Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images
Authors: Truong X. Tran, Marc L. Pusey, Ramazan S. Aygun
Organizations: Data Media Lab, Computer Science Department, The University of Alabama in Huntsville, USA. · XpressGenes, Inc, Huntsville, Alabama, USA. · Chemistry Departement, The University of Alabama in Huntsville, USA.
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.
Figures & tables
Figure 1: White light (top) and fluorescent images of non-crystal (left) and crystal (right) sample wells.
Figure 2: Basic analysis pipeline of protein crystallization (refer to Tran et al. (2017) ).
Figure 7: The framework of classifying TFL’d protein crystallization images.
Figure 8: Fluorescence spectra (simulated using SpectraViewer tool Inc (2018) ) when illuminating Carboxyrhodamine 6G - Succinimidyl Ester (CR-6G) with a 530nm Laser light.
Figure 9: Inspection pixel values for non-crystal and crystal regions.
Figure 10: Histogram of maximum red intensity for all three image categories.
Figure 11: Histogram of maximum green intensity for all three image categories.
Figure 12: Histogram of average gray image intensity for all three image categories.
Figure 13: The global intensity histogram threshold using Otsu’s method for all three image categories.
Figure 14Figure 15Figure 16
Hold-out 90%
3-fold
5-fold
10-fold
ACC (%)
94.56
93.75
93.65
94.30
MLC (%)
0
0.57
0.43
0.43
MC (%)
0
0.62
0.62
0.62
Table 1: Classification model testing result
Figure 21: Sample image of the 96-well plate (top) and 288-well plate (bottom).
Figure 22: Binary images of crystals (a,e) conducted by different thresholdings: Otsu (b, f); Green channel (c: DomG99, g: DomG97 with t max = 255 and t min = 40)); Red channel ( d: SubR99, h: Sub97 with t max = 255 and t min = 0).
Department of Electronic Engineering, Tsinghua University, Beijing, China · Beijing National Research Center for Information Science and Technology, Beijing, China