AutoFRET: An Image Processing-Based ROI Automated Selection Method for Quantitative FRET Measurements.

IF 3 4区 工程技术 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
Weijing Liang, Zhiyu Xiao, Lingmin Xie, Xingbang Xiong, Lei Liu, Min Hu, Zhengfei Zhuang
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引用次数: 0

Abstract

The emission-based fluorescence resonance energy transfer (E-FRET), renowned for its rapid detection, noninvasiveness towards fluorophores, and compatibility with both wide-field and confocal microscopy, is extensively employed in dynamically monitoring intermolecular interactions within living cells. However, E-FRET requires manual screening of hundreds to thousands of images for regions meeting specific criteria, a labor-intensive process devoid of mature automation solutions. In this article, we introduce AutoFRET, the automated and efficient solution tailored for E-FRET experimentation. AutoFRET harnesses image processing algorithms to swiftly and precisely identify target regions amidst vast image datasets. Furthermore, to mitigate the impact of dead cells in images on experimental results, we devise a novel cell morphology-based approach for their identification and exclusion. AutoFRET significantly reduces the time commitment for E-FRET experimental data analysis, condensing the entire process to the minute level. Comprehensive experimental evaluations reveal an average accuracy exceeding 95% for AutoFRET. This research presents a highly automated and reliable platform that expeditiously quantifies molecular interactions in living cells leveraging FRET technology, poised to contribute to advancements in quantitative biological research.

AutoFRET:基于图像处理的ROI自动选择方法定量FRET测量。
基于发射的荧光共振能量转移(E-FRET)以其快速检测,对荧光团的无创性以及与宽视场和共聚焦显微镜的兼容性而闻名,广泛用于动态监测活细胞内的分子间相互作用。然而,E-FRET需要手动筛选数百到数千张符合特定标准的区域的图像,这是一个劳动密集型的过程,缺乏成熟的自动化解决方案。在本文中,我们介绍AutoFRET,自动化和高效的解决方案量身定制的E-FRET实验。AutoFRET利用图像处理算法快速,准确地识别目标区域在庞大的图像数据集。此外,为了减轻图像中死细胞对实验结果的影响,我们设计了一种新的基于细胞形态的方法来识别和排除死细胞。AutoFRET显着减少了E-FRET实验数据分析的时间承诺,将整个过程压缩到分钟级别。综合实验评估显示平均精度超过95%的AutoFRET。这项研究提供了一个高度自动化和可靠的平台,可以利用FRET技术快速量化活细胞中的分子相互作用,为定量生物学研究的进步做出贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Microscopy and Microanalysis
Microscopy and Microanalysis 工程技术-材料科学:综合
CiteScore
1.10
自引率
10.70%
发文量
1391
审稿时长
6 months
期刊介绍: Microscopy and Microanalysis publishes original research papers in the fields of microscopy, imaging, and compositional analysis. This distinguished international forum is intended for microscopists in both biology and materials science. The journal provides significant articles that describe new and existing techniques and instrumentation, as well as the applications of these to the imaging and analysis of microstructure. Microscopy and Microanalysis also includes review articles, letters to the editor, and book reviews.
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