Photoactivatable cell sorting to link genetic variation with complex cellular phenotypes

可光激活的细胞分选将遗传变异与复杂的细胞表型联系起来

• 批准号: 10539111
• 负责人: Mark L Siegal
• 金额: $ 41.86万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10539111
• 关键词: Aging; Anaphase; Animal Model; Area; Awareness; Beds; Biological; Biological Assay; Biology; CRISPR/Cas technology; Cell Cycle; Cell Separation; Cell Shape; Cell Size; Cell division; Cells; Cellular Morphology; Cellular Structures; Cellular biology; Complex; Computing Methodologies; Disease; Disease Progression; Dyes; Environment; Eukaryotic Cell; Fluorescence-Activated Cell Sorting; Genetic; Genetic Crosses; Genetic Research; Genetic Variation; Genotype; Goals; Growth; Heritability; Human; Human Genetics; Image Analysis; Individual; Label; Laboratory Organism; Length; Lighting; Link; Malignant Neoplasms; Maps; Measurement; Measures; Methods; Microscope; Microscopy; Minor; Mitotic; Modeling; Morphology; Nuclear; Oak Tree; Organism; Phenotype; Population; Process; Proteins; Recombinants; Research Project Grants; Resistance; Resolution; Saccharomyces cerevisiae; Saccharomycetales; Sample Size; Sampling; Shapes; Source; Testing; Time; Variant; Wine; Yeasts; automated image analysis; cell behavior; cell dimension; cell type; cellular imaging; experimental study; flexibility; fluorophore; genetic analysis; genetic architecture; genetic variant; genome sequencing; genome wide association study; insight; interest; laboratory experience; laboratory experiment; meetings; method development; mutation screening; neutrophil; novel; photoactivation; precise genome editing; rapid technique; real-time images; trait

PROJECT SUMMARY/ABSTRACT Individuals differ from each other in many traits, and very few trait differences have simple genetic causes. Indeed, traits associated with common diseases in humans tend to be quite complex, with variation caused by the combined effects of many genetic variants as well as environmental influences and random chance. Determining the genetic contributions to variation in complex traits therefore remains challenging. One approach to meeting this challenge is to perform genetic analysis in laboratory organisms. Laboratory experiments can control for sources of variation that human studies cannot, and can serve as a test bed for developing new methods to determine genotypes and phenotypes at large scale. The budding yeast, Saccharomyces cerevisiae, long used as a model for eukaryotic cell biology, has emerged as a key organism for such experiments. Current yeast experiments achieve high statistical power for detecting genetic effects on trait variation by sampling thousands to millions of individuals. However, to achieve these sample sizes the experiments focus on traits that are easy to measure or select for, such as resistance to toxic environments. This limited repertoire leaves a big gap in understanding the genetic basis of differences in complex cellular traits such as morphological ones. The shapes and sizes of cells are highly relevant to various disease processes but are understudied by quantitative geneticists. To fill this gap, this project will use a combination of high- throughput microscopy, automated image analysis, and photoactivatable cell sorting to sample individuals for high-power genetic analysis. Genetic crosses between natural-isolate strains of budding yeast will generate large numbers of recombinant progeny. Real-time image analysis and microscope control will be used to identify cells with extreme trait values and label them via photoactivation of a genetically encoded or experimentally applied convertible fluorophore. Selected cells will then be recovered using fluorescence activated cell sorting and pooled for genome sequencing. Genetic variants that contribute to differences in cell morphology will be identified as those that are over-represented in selected pools relative to unselected pools. The project will produce a broadly applicable method for linking complex cellular traits with genetic differences. It will also yield new insights into the genetic basis of variation in such traits, and thereby advance understanding of the genetic underpinnings of complex diseases.

项目概要/摘要 个体在许多特征上存在差异，并且很少有特征差异是由简单的遗传原因造成的。 事实上，与人类常见疾病相关的特征往往相当复杂，由以下因素引起的变异： 许多遗传变异以及环境影响和随机机会的综合影响。 因此，确定复杂性状变异的遗传贡献仍然具有挑战性。一 应对这一挑战的方法是在实验室生物体中进行遗传分析。实验室 实验可以控制人类研究无法控制的变异来源，并且可以作为 开发大规模确定基因型和表型的新方法。发芽的酵母， 酿酒酵母（Saccharomyces cerevisiae）长期以来被用作真核细胞生物学的模型，现已成为一种关键生物体。 这样的实验。目前的酵母实验在检测遗传效应方面取得了很高的统计能力 通过对数千至数百万个体进行抽样来进行性状变异。然而，为了达到这些样本量 实验侧重于易于测量或选择的特征，例如对有毒环境的抵抗力。这 有限的功能在理解复杂细胞性状差异的遗传基础方面存在很大差距 比如形态学的。细胞的形状和大小与各种疾病过程高度相关，但 数量遗传学家正在对其进行研究。为了填补这一空白，该项目将结合使用高 通过通量显微镜、自动图像分析和光激活细胞分选来对个体进行采样 高功率遗传分析。芽殖酵母的天然分离菌株之间的遗传杂交将产生 大量重组后代。实时图像分析和显微镜控制将用于 识别具有极端特征值的细胞，并通过基因编码或光激活来标记它们 实验应用可转换荧光团。然后将使用荧光恢复选定的细胞 激活细胞分选并汇集用于基因组测序。导致细胞差异的遗传变异 形态将被识别为相对于未选择的池在选定的池中代表性过高的形态。 该项目将产生一种广泛适用的方法，将复杂的细胞特征与遗传联系起来。 差异。它还将对这些性状变异的遗传基础产生新的见解，从而推进 了解复杂疾病的遗传基础。