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Phenotypic map of porcine retinal ganglion cells.
Mol. Vis. 19, 904-916 (2013)
Purpose: Porcine retina is an excellent model for studying diverse retinal processes and diseases. The morphologies of porcine retinal ganglion cells (RGCs) have, however, not yet been described comprehensively. The aim of the present study was to create a classification of the RGCs using the 1, 1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine perchlorate (DiI) tracing method. Methods: About 170 RGCs were retrogradely labeled by injecting DiI into the optic nerve of postmortem eyes and statistically analyzed by two different clustering methods: Ward's algorithm and the K-means clustering. Major axis length of the soma, soma area size, and dendritic field area size were selected as main parameters for cluster classification. Results: RGC distribution in clusters was achieved according to their morphological parameters. It was feasible to combine both statistical methods, thereby obtaining a robust clustering distribution. Morphological analysis resulted in a classification of RGCs in three groups according to the soma size and dendritic field: A (large somas and large dendritic fields), B (medium to large somas and medium to large dendritic fields), C (medium to small somas and medium to small dendritic fields). Within groups, fine clustering defined several subgroups according to dendritic arborization and level of stratification. Additionally, cells stratifying in two different levels of the inner plexiform layer were observed within the clusters. Conclusions: This comprehensive study of RGC morphologies in the porcine retina provides fundamental knowledge about RGC cell types and provides a basis for functional studies toward selective RGC cell degeneration in retinal disorders.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Morphological Classification ; Experimental Glaucoma ; Mouse Retina ; In-vitro ; Pig ; Diversity ; Expression ; Death
ISSN (print) / ISBN
1090-0535
e-ISSN
1090-0535
Journal
Molecular Vision
Quellenangaben
Volume: 19,
Pages: 904-916
Publisher
Sun Yat-sen University, P.R. China
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
CF Metabolomics & Proteomics (CF-MPC)