Single-cell RNA seq mapping of chicken leukocytes
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA930653 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR23316541.fastq.gz | Fastqsanger.gz | |||
| SRR23316542.fastq.gz | Fastqsanger.gz | |||
| SRR23316543.fastq.gz | Fastqsanger.gz | |||
| SRR23316544.fastq.gz | Fastqsanger.gz |
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Project description:The immune system is a complex infrastructure where many cells interact with each other and perform duties depending on their type and function. When using traditional immunological methods in studying non-traditional model organisms, such as birds, challenges arise. These are often associated with a lack of knowledge surrounding the organism in question—particularly, the expected leukocytes, their cell-specific marker genes, and associated reagents. Single-cell transcriptomics allows us to study the immune system at the level of each singular cell and create a profile of each cell present in a sample without as much prior knowledge of the organism. This project aimed to investigate the possibility of using single-cell transcriptomics as an alternative to traditional methods in avian immunology and using this as a basis for further research into avian medicine. The study was performed by sequencing the mRNA in approximately twenty thousand individual chicken blood cells from 4 healthy adult birds, performing unsupervised clustering of the cells, and attempting to annotate clusters based on expression profiles. Most of this study has been performed using the R-based software Seurat and 10 x genomics software Cell Ranger. Resulting putative cell types include expected populations such as several different T-cells, B-cells, Monocytes, thrombocytes, red blood cells, and cells in various stages of the cell life cycle. After computational analysis, the number of cells per cell type corresponds to laboratory analysis of the cell types performed prior to sequencing. This indicates that the in-silico annotation of putative cell types is consistent with the actual cell types in the samples This study of chicken leukocytes highlights the possibility of the usage of single-cell transcriptomics within non-traditional model organism immunology. It shows that using modern single-cell sequencing and existing software, sequencing-based characterisation of immune cells is possible and could prove a robust option in immunology study cases where traditional methods are limited.
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Project description:BackgroundSingle-cell transcriptomics provides means to study cell populations at the level of individual cells. In leukocyte biology this approach could potentially aid the identification of subpopulations and functions without the need to develop species-specific reagents. The present study aimed to evaluate single-cell RNA-seq as a tool for identification of chicken peripheral blood leukocytes. For this purpose, purified and thrombocyte depleted leukocytes from 4 clinically healthy hens were subjected to single-cell 3' RNA-seq. Bioinformatic analysis of data comprised unsupervised clustering of the cells, and annotation of clusters based on expression profiles. Immunofluorescence phenotyping of the cell preparations used was also performed.ResultsComputational analysis identified 31 initial cell clusters and based on expression of defined marker genes 28 cluster were identified as comprising mainly B-cells, T-cells, monocytes, thrombocytes and red blood cells. Of the remaining clusters, two were putatively identified as basophils and eosinophils, and one as proliferating cells of mixed origin. In depth analysis on gene expression profiles within and between the initial cell clusters allowed further identification of cell identity and possible functions for some of them. For example, analysis of the group of monocyte clusters revealed subclusters comprising heterophils, as well as putative monocyte subtypes. Also, novel aspects of TCRγ/δ + T-cell subpopulations could be inferred such as evidence of at least two subtypes based on e.g., different expression of transcription factors MAF, SOX13 and GATA3. Moreover, a novel subpopulation of chicken peripheral B-cells with high SOX5 expression was identified. An overall good correlation between mRNA and cell surface phenotypic cell identification was shown.ConclusionsTaken together, we were able to identify and infer functional aspects of both previously well known as well as novel chicken leukocyte populations although some cell types. e.g., T-cell subtypes, proved more challenging to decipher. Although this methodology to some extent is limited by incomplete annotation of the chicken genome, it definitively has benefits in chicken immunology by expanding the options to distinguish identity and functions of immune cells also without access to species specific reagents.
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