Single-cell analyses of human pancreas: characteristics of two populations of acinar cells in chronic pancreatitis

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

Dynamic scRNA-seq of live human pancreatic slices reveals functional endocrine cell neogenesis through an intermediate ducto-acinar stage

Human pancreatic plasticity is implied from multiple single-cell RNA sequencing (scRNA-seq) studies. However, these have been invariably based on static datasets from which fate trajectories can only be inferred using pseudotemporal estimations. Furthermore, the analysis of isolated islets has resulted in a drastic underrepresentation of other cell types, hindering our ability to interrogate exocrine-endocrine interactions. The long-term culture of human pancreatic slices (HPSs) has presented the field with an opportunity to dynamically track tissue plasticity at the single-cell level. Combining datasets from same-donor HPSs at different time points, with or without a known regenerative stimulus (BMP signaling), led to integrated single-cell datasets storing true temporal or treatment-dependent information. This integration revealed population shifts consistent with ductal progenitor activation, blurring of ductal/acinar boundaries, formation of ducto-acinar-endocrine differentiation axes, and detection of transitional insulin-producing cells. This study provides the first longitudinal scRNA-seq analysis of whole human pancreatic tissue, confirming its plasticity in a dynamic fashion.

Integration of scRNA-Seq and Bulk RNA-Seq Reveals Molecular Characterization of the Immune Microenvironment in Acute Pancreatitis

Acute pancreatitis (AP) is an acute inflammatory disease of the exocrine pancreas. The pathogenesis of AP is still unclear, and there is currently no specific treatment. A variety of immune cells infiltrate in AP, which may play an important role in the progression of the disease. In this study, for the first time, scRNA-Seq and Bulk RNA-Seq data were used to show the characteristics of immune cell infiltration in AP, and to explore the specific molecular markers of different cell types. The present study also investigated cell-to-cell communication networks using the CellChat package, and AP-specific gene signatures (Clic1, Sat1, Serpina3n, Atf3, Lcn2, Osmr, Ccl9, Hspb1, Anxa2, Krt8, Cd44, Cd9, Hsp90aa1, Tmsb10, Hmox1, Fxyd5, Plin2, Pnp) were identified through integrative analysis of multiple sequencing datasets. We also defined disease-specific associated genes in different cell types, revealing dynamic changes through cell trajectory and pseudo-time analysis using the Monocle2 package. The results showed that macrophages were significantly increased in acute pancreatitis, and the number of interactions and interaction weight/strength of the macrophages in AP were significantly higher than those in the controls. The activities of various signaling pathways were abnormally regulated such as apoptosis, oxidative stress, lysosome, autophagy, ferroptosis, and inflammatory responses signaling pathways. In conclusion, this study comprehensively depicted the immune microenvironment of AP, explored the interaction network between different cell types, and defined AP-specific gene signatures, providing many new directions for basic research in AP.

Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer

Pancreatic ductal adenocarcinoma is a lethal disease with limited treatment options and poor survival. We studied 83 spatial samples from 31 patients (11 treatment-naïve and 20 treated) using single-cell/nucleus RNA sequencing, bulk-proteogenomics, spatial transcriptomics and cellular imaging. Subpopulations of tumor cells exhibited signatures of proliferation, KRAS signaling, cell stress and epithelial-to-mesenchymal transition. Mapping mutations and copy number events distinguished tumor populations from normal and transitional cells, including acinar-to-ductal metaplasia and pancreatic intraepithelial neoplasia. Pathology-assisted deconvolution of spatial transcriptomic data identified tumor and transitional subpopulations with distinct histological features. We showed coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin in tumor cells. Chemo-resistant samples contain a threefold enrichment of inflammatory cancer-associated fibroblasts that upregulate metallothioneins. Our study reveals a deeper understanding of the intricate substructure of pancreatic ductal adenocarcinoma tumors that could help improve therapy for patients with this disease.

Single-Cell Transcriptomic Analysis of the Mouse Pancreas: Characteristic Features of Pancreatic Ductal Cells in Chronic Pancreatitis

Chronic pancreatitis (CP) is a fibroinflammatory disorder of the pancreas. Our understanding of CP pathogenesis is partly limited by the incomplete characterization of pancreatic cell types. Here, we performed single-cell RNA sequencing on 3825 cells from the pancreas of one control mouse and mice with caerulein-induced CP. An analysis of the single-cell transcriptomes revealed 16 unique clusters and cell type-specific gene expression patterns in the mouse pancreas. Sub-clustering of the pancreatic mesenchymal cells from the control mouse revealed four clusters of cells with specific gene expression profiles (combinatorial expressions of Smoc2, Cxcl14, Tnfaip6, and Fn1). We observed that immune cells in the pancreas of the CP mice were abundant and diverse in cellular type. Compared to the control, 547 upregulated genes (including Mmp7, Ttr, Rgs5, Adh1, and Cldn2) and 257 downregulated genes were identified in ductal cells from the CP group. The elevated expression levels of MMP7 and TTR were further verified in the pancreatic ducts of CP patients. This study provides a preliminary description of the single-cell transcriptome profiles of mouse pancreata and accurately demonstrates the characteristics of pancreatic ductal cells in CP. The findings provide insight into novel disease-specific biomarkers and potential therapeutic targets of CP.