Background

Bone marrow-derived mesenchymal stem cells (BM-MSCs) and adipose tissue-derived mesenchymal stem cells (AT-MSCs) are potential cellular sources of therapeutic stem cells. MSCs are a multipotent population of cells capable of differentiating into a number of mesodermal lineages. Treatment using MSCs appears to be a helpful approach for structural restoration in regenerative medicine. Correct identification of these cells is necessary, but there is inadequate information on the MSC profile of cell surface markers and mRNA expression in dogs. In this study, we performed molecular characterization of canine BM-MSCs and AT-MSCs using immunological and mRNA expression analysis.

Results

Samples were confirmed to be multipotent based on their osteogenic and adipogenic differentiation. And these cells were checked as stem cell, hematopoietic and embryonic stem cell (ESC) markers by flow cytometry. BM- and AT-MSCs showed high expression of CD29 and CD44, moderate expression of CD90, and were negative for CD34, CD45, SSEA-3, SSEA-4, TRA-1-60, and TRA-1-81. SSEA-1 was expressed at very low levels in AT-MSCs. Quantitative real-time PCR (qRT-PCR) revealed expression of Oct3/4, Sox2, and Nanog in BM- and AT-MSCs. There was no significant difference in expression of Oct3/4 and Sox2 between BM-MSCs and AT-MSCs. However, Nanog expression was 2.5-fold higher in AT-MSCs than in BM-MSCs. Using immunocytochemical analysis, Oct3/4 and Sox2 proteins were observed in BM- and AT-MSCs.

Conclusion

Our results provide fundamental information to enable for more reproducible and reliable quality control in the identification of canine BM-MSCs and AT-MSCs by protein and mRNA expression analysis.

In one-shot color structured light systems, the color of stripe patterns are typically distorted with respect to color crosstalk, ambient light and the albedo of the scanned objects, leading to mismatch in the correspondence of color stripes between the projected and captured images. In this paper, an adaptive color calibration and Discrete Trend Transform algorithm are presented to achieve high-resolution 3D reconstructions. The adaptive color calibration, according to the relative albedo in RGB channels, can improve the accuracy of labeling stripe by alleviating the effect of albedo and ambient light while decoding the color. Furthermore, the Discrete Trend Transform in the M channel makes the color calibration an effective method for detecting weak stripes due to the uneven surfaces or reflectance characteristics of the scanned objects. With this approach, the presented system is suitable for scanning moving objects and generating high-resolution 3D reconstructions without the need of dark laboratory environments.