Deciphering the evolution of vertebrate immune cell types with single-cell RNA-seq
Author and article information
Abstract
Single-cell RNA-seq is revolutionizing our understanding of cell type heterogeneity in many fields of biology, ranging from neuroscience to cancer to immunology. In Immunology, one of the main promises of this approach is the ability to define cell types as clusters in the whole transcriptome space (i.e., without relying on specific surface markers), thereby providing an unbiased classification of immune cell types. So far, this technology has been mainly applied in mouse and human. However, technically it could be used for immune cell-type identification in any species without requiring the development and validation of species-specific antibodies for cell sorting. Here we review recent developments using single-cell RNA-seq to characterize immune cell populations in non-mammalian vertebrates, with a focus on zebrafish (Danio rerio). We advocate that single-cell RNA-seq technology is likely to provide key insights into our understanding of the evolution of the adaptive immune system.
Cite this as
2018. Deciphering the evolution of vertebrate immune cell types with single-cell RNA-seq. PeerJ Preprints 6:e26858v1 https://doi.org/10.7287/peerj.preprints.26858v1Author comment
Here we review recent developments using single-cell RNA-seq to characterize immune cell populations in non-mammalian vertebrates, with a focus on zebrafish
Sections
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Santiago J Carmona conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
David Gfeller conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Data Deposition
The following information was supplied regarding data availability:
This is a literature review article and therefore did not generate any data or code.
Funding
S.J.C is supported by SystemsX (MelanomX grant). D.G. is supported by the Swiss National Science Foundation (31003A_173156) and the Swiss Cancer League (KFS-3953-08-2016) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
