Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data. Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data

Single-cell RNA-seq (scRNA-seq) can be used to characterize cellular heterogeneity in thousands of cells. The reconstruction of a gene network based on coexpression patterns is a fundamental task in scRNA-seq analyses, and the mutual exclusivity of gene expression can be critical to understand such heterogeneity. Here, we propose an approach for detecting communities from a gene network constructed on the basis of coexpression properties. The community-based comparison of multiple coexpression networks enables the identification of functionally related gene clusters that cannot be fully captured through differential gene expression-based analysis. We also developed a novel metric referred to as the exclusively expressed index (EEI) that identifies mutually exclusive gene pairs from sparse scRNA-seq data. EEI quantifies and ranks the exclusive expression levels of all gene pairs from binary expression patterns while maintaining robustness against a low sequencing depth. We applied our methods to glioblastoma scRNA-seq data and found that gene communities are partially conserved after serum stimulation despite a considerable number of differentially expressed genes. We also demonstrate that the identification of mutually exclusive gene sets with EEI can improve the sensitivity of capturing cellular heterogeneity. Our methods complement existing approaches and provide new biological insights from even a large sparse dataset in the single-cell analysis field. Overall design: GSCs were cultured in Dulbecco’s modifiedEagle’s medium (DMEM)/F12 (Life Technologies) containing a B27 supplement minus vitamin A (Life Technologies), epidermal growth factor, and fibroblast growth factor 2 (20 ng/ml each; Wako Pure Chemicals Industries). For in vitro differentiation, GSCs were cultured in Dulbecco’s modified Eagle’s medium/F-12 medium (Life Technologies) containing 10% fetal bovine serum for the indicated times. Single-cell suspensions of GSCs or serum-induced differentiated GSCs were subjected to droplet-based scRNA-seq library preparation with the Chromium Single Cell 3' Reagent Kit v2 (10x Genomics), aiming for an estimated 2,000 cells per library and following the manufacturer’s instructions. The libraries were checked with a BioAnalyzer High Sensitivity Chip (Agilent), quantified with a KAPA Library Quantification kit (Roche), and then sequenced on the Illumina Hiseq 2500 platform in rapid mode.

单细胞RNA测序（single-cell RNA-seq，scRNA-seq）可用于表征数千个细胞中的细胞异质性。基于共表达模式构建基因网络是单细胞RNA测序分析中的核心任务，而基因表达的互斥性对于理解此类细胞异质性至关重要。本研究提出一种从基于共表达特征构建的基因网络中识别功能模块的方法。通过对多个共表达网络进行基于模块的比较，可识别出基于差异基因表达分析无法完全捕获的功能相关基因簇。本研究还开发了一种名为专属表达指数（exclusively expressed index，EEI）的新型量化指标，可从稀疏的单细胞RNA测序数据中识别互斥基因对。专属表达指数可基于二元表达模式对所有基因对的专属表达水平进行量化与排序，同时对低测序深度具有良好的鲁棒性。本研究将所提方法应用于胶质母细胞瘤的单细胞RNA测序数据，发现尽管存在大量差异表达基因，但血清刺激后基因功能模块仍存在部分保守性。本研究还证实，利用专属表达指数识别互斥基因集可提升细胞异质性捕获的灵敏度。本研究方法可作为现有分析手段的补充，即便针对单细胞分析领域中的大型稀疏数据集，也能提供全新的生物学见解。

实验设计：将胶质母细胞瘤干细胞（glioblastoma stem cells，GSCs）培养于添加了不含维生素A的B27添加剂（Life Technologies公司）、表皮生长因子及成纤维细胞生长因子2（每种20 ng/ml，Wako Pure Chemicals Industries公司）的杜尔贝科改良伊格尔培养基（Dulbecco’s modified Eagle’s medium，DMEM）/F12培养基（Life Technologies公司）中。体外诱导分化时，将胶质母细胞瘤干细胞培养于添加10%胎牛血清的杜尔贝科改良伊格尔培养基/F-12培养基（Life Technologies公司）中，培养时长按实验设计设置。将胶质母细胞瘤干细胞或血清诱导分化后的胶质母细胞瘤干细胞制备为单细胞悬液，依照制造商说明书，使用Chromium单细胞3'端试剂试剂盒v2（10x Genomics公司）进行基于液滴的单细胞RNA测序文库构建，目标为每个文库约2000个细胞。使用生物分析仪高灵敏度芯片（Agilent公司）对文库进行质量检测，通过KAPA文库定量试剂盒（Roche公司）完成文库定量，随后在Illumina Hiseq 2500平台以快速模式进行测序。