make all of its research and teaching resources publicly available and will continue to help to create other centres with similar aims. To promote a wider cultural change, the SABS:R^3 will also engage with the academic publishing industry (Elsevier, OUP, and Taylor & Francis). We will explore novel ways of disseminating the outputs of computational biomedical research, to engender trust in the released tools and software, facilitate more uptake and re-use.