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学和转录组学数据有所增加，但代谢组学一直缺乏分辨率和数据量。分析技术（特别是质谱法）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领域的兴趣，以及单个细胞的代谢网络如何影响环境并受环境影响。建立在多组学数据集的扩展有丰富的机会，新的计算和数学方法处理和提取有价值的见解，从两个代谢组学数据隔离和推断代谢状态从其他“组学”数据源。然而，多个基本问题仍然存在，包括整合在不同的空间和时间尺度上收集的数据，试图对公开数据集进行荟萃分析时的强烈批量效应，以及潜在的物种间差异，这些差异阻碍了知识从非人类动物模型转移到人类。该项目将寻求利用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交叉。将解决的关键挑战包括体内调控网络的分布式性质，其中在给定组织中产生的代谢物可以在另一个组织中使用，创建跨越多个尺度的网络，并且不能通过孤立地分析单个组织来完全捕获。一个相关的问题是来自全身与单细胞分辨率测量方法的数据的准确整合。具体而言，该项目将属于EPSRC类别的“生物信息学”，因为该项目将寻求开发整合不同数据模式的新方法，以建立对代谢通量的系统级理解，以及“数学生物学”，因为需要新的数学和/或统计方法来整合这些数据跨越空间和时间维度。除了这些主要类别之外，该项目还可能属于“人工智能技术”类别，因为现有的人工智能方法（包括自动编码器和图形神经网络）可能需要向新的方向发展，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推进我们对代谢网络如何在细胞之间整合以及与细胞生物学不同方面整合的理解。以及疾病状态下代谢失调的全身效应-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.