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Additional file 3: Figure S1. Ambient contribution and islet cell gene marker UMAP and violin plots in scRNA-seq and snRNA-seq in cultured human islets. (A,B) Box plots representing the levels of ambient RNA contamination in droplets of (A) scRNA-seq and (B) snRNA-seq datasets. (C) Violin plot representing the duplication rate in scRNA-seq (orange plot) and snRNA-seq (blue plot) methodologies. The rate was calculated by normalizing the average number of UMI per cell/nucleus on the average number of reads per cell/nucleus. (D, F, H, J) UMAP plots displaying expression levels of (D) GCG, (F) INS, (H) PPY, and (J) SST within the global distribution in scRNA-seq (left panels) and snRNA-seq (right panels). Expression levels are indicated as natural log of counts and range from 0 (gray) to 5 (purple). (E, G, I, K) Violin plots representing expression levels (natural log of counts, Y-axis) of (E) GCG, (G) INS, (I) PPY, and (K) SST within each cluster (X-axis) identified in scRNA-seq (orange plots) or snRNA-seq (blue plots) datasets. Figure S2. Correlation plots and fractional overlap estimation of islet cell types from scRNA-seq dataset with those from the reference dataset. (A) Scatter plots representing correlation of gene expression levels between scRNA-seq-derived (Y-axis) α-cells (first row from top), β-cells (second row from top), PP-cells (third row from top), and δ-cells (fourth row from top) and reference-derived (X-axis) α-cells (first column from left), β-cells (second column from left), PP-cells (third column from left) or δ-cells (fourth column from left). X-axis and Y-axis represent the expression levels in natural log of counts in the indicated datasets. The blue line in each plot represents the regression line, whose fit is indicated by the R2 value (the square of the Pearson correlation coefficient). The red circles indicate the islet cell specific marker genes driving the correlation between the same cell type from the two datasets. The red dotted squares highlight the correlation plots used in the main Fig. 3D. (B-E) Fractional overlap expressed in percentages (%, Y-axis) of the (B) top 100, (C) 200, (D) 500, or (E) 1000 genes between the indicated islet cell types from the reference dataset (X-axis) and the α-cells (yellow bars), β-cells (green bars), PP-cells (blue bars), or δ-cells (red bars) from the scRNA-seq dataset. Figure S3. Quality check parameters in the transplanted human islet snRNA-seq dataset. (A-D) Violin plots of (A) number of genes, (B) number of reads, (C) proportion of mitochondrial genes, and (D) proportion of mouse genes per nucleus across the 4 transplanted human islet samples in the snRNA-seq dataset. (E) Box plot of the levels of ambient RNA contamination in droplets of the in vivo snRNA-seq dataset within each human islet graft. (F) UMAP plot of nuclear cluster distribution and cell type prediction of in vivo snRNA-seq dataset following harmonization to the reference dataset. Figure S4. Islet cell type validation in human islet graft sections by immunofluorescence. Representative images of human islet cells identified as α-cells, β-cells or δ-cells according to the glucagon (GCG, green), insulin (INS, red) and somatostatin (SST, white) labeling. Nuclei are stained in blue. Scale bar is: 50 μm.

Additional file 1: Table S1. Characteristics of human islet donors and human islet preparations used for single-cell or single-nucleus transcriptomic analysis.

Additional file 2: Table S2. Number of different types of droplets, reads and genes generated by scRNA-seq and snRNA-seq in cultured or transplanted human islets.

Aims/hypothesisT cell protein tyrosine phosphatase (TCPTP, encoded by PTPN2) regulates cytokine-induced pancreatic beta cell apoptosis and may contribute to the pathogenesis of type 1 diabetes. However, the role of TCPTP in pancreatic endocrine function and insulin secretion remains largely unknown.MethodsTo investigate the endocrine role of pancreatic TCPTP we generated mice with pancreas Ptpn2/TCPTP deletion (panc-TCPTP KO).ResultsWhen fed regular chow, panc-TCPTP KO and control mice exhibited comparable glucose tolerance. However, when challenged with prolonged high fat feeding panc-TCPTP KO mice exhibited impaired glucose tolerance and attenuated glucose-stimulated insulin secretion (GSIS). The defect in GSIS was recapitulated in primary islets ex vivo and after TCPTP pharmacological inhibition or lentiviral-mediated TCPTP knockdown in the glucose-responsive MIN6 beta cells, consistent with this being cell autonomous. Reconstitution of TCPTP in knockdown cells reversed the defect in GSIS demonstrating that the defect was a direct consequence of TCPTP deficiency. The reduced insulin secretion in TCPTP knockdown MIN6 beta cells was associated with decreased insulin content and glucose sensing. Furthermore, TCPTP deficiency led to enhanced tyrosyl phosphorylation of signal transducer and activator of transcription 1 and 3 (STAT 1/3), and substrate trapping studies in MIN6 beta cells identified STAT 1/3 as TCPTP substrates. STAT3 pharmacological inhibition and small interfering RNA-mediated STAT3 knockdown in TCPTP deficient cells restored GSIS to control levels, indicating that the effects of TCPTP deficiency were mediated, at least in part, through enhanced STAT3 phosphorylation and signalling.Conclusions/interpretationThese studies identify a novel role for TCPTP in insulin secretion and uncover STAT3 as a physiologically relevant target for TCPTP in the endocrine pancreas.

Maturity-onset diabetes of the young 1 (MODY1) is a monogenic diabetes condition caused by heterozygous HNF4A mutations. We investigate how HNF4A haploinsufficiency from a MODY1/HNF4A mutation influences the development of foregut-derived liver and pancreatic cells through differentiation of human induced pluripotent stem cells from a MODY1 family down the foregut lineage. In MODY1-derived hepatopancreatic progenitors, which expressed reduced HNF4A levels and mislocalized HNF4A, foregut genes were downregulated, whereas hindgut-specifying HOX genes were upregulated. MODY1-derived hepatocyte-like cells were found to exhibit altered morphology. Hepatic and β cell gene signatures were also perturbed in MODY1-derived hepatocyte-like and β-like cells, respectively. As mutant HNF4A (p.Ile271fs) did not undergo complete nonsense-mediated decay or exert dominant negativity, HNF4A-mediated loss of function is likely due to impaired transcriptional activation of target genes. Our results suggest that in MODY1, liver and pancreas development is perturbed early on, contributing to altered hepatic proteins and β cell defects in patients.

Although compensatory islet hyperplasia in response to insulin resistance is a recognized feature in diabetes, the factor(s) that promote β cell proliferation have been elusive. We previously reported that the liver is a source for such factors in the liver insulin receptor knockout (LIRKO) mouse, an insulin resistance model that manifests islet hyperplasia. Using proteomics we show that serpinB1, a protease inhibitor, which is abundant in the hepatocyte secretome and sera derived from LIRKO mice, is the liver-derived secretory protein that regulates β cell proliferation in humans, mice, and zebrafish. Small-molecule compounds, that partially mimic serpinB1 effects of inhibiting elastase activity, enhanced proliferation of β cells, and mice lacking serpinB1 exhibit attenuated β cell compensation in response to insulin resistance. Finally, SerpinB1 treatment of islets modulated proteins in growth/survival pathways. Together, these data implicate serpinB1 as an endogenous protein that can potentially be harnessed to enhance functional β cell mass in patients with diabetes.

Dissertação de Mestrado em Biologia Clínica Laboratorial Um número elevado de artigos científicos sugere uma correlação directa entre obesidade e diabetes. Curiosamente, a maioria dos indivíduos obesos não desenvolve diabetes de uma forma notória, sugerindo que os indivíduos conseguem compensar o ambiente de insulino-resistência. Uma vez que a gravidez é, ela própria, um estímulo abrupto ao aumento da massa celular das células β, foi nosso objectivo identificar os mecanismos envolvidos nesse estímulo, assim como clarificar se os estímulos que caracterizam o aumento do número de células β na gravidez são os mesmos responsáveis pelo mesmo fenótipo em estados de insulino-resistência. Neste estudo, utilizaram-se murganhos fêmea controlo (Lox +/+ Cre -/-) e “knockout” para o receptor de insulina dos hepatócitos (“liver-specific insulin knockout” – LIRKO) não gestantes e gestantes nos dias gestacionais 15,5 (G15.5) e 17,5 (G17,5) e nos dias postpartum 0 (P0) e 4 (P4), (n = 3-6). Os níveis de glucose medidos foram mais altos em LIRKOs em G0 (p