Chicken Peripheral Blood Mononuclear Cells Response to Avian Leukosis Virus Subgroup J Infection Assessed by Single-Cell RNA Sequencing

Delineation of chicken immune markers in the era of omics and multicolor flow cytometry

Multiparameter flow cytometry is a routine method in immunological studies incorporated in biomedical, veterinary, agricultural, and wildlife research and routinely used in veterinary clinical laboratories. Its use in the diagnostics of poultry diseases is still limited, but due to the continuous expansion of reagents and cost reductions, this may change in the near future. Although the structure and function of the avian immune system show commonalities with mammals, at the molecular level, there is often low homology across species. The cross-reactivity of mammalian immunological reagents is therefore low, but nevertheless, the list of reagents to study chicken immune cells is increasing. Recent improvement in multicolor antibody panels for chicken cells has resulted in more detailed analysis by flow cytometry and has allowed the discovery of novel leukocyte cell subpopulations. In this article, we present an overview of the reagents and guidance needed to perform multicolor flow cytometry using chicken samples and common pitfalls to avoid.

Deciphering infected cell types, hub gene networks and cell-cell communication in infectious bronchitis virus via single-cell RNA sequencing

Infectious bronchitis virus (IBV) is a coronavirus that infects chickens, which exhibits a broad tropism for epithelial cells, infecting the tracheal mucosal epithelium, intestinal mucosal epithelium, and renal tubular epithelial cells. Utilizing single-cell RNA sequencing (scRNA-seq), we systematically examined cells in renal, bursal, and tracheal tissues following IBV infection and identified tissue-specific molecular markers expressed in distinct cell types. We evaluated the expression of viral RNA in diverse cellular populations and subsequently ascertained that distal tubules and collecting ducts within the kidney, bursal mucosal epithelial cells, and follicle-associated epithelial cells exhibit susceptibility to IBV infection through immunofluorescence. Furthermore, our findings revealed an upregulation in the transcription of proinflammatory cytokines IL18 and IL1B in renal macrophages as well as increased expression of apoptosis-related gene STAT in distal tubules and collecting duct cells upon IBV infection leading to renal damage. Cell-to-cell communication unveiled potential interactions between diverse cell types, as well as upregulated signaling pathways and key sender-receiver cell populations after IBV infection. Integrating single-cell data from all tissues, we applied weighted gene co-expression network analysis (WGCNA) to identify gene modules that are specifically expressed in different cell populations. Based on the WGCNA results, we identified seven immune-related gene modules and determined the differential expression pattern of module genes, as well as the hub genes within these modules. Our comprehensive data provides valuable insights into the pathogenesis of IBV as well as avian antiviral immunology.

Single-cell RNA-seq mapping of chicken peripheral blood leukocytes

BACKGROUND

Single-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.

RESULTS

Computational 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.

CONCLUSIONS

Taken 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.

Dissection of key factors correlating with H5N1 avian influenza virus driven inflammatory lung injury of chicken identified by single-cell analysis

Chicken lung is an important target organ of avian influenza virus (AIV) infection, and different pathogenic virus strains lead to opposite prognosis. Using a single-cell RNA sequencing (scRNA-seq) assay, we systematically and sequentially analyzed the transcriptome of 16 cell types (19 clusters) in the lung tissue of chickens infected with H5N1 highly pathogenic avian influenza virus (HPAIV) and H9N2 low pathogenic avian influenza virus (LPAIV), respectively. Notably, we developed a valuable catalog of marker genes for these cell types. Compared to H9N2 AIV infection, H5N1 AIV infection induced extensive virus replication and the immune reaction across most cell types simultaneously. More importantly, we propose that infiltrating inflammatory macrophages (clusters 0, 1, and 14) with massive viral replication, pro-inflammatory cytokines (IFN-β, IL1β, IL6 and IL8), and emerging interaction of various cell populations through CCL4, CCL19 and CXCL13, potentially contributed to the H5N1 AIV driven inflammatory lung injury. Our data revealed complex but distinct immune response landscapes in the lung tissue of chickens after H5N1 and H9N2 AIV infection, and deciphered the potential mechanisms underlying AIV-driven inflammatory reactions in chicken. Furthermore, this article provides a rich database for the molecular basis of different cell-type responses to AIV infection.

Expression of toll-like receptor 4 and its associated cytokines from peripheral blood mononuclear cells in Leghorn chickens

Background and Aim

Immune cells require toll-like receptor 4 (TLR4) to respond to lipopolysaccharides (LPS) by releasing pro-inflammatory cytokines. Peripheral blood mononuclear cells (PBMCs) are used to assess changes in cytokines released in response to diseases or pathogens. This study aimed to assess TLR4 gene expression in PBMCs from Leghorn chicken and the release of related cytokines.

Materials and Methods

Peripheral blood mononuclear cells were isolated from blood samples obtained from Leghorn chicks. The PBMC cultures were stimulated with various concentrations of LPS (0.01-1 μg/ml). Polymerase chain reaction was used to detect TLR4 expression. The production of tumor necrosis factor-alpha (TNF-α) and interleukins (IL-1β and IL-6) was quantified using an enzyme-linked immunosorbent assay.

Results

We found that TLR4 was expressed in both non-stimulated and stimulated Leghorn chicken PBMCs. In addition, the release of TNF-α, IL-1β, and IL-6 levels in Leghorn chicken PBMCs increased significantly with an increase in LPS concentration (0.01-1 μg/mL) (p < 0.05).

Conclusion

Although TLR4 was expressed in both non-stimulated and stimulated Leghorn chicken PBMCs, its expression was significantly higher in LPS-stimulated PBMCs Therefore, the chicken's endotoxin response can be assessed by evaluating the pro-inflammatory cytokine production from PBMCs.

The immune cell landscape and response of Marek's disease resistant and susceptible chickens infected with Marek's disease virus

Genetically resistant or susceptible chickens to Marek's disease (MD) have been widely used models to identify the molecular determinants of these phenotypes. However, these prior studies lacked the basic identification and understanding of immune cell types that could be translated toward improved MD control. To gain insights into specific immune cell types and their responses to Marek's disease virus (MDV) infection, we used single-cell RNA sequencing (scRNAseq) on splenic cells from MD resistant and susceptible birds. In total, 14,378 cells formed clusters that identified various immune cell types. Lymphocytes, specifically T cell subtypes, were the most abundant with significant proportional changes in some subtypes upon infection. The largest number of differentially expressed genes (DEG) response was seen in granulocytes, while macrophage DEGs differed in directionality by subtype and line. Among the most DEG in almost all immune cell types were granzyme and granulysin, both associated with cell-perforating processes. Protein interactive network analyses revealed multiple overlapping canonical pathways within both lymphoid and myeloid cell lineages. This initial estimation of the chicken immune cell type landscape and its accompanying response will greatly aid efforts in identifying specific cell types and improving our knowledge of host response to viral infection.

Advances in Single-Cell Sequencing Technology and Its Application in Poultry Science

Single-cell sequencing (SCS) uses a single cell as the research material and involves three dimensions: genes, phenotypes and cell biological mechanisms. This type of research can locate target cells, analyze the dynamic changes in the target cells and the relationships between the cells, and pinpoint the molecular mechanism of cell formation. Currently, a common problem faced by animal husbandry scientists is how to apply existing science and technology to promote the production of high-quality livestock and poultry products and to breed livestock for disease resistance; this is also a bottleneck for the sustainable development of animal husbandry. In recent years, although SCS technology has been successfully applied in the fields of medicine and bioscience, its application in poultry science has been rarely reported. With the sustainable development of science and technology and the poultry industry, SCS technology has great potential in the application of poultry science (or animal husbandry). Therefore, it is necessary to review the innovation of SCS technology and its application in poultry science. This article summarizes the current main technical methods of SCS and its application in poultry, which can provide potential references for its future applications in precision breeding, disease prevention and control, immunity, and cell identification.

Advances on genetic and genomic studies of ALV resistance

Avian leukosis (AL) is a general term for a variety of neoplastic diseases in avian caused by avian leukosis virus (ALV). No vaccine or drug is currently available for the disease. Therefore, the disease can result in severe economic losses in poultry flocks. Increasing the resistance of poultry to ALV may be one effective strategy. In this review, we provide an overview of the roles of genes associated with ALV infection in the poultry genome, including endogenous retroviruses, virus receptors, interferon-stimulated genes, and other immune-related genes. Furthermore, some methods and techniques that can improve ALV resistance in poultry are discussed. The objectives are willing to provide some valuable references for disease resistance breeding in poultry.