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Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types.

ABSTRACT: The immune system of vertebrate species consists of many different cell types that have distinct functional roles and are subject to different evolutionary pressures. Here, we first analyzed conservation of genes specific for all major immune cell types in human and mouse. Our results revealed higher gene turnover and faster evolution of trans-membrane proteins in NK cells compared with other immune cell types, and especially T cells, but similar conservation of nuclear and cytoplasmic protein coding genes. To validate these findings in a distant vertebrate species, we used single-cell RNA sequencing of lck:GFP cells in zebrafish and obtained the first transcriptome of specific immune cell types in a nonmammalian species. Unsupervised clustering and single-cell TCR locus reconstruction identified three cell populations, T cells, a novel type of NK-like cells, and a smaller population of myeloid-like cells. Differential expression analysis uncovered new immune-cell-specific genes, including novel immunoglobulin-like receptors, and neofunctionalization of recently duplicated paralogs. Evolutionary analyses confirmed the higher gene turnover of trans-membrane proteins in NK cells compared with T cells in fish species, suggesting that this is a general property of immune cell types across all vertebrates.

SUBMITTER: Carmona SJ

PROVIDER: S-EPMC5340972 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types.

Carmona Santiago J SJ Teichmann Sarah A SA Ferreira Lauren L Macaulay Iain C IC Stubbington Michael J T MJ Cvejic Ana A Gfeller David D

Genome research 20170113 3

The immune system of vertebrate species consists of many different cell types that have distinct functional roles and are subject to different evolutionary pressures. Here, we first analyzed conservation of genes specific for all major immune cell types in human and mouse. Our results revealed higher gene turnover and faster evolution of <i>trans</i>-membrane proteins in NK cells compared with other immune cell types, and especially T cells, but similar conservation of nuclear and cytoplasmic pr ...[more]

PMID: 28087841

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Project description:Single-cell analysis has the potential to provide us with a host of new knowledge about biological systems, but it comes with the challenge of correctly interpreting the biological information. While emerging techniques have made it possible to measure inter-cellular variability at the transcriptome level, no consensus yet exists on the most appropriate method of data analysis of such single cell data. Methods for analysis of transcriptional data at the population level are well established but are not well suited to single cell analysis due to their dependence on population averages. In order to address this question, we have systematically tested combinations of methods for primary data analysis on single cell transcription data generated from two types of primary immune cells, neutrophils and T lymphocytes. Cells were obtained from healthy individuals, and single cell transcript expression data was obtained by a combination of single cell sorting and nanoscale quantitative real time PCR (qRT-PCR) for markers of cell type, intracellular signaling, and immune functionality. Gene expression analysis was focused on hierarchical clustering to determine the existence of cellular subgroups within the populations. Nine combinations of criteria for data exclusion and normalization were tested and evaluated. Bimodality in gene expression indicated the presence of cellular subgroups which were also revealed by data clustering. We observed evidence for two clearly defined cellular subtypes in the neutrophil populations and at least two in the T lymphocyte populations. When normalizing the data by different methods, we observed varying outcomes with corresponding interpretations of the biological characteristics of the cell populations. Normalization of the data by linear standardization taking into account technical effects such as plate effects, resulted in interpretations that most closely matched biological expectations. Single cell transcription profiling provides evidence of cellular subclasses in neutrophils and leukocytes that may be independent of traditional classifications based on cell surface markers. The choice of primary data analysis method had a substantial effect on the interpretation of the data. Adjustment for technical effects is critical to prevent misinterpretation of single cell transcript data.

Dataset Information

Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types.

Publications

Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types.

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