Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.
Bioinformatics. 2022 Sep 2;38(17):4117-4126. doi: 10.1093/bioinformatics/btac447.
Intercellular communication (i.e. cell-cell communication) plays an essential role in multicellular organisms coordinating various biological processes. Previous studies discovered that feedback loops between two cell types are a widespread and vital signaling motif regulating development, regeneration and cancer progression. While many computational methods have been developed to predict cell-cell communication based on gene expression datasets, these methods often predict one-directional ligand-receptor interactions from sender to receiver cells and are not suitable to identify feedback loops.
Here, we describe ligand-receptor loop (LRLoop), a new method for analyzing cell-cell communication based on bi-directional ligand-receptor interactions, where two pairs of ligand-receptor interactions are identified that are responsive to each other and thereby form a closed feedback loop. We first assessed LRLoop using bulk datasets and found our method significantly reduces the false positive rate seen with existing methods. Furthermore, we developed a new strategy to assess the performance of these methods in single-cell datasets. We used the between-tissue interactions as an indicator of potential false-positive prediction and found that LRLoop produced a lower fraction of between-tissue interactions than traditional methods. Finally, we applied LRLoop to the single-cell datasets obtained from retinal development. We discovered many new bi-directional ligand-receptor interactions among individual cell types that potentially control proliferation, neurogenesis and/or cell fate specification.
An R package is available at https://github.com/Pinlyu3/LRLoop. The source code can be found at figshare (https://doi.org/10.6084/m9.figshare.20126138.v1). The datasets can be found at figshare (https://doi.org/10.6084/m9.figshare.20126021.v1).
Supplementary data are available at Bioinformatics online.
细胞间通讯（即细胞-细胞通讯）在多细胞生物中协调各种生物过程中起着至关重要的作用。先前的研究发现，两种细胞类型之间的反馈回路是调节发育、再生和癌症进展的广泛而重要的信号调节模式。虽然已经开发出许多基于基因表达数据集预测细胞-细胞通讯的计算方法，但这些方法通常预测从发送细胞到接收细胞的单向配体-受体相互作用，不适合识别反馈回路。
在这里，我们描述了配体-受体环（LRLoop），这是一种基于双向配体-受体相互作用分析细胞-细胞通讯的新方法，其中鉴定了两对配体-受体相互作用，它们相互响应，从而形成封闭的反馈环。我们首先使用批量数据集评估了 LRLoop，发现我们的方法显着降低了现有方法的假阳性率。此外，我们开发了一种新策略来评估这些方法在单细胞数据集上的性能。我们使用组织间相互作用作为潜在假阳性预测的指标，发现 LRLoop 产生的组织间相互作用比传统方法少。最后，我们将 LRLoop 应用于从视网膜发育中获得的单细胞数据集。我们发现单个细胞类型之间存在许多新的双向配体-受体相互作用，这些相互作用可能控制增殖、神经发生和/或细胞命运特化。
在 https://github.com/Pinlyu3/LRLoop 上提供了一个 R 包。源代码可在 figshare 上找到（https://doi.org/10.6084/m9.figshare.20126138.v1）。数据集可在 figshare 上找到（https://doi.org/10.6084/m9.figshare.20126021.v1）。
补充数据可在 Bioinformatics 在线获得。
