PNAS Nexus

Chronological age is a potent determinant of clinical events, but it is conventionally treated as a linear function of time rather than a dynamic process shaped by genetics and tissue-specific senescence. Deep learning models derived from cardiovascular imaging offer an opportunity to quantify biological age across multiple domains and to examine the extent to which these measures capture shared or distinct vulnerabilities. Here, we applied deep learning to estimate biological age from electrocardiograms, cardiac MRI, carotid ultrasound, and retinal imaging, capturing electrical, structural, macrovascular, and microvascular domains in more than 100,000 UK Biobank participants. Genome-wide association and cross-trait heritability analyses showed that cardiovascular aging is not a singular process but a modular phenotype with distinct genetic determinants across modalities. Polygenic risk scores supported these distinct trajectories, showing that different biological age measures capture partly divergent biological processes with corresponding differences in clinical associations. Modality-specific genes also showcased distinct cell-type enrichment patterns. By deconvoluting aging into electrical, structural, macrovascular, and microvascular components, our results demonstrate that AI-derived age metrics capture distinct, disease-specific aging pathways. Ultimately, this modular framework positions deep learning-derived aging models not as holistic measures of health, but as domain-specific biomarkers of cardiovascular vulnerability.

The mechanical origin of cerebrospinal fluid (CSF) and interstitial fluid (ISF) transport remains unresolved. High-frequency arterial pulsations ([~] 1 Hz) have long been proposed as a driver of CSF flow, yet multiple biomechanical analyses suggest that their ability to support deep bulk interstitial transport is severely limited by the poroelastic resistance of neural tissue. At the same time, slow-wave sleep is associated with large, synchronous CSF oscillations and enhanced clearance-related dynamics near [~] 0.05 Hz. What selects this low-frequency regime remains unclear. Here we propose a theoretical framework in which this frequency selection is not incidental, but mechanically necessary. When neural populations update their state, local thermodynamic demand induces microvascular dilation. Under intracranial volume constraints, this blood-volume expansion must, to leading order, be compensated by displacement of other intracranial volume components, including CSF. We model the poroelastic response of the interstitial matrix and obtain an effective low-pass filter for this displacement, with a nominal cut-off frequency in the slow-wave range (rc {approx} 0.05 Hz). This mechanical filter implies two distinct forcing regimes. During wakefulness, rapid commitment and sensorimotor resetting are hypothesized to generate spectrally sharp, high-frequency transients in vascular volume. Because this spectral content lies largely above the poroelastic passband, waking dynamics are predicted to be inefficient at driving deep bulk transport. Slow-wave sleep, by contrast, reduces rapid commitment-like transitions and permits smoother, more globally synchronized vascular-volume oscillations that fall within the passband and support larger-scale CSF motion. The framework yields several falsifiable predictions, including load-dependent modulation of sleep-associated CSF pulsation amplitudes, a BOLD-first / CSF-second temporal ordering during slow-wave events, and a mechanical discrepancy between deep interstitial transport and the rapid dispersion of superficial exogenous tracers. More generally, the theory advances a strong claim: the sleeping brain is mechanically privileged for large-scale CSF dynamics not because sleep introduces a new driver, but because sleep permits forcing in a frequency range that brain tissue can actually transmit.

Mainland China experienced multiple waves of COVID19 pandemic during 2020 2022, driven by emerging variants and changes in public health and social measures (PHSMs). We developed a hypergraph-based Susceptible Vaccinated Exposed Infectious Recovered Susceptible (SVEIRS) model to reconstruct epidemic dynamics across 31 provinces, capturing transmission heterogeneity associated with clustered contacts. We assessed key characteristics of transmission at national and provincial levels during four outbreak periods: initial, localized predelta, Delta, and widespread Omicron, which accounted for 96.7% of all infections. We found significant diversity in transmission contributions across cluster sizes, with a small fraction of larger clusters responsible for a disproportionate share of infections. Counterfactual analyses showed that reducing clustersize heterogeneity, while holding overall exposure constant, could have lowered national infections by 11.70 to 30.79%, with the largest effects during Omicron period. Ascertainment rates increased over time but remained spatially heterogeneous with a range: (14.40, 71.93)%. Population susceptibility declined following mass vaccination (to 42.49% in Aug 2021, nationally) and rebounded (to 89.89% in Nov 2022) due to waning immunity with variations across the provinces. Effective reproduction numbers displayed marked temporal and spatial variability, with higher estimates during Omicron. Overall, these results highlight critical role of group contact heterogeneity in shaping epidemic dynamics.

The sodium-potassium ATPase (NKA) consumes 19-28% of cellular ATP and is critical for maintaining ion homeostasis. Understanding its energetic efficiency is essential for comprehending cellular physiology and pathophysiology. We develop bond graph models of the NKA that ensure thermodynamic consistency by enforcing conservation of mass, charge, and energy. A simplified 6-state model captures biophysics comparable to a 15-state model while remaining computationally tractable. Through detailed energetic analysis, we demonstrate that under physiological conditions, approximately 65% of the energy from ATP hydrolysis is stored as chemical energy in ion gradients, 10% as electrical energy in the membrane potential, and 25% is dissipated as heat, yielding an overall efficiency of [~]75%. We investigate how the free energy of ATP hydrolysis ({Delta}GATP), intracellular Na+, and extracellular K+ affect NKA efficiency and activity. A critical threshold exists at {Delta}GATP {approx} - 48 kJ/mol below which chemoelectrical transduction drops dramatically, consistent with NKA inhibition under ischemic conditions. The bond graph framework enables quantitative comparison of different NKA models and provides a systematic approach for analyzing ion pumps. SIGNIFICANCEThe sodium-potassium ATPase is one of the bodys most energy-consuming enzymes, yet its energetic efficiency and mechanisms remain incompletely understood. This study presents the first comprehensive energetic analysis using bond graph modeling, guaranteeing thermodynamic consistency. By demonstrating that simplified 6-state models capture essential energetic behaviors of complex 15-state models, we establish bond graphs as a powerful, tractable tool for energetic analysis, model comparison, model selection and validation. The bond graph approach can be applied to other transporters, offering a powerful tool for systems physiology and drug discovery.

Cardiac arrhythmias are abnormal heart rhythms characterized by disordered electrical dynamics that impair cardiac function and pose a major global burden of morbidity and mortality. Early and accurate prediction of arrhythmic anomalies from physiological time series is crucial for effective intervention, yet remains challenging due to the nonlinear, nonstationary, and individualized nature of cardiac dynamics. Despite significant advances in machine learning-based arrhythmia detection, most existing methods operate as static classifiers on electrocardiographic signals and lack online prediction, patient-specific adaptation, and mechanistic interpretability. From a dynamical-systems perspective, arrhythmias represent qualitative regime transitions, often preceded by subtle, temporally extended deviations that are difficult to detect in real time. Here we introduce CASCADE (Chaotic Attractor Sensitivity for Cardiac Anomaly Detection), an online and personalized anomaly forecasting framework built on a special type of reservoir computing called Dynamical Systems Machine Learning (DynML). DynML employs ensembles of continuous-time nonlinear dynamical systems as chaotic reservoirs to reconstruct and forecast short-term cardiac dynamics on a beat-to-beat basis, training only a linear readout. This design enables efficient online adaptation without retraining the underlying dynamical model. Rather than relying on static beat-level classification, CASCADE identifies arrhythmic events as failures of short-term predictability, manifested as statistically significant deviations between predicted and observed dynamics relative to subject-specific baselines. Detection performance is governed by the intrinsic dynamical complexity of the reservoir, quantified by topological entropy. Reservoirs operating near critical entropy regimes optimally amplify subtle, temporally extended irregularities in heartbeat dynamics, rendering incipient arrhythmic signatures linearly separable at the readout level. Topological entropy thus serves both as a predictor of model performance and a principled control parameter for reservoir design. When evaluated on the MIT-BIH Arrhythmia dataset, CASCADE achieved consistently high F1 scores, precision, recall, and overall accuracy across diverse patient populations, demonstrating strong generalizability across clinical and real-world settings. By integrating chaotic reservoir computing, entropy-guided tuning, and online personalized forecasting, CASCADE reframes arrhythmia detection as a problem of dynamical regime transition rather than static classification. This perspective provides a scalable, interpretable, and computationally efficient framework for real-time cardiac monitoring and early-warning clinical decision support.

The number of offspring produced per reproductive cycle varies widely across animals and is constrained by the number of ovarian follicles that proceed to ovulation. In vertebrates, this phenomenon has been explained by a luteinizing hormone receptor (LHR)-threshold model, in which only follicles expressing sufficient levels of LHR respond to the LH surge and proceed to ovulation. Here we propose a novel mechanism that explains the difference between ovulatory (F1) and non-ovulatory (F2) follicles using the cloudy catshark as a model. The cloudy catshark possesses a hierarchical ovary and produces only two eggs per reproductive cycle. Both F1 and F2 follicles are capable of receiving and responding to LH, as evidenced by their comparable expression of lhr and the downregulation of lhr following LH surge. Nevertheless, LH stimulation selectively activates transcriptional programs associated with the ovulatory process exclusively in F1 follicles. These include progesterone production via star2 upregulation, as well as cancer-associated transcriptional pathways, including transcription factors runxs, prostaglandin-related genes (ptgs2 and ptger1), and matrix metalloproteinases. These results indicate that ovulatory and non-ovulatory follicles may exhibit qualitatively distinct transcriptional responses to the LH surge, potentially challenging the prevailing LHR-threshold model in vertebrates, in which LHR expression is considered a key determinant of ovulatory competence.

Non-pharmaceutical interventions (NPIs) have been important for controlling SARS-CoV-2 transmission, particularly before and during initial vaccine rollout. During the pandemic, the US Centers for Disease Control and Prevention issued isolation and masking guidance in case of COVID-19-like illness, a positive SARS-CoV-2 test, or known exposure to SARS-CoV-2. However, the impact of this guidance on mitigating transmission in office workplaces is unclear. We used a network-based mathematical model to estimate the impact of this guidance on SARS-CoV-2 transmission among office workers and their communities. The model represented social contacts in the home, office, and community. We used data from the CorporateMix study to parametrize social contacts among office workers and calibrated the model to represent the COVID-19 epidemic in Georgia, USA from January 2021 through August 2022. In the reference scenario (58% adherence to guidance among office workers and the broader population), workplace transmission accounted for a small fraction of total infections. Reducing adherence among office workers to 0% increased workplace transmissions by 27.1% and increasing adherence to 75% reduced workplace transmission by 7.0%. Increasing adherence to 75% among office workers had minimal impact on symptomatic cases and deaths; increasing it among the broader population was more effective in reducing office worker cases and deaths. In our model, moderate adherence to recommended NPIs in workplaces was effective in reducing transmission, but increasing adherence had limited benefit given workplaces that have low contact intensity and hybrid work arrangements. These results underscore the public health benefits of community-wide adoption of recommended NPIs.

While media consumption can be a solitary act, it produces a shared, socially coordinated experience where audiences bodies align in response to shared narrative events that are often social-affective in nature. Despite this recognition, traditional descriptive models of Galvanic skin response (GSR) have existed for decades, yet the socially coordinated aspect remains to be fully reflected in physiological models with the field of communication often treating the underlying generators of autonomic activity as a black box. To bridge this gap, we introduce a computational framework that models the underlying neural driver and its convolution to sweat gland physiology to explain how narrative events translate into measurable conductance. By leveraging multimodal AI models to "interpret" the social-cognitive content of a film, we generated a predictor timeline for a synthetic audience comprised of digital agents (i.e. artificial body systems responding to the film events with GSR responses). We then test this computational audience model by comparing its predictions against an empirical dataset collected as audience members (N = 96) processed the same stimulus, finding that AI-identified social triggers, like moments of comedic violence or shared emotional shifts, significantly predict the GSR time-course of audience engagement. In sum, this paper moves beyond simple and often retrospective labels like "arousal" to offer a computational account of how shared social narratives grip the human nervous system. We provide a scalable and expandable framework and a set of tools to predict media impact and understanding the psychophysiological basis of media.

Stratified epithelial tissues such as the skin epidermis maintain barrier integrity during development and homeostasis through the coordinated action of cell proliferation, differentiation, delamination, and tissue-scale mechanical forces. During development, the orientation of cell division within the basal layer plays a pivotal role in tissue stratification; however, the mechanical principles linking the orientation of the division plane to these processes across developmental stages remain poorly understood. Here, we expand a recently developed three-dimensional vertex model for stratified epithelia, composed of the basement membrane, basal, and suprabasal layers, to study the mechanical and structural impact of cell divisions with a wider range of orientations. The model integrates developmental stage via specific changes in heterotypic interfacial tensions (arising from actomyosin cortical contractility and adhesion molecules at the basal-suprabasal interface) and tissue stiffness that have been quantified previously in experiments. By systematically varying background mechanical parameters, we investigate how heterotypic tension, division orientation, and tissue fluidity collectively influence the outcome of cell division. Our goal is to uncover the strategies that the embryo may employ to generate stratified phenotypes at different developmental stages, recognizing that these strategies might evolve over time. Although our focus is on the embryonic developmental stages of the epidermis, this framework may also be extended to investigate transformed cells, such as in cancer, to explore how altered division orientation contributes to precancerous or transformed phenotypes.

Accurate inference of physiological state across the menstrual cycle has important applications in reproductive health and in understanding symptom dynamics, yet most non-hormonal approaches rely on wearable sensors or calendar-based tracking. Whether self-reported symptoms alone can support prospective, cross-subject phase classification remains unresolved. Here, we introduce a hybrid modelling framework that combines a gradient-boosted classifier with a Hidden Semi-Markov Model to infer four menstrual cycle phases (menstrual, follicular, fertile, and luteal) from self-reported data. The classifier captures non-linear symptom patterns, while the temporal model imposes biologically grounded constraints, including cyclic ordering and realistic phase durations. In a leave-one-subject-out evaluation using hormonally annotated data from 41 participants, the model achieved 67.6\% accuracy and a macro F1 score of 0.662. Features reflecting short-term symptom variability were more informative than absolute symptom levels, indicating that within-person fluctuation provides a more generalisable signal of cycle phase than symptom intensity alone. These findings demonstrate the feasibility of low-burden, device-free menstrual health monitoring, establish symptom dynamics as a basis for scalable digital biomarkers, and expand access to tracking in resource-constrained settings and populations underserved by wearable-based approaches.

The rising frequency and severity of multiyear droughts due to climate change poses a serious threat to tree growth and survival, compromising our terrestrial carbon sink. Although tree diversity is known to enhance forest biomass production, its role in mediating drought impacts remains elusive due to inconsistent evidence and limited understanding of species interactions. Using empirically parameterized pairwise interactions among eight tree species, we show that tree diversity buffers forest communities against repeated drought-induced biomass loss. This buffering arises from heterogeneous neighborhoods that promote both niche differentiation and facilitative interactions among species. We further demonstrate that strategic planting designs, such as random or single-line spatial arrangements, amplify these benefits by maximizing neighborhood heterogeneity, with single-line being more plausible when balancing management effort. Our simulation results suggest that increasing diversity could raise carbon sequestration rates by 18.8%. These findings corroborate tree diversity and spatial heterogeneity as actionable, climate-adaptive tools that simultaneously enhance forest productivity, drought resilience, and long-term carbon sequestration.

The end-Permian mass extinction (EPME) represents the most severe biotic crisis of the Phanerozoic Eon on Earth and has been well documented in marine taxa. However, its impact on terrestrial organisms and ecosystems remains incompletely understood. Here we present a high-resolution reconstruction of terrestrial diversification dynamics and spatial reorganization across the Permo-Triassic boundary (PTB) using comprehensive occurrence data of macroplants, sporomorphs and vertebrates. Terrestrial responses to the EPME show highly temporal, regional and taxonomic heterogeneities. Plants experienced a genus-level diversity loss of [~] 6.7%, across the PTB, whilst vertebrates, a lagged decline from the late Permian, peaking at a diversity loss of [~] 66.7%. Global distributions of plant and vertebrate show converging on similar latitudinal gradients post the PTB. Plant diversity loss is disproportionately high in low-latitude and tropical regions and progressively lower toward mid- and high-latitudes. Our study facilitates a fine-grained understanding to terrestrial macroevolution in geologic history through multi-analysis of a large volume of fossil data. Our findings challenge the long-held notion of global terrestrial collapse and mass extinction in plants during the PTB and offer a deep-time analogue for uneven response of extant terrestrial biodiversity to ongoing climate change.

In dense urban settings, delays between diagnostic sampling and effective isolation can sustain transmission during peak infectiousness. We define a waiting-window transmission externality arising when infectious individuals remain mobile while awaiting results, formalized as E = N{middle dot}P{middle dot}TR{middle dot}D, where N is daily testing volume, P test positivity, TR transmission during the waiting period, and D turnaround time. Using Monte Carlo simulation and a susceptible-infectious-recovered (SIR) framework, we quantify excess infections per 1,000 tests/day under multiple diagnostic workflows. A surge scenario incorporates positive coupling between TR and D ({rho} = 0.45), reflecting co-occurrence of laboratory saturation and elevated contacts during system stress. Under centralized 48-hour workflows, excess infections reach [~]80 at P = 10% and [~]401 at P = 50%, increasing to [~]628 under surge conditions. In contrast, near-patient rapid testing and home sampling reduce this to [~]5 and [~]25-26, respectively. Workflows that eliminate the waiting window--either through immediate isolation at sampling or through home-based PCR that returns results at the point of collection--effectively collapse the transmission term. These findings identify diagnostic delay as a modifiable driver of epidemic dynamics. Operational redesign of testing workflows, including decentralized sampling and home-based molecular diagnostics, offers a scalable pathway to improve epidemic controllability and reduce inequities in dense urban environments.

Keshan disease (KD) and Kashin-Beck disease (KBD) are geographically restricted disorders in rural China with overlapping environmental and dietary risk factors. Selenium deficiency alone cannot explain their regional heterogeneity. Maize, a dietary staple in endemic areas, represents a key exposure pathway for climate-sensitive foodborne fungi and their metabolites. We profiled maize-associated fungal communities from seven villages across KD-, KBD-, KD-KBD co-endemic, and non-endemic regions using ITS sequencing and integrative bioinformatics. Fungal diversity, composition, trophic structure, and predicted biosynthetic gene cluster potential differed markedly among regions. KD-endemic areas were enriched in saprotrophic taxa such as Penicillium and Aspergillus, KBD-endemic regions favored cold- and humidity-adapted fungi, and KD-KBD co-endemic areas exhibited the highest predicted mycotoxin potential. Fungal patterns were strongly associated with regional temperature and humidity. These findings support a climate-sensitive, foodborne exposome framework, suggesting that variation in maize-associated fungi may contribute to endemic disease risk and highlighting the need for fungal surveillance in public health strategies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/716289v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@77c24borg.highwire.dtl.DTLVardef@74d158org.highwire.dtl.DTLVardef@15c15c6org.highwire.dtl.DTLVardef@99aa0c_HPS_FORMAT_FIGEXP M_FIG C_FIG

The COVID-19 pandemic saw successive emergence and global spread of novel viral variants, exhibiting enhanced transmissibility or evasion of immunity. While the genotypic and phenotypic basis of SARS-CoV-2 variants have been extensively characterized, the evolutionary factors governing their patterns of emergence are less well understood. In this study we systematically investigated how the invasion dynamics of viral variants depend on variant phenotype (increased transmissibility or immune evasion), source (local evolution vs importation), the timing of introduction, the distribution of population susceptibility, and the contact network structure. Using a stochastic multi-strain epidemic model, we find that strains with only a transmission advantage are more likely to emerge earlier in the epidemic, and rapidly and predictably dominate the viral population. In contrast, immune-escape variants tend to linger at low prevalence for extended time periods after emergence, avoiding detection, until a critical amount of immunity has built up in the population and they begin to rapidly outcompete existing strains. We find that two common features of realistic human contact networks---heterogeneity in contacts (overdispersion) and clustering---lead to more punctuated evolutionary dynamics. This work provides insight into past dynamics of SARS-CoV-2 variants and can help define planning scenarios for future epidemic modeling efforts.

Beyond sustaining life, breathing is a vital physiological rhythm that shapes cognition, perception, emotional regulation, and mental health. Breathing has a direct effect on neuronal excitability and is coupled to neural oscillations across a variety of brain regions. Notably, both respiration and neural oscillations are asymmetric and not perfectly rhythmic: for example, every breath has a different shape and duration, and is interspersed with variable pauses. Here, we examined the coupling between breathing and the brain by quantifying the nonsinusoidal features of each breath and comparing it to the shape of each corresponding neural oscillation cycle. By leveraging invasive human brain recordings from 16 participants, we found respiration-neural waveform coupling on a breath-by-breath, cycle-by-cycle basis across limbic and cortical forebrain regions. For decades, the dominant perspective on cognition and mental health have focused on the brain, but recent work is highlighting the importance of brain-body interactions. Our results show that the coupling between breathing and neural activity is much richer than previously appreciated, and our approach opens new avenues for studying these peripheral-to-central nervous system interactions in a more robust, temporally precise manner.

We used 2492 whole genome sequences from Laos to investigate the molecular epidemiology of SARS-CoV-2 from 2021 through 2024, covering the major waves of COVID-19 disease in Laos including time periods of travel restrictions and after relaxation of travel across international borders. We identify successive waves of COVID-19 caused by shifts in the dominant lineage, beginning with the Alpha variant in April 2021 and continuing through the Delta and Omicron variants. We quantify a shift from a small number of viral introductions responsible for widespread transmission in early waves to a larger number of introductions for each variant after travel restrictions were lifted, and identify potential routes of introduction into the country. Our study underscores the importance of genomic surveillance to public health responses to characterize viral transmission dynamics during pandemics.

Network-based epidemic models have been instrumental in understanding how contact structure shapes infectious disease dynamics, yet widely used frameworks such as Erd[o]s-Renyi, configuration-model, and stochastic block networks do not explicitly capture the combination of fully accessible (saturated) within-group interactions and constrained between-group connectivity characteristic of many real-world settings. Here, we introduce the Multi-Clique (MC) network model, a generative framework in which individuals are organised into fully connected cliques representing stable contact groups (e.g., households, classrooms, or workplaces), with a limited number of external connections governing inter-group transmission. Using stochastic susceptible-infectious-recovered (SIR) simulations on degree-matched networks, we compare epidemic dynamics on MC networks with those on classical random graph models. Despite having an identical mean degree, MC networks exhibit systematically distinct behaviour, including slower epidemic growth, reduced peak prevalence, increased fade-out probability, and delayed time to peak. These effects arise from rapid within but constrained between clique transmission, creating structural bottlenecks that standard models do not capture. The MC framework provides an interpretable, data-driven representation of recurrent contact structure, with parameters that map directly to observable quantities such as household and classroom sizes. By isolating the role of intergroup connectivity, the model offers a basis for evaluating targeted intervention strategies that reduce between-group mixing while preserving within-group interactions. Our results highlight the importance of explicitly representing the real-life clique-based network structure in epidemic models and suggest that classical degree-matched networks may systematically overestimate epidemic speed and intensity in structured populations.

Human activity is transforming the shape, size, and resilience of Earths biosphere, degrading and augmenting Holocene baseline conditions at various scales, and replacing the wild biosphere with an anthropogenically modified one. We evaluate episodes of biosphere change throughout Earth history and compare them with contemporary and near-future anthropogenic changes, developing the concept of biosphere disruptors - agents that force global-scale macroevolutionary change. Transient disruptors are short-lived agents (mean 8.0x105 years), including massive volcanism and asteroid impacts. Persistent disruptors, including atmospheric and ocean oxygenation and land plant evolution, remain in the Earth System over long timescales (mean 1.6x108 years). In the geological record, transient disruptors are associated with temporary but sometimes massive biosphere degradation, whereas persistent disruptors are associated with sustained biosphere enhancement. Most anthropogenic biosphere impacts resemble those of past transient disruptors, globally degrading wild biomass and biodiversity. Humanity is driving the second highest rate of biosphere degradation in Earth history after the Cretaceous-Palaeogene bolide impact. However, humanity is the first disrupting agent capable of reflecting on and potentially transforming its impact on planetary habitability. If we can achieve this, humanity could drive the greatest rate of increase in planetary habitability in Earth history on centennial to millennial timescales.

Tendons transmit mechanical forces between muscles and bones through their highly aligned, collagen-rich extracellular matrix. When damaged, resident cells help restore this matrix. However, in tendinopathies, this repair response fails, leading to loss of proper tendon function. How altered mechanical states reshape tendon cell and matrix architecture remains poorly understood because existing methods do not readily capture tendon structure across relevant length scales. Here, we combine Fluorescent Labeling of Abundant Reactive Entities (FLARE) with Expansion Microscopy (ExM) to visualize cellular and extracellular structures in native tendon tissue. FLARE-ExM resolves the dense fibrillar matrix across multiple tendon types, including elastic fibers and glycan-rich cellular protrusions. In a tendon resection model, acute loss-of-tension was associated with increased fibril width and expansion of carbohydrate- and protein-rich regions. In ruptured human Achilles tendon, FLARE-ExM revealed extracellular disorganization and disrupted cellular architecture. These results establish FLARE-ExM as a useful approach for studying how mechanical perturbation remodels tendon architecture across physiological and disease contexts.