Statistical Methods for Single-Cell Transcriptomics

项目总结 细胞是多细胞生物体的基本生物单位。最近的技术突破取得了 有可能在单细胞水平上测量基因表达，从而为探索基因铺平道路。 细胞间表达的异质性。一个细胞中所有RNA物种的丰度的集合形成了它的 “分子指纹”，使得研究许多基本的生物学问题成为可能。 通过传统的批量RNA-SEQ实验是可能的。单细胞RNA-seq(scRNA-seq)使我们能够更好地 描述单个细胞的谱系和类型，描述跨细胞基因表达的随机性， 并提高我们对细胞在健康和疾病中的功能的理解。ScRNA-seq分析正在转变 生物医学科学，已经在神经科学和免疫学等领域产生了重大影响，以及 可以增强我们对许多其他情况下疾病发展的理解，包括心脏代谢 疾病。然而，scRNA-seq数据提出了标准分析方法所不具备的新挑战。 被设计用来对抗。目前的scRNA-seq协议很复杂，经常引入不同的技术偏见 跨单元格，如果不正确删除，可能会模糊单元格类型识别并导致在 下游分析。已发表的scrna-seq研究主要是原则验证研究，说明 Scrna-seq在细胞类型分类和其他基础生物学分析中的应用。然而，由于使用了 随着scRNA-seq的持续增长，研究人员开始探索它们在疾病基因发现中的作用。 以我们在统计方法开发方面的专业知识和在基因组学分析方面的经验为基础 关于人类心脏代谢性疾病的数据，在这项建议中，我们建议开发新的统计方法来 解决scRNA-seq分析中的一些关键分析挑战。我们将指导方法开发 通过分析与合作者持续合作产生的scRNA-seq数据， 宾夕法尼亚大学和哥伦比亚大学。我们提出了以下具体目标。目标1：发展 方法恢复基因表达，鉴定细胞类型。目标2：开发检测基因的方法 表达随细胞类型或条件的不同而变化。目标3：开发评估特定异构体的方法 基因表达和检测差异选择性剪接。目标4：开发特定等位基因模型的方法 转录爆发及其基因调控。该提案解决了scrna-seq中的关键挑战。 分析，它汇集了一支杰出的科学家团队，他们在统计学方面有良好的记录 基因组学、单细胞生物学和心脏代谢性疾病。这一项目的成功完成将使 研究人员更好地解开复杂的细胞异质性，精确地将基因组序列与基因联系起来 监管，并促进将基础研究成果转化为人类疾病的临床研究。

PROJECT SUMMARY Cells are the basic biological units of multicellular organisms. Recent technological breakthroughs have made it possible to measure gene expression at the single-cell level, thus paving the way for exploring gene expression heterogeneity among cells. The collection of abundances of all RNA species in a cell forms its “molecular fingerprint”, enabling the investigation of many fundamental biological questions beyond those possible by traditional bulk RNA-seq experiments. Single-cell RNA-seq (scRNA-seq) allows us to better describe the lineage and type of single cells, characterize the stochasticity of gene expression across cells, and improve our understanding of cellular function in health and disease. ScRNA-seq analysis is transforming biomedical sciences, and has already made great impact in fields such as neuroscience and immunology, and can enhance our understanding of disease development in numerous other contexts including cardiometabolic diseases. However, scRNA-seq data present new challenges for which standard analytical methods are not designed to confront. Current scRNA-seq protocols are complex, often introducing technical biases that vary across cells, which, if not properly removed, can obscure cell type identification and lead to biased results in downstream analyses. Published scRNA-seq studies have mainly been proof-of-principal studies illustrating the utility of scRNA-seq in cell type classification and other basic biological analyses. However, as the use of scRNA-seq continues to grow, researchers are beginning to explore their utility in disease gene discovery. Building upon our expertise in statistical methods development and our experience with analysis of genomics data for human cardiometabolic diseases, in this proposal, we propose to develop novel statistical methods to address some of the key analytical challenges in scRNA-seq analysis. We will guide methods development through the analysis of scRNA-seq data generated from ongoing collaborations with collaborators at the University of Pennsylvania and Columbia University. We propose the following specific aims. Aim 1: Develop methods to recover gene expression and identify cell types. Aim 2: Develop methods to detect gene expression changes between cell types or conditions. Aim 3: Develop methods to estimate isoform-specific gene expression and detect differential alternative splicing. Aim 4: Develop methods to model allele-specific transcriptional bursting and its genetic regulation. This proposal addresses critical challenges in scRNA-seq analysis, and it brings together an exceptional team of scientists with proven track record in statistical genomics, single-cell biology, and cardiometabolic disease. The successful completion of this project will allow researchers to better disentangle complex cellular heterogeneity, precisely relate genomic sequence to gene regulation, and facilitate the translation of basic research findings into clinical studies of human disease.