Construction of cell specific gene co-regulations signatures based on single cell transcriptomics analysis

基于单细胞转录组学分析的细胞特异性基因共调控特征的构建

• 批准号: 10015323
• 负责人: Qin Ma
• 金额: $ 26.24万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-10 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10015323
• 关键词: Algorithms; Cells; Cellular Structures; Computing Methodologies; Data; Data Analyses; Databases; Detection; Development; Disease; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Genes; Genetic Transcription; Immune; Individual; Knowledge; Malignant Neoplasms; Mammalian Cell; Mathematics; Measures; Methods; MicroRNAs; Modeling; Neoplasm Metastasis; Pattern; Phenotype; Population; Regulation; Sampling; Signal Transduction; Source; The Cancer Genome Atlas; Tissue Sample; Tissues; base; cell type; computer infrastructure; computerized tools; epigenomics; genetic signature; mathematical model; novel; single-cell RNA sequencing; transcription factor; transcriptomics; web server

The gene expression values measured on a tissue sample is the, ensemble expression of all its comprising cells. Studying gene expression derived from tissue samples is COl)lplicated by the fact that there exist heterogeneous cell types in the tissue. For a full exploration of how different cell types interactively impact the development of a tissue or a disease, and more generally, robust mechanism interpretations from tissue-based transcriptomic data analysis, it is important to decipher the observed tissue-level gene expressions to the combination of expression levels of its cell components. A gene's expression in an individual cell is regulated by ,a set of transcriptional regulatory signals (TRSs) such as transcription factors, miRNAs, lncRNA, and epigenomic regulators. Oeciphering cell-type specific expression contribution is equivalent to identifying the true cell-type specific TRSs in different cell components of a tissue sample. Considering that the highly diverse TRS types in mammalian cells cannot be simultaneously measured by current experimental methods, we will model and quantify cell-type specific TRSs via mathematically well-defined co-regulation modules of their regulated genes based on single-cell RNA-Seq data. We hypothesize that the genes co-regulated by a common TRS in multiple cells can be characterized in single-cell RNA-Seq data and form gene signatures of the TRS. Mathematically, such a problem can be formulated as detection of a submatrix in a single-cell expression malrix, where the genes share coherent expression patterns over certain single-cell samples. Our preliminary data demonstrated that this problem can be solved by a biclustering based local low-rank submatrix detection approach. In this project, we propose the development of a computational infrastructure to derive gene signatures of cell-type specific TRSs from single-cell RNA-Seq data and decompose a tissue transcriptomic data to the contributions of TRSs in its component cells. Specifically, we have the following three aims: (1) Mathematically model TRS and associated co-regulation gene modules through transcriptomic profiles of single cells; (2) Develop a novel bi-clustering algorithm for identifying condition/cell-type specific co-regulated gene modules in single-cell transcriptomic data; and (3) Identify and annotate the gene signatures for each TRS, and estimate the level of each TRS in independent tissue data. Recent studies revealed the crucial impact of stromal and immune cells on the progression and metastasis of cancer. We will apply the computational methods to TCGA tissue expression and single-cell expression data from other sources, to quantitatively estimate the level of cell type-specific TRSs for different cell types within a cancer tissue. All the developed computational tools and derived knowledge will be maintained into/ a web server/database for public utilization.

在组织样品上测量的基因表达值是其所有组成细胞的总体表达。 研究组织样本中的基因表达是复杂的，因为存在异质性细胞类型 在组织中。为了全面探索不同类型的细胞如何相互作用地影响组织或疾病的发展， 更一般地说，从基于组织的转录组学数据分析中获得可靠的机制解释，重要的是 将观察到的组织水平基因表达解读为其细胞组分的表达水平的组合。 基因在单个细胞中的表达受一组转录调控信号（TRS）的调控，例如 转录因子、miRNA、lncRNA和表观基因组调节因子。细胞类型特异性表达贡献是 等同于在组织样品的不同细胞组分中鉴定真正的细胞类型特异性TRS。考虑到 哺乳动物细胞中高度多样的TRS类型不能通过当前的实验方法同时测量，我们将 通过其调节基因的数学上明确定义的共调节模块来建模和量化细胞类型特异性TRS 基于单细胞RNA-Seq数据我们假设在多个细胞中由共同的TRS共同调控的基因可以被 其特征在于单细胞RNA-Seq数据并形成TRS的基因签名。从数学上讲，这样的问题可能是 公式化为检测单细胞表达malrix中的子矩阵，其中基因共享一致表达 在某些单细胞样本上的模式。我们的初步数据表明，这个问题可以解决的双聚类 的局部低秩子矩阵检测方法。 在这个项目中，我们提出了一个计算基础设施的发展，以获得细胞类型的基因签名 从单细胞RNA-Seq数据中提取特定的TRS，并将组织转录组学数据分解为TRS在其 组成细胞。具体而言，我们有以下三个目标：（1）数学模型的TRS和相关的协同调节 通过单细胞的转录组学谱的基因模块;（2）开发一种新的双聚类算法， 条件/细胞类型特异性共调节基因模块;以及（3）识别和注释条件/细胞类型特异性共调节基因模块。 每个TRS的基因特征，并估计独立组织数据中每个TRS的水平。 最近的研究揭示了基质细胞和免疫细胞对癌症的进展和转移的关键影响。 我们将把计算方法应用于TCGA组织表达和其他来源的单细胞表达数据， 定量估计癌组织内不同细胞类型的细胞类型特异性TRS的水平。所有发达 计算工具和衍生知识将保存在网络服务器/数据库中供公众使用。