基于单细胞多组学数据的基因功能与细胞表面蛋白关联分析

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

作者信息

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

机构信息

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.

First Affiliated Hospital of Wenzhou Medical University, Wenzhou Medical University, Wenzhou, 325000, China.

出版信息

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

DOI:10.1016/j.compbiomed.2023.106733

PMID:36924730

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.

摘要

单细胞转录组学为研究人员提供了一个强大的工具，用于解析单个细胞的转录组异质性。然而，这种方法在揭示蛋白质水平上的细胞异质性方面存在不足。以往的单细胞多组学研究主要集中在数据整合上，而不是充分挖掘多组学数据的潜力。在这里，我们引入了一个新的分析框架，即基因功能与蛋白质关联（GFPA），它可以从单细胞多模态数据中挖掘基因功能与细胞表面蛋白之间的可靠关联。将GFPA应用于人类外周血单个核细胞（PBMC），我们观察到上皮-间质转化（EMT）与CD4 T细胞中的CD99蛋白存在关联，这与先前的研究结果一致。我们的结果表明，GFPA在多种细胞亚型和PBMC样本中都是可靠的。GFPA的Python包和详细教程可在https://github.com/studentiz/GFPA上免费获取。