scSNVariome - single cell SNVs driving cancer dynamics

scSNVariome - 驱动癌症动态的单细胞 SNV

• 批准号: 10593251
• 负责人: Anelia Horvath
• 金额: $ 23.93万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-09 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10593251
• 关键词: Adrenal Glands; Adrenal Neuroblastoma; Alleles; Assessment tool; Automobile Driving; Bar Codes; Benchmarking; Biological; Catalogs; Cell Line; Cells; Communities; Complement; Computer software; DNA; Data; Data Set; Deposition; Development; Embryo; Gene Expression; Gene Expression Profile; Gene Frequency; Genetic Variation; Genomics; Goals; Individual; Knowledge; MCF7 cell; Malignant Neoplasms; Metadata; Methods; Modeling; Mutation; Outcome; Phenotype; Prevalence; Probability; RNA; RNA Splicing; Reporting; Resources; Sampling; Single Nucleotide Polymorphism; Time; Update; Variant; Visualization; analysis pipeline; cancer cell; cancer type; cell type; data integration; experimental study; fetal; genetic variant; improved; indexing; machine learning algorithm; neoplastic cell; novel; prostate cancer cell; single-cell RNA sequencing; stem; tool; tumor; tumor heterogeneity; two-dimensional; variant detection; variant of interest

Recently, Single Nucleotide Variants (SNV) detection from single cell RNA sequencing (scRNA-seq) experiments have started to emerge. These studies have demonstrated the utility of scRNA-seq SNV assessments to characterize intra-tumoral heterogeneity, define mutation-associated expression signatures, identify tumor cells displaying lineage infidelity, and evaluate the tumor differentiation state. However, currently, most of the SNV data from scRNA-seq cancer datasets (10x Genomics) is not obtained at cell-level and therefore lacks information on SNV-associated cell phenotypes. SNV assessments from scRNA-seq data can complement DNA-based SNV-studies and maximize the potential of scRNA-seq datasets. Importantly, they can provide crucial information on the SNV functionality through studying the variant allele specific dynamics and its correlation to phenotype. Given this wide application range, the knowledge on cell-level SNV expression and dynamics can be instrumental for any cancer scRNA-seq study. In the last year we have developed tools for assessment of Single Cell-specific Expressed SNVs (sceSNVs). SCExecute executes a user-provided command on barcode-stratified, extracted on-the-fly individual cell alignments. We apply scExecute in conjunction with variant callers to detect sceSNVs. For estimation of allele specific sceSNVs expression we apply SCReadCounts, which generates cell-SNV matrices with cell-level expressed variant allele frequency (VAFRNA). These cell-SNV matrices can be used as inputs for our other tools scReQTL, scRsQTL, and scSNPair, to correlate variant expression to gene expression, splicing, and other SNV's expression, respectively. The expression of sceSNVs of interest can be projected in two-dimensional projection space across all cells in a sample using scSNVis. Here, we propose to employ the above-described approaches on cancer scRNA-seq datasets with the aim to assess sceSNVs and to initiate a public Pan-Cancer scVariome catalogue (Aim 1). We will integrate new and existing (SCReadCounts, scReQTL, scRsQTL, scSNPair and scSNVis) tools for the discovery and analysis of sceSNVs from scRNA-Seq data in an end-to-end, integrated, containerized, publicly available pipeline. New tools will incorporate velocity and pseudotime inference analyses to study sceSNV associations with cell dynamics. Using this pipeline, we will supply functional sceSNV annotations to the catalogue (Aim 2). Furthermore, we will develop and incorporate a new tool that cross- references locally observed (by different teams) sceSNVs, indexing by loci, study, sample, and cellular barcode, providing the additional context of cell-type, study meta-data, and other annotation and summary results from the catalog, with the overarching goal to facilitate community annotation of sceSNVs in scRNA-Seq data (Aim 3).

最近，单细胞RNA测序（scRNA-seq）的单核苷酸变异（SNV）检测 实验已经开始出现。这些研究已经证明了scRNA-seq SNV的实用性 评估以表征肿瘤内异质性，定义突变相关的表达特征， 鉴定显示谱系失真的肿瘤细胞，并评估肿瘤分化状态。但是目前， 来自scRNA-seq癌症数据集（10 x Genomics）的大多数SNV数据不是在细胞水平获得的， 因此缺乏SNV相关细胞表型的信息。 来自scRNA-seq数据的SNV评估可以补充基于DNA的SNV研究，并最大限度地提高SNV评估的准确性。 scRNA-seq数据集的潜力。重要的是，它们可以提供有关SNV功能的关键信息 通过研究变异等位基因的特异性动态及其与表型的相关性。鉴于这一广泛 应用范围，细胞水平的SNV表达和动力学的知识可以有助于 任何癌症scRNA-seq研究。 在过去的一年中，我们开发了用于评估单细胞特异性表达SNV的工具， （sceSNV）。SCExecute对条形码分层、动态提取执行用户提供的命令 单个细胞排列。我们将scExecute与变体调用程序结合使用来检测sceSNV。为 为了估计等位基因特异性sceSNV表达，我们应用SCReadCounts，其生成细胞-SNV矩阵 细胞水平表达的变异等位基因频率（VAFRNA）。这些单元-SNV矩阵可以用作用于以下操作的输入： 我们的其他工具scReQTL、scRsQTL和scSNPair，将变异表达与基因表达相关联， 剪接和其他SNV的表达。感兴趣的sceSNV的表达可以被投影到 使用scSNVis在样品中的所有细胞上的二维投影空间。 在这里，我们建议在癌症scRNA-seq数据集上采用上述方法 目的是评估sceSNV并启动公共泛癌症scVariome目录（Aim 1）。我们 将整合新的和现有的（SCReadCounts，scReQTL，scRsQTL，scSNPair和scSNVis）工具， 从scRNA-Seq数据中发现和分析sceSNV，是一种端到端的、集成的、容器化的， 公共管道。新工具将结合速度和伪时间推断分析， 研究sceSNV与细胞动力学的关联。使用此管道，我们将提供功能sceSNV 目录注释（目标2）。此外，我们将开发并整合一种新的工具，跨- 参考当地观察到的（由不同团队）sceSNV，按基因座、研究、样本和细胞索引 条形码，提供细胞类型、研究元数据和其他注释的附加上下文， 目录的摘要结果，总体目标是促进社区注释， scRNA-Seq数据中的sceSNV（Aim 3）。