Tissue Engineering Part A最新文献

{"title":"Heterogeneity of Endothelial Cells Impacts the Functionality of Human Pancreatic <i>In Vitro</i> Models.","authors":"Max Urbanczyk, Athar Abuhelou, Marie Köninger, Abiramy Jeyagaran, Daniel Carvajal-Berrio, Ellie Kim, Julia Marzi, Peter Loskill, Shannon L Layland, Katja Schenke-Layland","doi":"10.1089/ten.tea.2024.0176","DOIUrl":"https://doi.org/10.1089/ten.tea.2024.0176","url":null,"abstract":"<p><p>Endothelial cells (ECs) play a crucial role in maintaining tissue homeostasis and functionality. Depending on their tissue of origin, ECs can be highly heterogeneous regarding their morphology, gene and protein expression, functionality, and signaling pathways. Understanding the interaction between organ-specific ECs and their surrounding tissue is therefore critical when investigating tissue homeostasis, disease development, and progression. <i>In vitro</i> models often lack organ-specific ECs, potentially limiting the translatability and validity of the obtained results. The goal of this study was to assess the differences between commonly used EC sources in tissue engineering applications, including human umbilical vein ECs (HUVECs), human dermal microvascular ECs (hdmvECs), and human foreskin microvascular ECs (hfmvECs), and organ-specific human pancreatic microvascular ECs (hpmvECs), and test their impact on functionality within an <i>in vitro</i> pancreas test system used for diabetes research. Utilizing high-resolution Raman microspectroscopy and Raman imaging in combination with established protein and gene expression analyses and exposure to defined physical signals within microfluidic cultures, we identified that ECs exhibit significant differences in their biochemical composition, relevant protein expression, angiogenic potential, and response to the application of mechanical shear stress. Proof-of-concept results showed that the coculture of isolated human islets of Langerhans with hpmvECs significantly increased the functionality when compared with control islets and islets cocultured with HUVECs. Our study demonstrates that the choice of EC type significantly impacts the experimental results, which needs to be considered when implementing ECs into <i>in vitro</i> models.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selection of Force Sensors for <i>In Situ</i> Measurement of Neotissue Microenvironments.","authors":"Marta Rodriguez Navas, Eric M Darling","doi":"10.1089/ten.tea.2024.0192","DOIUrl":"https://doi.org/10.1089/ten.tea.2024.0192","url":null,"abstract":"<p><p>Mechanical forces are a critical stimulus in both native and engineered tissues. Direct measurement of these microenvironmental forces has been challenging, particularly for cell-dense models. To address this, we previously developed hydrogel-based force sensors that are approximately the size of a cell and can be imaged over time to computationally assess the forces exerted by surrounding cells and matrix. The goal of this project was to identify how the physical characteristics of force sensors impact measurements. Sensors were varied in size, elastic modulus, and surface coating before being included in stem cell suspensions that then spontaneously self-assembled into spheroidal neotissues. Using this model of early mesenchymal condensation, we hypothesized that larger, softer sensors would provide greater sensitivity and precision, whereas protein coatings would influence the directionality of applied forces (tensile vs. compressive). These experiments were conducted using a high-content imaging system that allowed analysis of over a thousand sensors to evaluate the various conditions. Results indicated that measurement fidelity was highest for force sensors that had a diameter >20 µm and modulus ∼0.2 kPa. Extremely soft sensors deformed too much, whereas stiffer sensors deformed too little. Collagen and N-cadherin coatings, which replicated cell-matrix or cell-cell binding, respectively, allowed for tensile forces to be exerted on the sensors, with greater forces being observed for N-cadherin sensors in these highly cellular neotissue constructs. Uncoated sensors were universally compressed due to the lack of cell-sensor adhesion. Disruption of the actin cytoskeleton lessened microenvironmental forces, whereas disruption of microtubules had no measurable effect. Potential future applications of the technology include studies of <i>in situ</i> forces in developing tissues as well as a real-time sensor for monitoring the growth of engineered constructs.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual Role of Ibuprofen and Indomethacin in Promoting Peripheral Nerve Regeneration <i>In Vitro</i>.","authors":"Jarin Tusnim, Bryan J Pfister, Jonathan M Grasman","doi":"10.1089/ten.tea.2024.0224","DOIUrl":"https://doi.org/10.1089/ten.tea.2024.0224","url":null,"abstract":"<p><p>Peripheral nerve injuries (PNI) can result in significant losses of motor and sensory function. Although peripheral nerves have an innate capacity for regeneration, restoration of function after severe injury remains suboptimal. The gold standard for peripheral nerve regeneration (PNR) is autologous nerve transplantation, but this method is limited by the generation of an additional surgical site, donor-site morbidity, and neuroma formation at the site of harvest. Although targeted drug compounds have the potential to influence axonal growth, there are no drugs currently approved to treat PNI. Therefore, we propose to repurpose commonly used nonsteroidal anti-inflammatory drugs (NSAIDs) to enhance PNR, facilitating easier clinical translation. Additionally, calcium signaling plays a crucial role in neuronal connectivity and regeneration, but how specific drugs modulate this process remains unclear. We developed an <i>in vitro</i> hollow channel collagen gel platform that successfully supports neuronal network formation. This study evaluated the effects of commonly used NSAIDs, namely ibuprofen and indomethacin, in our <i>in vitro</i> model of axonal growth, regeneration, and calcium signaling as potential treatments for PNI. Our results demonstrate enhanced axonal growth and regrowth with both ibuprofen and indomethacin, suggesting a positive influence on PNR. Further, these drugs showed enhanced calcium signaling dynamics, which we posit is a crucial aspect for nerve repair. Taken together, these findings highlight the potential of ibuprofen and indomethacin to be used as treatment options for PNI, given their dual capability to promote axonal growth and enhance calcium signaling.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional Differences in Vascular Graft Degradation and Regeneration Contribute to Dilation.","authors":"Ziyu Wang, Suzanne M Mithieux, Kevin M Blum, Tai Yi, Yuichi Matsuzaki, Nguyen T H Pham, Brian S Hawkett, Toshiharu Shinoka, Christopher K Breuer, Anthony S Weiss","doi":"10.1089/ten.TEA.2024.0082","DOIUrl":"10.1089/ten.TEA.2024.0082","url":null,"abstract":"<p><p>Severe coronary artery disease is often treated with a coronary artery bypass graft using an autologous blood vessel. When this is not available, a commercially available synthetic graft can be used as an alternative but is associated with high failure rates and complications. Therefore, the research focus has shifted toward the development of biodegradable, regenerative vascular grafts that can convert into neoarteries. We previously developed an electrospun tropoelastin (TE)-polyglycerol sebacate (PGS) vascular graft that rapidly regenerated into a neoartery, with a cellular composition and extracellular matrix approximating the native aorta. We noted, however, that the TE-PGS graft underwent dilation until sufficient neotissue had been regenerated. This study investigated the mechanisms behind the observed dilation following TE-PGS vascular graft implantation in mice. We saw more pronounced dilation at the graft middle compared with the graft proximal and graft distal regions at 8 weeks postimplantation. Histological analysis revealed less degradation at the graft middle, although the remaining graft material appeared pitted, suggesting compromised structural and mechanical integrity. We also observed delayed cellular infiltration and extracellular matrix (ECM) deposition at the graft middle, corresponding with the area's reduced ability to resist dilation. In contrast, the graft proximal region exhibited greater degradation and significantly enhanced cellular infiltration and ECM regeneration. The nonuniform dilation was attributed to the combined effect of the regional differences in graft degradation and arterial regeneration. Consideration of these findings is crucial for graft optimization prior to its use in clinical applications.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Augmented Osteoarthritis Grading Standardization.","authors":"Lacksaya Nagarajan, Aadyant Khatri, Arnav Sudan, Raju Vaishya, Sourabh Ghosh","doi":"10.1089/ten.TEA.2023.0206","DOIUrl":"10.1089/ten.TEA.2023.0206","url":null,"abstract":"<p><p>Manual grading of cartilage histology images for investigating the extent and severity of osteoarthritis (OA) involves critical examination of the cell characteristics, which makes this task tiresome, tedious, and error prone. This results in wide interobserver variation, causing ambiguities in OA grade prediction. Such drawbacks of manual assessment can be overcome by implementing artificial intelligence-based automated image classification techniques such as deep learning (DL). Hence, we present the feasibility of training a deep neural network with cartilage histology images, which can grade the extent and severity of knee OA based on modified Mankin scoring system. The grading system used here for automating OA grading was simplified and modified based on the microscopic observations from the histology images, where three parameters (Safranin-O staining intensity, chondrocyte distribution and arrangement, and morphology) were considered for evaluating the OA progression. The histology images were tiled, labeled, and grouped together based on the developed grading system (Grade 0-3). Four different DL architectures were tried for image classification and the best performing model was selected by fivefold validation method. With a validation accuracy of ∼84%, 0.632 Cohen's kappa score, and an excellent receiver operating characteristic (ROC)-area under the ROC curve ranging between 0.89 and 0.99, DenseNet121 was selected among the four models as the best performing model, and was used for inferencing on new data. Final grades obtained from the models were in accordance with the grades provided by the medical experts. We hereby demonstrate that a DL architecture can be taught to interpret the degree of cartilage degradation, with excellent discriminatory ability across all four classes of OA severity. Unlike other works where radiographic images have been considered for grading of OA, we have considered histology images, which is a fundamental approach for grading OA extent and severity. This would bring a paradigm shift in histology-based assessment of OA, making this automated approach to be explored as an option for OA grading standardization. Ethical approval number-IAH-BMR-018/10-19.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":" ","pages":"591-604"},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89720905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial for Special Issue on Artificial Intelligence in Tissue Engineering and Biology.","authors":"Jason L Guo, Michael Januszyk, Michael T Longaker","doi":"10.1089/ten.TEA.2024.0240","DOIUrl":"10.1089/ten.TEA.2024.0240","url":null,"abstract":"","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":" ","pages":"589-590"},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning.","authors":"Elizaveta Chechekhina, Nikita Voloshin, Konstantin Kulebyakin, Pyotr Tyurin-Kuzmin","doi":"10.1089/ten.TEA.2024.0014","DOIUrl":"10.1089/ten.TEA.2024.0014","url":null,"abstract":"<p><p>In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are used for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology; its applications span diverse fields such as geology and material science. Although our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally applicable to other scientific domains. Furthermore, a number of the proposed solutions can be modified for use in entirely distinct computer vision cases.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":" ","pages":"627-639"},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140332354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia.","authors":"Chi T Viet, Michael Zhang, Neeraja Dharmaraj, Grace Y Li, Alexander T Pearson, Victoria A Manon, Anupama Grandhi, Ke Xu, Bradley E Aouizerat, Simon Young","doi":"10.1089/ten.TEA.2024.0096","DOIUrl":"10.1089/ten.TEA.2024.0096","url":null,"abstract":"<p><p>Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes for patients. The clinical challenge lies in identifying those patients at the highest risk of developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathological, histological, or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality in OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Biomarkers such as S100A7 demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple \"hidden\" layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":" ","pages":"640-651"},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11564848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hematoxylin and Eosin Architecture Uncovers Clinically Divergent Niches in Pancreatic Cancer.","authors":"Jason L Guo, David M Lopez, Shamik Mascharak, Deshka S Foster, Anum Khan, Michael F Davitt, Alan T Nguyen, Austin R Burcham, Malini S Chinta, Nicholas J Guardino, Michelle Griffin, Elisabeth Miller, Michael Januszyk, Shyam S Raghavan, Teri A Longacre, Daniel J Delitto, Jeffrey A Norton, Michael T Longaker","doi":"10.1089/ten.TEA.2024.0039","DOIUrl":"10.1089/ten.TEA.2024.0039","url":null,"abstract":"<p><p>Pancreatic ductal adenocarcinoma (PDAC) represents one of the only cancers with an increasing incidence rate and is often associated with intra- and peri-tumoral scarring, referred to as desmoplasia. This scarring is highly heterogeneous in extracellular matrix (ECM) architecture and plays complex roles in both tumor biology and clinical outcomes that are not yet fully understood. Using hematoxylin and eosin (H&E), a routine histological stain utilized in existing clinical workflows, we quantified ECM architecture in 85 patient samples to assess relationships between desmoplastic architecture and clinical outcomes such as survival time and disease recurrence. By utilizing unsupervised machine learning to summarize a latent space across 147 local (e.g., fiber length, solidity) and global (e.g., fiber branching, porosity) H&E-based features, we identified a continuum of histological architectures that were associated with differences in both survival and recurrence. Furthermore, we mapped H&E architectures to a CO-Detection by indEXing (CODEX) reference atlas, revealing localized cell- and protein-based niches associated with outcome-positive versus outcome-negative scarring in the tumor microenvironment. Overall, our study utilizes standard H&E staining to uncover clinically relevant associations between desmoplastic organization and PDAC outcomes, offering a translatable pipeline to support prognostic decision-making and a blueprint of spatial-biological factors for modeling by tissue engineering methods.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":" ","pages":"605-613"},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MyoFInDer: An AI-Based Tool for Myotube Fusion Index Determination.","authors":"Antoine Weisrock, Rebecca Wüst, Maria Olenic, Pauline Lecomte-Grosbras, Lieven Thorrez","doi":"10.1089/ten.TEA.2024.0049","DOIUrl":"10.1089/ten.TEA.2024.0049","url":null,"abstract":"<p><p>The fusion index is a key indicator for quantifying the differentiation of a myoblast population, which is often calculated manually. In addition to being time-consuming, manual quantification is also error prone and subjective. Several software tools have been proposed for addressing these limitations but suffer from various drawbacks, including unintuitive interfaces and limited performance. In this study, we describe MyoFInDer, a Python-based program for the automated computation of the fusion index of skeletal muscle. At the core of MyoFInDer is a powerful artificial intelligence-based image segmentation model. MyoFInDer also determines the total nuclei count and the percentage of stained area and allows for manual verification and correction. MyoFInDer can reliably determine the fusion index, with a high correlation to manual counting. Compared with other tools, MyoFInDer stands out as it minimizes the interoperator variability, minimizes process time and displays the best correlation to manual counting. Therefore, it is a suitable choice for calculating fusion index in an automated way, and gives researchers access to the high performance and flexibility of a modern artificial intelligence model. As a free and open-source project, MyoFInDer can be modified or extended to meet specific needs.</p>","PeriodicalId":56375,"journal":{"name":"Tissue Engineering Part A","volume":" ","pages":"652-661"},"PeriodicalIF":3.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141238837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}