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Quality control of mesenchymal stem cells is an important step before their clinical use in regenerative therapy. Among various characteristics of mesenchymal stem cells, reproducibility of population compositions should be analyzed according to characteristics, such as stem cell contents and differentiation stages. Such characterization may be possible by assessing the expression of several surface markers. Here we report our attempts to utilize antibody arrays for analyzing surface markers expressed in mesenchymal stem cell populations in a high-throughput manner. Antibody arrays were fabricated using a glass plate on which a micropatterned alkanethiol monolayer was formed. Various antibodies against surface markers including CD11b, CD31, CD44, CD45, CD51, CD73, CD90, CD105, and CD254 were covalently immobilized on the micropatterned surface in an array format to obtain an antibody array. To examine the feasibility of the array, cell binding assays were performed on the array using a mouse mesenchymal stem cell line. Our results showed that cell binding was observed on the arrayed spots with immobilized antibodies which exhibited reactivity to the cells in flow cytometry. It was further found that the density of cells attached to antibody spots was correlated to the mean fluorescent channel recorded in flow cytometry. These results demonstrate that data obtained by cell binding assays on the antibody array are comparable to those by the conventional flow cytometry, while throughput of the analysis is much higher with the antibody array-based method than flow cytometry. Accordingly, we concluded that the antibody array provides a high-throughput analytical method useful for the quality control of mesenchymal stem cells.

Effects of magnetic field (maximum field, 4 and 93 T(2) m(-1)) on the propagation speed of a chemical wavefront from the Belousov-Zhabotinsky reaction were studied in a thin glass tube. The downward and upward speed and the horizontal one are, respectively affected significantly by vertical and horizontal magnetic fields. Observations of the wavefront shape in magnetic fields showed that the magnetic force-induced convection causes the observed effects.

Conventional comparative molecular field analysis (CoMFA) requires at least 3 orders of experimental data, such as IC 50 and K i, to obtain a good model, although practically there are many screening assays where biological activity is measured only by rating scale. To improve three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis, we developed in this study a modified ordinal classification-oriented CoMFA using partial-least-squares generalized linear regression and ridge estimation. The modified Logistic CoMFA was validated using a corticosteroid binding globulin receptor binding data set, a benchmark for 3D-QSAR, and an acetylcholine esterase inhibitor data set. Our results show that modification of Logistic CoMFA enhanced both prediction accuracy and 3D graphical analysis. In addition, the 3D graphical analysis of the modified Logistic CoMFA was much improved. This improvement resulted in more accurate information on the binding mode between proteins and ligands than in the case of conventional CoMFA.

Comparative Molecular Field Analysis (CoMFA) is most widely used as one of the 3-dimensional QSAR (3D-QSAR) methods to identify the relationship between chemical structure and biological activity. Conventional CoMFA requires at least 3 orders of experimental data, such as IC50 and Ki, to obtain a good model, although practically there are many screening assays where biological activity is measured only by a rating scale. Hence, rating classification-oriented CoMFA coupled with ordinal logistic regression has been developed, and its predictive ability and 3D graphical analysis ability have been investigated. As a result, this novel CoMFA (Logistic CoMFA) has been found to be more robust than conventional CoMFAs in both predictive and 3D graphical analysis abilities. Furthermore, Logistic CoMFA is useful since it can provide the probability of each rank.