Statistical Methods for Single-Cell Transcriptomics

项目摘要 细胞是多细胞生物的基本生物单位。最近的技术突破 在单细胞水平上测量基因表达是可能的，从而为探索基因表达铺平了道路。 细胞间的表达异质性。细胞中所有RNA种类的丰度的集合形成其 “分子指纹”，使许多基本的生物学问题的调查超出了这些 通过传统的批量RNA-seq实验。单细胞RNA-seq（scRNA-seq）使我们能够更好地 描述单细胞的谱系和类型，表征跨细胞基因表达的随机性， 提高我们对健康和疾病中细胞功能的理解。ScRNA-seq分析正在改变 生物医学科学，并已经在神经科学和免疫学等领域产生了巨大的影响， 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无法解决的新挑战。 旨在对抗。目前的scRNA-seq协议很复杂，经常引入不同的技术偏见， 如果不适当去除，可能会模糊细胞类型识别，并导致偏倚结果， 下游分析。已发表的scRNA-seq研究主要是原理证明研究， scRNA-seq在细胞类型分类和其他基本生物学分析中的效用。然而，由于使用 scRNA-seq持续增长，研究人员开始探索其在疾病基因发现中的效用。 基于我们在统计方法开发方面的专业知识和我们在基因组学分析方面的经验， 数据的人类心脏代谢疾病，在这项建议中，我们建议开发新的统计方法， 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scRNA-seq数据，这些数据是与合作者在 宾夕法尼亚大学和哥伦比亚大学。我们提出以下具体目标。目标1：发展 恢复基因表达和鉴定细胞类型的方法。目的2：建立基因检测方法 表达在细胞类型或条件之间改变。目的3：开发估计异构体特异性的方法 基因表达和检测差异可变剪接。目的4：开发模拟等位基因特异性的方法 转录爆发及其遗传调控。该提案解决了scRNA-seq中的关键挑战 它汇集了一支杰出的科学家团队，在统计方面有着良好的记录， 基因组学、单细胞生物学和心脏代谢疾病。该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 监管，并促进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.