Spatial Transcriptomics in Chronic Rhinosinusitis: Deciphering Tissue Architecture, Immune Niches, and Precision Therapeutics
4 affiliations
- 1Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore 575018, Karnataka, India
- 2Centre for Health Informatics and Evidence Based Medicine (CHIEM), Yenepoya (Deemed to be University), Mangalore 575018, Karnataka, India
- 3Multiomics and Precision Medicine Lab (MPML), Yenepoya Research Centre (YRC), Yenepoya (Deemed to be University), Mangalore 575018, Karnataka, India
- 4⁴ Department of Bioinformatics, University of North Bengal, Siliguri 734013, West Bengal, India
Abstract
Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory disorder in which epithelial injury, immune activation, and stromal remodelling are organised into local tissue niches. Bulk and single-cell RNA sequencing have defined important cell states, but dissociation removes the anatomical relationships needed to determine where those states coexist. Spatial transcriptomics (ST) addresses this gap by mapping RNA, and in some platforms, protein, onto intact sinonasal tissue. This review provides an evidence-graded synthesis organised around a single translational chain: spatial niche to molecular signature to plausible biological mechanism to CRS phenotype/endotype to clinical implication to therapeutic relevance to validation status. Direct CRS ST evidence is separated from single-cell RNA sequencing support, computational prediction, protein or in situ evidence, experimental perturbation, clinical validation, and extrapolation from other inflammatory diseases. Current CRS studies support spatial compartmentalisation of eosinophil-rich epithelial lesions, ALOX15-positive macrophage programmes, GZMK-positive CD8 T-cell-fibroblast neighbourhoods, and treatment-associated epithelial and stromal changes. However, most ligand-receptor calls remain predictive, and spatial association alone does not establish functional causality. ST is therefore best viewed as a discovery and mechanistic-prioritisation technology whose clinical promises—biomarker selection, biologic matching, and response monitoring—require independent cohorts, locked assays, patient-level reproducibility, and prospective outcome studies. We compare study designs and platforms, examine statistical issues including spatial autocorrelation and pseudoreplication, and propose a cautious translational framework in which orthogonal and clinical validation convert spatial observations into actionable evidence.
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BibTeX
@article{ghosh2026spatial,
title = {{Spatial Transcriptomics in Chronic Rhinosinusitis: Deciphering Tissue Architecture, Immune Niches, and Precision Therapeutics}},
author = {Subhajit Ghosh and Inamul Hasan Madar and Koushik Bardhan and Punitha Muruganantham},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27219510},
url = {https://www.mdpi.com/1422-0067/27/21/9510},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Spatial Transcriptomics in Chronic Rhinosinusitis: Deciphering Tissue Architecture, Immune Niches, and Precision Therapeutics
AU - Subhajit Ghosh
AU - Inamul Hasan Madar
AU - Koushik Bardhan
AU - Punitha Muruganantham
PY - 2026
DA - 2026/10/24
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27219510
UR - https://www.mdpi.com/1422-0067/27/21/9510
AB - Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory disorder in which epithelial injury, immune activation, and stromal remodelling are organised into local tissue niches. Bulk and single-cell RNA sequencing have defined important cell states, but dissociation removes the anatomical relationships needed to determine where those states coexist. Spatial transcriptomics (ST) addresses this gap by mapping RNA, and in some platforms, protein, onto intact sinonasal tissue. This review provides an evidence-graded synthesis organised around a single translational chain: spatial niche to molecular signature to plausible biological mechanism to CRS phenotype/endotype to clinical implication to therapeutic relevance to validation status. Direct CRS ST evidence is separated from single-cell RNA sequencing support, computational prediction, protein or in situ evidence, experimental perturbation, clinical validation, and extrapolation from other inflammatory diseases. Current CRS studies support spatial compartmentalisation of eosinophil-rich epithelial lesions, ALOX15-positive macrophage programmes, GZMK-positive CD8 T-cell-fibroblast neighbourhoods, and treatment-associated epithelial and stromal changes. However, most ligand-receptor calls remain predictive, and spatial association alone does not establish functional causality. ST is therefore best viewed as a discovery and mechanistic-prioritisation technology whose clinical promises—biomarker selection, biologic matching, and response monitoring—require independent cohorts, locked assays, patient-level reproducibility, and prospective outcome studies. We compare study designs and platforms, examine statistical issues including spatial autocorrelation and pseudoreplication, and propose a cautious translational framework in which orthogonal and clinical validation convert spatial observations into actionable evidence.
SN - 1422-0067
ER -