Understanding how molecular mechanisms occur and shape brain function and dysfunction remains a central challenge in neuroscience. Although bulk omics methods have contributed significantly to the field, they fail to address the cellular and spatial heterogeneity of the brain. Single-cell and spatial multi-omics approaches emerged to address these limitations by enabling integrated, high-resolution profiling of molecular layers while preserving cellular and tissue context. However, despite their impact on basic neuroscience, the clinical translation of these methods remains limited by cost, technical complexity, and analytical challenges. In this review, we summarize recent advances in single-cell and spatial multi-omics applied to brain research, critically evaluating their technological capabilities, translational potential, and current limitations. We further highlight emerging directions, including spatiotemporal integration, morphomics, improved reproducibility, and the expansion of multi-omics research to biologically and environmentally diverse populations.

Once dismissed as mere cellular waste, extracellular vesicles (EVs) have undergone a conceptual redefinition, emerging as programmable therapeutic scaffolds with broad biomedical applications. Modern EV design has progressed past the conventional framework of localized cargo delivery to isolated recipient cells; instead, the focus has shifted toward systemic, multi-cellular niche remodeling aimed at restoring tissue-level homeostasis. This review provides a comprehensive analysis of the engineering strategies to overcome the biological bottlenecks of naive EVs, specifically rapid systemic clearance and inefficient cytosolic delivery. We detail current strategies for active loading and for bypassing endolysosomal entrapment to facilitate in-situ translation of therapeutic mRNA. Furthermore, we discuss how the synergy between engineered EVs and responsive biomaterial scaffolds provides the spatiotemporal control necessary for localized reprogramming of diseased microenvironments. Finally, by examining application paradigms across oncology, regenerative medicine, and neurodegeneration alongside existing regulatory classification frameworks, this review provides a roadmap for transitioning intelligent vesicle platforms from benchtop discovery to clinical-grade compliance.

Aims: Spatially resolved omics technologies enable investigation of cellular interactions within their local microenvironments (neighbourhoods) directly in situ. Although several computational methods have been developed for neighbourhood analysis, significant limitations remain in how neighbourhoods are defined and interrogated. Here, we present Kandinsky, a toolkit that provides a flexible and versatile framework for defining and analysing cell neighbourhoods.

Methods: We developed Kandinsky to improve flexibility in neighbourhood analysis and maximise compatibility with a wide range of spatial omics data. We therefore implemented multiple approaches for identifying cell- or spot-based neighbourhoods that serve as input for four analytical modules: differential gene or protein expression analysis, neighbourhood clustering, co-localisation/dispersion, and spatial hot and cold areas. In addition to its core functionality, Kandinsky enables the execution of external tools within the same analytical framework.

Results: We applied Kandinsky to real and simulated spatial datasets to benchmark its performance against existing methods and demonstrate its ability to uncover biologically meaningful spatial interactions. Kandinsky achieved competitive performance in terms of accuracy, memory usage, and runtime. In real datasets, it suggested transcriptional changes associated with acinar-to-beta cell reprogramming in the healthy pancreas; recapitulated stromal, immune, and tumour-associated clusters in pancreatic cancer; revealed the spatial co-localisation of myoepithelial cells with specific breast cancer subpopulations; and confirmed the association between regions of high CD74 expression and immune cell infiltration.

Conclusion: Kandinsky is a flexible and versatile toolkit for neighbourhood analysis that facilitates the exploration and interpretation of complex spatial omics data.

Metabolic stress driven by mitochondrial dysfunction underlies a wide range of human diseases, yet the same defect can be detrimental to some cells while sparing their neighbors. We argue that this paradox reflects lineage mosaicism. Tissues are built from diverse clonal lineages whose differences remain hidden until mitochondrial dysfunction unmasks them. Rather than failing uniformly, cells diverge, engaging distinct stress programs shaped by developmental history and local context. By applying lineage-resolved approaches to mitochondrial dysfunction, we can move beyond average cellular behavior to understand when, where, and why individual cells adapt, persist, or fail.

Regular aerobic exercise is associated with increased survival for patients suffering from solid tumor cancers. In the last decade, pre-clinical exercise oncology studies have begun to explore the mechanisms governing the protective effects of exercise, leading to the translation of exercise-based regimens into the clinic. However, many patients with intractable solid tumors or those diagnosed at a late stage may be physically unable to partake in exercise-based regimens, or lack access to them. In this perspective piece, authors argue that the value of pre-clinical exercise oncology work is not limited to direct translation, but should be considered as an additional means of discovery for novel anti-tumor mechanisms. When exercise-based pre-clinical work is considered as a discovery engine, mechanisms identified at the intersection of exercise physiology and tumor biology can be autonomously evaluated for their clinical potential, independent of the exercise intervention.

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.