Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology

Area of Science:

• Biochemistry
• Cell Biology
• Systems Biology

Background:

• Metabolic reprogramming is crucial for cancer and immune cell function.
• Understanding cellular metabolism in its tissue context is vital for biological insights.

Purpose of the Study:

• To introduce the Single Cell Spatially resolved Metabolic (scSpaMet) framework for joint protein-metabolite profiling.
• To analyze cell type-dependent metabolite profiles and spatial interactions in human tissues.
• To establish a tool for systems-level understanding of tissue biology.

Main Methods:

• Developed the scSpaMet pipeline integrating untargeted spatial metabolomics and targeted multiplexed protein imaging.
• Applied scSpaMet to profile single cells in human lung cancer, tonsil, and endometrium tissues.
• Utilized deep learning for joint embedding and trajectory inference of metabolic states.

Main Results:

• scSpaMet successfully profiled over 58,000 single cells across three human tissue types.
• Revealed cell type-specific metabolite profiles and competition among neighboring cells.
• Identified unique metabolite states within cell types and metabolic patterns along differentiation paths.

Conclusions:

• scSpaMet enables quantitative and visual cell-type specific, spatially resolved metabolic-protein mapping.
• The framework provides a powerful tool for systems-level investigation of tissue biology.
• This approach deepens the understanding of cellular metabolism in complex biological systems.