New software tools for differential analysis of single-cell genomics perturbation experiments

用于单细胞基因组扰动实验差异分析的新软件工具

• 批准号: 10735033
• 负责人: David Kimelman
• 金额: $ 63.88万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-22 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10735033
• 关键词: ATAC-seq; Algorithms; Animals; Atlases; Biological Assay; Biological Process; Bulla; Catalogs; Cells; Chemicals; Chromatin; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Computer software; DNA; DNA Methylation; DNA sequencing; Data; Data Set; Development; Developmental Biology; Disease; Disease Progression; Elements; Embryo; Embryonic Development; Engineering; Epigenetic Process; Evolution; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Transcription; Genomics; Goals; Individual; Informatics; Literature; Maps; Measurement; Measures; Mediating; Methods; Molecular; Output; Pathogenesis; Pathologic; Pathology; Phenotype; Physiological; Population; Publishing; Regression Analysis; Resolution; Sampling; Scientist; Software Tools; Specimen; Systems Development; Techniques; Technology; Time; Tissues; Transcriptional Regulation; Untranslated RNA; Work; Zebrafish; animal data; cell type; defined contribution; epigenome; experimental study; genetic analysis; genome editing; histone modification; human disease; insight; interest; knockout gene; programs; single cell analysis; single cell sequencing; single-cell RNA sequencing; software development; tool; tool development; transcriptome sequencing; vertebrate genome

Single-cell genomics technology has advanced at a blistering pace. The throughput of single-cell transcriptome sequencing has increased by four orders of magnitude in the past five years alone, enabling our group and others to catalog all of the cell types in a whole embryo within a single experiment. In parallel, assays for diverse aspects of the epigenome, including chromatin accessibility, DNA methylation, and histone modifications have been adapted to work in single cells and at scale. Furthermore, multiplexing techniques have raised the prospect of using single-cell genomics not only to catalog cell types, but to comprehensively study the effects of myriad perturbations of embryonic development, or to characterize the evolution of disease pathogenesis at whole-animal scale and molecular resolution. In principle, single-cell genomics could serve as an extraordinarily high-content means of phenotyping, but the volume and richness of datasets produced by such experiments poses new, daunting computational and statistical challenges. A lack of software tools for comparing specimens profiled as part of single-cell RNA-seq or ATAC-seq experiments constitutes a critical gap in the field. This proposal aims to fill that gap with software tools that will allow users to characterize how disease progression, genetic or chemical perturbations, or environmental effects alter the proportions and molecular states of cells in complex tissues or whole embryos. In order to establish the accuracy of our tools and the physiological relevance of their predictions, we will extensively validate their output through analysis of existing and newly generated single-cell sequencing data using the very tractable zebrafish embryonic development system. In our first Aim, we will develop software for detecting shifts within cell populations across healthy and pathological molecular states. In our second Aim, we will develop software for identifying genes that mediate or regulate cell-state transitions during development or disease pathogenesis. In our third Aim, we will develop methods for defining how chromatin states at regulatory DNA control transcriptional states. Upon completing these aims, we will have delivered new, widely applicable software for analyzing single-cell genomics experiments. We will also have produced new datasets that will serve both as a reference map for vertebrate embryogenesis and a platform for further development of tools for genetic analysis by our group and others. Our experiments will also yield new insights as to how vertebrate genomes encode developmental programs.

单细胞基因组学技术已经以惊人的速度发展。单细胞转录组的通量 仅在过去五年中，测序就增加了四个数量级，使我们的团队和 另一些人则在一次实验中对整个胚胎中的所有细胞类型进行分类。同时， 表观基因组的不同方面，包括染色质可及性，DNA甲基化和组蛋白 已经进行了修改以在单细胞中和大规模地工作。此外，复用技术 已经提出了使用单细胞基因组学不仅可以对细胞类型进行分类， 研究胚胎发育的无数扰动的影响，或描述疾病的演变 发病机制在整个动物规模和分子分辨率。原则上，单细胞基因组学可以作为 一个非常高的内容表型分析手段，但数量和丰富的数据集产生的 这些实验提出了新的、令人生畏的计算和统计挑战。缺乏软件工具， 比较作为单细胞RNA-seq或ATAC-seq实验一部分的样本构成了 外地的严重差距。该提案旨在用软件工具填补这一空白，使用户能够 描述疾病进展、遗传或化学扰动或环境影响如何改变 复杂组织或整个胚胎中细胞的比例和分子状态。为了建立 我们的工具的准确性和他们的预测的生理相关性，我们将广泛验证他们的 通过分析现有的和新生成的单细胞测序数据输出， 斑马鱼胚胎发育系统在我们的第一个目标中，我们将开发软件，用于检测 健康和病理分子状态的细胞群。在第二个目标中，我们将开发软件 用于鉴定在发育或疾病期间介导或调节细胞状态转变的基因 发病机制在我们的第三个目标中，我们将开发定义染色质在调控DNA中状态的方法。 控制转录状态。在完成这些目标后，我们将提供新的，广泛适用的 单细胞基因组学实验分析软件。我们还将制作新的数据集， 作为脊椎动物胚胎发生的参考图谱和进一步开发工具的平台， 我们和其他人的基因分析。我们的实验也将产生新的见解， 基因组编码发育程序。