Single-cell RNA sequencing for engineering and studying human islets

A systematic review of single-cell RNA sequencing applications and innovations

Bulk RNA sequencing is one type of RNA sequencing technique, as well as targeted RNA sequencing and whole transcriptome sequencing. It provides valuable insights into gene expression in specific cell populations or regions. However, these methods often miss the diversity of cells within complex tissues. This restriction is overcome by single-cell RNA sequencing, which records gene expression at the single-cell level. It offers a detailed picture of the diversity of cells. It is essential to study glucose homeostasis. It offers thorough explanations of cellular variation. Networks and Governance Dynamics The use of scRNA-seq in islet cells is reviewed in this study, along with sample preparation, sequencing, and computational analysis. It highlights advances in understanding cell types. Gene activity and cell interactions. Along with the challenges and limitations of scRNA-seq, this review highlights the importance of scRNA-seq in understanding complex biological processes and diseases. It is an essential resource for future research and method development in this field, which will help to build personalized treatment.

RNA-based diagnostic innovations: A new frontier in diabetes diagnosis and management

Background/Objective: Diabetes mellitus (DM) remains a major global health challenge due to its chronic nature and associated complications. Traditional diagnostic approaches, though effective, often lack the sensitivity required for early-stage detection. Recent advancements in molecular biology have identified RNA molecules, particularly non-coding RNAs such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), as promising biomarkers for diabetes. This review aims to explore the role of RNA-based biomarkers in the diagnosis, prognosis, and management of diabetes, highlighting their potential to revolutionize diabetes care.Method: A comprehensive literature review was conducted using electronic databases including PubMed, Scopus, and Web of Science. Articles published up to 2024 were screened and analyzed to extract relevant findings related to RNA-based diagnostics in diabetes. Emphasis was placed on studies demonstrating clinical utility, mechanistic insights, and translational potential of RNA molecules.Results: Numerous RNA species, particularly miRNAs such as miR-375, miR-29, and lncRNAs like H19 and MEG3, exhibit altered expression patterns in diabetic patients. These molecules are involved in key regulatory pathways of glucose metabolism, insulin resistance, and β-cell function. Circulating RNAs are detectable in various biofluids, enabling non-invasive diagnostic approaches. Emerging technologies, including RNA sequencing and liquid biopsy platforms, have enhanced the sensitivity and specificity of RNA detection, fostering the development of novel diagnostic tools and personalized therapeutic strategies.Conclusion: RNA-based biomarkers hold significant promise in advancing early detection, risk stratification, and therapeutic monitoring in diabetes care. Despite current challenges such as standardization and clinical validation, the integration of RNA diagnostics into routine clinical practice could transform diabetes management, paving the way for precision medicine approaches. Further research and multi-center trials are essential to validate these biomarkers and facilitate their regulatory approval and clinical implementation.

Controlling human stem cell-derived islet composition using magnetic sorting

Stem cell-derived islets (SC-islets) consists of multiple hormone-producing cell types and offer a promising therapeutic avenue for treating type 1 diabetes (T1D). Currently, the composition of cell types generated within these SC-islets currently cannot be controlled via soluble factors during this differentiation process and consist of off-target cell types. In this study, we devised a magnetic-activated cell sorting (MACS) protocol to enrich SC-islets for CD49a, a marker associated with functional insulin-producing β cells. SC-islets were generated from human pluripotent stem cells (hPSCs) using an adherent differentiation protocol and then sorted and aggregated into islet-like clusters to produce CD49a-enriched, CD49a-depleted, and unsorted SC-islets. Single-cell RNA sequencing (scRNA-seq) and immunostaining revealed that CD49a-enriched SC-islets had higher proportions of β cells and improved transcriptional identity compared to other cell types. Functional assays demonstrated that CD49a-enriched SC-islets exhibited enhanced glucose-stimulated insulin secretion both in vitro and in vivo following transplantation into diabetic mice. These findings suggest that CD49a-based sorting significantly improves β cell identity and the overall function of SC-islets, improving their effectiveness for T1D cell replacement therapies.

Identification of unique cell type responses in pancreatic islets to stress

Diabetes involves the death or dysfunction of pancreatic β-cells. Analysis of bulk sequencing from human samples and studies using in vitro and in vivo models suggest that endoplasmic reticulum and inflammatory signaling play an important role in diabetes progression. To better characterize cell type-specific stress response, we perform multiplexed single-cell RNA sequencing to define the transcriptional signature of primary human islet cells exposed to endoplasmic reticulum and inflammatory stress. Through comprehensive pair-wise analysis of stress responses across pancreatic endocrine and exocrine cell types, we define changes in gene expression for each cell type under different diabetes-associated stressors. We find that β-, α-, and ductal cells have the greatest transcriptional response. We utilize stem cell-derived islets to study islet health through the candidate gene CIB1, which was upregulated under stress in primary human islets. Our findings provide insights into cell type-specific responses to diabetes-associated stress and establish a resource to identify targets for diabetes therapeutics.

Comparative and integrative single cell analysis reveals new insights into the transcriptional immaturity of stem cell-derived β cells

Diabetes cell replacement therapy has the potential to be transformed by human pluripotent stem cell-derived β cells (SC-β cells). However, the precise identity of SC-β cells in relationship to primary fetal and adult β-cells remains unclear. Here, we used single-cell sequencing datasets to characterize the transcriptional identity of islets from in vitro differentiation, fetal islets, and adult islets. Our analysis revealed that SC-β cells share a core β-cell transcriptional identity with human adult and fetal β-cells, however SC-β cells possess a unique transcriptional profile characterized by the persistent expression and activation of progenitor and neural-biased gene networks. These networks are present in SC-β cells, irrespective of the derivation protocol used. Notably, fetal β-cells also exhibit this neural signature at the transcriptional level. Our findings offer insights into the transcriptional identity of SC-β cells and underscore the need for further investigation of the role of neural transcriptional networks in their development.

Human Pancreatic α-Cell Heterogeneity and Trajectory Inference Analysis Using Integrated Single Cell- and Single Nucleus-RNA Sequencing Platforms

Prior studies have shown that pancreatic α-cells can transdifferentiate into β-cells, and that β-cells de-differentiate and are prone to acquire an α-cell phenotype in type 2 diabetes (T2D). However, the specific human α-cell and β-cell subtypes that are involved in α-to-β-cell and β-to-α-cell transitions are unknown. Here, we have integrated single cell RNA sequencing (scRNA-seq) and single nucleus RNA-seq (snRNA-seq) of isolated human islets and human islet grafts and provide additional insight into α-β cell fate switching. Using this approach, we make seven novel observations. 1) There are five different GCG -expressing human α-cell subclusters [α1, α2, α-β-transition 1 (AB-Tr1), α-β-transition 2 (AB-Tr2), and α-β (AB) cluster] with different transcriptome profiles in human islets from non-diabetic donors. 2) The AB subcluster displays multihormonal gene expression, inferred mostly from snRNA-seq data suggesting identification by pre-mRNA expression. 3) The α1, α2, AB-Tr1, and AB-Tr2 subclusters are enriched in genes specific for α-cell function while AB cells are enriched in genes related to pancreatic progenitor and β-cell pathways; 4) Trajectory inference analysis of extracted α- and β-cell clusters and RNA velocity/PAGA analysis suggests a bifurcate transition potential for AB towards both α- and β-cells. 5) Gene commonality analysis identifies ZNF385D, TRPM3, CASR, MEG3 and HDAC9 as signature for trajectories moving towards β-cells and SMOC1, PLCE1, PAPPA2, ZNF331, ALDH1A1, SLC30A8, BTG2, TM4SF4, NR4A1 and PSCK2 as signature for trajectories moving towards α-cells. 6) Remarkably, in contrast to the events in vitro , the AB subcluster is not identified in vivo in human islet grafts and trajectory inference analysis suggests only unidirectional transition from α-to-β-cells in vivo . 7) Analysis of scRNA-seq datasets from adult human T2D donor islets reveals a clear unidirectional transition from β-to-α-cells compatible with dedifferentiation or conversion into α-cells. Collectively, these studies show that snRNA-seq and scRNA-seq can be leveraged to identify transitions in the transcriptional status among human islet endocrine cell subpopulations in vitro , in vivo , in non-diabetes and in T2D. They reveal the potential gene signatures for common trajectories involved in interconversion between α- and β-cells and highlight the utility and power of studying single nuclear transcriptomes of human islets in vivo . Most importantly, they illustrate the importance of studying human islets in their natural in vivo setting.

Inferring regulators of cell identity in the human adult pancreas

Cellular identity during development is under the control of transcription factors that form gene regulatory networks. However, the transcription factors and gene regulatory networks underlying cellular identity in the human adult pancreas remain largely unexplored. Here, we integrate multiple single-cell RNA-sequencing datasets of the human adult pancreas, totaling 7393 cells, and comprehensively reconstruct gene regulatory networks. We show that a network of 142 transcription factors forms distinct regulatory modules that characterize pancreatic cell types. We present evidence that our approach identifies regulators of cell identity and cell states in the human adult pancreas. We predict that HEYL, BHLHE41 and JUND are active in acinar, beta and alpha cells, respectively, and show that these proteins are present in the human adult pancreas as well as in human induced pluripotent stem cell (hiPSC)-derived islet cells. Using single-cell transcriptomics, we found that JUND represses beta cell genes in hiPSC-alpha cells. BHLHE41 depletion induced apoptosis in primary pancreatic islets. The comprehensive gene regulatory network atlas can be explored interactively online. We anticipate our analysis to be the starting point for a more sophisticated dissection of how transcription factors regulate cell identity and cell states in the human adult pancreas.

Single-nucleus multi-omics of human stem cell-derived islets identifies deficiencies in lineage specification

Insulin-producing β cells created from human pluripotent stem cells have potential as a therapy for insulin-dependent diabetes, but human pluripotent stem cell-derived islets (SC-islets) still differ from their in vivo counterparts. To better understand the state of cell types within SC-islets and identify lineage specification deficiencies, we used single-nucleus multi-omic sequencing to analyse chromatin accessibility and transcriptional profiles of SC-islets and primary human islets. Here we provide an analysis that enabled the derivation of gene lists and activity for identifying each SC-islet cell type compared with primary islets. Within SC-islets, we found that the difference between β cells and awry enterochromaffin-like cells is a gradient of cell states rather than a stark difference in identity. Furthermore, transplantation of SC-islets in vivo improved cellular identities overtime, while long-term in vitro culture did not. Collectively, our results highlight the importance of chromatin and transcriptional landscapes during islet cell specification and maturation.

Partners in Crime: Beta-Cells and Autoimmune Responses Complicit in Type 1 Diabetes Pathogenesis

Type 1 diabetes (T1D) is an autoimmune disease characterized by autoreactive T cell-mediated destruction of insulin-producing pancreatic beta-cells. Loss of beta-cells leads to insulin insufficiency and hyperglycemia, with patients eventually requiring lifelong insulin therapy to maintain normal glycemic control. Since T1D has been historically defined as a disease of immune system dysregulation, there has been little focus on the state and response of beta-cells and how they may also contribute to their own demise. Major hurdles to identifying a cure for T1D include a limited understanding of disease etiology and how functional and transcriptional beta-cell heterogeneity may be involved in disease progression. Recent studies indicate that the beta-cell response is not simply a passive aspect of T1D pathogenesis, but rather an interplay between the beta-cell and the immune system actively contributing to disease. Here, we comprehensively review the current literature describing beta-cell vulnerability, heterogeneity, and contributions to pathophysiology of T1D, how these responses are influenced by autoimmunity, and describe pathways that can potentially be exploited to delay T1D.

Applications of iPSC-derived beta cells from patients with diabetes

Improved stem cell-derived pancreatic islet (SC-islet) differentiation protocols robustly generate insulin-secreting β cells from patient induced pluripotent stem cells (iPSCs). These advances are enabling in vitro disease modeling studies and the development of an autologous diabetes cell replacement therapy. SC-islet technology elucidates key features of human pancreas development and diabetes disease progression through the generation of pancreatic progenitors, endocrine progenitors, and β cells derived from diabetic and nondiabetic iPSCs. Combining disease modeling with gene editing and next-generation sequencing reveals the impact of diabetes-causing mutations and diabetic phenotypes on multiple islet cell types. In addition, the supply of SC-islets, containing β and other islet cell types, is unlimited, presenting an opportunity for personalized medicine and overcoming several disadvantages posed by donor islets. This review highlights relevant studies involving iPSC-β cells and progenitors, encompassing new conclusions involving cells from patients with diabetes and the therapeutic potential of iPSC-β cells.

Improved differentiation protocols generate pancreatic islet from patient stem cells

Diabetic stem cell-derived islet studies identified key markers for cell function

Gene editing aims to address unmet needs for stem cell therapy field

Stem cell-derived islets are a promising source for diabetes stem cell therapy

Single-Cell Transcriptome Profiling Reveals β Cell Maturation in Stem Cell-Derived Islets after Transplantation

Human pluripotent stem cells differentiated to insulin-secreting β cells (SC-β cells) in islet organoids could provide an unlimited cell source for diabetes cell replacement therapy. However, current SC-β cells generated in vitro are transcriptionally and functionally immature compared to native adult β cells. Here, we use single-cell transcriptomic profiling to catalog changes that occur in transplanted SC-β cells. We find that transplanted SC-β cells exhibit drastic transcriptional changes and mature to more closely resemble adult β cells. Insulin and IAPP protein secretions increase upon transplantation, along with expression of maturation genes lacking with differentiation in vitro, including INS, MAFA, CHGB, and G6PC2. Other differentiated cell types, such as SC-α and SC-enterochromaffin (SC-EC) cells, also exhibit large transcriptional changes. This study provides a comprehensive resource for understanding human islet cell maturation and provides important insights into maturation of SC-β cells and other SC-islet cell types to enable future differentiation strategy improvements.