![]() However, variation in the aforementioned foci properties, and in cellular background signal or other relevant cellular contexts, can make foci quantification resistant to efficient, automated, analysis. An accurate measure of foci number, size, intensity, geometry or colocalization with other cellular foci or compartments may often reveal variations in intriguing underlying biological phenomena. The ability to accurately identify and quantify objects (i.e., foci) in microscopy data is thus key, and yet routinely papers are published with microscopy data that suffers from a lack of rigorous, unbiased analysis of foci of interest. Live and fixed cell fluorescence microscopy is a common approach utilized by biologists to elucidate understanding of cellular function. ![]() HARLEY is open source and can be downloaded from. It allows batch processing of foci detection and quantification, and the ability to run various geometry-based and pixel-based colocalization analyses to uncover trends or correlations in foci-related data. HARLEY is an intuitive tool aimed at yeast microscopy users without much technical expertise. For less ambiguous foci datasets, a parametric selection is available. The trained classifier is then used to create consistent results across datasets. Candidate features are annotated with a set of geometrical and intensity-based properties to train a kernel Support Vector Machine to recognize features of interest. Since no shape is implied, this method is not limited to round features, as is often the case with other algorithms. After a brief model training onβ~β20 cells the detection and quantification of foci is fully automated and based on closed loops in intensity contours, constrained only by the a priori known size of the features of interest. To facilitate consistent results we developed HARLEY (Human Augmented Recognition of LLPS Ensembles in Yeast), a customizable software for detection and quantification of stress granules in S. In our studies on stress granules in yeast, users displayed a striking variation of up to 3.7-fold in foci calls and were only able to replicate their results with 62β78% accuracy, when re-quantifying the same images. Unfortunately, numerous cellular structures present unique challenges in their ability to be unbiasedly and accurately detected and quantified. ![]() Quantification of cellular structures in fluorescence microscopy data is a key means of understanding cellular function. ![]()
0 Comments
Leave a Reply. |