Single-cell RNA sequencing of meningiomas. Single-cell RNA sequencing of meningiomas

Gene expression profiling via RNA-sequencing has become standard for measuring and analyzing the gene activity in bulk and at single cell level. Increasing sample sizes and cell counts provides substantial information about transcriptional architecture of samples. In addition to quantification of expression at cellular level, RNA-seq can be used for detecting of variants, including single nucleotide variants and small insertions/deletions and also large variants such as copy number variants. The joint analysis of variants with transcriptional state of cells or samples can provide insight about impact of mutations. To provide a comprehensive method to jointly analyze the genetic variants and cellular states, we introduce XCVATR, a method that can identify variants, detect local enrichment of expressed variants, within embedding of samples and cells. The embeddings provide information about cellular states among cells by defining a cell-cell distance metric. Unlike clustering algorithms, which depend on a cell-cell distance and use it to define clusters that explain cell clusters globally, XCVATR detects the local enrichment of expressed variants in the embedding space such that embedding can be computed using any type of measurement or method, for example by PCA or tSNE of the expression levels. XCVATR searches local patterns of association of each variant with the positions of cells in an embedding of the cells. XCVATR also visualizes the local clumps of small and large-scale variant calls in single cell and bulk RNA-sequencing datasets. We perform simulations and demonstrate that XCVATR can identify the enrichments of expressed variants. We also apply XCVATR on single cell and bulk RNA-seq datasets and demonstrate its utility. Overall design: We report single-cell RNA sequencing from one meningioma patient. This one patient had two meningioma tissues from postal and frontal regions.

通过RNA测序（RNA-sequencing）进行基因表达谱分析，已成为批量样本与单细胞水平基因活性检测与分析的标准方法。扩大样本量与细胞数目，可获取关于样本转录组架构的丰富信息。除可实现细胞水平的表达定量外，RNA-seq还可用于变异检测，涵盖单核苷酸变异（single nucleotide variants）、小型插入/缺失变异，以及拷贝数变异（copy number variants）等多种变异类型。将变异与细胞或样本的转录状态进行联合分析，可深入揭示突变的生物学影响。为开发一种可联合分析遗传变异与细胞状态的综合性分析方法，我们提出了XCVATR工具：该方法可在样本与细胞的嵌入表征中识别变异，并检测表达变异的局部富集情况。嵌入表征通过定义细胞间距离度量，可反映细胞群体内的细胞状态分布特征。与依赖细胞间距离并以此全局定义细胞簇的聚类算法不同，XCVATR可在嵌入空间中检测表达变异的局部富集，且嵌入表征可通过任意类型的测量方式或计算方法生成，例如基于表达水平的主成分分析（PCA）或t分布邻域嵌入（tSNE）。XCVATR会在细胞嵌入表征中，搜索每个变异与细胞空间位置之间的关联局部模式。此外，XCVATR还可对单细胞与批量RNA测序数据集中小规模、大规模变异检测结果的局部聚集情况进行可视化展示。我们通过模拟实验验证了XCVATR可有效识别表达变异的富集情况，并将其应用于单细胞与批量RNA-seq数据集，证实了该工具的实用性。研究设计：本研究报道了1例脑膜瘤患者的单细胞RNA测序数据，该患者的两处脑膜瘤组织分别取自postal区与额叶区。