Cell-to-cell communication is vital for our bodies to function properly. While scientists have long studied how proteins help cells communicate, they’ve had a much harder time understanding how metabolites, the small molecules involved in metabolism, carry messages between cells. A new tool called MEBOCOST, developed by researchers at Boston Children’s Hospital, offers a solution by using RNA sequencing data to detect these metabolite-based signals.

MEBOCOST stands for “Metabolite-Based Cell-Cell Signaling Tool.” It combines single-cell RNA sequencing (scRNA-seq) with a type of mathematical modeling called metabolic flux balance analysis. This lets researchers map how cells exchange small molecules that influence each other’s behavior. In other words, MEBOCOST can figure out which cells are “talking” to which, and what metabolic signals they’re using.

MEBOCOST detects metabolite-sensor communications between cells with scRNA-seq

(A) Cartoon to show that MEBOCOST predicts metabolite-mediated cell-cell communications, in which sender cells secret metabolites and receiver cells receive metabolites through three types of sensor proteins, including cell surface transporter, cell surface receptor, and nuclear receptor. (B) Cartoons to show the seven steps in MEBOCOST to identify cell-cell metabolite-sensor communications. [1] The RNA expression data and cell type annotation obtained from scRNA-seq were taken as the input data to MEBOCOST. [2] A knowledgebase of enzymes (2.1) and sensor proteins (2.2) for metabolites is incorporated into the MEBOCOST algorithm. [3] The RNA expression values of the metabolite enzymes were extracted from scRNA-seq data. [4] The RNA expression values of sensor proteins for metabolites were extracted from the scRNA-seq data. [5] Calculate the enzyme-sensor co-expression score between two cell groups by taking the product of the mean of enzyme expression values in the sender cells and the mean of sensor expression values in the receiver cells. [6] Shuffling single cell labels to generate a statistical null distribution to calculate the P-value for the enzyme-sensor co-expression score. [7] Incorporating metabolite efflux rates to indicate secretion in sender cells and influx rates to indicate uptake activity in receiver cells, respectively, for combining with the enzyme-sensor co-expression score to identify communication events.

The research team validated MEBOCOST using several types of biological data, including lab experiments, spatial tissue analysis, CRISPR screens, and patient samples. The results consistently showed that MEBOCOST could identify meaningful and biologically relevant metabolic communication events.

For example, when the team analyzed single-cell RNA sequencing data from human white fat tissue, they found that macrophages, a type of immune cell, were the main drivers of metabolic signaling changes in obese patients. In another experiment using brown fat tissue from mice, the tool identified a new communication link between endothelial cells and fat cells involving glutamine, a key metabolite. This specific interaction was found to play a role in how fat cells develop, and the researchers confirmed this in the lab.

These discoveries highlight how important metabolite-mediated communication (mCCC) is to health and disease, particularly in areas like obesity and metabolism. MEBOCOST makes it easier for researchers to explore these hidden cellular conversations, which could eventually lead to new therapies for metabolic disorders.

Availability – MEBOCOST is freely available at https://github.com/kaifuchenlab/MEBOCOST.