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Browsing by Author "Ghosh, Soham, committee member"

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    A QuPath workflow utilizing machine learning to analyze homing protein specificity and penetration into lung granulomas of Mycobacterium tuberculosis infected mice
    (Colorado State University. Libraries, 2024) Patterson, John, author; Gonzalez-Juarrero, Mercedes, advisor; Lyons, Mike, committee member; Ghosh, Soham, committee member
    Targeted delivery of drugs to the lungs can improve TB chemotherapy and thus our goal is to develop TB-drug loaded nanoparticles tagged to pulmonary homing peptides. In a previous study, homing peptides to the lungs of TB diseased animals were identified using preclinical TB models (Balb/c and C3HeB/FeJ mice). The selection of homing peptides was carried out using a phage library containing peptides with known homing affinity in other diseases (e.g. cancer). Having identified and selected the homing peptide PL1 (PPRRGLIKLKTS) to granulomas present in the lungs of murine TB models, the PL1 peptide and a negative control (scrambled LinnTT peptide) were tagged to Fluorescein Amidites (FAM). To facilitate tracking in vivo of the nanoparticles to be loaded with TB drugs, silver nanoparticles (SNP) were conjugated to Cy3 fluorochrome, a fluorescent marker used in in vivo tracking studies, followed by functionalization with the PL1 homing peptide (PL1-SNP) or biotin as negative control (Ctrl-SNP). Tracking and homing of the PL1 peptide to granulomas was possible after in vivo administration via intraperitoneal (IP) or intravenous (IV) route of either the FAM tagged synthetic peptides or Cy3-SNPs to Mycobacterium tuberculosis (Mtb) infected C3HeB/FeJ mice. Visualization of the fluorescence-tagged carriers within the lungs was performed using microscopic slides affixed with lung sections from each mouse followed by whole slide imaging. The semi-quantitative analysis of the fluorescence whole slide images performed using the QuPath workflow confirmed that PL1-FAM, or PL1-SNP homed to the granulomas. Thereafter, a QuPath workflow was developed that uses machine learning approaches (MLP) for unbiased identification of tissue types. Other tools were used for characterization and quantification of FAM (synthetic peptides) and Cy3 (SNP) positive cells within granulomatous lesions of the C3HeB/FeJ TB mouse model. Moreover, it was important to quantify the penetration capacity of the FAM tagged peptide as well as the peptide coated SNP into granulomas. QuPath also includes a built in MLP pixel classifier for unbiased segmentation of the whole slide. In addition, a modified QuPath script was developed to segment the granulomas into concentric regions (outer, inner and center) followed by detection and quantification of positive cells for either fluorochrome within each region. Specific colocalization of PL1 with its known receptor (FN-EDB), either as a synthetic peptide or coupled to the SNP, was also studied using lung sections from mice treated with PL1-FAM or PL1-SNP and counter stained with Alexa 647 conjugated anti-FN-EDB monoclonal antibodies. The modified QuPath script was trained to quantify fluorescence from Alexa 647 in cells within granulomas and the Pearson coefficient and QuPath script was used to assess PL1 and FN-EDB colocalization within each region of the granuloma. The results demonstrated that when compared to their respective control samples, the IP route of administration provides equal or better homing of PL1 peptide to the granulomas than the IV route. Both the PL1-FAM and PL1-SNP home to the granulomas and specifically colocalize with its receptor FN-EDB. The FAM tagged peptide and SNP penetrate to the inner and center regions of the granuloma whereas the control SNP were unable to penetrate the barrier in the outer region of the granulomas. The QuPath workflow developed here can be used for tracking and quantification of other homing peptides and nanoparticles for development of new TB therapeutics.
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    Interaction of erythrocytes (RBC's) with nanostructured surfaces
    (Colorado State University. Libraries, 2022) Virk, Harvinder Singh, author; Popat, Ketul C., advisor; Ghosh, Soham, committee member; Li, Vivian, committee member
    Titanium and its alloys are used to make different blood-contacting medical devices such as stents, artificial heart valves, and catheters for cardiovascular diseases due to their superior biocompatibility. Thrombus formation begins on the surface of these devices as soon as they encounter blood. This leads to the formation of blood clots, which obstructs the flow of blood that leads to severe complications. Recent advancements in nanoscale fabrication and superhydrophobic surface modification techniques have demonstrated that these surfaces have antiadhesive properties and the ability to reduce thrombosis. In this study, the interaction of erythrocytes and whole blood clotting kinetics on superhydrophobic titanium nanostructured surfaces was investigated. These surfaces were characterized for their wettability (contact angle), surface morphology and topography (scanning electron microscopy (SEM)), and crystallinity (glancing angled X-Ray diffraction (GAXRD)). Erythrocyte morphology on different surfaces was characterized using SEM and overall cell viability was demonstrated through fluorescence microscopy. The hemocompatibility of these surfaces was characterized using commercially available assays: thrombin generation assay --> thrombin generation, hemolytic assay --> hemolysis, and complement convertase assay --> complement activity. The results indicate that superhydrophobic titanium nanostructured surfaces had lower erythrocyte adhesion, less morphological changes in adhered cells, lower thrombin generation, lower complement activation, and were less cytotoxic compared to control surfaces. Thus, superhydrophobic titanium nanostructured surfaces may be a promising approach to prevent thrombosis for several blood-contacting medical devices.
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    The path from injury to degeneration: multi-modal characterization of chronic rotator cuff degeneration
    (Colorado State University. Libraries, 2021) Johnson, James W., author; McGilvray, Kirk C., advisor; Puttlitz, Christian, committee member; Ghosh, Soham, committee member; Easley, Jeremiah, committee member
    Rotator cuff tendon tears are a prevalent issue worldwide; tears to these tendons can reduce arm mobility, cause pain, and decrease quality of life. Unfortunately, rotator cuff tendon tear repair surgeries experience unacceptable failures rates, with comorbidities such as age, chronic rotator cuff degeneration, or osteoporosis exacerbating these failures. The etiology of chronic degeneration is not fully understood, and there are no therapies or treatment capable of reversing or healing that condition. Furthermore, research is hindered due to the inability of current large animal translational models to faithfully recapitulate the wide range of changes noted in chronic degeneration. With that in mind, this work sought to improve the understanding of chronic rotator cuff degeneration through development of a clinically translatable large animal model and study of the injury and degeneration cascade. Specifically, this work has five components that will contribute to this body of knowledge. The first aim was to generate a model through transection of one half of the width of the tendon; unfortunately, this was found to result in differential changes on the two halves of the tendon that did not match the embodiment of changes seen clinically. The inadequacy and learnings from this model led to the generation of aims two and three. It has been hypothesized that chronic degeneration can result from untreated partial tears that are not diagnosed or treated with any conservative treatment. Aim 2 was focused on generating a chronic degeneration model through a clinically relevant bursal-side partial tear. Whereas Aim 3 was focused on creating a similar model without damaging the tendon insertion, providing the opportunity to screen therapies intended at halting or reversing the degeneration cascade. Aim 4 involved assessing tendons in an ovine model of osteoporosis for signs of degeneration as a means of determining the underlying cause for increased prevalence of rotator cuff repair failure in patients with osteoporosis. Aim 5 included characterization of the biomechanical, histological, and gene expression changes in cadaveric human rotator cuff tendons across a spectrum of ages as a means of better understanding the manifestation of chronic degeneration with the human rotator cuff. This aim was utilized as positive validation of the ovine models and as a means to generate design targets for repair scaffold mechanical properties. Aim 6 entailed generating a preliminary design of a scaffold capable of recapitulating the biomechanical properties of the healthy human supraspinatus tendons tested in Aim 5. Together, these proposed Aims provide new models of chronic rotator cuff degeneration, unique and novel data illuminating the multifactorial degeneration cascade in humans, and a prototype scaffold aimed at improving repair prognosis.
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    Using finite state projection and Fisher information to improve single-cell experiment design to gain better understanding of DUSP1 transcription dynamics
    (Colorado State University. Libraries, 2023) Cook, Joshua A., author; Munsky, Brian, advisor; Chong, Edwin, committee member; Ghosh, Soham, committee member
    Many recent studies have combined fluorescent biochemical labels, single-cell microscopy, and discrete stochastic modeling to understand and predict how organisms react to environmental changes to control gene expression. The experimental data used in these studies is often collected using intuitively-designed applications of techniques such as single-cell immunnocytochemistry (ICC) to measure protein expression and transport or single-molecule Fluorescence in situ Hybridization (smFISH) to measure the number and position of transcribed mRNA. Once collected, these single-cell data are then analyzed using discrete stochastic models, often based on the framework of the Chemical Master Equation (CME), which can be solved using the Finite State Projection (FSP) algorithm. Unfortunately, these experiments can be expensive and labor intensive to perform, primarily due to long imaging and image analysis times, and it is not clear how these experiments must be designed to obtain the most information when their results are later analyzed using the FSP techniques. The recently discovered Finite State Projection based Fisher information Matrix (FSP-FIM) provides a potential and practical solution to this experiment design challenge by providing direct estimates for how well any potential experiment should be expected to constrain parameters for a given model or set of models. In this report, we examine this challenge of experiment design in the situation where multiple different types of experiments (i.e., ICC and smFISH) are possible, for different time points, for different numbers of measurements per time point, for different environmental inputs, and for different assumed models and combinations of unknown parameters. We extend the previous FSP-FIM theory to address these multiple challenges, and we introduce new computational tools in the form of advances to the Stochastic System Identification Toolkit (in Mathworks Matlab) that allow users to easily and efficiently compute the FSP and FIM for each of these circumstances. Using experimental smFISH data, we demonstrate the effectiveness of the FSP tools to quantitatively reproduce the single-cell transcription dynamics of the Dual Specific Phosphatase 1 (DUSP1) gene under stimulation by Dexamethasone (Dex), and we show how the FSP-FIM can be used to design optimal combinations of ICC and smFISH to further improve quantification of this gene regulatory process, including predicting the optimal allocation of measurement times to obtain the most amount of information from each experiment. To probe the generality of our results, these FSP and FSP-FIM analyses are conducted for different models, under different assumptions on known and unknown parameters, and under different drug dosage regimens. The approach developed in this work is expected to have substantial impact on how computational models can be employed to improve the selection and design of future single-cell experiments.