Photoactivatable cell sorting to link genetic variation with complex cellular phenotypes

项目摘要/摘要 个体在许多特征上存在差异，很少有特征差异具有简单的遗传原因。 事实上，与人类常见疾病相关的特征往往是相当复杂的，变异是由 许多遗传变异以及环境影响和随机机会的综合作用。 因此，确定遗传因素对复杂性状变异的影响仍然具有挑战性。一 应对这一挑战的方法是在实验室生物体中进行基因分析。实验室 实验可以控制人类研究无法控制的变异来源，并可以作为研究的试验台 开发新的方法来大规模确定基因类型和表型。萌芽的酵母， 酿酒酵母长期以来一直被用作真核细胞生物学的模型，现已成为 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 通过对数千到数百万个个体进行抽样来研究特征变异。然而，为了达到这些样本量， 实验的重点是那些容易测量或选择的特征，比如对有毒环境的抵抗力。这 在理解复杂细胞特性差异的遗传基础方面，有限的谱系留下了很大的差距 比如形态上的。细胞的形状和大小与各种疾病过程高度相关，但 数量遗传学家对此的研究不足。为了填补这一空白，该项目将结合使用高- 吞吐量显微镜、自动图像分析和可光激活细胞分类，用于对个体进行采样 高功率遗传分析。自然分离的发芽酵母菌株之间的遗传杂交将产生 大量的重组后代。实时图像分析和显微镜控制将用于 识别具有极端特性值的细胞，并通过对基因编码的或 实验应用的可转换荧光团。然后将使用荧光技术恢复选定的细胞 启动细胞分选，并汇集用于基因组测序。导致细胞差异的遗传变异 形态将被识别为在选定池中相对于未选定池过多表示的那些形态。 该项目将产生一种广泛适用的方法，将复杂的细胞特征与基因联系起来 不同之处。它还将对这些性状变异的遗传基础产生新的见解，从而推动 对复杂疾病的基因基础的理解。

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.