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At least 19 recordsLinked to original sources
PubMed2026

Accurately Deciphering Tissue Heterogeneity From Spatial Multi-Modal and Multi-Omics With STransformer.

Advances in spatially resolved technologies enable the simultaneous acquisition of diverse data modalities within a tissue slice while preserving critical spatial context, which presents unprecedented opportunities to decipher intricate tissue heterogeneity. However, existing computational approaches lack the intrinsic flexibility to universally process both spatial multi-modal and multi-omics data. Here, we introduce STransformer, a unified deep learning framework designed to seamlessly accommodate a comprehensive landscape of spatial data. By simultaneously capturing short-range cellular interactions and tissue-wide semantic patterns, it extracts robust representations to accurately dissect complex tissue heterogeneity. Systematic evaluations across diverse species, tissue types, and data modalities highlight its profound versatility. For spatial multi-modal data, STransformer delineates intricate anatomical structures in the human cortex, uncovers pathological mechanisms in Alzheimer's disease, and characterizes dynamic spatiotemporal developmental trajectories during chicken cardiogenesis. Scaling to spatial multi-omics data, STransformer synergizes spatial transcriptomic and proteomic profiles to decipher intricate immune microenvironments within the human tonsil, and jointly analyzes spatial epigenomic and transcriptomic data to infer regulatory mechanisms in the mouse embryonic brain. Consequently, STransformer serves as a highly versatile and robust analytical framework for advancing our understanding of tissue heterogeneity and disease pathogenesis.

Multiomics↗
PubMed2025

Polygenic enrichment analysis in multi-omics levels identifies cell/tissue specific associations with schizophrenia based on single-cell RNA sequencing data.

OBJECTIVE: Understanding the specific cellular origin and tissue heterogeneity in schizophrenia is critically important for exploring the disease etiology. This study aims to investigate these aspects by performing multiple analyses based on omics data. METHOD: We performed single-cell disease relevance score (scDRS) algorithm to link brain single-cell RNA sequencing (scRNA-seq) with schizophrenia risk across multi-omics scales at single-cell resolution. This approach identified cell types with overexpression of schizophrenia-related genes implicated by multi-omics panels (ATAC-seq, RNA-seq, TWAS, and GWAS). Schizophrenia-related genes from these multi-omics panels were extracted and combined with scRNA-seq data to calculate scDRS. Subsequently, the cell-type vs. disease association and tissue heterogeneity were assessed using scDRS for each omics panel. RESULTS: We identified two novel cell subpopulations in the brain that differentially express SCUBE3 (59 cells, 7.0 %) and FN1 (21 cells, 2.5 %). At the individual cell level, schizophrenia-associated cell subpopulations included microglial cell associated with ATAC-seq panel (Passociation = 0.002, Pheterogeneity = 0.009) and deep layer neuron suggestively associated with GWAS panel (Passociation = 0.033, Pheterogeneity = 0.017). At the brain tissue level, microglial cell was significantly associated with cortical plate in ATAC-seq panel (Passociation = 0.002, Pheterogeneity = 0.011). Gene level analysis identified several genes associated with schizophrenia across multi-omics panels. CONCLUSIONS: Our study outlines the signature of cell subpopulations, brain regions, and disease risk genes in schizophrenia at single-cell resolution across multi-omics scales. These findings provide a reference for future precision medicine approaches targeting specific cell types and brain regions in schizophrenia.

Schizophrenia↗
PubMed2026

Multi-omics approaches in idiopathic pulmonary fibrosis: from molecular mechanisms to therapeutic targets and precision medicine.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with limited therapeutic options and marked molecular heterogeneity. Despite available antifibrotic therapies, disease progression remains poorly predictable, highlighting the need for improved mechanistic understanding and therapeutic targeting. This review summarizes recent advances in multi-omics research to elucidate the molecular mechanisms underlying IPF and to identify potential biomarkers and pharmacological targets. Multi-omics studies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome profiling, and single-cell sequencing, have revealed key pathogenic mechanisms in IPF. Genetic susceptibility factors such as MUC5B promoter variants and telomere-related genes contribute to disease risk. Epigenetic regulation, including DNA methylation, histone modifications, and non-coding RNAs, plays a central role in fibrotic remodeling. Transcriptomic and proteomic analyses have identified dysregulated signaling pathways, including TGF-β, mTOR, cellular senescence, and extracellular matrix remodeling. Metabolomic alterations indicate disrupted lipid and amino acid metabolism. Importantly, integration of multi-omics datasets enables the identification of molecular endotypes, candidate biomarkers, and potential therapeutic targets. However, challenges including data integration, tissue heterogeneity, limited cohort size, and the need for functional validation remain important barriers to clinical translation. Continued development of multi-omics approaches may facilitate more accurate disease classification and support the development of personalized therapeutic strategies for IPF.

biomarkers↗
PubMed2025

Transcript-guided targeted cell enrichment for scalable single-nucleus RNA sequencing.

Large-scale single-cell atlases have revealed many aging- and disease-associated cell types, yet these populations are often underrepresented in heterogeneous tissues, limiting detailed molecular analyses. To address this, we developed EnrichSci-a scalable, microfluidics-free platform that combines hybridization chain reaction RNA fluorescence in situ hybridization (FISH) with combinatorial indexing to profile single-nucleus transcriptomes of target cell types with full gene-body coverage. Applied to oligodendrocytes in the aging mouse brain, EnrichSci uncovered aging-associated molecular dynamics across distinct oligodendrocyte subtypes, revealing both shared and subtype-specific gene expression changes. Additionally, we identified aging-associated exon-level signatures missed by conventional gene-level analyses, highlighting post-transcriptional regulation as a critical dimension of cell-state dynamics in aging. By coupling transcript-guided enrichment with a scalable sequencing workflow, EnrichSci provides a versatile approach to decode dynamic regulatory landscapes in diverse cell types from complex tissues.

Animals↗
PubMed2025

Using Callus as an Ex Vivo System for Chromatin Analysis.

Next-generation sequencing has revolutionized epigenetics research, enabling a comprehensive analysis of DNA methylation and histone modification profiles to explore complex biological systems at unprecedented depth. Deciphering the intricate epigenetic mechanisms that regulate gene activity presents significant challenges, including the issue of analyzing heterogeneous cell populations in bulk. Bulk analysis introduces bias and can obscure crucial information by averaging readouts from distinct cells. Various approaches have been developed to address this issue, such as cell-type-specific enrichment or single-cell sequencing techniques. However, the need for transgenic lines with fluorescent markers, along with technical challenges such as efficient protoplast isolation and low yield, limits their widespread adoption and use in multi-omic studies. This review discusses the pros and cons of these approaches, providing a valuable basis for selecting the most suitable strategy to minimize heterogeneity. We will also highlight the use of cotyledon-derived callus as an ex vivo system as a simple, accessible, and robust platform for enabling high-throughput multi-omic analyses.

Chromatin↗
PubMed2026

Unbiased Spatial Proteomics Uncovers Hepatic in Situ Regulation in Alcohol-Associated Hepatitis.

Alcohol-associated hepatitis (AH) is an acute inflammatory form of alcohol-associated liver disease. Previous studies have explored molecular mechanisms associated with AH pathogenesis through bulk liver tissue analysis; however, the heterogeneity of liver tissue and hence the spatial regulation within the AH liver microenvironment remained unaddressed. Here, an unbiased spatial proteomics analysis on the pathologic regions (PRs) of AH liver tissue is presented, including immune cell infiltration foci, lipid droplets, chicken-wire fibrosis, and fibrotic bands. Through combining a highly efficient nanodroplet processing in one pot for trace samples platform with ultrasensitive liquid chromatography-mass spectrometry, this study identified and quantified a total of 5186 unique proteins from PRs isolated in 200-μm-long × 200-μm-wide × 10-μm-thick areas. This in-depth spatial proteome coverage allowed us to discover mechanistic regulations within individual PRs, including compromised resolution of inflammation with infiltrated neutrophils at infiltration foci, increase of mitochondrial and peroxisomal fatty acid β-oxidation at lipid droplets, and differential cellular and extracellular regulations between chicken-wire fibrosis and fibrotic bands. Overall, this study demonstrated a new capability for AH research, revealed the significance of understanding spatial regulation within AH liver tissue, and further facilitated the development of therapeutic strategies at high resolution.

Proteomics↗
PubMed2024

Quantification of escape from X chromosome inactivation with single-cell omics data reveals heterogeneity across cell types and tissues.

Several X-linked genes escape from X chromosome inactivation (XCI), while differences in escape across cell types and tissues are still poorly characterized. Here, we developed scLinaX for directly quantifying relative gene expression from the inactivated X chromosome with droplet-based single-cell RNA sequencing (scRNA-seq) data. The scLinaX and differentially expressed gene analyses with large-scale blood scRNA-seq datasets consistently identified the stronger escape in lymphocytes than in myeloid cells. An extension of scLinaX to a 10x multiome dataset (scLinaX-multi) suggested a stronger escape in lymphocytes than in myeloid cells at the chromatin-accessibility level. The scLinaX analysis of human multiple-organ scRNA-seq datasets also identified the relatively strong degree of escape from XCI in lymphoid tissues and lymphocytes. Finally, effect size comparisons of genome-wide association studies between sexes suggested the underlying impact of escape on the genotype-phenotype association. Overall, scLinaX and the quantified escape catalog identified the heterogeneity of escape across cell types and tissues.

X Chromosome Inactivation↗
PubMed2026

The brain as an HIV reservoir: Recent findings using autopsy tissues from people with HIV.

HIV persistence within anatomical reservoirs remains the primary barrier to achieving an HIV cure. While antiretroviral therapy effectively suppresses plasma viremia, it does not eliminate integrated proviral genomes that persist in long-lived cellular compartments. The central nervous system (CNS) is a clinically important HIV reservoir, characterized by immune privilege and the persistence of tissue-resident infection despite effective antiretroviral therapy (ART). Evidence from postmortem studies reveals that HIV DNA, RNA, and even intact replication-competent proviruses remain detectable in brain tissue from virally suppressed people with HIV. Evidence derived primarily from in situ approaches and viable-cell studies supports myeloid-lineage reservoirs, particularly microglia and CNS-associated macrophages, as key cellular sources of persistence, while the extent and biological relevance of astrocyte infection remains debated. These reservoirs exhibit transcriptional activity and are associated with chronic neuroinflammation, which may contribute to HIV-associated neurocognitive disorders, despite systemic viral suppression. Here, we synthesize recent findings from autopsy brain studies, including work enabled by major biorepositories, such as the National NeuroHIV Tissue Consortium and rapid-autopsy programs, including the Last Gift, both of which are essential for studying HIV reservoirs in the CNS. We summarize methodologies for detecting and characterizing HIV in brain tissue, highlight heterogeneous patterns of regional distribution and compartmentalization, and review emerging links between CNS persistence and neuroinflammation. We conclude with priorities for harmonized tissue processing, multi-modal single-cell and spatial profiling, and coordinated cross-cohort analyses to clarify the contribution of CNS reservoirs to neuroHIV pathogenesis and systemic rebound.

Humans↗
PubMed2026

Transcriptomic Association Between Poliovirus Receptor (PVR/CD155) and Claudin Signaling Pathways in Colorectal Cancer.

BACKGROUND/AIM: Enterotoxigenic Bacteroides fragilis promotes colorectal carcinogenesis through toxin-mediated cleavage of E-cadherin, a process facilitated by membrane-associated Claudin-4 (CLDN4). Separately, the poliovirus receptor (PVR/CD155) modulates tumor epithelial and immune dynamics. This study explored potential transcriptomic interactions and co-expression frameworks between PVR and claudin signaling pathways in colorectal cancer. MATERIALS AND METHODS: Transcriptomic and proteomic data from the The Cancer Genome Atlas-colon adenocarcinoma cohort (TCGA-COAD) were evaluated. An exploratory E-cadherin Cleavage Index was modeled to capture transcript-protein discordance. To control for tissue composition heterogeneity without mathematical circularity, a de-circularized, non-parametric partial rank residual model adjusted for independent CLDN4 expression was deployed within the stable microsatellite-stable (MSS) sub-cohort (N=473). RESULTS: Multivariable survival models showed no independent associations between overall survival and continuous PVR (p=0.79) or CLDN3 (p=0.56) expression. Robust linear modeling revealed no significant baseline interaction between PVR and CLDN4 regarding the exploratory Cleavage Index (p=0.82). However, de-circularized partial correlation analysis revealed a highly stable, positive co-expression between PVR and CLDN3 (rho=0.2459, p=3.23×10-7). Both epithelial markers retained modest inverse correlations with the infiltrating lymphocytic axis (TIGIT and CD96). CONCLUSION: Baseline PVR expression is coordinated with CLDN3 tissue programs independent of general epithelial cellularity but does not interact with the CLDN4 axis or impact overall survival in an unexposed cohort. Because TCGA lacks virome or active microbial exposure tracking, these findings serve as baseline benchmarks for future context-dependent mechanistic studies.

Bacteroides fragilis toxin↗
PubMed2025

Growth-limiting drought increases sensitivity of Asian rice (Oryza sativa) leaves to heat shock through physiological and spatially distinct transcriptomic responses.

Growth-limiting droughts (GLD) impair tissue expansion and delay developmental transitions but are often not considered as stressors, as many physiological traits are only slightly altered relative to well-watered counterparts. Concurrently, cell size, biochemical makeup, and transcriptome profiles vary along the leaf blade in accordance with the partitioning of distinct functions to spatially defined regions of the leaf. This suggests that because different parts of the leaf have underlying differences in their transcriptome profiles, they might respond to GLD in distinctive ways. Moreover, how antagonistic stressors influence physiology and gene expression in different zones of leaves is an open question. In this study, we profiled growth, anatomy, and gas exchange in Asian rice (Oryza sativa) leaves developed in well-watered and GLD conditions, with or without a secondary heat shock. We dissected leaves into seven equal-length segments for transcriptome analysis in these conditions. We hypothesized that GLD would make the leaves more sensitive to heat shock and would disrupt the underlying heterogeneity of the leaf transcriptome. GLD plants were more strongly affected by heat shock with respect to gas exchange and the number and types of genes that were differentially expressed and that these differences varied along the leaf blade. We developed an eFP browser tool with these data to facilitate exploration and hypothesis testing. These findings show that even mild drought treatments are sufficient to impact responses to antagonistic stressors and that substantial within-organ variance exists with respect to stress responses.

Oryza↗
PubMed2026

Representation learning for multi-modal spatially resolved transcriptomics data.

MOTIVATION: Spatial transcriptomics enables in-depth molecular characterization of samples on a morphology and RNA level while preserving spatial location. Integrating the resulting multi-modal data is an unsolved problem, and developing new solutions in precision medicine depends on improved methodologies. RESULTS: We introduce AESTETIK, a convolutional deep learning model that jointly integrates spatial, transcriptomics, and morphology information to learn accurate spot representations. AESTETIK yielded substantially improved cluster assignments on widely adopted technology platforms (e.g. 10x Genomics™, NanoString™) across multiple datasets. We achieved performance enhancement on structured tissues (e.g. brain) with a 21% increase in median ARI over previous state-of-the-art methods. Notably, AESTETIK also demonstrated superior performance on cancer tissues with heterogeneous cell populations, showing a 2-fold increase in breast cancer, 79% in melanoma, and 21% in liver cancer. We expect that these advances will enable a multi-modal understanding of key biological processes. AVAILABILITY AND IMPLEMENTATION: AESTETIK is implemented in Python 3 and is available as open source software at http://www.github.com/ratschlab/aestetik. The Snakemake pipeline for reproducing the results is available at http://www.github.com/ratschlab/st-rep.

Spatial Transcriptomics↗
PubMed2024

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials.

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Humans↗
PubMed2026

Age and early life adversity shape heterogeneity of the epigenome across tissues in macaques.

Age and early life adversity (ELA) are key determinants of health, but whether they affect similar physiological mechanisms across tissues is unknown. We generated DNA methylation (DNAm) profiles across 14 tissues in 237 semi-free-ranging rhesus macaques with naturally occurring ELA. Age-associated DNAm was predominantly tissue dependent, yet tissue-specific epigenetic clocks showed that epigenetic aging was relatively consistent within individuals. ELA effects were adversity dependent, but each ELA exerted coordinated effects across tissues. Although ELA targeted many of the same loci as age, the directions of effects differed, which indicates that ELA does not uniformly increase epigenetic age. Instead, ELA leaves a coordinated, cross-tissue epigenetic signature that is distinct from-yet intertwined with-age-related differences, which advances our understanding of how early environments sculpt the molecular foundations of aging and disease.

Animals↗
PubMed2026

CAGNet: a structure-aware clustering-alternated graph network for cell-cell interaction inference in spatial transcriptomics.

MOTIVATION: Understanding cell-cell interactions (CCIs) in spatial transcriptomics is crucial for uncovering the spatial organization and functional heterogeneity of tissues. However, existing graph-based models typically rely on static clustering or fixed adjacency structures, which limits their ability to capture dynamic cellular relationships. RESULTS: We propose CAGNet, a two-stage framework for CCI inference from spatial transcriptomics data. In Stage 1, a Graph Attention Network encoder with joint feature and graph reconstruction learns structure-aware node embeddings from spatial gene expression profiles. In Stage 2, an alternating optimization mechanism iteratively updates cluster centers via KL-guided soft assignment and refines node embeddings through spatial graph reconstruction, establishing a closed-loop between representation learning and clustering. Experiments on three 10x Genomics Visium datasets demonstrate that CAGNet consistently outperforms six CCI inference baselines across ACC, AUC, AP, Precision, Recall, and F1. CAGNet also achieves the highest Adjusted Rand Index on all three datasets against six spatial domain identification methods, confirming that the learned embeddings capture biologically relevant spatial organization. Information-theoretic analysis further shows that CAGNet retains the highest mutual information between input features and learned embeddings among all compared methods. Ablation studies and 5-fold cross-validation confirm the contribution of each component and the reproducibility of the results. AVAILABILITY: The proposed method is implemented in the CAGNet package available at http://github.com/mahan1233333-maker/CAGNet .

Spatial Transcriptomics↗
PubMed2026

Recent advances in molecular mechanisms to improve the efficacy of CAR-T cell therapy for viral diseases, cancer, and autoimmune diseases.

Chimeric antigen receptor (CAR)-T cell therapy has transformed the treatment of hematological malignancies, yet its broader application to solid tumors, chronic viral infections, and autoimmune diseases remains constrained by antigen heterogeneity, immunosuppressive tissue microenvironments, T-cell exhaustion, limited persistence, and treatment-associated toxicities. These challenges have shifted the field from optimizing individual receptor constructs toward engineering CAR-T cells as programmable immune systems capable of adapting to diverse disease contexts. This review synthesizes recent advances in molecular engineering strategies that enhance CAR-T cell function beyond conventional receptor design. We discuss how receptor engineering, genome editing, transcriptional and epigenetic regulation, metabolic reprogramming, synthetic gene circuits, and safety-control platforms collectively reshape CAR-T cell fate, persistence, and therapeutic efficacy. Rather than functioning independently, these engineering strategies are increasingly integrated to generate context-specific cellular therapies capable of adapting to diverse disease environments, including cancer, autoimmune diseases, and chronic viral infections. We also highlight the potential for translation into clinical practice or clinical translation and discuss the major challenges associated with clinical implementation. Next-generation CAR-T therapies will increasingly integrate molecular engineering strategies or will rely on molecular engineering strategies to integrate antigen recognition, cellular fitness, immune regulation, and longevity rather than simply maximizing cytotoxic activity. Recent advances in programmable cellular engineering coupled with rigorous clinical evaluation as well as scalable manufacturing technologies or scalable manufacturing platforms in the treatment of other diseases beyond oncology will facilitate the development of safer, more durable, and broadly applicable cellular therapies.

Humans↗
PubMed2026

Integrating Radiogenomics and CSF-Based Liquid Biopsy Sequencing for Precision Neuro-Oncology.

Glioblastoma and diffuse gliomas pose major therapeutic challenges due to marked intratumoral heterogeneity, limited tissue accessibility, and the blood-brain barrier. Tissue-based next-generation sequencing (NGS) remains essential for WHO CNS5 molecular classification, yet it is invasive and poorly suited to serial monitoring. Two complementary non- or minimally invasive approaches have advanced rapidly: radiogenomics, which correlates multiparametric MRI features with genomic alterations, and cerebrospinal fluid (CSF) liquid biopsy sequencing, which detects circulating tumor DNA with high tissue concordance. This review examines the independent progress and synergistic integration of radiogenomics and CSF-NGS. Imaging signatures can non-invasively predict key drivers (IDH1/2, EGFR, TERT, PTEN, TP53) and molecular subtypes, while CSF-ctDNA sequencing enables real-time assessment of clonal evolution, therapy resistance (including post-temozolomide hypermutation), and residual disease. We discuss technical considerations, performance metrics, multimodal artificial-intelligence fusion, and emerging clinical applications for diagnosis, prognosis, treatment selection, and longitudinal surveillance. Critical challenges, standardization, prospective validation, and workflow integration are highlighted. By combining the spatial phenotypic information of radiogenomics with the temporal genomic resolution of CSF sequencing, this multimodal strategy offers a promising path toward precision neuro-oncology and reduced reliance on repeated invasive sampling.

Humans↗
PubMed2025

RNA sequencing offers new diagnostic opportunities in neurodevelopmental disorders: A systematic review.

PURPOSE: Transcriptomics by way of RNA sequencing (RNAseq) has emerged as a means to increase the diagnostic yield in genetic conditions. In this systematic review, we focus on the contribution of transcriptomics to improve the diagnostic yield in neurodevelopmental disorders. METHODS: We performed a systematic literature search in PubMed until January 2024, including articles describing diagnostic RNAseq on at least 1 individual with a primary neurodevelopmental phenotype. We extracted data on cohort size, phenotype, sample tissue, previously used diagnostic methods, added diagnostic yield of RNAseq, the use of control samples, and technical aspects of the RNA sequencing methodology. RESULTS: A total of 17 articles were eligible for inclusion in the systematic review. We found an average added diagnostic yield of 15.5% through RNA sequencing for individuals with neurodevelopmental disorders. There is heterogeneity in the tissue type, reported quality measures, and the computational pipeline. CONCLUSION: The significantly increased diagnostic yield demonstrates the value of this novel tool in the diagnostic setting of neurodevelopmental disorders. Our results offer an overview of common methodologies for RNAseq and allow us to formulate recommendations for genetic labs and clinicians when implementing RNAseq as a diagnostic tool. Lastly, we provide recommendations for future publications to increase transparency and reproducibility.

Humans↗
PubMed2026

iMSC-derived extracellular vesicles and their miRNA cargo influence inflammation and oxidative damage in an in vitro osteoarthritis model.

Osteoarthritis is a multifactorial chronic joint disease characterized by progressive cartilage degradation and inflammation. Since there is no effective cure, emerging therapeutic approaches, such as mesenchymal stromal cells (MSCs) transplantation, are currently under investigation. However, the clinical translation of MSC-based therapies is hampered by several limitations, such as donor-dependent variability and heterogeneity related to tissue sources. To address these issues, MSCs derived from induced pluripotent stem cells (iMSCs) have been proposed as a more standardized and scalable alternative. Due to the risks of cell-based therapy, extracellular vesicles (EVs), particularly iMSC-EVs (iEVs), could represent a promising cell-free approach for OA treatment. The present study aimed at characterizing iMSC-derived EVs and evaluating their functional role in modulating inflammatory responses and redox balance in an in vitro OA model. Notably, recent evidence highlights the central role of EV-encapsulated microRNAs (EV-miRNAs) in mediating these effects. EVs isolated from iMSC conditioned media were characterized, and their miRNA content was analyzed at different culture passages. Selected miRNAs were subsequently assessed for their biological activity in an in vitro OA model, with a focus on their impact on inflammatory mediators and oxidative stress parameters. Specifically, six miRNAs such as hsa-miR-17-5p, hsa-miR-20a-5p, hsa-miR-21-5p, hsa-miR-29a-3p, hsa-miR-29b-3p, and hsa-miR-29c-3p differentially reflect the anti-inflammatory and antioxidant effects of iMSCs-EVs treatment, suggesting possible synergistic effects. Their combined effect in the in vitro model confirmed their potential modulation in the expression of pro-inflammatory cytokines. Furthermore, their treatment markedly reduced ROS accumulation and oxidative damage, while restoring antioxidant defense systems. These findings support the therapeutic potential of iMSC-derived EVs as a cell-free strategy for OA treatment. The miRNA cargo encapsulated within iEVs appears to play a pivotal role in modulating inflammation and oxidative stress, emphasizing their promise as a novel, minimally invasive approach for disease modification in OA.

MicroRNAs↗