Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

单细胞 RNA 测序的最新进展显示了肿瘤中非癌细胞的异质细胞类型和基因表达状态。跨肿瘤的多个 scRNA-seq 数据集的整合可以指示肿瘤微环境 (TME) 中的常见细胞类型和状态。我们开发了一个数据驱动框架 MetaTiME，以克服使用已知基因标记进行手动标记所导致的分辨率和一致性限制。 MetaTiME 使用数百万个 TME 单细胞来学习元组件，这些元组件编码在各种癌症类型中观察到的基因表达的独立组件。元成分在生物学上可解释为细胞类型、细胞状态和信号活动。通过投影到 MetaTiME 空间，我们提供了一个工具来注释 TME scRNA-seq 数据的细胞状态和签名连续体。 MetaTiME 利用表观遗传学数据揭示了细胞状态的关键转录调节因子。总体而言，MetaTiME 学习数据驱动的元组件，这些元组件描述肿瘤免疫和癌症免疫治疗的细胞状态和基因调节因子。