Cha Junha, Lavi Michael, Kim Junhan, Shomron Noam, Lee Insuk
Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea.
Faculty of Medicine and Edmond J Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 69978, Israel.
Comput Struct Biotechnol J. 2023 Mar 22;21:2296-2304. doi: 10.1016/j.csbj.2023.03.036. eCollection 2023.
Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis.
单细胞转录组数据为探索特定细胞类型的基因网络提供了独特的机会。然而，单细胞RNA测序（scRNA-seq）数据的捕获率不足和高维度对细胞类型特异性基因网络（CGN）的重建提出了挑战。在这里，我们证明了scRNA-seq数据的插补能够通过有效检索基因功能关联来重建CGN。我们使用插补后的scRNA-seq数据为7种原发性和9种转移性乳腺癌细胞系重建了CGN。基于网络中心性度量和假定为细胞类型特征主要决定因素的CGN枢纽基因，对原发性或转移性细胞系的关键基因进行了优先级排序。为了识别乳腺癌转移中的新基因，我们使用了原发性和转移性细胞系之间中心性的平均排名差异。与使用差异表达预测的基因相比，使用CGN中心性分析预测的基因在已知乳腺癌转移基因中更富集。在对几个候选基因进行敲低试验期间，分子伴侣被鉴定为乳腺癌转移的新基因。总体而言，我们的研究证明了一种有效的CGN重建技术，即插补scRNA-seq数据，以及使用单细胞网络分析识别特定细胞亚群关键基因的可行性。
