Systems-based approaches to study immunometabolism

Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of, and challenges faced by metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.

Automated summary

Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), have uncovered new levels of complexity in cellular systems. The study of immune cell metabolism has emerged as a new area of investigation using the systems biology approach. However, there are several challenges, including the diversity of immune cell phenotypes, sparse immune cell numbers in vivo, limitations in metabolite identification, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and dependence on the local milieu. This review discusses the systemic nature of immunometabolism, cell- and system-based approaches, mathematical modeling techniques, and applications and challenges in metabolic modeling.