Spatial omics is a broad term referring to technologies that allow for biomolecules to be observed within their native tissue context. These technologies have been used by biomedical researchers to gain a better understanding of cellular interactions, tumor microenvironment dynamics, and immune cell infiltration. While the basic outputs, such as spatial coordinates, segmentation masks, and transcript/protein matrices, are provided by the instrument software, the true biological insights come from several downstream, specialized analysis steps. Since spatial omics remains a relatively new field, no unified analysis pipeline has yet been established to encompass all platforms. Most workflows are adapted from single-cell RNA sequencing analysis frameworks, while incorporating additional steps that are specific to spatial data, especially for imaging-based technologies. At the same time, the diversity of platforms, data modalities, and output formats has introduced substantial challenges for data representation, interoperability, and cross-platform integration, highlighting the need for flexible, spatially aware, and user-friendly data structures made specifically for imaging-based data, not merely adapted from other methods. This review summarizes the general analytical steps following spatial omics data acquisition, commonly used data infrastructures and tools, existing gaps, and future directions in the field.

Spatial transcriptomics (ST) maps gene expression within intact tissues, but its cost and technical complexity limit broad adoption. A growing body of deep-learning methods now seeks to infer ST directly from routine histology images, especially hematoxylin and eosin (H&E)-stained slides, with the aim of turning archived pathology material into virtual molecular data. This review examines more than 40 ST prediction models, comparing their data requirements, modelling strategies, evaluation practices, and translational potential. Recent advances in model architecture, histology foundation models, and higher-resolution in situ ST platforms have improved prediction performance and expanded the range of potential applications. However, most current methods still show limited robustness and generalisability, with performance constrained primarily by the quantity, quality, and diversity of available ST training data. Comparisons between models also remain difficult due to inconsistent pre-processing pipelines, training datasets and evaluation strategies. The field is increasingly recognising that progress depends not only on more sophisticated models, but also on standardised benchmarks, improved data harmonisation and clearer evaluation frameworks. Despite these limitations, ST prediction is already showing utility in research settings, including biomarker discovery, tissue domain inference, molecular super-resolution and large-scale analysis of archived histology cohorts. Emerging applications also include patient stratification, virtual molecular profiling and multi-modal pathology systems that integrate histology, transcriptomics and language models. As datasets continue to expand and models become more reliable, ST prediction has the potential to become an important component of next-generation digital pathology workflows.

Secreted proteins mediate intercellular and inter-organ communication and are essential for coordinating physiological processes across tissues. Advances in proteomics and proximity labeling have greatly expanded the catalog of circulating secreted factors; however, for many of these molecules, their cognate receptors and mechanisms of action remain unknown. This lack of receptor annotation represents a major bottleneck in understanding systemic signaling networks and translating secretome discoveries into biological insights. In this review, we summarize and evaluate the strengths and limitations of current strategies for deorphanizing secreted proteins, including 1) biochemical approaches such as affinity purification–mass spectrometry and crosslinking-based receptor capture, 2) genetic screening strategies in both in vivo and in vitro systems, including RNA interference and Clustered Regularly-Interspaced Short Palindromic Repeats (CRISPR)-based perturbation and activation platforms, and 3) computational frameworks based on AI-driven protein structure modeling. Finally, we outline future directions aimed at accelerating ligand–receptor identification, including multiplexed screening platforms, approaches to improve sensitivity for low-affinity interactions, synthetic biology tools that convert transient binding events into stable readouts, and integration with single-cell and spatial transcriptomic technologies. Together, these advances provide a roadmap for transforming classical ligand deorphanization into a scalable, context-aware framework for decoding inter-organ communication.

Background: Cystic fibrosis (CF) is a progressive genetic disease characterized by defective ion transport, mucus accumulation, chronic infection, and inflammation that drive airway damage and ultimately end-stage lung failure. Previous studies show that high levels of proteolytic enzymes in the sputum of CF patients correlate with declining lung function, but the related effects on distal lung extracellular matrix (ECM) and immune responses are unclear.

Methods: To address this gap, induced pluripotent stem cell (iPSC) lines from healthy donors and CF patients were differentiated into macrophages, and stimulated with lipopolysaccharide (LPS) to compare their inflammatory responses. Bulk RNA sequencing, functional assays, and secreted protein profiling revealed key differences between healthy and CF-derived macrophages, providing insight into how these cells may contribute to inflammatory responses in CF patients. Further, human lung ECM from distal CF lung tissue was isolated, used to generate ECM biomaterials, and combined with iPSC-derived macrophages from healthy and CF donors in vitro. Macrophage phenotype was evaluated through cytokine profiling and RNA sequencing.

Results: CF macrophage inflammation was dysregulated, with elevated baseline IL-8, IL-18, and MCP-1 expression, and a blunted inflammatory response to CF ECM compared to healthy macrophages. By using CF ECM and healthy macrophages, we characterized how healthy cells may be altered in a persistent CF milieu after anticipated CFTR modulator therapy.

Conclusion: These findings reveal altered innate immune behavior in CF and demonstrate the utility of iPSC-derived macrophages for modeling extrinsic immune-ECM interactions in disease.

Spatial omics is a broad term referring to technologies that allow for biomolecules to be observed within their native tissue context. These technologies have been used by biomedical researchers to gain a better understanding of cellular interactions, tumor microenvironment dynamics, and immune cell infiltration. While the basic outputs, such as spatial coordinates, segmentation masks, and transcript/protein matrices, are provided by the instrument software, the true biological insights come from several downstream, specialized analysis steps. Since spatial omics remains a relatively new field, no unified analysis pipeline has yet been established to encompass all platforms. Most workflows are adapted from single-cell RNA sequencing analysis frameworks, while incorporating additional steps that are specific to spatial data, especially for imaging-based technologies. At the same time, the diversity of platforms, data modalities, and output formats has introduced substantial challenges for data representation, interoperability, and cross-platform integration, highlighting the need for flexible, spatially aware, and user-friendly data structures made specifically for imaging-based data, not merely adapted from other methods. This review summarizes the general analytical steps following spatial omics data acquisition, commonly used data infrastructures and tools, existing gaps, and future directions in the field.

Spatial transcriptomics (ST) maps gene expression within intact tissues, but its cost and technical complexity limit broad adoption. A growing body of deep-learning methods now seeks to infer ST directly from routine histology images, especially hematoxylin and eosin (H&E)-stained slides, with the aim of turning archived pathology material into virtual molecular data. This review examines more than 40 ST prediction models, comparing their data requirements, modelling strategies, evaluation practices, and translational potential. Recent advances in model architecture, histology foundation models, and higher-resolution in situ ST platforms have improved prediction performance and expanded the range of potential applications. However, most current methods still show limited robustness and generalisability, with performance constrained primarily by the quantity, quality, and diversity of available ST training data. Comparisons between models also remain difficult due to inconsistent pre-processing pipelines, training datasets and evaluation strategies. The field is increasingly recognising that progress depends not only on more sophisticated models, but also on standardised benchmarks, improved data harmonisation and clearer evaluation frameworks. Despite these limitations, ST prediction is already showing utility in research settings, including biomarker discovery, tissue domain inference, molecular super-resolution and large-scale analysis of archived histology cohorts. Emerging applications also include patient stratification, virtual molecular profiling and multi-modal pathology systems that integrate histology, transcriptomics and language models. As datasets continue to expand and models become more reliable, ST prediction has the potential to become an important component of next-generation digital pathology workflows.

Extracellular vesicles (EVs) are membrane-enclosed particles released by cells carrying proteins, lipids, metabolites and nucleic acids that can alter the behavior of recipient cells. In mammalian systems, EVs have been studied extensively as important mediators of intercellular and interorgan communication in development, tissue homeostasis, immunity, regeneration, metabolism, cancer and neurobiology. In parallel, Drosophila has emerged as a powerful in vivo model for EV research owing to its genetic tractability and the availability of well-established tools for studying interorgan communication. Work in Drosophila has shown that EVs participate in synaptic cargo transfer, developmental and reproductive signaling, neuronal homeostasis and systemic immune responses. Importantly, most of the pathways that regulate endosomal sorting, multivesicular body dynamics, membrane budding and vesicle secretion are conserved between flies and mammals. This review summarizes current understanding of EV nomenclature, biogenesis, cargo selection and biological function, with emphasis on points of convergence and divergence between mammalian and Drosophila systems. It further discusses the strengths and limitations of Drosophila as a model for mammalian EV biology and highlights how comparative approaches can sharpen mechanistic insight and translational EV studies.

Background: Cystic fibrosis (CF) is a progressive genetic disease characterized by defective ion transport, mucus accumulation, chronic infection, and inflammation that drive airway damage and ultimately end-stage lung failure. Previous studies show that high levels of proteolytic enzymes in the sputum of CF patients correlate with declining lung function, but the related effects on distal lung extracellular matrix (ECM) and immune responses are unclear.

Methods: To address this gap, induced pluripotent stem cell (iPSC) lines from healthy donors and CF patients were differentiated into macrophages, and stimulated with lipopolysaccharide (LPS) to compare their inflammatory responses. Bulk RNA sequencing, functional assays, and secreted protein profiling revealed key differences between healthy and CF-derived macrophages, providing insight into how these cells may contribute to inflammatory responses in CF patients. Further, human lung ECM from distal CF lung tissue was isolated, used to generate ECM biomaterials, and combined with iPSC-derived macrophages from healthy and CF donors in vitro. Macrophage phenotype was evaluated through cytokine profiling and RNA sequencing.

Results: CF macrophage inflammation was dysregulated, with elevated baseline IL-8, IL-18, and MCP-1 expression, and a blunted inflammatory response to CF ECM compared to healthy macrophages. By using CF ECM and healthy macrophages, we characterized how healthy cells may be altered in a persistent CF milieu after anticipated CFTR modulator therapy.

Conclusion: These findings reveal altered innate immune behavior in CF and demonstrate the utility of iPSC-derived macrophages for modeling extrinsic immune-ECM interactions in disease.