Discharge competence and pattern formation in peatlands: a meta-ecosystem model of the Everglades ridge-slough landscape.

Regular landscape patterning arises from spatially-dependent feedbacks, and can undergo catastrophic loss in response to changing landscape drivers. The central Everglades (Florida, USA) historically exhibited regular, linear, flow-parallel orientation of high-elevation sawgrass ridges and low-elevation sloughs that has degraded due to hydrologic modification. In this study, we use a meta-ecosystem approach to model a mechanism for the establishment, persistence, and loss of this landscape. The discharge competence (or self-organizing canal) hypothesis assumes non-linear relationships between peat accretion and water depth, and describes flow-dependent feedbacks of microtopography on water depth. Closed-form model solutions demonstrate that 1) this mechanism can produce spontaneous divergence of local elevation; 2) divergent and homogenous states can exhibit global bi-stability; and 3) feedbacks that produce divergence act anisotropically. Thus, discharge competence and non-linear peat accretion dynamics may explain the establishment, persistence, and loss of landscape pattern, even in the absence of other spatial feedbacks. Our model provides specific, testable predictions that may allow discrimination between the self-organizing canal hypotheses and competing explanations. The potential for global bi-stability suggested by our model suggests that hydrologic restoration may not re-initiate spontaneous pattern establishment, particularly where distinct soil elevation modes have been lost. As a result, we recommend that management efforts should prioritize maintenance of historic hydroperiods in areas of conserved pattern over restoration of hydrologic regimes in degraded regions. This study illustrates the value of simple meta-ecosystem models for investigation of spatial processes.

Micro-Anatomical Quantitative Imaging Towards Enabling Automated Diagnosis of Thick Tissues at the Point of Care

Histopathology is the clinical standard for tissue diagnosis. However, histopathology has several limitations including that it requires tissue processing, which can take 30 minutes or more, and requires a highly trained pathologist to diagnose the tissue. Additionally, the diagnosis is qualitative, and the lack of quantitation leads to possible observer-specific diagnosis. Taken together, it is difficult to diagnose tissue at the point of care using histopathology.

Several clinical situations could benefit from more rapid and automated histological processing, which could reduce the time and the number of steps required between obtaining a fresh tissue specimen and rendering a diagnosis. For example, there is need for rapid detection of residual cancer on the surface of tumor resection specimens during excisional surgeries, which is known as intraoperative tumor margin assessment. Additionally, rapid assessment of biopsy specimens at the point-of-care could enable clinicians to confirm that a suspicious lesion is successfully sampled, thus preventing an unnecessary repeat biopsy procedure. Rapid and low cost histological processing could also be potentially useful in settings lacking the human resources and equipment necessary to perform standard histologic assessment. Lastly, automated interpretation of tissue samples could potentially reduce inter-observer error, particularly in the diagnosis of borderline lesions.

To address these needs, high quality microscopic images of the tissue must be obtained in rapid timeframes, in order for a pathologic assessment to be useful for guiding the intervention. Optical microscopy is a powerful technique to obtain high-resolution images of tissue morphology in real-time at the point of care, without the need for tissue processing. In particular, a number of groups have combined fluorescence microscopy with vital fluorescent stains to visualize micro-anatomical features of thick (i.e. unsectioned or unprocessed) tissue. However, robust methods for segmentation and quantitative analysis of heterogeneous images are essential to enable automated diagnosis. Thus, the goal of this work was to obtain high resolution imaging of tissue morphology through employing fluorescence microscopy and vital fluorescent stains and to develop a quantitative strategy to segment and quantify tissue features in heterogeneous images, such as nuclei and the surrounding stroma, which will enable automated diagnosis of thick tissues.

To achieve these goals, three specific aims were proposed. The first aim was to develop an image processing method that can differentiate nuclei from background tissue heterogeneity and enable automated diagnosis of thick tissue at the point of care. A computational technique called sparse component analysis (SCA) was adapted to isolate features of interest, such as nuclei, from the background. SCA has been used previously in the image processing community for image compression, enhancement, and restoration, but has never been applied to separate distinct tissue types in a heterogeneous image. In combination with a high resolution fluorescence microendoscope (HRME) and a contrast agent acriflavine, the utility of this technique was demonstrated through imaging preclinical sarcoma tumor margins. Acriflavine localizes to the nuclei of cells where it reversibly associates with RNA and DNA. Additionally, acriflavine shows some affinity for collagen and muscle. SCA was adapted to isolate acriflavine positive features or APFs (which correspond to RNA and DNA) from background tissue heterogeneity. The circle transform (CT) was applied to the SCA output to quantify the size and density of overlapping APFs. The sensitivity of the SCA+CT approach to variations in APF size, density and background heterogeneity was demonstrated through simulations. Specifically, SCA+CT achieved the lowest errors for higher contrast ratios and larger APF sizes. When applied to tissue images of excised sarcoma margins, SCA+CT correctly isolated APFs and showed consistently increased density in tumor and tumor + muscle images compared to images containing muscle. Next, variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82% and 75%. The utility of this approach was further tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78% and 82%. The results indicate that SCA+CT can accurately delineate APFs in heterogeneous tissue, which is essential to enable automated and rapid surveillance of tissue pathology.

Two primary challenges were identified in the work in aim 1. First, while SCA can be used to isolate features, such as APFs, from heterogeneous images, its performance is limited by the contrast between APFs and the background. Second, while it is feasible to create mosaics by scanning a sarcoma tumor bed in a mouse, which is on the order of 3-7 mm in any one dimension, it is not feasible to evaluate an entire human surgical margin. Thus, improvements to the microscopic imaging system were made to (1) improve image contrast through rejecting out-of-focus background fluorescence and to (2) increase the field of view (FOV) while maintaining the sub-cellular resolution needed for delineation of nuclei. To address these challenges, a technique called structured illumination microscopy (SIM) was employed in which the entire FOV is illuminated with a defined spatial pattern rather than scanning a focal spot, such as in confocal microscopy.

Thus, the second aim was to improve image contrast and increase the FOV through employing wide-field, non-contact structured illumination microscopy and optimize the segmentation algorithm for new imaging modality. Both image contrast and FOV were increased through the development of a wide-field fluorescence SIM system. Clear improvement in image contrast was seen in structured illumination images compared to uniform illumination images. Additionally, the FOV is over 13X larger than the fluorescence microendoscope used in aim 1. Initial segmentation results of SIM images revealed that SCA is unable to segment large numbers of APFs in the tumor images. Because the FOV of the SIM system is over 13X larger than the FOV of the fluorescence microendoscope, dense collections of APFs commonly seen in tumor images could no longer be sparsely represented, and the fundamental sparsity assumption associated with SCA was no longer met. Thus, an algorithm called maximally stable extremal regions (MSER) was investigated as an alternative approach for APF segmentation in SIM images. MSER was able to accurately segment large numbers of APFs in SIM images of tumor tissue. In addition to optimizing MSER for SIM image segmentation, an optimal frequency of the illumination pattern used in SIM was carefully selected because the image signal to noise ratio (SNR) is dependent on the grid frequency. A grid frequency of 31.7 mm-1 led to the highest SNR and lowest percent error associated with MSER segmentation.

Once MSER was optimized for SIM image segmentation and the optimal grid frequency was selected, a quantitative model was developed to diagnose mouse sarcoma tumor margins that were imaged ex vivo with SIM. Tumor margins were stained with acridine orange (AO) in aim 2 because AO was found to stain the sarcoma tissue more brightly than acriflavine. Both acriflavine and AO are intravital dyes, which have been shown to stain nuclei, skeletal muscle, and collagenous stroma. A tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER segmentation output. Specifically, a logistic regression model was used to classify each localized region. The logistic regression model yielded an output in terms of probability (0-100%) that tumor was located within each 75x75 µm region. The model performance was tested using a receiver operator characteristic (ROC) curve analysis that revealed 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that with a tumor probability threshold of 50%, 8% of all regions from negative margins exceeded this threshold, while over 17% of all regions exceeded the threshold in the positive margins. Thus, 8% of regions in negative margins were considered false positives. These false positive regions are likely due to the high density of APFs present in normal tissues, which clearly demonstrates a challenge in implementing this automatic algorithm based on AO staining alone.

Thus, the third aim was to improve the specificity of the diagnostic model through leveraging other sources of contrast. Modifications were made to the SIM system to enable fluorescence imaging at a variety of wavelengths. Specifically, the SIM system was modified to enabling imaging of red fluorescent protein (RFP) expressing sarcomas, which were used to delineate the location of tumor cells within each image. Initial analysis of AO stained panels confirmed that there was room for improvement in tumor detection, particularly in regards to false positive regions that were negative for RFP. One approach for improving the specificity of the diagnostic model was to investigate using a fluorophore that was more specific to staining tumor. Specifically, tetracycline was selected because it appeared to specifically stain freshly excised tumor tissue in a matter of minutes, and was non-toxic and stable in solution. Results indicated that tetracycline staining has promise for increasing the specificity of tumor detection in SIM images of a preclinical sarcoma model and further investigation is warranted.

In conclusion, this work presents the development of a combination of tools that is capable of automated segmentation and quantification of micro-anatomical images of thick tissue. When compared to the fluorescence microendoscope, wide-field multispectral fluorescence SIM imaging provided improved image contrast, a larger FOV with comparable resolution, and the ability to image a variety of fluorophores. MSER was an appropriate and rapid approach to segment dense collections of APFs from wide-field SIM images. Variables that reflect the morphology of the tissue, such as the density, size, and shape of nuclei and nucleoli, can be used to automatically diagnose SIM images. The clinical utility of SIM imaging and MSER segmentation to detect microscopic residual disease has been demonstrated by imaging excised preclinical sarcoma margins. Ultimately, this work demonstrates that fluorescence imaging of tissue micro-anatomy combined with a specialized algorithm for delineation and quantification of features is a means for rapid, non-destructive and automated detection of microscopic disease, which could improve cancer management in a variety of clinical scenarios.

Delivery and Scavenging of Nucleic Acids by Polycationic Polymers

Electrostatic interaction is a strong force that attracts positively and negatively charged molecules to each other. Such an interaction is formed between positively charged polycationic polymers and negatively charged nucleic acids. In this dissertation, the electrostatic attraction between polycationic polymers and nucleic acids is exploited for applications in oral gene delivery and nucleic acid scavenging. An enhanced nanoparticle for oral gene delivery of a human Factor IX (hFIX) plasmid is developed using the polycationic polysaccharide, chitosan (Ch), in combination with protamine sulfate (PS) to treat hemophilia B. For nucleic acid scavenging purposes, the development of an effective nucleic acid scavenging nanofiber platform is described for dampening hyper-inflammation and reducing the formation of biofilms.

Non-viral gene therapy may be an attractive alternative to chronic protein replacement therapy. Orally administered non-viral gene vectors have been investigated for more than one decade with little progress made beyond the initial studies. Oral administration has many benefits over intravenous injection including patient compliance and overall cost; however, effective oral gene delivery systems remain elusive. To date, only chitosan carriers have demonstrated successful oral gene delivery due to chitosan’s stability via the oral route. In this study, we increase the transfection efficiency of the chitosan gene carrier by adding protamine sulfate to the nanoparticle formulation. The addition of protamine sulfate to the chitosan nanoparticles results in up to 42x higher in vitro transfection efficiency than chitosan nanoparticles without protamine sulfate. Therapeutic levels of hFIX protein are detected after oral delivery of Ch/PS/phFIX nanoparticles in 5/12 mice in vivo, ranging from 3 -132 ng/mL, as compared to levels below 4 ng/mL in 1/12 mice given Ch/phFIX nanoparticles. These results indicate the protamine sulfate enhances the transfection efficiency of chitosan and should be considered as an effective ternary component for applications in oral gene delivery.

Dying cells release nucleic acids (NA) and NA-complexes that activate the inflammatory pathways of immune cells. Sustained activation of these pathways contributes to chronic inflammation related to autoimmune diseases including systemic lupus erythematosus, rheumatoid arthritis, and inflammatory bowel disease. Studies have shown that certain soluble, cationic polymers can scavenge extracellular nucleic acids and inhibit RNA-and DNA-mediated activation of Toll-like receptors (TLRs) and inflammation. In this study, the cationic polymers are incorporated onto insoluble nanofibers, enabling local scavenging of negatively charged pro-inflammatory species such as damage-associated molecular pattern (DAMP) molecules in the extracellular space, reducing cytotoxicity related to unwanted internalization of soluble cationic polymers. In vitro data show that electrospun nanofibers grafted with cationic polymers, termed nucleic acid scavenging nanofibers (NASFs), can scavenge nucleic acid-based agonists of TLR 3 and TLR 9 directly from serum and prevent the production of NF-ĸB, an immune system activating transcription factor while also demonstrating low cytotoxicity. NASFs formed from poly (styrene-alt-maleic anhydride) conjugated with 1.8 kDa branched polyethylenimine (bPEI) resulted in randomly aligned fibers with diameters of 486±9 nm. NASFs effectively eliminate the immune stimulating response of NA based agonists CpG (TLR 9) and poly (I:C) (TLR 3) while not affecting the activation caused by the non-nucleic acid TLR agonist pam3CSK4. Results in a more biologically relevant context of doxorubicin-induced cell death in RAW cells demonstrates that NASFs block ~25-40% of NF-ĸβ response in Ramos-Blue cells treated with RAW extracellular debris, ie DAMPs, following doxorubicin treatment. Together, these data demonstrate that the formation of cationic NASFs by a simple, replicable, modular technique is effective and that such NASFs are capable of modulating localized inflammatory responses.

An understandable way to clinically apply the NASF is as a wound bandage. Chronic wounds are a serious clinical problem that is attributed to an extended period of inflammation as well as the presence of biofilms. An NASF bandage can potentially have two benefits in the treatment of chronic wounds by reducing the inflammation and preventing biofilm formation. NASF can prevent biofilm formation by reducing the NA present in the wound bed, therefore removing large components of what the bacteria use to develop their biofilm matrix, the extracellular polymeric substance, without which the biofilm cannot develop. The NASF described above is used to show the effect of the nucleic acid scavenging technology on in vitro and in vivo biofilm formation of P. aeruginosa, S. aureus, and S. epidermidis biofilms. The in vitro studies demonstrated that the NASFs were able to significantly reduce the biofilm formation in all three bacterial strains. In vivo studies of the NASF on mouse wounds infected with biofilm show that the NASF retain their functionality and are able to scavenge DNA, RNA, and protein from the wound bed. The NASF remove DNA that are maintaining the inflammatory state of the open wound and contributing to the extracellular polymeric substance (EPS), such as mtDNA, and also removing proteins that are required for bacteria/biofilm formation and maintenance such as chaperonin, ribosomal proteins, succinyl CoA-ligase, and polymerases. However, the NASF are not successful at decreasing the wound healing time because their repeated application and removal disrupts the wound bed and removes proteins required for wound healing such as fibronectin, vibronectin, keratin, and plasminogen. Further optimization of NASF treatment duration and potential combination treatments should be tested to reduce the unwanted side effects of increased wound healing time.