Real-Time Volatile Metabolomics Analysis of Dendritic Cells
Introduction:
Dendritic cells (DCs) actively sample and present antigen to cells of the adaptive immune system and are thus vital for successful immune control and memory formation. Immune cell metabolism and function are tightly interlinked, and a better understanding of this interaction offers potential to develop immunomodulatory strategies. However, current approaches for assessing the immune cell metabolome are often limited by endpoint measurements, may involve laborious sample preparation, and may lack unbiased, temporal resolution of the metabolome. In this study, we present a novel setup coupled to a secondary electrospray ionization-high resolution mass spectrometric (SESIHRMS) platform allowing headspace analysis of immature and activated DCs in real-time with minimal sample preparation and intervention, with high technical reproducibility and potential for automation. Distinct metabolic signatures of DCs treated with different supernatants (SNs) of bacterial cultures were detected during real-time analyses over 6 h compared to their respective controls (SN only). Furthermore, the technique allowed for the detection of 13C incorporation into volatile metabolites, opening the possibility for real-time tracing of metabolic pathways in DCs. Moreover, differences in the metabolic profile of naive and activated DCs were discovered, and pathway-enrichment analysis revealed three significantly altered pathways, including the TCA cycle, α- linolenic acid metabolism, and valine, leucine, and isoleucine degradation.
Results:
Study I: Stimulation of Cells by Bacterial SN:
The study explored the capacity of a method to detect volatile organic compounds (VOCs) emitted by dendritic cells (DCs) and to differentiate the cellular responses to various bacterial supernatants (SNs). A significant difference was observed in the VOC profiles between cells exposed to two different SNs, both with and without DC presence, identifying 71 VOCs for SN1 and 59 VOCs for SN2, all showing a significant change in abundance. In total, 108 unique VOCs were analyzed using t-distributed stochastic neighbor embedding (tSNE) and hierarchical clustering, which demonstrated distinct group segregations based on the presence of DCs and the type of SN. The study also uncovered specific VOCs that either increased or decreased over time in DC samples, indicating active metabolism or production by DCs. One compound, identified as 2-(methylthio) benzothiazole (SCH3-BTH), was found in higher abundance in SN1 + DC samples, suggesting an antimicrobial strategy by the DCs. The method exhibited high technical and biological reproducibility, with low intra-experimental variation, and maintained cell integrity during analysis. It also allowed for the efficient measurement of multiple conditions in a single run, enhancing the experiment's overall efficiency.
Study II: Monitoring 13 C-Incorporation from Labeled Glucose into VOCs in Real-Time:
The study demonstrated the capability of a method to accurately differentiate between various experimental setups with strong consistency. It further explored if this method could track the real-time incorporation of 13C into volatile metabolites over 4 hours. Through observing the 13C/12C ratio in three biological replicates of dendritic cells exposed to 13C-labeled and standard glucose, a significant incorporation of 13C was observed immediately after glucose introduction, which then declined to nearly zero after approximately 2 hours. For cells exposed to standard glucose (12C6), the 13C /12C ratios remained at natural levels, as expected. The metabolite observed at m/z 75.04404 was tentatively identified as lactaldehyde, which is involved in essential metabolic pathways such as pyruvate and carbohydrate metabolism. The study notes that only one metabolite consistently showed 13C incorporation, possibly due to dendritic cells' reliance on internal glycogen reserves for metabolic activities and the time needed for intracellular metabolites to achieve isotopic equilibrium. This observation underlines the method's reliability across different samples and its potential for tracing 13C incorporation in real time. The technique's adaptability for use with other isotopic tracers, like deuterium and 13C, points to its broader applicability in metabolic research, especially for analyzing cells with slower metabolic rates or those dependent on intrinsic energy reserves.
Study III: 13 C-Incorporation with and without long-time LPS stimulation:
This study evaluates a novel method for real-time monitoring of 13C incorporation into volatile organic compounds (VOCs) to detect metabolic changes in dendritic cells (DCs) in response to lipopolysaccharide (LPS) stimulation. By exposing DCs to 13C-labeled glucose (13C6-Glc) with and without LPS for 24 hours, and comparing them to controls exposed to naturally abundant 12C-glucose (12C6-Glc), significant 13C incorporation was observed, highlighting metabolic reprogramming upon LPS stimulation. Principal component analysis (PCA) and analysis of variance (ANOVA) showed clear metabolic profile distinctions among groups, with pathway enrichment analysis revealing key metabolic pathway alterations, including in α-linolenic acid metabolism and the tricarboxylic acid (TCA) cycle. The study notes technical limitations such as the need for automated processes and acknowledges that further research is needed to confirm the identified features and explore their metabolic roles.
Conclusion:
The proposed mass spectrometric platform allows the monitoring of metabolic trajectories emitted by DCs in realtime. The setup poses an attractive complementary approach
to standard metabolomics methods due to its short analysis time, sensitive detection, minimal sample preparation, and high efficiency in measuring multiple probes simultaneously. In combination with labeled substrates, the technique has the potential to provide new insights into metabolic pathways that play a key role in immunological responses triggered by different types of cells.
