Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.

单细胞 RNA 测序 (scRNA-seq) 可识别组织内的细胞亚群，但不会捕获它们的空间分布，也不会揭示原位细胞间通讯的局部网络。最近开发的一套在组织内定位 RNA 的技术，包括多重原位杂交和原位测序（这里定义为高复杂 RNA 成像）和空间条形码，可以帮助解决这个问题。然而，目前没有任何方法能像 scRNA-seq 那样提供完整的转录组范围，这强调了整合单细胞和空间数据的方法的必要性。在这里，我们回顾了将 scRNA-seq 与空间转录组学相结合的努力，包括新兴的综合计算方法，并提出了有效结合当前方法的方法。