Deep-coverage single-cell metabolomics enabled by ion mobility-resolved mass cytometry
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.
Authors
- Xinhao Zeng
- Yandong Yin (ORCID: https://orcid.org/0000-0003-2315-5593)
- S. Kevin Zhou (ORCID: https://orcid.org/0000-0002-6881-4444)
- Junhao Hu (ORCID: https://orcid.org/0000-0003-3538-2785)
- Xuebin Zhu (ORCID: https://orcid.org/0000-0001-9484-8253)
- Tianzhang Kou
- Mingdu Luo
- Zheng-Jiang Zhu
Institutions
- Shanghai University (CN)
- Chinese Academy of Sciences (CN)
- Shanghai Institute of Organic Chemistry (CN)
- Shanghai Academy of Environmental Sciences (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Nature Methods
- Published
- 2026-02-09
- DOI
- https://doi.org/10.1038/s41592-025-02970-2
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00