Harmine and exendin-4 combination therapy safely expands human ß cell mass in vivo in a mouse xenograft system.

Five hundred thirty-seven million people globally suffer from diabetes. Insulin-producing ß cells are reduced in number in most people with diabetes, but most individuals still have some residual ß cells. However, none of the many diabetes drugs in common use increases human ß cell numbers. Recently, small molecules that inhibit dual tyrosine-regulated kinase 1A (DYRK1A) have been shown to induce immunohistochemical markers of human ß cell replication, and this is enhanced by drugs that stimulate the glucagon-like peptide 1 (GLP1) receptor (GLP1R) on ß cells. However, it remains to be demonstrated whether these immunohistochemical findings translate into an actual increase in human ß cell numbers in vivo. It is also unknown whether DYRK1A inhibitors together with GLP1R agonists (GLP1RAs) affect human ß cell survival. Here, using an optimized immunolabeling-enabled three-dimensional imaging of solvent-cleared organs (iDISCO+) protocol in mouse kidneys bearing human islet grafts, we demonstrate that combination of a DYRK1A inhibitor with exendin-4 increases actual human ß cell mass in vivo by a mean of four- to sevenfold in diabetic and nondiabetic mice over 3 months and reverses diabetes, without alteration in human α cell mass. The augmentation in human ß cell mass occurred through mechanisms that included enhanced human ß cell proliferation, function, and survival. The increase in human ß cell survival was mediated, in part, by the islet prohormone VGF. Together, these findings demonstrate the therapeutic potential and favorable preclinical safety profile of the DYRK1A inhibitor-GLP1RA combination for diabetes treatment.

LGR4 is essential for maintaining ß-cell homeostasis through suppression of RANK.

Pancreatic ß-cell stress contributes to diabetes progression. This study demonstrates that Leucine-rich repeat-containing G-protein-coupled-receptor-4 (LGR4) is critical for maintaining ß-cell health and is modulated by stressors. In vitro , Lgr4 knockdown decreases proliferation and survival in rodent ß-cells, while overexpression protects against cytokine-induced cell death in rodent and human ß-cells. Mechanistically, LGR4 suppresses Receptor Activator of Nuclear Factor Kappa B (NFκB) (RANK) and its subsequent activation of NFκB to protect ß-cells. ß-cell-specific Lgr4 -conditional knockout (cko) mice exhibit normal glucose homeostasis but increased ß-cell death in both sexes and decreased proliferation only in females. Male Lgr4 cko mice under stress display reduced ß-cell proliferation and a further increase in ß-cell death. Upon aging, both male and female Lgr4 cko mice display impaired ß-cell homeostasis, however, only female mice are glucose intolerant with decreased plasma insulin. We show that LGR4 is required for maintaining ß-cell health under basal and stress-induced conditions, through suppression of RANK. Teaser: LGR4 receptor is critical for maintaining ß-cell health under basal and stressed conditions, through suppression of RANK.

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 ß-cells, and that ß-cells de-differentiate and are prone to acquire an α-cell phenotype in type 2 diabetes (T2D). However, the specific human α-cell and ß-cell subtypes that are involved in α-to-ß-cell and ß-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-ß 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-ß-transition 1 (AB-Tr1), α-ß-transition 2 (AB-Tr2), and α-ß (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 ß-cell pathways; 4) Trajectory inference analysis of extracted α- and ß-cell clusters and RNA velocity/PAGA analysis suggests a bifurcate transition potential for AB towards both α- and ß-cells. 5) Gene commonality analysis identifies ZNF385D, TRPM3, CASR, MEG3 and HDAC9 as signature for trajectories moving towards ß-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-ß-cells in vivo . 7) Analysis of scRNA-seq datasets from adult human T2D donor islets reveals a clear unidirectional transition from ß-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 ß-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.

Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes ß-cell subpopulations with dynamic transcriptome profiles.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. METHODS: We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. RESULTS: First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (ß-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three ß-cell sub-clusters: an INS pre-mRNA cluster (ß3), an intermediate INS mRNA cluster (ß2), and an INS mRNA-rich cluster (ß1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (ß1) becomes the predominant sub-cluster in vivo. CONCLUSIONS: In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo.