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February 11, 2026Nature Methods6 citationsOpen Access

Deep-coverage single-cell metabolomics enabled by ion mobility-resolved mass cytometry

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MLMingdu LuoTKTianzhang KouYYYandong Yin

Key Points

  • To enhance sensitivity and metabolic coverage in single-cell metabolomics using advanced technology.
  • Developed ion mobility-resolved mass cytometry technology for high-throughput analysis
  • Implemented selective ion accumulation and cell superposition-based amplification
  • Utilized computational tool MetCell for data analysis
  • Detected over 5,000 metabolic peaks and approximately 800 metabolites per cell
  • Achieved 3- to 10-fold improvement in metabolite coverage compared to previous methods
  • Revealed distinct metabolic states and heterogeneity in hepatocytes during aging

Abstract

Current single-cell metabolomics approaches are limited by insufficient sensitivity, robustness and metabolite coverage. We present an ion mobility-resolved mass cytometry technology that integrates high-throughput single-cell injection with ion mobility–mass spectrometry for multidimensional metabolomic profiling. Ion mobility-enabled selective ion accumulation and cell superposition-based amplification strategies substantially enhance sensitivity, robustness and overall analytical performance. Combined with our computational tool, MetCell, this technology allows high-throughput analysis while achieving exceptional profiling depth, detecting over 5,000 metabolic peaks and annotating approximately 800 metabolites per cell—representing a 3-fold to 10-fold improvement over existing methods. It offers attomole-level sensitivity and captures a broad dynamic range of metabolites within individual cells. Applied to 45,603 primary liver cells from aging mice, it enabled accurate cell-type and cell-subtype annotation and revealed distinct metabolic states and heterogeneity in hepatocytes during aging. This platform sets a new benchmark for high-throughput single-cell metabolomics, advancing our understanding of metabolic heterogeneity at single-cell resolution.

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Cite This Study

Luo et al. (2026) studied this question.

synapsesocial.com/papers/698be001058ab1890a13b9e3https://doi.org/10.1038/s41592-025-02970-2
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