Inferring mitochondrial and cytosolic metabolism by coupling isotope tracing and deconvolution

Area of Science:

• Cellular Metabolism
• Systems Biology
• Biochemistry

Background:

• Understanding eukaryotic cell metabolism is hindered by the inability to study distinct subcellular compartments.
• Analyzing isolated organelles introduces significant metabolic bias.
• Existing methods lack the resolution to accurately assess compartmentalized metabolic activities.

Purpose of the Study:

• To develop a novel computational method for inferring physiological metabolic fluxes and metabolite concentrations in mitochondria and cytosol within intact cells.
• To overcome the limitations of organelle isolation and improve the accuracy of metabolic analysis.
• To provide a quantitative view of subcellular metabolic activities without cell fractionation.

Main Methods:

• Utilized isotope tracing experiments in intact cells.
• Employed computational deconvolution of metabolite isotopic labeling patterns and concentrations.
• Integrated metabolic and thermodynamic modeling to infer compartmentalized fluxes and concentrations.

Main Results:

• Significantly reduced uncertainty in compartmentalized fluxes (one order of magnitude) and concentrations (three orders of magnitude) compared to existing approaches.
• Provided a quantitative view of mitochondrial and cytosolic metabolic activities in central carbon metabolism across cultured cell lines.
• Identified major variability in compartmentalized malate-aspartate shuttle fluxes.

Conclusions:

• The developed method enables accurate inference of metabolism at a subcellular resolution in intact cells.
• This approach is crucial for studying metabolic dysfunction in human diseases.
• The method holds promise for applications in bioengineering and metabolic research.