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

Abstract

Recent advances in single-cell RNA sequencing and bioinformatics have drastically increased our ability to interrogate the cellular composition of traditionally difficult to study organs, such as the pancreas. With the advent of these technologies and approaches, the field has grown, in just a few years, from profiling pancreas disease states to identifying molecular mechanisms of therapy resistance in pancreatic ductal adenocarcinoma, a particularly deadly cancer. Single-cell transcriptomics and related spatial approaches have identified previously undescribed epithelial and stromal cell types and states, how these populations change with disease progression, and potential mechanisms of action which will serve as the basis for designing new therapeutic strategies. Here, we review the recent literature on how single-cell transcriptomic approaches have changed our understanding of pancreas biology and disease progression.

Reorganization of pancreas circadian transcriptome with aging

“…This previously unknown temporal re-organization of pancreatic cellular microenvironment, centered on fibroblasts markers points to a fibroblast-centered circadian re-organization of the pancreas with aging. These findings are particularly interesting since fibroblasts are the major stromal population that expand in pancreatic adenocarcinoma, an aging-related pancreas pathology [ 28 ]. Whether the circadian re-ordering of pancreatic microenvironment by aging could predispose pancreas to cancer by age, or whether similar circadian gain of the ECM and fibroblast signaling occurs in pancreatic cancer remains to be studied.…”

ROR2 regulates cellular plasticity in pancreatic neoplasia and adenocarcinoma

“…Our findings reveal that in neoplasia, Ror2 remains associated with and promotes a proliferative neck cell/SPEM-like identity. In the stomach and the pancreas, SPEM is associated with a wound healing function, suggesting that in neoplasia SPEM is part of a repair program responding to the perceived wound induced by transformation (Cephas et al, 2023). Indeed, ablation of Ror2 in mouse neoplasia showed more extensive transformation, with early neoplastic lesions being highly senescent and in late neoplasia became dominated by a gastric pit cell-like cell identity.…”

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

“…Accuracy refers to the proportion of correctly predicted samples out of the total samples. Precision = TP TP + FP (10) Recall = TP TP + FN (11) Equation (10) represents the calculation process for precision, while Equation (11) represents the calculation process for recall. In this context, FP stands for false positives, FN represents false negatives, and TP denotes true positives.…”

“…It can also provide new insights into how cells interact and function in complex systems [7][8][9]. Moreover, scRNAseq can support research in healthcare, drug development, and biotechnology [10][11][12][13]. Single-cell RNA sequencing (scRNA-seq) has enabled powerful and straightforward access to the transcriptomes of individual cells [1].…”