No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell–cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell–cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

没有细胞生活在真空中，细胞之间的分子相互作用决定了大多数表型。转录组学提供了丰富的信息来推断细胞间的相互作用和通讯，从而加速发现细胞在其群落中的作用。此类研究在很大程度上依赖于推断哪些细胞正在相互作用以及所涉及的配体和受体的算法。不同研究领域的特定压力正在推动下一代计算工具的发展，带来新的概念机会和技术进步。现在，更复杂的算法可以解释细胞的异质性和空间组织、多种配体类型和细胞内信号传导事件，并能够使用更大、更复杂的数据集，包括单细胞和空间转录组学。同样，新的高通量实验方法正在增加可同时分析的相互作用的数量和分辨率。在这里，我们探讨了细胞与细胞相互作用研究的最新进展，并强调了下一代工具的多样化，这些工具为不同的应用产生了丰富的工具生态系统，并带来了宝贵的发现。