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Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing.

ABSTRACT: Pigs are a valuable human biomedical model and an important protein source supporting global food security. The transcriptomes of peripheral blood immune cells in pigs were defined at the bulk cell-type and single cell levels. First, eight cell types were isolated in bulk from peripheral blood mononuclear cells (PBMCs) by cell sorting, representing Myeloid, NK cells and specific populations of T and B-cells. Transcriptomes for each bulk population of cells were generated by RNA-seq with 10,974 expressed genes detected. Pairwise comparisons between cell types revealed specific expression, while enrichment analysis identified 1,885 to 3,591 significantly enriched genes across all 8 cell types. Gene Ontology analysis for the top 25% of significantly enriched genes (SEG) showed high enrichment of biological processes related to the nature of each cell type. Comparison of gene expression indicated highly significant correlations between pig cells and corresponding human PBMC bulk RNA-seq data available in Haemopedia. Second, higher resolution of distinct cell populations was obtained by single-cell RNA-sequencing (scRNA-seq) of PBMC. Seven PBMC samples were partitioned and sequenced that produced 28,810 single cell transcriptomes distributed across 36 clusters and classified into 13 general cell types including plasmacytoid dendritic cells (DC), conventional DCs, monocytes, B-cell, conventional CD4 and CD8 αβ T-cells, NK cells, and γδ T-cells. Signature gene sets from the human Haemopedia data were assessed for relative enrichment in genes expressed in pig cells and integration of pig scRNA-seq with a public human scRNA-seq dataset provided further validation for similarity between human and pig data. The sorted porcine bulk RNAseq dataset informed classification of scRNA-seq PBMC populations; specifically, an integration of the datasets showed that the pig bulk RNAseq data helped define the CD4CD8 double-positive T-cell populations in the scRNA-seq data. Overall, the data provides deep and well-validated transcriptomic data from sorted PBMC populations and the first single-cell transcriptomic data for porcine PBMCs. This resource will be invaluable for annotation of pig genes controlling immunogenetic traits as part of the porcine Functional Annotation of Animal Genomes (FAANG) project, as well as further study of, and development of new reagents for, porcine immunology.

SUBMITTER: Herrera-Uribe J

PROVIDER: S-EPMC8261551 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundUlcerative colitis (UC) is a complicated disease caused by the interaction between genetic and environmental factors that affects mucosal homeostasis and triggers an inappropriate immune response. Single-cell RNA sequencing (scRNA-seq) can be used to rapidly obtain the precise gene expression patterns of thousands of cells in the intestine, analyze the characteristics of cells with the same phenotype, and provide new insights into the growth and development of intestinal organs, the clonal evolution of cells, and immune cell changes. These findings can provide new ideas for the diagnosis and treatment of intestinal diseases.AimTo identify clinical phenotypes and biomarkers that can predict the response of UC patients to specific therapeutic drugs and thus aid the diagnosis and treatment of UC.MethodsUsing the Gene Expression Omnibus (GEO) database, we analyzed peripheral blood cell subtypes of patients with UC by scRNA-seq combined with bulk RNA sequencing (RNA-seq) to reveal the core genes of UC. We then combined weighted gene correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analysis to reveal diagnostic markers of UC.ResultsAfter processing the scRNA-seq data, we obtained data from approximately 24340 cells and identified 17 cell types. Through intercellular communication analysis, we selected monocyte marker genes as the candidate gene set for the prediction model. Construction of a WGCNA coexpression network identified RhoB, cathepsin D (CTSD) and zyxin (ZYX) as core genes. Immune infiltration analysis showed that these three core genes were strongly correlated with immune cells. Functional enrichment analysis showed that the differentially expressed genes were closely related to immune and inflammatory responses, which are associated with many challenges in the diagnosis and treatment of UC.ConclusionThrough scRNA-seq analysis, LASSO diagnostic model building and WGCNA, we identified RhoB, CTSD and ZYX as core genes of UC that are closely related to monocyte infiltration that may serve as diagnostic markers and molecular targets for UC therapeutic intervention.

Project description:Background: Osteosarcoma (OS) is a kind of solid tumor with high heterogeneity at tumor microenvironment (TME), genome and transcriptome level. In view of the regulatory effect of metabolism on TME, this study was based on four metabolic models to explore the intertumoral heterogeneity of OS at the RNA sequencing (RNA-seq) level and the intratumoral heterogeneity of OS at the bulk RNA-seq and single cell RNA-seq (scRNA-seq) level. Methods: The GSVA package was used for single-sample gene set enrichment analysis (ssGSEA) analysis to obtain a glycolysis, pentose phosphate pathway (PPP), fatty acid oxidation (FAO) and glutaminolysis gene sets score. ConsensusClusterPlus was employed to cluster OS samples downloaded from the Target database. The scRNA-seq and bulk RNA-seq data of immune cells from GSE162454 dataset were analyzed to identify the subsets and types of immune cells in OS. Malignant cells and non-malignant cells were distinguished by large-scale chromosomal copy number variation. The correlations of metabolic molecular subtypes and immune cell types with four metabolic patterns, hypoxia and angiogenesis were determined by Pearson correlation analysis. Results: Two metabolism-related molecular subtypes of OS, cluster 1 and cluster 2, were identified. Cluster 2 was associated with poor prognosis of OS, active glycolysis, FAO, glutaminolysis, and bad TME. The identified 28608 immune cells were divided into 15 separate clusters covering 6 types of immune cells. The enrichment scores of 5 kinds of immune cells in cluster-1 and cluster-2 were significantly different. And five kinds of immune cells were significantly correlated with four metabolic modes, hypoxia and angiogenesis. Of the 28,608 immune cells, 7617 were malignant cells. The four metabolic patterns of malignant cells were significantly positively correlated with hypoxia and negatively correlated with angiogenesis. Conclusion: We used RNA-seq to reveal two molecular subtypes of OS with prognosis, metabolic pattern and TME, and determined the composition and metabolic heterogeneity of immune cells in OS tumor by bulk RNA-seq and single-cell RNA-seq.

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Reference Transcriptomes of Porcine Peripheral Immune Cells Created Through Bulk and Single-Cell RNA Sequencing.

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