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.

单细胞基因组技术以起泡的速度发展。单细胞转录组的吞吐量 仅在过去的五年中，测序就增加了四个数量级，使我们的小组和能够 其他人将整个胚胎中的所有细胞类型分类在单个实验中。并行，测定 表观基因组的各个方面，包括染色质可及性，DNA甲基化和组蛋白 修改已适应单个细胞和大规模工作。此外，多路复用技术 已经提高了将单细胞基因组学不仅用于目录细胞类型的前景，而且可以全面 研究胚胎发育的无数扰动的影响，或表征疾病的演变 全动物量表和分子分辨率的发病机理。原则上，单细胞基因组学可以用作 表型的一种非常高的表型手段，但数据集的数量和丰富度是由 这样的实验带来了新的，艰巨的计算和统计挑战。缺乏软件工具用于 比较作为单细胞RNA-seq或ATAC-Seq实验的一部分的样品构成了A 该领域的关键差距。该建议旨在用软件工具来填补这一空白，以允许用户 表征疾病进展，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态。为了建立 我们的工具的准确性和预测的生理相关性，我们将广泛验证其 通过分析现有和新生成的单细胞测序数据的输出 斑马鱼胚胎开发系统。在我们的第一个目标中，我们将开发用于检测内部变化的软件 跨健康和病理分子状态的细胞群体。在我们的第二个目标中，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态过渡的基因 发病。在我们的第三个目标中，我们将开发定义染色质如何在调节性DNA处的方法 控制转录状态。完成这些目标后，我们将提供新的，广泛的适用 用于分析单细胞基因组学实验的软件。我们还将生成新数据集 既用作脊椎动物胚胎发生的参考图，又是进一步开发工具的平台 我们小组和其他人的遗传分析。我们的实验还将产生有关脊椎动物的新见解 基因组编码发展程序。