Directed differentiation into insulin-producing cells using microRNA manipulation

Our commentary is focused on three studies that used microRNA overexpression methods for directed differentiation of stem cells into insulin-producing cells. Islet transplantation is the only cell-based therapy used to treat type 1 diabetes mellitus. However, due to the scarcity of cadaveric donors and limited availability of good quality and quantity of islets for transplant, alternate sources of insulin-producing cells are being studied and used by researchers. This commentary provides an overview of distinct studies focused on manipulating microRNA expression to optimize differentiation of embryonic stem cells or induced pluripotent stem cells into insulin-producing cells. These studies have used different approaches to overexpress micro-RNAs that are highly abundant in human islets (such as miR-375 and miR-7) in their differentiation protocol to achieve better differentiation into functional islet beta (β)-cells

An optimised step-by-step protocol for measuring relative telomere length

Telomeres represent the nucleotide repeat sequences at the ends of chromosomes and are essential for chromosome stability. They can shorten at each round of DNA replication mainly because of incomplete DNA synthesis of the lagging strand. Reduced relative telomere length is associated with aging and a range of disease states. Different methods such as terminal restriction fragment analysis, real-time quantitative PCR (qPCR) and fluorescence in situ hybridization are available to measure telomere length; however, the qPCR-based method is commonly used for large population-based studies. There are multiple variations across qPCR-based methods, including the choice of the single-copy gene, primer sequences, reagents, and data analysis methods in the different reported studies so far. Here, we provide a detailed step-by-step protocol that we have optimized and successfully tested in the hands of other users. This protocol will help researchers interested in measuring relative telomere lengths in cells or across larger clinical cohort/study samples to determine associations of telomere length with health and disease

Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows

MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a machine learning algorithm. We then provide optimized techniques to validate the identified miRNAs through over-expression/loss-of-function studies. Overall, these protocols apply to any field in biology where high-dimensional data are produced. For complete details on the use and execution of this protocol, please refer to Wong et al. (2021a)

Epigenetic and transcriptome profiling identifies a population of visceral adipose-derived progenitor cells with potential to differentiate into an endocrine pancreatic lineage

Type 1 diabetes (T1D) is characterized by the loss of insulin-producing β-cells in the pancreas. T1D can be treated using cadaveric islet transplantation, but this therapy is severely limited by a lack of pancreas donors. To develop an alternative cell source for transplantation therapy, we carried out the epigenetic characterization in nine different adult mouse tissues and identified visceral adipose-derived progenitors as a candidate cell population. Chromatin conformation, assessed using chromatin immunoprecipitation (ChIP) sequencing and validated by ChIP-polymerase chain reaction (PCR) at key endocrine pancreatic gene promoters, revealed similarities between visceral fat and endocrine pancreas. Multiple techniques involving quantitative PCR, in-situ PCR, confocal microscopy, and flow cytometry confirmed the presence of measurable (2–1000-fold over detectable limits) pancreatic gene transcripts and mesenchymal progenitor cell markers (CD73, CD90 and CD105; >98%) in visceral adipose tissue-derived mesenchymal cells (AMCs). The differentiation potential of AMCs was explored in transgenic reporter mice expressing green fluorescent protein (GFP) under the regulation of the Pdx1 (pancreatic and duodenal homeobox-1) gene promoter. GFP expression was measured as an index of Pdx1 promoter activity to optimize culture conditions for endocrine pancreatic differentiation. Differentiated AMCs demonstrated their capacity to induce pancreatic endocrine genes as evidenced by increased GFP expression and validated using TaqMan real-time PCR (at least 2–200-fold relative to undifferentiated AMCs). Human AMCs differentiated using optimized protocols continued to produce insulin following transplantation in NOD/SCID mice. Our studies provide a systematic analysis of potential islet progenitor populations using genome-wide profiling studies and characterize visceral adipose-derived cells for replacement therapy in diabetes

Generation of human islet progenitor cells via epithelial-to-mesenchymal transition

Epithelial to mesenchymal transition (EMT) has been shown to occur during generation of human islet-derived progenitor cells (hIPCs), which have been demonstrated to retain potential to differentiate into insulin-producing cells. EMT is a biological process where epithelial cells go through a phenotypic change to become more mesenchymal-like. EMT is reported to form the basis of three distinct physiological and pathological processes: embryo formation/implantation, tissue repair and carcinoma/metastasis. We demonstrated that human islets undergo EMT when exposed to growth-promoting conditions in vitro. Here, we provide an overview of EMT, generation of hIPCs and other stem cells with this phenomenon, the debate surrounding the origin of lineage-committed progenitor cells and finally the role of microRNAs in regulating EMT in hIPCs

Connexins and microRNAs : interlinked players in regulating islet function?

Pancreatic β-cells are connected to neighboring endocrine cells through the adherin proteins and gap junctions. Connexin 36 (Cx36) is one of the most well-studied and abundantly expressed gap-junction proteins within rodent islets, which is important in coordinated insulin secretion. The expression of connexins is regulated at various levels and by several mechanisms; one of which is via microRNAs. In past 2 decades, microRNAs (miRNAs) have emerged as key molecules in developmental, physiologic and pathological processes. However, very few studies have demonstrated miRNA-mediated regulation of connexins. Even though there are no reports yet on miRNAs and Cx36; we envisage that considering the important role of connexins and microRNAs in insulin secretion, there would be common pathways interlinking these biomolecules. Here, we discuss the current literature on connexins and miRNAs specifically with reference to islet function

Probe-based real-time PCR approaches for quantitative measurement of microRNAs

Probe-based quantitative PCR (qPCR) is a favoured method for measuring transcript abundance, since it is one of the most sensitive detection methods that provides an accurate and reproducible analysis. Probe-based chemistry offers the least background fluorescence as compared to other (dye-based) chemistries. Presently, there are several platforms available that use probe-based chemistry to quantitate transcript abundance. qPCR in a 96 well plate is the most routinely used method, however only a maximum of 96 samples or miRNAs can be tested in a single run. This is time-consuming and tedious if a large number of samples/miRNAs are to be analyzed. High-throughput probe-based platforms such as microfluidics (e.g. TaqMan Array Card) and nanofluidics arrays (e.g. OpenArray) offer ease to reproducibly and efficiently detect the abundance of multiple microRNAs in a large number of samples in a short time. Here, we demonstrate the experimental setup and protocol for miRNA quantitation from serum or plasma-EDTA samples, using probe-based chemistry and three different platforms (96 well plate, microfluidics and nanofluidics arrays) offering increasing levels of throughput

Promoting pro-endocrine differentiation and graft maturation following surgical resection of the mouse pancreas

Type 1 diabetes (T1D) is an autoimmune disease, where insulin-producing β-cells in the pancreas are inappropriately recognized and destroyed by immune cells. Islet transplantation is the most successful cell-based therapy for T1D individuals who experience frequent and severe life-threatening hypoglycemia. However, this therapy is extremely restricted owing to the limited availability of donor pancreas. In recent years, significant progress has been made in generating β-cells from stem/progenitor cells using different approaches of in vitro differentiation. The insulin production from such in vitro generated β-cells is still far less than that observed in islet β-cells. We employed a novel strategy to improve the efficiency of progenitor cell differentiation by performing partial mouse pancreas resection after transplanting in vitro generated insulin-producing cells under the kidney capsule of these mice. Pancreas resection (pancreatectomy) has been shown to induce regenerative pathways, leading to regeneration of almost the entire resected pancreas over 3–5 weeks in mice. We found that in our method, regenerating mouse pancreas promotes better graft differentiation/maturation and insulin production from transplanted cells. In this chapter, we detail the protocols used for transplantation of in vitro differentiated cells in immunocompromised mice, partial pancreatectomy in host (NOD scid) mice, and assessment of graft function. We believe that our protocols provide a solid platform for further studies aimed at understanding growth/differentiation molecules secreted from regenerating pancreas that promote graft maturation

[In Press] Short-chain fatty acids on insulin sensitivity : a systematic review and meta-analysis

Context: There is substantial evidence that reduced short-chain fatty acids (SCFAs) in the gut are associated with obesity and type 2 diabetes, although findings from clinical interventions that can increase SCFAs are inconsistent. Objective: This systematic review and meta-analysis aimed to assess the effect of SCFA interventions on fasting glucose, fasting insulin, and homeostatic model assessment of insulin resistance (HOMA-IR). Data Sources: Relevant articles published up to July 28, 2022, were extracted from PubMed and Embase using the MeSH (Medical Subject Headings) terms of the defined keywords [(short-chain fatty acids) AND (obesity OR diabetes OR insulin sensitivity)] and their synonyms. Data analyses were performed independently by two researchers who used the Cochrane meta-analysis checklist and the PRISMA guidelines. Data Extraction: Clinical studies and trials that measured SCFAs and reported glucose homeostasis parameters were included in the analysis. Standardized mean differences (SMDs) with 95%CIs were calculated using a random-effects model in the data extraction tool Review Manager version 5.4 (RevMan 5.4). The risk-of-bias assessment was performed following the Cochrane checklist for randomized and crossover studies. Data Analysis: In total, 6040 nonduplicate studies were identified, 23 of which met the defined criteria, reported fasting insulin, fasting glucose, or HOMA-IR values, and reported change in SCFA concentrations post intervention. Meta-analyses of these studies indicated that fasting insulin concentrations were significantly reduced (overall effect: SMD = −0.15; 95%CI = −0.29 to −0.01, P = 0.04) in treatment groups, relative to placebo groups, at the end of the intervention. Studies with a confirmed increase in SCFAs at the end of intervention also had a significant effect on lowering fasting insulin (P = 0.008). Elevated levels of SCFAs, compared with baseline levels, were associated with beneficial effects on HOMA-IR (P < 0.00001). There was no significant change in fasting glucose concentrations. Conclusion: Increased postintervention levels of SCFAs are associated with lower fasting insulin concentrations, offering a beneficial effect on insulin sensitivity

Comparative analysis of diagnostic platforms for measurement of differentially methylated insulin DNA

Circulating cell-free DNA (cfDNA) has been intensively investigated as a diagnostic and prognostic marker for various cancers. In recent years, presence of unmethylated insulin cfDNA in the circulation has been correlated with pancreatic β-cell death and risk of developing type 1 diabetes. Digital (d)PCR is an increasingly popular method of quantifying insulin cfDNA due to its ability to determine absolute copy numbers, and its increased sensitivity when compared to the more routinely used quantitative PCR. Multiple platforms have been developed to carry out dPCR. However, not all technologies perform comparably, thereby necessitating evaluation of each platform. Here, we compare two dPCR platforms: the QuantStudio 3D (QS3D, Applied Biosystems) and the QX200 (Bio-Rad), to measure copies of unmethylated/methylated insulin plasmids. The QS3D detected greater copy numbers of the plasmids than the QX200 (manual mode), whereas QX200 demonstrated minimal replicate variability, increased throughput, ease of use and the potential for automation. Overall, the performance of QX200, in our hands, was better suited to measure differentially methylated insulin cfDNA