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Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images

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Abstract

The accuracy of detecting protein crystals for fluorescence microscopy images is very critical for high throughput and automated systems. Although the trace fluorescent labeling method could highlight protein crystals, reflection and emission from the fluorescence dye is not always due to crystal regions. Therefore, the analysis of the peak wavelength in the emission spectra of a fluorophore may not always yield effective results. In this paper, we show that using the subordinate color intensity corresponding to longer wavelengths than the peak wavelength of the emission spectra could improve the accuracy of protein crystal detection. Hence, we have built a segmentation method based on the percentile intensity of the subordinate color for trace fluorescently labeled (TFL’d) protein crystallization trial images. Compared to using the dominant color channel, our segmentation method on subordinate color channel was able to reduce the misclassification rate of likely-leads or crystals as non-crystals by the percentage of from 9.71% to 2.02% depending on the classifier. Similarly, the accuracy of classifiers were increased by the percentage of from 1.77% to 5.53%. Our method reached around 94% accuracy while keeping misclassification of likely-leads and crystals as non-crystals below 1%. Moreover, to evaluate the generalizability of our method, we have conducted new wet lab experiments on two proteins, Concanavalin A (Con A) and Ab inorganic pyrophosphate (AbIPPase), and the misclassification rate was below 1%. Our experiments show that using the subordinate channel may be more helpful for TFL’d protein trial image classification.

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Acknowledgment

This research was supported by National Institutes of Health (GM116283) grant. This paper is an extension of our previous work “T. X. Tran and R. S. Aygun and M. L. Pusey, Classifying protein crystallization trial images using subordinate color channel, in 2017 IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), Nov. 2017, page 1546-1553, DOI 10.1109/BIBM.2017.8217890”. ⒸIEEE 2017. Reprinted, with permission from T. X. Tran and R. S. Aygun and M. L. Pusey, Classifying protein crystallization trial images using subordinate color channel, in 2017 IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), Nov. 2017, page 1546-1553, DOI 10.1109/BIBM.2017.8217890.

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Authors and Affiliations

  1. Data Media Lab, Computer Science Department, The University of Alabama in Huntsville, Huntsville, AL, USA

    Truong X. Tran & Ramazan S. Aygun

  2. XpressGenes, Inc, Huntsville, AL, USA

    Marc L. Pusey

  3. Chemistry Department, The University of Alabama in Huntsville, Huntsville, AL, USA

    Marc L. Pusey

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  1. Truong X. Tran
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  2. Marc L. Pusey
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  3. Ramazan S. Aygun
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Correspondence to Truong X. Tran.

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Tran, T.X., Pusey, M.L. & Aygun, R.S. Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images. J Fluoresc 30, 637–656 (2020). https://doi.org/10.1007/s10895-020-02500-7

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  • DOI: https://doi.org/10.1007/s10895-020-02500-7

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