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

基本信息

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
项目类别：
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

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

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项目摘要

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.

项目摘要/摘要个体在许多特征中彼此不同，很少的性状差异具有简单的遗传原因。实际上，与人类常见疾病相关的特征往往很复杂，并且由于许多遗传变异以及环境影响和随机机会的综合作用。因此，确定对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的变异来源，并且可以用作测试床开发新方法以大规模确定基因型和表型。萌芽的酵母，长期用作真核细胞生物学模型的酿酒酵母已成为关键的生物体这样的实验。当前的酵母实验获得了高统计能力来检测遗传影响特质差异是通过对数百万人进行抽样。但是，要实现这些样本大小实验着眼于易于测量或选择的性状，例如对有毒环境的抗性。这有限的曲目在理解复杂细胞性状差异的遗传基础方面留下了很大的差距例如形态学。细胞的形状和大小与各种疾病过程高度相关，但被定量遗传学家研究了。为了填补这一空白，该项目将结合使用高吞吐量显微镜，自动图像分析和光活化细胞对样品个体的分类高功率遗传分析。自然分析酵母菌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性状值的细胞，并通过光激活遗传编码或实验施用可转换的荧光团。然后，将使用荧光回收选定的细胞活化的细胞分选并合并以进行基因组测序。导致细胞差异的遗传变异形态将被确定为相对于未选择池的选定池中代表过多的形态。该项目将产生一种将复杂细胞性状与遗传联系起来的广泛适用方法差异。它还将产生对此类特征变异遗传基础的新见解，从而提高了解复杂疾病的遗传基础。