Studies on diabetes have long been hampered by a lack of authentic disease models that, ideally, should be unlimited and able to recapitulate the abnormalities involved in the development, structure, and function of human pancreatic islets under pathological conditions. Stem cell-based islet organoids faithfully recapitulate islet development invitroand provide large amounts of three-dimensional functional islet biomimetic materials with a morphological structure and cellular composition similar to those of native islets. Thus, islet organoids hold great promise for modeling islet development and function, deciphering the mechanisms underlying the onset of diabetes, providing an invitrohuman organ model for infection of viruses such as SARS-CoV-2, and contributing to drug screening and autologous islet transplantation. However, the currently established islet organoids are generally immature compared with native islets, and further efforts should be made to improve the heterogeneity and functionality of islet organoids, making it an authentic and informative disease model for diabetes. Here, we review the advances and challenges in the generation of islet organoids, focusing on human pluripotent stem cell-derived islet organoids, and the potential applications of islet organoids as disease models and regenerative therapies for diabetes.
The eukaryotic genome is folded into higher-order conformation accompanied with constrained dynamics for coordinated genome functions. However, the molecular machinery underlying these hierarchically organized three-dimensional (3D) chromatin architecture and dynamics remains poorly understood. Here by combining imaging and sequencing, we studied the role of lamin B1 in chromatin architecture and dynamics. We found that lamin B1 depletion leads to detachment of lamina-associated domains (LADs) from the nuclear periphery accompanied with global chromatin redistribution and decompaction. Consequently, the interchromosomal as well as inter-compartment interactions are increased, but the structure of topologically associating domains (TADs) is not affected. Using live-cell genomic loci tracking, we further proved that depletion of lamin B1 leads to increased chromatin dynamics, owing to chromatin decompaction and redistribution toward nucleoplasm. Taken together, our data suggest that lamin B1 and chromatin interactions at the nuclear periphery promote LAD maintenance, chromatin compaction, genomic compartmentalization into chromosome territories and A/B compartments and confine chromatin dynamics, supporting their crucial roles in chromatin higher-order structure and chromatin dynamics.
A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or offtarget effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.