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

基本信息

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
项目类别：
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

项目摘要

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正在以惊人的速度发展。单细胞转录组通量仅在过去五年中，测序就增加了四个数量级，使我们的团队和其他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，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于在单细胞中大规模发挥作用。此外，多路复用技术提出了使用单细胞基因组学的前景，不仅可以对细胞类型进行编目，而且可以全面地研究胚胎发育的无数扰动的影响，或描述疾病的演变全动物规模和分子分辨率的发病机制。原则上，单细胞基因组学可以作为一种内容非常丰富的表型分析方法，但产生的数据集的数量和丰富性此类实验提出了新的、令人畏惧的计算和统计挑战。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该领域的关键差距。该提案旨在通过软件工具来填补这一空白，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及其预测的生理相关性，我们将广泛验证他们的通过使用非常易于处理的方法分析现有和新生成的单细胞测序数据来输出斑马鱼胚胎发育系统。在我们的第一个目标中，我们将开发用于检测内部变化的软件健康和病理分子状态的细胞群。在我们的第二个目标中，我们将开发软件用于识别在发育或疾病过程中介导或调节细胞状态转变的基因发病。在我们的第三个目标中，我们将开发方法来定义染色质在调控 DNA 中的状态控制转录状态。完成这些目标后，我们将交付新的、广泛适用的用于分析单细胞基因组学实验的软件。我们还将生成新的数据集既可以作为脊椎动物胚胎发生的参考图谱，也可以作为进一步开发工具的平台我们小组和其他人进行的遗传分析。我们的实验还将产生关于脊椎动物如何基因组编码发育程序。