降噪
免疫染色
信号(编程语言)
噪音(视频)
信噪比(成像)
声学
计算机科学
算法
模式识别(心理学)
人工智能
数学
物理
医学
统计
病理
免疫组织化学
图像(数学)
程序设计语言
作者
Lucio Azzari,Minnamari Vippola,Soile Nymark,Teemu O. Ihalainen,Elina Mäntylä
出处
期刊:Bio-protocol
[Bio-Protocol]
日期:2024-01-01
卷期号:14 (1353)
标识
DOI:10.21769/bioprotoc.5072
摘要
Expansion microscopy (ExM) has significantly reformed the field of super-resolution imaging, emerging as a powerful tool for visualizing complex cellular structures with nanoscale precision. Despite its capabilities, the epitope accessibility, labeling density, and precision of individual molecule detection pose challenges. We recently developed an iterative indirect immunofluorescence (IT-IF) method to improve the epitope labeling density, improving the signal and total intensity. In our protocol, we iteratively apply immunostaining steps before the expansion and exploit signal processing through noise estimation, denoising, and deblurring (NEDD) to aid in quantitative image analyses. Herein, we describe the steps of the iterative staining procedure and provide instructions on how to perform NEDD-based signal processing. Overall, IT-IF in ExM-laser scanning confocal microscopy (LSCM) represents a significant advancement in the field of cellular imaging, offering researchers a versatile tool for unraveling the structural complexity of biological systems at the molecular level with an increased signal-to-noise ratio and fluorescence intensity. Key features • Builds upon the method developed by Mäntylä et al. [1] and introduces the IT-IF method and signal-processing platform for several nanoscopy imaging applications. • Retains signal-to-noise ratio and significantly enhances the fluorescence intensity of ExM-LSCM data. • Automatic estimation of noise, signal reconstruction, denoising, and deblurring for increased reliability in image quantifications. • Requires at least seven days to complete.
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