Photoactivatable cell sorting to link genetic variation with complex cellular phenotypes

项目总结/摘要 个体在许多性状上彼此不同，很少有性状差异是由简单的遗传原因引起的。 事实上，与人类常见疾病相关的特征往往相当复杂， 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，并可以作为测试平台， 开发新的方法来大规模确定基因型和表型。芽殖酵母， 酿酒酵母，长期以来被用作真核细胞生物学的模型，已经成为一种关键的生物体， 这样的实验。目前的酵母实验在检测遗传效应方面具有很高的统计功效， 通过对数千到数百万个体进行取样来分析性状变异。然而，为了达到这些样本量， 实验集中在易于测量或选择的特性上，例如对有毒环境的抵抗力。这 在理解复杂细胞特征差异的遗传基础方面，有限的剧目留下了很大的空白 例如形态学的。细胞的形状和大小与各种疾病过程高度相关， 被数量遗传学家忽视了。为了填补这一空白，该项目将使用高- 通过显微镜、自动图像分析和光活化细胞分选对个体进行取样， 高倍基因分析天然分离的芽殖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 识别具有极端性状值的细胞，并通过光活化遗传编码的或 实验应用的可转换荧光团。然后使用荧光技术回收选定的细胞 激活细胞分选并合并用于基因组测序。导致细胞差异的遗传变异 形态学将被确定为在所选择的池中相对于非细胞池过度代表的那些。 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特征联系起来。 差异它还将对这些性状变异的遗传基础产生新的见解，从而促进 了解复杂疾病的遗传基础。

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.