scSNVariome - single cell SNVs driving cancer dynamics

最近，从单细胞RNA测序(scRNA-seq)中检测单核苷酸变异(SNV) 实验已经开始出现。这些研究证明了scRNA-seq SNV的实用性。 评价肿瘤内的异质性，定义突变相关的表达特征， 鉴定表现出谱系不忠的肿瘤细胞，并评估肿瘤分化状态。不过，目前， 来自scRNA-seq癌症数据集(10倍基因组学)的大部分SNV数据不是在细胞水平上获得的，并且 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 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力学及其与表型的相关性。考虑到这么宽 应用范围、细胞水平SNV表达和动力学的知识可以帮助 任何癌症scRNA-seq研究。 在过去的一年里，我们开发了用于评估单细胞特异性表达的SNV的工具 (SceSNvs)。SCExecute对动态提取的分层条形码执行用户提供的命令 单个单元格对齐。我们结合使用scExecute和不同的调用方来检测sceSNV。为 等位基因特异性sceSNV表达的估计我们应用SCReadCounts，它生成细胞-SNV矩阵 与细胞水平表达的变异等位基因频率(VAFRNA)。这些单元-SNV矩阵可以用作输入 我们的其他工具scReQTL、scRsQTL和scSNPair，将变体表达与基因表达相关联， 剪接，以及其他SNV的表达。感兴趣的sceSNV的表达可以投射在 使用scSNVis在样本中的所有单元格之间的二维投影空间。 在这里，我们建议在癌症scrna-seq数据集上使用上述方法。 目的是评估SCeSNV，并启动一个公开的泛癌scVariome目录(目标1)。我们 将集成新的和现有的(SCReadCounts、scReQTL、scRsQTL、scSNPair和scSNVis)工具 在端到端的、集成的、集装化的、 可公开使用的管道。新工具将结合速度和伪时间推断分析来 研究sceSNV与细胞动力学的关系。使用这条管道，我们将提供功能性的sceSNV 对目录的注释(目标2)。此外，我们将开发和整合一种新的工具，该工具可以交叉- 参考文献本地观察(由不同团队)sceSNV，按位置、研究、样本和细胞编制索引 条形码，提供单元格类型、研究元数据和其他注释的附加上下文 来自目录的结果摘要，首要目标是促进社区注释 ScRNA-Seq数据中的sceSNV(目标3)。

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).