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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, &gt;2 μm 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.

A Practical Approach to Quantitative Processing and Analysis of Small Biological Structures by Fluorescent Imaging