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利用高通量荧光显微镜成像技术量化酵母中的竞争适应性

Quantifying Competitive Fitness in Yeast with High-Throughput Fluorescence Microscopy Imaging.

作者信息

Sumanarathne Aruni S, Gerstein Aleeza C

机构信息

Department of Microbiology, University of Manitoba, Winnipeg, Manitoba, Canada.

Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada.

出版信息

Curr Protoc. 2025 Jan;5(1):e70093. doi: 10.1002/cpz1.70093.

Abstract

Competitive fitness is a fundamental concept in evolutionary biology that captures the ability of organisms to survive, reproduce, and compete for resources in their environment. Competitive fitness is typically assessed in the lab by growing two or more competitors together and measuring the frequency of each at multiple time points. Traditional microbial competitive fitness assays are labor intensive and involve plating on solid medium and counting colonies. Here, we describe a method to quantitatively measure competitive fitness using fluorescence microscopic imaging and machine-learning-enabled image analysis to directly count the number of cells from each competitor in the mixed population. This high-throughput, primarily automated, and efficient process gives accurate and reproducible results for competitive fitness. Here, we describe the entire process, from sample preparation through microscopy to quantification, and provide instructions and scripts for the image analysis, fitness calculations, and sample data visualizations. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Sample preparation Basic Protocol 2: Photographing fluorescing and non-fluorescing cells using an EVOS microscope Basic Protocol 3: Counting fluorescing and non-fluorescing cells with Orbit Image Analysis Basic Protocol 4: Getting the average cell counts per well and changing the file names Basic Protocol 5: Calculating competitive fitness using R.

摘要

竞争适应性是进化生物学中的一个基本概念,它体现了生物体在其环境中生存、繁殖以及竞争资源的能力。在实验室中,通常通过将两个或更多的竞争者共同培养,并在多个时间点测量每个竞争者的频率来评估竞争适应性。传统的微生物竞争适应性测定方法劳动强度大,包括在固体培养基上平板接种和计数菌落。在此,我们描述了一种使用荧光显微镜成像和机器学习辅助图像分析来直接计数混合群体中每个竞争者的细胞数量,从而定量测量竞争适应性的方法。这个高通量、主要自动化且高效的过程能够为竞争适应性提供准确且可重复的结果。在此,我们描述了从样品制备到显微镜观察再到定量分析的整个过程,并提供了图像分析、适应性计算和样品数据可视化的说明及脚本。© 2025作者。由Wiley Periodicals LLC出版的《当前实验方案》。基本方案1:样品制备 基本方案2:使用EVOS显微镜拍摄荧光和非荧光细胞 基本方案3:使用Orbit图像分析对荧光和非荧光细胞进行计数 基本方案4:获取每个孔的平均细胞计数并更改文件名 基本方案5:使用R计算竞争适应性

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e69/11771579/2343ed6f892a/CPZ1-5-0-g009.jpg

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