Integrative Biology

Protein tyrosine phosphatase receptor J (PTPRJ) restrains cell proliferation and migration by dephosphorylating receptor tyrosine kinases (RTKs) including the epidermal growth factor receptor (EGFR). PTPRJ is a purported tumor suppressor, and alterations to its expression and/or function are associated with colorectal, breast, lung, and other cancers. While there is interest in controlling PTPRJ-regulated phenotypes, efforts are limited by the complexity of PTPRJ-mediated signaling. PTPRJ dephosphorylates multiple RTKs, and the degree to which PTPRJ control of signaling and phenotypes depends on local cellular RTK activation profiles is unknown. To probe the context dependence of PTPRJ signaling regulation, we collected signaling measurements across 16 pathway nodes at two time points in a panel of HSC3 carcinoma cells engineered with different PTPRJ expression profiles. Cells were treated with three different RTK ligands, and paired phenotype measurements (viability, wound healing, xCELLigence cell index) were made. Partial least squares regression models were developed to predict relationships between PTPRJ-regulated signaling pathways and cell phenotypes. The model effectively separated contributions to variance arising from the PTPRJ expression background and growth factor context. In testing model predictions, we demonstrated that PTPRJ suppressed MET-induced cell cell proliferation via regulation of a HER3/AKT signaling axis that stabilized PTPRJ expression through an unanticipated feedback mechanism. We also found that PTPRJ regulated HSC3 cell migration via JNK signaling that was preferentially activated by MET. Our results identify new regulatory nodes through which PTPRJ influences cancer cell phenotypes and demonstrates that these processes preferentially occur in the context of distinct RTK activation states.

Glioblastoma is an aggressive brain cancer whose cells can switch between different modes of invasion in response to their surroundings, making the disease difficult to predict and treat. How physical forces influence this adaptability remains poorly understood. Here, we used simulated microgravity together with engineered hydrogels that independently control adhesion, degradability, and mechanical properties to test how gravity affects glioblastoma invasion. Microgravity strongly reduced invasion and shifted cells from elongated, protrusive behavior to a more cohesive state. Proteomic analysis showed reduced invasive signaling together with increased cell-matrix and cell-cell adhesion, consistent with a redistribution of contractile forces toward the cell edge. Under normal gravity, blocking CD44, integrin {beta}1, or N-cadherin reduced matrix-dependent invasion. In contrast, under microgravity, inhibiting these same adhesion pathways restored invasion, indicating that microgravity traps cells in an overly adhesive, cohesive state that limits movement rather than motility itself. These findings show that gravity is an important regulator of cancer cell plasticity and reveal a mechanically induced vulnerability in glioblastoma invasion. More broadly, combining defined biomaterials with gravitational modulation provides a new way to study how physical forces shape tumor behavior.

BackgroundDiscrete subaortic stenosis (DSS) is a pediatric cardiovascular disease marked by fibrotic growth within the left ventricular outflow tract (LVOT), leading to severe complications, including left ventricular hypertrophy, aortic regurgitation, and arrhythmias. Despite surgical intervention, a 20-30% recurrence rate suggests a complex underlying pathophysiology. Elevated flow and resultant hemodynamic shear stress within the LVOT are key factors in DSS development. While effects of shear stress on endothelial cells have been studied, the impact on macrophages and their interactions with endothelial cells remains unclear. MethodsIn this study, human monocyte-derived macrophages (MDMs) and human aortic endothelial cells (HAECs) were subjected to shear using a cone-and-plate viscometer. Cellular crosstalk was evaluated through conditioned media (CM) transfers. Gene expression, permeability and chemotaxis assays, immunofluorescent staining, and ELISAs assessed cellular responses. ResultsMDMs exposed to shear stress exhibited a pro-inflammatory response with upregulated TNF and CXCL8 genes. HAECs exposed to MDM-CM showed increased expression of inflammatory markers (VCAM-1, ICAM-1) and decreased VE-Cadherin and CD31, indicating increased permeability. Permeability assays confirmed that HAECs became more permeable when exposed to MDM-CM. Chemotaxis assays showed time-dependent monocyte migration in both MDM-CM and HAEC-CM. Immunofluorescent staining revealed diminished VE-Cadherin and CD31 in HAECs exposed to MDM-CM. ConclusionsOverall, pathological shear stress induced macrophages to secrete factors that increased endothelial permeability and perpetuated an inflammatory response. This interaction likely exacerbates fibrosis in DSS, promoting recurrence post-surgery. Understanding these mechanisms opens potential therapeutic avenues targeting inflammatory crosstalk between macrophages and endothelial cells, which could mitigate fibrosis and improve patient outcomes.

Cells communicate via extracellular ligands, such as hormones, which bind to plasma membrane receptors and trigger intracellular signaling cascades. G Protein-Coupled Receptors (GPCRs) exemplify this mechanism by initiating signaling both at the cell surface and, from intracellular compartments such as endosomes. The kinetics and spatial localization of these signals are critical determinants of cellular responses, yet receptor trafficking-including internalization, endosomal sorting, and recycling-remains a pivotal but often overlooked component of theoretical GPCR models. In this study, we present a mathematical framework that integrates receptor trafficking and signaling compartmentalization into generic GPCR dynamic models. Using a compartmentalized approach based on systems of ordinary differential equations (Chemical Reaction Networks), we analyze how receptor internalization and recycling modulate ligand-induced responses. Our results show that the balance between plasma membrane and endosomal signaling can significantly enhance or diminish ligand efficacy. Calibrated with high-throughput kinetic data, our model offers a refined tool for ligand pharmacological characterization and advances the understanding of GPCR signaling spatial organization.

Glioblastoma (GBM) is a highly aggressive brain tumor with a five-year survival rate of less than 5%. The current standard of care established 20 years ago includes maximal surgical resection and administration of alkylating agent temozolomide (TMZ). GBM is highly invasive, and GBM cells that evade surgical resection can become resistant to TMZ and develop new aggressive secondary tumors. Post-relapse there are few treatment options available to patients. Tissue engineering approaches suggest the opportunity to develop in vitro models of the GBM tumor microenvironment that may accelerate the discovery of novel therapies for GBM. Here, we report the adaptation of hydrogel microdroplets (microgels) to encapsulate GBM cells in a tailorable 3D matrix to assess patterns of growth and to screen TMZ drug response using patient-derived xenograft (PDX) specimens. We exploit a unique aspect of the microgel system to account for the cellular heterogeneity within the tumor microenvironment (TME). We combine cell-laden microgels generated from TMZ-resistant and TMZ responsive variants of the same PDX specimens to create heterogeneous populations with varying levels of drug sensitivity. We demonstrate a range of drug resistance phenotypes as a function of the ratio of TMZ-responsive to resistance cells and identify the population required for TMZ-resistance to overtake take the response. We then investigate the influence of tumor mimetic shifts in hyaluronic acid bioavailability and hypoxia on patterns of TMZ resistance. We show exposure to matrix-bound hyaluronan increases TMZ resistance and the glioma stem cell population in both cell variants. Lastly, we report an increase in TMZ sensitivity but divergent changes in the GSC subfraction for TMZ resistant vs responsive GBM in the presence of hypoxia. Together, we demonstrate the versatility of cell-laden microgel approach to replicate heterogenous tumor populations, model shifts in the tumor microenvironment, and rapidly screen therapeutic response.

IntroductionCancer progression is driven not only by tumor cells but also by interactions between the extracellular matrix (ECM), stromal cells, and immune cells within the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs) are major drivers of ECM remodeling, assembling ECM with aberrant organization. Extra domain A fibronectin (EDA-FN), a cellular FN containing an extra type III domain, is upregulated in the TME. EDA-FN regulates cellular behavior and has been associated with poor patient prognosis. Macrophages are among the most abundant immune cells within the TME, where they contribute to TME remodeling and inflammation to promote cancer cell invasion and metastasis. However, how tumor-associated matrix-specific cues regulate macrophage behavior remains largely understudied. PurposeHere, we developed a fibroblast-derived matrix platform that captures the structural imprint of tumor-associated EDA-enriched matrices and investigated how matrix-specific cues regulate macrophage behavior in the absence of ongoing soluble factor cues. MethodHuman mammary fibroblasts (HMFs) preconditioned in incubated low-serum media (lNC, or control) and MDA-MB231 metastatic breast cancer cell-conditioned media (mTCM) were cultured on polyacrylamide gels of 2 kPa and 20 kPa, respectively, followed by decellularization. Matrix organization, including fiber alignment, width, and intrafibrillar spacing, was quantified from confocal images. Decellularized EDA-FN-enriched matrices were subsequently reseeded with macrophages to assess macrophage morphology, phenotype, and matrix interactions. ResultsThe combined effects of tumor-derived soluble factors and pathological stiffness induced a CAF-like phenotype in HMFs, accompanied by cytoskeletal reorganization and microarchitectural alterations of EDA-FN-enriched matrices. Tumor-associated matrices exhibited increased alignment, narrower fiber width, and enlarged intrafibrillar spacing compared to control matrices. These aberrant, tumor-associated matrix-derived features were associated with altered macrophage behavior, including heterogeneous morphology, enhanced localized EDA-FN matrix loss beneath the cell body, and a hybrid phenotype with a shift toward a CD206-dominant profile. ConclusionsThese findings demonstrate the feasibility of obtaining EDA-FN-enriched matrices to isolate matrix-specific cues for investigating macrophage-ECM interactions. Furthermore, this platform can be leveraged to identify matrix-targeting therapeutic approaches for modulating macrophage function within the TME.

The development of cancer immunotherapies is hindered by the lack of human-relevant models that accurately translate to patient outcomes. We combine patient-derived colorectal tumor organoids (PDOs) and cancer-associated fibroblasts (CAFs) into floating extracellular matrix drops to form miniature colorectal tumors. These SHaking Organoid COcultures (SHOCOs) maintain immune cells in numbers, states and functional interactions that are more physiologically accurate than traditional PDO-based co-culture models. Immunocompetent SHOCOs treated with T-cell bispecific antibodies exhibited a robust anti-tumor response, in a concentration- and duration of treatment-dependent manner. By varying the stromal content, we found that fibroblasts present a physical barrier that hinders intratumoral T-cell infiltration. We also demonstrate that tumor-associated stroma can be exploited therapeutically in potentiating anti-tumor immune responses. SHOCOs could aid the battle against cancer both by providing fundamental insights into immune and stromal tumor biology, and by catalyzing the discovery of novel therapeutic approaches. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/729459v1_ufig1.gif" ALT="Figure 1"> View larger version (78K): org.highwire.dtl.DTLVardef@1aed309org.highwire.dtl.DTLVardef@a11696org.highwire.dtl.DTLVardef@1d1d74dorg.highwire.dtl.DTLVardef@18e2c3f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 0C_FLOATNO / Graphical Abstract: Illustration of the SHOCO Model. (A) Overview of the components used in creating SHOCOs and the complex tumor microenvironment they replicate. Epithelial cells (pink), CAFs (red), CD8 T cell (blue), CD4 T cell (green). Characteristics of SHOCOs: (B) Emphasizing the role of CAFs in contracting and creating a denser microenvironment. (C) CAFs act as a physical barrier, preventing CD8 T cells from reaching tumor niches. (D) SHOCOs maintain a substantial myeloid population (violet) capable of performing antigen presentation. (E) T cells within SHOCOs mount an anti-tumor cytotoxic immune response as a result of immunotherapy. Granzymes (orange), cleaved Caspase-3/7 (yellow). C_FIG

BackgroundThe complexity of gene regulatory networks, involving thousands of genes, poses a fundamental challenge to understanding cancer phenotype reversal. However, recent evidence suggests that the effective dimensionality of normal and tumor transcriptional manifolds is low, and that small panels of genes can discriminate perfectly between normal and tumor samples. MethodsWe build upon two previously developed concepts: (i) highly accurate normal and tumor gene markers (namely, N-and T-markers), defined as genes with exclusive expression intervals in normal and tumor samples, respectively; and (ii) gene deregulation networks (GDNs), represented as directed acyclic graphs encoding causal relationships between gene deregulation events. A subset of genes appearing in both marker classes (NT-markers) act as bridging nodes between the N-and T-GDNs. Starting from these elements, we introduce a quantitative dynamical model based on node frequency and connectivity to assess how gene intervention effects propagate through the GDN and thereby predict their overall impact on the tumor tissue. ResultsAccording to the model, interventions on pure T-markers (T-markers that are not NT-markers) produce effects largely confined to the T-GDN, with a minimal perturbation of the N-network. Interventions on pure N-markers (N-markers that are not NT-markers) generate a perturbation of both networks, but with limited effect. In contrast, interventions on NT-markers with high activation frequency in both tumors and normal state (e.g., AGER in lung adenocarcinoma: 98% in tumor samples, 75% in normal samples) can induce bidirectional phenotype shifts. For an effective combination of targets, coverage across tumor samples must be maximized. At the same time, in the T-GDN the number of nodes unreached by the reverse cascade following the intervention must be minimized, as these regions may act as escape routes for the tumor. Escape probability further depends on the tumor stage and the tumors activation rate of new T-genes. When targeting NT genes, high frequency in normal samples should also be prioritized. ConclusionsHigh-frequency NT-genes, due to dual network connectivity and tissue relevance, represent optimal targets for achieving at least partial phenotype reversal. This framework provides a quantitative guide for prioritizing gene therapy targets and designing combination strategies that balance coverage, escape minimization, and normal tissue relevance.

The development of large-scale, three-dimensional human tissues is crucial for various applications in therapeutic tissue engineering, disease modeling, and drug testing. However, due to the diffusion limit of oxygen, the lack of functional vascular networks is a significant limitation in maintaining these engineered tissues in the laboratory. To address this challenge, we present a systematic, model-based design process for artificial supply networks that can ensure a sufficient supply of oxygen and nutrients to engineered human tissue. Our approach combines mathematical models of fluid dynamics, cell metabolism, and network properties to identify key parameters influencing the supply performance. We demonstrate the applicability and possibilities of this design process by simulating different network structures, including cuboid and rhombic do-decahedral honeycombs, under various conditions. Our results show that the structure of the artificial supply network, oxygen concentration, and solute flow within the network strongly influence cellular metabolic activity and viability. We also examine the effects of non-uniform cell density, channel blockage, and long channel length on the oxygen distribution inside the cell-containing tissue compartment. Our findings highlight the importance of considering these factors in the design of artificial supply networks for large-scale engineered human tissues. This study provides a promising approach for quickly exploring the vast design space of possible network structures under different conditions for desired cell and tissue states, ultimately contributing to the development of more efficient and effective tissue engineering strategies.

IL-10 is a key anti-inflammatory cytokine whose activity is impaired in autoimmune diseases. However, IL-10 also promotes inflammation under certain conditions, limiting the efficacy of IL-10-based therapies. Because the principles underlying these opposing effects remain unclear, we developed a mathematical model of the IL-10 signaling pathway to understand how such pleiotropic responses arise. Considering that STAT3 signaling is buffered against changes in IL-10RB receptor affinity, we provide a minimal mechanistic explanation for IL-10 variants with anti-inflammatory or pro-inflammatory biased responses produced by an altered receptor affinity. By linking model-predicted pSTAT1 and pSTAT3 abundances with transcriptomic changes, we identified IL-10-responsive genes that are regulated at different pSTAT thresholds. This could explain how IL-10 elicits distinct downstream responses at different signaling strengths, and suggests that, depending on the required response, the affinity of IL10 for its receptors should not always be enhanced. Overall, our study highlights how modeling can help disentangle IL-10 pleiotropy to support the rational development of more effective IL-10-based therapies.

The vascular system exhibits complex, non-planar geometries that become further distorted during pathological remodeling, including arterial tortuosity and aneurysms. Although hemodynamic shear stress is a well-established regulator of vascular function, the direct effects of curvature as an intrinsic geometric cue remain poorly defined. This is largely because existing in vitro models are static and fail to capture the dynamic changes that accompany disease progression. To address this gap, we used a magnetoactive hydrogel platform that enables real-time, on-demand curvature of endothelial monolayers to reproduce clinically established tortuosity metrics. Using this system, we found that elevated curvature increased nuclear localization of yes-associated protein (YAP), with the strongest response in convex relative to concave regions of highly tortuous endothelial monolayers. This mechanosensitive response was accompanied by reduced VE-Cadherin junctional thickness and increased membrane localization of endothelial nitric oxide synthase. Together, these findings identify local curvature, independent of shear stress, as a regulator of endothelial cell mechanosensing and function, and establish a dynamic hydrogel platform for isolating geometric regulation from shear stress inputs in vascular mechanobiology.

Chimeric antigen receptor (CAR) T-cells have represented a groundbreaking advance in the control of hematological cancers. However, their efficacy in controlling solid tumors has been rather limited, highlighting the importance of new cell-based therapies strategies to curb the progression of solid cancers. Here, we generated functional macrophages from human umbilical cord blood derived CD34+ hematopoietic stem cells (HSCs) engineered to express CARs. Approximately 50% of the CAR-MacCD34 population expressed anti-HER2 CARs and maintained high viability throughout differentiation. Mass spectrometry (MS) and multiparametric flow cytometry analysis revealed upregulation of proteins associated with phagocytosis, matrix remodeling, and degradation, indicating enhanced tumor infiltration potential. In vitro, CAR-MacCD34 exhibited a significantly higher capacity to phagocytose HER2-positive tumor cells compared to untransduced MacCD34 cells. Additionally, CAR-MacCD34 cells that phagocytosed cancer cells showed increased nuclear translocation of NF-kB, suggesting CAR-mediated intracellular signaling. To assess functionality in a more physiologically relevant context, we used tumor spheroids embedded in a dense 3D collagen matrix. Confocal microscopy and live imaging revealed that CAR-MacCD34 cells exhibited superior infiltration of dense tumor spheroids compared to untransduced MacCD34 cells. Notably, we observed multiple instances of tumor cell phagocytosis by CAR-MacCD34 cells in this 3D model. In addition, we employed in vivo zebrafish larvae models of HER2-positive tumors. We noted that CAR-MacCD34 cells persisted for over 8 days post-injection and demonstrated significantly greater efficacy in controlling tumor growth compared to untransduced MacCD34 cells. Our findings introduce a novel CAR-macrophage therapeutic approach with promising clinical potential, leveraging a renewable and accessible cellular source. Optimizing CAR-MacCD34 functionality in combination with existing therapies may lead to durable and effective anti-tumor responses for patients with solid tumors.

Experimental studies show that tumor cells adopt migratory or proliferative phenotypes depending on the local extracellular matrix (ECM). In this work, we propose a minimal go-or-grow invasion model, comprising two specialist cell phenotypes: proliferating and migratory, with phenotypic switching and cell migration depending on local ECM density. Numerical simulations of this model reveal that the spatial arrangement of proliferative and migratory cells depends on the choice of phenotypic switching function. We then ask whether this specialist cell-phenotype model can be reduced to a generalist cell-phenotype model. We derive a relationship between the reduced model and go-or-grow model in the fast phenotypic switching regime. We observe that the reduced model captures the dynamics of the original model, for a range of realistic phenotypic switching functions. We analytically derive the minimum traveling wave speed of the reduced model in a homogeneous ECM bed. Moreover, using linear stability analysis on the go-or-grow model, we recover the same wave speed expression. In addition, we numerically explore how the key parameters influence the traveling wave speed profile. Our analysis indicated the counter-intuitive result that the wave speed is independent of the matrix degradation rate, and our simulations show that, at most, the speed is weakly dependent on this parameter.

Preserving T cell cytotoxic function during ex vivo expansion remains a major challenge for adoptive cancer immunotherapy. A synthetic immune niche (SIN) composed of immobilized CCL21 and ICAM1 was shown to improve T cell expansion while preserving cytotoxicity; however, it remains unclear which specific step of the T cell killing process is enhanced by the SIN stimulation. Here, we combined advanced imaging based on time-lapse microscopy with a mean-field model to analyze the distinct stages in killing of B16 melanoma cells by CD8+ T cells. This framework enabled us to resolve the tumor cell killing process into discrete steps throughout target cell engagement with SIN-treated T cells and lytic hit delivery. We found that tumor cell death is best explained by a multi-hit process, requiring approximately four discrete hits to trigger cell death. Model-based analysis identified an increase in the lytic hit delivery as the parameter that best accounts for the enhanced cytotoxicity of SIN-treated T cells, a difference not explained by changes in target encounter frequency or conjugate stability. Global sensitivity analysis further showed that tumor control is more strongly improved by enhancing lytic hit delivery than by increasing target encounter rates. These findings fundamentally reorient our understanding of optimized T cell manufacturing, suggesting that the lytic execution step may be the primary rate-limiting bottleneck in this in-vitro system, and that engineering strategies targeting granule polarization or discharge warrant prioritization alongside affinity-enhancement approaches.

Acquired resistance to osimertinib remains a major barrier in EGFR-mutant lung adenocarcinoma (LUAD), and in many patients cannot be explained by secondary targetable mutations. This pattern highlights a central role for non-genetic plasticity programs, including epithelial-mesenchymal transition (EMT), drug tolerance, immune evasion, and lineage switch. Here, we used a systems-level framework to define how these processes are coordinated. We constructed a minimal gene regulatory network integrating core EMT regulators with AXL, RB1, PD-L1, and NF-{kappa}B, and analysed its emergent behaviour using dynamical simulations. The network resolved into two mutually inhibitory, self-reinforcing "teams": an epithelial/sensitive team centred on RB1, miR-200, miR-34, p53, and E-cadherin, and a mesenchymal/resistant team centred on ZEB1, SNAIL, AXL, PD-L1, and NF-{kappa}B. Simulations predicted a strong coupling between EMT and osimertinib resistance, which was validated across bulk transcriptomic datasets from NSCLC cell lines, EGFR-mutant patient cohorts, and perturbation experiments. Inducing EMT increased RB1-loss programs, whereas osimertinib exposure induced AXL and EMT programs, supporting bidirectional regulation and reinforcement. Single-cell and spatial transcriptomic analyses further showed that EMT, AXL, PD-L1 activity, and reduced RB1 signaling co-occur within tumors. Clinically, activation of individual axes such as EMT, RB1 loss, or PD-L1 upregulation was associated with worse outcomes, while combined activation produced markedly poorer survival than any single axis alone. Extending the network to incorporate lineage regulators further linked a partial LUAD-to-LUSC shift with EMT, RB1 loss, and resistance. Together, these findings identify a network topology that coordinates multiple plasticity programs driving osimertinib resistance and suggest that disrupting this cooperative architecture may offer a therapeutic strategy in EGFR-mutant LUAD.

Multivalent ligand-receptor interactions underlie most forms of cell-cell communication, yet a general quantitative framework for "avidity" has remained elusive for over a century. Here, we derive closed-form expressions for signaling potency (EC50) in multivalent systems directly from first principles, extending exact analytical models of ternary complex equilibria to account for receptor confinement at cell surfaces. These equations unify antibody-antigen and cytokine-receptor interactions under a common mathematical framework in which potency emerges as a function of binding constants and receptor density. In contrast to monovalent models, EC50 is no longer equal to the dissociation constant (Kd), but instead reflects receptor-dependent avidity effects that vary across cellular contexts. We validate these predictions across biophysical measurements, in vitro binding and signaling assays, in vivo murine cytokine perturbation data, and human spatial transcriptomic datasets. The framework explains longstanding empirical observations, including enhanced antibody potency through avidity and asymmetric control of cytokine signaling by receptor subunits. By embedding these equations within a regression-compatible formulation, we enable inference of signaling drivers from single-cell and spatial transcriptomic data. This work establishes a mechanistic bridge between molecular binding, receptor context, and tissue-level signaling, providing a quantitative foundation for interpreting and modeling intercellular communication in health and disease.

High-risk neuroblastoma patients face poor survival despite intensive treatment. Drug testing using patient-derived models can support therapy prioritization for precision medicine and drug development. Models incorporating tumour microenvironmental components, such as co-cultures and short-term cultured patient samples containing substantial non-malignant cell fractions, could better recapitulate microenvironment-dependent drug responses. However, conventional viability assays measure the combined signal from all viable cells in a well and therefore cannot determine tumour-specific drug responses. Here, we establish a microscopy-based readout to quantify cell-type-specific viability in two complementary settings: neuroblastoma-PBMC co-cultures and freshly dissociated patient tumour samples. In the co-cultures, PBMCs were pre-labelled with a cell-tracking dye, and Calcein staining was used to independently quantify the viability of tumour cells and PBMCs in the same well. The Calcein-based viability readout correlated strongly with conventional CellTiter-Glo measurements and was compatible with automated high-throughput drug screening. The imaging workflow enabled identification of compounds with differential efficacy in co-culture versus monoculture and distinguished tumour-specific effects from PBMC toxicity. The microscopy-based viability readout was further adapted to short-term cultured patient samples. Neuroblastoma tumour cells were distinguished from the non-malignant cells using a combination of tumour-specific surface markers NCAM, L1CAM and B7H3. This enabled determination of tumour fractions and measurement of tumour-specific drug responses. Tumour fractions varied substantially between patient samples, highlighting the importance of tumour-specific viability measurements. Together, the microscopy-based viability readout for co-cultures and patient samples enables scalable assessment of tumour-specific drug responses.

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

Control of fluids is a hallmark of microfluidic systems and fundamental for the successful application of microfluidic devices. Trigger valves use geometric features to autonomously control the release of fluids in microfluidic devices. Our previous work has adapted geometries used in closed trigger valve systems to enable use in open systems, allowing for open microfluidic devices with up to three trigger valves. Here, we focus on the parallel co-flows produced by sequential release of trigger valves and present a model that predicts their layer widths as a function of the geometric characteristics of the different side channels of each trigger valve. We show layered co-flows with widths as low as 50 microns. Additionally, we expand the use of trigger valves in open microfluidic devices by incorporating 1) varied step heights, 2) devices with up to seven trigger valves, and 3) use of varied fluids and plastics. To validate the implementation and use of these trigger valves in open systems, we have developed a theoretical framework to compare predicted outcomes (i.e., fluid travel distance, velocity, and layering width) with our experimental values. This theoretical work offers applications in various fields, including hydrogel patterning for 3D cell culture, organ-on-a-chip models, at-home sample preparation, and autonomous microfluidic systems for biosensing.

Accurate cell counting is essential in tissue engineering and cancer research. The ongoing transition towards advanced 3D in vitro tumour models raises a question about the validity of the standard cell counting protocols, particularly in the systems containing extracellular matrix-based scaffolds. Here, we provide a quantitative analysis of the performance of three popular plate reader-based cell counting/viability assays, such as the Alamar Blue, MTT, CellTiter Glo 3D assays, in 2D monolayer and 3D scaffold-based cultures of U251 human glioblastoma cells, including cell-laden Matrigel plugs, and original tissue engineering constructs based on the decellularised sheep brain scaffolds. We quantitatively characterized the assays linearity, precision, biological and technical reproducibility, proportionality, and inter-assay agreement. The study revealed that assays performance is highly platform-dependent, with 2D cultures allowing significantly more precise and reliable measurements than in 3D ECM scaffold-based cultures. The numerical results provided in this study can help researchers make informed decisions when working with 3D scaffold-based in vitro tumour models and for other tissue engineering purposes where precise cell counting is essential. ToC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/720021v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@16018d9org.highwire.dtl.DTLVardef@1ff7d6dorg.highwire.dtl.DTLVardef@838021org.highwire.dtl.DTLVardef@1510d5b_HPS_FORMAT_FIGEXP M_FIG C_FIG