Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-13T07:01:59.624Z Has data issue: false hasContentIssue false
  • English
  • Français

Application of Gray Level co-Occurrence Matrix Algorithm for Detection of Discrete Structural Changes in Cell Nuclei After Exposure to Iron Oxide Nanoparticles and 6-Hydroxydopamine

Published online by Cambridge University Press:  18 June 2019

Dubravka Nikolovski ,
Jelena Cumic  and
Igor Pantic
Dubravka Nikolovski
Affiliation:
Institute of Public Health Pancevo, Pasterova 2, Pancevo, Serbia
Jelena Cumic
Affiliation:
Clinical Center of Serbia, School of Medicine, University in Belgrade, Dr.KosteTodorovića 8, RS-11129, Belgrade, Serbia
Igor Pantic*
Affiliation:
University of Belgrade, Faculty of Medicine, Institute of Medical Physiology, Laboratory for cellular physiology, Visegradska 26/II, RS-11129, Belgrade, Serbia University of Haifa, 199 Abba Hushi Blvd., Mount Carmel, Haifa, IL-3498838, Israel
*
*Author for correspondence: Igor Pantic, E-mail: igorpantic@gmail.com
Get access

Abstract

The gray level co-occurrence matrix (GLCM) algorithm is a contemporary computational biology method which, today, is frequently used to detect small changes in texture that are not visible using conventional techniques. We demonstrate that the toxic compound 6-hydroxydopamine (6-OHDA) and iron oxide nanoparticles (IONPS) have opposite effects on GLCM features of cell nuclei. Saccharomyces cerevisiae yeast cells were treated with 6-OHDA and IONPs, and imaging with GLCM analysis was performed at three different time points: 30 min, 60 min, and 120 min after the treatment. A total of 200 cell nuclei were analyzed, and for each nucleus, 5 GLCM parameters were calculated: Angular second moment (ASM), Inverse difference moment (IDM), Contrast (CON), Correlation (COR) and Sum Variance (SVAR). Exposure to IONPs was associated with the increase of ASM and IDM while the values of SVAR and COR were reduced. Treatment with 6-OHDA was associated with the increase of SVAR and CON, while the values of nuclear ASM and IDM were reduced. This is the first study to indicate that IONPs and 6-OHDA have opposite effects on nuclear texture. Also, to the best of our knowledge, this is the first study to apply the GLCM algorithm in Saccharomyces cerevisiae yeast cells in this experimental setting.

Keywords

cellnanomaterialnucleusoxidopaminestructuretexture
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 25 , Issue 4 , August 2019 , pp. 982 - 988
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

Andersen, B & Stevens, RC (1998). The human D1A dopamine receptor: Heterologous expression in Saccharomyces cerevisiae and purification of the functional receptor. Protein Expr Purif 13(1), 111–119.10.1006/prep.1998.0862Google Scholar
Chung, TH, Hsu, SC, Wu, SH, Hsiao, JK, Lin, CP, Yao, M & Huang, DM (2018). Dextran-coated iron oxide nanoparticle-improved therapeutic effects of human mesenchymal stem cells in a mouse model of Parkinson's disease. Nanoscale 10(6), 2998–3007.10.1039/C7NR06976FGoogle Scholar
Del Rio, MJ & Velez-Pardo, C (2002). Monoamine neurotoxins-induced apoptosis in lymphocytes by a common oxidative stress mechanism: Involvement of hydrogen peroxide (H(2)O(2)), caspase-3, and nuclear factor kappa-B (NF-kappaB), p53, c-Jun transcription factors. Biochem Pharmacol 63(4), 677–688.Google Scholar
Gupta, S, Goswami, P, Biswas, J, Joshi, N, Sharma, S, Nath, C & Singh, S (2015).6-Hydroxydopamine and lipopolysaccharides induced DNA damage in astrocytes: Involvement of nitric oxide and mitochondria. Mutat Res Genet Toxicol Environ Mutagen 778, 22–36.Google Scholar
Haralick, R, Shanmugam, K & Dinstein, I (1973). Textural features of image classification. IEEE Trans Syst Man Cybern 3, 610–621.Google Scholar
Hegazy, MA, Maklad, HM, Samy, DM, Abdelmonsif, DA, El Sabaa, BM & Elnozahy, FY (2017). Cerium oxide nanoparticles could ameliorate behavioral and neurochemical impairments in 6-hydroxydopamine induced Parkinson's disease in rats. Neurochem Int 108, 361–371.10.1016/j.neuint.2017.05.011Google Scholar
Huang, Z, Xu, B, Huang, X, Zhang, Y, Yu, M, Han, X, Song, L, Xia, Y, Zhu, Z, Wang, X, Chen, M & Lu, C (2019) Metabolomics reveals the role of acetyl-l-carnitine metabolism in γ-Fe(2)O(3) NP-induced embryonic development toxicity via mitochondria damage. Nanotoxicology. Jan 21, 1–17. In press. doi: 10.1080/17435390.2018.1537411.Google Scholar
Joseph, GB, Baum, T, Carballido-Gamio, J, Nardo, L, Virayavanich, W, Alizai, H, Lynch, JA, McCulloch, CE, Majumdar, S & Link, TM (2011). Texture analysis of cartilage T2 maps: Individuals with risk factors for OA have higher and more heterogeneous knee cartilage MR T2 compared to normal controls--data from the osteoarthritis initiative. Arthritis Res Ther 13(5), R153.10.1186/ar3469Google Scholar
Kim, TY, Cho, NH, Jeong, GB, Bengtsson, E & Choi, HK (2014). 3D texture analysis in renal cell carcinoma tissue image grading. Comput Math Methods Med 2014, 536217.10.1155/2014/536217Google Scholar
Laffon, B, Fernandez-Bertolez, N, Costa, C, Brandao, F, Teixeira, JP, Pasaro, E & Valdiglesias, V (2018). Cellular and molecular toxicity of ironoxide nanoparticles. Adv Exp Med Biol 1048, 199–213.10.1007/978-3-319-72041-8_12Google Scholar
Loizou, CP, Petroudi, S, Seimenis, I, Pantziaris, M & Pattichis, CS (2014). Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome. J Neuroradiol 42, 99–114.10.1016/j.neurad.2014.05.006Google Scholar
Maani, R, Kalra, S & Yang, YH (2014). Robust volumetric texture classification of magnetic resonance images of the brain using local frequency descriptor. IEEE Trans Image Process 23(10), 4625–4636.10.1109/TIP.2014.2351620Google Scholar
Macreadie, I, Bartone, N & Sparrow, L (2010). Inhibition of respiratory growth and survival in yeast by dopamine and counteraction with ascorbate or glutathione. J Biomol Screen 15, 297–301.10.1177/1087057109358920Google Scholar
Mohanaiah, P, Sathyanarayana, P & GuruKumar, L (2003).Image texture feature extraction using GLCM approach.Int J Scie Res Pub 3(5), 1–5.Google Scholar
Nikolovski, D, Dugalic, S & Pantic, I (2017). Iron oxide nanoparticles decrease nuclear fractal dimension of buccal epithelial cells in a time-dependent manner. J Microsc 268(1), 45–52.10.1111/jmi.12585Google Scholar
Özgür, ME, Ulu, A, Balcıoğlu, S, Özcan, İ, Köytepe, S &Ateş, B (2018) The toxicity assessment of iron oxide (Fe₃O₄) nanoparticles on physical and biochemical quality of rainbow trout spermatozoon. Toxics 6, 1–6.Google Scholar
Pantic, I, Dimitrijevic, D, Nesic, D & Petrovic, D (2016). Gray level co-occurrence matrix algorithm as pattern recognition biosensor for oxidopamine-induced changes in lymphocyte chromatin architecture. J Theor Biol 406, 124–128.10.1016/j.jtbi.2016.07.018Google Scholar
Pantic, I, Pantic, S, Paunovic, J & Perovic, M (2013 a). Nuclear entropy, angular second moment, variance and texture correlation of thymus cortical and medullar lymphocytes: Gray level co-occurrence matrix analysis. An Acad Bras Cienc 85(3), 1063–1072.10.1590/S0001-37652013005000045Google Scholar
Pantic, I, Paunovic, J, Perovic, M, Cattani, C, Pantic, S, Suzic, S, Nesic, D & Basta-Jovanovic, G (2013 b).Time-dependent reduction of structural complexity of the buccal epithelial cell nuclei after treatment with silver nanoparticles. J Microsc 252(3), 286–294.Google Scholar
Saraiva, C, Paiva, J, Santos, T, Ferreira, L & Bernardino, L (2016). MicroRNA-124 loaded nanoparticles enhance brain repair in Parkinson's disease. J Control Release 235, 291–305.Google Scholar
Shamir, L, Wolkow, CA & Goldberg, IG (2009). Quantitative measurement of aging using image texture entropy. Bioinformatics 25(23), 3060–3063.10.1093/bioinformatics/btp571Google Scholar
Song, CI, Ryu, CH, Choi, SH, Roh, JL, Nam, SY & Kim, SY (2013). Quantitative evaluation of vocal-fold mucosal irregularities using GLCM-based texture analysis. Laryngoscope 123(11), E45–E50.Google Scholar
Szczypinski, P, Strzelecki, M & Materka, A (2007) MaZda—a software for texture analysis. In 2007 International Symposium on Information Technology Convergence (ISITC 2007), 23–24 November 2007. IEEE: Joenju, South Korea. DOI: 10.1109/ISITC.2007.15.Google Scholar
Szczypinski, PM, Strzelecki, M, Materka, A & Klepaczko, A (2009). MaZda--a software package for image texture analysis.Comput Methods Prog Biomed 94(1), 66–76.Google Scholar
Tsao, CW, Cheng, JT & Lin, YS (2002). Down-regulation of Bcl-2, activation of caspases, and involvement of reactive oxygen species in 6-hydroxydopamine-induced thymocyte apoptosis. Neuroimmunomodulation 10(6), 328–336.Google Scholar
Umarao, P, Bose, S, Bhattacharyya, S, Kumar, A & Jain, S (2016). Neuroprotective potential of superparamagnetic iron oxide nanoparticles along with exposure to electromagnetic field in 6-OHDA Rat model of Parkinson's disease. J Nanosci Nanotechnol 16(1), 261–269.Google Scholar
Veskovic, M, Labudovic-Borovic, M, Zaletel, I, Rakocevic, J, Mladenovic, D, Jorgacevic, B, Vucevic, D & Radosavljevic, T (2018). The effects of betaine on the nuclear fractal dimension, chromatin texture, and proliferative activity in hepatocytes in mouse model of nonalcoholic fatty liver disease. Microsc Microanal 24(2), 132–138.10.1017/S1431927617012806Google Scholar
Wei, L, Gan, Q & Ji, T (2017). Cervical cancer histology image identification method based on texture and lesion area features. Comput Assist Surg (Abingdon) 22(Supp1), 186–199.10.1080/24699322.2017.1389397Google Scholar
Zanganeh, S, Hutter, G, Spitler, R, Lenkov, O, Mahmoudi, M, Shaw, A, Pajarinen, JS, Nejadnik, H, Goodman, S, Moseley, M, Coussens, LM & Daldrup-Link, HE (2016). Iron oxide nanoparticles inhibit tumour growth by inducing pro-inflammatory macrophage polarization in tumour tissues. Nat Nanotechnol 11(11), 986–994.Google Scholar
Zhang, Y, Wang, Z, Li, X, Wang, L, Yin, M, Wang, L, Chen, N, Fan, C & Song, H (2016). Dietary ironoxide nanoparticles delay aging and ameliorate neurodegeneration in drosophila. Adv Mater 28(7), 1387–1393.Google Scholar
Zhao, YZ, Jin, RR, Yang, W, Xiang, Q, Yu, WZ, Lin, Q, Tian, FR, Mao, KL, Lv, CZ, Wang, YX & Lu, CT (2016). Using gelatin nanoparticle mediated intranasal delivery of neuropeptide substance P to enhance neuro-recovery in hemiparkinsonian rats. PLoS One 11(2), e0148848.Google Scholar