Validation of a color deconvolution method to quantify MSC tri-lineage differentiation across species
Emma Heyman, Marguerite Meeremans, Bert Devriendt, Maria Olenic, Koen Chiers, Catharina De Schauwer
Abstract
Mesenchymal stem cells (MSCs) are a promising candidate for both human and veterinary regenerative medicine applications because of their abundance and ability to differentiate into several lineages. Mesenchymal stem cells are however a heterogeneous cell population and as such, it is imperative that they are unequivocally characterized to acquire reproducible results in clinical trials. Although the tri-lineage differentiation potential of MSCs is reported in most veterinary studies, a qualitative evaluation of representative histological images does not always unambiguously confirm tri-lineage differentiation. Moreover, potential differences in differentiation capacity are not identified. Therefore, quantification of tri-lineage differentiation would greatly enhance proper characterization of MSCs. In this study, a method to quantify the tri-lineage differentiation potential of MSCs is described using digital image analysis, based on the color deconvolution plug-in (ImageJ). Mesenchymal stem cells from three species, i.e., bovine, equine, and porcine, were differentiated toward adipocytes, chondrocytes, and osteocytes. Subsequently, differentiated MSCs were stained with Oil Red O, Alcian Blue, and Alizarin Red S, respectively. Next, a differentiation ratio (DR) was obtained by dividing the area % of the differentiation signal by the area % of the nuclear signal. Although MSCs isolated from all donors in all species were capable of tri-lineage differentiation, differences were demonstrated between donors using this quantitative DR. Our straightforward, simple but robust method represents an elegant approach to determine the degree of MSC tri-lineage differentiation across species. As such, differences in differentiation potential within the heterogeneous MSC population and between different MSC sources can easily be identified, which will support further optimization of regenerative therapies.