Comparing cell types across mutants and species at single cell resolution

以单细胞分辨率比较突变体和物种之间的细胞类型

• 批准号: 10474563
• 负责人: John Isaac Murray
• 金额: $ 44.38万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10474563
• 关键词: 4D Imaging; Adopted; Alleles; Animal Model; Animals; Atlases; Auxins; Biological; CRISPR imaging; Caenorhabditis; Caenorhabditis elegans; Cell Lineage; Cell physiology; Cells; Data; Data Set; Defect; Development; Developmental Gene; Diabetes Mellitus; Disease; Embryo; Evolution; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic Models; Genetic Transcription; Genetic study; Genome; Image; Individual; Lead; Light; Malignant Neoplasms; Measures; Mesoderm Cell; Methods; Modeling; Molecular; Muscle; Nematoda; Organism; Pattern; Persons; Pharmacology; Pharyngeal structure; RNA; Regulator Genes; Resolution; Role; Scientist; Site; Switch Genes; System; Testing; Tissues; Trees; Work; base; cell fate specification; cell type; comparative; experience; experimental study; genetic manipulation; genome-wide; human tissue; imaging approach; improved; insight; mutant; novel; progenitor; quantitative imaging; single cell analysis; single molecule; single-cell RNA sequencing; transcriptome

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.

抽象的 现在可以常规地为人体组织和整个模型生成单细胞基因表达图谱 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。尽管 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，以重点关注 研究表明，需要进一步的工作来确定监管机制和功能重要性 大规模观察表达模式。一个关键问题是如何识别数据集中的同源细胞 它们的表达可能会改变，例如来自同一组织的进化数据或来自动物的数据 经历过遗传或药理学扰动的人。该项目将使用广泛使用的模型 秀丽隐杆线虫生物体开发和测试方法来比较这些条件下的细胞。我们的 重点是两个生物学问题。在目标 1 中，我们将比较秀丽隐杆线虫和 其他四个相关线虫胚胎。这些线虫物种与线虫具有几乎相同的胚胎谱系。 尽管存在显着的序列差异（每个中性位点 > 1 次替换），但线虫仍然是理想的测试对象 跨进化过程中单细胞数据集对齐的情况。我们将生成大型单细胞 RNA 测序 每个物种的胚胎数据集（C. remanei、C. brenneri、C. briggsae 和 C. nigoni）。我们将比较两者 自动同源转移和从头谱系推断方法来识别每个物种的细胞类型。我们 将使用定量成像方法（smFISH 和 GFP 敲入的实时成像）来验证结果 单细胞实验。由此产生的数据将使我们能够通过保守性对基因和细胞类型进行分类 它们的基因表达，提供对细胞类型进化的洞察。在目标 2 中，我们将测试以下角色： 中胚层“MS”谱系（产生 咽、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 使用生长素降解决定子方法条件性丢失这些中胚层调节因子后的 scRNA-seq。正如目标 1 中那样， 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。结果数据将 让我们能够区分同源命运转变和新细胞状态的形成，以区分可能的 每个调节器的间接目标的直接或上下文特定目标，并生成全基因组 发育中的动物胚胎的中胚层调节网络。