Yu Alice, Li Yuanyuan, Li Irene, Ozawa Michael G, Yeh Christine, Chiou Aaron E, Trope Winston L, Taylor Jonathan, Shrager Joseph, Plevritis Sylvia K
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Department of Radiology, Stanford University, Stanford, CA, USA.
Sci Adv. 2022 Mar 18;8(11):eabi4757. doi: 10.1126/sciadv.abi4757.
Cellular cross-talk in tissue microenvironments is fundamental to normal and pathological biological processes. Global assessment of cell-cell interactions (CCIs) is not yet technically feasible, but computational efforts to reconstruct these interactions have been proposed. Current computational approaches that identify CCI often make the simplifying assumption that pairwise interactions are independent of one another, which can lead to reduced accuracy. We present REMI (REgularized Microenvironment Interactome), a graph-based algorithm that predicts ligand-receptor (LR) interactions by accounting for LR dependencies on high-dimensional, small-sample size datasets. We apply REMI to reconstruct the human lung adenocarcinoma (LUAD) interactome from a bulk flow-sorted RNA sequencing dataset, then leverage single-cell transcriptomics data to increase the cell type resolution and identify LR prognostic signatures among tumor-stroma-immune subpopulations. We experimentally confirmed colocalization of CTGF:LRP6 among malignant cell subtypes as an interaction predicted to be associated with LUAD progression. Our work presents a computational approach to reconstruct interactomes and identify clinically relevant CCIs.
组织微环境中的细胞间相互作用对于正常和病理生物学过程至关重要。对细胞 - 细胞相互作用（CCI）进行全面评估在技术上尚不可行，但已有人提出通过计算来重建这些相互作用。目前识别CCI的计算方法通常做出简化假设，即成对相互作用彼此独立，这可能导致准确性降低。我们提出了REMI（正则化微环境相互作用组），这是一种基于图的算法，通过考虑高维、小样本量数据集上的配体 - 受体（LR）依赖性来预测LR相互作用。我们应用REMI从大量流式分选的RNA测序数据集中重建人类肺腺癌（LUAD）相互作用组，然后利用单细胞转录组学数据提高细胞类型分辨率，并在肿瘤 - 基质 - 免疫亚群中识别LR预后特征。我们通过实验证实了CTGF:LRP6在恶性细胞亚型中的共定位是一种预计与LUAD进展相关的相互作用。我们的工作提出了一种重建相互作用组并识别临床相关CCI的计算方法。
