APL Bioengineering最新文献

Preterm labor is a prevalent public health problem and occurs when the myometrium, the smooth muscle layer of the uterus, begins contracting before the fetus reaches full term. Abnormal contractions of the myometrium also underlie painful menstrual cramps, known as dysmenorrhea. Both disorders have been associated with increased production of prostaglandins and cytokines, yet the functional impacts of inflammatory mediators on the contractility of human myometrium have not been fully established, in part due to a lack of effective model systems. To address this, we engineered human myometrial microtissues (μmyometrium) on compliant hydrogels designed for traction force microscopy. We then measured μmyometrium contractility in response to a panel of compounds with known contractile effects and inflammatory mediators. We observed that prostaglandin F2α, interleukin 6, and interleukin 8 induced contraction, while prostaglandin E1 and prostaglandin E2 induced relaxation. Our data suggest that inflammation may be a key factor modulating uterine contractility in conditions including, but not limited to, preterm labor or dysmenorrhea. More broadly, our μmyometrium model can be used to systematically identify the functional impact of many small molecules on human myometrium.

Cervical cancer (CC) remains a leading cause of female cancer mortality globally. Immunogenic cell death (ICD) influences the tumor microenvironment (TME) and adaptive immune responses. Cancer-associated fibroblasts (CAFs) within the TME suppress anti-tumor immunity and contribute to CC progression. This study identified three ICD-related CAF clusters linked to patient survival, including IL6+CAF and ILR1+CAF, which were associated with clinical outcomes. Using a nine-gene risk model, patients were stratified into risk groups, with high-risk individuals showing worse survival and correlations with pathways such as hypoxia and TGFβ. The model also predicted immunotherapy responses, highlighting immune infiltration differences across risk groups. These findings provide insights into the role of CAF clusters in CC and present a risk model that supports prognosis prediction and personalized therapy.

Hyperspectral imaging (HSI) has become an evident transformative apparatus in medical diagnostics. The review aims to appraise the present advancement and challenges in HSI for medical applications. It features a variety of medical applications namely diagnosing diabetic retinopathy, neurodegenerative diseases like Parkinson's and Alzheimer's, which illustrates its effectiveness in early diagnosis, early caries detection in periodontal disease, and dermatology by detecting skin cancer. Regardless of these advances, the challenges exist within every aspect that limits its broader clinical adoption. It has various constraints including difficulties with technology related to the complexity of the HSI system and needing specialist training, which may act as a drawback to its clinical settings. This article pertains to potential challenges expressed in medical applications and probable solutions to overcome these constraints. Successful companies that perform advanced solutions with HSI in terms of medical applications are being emphasized in this study to signal the high level of interest in medical diagnosis for systems to incorporate machine learning ML and artificial intelligence AI to foster precision diagnosis and standardized clinical workflow. This advancement signifies progressive possibilities of HSI in real-time clinical assessments. In conclusion despite HSI has been presented as a significant advanced medical imaging tool, addressing its limitations and probable solutions is for broader clinical adoption.

The high mortality associated with certain cancers can be attributed to the invasive nature of the tumor cells. Yet, the complexity of studying invasion hinders our understanding of how the tumor spreads. This work presents a microengineered three-dimensional (3D) in vitro model for studying cancer cell invasion and interaction with endothelial cells. The model was generated by printing a biomimetic hydrogel scaffold directly on a chip using 2-photon polymerization that simulates the brain's extracellular matrix. The scaffold's geometry was specifically designed to facilitate the growth of a continuous layer of endothelial cells on one side, while also allowing for the introduction of tumor cells on the other side. This arrangement confines the cells spatially and enables in situ microscopy of the cancer cells as they invade the hydrogel scaffold and interact with the endothelial layer. We examined the impact of 3D printing parameters on the hydrogel's physical properties and used patient derived glioblastoma cells to study their effect on cell invasion. Notably, the tumor cells tended to infiltrate faster when an endothelial cell barrier was present. The potential for adjusting the hydrogel scaffold's properties, coupled with the capability for real-time observation of tumor-endothelial cell interactions, offers a platform for studying tumor invasion and tumor-endothelial cell interactions.

Regenerative medicine is moving from the nascent to the transitional stage as researchers are actively engaged in creating mini-organs from pluripotent stem cells to construct artificial models of physiological and pathological conditions. Currently, mini-organs can express higher-order functions, but their size is limited to the order of a few millimeters. Therefore, one of the ultimate goals of regenerative medicine, "organ replication and transplantation with organoid," remains a major obstacle. Three-dimensional (3D) bioprinting technology is expected to be an innovative breakthrough in this field, but various issues have been raised, such as cell damage, versatility of bioink, and printing time. In this study, we established a method for fabricating, connecting, and assembling organoid units of various shapes independent of cell type, extracellular matrix, and adhesive composition (unit construction method). We also fabricated kidney tissue-like structures using three types of parenchymal and interstitial cells that compose the human kidney and obtained findings suggesting the possibility of crosstalk between the units. This study mainly focuses on methods for reproducing the structure of organs, and there are still issues to be addressed in terms of the expression of their higher-order functions. We anticipate that engineering innovation based on this technique will bring us closer to the realization of highly efficient and rapid fabrication of full-scale organoids that can withstand organ transplantation.

Gas vesicles (GVs) based on acoustic reporter genes have emerged as potent contrast agents for cellular and molecular ultrasound imaging. These air-filled, genetically encoded protein nanostructures can be expressed in a variety of cell types in vivo to visualize cell location and activity or injected systemically to label and monitor tissue function. Distinguishing GV signal from tissue deep inside intact organisms requires imaging approaches such as amplitude modulation (AM) or collapse-based pulse sequences. However, these approaches have limitations either in sensitivity or require the destruction of GVs, restricting the imaging of dynamic cellular processes. To address these limitations, we developed harmonic imaging to enhance the sensitivity of nondestructive GV imaging. We hypothesized that harmonic imaging, integrated with AM, could significantly elevate GV detection sensitivity by leveraging the nonlinear acoustic response of GVs. We tested this hypothesis by imaging tissue-mimicking phantoms embedded with purified GVs, mammalian cells genetically modified to express GVs, and mice liver in vivo post-systemic infusion of GVs. Our findings reveal that harmonic cross-propagating wave AM (HxAM) imaging markedly surpasses traditional xAM in isolating GVs' nonlinear acoustic signature, demonstrating significant (p < 0.05) enhancements in imaging performance. HxAM imaging improves detection of GV producing cells up to three folds in vitro, enhances in vivo imaging performance by over 10 dB, while extending imaging depth by up to 20%. Investigation into the backscattered spectra further elucidates the advantages of harmonic imaging. These advancements bolster ultrasound's capability in molecular and cellular imaging, underscoring the potential of harmonic signals to improve GV detection.

RNA translation to protein is paramount to creating life, yet RNA and protein correlations vary widely across tissues, cells, and species. To investigate these perplexing results, we utilize a time-series fixation method that combines static stimulation and a programmable formaldehyde perfusion to map pseudo-Signaling with Omics signatures (pSigOmics) of single-cell data from hundreds of thousands of cells. Using the widely studied nuclear factor kappa B (NFκB) mammalian signaling pathway in mouse fibroblasts, we discovered a novel asynchronous pseudotime regulation (APR) between RNA and protein levels in the quintessential NFκB p65 protein using single molecule spatial imaging. Prototypical NFκB dynamics are successfully confirmed by the rise and fall of NFκB response as well as A20 negative inhibitor activity by 90 min. The observed p65 translational APR is evident in both statically sampled timepoints and dynamic response gradients from programmable formaldehyde fixation, which successfully creates continuous response measurements. Finally, we implement a graph neural network model capable of predicting APR cell subpopulations from GAPDH RNA spatial expression, which is strongly correlated with p65 RNA signatures. Successful decision tree classifiers on Potential of Heat-diffusion for Affinity-based Trajectory Embedding embeddings of our data, which illustrate partitions of APR cell subpopulations in latent space, further confirm the APR patterns. Together, our data suggest an RNA-protein regulatory framework in which translation adapts to signaling events and illuminates how immune signaling is timed across various cell subpopulations.

Conducting polymer hydrogels (CPHs) are composite polymeric materials with unique properties that combine the electrical capabilities of conducting polymers (CPs) with the excellent mechanical properties and biocompatibility of traditional hydrogels. This review aims to highlight how the unique properties CPHs have from combining their two constituent materials are utilized within the biomedical field. First, the synthesis approaches and applications of non-CPH conductive hydrogels are discussed briefly, contrasting CPH-based systems. The synthesis routes of hydrogels, CPs, and CPHs are then discussed. This review also provides a comprehensive overview of the recent advancements and applications of CPHs in the biomedical field, encompassing their applications as biosensors, drug delivery scaffolds (DDSs), and tissue engineering platforms. Regarding their applications within tissue engineering, a comprehensive discussion of the usage of CPHs for skeletal muscle prosthetics and regeneration, cardiac regeneration, epithelial regeneration and wound healing, bone and cartilage regeneration, and neural prosthetics and regeneration is provided. Finally, critical challenges and future perspectives are also addressed, emphasizing the need for continued research; however, this fascinating class of materials holds promise within the vastly evolving field of biomedicine.

Quantitative micro-elastography (QME) is a compression-based optical coherence elastography technique enabling the estimation of tissue mechanical properties on the micro-scale. QME utilizes a compliant layer as an optical stress sensor, placed between an imaging window and tissue, providing quantitative estimation of elasticity. However, the implementation of the layer is challenging and introduces unpredictable friction conditions at the contact boundaries, deteriorating the accuracy and reliability of elasticity estimation. This has largely limited the use of QME to ex vivo studies and is a barrier to clinical translation. In this work, we present a novel implementation by affixing the stress sensing layer to the imaging window and optimizing the layer thickness, enhancing the practical use of QME for in vivo applications by eliminating the requirement for manual placement of the layer, and significantly reducing variations in the friction conditions, leading to substantial improvement in the accuracy and repeatability of elasticity estimation. We performed a systematic validation of the integrated layer, demonstrating >30% improvement in sensitivity and the ability to provide mechanical contrast in a mechanically heterogeneous phantom. In addition, we demonstrate the ability to obtain accurate estimation of elasticity (<6% error compared to <14% achieved using existing QME) in homogeneous phantoms with mechanical properties ranging from 40 to 130 kPa. Furthermore, we show the integrated layer to be more robust, exhibiting increased temporal stability, as well as improved conformity to variations in sample surface topography, allowing for accurate estimation of elasticity over acquisition times 3× longer than current methods. Finally, when applied to ex vivo human breast tissue, we demonstrate the ability to distinguish between healthy and diseased tissue features, such as stroma and cancer, confirmed by co-registered histology, showcasing the potential for routine use in biomedical applications.