Single-Cell RNA Sequencing Reveals Microevolution of the Stickleback Immune System. (11th April 2023)

• Record Type: Journal Article
• Authors: Fuess, Lauren E; Bolnick, Daniel I
• Editors: Yi, Soojin
• Is Part Of: Genome biology and evolution. Volume 15:Number 4(2023)
• Publication Date: 2023-04-11
• Subjects: evolutionary immunology -- host–parasite interactions -- ecoimmunology; Genomics -- Periodicals; Genes -- Periodicals; 572.8605
• DOI: 10.1093/gbe/evad053
• Languages: English
• ISSNs: 1759-6653

Abstract

Abstract: The risk and severity of pathogen infections in humans, livestock, or wild organisms depend on host immune function, which can vary between closely related host populations or even among individuals. This immune variation can entail between-population differences in immune gene coding sequences, copy number, or expression. In recent years, many studies have focused on population divergence in immunity using whole-tissue transcriptomics. But, whole-tissue transcriptomics cannot distinguish between evolved differences in gene regulation within cells, versus changes in cell composition within the focal tissue. Here, we leverage single-cell transcriptomic approaches to document signatures of microevolution of immune system structure in a natural system, the three-spined stickleback ( Gasterosteus aculeatus ). We sampled nine adult fish from three populations with variability in resistance to a cestode parasite, Schistocephalus solidus, to create the first comprehensive immune cell atlas for G. aculeatus. Eight broad immune cell types, corresponding to major vertebrate immune cells, were identified. We were also able to document significant variation in both abundance and expression profiles of the individual immune cell types among the three populations of fish. Furthermore, we demonstrate that identified cell type markers can be used to reinterpret traditional transcriptomic data: we reevaluate previously published whole-tissue transcriptome data from a quantitative genetic experimental infection study to gain better resolution relating infection outcomes to inferred cell type variation. Our combined study demonstrates the power of single-cell sequencing to not only document evolutionary phenomena (i.e., microevolution of immune cells) but also increase the power of traditional transcriptomic data sets.