Identification of innate lymphoid cells in single-cell RNA-Seq data.
Details
Serval ID
serval:BIB_746D02A9A722
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Identification of innate lymphoid cells in single-cell RNA-Seq data.
Journal
Immunogenetics
ISSN
1432-1211 (Electronic)
ISSN-L
0093-7711
Publication state
Published
Issued date
07/2017
Peer-reviewed
Oui
Volume
69
Number
7
Pages
439-450
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Publication Status: ppublish
Abstract
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.
Keywords
Cell type predictions, Immune cell type evolution, Immunogenomics, Innate lymphoid cells, Single-cell RNA-Seq
Pubmed
Web of science
Create date
30/05/2017 17:11
Last modification date
20/08/2019 15:32