Abstract

Macrophages consist of a heterogeneous population of functionally distinct cells that participate in many physiological and pathological processes. They exhibit prominent plasticity by changing their different functional phenotypes represented by proinflammatory (M1) and anti-inflammatory (M2) in response to different environmental stimuli. Emerging evidence illustrates the importance of intracellular metabolic pathways in macrophage polarizations and functions. In the tumor microenvironment (TME), macrophages tend to M2 polarization, which promotes tumor growth and leads to adverse physiological effects. Due to the lack of highly specific antigens in M1 and M2 macrophages, significant challenges present in isolating these subtypes from clinical samples or in vitro coculture models of tumor-immune cells. In reverse, the single-cell technique provides the possibility to investigate the factors influencing macrophage polarization in the TME. In this research, we employed inertial microfluidic chip-mass spectrometry (IMC-MS) to conduct single-cell metabolomics analysis of macrophages polarized into the two major phenotypes, respectively, and 213 metabolites were identified in total. Subsequently, differential metabolites between macrophage phenotypes were analyzed using volcano plots and binary logistic regression models. Glutamine was pinpointed as a key metabolite for the M1 and M2 phenotypes. Experimental results from both monoculture and coculture cell models demonstrated that M1 polarization is more reliant on the presence of glutamine in the culture environment than M2 polarization. Glutamine deficiency resulted in failed M1 polarization, while its absence had a less pronounced effect on M2 polarization. Replenishing an appropriate amount of glutamine during the intermediate stages of coculture models significantly enhanced the proportion of M1 polarization and suppressed the growth of tumor cells. This research elucidated glutamine as a key factor influencing macrophage polarization in the TME via single-cell metabolomics based on IMC-MS, offering promising insights and targets for tumor therapies.

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