Toward modeling metabolic state from single-cell transcriptomics.

Area of Science:

• Biochemistry
• Systems Biology
• Computational Biology

Background:

• Single-cell metabolic studies offer unique insights into cellular functions and heterogeneity not captured by other omics layers.
• Direct single-cell metabolic measurements face challenges in scalability, sensitivity, and parallelization with transcriptomics.
• Interconnectedness of omics layers allows for metabolic predictions using accessible omics data and prior metabolic network knowledge.

Purpose of the Study:

• To review current single-cell metabolic measurement and modeling approaches.
• To highlight the utility of computational techniques for single-cell metabolism prediction.
• To propose future directions for single-cell metabolic modeling.

Main Methods:

• Review of computational methods for single-cell metabolism prediction, including pathway-level analysis, constraint-based modeling, and kinetic modeling.
• Discussion of challenges in transitioning from bulk to single-cell metabolic modeling.
• Identification of potential model extensions and computational methods.

Main Results:

• Single-cell metabolic modeling is an emerging field offering novel perspectives on cellular functions.
• Modeling approaches differ in input needs, assumptions, scalability, and the insights they provide.
• Integration of prior metabolic knowledge enhances prediction robustness.

Conclusions:

• Computational modeling is crucial for advancing single-cell metabolism studies.
• Prior metabolic knowledge is key for robust and interpretable predictions.
• Future work should focus on developing advanced computational methods for single-cell metabolic modeling.