Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Area of Science:

• Computational Biology
• Metabolic Engineering
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-Seq) provides high-resolution gene expression data.
• Genome-scale metabolic models (GEMs) offer a framework for understanding cellular metabolism.
• Integrating scRNA-Seq with GEMs requires advanced computational tools to analyze cell-specific metabolic functions.

Purpose of the Study:

• To develop and validate computational methods for generating cell-type-specific GEMs from scRNA-Seq data.
• To assess the metabolic heterogeneity within different cell populations and states.
• To identify metabolic differences between healthy and diseased cells, such as in cancer.

Main Methods:

• Developed a method to determine the minimum cell pool size for stable model generation.
• Implemented a bootstrapping strategy for robust statistical inference.
• Adapted and accelerated the task-driven integrative network inference for tissues (TINX) algorithm for context-specific GEM reconstruction.
• Evaluated the impact of RNA-Seq normalization techniques on model topology.

Main Results:

• Generated cell-type-specific GEMs from mouse cortex neurons and human lung cancer tumor microenvironment cells.
• Demonstrated that nearly every cell subtype possesses a distinct metabolic profile.
• Successfully identified cancer-associated metabolic alterations distinguishing cancer cells from healthy cells.
• Contextualized metabolic models for 202 single-cell clusters across 19 human organs, creating the Metabolic Atlas resource.

Conclusions:

• The integration of scRNA-Seq and GEMs offers a powerful approach to dissecting cellular metabolism at single-cell resolution.
• The developed computational framework enables the discovery of novel metabolic insights in various biological contexts.
• The Metabolic Atlas provides a valuable, accessible resource for studying human metabolism.