Single-cell metabolomics provides a powerful way for characterizing metabolic heterogeneity at the individual-cell level which is obscured in bulk analyses. Mass spectrometry-based single-cell metabolomics enables the label-free, high-throughput detection of metabolites. However, existing methods are not sensitive enough to detect low-abundance metabolites, and show high technical variability and restricted coverage and annotation confidence.

A study published in Nature Methods and led by Prof. ZHU Zhengjiang from Shanghai Institute of Organic Chemistry (SIOC) of the Chinese Academy of Sciences developed an ion mobility-resolved mass cytometry technology for single-cell metabolomics. This single-cell metabolomics method achieved high throughput while maintaining deep coverage by integrating data acquisition with advanced processing strategies.

Researchers first established an ion mobility-resolved mass cytometry platform by integrating flow cytometry with ion mobility-mass spectrometry (IM-MS). The platform continuously injected live cells into the IM-MS for direct electrospray ionization, transferred the pulsed single-cell ions that are generated into an ion trap for accumulation, and performed ion mobility separation with multidimensional measurements.

To improve the detection of low-mass metabolites and reduce interference from cellular lipids, researchers implemented selective ion accumulation in the low-mass range on trapped ion mobility spectrometry, which enhanced the abundance of low-mass ions and then increased the sensitivity of detection for low-mass metabolites by 20-fold compared to the default condition.

Moreover, researchers developed a cell superposition strategy which leverages distinctive ion signatures characterized by mass-to-charge ratio and ion mobility values to aggregate identical ions across multiple cells. The strategy enhanced peak detection and guided targeted ion extraction in individual cells, improving robustness while maintaining single-cell resolution. The selective ion accumulation and cell superposition enabled attomole-level sensitivity and a broad dynamic range at the single-cell level.

MetCell, an end-to-end computational tool optimized for ion mobility-resolved single-cell metabolomics, was developed by researchers. It can detect over 5,000 metabolic peaks and annotate approximately 800 metabolites per cell, representing a 3-fold to 10-fold improvement over existing methods. Notably, 389 metabolites were identified with level one confidence, which is the highest number reported at this level in single-cell metabolomics.

Using MetCell, researchers established a metabolic single-cell atlas containing 45,603 primary liver cells from aging mice. They demonstrated accurate cell type and subtype annotation using single-cell metabolomics and unveiled distinct metabolic states and heterogeneity of hepatocytes during aging.