Department of Genetics, Evolution and Environment, Institute of Healthy Ageing, University College London, London, UK.
Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
Methods Mol Biol. 2022;2477:381-397. doi: 10.1007/978-1-0716-2257-5_21.
Colony fitness screens are powerful approaches for functional genomics and genetics. This protocol describes experimental and computational procedures for assaying the fitness of thousands of microbial strains in numerous conditions in parallel. Data analysis is based on pyphe, an all-in-one bioinformatics toolbox for scanning, image analysis, data normalization, and interpretation. We describe a standard protocol where endpoint colony areas are used as fitness proxy and two variations on this, one using colony growth curves and one using colony viability staining with phloxine B. Different strategies for experimental design, normalization and quality control are discussed. Using these approaches, it is possible to collect hundreds of thousands of data points, with low technical noise levels around 5%, in an experiment typically lasting 2 weeks or less.
集落适合度筛选是功能基因组学和遗传学的强大方法。本方案描述了在多种条件下平行测定数千种微生物菌株适合度的实验和计算程序。数据分析基于 pyphe，这是一个用于扫描、图像分析、数据归一化和解释的一体化生物信息学工具包。我们描述了一个标准方案，其中使用终点集落面积作为适合度替代物，并对其进行了两种变体，一种使用集落生长曲线，另一种使用荧光素 B 对集落活力进行染色。讨论了不同的实验设计、归一化和质量控制策略。使用这些方法，在通常持续 2 周或更短时间的实验中，可以收集数十万具有低技术噪声（约 5%）的数据点。
