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

尽管在单细胞水平上表征免疫细胞的基因组和转录组数据有所增加，但代谢组学在数据的分辨率和数量方面仍然缺乏。分析技术(特别是质谱学)的不断进步已经开始改善这些缺陷，激发了人们对免疫代谢以及单个细胞的代谢网络如何影响和受环境影响的兴趣。建立在多组数据集扩展的基础上，有大量的机会为新的计算和数学方法处理并从孤立的代谢数据中提取有价值的见解，以及从其他‘组学’数据源中推断代谢状态。然而，仍然存在多个基本问题，包括整合在不同空间和时间尺度上收集的数据，在试图对公开可用的数据集进行元分析时产生强烈的批次效应，以及阻碍从非人类动物模型向人类转移知识的潜在物种间差异。该项目将寻求开发新的空间组学方法开发、系统免疫学和新的开源计算工具开发的高度跨学科的交叉。将解决的关键挑战包括体内调节网络的分布式性质，在这种网络中，在一个特定组织中产生的代谢物可能被另一个组织利用，形成跨越多个规模的网络，而不是通过孤立地分析单个组织来完全捕获。一个相关的问题是，从测量全身和单个细胞分辨率的方法中获得的数据的准确整合。具体地说，该项目将属于EPSRC的“生物信息学”类别，因为该项目将寻求开发新的方法来整合不同的数据模式，以便建立对代谢通量和“数学生物学”的系统级理解，因为将需要新的数学和/或统计方法来整合这些跨越空间和时间维度的数据。除了这些主要类别外，该项目也有可能被归入“人工智能技术”类别，因为包括自动编码器和图形神经网络在内的现有人工智能方法可能需要在新的方向上发展，以利用项目中使用的特定数据中的内在结构。开发的方法将有助于促进我们对代谢网络如何在细胞之间以及与细胞生物学的不同方面整合的理解。以及疾病状态下代谢失调的系统性影响--特别是涉及免疫系统的那些。

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.