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

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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.