Abstract
Recent advances in single-cell RNA sequencing have revealed heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can reveal common cell types and states in the tumor microenvironment (TME). We developed 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.
Competing Interest Statement
MB is a consultant to and receives sponsored research support from Novartis. MB serves on the SAB of H3 Biomedicine, Kronos Bio, and GV20 Oncotherapy. X.S.L conducted the work while being on the faculty at DFCI, and is currently a board member and CEO of GV20 Therapeutics. The remaining authors declare no competing interests.
Footnotes
Meta-component annotation is updated; Main text and Figures revised; Supplemental files revised.