Genome-wide association studies are a powerful approach for identifying determinants of disease. For infectious diseases, high throughput assays are required for measuring the variance in multiple virulence-related phenotypes of large bacterial isolate collections and for association of this phenotypic variance with genotype. The primary limiting factors are cost, effectiveness and a standardized inoculum. A method was developed to create an inoculum array of multiple isolates that could be used for a series of high-throughput multi-isolate phenotypic investigations in a laboratory setting. A key starting point was the standardisation of the inoculum by production of identical batches of each isolate from cells grown to mid-log phase. Cultures with pre-determined optical densities were aliquoted in set patterns into multiple multi-well plates containing 50% glycerol and stored at -80 °C. Prior to a specific assay, an inoculum plate was defrosted and subjected to a brief period of incubation. Control strains can be placed on each plate in order to control for intra-assay variability. A high throughput screen is described in detail for quantification of biofilm formation. This example utilised the crystal violet staining method and multi-assay stock plates containing 16 meningococcal isolates.â¢Multi-assay stock plate of exponentially growing isolates is cost-effective and simple to implement in a laboratory setting.â¢This method would predict realistic standard deviations for multiple isolates in phenotypic assays and generate data for performance of power calculations for genotyping.â¢This method has the potential to identify both known and unknown genetic determinants of phenotypic variability for each tested isolate when paired with genetic analysis of whole genome sequencing data.
Development of a robust and quantitative high-throughput screening method for assessing phenotypic variation in large Neisseria meningitidis isolate collections.
开发一种稳健的定量高通量筛选方法,用于评估大型脑膜炎奈瑟菌分离株库中的表型变异
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作者:Farzand Robeena, Croix Megan De Ste, Dave Neelam, Bayliss Christopher D
| 期刊: | MethodsX | 影响因子: | 1.900 |
| 时间: | 2023 | 起止号: | 2023 Mar 2; 10:102091 |
| doi: | 10.1016/j.mex.2023.102091 | 研究方向: | 免疫/内分泌 |
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