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Biology subjects

Ana Cvejic

Publications and source records attributed to Ana Cvejic.

2 recordsLinked to original sources

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

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 analysed gene conservation of 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 to other immune cell populations, 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 to obtain the first transcriptome of specific immune cell types in a non-mammalian 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 a higher gene turnover and lower conservation of trans-membrane proteins in NK cells compared to T cells in fish species, suggesting that this is a general property of immune cell types across all vertebrates.

Immunology

Power Analysis of Single Cell RNA‐Sequencing Experiments

High-throughput single cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, and has revealed new cell types, and new insights into developmental process and stochasticity in gene expression. There are now several published scRNA-seq protocols, which all sequence transcriptomes from a minute amount of starting material. Therefore, a key question is how these methods compare in terms of sensitivity of detection of mRNA molecules, and accuracy of quantification of gene expression. Here, we assessed the sensitivity and accuracy of many published data sets based on standardized spike-ins with a uniform raw data processing pipeline. We developed a flexible and fast UMI counting tool (https://github.com/vals/umis) which is compatible with all UMI based protocols. This allowed us to relate these parameters to sequencing depth, and discuss the trade offs between the different methods. To confirm our results, we performed experiments on cells from the same population using three different protocols. We also investigated the effect of RNA degradation on spike-in molecules, and the average efficiency of scRNA-seq on spike-in molecules versus endogenous RNAs.

Genomics