Magnetic resonance imaging (MRI) provides an effective approach to track the labeled pluripotent stem cell (PSC)-derived neural progenitor cells (NPCs) for neural transplantation and neurological disorder treatments. However, labeling the thawed cells after cryopreservation can be limited by the inefficient intracellular labeling and variations in labeling efficiency. Therefore, cryopreservation of the pre-labeled cells can provide uniform cell population and operational convenience for the following in vitro and in vivo investigations. In this study, the feasibility of cryopreserving PSC-derived NPC aggregates labeled with micron-sized particles of iron oxide (MPIO) was investigated. The NPC aggregates derived from embryoid body formation were labeled in suspension with different concentrations of MPIO in the range of 0-100 ug Fe per mL. The results indicated that intracellular MPIO incorporation was retained after cryopreservation (70-80% labeling efficiency), which did not significantly affect cell recovery, proliferation, cytotoxicity and neural lineage commitment. MRI analysis was performed in the phantom tissue environment using cell layers with different MPIO exposures separated by agarose gels. The results showed comparable detectability for the MPIO-labeled cells before and after cryopreservation indicated by T2 and T2* relaxation rates. These findings indicate the feasibility of cryopreserving MPIO-labeled PSC-derived NPC aggregates for potential cell banking toward various in vitro and in vivo cell tracking studies. / A Thesis submitted to the Department of Chemical and Biomedical Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Fall Semester, 2014. / November 5, 2014. / Cryopreservation, Iron oxide particle, Magnetic resonnance imaging, Neural progenitor aggregate, Pluripotent stem cell / Includes bibliographical references. / Yan Li, Professor Directing Thesis; Samuel C. Grant, Committee Member; Jingjiao Guan, Committee Member.

archives@tulane.edu / Prostate cancer is the second leading cause of death due to cancer for men in the United States. A common form of treatment for prostate cancer is a surgical procedure known as a radical prostatectomy, the full resection of the prostate from the patient. The success of this procedure is quantified by the presence of diseased tissue at the surface of the resected specimen. The ideal outcome of this procedure is known as a negative surgical margin (NSM), indicating that there is no diseased tissue along the margin of the specimen, however positive surgical margins (PSM) occur regularly. Currently, the method for intra-operative detection is Frozen Section Analysis (FSA) but it suffers from a high false negative rate due to a small sampling size. Our group is developing a new method of ex vivo imaging of the entire radical prostatectomy surgical margins using video-rate structured illumination microscopy (VR-SIM) paired with a custom-built sample position system. The goal of this method is to rapidly image the entire circumference of the resected specimen in order to correct surgical margins intraoperatively. Currently, the custom-built apparatus used for manipulating the specimen, known as the Automated Prostate Positioning System (APPS), faces several challenges in order for the machine to be considered adoptable in a point-of-care environment. The goal of this work is to further refine the APPS with the intent of implementing this system for point-of-care applications. This will be achieved through three specific aims: (1) identify the existing challenges faced in clinical applications of the current APPS configuration, (2) use the challenges identified with the current configuration of the APPS to implement structural and software changes to better suit clinical applications, and (3) verify that the new APPS configuration is an improvement of the prior iteration through bench experiments. / 1 / Max Cooper

Kidney-on-chip (KOC) is an emerging technology aiming to facilitate pharmaceutical development by providing more robust prediction on drug nephrotoxicity and efficacy during preclinical stage. Proximal tubule is the primary site of investigation in the kidney for its role in drug excretion and vulnerability against drug-induced toxicity. Exposed to shearing stress of fluid flow, renal proximal tubule epithelial cells cultured in the microfluidic-based KOC platform exhibit improved long-term viability and express morphological and functional characteristics similar to proximal tubule epithelium in vivo, such as apical-basolateral membrane polarization, enhanced reabsorption function and appropriate injury response, which are deficient in conventional cell culture model. However, many KOC platforms utilizing larger-sized channels yet to fully represent human proximal tubule structure, while topography resembling tubular curvature of human proximal tubule have shown similar morphological improvement as fluid-driven KOC platforms. Therefore, a potential direction for KOC platform is through merging of physiologically relevant tubular structure and flow rate. In this study, a microfluidic device containing microchannels with dimension analogous to human proximal tubules was designed and fabricated with photo-lithography, thermal reflow, and soft lithography techniques. The fluid dynamics, particularly the shear stress performance, was analyzed with COMSOL, ensuring fluid delivered to such a device could induce desired shear stress. In addition, preliminary study on electrode design was performed, demonstrating the capability to integrate trans-epithelial electrical resistance sensing mechanism for real time monitoring the tissue integrity cultured in such devices. / 2021-05-15T00:00:00Z

In vivo imaging of the human cornea and retina is typically performed in a reflection geometry. Images are formed from light that has backscattered off corneal microstructures or backreflected from the retina. In this configuration, artifacts caused by superficial surface reflections are often encountered. These unwanted reflections can either globally overwhelm the signal or cause local glare, complicating reliable image quantification. This thesis describes a pair of alternative ophthalmic imaging techniques based instead on transmitted light, which inherently avoids these artifacts. For retinal (i.e. fundus) imaging, we describe a mesoscopic transmission imaging method, which we call transcranial fundus imaging. The method uses deeply penetrating near-infrared light delivered transcranially from the side of the head, and exploits multiple scattering to redirect a portion of the light towards the posterior eye. This unique transmission geometry simplifies absorption measurements and enables flash-free, non-mydriatic imaging as deep as the choroid. We use multispectral image sets taken with this new transillumination approach to estimate oxygen saturation in retinal blood vessels. In the cornea, we describe a new technique for non-contact phase-contrast microscopic imaging. It is based on fundus retro-reflection and back-illumination of the crystalline lens and cornea. To enhance phase-gradient contrast, we apply asymmetric illumination by illuminating one side of the fundus. The technique produces micron-scale lateral resolution across a 1-mm diagonal field of view. We show representative images of the epithelium, the subbasal nerve plexus, large stromal nerves, dendritic immune cells, endothelial nuclei, and the anterior crystalline lens, demonstrating the potential of this instrument for clinical applications.

The present study aimed to accelerate and improve accuracy of ɣ-aminobutyric acid (GABA) and glutathione (GSH) quantification. These metabolites, present at low concentrations in the brain, are challenging to detect using MR spectroscopy due to the fact that their resonance frequencies overlap with those of other more abundant metabolites. The advanced spectral editing techniques involving J-difference editing that are required to resolve the overlapping signals of these low concentration metabolites are not routinely available on clinical MRI scanners. In this work we implemented on a 3T Siemens Skyra MRI a novel MRS technique called Hadamard Encoding and Reconstruction of MEGA-Edited Spectroscopy (HERMES) to simultaneously detect GABA and GSH, developed a novel postprocessing technique that simultaneously models the sum and various difference spectra, and evaluated the performance of the sequence and processing method both in phantoms and in vivo. HERMES was implemented by modifying the Siemens GABA-edited MEGA-PRESS WIP sequence to include two additional sub-experiments – one editing GSH with a single lobe pulse and one editing both GABA and GSH using a dual lobe pulse, and replacing the Siemens pulses with ‘universal' pulses similar to those used in a previous implementation of HERMES on a Philips platform. Performance was assessed in a phantom and 22 healthy adults, 15 of whom provided usable data (7 male, mean age 25.6 ± 2.7 yr). Three of the subjects were scanned 3 times to assess reproducibility. Data were processed and compared using a set of custom scripts in MATLAB. Following frequency and phase correction of individual averages with GANNET, we applied our custom simultaneous linear combination model that iteratively fit the concatenated sum and difference spectra using a least squares routine. SPM was used for tissue segmentation of structural images and FID-A to simulate high-resolution basis sets. The simultaneous modelling technique provided absolute quantification results for 15 metabolite moieties using internal unsuppressed water as a reference. The performance of the simultaneous fitting approach was compared to multiple independent fittings for HERCULES (Hadamard Editing Resolves Chemicals Using Linear-combination Estimation of Spectra) data that had been previously acquired on a 3T Philips Achieva MRI. Although the HERMES sequence implemented on the Siemens platform as part of this project was able to successfully edit both GABA and GSH, and generate line shapes consistent with the work by Saleh et al. (2016), quantification accuracy was disappointing. In the phantom data, GSH and GABA concentrations were both roughly 50% of known levels. Since the actual concentrations in vivo were not known, we were not able to establish accuracy, but quantification agreement between the MEGA-PRESS and HERMES sequences was poor for most metabolites. Specifically, GABA levels were two to three times higher than expected values using both HERMES and GABA-edited MEGA-PRESS. Despite poor absolute agreement, concentrations from HERMES and MEGA-PRESS data were moderately correlated, and HERMES data showed lower coefficients of variation across subjects, suggesting that it may be more reliable. HERMES was also more reproducible across scanning sessions and participants for more metabolites than GABA- or GSH-edited MEGA-PRESS. Our findings also showed that simultaneous fitting using the sum and difference spectra produces lower coefficients of variation for most metabolites than fittings to sum and difference spectra separately.

Introduction: HIV-related brain alterations can be identified using neuroimaging modalities such as proton magnetic resonance spectroscopy (1H-MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI). However, few studies have combined multiple MRI measures/features to identify a multivariate neuroimaging signature that typifies HIV infection. Elastic net (EN) regularisation uses penalised regression to perform variable selection, shrinking the weighting of unimportant variables to zero. We chose to use the embedded feature selection of EN logistic regression to identify a set of neuroimaging features characteristic of paediatric HIV infection. We aimed to determine 1) the most useful features across MRI modalities to separate HIV+ children from HIV- controls and 2) whether better classification performance is obtained by combining multimodal MRI features rather than using features from a single modality. Methods: The study sample comprised 72 HIV+ 7-year-old children from the Children with HIV Early Antiretroviral Therapy (CHER) trial in Cape Town, who initiated combination antiretroviral therapy (cART) in infancy and had their viral loads suppressed from a young age, and 55 HIV- control children. Neuroimaging features were extracted to generate 7 MRI-derived sets. For sMRI, 42 regional brain volumes (1st set), mean cortical thickness and gyrification in 68 brain regions (2nd and 3rd set) were used. For DTI data: radial (RD), axial (AD), mean (MD) diffusivities, and fractional anisotropy (FA) in each of 20 atlas regions were extracted for a total of 80 DTI features (4th set). For 1H-MRS, concentrations of 14 metabolites and their ratios to creatine in the basal ganglia, peritrigonal white matter, and midfrontal gray matter voxels (5th, 6th and 7th set) were considered. A logistic EN regression model with repeated 10-fold cross validation (CV) was implemented in R, initially on each feature set separately. Sex, age and total intracranial volume (TIV) were included as confounders with no shrinkage penalty. For each model, the classification performance for HIV+ vs HIV- was assessed by computing accuracy, specificity, sensitivity, and mean area under the receiver operator characteristic curve (AUC) across 10 CV folds and 100 iterations. To combine feature sets, the best performing set was concatenated with each of the other sets and further EN regressions were run. The combination giving the largest AUC was combined with each of the remaining sets until there was no further increase in AUC. Two concatenation techniques were explored: nested and non-nested modelling. All models were assessed for their goodness of fit using χ 2 likelihood ratio tests for non-nested models and Akaike information criterion (AIC) for nested models. To identify features most useful in distinguishing HIV infection, the EN model was retrained on all the data, to find features with non-zero weights. Finally, multivariate imputation using chained equations (MICE) was explored to investigate the effect of increased sample size on classification and feature selection. Results: The best performing modality in the single modality analysis was sMRI volumes

Obesity poses a public health threat worldwide and is associated with a higher mortality, increased likelihood of diabetes, and an increased risk of cancer. When treating obesity, regular monitoring of metrics such as body mass index (BMI) and waist circumference has been found to result in improved health outcomes for patients. Three-dimensional (3D) scanners provide a useful tool to provide body measurements based on 3D images in obesity management. However, such scanners are often inaccessible due to cost. A smartphone image-based method able to produce 3D images may provide a more accessible measuring tool. As a step towards developing such a smartphone application, this project developed a method for 3D reconstruction of body images from two-dimensional (2D) images, using a full body 3D Gaussian process morphable model (GPMM). Separate GPMMs were trained to learn the shape of female and male human bodies. Gaussian process regression of the three-dimensional (3D) GPMM models onto two-dimensional (2D) images is performed. Corresponding landmarks on the 3D shapes and in the 2D images are employed in reconstruction. Measurements of body volume, waist circumference and height are then performed to extract information that is useful in obesity management. Different model configurations (shape model with arms; modified shape model with arms; shape model without arms; marginalised shape model without arms; shape model with different landmarks) were used to ascertain the most promising approach for the reconstruction. Each reconstructed body was tested for accuracy using the surface-tosurface distance per vertex, modified Hausdorff distance, and assessment of the measurements. Tests were performed using data from the same dataset used to build the model and generalised data from a different dataset. In all test cases, the best performing approach used shape models without arms when considering surface distances. However, the surface-to-surface distances errors were larger than those seen in literature. For body measurements, the best performing models varied with different models performing best for different measurements. For the measurements, the errors were larger than the allowable errors and larger than those found in literature. Landmark positions were evaluated separately and found to be imprecise. There are a few sources that contribute towards the reconstruction errors. Possible sources of error include an inability to interpret pose and landmark position errors. The major recommendations for future work are to use a model that incorporates both shape and pose and to use automatic landmarking methods. Regarding a pathway to a smartphone app, camera parameter information should be considered to improve processing of the images and smartphone orientation information should be considered to correct for distortions due to a tilted phone.

Rapid and sensitive detection of viral infections is of significant importance for improving patient care and containing outbreaks that threaten public health. Although there has been an enormous effort to develop point-of-care biosensors for viral diagnostics applications, sensitive, robust and easily portable platforms have yet to be realized. This dissertation focuses on optimization of a multiplexed immunoassay platform for viral diagnostics applications using a label-free optical biosensor termed Single-Particle Interferometric Reflectance Imaging Sensor (SP-IRIS). SP-IRIS utilizes an antibody microarray that captures the target viruses and an optical instrument that allows visualization of individual captured virus particles. Since this platform relies on capture of whole viruses, it is crucial to identify high-affinity antibodies that are capable of recognizing intact virions. For this purpose, we screened various antibodies for their performance on the SP-IRIS platform. By screening 43 different antibodies for three different viruses, we demonstrated specific and sensitive detection of different viruses and different subtypes of the same virus. This work allowed us to assemble an antibody microarray capable of multiplexed detection that has been tested in our laboratory as well as at two separate high-containment facilities. Next, we adapted a different antibody immobilization technique, DNA-directed antibody immobilization (DDI), to the SP-IRIS platform as a means to improve the sensitivity and robustness of the assay. First, we characterized the elevation of the antibodies conjugated to a DNA sequence on a three-dimensional polymeric surface using a fluorescence axial localization technique, Spectral Self-Interference Fluorescence Microscopy, determining the optimal length of the DNA linkers for SP-IRIS substrates. We subsequently showed the specific detection of Vesicular Stomatitis Virus (VSV) expressing Ebola glycoprotein on SP-IRIS platform using the DDI approach. We showed that DNA-conjugated antibodies improve the capture efficiency resulting in over a ten-fold improvement in assay sensitivity compared to directly immobilized antibodies. To demonstrate the feasibility of the DDI technique for multiplexed virus detection utilizing SP-IRIS, we used VSVs expressing Ebola, Marburg or Lassa surface glycoproteins and successfully demonstrated specific and multiplexed detection using a DNA microarray surface. We also combined this approach with a passive microfluidic cartridge, demonstrating the feasibility of SP-IRIS as a rapid testing technique that is well suited for point-of-care applications.

Bacterial transcription factors (TFs) affect gene regulation by recognizing and binding regulatory DNA sequences and subsequent recruitment of transcriptional machinery. The emergence of new genomic techniques has made global mapping of TF-DNA binding sites more feasible. The advent of high-throughput, next-generation sequencing (NGS) methods such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) provides high-resolution genome-wide binding data that helps increase our understanding of bacterial gene regulatory networks. This technology has so far been applied on a handful of bacterial TFs, leading to more transcription factor binding sites (TFBS) open for discovery. Despite being one of the most studied microorganisms in scientific research, only a select few Escherichia coli TFs have been studied on a genome-wide scale. In order to generate a more accurate map of E.coli TF binding locations, this work uses a combination of ChIP-based protocols both done in vivo and in vitro to properly identify all possible binding locations for TFs being studied. Results show that our methods not only find strong, well-known reported binding sites but also discover many more novel, biologically relevant ones. Our ability to properly evaluate and characterize TFBS based on the analysis of large-scale data sets generated from all the ChIP experiments improves how binding sites are being called in high-throughput data, constructing a comprehensive framework to build and model the regulatory network on. The successful implementation of the developed in vitro ChIP method shows good reproducibility with in vivo ChIP data, and also captures more actual binding occurrences, fairly complementing data generated from motif-based computationally predicted binding sites. Assessment of the differential binding patterns observed highlight the role that binding motif sequence and DNA accessibility play in determining TF binding in the cell. This work also demonstrates the unprecedented ability of using both in vivo and in vitro binding methods in parallel to generate a more complete model of TF binding. Over-all, the results present the potential to further our understanding of bacterial regulatory networks through an integrated analysis and characterization of TF–DNA binding behavior across the genome. / 2021-09-28T00:00:00Z

Neural stem cells have the capacity to self-renew and differentiate into multiple specialized neural phenotypes, providing a promising therapeutic strategy to replace damaged or lost neurons in neurological diseases. Preliminary clinical trials have demonstrated that grafting neural stem cells in brain tissue could achieve some therapeutic effects. However, the outcomes are highly variable with various adverse effects. Unfortunately, an understanding of which factors underlie success or failure remains elusive.  To facilitate the development of safe and effective clinical therapies using grafted neural stem cells, it will be informative to improve our understanding of the environmental factors and means of controlling the process of integration. It is currently difficult to observe the characteristics of neural stem cell integration into host tissue continuously; the integration process is thus often deduced from snapshots of post-mortem tissue requiring sacrifice of transplanted animals at distinct time points, which is often inefficient and impractical to carry out. This dissertation addresses the problem by describing the development and use of a reliable experimental platform that enables continuous observation of stem cells grafted in mouse cortex throughout the course of integration. Current attempts to image neocortical regions on the surface of mouse brain typically use a small glass disc attached to the cranial surface. This approach, however, is often challenged by progressive deterioration in optical quality and permits limited tissue access after its initial implantation. I describe a design and demonstrate a two-stage cranial implant device developed with a remarkably versatile material, polydimethylsiloxane, which facilitates longitudinal imaging experiments in mouse cortex. The system was designed considering biocompatibility and optical performance. This enabled us to achieve sustained periods of optical quality, extending beyond a year in some mice, and allows imaging at high spatiotemporal resolution using wide-field microscopy. Additionally, the two-part system, consisting of a fixed headplate with integrated neural access chamber and optical insert, allowed flexible access to the underlying tissue. Finally, I demonstrate the technical feasibility of rapid adaptation of the system to accommodate varying applications requiring long-term ability to visualize and access neural tissue. Utilizing the two-part cranial window system, two distinct sources of neural stem cells dissected from distinct anatomical regions within mouse embryo and labeled with genetically encoded calcium indicators were transplanted into an adult mouse cortex. The cellular dynamics across hundreds of transplants were acquired periodically across several months using wide-field epifluorescence microscopy. This allowed longitudinal comparisons of cell and network activity from each animal. Immediately after transplantation, in both cell populations, the spontaneous network activity was dominated by a highly recurrent pattern of synchronous bursts, similar to the characteristic activity observed during early development of endogenous cells. Gradually, the network activity diversified and matured into complex activation patterns — network states with better information processing capacities. In an attempt to quantify functional integration of grafted cell-derived neurons with host neural network, several strategies were employed to capture the evolution in dynamic patterns of network activation, including cross-correlation, entropy, and information carrying capacity. Future work using such approach to analyze environmental factors on impacting neural stem cell integration in the native context will contribute to advanced stem cell therapy for neurological disorders. / 2020-09-28T00:00:00Z