Integrative Biology

Anticancer drug resistance is challenging to overcome because it can arise through both intrinsic and acquired mechanisms, each driven by distinct cellular machinery. In particular, there is a sharp need for therapies that target hormone-insensitive prostate tumors due to the growing incidence of castration-resistant prostate cancer. Optimizing the pathways that regulate apoptosis in prostate cancer offers a promising strategy to induce apoptosis and inhibit tumor progression, since these mechanisms do not depend on hormonal signaling. Here, we identified strategies to enhance apoptosis in prostate cancer cells. We used several computational tools (including sensitivity analysis, particle swarm optimization, and ImageJ) to design an ordinary differential equation model of caspase-mediated prostate cancer apoptosis signaling. We apply the model to identify key modalities that increase the propensity toward apoptosis across three separate pro-apoptotic drugs (Tocopheryloxybutyrate, Narciclasine, and Celecoxib). Overall, we demonstrate that apoptosis dynamics can be accurately captured in response to each of the three drugs and identify which features of the model represent viable targets for overcoming intrinsic drug resistance.

BackgroundEpithelial-mesenchymal transition (EMT) and its reverse process Mesenchymal-Epithelial Transition (MET) are crucial during metastasis and therapy resistance. While the dynamics and master regulators of EMT are well-studied, the transcription factors that can prevent EMT or promote MET are relatively less understood. ResultsHere, by integrating bulk and spatial transcriptomic data analysis from cell lines and patient samples, with mechanism-based dynamical modelling, we identify IRF6 as a factor that strongly associates with an epithelial phenotype and is often inhibited during EMT. In vitro experiments in multiple cancer cell lines demonstrate the progression to a mesenchymal phenotype upon IRF6 knock-down, suggesting a role as an inhibitor of EMT. Finally, we observe that IRF6 expression levels correlates with worse patient survival in a subset of solid tumour types. ConclusionOur integrated computational-experimental systems-level analysis suggests that IRF6 is frequently downregulated during EMT and can also prevent the progression towards a complete EMT, underscoring its role as an MET stabilizing factor.

BackgroundDespite improved cancer outcomes with kinase inhibitors (KIs), their cardiotoxicity remains a significant clinical challenge. Current approaches to predict and prevent KI-induced cardiac adverse events (CAEs) are limited by an incomplete understanding of underlying mechanisms, including the contribution of off-target kinase engagement. ObjectivesTo establish links between kinase inhibition profiles and cardiotoxic phenotypes using empirical proteomic data, and to leverage these profiles in machine learning (ML) models capable of predicting KI cardiotoxicity. MethodsWe curated a database connecting kinome-wide target binding profiles of FDA-approved KIs (n=44) with documented incidence rates of six distinct CAEs. Binding profiles were derived from unbiased chemoproteomics and used to assess associations between KI selectivity, specific kinase targets, and CAEs. Profiles were further used to develop ML models to predict CAE risk, with SHAP-based model interpretation applied to identify cardiotoxicity-associated kinases. ResultsKI promiscuity was not a significant predictor of cardiotoxicity across all six CAEs. Frequency analysis revealed that kinases including RET, PDGFRB, and DDR1 are recurrently inhibited across CAE-linked compounds, with nearly all identified as off-targets not annotated by the FDA. Network and pathway enrichment analyses supported a systems-level model in which cardiotoxicity arises from coordinated disruption of cardiac-relevant signaling networks. ML models achieved 66-84% cross-validated accuracy (ROC-AUC 0.75-0.8) across CAE endpoints, with SHAP analysis identifying PDGFRB, EGFR, and MEK1/2 among the most predictive kinases. ConclusionsProteomic kinome profiling combined with machine learning provides a mechanistically grounded framework for predicting KI cardiotoxicity and supports off-target-aware drug design to minimize cardiovascular risk.

Elastic and viscoelastic properties of extracellular matrices (ECM) are known to regulate cellular behavior and mechanosensation differently, with implications for morphogenesis, wound healing, and pathophysiology. Most in vitro cellular processes, including cell migration, are studied on linear-elastic substrates to mimic extracellular matrices. However, most tissues are viscoelastic and display a loss modulus (G) that may be 10-20% of their storage modulus (G) under biophysically relevant conditions. Recent research has shown that cells can distinguish between elastic and viscoelastic ECM, leading to alterations in their cellular morphology, migration rates, and contractility. Here, we present a protocol for creating PAH-based model ECMs that enables the fabrication of viscoelastic substrates with storage moduli similar to those of their elastic counterparts. To explore how G influences epithelial cell mechanobiology, we fabricated tunable viscoelastic model ECMs with G of 3 kPa, 8 kPa, and 12 kPa, and for each, independently tuned G values to approximately 300 Pa, 500 Pa, and 700 Pa, respectively. We found that A549 cells cultured on stiff elastic model ECMs migrated [~]30% slower and formed larger focal adhesions compared to their viscoelastic counterparts. Conversely, A549 cells on intermediate viscoelastic model ECMs exhibited a [~]54% reduction in migration speed, with no significant difference in focal adhesion size relative to their elastic counterparts. These findings highlight the complex interplay between substrate (ECM) elastic and viscoelastic properties in regulating epithelial cell mechanobiology and emphasize the importance of time-dependent matrix mechanics in governing epithelial responses.

Cancer-associated fibroblasts (CAFs) are a central component of the tumor microenvironment that facilitate a supportive niche for cancer progression and metastasis. Experimental evidence suggests that CAFs can facilitate estrogen-independent tumor growth, thereby reducing the efficacy of anti-hormonal therapies. Understanding and quantifying the complex interactions between tumor cells, hormonal signalling, and the microenvironment are crucial for designing more effective and individualized treatment strategies. We propose a mathematical framework to explore the influence of CAFs on ER+ breast cancer progression and to evaluate strategies to mitigate their impact. We develop a system of nonlinear ordinary differential equations that substantiates the experimental observations by providing a mechanistic basis for the role of CAFs in regulating estrogen-independent growth dynamics. We then employ optimal control theory to evaluate distinct therapeutic approaches involving monotherapy or combinations of: (i) inhibition of tumor-to-CAF signaling, (ii) inhibition of CAF-to-tumor proliferative signaling, and (iii) endocrine therapy. Taken together, our results demonstrate that CAF-targeted strategies can enhance treatment efficacy across various estrogen dosing regimens. Our study provides new insights into the potential of CAF as a therapeutic target that could help to improve existing approaches for endocrine therapies.

Intravasation is the process by which cancer cells breach the physical boundaries of a primary tumor and enter blood or lymphatic vessels. In this work, MCF-7 breast cancer cells were cultured within polymer-based microcapsules (here referred to as artificial microtumors) to investigate the transcriptomic and morpho-mechanical changes occurring in cancer cells during their release from these matrices, mimicking in vitro the process of intravasation. Our results show that even confined and released cancer cells share approximately 95% of their global transcriptomic profiles, intravasation-like cells exhibited marked differences in the expression of pathogenic hallmarks, including pathways associated with cell proliferation, immunosurveillance, and dormancy. Notably, a clear upregulation of YAP/TAZ signaling was observed in released cells, a result further supported by single-cell traction force microscopy assays, demonstrating that those cells exhibit higher biomechanical activity compared to cells located within artificial microtumors or those cultured on conventional 2D flasks, as shown for intravasated cells in vivo. To further enrich our investigation, the mechanotranscriptomic activity of MCF-7 cells was compared with suspended spheroids cultured on non-adherent surfaces (i.e., agarose hydrogels). Our results show that released cells displayed increased biomechanical activity and elevated expression of malignant markers, indicating that mechanical stress, beyond cell-cell contact alone, is required to trigger malignant responses. These observations were further supported by co-culture studies of MCF-7 cells with human fibroblasts and endothelial cells, which showed reduced proliferative and invasive capacities under confinement. Overall, our findings demonstrate that shifts in mechanical and metabolic stress, as experienced during intravasation, act as critical stimuli driving mechanotranscriptomic programs associated with cancer progression.

BackgroundActivated platelets release polyphosphate (polyP), a linear polymer of inorganic phosphate residues, from dense granules. Experiments performed under no-flow conditions show that polyP alters the kinetics of tissue factor (TF) pathway reactions, accelerating FXI activation by thrombin and FV activation by FXa and thrombin, and may impact inhibition by tissue factor pathway inhibitor (TFPI). How polyP influences this pathway in conjunction with platelet deposition under flow remains understudied. ObjectivesTo investigate how polyP-mediated acceleration of FV and FXI activation modulates thrombin generation under flow in TF-initiated coagulation. MethodsWe extended a previously validated mathematical model of platelet deposition and coagulation under flow to examine polyP-mediated effects following a small vascular injury during intravascular clotting. Simulations varied the surface density of TF exposed, wall shear rate, and plasma TFPI concentration. ResultsPolyP shifts the threshold TF density for a thrombin burst to lower TF densities. For TF densities above this threshold, polyP shortens the lag time to thrombin generation in a TF- and shear-rate-dependent manner. Although no explicit effect of polyP on TFPI function was included in the model, thrombin generation was much less sensitive to TFPI concentration with polyP, in a TF-dependent manner. Relative contributions of accelerations of FV and FXI activations depend on incompletely known enhancements by polyP. ConclusionsThe experimentally observed influence of polyP on TFPI function depends on TF density and may arise indirectly from accelerated FV and FXI activation, with the dominant effect arising through accelerated thrombin-mediated conversion of FV to FVa.

Most cellular therapies, like CAR T cells, remain ineffective in solid tumors. This is primarily due to a complex tumor microenvironment (TME), which creates biochemically hostile and often immunosuppressive conditions that limit efficacy of immunotherapies. Besides, cellular therapy efficacy is still often established in traditional 2D cultures that fail to simulate relevant aspects of solid tumor biology. Recent advances in three-dimensional (3D) and organ-on-chip culture systems have provided more physiologically relevant models for immunotherapy testing. These microphysiological systems (MPS) not only offer a 3D environment that alters tumor cell sensitivity to therapy but also enable inclusion of TME components and assessment of processes such as extravasation and infiltration, key steps in CAR T cell activity in vivo. This study focuses on applying an advanced culture technique and further building on the use of a scalable on-chip platform, the OrganoPlate, to grow EpCAM-positive and EpCAM-negative tumor cells in co-culture with an endothelial vessel to study EpCAM-targeting CAR T cell migration and killing kinetics. The CAR T cells specifically targeted and killed EpCAM-positive HT-29 tumor cells while EpCAM-negative A375 tumor cells were not affected. In addition, target cell killing was dependent on the ratio between CAR T and tumor cells (E:T ratio) and was enhanced by addition of IL-2. Inflammatory cytokines like INF-{gamma}, TNF and IL-6 increased overtime in cultures containing CAR T cells. Morphometric analyses of the endothelial compartment showed E:T ratio dependent disruption of endothelial vessels. Additionally, this system was able to distinguish EpCAM ScFv-CD28-CD3z and EpCAM ScFv-TM-4-1BB-CD3z CAR T cells killing abilities and was used for studying the effect of immune checkpoint inhibitors and Temozolomide, a DNA targeting drug, on CAR T cell performance. Altogether, this work adds to the available advanced culture techniques for immunotherapy developers by describing a model that is modular, scalable, and suitable for phenotypic and functional characterization of CAR T cells.

BackgroundTherapeutic agents targeting the PD1-PDL1 interaction are of great clinical value, however accurately predicting which patients are most likely to benefit is challenging. Improved predictive biomarkers for anti-PD1 therapy are clearly needed. Quantifying PD1 saturation by PDL1 in tumor tissue has the potential to serve as such a biomarker. Here we report a novel bioassay called the PD1 Ligand Receptor Complex Aptamer (LIRECAP) assay and demonstrate it can be used to quantify the saturation of PD1 by PDL1 in formalin-fixed paraffin-embedded tumor biospecimens. ResultsThe PD1 LIRECAP assay was developed by identifying a pair of RNA aptamers. One aptamer preferentially binds to unoccupied PD1 (P aptamer) and the other to the PD1-PDL1 complex (C aptamer). P and C aptamers were added together to a formalin-fixed sample, and bound aptamer extracted. A 2-color qRT-PCR assay using a single set of primers was used to determine the ratio of the sample-bound C to P aptamers (C:P ratio) which reflected PD1 saturation by PDL1 in the sample. Quantification of PD1 saturation by PDL1 as determined by the PD1 LIRECAP assay correlated closely with PD1-mediated signaling and PD1-PDL1 proximity. Analysis of sarcoma FFPE biospecimens confirmed the assay is technically reproducible on clinical biospecimens. There were significant differences in PD1 saturation by PDL1 between patients as well as considerable intratumoral heterogeneity. ConclusionsThe PD1 LIRECAP assay is novel assay that can be used to quantify PD1 saturation by PDL1 in clinical biospecimens. The assay is technically feasible, reproducible, and has the potential to serve as a superior predictive biomarker for PD1/PDL1-based therapy. Similar assays based on this platform could be used in other systems and settings to quantify interaction between two molecules.

Cellular immunotherapy has revolutionized cancer treatment by enabling more targeted and personalized disease management. As the field progresses, there is an increasing need for high-throughput in vitro assays to efficiently assess the cytotoxicity of therapeutic cells. Conventional cytotoxicity assays pose various limitations in the workflow and scalability. Here, we present an mRNA lipid nanoparticle (mRNA-LNP) approach to efficiently and robustly deliver reporter genes to target cells for assessing immune effector cell-mediated cytotoxicity. This approach enables the rapid, homogenous reporter expression without compromising the viability of target cells. The cytotoxicity results obtained using mRNA-LNP-transfected cells are highly consistent and comparable to those obtained using cell lines with stable reporter gene expression. Finally, we highlight the mRNA-LNP approachs compatibility across a diverse range of tumor models, including primary tumor-derived models, enabling rapid and high-throughput assessment of the potency of various cytotoxic therapeutic cells.

Chemotaxis plays a critical role in the metastatic progression of breast cancer. The chemokine CXCL12 is well recognized as an essential component of chemotactic migration in triple-negative breast cancer (TNBC) cells in vivo. The purpose of this study is to determine how the highly metastatic TNBC cell line, MDA-MB-231, migrates in response to well-defined CXCL12 gradients in vitro. Traditional 2D transwell migration assays were optimized to gauge the MDA-MB-231 cells responsiveness to various CXCL12 concentrations. The optimum chemoattractant concentrations were applied to a commercially available 3D chemotaxis assay as stable linearly diffused gradients. Cells were embedded in type 1 bovine collagen at two different collagen concentrations, and individual unlabeled cells were monitored for 24 hours using brightfield microscopy. Time-lapse videos were used to track cell movement and shape. Quantitative data analysis was performed using an automated tracking software to measure chemotactic parameters based on cell morphology. MDA-MB-231 cells were responsive to CXCL12 concentrations greater than 200 ng/mL in 2D and 3D systems. In 3D systems, significant directed migration was observed in denser collagen matrices. It was observed that in 3D matrices a range of cell morphologies was present. Therefore, chemotaxis was evaluated as a function of cell shape revealing some differences between sub cellular populations. Our findings show the cells shape influences the chemotactic sensing towards CXCL12 gradients.

Cardiac fibrosis is a pathological process in which the myocardium stiffens due to the overproduction of extracellular matrix (ECM) proteins. Cardiac fibroblasts activate to myofibroblasts in response to the inflammatory cytokine transforming growth factor beta1 (TGF-{beta}1) to promote fibrotic scarring. Biological sex also influences cardiac fibrosis progression and patient outcomes, where males exhibit increased fibrotic scarring after acute inflammation relative to females. At the cellular level, sex differences in TGF-{beta}1-mediated cardiac myofibroblast activation processes have not been clearly defined. We hypothesized that TGF-{beta}1 would cause sex-specific cardiac myofibroblast activation levels and alter the secretion of bioactive molecules to modulate sex differences in cardiac fibrosis. Primary left ventricle cardiac fibroblasts were isolated from male and female C57BL/6J mice and cultured on hydrogel biomaterials mimicking native myocardial ECM stiffness and treated with TGF-{beta}1 and/or the TGF-{beta}1 receptor inhibitor SD208. Male myofibroblasts exhibited increased -SMA stress fiber formation, increased SMAD2/3 localization, and greater resistance to SD208 inhibition compared to female myofibroblasts on hydrogels at various time points tested. Sex differences in relative secreted cytokine abundance were also determined, with male CFs secreting increased vascular endothelial growth factor (VEGF) and female CFs producing increased periostin and fibroblast growth factor 21 in response to TGF-{beta}1. Our findings establish that TGF-{beta}1 mediates sex differences in cardiac myofibroblast activation on hydrogels and secreted factors that may modulate the myocardial microenvironment. Our work underscores the importance of using hydrogels as cell culture platforms to recapitulate sex-specific cardiac fibrosis phenotypes as a steppingstone towards identifying sex-dependent therapeutic interventions for cardiac fibrosis.

AbstractsO_ST_ABSBackgroundC_ST_ABSQuantitative systems pharmacology (QSP) models provide mechanistic insight into drug response but are limited by labor-intensive, expert-driven workflows. We developed an AI-assisted QSP (AI-QSP) framework that integrates large language models (LLMs) with SBML-based modeling to enable automated reconstruction, extension, and calibration of mechanistic models. MethodsThe framework was applied to a published CAR-T QSP model. The model was reconstructed in SBML and extended via LLM-guided prompts to incorporate key resistance mechanisms: T-cell exhaustion, PD-1/PD-L1 checkpoint regulation, and tumor antigen escape. Model development followed an iterative expert-in-the-loop workflow. The resulting model (21 reactions, 9 species) was calibrated to synthetic benchmark data using 19-parameter optimization. Model credibility was assessed using ASME V&V 40 and ICH M15 principles, including global sensitivity and profile-likelihood analyses. ResultsThe calibrated model reproduced benchmark dynamics with high accuracy (mean log-RMSE = 0.132). Sensitivity analysis identified CAR-T killing and bystander cytotoxicity as dominant drivers of tumor response. Profile-likelihood analysis showed 71% of parameters were practically identifiable, with remaining parameters prioritised for future data-driven refinement. ConclusionsAI-assisted QSP modeling enables reproducible, scalable model reconstruction and evolution while maintaining mechanistic transparency and regulatory alignment. This framework provides a foundation for accelerating model-informed drug development in cell and gene therapies.

Mechanical homeostasis indicates the remarkable ability displayed by cells in tissues to maintain their mechanical properties near a stable homeostatic set-point. Experimental investigations and theoretical studies indicate that mechanical stress represents a key homeostatic target that stromal cells, such as fibroblasts, seek to maintain by tuning the intracellular structure and by remodeling the extracellular matrix. Much of what is known about mechanical homeostasis of tissues under tension, or tensional homeostasis, is based on experiments on tissue equivalents, that is fibroblast-populated collagen gels. However, existing platforms used to investigate tensional homeostasis cannot infer mechanical stress dynamically. Here we developed an integrated biomechanical bioreactor combining force sensing with confocal microscopy to dissect the mechanobiological mechanisms of tensional homeostasis. We used our novel platform to test the hypothesis that fibroblasts maintain a constant state of stress across varying collagen concentrations. Contrary to this assumption, synchronized force and imaging measurements revealed that stress is not constant but rather elevated at low collagen concentrations, where fibroblast contraction drives earlier collagen fiber alignment and greater tissue compaction. Conversely, force generation and -SMA expression increase with increasing collagen concentration, accompanied by modest transcriptional changes. However, at the highest collagen concentration, this homeostatic balance is disrupted, with lower force generation and -SMA expression, as gene expression shifts toward VEGFC-mediated autocrine survival signaling. These findings demonstrate that tensional homeostasis emerges from a dynamic balance between cellular contractility and extracellular matrix densification rather than stress maintenance, and reveal that excessive matrix density disrupts this balance by triggering a pro-survival response.

The endometrium and menstrual disorders, such as endometriosis and adenomyosis, are difficult to study, partly because menstruation depends on interactions between multiple cell types through complex molecular mechanisms. To help understand this system, researchers need humanized experimental and computational models that can interrogate how endometrial cell populations impact each other in both physiological and pathological conditions. Here, we use ordinary differential equations (ODEs) to model changes in the rates of endometrial cell proliferation and death in response to hormones, cytokines, and the specific cell types present. To calibrate this computational model, we used previous-published experimental datasets from a 3D co-culture platform supporting primary human endometrial epithelial organoids and endometrial stromal cells. Our ODE-based model can simulate the size of endometrial epithelial organoids and the density of stromal cells over time under multiple hormone/cytokine conditions in mono- and co-cultures. We further created a second, partial differential equation (PDE)-based model that simulates the diffusion of molecules added to these 3D cultures and their uptake by proliferating endometrial cells using the predicted cell densities from the ODE model as inputs to the PDE simulations. We show that the exposure to hormones and cytokines used in the experiments is reasonably homogenous throughout the 3D culture and identify conditions where this would not be true. Altogether we use these models to quantify the influence of stromal cells on epithelial cell proliferation and vice versa, to identify differences across cells from different donors, and to provide a quantitative assessment of experimental designs.

Microfluidic devices with surface-bound biomolecular patterns enable localised detection arrays, enzymatic catalysis, and gene expression. Photolithography is a contactless patterning method with high spatial control. However, while patterning open surfaces by photolithography is well-established, patterning enclosed microfluidic channels remains technically challenging. Such capability would enable in situ surface modification and precise pattern alignment to channel geometries. Here, we present a photolithographic method using commercially available reagents to pattern sealed microfluidic devices. We first coat surfaces with (3-Aminopropyl)triethoxysilane (APTES) to bond microfluidic chips and provide surface amine groups onto which photocleavable polyethylene glycol (PC PEG) compounds are bound. UV exposure using standard photolithography equipment selectively deprotects the amine groups, which can subsequently bind amine-reactive cargos. We demonstrate this methods versatility by patterning both glass and poly(dimethylsiloxane) (PDMS) surfaces with diverse cargoes: DNA, proteins and gold nanoparticles. We also compare covalent versus noncovalent DNA patterning. Covalently bound DNA patterns were denser and could be used for sequence-specific target DNA capture. However, noncovalently bound DNA yielded higher cell-free gene expression from surface-bound GFP templates.

Adoptive cell therapies are transforming the treatment of cancer and autoimmunity by enhancing patients own immune cells to fight disease. In cell therapy manufacturing, immunomagnetic beads are used to isolate and activate target cells for gene transfer but must be removed downstream to [&le;]10 beads per 300,000 cells. Current quantification requires time-intensive and error-prone manual counting using brightfield microscopy, while existing automated approaches struggle with variable bead-cell morphology and tedious sample preparation steps. Raman spectroscopy offers rapid, morphology-independent detection using molecular signatures generated by inelastic light scattering. Here, we leverage immunomagnetic beads strong Raman signatures to quantify them in area scans from dried samples, achieving single bead resolution and accurate counting of bead clusters with and without cells. Using low power ([&le;]7 mW) and exposure times ([&ge;]0.5 s), the average area under 3 signature Raman peaks (1110 cm-1, 1346 cm-1, and 1595 cm-1) are measured and input to a linear regression model, achieving a mean squared error (MSE) of <0.2 beads. Our results show Raman spectroscopy as a robust, automated approach for bead counting in existing pipelines with potential to improve the safety and throughput of cell therapies.

The extracellular matrix (ECM) plays a pivotal role in lymphatic vasculature physiology, yet the specific contribution of individual ECM components to lymphatic endothelial permeability remains poorly understood, limiting the development of physiologically relevant in vitro models for lymphatic disease research and therapeutic development. Here, we used an in vitro transwell platform to systematically investigate how four clinically relevant ECM proteins, collagen I, fibronectin, fibrin, and laminin, regulate human lymphatic endothelial cell (LEC) barrier function and junctional integrity. Fibrin and collagen I substrates enhanced barrier integrity, demonstrating 80% and 67% increases in transendothelial electrical resistance (TEER), respectively, compared to uncoated controls. FITC-dextran transport assays confirmed these findings, with fibrin and collagen I reducing permeability by 20% and 10%, respectively. Immunofluorescence analysis revealed elevated ZO-1 expression on fibrin, fibronectin, and laminin matrices, while VE-cadherin levels remained unchanged across conditions. Quantitative junctional analysis demonstrated that fibrin increased ZO-1 junction continuity by [~]35%, while collagen I and fibronectin enhanced continuity by [~]22%, with all ECM coatings reducing discontinuous junctions by 60-80%. Mechanistically, RhoA expression was reduced in LECs cultured on fibrin, suggesting decreased stress fiber formation contributes to enhanced barrier function, though overall actin cytoskeletal anisotropy remained unchanged. These findings demonstrate that ECM composition modulates LEC junctional organization and barrier integrity, with fibrin and collagen I exerting the most pronounced barrier-enhancing effects. This engineered platform provides a foundation for developing next-generation in vitro models of lymphatic vasculature that more accurately recapitulate physiological conditions, with applications in lymphedema research, cancer metastasis studies, and immune cell trafficking investigations.

Osteoarthritis (OA) is a multifactorial joint disease driven by complex interactions among chondrocytes, osteoblasts, fibroblasts, and immune cells across cartilage, bone, and synovial tissues. Conventional monoculture systems are unable to capture this crosstalk, limiting their physiological relevance. Building on our previously established joint-on-a-chip platform, this study evaluated multicellular communication and assessed whether a microfluidic co-culture provides a more realistic representation of joint inflammation compared with monoculture models. Two configurations were established: a healthy, low-inflammation model containing M0 macrophages and an OA-like, high-inflammation model with M1 macrophages. In healthy models, co-culture significantly increased MMP-1 ([~]4-fold), MMP-3 ([~]15-fold), TIMP-2 ([~]5-fold), IL-6 ([~]6-fold), and IL-8 ([~]5-fold) relative to monoculture, indicating that endogenous signaling initiates basal matrix remodeling and inflammatory pathways. In disease models, M1-driven co-culture elevated MMP-10 ([~]300-fold) and MMP-13 ([~]60-fold), along with TIMP-2 ([~]5-fold), compared with monoculture, reflecting amplified catabolic activation. Direct comparison of disease versus healthy co-culture revealed additional increases in MMP-10 ([~]55-fold), MMP-13 ([~]95-fold), MCP-1 ([~]1.6-fold), MMP-1 ([~]1.6-fold), MMP-3 ([~]1.8-fold), TIMP-1 ([~]1.4-fold), and TIMP-2 ([~]1.5-fold), representing a macrophage-mediated shift from homeostasis to OA-like pathology. However, neither IL-1 nor TNF, each a key inflammatory mediator of OA, differed measurably between healthy and disease models under either monoculture or co-culture conditions. Thus, the microfluidic joint inflammation-on-a-chip model presented here more faithfully recapitulates the pathogenic MMP profile of OA than monoculture systems, but it does not yet fully recapitulate the pathogenic inflammatory environment of OA.

BackgroundMET overexpression is associated with poor prognosis in many solid tumors due to its central role in tumor survival, invasion, metastasis, and chemoresistance. While targeting MET with antibody-drug conjugates has shown promising results, engineered cellular immunotherapeutic approaches have not been extensively explored. Compared to conventional single-chain variable fragments (scFv), naturally occurring single-domain antibodies consisting of variable heavy chains only (VHH or nanobodies) are smaller, retain high specificity, and exhibit remarkable biochemical stability. In this study, we tested the efficacy of MET-targeting VHH-CAR-T (chimeric antigen receptor T cells). MethodsWe generated a panel of VHH-CAR-Ts using mRNA electroporation. VHH-CAR-T cells were evaluated in functional assays including cell binding avidity, cytokine production profiles, hydrogel microwell-based cellular kinetics, and in vitro cytotoxicity. We also assessed the therapeutic efficacy of VHH-CAR-T in an in vivo mouse model of metastatic triple negative breast cancer (TNBC). ResultsAmong the tested VHH, we identified those with intermediate avidity as most effective for in vitro tumor killing. VHH-CAR-Ts with CD28 costimulatory domains demonstrated augmented cytotoxicity with favorable selectivity, requiring a minimum antigen density threshold for activation. Mechanistically, VHH-CAR-Ts demonstrated low tonic signaling, high avidity, potent cytokine production, and rapid tumor killing kinetics. When administered in an mRNA format, VHH-CAR-Ts exhibited potent and prolonged control of tumor growth in an in vivo metastatic model of TNBC. ConclusionTaken together, these results demonstrate that VHH-CAR-Ts exhibit robust MET specificity and potent therapeutic efficacy both in vitro and in vivo. Thus, VHH-CAR-T cell therapy represents a promising immunotherapeutic strategy for targeting MET-overexpressing solid tumors. What is already known on this topicMET signaling is an important contributor to the aggressiveness of many solid tumors, and targeting MET by antibody-drug conjugates has shown efficacy and safety. Targeting MET by CAR-T cells has been under study, though with limited potency. What this study addsThis study is the first to demonstrate effectiveness of anti-MET VHH-CAR-T cells. Compared with other antigen binding domains, VHH-incorporated CAR-T cells show low tonic signaling, a favorable cytokine profile, and potent tumor killing. How this study might affect research, practice or policyWith the multiple advantages of VHHs including small size, stability, and low potential for tonic signaling, VHH-CAR-T cells represent a promising approach for CAR-T design against solid tumors.