APL Bioengineering

Scaling up microvessel culture systems is essential for producing vascularized clinically relevant tissues, yet current platforms offer little guidance on how to preserve flow conditions during scale-up. Here, we present a computational-experimental framework using computational fluid dynamics (CFD) to guide the design and scaling of microvessel bioreactors. Interstitial flow distributions were pre-dicted in two perfusion-based platforms-a permeable insert and a rhomboidal microfluidic chamber-across multiple scaling factors and hydrostatic pressures. CFD identified IF ranges conducive to vascu-logenesis and quantified how geometry and pressure modulate flow uniformity. Scaled-up bioreactors generated microvessel networks with consistent morphology and connectivity over a 30-fold increase in culture volume, confirming that maintaining equivalent IF ensures reproducible outcomes. The permeable insert platform maintained uniform IF across scales, while the rhomboidal chamber produced spatially varying IF resulting in heterogeneous but physiologically relevant networks. These findings establish CFD as a predictive tool for rationally scaling perfusion bioreactors, enabling microvessel production at clinically relevant scales with controllable morphology. Significance StatementScaling up microvessel bioreactors is critical for engineering large pre-vascularized tissues. However, larger scales may disrupt flow conditions that drive vessel formation. This study demonstrates that computational fluid dynamics (CFD) can predict interstitial flow and guide the rational scale-up while preserving the vasculogenic microenvironment. Experiments across 30+-fold size increase confirmed that matching inter-stitial flow results in morphologically identical microvessel networks. By linking simulation-based design with experimental validation, this work establishes CFD as design tool for scalable perfusion bioreactors for production of microvessel networks at clinically relevant scales.

Maintaining a confluent, antithrombotic endothelium on cardiovascular biomaterial surfaces remains a major barrier to long-term hemocompatibility, as endothelial cells (ECs) rapidly denude under supraphysiological shear in prosthetic devices. Here, we hypothesized that mesoscale surface geometry ([~]100-200 {micro}m) could reorganize near-wall hemodynamics, preserving endothelial coverage and function under extreme shear. Engineered microtrenches were introduced onto an implant biomaterial to generate spatially defined shear environments. Under supraphysiological near-wall shear ([~]250 dyn/cm{superscript 2}), microtrenched geometries created attenuated shear and vorticity gradients. Endothelial monolayers were sustained in these flow domains for 120 hours, whereas flat controls rapidly denuded. Endothelial retention in 22.5{degrees} angled trenches increased dramatically, from an EC of 33 to 101 dyn/cm{superscript 2}. 45{degrees} angled trenches further increased endothelial shear resistance to an EC of 207 dyn/cm{superscript 2}. Endothelial monolayers demonstrated collective mechano-adaptation to ultra-high shear through VE-cadherin junction thickening and coordinated cytoskeletal and nuclear alignment. Mechanoadapted monolayers exhibited increased eNOS expression correlated with local shear and elevated nitrite production (45{degrees}: 50.4 {+/-} 6.1 {micro}M; 22.5{degrees}: 35.7 {+/-} 3.3 {micro}M; 0{degrees}: 28.4 {+/-} 6.8 {micro}M). In contrast, interfaces with abrupt shear transitions or elevated rotational flow exhibited reduced coverage, junctional thinning, and re-emergence of VCAM-1 and PAI-1, indicating inflammatory and pro-thrombotic activation. Structural, functional, and inflammatory readouts exhibited peak responses within a shared shear-vorticity regime. Multivariate regression identified shear-vorticity coupling as the dominant predictor of endothelial persistence, with optima clustering within a mechanical range ({approx}0.8-2.9 x 10 dyn{middle dot}cm-{superscript 2}{middle dot}s-{superscript 1}). These findings establish geometry-driven modulation of near-wall flow as a predictive, material-agnostic strategy for endothelialization and vasoprotection of high-shear cardiovascular implants.

Caveolae, flask-shaped membrane invaginations highly enriched in endothelial cells, play a central role in buffering membrane tension, yet the principles governing their spatial organization remain elusive. This investigation sought to generate the most comprehensive and systematic analysis of blood vessel caveolar spatial organization. To do so, our group leveraged micropatterning technologies to impose precise biophysical constraints on endothelial cell geometry to probe how caveolae are organized under defined tensional and polarity environments. These experiments were integrated with a high-throughput spatial cell mapping computational pipeline for analyzing thousands of caveolae, providing an extremely high-fidelity analysis. Our results provide a governing framework of how total cellular caveolae are spatially organized during random and directional migration, non-motile polarized, nascent and stable monolayers with differing confinement levels as well as in angiogenic vasculature in vivo. Broadly, our results demonstrated caveolae preferentially organized in the rear of migrating and polarized endothelial cells. In differing monolayer configurations, caveolae default to a peri-junctional spatial organization. Lastly, in mouse retinal blood vessels caveolae are most prominent in the vascular front due to their responsiveness to vascular endothelial growth factor signaling. Overall, these results strongly suggest that caveolae cellular arrangement and number are highly predictive of vascular stability and remodeling states.

Precision-cut lung slices (PCLS) retain the native cells and extracellular matrix that contribute to the structural and functional integrity of lung tissue. This technique enables the study of cell-matrix interactions and is particularly useful for pre-clinical pharmacological studies. More specifically, PCLS are widely used to model the complex pathophysiology of pulmonary fibrosis, an uncurable and progressive interstitial lung disease. Current ex vivo pulmonary fibrosis models expose PCLS to pro-fibrotic biochemical cues over a short timeframe (hours to days) and quickly collect samples for analysis due to viability concerns. This condensed timeline is a limitation to understanding chronic disease mechanisms. To extend the utility of ex vivo pulmonary fibrosis models, PCLS were embedded in engineered hydrogels and exposed to pro-fibrotic biochemical and biophysical cues. Hydrogel-embedded PCLS maintained greater than 80% total cell viability over 3 weeks in culture. Gene expression patterns in samples exposed to pro-fibrotic cues matched trends measured in human fibrotic lung tissue. Finally, treatment with Nintedanib, a Food and Drug Administration approved pulmonary fibrosis drug, moderately reduced fibroblast activation and influenced epithelial cell differentiation. Collectively, these results show that hydrogel-embedded PCLS models of pulmonary fibrosis extend our ability to study fibrotic processes ex vivo and, when applied to human tissues, present a new approach methodology for studying lung disease and treatment.

Glioblastoma (GBM) lethality arises from aggressive invasion and diffuse infiltration of brain tissue. Conventional GBM preclinical models often fail to predict clinical therapeutic efficacy because they do not recapitulate the pathological extracellular matrix (ECM) cues that drive tumor invasion. Here, we present an ECM mimetic 3D platform using a fibrin scaffold to recapitulate the hemorrhagic, pro-thrombotic tumor microenvironment characteristic of high-grade gliomas. This fibrin scaffold induces a pro-invasive phenotype in GBM spheroids by upregulating proliferation/cell cycle- (MYC, FOXOM1, CCND1) and invasion-associated-(CTSS, FOXM1, CCND1) genes. Traditional cell morphology quantification methods (e.g., circularity) distil complex shapes into singular metrics and cannot capture the nuances of invasion. To address this limitation, we have applied a deep-learning segmentation pipeline (MARS-Net) and high-content morphodynamic descriptors. By using the Preserving Heterogeneity (PHet) algorithm, the 3D platform accurately classifies invasiveness levels and captures the invasion-inhibitory effects of potential repurposable drug candidates. We demonstrate that our model can predict a spheroids long-term invasive fate with high accuracy using only partial image sets from early time-points, rather than the complete time-course images. Our work presents an in vivo-like, scalable 3D platform integrated with a quantitative high-throughput pipeline to elucidate GBM invasion mechanisms and to evaluate anti-invasive compounds.

The spatial arrangement of cells is fundamental to the mechanical and functional integrity of three-dimensional (3D) tissues, yet engineering spatially well-controlled tissue architectures remains challenging. Here, we computationally investigated how layered tissue architectures can be designed by modulating cell-cell interfacial tension. We performed simulations using a 3D vertex model and systematically varied interfacial tension magnitudes. The simulations generated a range of layered tissue architectures, including planar monolayers, bilayers, and structurally stratified states. In homogeneous cell populations, increasing interfacial tension drove transitions from monolayer to structurally stratified configurations. In heterogeneous populations, differential interfacial tensions induced out-of-plane cell sorting and the formation of compositionally sorted multilayers. Moreover, a recursive tension design strategy enabled hierarchical organization of multiple cell types into separate layers. Notably, this recursive scheme uses only two tension levels (high vs. low) assigned across interfaces and can, in principle, be extended to specify layered architectures with an arbitrary number of layers. Together, these results identify cell-cell interfacial tension as a tunable mechanical parameter for regulating layered tissue architecture and provide design principles for layered tissue engineering and regenerative medicine.

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

Nucleus shape is a sensitive indicator of cell state, influenced by numerous bio-chemical and physiological factors. While prior work has cataloged how perturbations alter nucleus morphology, we address the inverse: inferring underlying molecular changes from nucleus shape alone. We previously developed a mechanical model yielding two nondimensional parameters: flatness index and scale factor, which are surrogate measures for cortical actin tension and nuclear envelope compliance respectively. In this study, we apply these parameters to investigate the dynamics in cellular mechanics during confined migration. We fabricated polydimethylsiloxane (PDMS) microchannels with widths of 3 {micro}m (high confinement) and 10 {micro}m (low confinement) and tracked cells migrating through them. We captured high-frequency 3D nucleus shapes via double fluorescence exclusion microscopy and custom image analysis. Fitting the model and estimating flatness index and scale factor to time-resolved shapes revealed dynamic regulation in 3 {micro}m channels: actin tension decreased and nucleus compliance increased immediately before nucleus entry into the constriction, with rapid restoration to baseline upon exit. No such changes occurred in 10 {micro}m channels, indicating active, confinement-dependent cytoskeletal adaptation. Immunostaining for YAP and lamin-A,C confirmed these model inferences. Our results uncover mechanostasis, active mechanical homeostasis, during confined migration and establish the combination of double fluorescence exclusion microscopy and nondimensional nucleus shape parameters as a powerful, non-invasive tool for single-cell mechanobiology studies.

Respiratory infectious diseases are among the leading causes of global morbidity and mortality and remain a major public health concern. Progress in understanding early host-pathogen interactions has been hampered by the limited physiological relevance of existing experimental systems. Different models mimicking the human respiratory epithelium have been developed to study viral infections in vitro, such as tridimensional (3D) tissue models and organoids. However, many lack key features of human tissue architecture, particularly the lamina propria or immune cells. To address these limitations, we established an immunocompetent 3D model of the human respiratory mucosa by combining nasal epithelial cells isolated from nasal brushings, fibroblasts from mid-turbinate nasal biopsies, and macrophages derived from blood monocytes. These cells were sequentially seeded into collagen-chitosan scaffolds, resulting in a reconstructed respiratory mucosa closely resembling the in vivo nasal tissues. To further confirm the physiological relevance of the model, we infected it with influenza A virus. The mucosa model supported viral replication in the epithelium and consequently showed increased secretion of inflammatory cytokines and upregulation of type I interferon related genes, enabling the monitoring of early antiviral innate immune responses in a physiologically relevant context.

Small airways are the primary sites of airflow obstruction in chronic obstructive pulmonary disease. Effective delivery of aerosolized drug particles to these regions is crucial to maximize treatment efficacy while minimizing side effects. However, conventional inhalation therapy approaches (i.e., full-mouth particle release and inhalation (FMD)) typically result in insufficient drug deposition in the small airways and an uneven distribution across the five lung lobes. To address such deficiencies, the goals of this study are triple folds: (1) to develop a fast and accurate framework to secure target drug delivery (TDD) nozzle diameter and location based on the conventional computational fluid particle dynamics (CFPD)-FMD simulations, (2) to develop a CFPD-informed machine learning (ML) inverse-design framework that predicts optimal inhaler nozzle parameters based on patient-specific breathing patterns and drug properties, and (3) to demonstrate the feasibility of embedding this framework into a user-centered smart inhaler prototype to improve uniform TTD to the small airways across all five lung lobes. Specifically, a subject-specific mouth-to-generation-10 human respiratory system was employed, and 108 high-fidelity CFPD-FMD simulations were performed under varied physiological and design parameters, including tidal volume, particle diameter, release location, and release timing. Particle release maps generated from those CFPD-FMD simulations via backtracking identified optimal nozzle diameters and locations that promote uniform multi-lobe drug delivery while limiting off-target deposition. Accordingly, a dataset was compiled with inputs (i.e., flow rate, particle size, release z-coordinate, release time) and targets (i.e., nozzle center x- and y-coordinates, nozzle diameter). These inputs and targets form the CFPD-TDD dataset, on which 16 ML models were trained to learn inverse mapping from patient- and drug-specific inputs to optimal nozzle design parameters. Performance was evaluated using mean squared error (MSE) and mean absolute error (MAE) overall and per target feature. Parametric analysis using CFPD-FMD simulations was conducted to determine how patient-specific and drug-specific factors affect pulmonary air-particle transport dynamics and to explain why achieving CFPD-TDD in small airways with CFPD-FMD strategies remains challenging. Furthermore, the ML evaluation in this feasibility study demonstrated robust learning of the inverse mapping from patient-specific inputs to optimal nozzle parameters. Four top-performing models showed consistently low MSE/MAE across cases, and an ensemble (i.e., mixed model (MixModel)) combining their strengths was formulated. Independent CFPD-TDD simulations beyond the training and testing datasets were used as the ground truth to validate ML-predicted nozzle configurations. Compared with conventional CFPD-FMD strategies, ML-guided nozzle designs significantly improved inter-lobar deposition uniformity and reduced off-target deposition in the upper airways, demonstrating the feasibility of ML-enabled TDD to the small airways. Overall, this study establishes a CFPD-informed ML inverse-design framework as a viable algorithmic foundation for user-centered smart inhalers, enabling adaptive, patient-specific TDD to the small airways with improved deposition uniformity across all five lung lobes. By integrating first-principle-based CFPD with ML, this work provides a methodological pathway toward next-generation smart inhalers for more effective treatment of small airway diseases.

Understanding how cells migrate through confined environments is crucial for elucidating fundamental biological processes, including cancer invasion, immune surveillance, and tissue morphogenesis. The nucleus, as the largest and stiffest cellular organelle, often limits cellular deformability, making it a key factor in migration through narrow pores or highly constrained spaces. In this work, we introduce a geometric surface partial differential equation (GS-PDE) model in which the cell plasma membrane and nuclear envelope are described as evolving energetic closed surfaces governed by force-balance equations. We replicate the results of a biophysical experiment, where a microfluidic device is used to impose compressive stresses on cells by driving them through narrow microchannels under a controlled pressure gradient. The model is validated by reproducing cell entry into the microchannels. A parametric sensitivity analysis highlights the dominant influence of specific parameters, whose accurate estimation is essential for faithfully capturing the experimental setup. We found that surface tension and confinement geometry emerge as key determinants of translocation efficiency. Although tailored to this specific setup for validation purposes, the framework is sufficiently general to be applied to a broad range of cell mechanics scenarios, providing a robust and flexible tool for investigating the interplay between cell mechanics and confinement. It also offers a solid foundation for future extensions integrating more complex biochemical processes such as active confined migration.

Experimental methods for characterizing single cells and cell populations have improved tremendously over the past decades. This progress has enabled the development of quantitative, mechanistic models for cellular processes based on either single cell or bulk data. However, coherent statistical frameworks for the model-based integration of different data types at the single-cell and population levels are still missing. In this work, we present a mathematical modeling approach for integrating single-cell time-lapse, single-cell snapshot, single-cell time-to-event and population-average data. Utilizing a formulation based on nonlinear mixed-effect modeling, we enable the description of multiple data types, with and without single-cell resolution, and we propose a tailored parameter estimation method. Furthermore, we propose a tailored parameter estimation scheme that facilitates the assessment of underlying process parameters. Our study demonstrates that the proposed approach can reliably integrate diverse data types, thereby improving parameter identifiability and prediction accuracy. Applying this framework of extrinsic apoptosis reveals that simultaneously considering multiple data types can be essential, particularly when experimental constraints limit data availability. The proposed approach is broadly applicable and may significantly advance our understanding of complex biological processes.

Low- to moderate-intensity ultrasound (US) technologies are increasingly being used to non-invasively modulate biological function in both clinical and laboratory settings. Realizing the full potential of these approaches requires a detailed mechanistic understanding of how ultrasound interacts with living cells. Here, we developed a well-controlled experimental platform to expose adherent cells to ultrasound stimulation while monitoring cellular activation via calcium imaging. We show that cell activation is dependent on cell type and identify NIH3T3 fibroblasts as a particularly robust responder. Our findings indicate that acoustic streaming is the primary mechanism underlying ultrasound-induced activation in our in vitro experiments. Surprisingly, the investigation of calcium dynamics revealed that the observed cytoplasmic calcium elevation originates predominantly from intracellular stores rather than extracellular influx, with membrane ion channels not contributing directly to the response. Notably, the biomechanical property of the cell-cortex emerges as a critical determinant of the cells sensitivity to ultrasound. Overall, our results provide clear evidence that the underlying mechanistic response involves external and internal factors that modulate the ultrasound-cell interaction and highlight important mechanistic considerations for ultrasound-based strategies aimed at cellular stimulation.

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

Metastasis is the leading cause of death in breast cancer patients, yet there are no drugs specifically designed to block cancer cell intravasation, an early step of the metastatic cascade that originates circulating tumour cells (CTCs). A major challenge in developing anti-intravasation drugs is the scarcity of relevant in vitro platforms suitable for predictable drug discovery. Intravasation is a fundamental step of metastasis and involves the crossing of cancer cells through an endothelial barrier to enter the blood circulation. Here we developed an intravasation-on-a-chip model with controlled extracellular matrix composition, fluid flow and shear stress, which mimics the dynamic tumour-endothelium interface. The systems allows real-time imaging of intravasation and the isolation and quantification of intravasated cancer cells. As a proof-of-concept for drug testing, we show that perfusion with the PI3K/mTOR inhibitor Dactolisib, significantly reduced intravasation without compromising endothelial cell viability. The system also provides the capability to evaluate inhibitor on-target activity via imaging analysis. This intravasation-on-a-chip model offers a powerful, scalable, and imaging-compatible platform for discovering and evaluating anti-intravasation compounds.

Fungal substrates are promising candidates for unconventional computing, but specimen-to-specimen variability makes logic-gate fabrication difficult to reproduce. This paper presents a digital-twin workflow for fungal excitable networks and evaluates three components needed for computeraided design: identifying parameter regimes that support XOR computation, inferring latent biophysical parameters from electrical characterization data, and refining those inferred parameters by waveform matching. The model represents mycelium as a random geometric graph with FitzHugh-Nagumo node dynamics and memristive edge conductances. A systematic optimization study over 160 simulated specimens identifies a viable XOR subspace defined by tuned biophysical parameters, electrode geometry, and stimulus timing. A characterization study over 400 simulated specimens uses step-response, paired-pulse, and triangle-sweep protocols to extract 94 response features. Random forest regressors recover several latent parameters reliably (R2 = 0.912 for{tau} v, 0.816 for{tau} w, 0.717 for a), while vscale, Ron, and Roff remain weakly identifiable. On a preliminary rediscovery validation using 15 optimized specimens (20-50 nodes), ML initialization followed by local waveform-matching refinement reduces mean waveform mismatch from 1.070 to 0.042 (96.0%; one-sided Wilcoxon p = 3.1 x 10-5) and reduces mean core-parameter error from 16.6% to 8.8% (p = 6.1 x 10-5). A sensitivity analysis on 72 viable specimens reveals that{tau} w and are the most consequential parameters for XOR twin accuracy, while vscale and Roff are both hard to identify and tolerant to error. These results show that fungal digital twins can already narrow the search for viable computational substrates, partially recover the excitable dynamics that govern them, and support small-scale specimen-specific refinement without yet claiming full XOR transfer.

The epithelial-mesenchymal transition (EMT) alters cell-cell interactions to facilitate collective or individual migration during embryonic development, wound repair, or tumor invasion. Epithelial cells are typically cohesive and stationary while mesenchymal cells are individually dispersed and motile. Additional "partial" EMT states are thought to occur with distinct adhesive and migratory behaviors, but these functional phenotypes are poorly understood. Here, we show that cells treated with moderate TGF-{beta} concentration exhibit collective migration that is fast and directionally persistent despite heterogeneity in epithelial, partial, and mesenchymal states. We find cells coordinate their motility by reorienting in similar directions after transient contacts, a distinct "collision guidance" mechanism that differs from epithelial arrest or mesenchymal repulsion. Moreover, partial EMT cells sustain collision guidance when interacting with epithelial or mesenchymal cells, which otherwise have increased tendency to repel. We corroborate these experimental observations with a computational model using self-propelled interacting particles that align their motion or repel upon contact. Finally, we show that partial EMT enables tissue monolayer fronts to overwhelm and displace monolayers of other cell types after collision. Overall, these results reveal that partial EMT promotes coherent and emergent behaviors that bridge from cell to tissue length scales, with potential implications for shaping epithelial tissue formation, regeneration, or disorganization.

Cancer cells in hypoxic environments often proliferate less but exhibit enhanced migration relative to their normoxic counterparts. Recent in vitro and in silico studies have characterized the role of hypoxic memory - the ability of cancer cells to retain their hypoxic phenotype even when reoxygenated - in tumor invasion. However, the observations have been limited either to exposing cancer cells to hypoxia for a fixed duration or by assuming a fixed-time persistence of the hypoxic state upon reoxygenation independent of the duration of hypoxia exposure. Thus, time-dependent cell-state changes during hypoxia and their impact on hypoxic memory remains unclear. Here, we first analyze transcriptomic data from breast cancer samples to show that the genes upregulated at transcriptional level and hypomethylated at epigenetic level are enriched in cell invasion, indicating hypoxic memory-driven process of tumor invasion. Next, we used a computational model to investigate how the spatial-temporal dynamics of oxygen levels in a tumor drive time-dependent changes in hypoxic memory and influence tumor invasion dynamics. Our simulation results show that such dynamic hypoxic memory can drive enhanced tumor invasion over a fixed hypoxic memory by a) enriching hypoxic cell density at the tumor front, b) reducing sensitivity of hypoxic cell state to fluctuations in oxygen supply, and c) enhancing effective diffusion of hypoxic cells. Our results highlight the crucial role of dynamic hypoxic memory in shaping tumor invasion dynamics, underscoring the need to elucidate its underlying mechanisms in future studies.

The blood vasculature has a high capacity for structural regeneration, driven by the blood endothelial cells (BECs) that comprise it. This regenerative process, which involves BEC migration and proliferation to form these complex tissues, is linked to low frequency (< 0.1 Hz) calcium spiking that precedes these activities. However, we need new approaches to stimulating angiogenic responses in tissue engineering applications. By conducting experiments that manipulate local ionic concentrations and developing a simple, yet powerful, computational analysis, we demonstrate that sodium-calcium cross-talk is a crucial component that regulates the calcium signaling and downstream angiogenic responses. Activation and deactivation of the inositol triphosphate 3 receptors (IP3Rs) on the endoplasmic reticulum (ER) and the switch between forward and reverse modes of the sodium-calcium exchanger (NCX) are proposed to be the key mechanisms underlying calcium oscillations when cells are exposed to temporary cationic depletion. The spiking is suggested to be a release of intracellular calcium mediated by IP3R activity, and transport in or out of the cell is driven by NCX for the calcium oscillatory signaling pattern. The NCX and IP3R both contribute to manage intracellular calcium and ionic concentration as initially there is a long ER deactivation period while intracellular sodium slowly increases until a sudden onset of calcium is released by the ER. Other calcium and sodium ion channels can change this resonant coupling of ER and NCX to alter the inter-spike duration. Synchronization of the spiking intervals between cells is triggered by stimulating with vascular endothelial growth factor (VEGF), which induces a propagating wave of intracellular calcium across the 2D tissue culture, prior to coordinated cell migration and proliferation towards the VEGF source. This wave, which can be artificially induced and studied using electrical stimulation, suggests that the underlying sodium-calcium crosstalk mechanism introduces intracellular calcium polarization, whose orientation is transferred across cells through spike synchronization. Thus, control of calcium signaling dynamics through regulation of ionic depletion can serve as useful method for generating angiogenic responses in engineered tissue constructs.

Digital volume correlation (DVC) has become the benchmark experimental technique for full-field strain measurement in bone mechanics. In our previous work we developed a novel data-driven image mechanics (D2IM) approach that learns from DVC data and predicts displacement fields directly from undeformed X-ray computed tomography (XCT) images, deriving strain fields from such predictions. However, strain fields derived through numerical differentiation of displacement fields amplify high-frequency noise, and regularization techniques compromise spatial resolution while incurring substantial computational costs. Here we propose the upgrade D2IM-Strain to predict strain fields directly from XCT images of bone. Two prediction strategies were compared: displacement-derived strain and direct strain prediction. The direct strain prediction model significantly improved accuracy particularly for strain magnitudes below 10000{micro}{varepsilon}, taken as a representative threshold value for bone tissue yielding in compression. In addition, the direct approach reduced false-positive high-strain classifications by 75%. By eliminating numerical differentiation, the approach reduces noise amplification while maintaining computational efficiency. These findings represent a critical step toward developing robust data-driven volume correlation methods for hierarchical materials.