It is better to light a candle than to curse the darkness: single-cell transcriptomics sheds new light on pancreas biology and disease

Novel Approach for Pancreas Transcriptomics Reveals the Cellular Landscape in Homeostasis and Acute Pancreatitis

BACKGROUND & AIMS

Acinar cells produce digestive enzymes that impede transcriptomic characterization of the exocrine pancreas. Thus, single-cell RNA-sequencing studies of the pancreas underrepresent acinar cells relative to histological expectations, and a robust approach to capture pancreatic cell responses in disease states is needed. We sought to innovate a method that overcomes these challenges to accelerate study of the pancreas in health and disease.

METHODS

We leverage FixNCut, a single-cell RNA-sequencing approach in which tissue is reversibly fixed with dithiobis(succinimidyl propionate) before dissociation and single-cell preparation. We apply FixNCut to an established mouse model of acute pancreatitis, validate findings using GeoMx whole transcriptome atlas profiling, and integrate our data with prior studies to compare our method in both mouse and human pancreas datasets.

RESULTS

FixNCut achieves unprecedented definition of challenging pancreatic cells, including acinar and immune populations in homeostasis and acute pancreatitis, and identifies changes in all major cell types during injury and recovery. We define the acinar transcriptome during homeostasis and acinar-to-ductal metaplasia and establish a unique gene set to measure deviation from normal acinar identity. We characterize pancreatic immune cells, and analysis of T-cell subsets reveals a polarization of the homeostatic pancreas toward type-2 immunity. We report immune responses during acute pancreatitis and recovery, including early neutrophil infiltration, expansion of dendritic cell subsets, and a substantial shift in the transcriptome of macrophages due to both resident macrophage activation and monocyte infiltration.

CONCLUSIONS

FixNCut preserves pancreatic transcriptomes to uncover novel cell states during homeostasis and following pancreatitis, establishing a broadly applicable approach and reference atlas for study of pancreas biology and disease.

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data

Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations is challenging and requires stable and interpretable methods. However, the current cell type identification methods have limited performance, mainly due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, a multi-view graph convolutional network model (scMGCN) that integrates multiple graph structures from raw scRNA-seq data and applies graph convolutional networks with attention mechanisms to learn cell embeddings and predict cell labels. We evaluate our model on single-dataset, cross-species, and cross-platform experiments and compare it with other state-of-the-art methods. Our results show that scMGCN outperforms the other methods regarding stability, accuracy, and robustness to batch effects. Our main contributions are as follows: Firstly, we introduce multi-view learning and multiple graph construction methods to capture comprehensive cellular information from scRNA-seq data. Secondly, we construct a scMGCN that combines graph convolutional networks with attention mechanisms to extract shared, high-order information from cells. Finally, we demonstrate the effectiveness and superiority of the scMGCN on various datasets.

ROR2 regulates cellular plasticity in pancreatic neoplasia and adenocarcinoma

Cellular plasticity is a hallmark of pancreatic ductal adenocarcinoma (PDAC) starting from the conversion of normal cells into precancerous lesions to the progression of carcinoma subtypes associated with aggressiveness and therapeutic response. We discovered that normal acinar cell differentiation, maintained by the transcription factor Pdx1, suppresses a broad gastric cell identity that is maintained in metaplasia, neoplasia, and the classical subtype of PDAC in mouse and human. We have identified the receptor tyrosine kinase Ror2 as marker of a gastric metaplasia (SPEM)-like identity in the pancreas. Ablation of Ror2 in a mouse model of pancreatic tumorigenesis promoted a switch to a gastric pit cell identity that largely persisted through progression to the classical subtype of PDAC. In both human and mouse pancreatic cancer, ROR2 activity continued to antagonize the gastric pit cell identity, strongly promoting an epithelial to mesenchymal transition, conferring resistance to KRAS inhibition, and vulnerability to AKT inhibition.

Imaging in Type 1 Diabetes, Current Perspectives and Directions

Type 1 diabetes (T1D) is characterized by the autoimmune-mediated attack of insulin-producing beta cells in the pancreas, leading to reliance on exogenous insulin to control a patient's blood glucose levels. As progress is being made in understanding the pathophysiology of the disease and how to better develop therapies to treat it, there is an increasing need for monitoring technologies to quantify beta cell mass and function throughout T1D progression and beta cell replacement therapy. Molecular imaging techniques offer a possible solution through both radiologic and non-radiologic means including positron emission tomography, magnetic resonance imaging, electron paramagnetic resonance imaging, and spatial omics. This commentary piece outlines the role of molecular imaging in T1D research and highlights the need for further applications of such methodologies in T1D.

Reorganization of pancreas circadian transcriptome with aging

The evolutionarily conserved circadian system allows organisms to synchronize internal processes with 24-h cycling environmental timing cues, ensuring optimal adaptation. Like other organs, the pancreas function is under circadian control. Recent evidence suggests that aging by itself is associated with altered circadian homeostasis in different tissues which could affect the organ's resiliency to aging-related pathologies. Pancreas pathologies of either endocrine or exocrine components are age-related. Whether pancreas circadian transcriptome output is affected by age is still unknown. To address this, here we profiled the impact of age on the pancreatic transcriptome over a full circadian cycle and elucidated a circadian transcriptome reorganization of pancreas by aging. Our study highlights gain of rhythms in the extrinsic cellular pathways in the aged pancreas and extends a potential role to fibroblast-associated mechanisms.