Optimization of endocrine pancreas fluorescence analysis using machine methods

Authors

DOI:

https://doi.org/10.14739/2310-1237.2022.1.254173

Keywords:

fluorescence microscopy, digital image analysis system, pancreas

Abstract

The study aims to establish the appropriate parameters of UV excitation intensity and permanent excitation time on the pancreatic islets photobleaching, the ratio of the intensity of the useful signal in the region of interest to the intensity of nonspecific background fluorescence.

Materials and methods. The pancreas of three adult Wistar rats was fixed in Bouin solution (20 hours) and poured into paraplast after standard histological processing. The study was carried out on paraffin sections of the pancreas. The islets’ insulin and glucagon were determined by immunofluorescence method using monoclonal antibodies (Santa Cruz Biotechnology). The immunofluorescence reaction was studied using an AxioImager-M2 fluorescent microscope. AxioVision digital image analysis system was used for fluorescence imaging, and ImageJ 64-bit image analysis system was used for image quantification. 30 pancreatic islets with an area from 3000 µm2 to 5000 µm2 (8–13 % of the frame area) were analyzed.

Results. Measurements carried out at constant values of hormone concentration in endocrinocytes showed a different estimate of the average fluorescence intensity for insulin and glucagon, which depended on the intensity of UV radiation. As the intensity of UV radiation increased, the average fluorescence intensity in the region of interest for insulin and glucagon increased, but when the camera exposure was corrected, it became almost the same. Regardless of this, the intensity of nonspecific background fluorescence increased monotonically. The use of the ratio of the logarithms of the background fluorescence of the drug and the fluorescence of endocrinocytes in the calculations gives a stable estimate of the relative concentration of hormones, which does not depend on the intensity of the selected UV radiation regime, as well as on the duration of UV irradiation of the drug. This makes it possible to neutralize the effect of photodynamic discoloration of the preparation caused by continuous irradiation. Methods for machine selection of the region of interest by various algorithms of the ImageJ program lead to different estimates of its area, integral, and average fluorescence values. At the same time, the result closest to the “ideal” interactive method of highlighting the area of interest for insulin and glucagon was shown by Otsu’s algorithm.

Conclusions. In immunofluorescent examination of the pancreas, a moderate UV radiation mode should be selected, exposure correction of the CCD camera before taking each frame, and the total time for examining the visual field of the sample should be limited to 1–2 minutes. To highlight the area of interest for insulin and glucagon in automatic analysis, it is recommended to use the Otsu algorithm. To obtain a quantitative estimate of the average fluorescence intensity in the region of interest, it is recommended to use the ratio of the logarithms of the background fluorescence of the drug and endocrinocytes in the calculations.

Author Biographies

T. V. Ivanenko, Zaporizhzhia State Medical University, Ukraine

MD, PhD, Associate Professor of the Department of Pathological Physiology with Course of Normal Physiology

A. V. Abramov, Zaporizhzhia State Medical University, Ukraine

MD, PhD, DSc, Professor of the Department of Pathological Physiology with Course of Normal Physiology

References

Dunst, S., & Tomancak, P. (2019). Imaging Flies by Fluorescence Microscopy: Principles, Technologies, and Applications. Genetics, 211(1), 15-34. https://doi.org/10.1534/genetics.118.300227

Fang, J., Swain, A., Unni, R., & Zheng, Y. (2021). Decoding Optical Data with Machine Learning. Laser & photonics reviews, 15(2), 2000422. https://doi.org/10.1002/lpor.202000422

Herman B. (2002). Fluorescence microscopy. Current protocols in cell biology, Chapter 4. https://doi.org/10.1002/0471143030.cb0402s13

Lichtman, J. W., & Conchello, J. A. (2005). Fluorescence microscopy. Nature methods, 2(12), 910-919. https://doi.org/10.1038/nmeth817

Campbell-Thompson, M., & Tang, S. C. (2021). Pancreas Optical Clearing and 3-D Microscopy in Health and Diabetes. Frontiers in endocrinology, 12, 644826. https://doi.org/10.3389/fendo.2021.644826

De Boer, P., & Giepmans, B. N. (2021). State-of-the-art microscopy to understand islets of Langerhans: what to expect next?. Immunology and cell biology, 99(5), 509-520. https://doi.org/10.1111/imcb.12450

Dybala, M. P., Olehnik, S. K., Fowler, J. L., Golab, K., Millis, J. M., Golebiewska, J., Bachul, P., Witkowski, P., & Hara, M. (2019). Pancreatic beta cell/islet mass and body mass index. Islets, 11(1), 1-9. https://doi.org/10.1080/19382014.2018.1557486

Starlch, G. H., Zafiroya, M., Jablenska, R., Petkoy P., & Lardinois, C. K. (1991). A morphological and immunohistochemical investigation of endocrine pancreata from obese ob+/ob+ mice. Acta Histochemical, 90(1), 93-101. https://doi.org/10.1016/S0065-1281(11)80167-4

Schneider, B. S., Hastings, H. M., & Maytal, G. (1996). The Spatial Distribution of Pancreatic Islets Follows a Universal Power Law. Proceedings the Royal Society (London), 263, 129-131. https://doi.org/10.1098/rspb.1996.0020

Parween, S., Kostromina, E., Nord, C., Eriksson, M., Lindström, P., & Ahlgren, U. (2016). Intra-islet lesions and lobular variations in β-cell mass expansion in ob/ob mice revealed by 3D imaging of intact pancreas. Scientific reports, 6, 34885. https://doi.org/10.1038/srep34885

Wang, L. J., & Kaufman, D. B. (2016). Digital Image Analysis to Assess Quantity and Morphological Quality of Isolated Pancreatic Islets. Cell transplantation, 25(7), 1219-1225. https://doi.org/10.3727/096368915X689947

Rickels, M. R., & Robertson, R. P. (2019). Pancreatic Islet Transplantation in Humans: Recent Progress and Future Directions. Endocrine reviews, 40(2), 631-668. https://doi.org/10.1210/er.2018-00154

González, R. C., & Woods, R. E. (2018). Digital Image Processing (4th ed.). Pearson Education Limited.

McNamara, G., Difilippantonio, M., Ried, T., & Bieber, F. R. (2017). Microscopy and image analysis. Current Protocols in Human Genetics, 2017, 4.4.1-4.4.89. https://doi.org/10.1002/cphg.42

Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1) 62-69. https://doi.org/10.1109/TSMC.1979.4310076

Sezgin, M., & Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 13(1), 146-165. https://doi.org/10.1117/1.1631315

Nichele, L., Persichetti, V., Lucidi, M., & Cincotti, G. (2020). Quantitative evaluation of ImageJ thresholding algorithms for microbial cell counting. OSA Continuum, 3(6), 1417-1427. https://doi.org/10.1364/OSAC.393971

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Published

2022-04-15

How to Cite

1.
Ivanenko TV, Abramov AV. Optimization of endocrine pancreas fluorescence analysis using machine methods. Pathologia [Internet]. 2022Apr.15 [cited 2024Nov.29];19(1):24-31. Available from: http://pat.zsmu.edu.ua/article/view/254173

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Original research