Biomechanics and Modeling in Mechanobiology

The control of uterine activity during pregnancy is a complex process that involves regulating myometrial excitability across multiple scales. While numerous studies have investigated various regulatory mechanisms and established the contributions of ion channels and gap junctions, how these mechanisms interact to produce observed changes in uterine activity remains poorly understood. Pivotal to these efforts are computational models that effectively capture gestational changes in excitability across scales. In this study, we propose a multiscale computational modeling framework that can reproduce measured activity at the cellular and tissue scales at a given gestational stage. At the cellular level, we identify key ion currents underlying the observed electrophysiological properties based on a literature review of their regulation and a sensitivity analysis of the Tong 2011 uterine smooth muscle cell activation model. The conductances of these ion currents are then fit to reproduce characteristic resting membrane potentials and burst properties using Bayesian optimization. To extend to the tissue level, we employ an anisotropic monodomain model, parameterized by the resistivity of late pregnancy uterine muscle, to investigate electrical propagation in a two-dimensional section of uterine tissue. We then apply the multiscale model to study myometrial activation in late pregnancy and elucidate the contributions of ion channel and gap junction regulation in transitioning the uterus from a quiescent state to labor. Our resulting model successfully reproduces measured electrophysiological properties at the cellular level and characteristic single-spike and burst-propagation patterns at the tissue level across the three late-pregnant time points analyzed (days 16/17, 18/19, and 20/21) in a murine model. Furthermore, our results suggest that the regulation of the conductances of the voltage-dependent potassium current (IK1), L-type calcium current (ICaL), and sodium current (INa) is most important in determining preterm uterine excitability. The framework established here will promote the development of more gestationally relevant models to better understand labor progression and the factors involved in dysfunctional labor.

The osteocyte network in bone is believed to play an important role for how bone tissues sense and respond to mechanical stimulation. Yet, bone adaptation to mechanical loads is often conceptualised as a simple response to mechanical stimuli, such as Wolffs law, which is based on mechanical variables only and takes no account of the cellular basis of mechanosensation. Wolffs law presumes the existence of a reference mechanical stimulus, the mechanical setpoint, above which bone is consolidated, and under which bone is removed. In this paper, we develop a theory of bone tissue sensing and adaptation based on osteocytes to provide new understanding of the role played by osteocyte signals in mechanical adaptation. In this theory, the mechanical setpoint of Frosts mechanostat is explicitly embodied as osteocyte properties involved in mechanotransduction. The mechanical setpoint is allowed to adapt due to the replacement of osteocytes during remodelling, making the setpoint space and time dependent. We propose a mathematical model to implement this new theory of bone adapation and present numerical simulations of this model to explore how mechanobiological response curves (effective Wolffs laws) are modulated by setpoint adaptation during remodelling. By accounting for varying osteocyte populations within bone tissue, we explore bone adaptation under osteocyte disruptions, which is particularly relevant to age-related bone loss. Our model suggests that biological disruptions of remodelling balance cannot always be compensated by mechanical feedback, and that setpoint adaptation during remodelling may have significant observable consequences, such as hysteresis in bone response signatures that resemble lazy zones.

PurposeComputational models of the uterus during pregnancy enable analysis of electro-chemo-mechanical pathways to predict labor timing and guide treatment planning. We aim to develop a robust image-based modeling pipeline to investigate uterine passive mechanics during late pregnancy. MethodsA parametric model of the uterus and cervix was created using a patients MRI measurements at 38 weeks of gestation. Inspired by advances in cardiac mechanics models, we created Laplace-Dirichlet solutions to inform tissue domains, fiber structure within the uterus and cervix, and spatially varying Robin boundary conditions. Prior imaging and mechanical testing data were used to fit material parameters. Boundary condition parameters were tuned to match the displacements of a previously established approach that employed contact with surrounding tissue. The tissue mechanical response to a physiologic load was assessed across varying material properties and fiber architectures. ResultsDiscrepancies in nodal displacements between the current approach and the contact-based model were limited to 3.4 {+/-} 1.8 mm, yielding nearly 90 % computational savings. Uterine tensile strains were more sensitive to ground substance elastic modulus (E) compared to fiber properties. Reduced E and fiber stiffness increased cervical strains and compression. Fiber dispersion and architecture modulated the opening of the cervical internal ostium but had a reduced impact on compression. ConclusionWe developed a novel workflow for modeling passive uterine mechanics, informed by patient-specific measurements and in vitro mechanical tests. The robust workflow may prove useful for studying labor progression and conducting longitudinal studies to enhance our understanding of normal and pathological pregnancies.

Cell shape and mechanics are intricately connected and tightly regulated by mechanochemical events including biochemical signaling, cytoskeletal remodeling, and plasma membrane mechanics. While experimental advances in microscopy have shed light on the intricate coordination involved in cell shape change in response to different cues, the ability to conduct three-dimensional simulations in realistic geometries remains an open computational challenge. In this work, we develop a finite-element framework that incorporates advection-diffusion-reaction equations coupled with equations governing the kinematics of a deformable interface representing the cell membrane. We applied this framework to three distinct coupled mechanochemical systems, each governed by geometric partial differential equations, resulting in large deformations of the interface. In all three examples, our simulations revealed the emergence of feedback between cellular signaling, cytoskeletal organization, and cell shape. In our first two sets of simulations, we observed that cell migration and neutrophil protrusion were regulated by membrane tension-mediated feedback. In our final application, we predicted shape changes of a dendritic spine starting from a realistic geometry, and found that the complex shape of the spine gives rise to localized regimes of actin cytoskeleton remodeling not previously observed with idealized geometries. Thus, our finite-element framework allows us to generate new mechanistic insights for biophysical problems.

Identification and treatment of pregnancies at risk for preterm birth is a central challenge in obstetric research. Many of the known causes of preterm birth originate from mechanical failure in reproductive tissues. To better understand the biomechanical environment of the gravid uterus and its potential contribution to preterm birth, this computational study presents a parametric method for modeling maternal reproductive anatomy during the early second trimester. A finite element modeling approach was built using existing sonographic measurements from early second-trimester maternal anatomy and material properties from published mechanical tests. We applied the same physiologically relevant intrauterine pressure to all models and quantified the resulting tissue stretch. The sensitivity of the stretch in the proximal cervix was explored by varying material properties and sonographic maternal anatomy dimensions. Cervical material properties, particularly the fiber stiffness modulus and ground substance Youngs modulus, were found to have the greatest effect on proximal cervix stretch compared to other material properties and sonographic dimensions. Among the sonographic dimension measurements, those defining the region surrounding the proximal cervix had the greatest effect on proximal cervix stretch, including the curvature of the posterior uterine wall and the thickness of the lower uterine segment. The computational modeling approach presented here enables future patient-specific studies of gravid reproductive tissues to elucidate differences between individuals who do and do not deliver preterm. Additionally, this study is foundational for building digital twins to support future virtual clinical studies on diagnostic and therapeutic device design to prevent preterm birth.

PurposeFunctional tricuspid regurgitation (FTR) is driven by right ventricular (RV) remodeling, annular dilation, and papillary muscle dislocation. Free wall approximation (FWA) has been proposed to treat FTR by addressing RV dilation, but its effects on tricuspid valve (TV) biomechanics remain unclear. We present a real-time 3D echocardiographic (rt3DE)-based finite element framework to quantify TV biomechanics under FTR, and preliminarily apply it to assess FWA effects. MethodsSubject-specific models were developed from rt3DE data of three dilated porcine hearts in an ex-vivo mock-loop. TV geometries at end-diastole and peak systole (PS) were complemented by parametric chordae tendineae and hyperelastic tissue properties. TV closure was simulated under a standard pressure load and image-based annular motion. After tuning chordae length to replicate the PS ground truth in FTR, FWA was simulated as 30% and 60% approximations along three anatomical directions (anterior-posterior, A-P; anterior-septal, A-S; anterior-septal wall, A-SW). ResultsIn FTR simulations, median geometric errors ranged from 1.16 to 1.26 mm; median stress ranged from 56.4 to 74.7 kPa. FWA simulations predicted regurgitant orifice area (ROA) reductions by 53-99%, albeit overestimating the residual ROA vs. in vitro ground truth when starting from particularly extreme FTR conditions; concomitantly, a median stress reduction by 8-43% vs. FTR conditions was predicted. ConclusionPreliminary data suggest that our rt3DE-based framework can reliably quantify FTR-related TV biomechanics and that post-FWA biomechanics depends on initial FTR conditions. A larger cohort is required to verify the method and obtain statistically significant results.

Constitutive models of myocardial mechanics form a cornerstone of personalized cardiac simulations and cardiac digital twins. Researchers traditionally prescribe these models a priori and calibrate them from ex vivo tissue experiments, even though tissue excision alters loading conditions, removes residual stresses, and eliminates important physiological interactions. Multimodal cardiac MRI now provides subject-specific ventricular geometry, deformation, and myocardial microstructure, yet current inverse approaches still rely on predefined constitutive laws. Here we present the first framework to discover constitutive models of passive myocardial mechanics directly from in vivo cardiac imaging data by embedding a constitutive artificial neural network within a nonlinear finite element model of ventricular filling. Using multimodal cardiac MRI that combines ventricular geometry, deformation, and microstructure from a representative healthy individual, the framework identifies sparse, mechanically admissible strain-energy functions without prescribing their form a priori. The best-performing model contains only two fiber- and two sheet-invariant terms, achieves a mean displacement error of 1.62 mm, and reduces the error of the widely used Guccione and Holzapfel models by 34.14% and 26.01%. The discovered models indicate that fiber- and sheet-related anisotropic mechanisms dominate the passive mechanical response during physiological ventricular filling. More broadly, this work establishes a non-invasive strategy for subject-specific constitutive discovery from cardiac imaging data and lays the foundation for personalized cardiac simulations and cardiac digital twins.

The endothelial cells (ECs) that line blood vessels continuously sense and respond to the physical forces exerted by blood flow. In vivo, pulsatile arterial flow interacts with vessel curvature, branching and other anatomical features to generate complex local hemodynamic environments that dictate the magnitude, direction, pulsatility, and oscillatory nature of wall shear stress experienced by ECs. Currently, accessible and reproducible in vitro models of complex pulsatile flow that recapitulate in vivo vascular anatomy remain limited. Here, we combine a novel rotational-flow endothelial culture platform with detailed computational fluid dynamics (CFD) modeling to characterize four well geometries designed to generate distinct hemodynamic environments. CFD analyses demonstrate that these geometries intrinsically generate pulsatile flow and produce reproducible spatially distinct regions of wall shear stress magnitude, pulsatility, and oscillatory shear within a single culture well. Endothelial alignment mapping and functional assays reveal region-specific cellular responses to the predicted local flow conditions that closely corresponded to the predicted local hemodynamic environment, linking complex flow patterns to endothelial adaptation. The technical advancements of our modeling efforts should support a faster, cheaper, simpler, and--importantly--validated framework for future investigation into EC mechanobiology under complex flow conditions. HIGHLIGHTSO_LISimple engineered well geometries generate distinct hemodynamic microenvironments, mimicking in vivo vascular structures, using a conventional orbital shaker. C_LIO_LIComputational fluid dynamics (CFD) reveals spatially distinct patterns of wall shear stress, pulsatility, and oscillatory shear applied to ECs within individual culture wells. C_LIO_LIHigh average wall shear stress and elevated oscillatory shear index induces a unique perpendicular alignment of ECs to the dominant flow vector. C_LI

Acute subdural hematoma (ASDH) is a prevalent injury with high mortality and morbidity, often resulting from bridging vein (BV) disruption secondary to cortical relative motion. As a thin membrane enveloping the brain surface and anchoring BVs, the pia mater is hypothesized to play a critical mechanical role in cortical response and hence ASDH pathogenesis. Finite element (FE) head models are valuable tools to predict ASDH occurrence during impacts. However, the pia mater is often represented as an elastic material in existing FE head models, despite experimental evidence reporting its nonlinear mechanical behavior. In this study, both linear (Young's modulus of 11.5 MPa) and nonlinear (the stress-strain curve derived from pial tension tests) material models of the pia mater were implemented in one FE head model. The models were subjected to three experimental impact loadings, one of which was known to cause ASDH and two of which were not. Results demonstrated that, across all simulated impacts, the model with nonlinear pia mater properties predicted larger cortical displacements and BV responses than the linear model. For the impact with known ASDH occurrence, the predicted BV strain was 0.17 for the nonlinear model and 0.094 for the linear model, with only the former approaching the reported rupture strain range of the BV-superior sagittal sinus complex (0.29 {+/-} 0.13). These findings verified the mechanical importance of the pia mater in cortical responses and hence the prediction of ASDH, suggesting that conventional linear pia modeling might over-constrain cortical motion, leading to underestimation of BV strain and ASDH risk. The current study supported the adoption of experimentally derived nonlinear pia mater properties in FE head models to improve the reliability of ASDH prediction.

Objective: To identify brain injury metrics suitable for supporting sports head injury assessment by evaluating their association with brain tissue strain and consistency across sports. Methods: Head kinematics from 3,139 impacts in boxing, mixed martial arts, and rugby matches were recorded using instrumented mouthguards and used to calculate nine brain injury metrics. Impacts were simulated using an anatomically detailed finite element brain model to estimate peak 95th-percentile maximum principal strain (MPS) in the brain and brainstem, a measure of tissue deformation associated with long-term pathology. Sport-specific ordinary least squares models estimated xE, the metric value equivalent to a reference MPS of 0.21. Metric-MPS correlations and xE uncertainties were quantified using 5000 bootstrap resamples. Cross-sport consistency was assessed using the coefficient of variation (CV) of sport-specific median xE values, and uncertainty using the normalised confidence interval size (NCIS). Results: XGB, an extreme gradient boosting strain-prediction model, showed the strongest and most consistent correlations with whole-brain (r=0.924-0.974) and brainstem MPS (r=0.887-0.954) across all sports. PRV, BrIC and UBrIC also correlated strongly with whole-brain (r=0.724-0.930) and brainstem MPS (r=0.739-0.900), whereas HIC15 and HARM showed weaker correlation with MPS, particularly in rugby. XGB showed the lowest cross-sport variability (CV=0.034) and uncertainty (median NCIS=0.056). HARM, DAMAGE and HIC15 showed the greatest sport dependence (CV=0.575-0.588) and uncertainty (median NCIS=0.331-0.791). Conclusions: XGB, BrIC, and UBrIC demonstrated the strongest associations with brain tissue strain and the greatest consistency across sports. This study provides a biomechanically informed framework for selecting suitable metrics for sports HIA protocols.

Predictions of skeletal muscle energy consumption under a diverse range of muscle contractile conditions are critical for improving our understanding of locomotion. Existing mathematical models, while capturing the mechanical dependence of energy consuming processes, neglect the time-dependent behaviour and recovery costs associated with regenerating ATP. This time-dependence is important for predicting the energetic response of muscles during repetitive or cyclical tasks like locomotion, where muscle undergoes many contraction cycles. This study presents a novel model to predict energetic rates based on physiological processes: Ca2+ transport costs, cross-bridge cycling costs, and ATP regeneration. Previous mathematical models include the dependence on Ca2+ transport and cross-bridge cycling, but neglect the time-dependent response and the subsequent recovery of ATP following the contraction. Model parameters were obtained from existing data on isolated muscle preparations, and predicted energetic rates were validated on separate datasets across a range of contractile conditions including dynamic, sub-maximal, and twitch contractions. The time-dependent model was able to capture the influence of contraction frequency on peak energetic rates and the time-course of energetic recovery observed experimentally. The model captures key physiological processes while maintaining a minimal number of free parameters and low computational cost. This enables generalisability across muscles and species, and implementation into larger scale musculoskeletal models.

Background: Acute pulmonary embolism (PE) is a leading cause of morbidity and mortality with persistent difficulties in choosing interventions and predicting outcomes for patients defined clinically as intermediate risk. Computational fluid dynamics (CFD) tools have been used to understand the hemodynamic environment and plan interventions in the pulmonary arteries across a variety of disease conditions. Several biomechanical metrics have been used to evaluate risk in narrowed vessels, including hemodynamic resistance, power dissipation, and fractional flow reserve (FFR). In this study, we evaluate differences in these CFD-derived biomarkers between healthy controls (HC) and intermediate risk, acute PE patients. Additionally, we examine the response of patient hemodynamics to mechanical thrombectomy and compare values of these biomarkers across post-intervention pressure status. Methods: A CFD framework was developed to simulate patient-specific hemodynamics within the pulmonary vasculature identifiable from clinical imaging. The pipeline involved reconstructing three-dimensional (3D) structures of the pulmonary arteries and modeling blood flow with the finite element method. Patient-specific boundary conditions were derived from matching pre-intervention inlet mPAP to the patient's measured value given their measured CO as steady inflow. Converged simulations allowed for precise quantification of primary hemodynamic characteristics (flow and pressure) as well as secondary flow phenomena, primarily wall shear stress (WSS) and simulated pressure metrics such as fractional flow reserve (FFR). Results: Our simulations revealed significant elevations in resistance, power dissipation, and the number of vessels with low FFR in those patients with acute PE (n=6) compared to HC (n=3). Occlusions of hemodynamic significance were generally found in segmental pulmonary arteries. For patients with normalized pulmonary pressures post-thrombectomy (n=3), we found significantly higher proximal power dissipation and counts of low FFR vessels in comparison to those with elevated pressures after intervention (n=3). Distal resistance, which was derived from the portion of resistance attributed to the outflow boundary conditions, was significantly higher in patients with elevated pressures post-intervention. Across all PE patients, FFR count was significantly correlated with post-thrombectomy pulmonary pressure and cardiac index. Discussion: CFD-derived biomarkers offer a promising tool for understanding disease severity in acute PE. Differences between HCs and acute PE patients reveal expected increases in metrics associated with proximal disease burden. Yet, in examining acute PE patients with varying post-intervention hemodynamics, we found that these metrics of proximal disease burden could also be useful to predict the efficacy of mechanical thrombectomy. Those patients with normalized pressures had higher values for proximal disease metrics and lower values for distal disease metrics than those with continued elevations in pressure. This suggests that accessibility of hemodynamically-significant emboli to thrombectomy may be useful as a predictor for outcomes.

Vascular endothelial cells (ECs) form a monolayer lining blood vessels and serve as a barrier between blood and tissues. Clathrin-mediated endocytosis (CME) is a major internalization pathway that involves a physical conformational change of the plasma membrane to form a vesicle and is therefore sensitive to the local environment. ECs are subjected to a myriad of fluid shear stress (FSS) rates from circulating blood, which we hypothesize affects CME. To test this, we used simultaneous two-wavelength axial ratiometry (STAR) microscopy, which provides nanoscale axial resolution, to determine the frequency and morphology of clathrin-coated vesicles as they form. Human umbilical vein endothelial cells (HUVECs) were transfected with dual-tagged clathrin light chain a (CLCa-iRFP-EGFP) and cultured under 10 dyn/cm2 FSS. CME activity was elevated in cells cultured under flow and assayed in static or flow conditions compared to statically cultured and imaged controls, indicating that FSS-induced changes to CME were maintained shortly after flow cessation. Single vesicle analysis showed cells cultured in FSS had a slight preference for vesicle formation with a flat-to-curved clathrin transition compared to control. Next, to assess the impact of different FSS rates, we cultured HUVECs at 20 and 40 dyn/cm2 FSS. We found total CME frequency was elevated compared to control at 20 dyn/cm2, but not 40 dyn/cm2. HUVECs cultured at both 20 and 40 dyn/cm2 had vesicles with increased lifetime and enhanced stability, as well as a higher proportion of vesicles formed through a flat-to-curved transition of clathrin.

Tricuspid valve regurgitation is a frequent valve lesion and, if severe, an independent predictor of mortality. In most patients, the valve itself has historically been considered intact. Yet, we have previously shown that the valve may not be an innocent bystander. In multiple sheep models, we have shown that the tricuspid valve thickens and stiffens. This remodeling may contribute to valve disease. Our goal is to extend our investigation of tricuspid valve remodeling to a rodent model, potentially opening scientific opportunity and enabling scaling our studies. To this end, we used pulmonary artery banding (PAB) in male rats to induce pressure overload and right ventricular remodeling. After excising the tricuspid valve, we quantified anterior leaflet morphology, mapped anterior leaflet thickness using optical coherence tomography, and evaluated anterior leaflet belly mechanics using a custom bulge testing system. Compared with SHAM controls, PAB increased anterior leaflet area. Moreover, anterior leaflets in PAB animals exhibited region-specific thickening, with the largest increases near the annulus. Finally, anterior leaflets in PAB animals were significantly less compliant. However, leaflet stiffening stemmed from aforementioned thickening, i.e., structural stiffening, not constitutive stiffening. Our findings demonstrate that we can reliably quantify leaflet area, thickness, and stiffness in the minuscule tricuspid valves of rats. We also show that tricuspid valve remodeling is not ovine-specific, but also affects the tricuspid valves of rats. Together, our findings support our hypothesis that tricuspid valves are not innocent bystanders in regurgitation, and that rats may serve as a scalable model system for future investigations. NEW & NOTEWORTHYUsing a rat pulmonary artery banding model of pulmonary hypertension, we show that chronic right ventricular pressure overload induces leaflet enlargement and region-specific thickness remodeling of the tricuspid valve. Although structural mechanical metrics change under pressure loading, normalization by thickness reveals that geometric remodeling rather than intrinsic material stiffening predominates. These findings highlight leaflet structural (mal)adaptation as a potential contributor to functional tricuspid regurgitation and underscore the importance of considering leaflet geometry in therapeutic strategies.

Accurate estimation of body segment inertial properties is essential for biomechanical analyses, yet commonly used scaling methods rely on limited datasets and do not generalize well across diverse adult body morphologies. We developed a data-driven framework that estimates segment lengths, masses, centers of mass, and moments of inertia using regression models trained on large anthropometric datasets (ANSUR II and NHANES) combined with a geometric representation of 16 body segments. The framework uses height, weight, and sex as primary inputs and incorporates waist and hip circumferences or other length and cross-sectional measurements when available to refine body-shape predictions. For individuals with obesity, additional geometric rules redistribute excess mass based on segment-specific volume changes. The resulting models reproduced segment lengths, cross-sectional dimensions, and lumped segment masses within the ranges observed in the training datasets and outperformed published regression equations, particularly at higher body mass index (BMI) values. To promote broad adoption, we provide an open-source API in Python that performs the full parameter estimation using the trained models. This framework offers an accurate and accessible method for estimating adult body segment properties across a wide range of body sizes and shapes, supporting improved motion analysis, musculoskeletal simulation, and clinical biomechanics.

Epithelial sheets must maintain robust barrier function while enduring severe mechanical deformations across various physiological environments. While baseline actomyosin contractility is understood to stabilize intercellular junctions and hence cell-cell contact integrity, how cell-generated active forces interact with external physical strain to dictate contact integrity remains poorly understood. In this study, we investigated the biophysical trade-offs between actomyosin contractility and barrier resilience when Madin-Darby Canine Kidney (MDCK) cell islands are subject to large stretch. In contrast to a high concentration (50 M) of the non-muscle myosin II inhibitor blebbistatin that disrupted cell-cell contacts, we first identified a lower concentration (10 M) that maintained cell-cell contact integrity in the absence of any stretch. Such moderate inhibition of non-muscle myosin II reduced, but preserved some level of actin bundle organization. Remarkably, when challenged with a pathological 38% linear stretch using a custom-built biaxial stretching device, 10 M blebbistatin treated epithelial islands exhibited significantly fewer cell-cell contact ruptures than untreated controls, demonstrating a potent protective effect against mechanical strain. Traction force microscopy revealed diminished cell-generated strain energy by over 60% indicating a partial but significant reduction in contractility upon 10 M blebbistatin treatment. Nanoindentation measurements revealed that moderate contractility inhibition decreased the cellular Young's modulus by more than 40%. Consequently, moderate contractility inhibition safeguards epithelial junctions through a dual mechanical effect: it simultaneously reduces baseline active tensile stresses due to cell contractility and lowers the passive elastic forces generated within the softened cell island during external stretch. Our findings indicate that this systemic reduction in forces dominates over any loss of biochemical adhesion strength at cell-cell contacts. We propose that shifting the epithelium from a rigid, highly stressed continuum to a more compliant, relaxed state by moderate contractility inhibition can serve as a general biophysical mechanism to preserve barrier integrity under severe mechanical challenge.

Cell states are increasingly conceptualized as attractors of high-dimensional dynamical systems, yet quantitative approaches for integrating phenotypic information into this framework remain limited. Here, we take an image-based approach that combines unsupervised machine learning (ML) with timelapse imaging to extract and characterize the temporal dynamics of morphological features. Using a cell line with endogenously tagged VE-cadherin, we acquired brightfield and fluorescence timelapse images of human induced pluripotent stem cell-derived endothelial cell (hiPSC-EC) monolayers, which adopt distinct phenotypes at two different magnitudes of shear stress in terms of their morphology, behavior, and VE-cadherin organization. To quantify these phenotypic cell states without segmentation, we trained a diffusion autoencoder to predict VE-cadherin signal from brightfield images. We identified interpretable ML-based features representing cell orientation, elongation, and local density. Treating these variables as dimensions of a morphological state space, we estimated a data-driven vector field and found that the two observed phenotypic cell states correspond to stable fixed points of the inferred dynamical system. Mapping measured cell migration coherence onto this space further distinguished the states. Imaging cells across intermediate shear stresses revealed a regime of bistability in which both states coexist, indicating that the shear-stress-dependent transition between endothelial cell states occurs as a bifurcation of the inferred dynamical system. Finally, we applied this method to study an N-terminal truncation of VE-cadherin, finding that mutated cells preserve alignment and coherent migration, but exhibit altered morphology and increased migration speed. This work demonstrates the applicability of a dynamical systems approach to quantitatively characterize morphological aspects of cell state from interpretable ML-based features.

The cytoplasm is a crowded and dynamic fluid within which cellular building blocks such as mRNA, proteins, or organelles undergo transport and mixing. Although small things like proteins can eventually mix through diffusion, the high viscosity of cytoplasm means that it should be difficult to obtain significant mixing for structures in the size range of mRNA, multi-protein complexes or organelles. In large amoeboid cells, the cytoplasm undergoes active streaming coupled to cell motility, but this streaming is laminar flow which should not be effective for mixing. In this work we used a combination of live cell tracking of injected beads and computational analysis of motion and mixing in giant amoeba Chaos carolinensis with the initial goal of testing the possibility that large-scale cellular deformations during pseudopod formation might implement chaotic mixing by a Baker-transform like process. Instead, we found that Chaos carolinensis accelerates cytoplasmic mixing using a novel cytoplasmic gel state capture and release strategy. While it was previously thought that the amoeba sol to gel state transitions only occur at the trailing and leading edge of the cell body, our work indicates that these transitions occur frequently throughout the mid-cell region, driving the cytoplasmic mixing of beads and organelles. These results indicate that amoeba achieves nearly complete mixing between 1 and 2 cytoplasmic stream/flow cycle, effectively approximating the Bernoulli mixing regime and thus representing one of the theoretically fastest possible mixers.

The human spine is a complex, coordinated biomechanical system. Physiologically, its tissues are also highly interdependent in terms of function and viability. The interaction between mechanical stress and biological/biochemical activity over time constitutes a key driver of spinal degeneration. Research to date providing mechanistic insights into this process has focused on individual components (vertebra and disc tissue analogues), in isolation or as basic functional units. However, many observations from individual units will not translate to whole spine behaviour. The intricate complexity of the spine requires novel experimental models (synthetic and biotic), which must consider the spine at an organ level and adopt an integrative approach that can capture the dynamics which govern its function. Here, we report the development of a biomimetic spinal model prototype, amenable to cellular integration, which is miniaturised to the in vitro scale to provide a controlled environment and testbed for axial biological mechanics. The research presented here encompasses more than a decade of systematic investigations during which the gradual emergence of key manufacturing innovations progressively enabled addressing an exceptionally complex bioengineering challenge - organotypic spine engineering. The model comprises the full anatomical range of spinal vertebrae/bones (C1 to Sacrum & Coccyx), reproduced using bioceramic materials, assembled in sequence into a relevant columnar architecture and mechanically connected end-to-end by biochemically active interfaces. A range of assessments examining anatomical design, material behaviour and manufacturing processes is presented. The work explores concepts such as longitudinal mechanobiology and multi-segment coupling as well as manufacturing strategies using autonomous materials and instrumentation. This prototype introduces for the first time columnar level behaviour and the ability to study time dependent adaptations. This model is important because it can support tissue maturation, evolving mechanical properties and adaptive behaviour and it represents an intermediate step between isolated skeletal tissue models and future organ-level spinal constructs.

BackgroundQuantitative dynamic contrast-enhanced myocardial perfusion cardiovascular magnetic resonance (CMR) enables estimation of myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). These measurements require an arterial input function (AIF), which is typically derived from the left ventricular blood pool. However, the contrast agent bolus undergoes dispersion during transport through the coronary vasculature before reaching the myocardial microcirculation. This may introduce systematic and spatially heterogeneous errors in MBF and MPR estimates. PurposeThis work provides an extended segmental analysis of bolus-dispersion-induced errors in quantitative myocardial perfusion MRI using previously established computational fluid dynamics (CFD) simulations in realistic porcine coronary artery models. The focus of the present analysis is the assignment of coronary outlets to myocardial segments and the resulting segmental variability of MBF and MPR errors. MethodsRealistic three-dimensional models of the left and right coronary arteries were extracted from an ex-vivo porcine imaging cryomicrotome dataset. The models extended down to the pre-arteriolar level and included 364 outlets for the left coronary artery and 104 outlets for the right coronary artery, with an average outlet diameter of 383 {+/-} 85 {micro}m. Blood flow was simulated under rest and stress conditions using OpenFOAM. Contrast agent transport was then modeled by solving the advection-diffusion equation using a gamma-variate bolus as input. Outlet concentration-time curves were analyzed using an indicator-dilution model to estimate MBF and MPR errors. Outlets were assigned to standardized myocardial segments, and segmental averages were evaluated with respect to coronary supply territory and travel distance from the model inlet. ResultsThe simulations demonstrated marked segmental heterogeneity of volume blood flow and bolus-dispersion-induced MBF and MPR errors. Errors increased with travel distance from the coronary artery inlet and were more pronounced in regions supplied by the right coronary artery, consistent with lower flow velocities and stronger bolus dispersion. The resulting systematic errors led to underestimation of MBF and overestimation of MPR, with segmental deviations reaching up to approximately 60%. ConclusionBolus dispersion in the coronary vasculature may lead to substantial segmental and location-dependent errors in quantitative myocardial perfusion MRI. This extended analysis indicates that dispersion-related bias is not spatially uniform, but depends on coronary supply territory, travel distance, and flow conditions. These effects should be considered when interpreting regional MBF and MPR estimates, particularly as automated quantitative myocardial perfusion CMR becomes more widely used.