Computational Analysis of Histological Images of Tissue Engineered Cartilage for Evaluation of Scaffold Cell Migration
DOI:
https://doi.org/10.14738/jbemi.46.3854Keywords:
image analysis, histological images, cartilage, scaffolds, bioreactorAbstract
Human chondrocytes were seeded on porcine collagen scaffolds and cultivated for up to six weeks in a cartilage bioreactor. To evaluate the influence of cultivation parameters on the proliferation and migration of the cells into the scaffolds, microscopic images from histological and immunohistochemical stainings were taken and digitalized. For evaluation of these pictures, image processing algorithms have been developed that enable quantitative conclusions with regards to aggrecan and collagen type I concentrations as well as the number of cell nuclei within the scaffold and respective migration depths. Furthermore, the number of scaffold lacunae and their orientation relative to the scaffold´s surface can be determined. A total of 85 images of different cultivations under various conditions were processed and the results evaluated by an expert. Additionally, the findings were related to results of available conventional biochemical laboratory results. The outcomes showed very few minor flaws but were valid in most cases. Some findings - as the distribution of the total cell number between cells on the surface and inside the scaffold - are superior to conventional laboratory methods that do not give this insight. A further advantage compared to the established common expert evaluation of these images, is that this approach is faster and less dependent on the judgement of the individual expert and offers quantitative results. The software development will be continued and applied for further optimizing of cartilage culture conditions.References
(1) N. Rotter, M. Bücheler, A. Haisch et al., “Cartilage tissue engineering using resorbable scaffolds,” Journal of Tissue Engineering and Regenerative Medicine, vol. 1, no. 6, pp. 411–416, 2007.
(2) A. Salerno, E. Di Maio, S. Iannace et al., “Tailoring the pore structure of PCL scaffolds for tissue engineering prepared via gas foaming of multi-phase blends,” Journal of Porous Materials, vol. 19, no. 2, pp. 181–188, 2012.
(3) S. J. Hollister, “Porous scaffold design for tissue engineering,” Nature Materials, vol. 4, no. 7, pp. 518–524, 2005.
(4) S. Schwarz, L. Koerber, A. F. Elsaesser et al., “Decellularized cartilage matrix as a novel biomatrix for cartilage tissue-engineering applications,” Tissue Engineering. Part A, vol. 18, 21-22, pp. 2195–2209, 2012.
(5) M. N. Gurcan, L. E. Boucheron, A. Can et al., “Histopathological image analysis: A review,” IEEE Reviews in Biomedical Engineering, vol. 2, pp. 147–171, 2009.
(6) J. M. Haggerty, X. N. Wang, A. Dickinson et al., “Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin,” BMC Medical Imaging, vol. 14, p. 7, 2014.
(7) T. J. Fuchs and J. M. Buhmann, “Computational pathology: Challenges and promises for tissue analysis,” Computerized Medical Imaging and Graphics, vol. 35, no. 7, pp. 515–530, 2011.
(8) G. Matheron, Random sets and integral geometry, Wiley, New York, 1975.
(9) S. Princz, U. Wenzel, H. Tritschler et al., “Automated bioreactor system for cartilage tissue engineering of human primary nasal septal chondrocytes,” Biomedizinische Technik. Biomedical Engineering, vol. 62, no. 5, pp. 481–486, 2017.
(10) S. Princz, U. Wenzel, S. Schwarz et al., “New bioreactor vessel for tissue engineering of human nasal septal chondrocytes,” Current Directions in Biomedical Engineering, vol. 2, no. 1, p. 1415, 2016.
(11) A. F. Elsaesser, C. Bermueller, S. Schwarz et al., “In vitro cytotoxicity and in vivo effects of a decellularized xenogeneic collagen scaffold in nasal cartilage repair,” Tissue Engineering. Part A, vol. 20,
-12, pp. 1668–1678, 2014.
(12) A. F. Elsaesser, S. Schwarz, H. Joos et al., “Characterization of a migrative subpopulation of adult human nasoseptal chondrocytes with progenitor cell features and their potential for in vivo cartilage regeneration strategies,” Cell & Bioscience, vol. 6, p. 11, 2016.
(13) K. ElDahshan, M. Youssef, E. Masameer et al., “Comparison of Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis Using RGB and HSV Color Spaces,” Journal of Biomedical Engineering and Medical Imaging, vol. 2, no. 2, 2015.
(14) R. Sedgewick, Fundamentals, data structures, sorting, searching, Addison-Wesley, Boston, Mass., 2009.
(15) J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986.
(16) I. Barbosa, S. Garcia, V. Barbier-Chassefière et al., “Improved and simple micro assay for sulfated glycosaminoglycans quantification in biological extracts and its use in skin and muscle tissue studies,” Glycobiology, vol. 13, no. 9, pp. 647–653, 2003.
