Automated digital image quantification of histological staining for the analysis of the trilineage differentiation potential of mesenchymal stem cells

Abstract

Background Multipotent mesenchymal stem cells (MSCs) have the potential to repair and regenerate damaged tissues and are considered as attractive candidates for the development of cell-based regenerative therapies. Currently, there are more than 200 clinical trials involving the use of MSCs for a wide variety of indications. However, variations in their isolation, expansion, and particularly characterization have made the interpretation of study outcomes or the rigorous assessment of therapeutic efficacy difficult. An unbiased characterization of MSCs is of major importance and essential to guaranty that only the most suitable cells will be used. The development of standardized and reproducible assays to predict MSC potency is therefore mandatory. The currently used quantification methodologies for the determination of the trilineage potential of MSCs are usually based on absorbance measurements which are imprecise and prone to errors. We therefore aimed at developing a methodology first offering a standardized way to objectively quantify the trilineage potential of MSC preparations and second allowing to discriminate functional differences between clonally expanded cell populations. Method MSCs originating from several patients were differentiated into osteoblasts, adipocytes, and chondroblasts for 14, 17, and 21 days. Differentiated cells were then stained with the classical dyes: Alizarin Red S for osteoblasts, Oil Red O for adipocytes, and Alcian Blue 8GX for chondroblasts. Quantification of differentiation was then performed with our newly developed digital image analysis (DIA) tool followed by the classical absorbance measurement. The results from the two techniques were then compared. Result Quantification based on DIA allowed highly standardized and objective dye quantification with superior sensitivity compared to absorbance measurements. Furthermore, small differences between MSC lines in the differentiation potential were highlighted using DIA whereas no difference was detected using absorbance quantification. Conclusion Our approach represents a novel method that simplifies the laboratory procedures not only for the quantification of histological dyes and the degree of differentiation of MSCs, but also due to its color independence, it can be easily adapted for the quantification of a wide range of staining procedures in histology. The method is easily applicable since it is based on open source software and standard light microscopy. Electronic supplementary material The online version of this article (10.1186/s13287-019-1170-8) contains supplementary material, which is available to authorized users.

Identification of ALP+/CD73+ defining markers for enhanced osteogenic potential in human adipose-derived mesenchymal stromal cells by mass cytometry

“…1 and S1 A) at days 14, 17, and 21. Staining intensity was quantified using a highly standardized, automated digital image quantification approach [ 35 ]. This approach takes into consideration not only the amount of deposited dye in the whole cell culture dish but also the time needed for differentiation [ 35 ].…”

Corylin, a flavonoid derived from Psoralea Fructus, induces osteoblastic differentiation via estrogen and Wnt/β‐catenin signaling pathways

“…The photos of stained micromass were taken using Nikon Ni‐U upright fluorescence microscope equipped with MBF Stereo Investigator Software and a colour camera. The quantitation of histological staining was performed using digital image analysis (DIA) method …”

“…The quantitation of histological staining was performed using digital image analysis (DIA) method. 23…”

Effect of local anesthetics on viability and differentiation of various adult stem/progenitor cells

“…Alizarin Red (AR) staining and Oil Red O (ORO) staining were used to evaluate osteogenesis and adipogenesis, respectively, following our protocols [19,20,22]. The collagen deposition, AR-stained calcification, AR-stained proteoglycan, and ORO-positive lipid droplets were indirectly quantified guided by the previously validated digital imaging processing protocol [28,29]. For the imaging-based matrix quantification, a total of 10-15 areas of interest were randomly selected from the tissue sections, and subsequently, pre-validated quantification measures for the color intensity of pixels were employed.…”