Simple large-scale quantitative phenotyping and antimicrobial susceptibility testing with Q-PHAST.

IF 13.1 1区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Juan Carlos Nunez-Rodriguez, Miquel Àngel Schikora-Tamarit, Ewa Ksiezopolska, Toni Gabaldón
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引用次数: 0

Abstract

The characterization of antimicrobial susceptibility and other relevant phenotypes in large collections of microbial isolates is a common need across research and clinical microbiology laboratories. Robotization provides unprecedented throughput but involves costs that are prohibitive for the average laboratory. Here, using affordable materials and open-source software, we developed Q-PHAST (Quantitative PHenotyping and Antimicrobial Susceptibility Testing), a unique solution for cost-effective, large-scale phenotyping in a standard microbiology laboratory. Single colonies are grown in a deep 96-well master plate, from which diluted aliquots are used to generate 96 spots on different experimental plates containing solid medium with the substance and concentration of interest. These plates are incubated on inexpensive flatbed scanners that monitor the growth of each spot by obtaining images every 15 min. A simple, python-based software, which can be used via a graphical interface on various operating systems ( https://github.com/Gabaldonlab/Q-PHAST ), analyzes the images to infer growth, fitness (e.g., doubling rate) and susceptibility (e.g., minimum inhibitory concentration) measures. With <120 min of hands-on time per day for three consecutive days, ready-to-use results are obtained and presented in tables or graphs. This solution enables non-experts with limited resources to perform accurate quantitative phenotyping on hundreds of strains in parallel.

Q-PHAST简易大规模定量表型及药敏试验。
在大量微生物分离物中对抗菌药物敏感性和其他相关表型进行表征是研究和临床微生物学实验室的共同需求。机器人化提供了前所未有的吞吐量,但涉及的成本对一般实验室来说是令人望而却步的。在这里,使用价格合理的材料和开源软件,我们开发了Q-PHAST(定量表型和抗菌药物敏感性测试),这是一个独特的解决方案,具有成本效益,在标准微生物实验室进行大规模表型分析。单个菌落在深96孔母板中生长,用稀释的等分液在不同的实验板上产生96个斑点,实验板上含有感兴趣的物质和浓度的固体培养基。这些板在廉价的平板扫描仪上孵育,通过每15分钟获得图像来监测每个斑点的生长。一个简单的,基于python的软件,可以通过各种操作系统(https://github.com/Gabaldonlab/Q-PHAST)的图形界面使用,分析图像以推断生长,适应性(例如,倍增率)和敏感性(例如,最小抑制浓度)措施。与
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来源期刊
Nature Protocols
Nature Protocols 生物-生化研究方法
CiteScore
29.10
自引率
0.70%
发文量
128
审稿时长
4 months
期刊介绍: Nature Protocols focuses on publishing protocols used to address significant biological and biomedical science research questions, including methods grounded in physics and chemistry with practical applications to biological problems. The journal caters to a primary audience of research scientists and, as such, exclusively publishes protocols with research applications. Protocols primarily aimed at influencing patient management and treatment decisions are not featured. The specific techniques covered encompass a wide range, including but not limited to: Biochemistry, Cell biology, Cell culture, Chemical modification, Computational biology, Developmental biology, Epigenomics, Genetic analysis, Genetic modification, Genomics, Imaging, Immunology, Isolation, purification, and separation, Lipidomics, Metabolomics, Microbiology, Model organisms, Nanotechnology, Neuroscience, Nucleic-acid-based molecular biology, Pharmacology, Plant biology, Protein analysis, Proteomics, Spectroscopy, Structural biology, Synthetic chemistry, Tissue culture, Toxicology, and Virology.
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