Abstract
Background:
As an excellent model for many animal and human diseases, rabbits are the third-most used mammal model after mice and rats. A plethora of studies on the exploration of rabbit mesenchymal stem cells still face discrepancies, especially in the standardization of phenotype and genotype characteristics to support reproducibility in both biomedical and translational research.
Aim:
This study is aimed to evaluate the characterization and differentiation potential of visceral rabbit adipose-derived mesenchymal stem cells (Rab-ADMSC).
Methods:
Visceral adipose tissue was obtained from three healthy male White New Zealand rabbits. Cells were further processed and cultivated aseptically. Phenotype and genotype assessments, including morphological observation, proliferation capacity, population doubling time, stemness- and senescence-related genes determination, a set panel of mesenchymal stem/stromal cell (MSC) surface markers evaluation, and multilineage differentiation, were performed in this study.
Results:
Visceral Rab-ADMSC exhibited fibroblast-like shape morphology and had a plastic adherent ability, expressed stemness- (NANOG, SOX2) and senescence-related (TP53, CDKN1A) markers. Visceral Rab-ADMSC performs high expression of CD9, moderate expression of CD44 and CD49f, dimly expression of CD105, CD90, and CD73, and negative expression of CD13 and CD133 as well as CD45 as a hematopoietic stem cell marker. Despite these discrepancies, visceral Rab-ADMSC maintained its ability to differentiate into osteocytes, adipocytes, and chondrocytes.
Conclusion:
To recapitulate, visceral Rab-ADMSC reveals the phenotype and genotype characteristics of adult mesenchymal stem cells. The study emphasizes how variations in tissue sources, culture conditions, and techniques can affect the reproducibility and validity of MSC obtained from different specific anatomical depots and species. Thus, the utilization of rabbit MSC as an animal model in biomedical and translational studies should be done with full caution to avoid data misinterpretation.