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-$γ$ receptor subunits.

翻译：细胞间通讯通常通过配体-受体共表达来推断，这种关联性范式无法区分因果性信号传导与共享调控或混杂效应。本文提出MR-CCC框架，该贝叶斯孟德尔随机化方法以顺式eQTL作为配体与受体表达的工具变量，并通过交互项显式建模受体调控的配体效应，使得配体的因果效应可随受体丰度变化。采用尖峰-板先验可量化因果性信号传导证据的后验包含概率，而高效吉布斯采样器则提供可扩展的推断能力。相比朴素回归、MVMR和MR-BMA等基准方法，MR-CCC在控制混杂因素导致的假阳性发现的同时保持高统计效力，并能独特地同时估计配体主效应和受体调控的交互效应。将该方法应用于OneK1K数据集的NK细胞→单核细胞轴分析，MR-CCC在GABA、干扰素、白细胞介素和前列腺素信号通路中识别出八项发现，包括IL-18两个受体亚基的化学计量依赖性解离，以及强制型IFN-γ受体两个亚基的协同发现。