A mass spectrometry workflow integrates ion mobility separation with mass cytometry to enable high-throughput single-cell metabolomics with improved metabolite coverage.
Single-cell metabolomics has been limited by low sensitivity, sparse metabolite detection, and high technical variability compared with single-cell transcriptomic and proteomic methods. To address these challenges, the researchers integrated trapped ion mobility spectrometry with time-of-flight mass cytometry – enabling the detection and annotation of hundreds of metabolites across large single-cell datasets while adding an additional separation dimension to reduce chemical interference.
A central feature of the method is ion mobility-enabled selective ion accumulation, which enriches low-mass metabolites and suppresses signals from abundant background species such as lipids. This was paired with a “cell superposition” strategy, where signals from many cells are first combined to identify genuine metabolite peaks, which are then extracted back to individual cells. This approach improves signal-to-noise ratios and reduces missing values while retaining single-cell resolution.
To handle the resulting datasets, the researchers developed a dedicated computational workflow, MetCell, tailored for ion mobility-resolved single-cell metabolomics. The pipeline integrates peak detection, targeted feature extraction, and metabolite annotation based on mass-to-charge ratio, collision cross-section, and in situ MS/MS data. Applied across multiple cell lines, the approach detected several thousand metabolic features per cell and enabled annotation of around 800 metabolites.
The researchers demonstrated the method by profiling more than 45,000 primary liver cells from young and aged mice. Analysis of the data resolved major liver cell populations and revealed metabolic variation within hepatocytes, including differences in lipid handling and glycogen-associated pathways that varied with age.
The authors note that combining ion mobility separation with selective ion accumulation and tailored computational analysis provides a practical route to more comprehensive single-cell metabolite profiling.
They suggest that the approach could support studies of cellular metabolism in aging, development, and disease – and may be adaptable to other ion mobility-based single-cell mass spectrometry platforms.
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