Department of Mathematics and Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC, USA.
Department of Mathematics, The George Washington University, Washington, DC, USA.
Nat Methods. 2023 Feb;20(2):218-228. doi: 10.1038/s41592-022-01728-4. Epub 2023 Jan 23.
Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell-cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development.
空间转录组学技术和带有空间注释的单细胞 RNA 测序数据集为剖析细胞间通讯（CCC）提供了前所未有的机会。然而，在重建 CCC 时纳入空间信息和复杂的生化过程仍然是一个主要挑战。在这里，我们提出了 COMMOT（通过最优传输进行通讯分析）来推断空间转录组学中的 CCC，它考虑了不同配体和受体种类之间的竞争以及细胞之间的空间距离。我们开发了一种集体最优传输方法来处理复杂的分子相互作用和空间约束。此外，我们引入了下游分析工具，使用机器学习模型推断空间信号的方向性和受信号调控的基因。我们将 COMMOT 应用于模拟数据和使用五种不同技术获得的八个空间数据集，以展示其在不同空间分辨率和基因覆盖率的数据中识别空间 CCC 的有效性和鲁棒性。最后，COMMOT 在人类表皮发育的一个案例研究中识别了皮肤形态发生过程中的新 CCC。
