Cell-based models of the blood-brain barrier (BBB) are important for increasing the knowledge of BBB formation, degradation and brain exposure of drug substances. Human models are preferred over animal models because of inter-species differences in BBB structure and function. However, access to human primary BBB tissue is limited and has shown degeneration of BBB functions in vitro. Human induced pluripotent stem cells (iPSCs) can be used to generate relevant cell types to model the BBB with human tissue. We generated a human iPSC-derived model of the BBB that includes endothelial cells in co-culture with pericytes, astrocytes and neurons. Evaluation of barrier properties showed that the endothelial cells in our co-culture model have high transendothelial electrical resistance, functional efflux and ability to discriminate between CNS permeable and non-permeable substances. Whole genome expression profiling revealed transcriptional changes that occur in co-culture, including upregulation of tight junction proteins such as claudins and neurotransmitter transporters. Pathway analysis implicated changes in the WNT, TNF and PI3K-Akt pathways upon co-culture. Our data suggests that co-culture of iPSC-derived endothelial cells promotes barrier formation on a functional and transcriptional level. The information about gene expression changes in co-culture can be used to further improve iPSC-derived BBB models through selective pathway manipulation.
Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to e.g., distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation towards hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell cultures.
Extracellular matrix (ECM) components govern a range of cell functions such as migration, proliferation, maintenance of stemness and differentiation. Cell niches that harbor stem-/progenitor cells, with matching ECM, have been shown in a range of organs, although their presence in the heart is still under debate. Determining niches depends on a range of in vitro and in vivo models and techniques, where animal models are powerful tools for studying cell-ECM dynamics, however, they are costly and time-consuming to use. In vitro models based on recombinant ECM proteins lack the complexity of the in vivo ECM. To address these issues, we present the Spatiotemporal Extracellular Matrix Model (StEMM) for studies of cell-ECM dynamics, such as cell niches. This model combines gentle decellularization and sectioning of cardiac tissue, allowing retention of a complex ECM, with recellularization and subsequent image processing using image stitching, segmentation, automatic binning and generation of cluster maps. We have thereby developed an in situ representation of the cardiac ECM that is useful for assessment of repopulation dynamics and to study the effect of local ECM composition on phenotype preservation of reseeded mesenchymal progenitor cells. This model provides a platform for studies of organ-specific cell-ECM dynamics and identification of potential cell niches. © AlphaMed Press 2021 SIGNIFICANCE STATEMENT: Stem cells reside in adult organs within specific microenvironments called cell niches. The heart is a complex organ and so far, the presence and localization of stem-/progenitor cell niches are subject to constant debate. To address these issues, the authors have developed the Spatiotemporal Extracellular Matrix Model (StEMM), which combines a modified protocol for decellularization, with cryo-sectioning, recellularization, and subsequent image processing including automatic binning and generation of cluster maps. StEMM was developed within a cardiac context and validated using syngeneic mesenchymal progenitor cells. However, this model is not restricted with regard to species or organs.
Housekeeping genes (HKGs) are involved in basic functions needed for the sustenance of the cell and are assumed to be constitutively expressed at a constant level. Based on these features, HKGs are frequently used for normalization of gene expression data. In the present study, we used the CodeLink Gene Expression Bioarray system to interrogate changes in gene expression occurring during differentiation of human ESCs (hESCs). Notably, in the three hESC lines used for the study, we observed that the RNA levels of 56 frequently used HKGs varied to a degree that rendered them inappropriate as reference genes. Therefore, we defined a novel set of HKGs specifically for hESCs. Here we present a comprehensive list of 292 genes that are stably expressed (coefficient of variation <20%) in differentiating hESCs. These genes were further grouped into high-, medium-, and low-expressed genes. The expression patterns of these novel HKGs show very little overlap with results obtained from somatic cells and tissues. We further explored the stability of this novel set of HKGs in independent, publicly available gene expression data from hESCs and observed substantial similarities with our results. Gene expression was confirmed by real-time quantitative polymerase chain reaction analysis. Taken together, these results suggest that differentiating hESCs have a unique HKG signature and underscore the necessity to validate the expression profiles of putative HKGs. In addition, this novel set of HKGs can preferentially be used as controls in gene expression analyses of differentiating hESCs.
Human embryonic stem cells (hESCs) can differentiate in vitro into spontaneously contracting cardiomyocytes (CMs). These cells may prove extremely useful for various applications in basic research, drug discovery, and regenerative medicine. To fully use the potential of the cells, they need to be extensively characterized, and the regulatory mechanisms that control hESC differentiation toward the cardiac lineage need to be better defined. In this study, we used microarrays to analyze, for the first time, the global gene expression profile of isolated hESC-derived CM clusters. By comparing the clusters with undifferentiated hESCs and using stringent selection criteria, we identified 530 upregulated and 40 downregulated genes in the contracting clusters. To further characterize the family of upregulated genes in the hESC-derived CM clusters, the genes were classified according to their Gene Ontology annotation. The results indicate that the hESC-derived CM clusters display high similarities, on a molecular level, to human heart tissue. Moreover, using the family of upregulated genes, we created protein interaction maps that revealed topological characteristics. We also searched for cellular pathways among the upregulated genes in the hESC-derived CM clusters and identified eight significantly upregulated pathways. Real-time quantitative polymerase chain reaction and immunohistochemical analysis confirmed the expression of a subset of the genes identified by the microarrays. Taken together, the results presented here provide a molecular signature of hESC-derived CM clusters and further our understanding of the biological processes that are active in these cells.