Gene function and cell surface protein association analysis based on single-cell multiomics data

Huan Hu; Zhen Feng; Hai Lin; Jinyan Cheng; Jie Lyu; Yaru Zhang; Junjie Zhao; Fei Xu; Tao Lin; Qi Zhao; Jianwei Shuai

doi:10.1016/j.compbiomed.2023.106733

Gene function and cell surface protein association analysis based on single-cell multiomics data

Comput Biol Med. 2023 May:157:106733. doi: 10.1016/j.compbiomed.2023.106733. Epub 2023 Mar 1.

Authors

Huan Hu¹, Zhen Feng², Hai Lin³, Jinyan Cheng³, Jie Lyu³, Yaru Zhang⁴, Junjie Zhao⁵, Fei Xu⁶, Tao Lin⁷, Qi Zhao⁸, Jianwei Shuai⁹

Affiliations

¹ Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, 361005, China; National Institute for Data Science in Health and Medicine, State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen University, Xiamen, 361005, China; Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001, China.
² First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, 325000, China.
³ Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001, China.
⁴ Institute of Biomedical Big Data, School of Ophthalmology & Optometry and Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, 325027, China.
⁵ Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001, China; Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510000, China.
⁶ Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, 361005, China; Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001, China.
⁷ Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, 325001, China.
⁸ School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China. Electronic address: zhaoqi@lnu.edu.cn.
⁹ Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, 361005, China; National Institute for Data Science in Health and Medicine, State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, Xiamen University, Xiamen, 361005, China; Wenzhou Institute and Wenzhou Key Laboratory of Biophysics, University of Chinese Academy of Sciences, Wenzhou, 325001, China; Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, 325001, China. Electronic address: jianweishuai@xmu.edu.cn.

PMID: 36924730
DOI: 10.1016/j.compbiomed.2023.106733

Abstract

Single-cell transcriptomics provides researchers with a powerful tool to resolve the transcriptome heterogeneity of individual cells. However, this method falls short in revealing cellular heterogeneity at the protein level. Previous single-cell multiomics studies have focused on data integration rather than exploiting the full potential of multiomics data. Here we introduce a new analysis framework, gene function and protein association (GFPA), that mines reliable associations between gene function and cell surface protein from single-cell multimodal data. Applying GFPA to human peripheral blood mononuclear cells (PBMCs), we observe an association of epithelial mesenchymal transition (EMT) with the CD99 protein in CD4 T cells, which is consistent with previous findings. Our results show that GFPA is reliable across multiple cell subtypes and PBMC samples. The GFPA python packages and detailed tutorials are freely available at https://github.com/studentiz/GFPA.

Keywords: Association analysis; Cell surface protein; Computing framework; Multiomics; Single-cell.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Gene Expression Profiling / methods
Humans
Leukocytes, Mononuclear*
Membrane Proteins
Multiomics*
Transcriptome

Substances

Membrane Proteins