MR-CCC: Bayesian Mendelian Randomization for Causal Cell--Cell Communication

TL;DR

MR-CCC introduces a Bayesian Mendelian randomization method to infer causal cell-cell communication, accounting for receptor modulation and controlling false discoveries.

Contribution

It models receptor-modulated ligand effects using cis-eQTLs and a spike--and--slab prior, providing scalable inference and novel causal insights.

Findings

Identified eight causal signaling discoveries in NK cell to monocyte communication.

Controlled false positives under confounding compared to other methods.

Estimated both ligand effects and receptor-modulated interactions.

Abstract

Cell--cell communication (CCC) is commonly inferred from ligand--receptor co-expression, an associational paradigm that cannot distinguish causal signaling from shared regulation or confounding. We propose MR-CCC, a Bayesian Mendelian randomization framework that uses cis-eQTLs as instruments for ligand and receptor expression and explicitly models receptor-modulated ligand effects through an interaction term, so the causal effect of a ligand can vary with receptor abundance. A spike--and--slab prior yields posterior inclusion probabilities quantifying evidence for causal signaling, and an efficient Gibbs sampler provides scalable inference. Benchmarked against naive regression, MVMR, and MR-BMA, MR-CCC controls false discoveries under confounding while retaining high power, and uniquely estimates both the ligand main and receptor-modulated interaction effects. Applied to the OneK1K NK cells $\to$ monocytes axis, MR-CCC identifies eight discoveries across GABA, interferon, interleukin, and prostaglandin signaling, including a stoichiometry-dependent dissociation of the two IL-18 receptor chains and co-discovery of both obligate IFN-$\gamma$ receptor subunits.