Interrogating fundamental cell biology principles that govern tissue morphogenesis is critical to better understanding of developmental biology and engineering novel multicellular systems. Recently, functional micro-tissues derived from pluripotent embryonic stem cell (ESC) aggregates have provided novel platforms for experimental investigation; however elucidating the factors directing emergent spatial phenotypic patterns remains a significant challenge. Computational modelling techniques offer a unique complementary approach to probe mechanisms regulating morphogenic processes and provide a wealth of spatio-temporal data, but quantitative analysis of simulations and comparison to experimental data is extremely difficult. Quantitative descriptions of spatial phenomena across multiple systems and scales would enable unprecedented comparisons of computational simulations with experimental systems, thereby leveraging the inherent power of computational methods to interrogate the mechanisms governing emergent properties of multicellular biology. To address these challenges, we developed a portable pattern recognition pipeline consisting of: the conversion of cellular images into networks, extraction of novel features via network analysis, and generation of morphogenic trajectories. This novel methodology enabled the quantitative description of morphogenic pattern trajectories that could be compared across diverse systems: computational modelling of multicellular structures, differentiation of stem cell aggregates, and gastrulation of cichlid fish. Moreover, this method identified novel spatio-temporal features associated with different stages of embryo gastrulation, and elucidated a complex paracrine mechanism capable of explaining spatiotemporal pattern kinetic differences in ESC aggregates of different sizes.
Recent studies have found that uncontrolled diabetes and consequential hyperglycemic conditions can lead to an increased incidence of osteoporosis. Osteoblasts, adipocytes, and mesenchymal stem cells (MSCs) are all components of the bone marrow microenvironment and thus may have an effect on diabetes-related osteoporosis. However, few studies have investigated the influence of these three cell types on each other, especially in the context of hyperglycemia. Thus, we developed a hydrogel-based 3D culture platform engineered to allow live-cell retrieval in order to investigate the interactions between MSCs, osteoblasts, and adipocytes in mono-, co-, and tri-culture configurations under hyperglycemic conditions for 7 days of culture. Gene expression, histochemical analysis of differentiation markers, and cell viability were measured for all cell types, and MSC-laden hydrogels were degraded to retrieve cells to assess their colony-forming capacity. Multivariate models of gene expression data indicated that primary discrimination was dependent on the neighboring cell type, validating the need for co-culture configurations to study conditions modeling this disease state. MSC viability and clonogenicity were reduced when mono- and co-cultured with osteoblasts at high glucose levels. In contrast, MSCs showed no reduction of viability or clonogenicity when cultured with adipocytes under high glucose conditions, and the adipogenic gene expression indicates that cross-talk between MSCs and adipocytes may occur. Thus, our unique culture platform combined with post-culture multivariate analysis provided a novel insight into cellular interactions within the MSC microenvironment and highlights the necessity of multi-cellular culture systems for further investigation of complex pathologies such as diabetes and osteoporosis.