用于在线E-FRET定量成像的自动背景识别与数据筛选
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摘要
因灵敏性高、无损伤和测量速度快等特性,基于3-cube的荧光能量共振转移(E-FRET)显微成像术是目前最流行的活细胞定量FRET成像技术。为了实现活细胞在线实时FRET定量成像,首先提出了一种细胞图像背景自动识别与图像阈值设定的方法:逐像素统计灰度值出现的次数,第1个峰值处的灰度值确定为背景值;将背景值的β(经验常数)倍设为阈值,将扣除背景的供体激发供体探测通道图像和受体激发受体探测通道图像再次扣除阈值,负值置零后进行逻辑与运算制作用于数据筛选的布尔逻辑模板,并将其用于FRET效率和供受体浓度比的数据筛选。利用所提出的方法对转染了不同FRET质粒的细胞进行活细胞在线动态定量E-FRET成像,得到了与期望值一致的测量结果。
Three-cube-based fluorescence resonance energy transfer(E-FRET) microscopy is the most popular live-cell quantitative FRET imaging technique owing to its high sensitivity, no damage and fast measurement speed. To realize live-cell online real-time quantitative FRET imaging, we propose an automatic cell imaging background recognition and threshold setting method that counts gray values of an image pixel by pixel and assign the first peak gray value in the corresponding gray value-count plot as the background. The β(the empirical constant) times of the background value are set as a threshold. The corrected donor-excitation and donor-detection, and acceptor-excitation and acceptor-detection images obtained by subtracting the corresponding threshold from the raw images are used to create a Boolean logic template for data filtering of the FRET efficiency and relative concentration ratio between the acceptor and the donor via logical and operation. The results obtained through online dynamic quantitative E-FRET images of live cells expressing different plasmids on our self-assembled automatic E-FRET microscope are consistent with the expected values.
Three-cube-based fluorescence resonance energy transfer(E-FRET) microscopy is the most popular live-cell quantitative FRET imaging technique owing to its high sensitivity, no damage and fast measurement speed. To realize live-cell online real-time quantitative FRET imaging, we propose an automatic cell imaging background recognition and threshold setting method that counts gray values of an image pixel by pixel and assign the first peak gray value in the corresponding gray value-count plot as the background. The β(the empirical constant) times of the background value are set as a threshold. The corrected donor-excitation and donor-detection, and acceptor-excitation and acceptor-detection images obtained by subtracting the corresponding threshold from the raw images are used to create a Boolean logic template for data filtering of the FRET efficiency and relative concentration ratio between the acceptor and the donor via logical and operation. The results obtained through online dynamic quantitative E-FRET images of live cells expressing different plasmids on our self-assembled automatic E-FRET microscope are consistent with the expected values.
引文
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[2] Adams C L, Chen Y T, Smith S J, et al. Mechanisms of epithelial cell-cell adhesion and cell compaction revealed by high-resolution tracking of E-cadherin-green fluorescent protein[J]. Journal of Cell Biology, 1998, 142(4): 1105-1119.
[3] Chang H, Qi C, Yi Q L, et al. P53 gene deletion detected by fluorescence in situ hybridization is an adverse prognostic factor for patients with multiple myeloma following autologous stem cell transplantation[J]. Blood, 2005, 105(1): 358-360.
[4] Tsien R Y. The green fluorescent protein[J]. Annual Review of Biochemistry, 1998, 67(1): 509-544.
[5] Shaner N C, Steinbach P A, Tsien R Y. A guide to choosing fluorescent proteins[J]. Nature Methods, 2005, 2(12): 905-909.
[6] Zal T, Gascoigne N R J. Photobleaching-corrected FRET efficiency imaging of live cells[J]. Biophysical Journal, 2004, 86(6): 3923-3939.
[7] Canny J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698.
[8] Ramesh V, Haralick R M. An integrated gradient edge detector-theory and performance evaluation[C]. Proceedings of the 1994 ARPA Image Understanding Workshop, 1994: 689-702.
[9] Haralick R M. Edge and region analysis for digital image data[J]. Computer Graphics and Image Processing, 1980, 12(1): 60-73.
[10] Demigny D, Lorca F G, Kessal L. Evaluation of edge detectors performances with a discrete expression of Canny’s criteria[C]. Proceedings of International Conference on Image Processing, Washington, 1995: 169-172.
[11] Senthilkumaran N, Rajesh R. Edge detection techniques for image segmentation—a survey of soft computing approaches[J]. International Journal of Recent Trends in Engineering, 2009, 1(2): 250-254.
[12] Meyer F. Iterative image transformations for an automatic screening of cervical smears[J]. Journal of Histochemistry & Cytochemistry, 1979, 27(1): 128-135.
[13] Beucher S. The watershed transformation applied to image segmentation[J]. Scanning Microscopy, 2000(s6): 299-314.
[14] Vincent L. Morphological grayscale reconstruction in image analysis: applications and efficient algorithms[J]. IEEE Transactions on Image Processing, 1993, 2(2): 175-201.
[15] Serra J. Image analysis and mathematical morphology[M]. 1st ed. Orlando: Academic Press, 1983: 446-472.
[16] Butz E S, Ben-Johny M, Shen M, et al. Quantifying macromolecular interactions in living cells using FRET two-hybrid assays[J]. Nature Protocols, 2016, 11(12): 2470-2498.
[17] Chen H, Puhl H L, Koushik S V, et al. Measurement of FRET efficiency and ratio of donor to acceptor concentration in living cells[J]. Biophysical Journal, 2006: 91(5): 39-41.
[18] Chai L Y, Zhang J W, Zhang L L, et al. Miniature fiber optic spectrometer-based quantitative fluorescence resonance energy transfer measurement in single living cells[J]. Journal of Biomedical Optics, 2015, 20(3): 037008.
[19] Zhang J W, Chai L Y, Zhang J, et al. Quantitative FRET measurement using emission-spectral unmixing with independent excitation crosstalk correction[J]. Journal of Microscopy, 2015, 257(2): 104-116.
[20] Zhang J, Lin F R, Chai L Y, et al. IIem-spFRET: improved Iem-spFRET method for robust FRET measurement[J]. Journal of Biomedical Optics, 2016, 21(10): 105003.
[21] Du M Y, Zhang L L, Xie S S, et al. Wide-field microscopic FRET imaging using simultaneous spectral unmixing of excitation and emission spectra[J]. Optics Express, 2016, 24(14): 16037-16051.