APL Bioengineering最新文献

Resistance to and associated toxic side effects of neoadjuvant chemotherapy remain major obstacles to improving the prognosis of osteosarcoma patients. Consequently, there is an urgent need to discover effective therapeutic agents with lower toxicity. In this study, the patient-derived xenograft (PDX) model was established and single-cell multi-omics sequencing was performed to comprehensively analyze changes in cellular heterogeneity and gene expression patterns of under formononetin treatment. We found that formononetin can significantly inhibit tumor growth in the osteosarcoma PDX model, on which the single-cell sequencing identified MYO1B as a key target mediating the anti-osteosarcoma effects of formononetin. In vitro experiments demonstrated that MYO1B overexpression enhanced the proliferation, invasion, and migration of osteosarcoma cells, while MYO1B silencing exhibited the opposite effects. Further investigation revealed that formononetin treatment markedly downregulated MYO1B expression, effectively suppressing the proliferative, invasive, and migratory phenotypes of osteosarcoma cells. Moreover, single-cell transcriptomic analysis of murine-derived cells showed that formononetin enhanced the cytotoxic activity of NK cells, promoted M1 macrophage polarization and inhibited M2 polarization, and reduced the proportion of senescent neutrophils, thereby alleviating the immunosuppressive state of the tumor microenvironment. Overall, our findings provide a comprehensive single-cell-level elucidation of the molecular mechanisms underlying the anti-osteosarcoma effects of formononetin, primarily involving downregulating the expression of MYO1B and remodeling the tumor immune microenvironment.

To address the issues of insufficient utilization of multimodal information, modal heterogeneity, and data gaps leading to poor model generalization in early prediction of chronic obstructive pulmonary disease (COPD), a deep learning-based multimodal dynamic fusion network (MMDF-Net) is proposed. This model integrates chest CT images, pulmonary function indicators, and environmental exposure data. It aligns image and non-image features via a dual-tower cross-modal contrastive learning module, mitigating semantic differences across modalities. A conditional generative adversarial network is used to generate high-fidelity environmental exposure data, reducing reliance on data completeness. A dynamic gating fusion mechanism adaptively adjusts multimodal weights based on patient smoking history, age, and other attributes to suppress noise. On the COPD Gene dataset, MMDF-Net achieves an area under the curve (AUC) of 0.92, a sensitivity of 92.3%, and a specificity of 88.7%, significantly outperforming single-modal models and dynamically adjusting weights according to disease stage. These results demonstrate that this multimodal dynamic fusion strategy can effectively address data heterogeneity and individual differences, providing technical support for precise early intervention in COPD.

Oral Squamous Cell Carcinoma (OSCC) contains diverse communities of cells within the oral mucosa. A subset of the epithelia is highly responsive to changing niche conditions, resulting in their loss of polarity, epithelial-to-mesenchymal transition (EMT), and invasion in tumor-adjacent stroma. Given the range of cell states, we sought to understand how cytokine-mediated signaling from mesenchymal SCC25 cells or stiffness-induced mesenchymal (simCal27) cells caused EMT in naïve Cal27 epithelial cells. Media conditioned by SCC25 enhanced Cal27 cell migration, nuclear localization of EMT markers, and caused transcriptomic changes related to cytokine response ontological terms. SCC and simCal27 cells have unique cytokine profiles, which when regressed against transcriptomic changes, suggested that higher expression of IL-1a, IL-6, IL-8, Angiogenin, and PAI-1 in conditioned media could drive EMT; upregulation of these cytokines also appears impactful for overall survival and progression-free interval. However, depletion and supplementation assays clearly show that the presence of these specific cytokines is critical to induce a migratory phenotype and that naïve Cal27's motility is regulated by MAPK and AKT signaling pathways; loss or inhibition of these pathways reduced migration. These data suggest that paracrine signals from stiffness-induced mesenchymal cells act via distinct kinase pathways and may be necessary for cooperative dissemination of OSCC.

Regenerative medicine is transforming how we restore tissue function, leveraging advances in cell and molecular biology, biomaterials, and engineered microenvironments. While there have been notable advances and rapid progress over the past few decades, ongoing challenges persist in the technical development and effective translation of these advancements to clinical care. This perspective highlights clinically promising examples and critically assesses present challenges in translating tissue regenerative medicine therapies from the bench to the clinic. We further examine the evolving landscape of regenerative medicine by describing strategies to optimize the cellular microenvironment, the impact of patient demographics, and the use of artificial intelligence to shape the future of this field.

Successful development of tissue structures requires different collective cell migration patterns or phenotypes. Two examples of collective migration phenotypes in epithelial morphogenic processes, such as tubulogenesis, are rotational and invasive. Rotational collective migration phenotypes (RCM) typically lead to acinar structures, and invasive collective migration (ICM) phenotypes lead to duct-like structures. How cells adopt these different phenotypes is still largely unknown. Here, we investigate how cell-cell adhesion marker P-cadherin (CDH3) and mechanical cell-matrix interactions, including matrix deformations, protrusions, and focal adhesions, control rotational or invasive phenotypes during tubulogenesis. To accomplish our objective, we created a custom 3D microfluidic assay to perform live-cell imaging of epithelial clusters or cysts [wild-type (WT) and CDH3-depleted (CDH3^-/-)] undergoing tubulogenesis, while simultaneously measuring matrix deformation rates. Our findings reveal WT epithelial cysts maintain rotational phenotypes, but transition to an invasive phenotype to undergo tubulogenesis. Furthermore, we demonstrate ICM phenotypes correlate with higher matrix deformation rates compared to rotational phenotypes. Our studies reveal CDH3 is required for epithelial cysts to transition from rotational to ICM phenotypes, associated with decreased matrix deformation rates. Without CDH3, epithelial cysts lose their ability to adopt ICM phenotypes, which can be rescued by RhoA activation. Finally, we demonstrate that the RhoA-rescued ICM phenotype is mediated, in part, by increased matrix deformation rates and vinculin recruitment to focal adhesion sites.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer accounting for a majority of cases. Despite advances in targeted therapies and immunotherapy, challenges such as tumor heterogeneity, resistance mechanisms, and limited preclinical models hinder treatment efficacy. Traditional cancer models, including 2D cell cultures and animal models, often fail to accurately replicate the lung's complex architecture, microenvironment, and biomechanical cues, leading to poor predictive performance in drug development. Microfluidic-based organ-on-a-chip technology offers a promising alternative by integrating human-derived cells with precisely controlled perfusion, mechanical cues, and tumor-stroma interactions in physiologically relevant 3D models. These platforms enable the study of lung cancer biology, drug responses, and patient-specific therapeutic outcomes with improved accuracy. In this review, we discuss recent advancements in microfluidic systems for recapitulating normal lung physiology and 3D lung cancer microenvironment, covering various microfluidic platforms with applications in disease modeling and drug testing. Unlike other review articles, we bring first-hand insights from clinicians about the current treatment practice for lung cancer and the clinical utilities of lung cancer-on-a-chip models, which bioengineers have been seeking. We also highlight the translational potential of these systems in personalized oncology and the need for interdisciplinary collaborations, particularly with clinicians, to enhance their clinical impact.

Idiopathic pulmonary fibrosis (IPF), a progressive and fatal lung disease, significantly increases the risk of lung cancer, particularly lung adenocarcinoma (LUAD). However, the shared genetic mechanisms driving IPF and LUAD comorbidities remain poorly understood, necessitating integrated multi-omics investigations. Through bulk and single-cell transcriptomic analyses, we identified 308 shared differentially expressed genes enriched in lipid metabolism and immune-inflammatory processes. Additionally, single-cell profiling revealed significant alterations in epithelial cells and macrophage populations between LUAD and IPF tissues, underscoring their role in disease progression. Furthermore, the copy number variation profiling identified a premalignant epithelial subpopulation in IPF exhibiting transcriptional signatures resembling LUAD malignant epithelial cells, and trajectory analysis illustrated a potential temporal progression toward malignancy. To identify co-causal genes, we performed weighted gene coexpression network analysis, defining modules associated with key cell types involved in comorbidities. Moreover, leveraging 101 algorithm combinations across ten machine learning approaches, we constructed a robust prognostic model, pinpointing CHST6 as a top prognostic gene consistently upregulated in both LUAD and IPF. Functional validation confirmed that CHST6 promotes lung cancer cell proliferation, migration, and invasion. In conclusion, our findings elucidate the shared molecular landscape of LUAD and IPF and propose that CHST6 is a promising co-disease therapeutic target.

Mechanomedicine is an emerging interdisciplinary field that applies the principles of mechanobiology to understand, diagnose, and treat disease. Recent advances reveal how mechanical cues such as stiffness, flow, and compression shape cell behavior, tissue function, and disease progression. Leveraging diverse tools, including organ-on-chip platforms, high-resolution force imaging, and synthetic mechanosensors, researchers have uncovered critical links between mechanotransduction and processes such as inflammation, aging, fibrosis, and tumor invasion. From reversible mechanomemory to programmable force-responsive circuits, these discoveries highlight the translational potential of targeting cellular mechanosensing for therapeutic innovation. Moving forward, integrating molecular biology, bioengineering, physics, and medicine will be essential to develop therapies that directly leverage the language of mechanical forces.

Living systems process a broad range of internal and external stimuli, respond to environmental constraints, and adapt to various conditions through tight coordination between signaling networks and cellular mechanics. Among these, calcium signaling and cytoskeletal regulation form an essential interplay that spans multiple scales of biological organization-from ion-protein interactions to intercellular communication and tissue-level behaviors. Calcium ions (Ca²⁺) act as universal messengers, integrating a wide range of cellular signaling inputs to modulate a broad range of cellular structures and functions through the spatiotemporal dynamics of their concentration changes. Ca²⁺ signals follow conserved principles, despite their diverse roles, that define regulatory "Rules of Life" (RoLs)-generalized mechanisms that operate across biological contexts. This review focuses on how Ca²⁺ regulates and is regulated by cytoskeletal dynamics, with a particular emphasis on computational modeling for predictive simulations. As key examples, we highlight three specific RoLs: (1) Ca²⁺ dynamics facilitate cytoskeletal reorganization following stress and damage, (2) Ca²⁺ regulates actin dynamics to control synapse processes supporting both synapse formation and exocytosis, and (3) reciprocal coupling of spatiotemporal Ca²⁺ signaling and cellular dynamics defines distinct cellular roles in emergent multicellular behavior. Finally, we outline future directions toward developing multimodal computational simulations for identifying new RoLs, integrating them into multi-scale computational frameworks, and applications in bioengineering, pharmacology, and regenerative medicine.

Neuropsychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BPD) remain challenging to diagnose due to the absence of objective biomarkers, with current assessments relying largely on subjective clinical evaluations. In this study, we present a computational analysis pipeline designed to identify disease-specific electrophysiological signatures from multi-electrode array (MEA) recordings of patient-derived cerebral organoids (COs) and two-dimensional cortical interneuron cultures (2DNs). Using a Support Vector Machine classifier optimized for high-dimensional data, we achieved 95.8% classification accuracy in distinguishing SCZ from control samples in 2DNs under both baseline and post-electrical-stimulation (PES) conditions with the extracted electrophysiological signatures. In COs, classification accuracy improved from 83.3% at baseline to 91.6% following PES, enabling robust separation of control, SCZ, and BPD cohorts. Key discriminative features included channel-specific measures of network activity, with PES significantly enhancing classification performance, particularly for BPD. These results underscore the potential of MEA-based functional phenotyping, coupled with machine learning, to uncover reliable, stimulation-sensitive electrophysiological biomarkers, offering a path toward more objective diagnosis and personalized treatment strategies for neuropsychiatric disorders.