Biology subjects

Budday, S.

Publications and source records attributed to Budday, S..

14 recordsLinked to original sources

bioRxiv2026

Mechanochemical synchronization of force oscillations in connective tissue models

Cells in a tissue coordinate their behavior collectively by mechanical and chemical feedback with their surroundings. To study how such feedback mechanisms interact, we embed multicellular spheroids from mouse and primary human fibroblasts of different origin in 3D collagen matrices. Immediately after embedding, spheroids apply steadily increasing, radially symmetric, inward-directed forces to the surrounding matrix. Typically, within a few hours, collective force oscillations emerge with an average time interval of {approx}30 min. Similar force oscillations also arise in engineered, parallel-aligned microtissues attached to flexible pillars. Force oscillations propagate between neighboring cells as a mechanical wave, accompanied by calcium waves that travel at comparable speeds of several hundred m/min. Blocking gap junctions or mechanosensitive Piezo1 channels impairs these oscillations, indicating that calcium signaling contributes to their propagation. Their amplitude and frequency are regulated by integrin-mediated feedback in actin-rich protrusions and by ERK signaling, which also controls collective invasion into the surrounding matrix. Neighboring spheroids mechanically synchronize their force oscillations in phase and frequency over distances of up to {approx}2 mm without direct contact. Together, our results show that oscillatory mechanochemical feedback enables multicellular systems to coordinate force generation and invasive migration across cellular and millimeter length scales in 3D matrices.

biophysics↗

bioRxiv2026

Tuning the mechanical properties of polymer-based surrogate materials for articular cartilage and vocal fold repair

The macroscopic biomechanical characteristics of soft and ultrasoft tissues, such as articular cartilage and vocal folds, significantly determine their physiological function. Treatments of widespread tissue degradations due to osteoarthritis in the knee or vocal fold impairment remain an unresolved challenge. For the design of implants for tissue repair after injury or disease, it is key to thoroughly understand the unique biomechanical properties of native tissues and potential substitute materials. We use multimodal mechanical testing methods combined with hyperelastic nonlinear continuum mechanics modeling, and finite element simulations to determine the macroscopic behavior of surrogate materials for human articular cartilage in the knee and human vocal folds. Our cyclic loading experiments reveal qualitative similarities for both tissues and their surrogates, including a nonlinear stress-strain behavior, hysteresis, and conditioning. We demonstrate the tunability of biomimetic and biosimilar stiffnesses of synthetic articular cartilage and vocal fold surrogates through tissue-specific process-material combinations. Our results demonstrate the feasibility of synthetic metamaterials in replicating essential passive biomechanical functions with great potential for future treatment options.

bioengineering↗

bioRxiv2026

Spatial scale of indentation explains shift in ratio between spinal cord gray and white matter stiffness

The structural integrity of spinal cord tissue and the transmission of mechanical stimuli across the different levels of tissue microarchitecture and varying spatial scales of mechanical loading challenge experimental and computational efforts to accurately model, simulate and interpret tissue mechanics, leading to conflicting findings in existing literature. Here, we demonstrate that the bead size used in spherical indentation tests significantly affects the stiffness ratio of spinal cord gray to white matter, a dependence which we only observe on the transverse plane and not the coronal plane of the tissue. Our study reveals a shift in stiffness ratio such that for smaller spherical indenters gray matter is stiffer than white matter, while for larger indenters, white matter is stiffer than gray matter. The mean relative change from the 100 {micro}m bead to the 500 {micro}m bead differed between anatomical planes, with transverse sections showing a decrease in gray matter (-13.3%) and an increase in white matter stiffness (+26.9%), accompanied by a reduction in the gray-to-white matter stiffness ratio from 1.07 to 0.76, whereas coronal sections exhibited increases in both gray (+21.0%) and white matter (+33.8%), along with a change in the ratio from 0.99 to 1.14. These findings contribute to explaining previously contradictory results in the literature and underscore the relevance of spatial scales in mechanical characterization studies.

biophysics↗

bioRxiv2026

In silico model of neuronal pathfinding during spinal cord regeneration in zebrafish larvae

Functional spinal cord repair in zebrafish is governed by regeneration-favorable biochemical and mechanical cues within the lesion microenvironment. Alterations in extracellular matrix composition and stiffness are closely associated with axon regeneration. However, experimentally dissecting the interplay between mechanical signals and axonal regrowth in vivo remains technically challenging. Here, we present an agent-based modeling framework to simulate stiffness-mediated axonal growth trajectories across the lesion. We use this model to explore potential mechanisms underlying the characteristic growth patterns observed during zebrafish spinal cord regeneration. Computational predictions were qualitatively compared with confocal imaging data obtained from larval zebrafish. These phenomenological comparisons revealed a close agreement between simulated and experimentally observed axon growth, indicating that experimentally observed patterns could be governed by transient changes in the stiffness profile of the spinal cord and lesion microenvironment. Hence, our computational framework provides an in silico platform for investigating the role of mechanical cues in axon regeneration in the injured spinal cord.

biophysics↗

bioRxiv2026

The Mechanical Fingerprint of Hippocampal Sclerosis Linking Neuronal Cell Loss and Gliosis to Tissue Stiffness

Hippocampal sclerosis (HS) is the most common pathology in drug-resistant temporal lobe epilepsy (TLE). However, clinical diagnosis, prevalent epileptogenicity, and drug drug-resistance in individuals with HS remain an ongoing challenge demanding multidisciplinary research efforts. In this study, we examined the mechanical properties of neurosurgically en bloc resected HS specimens (n=8) ex vivo under compression, tension, and torsional shear. We fitted a two-term Ogden hyperelastic model to the measured mechanical responses to quantify nonlinear mechanical tissue properties. The resulting parameters revealed higher strain stiffening under compression in HS compared to hippocampus obtained post mortem (n=7). The distinction was most noticeable in the large-strain regime, which has important implications for using mechanical tissue properties as valuable diagnostic biomarker. Furthermore, we correlated the tissue microstructure with mechanical parameters. We trained a deep-learning histopathology classifier to detect and classify neurons and glial cells from hematoxylin-stained whole slide images (WSI). We identified a strong association between the small-strain stiffness (shear modulus {micro}) and the overall cell density as well as the glial cell density. The negative relationship between the neuron-to-glia ratio and shear modulus is consistent with the hypothesis that neuronal cell loss and gliosis drives tissue stiffening, respectively. Magnetic resonance imaging (MRI) analysis of the specimens confirmed the previously reported negative association between MRI-derived fractional anisotropy and shear modulus {micro}. Taken together, our study establishes a direct link between tissue mechanics and microstructure, suggesting nonlinear continuum mechanics models as promising new tools for clinical diagnosis and novel research strategies.

bioengineering↗

bioRxiv2026

Towards patient-specific biomechanical human brain models

Reliable characterization of spatial variations in brain tissue stiffness is essential for predictive biomechanical modeling, yet most current methods rely on coarse regional parameter assignments based on invasive mechanical testing. In this study, we propose a new approach to obtain subject-specific mechanical properties at voxel resolution from in vivo diffusion tensor magnetic resonance imaging (DTI) based on a linear regression between the fractional anisotropy (FA) from DTI and experimentally measured stiffness values. To assess how such heterogeneity in mechanical properties influences simulated brain deformation, we construct a finite element model based on two material parameterizations of the same human brain: one employing nine anatomically defined regions, each with uniform material parameters, and another in which the shear modulus is assigned voxel-wise on the corresponding FA value. Applying this FA-stiffness mapping yields a smoothly varying mechanical property distribution that better captures local microstructural differences not represented by region-wise parameterizations. Both parameterizations are subjected to an identical atrophy-driven loading scenario. They exhibit comparable overall volume loss, but diverge in regional behavior. The voxel-resolved parameterization predicts more pronounced ventricular expansion and differs in the displacement and stretch distributions, indicating that variability in stiffness can alter local predicted responses even when global outcomes appear similar. This work presents a pipeline for estimating individualized mechanical properties directly from imaging protocols that are routinely performed on patients, with important implications for brain biomechanics. While the approach depends on a simplified linear FA-stiffness relation and assumes isotropic constitutive behavior, it provides a framework for integrating imaging-based microstructure into subject-specific simulations. Future validation against in vivo or experimental deformation data is needed to determine the fidelity and clinical utility of FA-derived stiffness fields.

neuroscience↗

bioRxiv2026

Fractional Anisotropy as a Surrogate Marker of Brain Mechanics

Understanding the mechanical properties of brain tissue may provide crucial insights into brain development, injury, disease and surgical planning. Conventionally, these properties are measured ex vivo or in vivo during surgical procedures, while non-invasive in vivo alternatives are sparse. This study investigates whether fractional anisotropy (FA) derived from diffusion-weighted magnetic resonance imaging can serve as a surrogate marker for brain tissue stiffness in healthy human brains. MRI data were collected from three body donor brains, 28 healthy adults, and a publicly available independent dataset of 26 adults. FA values were compared with mechanical properties from ex vivo mechanical testing of brain tissue. Statistical analysis revealed a strong negative correlation between FA and the mechanical response for small strains expressed as shear modulus of a one-term hyperelastic Ogden model, indicating that higher FA values are associated with lower tissue stiffness. The nonlinearity parameter alpha exhibited a qualitatively similar, but considerably weaker correlation with FA. These findings were consistent across datasets. The findings suggest that FA can be a robust, non-invasive marker for estimating mechanical properties of brain tissue, with potential applications in clinical diagnosis and computational modeling of brain mechanics and the study of brain development. Further research is needed to clarify the relationship in lesional tissues and to optimize clinical utility.

neuroscience↗

bioRxiv2025

Integration of customizable 3D printed mixing printheads for controlled and tuneable material and stiffness gradient fabrication with high cell viability in extrusion-based bioprinting

The fabrication of native tissue-like structures with gradual transitions in material properties, cell types and growth factors remains a major challenge in biofabrication. Particularly the complex hierarchical organization, present in living tissues, has to be mimicked as close as possible for the created models to fulfil the desired function. However, the fabrication of gradual structures incorporating several materials ensuring high cell survival and subsequent unaffected tissue maturation is highly challenging. To get a step closer to the goal of generating tissue models containing controlled gradient structures, here we show a novel approach combining extrusion- based 3D bioprinting with static mixing, using self-designed Digital Light Processing (DLP) printed mixing units to fabricate defined gradual structures. Two passive mixing geometries, sinusoidal and obstacle-based, are designed and fabricated and benchmarked against commercially available static mixers. The mixing performance is assessed pixelwise using dyed alginate solutions that are extruded through the mixers using an adapted 3D printer equipped with cavity pumps. The obstacle structure exhibits the highest mixing rate compared to the sinusoidal design and commercially available static mixers. Beyond mixing efficiency, the biological compatibility of the systems is assessed by evaluating the viability of U87 and NIH3T3 cells after extrusion. Two distinct polymer solutions of differing viscosities, composed of allyl-modified gelatin (gelAGE), polyethylene glycol dithiol (PEG-2-SH), and Matrigel, are extruded through the static mixer, revealing significantly higher cell viability in the self-designed printheads compared to commercial mixers. Subsequently, graded structures mimicking the mechanical profile of native brain tissue are fabricated and mechanically characterized using nanoindentation, confirming the successful generation of continuous stiffness gradients. In summary, this study demonstrates that combining extrusion-based 3D printing with customizable mixing units enables the controlled fabrication of tissue-like gradient structures with enhanced mixing rates and cell viability. The design flexibility and rapid fabrication of DLP-printed printheads enable the adaptation to specific tissue requirements, providing a robust platform for future developments in biofabrication and tissue engineering. This work serves as foundation for the creation of increasingly complex and functional tissue models.

bioengineering↗

bioRxiv2025

Mechanical impact on neural stem cell lineage decisions in human brain organoids

During neurodevelopment neural stem cells give rise to a spatially patterned tissue in which a regionally differentially regulated balance between proliferation and differentiation produces the fine-tuned number of neurons and macroglia necessary for a functional central nervous system. The cells driving these highly intricated developmental processes of patterning, growth and differentiation are constantly exposed to a mechanical environment that is however variable between different brain regions and along differentiation trajectories. Here we demonstrate that both, acute mechanical manipulations as well as a persistent change in the mechanical environment provided to human brain organoids, instruct neural stem cell lineage decisions. Furthermore, we dissect the underlying changes in the molecular program of organoid-resident cells by bulk- and single cell RNA-sequencing and reveal that mechanical manipulations impact on molecular programs governing early patterning events as well as cell-type specifically alter cellular metabolism. Thus, our results unravel a regulatory network linking mechanics and neural stem cell lineage decisions.

neuroscience↗

bioRxiv2023

Fiber alignment in 3D collagen networks as a biophysical marker for cell contractility

Cells cultured in 3D fibrous biopolymer matrices exert traction forces on their environment that induce deformations and remodeling of the fiber network. By measuring these deformations, the traction forces can be reconstructed if the mechanical properties of the matrix and the force-free matrix configuration are known. These requirements severely limit the applicability of traction force reconstruction in practice. In this study, we test whether force-induced matrix remodeling can instead be used as a proxy for cellular traction forces. We measure the traction forces of hepatic stellate cells and different glioblastoma cell lines and quantify matrix remodeling by measuring the fiber orientation and fiber density around these cells. In agreement with simulated fiber networks, we demonstrate that changes in local fiber orientation and density are directly related to cell forces. By resolving Rho-kinase (ROCK) Inhibitor-induced changes of traction forces and fiber alignment and density in hepatic stellate cells, we show that the method is suitable for drug screening assays. We conclude that differences in local fiber orientation and density, which are easily measurable, can be used as a qualitative proxy for changes in traction forces. The method is available as an open-source Python package with a graphical user interface.

biophysics↗

bioRxiv2023

Immune cells employ traction forces to overcome steric hindrance in 3D biopolymer networks

To reach targets outside the bloodstream, immune cells can extravasate and migrate through connective tissue. During tissue infiltration, immune cells migrate in an amoeboid fashion, characterized by weak matrix adhesions and low traction forces, that allows them to achieve high migration speeds of up to 10 {micro}m/min. How immune cells reconcile amoeboid migration with the need to overcome steric hindrance in dense matrices is currently not understood. Here we show that NK92 (natural killer) cells can switch from their default amoeboid migration mode to a contractile, mesenchymal-like migration mode when moving through fibrous human amniotic membrane (HAM) tissue. We subsequently study immune cell migration in reconstituted 3D collagen networks with known mechanical properties and pore sizes and apply time-lapse confocal reflection microscopy to obtain simultaneous measurements of migration speed, directional persistence, and cell contractility. We find that NK92 cells are highly mechanoresponsive and exert substantial acto-myosin driven, integrin-mediated contractile forces of up to 100 nN on the extracellular matrix during short contractile phases. This burst-like contractile behavior is also found in primary B, T, NK cells, neutrophils, and monocytes, and is tightly related to the fraction of cells that appear to become stuck in narrow pores of the surrounding matrix. Our results demonstrate that steric hindrance guides the rapid regulation of integrin-mediated adhesion to the ECM in a large number of immune cell subtypes.

biophysics↗

bioRxiv2022

Inverse identification of region-specific hyperelastic material parameters for human brain tissue

The identification of material parameters accurately describing the region-dependent mechanical behavior of human brain tissue is crucial for computational models used to assist, e.g., the development of safety equipment like helmets or the planning and execution of brain surgery. While the division of the human brain into different anatomical regions is well established, knowledge about regions with distinct mechanical properties remains limited. Here, we establish an inverse parameter identification scheme using a hyperelastic Ogden model and experimental data from multi-modal testing of tissue from 19 anatomical human brain regions to identify mechanically distinct regions and provide the corresponding material parameters. We assign the 19 anatomical regions to nine governing regions based on similar parameters and microstructures. Statistical analyses confirm differences between the regions and indicate that at least the corpus callosum and the corona radiata should be assigned different material parameters in computational models of the human brain. We provide a total of four parameter sets based on the two initial Poissons ratios of 0.45 and 0.49 as well as the pre- and unconditioned experimental responses, respectively. Our results highlight the close interrelation between the Poissons ratio and the remaining model parameters. The identified parameters will contribute to more precise computational models enabling spatially resolved predictions of the stress and strain states in human brains under complex mechanical loading conditions.

bioengineering↗

bioRxiv2022

Dynamic traction force measurements of migrating immune cells in 3D matrices

Immune cells such as natural killer (NK) cells migrate with high speeds of several {micro}m/min through dense tissue, but the traction forces are unknown. We present a method to measure dynamic traction forces of fast migrating cells in non-linear biopolymer matrices. The method accounts for the mechanical non-linearity of the 3D tissue matrix and can be applied to time series of confocal or bright-field image stacks. The method is highly sensitive over a large range of forces and object sizes, from [~]1 nN for axon growth cones up to [~]10 {micro}N for mouse intestinal organoids. We find that NK cells display bursts of large traction forces that increase with matrix stiffness and facilitate migration through tight constrictions.

biophysics↗

bioRxiv2022

Exploring the role of the outer subventricular zone during cortical folding through a physics-based model

The human brain has a highly complex structure both on the microscopic and macroscopic scales. Increasing evidence has emphasized the role of mechanical forces for cortical folding - a classical hallmark of the human brain. However, the link between cellular processes at the microscale and mechanical forces at the macroscale remains insufficiently understood. Recent findings suggest that an additional proliferating zone, the outer subventricular zone (OSVZ), is decisive for the particular size and complexity of the human cortex. To better understand how the OSVZ affects cortical folding, we establish a multifield computational model that couples cell proliferation and migration at the cell scale with growth and cortical folding at the organ scale by combining an advection-diffusion model with the theory of finite growth. We validate our model based on data from histologically stained sections of the human fetal brain. Finally, we address open questions regarding the role of the OSVZ for the formation of cortical folds. The presented framework not only improves our understanding of human brain development, but could eventually help diagnose and treat neuronal disorders arising from disruptions in cellular development and associated malformations of cortical development.

neuroscience↗