Project I - Transcriptomic Analysis of Structural Birth Defects in Mouse Developmental Mutants

项目总结 在这个项目中，我们计划应用一种强大且可扩展的组合索引技术来表征 携带已知或可能导致突变的小鼠在单细胞水平上的基因表达 结构性先天缺陷。我们建议评估单细胞表达是否可以作为一种表型； 具体地说，我们的目标是组织这些数据集，以评估是否有单细胞基因的签名 便于根据推测的发育信号通路对突变系进行分组的表达 微扰。这一分析将得到项目2中提出的解剖分析的补充。 考虑到这种方法的新颖性，我们将首先分析Shh中基因突变的10个品系 信号通路，以便将单细胞转录数据与研究良好的发育相关 表型。为了最大限度地增加新基因发现的机会，我们还将研究具有 之前没有关于人类结构性出生缺陷的注释。具体地说，使用对 人类外显子组测序数据，我们已经确定了一大批可能是单倍体不足的基因；即， 当杂合子为空时，它们与生存不相容。我们还进一步培育出一种杂合子 选择(SHET)与人类疾病严重程度密切相关的统计数据。我们的目标是描述75个 顶端五分之一集合中具有有限功能注释的行；这些基因将被选为a) 基于胚胎发育过程中单细胞表达的证据(曹等人)。2019年)，它们是新的细胞- 类型特定的指标基因或b)是已知的致死基因(在小鼠中)，具有较高的蛋白质频率。 互动。 作为这项工作的一部分，我们将开发生物信息学工具，以促进不同数据集的比较。 这些可以识别具有常见发育信号异常的突变系，以及潜在的 作为了解人类先天畸形的机制基础的一种手段。

PROJECT SUMMARY In this project we plan to apply a powerful and scaleable technique of combinatorial indexing to characterize gene expression at the single-cell level in mice carrying mutations that are either known to or likely to result in structural birth defects. We propose to assess whether single-cell expression can be utilized as a phenotype; specifically, we aim to organize these data sets to assess whether there are signatures of single-cell gene expression that facilitate grouping mutant lines based on presumptive pathways of developmental signaling perturbation. This analysis will be complemented by the anatomical analysis that is proposed in Project 2. Given the novelty of this method, we will initially analyze 10 lines that are mutated for genes in the Shh signaling pathway, in order to correlate single-cell transcriptomic data with well-studied developmental phenotypes. To maximize the opportunity for new gene discovery, we will also examine novel genes that have not been previously annotated with respect to human structural birth defects. Specifically, using an analysis of human exome sequencing data, we have identified a large cohort of genes that are likely haploinsufficient; i.e., they are not compatible with survival when heterozygous null. We have furthermore developed a heterozygote selection (shet) statistic that correlates remarkably well with human disease severity. We aim to characterize 75 lines from the top quintile shet set that have limited functional annotation; these genes will be chosen either a) based on evidence from single-cell expression during embryogenesis (Cao et al. 2019) that they are novel cell- type-specific index genes or b) are known lethal genes (in mice) that have a high frequency of protein interactions. As part of this effort we will develop bioinformatic tools to facilitate comparisons across different datasets. These can identify mutant lines with common abnormalities of developmental signaling, as well as potentially serving as a means to understand the mechanistic basis for human congenital abnormalities.