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: 10677887
• 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/10677887
• 关键词: 4D Imaging; Adopted; Alleles; Animals; Atlases; Auxins; Biological; CRISPR imaging; Caenorhabditis; Caenorhabditis elegans; Cell Lineage; Cell physiology; Cells; Classification; 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; Malignant Neoplasms; Measures; Mesoderm Cell; Methods; Modeling; Molecular; Muscle; Nematoda; Organism; Pattern; Persons; Pharyngeal structure; RNA; Regulator Genes; Resolution; Role; Scientist; Site; Specific qualifier value; Switch Genes; System; Testing; Tissues; Trees; Work; cell fate specification; cell type; comparative; experience; experimental study; genetic manipulation; genome-wide; human tissue; imaging approach; improved; insight; model organism; mutant; novel; pharmacologic; 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，单细胞基因表达图谱是针对人组织和整个模型的常规生成的 生物体胚胎，并阐明了细胞类型的多样性和基因表达的调节。尽管 这些野生型单细胞图谱可以预测跨发育的候选调节基因 研究，需要进一步的工作来确定调节机制和功能重要性 观察到的表达模式在规模上。一个关键问题是如何识别数据集之间的同源单元 它们的表达可能会改变，例如来自相同组织的数据，或者来自动物 经历了遗传或药物扰动。该项目将使用广泛使用的模型 秀丽隐杆线虫的生物体开发和测试方法以比较在这种情况下的细胞。我们的 重点是两个生物学问题。在AIM 1中，我们将比较秀丽隐杆线虫和 其他四个相关的线虫胚胎。这些线虫物种与C的胚胎线几乎相同。 秀丽隐藏目的地的实质序列发散（每个中性位点> 1替代），使其成为理想的测试 跨进化对单元数据集对齐的情况。我们将生成大型的单细胞RNA测序 每个物种胚胎的数据集（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理方法以识别每个物种中的细胞类型。我们 将使用定量成像方法（Smfish和GFP敲击INS的实时成像）来验证 最终的数据将使我们能够通过保护对基因和细胞类型进行分类 它们的基因表达，提供对细胞类型演变的见解。在AIM 2中，我们将测试 中胚层“ MS”谱系的规范和多样化中保守的调节剂（产生 咽，身体壁肌和一些专门的中胚层细胞类型）。我们将通过 这些中胚层调节剂有条件丢失后使用生长素degron方法有条件地丢失。就像在AIM 1中一样 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。结果数据将 允许我们区分同型脂肪转化与新细胞状态的形成，以区分可能 来自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。