Comparing cell types across mutants and species at single cell resolution

摘要 现在常规地为人体组织和整个模型生成单细胞基因表达图谱 并阐明了细胞类型的多样性和基因表达的调控。而当 这些野生型单细胞图谱可以预测不同发育阶段的候选调控基因 随着研究的深入，还需要进一步的工作来确定该蛋白的调节机制和功能重要性。 观察到规模化的表达模式。一个关键问题是如何识别数据集之间的同源细胞 它们的表达可以被改变，例如来自相同组织的进化数据，或者来自动物的数据 那些经历了遗传或药理学干扰的人。本项目将使用广泛使用的模型 生物体秀丽线虫开发和测试方法来比较这种条件下的细胞。我们的 重点放在两个生物问题上。在目标1中，我们将比较线虫和线虫在单个细胞中的表达 另外四个相关的线虫胚胎。这些线虫物种的胚胎谱系与C. 尽管有很大的序列差异(每个中性位点有1个替换)，但优雅使它们成为理想的测试 跨进化的单细胞数据集的比对案例。我们将产生大型单细胞RNA测序 每种(C.remanei、C.Brenneri、C.briggsae和C.nigoni)胚胎的数据集。我们将对两者进行比较 自动同源转移和从头谱系推断方法，以确定每个物种的细胞类型。我们 将使用定量成像方法(smFISH和GFP敲打蛋白的实时成像)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 他们的基因表达，提供了对细胞类型进化的洞察。在目标2中，我们将测试 在中胚层“MS”谱系的规范和多样化方面保守的调节者(产生 咽部、体壁肌肉和一些特殊的中胚层细胞类型)。我们将通过以下方式测量基因表达 在使用生长素降解方法有条件地失去这些中胚层调节因子后，scRNA-seq。就像目标1一样， 我们将测试和验证这些数据集的自动对齐方法，以识别细胞。生成的数据将 使我们能够区分同源异型命运转换和新细胞状态的形成，以区分可能的 来自每个调节器的间接靶标的直接或上下文特定靶标，并产生全基因组范围的 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.