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态的理解。最近，空间转录组学已成为一种工具，可以将多细胞邻域中的单细胞置于背景中并识别空间重复的表型或生态型。这些技术已经生成了包含数百至数百万个细胞的靶向转录组和全转录组概况的大量数据集。这些数据为发育层次、细胞可塑性和多样化组织微环境提供了新的见解，并刺激了单细胞分析计算方法的大量创新。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、持续挑战和前景。我们讨论了样本处理、数据集成、微妙细胞状态识别、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数据中的日益增长的应用。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学研究及其临床转化中的未来。