MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment

Yi Zhang, Guanjue Xiang, Alva Yijia Jiang, Allen Lynch, Zexian Zeng, Chenfei Wang, Wubing Zhang, Jingyu Fan, Jiajinlong Kang, Shengqing Stan Gu, Changxin Wan, Boning Zhang, X. Shirley Liu (), Myles Brown () and Clifford A. Meyer ()
Additional contact information
Yi Zhang: Dana-Farber Cancer Institute
Guanjue Xiang: Dana-Farber Cancer Institute
Alva Yijia Jiang: Dana-Farber Cancer Institute
Allen Lynch: Dana-Farber Cancer Institute
Zexian Zeng: Dana-Farber Cancer Institute
Chenfei Wang: Dana-Farber Cancer Institute
Wubing Zhang: Dana-Farber Cancer Institute
Jingyu Fan: Dana-Farber Cancer Institute
Jiajinlong Kang: Dana-Farber Cancer Institute
Shengqing Stan Gu: Dana-Farber Cancer Institute
Changxin Wan: Dana-Farber Cancer Institute
Boning Zhang: Dana-Farber Cancer Institute
X. Shirley Liu: Dana-Farber Cancer Institute
Myles Brown: Dana-Farber Cancer Institute
Clifford A. Meyer: Dana-Farber Cancer Institute

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract 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.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-023-38333-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38333-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-38333-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().