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

单细胞基因组学技术以惊人的速度发展。单细胞转录组的产量 仅在过去五年中，测序就增加了四个数量级，使我们集团和 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，对 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 改造已经适应于在单个细胞中和在规模上工作。此外，多路复用技术 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，而且可以全面 研究胚胎发育的无数干扰的影响，或描述疾病的进化 全动物尺度的致病机制和分子分辨。原则上，单细胞基因组学可以作为 一种非常高含量的表型分析手段，但由 这样的实验带来了新的、令人生畏的计算和统计挑战。软件工具的缺乏 比较作为单细胞rna-seq或atac-seq实验一部分的标本构成 该领域的关键差距。这项提议旨在用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 我们工具的准确性和他们预测的生理相关性，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，使用非常易于处理的 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移动的软件 健康和病理分子状态下的细胞群。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期间调节或调节细胞状态转换的基因 发病机制。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节dna中的状态。 控制转录状态。在实现这些目标后，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育的参考图，也可以作为进一步开发工具的平台 我们团队和其他人进行的基因分析。我们的实验还将对脊椎动物如何 基因组编码发育程序。

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.