Single cell and deep transcriptomic analysis identify common and cell type specific genes in porcine immune cell

Authors: HERRERA-URIBE, JUBER - Iowa State University; Byrne, Kristen; LIU, HAIBO - Iowa State University; SIVASANKARAN, SATHESH - US Department Of Agriculture (USDA); WIARDA, JAYNE - Orise Fellow; YANG, PENGXIN - Iowa State University; Loving, Crystal

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 11/23/2020
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Pigs are important to global agricultural livelihoods and the diet of millions of people worldwide, yet this network is challenged by highly transmissible disease. Additionally, pigs have arisen as a human biomedical model given that the porcine immune system shares many similarities with humans. However, the porcine immune cell transcriptome has not been comprehensively studied. Here, we have performed bulk RNA sequencing on flow-sorted porcine immune cells and single cell RNA sequencing (scRNA-seq) on porcine peripheral blood mononuclear cells (PBMCs) to create a deep understanding of the porcine immune system. PBMCs were isolated from nine healthy pigs. Isolated RNA from sorted immune cells (monocytes, neutrophils, NK cells and specific populations of T and B cells) from two pigs were used for bulk RNA-seq, while PBMCs were used for scRNA-seq using the 10X platform on the remaining seven independent samples. Deep transcriptomes (around 465 million total reads pair-end sequences across samples identifying in average 10,422 genes per cell) from sorted cells were used to determine enriched and specific genes among sorted immune cell populations. Highly enriched genes identified biological processes related to the nature of each cell type using Gene Ontology analysis and comparison with human immune cells was performed. Kmeans cluster analysis was performed to identify co-expression clusters among cell types that were used for transcription factor binding enrichment within clusters and for further integration with scRNA data. On average, scRNAseq of 5479 cells were sequenced and 789 genes per cell were detected. scRNA data allowed the identification of 15 cell clusters. These clusters were annotated using specific cell markers, and differentially expressed genes across clusters were calculated. Taken together, the gene expression profiles and single cell transcriptomic analysis reported here is the first comprehensive transcriptomic study of circulating porcine immune cell types and provides a valuable resource to elucidate molecular markers for porcine immune cell identity and function.