Spatial Immunophenotyping of the Chicken Respiratory Tract: A Spatial Multimodal Approach to Avian Respiratory Immunity to Influenza Virus Infection

About the Project

This study integrates mIHC, AI-driven analysis, and spatial transcriptomics to map the innate immune response in the chicken respiratory tract following H5N1 HPAIV and H7N9 LPAIV infection. By leveraging archived animal samples, the project maximizes ethical and economic efficiency.

The proposed work is explicitly aimed at validating novel methodologies and understanding immunopathological differences in susceptibility to avian influenza viruses.

Background:

Avian infectious diseases pose significant threats to global poultry production, food security, and public health. Outbreaks of avian influenza viruses (AIV), particularly zoonotic strains like H5N1 and H7N9, have led to mass culling and economic disruption. The current spread of H5N1 high pathogenicity avian influenza (HPAI) in wild birds and poultry, with recent zoonotic cases in U.S. dairy cattle and workers, underscores the urgency of understanding avian immune responses.

Despite the clear link between avian viral infections and their impact on animal and human health, our understanding of avian immunology remains limited. The avian respiratory and immune systems differ markedly from those of mammals. The upper respiratory mucosa relies on a complex interplay of antibodies and immune cells to combat viral threats. In mammals, mucosal interferon-λ signalling and spatial immune cell distribution are critical for viral control and CD8+ cell activation.Similarly, evidence suggests that the avian respiratory mucosa’s innate immune response plays a role against respiratory pathogens. Recent studies, have shown that the chicken respiratory tract plays a central role in innate immune responses to AIV. For example, H7N9 infection in chickens, quail, and ducks revealed cytokine and chemokine expression patterns in the trachea that correlated with disease severity. Other studies have reported increased NK cells, KUL01+ macrophages, and activated γδ and CD8 T cells in chicken lungs post-infection. These findings highlight the need to map immune cell distribution and function in the avian respiratory tract.

While multiplexed histology has advanced mammal lymphoid cell characterization, similar studies in birds are lacking. This project aims to fill that gap by integrating multiplex immunohistochemistry (mIHC), spatial transcriptomics, and AI-driven digital pathology to characterize host–virus interactions in the chicken trachea.

Aims, objectives and methods:

Preliminary Experiment: Optimization of mIHC and Computational Pathology

Aim:

To standardize mIHC protocols for detecting key immune cell populations in chicken nasal turbinate and tracheal tissue and validate an AI-based pipeline for automated quantification and spatial analysis of immune cells in whole slide images.

Hypothesis:

Immune cell spatial organization within mucosal-associated lymphoid tissues (MALT) and the lamina propria/submucosa varies within respiratory compartments.

Design:

Optimize mIHC for formalin-fixed, paraffin-embedded (FFPE) chicken tissues.

• Tissue Preparation: FFPE trachea and selected lymphoid organs samples from healthy chickens (via University of Bristol (UoB) Veterinary Pathology Service) will be sectioned at 3–5 μm and deparaffinized.
• Staining Protocol: Cross-reactive and chicken-specific antibodies targeting T cells, B cells, macrophages, and dendritic cells will be optimized in lymphoid organs, according to bibliography.14 Sections will be counterstained with haematoxylin for morphological clarity.
• Digital Analysis: Slides will be scanned using a high-resolution whole slide scanner (Motic EasyScan One, 80x objective). AI software (Cellprofiler/QuPath) will be validated for quantifying and analysing immune cell populations. Validation will be supported by our AI image analysis collaborators at University of Oxford and UoB.

Experiment 1: Spatial Analysis of nasal turbinate and tracheal Innate Immune Responses to H5N1 and H7N9 Infection

Aim:

To characterize and compare spatial distribution of innate immune cells in H5N1 HPAIV-infected versus sham control chickens using optimized mIHC and AI analysis.

Hypothesis:

Spatial distribution and proximity of immune cell subsets correlate with their functional states and responses to viral proteins in respiratory mucosal microstructures.

Design:

AI-based image analysis of viral-immune interactions in FFPE tracheal tissues.

• Tissue Preparation: Archived FFPE samples from H5N1 and H7N9 infected chickens (1, 3, 5 and, 7 days post-inoculation) and sham controls provided by our collaborators at (IRTA-CReSA, Barcelona) will be sectioned and deparaffinized.
• Staining Protocol: Sections will be stained using the optimized mIHC protocol, including viral nucleoprotein.10
• Digital Analysis: CellProfiler/QuPath will quantify immune cell populations and analyze spatial distribution, density, and proximity in infected versus control tissues.

Experiment 2: Correlation Between Spatial Transcriptomics and Immune Cell Mapping

Aim:

To conduct a proof-of-concept application of spatial transcriptomics on selected FFPE chicken tracheal tissues, validating the feasibility of this technique in avian samples and establishing a foundation for future, more comprehensive gene expression studies.

Hypothesis:

Differential spatial distribution of mucosal immune populations correlates with host and viral gene signatures, revealing insights into innate immune responses and disease outcomes.

Design:

This pilot study will test the compatibility of the Xenium platform with chicken FFPE tissues and integrate genetic data with mIHC-based immune cell mapping.

• Tissue Preparation: A small number of FFPE sections from Experiment 1 (both infected and control) will be selected based on mIHC results. These will be processed using the Xenium platform to evaluate technical feasibility and signal quality.
• Workflow: 5 μm sections will be mounted on Xenium-compatible slides.
• Library Preparation and Sequencing: Libraries will be amplified, fragmented, and sequenced using paired-end reads. A gene panel will be used, focusing on immune and viral response markers.
• Data Analysis: Differential gene expression will be analysed with support from the UoB Bioinformatics and Computational Genomics Laboratory results will be integrated with mIHC data to explore relationships between immune cell abundance, viral localization, and gene expression.

Supervisors: Dr. Beatriz Vidana, Dr. Benedetta Amato and Prof. Shahriar Behboudi

Start date: September 2026