HARLEY mitigates user bias and facilitates efficient quantification and co-localization analyses of foci in yeast fluorescence images.
HARLEY mitigates user bias and facilitates efficient quantification and co-localization analyses of foci in yeast fluorescence images.
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DOI：
10.1038/s41598-022-16381-2
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发表时间：
2022-07-18
影响因子：
4.6
通讯作者：
Buchan JR
中科院分区：
文献类型：
--
作者：
Shabanov I;Buchan JR
荧光显微镜数据中细胞结构的定量是理解细胞功能的关键手段。不幸的是，许多细胞结构在其无偏和准确检测和定量的能力方面存在独特的挑战。在我们对酵母中的应激颗粒的研究中，用户在焦点调用中显示出高达3.7倍的惊人变化，并且在重新量化相同图像时，只能以62-78%的准确度复制结果。为了促进一致的结果，我们开发了哈雷（酵母中LLPS集合的人类增强识别），这是一种可定制的软件，用于检测和定量S.啤酒。在对约20个细胞进行简短的模型训练后，病灶的检测和量化是完全自动化的，并且基于强度轮廓中的闭合环，仅受感兴趣特征的先验已知大小的约束。由于没有隐含形状，因此该方法不限于圆形特征，这与其他算法通常的情况不同。候选特征用一组基于几何和强度的属性来标注，以训练核支持向量机来识别感兴趣的特征。然后，训练好的分类器用于在数据集上创建一致的结果。对于不太模糊的焦点数据集，可以使用参数选择。哈雷是一个直观的工具，针对酵母显微镜用户没有太多的技术专长。它允许对病灶检测和量化进行批处理，并能够运行各种基于几何和基于像素的共定位分析，以揭示病灶相关数据的趋势或相关性。哈雷是开源的，可以从https://github.com/lnilya/harley下载。
Quantification of cellular structures in fluorescence microscopy data is a key means of understanding cellular function. Unfortunately, numerous cellular structures present unique challenges in their ability to be unbiasedly and accurately detected and quantified. 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 62–78% accuracy, when re-quantifying the same images. 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. cerevisiae. After a brief model training on ~ 20 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. Since no shape is implied, this method is not limited to round features, as is often the case with other algorithms. 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. The trained classifier is then used to create consistent results across datasets. For less ambiguous foci datasets, a parametric selection is available. HARLEY is an intuitive tool aimed at yeast microscopy users without much technical expertise. 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 open source and can be downloaded from https://github.com/lnilya/harley.
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作者：
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作者：
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DOI：
10.1016/j.bbapap.2017.09.013
发表时间：
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影响因子：
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作者：
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