利用高通量荧光显微镜成像技术定量分析酵母的竞争适应度。

IF 2.2
Aruni S. Sumanarathne, Aleeza C. Gerstein
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引用次数: 0

摘要

竞争适应度是进化生物学中的一个基本概念,它捕获了生物体在其环境中生存、繁殖和竞争资源的能力。竞争适应度通常是通过在实验室中培养两个或更多的竞争者,并在多个时间点测量每个竞争者的频率来评估的。传统的微生物竞争适应度测定是劳动密集型的,需要在固体培养基上进行电镀和菌落计数。在这里,我们描述了一种定量测量竞争适应度的方法,使用荧光显微成像和机器学习支持的图像分析来直接计算混合种群中每个竞争对手的细胞数量。这种高通量、主要自动化和高效的过程为竞技健身提供了准确和可重复的结果。在这里,我们描述了整个过程,从样品制备到显微镜到定量,并提供了图像分析,适应度计算和样品数据可视化的说明和脚本。©2025作者。Wiley期刊有限责任公司发布的当前协议。基本协议1:样品制备基本协议2:使用EVOS显微镜拍摄荧光和非荧光细胞基本协议3:使用轨道图像分析计数荧光和非荧光细胞基本协议4:获得每孔平均细胞计数并更改文件名基本协议5:使用R计算竞争适应度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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