A Practical Approach to Quantitative Processing and Analysis of Small Biological Structures by Fluorescent Imaging.
A Practical Approach to Quantitative Processing and Analysis of Small Biological Structures by Fluorescent Imaging.
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DOI：
10.7171/jbt.16-2703-001
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发表时间：
2016-09-01
期刊：
影响因子：
--
通讯作者：
Mendez, Armando J
中科院分区：
文献类型：
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作者：
Noller, Crystal M;Boulina, Maria;Mendez, Armando J
定量荧光成像的标准认识模糊，讨论不足。常见的最佳实践是以奈奎斯特速率采集图像，其中最高信号频率被假定为成像系统的最高可获得分辨率。然而，这个特定的标准是为了确保所有可获得的信息都被收集。当前研究的目的是证明，出于量化目的，这些正确设置的采集速率可能是多余的;相反，感兴趣对象的线性尺寸可用于计算图像中的足够信息密度。我们描述了优化的图像采集参数和无偏的方法，用于处理和量化中等大小的细胞结构。对兔髂动脉切片进行化学染色，以识别和定量直径>2 μ m的交感静脉曲张。使用免费的开放获取软件对图像进行处理，以减少背景噪音并分割物体。实验的最佳采样率的计算是基于感兴趣的对象的大小。不同的采样率和处理技术对对象量化的影响进行了演示。过度采样导致文件大小大幅增加，而采样不足则阻碍了可靠的量化。对原始和错误处理的图像进行量化会产生错误的结构，从而歪曲基础数据。目前的研究强调了根据感兴趣的结构定义图像采集参数的重要性。所提出的采集后处理步骤有效地去除了背景和噪声，允许可靠的量化，并消除了用户偏差。这种可定制的、可靠的背景减除和结构量化方法为跨生物学科的研究人员提供了一种可重复的工具。
Standards in quantitative fluorescent imaging are vaguely recognized and receive insufficient discussion. A common best practice is to acquire images at Nyquist rate, where highest signal frequency is assumed to be the highest obtainable resolution of the imaging system. However, this particular standard is set to insure that all obtainable information is being collected. The objective of the current study was to demonstrate that for quantification purposes, these correctly set acquisition rates can be redundant; instead, linear size of the objects of interest can be used to calculate sufficient information density in the image. We describe optimized image acquisition parameters and unbiased methods for processing and quantification of medium-size cellular structures. Sections of rabbit aortas were immunohistochemically stained to identify and quantify sympathetic varicosities, >2 mum in diameter. Images were processed to reduce background noise and segment objects using free, open-access software. Calculations of the optimal sampling rate for the experiment were based on the size of the objects of interest. The effect of differing sampling rates and processing techniques on object quantification was demonstrated. Oversampling led to a substantial increase in file size, whereas undersampling hindered reliable quantification. Quantification of raw and incorrectly processed images generated false structures, misrepresenting the underlying data. The current study emphasizes the importance of defining image-acquisition parameters based on the structure(s) of interest. The proposed postacquisition processing steps effectively removed background and noise, allowed for reliable quantification, and eliminated user bias. This customizable, reliable method for background subtraction and structure quantification provides a reproducible tool for researchers across biologic disciplines.