Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter April 5, 2019

Obtaining the sGAG distribution profile in articular cartilage color images

  • Carla Iglesias , Lu Luo , Javier Martínez EMAIL logo , Daniel J. Kelly , Javier Taboada and Ignacio Pérez

Abstract

The articular cartilage tissue is an essential component of joints as it reduces the friction between the two bones. Its load-bearing properties depend mostly on proteoglycan distribution, which can be analyzed through the study of the presence of sulfated glycosaminoglycan (sGAG). Currently, sGAG distribution in articular cartilage is not completely known; it is calculated by means of laboratory tests that imply the inherent inaccuracy of a manual procedure. This paper presents an easy-to-use desktop software application for obtaining the sGAG distribution profile in tissue. This app uses color images of stained cartilage tissues taken under a microscope, so researchers at the Trinity Centre for Bioengineering (Dublin, Ireland) can understand the qualitative distribution of sGAG with depth in the studied tissues.

  1. Author Statement

  2. Research funding: C. Iglesias acknowledges the Spanish Ministry of Education, Culture and Sport for the FPU 12/02283 grant. This research has been partially funded by the Spanish Ministry of Economy and Competitiveness through the research project TIN2016-76770-R.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Informed consent: Informed consent is not applicable.

  5. Ethical approval: The conducted research is not related to either human or animal use.

References

[1] Hardingham T. Articular cartilage. In: Maddison PJ, Isenberg DA, Woo P, Glass DN, eds. Oxford Textbook of Rheumatology. Vol 1st ed. Oxford: Oxford Medical Publications; 1998:405–20.Search in Google Scholar

[2] Bhosale AM, Richardson JB. Articular cartilage: structure, injuries and review of management. Br Med Bull 2008;87: 77–95.10.1093/bmb/ldn025Search in Google Scholar PubMed

[3] Landinez-Parra NS, Garzón-Alvarado DA, Vanegas-Acosta JC. A phenomenological mathematical model of the articular cartilage damage. Comput Methods Programs Biomed 2011;104:58–74.10.1016/j.cmpb.2011.02.003Search in Google Scholar

[4] Mouw JK, Case ND, Guldberg RE, Plaas AHK, Levenston ME. Variations in matrix composition and GAG fine structure among scaffolds for cartilage tissue engineering. Osteoarthr Cartil 2005;13:828–36.10.1016/j.joca.2005.04.020Search in Google Scholar PubMed

[5] Hoshi H, Shimawaki K, Takegawa Y, Ohyanagi T, Amano M, Hinou H, et al. Molecular shuttle between extracellular and cytoplasmic space allows for monitoring of GAG biosynthesis in human articular chondrocytes. Biochim Biophys Acta Gen Subj 2012;1820:1391–8.10.1016/j.bbagen.2012.01.004Search in Google Scholar

[6] Grushko G, Schneiderman R, Maroudas A. Some biochemical and biophysical parameters for the study of the pathogenesis of osteoarthritis: a comparison between the processes of ageing and degeneration in human hip cartilage. Connect Tissue Res 1989;19:149–76.10.3109/03008208909043895Search in Google Scholar

[7] Gannon AR, Nagel T, Kelly DJ. The role of the superficial region in determining the dynamic properties of articular cartilage. Osteoarthr Cartil 2012;20:1417–25.10.1016/j.joca.2012.08.005Search in Google Scholar PubMed

[8] Samosky JT, Burstein D, Grimson WE, Howe R, Martin S, Gray ML. Spatially-localized correlation of dGEMRIC-measured GAG distribution and mechanical stiffness in the human tibial plateau. J Orthop Res 2005;23:93–101.10.1016/j.orthres.2004.05.008Search in Google Scholar PubMed

[9] Hardingham T, Bayliss M. Proteoglycans of articular cartilage: changes in aging and in joint disease. Semin Arthritis Rheum 1990;20:12–33.10.1016/0049-0172(90)90044-GSearch in Google Scholar PubMed

[10] Bansal PN, Joshi NS, Entezari V, Grinstaff MW, Snyder BD. Contrast enhanced computed tomography can predict the glycosaminoglycan content and biomechanical properties of articular cartilage. Osteoarthr Cartil 2010;18:184–91.10.1016/j.joca.2009.09.003Search in Google Scholar PubMed

[11] Keenan KE, Besier TF, Pauly JM, Han E, Rosenberg J, Smith RL, et al. Prediction of glycosaminoglycan content in human cartilage by age, T1p and T2 MRI. Osteoarthr Cartil 2011;19:171–9.10.1016/j.joca.2010.11.009Search in Google Scholar PubMed PubMed Central

[12] Aula AS, Jurvelin JS, Töyräs J. Simultaneous computed tomography of articular cartilage and subchondral bone. Osteoarthr Cartil 2009;17:1583–8.10.1016/j.joca.2009.06.010Search in Google Scholar PubMed

[13] Seifzadeh A, Oguamanam DCD, Trutiak N, Hurtig M, Papini M. Determination of nonlinear fibre-reinforced biphasic poroviscoelastic constitutive parameters of articular cartilage using stress relaxation indentation testing and an optimizing finite element analysis. Comput Methods Programs Biomed 2012;107:315–26.10.1016/j.cmpb.2011.07.004Search in Google Scholar PubMed

[14] Saarakkala S, Julkunen P, Kiviranta P, Mäkitalo J, Jurvelin JS, Korhonen RK. Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics. Osteoarthr Cartil 2010;18:73–81.10.1016/j.joca.2009.08.003Search in Google Scholar PubMed

[15] Bashir A, Gray ML, Hartke J, Burstein D. Nondestructive imaging of human cartilage glycosaminoglycan concentration by MRI. Magn Reson Med 1999;41:857–65.10.1002/(SICI)1522-2594(199905)41:5<857::AID-MRM1>3.0.CO;2-ESearch in Google Scholar PubMed

[16] Vanni S, Lagerholm BC, Otey C, Taylor DL, Lanni F. Internet-based image analysis quantifies contractile behavior of individual fibroblasts inside model tissue. Biophys J 2003;84:2715–27.10.1016/S0006-3495(03)75077-2Search in Google Scholar PubMed

[17] Baxter SC, Rekers TG, Goldsmith EC. Contraction in collagen-fibroblast gels: strain measurements using digital image correlation. In: Proceedings of the 2005 Summer Bioengineering Conference, Vail, CO, USA; 2005:964–5.Search in Google Scholar

[18] Zamir EA, Czirok A, Rongish BJ, Little CD. A digital image-based method for computational tissue fate mapping during early avian morphogenesis. Ann Biomed Eng 2005;33:854–65.10.1007/s10439-005-3037-7Search in Google Scholar PubMed

[19] Ganesan K, Martis RJ, Acharya UR, Chua CK, Min LC, Ng EY, et al. Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images. Med Biol Eng Comput 2014;52:663–72.10.1007/s11517-014-1167-5Search in Google Scholar PubMed

[20] Menchón-Lara R-M, Bastida-Jumilla M-C, Morales-Sánchez J, Sancho-Gómez J-L. Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks. Med Biol Eng Comput 2014;52:169–81.10.1007/s11517-013-1128-4Search in Google Scholar PubMed

[21] Carroll SF, Buckley CT, Kelly DJ. Cyclic tensile strain can play a role in directing both intramembranous and endochondral ossification of mesenchymal stem cells. Front Bioeng Biotechnol 2017;5:73.10.3389/fbioe.2017.00073Search in Google Scholar PubMed

[22] Gannon AR, Naggel T, Kelly DJ. The role of the superficial region in determining the dynamic properties of articular cartilage. Osteoarthr Cartil 2012;20:1417–25.10.1016/j.joca.2012.08.005Search in Google Scholar PubMed

Received: 2018-04-09
Accepted: 2018-12-05
Published Online: 2019-04-05
Published in Print: 2019-09-25

©2019 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 25.4.2024 from https://www.degruyter.com/document/doi/10.1515/bmt-2018-0055/html
Scroll to top button