Moderate beta-cell ablation triggers synergic compensatory mechanisms even in the absence of overt metabolic disruption.

Regeneration, the ability to replace injured tissues and organs, is a phenomenon commonly associated with lower vertebrates but is also observed in mammals, in specific tissues. In this study, we investigated the regenerative potential of pancreatic islets following moderate beta-cell loss in mice. Using a rapid model of moderate ablation, we observed a compensatory response characterized by transient inflammation and proliferation signatures, ultimately leading to the recovery of beta-cell identity and function. Interestingly, this proliferative response occurred independently of inflammation, as demonstrated in ablated immunodeficient mice. Furthermore, exposure to high-fat diet stimulated beta-cell proliferation but negatively impacted beta-cell function. In contrast, an equivalent slower ablation model revealed a delayed but similar proliferative response, suggesting proliferation as a common regenerative response. However, high-fat diet failed to promote proliferation in this model, indicating a differential response to metabolic stressors. Overall, our findings shed light on the complex interplay between beta-cell loss, inflammation, and stress in modulating pancreatic islet regeneration. Understanding these mechanisms could pave the way for novel therapeutic strategies based on beta-cell proliferation.

Molecular profiling of NOD mouse islets reveals a novel regulator of insulitis onset.

Non-obese diabetes (NOD) mice are an established, spontaneous model of type 1 diabetes in which diabetes develops through insulitis. Using next-generation sequencing, coupled with pathway analysis, the molecular fingerprint of early insulitis was mapped in a cohort of mice ranging from 4 to 12 weeks of age. The resulting dynamic timeline revealed an initial decrease in proliferative capacity followed by the emergence of an inflammatory signature between 6 and 8 weeks that increased to a regulatory plateau between 10 and 12 weeks. The inflammatory signature is identified by the activation of central immunogenic factors such as Infg, Il1b, and Tnfa, and activation of canonical inflammatory signaling. Analysis of the regulatory landscape revealed the transcription factor Atf3 as a potential novel modulator of inflammatory signaling in the NOD islets. Furthermore, the Hedgehog signaling pathway correlated with Atf3 regulation, suggesting that the two play a role in regulating islet inflammation; however, further studies are needed to establish the nature of this connection.

The age-dependent regulation of pancreatic islet landscape is fueled by a HNF1a-immune signaling loop.

Animal longevity is a function of global vital organ functionality and, consequently, a complex polygenic trait. Yet, monogenic regulators controlling overall or organ-specific ageing exist, owing their conservation to their function in growth and development. Here, by using pathway analysis combined with wet-biology methods on several dynamic timelines, we identified Hnf1a as a novel master regulator of the maturation and ageing in the adult pancreatic islet during the first year of life. Conditional transgenic mice bearing suboptimal levels of this transcription factor in the pancreatic islets displayed age-dependent changes, with a profile echoing precocious maturation. Additionally, the comparative pathway analysis revealed a link between Hnf1a age-dependent regulation and immune signaling, which was confirmed in the ageing timeline of an overly immunodeficient mouse model. Last, the global proteome analysis of human islets spanning three decades of life largely backed the age-specific regulation observed in mice. Collectively, our results suggest a novel role of Hnf1a as a monogenic regulator of the maturation and ageing process in the pancreatic islet via a direct or indirect regulatory loop with immune signaling.

The GLI code controls HNF1A levels during foregut differentiation.

Differentiation of human induced pluripotent stem cells towards pancreatic islet endocrine cells is a complex process, involving the stepwise modulation of key developmental pathways, such as the Hedgehog signaling inhibition during early differentiation stages. In tandem with this active inhibition, key transcription factors for the islet endocrine cell fate, such as HNF1A, show specific changes in their expression patterns. Here we designed a pilot study aimed at investigating the potential interconnection between HH-signaling inhibition and the increase in the HNF1A expression during early regeneration, by inducing changes in the GLI code. This unveiled a link between the two, where GLI3-R mediated Hedgehog target genes inhibition is apparently required for HNF1A efficient expression.

Global proteomics reveals insulin abundance as a marker of human islet homeostasis alterations.

AIM: The variation in quality between the human islet samples represents a major problem for research, especially when used as control material. The assays assessing the quality of human islets used in research are non-standardized and limited, with many important parameters not being consistently assessed. High-throughput studies aimed at characterizing the diversity and segregation markers among apparently functionally healthy islet preps are thus a requirement. Here, we designed a pilot study to comprehensively identify the diversity of global proteome signatures and the deviation from normal homeostasis in randomly selected human-isolated islet samples. METHODS: By using Tandem Mass Tag 16-plex proteomics, we focused on the recurrently observed disparity in the detected insulin abundance between the samples, used it as a segregating parameter, and analyzed the correlated changes in the proteome signature and homeostasis by pathway analysis. RESULTS: In this pilot study, we showed that insulin protein abundance is a predictor of human islet homeostasis and quality. This parameter is independent of other quality predictors within their acceptable range, thus being able to further stratify islets samples of apparent good quality. Human islets with low amounts of insulin displayed changes in their metabolic and signaling profile, especially in regard to energy homeostasis and cell identity maintenance. We further showed that xenotransplantation into diabetic hosts is not expected to improve the pre-transplantation signature, as it has a negative effect on energy balance, antioxidant activity, and islet cell identity. CONCLUSIONS: Insulin protein abundance predicts significant changes in human islet homeostasis among random samples of apparently good quality.

Glucagon-like peptide-1 treatment reduces the risk of diabetes-type 2 related amputations: A cohort study in Denmark.

AIMS: To assess the impact of Glucagon-like peptide-1 (GLP-1) agonists on the risk of lower extremity amputations in patients with type 2 diabetes mellitus (DM2). METHODS: We conducted a cohort study on 309,116 patients with DM2 using Danish National Register and Diabetes Database. We tracked the GLP-1 agonists over time along with the medication dose. Time-varying models are used to assess the risk of amputation for patients with/without GLP-1 treatment. RESULTS: Patients on GLP-1 treatment experience a notable reduction in the risk of amputation compared to those without the treatment with a hazard ratio (HR) of 0.5, 95% CI [0.54-0.74], indicating a statistically significant difference (p <.005). This risk reduction was consistent across different age groups, but notably most pronounced among middle income patients. The findings were further validated by using time-varying Cox models, which considered the patient's comorbidity history. CONCLUSIONS: Our analysis reveals compelling evidence of a reduced risk of amputation among patients receiving GLP-1 therapy, an effect dominated by liraglutide, compared to those without the treatment, even after adjusting for various socio-economic factors. However, further investigation is required to identify and account for any other potential confounding variables that may impact the outcome.

Chronically Elevated Exogenous Glucose Elicits Antipodal Effects on the Proteome Signature of Differentiating Human iPSC-Derived Pancreatic Progenitors.

The past decade revealed that cell identity changes, such as dedifferentiation or transdifferentiation, accompany the insulin-producing ß-cell decay in most diabetes conditions. Mapping and controlling the mechanisms governing these processes is, thus, extremely valuable for managing the disease progression. Extracellular glucose is known to influence cell identity by impacting the redox balance. Here, we use global proteomics and pathway analysis to map the response of differentiating human pancreatic progenitors to chronically increased in vitro glucose levels. We show that exogenous high glucose levels impact different protein subsets in a concentration-dependent manner. In contrast, regardless of concentration, glucose elicits an antipodal effect on the proteome landscape, inducing both beneficial and detrimental changes in regard to achieving the desired islet cell fingerprint. Furthermore, we identified that only a subgroup of these effects and pathways are regulated by changes in redox balance. Our study highlights a complex effect of exogenous glucose on differentiating pancreas progenitors characterized by a distinct proteome signature.

In vivo Environment Swiftly Restricts Human Pancreatic Progenitors Toward Mono-Hormonal Identity via a HNF1A/HNF4A Mechanism.

Generating insulin-producing ß-cells from human induced pluripotent stem cells is a promising cell replacement therapy for improving or curing insulin-dependent diabetes. The transplantation of end-stages differentiating cells into living hosts was demonstrated to improve ß-cell maturation. Nevertheless, the cellular and molecular mechanisms outlining the transplanted cells' response to the in vivo environment are still to be properly characterized. Here we use global proteomics and large-scale imaging techniques to demultiplex and filter the cellular processes and molecular signatures modulated by the immediate in vivo effect. We show that in vivo exposure swiftly confines in vitro generated human pancreatic progenitors to single hormone expression. The global proteome landscape of the transplanted cells was closer to native human islets, especially in regard to energy metabolism and redox balance. Moreover, our study indicates a possible link between these processes and certain epigenetic regulators involved in cell identity. Pathway analysis predicted HNF1A and HNF4A as key regulators controlling the in vivo islet-promoting response, with experimental evidence suggesting their involvement in confining islet cell fate following xeno-transplantation.

In vivo hyperglycaemia exposure elicits distinct period-dependent effects on human pancreatic progenitor differentiation, conveyed by oxidative stress.

AIM: The loss of insulin-secreting ß-cells, ultimately characterizing most diabetes forms, demands the development of cell replacement therapies. The common endpoint for all ex vivo strategies is transplantation into diabetic patients. However, the effects of hyperglycaemia environment on the transplanted cells were not yet properly assessed. Thus, the main goal of this study was to characterize global effect of brief and prolonged in vivo hyperglycaemia exposure on the cell fate acquisition and maintenance of transplanted human pancreatic progenitors. METHODS: To rigorously study the effect of hyperglycaemia, in vitro differentiated human-induced pluripotent stem cells (hiPSC)-derived pancreatic progenitors were xenotransplanted in normoglycaemic and diabetic NSG rat insulin promoter (RIP)-diphtheria toxin receptor (DTR) mice. The transplants were retrieved after 1-week or 1-month exposure to overt hyperglycaemia and analysed by large-scale microscopy or global proteomics. For this study we pioneer the use of the NSG RIP-DTR system in the transplantation of hiPSC, making use of its highly reproducible specific and absolute ß-cell ablation property in the absence of inflammation or other organ toxicity. RESULTS: Here we show for the first time that besides the presence of an induced oxidative stress signature, the cell fate and proteome landscape response to hyperglycaemia was different, involving largely different mechanisms, according to the period spent in the hyperglycaemic environment. Surprisingly, brief hyperglycaemia exposure increased the bihormonal cell number by impeding the activity of specific islet lineage determinants. Moreover, it activated antioxidant and inflammation protection mechanisms signatures in the transplanted cells. In contrast, the prolonged exposure was characterized by decreased numbers of hormone + cells, low/absent detoxification signature, augmented production of oxygen reactive species and increased apoptosis. CONCLUSION: Hyperglycaemia exposure induced distinct, period-dependent, negative effects on xenotransplanted human pancreatic progenitor, affecting their energy homeostasis, cell fate acquisition and survival.

Toward Machine-Learning-Based Decision Support in Diabetes Care: A Risk Stratification Study on Diabetic Foot Ulcer and Amputation.

Diabetes mellitus is associated with serious complications, with foot ulcers and amputation of limbs among the most debilitating consequences of late diagnosis and treatment of foot ulcers. Thus, prediction and on-time treatment of diabetic foot ulcers (DFU) are of great importance for improving and maintaining patients' quality of life and avoiding the consequent socio-economical burden of amputation. In this study, we use Danish national registry data to understand the risk factors of developing diabetic foot ulcers and amputation among patients with diabetes. We analyze the data of 246,705 patients with diabetes to assess some of the main risk factors for developing DFU/amputation. We study the socioeconomic information and past medical history of the patients. Factors, such as low family disposable income, cardiovascular disorders, peripheral artery, neuropathy, and chronic renal complications are among the important risk factors. Mental disorders and depression, albeit not as pronounced, still pose higher risks in comparison to the group of people without these complications. We further use machine learning techniques to assess the practical usefulness of such risk factors for predicting foot ulcers and amputation. Finally, we outline the limitations of working with registry data sources and explain potentials for combining additional public and private data sources in future applications of artificial intelligence (AI) to improve the prediction of diabetic foot ulcers and amputation.

Reprogrammed Cells Display Distinct Proteomic Signatures Associated with Colony Morphology Variability.

Human induced pluripotent stem cells (hiPSCs) are of high interest because they can be differentiated into a vast range of different cell types. Ideally, reprogrammed cells should sustain long-term culturing in an undifferentiated state. However, some reprogrammed cell lines represent an unstable state by spontaneously differentiating and changing their cellular phenotype and colony morphology. This phenomenon is not fully understood, and no method is available to predict it reliably. In this study, we analyzed and compared the proteome landscape of 20 reprogrammed cell lines classified as stable and unstable based on long-term colony morphology. We identified distinct proteomic signatures associated with stable colony morphology and with unstable colony morphology, although the typical pluripotency markers (POU5F1, SOX2) were present with both morphologies. Notably, epithelial to mesenchymal transition (EMT) protein markers were associated with unstable colony morphology, and the transforming growth factor beta (TGFB) signalling pathway was predicted as one of the main regulator pathways involved in this process. Furthermore, we identified specific proteins that separated the stable from the unstable state. Finally, we assessed both spontaneous embryonic body (EB) formation and directed differentiation and showed that reprogrammed lines with an unstable colony morphology had reduced differentiation capacity. To conclude, we found that different defined patterns of colony morphology in reprogrammed cells were associated with distinct proteomic profiles and different outcomes in differentiation capacity.

Feasibility of single-cell analysis of model cancer and foetal cells in blood after isolation by cell picking.

The objective of the present feasibility study was to transfer single cell line cells to either microscopy slides for downstream immune characterization or to polymerase chain reaction tubes for downstream DNA quantitation. Tumour cell lines, SKBR3 and MCF7 and trophoblast cell line JEG-3 were spiked in healthy donor blood. The CytoTrack system was used to scan the spiked blood samples to identify target cells. Individual target cells were identified, picked by use of a CytoPicker and deposited to either a microscopic slide or a polymerase chain reaction tube (PCR). Single tumour cells on microscopic slides were further immunostained with human epidermal growth factor receptor 2 (Her2) and epithelial cell adhesion molecule (EpCAM). From the picked cells in polymerase chain reaction tubes, DNA was amplified, quantified and used for Short Tandem Repeat genotyping. Depositing rare cells to microscopy slides was laborious with only five cells per hour. In this study with a trained operator, the picked cells had an 80.5% recovery rate. Depositing single trophoblast cells in PCR tubes was a faster process with 10 cells in 5 min. Immunostaining of isolated cells by both Her2 and EpCAM was possible but showed varying staining intensity. Presence of trophoblasts and contaminating white blood cells in PCR tubes after cell picking was confirmed based on DNA yield and mixed Short Tandem Repeat profiles in five out of eight samples. Using the CytoPicker tool, single tumour and trophoblast cells were successfully isolated and moved from blood samples, allowing subsequent immunostaining or Short Tandem Repeat genotyping.

Vistrates: A Component Model for Ubiquitous Analytics.

Visualization tools are often specialized for specific tasks, which turns the user's analytical workflow into a fragmented process performed across many tools. In this paper, we present a component model design for data visualization to promote modular designs of visualization tools that enhance their analytical scope. Rather than fragmenting tasks across tools, the component model supports unification, where components-the building blocks of this model-can be assembled to support a wide range of tasks. Furthermore, the model also provides additional key properties, such as support for collaboration, sharing across multiple devices, and adaptive usage depending on expertise, from creating visualizations using dropdown menus, through instantiating components, to actually modifying components or creating entirely new ones from scratch using JavaScript or Python source code. To realize our model, we introduce VISTRATES, a literate computing platform for developing, assembling, and sharing visualization components. From a visualization perspective, Vistrates features cross-cutting components for visual representations, interaction, collaboration, and device responsiveness maintained in a component repository. From a development perspective, Vistrates offers a collaborative programming environment where novices and experts alike can compose component pipelines for specific analytical activities. Finally, we present several Vistrates use cases that span the full range of the classic "anytime" and "anywhere" motto for ubiquitous analysis: from mobile and on-the-go usage, through office settings, to collaborative smart environments covering a variety of tasks and devices.