Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

单细胞转录组学拓宽了我们对健康和患病组织中细胞多样性和基因表达动力学的理解。最近，空间转录组学已成为一种工具，用于将多细胞邻域中的单细胞置于上下文中，并识别空间上反复出现的表型或生态型。这些技术已经生成了大量数据集，其中包含数百到数百万个细胞的靶向转录组和全转录组图谱。这些数据为发育层次结构、细胞可塑性和多样化的组织微环境提供了新的见解，并激发了单细胞分析计算方法的爆发式创新。在这篇综述中，我们讨论了识别和表征细胞状态和多细胞邻域的最新进展、持续的挑战和前景。我们讨论了样本处理、数据集成、细微细胞状态识别、轨迹建模、反卷积和空间分析方面的最新进展。此外，我们还讨论了深度学习（包括基础模型）在分析单细胞和空间转录组学数据方面的日益广泛应用。最后，我们讨论了这些工具在干细胞生物学、免疫学和肿瘤生物学领域的最新应用，以及单细胞和空间转录组学在生物研究中的未来及其在临床上的转化。