Comparing cell types across mutants and species at single cell resolution

摘要 单细胞基因表达图谱现在是常规产生的人类组织和整个模型 生物体胚胎，并揭示了细胞类型的多样性和基因表达的调控。而 这些野生型单细胞图谱可以预测整个发育过程中的候选调控基因， 研究，需要进一步的工作，以确定监管机制和功能的重要性， 观察到大规模的表达模式。一个关键的问题是如何识别数据集之间的同源细胞， 它们的表达可能被改变，例如来自进化过程中相同组织的数据，或来自动物的数据， 经历过遗传或药理学干扰的人本项目将使用广泛使用的模型 生物秀丽隐杆线虫开发和测试方法，以比较细胞在这种条件下。我们 重点是两个生物学问题。在目标1中，我们将比较C.线虫和 四个相关的线虫胚胎这些线虫的胚胎谱系与C. 尽管存在大量的序列差异（每个中性位点>1个取代），但它们仍然是理想的测试方法 在整个进化过程中对齐单细胞数据集的案例。我们将产生大的单细胞RNA测序 每个物种的胚胎数据集（C. remanei，C. brenneri，C. briggsae和C. nigoni）。我们将两者进行比较 自动同源转移和从头谱系推断方法来鉴定每个物种中的细胞类型。我们 将使用定量成像方法（smFISH和GFP敲入的实时成像）来验证 单细胞实验由此产生的数据将使我们能够根据保守性对基因和细胞类型进行分类。 它们的基因表达，提供了深入了解细胞类型的进化。在目标2中，我们将测试 中胚层“MS”谱系的特化和多样化中的保守调节因子（其产生 咽、体壁肌肉和一些特化的中胚层细胞类型）。我们将测量基因表达， 使用生长素降解决定子方法在这些中胚层调节剂的条件性损失之后的scRNA-seq。如目标1所示， 我们将测试和验证这些数据集的自动比对方法，以识别细胞。结果数据将 使我们能够区分同源异型的命运转变与新的细胞状态的形成， 直接或环境特异性靶点与每个调节因子的间接靶点分离，并产生全基因组 发育中的动物胚胎的中胚层调节网络。

ABSTRACT Single cell gene expression atlases are now routinely generated for human tissues and entire model organism embryos and have shed light on the diversity of cell types and regulation of gene expression. While these wild-type single cell atlases can predict candidate regulatory genes across development for focused studies, further work is needed to determine the regulatory mechanisms and functional importance of the observed expression patterns at scale. A key problem is how to identify homologous cells between datasets in which their expression may be altered, for example data from the same tissue across evolution, or from animals that have experienced a genetic or pharmacological perturbation. This project will use the widely used model organism Caenorhabditis elegans to develop and test methods to compare cells across such conditions. Our focus is on two biological problems. In Aim 1, we will compare expression in single cells between C. elegans and four other related nematode embryos. These nematode species have nearly identical embryonic lineages to C. elegans despite substantial sequence divergence (>1 substitution per neutral site), making them an ideal test case for alignment of single cell datasets across evolution. We will generate large single cell RNA-sequencing datasets for embryos of each species (C. remanei, C. brenneri, C. briggsae and C. nigoni). We will compare both automated homology transfer and de novo lineage inference methods to identify cell types in each species. We will use quantitative imaging approaches (smFISH and live imaging of GFP knock ins) to validate the results of the single cell experiments. The resulting data will allow us to classify genes and cell types by the conservation of their gene expression, providing insight into the evolution of cell types. In Aim 2, we will test the role of conserved regulators in the specification and diversification of the mesodermal “MS” lineage (which produces pharynx, body wall muscle, and some specialized mesodermal cell types). We will measure gene expression by scRNA-seq after conditional loss of these mesodermal regulators using an auxin degron approach. As in Aim 1, we will test and validate automated alignment methods for these datasets to identify cells. The resulting data will allow us to distinguish homeotic fate transformations from the formation of novel cell states, to distinguish likely direct or context specific targets from indirect targets of each regulator, and to generate a genome-wide mesodermal regulatory network of a developing animal embryo.