Self-Organization Provides Cell Fate Commitment in MSC Sheet Condensed Areas via ROCK-Dependent Mechanism

Multipotent mesenchymal stem/stromal cells (MSC) are one of the crucial regulators of regeneration and tissue repair and possess an intrinsic program from self-organization mediated by condensation, migration and self-patterning. The ability to self-organize has been successfully exploited in tissue engineering approaches using cell sheets (CS) and their modifications. In this study, we used CS as a model of human MSC spontaneous self-organization to demonstrate its structural, transcriptomic impact and multipotent stromal cell commitment. We used CS formation to visualize MSC self-organization and evaluated the role of the Rho-GTPase pathway in spontaneous condensation, resulting in a significant anisotropy of the cell density within the construct. Differentiation assays were carried out using conventional protocols, and microdissection and RNA-sequencing were applied to establish putative targets behind the observed phenomena. The differentiation of MSC to bone and cartilage, but not to adipocytes in CS, occurred more effectively than in the monolayer. RNA-sequencing indicated transcriptional shifts involving the activation of the Rho-GTPase pathway and repression of SREBP, which was concordant with the lack of adipogenesis in CS. Eventually, we used an inhibitory analysis to validate our findings and suggested a model where the self-organization of MSC defined their commitment and cell fate via ROCK1/2 and SREBP as major effectors under the putative switching control of AMP kinase.

The Space–Time Kernel-Based Numerical Method for Burgers’ Equations

It is well known that major error occur in the time integration instead of the spatial approximation. In this work, anisotropic kernels are used for temporal as well as spatial approximation to construct a numerical scheme for solving nonlinear Burgers’ equations. The time-dependent PDEs are collocated in both space and time first, contrary to spatial discretization, and time stepping procedures for time integration are then applied. Physically one cannot in general expect that the spatial and temporal features of the solution behaves on the same order. Hence, one should have to incorporate anisotropic kernels. The nonlinear Burgers’ equations are converted by nonlinear transformation to linear equations. The spatial discretizations are carried out to construct differentiation matrices. Comparisons with most available numerical methods are made to solve the Burgers’ equations.