Identification of innate lymphoid cells in single-cell RNA-Seq data
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Innate lymphoid cells (ILCs) consist of natural killer (NK) cells and non-cytotoxic ILCs that are broadly classified into ILC1, ILC2, and ILC3 subtypes. These cells recently emerged as important early effectors of innate immunity for their roles in tissue homeostasis and inflammation. Over the last few years, ILCs have been extensively studied in mouse and human at the functional and molecular level, including gene expression profiling. However, sorting ILCs with flow cytometry for gene expression analysis is a delicate and time-consuming process. Here we propose and validate a novel framework for studying ILCs at the transcriptomic level using single-cell RNA-Seq data. Our approach combines unsupervised clustering and a new cell type classifier trained on mouse ILC gene expression data. We show that this approach can accurately identify different ILCs, especially ILC2 cells, in human lymphocyte single-cell RNA-Seq data. Our new model relies only on genes conserved across vertebrates, thereby making it in principle applicable in any vertebrate species. Considering the rapid increase in throughput of single-cell RNA-Seq technology, our work provides a computational framework for studying ILC2 cells in single-cell transcriptomic data and may help exploring their conservation in distant vertebrate species.
KeywordsInnate lymphoid cells Single-cell RNA-Seq Immune cell type evolution Immunogenomics Cell type predictions
D.G. is supported by the Ludwig Institute for Cancer Research (LICR) and the Center for Advanced Modeling Science (CADMOS); S.J.C. is supported by SystemsX (MelamonX grant). C.J is supported by the Swiss National Science Foundation (Ambizione Fellowship PZOOP3_161459).
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Conflict of interest
The authors declare that they have no conflict of interest.
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