Biology subjects

Otero, M.

Publications and source records attributed to Otero, M..

9 recordsLinked to original sources

bioRxiv2026

Single-nucleus multiome profiling reveals osteocyte-centered remodeling of human subchondral bone across osteoarthritis severity

Objective: Subchondral bone remodeling is a hallmark of osteoarthritis (OA), but osteocytes, the principal regulators of bone homeostasis, have been inaccessible to single-cell studies because they are embedded in mineralized matrix. We aimed to define the cellular, transcriptional and chromatin changes of human subchondral bone across OA severity, osteocytes included, and to identify candidate signals between cartilage and bone. Design: Subchondral bone from the medial femoral condyle of human knees graded by the Outerbridge system was analyzed by bulk RNA sequencing (89 knees) and by single-nucleus multiome sequencing of gene expression and chromatin accessibility after mechanical nuclear isolation (16 donors). Three hypotheses were prespecified: with increasing OA severity, (1) the osteoprogenitor fraction increases, (2) the osteocyte fraction increases, and (3) osteocyte SMAD2/3 motif activity increases. Results: With OA severity, bulk expression shifted toward extracellular matrix and ossification programs. The atlas resolved 26 cell states among 61,570 nuclei, including 906 osteocytes. Osteoprogenitors (p = 0.003) and osteocytes (p = 0.036) increased in share along an osteogenic continuum that starts from CXCL12-abundant reticular cells. Chondrocytes at the osteochondral junction upregulated FN1, FGF1, FGF2 and TNFSF11 without increased hypertrophic gene expression, and their share of the inferred FN1 signal received by osteocytes rose from 4% in normal knees to 33% in severe OA. Osteocyte SMAD2/3 motif activity increased with severity (rho = 0.70, p = 0.003), and all three hypotheses remained significant after Holm correction. Across the osteogenic lineage, SMAD2/3 activity correlated with junction chondrocyte FN1 (rho = 0.63, p = 0.017), and in osteocytes it was accompanied by TGF-{beta}-responsive matrix genes and perilacunar-remodeling genes. Conclusions: This osteocyte-inclusive atlas of human OA subchondral bone shows expansion of the osteogenic compartment and a rise of osteocyte SMAD2/3-linked matrix remodeling with disease, and identifies chondrocyte-derived FN1 as a candidate contributor to cartilage-bone communication associated with osteocyte TGF-{beta}/SMAD2/3 signaling.

molecular biology↗

bioRxiv2026

Loss of connexin 43 in cartilage causes mitochondrial dysfunction and accelerates post-traumatic osteoarthritis progression

Osteoarthritis (OA) is a major cause of chronic pain and disability worldwide, characterized by progressive degeneration of cartilage and subchondral bone. Post-traumatic OA (PTOA) develops in as many as 25-50% of individuals following major joint injury, making it a leading cause of OA in younger and otherwise healthy populations.1,2 Connexin 43 (Cx43), a gap junction protein involved in intercellular communication and cellular stress responses, has been linked to OA; however, its role in the progression of PTOA remains unclear. Here, we examined how cartilage-specific loss of Cx43 influences PTOA and chondrocyte metabolic function. Using a murine model of conditional Cx43 deletion in cartilage, we demonstrate that male knockout mice exhibited severe cartilage surface damage and matrix loss, whereas female knockout mice showed cartilage thinning accompanied by reduced chondrocyte hypertrophy, decreased subchondral bone density, and increased osteophyte formation. Thus, loss of Cx43 disrupts cartilage integrity and osteochondral remodeling in a sex-specific manner, predisposing joints to maladaptive bone changes and cartilage degeneration. Complementary mechanistic studies in human articular chondrocytes revealed that Cx43 deficiency impairs mitochondrial respiration, reduces spare respiratory capacity, and lowers ATP production, consistent with compromised cellular bioenergetics. Together, these findings identify Cx43 as an important coordinator of metabolic and structural responses to joint injury. These results position Cx43 as a context-dependent regulator of joint homeostasis and suggest that maintenance of Cx43 expression may support cartilage resilience following injury.

cell biology↗

bioRxiv2026

Synovial transcriptional clusters link cartilage degeneration to cell-type-specific gene expression in knee osteoarthritis

ObjectivesTo identify synovial transcriptional clusters in human knee osteoarthritis (OA) and determine how these relate to synovial histologic features, cell-type-associated gene expression, and cartilage degeneration severity. MethodsBulk RNA sequencing (RNA-seq) of synovial tissue from n = 135 patients with knee OA was analyzed using consensus clustering. Clusters were compared by clinical and histologic features, including cartilage degeneration severity (OARSI score). Single-cell RNA-seq (n = 18) and spatial transcriptomics were used to relate cartilage degeneration-associated gene expression patterns to synovial cell populations. ResultsFour synovial transcriptional clusters that differed in synovial histologic features and cartilage degeneration severity were identified. Greater cartilage degeneration was associated with enrichment of lining fibroblast- and inflammatory myeloid-associated gene expression, whereas lesser cartilage degeneration was associated with enrichment of sublining fibroblast, endothelial, mural cell, and adipocyte-associated gene expression. ConclusionsHuman knee OA synovium segregates into transcriptional clusters associated with cartilage degeneration severity. Synovial transcriptional heterogeneity corresponds to cell-type-associated gene expression. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIOsteoarthritis synovium exhibits marked histologic and molecular heterogeneity. C_LIO_LISynovial inflammation detected by MRI correlates with cartilage degeneration and predicts progressive cartilage loss in knee OA. C_LIO_LIPrior transcriptomic studies have identified molecular subsets of OA synovium, but their relationship to cartilage degeneration severity remains unclear. C_LI What this study addsO_LIOA synovium segregates into four transcriptional clusters: Sublining (C1), Lymphomyeloid (C2), Myeloid (C3), and Major trauma (C4). C_LIO_LIGreater cartilage degeneration is associated with enrichment of inflammatory myeloid and lining fibroblast gene expression, whereas lesser degeneration is associated with enrichment of adipocyte, sublining fibroblast, endothelial, and mural cell-associated gene expression. C_LI How this study might affect research, practice or policyO_LIProvides a framework for a clinically relevant biological stratification of OA patients based on synovial molecular features. C_LIO_LIInforms future efforts to link synovial biology with OA prognosis, cartilage degeneration, treatment allocation, and development of targeted therapeutic strategies. C_LI

genomics↗

bioRxiv2025

Frequency-Dependent Neurostimulation in Whole Brain Network models

BackgroundBrain stimulation at specific frequencies has been shown to have therapeutic effects for certain neurological disorders. However, it remains unclear how these oscillatory inputs interact with neuronal dynamics and what drives this frequency selectivity. Here, we propose that this is achieved by targeting specific brain circuits through frequency-selective network entrainment. MethodsTo demonstrate that this type of entrainment can occur in the brain connectome structure, we use a minimal physics-based model of coupled oscillators that preserves only the essential structural connectivity of real brain networks. Using this model, we test how periodic stimulation influences network oscillatory dynamics, identifying sub-networks that synchronize with specific stimulation frequencies. Furthermore, we validate the suitability of the model to reproduce well-known frequency-selective entrainment using visual and auditory periodic stimulation. ResultsOur model reproduces selective network entrainment, whereby distinct stimulation frequencies favor the synchrony of different sub-networks. Crucially, entrainment exhibits a physics-predicted inverse relationship with frequency: lower-frequency stimulation favors broader synchrony across the network, while higher-frequency inputs produce spatially confined effects. These patterns emerge purely from network structure and oscillatory physics, independent of biological details. ConclusionThis physics-based approach elucidates the fundamental mechanistic principles governing frequency-selective brain network stimulation. Our minimal model demonstrates that selective entrainment of specific subnetworks can be achieved through frequency selection alone, largely determined by network structure and oscillatory dynamics. These findings provide a theoretical foundation for understanding how network architecture determines stimulation selectivity, supporting the development of principled approaches to targeted neuromodulation. Author summaryIn this work, we explore how external rhythmic stimulation interacts with the brains own rhythms. We use a physics-based model that represents the brain as a network of interconnected oscillators, linked according to the human connectome. Through this model, we show that stimulation effects are not limited to the targeted region but spread across the network, depending on the stimulation frequency, its spatial location and the underlying network dynamics. Our results reveal that low-frequency rhythms promote large-scale synchrony, while high-frequency rhythms engage more localized areas, all emerging from the brains structural connections and communication delays. The simulated cortical activation exhibits a frequency-selective profile that aligns strongly with that estimated from sensory-evoked electroencephalographic activity. This agreement suggests that differences between individuals in how their brain networks operate may explain why stimulation affects each person differently. Our findings offer a mechanistic framework for designing personalized stimulation protocols, illustrating how computational modeling can guide the optimization of noninvasive brain stimulation in both research and clinical contexts.

bioengineering↗

bioRxiv2025

Effects of Electrocardiograms QRS Detection Algorithms in Heart Rate Variability Metrics

Heart Rate Variability (HRV) is a marker used for assessing autonomic nervous system function, derived from the timing between R-peaks in electrocardiogram (ECG) signals. Accurate detection of QRS complexes is essential for reliable HRV computation. While many R-wave detection algorithms exist, their impact on the accuracy of HRV metrics remains underexplored. This study addresses this gap by assessing how QRS detection errors affect HRV analysis across different algorithms and recording setups. We evaluated eight widely used QRS detectors using ECG recordings from 25 healthy participants under rest, cognitive load, and physical activity conditions. Two acquisition setups were considered: "chest strap" and "loose cables." We used the manually annotated R-peaks to calculate the ground-truth HRV metric values. The relationship between the detector performance and HRV errors was evaluated for 11 metrics using the concordance correlation coefficient (CCC). Results showed significant variability in detector performance across algorithms and setups. No single QRS detection algorithm outperformed across all scenarios. Loose cable recordings yielded higher CCC values than chest straps, particularly for MeanNN and LF power. These findings highlight the critical role of QRS detector selection and signal acquisition conditions in HRV analysis. They underscore the need for context-specific benchmarking, particularly for wearable and ambulatory applications where signal quality can vary. Ultimately, this study offers practical recommendations for clinicians and researchers on selecting QRS detection algorithms that best align with their specific analytical objectives and recording conditions.

bioengineering↗

bioRxiv2024

Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome-coupled oscillators model

Large-scale brain models with biophysical or biophysically inspired parameters generate brain-like dynamics with multi-state metastability. Multi-state metastability reflects the capacity of the brain to transition between different network configurations and cognitive states in response to changing environments or tasks, thus relating to cognitive flexibility. To study this phenomenon, we used a Kuramoto network of oscillators corresponding to a human brain atlas of 90 nodes, each with an intrinsic frequency of 40 Hz. The networks nodes were interconnected based on the structural connectivity strengths and delays found in the human brain. We identified global coupling and delay scale parameters corresponding to maximum spectral entropy, a proxy for maximal multi-state metastability. At this point, we show that multiple coherent (functional) sub-networks spontaneously emerge across multiple oscillatory modes, and persist in time for periods between 140 and 4389 ms. Most nodes in the network exhibit broad frequency spectra away from their intrinsic frequency, and switch between modes, in a manner similar to that reported in empirical resting-state neuroimaging data. We suggest that the obtained dynamics at maximum metastability is a suitable model of the awake brain. Further, we show that global coupling and delay scale parameters away from maximum metastability yield dynamical features similar to other brain states such as sleep and anesthesia. Therefore, spectral entropy also correlates with wakefulness and the synchrony of functional networks.

neuroscience↗

bioRxiv2023

A High-Resolution LED Display for Steady State Visual Stimulation: Customized, Affordable, and Open Source

Visually evoked steady-state potentials (SSVEPs) are neural responses elicited by visual stimuli oscillating at specific frequencies. In this study, we introduce a novel LED display system designed specifically for steady-state visual stimulation, offering precise control over visual stimulus parameters, including frequency resolution, luminance, and the ability to control the phase at the end of the stimulation. The LED display provides a personalized, modular, and affordable option for experimental setups. Based on the Teensy 3.2 board, the display utilizes Direct Digital Synthesis and Pulse Width Modulation techniques to control the LEDs. Its performance is validated through four experiments: the first two measure LED light intensities directly, while the last two assess the displays impact on EEG recordings. The results demonstrate that the display can deliver a stimulus suitable for generating SSVEPs with the desired frequency and phase resolution. We provide comprehensive documentation, including all necessary codes and electrical diagrams, as an open-source resource. This facilitates the replication and adaptation of the system for specific experimental requirements, enhancing its potential for widespread use in the field of neuroscience.

bioinformatics↗

bioRxiv2021

Modelling neural entrainment and its persistence: influence of frequency of stimulation and phase at the stimulus offset

The entrainment (synchronization) of brain oscillations to the frequency of sensory stimuli is a key mechanism that shapes perceptual and cognitive processes, such that atypical neural entrainment leads to neuro-psychological deficits. ObjectiveWe investigated the dynamic of neural entrainment. Particular attention was paid to the oscillatory behavior that succeed the end of the stimulation, since the persistence (reverberation) of neural entrainment may condition future sensory representations based on predictions about stimulus rhythmicity. ApproachA modified Jansen-Rit neural mass model of coupled cortical columns generated a time series whose frequency spectrum resembled that of the electroencephalogram. We evaluated spectro-temporal features of entrainment, during and after rhythmic stimulation of different frequencies, as a function of the resonance frequency of the neural population and the coupling strength between cortical columns. We tested if the duration of the entrainment persistence depended on the state of the neural network at the time the stimulus ends. Main ResultsThe entrainment of the column that received the stimulation was maximum when the frequency of the entrainer was within a narrow range around the resonance frequency of the column. When this occurred, entrainment persisted for several cycles after the stimulus terminated, and the propagation of the entrainment to other columns was facilitated. Propagation depended on the resonance frequency of the second column, and the coupling strength between columns. The duration of the persistence of the entrainment depended on the phase of the neural oscillation at the time the entrainer terminated, such that falling phases (from{pi} /2 to 3{pi}/2 in a sine function) led to longer persistence than rising phases (from 0 to{pi} /2 and 3{pi}/2 to 2{pi}). SignificanceThe study bridges between models of neural oscillations and empirical electrophysiology, and provides insights to the use of rhythmic sensory stimulation for neuroenhancement.

bioengineering↗

bioRxiv2018

Functional Asymmetries between Central and Peripheral Retinal Ganglion Cells in a Diurnal Rodent

The segregated properties of the visual system processing central or peripheral regions of the visual field have been widely studied in the visual cortex and the LGN, but rarely reported in retina. The retina performs complex computational strategies to extract spatial-temporal features that are in coherence with animal behavior and survival. Even if a big effort has been done to functionally characterize different retinal ganglion cell (RGC) types, a clear account of the particular functionality of central and peripheral cells is still missing. Here, using electrophysiological data obtained with a 256-MEA recording system on female diurnal rodent retinas (Octodon degus), we evidenced that peripheral RGCs have larger receptive fields, more sustained, faster and shorter temporal responses and sensitive to higher temporal frequencies with a broader frequency bandwidth than the center. Additionally, we also compared the asymmetries between ON and OFF cell populations present in each region, reporting that these asymmetries are dependent on the eccentricity. Finally, the presence of the asymmetries here reported emphasizes even more the complexity of computational strategies performed by the retina, which could serve as inspiration for the development of artificial visual systems.

neuroscience↗