Advanced Science

Cervical spinal cord injury (SCI) causes profound and persistent loss of hand function, and effective neuromodulation strategies remain limited. We report the first-in-human implantation of a 32-contact cervical epidural paddle array in two individuals with severe chronic SCI. Individualized motor pool recruitment maps, derived from systematic bipolar and multipolar configurations, enabled person-specific stimulation parameters. Optimized stimulation restored volitional hand opening, closing and coordinated upper-limb movements that were previously unattainable. This approach achieved a >91% success rate in complex reach-grasp-lift-release sequences, supported by substantial gains in range of motion, grip, and pinch strength. Electrophysiological and kinematic analyses demonstrated parameter-dependent, selective recruitment of flexor and extensor motor pools. Personalized stimulation programs integrated with goal-directed activities enabled functional hand use in home and community settings, sustained over several months of continued autonomous use. These findings establish a mechanistically grounded and translational framework for restoring upper-limb function after chronic severe SCI.

Glaucoma is characterized by progressive stiffening of the trabecular meshwork (TM), which elevates intraocular pressure and contributes to tissue dysfunction. Although substrate stiffness and mechanical stimulation both regulate TM homeostasis, their combined effects remain poorly understood. Here, a hydrogel-integrated microfluidic platform is presented that enables simultaneous control of substrate stiffness via tunable gelatin methacryloyl (GelMA) hydrogels and equi-biaxial quasi-static stretch via hydraulic actuation. Finite element analysis validates the applied strain field, and optimized crosslinking ensures structural stability. Primary normal TM (nTM) and glaucomatous TM (gTM) cells cultured under coupled conditions exhibit selective mechanotransduction dysregulation rather than global mechanosensory impairment. While nTM cells dynamically regulate -smooth muscle actin (-SMA), myocilin (MYOC), matrix metalloproteinase-2 (MMP2), and collagen type I (COL1), gTM cells display constitutively elevated -SMA, loss of mechanical regulation of MMP2, and impaired stretch-mediated COL1 suppression, while retaining stiffness-dependent focal adhesion kinase and MYOC sensitivity. Key differences between normal and glaucomatous cells emerge only under combined stiff and stretched conditions, underscoring the importance of coupled mechanical cues in revealing disease-relevant phenotypes. These findings implicate tissue stiffening in selective pathway dysregulation and highlight mechanotransduction-targeted therapeutic strategies.

Persistent hyperglycemia impairs wound healing in diabetic patients, and severe cases may even lead to disability or death. Glycemic control alone cannot effectively prevent the occurrence of diabetic foot ulcers, a serious complication of diabetes. However, safe, efficient, and cost-effective therapies remain unavailable and are urgently needed. Using a novel sports medicine paradigm, we predicted, based on reverse-transcriptomics, that exercise-induced sweat has the potential to promote would healing in diabetic foot ulcers. Subsequent animal experiments demonstrated that sweat can indeed promote re-epithelialization and collagen deposition, upregulate the expression of the proliferation marker Ki-67, the angiogenesis marker CD31, and -SMA, and significantly accelerate wound healing in a mouse model of diabetic foot ulcers. This study provides a new direction for sports medicine and offers a novel therapeutic strategy for patients with diabetic foot ulcers.

Spatial transcriptomics enables investigation of tissue organization while preserving molecular and spatial information within intact tissues. However, existing computational methods primarily focus on clustering and batch integration and provide limited characterization of higher-order spatial organization and transferable tissuestate dynamics across heterogeneous biological systems. This study introduces a cross-domain spatial transcriptomic framework centered on recurrence-based latent tissuestate analysis, pathological fragmentation quantification, and transferable representation learning between wound repair and tumor-associated microenvironments. Human spatial transcriptomic datasets spanning cutaneous wound healing, oral squamous cell carcinoma, and head and neck squamous cell carcinoma were integrated within a graph-based latent embedding framework. Recurrence analysis was applied within latent transcriptomic space to characterize spatial organization and remodeling dynamics. A pathological fragmentation index quantified intra-tissue spatial disorganization from recurrence structure. The learned latent embeddings achieved a mean silhouette score of 0.79, demonstrating coherent separation of tissue states. Recurrence analysis revealed progressive restoration of spatial organization during wound remodeling, whereas tumor-associated tissues exhibited increased fragmentation and heterogeneous recurrence structure. Independent single-cell RNA-seq reference atlases demonstrated reproducible multicellular enrichment patterns within latent spatial niches. The proposed framework demonstrates that recurrence-inspired latent spatial analysis may provide biologically interpretable characterization of tissue organization and pathological remodeling across heterogeneous biological systems.

Chronic inflammation drives tissue dysfunction and aging, yet the dynamic interplay between persistent inflammatory signaling and structural deterioration remains difficult to study in human-relevant systems. Here, an advanced long-term human skin platform is presented that preserves native tissue architecture and epidermal, stromal, and immune-associated molecular programs for up to 4 weeks. Using this system, sustained cytokine-driven inflammation was modeled, demonstrating chronic inflammatory transcriptional programs, progressive histopathological changes, and persistent inflammatory mediator secretion that were broadly suppressed by the JAK inhibitor tofacitinib. Using aged donor tissue, prolonged senolytic-associated treatment attenuated inflammatory and remodeling pathways. Finally, UVB exposure triggered coordinated stress and inflammatory responses that were partially mitigated using topical sunscreen, demonstrating compatibility with environmental stress modeling and topical intervention within preserved tissue architecture. Together, these findings establish a versatile human skin platform for modeling chronic inflammation, aging-associated tissue remodeling, and environmental stress, providing a translational framework for investigating skin tissue dysfunction and evaluating therapeutic interventions.

Periodontal disease is characterized by progressive degradation of the gingival extracellular matrix and loss of the physical confinement it imposes on resident stromal cells. In human periodontal tissue, ECM collagen integrity is inversely correlated with facultative nuclear histone acetylation in stromal cells. We hypothesized that matrix stiffness directly coordinates an epigenomic shift in stromal cells. We use a three-dimensional mechanically tunable hydrogel system to independently tune the storage moduli across the mechanical range of healthy and periodontitis-affected gingival tissue. Matrix stiffness drives a genome-wide response in donor-derived human gingival fibroblasts. Matrix-induced confinement leads to an isotropic nuclear geometry and a folded nuclear envelope architecture compared with more permissive, soft matrices. H3K27Ac is suppressed through a stiffness and actomyosin contractility-dependent mechanism. DNMT inhibition in stiff matrices restores the high-acetylation chromatin state with persistent nuclear envelope folding. At the genomic level, stiff matrix confinement drives global CpG methylation gain concentrated at pericentromeric satellite repeats and repeat-dense regions, while collagen synthesis gene promoters and CTCF binding sites are selectively hypomethylated. Non-canonical NF-{kappa}B inflammatory signaling is attenuated through promoter methylation of MAP3K14, and pharmacological NIK inhibition reduces TLR2-stimulated IL-6 secretion in soft-matrix fibroblasts to levels comparable to the stiff condition. These findings identify the gingival ECM as an active epigenomic regulator of stromal inflammatory competence and provide a mechanistic rationale for targeting matrix mechanics to restore stromal homeostasis in periodontitis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/728299v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@131db0borg.highwire.dtl.DTLVardef@23ba0borg.highwire.dtl.DTLVardef@18b67d9org.highwire.dtl.DTLVardef@14ede81_HPS_FORMAT_FIGEXP M_FIG C_FIG The mechanobiological state of human gingival fibroblasts differs between healthy, stiff extracellular matrices and degraded, soft matrices characteristic of periodontal disease. In a healthy environment, stiff matrices impose physical confinement that enforces an isotropic nuclear geometry, driving dense heterochromatin formation, high global CpG methylation, and reduced histone acetylation. Conversely, the loss of mechanical confinement in soft matrices enables cell spreading and an open euchromatin state, fundamentally rewiring the cellular epigenome to promote non-canonical NF-{kappa}B signaling and chronic inflammation.

The thymus plays a pivotal role in the generation of immunocompetent T cells. Although its function is dependent on its complex extracellular matrix, its 3D architecture and mechanical properties remain poorly characterised This knowledge gap limits efforts to model and engineer the organ, which is a critical step towards the development of strategies for the treatment of many haematological and autoimmune diseases. Here, we provide the first comprehensive multiscale dataset of bovine thymic extracellular matrix architecture and viscoelastic behaviour, including quantitiative descriptors such as relaxation times, instantaneous and equilibrium elastic moduli, storage and loss moduli, and spatial mechanical heterogeneity. Taken together, our data define the thymus as a compliant, highly dissipative viscoelastic organ with a fibrillar architecture. They also represent a unique database, which, for the first time, paves the way for quantitative thymus tissue engineering.

The molecular characterization of human solid tumors has introduced immense genomic complexity and intra-tumoral diversification. Converting these detailed, multi-omic profiles right into workable, broad-spectrum therapeutics continues to be an formidable bottleneck in precision oncology. Traditional computational drug repurposing strategies largely rely on single-modality chemical descriptors, which frequently fail to capture the systemic transcriptomic interactions within the highly dynamic tumor microenvironment. Here, this study presents a robust multi-modal deep learning framework that synergistically integrates two-dimensional (2D) molecular graphs via Graph Neural Networks (GNNs) and chemical functional group patterns via self-attention Transformers. By mapping this dual-stream chemical feature space to the perturbational transcriptomic signatures (LINCS L1000) of 22 distinct cancer types from The Cancer Genome Atlas (TCGA), a vast library of over 28,000 small-molecule compounds was computationally screened. The developed multi-modal architecture achieved state-of-the-art predictive accuracy, significantly outperforming traditional single-modality baseline models. Strikingly, the comprehensive pan-cancer transcriptomic reversal landscape identified a persistent convergence of non-oncology drugs exhibiting potent broad-spectrum anti-tumor potential. Specifically, Class I Histone Deacetylase (HDAC) inhibitorsmost notably TC-H-106, RG2833, and Tianeptinaline, agents originally developed to penetrate the blood-brain barrier for neurodegenerative and psychiatric disordersemerged as top therapeutic candidates across lung adenocarcinoma (LUAD), bladder urothelial carcinoma (BLCA), and rectum adenocarcinoma (READ). Subsequent high-dimensional network pharmacology and functional enrichment analyses confirmed that these agents robustly suppress essential oncogenic pathways, specifically collapsing the G1/S phase transition and DNA damage repair machineries. Furthermore, structural validation via molecular docking and force-field thermodynamics confirmed the highly stable physical binding affinity (Vina score: -7.0 kcal/mol, MMFF94 Energy: 64.76 kcal/mol) of TC-H-106 to the HDAC1 catalytic pocket. Kaplan-Meier survival analysis based on TCGA gene expression stratification underscored the significant prognostic benefit of targeting this epigenetic axis. Collectively, these findings introduce a powerful multi-modal AI framework for systems-level drug repurposing and highlight brain-penetrant Class I HDAC inhibitors as highly promising candidates for pan-cancer epigenetic therapy.

Meningeal lymphatic vessels (mLVs) are vital for brain waste clearance, making them a promising therapeutic target. However, effective modulation strategies for mLVs with translational potential remain underdeveloped. Here, we develop a low-intensity focused ultrasound (LIFU) strategy that precisely targets the vault cranial meninges to non-invasively facilitate mLVs drainage. Using models of Alzheimers disease (AD) and aging, we demonstrate that this approach promotes CSF drainage, prevents cognitive decline, and reduces pathological biomarkers. Mechanistically, RNA sequencing combined with calcium imaging in vitro reveals that LIFU activates the Piezo1 ion channel in lymphatic endothelial cells, whereas pharmacological inhibition of Piezo1 abolishes LIFUs therapeutic effects. Compliant with FDA safety guidelines, this LIFU protocol demonstrates strong clinical translatability. If its efficacy is clinically confirmed, LIFU offers a promising therapy for neurodegenerative diseases triggered by waste accumulation.

The development of neural probes has enabled a deeper understanding and improved treatment for neurological disorders. Microneurography is currently the gold standard for assessing the electrophysiological signature of pain mechanisms in the human peripheral nervous system. However, its clinical utility is limited by the low recording yield and signal-to-noise ratio of single-electrode probes. To overcome these limitations, we developed mechanically robust, multi-electrode probes designed for acute percutaneous insertion and recording in peripheral nerves. The electrical and mechanical stability of these probes was confirmed through repeated insertions in artificial human skin and rat peripheral nerves. In addition, ex vivo and in vivo experiments demonstrated enhanced functional performance, with multi-site recordings enabling the isolation of single-fiber activity. Importantly, our probes can be operated analogously to conventional microneurography needles while substantially increasing the information yield, providing enhanced capabilities for minimally invasive peripheral nerve assessment.

Human induced pluripotent stem cells (hiPSCs) offer a powerful platform to model chondrogenesis and enable regenerative strategies, yet regulation of cell-fate commitment remains elusive. Here, we systematically mapped cell-fate trajectories from 7 time points during a 49-day chondrogenic hiPSC differentiation protocol using single-nucleus multimodal transcriptomic and chromatin accessibility profiling (scRNA-seq and scATAC-seq). Integrative analysis of dynamics revealed branching differentiation trajectories with clear bifurcation points and distinct cell-fates. Notably, the chondrogenic trajectory originated at day 6 as a neurogenic development and branched off at day 21 to a chondrogenic cell-fate. Through transcription factor activity analysis (TFAA) and cis-co-accessibility networks, we found that NFIA and NFIB drove chondrogenic distinction, exhibited in both modalities as directly targeting chondrogenic genes such as COMP, FIBIN, VIM. This was then confirmed by experimental validation as modulation of NFIA expression at this point further enhanced chondrocyte formation. Together, our study provides a high-resolution multimodal atlas of chondrogenic differentiation and identified critical transcriptional regulators that can now be leveraged to implement regenerative cartilage therapies from hiPSCs.

The superior stealth properties and high information density make DNA a sought-after candidate in the field of molecular steganography. Here, we developed the InfinMark end-to-end DNA steganography framework for anti-counterfeiting applications by combining the characteristics of both the Internet of Things (IoT) and DNA-of-Things (DoT). InfinMark includes five modules: Information Transcoding, Fingerprint Writing, Nano-encapsulation, Invisible Marking, and Multi-level Rapid Authentication. It ensures precise anti-counterfeiting information reading and writing through a dynamic DNA-compatible transcoding algorithm, achieves seamless embedding by developing scalable nanoparticle manufacture methods, and supports cross-scenario on-site verification, ultimately granting it comprehensive anti-counterfeiting capabilities spanning from source labeling to terminal tracing. By addressing the bottlenecks in IoT and DoT integration, lifecycle tracking, as well on-site product authentication, this research constructs a full-chain bimodal anti-counterfeiting system, thereby showcasing the practical application of informational DNA nanoparticles in various aspects of production and daily life.

Chronic periodontitis represents one of the most prevalent inflammatory tissue-destructive conditions in humans, yet the molecular thresholds separating reversible inflammation from permanent structural collapse remain undefined. Using single-cell RNA sequencing data from 12,104 cells (GSE152042) spanning three disease states -- healthy gingival tissue, mild periodontitis, and severe periodontitis -- we constructed a variational autoencoder (VAE)-derived 20-dimensional latent disease manifold and applied formal hysteresis quantification to measure transcriptional irreversibility. Chi-square analysis across 9,163 cells occupying transitional pseudotime bins yielded {chi}{superscript 2} = 11,971 (p < 10-300, df = 4), with Cramers V = 0.81, confirming strong state-memory effects inconsistent with freely reversible disease dynamics. Non-negative matrix factorisation (NMF; k = 15) identified biologically coherent gene programs whose co-activation topology was encoded as a hypergraph constraint network; in severe disease, 16 of 76 healthy constraints collapsed by more than 60%, and the Fibroblast-Epithelial coupling (Programs 1-4) was reduced by 84%. A six-agent agentic AI simulation faithfully recapitulated observed shifts in cellular composition and established a temporal threshold beyond which tissue damage trajectories diverge irreversibly. We introduce the Regenerative Permission Index (RPI), a composite single-cell metric (range: 0.060-0.644), whose mean in severe periodontitis (0.323) falls well below the 0.50 permissibility threshold, indicating that all tested biomaterial interventions will fail. Five-fold cross-validated classification achieved 88% accuracy (Random Forest, AUC = 0.992), and permutation testing confirmed that constraint network patterns are biologically specific rather than artefactual (p < 0.01). Together, these findings provide a quantitative basis for understanding periodontal irreversibility and position RPI-guided decision-making as a framework for precision regenerative medicine.

The global crisis of multidrug-resistant pathogens necessitates innovative antimicrobial peptide (AMP) discovery. Deep-sea microbiomes represent an underexplored resource for novel AMPs, but their mining is hindered by biases in current prediction methods, including sequence length imbalance, N-terminal methionine artifacts, and lack of microbial optimization. To overcome these, we developed XAMP, a dual-engine predictor integrating XAMP-E (based on ESM-2 for high-accuracy feature representation) and XAMP-T (a one-layer Transformer for accelerated screening). By training on debiased datasets, XAMP achieved a median AUC of 0.972, an approximately 10% improvement over state-of-the-art tools, with XAMP-T operating 5 to 40 times faster. Applying this pipeline to deep-sea metagenomes, we identified 2,355 promising AMP candidates. Experimental validation of six synthesized peptides against ESKAPE pathogens demonstrated potent, broad-spectrum activity, particularly against Gram-negative bacteria, which dominate deep-sea ecosystems and represent a major challenge in nosocomial infections. This study establishes a robust computational-experimental framework for discovering therapeutic candidates from extreme environments to combat antibiotic resistance crises.

Cells dynamically integrate biochemical and mechanical signals arising from their surrounding microenvironment to regulate morphology and behavior. Mechanical cues like matrix stiffness, surface topography, and other physical perturbations modify biophysical signals. Surface topography, particularly curvature regime acts as any important mediator of mechanotransduction by coordinating cytoskeletal organization, focal adhesion dynamics, and nuclear architecture. Curvature response has been demonstrated at broader length scales and influences nucleus shape change, chromatin organization, and gene regulation, positioning the nucleus as an active mechanosensitive hub. Bone tissue consists of a curvature-rich microenvironment defined by a trabecular architecture at tissue scale and by resorption cavities such as Howships lacunae at cellular scale. While these geometries are essential for homeostasis, their role in pathological context remains poorly understood. Osteosarcoma develops within this mechanically complex multiscale architecture, but how bone-inspired curvature regulates nuclear behavior and signaling in osteosarcoma cells remains unclear. Here, we engineered three-dimensional (3D) concave hemispherical substrates that recapitulate nucleus-scale bone micro-curvature and assessed their effects on human SaOS-2 osteosarcoma cells. In comparison with flat surfaces, concave confinement resulted in pronounced nuclear rounding and softening, accompanied by Lamin A/C reorganization and increased heterochromatin compaction marked by H3K9me3. Curvature-driven nuclear remodeling selectively modulated Hippo pathway main effectors YAP/TAZ without activating NF-{kappa}B mediated canonical inflammatory responses. Furthermore, cells maintained overall viability without elevated pathological DNA damage or apoptotic signaling, suggesting an adaptive, damage-tolerant nuclear response. Overall, these findings indicate nucleus-scale curvature as a critical regulator within the bone microenvironment that governs nuclear modelling and mechanosensitive signaling in osteosarcoma cells. Incorporating physiologically relevant geometry into in vitro models establishes new insight into cancer microenvironment crosstalk and highlights nuclear interior and outer architecture as a key regulator of tumor cell behavior.

Matrix stiffness serves as a pivotal biophysical cue that profoundly dictates exosome biogenesis and cellular internalization, yet often creates a functional trade-off that impedes clinical translation. Herein, we developed a mechano-chemo-transductive strategy to engineer mesenchymal stem cell (MSC) exosomes endowed with robust biogenesis and superior delivery potency. Specifically, we revealed that MSCs cultured on soft matrices secreted a significantly elevated exosome yield and demonstrated enhanced competence to drive macrophage towards anti-inflammatory M2 polarization. Conversely, stiff matrices upregulated ATP-binding cassette transporter A1 (ABCA1) expression, enriching exosomal membrane cholesterol and facilitating cellular internalization by recipient cells. By taking advantages of these unique mechano-responses, we engineered MSCs via substrate softening combined with ABCA1 modulation to generate mechanochemically reprogrammed exosomes with concurrently enhanced yield and internalization efficiency. In a murine model of pulmonary fibrosis characterized by restrictive biological barriers, inhaled mechanochemically reprogrammed exosomes treatment demonstrated superior lung retention and deep tissue penetration. Furthermore, they effectively orchestrated immune homeostasis by repolarizing alveolar macrophages to reverse fibrotic remodeling and restore lung function. Collectively, by reconciling the intrinsic trade-off between biogenesis and cellular uptake, this strategy represents a paradigm shift in exosome engineering and paves the way for next-generation therapeutics against refractory fibrotic diseases.

Connexin36 (Cx36) is broadly expressed in neurons and serves as the principal protein that forms interneuronal gap junctions (GJs), also known as electrical synapses. Recent high-resolution structures of human Cx36 GJ have revealed crucial electrostatic interactions (ESIs) of charged residues between two docked Cx36 hemichannels at the second extracellular (E2) loops. Despite their structural importance, the mechanistic roles of these ESIs remain poorly understood. To investigate their significance, we systematically designed and tested a series of missense variants targeting key E2 interface residues, aiming to disrupt or modulate the electrostatic landscape at the docking interface. Based on the ESI pairs defined from the crystal structure, our combined computational calculations and dual patch-clamp experiments in engineered HEK293 cell pairs suggest that at least three ESI residual pairs per E2-E2 interface are required to support functional GJ formation. Furthermore, we found that these unique ESIs of Cx36 could play a role in its docking specificity to itself, as they rarely form heterotypic GJs with other brain connexins. Overall, these findings provide essential molecular and functional insights into the mechanisms governing Cx36 GJ formation and partner specificity, paving the way for future therapeutic approaches targeting connexin dysfunction in human diseases.

Hydrogels are increasingly recognized as promising therapeutics for arthritic joints, extending their traditional role as mechanical lubricants to modulators of joint immunity. However, the rational design of these materials remains challenging, with progress largely driven by empirical experimentation. To address this, we curated a comprehensive database of 220 hydrogel formulations from 317 published studies and applied an interpretable machine learning (ML) framework to uncover the relationships between hydrogel design parameters and the arthritis severity score. Using a Random Forest algorithm, our model achieved an external validation accuracy of 0.67 in predicting effective hydrogel therapies for arthritis. Analysis revealed a clear hierarchy of design principles: the choice of functional agent, base polymer, and elastic modulus were the most influential predictors of therapeutic efficacy, with composite agents, protein-based polymers, and softer hydrogels most strongly associated with positive therapeutic outcomes. Mechanistic investigations further demonstrated that successful hydrogels promote an anti-inflammatory M2 macrophage phenotype. Benchmarking against classical statistical methods and a large language model framework showed that our ML approach provided more robust, nuanced insights into complex feature interactions. This data-driven framework offers a generalizable blueprint for the rational design of next-generation immunomodulatory hydrogels, paving the way for more effective arthritis therapies.

Brain-body interactions (BBIs) are fundamental to cognition and mental health, but their continuous multimodal dynamics remain difficult to extract. Previous approaches have been largely observational, and few frameworks enable these interacting processes to be modeled within an integrated generative system. Here, we applied a Predictive-Coding-Inspired Variational RNN (PV-RNN) to simultaneous EEG, ECG, and respiration recordings obtained from 33 participants during exteroceptive and interoceptive attention. The model learned a temporal hierarchy spanning modality-specific dynamics, multimodal associative integration, and sequence-level global states, and accurately reconstructed unseen physiological sequences. Specifically, the intermediate associative layer successfully captured the core complexities of BBI by extracting multiscale, nonlinear, and bidirectional coupling dynamics with variable temporal lags. Furthermore, the estimated precision (inverse variance) of latent variables representing BBI dynamics within this multimodal associative layer increased significantly during interoceptive attention. The magnitude of this condition-dependent precision enhancement correlated positively with subjective adaptive body controllability and negatively with psychiatric vulnerabilities, including rumination and trait anxiety. These findings identify a latent physiological signature of interoceptive attention and establish hierarchical generative modeling as an interpretable framework for extracting continuous BBI dynamics and linking multimodal physiology to cognitive and clinical characteristics.

Cell niches play critical roles in tissue organization and orchestrate homeostasis, development, and disease progression. Advances in spatial omics technologies now allow diverse molecular and image-derived data to be jointly captured while preserving spatial context, but deciphering cell niches from such spatial multimodal and multi-omics data remains challenging. Existing computational methods are still limited in their flexibility across variable combinations of spatial modalities and omics data. Here we introduce SpaNECT, a unified and flexible framework designed to accommodate spatial multimodal and multi-omics data for cell niche characterization. SpaNECT further incorporates reference-informed cell-type information to support biologically interpretable niche analysis. Systematic evaluations across diverse tissues, disease conditions, and developmental stages showed that SpaNECT consistently outperformed representative methods in resolving cell niches. In mouse brain spatial multi-omics data, SpaNECT uncovered niche-associated molecular and regulatory programs; in developing chick heart, it tracked cross-stage niche reorganization and progressive remodeling of ventricular-associated cell states during maturation. Overall, SpaNECT establishes a general and robust framework for characterizing cell niches across spatial multimodal and multi-omics data.