Aging- and alcohol-associated spatial transcriptomic signature in mouse acute pancreatitis reveals heterogeneity of inflammation and potential pathogenic factors

Abstract

The rapidly aging population is consuming more alcohol, leading to increased alcohol-associated acute pancreatitis (AAP) with high mortality. However, the mechanisms remain undefined, and currently there are no effective therapies available. This study aims to elucidate aging- and alcohol-associated spatial transcriptomic signature by establishing an aging AAP mouse model and applying Visium spatial transcriptomics for understanding of the mechanisms in the context of the pancreatic tissue. Upon alcohol diet feeding and caerulein treatment, aging mice (18 months) developed significantly more severe AAP with 5.0-fold increase of injury score and 2.4-fold increase of amylase compared to young mice (3 months). Via Visium spatial transcriptomics, eight distinct tissue clusters were revealed from aggregated transcriptomes of aging and young AAP mice: five acinar, two stromal, and one islet, which were then merged into three clusters: acinar, stromal, and islet for the comparative analysis. Compared to young AAP mice, > 1300 differentially expressed genes (DEGs) and approximately 3000 differentially regulated pathways were identified in aging AAP mice. The top five DEGs upregulated in aging AAP mice include Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp with heterogeneous distributions among the clusters. Taken together, this study demonstrates spatial heterogeneity of inflammatory processes in aging AAP mice, offering novel insights into the mechanisms and potential drivers for AAP development. KEY MESSAGES: Mechanisms regarding high mortality of AAP in aging remain undefined. An aging AAP mouse model was developed recapturing clinical exhibition in humans. Spatial transcriptomics identified contrasted DEGs in aging vs. young AAP mice. Top five DEGs were Mmp8, Ppbp, Serpina3m, Cxcl13, and Hamp in aging vs. young AAP mice. Our findings shed insights for identification of molecular drivers in aging AAP.

Keywords: Acute pancreatitis; Aging; Alcohol; Alcohol-associated acute pancreatitis mouse model; Differentially expressed genes; Visium spatial transcriptomics.

Fig. 1

Alcohol feeding sensitizes aging mice to a low-dose caerulein-induced acute pancreatitis. Mice were acclimated to Lieber-DeCarli CON diet and then fed incrementally increased EtOH from 2, 4, and 5% for 1 week and remained on 5% for 2 weeks, and the control mice were fed CON diet. CAE was injected (50 µg/kg, 3 hourly injections, ip) at the end of two week, and PBS injections served as the control of CAE. The mice were euthanized 16 h after CAE injections; the blood and pancreata were harvested. a Body weight. b Serum EtOH level at the experiment endpoint. c H&E images of the pancreatic sections. d AP score. e Serum amylase level. Data are presented as mean ± SEM. n = 4–6 mice/group. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001

Fig. 2

Visium spatial transcriptomics identifies distinct pancreatic clusters in AAP. a H&E images. b Clustering overlay on H&E images. c, d UMAP and t-SNE plots illustrating the annotated tissue clusters and tissue sample information within the integrated spatial transcriptomics data. The spleen in Young_AAP2 was excluded from sequencing data analysis; n = 2 mice/group

Fig. 3

Major pancreatic tissue types are identified in each cluster. a–d Violin plot (left panel) and UMAP (right panel) showing specific gene expression levels and distribution patterns

Fig. 4

Major pancreatic tissue types in each cluster are validated by scRNA-seq integration. a Bar plot showing major cell types in each cluster identified with scRNA-seq data before and after cluster data merging. b Spatial figure plot visualizing major cell types in each sample. c Cluster composition

Fig. 5

Inflammation signature genes are upregulated in Aging_AAP compared with Young_AAP. a Dot-plot of the inflammatory DEGs in Aging_AAP compared to Young_AAP. b Spatial figure plot depicting the gene expression. c qPCR validation. n = 4 mice/group. *p < 0.05

Fig. 6

Differential pathways are upregulated in Aging_AAP compared with Young_AAP. a Dot plot of the top 20 differential pathways in Aging_AAP compared to Young_AAP. b Interaction plot of the top 12 differential pathways from all clusters. Size: number of DEGs