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Postnatal Developmental Changes in Fractal Complexity of Giemsa-Stained Chromatin in Mice Spleen Follicular Cells

Published online by Cambridge University Press:  18 September 2017

Igor Pantic Open the ORCID record for Igor Pantic [Opens in a new window] ,
Jovana Paunovic ,
Danijela Vucevic ,
Tatjana Radosavljevic ,
Stefan Dugalic ,
Anita Petkovic ,
Sanja Radojevic-Skodric  and
Senka Pantic
Igor Pantic*
Affiliation:
Laboratory for Cellular Physiology, Institute of Medical Physiology, Faculty of Medicine, University of Belgrade, Visegradska 26/II, RS-11129 Belgrade, Serbia University of Haifa, 199 Abba Hushi Blvd, Mount Carmel, Haifa 3498838, Israel
Jovana Paunovic
Affiliation:
Institute of pathological physiology, Faculty of Medicine, University of Belgrade, Dr Subotica 9, RS-11129 Belgrade, Serbia
Danijela Vucevic
Affiliation:
Institute of pathological physiology, Faculty of Medicine, University of Belgrade, Dr Subotica 9, RS-11129 Belgrade, Serbia
Tatjana Radosavljevic
Affiliation:
Institute of pathological physiology, Faculty of Medicine, University of Belgrade, Dr Subotica 9, RS-11129 Belgrade, Serbia
Stefan Dugalic
Affiliation:
Clinic for Gynecology and Obstetrics, Clinical Center of Serbia, Faculty of Medicine, University of Belgrade, Dr Koste Todorovica 26, RS-11000 Belgrade, Serbia
Anita Petkovic
Affiliation:
Clinic for infectious diseases, Clinical Center of Serbia, Faculty of Medicine, University of Belgrade, Bulevar oslobodjenja 16, RS-11000 Belgrade, Serbia
Sanja Radojevic-Skodric
Affiliation:
Institute of Pathology, Faculty of Medicine, University of Belgrade, Dr Subotica 1, 11000 Belgrade, Serbia
Senka Pantic
Affiliation:
Institute of Histology and Embryology, Faculty of Medicine, University of Belgrade, Visegradska 26/II, RS-11129 Belgrade, Serbia
*
*Corresponding author. [email protected]
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Abstract

Although there are numerous recent works focusing on fractal properties of DNA and chromatin, many issues regarding changes in chromatin fractality during physiological aging remain unclear. In this study, we present results indicating that in mice, there is an age-related reduction of chromatin fractal complexity in a population of spleen follicular cells (SFCs). Spleen tissue was obtained from 16 mice and fixated in Carnoy solution. The youngest animal was newborn, and each animal was exactly 1 month older than the previous. We performed fractal analysis of SFC chromatin structure, stained using Giemsa technique. Fractal analysis was done in a plugin algorithm of ImageJ software. We also performed gray-level co-occurrence matrix (GLCM) analysis of all chromatin structures with the calculation of parameters such as angular second moment and inverse difference moment. Giemsa-stained SFC chromatin exhibited an age-dependent reduction of fractal dimension with statistically significant (p<0.01) linear trend. Moreover, there was a statistically significant increase of SFC chromatin lacunarity. The chromatin GLCM parameters did not significantly change. To our knowledge, this is the first study to perform fractal and GLCM analyses of SFC chromatin and to investigate potential changes of fractal parameters during postnatal development.

Keywords

spleenagingmicroscopymicrographlymphocyte
Type
Biological Science Applications
Information
Microscopy and Microanalysis , Volume 23 , Issue 5 , October 2017 , pp. 1024 - 1029
Copyright
© Microscopy Society of America 2017 

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References

Abramoff, M.D., Magalhaes, P.J. & Ram, S.J. (2004). Image processing with imagej. Biophoton Int 11(7), 36–42.Google Scholar
Adam, R.L., Silva, R.C., Pereira, F.G., Leite, N.J., Lorand-Metze, I. & Metze, K. (2006). The fractal dimension of nuclear chromatin as a prognostic factor in acute precursor B lymphoblastic leukemia. Cell Oncol 28(1–2), 55–59.Google ScholarPubMed
Badea, A.F., Lupsor Platon, M., Crisan, M., Cattani, C., Badea, I., Pierro, G., Sannino, G. & Baciut, G. (2013). Fractal analysis of elastographic images for automatic detection of diffuse diseases of salivary glands: Preliminary results. Comput Math Methods Med 2013, 347238.CrossRefGoogle ScholarPubMed
Bancaud, A., Huet, S., Daigle, N., Mozziconacci, J., Beaudouin, J. & Ellenberg, J. (2009). Molecular crowding affects diffusion and binding of nuclear proteins in heterochromatin and reveals the fractal organization of chromatin. EMBO J 28(24), 3785–3798.Google Scholar
Bancaud, A., Lavelle, C., Huet, S. & Ellenberg, J. (2012). A fractal model for nuclear organization: Current evidence and biological implications. Nucleic Acids Res 40(18), 8783–8792.CrossRefGoogle ScholarPubMed
Delinasios, J.G., Angeli, F., Koumakis, G., Kumar, S., Kang, W.H., Sica, G., Iacopino, F., Lama, G., Lamprecht, S., Sigal-Batikoff, I., Tsangaris, G.T., Farfarelos, C.D., Farfarelos, M.C., Vairaktaris, E., Vassiliou, S. & Delinasios, G.J. (2015). Proliferating fibroblasts and HeLa cells co-cultured in vitro reciprocally influence growth patterns, protein expression, chromatin features and cell survival. Anticancer Res 35(4), 1881–1916.Google Scholar
D’emerico, S., Pignone, D., Vita, F. & Scrugli, A. (2003). Karyomorphological analyses and chromatin characterization by banding techniques in Euphorbia characias L. and E. wulfenii Hoppe. Caryologia 56(4), 501–508.CrossRefGoogle Scholar
Ferro, D.P., Falconi, M.A., Adam, R.L., Ortega, M.M., Lima, C.P., de Souza, C.A., Lorand-Metze, I. & Metze, K. (2011). Fractal characteristics of May-Grunwald-Giemsa stained chromatin are independent prognostic factors for survival in multiple myeloma. PLoS One 6(6), e20706.Google Scholar
Fetit, A.E., Novak, J., Peet, A.C. & Arvanitis, T.N. (2014). 3D texture analysis of MR images to improve classification of paediatric brain tumours: a preliminary study. Stud Health Technol Inform 202, 213–216.Google ScholarPubMed
Jin, K. (2010). Modern biological theories of aging. Aging Dis 1(2), 72–74.Google Scholar
Jitaree, S., Phinyomark, A., Boonyaphiphat, P. & Phukpattaranont, P. (2015). Cell type classifiers for breast cancer microscopic images based on fractal dimension texture analysis of image color layers. Scanning 37(2), 145–151.CrossRefGoogle ScholarPubMed
Karperien, A. (1999–2007). FracLac for ImageJ, version 2.5. Available at http://rsb.info.nih.gov/ij//fraclac/FLHelp/Introduction.htm (retrieved March 31, 2017).Google Scholar
Lilli, R.D. (1965). Histopathologic Technic and Practical Histochemistry. New York: Mcgraw-Hill Book Company.Google Scholar
Lipsitz, L.A. & Goldberger, A.L. (1992). Loss of “complexity” and aging. Potential applications of fractals and chaos theory to senescence. JAMA 267(13), 1806–1809.CrossRefGoogle ScholarPubMed
Liu, B., Yip, R. & Zhou, Z. (2012). Chromatin remodeling, DNA damage repair and aging. Curr Genomics 13(7), 533–547.CrossRefGoogle ScholarPubMed
McNally, J.G. & Mazza, D. (2010). Fractal geometry in the nucleus. EMBO J 29(1), 2–3.Google Scholar
Metze, K. (2010). Fractal dimension of chromatin and cancer prognosis. Epigenomics 2(5), 601–604.Google Scholar
Metze, K. (2013). Fractal dimension of chromatin: Potential molecular diagnostic applications for cancer prognosis. Expert Rev Mol Diagn 13(7), 719–735.Google Scholar
Milosevic, N.T., Ristanovic, D., Gudovic, R., Rajkovic, K. & Maric, D. (2007). Application of fractal analysis to neuronal dendritic arborisation patterns of the monkey dentate nucleus. Neurosci Lett 425(1), 23–27.Google Scholar
Mirny, L.A. (2011). The fractal globule as a model of chromatin architecture in the cell. Chromosome Res 19(1), 37–51.Google Scholar
Moreira, R.D., Moriel, A.R., Murta Junior, L.O., Neves, L.A. & Godoy, M.F. (2011). Fractal dimension in quantifying the degree of myocardial cellular rejection after cardiac transplantation. Rev Bras Cir Cardiovasc 26(2), 155–163.CrossRefGoogle ScholarPubMed
Pantic, I., Basta-Jovanovic, G., Starcevic, V., Paunovic, J., Suzic, S., Kojic, Z. & Pantic, S. (2013 a). Complexity reduction of chromatin architecture in macula densa cells during mouse postnatal development. Nephrology (Carlton) 18(2), 117–124.Google Scholar
Pantic, I., Dacic, S., Brkic, P., Lavrnja, I., Jovanovic, T., Pantic, S. & Pekovic, S. (2015). Discriminatory ability of fractal and grey level co-occurrence matrix methods in structural analysis of hippocampus layers. J Theor Biol 370, 151–156.Google Scholar
Pantic, I., Pantic, S. & Paunovic, J. (2012). Aging increases nuclear chromatin entropy of erythroid precursor cells in mice spleen hematopoietic tissue. Microsc Microanal 18(5), 1054–1059.CrossRefGoogle ScholarPubMed
Pantic, I., Paunovic, J., Basta-Jovanovic, G., Perovic, M., Pantic, S. & Milosevic, N.T. (2013b). Age-related reduction of structural complexity in spleen hematopoietic tissue architecture in mice. Exp Gerontol 48(9), 926–932.Google Scholar
Pantic, I., Petrovic, D., Paunovic, J., Vucevic, D., Radosavljevic, T. & Pantic, S. (2016). Age-related reduction of chromatin fractal dimension in toluidine blue – Stained hepatocytes. Mech Ageing Dev 157, 30–34.CrossRefGoogle ScholarPubMed
Schneider, C.A., Rasband, W.S. & Eliceiri, K.W. (2012). NIH image to imagej: 25 years of image analysis. Nat Methods 9(7), 671–675.CrossRefGoogle ScholarPubMed
Stockert, J.C., Blazquez-Castro, A. & Horobin, R.W. (2014). Identifying different types of chromatin using Giemsa staining. Methods Mol Biol 1094, 25–38.CrossRefGoogle ScholarPubMed
Warren, A., Chaberek, S., Ostrowski, K., Cogger, V.C., Hilmer, S.N., McCuskey, R.S., Fraser, R. & Le Couteur, D.G. (2008). Effects of old age on vascular complexity and dispersion of the hepatic sinusoidal network. Microcirculation 15(3), 191–202.Google Scholar
Wood, J.G. & Helfand, S.L. (2013). Chromatin structure and transposable elements in organismal aging. Front Genet 4, 274.Google Scholar
Zhang, L., Dean, D., Liu, J.Z., Sahgal, V., Wang, X. & Yue, G.H. (2007). Quantifying degeneration of white matter in normal aging using fractal dimension. Neurobiol Aging 28(10), 1543–1555.Google Scholar