scFBA

scFBA predicts metabolic fluxes from single-cell RNA-seq data to analyze metabolic reprogramming and cellular heterogeneity in cancer, including lung adenocarcinoma and breast cancer.

Key Features:

• Single-Cell Resolution: Estimates metabolic fluxes for individual cells using single-cell RNA-seq-derived constraints to capture intra-population variability.
• Integration with Multi-Scale Stoichiometric Models: Integrates single-cell RNA-seq profiles into multi-scale stoichiometric models to narrow the feasible space of single-cell fluxomes.
• Identification of Cellular Heterogeneity: Identifies clusters of cells with distinct growth rates and metabolic states from cancer samples such as lung adenocarcinoma and breast cancer.
• Metabolic Interaction Analysis: Infers potential metabolic interactions among cells through exchange of metabolites to reveal cooperative or competitive dynamics.

Scientific Applications:

• Cancer Research: Characterizes metabolic heterogeneity in tumors to inform studies of tumorigenesis and metabolic reprogramming.
• Drug Resistance Studies: Links single-cell metabolic pathways to mechanisms of drug resistance to support identification of resistance-associated metabolic states.
• Metabolic Pathway Analysis: Maps active metabolic pathways within individual cells to elucidate reprogramming that supports rapid growth and survival.

Methodology:

Translates single-cell RNA-seq data into metabolic flux predictions by integrating transcriptomic profiles with multi-scale stoichiometric models and constraining feasible fluxomes using a suite of MATLAB functions.