Single-cell transcriptome in silico analysis reveals conserved regulatory programs in macrophages/monocytes of abdominal aortic aneurysm from multiple mouse models and human

Abstract

Abdominal aortic aneurysm (AAA) is a life-threatening disease and there is currently a lack of effective treatment to prevent it rupturing. ScRNA-seq studies of AAA are still lacking. In the study, we analyzed the published AAA scRNA-seq datasets from the mouse elastase-induced model, CaCl2 treatment model, Ang II-induced model and human by using bioinformatic approaches and in silico analysis. A total of 26 cell clusters were obtained and 11 cell types were identified from multiple mouse AAA models. Also, the proportion of Mφ/Mo increased in the AAA group and Mφ/Mo was divided into seven subtypes. There were significant differences in transcriptional regulation patterns of Mφ/Mo in different AAA models. The enrichment pathways of upregulated or downregulated genes from Mφ/Mo in the three mouse datasets were different. The actived regulons of Mφ/Mo had strong specificity and the repressed regulons showed high consistency. The co-upregulated genes as well as actived regulons and co-downregulated genes as well as repressed regulons were closely correlated and formed regulatory networks. Mφ/Mo from human AAA dataset was divided into five subtypes. The proportion of three macrophage subpopulations increased but the proportion of two monocyte subpopulations decreased. In the AAA group, the upregulated or downregulated genes of Mφ/Mo were enriched in different pathways. After further analyzing the genes in Mφ/Mo of both mouse and human scRNA-seq datasets, two genes were upregulated in the four datasets, IL-1B and THBS1. In conclusion, in silico analysis of scRNA-seq revealed that Mφ/Mo and their regulatory related genes as well as interaction networks played an important role in the pathogenesis of AAA.

Integrating single-cell RNA-seq datasets with substantial batch effects

“…For example, pooling of datasets generated in different studies enabled cross-condition comparisons 1,2 , population-level analysis 3,4 , and revealed evolutionary relationships between individual cell types 5 . The selection of pre-clinical models, such as organoids and animals, and the characterization of their limitations likewise rely on comparison with human tissues [6][7][8][9][10][11][12][13][14] . Similarly, the selection of the optimal sequencing protocols requires a comparison of datasets generated with different protocols 15,16 .…”