Quantitative and qualitative evaluation of the hippocampal cytoarchitecture in adult cats with regard to the pathological diagnosis of hippocampal sclerosis.

Cats are known to be affected by hippocampal sclerosis, potentially causing antiseizure drug(s) resistance. In order to lay the foundation for a standardized, systematic classification and diagnosis of this pathology in cats, this prospective study aimed at evaluating normal reference values of cellular densities and the cytoarchitecture of the feline hippocampus. Three transverse sections (head, body and tail) of each left hippocampus were obtained from 17 non-epileptic cats of different brachycephalic and mesocephalic breeds and age classes (range: 3-17 years). Histological (hematoxylin and eosin, Nissl) and immunohistochemical (NeuN, GFAP) staining was performed to investigate neuron and astroglial cell populations, as well as the layer thickness of the pyramidal cell layer and granule cell layer. Significant differences in neuronal density (in CA2-CA4 and the granule cell layer) and layer thickness (in CA1-CA3 and the granule cell layer) were evidenced throughout the longitudinal hippocampal axis (p<0.05); on the other hand, the astrocyte density did not differ. Moreover, reference ranges were defined for these parameters in the pyramidal cell layer and in the granule cell layer. The findings did not differ according to breed or age. In veterinary medicine these parameters have not been evaluated in cats so far. As surgical treatment may become a therapeutic option for cats with temporal lobe epilepsy, estimating normal values of the hippocampal cytoarchitecture will help in the standardized histopathological examination of resected hippocampal specimens to reach a diagnosis of hippocampal sclerosis.

Citation

Jessica Zilli, Anne Schänzer, Kathrin Büttner, Monika Kressin, Martin J Schmidt. Quantitative and qualitative evaluation of the hippocampal cytoarchitecture in adult cats with regard to the pathological diagnosis of hippocampal sclerosis. PloS one. 2022;17(5):e0268010

Mesh Tags

PMID: 35560321

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