Lung metastases represent the most adverse clinical factor and rank as the leading cause of osteosarcoma‐related death. Nearly 80% of patients present lung micrometastasis at diagnosis not detected with current clinical tools. Herein, an exosome (EX)‐based imaging tool is developed for lung micrometastasis by positron emission tomography (PET) using osteosarcoma‐derived EXs as natural nanocarriers of the positron‐emitter copper‐64 (64Cu). Exosomes are isolated from metastatic osteosarcoma cells and functionalized with the macrocyclic chelator NODAGA for complexation with 64Cu. Surface functionalization has no effect on the physicochemical properties of EXs, or affinity for donor cells and endows them with favorable pharmacokinetics for in vivo studies. Whole‐body PET/magnetic resonance imaging (MRI) images in xenografted models show a specific accumulation of 64Cu‐NODAGA‐EXs in metastatic lesions as small as 2–3 mm or in a primary tumor, demonstrating the exquisite tropism of EXs for homotypic donor cells. The targetability for lung metastasis is also observed by optical imaging using indocyanine green (ICG)‐labeled EXs and D‐luciferin‐loaded EXs. These findings show that tumor‐derived EXs hold great potential as targeted imaging agents for the noninvasive detection of small lung metastasis by PET. This represents a step forward in the biomedical application of EXs in imaging diagnosis with increased translational potential.

Lung metastasis represents the leading cause of osteosarcoma-related death. Progress in preventing lung metastasis is pretty modest due to the inherent complexity of the metastatic process and the lack of suitable models. Herein, we provide mechanistic insights into how osteosarcoma systemically reprograms the lung microenvironment for metastatic outgrowth using metastatic mouse models and a multi-omics approach. We found that osteosarcoma-bearing mice or those preconditioned with cell-secretome harbour profound lung structural alteration with airways damage, inflammation, neutrophil infiltration, and remodelling of the extracellular matrix with deposition of fibronectin and collagen by stromal activated fibroblasts for tumour cell adhesion. These changes, supported by transcriptomic and histological data, promoted and accelerated the development of lung metastasis. Comparative proteome profiling of the cell secretome and mouse plasma identified a large number of proteins engaged in the extracellular-matrix organization, cell-matrix adhesion, neutrophil degranulation, and cytokine-mediated signalling, which were consistent with the observed lung microenvironmental changes. Moreover, we identified EFEMP1, a secreted extracellular matrix glycoprotein, as a potential risk factor for lung metastasis and a poor prognosis factor in osteosarcoma patients.

Artificial substrates have been implemented to overcome the problems associated with quantitative sampling of marine epifaunal assemblages. These substrates provide artificial habitats that mimic natural habitat features, thereby standardizing the sampling effort and enabling direct comparisons among different sites and studies. This paper explores the potential of the “Artificial Seaweed Monitoring System” (ASMS) sampling methodology to evaluate the natural variability of assemblages along a coastline of more than 200 km, by describing the succession of the ASMS’ associated macrofauna at two Rías of the Galician Coast (NW Iberian Peninsula) after 3, 6, 9, and 12 months after deployment. The results show that macrofauna assemblages harbored by ASMS differ between locations for every type of data. The results also support the hypothesis that succession in benthic communities is not a linear process, but rather a mixture of different successional stages. The use of the ASMS is proved to be a successful standard monitoring methodology, as it is sensitive to scale-dependent patterns and captures the temporal variability of macrobenthic assemblages. Hence, the ASMS can serve as a replicable approach contributing to the “Good Environmental Status” assessment through non-destructive monitoring programs based on benthic marine macrofauna monitoring, capturing the variability in representative assemblages as long as sampling deployment periods are standard.