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.

Motivation: Deep learning has rapidly become essential for predicting biomolecular interactions; however, most web-tools expose only a single, pre-built model with a fixed, non-configurable architecture that users cannot redesign, retrain on their own data, or compare; they are typically dedicated to one interaction type and often one species, and report prediction scores with little interpretability. These constraints force researchers across several disconnected, single-purpose tools and limit the flexibility, reproducibility, and long-term usability of existing platforms. Results: We present Deep-Interact Studio, a unified, web-based deep-learning platform that addresses these limitations by shifting interaction prediction from a model-centric to a user-driven, comparative, and interpretable paradigm. Within a single interface spanning all four interaction classes, namely protein-protein, drugtarget, RNA-protein, and protein-DNA, users design their own model architectures layer by layer, configure training hyperparameters, and train them on their own data, including custom, species-specific datasets. Multiple user-built models can then be trained under identical conditions and compared side by side at both the training and inference levels, while integrated interpretability, including SHAP-based feature attribution, embedding-space visualization, and interaction hub analysis, turns predictions into auditable, mechanistically grounded results. Deep-Interact Studio is, to our knowledge, the only such platform to combine fine-grained per-layer model customization with multi-model comparison and interpretability, offering a flexible and transparent alternative to fixed, single-purpose tools. Availability and implementation: Deep-Interact Studio is freely available as a web application at https://deepinteract.compbiosysnbu.in/ , with no login or installation required.