Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-29T15:59:20.333Z Has data issue: false hasContentIssue false

Application of Gray Level Co-Occurrence Matrix Analysis as a New Method for Enzyme Histochemistry Quantification

Published online by Cambridge University Press:  04 February 2019

Milorad Dragić*
Affiliation:
Department for General Physiology and Biophysics, Faculty of Biology, University of Belgrade, Belgrade, Studentski trg 3, 11001 Belgrade, Serbia Department of Molecular Biology and Endocrinology, Vinča Institute of Nuclear Sciences, University of Belgrade, Mike Petrovića Alasa 12-14, 11001 Belgrade, Serbia
Marina Zarić
Affiliation:
Department of Molecular Biology and Endocrinology, Vinča Institute of Nuclear Sciences, University of Belgrade, Mike Petrovića Alasa 12-14, 11001 Belgrade, Serbia
Nataša Mitrović
Affiliation:
Department of Molecular Biology and Endocrinology, Vinča Institute of Nuclear Sciences, University of Belgrade, Mike Petrovića Alasa 12-14, 11001 Belgrade, Serbia
Nadežda Nedeljković
Affiliation:
Department for General Physiology and Biophysics, Faculty of Biology, University of Belgrade, Belgrade, Studentski trg 3, 11001 Belgrade, Serbia
Ivana Grković
Affiliation:
Department of Molecular Biology and Endocrinology, Vinča Institute of Nuclear Sciences, University of Belgrade, Mike Petrovića Alasa 12-14, 11001 Belgrade, Serbia
*
*Author for correspondence: Milorad Dragić, E-mail: [email protected]
Get access

Abstract

Enzyme histochemistry is a valuable histological method which provides a connection between morphology, activity, and spatial localization of investigated enzymes. Even though the method relies purely on arbitrary evaluations performed by the human eye, it is still wildly accepted and used in histo(patho)logy. Texture analysis emerged as an excellent tool for image quantification of subtle differences reflected in both spatial discrepancies and gray level values of pixels. The current study of texture analysis utilizes the gray-level co-occurrence matrix as a method for quantification of differences between ecto-5′-nucleotidase activities in healthy hippocampal tissue and tissue with marked neurodegeneration. We used the angular second moment, contrast (CON), correlation, inverse difference moment (INV), and entropy for texture analysis and receiver operating characteristic analysis with immunoblot and qualitative assessment of enzyme histochemistry as a validation. Our results strongly argue that co-occurrence matrix analysis could be used for the determination of fine differences in the enzyme activities with the possibility to ascribe those differences to regions or specific cell types. In addition, it emerged that INV and CON are especially useful parameters for this type of enzyme histochemistry analysis. We concluded that texture analysis is a reliable method for quantification of this descriptive technique, thus removing biases and adding it a quantitative dimension.

Type
Software and Instrumentation
Copyright
Copyright © Microscopy Society of America 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adzic, M & Nedeljkovic, N (2018). Unveiling the role of ecto-5′-nucleotidase/CD73 in astrocyte migration by using pharmacological tools. Front Pharmacol 9, 153.Google Scholar
Antonioli, L, Blandizzi, C, Pacher, P & Hasko, G (2013 a). Immunity, inflammation and cancer: A leading role for adenosine. Nat Rev Cancer 13, 842857.Google Scholar
Antonioli, L, Pacher, P, Vizi, ES & Hasko, G (2013 b). CD39 and CD73 in immunity and inflammation. Trends Mol Med 19, 355367.Google Scholar
Balaban, CD, O'Callaghan, JP & Billingsley, ML (1988). Trimethyltin-induced neuronal damage in the rat brain: Comparative studies using silver degeneration stains, immunocytochemistry and immunoassay for neuronotypic and gliotypic proteins. Neuroscience 26, 337361.Google Scholar
Baraldi, A & Parmiggiani, F (1995). An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Trans Geosci Remote Sens 33, 293304.Google Scholar
Bjelobaba, I, Parabucki, A, Lavrnja, I, Stojkov, D, Dacic, S, Pekovic, S, Rakic, L, Stojiljkovic, M & Nedeljkovic, N (2011). Dynamic changes in the expression pattern of ecto-5′-nucleotidase in the rat model of cortical stab injury. J Neurosci Res 89, 862873.Google Scholar
Bonan, CD (2012). Ectonucleotidases and nucleotide/nucleoside transporters as pharmacological targets for neurological disorders. CNS Neurol Disord Drug Targets 11, 739750.10.2174/187152712803581092Google Scholar
Bonan, CD, Walz, R, Pereira, GS, Worm, PV, Battastini, AM, Cavalheiro, EA, Izquierdo, I & Sarkis, JJ (2000). Changes in synaptosomal ectonucleotidase activities in two rat models of temporal lobe epilepsy. Epilepsy Res 39, 229238.Google Scholar
Braun, N, Zhu, Y, Krieglstein, J, Culmsee, C & Zimmermann, H (1998). Upregulation of the Enzyme Chain Hydrolyzing Extracellular ATP after Transient Forebrain Ischemia in the Rat. Journal of Neuroscience 18, 48914900.Google Scholar
Fatima, K, Arooj, A & Majeed, H (2014). A new texture and shape based technique for improving meningioma classification. Microsc Res Tech 77, 862873.10.1002/jemt.22409Google Scholar
Gampe, K, Stefani, J, Hammer, K, Brendel, P, Potzsch, A, Enikolopov, G, Enjyoji, K, Acker-Palmer, A, Robson, SC & Zimmermann, H (2015). NTPDase2 and purinergic signaling control progenitor cell proliferation in neurogenic niches of the adult mouse brain. Stem Cells 33, 253264.10.1002/stem.1846Google Scholar
Ganderman, M, Peluffo, H, Beckman, JS, Cassina, P & Barbeito, L (2010). Extracellular ATP and the P2X7 receptor in astrocyte-mediated motor neuron death: Implications for amyotrophic lateral sclerosis. J Neuroinflammation 7, 33.Google Scholar
Gasparova, Z, Janega, P, Stara, V & Ujhazy, E (2012). Early and late stage of neurodegeneration induced by trimethyltin in hippocampus and cortex of male Wistar rats. Neuro Endocrinol Lett 33, 689696.Google Scholar
Geloso, MC, Corvino, V & Michetti, F (2011). Trimethyltin-induced hippocampal degeneration as a tool to investigate neurodegenerative processes. Neurochem Int 58, 729738.Google Scholar
Grkovic, I, Bjelobaba, I, Nedeljkovic, N, Mitrovic, N, Drakulic, D, Stanojlovic, M & Horvat, A (2014). Developmental increase in ecto-5′-nucleotidase activity overlaps with appearance of two immunologically distinct enzyme isoforms in rat hippocampal synaptic plasma membranes. J Mol Neurosci 54, 109118.Google Scholar
Grkovic, I, Mitrovic, N, Dragic, M, Adzic, M, Drakulic, D & Nedeljkovic, N (2018). Spatial distribution and expression of ectonucleotidases in rat hippocampus after removal of ovaries and estradiol replacement. Mol Neurobiol. doi: 10.1007/s12035-018-1217-3.Google Scholar
Haralick, R, Shanmugam, K & Dinstein, I (1973). Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3, 610621.Google Scholar
Kassner, A & Thornhill, RE (2010). Texture analysis: A review of neurologic MR imaging applications. AJNR Am J Neuroradiol 31, 809816.Google Scholar
Kather, JN, Weis, CA, Bianconi, F, Melchers, SM, Schad, LR, Gaiser, T, Marx, A & Zollner, FG (2016). Multi-class texture analysis in colorectal cancer histology. Sci Rep 6, 27988.Google Scholar
Kim, SY, Kim, EK, Moon, HJ, Yoon, JH & Kwak, JY (2015). Application of texture analysis in the differential diagnosis of benign and malignant thyroid nodules: Comparison with gray-scale ultrasound and elastography. AJR Am J Roentgenol 205, W343W351.Google Scholar
Kocinski, M, Klepaczko, A, Materka, A, Chekenya, M & Lundervold, A (2012). 3D image texture analysis of simulated and real-world vascular trees. Comput Methods Programs Biomed 107, 140154.10.1016/j.cmpb.2011.06.004Google Scholar
Koczyk, D & Oderfeld-Nowak, B (2000). Long-term microglial and astroglial activation in the hippocampus of trimethyltin-intoxicated rat: Stimulation of NGF and TrkA immunoreactivities in astroglia but not in microglia. Int J Dev Neurosci 18, 591606.Google Scholar
Langer, D, Hammer, K, Koszalka, P, Schrader, J, Robson, S & Zimmermann, H (2008). Distribution of ectonucleotidases in the rodent brain revisited. Cell Tissue Res 334, 199217.Google Scholar
Lavrnja, I, Laketa, D, Savic, D, Bozic, I, Bjelobaba, I, Pekovic, S & Nedeljkovic, N (2015). Expression of a second ecto-5′-nucleotidase variant besides the usual protein in symptomatic phase of experimental autoimmune encephalomyelitis. J Mol Neurosci 55, 898911.10.1007/s12031-014-0445-xGoogle Scholar
Losa, GA & Castelli, C (2005). Nuclear patterns of human breast cancer cells during apoptosis: Characterisation by fractal dimension and co-occurrence matrix statistics. Cell Tissue Res 322, 257267.10.1007/s00441-005-0030-2Google Scholar
Lubner, MG, Stabo, N, Lubner, SJ, Del Rio, AM, Song, C, Halberg, RB & Pickhardt, PJ (2015). CT textural analysis of hepatic metastatic colorectal cancer: Pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes. Abdom Imaging 40, 23312337.Google Scholar
Markwell, MA, Haas, SM, Bieber, LL & Tolbert, NE (1978). A modification of the Lowry procedure to simplify protein determination in membrane and lipoprotein samples. Anal Biochem 87, 206210.Google Scholar
Meier-Ruge, WA & Bruder, E (2008). Current concepts of enzyme histochemistry in modern pathology. Pathobiology 75, 233243.Google Scholar
Metz, CE (1978). Basic principles of ROC analysis. Semin Nucl Med 8, 283298.Google Scholar
Mitrovic, N, Zaric, M, Drakulic, D, Martinovic, J, Sevigny, J, Stanojlovic, M, Nedeljkovic, N & Grkovic, I (2017). 17beta-Estradiol-induced synaptic rearrangements are accompanied by altered ectonucleotidase activities in male rat hippocampal synaptosomes. J Mol Neurosci MN 61(3), 412422.10.1007/s12031-016-0877-6Google Scholar
Mitrovic, N, Zaric, M, Drakulic, D, Martinovic, J, Stanojlovic, M, Sevigny, J, Horvat, A, Nedeljkovic, N & Grkovic, I (2016). 17beta-Estradiol upregulates ecto-5′-nucleotidase (CD73) in hippocampal synaptosomes of female rats through action mediated by estrogen receptor-alpha and -beta. Neuroscience 324, 286296.10.1016/j.neuroscience.2016.03.022Google Scholar
Mohanaiah, P, Sathyanaravana, P & Gurukumar, L (2013). Image texture feature extraction using GLCM approach. Int J Sci Res Publ 3.Google Scholar
Mostaco-Guidolin, LB, Ko, AC, Wang, F, Xiang, B, Hewko, M, Tian, G, Major, A, Shiomi, M & Sowa, MG (2013). Collagen morphology and texture analysis: From statistics to classification. Sci Rep 3, 2190.10.1038/srep02190Google Scholar
Nedeljkovic, N, Bjelobaba, I, Subasic, S, Lavrnja, I, Pekovic, S, Stojkov, D, Vjestica, A, Rakic, L & Stojiljkovic, M (2006). Up-regulation of ectonucleotidase activity after cortical stab injury in rats. Cell Biol Int 30, 541546.10.1016/j.cellbi.2006.03.001Google Scholar
Pantic, I, Dacic, S, Brkic, P, Lavrnja, I, Pantic, S, Jovanovic, T & Pekovic, S (2014). Application of fractal and grey level co-occurrence matrix analysis in evaluation of brain corpus callosum and cingulum architecture. Microsc Microanal 20, 13731381.10.1017/S1431927614012811Google Scholar
Pantic, I, Pantic, S & Basta-Jovanovic, G (2012). Gray level co-occurrence matrix texture analysis of germinal center light zone lymphocyte nuclei: Physiology viewpoint with focus on apoptosis. Microsc Microanal 18, 470475.Google Scholar
Patrick, WJ, Besley, GT & Smith, II (1980). Histochemical diagnosis of Hirschsprung's disease and a comparison of the histochemical and biochemical activity of acetylcholinesterase in rectal mucosal biopsies. J Clin Pathol 33, 336343.10.1136/jcp.33.4.336Google Scholar
Rajkovic, N, Kolarevic, D, Kanjer, K, Milosevic, NT, Nikolic-Vukosavljevic, D & Radulovic, M (2016). Comparison of monofractal, multifractal and gray level Co-occurrence matrix algorithms in analysis of breast tumor microscopic images for prognosis of distant metastasis risk. Biomed Microdevices 18, 83.10.1007/s10544-016-0103-xGoogle Scholar
Rosenthal, AS, Moses, HL, Ganote, CE & Tice, L (1969). The participation of nucleotide in the formation of phosphatase reaction product: A chemical and electron microscope autoradiographic study. J Histochem Cytochem 17, 839847.Google Scholar
Sadej, R, Inai, K, Rajfur, Z, Ostapkowicz, A, Kohler, J, Skladanowski, AC, Mitchell, BS & Spychala, J (2008). Tenascin C interacts with ecto-5′-nucleotidase (eN) and regulates adenosine generation in cancer cells. Biochem Biophys Acta 1782, 3540.Google Scholar
Sharma, N, Ray, AK, Sharma, S, Shukla, KK, Pradhan, S & Aggarwal, LM (2008). Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network. J Med Phys 33, 119126.10.4103/0971-6203.42763Google Scholar
Stankovic, M, Pantic, I, DE Luka, SR, Puskas, N, Zaletel, I, Milutinovic-Smiljanic, S, Pantic, S & Trbovich, AM (2016). Quantification of structural changes in acute inflammation by fractal dimension, angular second moment and correlation. J Microsc 261, 277284.10.1111/jmi.12330Google Scholar
Stanojevic, I, Bjelobaba, I, Nedeljkovic, N, Drakulic, D, Petrovic, S, Stojiljkovic, M & Horvat, A (2011). Ontogenetic profile of ecto-5′-nucleotidase in rat brain synaptic plasma membranes. Int J Dev Neurosci 29, 397403.Google Scholar
Tesic, V, Perovic, M, Zaletel, I, Jovanovic, M, Puskas, N, Ruzdijic, S & Kanazir, S (2017). A single high dose of dexamethasone increases GAP-43 and synaptophysin in the hippocampus of aged rats. Exp Gerontol 98, 6268.10.1016/j.exger.2017.08.010Google Scholar
Tixier, F, Hatt, M, Le Rest, CC, Le Pogam, A, Corcos, L & Visvikis, D (2012). Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med 53, 693700.Google Scholar
Villamonte, ML, Torrejon-Escribano, B, Rodriguez-Martinez, A, Trapero, C, Vidal, A, Gomez De Aranda, I, Sevigny, J, Matias-Guiu, X & Martin-Satue, M (2018). Characterization of ecto-nucleotidases in human oviducts with an improved approach simultaneously identifying protein expression and in situ enzyme activity. Histochem Cell Biol 149, 269276.Google Scholar
Wachstein, M & Meisel, E (1957). Histochemistry of hepatic phosphatases of a physiologic pH; with special reference to the demonstration of bile canaliculi. Am J Clin Pathol 27, 1323.Google Scholar
Wagner, RC, Kreiner, P, Barrnett, RJ & Bitensky, MW (1972). Biochemical characterization and cytochemical localization of a catecholamine-sensitive adenylate cyclase in isolated capillary endothelium. Proc Natl Acad Sci USA 69, 31753179.Google Scholar
Xu, S, Shao, QQ, Sun, JT, Yang, N, Xie, Q, Wang, DH, Huang, QB, Huang, B, Wang, XY, Li, XG & Qu, X (2013). Synergy between the ectoenzymes CD39 and CD73 contributes to adenosinergic immunosuppression in human malignant gliomas. Neuro Oncol 15, 11601172.Google Scholar
Zimmermann, H, Zebisch, M & Strater, N (2012). Cellular function and molecular structure of ecto-nucleotidases. Purinergic Signal 8, 437502.10.1007/s11302-012-9309-4Google Scholar
Zweig, MH & Campbell, G (1993). Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clin Chem 39, 561577.Google Scholar