OME-Zarr: a cloud-optimized bioimaging file format with international community support.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.

Modeling, validation and verification of three-dimensional cell-scaffold contacts from terabyte-sized images.

BACKGROUND: Cell-scaffold contact measurements are derived from pairs of co-registered volumetric fluorescent confocal laser scanning microscopy (CLSM) images (z-stacks) of stained cells and three types of scaffolds (i.e., spun coat, large microfiber, and medium microfiber). Our analysis of the acquired terabyte-sized collection is motivated by the need to understand the nature of the shape dimensionality (1D vs 2D vs 3D) of cell-scaffold interactions relevant to tissue engineers that grow cells on biomaterial scaffolds. RESULTS: We designed five statistical and three geometrical contact models, and then down-selected them to one from each category using a validation approach based on physically orthogonal measurements to CLSM. The two selected models were applied to 414 z-stacks with three scaffold types and all contact results were visually verified. A planar geometrical model for the spun coat scaffold type was validated from atomic force microscopy images by computing surface roughness of 52.35 nm ±31.76 nm which was 2 to 8 times smaller than the CLSM resolution. A cylindrical model for fiber scaffolds was validated from multi-view 2D scanning electron microscopy (SEM) images. The fiber scaffold segmentation error was assessed by comparing fiber diameters from SEM and CLSM to be between 0.46% to 3.8% of the SEM reference values. For contact verification, we constructed a web-based visual verification system with 414 pairs of images with cells and their segmentation results, and with 4968 movies with animated cell, scaffold, and contact overlays. Based on visual verification by three experts, we report the accuracy of cell segmentation to be 96.4% with 94.3% precision, and the accuracy of cell-scaffold contact for a statistical model to be 62.6% with 76.7% precision and for a geometrical model to be 93.5% with 87.6% precision. CONCLUSIONS: The novelty of our approach lies in (1) representing cell-scaffold contact sites with statistical intensity and geometrical shape models, (2) designing a methodology for validating 3D geometrical contact models and (3) devising a mechanism for visual verification of hundreds of 3D measurements. The raw and processed data are publicly available from https://isg.nist.gov/deepzoomweb/data/ together with the web -based verification system.

Removal of Retained Electrospinning Solvent Prolongs Drug Release from Electrospun PLLA Fibers.

A major challenge in developing drug-releasing electrospun nanofibers is obtaining long-term drug release over many weeks with no burst release of drug. Here, we present new methods capable of prolonging the diffusive release of small molecule drugs from electrospun poly-L-lactic acid (PLLA) nanofibers. The methods focus on removal of retained electrospinning solvent through fiber heating, maintaining fibers in a laboratory setting, or a combination of these methods. These post-fabrication methods altered the release characteristics of a model small molecule drug, 6-aminonicotinamide (6AN), from PLLA fibers. Specifically, untreated fibers released 6AN over 9 days, and fibers that underwent a combined treatment of maintenance in a laboratory setting and heating released 6AN over 44 days. The unique and simple method presented here prolongs diffusive release of a small molecule drug from electrospun fibers and has potential to assist in lengthening small molecule drug release from a variety of polymeric nanomaterials.

Reduced astrocyte viability at physiological temperatures from magnetically activated iron oxide nanoparticles.

Superparamagnetic iron oxide nanoparticles (SPIONs) can generate heat when subjected to an alternating magnetic field (AMF). In the European Union, SPIONs actuated by AMF are used in hyperthermia treatment of glioblastoma multiforme, an aggressive form of brain cancer. Current data from clinical trials suggest that this therapy improves patient life expectancy, but their effect on healthy brain cells is virtually unknown. Thus, a viability study involving SPIONs subjected to an AMF was carried out on healthy cortical rat astrocytes, the most abundant cell type in the mammalian brain. The cells were cultured with aminosilane- or starch-coated SPIONs with or without application of an AMF. Significant cell death (p < 0.05) was observed only when SPIONs were added to astrocyte cultures and subjected to an AMF. Unexpectedly, the decrease in astrocyte viability was observed at physiological temperatures (34-40 °C) with AMF. A further decrease in astrocyte viability was found only when bulk temperatures exceeded 45 °C. To discern differences in the astrocyte structure when astrocytes were cultured with particles with or without AMF, scanning electron microscopy (SEM) was performed. SEM images revealed a change in the structure of the astrocyte cell membrane only when astrocytes were cultured with SPIONs and actuated with an AMF. This study is the first to report that astrocyte death occurs at physiological temperatures in the presence of magnetic particles and AMF, suggesting that other mechanisms are responsible for inducing astrocyte death in addition to heat.

A novel nomogram for peritoneal mesothelioma predicts survival.

BACKGROUND: Malignant peritoneal mesothelioma (MPM) is a rare disease treated with cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC). Estimation of personalized survival times can potentially guide treatment and surveillance. METHODS: We analyzed 104 patients who underwent CRS and cisplatin-based HIPEC for MPM. By means of 25 demographic, laboratory, operative, and histopathological variables, we developed a novel nomogram using machine-learned Bayesian belief networks with stepwise training, testing, and cross-validation. RESULTS: The mean peritoneal carcinomatosis index (PCI) was 15, and 66 % of patients had a completeness of cytoreduction (CC) score of 0 or 1. Eighty-seven percent of patients had epithelioid histology. The median follow-up time was 49 (1-195) months. The 3- and 5-year overall survivals (OS) were 58 and 46 %, respectively. The histological subtype, pre-CRS PCI, and preoperative serum CA-125 had the greatest impact on OS and were included in the nomogram. The mean areas under the receiver operating characteristic curve for the 10-fold cross-validation of the 3- and 5-year models were 0.77 and 0.74, respectively. The graphical calculator or nomogram uses color coding to assist the clinician in quickly estimating individualized patient-specific survival before surgery. CONCLUSIONS: Machine-learned Bayesian belief network analysis generated a novel nomogram predicting 3- and 5-year OS in patients treated with CRS and HIPEC for MPM. Pre-CRS estimation of survival times may potentially individualize patient care by influencing the use of systemic therapy and frequency of diagnostic imaging, and might prevent CRS in patients unlikely to achieve favorable outcomes despite surgical intervention.

Measuring dimensionality of cell-scaffold contacts of primary human bone marrow stromal cells cultured on electrospun fiber scaffolds.

The properties and structure of the cellular microenvironment can influence cell behavior. Sites of cell adhesion to the extracellular matrix (ECM) initiate intracellular signaling that directs cell functions such as proliferation, differentiation, and apoptosis. Electrospun fibers mimic the fibrous nature of native ECM proteins and cell culture in fibers affects cell shape and dimensionality, which can drive specific functions, such as the osteogenic differentiation of primary human bone marrow stromal cells (hBMSCs), by. In order to probe how scaffolds affect cell shape and behavior, cell-fiber contacts were imaged to assess their shape and dimensionality through a novel approach. Fluorescent polymeric fiber scaffolds were made so that they could be imaged by confocal fluorescence microscopy. Fluorescent polymer films were made as a planar control. hBSMCs were cultured on the fluorescent substrates and the cells and substrates were imaged. Two different image analysis approaches, one having geometrical assumptions and the other having statistical assumptions, were used to analyze the 3D structure of cell-scaffold contacts. The cells cultured in scaffolds contacted the fibers in multiple planes over the surface of the cell, while the cells cultured on films had contacts confined to the bottom surface of the cell. Shape metric analysis indicated that cell-fiber contacts had greater dimensionality and greater 3D character than the cell-film contacts. These results suggest that cell adhesion site-initiated signaling could emanate from multiple planes over the cell surface during culture in fibers, as opposed to emanating only from the cell's basal surface during culture on planar surfaces.

SEQUIN is an R/Shiny framework for rapid and reproducible analysis of RNA-seq data.

SEQUIN is a web-based application (app) that allows fast and intuitive analysis of RNA sequencing data derived for model organisms, tissues, and single cells. Integrated app functions enable uploading datasets, quality control, gene set enrichment, data visualization, and differential gene expression analysis. We also developed the iPSC Profiler, a practical gene module scoring tool that helps measure and compare pluripotent and differentiated cell types. Benchmarking to other commercial and non-commercial products underscored several advantages of SEQUIN. Freely available to the public, SEQUIN empowers scientists using interdisciplinary methods to investigate and present transcriptome data firsthand with state-of-the-art statistical methods. Hence, SEQUIN helps democratize and increase the throughput of interrogating biological questions using next-generation sequencing data with single-cell resolution.

OME-Zarr: a cloud-optimized bioimaging file format with international community support.

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

Quantitative, traceable determination of cell viability using absorbance microscopy.

Cell viability, an essential measurement for cell therapy products, lacks traceability. One of the most common cell viability tests is trypan blue dye exclusion where blue-stained cells are counted via brightfield imaging. Typically, live and dead cells are classified based on their pixel intensities which may vary arbitrarily making it difficult to compare results. Herein, a traceable absorbance microscopy method to determine the intracellular uptake of trypan blue is demonstrated. The intensity pixels of the brightfield images are converted to absorbance images which are used to calculate moles of trypan blue per cell. Trypan blue cell viability measurements, where trypan blue content in each cell is quantified, enable traceable live-dead classifications. To implement the absorbance microscopy method, we developed an open-source AbsorbanceQ application that generates quantitative absorbance images. The validation of absorbance microscopy is demonstrated using neutral density filters. Results from four different microscopes demonstrate a mean absolute deviation of 3% from the expected optical density values. When assessing trypan blue-stained Jurkat cells, the difference in intracellular uptake of trypan blue in heat-shock-killed cells using two different microscopes is 3.8%. Cells killed with formaldehyde take up ~50% less trypan blue as compared to the heat-shock-killed cells, suggesting that the killing mechanism affects trypan blue uptake. In a test mixture of approximately 50% live and 50% dead cells, 53% of cells were identified as dead (±6% standard deviation). Finally, to mimic batches of low-viability cells that may be encountered during a cell manufacturing process, viability was assessed for cells that were 1) overgrown in the cell culture incubator for five days or 2) incubated in DPBS at room temperature for five days. Instead of making live-dead classifications using arbitrary intensity values, absorbance imaging yields traceable units of moles that can be compared, which is useful for assuring quality for biomanufacturing processes.

Designing Trojan Detectors in Neural Networks Using Interactive Simulations.

This paper addresses the problem of designing trojan detectors in neural networks (NNs) using interactive simulations. Trojans in NNs are defined as triggers in inputs that cause misclassification of such inputs into a class (or classes) unintended by the design of a NN-based model. The goal of our work is to understand encodings of a variety of trojan types in fully connected layers of neural networks. Our approach is (1) to simulate nine types of trojan embeddings into dot patterns, (2) to devise measurements of NN states, and (3) to design trojan detectors in NN-based classification models. The interactive simulations are built on top of TensorFlow Playground with in-memory storage of data and NN coefficients. The simulations provide analytical, visualization, and output operations performed on training datasets and NN architectures. The measurements of a NN include (a) model inefficiency using modified Kullback-Liebler (KL) divergence from uniformly distributed states and (b) model sensitivity to variables related to data and NNs. Using the KL divergence measurements at each NN layer and per each predicted class label, a trojan detector is devised to discriminate NN models with or without trojans. To document robustness of such a trojan detector with respect to NN architectures, dataset perturbations, and trojan types, several properties of the KL divergence measurement are presented. For the general use, the web-based simulations is deployed via GitHub pages at https://github.com/usnistgov/nn-calculator.

T Cell-Mediated Antitumor Immunity Cooperatively Induced By TGFßR1 Antagonism and Gemcitabine Counteracts Reformation of the Stromal Barrier in Pancreatic Cancer.

The desmoplastic stroma of pancreatic cancers forms a physical barrier that impedes intratumoral drug delivery. Attempts to modulate the desmoplastic stroma to increase delivery of administered chemotherapy have not shown positive clinical results thus far, and preclinical reports in which chemotherapeutic drugs were coadministered with antistromal therapies did not universally demonstrate increased genotoxicity despite increased intratumoral drug levels. In this study, we tested whether TGFß antagonism can break the stromal barrier, enhance perfusion and tumoral drug delivery, and interrogated cellular and molecular mechanisms by which the tumor prevents synergism with coadministered gemcitabine. TGFß inhibition in genetically engineered murine models (GEMM) of pancreas cancer enhanced tumoral perfusion and increased intratumoral gemcitabine levels. However, tumors rapidly adapted to TGFß-dependent stromal modulation, and intratumoral perfusion returned to pre-treatment levels upon extended TGFß inhibition. Perfusion was governed by the phenotypic identity and distribution of cancer-associated fibroblasts (CAF) with the myelofibroblastic phenotype (myCAFs), and myCAFs which harbored unique genomic signatures rapidly escaped the restricting effects of TGFß inhibition. Despite the reformation of the stromal barrier and reversal of initially increased intratumoral exposure levels, TGFß inhibition in cooperation with gemcitabine effectively suppressed tumor growth via cooperative reprogramming of T regulatory cells and stimulation of CD8 T cell-mediated antitumor activity. The antitumor activity was further improved by the addition of anti-PD-L1 immune checkpoint blockade to offset adaptive PD-L1 upregulation induced by TGFß inhibition. These findings support the development of combined antistroma anticancer therapies capable of impacting the tumor beyond the disruption of the desmoplastic stroma as a physical barrier to improve drug delivery.

A method to rapidly analyze the simultaneous release of multiple pharmaceuticals from electrospun fibers.

Electrospun fibers are a commonly used cell scaffold and have also been used as pharmaceutical delivery devices. In this study, we developed a method to analyze the release of multiple pharmaceuticals from a single electrospun fiber scaffold and determine how each pharmaceutical's loading concentration affects the release rate of each pharmaceutical. Our analysis methods were tested on electrospun fibers loaded with two pharmaceuticals: 6-aminonicotinamide (6AN) and ibuprofen. Pharmaceutical concentration in electrospun fibers ranged from 1.5% to 8.5% by weight. We found that 6AN release was dependent on the concentration of 6AN and ibuprofen loaded into the fibers, while ibuprofen release was only dependent on the loading concentration of ibuprofen but not 6AN. Unexpectedly, ibuprofen release became dependent on both 6AN and ibuprofen loading concentrations when fibers were aged for 1-month post-fabrication at room temperature in the laboratory followed by a 4-hour incubation inside the cell culture incubator at 37 °C and 5% CO2. One additional discovery was an unknown signal that was attributed to the medical grade syringes used for electrospinning, which was easily removed using our method. These results demonstrate the utility of the methods developed here and indicate multiple agents can be released concomitantly from electrospun fibers to meet the demands of more complex tissue engineering approaches. Future work will focus on analysis of pharmaceutical release profiles to exploit the dependencies on pharmaceutical loading concentrations.

Comparison of Artificial Intelligence based approaches to cell function prediction.

Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels.

Differential expression of genes involved in the acute innate immune response to intracortical microelectrodes.

Higher order tasks in development for brain-computer interfacing applications require the invasiveness of intracortical microelectrodes. Unfortunately, the resulting inflammatory response contributes to the decline of detectable neural signal. The major components of the neuroinflammatory response to microelectrodes have been well-documented with histological imaging, leading to the identification of broad pathways of interest for its inhibition such as oxidative stress and innate immunity. To understand how to mitigate the neuroinflammatory response, a more precise understanding is required. Advancements in genotyping have led the development of new tools for developing temporal gene expression profiles. Therefore, we have meticulously characterized the gene expression profiles of the neuroinflammatory response to mice implanted with non-functional intracortical probes. A time course of differential acute expression of genes of the innate immune response were compared to naïve sham mice, identifying significant changes following implantation. Differential gene expression analysis revealed 22 genes that could inform future therapeutic targets. Particular emphasis is placed on the largest changes in gene expression occurring 24 h post-implantation, and in genes that are involved in multiple innate immune sets including Itgam, Cd14, and Irak4. STATEMENT OF SIGNIFICANCE: Current understanding of the cellular response contributing to the failure of intracortical microelectrodes has been limited to the evaluation of cellular presence around the electrode. Minimal research investigating gene expression profiles of these cells has left a knowledge gap identifying their phenotype. This manuscript represents the first robust investigation of the changes in gene expression levels specific to the innate immune response following intracortical microelectrode implantation. To understand the role of the complement system in response to implanted probes, we performed gene expression profiling over acute time points from implanted subjects and compared them to no-surgery controls. This manuscript provides valuable insights into inflammatory mechanisms at the tissue-probe interface, thus having a high impact on those using intracortical microelectrodes to study and treat neurological diseases and injuries.

Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy.

Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and noninvasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to noninvasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell-derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine-learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity. These results demonstrate noninvasive cell therapy characterization can be achieved with QBAM and machine learning.

Changes in male breast cancer over a 30-year period.

Male breast cancer is a rare clinical entity accounting for approximately 1 per cent of all breast cancers. The present study investigated changes in patient characteristics, disease patterns, treatment, and outcomes over a 30-year period. A retrospective chart review was performed on male breast cancer patients treated between 1975 and 2005 at Eastern Virginia Medical School, Norfolk, VA. Demographic, pathologic, treatment, and survival information was collected. To facilitate comparison of trends, the patients were divided into two groups: Cohort A (1972-1991, previously reported) and Cohort B (1992-2005). Both cohorts included 28 male patients. Comparing the cohorts, no statistical differences were noted in median age, ethnicity, presenting symptoms, or progesterone receptor status. In Cohort A, 70 per cent of patients were estrogen receptor positive, compared with 100 per cent of Cohort B (P = 0.02). Her2/neu was positive in three of five patients in Cohort B. There was a trend toward more conservative surgery, with no radical mastectomy or orchiectomy performed in Cohort B. Only two patients had sentinel lymph node mapping, both from Cohort B. Infiltrating ductal carcinoma was more prevalent in Cohort B (P = 0.04). For Cohort A and B, 5-year survival was 43 per cent and 51 per cent, respectively, which was not statistically significant. For male breast cancer, radical mastectomy is no longer a common treatment modality. Male breast cancer of today is more hormonally responsive which may have important implications for therapy. Survival has not significantly improved over the previous 30 years. Compilation of multi-institutional data of male breast cancer is needed to advance the treatment of this uncommon disease.

Combining electrospun nanofibers with cell-encapsulating hydrogel fibers for neural tissue engineering.

A promising component of biomaterial constructs for neural tissue engineering are electrospun fibers, which differentiate stem cells and neurons as well as direct neurite growth. However, means of protecting neurons, glia, and stem cells seeded on electrospun fibers between lab and surgical suite have yet to be developed. Here we report an effort to accomplish this using cell-encapsulating hydrogel fibers made by interfacial polyelectrolyte complexation (IPC). IPC-hydrogel fibers were created by interfacing acid-soluble chitosan (AsC) and cell-containing alginate and spinning them on bundles of aligned electrospun fibers. Primary spinal astrocytes, cortical neurons, or L929 fibroblasts were mixed into alginate hydrogels prior to IPC-fiber spinning. The viability of each cell type was assessed at 30 min, 4 h, 1 d, and 7 d after encapsulation in IPC hydrogels. Some neurons were encapsulated in IPC-hydrogel fibers made from water-soluble chitosan (WsC). Neurons were also stained with Tuj1 and assessed for neurite extension. Neuron survival in AsC-fibers was worse than astrocytes in AsC-fibers (p < 0.05) and neurons in WsC-fibers (p < 0.05). As expected, neuron and glia survival was worse than L929 fibroblasts (p < 0.05). Neurons in IPC-hydrogel fibers fabricated with WsC extended neurites robustly, while none in AsC fibers did. Neurons remaining inside IPC-hydrogel fibers extended neurites inside them, while others de-encapsulated, extending neurites on electrospun fibers, which did not fully integrate with IPC-hydrogel fibers. This study demonstrates that primary neurons and astrocytes can be encapsulated in IPC-hydrogel fibers at good percentages of survival. IPC hydrogel technology may be a useful tool for encapsulating neural and other cells on electrospun fiber scaffolds.

Optimizing ultrasound molecular imaging of secreted frizzled related protein 2 expression in angiosarcoma.

Secreted frizzled related protein 2 (SFRP2) is a tumor endothelial marker expressed in angiosarcoma. Previously, we showed ultrasound molecular imaging with SFRP2-targeted contrast increased average video pixel intensity (VI) of angiosarcoma vessels by 2.2 ± 0.6 VI versus streptavidin contrast. We hypothesized that redesigning our contrast agents would increase imaging performance. Improved molecular imaging reagents were created by combining NeutrAvidin™-functionalized microbubbles with biotinylated SFRP2 or IgY control antibodies. When angiosarcoma tumors in nude mice reached 8 mm, time-intensity, antibody loading, and microbubble dose experiments optimized molecular imaging. 10 minutes after injection, the control-subtracted time-intensity curve (TIC) for SFRP2-targeted contrast reached a maximum, after subtracting the contribution of free-flowing contrast. SFRP2 antibody-targeted VI was greater when contrast was formulated with 10-fold molar excess of maleimide-activated NeutrAvidin™ versus 3-fold (4.5 ± 0.18 vs. 0.32 ± 0.15, VI ± SEM, 5 x 106 dose, p < 0.001). Tumor vasculature returned greater average video pixel intensity using 5 x 107 versus 5 x 106 microbubbles (21.2 ± 2.5 vs. 4.5 ± 0.18, p = 0.0011). Specificity for tumor vasculature was confirmed by low VI for SFRP2-targeted, and control contrast in peri-tumoral vasculature (3.2 ± 0.52 vs. 1.6 ± 0.71, p = 0.92). After optimization, average video pixel intensity of tumor vasculature was 14.2 ± 3.0 VI units higher with SFRP2-targeted contrast versus IgY-targeted control (22.1 ± 2.5 vs. 7.9 ± 1.6, p < 0.001). After log decompression, 14.2 ΔVI was equal to ~70% higher signal, in arbitray acoustic units (AU), for SFRP2 versus IgY. This provided ~18- fold higher acoustic signal enhancement than provided previously by 2.2 ΔVI. Basing our targeted contrast on NeutrAvidin™-functionalized microbubbles, using IgY antibodies for our control contrast, and optimizing our imaging protocol significantly increased the SFRP2-specific signal returned from angiosarcoma vasculature, and may provide new opportunities for targeted molecular imaging.

The effect of engineered nanotopography of electrospun microfibers on fiber rigidity and macrophage cytokine production.

Currently, it is unknown how the mechanical properties of electrospun fibers, and the presentation of surface nanotopography influence macrophage gene expression and protein production. By further elucidating how specific fiber properties (mechanical properties or surface properties) alter macrophage behavior, it may be possible to create electrospun fiber scaffolds capable of initiating unique cellular and tissue responses. In this study, we determined the elastic modulus and rigidity of fibers with varying topographies created by finely controlling humidity and including a non-solvent during electrospinning. In total,five fiber scaffold types were produced. Analysis of fiber physical properties demonstrated no change in fiber diameter amongst the five different fiber groups. However, the four different fibrous scaffolds with nanopits or divots each possessed different numbers of pits with different nanoscale dimensions. Unpolarized bone marrow derived murine macrophages (M0), macrophages polarized towards a pro-inflammatory phenotype (M1), or macrophages polarized towards anti-inflammatory phenotype (M2b) were placed onto each of the scaffolds and cytokine RNA expression and protein production were analyzed. Specific nanotopographies did not appreciably alter cytokine production from undifferentiated macrophages (M0) or anti-inflammatory macrophages (M2b), but a specific fiber (with many small pits) did increase IL-12 transcript and IL-12 protein production compared to fibers with small divots. When analyzing the mechanical properties between fibers with divots or with many small pits,divoted fibers possessed similar elastic moduli but different stiffness values. In total,we present techniques capable of creating unique electrospun fibers. These unique fibers have varying fiber mechanical characteristics and modestly modulate macrophage cytokine expression.

Electrospun Fibers for Spinal Cord Injury Research and Regeneration.

Electrospinning is the process by which a scaffold containing micrometer and nanometer diameter fibers are drawn from a polymer solution or melt using a large voltage gradient between a polymer emitting source and a grounded collector. Ramakrishna and colleagues first investigated electrospun fibers for neural applications in 2004. After this initial study, electrospun fibers are increasingly investigated for neural tissue engineering applications. Electrospun fibers robustly support axonal regeneration within in vivo rodent models of spinal cord injury. These findings suggest the possibility of their eventual use within patients. Indeed, both spinal cord and peripheral nervous system regeneration research over the last several years shows that physical guidance cues induce recovery of limb, respiration, or bladder control in rodent models. Electrospun fibers may be an alternative to the peripheral nerve graft (PNG), because PNG autografts injure the patient and are limited in supply, and allografts risk host rejection. In addition, electrospun fibers can be engineered easily to confront new therapeutic challenges. Fibers can be modified to release therapies locally or can be physically modified to direct neural stem cell differentiation. This review summarizes the major findings and trends in the last decade of research, with a particular focus on spinal cord injury. This review also demonstrates how electrospun fibers can be used to study the central nervous system in vitro.