• PDX1
• chemical screening
• differentiation
• endocrine progenitor
• insulin
• pancreatic progenitor
• pluripotent stem cells

• Insulin-Secreting Cells/metabolism
• Insulin-Secreting Cells/drug effects
• Insulin-Secreting Cells/cytology
• Humans
• Cell Differentiation/drug effects
• Homeodomain Proteins/metabolism
• Homeodomain Proteins/genetics
• Trans-Activators/metabolism
• Trans-Activators/genetics
• Pluripotent Stem Cells/metabolism
• Pluripotent Stem Cells/drug effects
• Pluripotent Stem Cells/cytology
• Insulin/metabolism
• Small Molecule Libraries/pharmacology

• LGR5
• acinar cells
• development
• endocrine cells
• fetal pancreas
• human organoids
• organoids
• pancreas
• stem cell
• tripotent stem cell

• Humans
• Cell Lineage
• Pancreas/cytology
• Pancreas/embryology
• Organoids/cytology
• Organoids/metabolism
• Fetus/cytology
• Cell Differentiation
• Receptors, G-Protein-Coupled/metabolism
• Fetal Stem Cells/cytology
• Fetal Stem Cells/metabolism
• Cell Proliferation
• Single-Cell Analysis
• Islets of Langerhans/cytology
• Islets of Langerhans/embryology
• Islets of Langerhans/metabolism
• Mice
• Animals
• Acinar Cells/cytology
• Acinar Cells/metabolism

• chemoresistance
• digestive cancers
• genomic instability
• integrin α2
• tumor microenvironment

• CD8+ Tex cells
• Hepatocholangiocarcinoma (H-ChC)
• biphenotypic
• immunosuppression
• plasticity

• Diabetes mellitus
• Genes
• Pancreas
• Transcription factors
• β-cells

• Humans
• Pancreas
• Diabetes Mellitus
• Insulin-Secreting Cells
• Cell Differentiation/genetics
• Pluripotent Stem Cells
• Insulin

• RNA Velocity
• acinar cells
• ductal cells
• endocrine regeneration
• human pancreatic slices
• islet regeneration
• pancreatic progenitors
• scRNA-seq
• type 1 diabetes
• β cells

• Humans
• Single-Cell Gene Expression Analysis
• Pancreas
• Cell Differentiation
• Endocrine Cells

• Wilms tumor
• cancer
• nephron progenitors

• cis-regulation
• pancreas development
• pancreatic progenitor
• ptf1a

• Animals
• Gene Expression Regulation, Developmental
• Humans
• Mice
• Organogenesis/genetics
• Pancreas/metabolism
• Transcription Factors/metabolism
• Zebrafish/genetics
• Zebrafish/metabolism

• H2020 European Research Council
• Fundação para a Ciência e a Tecnologia

Transcriptome analysis is used to study gene expression in human tissues. It can promote the discovery of new therapeutic targets for related diseases by characterizing the endocrine function of pancreatic physiology and pathology, as well as the gene expression of pancreatic tumors. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) can detect transcriptional activity within a single cell. The scRNA-seq had an invaluable contribution to discovering previously unknown cell subtypes in normal and diseased pancreases, studying the functional role of rare islet cells, and studying various types of cells in diabetes as well as cancer. Here, we review the recent in vitro and in vivo advances in understanding the pancreatic physiology and pathology associated with single-cell sequencing technology, which may provide new insights into treatment strategy optimization for diabetes and pancreatic cancer.

One of the most challenging tasks of modern biology concerns the real-time tracking and quantification of mRNA expression in living cells. On this matter, a novel platform called SmartFlare^TM has taken advantage of fluorophore-linked nanoconstructs for targeting RNA transcripts. Although fluorescence emission does not account for the spatial mRNA distribution, NanoFlare technology has grown a range of theranostic applications starting from detecting biomarkers related to diseases, such as cancer, neurodegenerative pathologies or embryonic developmental disorders.

To investigate the potential of SmartFlare^TM in determining time-dependent mRNA expression of prominin 1 (CD133) and octamer-binding transcription factor 4 (OCT4) in single living cells through differentiation.

Brain fragments from the striatum of aborted human fetuses aged 8 wk postconception were processed to obtain neurospheres. For the in vitro differentiation, neurospheres were gently dissociated with Accutase solution. Single cells were resuspended in a basic medium enriched with fetal bovine serum, plated on poly-L-lysine-coated glass coverslips, and grown in a lapse of time from 1 to 4 wk. Live cell mRNA detection was performed using SmartFlare^TM probes (CD133, Oct4, Actin, and Scramble). All the samples were incubated at 37 °C for 24 h. For nuclear staining, Hoechst 33342 was added. SmartFlare^TM CD133- and OCT4-specific fluorescence signal was assessed using a semiquantitative visual approach, taking into account the fluorescence intensity and the number of labeled cells.

In agreement with previous PCR experiments, a unique expression trend was observed for CD133 and OCT4 genes until 7 d in vitro (DIV). Fluorescence resulted in a mixture of diffuse cytoplasmic and spotted-like pattern, also detectable in the contacting neural branches. From 15 to 30 DIV, only few cells showed a scattered fluorescent pattern, in line with the differentiation progression and coherent with mRNA downregulation of these stemness-related genes.

SmartFlare^TM appears to be a reliable, easy-to-handle tool for investigating CD133 and OCT4 expression in a neural stem cell model, preserving cell biological properties in anticipation of downstream experiments.

• mRNA detection
• SmartFlare^TM
• NanoFlare
• Live staining
• Nanotechnology
• Neural stem cell genes.

• diabetes
• glucagon
• glucose
• insulin
• islet
• α cell
• β cell

• Diabetes Mellitus
• Female
• Humans
• Insulin
• Islets of Langerhans
• Islets of Langerhans Transplantation
• Pregnancy

• UC4 DK104211 NIDDK NIH HHS
• U24 DK098085 NIDDK NIH HHS
• UC4 DK112232 NIDDK NIH HHS
• U01 DK089572 NIDDK NIH HHS
• R33 DK066636 NIDDK NIH HHS
• T32 GM152284 NIGMS NIH HHS
• R24 DK106755 NIDDK NIH HHS
• R01 DK097829 NIDDK NIH HHS
• U01 DK123716 NIDDK NIH HHS
• UC4 DK098085 NIDDK NIH HHS
• UC4 DK108120 NIDDK NIH HHS
• U01 DK072473 NIDDK NIH HHS
• U01 DK120456 NIDDK NIH HHS
• T32 GM007347 NIGMS NIH HHS
• I01 BX000666 BLRD VA
• R01 DK094199 NIDDK NIH HHS
• P30 DK020593 NIDDK NIH HHS
• U01 DK123743 NIDDK NIH HHS
• F30 DK118830 NIDDK NIH HHS
• R01 DK117147 NIDDK NIH HHS
• R21 DK066636 NIDDK NIH HHS
• National Institute of Diabetes and Digestive and Kidney Diseases
• Human Islet Research Network
• Vanderbilt Diabetes Research and Training Center
• Department of Veterans Affairs
• National Institutes of Health

• Acinar Cells/metabolism
• Carcinoma, Pancreatic Ductal/mortality
• Carcinoma, Pancreatic Ductal/physiopathology
• Humans
• Survival Analysis

• ZIA BC011981 Intramural NIH HHS

• beta cell
• diabetes
• human
• in vitro differentiation
• modeling
• monogenic
• pancreas
• pluripotent stem cell

• Animals
• Cell Differentiation
• Diabetes Mellitus
• Humans
• Insulin-Secreting Cells/cytology
• Models, Biological
• Pancreas/cytology
• Pancreas/growth & development
• Pluripotent Stem Cells/cytology

• MC_PC_17230 Medical Research Council
• Wellcome Trust

• human pancreatic slices
• pancreatic regeneration
• pseudotime
• single-cell RNA sequencing
• β-cell neogenesis

• Cell Differentiation
• Cell Lineage
• Humans
• Pancreas
• Regeneration

• U01 DK120393 NIDDK NIH HHS

Human organogenesis remains relatively unexplored for ethical and practical reasons. Here, we report the establishment of a single-cell transcriptome atlas of the human fetal pancreas between 7 and 10 post-conceptional weeks of development. To interrogate cell–cell interactions, we describe InterCom, an R-Package we developed for identifying receptor–ligand pairs and their downstream effects. We further report the establishment of a human pancreas culture system starting from fetal tissue or human pluripotent stem cells, enabling the long-term maintenance of pancreas progenitors in a minimal, defined medium in three-dimensions. Benchmarking the cells produced in 2-dimensions and those expanded in 3-dimensions to fetal tissue identifies that progenitors expanded in 3-dimensions are transcriptionally closer to the fetal pancreas. We further demonstrate the potential of this system as a screening platform and identify the importance of the EGF and FGF pathways controlling human pancreas progenitor expansion.

From single-cell transcriptome analyses to defining culture media for spheroids, the authors provide a census of information to understand the development of human pancreatic progenitors. This approach identifies signalling pathways (EGF and FGF) regulating progenitor proliferation.

Cancer subtype switching, which involves unclear cancer cell origin, cell fate decision, and transdifferentiation of cells within a confined tumor microenvironment, remains a major problem in pancreatic cancer (PDA).

By analyzing PDA subtypes in The Cancer Genome Atlas, we identified that epigenetic silencing of apoptosis-associated tyrosine kinase (AATK) inversely was correlated with mRNA expression and was enriched in the quasi-mesenchymal cancer subtype. By comparing early mouse pancreatic lesions, the non-invasive regions showed AATK co-expression in cells with acinar-to-ductal metaplasia, nuclear VAV1 localization, and cell cycle suppression; but the invasive lesions conversely revealed diminished AATK expression in those with poorly differentiated histology, cytosolic VAV1 localization, and co-expression of p63 and HNF1α. Transiently activated AATK initiates acinar differentiation into a ductal cell fate to establish apical-basal polarization in acinar-to-ductal metaplasia. Silenced AATK and ectopically expressed p63 and HNF1α allow the proliferation of ductal PanINs in mice.

Epigenetic silencing of AATK regulates the cellular transdifferentiation, proliferation, and cell cycle progression in converting PDA-subtypes.

• AATK
• DNA methylation
• KPC model
• Pancreatic cancer
• TP63

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine–Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by “druggability” prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types.

• drug targets
• integrins
• pancreatic ductal adenocarcinoma
• protein-protein interactions
• structural dynamics
• support vector machine–recursive feature elimination