Rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are chronic autoimmune diseases that result from the combined influence of genetic and environmental factors that promotes the loss of tolerance to cellular components. The complexity of these diseases converts them into a major challenge at the diagnostic and treatment level. Therefore, it is convenient to implement the use of tools for a better understanding of the physiopathology of these diseases to propose reliable biomarkers. The “omics” disciplines like metabolomics and lipidomics allow to study RA and SLE in a higher degree of detail since they evaluate the metabolites and metabolic pathways involved in disease pathogenesis. This review has compiled the information of metabolomics and lipidomics studies where samples obtained from RA and SLE patients were evaluated to find the metabolites and pathways differences between patients and healthy controls. In both diseases, there is a decrease in several amino acids and oxidative stress-related metabolites like glutathione. These findings may be useful for functional metabolomics studies aiming to reprogram the metabolism in a disease setting to recover normal immune cell homeostasis and function.

Extracellular vesicles (EVs) have emerged as essential mediators of intercellular communication, transporting a complex repertoire of lipids, proteins, and nucleic acids that mirror the physiological and pathological status of their parent cells. This review provides a comprehensive overview of EVs from their biogenesis and molecular composition to their translational potential in human disease. This review outlines the major classes of EVs, including exosomes, microvesicles, apoptotic bodies, and oncosomes, together with recent developments in their isolation, molecular characterization, and omics-based profiling. Special focus is given to the role of EVs in viral infection, inflammation, and immune regulation, as well as their contribution to disease development and cancer biology. Moreover, we highlight the emerging clinical applications of mesenchymal stem cell-derived EVs (MSC-EVs) in regenerative medicine and oncology, alongside the therapeutic modulation of EV signaling by photobiomodulation (PBM). Finally, we address key translational challenges related to standardization, scalability, and regulatory validation. As exosome-based therapeutics fall under strict FDA and EMA oversight, their translation further depends on harmonized quality controls and robust safety evaluation. By integrating molecular mechanisms with clinical applications, this review emphasizes the transformative potential of EVs as next-generation diagnostic and therapeutic tools in precision medicine.

Extracellular vesicles (EVs) are membrane-bound nanoparticles released by almost all cell types into the extracellular space, acting as important mediators of intercellular communication by transferring proteins, lipids, and nucleic acids horizontally. EVs are generally classified into small EVs (200 nm), microvesicles, and apoptotic bodies, with current classification methods focusing on physical properties, molecular composition, and cellular origin, as detailed in the MISEV2023 guidelines. EVs are highly promising for diagnostic and therapeutic applications due to their intrinsic biocompatibility, stability in biological fluids, capacity to carry diverse molecular cargo, and potential for drug delivery and functionalization to enable targeted delivery and tissue repair. This narrative review discusses the emerging roles of EVs across various medical fields, including obstetrics and gynecology, ophthalmology, otorhinolaryngology, urology, oncology, orthopedics, neurology, immunology, wound healing, chronic pain management, dermatology, and cardiology. In each discipline, EVs show potential as biomarkers for diagnosing physiological or pathological conditions and as carriers for targeted drug delivery and regenerative treatments. Exosomes, a major type of small EVs, have especially attracted attention as versatile nanocarriers for precision medicine. However, translation into clinical practice requires addressing key pitfalls, including the standardization of isolation and characterization protocols, dose definition, GMP-compliant large-scale production, and regulatory approval. Ongoing interdisciplinary collaboration across disciplines and thorough clinical testing will be essential to unlock the full biomedical potential of EVs and establish them as transformative tools in personalized healthcare.

Spatial transcriptomics provides researchers with a powerful tool to investigate gene expression patterns within their native positional context in tissues, revealing intricate cellular relationships. However, analyzing spatial transcriptomics data presents unique challenges due to their high dimensionality and complexity. Several competitive tools have emerged, each aiming to integrate spatial dependencies into their workflow. Yet, the overall performance of these approaches remains constrained by limitations in prediction accuracy and compatibility with specific data types. Here, we introduce the third version of the KODAMA algorithm, specifically tailored for spatial transcriptomics analysis. This upgraded version incorporates a novel approach that effectively reduces data dimensionality while preserving spatial information. At its core, the KODAMA algorithm employs parallel iterations that enforce spatial constraints throughout an embedded clustering process. Our method provides the flexibility to simultaneously analyze multiple samples, streamlining analysis workflows. Additionally, it extends beyond traditional 2D analysis to accommodate multidimensional datasets, including 3D spatial information. KODAMA seamlessly integrates into various pipelines, such as Seurat and Giotto. Extensive evaluations of KODAMA on 10x Visium, Visium HD, and image-based 3D datasets demonstrate its high accuracy in spatial domain prediction. Comparative analyses against alternative dimensionality reduction techniques and spatial analysis tools consistently validate and highlight KODAMA's superior performance in unraveling the spatial organization of cellular components across both single and integrated tissue samples.

Neuropsychiatric Systemic Lupus Erythematosus (NPSLE) has multiple pathogenic mechanisms that cause diverse manifestations and whose diagnosis is challenging because of the absence of appropriate diagnostic tests. In the present study the application of proteomics using two-dimensional electrophoresis (2D) and mass spectrometry (MS) allowed the comparison of the protein profile of the serum low and high abundance protein fractions of NPSLE patients (NPSLE group) and SLE without neuropsychiatric syndromes (SLE group), Neuropsychiatric syndromes not associated with SLE (NPnoSLE groups), and healthy controls (CTRL group). The gels obtained were digitalized and analyzed with the PDQuest software. The statistical analysis of the spots was performed using the nonparametric Kruskal Wallis and Dunn's multiple comparison tests. Two spots showed significant differences and were identified by MS. Spot 4009 was significantly lower in NPSLE with regard to NPnoSLE (p= 0,004) and was identified as apolipoprotein A1 (APOA1) (score 809-1132). Spot 8001 was significantly higher in NPSLE regarding CTRL and NPnoSLE (p= 0,01 y 0,03, respectively) and was identified as serum amyloid A (SAA) (score 725-2488). The proinflammatory high density lipoproteins (HDL) have been described in SLE. In this HDL the decrease of APOA1 is followed by an increase in SAA. This altered level of both proteins may be related to the inflammatory state that is characteristic of an autoimmune disease like SLE, but this is not specific for NPSLE.