Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

Integration of Clinical Trial Spatial Multiomics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery.

Due to the lack of treatment options, there remains a need to advance new therapeutics in hepatocellular carcinoma (HCC). The traditional approach moves from initial molecular discovery through animal models to human trials to advance novel systemic therapies that improve treatment outcomes for patients with cancer. Computational methods that simulate tumors mathematically to describe cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico, potentially greatly accelerating delivery of new therapeutics to patients. To facilitate the design of dosing regimens and identification of potential biomarkers for immunotherapy, we developed a new computational model to track tumor progression at the organ scale while capturing the spatial heterogeneity of the tumor in HCC. This computational model of spatial quantitative systems pharmacology was designed to simulate the effects of combination immunotherapy. The model was initiated using literature-derived parameter values and fitted to the specifics of HCC. Model validation was done through comparison with spatial multiomics data from a neoadjuvant HCC clinical trial combining anti-PD1 immunotherapy and a multitargeted tyrosine kinase inhibitor cabozantinib. Validation using spatial proteomics data from imaging mass cytometry demonstrated that closer proximity between CD8 T cells and macrophages correlated with nonresponse. We also compared the model output with Visium spatial transcriptomics profiling of samples from posttreatment tumor resections in the clinical trial and from another independent study of anti-PD1 monotherapy. Spatial transcriptomics data confirmed simulation results, suggesting the importance of spatial patterns of tumor vasculature and TGFß in tumor and immune cell interactions. Our findings demonstrate that incorporating mathematical modeling and computer simulations with high-throughput spatial multiomics data provides a novel approach for patient outcome prediction and biomarker discovery. Significance: Incorporating mathematical modeling and computer simulations with high-throughput spatial multiomics data provides an effective approach for patient outcome prediction and biomarker discovery.

Antibacterial polyurethane foams with quaternized-chitosan as a chain extender for nasal packing and hemostasis.

Endoscopic surgery is an effective and common clinical practice for chronic sinusitis. Nasal packing materials are applied in nasal surgery to prevent hemorrhage and promote wound healing. In this study, a degradable polyurethane foam dressing is successfully developed as a promising nasal packing material with good biocompatibility and antibacterial capability. Specifically, quaternized chitosan (QCS) serves as the crosslinker instead of polyols to offer polyurethane foam (PUF-QCS) antibacterial capability. The PUF-QCS2.0 % (with 2.0 wt% QCS) exhibits satisfactory liquid absorption capacity (19.4 g/g), high compressive strengths at both wet (14.5 kPa) and dry states (7.7 kPa), and a good degradation rate (8.3 %) within 7 days. Meanwhile, PUF-QCS2.0 % retains long-term antibacterial activity for 7 days and kills 97.3 % of S. aureus and 91.8 % of E. coli within 6 hours in antibacterial testing. Furthermore, PUF-QCS2.0 % demonstrates a positive hemostatic response in the rabbit nasal septum mucosa trauma model by reducing hemostatic time over 50.0 % and decreasing blood loss up to 76.1 % compared to the commercial PVA nasal packing sponge. Importantly, PUF-QCS also exhibits a significant antibacterial activity in nasal cavity. This nasal packing material has advantages in post-surgery bleeding control and infection prevention. STATEMENT OF SIGNIFICANCE: The performance of a nasal packing sponge requires good mechanical properties, fast and high liquid absorption rate, effective degradability and strong antibacterial activity. These features are helpful for improving the postoperative recovery and patient healing. However, integrating these into a single polyurethane foam is a challenge. In this study, quaternized chitosan (QCS) is synthesized and used as a chain extender and antibacterial agent in preparing a degradable polyurethane foam (PUF-QCS) dressing. PUF-QCS undergoes partial degradation and exhibits effective broad-spectrum antibacterial activity in 7 days. The reduction of postoperative bleeding and infection observed in the animal experiment further demonstrates that the PUF-QCS developed here outperforms the existing commercial nasal packing materials.

Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial.

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders' samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFß in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.

Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model.

Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon's entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as "cold", "compartmentalized" and "mixed", which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes.

Spatial and Compositional Biomarkers in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between anti-tumor immunity and clinical outcomes, yet such connections remain underexplored. Here we employed a dataset derived from imaging mass cytometry of 58 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multi-scale computational algorithms. We detected distinct cell distribution patterns among clinical subgroups, potentially stemming from different infiltration related to tumor vasculature and fibroblast heterogeneity. Spatial analysis also identified ten recurrent cellular neighborhoods (CNs) - a collection of local TME characteristics with unique cell components. Coupling of the prevalence of pan-immune and perivasculature immune hotspot CNs, enrichment of inter-CN interactions was associated with improved survival. Using a deep learning model trained on engineered spatial data, we can with high accuracy (mean AUC of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles, and suggest novel imaging-based biomarkers for treatment development in the context of TNBC.

Quantitative Spatial Profiling of Immune Populations in Pancreatic Ductal Adenocarcinoma Reveals Tumor Microenvironment Heterogeneity and Prognostic Biomarkers.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with poor 5-year survival rates, necessitating identification of novel therapeutic targets. Elucidating the biology of the tumor immune microenvironment (TiME) can provide vital insights into mechanisms of tumor progression. In this study, we developed a quantitative image processing platform to analyze sequential multiplexed IHC data from archival PDAC tissue resection specimens. A 27-plex marker panel was employed to simultaneously phenotype cell populations and their functional states, followed by a computational workflow to interrogate the immune contextures of the TiME in search of potential biomarkers. The PDAC TiME reflected a low-immunogenic ecosystem with both high intratumoral and intertumoral heterogeneity. Spatial analysis revealed that the relative distance between IL10+ myelomonocytes, PD-1+ CD4+ T cells, and granzyme B+ CD8+ T cells correlated significantly with survival, from which a spatial proximity signature termed imRS was derived that correlated with PDAC patient survival. Furthermore, spatial enrichment of CD8+ T cells in lymphoid aggregates was also linked to improved survival. Altogether, these findings indicate that the PDAC TiME, generally considered immuno-dormant or immunosuppressive, is a spatially nuanced ecosystem orchestrated by ordered immune hierarchies. This new understanding of spatial complexity may guide novel treatment strategies for PDAC. SIGNIFICANCE: Quantitative image analysis of PDAC specimens reveals intertumoral and intratumoral heterogeneity of immune populations and identifies spatial immune architectures that are significantly associated with disease prognosis.

Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model.

Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front.

Multi-Scale Spatial Analysis of the Tumor Microenvironment Reveals Features of Cabozantinib and Nivolumab Efficacy in Hepatocellular Carcinoma.

Background: Concomitant inhibition of vascular endothelial growth factor (VEGF) and programmed cell death protein 1 (PD-1) or its ligand PD-L1 is a standard of care for patients with advanced hepatocellular carcinoma (HCC), but only a minority of patients respond, and responses are usually transient. Understanding the effects of therapies on the tumor microenvironment (TME) can provide insights into mechanisms of therapeutic resistance. Methods: 14 patients with HCC were treated with the combination of cabozantinib and nivolumab through the Johns Hopkins Sidney Kimmel Comprehensive Cancer Center. Among them, 12 patients (5 responders + 7 non-responders) underwent successful margin negative resection and are subjects to tissue microarray (TMA) construction containing 37 representative tumor region cores. Using the TMAs, we performed imaging mass cytometry (IMC) with a panel of 27-cell lineage and functional markers. All multiplexed images were then segmented to generate a single-cell dataset that enables (1) tumor-immune compartment analysis and (2) cell community analysis based on graph-embedding methodology. Results from these hierarchies are merged into response-associated biological process patterns. Results: Image processing on 37 multiplexed-images discriminated 59,453 cells and was then clustered into 17 cell types. Compartment analysis showed that at immune-tumor boundaries from NR, PD-L1 level on tumor cells is significantly higher than remote regions; however, Granzyme B expression shows the opposite pattern. We also identify that the close proximity of CD8+ T cells to arginase 1hi (Arg1hi) macrophages, rather than CD4+ T cells, is a salient feature of the TME in non-responders. Furthermore, cell community analysis extracted 8 types of cell-cell interaction networks termed cellular communities (CCs). We observed that in non-responders, macrophage-enriched CC (MCC) and lymphocyte-enriched CC (LCC) strongly communicate with tumor CC, whereas in responders, such communications were undermined by the engagement between MCC and LCC. Conclusion: These results demonstrate the feasibility of a novel application of multiplexed image analysis that is broadly applicable to quantitative analysis of pathology specimens in immuno-oncology and provides further evidence that CD163-Arg1hi macrophages may be a therapeutic target in HCC. The results also provide critical information for the development of mechanistic quantitative systems pharmacology models aimed at predicting outcomes of clinical trials.

Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer using nuclear morphology and tissue architecture.

Characterizing likelihood of response to neoadjuvant chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) is an important yet unmet challenge. In this study, a machine-learning framework is developed using imaging of biopsy pathology specimens to generate models of likelihood of NAC response. Developed using cross-validation (evaluable N = 66) and an independent validation cohort (evaluable N = 56), our models achieve promising results (65%-73% accuracy). Interestingly, one model-using features derived from hematoxylin and eosin (H&E)-stained tissues in conjunction with clinico-demographic features-is able to stratify the cohort into likely responders in cross-validation and the validation cohort (response rate of 65% for predicted responder compared with the 41% baseline response rate in the validation cohort). The results suggest that computational approaches applied to routine pathology specimens of MIBC can capture differences between responders and non-responders to NAC and should therefore be considered in the future design of precision oncology for MIBC.

Versatile Janus Composite Nonwoven Solar Absorbers with Salt Resistance for Efficient Wastewater Purification and Desalination.

Solar steam generation is an efficient way of harvesting solar energy for water purification. Developing a versatile solar absorber with salt resistance and the capability to purify an oil-in-water emulsion is a grand challenge. Herein, a polypropylene (PP) nonwoven fabric-based photothermal absorber is fabricated by the combination of carbon nanotubes (CNTs), polypyrrole (PPy), and a fluorinated hydrophobic coating in a layer-by-layer approach. The specially designed architecture displays a hierarchical microstructure and Janus wetting properties, facilitating solar absorption and heat generation on the evaporation surface, and can effectively prevent salt crystallization. The water layer formed on the superhydrophilic/underwater superoleophobic bottom surface could repel oil droplets and form a channel to advect concentrated salt back into bulk water, which enabled high purity separation of an oil-in-water emulsion and continuous desalinization of seawater without the reduction of the evaporation rate. As a result, the solar absorber can achieve a remarkable evaporation rate of 1.61 kg m-2 h-1 and an energy efficiency of 91.2% under 1 sun irradiation and shows extraordinary performance in the purification of contaminated wastewater (over 99.8% purity). The strategy proposed provides a pathway for developing versatile high-performance solar absorbers for the sustainable treatment of saline water, wastewater, and oil-containing water.

Shish-Kebab-Structured UHMWPE Coating for Efficient and Cost-Effective Oil-Water Separation.

High-performance low-cost superhydrophobic sponges are desired for selective recycling of leaking oils from open water. Herein, an ingenious method is proposed to fabricate an ultrathin superhydrophobic coating layer on a commercial sponge. The coating layer is composed of a shish-kebab-structured porous ultrahigh molecular weight polyethylene (UHMWPE) film that is fabricated from a UHMWPE/xylene solution by shear flow-induced crystallization. A strong relationship between the shish-kebab crystallite morphology and the superwetting performance is confirmed. The UHMWPE coating layer fabricated at a 900 rpm rotation rate possesses a lamellae size of 95.1 nm and a lamellae distance of 27.4 nm, which lead to a high water contact angle of 157° and a low contact angle hysteresis of 4.5°. The UHMWPE layer prepared in 4 min of treatment is thick enough to prevent the intrusion of water even under vacuum and remain superoleophilic. The developed UHMWPE-coated sponge (UCS) exhibited a high absorption capability of 70-191 g/g toward various oils and solvents, which is comparable with the neat melamine sponge. Its excellent compressibility and durability enabled fast recovery of absorbed oil with a high recovery rate (over 85%) by mechanical squeezing. The UCS could be assembled into small devices to selectively collect oil from open water and a water/oil mixture using a pump, which manifests its promising practical applicability. Apart from these extraordinary properties, the approach developed has the lowest material cost and the shortest processing time hitherto.

Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer.

Overwhelming evidence has shown the significant role of the tumor microenvironment (TME) in governing the triple-negative breast cancer (TNBC) progression. Digital pathology can provide key information about the spatial heterogeneity within the TME using image analysis and spatial statistics. These analyses have been applied to CD8+ T cells, but quantitative analyses of other important markers and their correlations are limited. In this study, a digital pathology computational workflow is formulated for characterizing the spatial distributions of five immune markers (CD3, CD4, CD8, CD20, and FoxP3) and then the functionality is tested on whole slide images from patients with TNBC. The workflow is initiated by digital image processing to extract and colocalize immune marker-labeled cells and then convert this information to point patterns. Afterward invasive front (IF), central tumor (CT), and normal tissue (N) are characterized. For each region, we examine the intra-tumoral heterogeneity. The workflow is then repeated for all specimens to capture inter-tumoral heterogeneity. In this study, both intra- and inter-tumoral heterogeneities are observed for all five markers across all specimens. Among all regions, IF tends to have higher densities of immune cells and overall larger variations in spatial model fitting parameters and higher density in cell clusters and hotspots compared to CT and N. Results suggest a distinct role of IF in the tumor immuno-architecture. Though the sample size is limited in the study, the computational workflow could be readily reproduced and scaled due to its automatic nature. Importantly, the value of the workflow also lies in its potential to be linked to treatment outcomes and identification of predictive biomarkers for responders/non-responders, and its application to parameterization and validation of computational immuno-oncology models.

Highly Durable Superhydrophobic Polymer Foams Fabricated by Extrusion and Supercritical CO2 Foaming for Selective Oil Absorption.

The severe water contamination caused by oil leakage is calling for low-cost and high-performance absorbent materials for selective oil removal. In this study, a scalable green method was proposed to produce polypropylene (PP)/poly(tetrafluoroethylene) (PTFE) composite foams via conventional processing techniques including twin-screw extrusion and supercritical carbon dioxide foaming. To produce the superhydrophobic foam, micro- and nanosized PTFE particles were melt blended with PP and subsequently foamed. Ascribed to the nanofibrillation of microsized PTFE during processing, the fabricated foam exhibited a special highly porous structure with PTFE nanofibrils and nanoparticles uniformly distributed on the pore surfaces within the PP matrix, which resulted in a remarkably high water contact angle of 156.8° and a low contact angle hysteresis of 1.9°. Unlike traditional surface-modified superhydrophobic absorbers, the foams prepared are entirely superhydrophobic, which means that they remain superhydrophobic when being fractured or cut. Moreover, they are highly durable and maintained the superhydrophobicity when subjected to ultrasonication and mechanical sanding. When used in selective oil absorption, the durable foams exhibited excellent absorption efficiency and high stability in repetitive and long-term use. These advantages make the PP/PTFE foam a promising superabsorbent material for water remediation.

Controlling Superwettability by Microstructure and Surface Energy Manipulation on Three-Dimensional Substrates for Versatile Gravity-Driven Oil/Water Separation.

Superwettable materials have gained tremendous attention because of their special wetting abilities. The key to obtaining and tuning superwettability is to precisely control the interfacial microstructures and surface energies of materials. Herein, we propose a novel approach to controlling the superwettability of three-dimensional foams. The surface microstructure was manipulated by the layer-by-layer covalent grafting of multidimensional nanoparticles (e.g., silica, carbon nanotubes, and graphene oxide), and the surface energy was tailored by grafting chemicals with different functional groups. This grafting approach improved the mechanical performance, reduced particle loading, and prevented particle disassociation, thereby increasing the absorption capacity and durability of the functionalized foams. More importantly, superhydrophobic/superoleophilic foams were obtained after heptanol grafting. They showed water contact angles of 153° in air and 158° in oil, an absorption capacity 113 times their weight gain, and a remarkable flux of 32.6 L m-2 s-1 for the separation of oil from water driven by gravity. Polydopamine grafting resulted in superhydrophilic/underwater superoleophobic foams that had an oil contact angle of 152° under water and a high flux of 19.3 L m-2 s-1 for the separation of water from oil. Thus, this study offers not only intelligent materials for versatile oil/water separation but also a profound approach for engineering high-performance superwettable materials.

Comparison between PCL/hydroxyapatite (HA) and PCL/halloysite nanotube (HNT) composite scaffolds prepared by co-extrusion and gas foaming.

In this work, three-dimensional poly(caprolactone) (PCL) tissue engineering scaffolds were prepared by co-extrusion and gas foaming. Biocompatible hydroxyapatite (HA) and halloysite nanotubes (HNT) were added to the polymer matrix to enhance the mechanical properties and bioactivity of the composite scaffolds. The effects of HA and HNT on the rheological behavior, microstructure, and mechanical properties of the composite scaffolds were systematically compared. It was found that the HNT improved viscosity more significantly than HA, and reduced the pore size of scaffolds, while the mechanical performance of PCL/HNT scaffolds was higher than PCL/HA scaffolds with the same filler content. Human mesenchymal stem cells (hMSCs) were used as the cell model to compare the biological properties of two composite scaffolds. The results demonstrated that cells could survive on all scaffolds, and showed a more flourishing living state on the composite scaffolds. The cell differentiation for 5% HA and 1% HNT scaffolds were significantly higher than other scaffolds, while the differentiation of 5% HNT scaffolds was lower than that of 1% HNT scaffolds mainly because of the reduced pore size and pore interconnectivity. Therefore, this study suggested that, with proper filler content and control of microstructure through processing, HNT could be a suitable substitute for HA for bone tissue engineering to reduce the cost and improve mechanical performance.

Electrospinning of unidirectionally and orthogonally aligned thermoplastic polyurethane nanofibers: fiber orientation and cell migration.

Unidirectionally and orthogonally aligned thermoplastic polyurethane (TPU) nanofibers were electrospun using a custom-built electrospinning device. The unidirectionally aligned fibers were collected using two parallel copper plates, and the orthogonally aligned fibers were collected using two orthogonal sets of parallel copper plates with alternate negative connections. Carbon nanotubes (CNT) and polyacrylic acid (PAA) were added to modify the polymer solution. It was found that both CNT and PAA were capable of increasing solution conductivity. The TPU/PAA fiber showed the highest degree of fiber orientation with more than 90% of the fibers having an orientation angle between -10° and 10° for unidirectionally aligned fibers, and for orthogonally aligned fibers, the orientation angle of 50% fibers located between -10° and 10° and 48% fibers located between 80° and 100°. Viability assessment of 3T3 fibroblasts cultured on TPU/PAA fibers suggested that the material was cytocompatible. The cells' orientation and migration direction closely matched the fibers' orientation. The cell migration velocity and distance were both enhanced with the guidance of fibers compared with cells cultured on random fibers and common tissue culture plastic. Controlling cell migration velocity and directionality may provide ways to influence differentiation and gene expression and systems that would allow further exploration of wound repair and metastatic cell behavior.

Thermoplastic polyurethane/hydroxyapatite electrospun scaffolds for bone tissue engineering: effects of polymer properties and particle size.

Thermoplastic polyurethane (TPU)/hydroxyapatite (HA) scaffolds were fabricated via electrospinning. The effects of TPU properties and HA particle size on scaffold physical properties and osteoblast-like cell performance were investigated. It was found that the addition of micro-HA (mHA), which was inlayed in the fiber, decreased the electrospun fiber diameter. On the contrary, nano-HA (nHA), which was either embedded or existed inside of the fiber, increased the fiber diameter for both soft and hard TPUs. The soft TPU had a much lower Young's modulus and higher strain-at-break than the hard TPU. The addition of both mHA and nHA decreased the tensile properties; this decrease was more significant with mHA. The cells on the hard scaffolds actively proliferated and migrated compared to those on the soft scaffolds. On the other hand, cells on the soft scaffolds more effectively induced osteogenesis of human mesenchymal stem cells (hMSCs) than those on the hard scaffolds. In addition, our data suggest that the soft scaffolds with supplementation of nHA further enhanced osteogenesis of hMSCs compared to those without nHA. The soft TPU scaffolds containing nano-HA have the potential to be used in bone tissue engineering applications.