Matching Items (16)

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Biology-based matched signal processing and physics-based modeling for improved detection

Description

Peptide microarrays have been used in molecular biology to profile immune responses and develop diagnostic tools. When the microarrays are printed with random peptide sequences, they can be used

Peptide microarrays have been used in molecular biology to profile immune responses and develop diagnostic tools. When the microarrays are printed with random peptide sequences, they can be used to identify antigen antibody binding patterns or immunosignatures. In this thesis, an advanced signal processing method is proposed to estimate epitope antigen subsequences as well as identify mimotope antigen subsequences that mimic the structure of epitopes from random-sequence peptide microarrays. The method first maps peptide sequences to linear expansions of highly-localized one-dimensional (1-D) time-varying signals and uses a time-frequency processing technique to detect recurring patterns in subsequences. This technique is matched to the aforementioned mapping scheme, and it allows for an inherent analysis on how substitutions in the subsequences can affect antibody binding strength. The performance of the proposed method is demonstrated by estimating epitopes and identifying potential mimotopes for eight monoclonal antibody samples.

The proposed mapping is generalized to express information on a protein's sequence location, structure and function onto a highly localized three-dimensional (3-D) Gaussian waveform. In particular, as analysis of protein homology has shown that incorporating different kinds of information into an alignment process can yield more robust alignment results, a pairwise protein structure alignment method is proposed based on a joint similarity measure of multiple mapped protein attributes. The 3-D mapping allocates protein properties into distinct regions in the time-frequency plane in order to simplify the alignment process by including all relevant information into a single, highly customizable waveform. Simulations demonstrate the improved performance of the joint alignment approach to infer relationships between proteins, and they provide information on mutations that cause changes to both the sequence and structure of a protein.

In addition to the biology-based signal processing methods, a statistical method is considered that uses a physics-based model to improve processing performance. In particular, an externally developed physics-based model for sea clutter is examined when detecting a low radar cross-section target in heavy sea clutter. This novel model includes a process that generates random dynamic sea clutter based on the governing physics of water gravity and capillary waves and a finite-difference time-domain electromagnetics simulation process based on Maxwell's equations propagating the radar signal. A subspace clutter suppression detector is applied to remove dominant clutter eigenmodes, and its improved performance over matched filtering is demonstrated using simulations.

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Date Created
  • 2014

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Adaptive learning and unsupervised clustering of immune responses using microarray random sequence peptides

Description

Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from

Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can potentially provide pre-symptomatic diagnosis for infectious diseases or detection of biological threats. Currently, traditional bioinformatics tools, such as data mining classification algorithms, are used to process the large amount of peptide microarray data. However, these methods generally require training data and do not adapt to changing immune conditions or additional patient information. This work proposes advanced processing techniques to improve the classification and identification of single and multiple underlying immune response states embedded in immunosignatures, making it possible to detect both known and previously unknown diseases or biothreat agents. Novel adaptive learning methodologies for un- supervised and semi-supervised clustering integrated with immunosignature feature extraction approaches are proposed. The techniques are based on extracting novel stochastic features from microarray binding intensities and use Dirichlet process Gaussian mixture models to adaptively cluster the immunosignatures in the feature space. This learning-while-clustering approach allows continuous discovery of antibody activity by adaptively detecting new disease states, with limited a priori disease or patient information. A beta process factor analysis model to determine underlying patient immune responses is also proposed to further improve the adaptive clustering performance by formatting new relationships between patients and antibody activity. In order to extend the clustering methods for diagnosing multiple states in a patient, the adaptive hierarchical Dirichlet process is integrated with modified beta process factor analysis latent feature modeling to identify relationships between patients and infectious agents. The use of Bayesian nonparametric adaptive learning techniques allows for further clustering if additional patient data is received. Significant improvements in feature identification and immune response clustering are demonstrated using samples from patients with different diseases.

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Created

Date Created
  • 2013

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An integrated microarray printing and detection system to study protein-protein interactions

Description

In this thesis, a breadboard Integrated Microarray Printing and Detection System (IMPDS) was proposed to address key limitations of traditional microarrays. IMPDS integrated two core components of a high-resolution surface

In this thesis, a breadboard Integrated Microarray Printing and Detection System (IMPDS) was proposed to address key limitations of traditional microarrays. IMPDS integrated two core components of a high-resolution surface plasmon resonance imaging (SPRi) system and a piezoelectric dispensing system that can print ultra-low volume droplets. To avoid evaporation of droplets in the microarray, a 100 μm thick oil layer (dodecane) was used to cover the chip surface. The interaction between BSA (Bovine serum albumin) and Anti-BSA was used to evaluate the capability of IMPDS. The alignment variability of printing, stability of droplets array and quantification of protein-protein interactions based on nanodroplet array were evaluated through a 10 x 10 microarray on SPR sensor chip. Binding kinetic constants obtained from IMPDS are close with results from commercial SPR setup (BI-3000), which indicates that IMPDS is capable to measure kinetic constants accurately. The IMPDS setup has following advantages: 1) nanoliter scale sample consumption, 2) high-throughput detection with real-time kinetic information for biomolecular interactions, 3) real-time information during printing and spot-on-spot detection of biomolecular interactions 4) flexible selection of probes and receptors (M x N interactions). Since IMPDS studies biomolecular interactions with low cost and high flexibility in real-time manner, it has great potential in applications such as drug discovery, food safety and disease diagnostics, etc.

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Date Created
  • 2017

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New methods for biological and environmental protein fingerprinting: from traditional techniques to new technology

Description

A new challenge on the horizon is to utilize the large amounts of protein found in the atmosphere to identify different organisms from which the protein originated. Included here is

A new challenge on the horizon is to utilize the large amounts of protein found in the atmosphere to identify different organisms from which the protein originated. Included here is work investigating the presence of identifiable patterns of different proteins collected from the air and biological samples for the purposes of remote identification. Protein patterns were generated using high performance liquid chromatography (HPLC). Patterns created could identify high-traffic and low-traffic indoor spaces. Samples were collected from the air using air pumps to draw air through a filter paper trapping particulates, including large amounts of shed protein matter. In complimentary research aerosolized biological samples were collected from various ecosystems throughout Ecuador to explore the relationship between environmental setting and aerosolized protein concentrations. In order to further enhance protein separation and produce more detailed patterns for the identification of individual organisms of interest; a novel separation device was constructed and characterized. The separation device incorporates a longitudinal gradient as well as insulating dielectrophoretic features within a single channel. This design allows for the production of stronger local field gradients along a global gradient allowing particles to enter, initially transported through the channel by electrophoresis and electroosmosis, and to be isolated according to their characteristic physical properties, including charge, polarizability, deformability, surface charge mobility, dielectric features, and local capacitance. Thus, different types of particles are simultaneously separated at different points along the channel distance given small variations of properties. The device has shown the ability to separate analytes over a large dynamic range of size, from 20 nm to 1 μm, roughly the size of proteins to the size of cells. In the study of different sized sulfate capped polystyrene particles were shown to be selectively captured as well as concentrating particles from 103 to 106 times. Qualitative capture and manipulation of β-amyloid fibrils were also shown. The results demonstrate the selective focusing ability of the technique; and it may form the foundation for a versatile tool for separating complex mixtures. Combined this work shows promise for future identification of individual organisms from aerosolized protein as well as for applications in biomedical research.

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Date Created
  • 2011

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Early detection and treatment of breast cancer by random peptide array in neuN transgenic mouse model

Description

Breast cancer is the most common cancer and currently the second leading cause of death among women in the United States. Patients’ five-year relative survival rate decreases from 99% to

Breast cancer is the most common cancer and currently the second leading cause of death among women in the United States. Patients’ five-year relative survival rate decreases from 99% to 25% when breast cancer is diagnosed late. Immune checkpoint blockage has shown to be a promising therapy to improve patients’ outcome in many other cancers. However, due to the lack of early diagnosis, the treatment is normally given in the later stages. An early diagnosis system for breast cancer could potentially revolutionize current treatment strategies, improve patients’ outcomes and even eradicate the disease. The current breast cancer diagnostic methods cannot meet this demand. A simple, effective, noninvasive and inexpensive early diagnostic technology is needed. Immunosignature technology leverages the power of the immune system to find cancer early. Antibodies targeting tumor antigens in the blood are probed on a high-throughput random peptide array and generate a specific binding pattern called the immunosignature.

In this dissertation, I propose a scenario for using immunosignature technology to detect breast cancer early and to implement an early treatment strategy by using the PD-L1 immune checkpoint inhibitor. I develop a methodology to describe the early diagnosis and treatment of breast cancer in a FVB/N neuN breast cancer mouse model. By comparing FVB/N neuN transgenic mice and age-matched wild type controls, I have found and validated specific immunosignatures at multiple time points before tumors are palpable. Immunosignatures change along with tumor development. Using a late-stage immunosignature to predict early samples, or vice versa, cannot achieve high prediction performance. By using the immunosignature of early breast cancer, I show that at the time of diagnosis, early treatment with the checkpoint blockade, anti-PD-L1, inhibits tumor growth in FVB/N neuN transgenic mouse model. The mRNA analysis of the PD-L1 level in mice mammary glands suggests that it is more effective to have treatment early.

Novel discoveries are changing understanding of breast cancer and improving strategies in clinical treatment. Researchers and healthcare professionals are actively working in the early diagnosis and early treatment fields. This dissertation provides a step along the road for better diagnosis and treatment of breast cancer.

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Created

Date Created
  • 2015

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Exploring the nature of protein-peptide interactions on surfaces

Description

Protein-surface interactions, no matter structured or unstructured, are important in both biological and man-made systems. Unstructured interactions are more difficult to study with conventional techniques due to the lack of

Protein-surface interactions, no matter structured or unstructured, are important in both biological and man-made systems. Unstructured interactions are more difficult to study with conventional techniques due to the lack of a specific binding structure. In this dissertation, a novel approach is employed to study the unstructured interactions between proteins and heterogonous surfaces, by looking at a large number of different binding partners at surfaces and using the binding information to understand the chemistry of binding. In this regard, surface-bound peptide arrays are used as a model for the study. Specifically, in Chapter 2, the effects of charge, hydrophobicity and length of surface-bound peptides on binding affinity for specific globular proteins (&beta-galactosidase and &alpha1-antitrypsin) and relative binding of different proteins were examined with LC Sciences peptide array platform. While the general charge and hydrophobicity of the peptides are certainly important, more surprising is that &beta-galactosidase affinity for the surface does not simply increase with the length of the peptide. Another interesting observation that leads to the next part of the study is that even very short surface-bound peptides can have both strong and selective interactions with proteins. Hence, in Chapter 3, selected tetrapeptide sequences with known binding characteristics to &beta-galactosidase are used as building blocks to create longer sequences to see if the binding function can be added together. The conclusion is that while adding two component sequences together can either greatly increase or decrease overall binding and specificity, the contribution to the binding affinity and specificity of the individual binding components is strongly dependent on their position in the peptide. Finally, in Chapter 4, another array platform is utilized to overcome the limitations associated with LC Sciences. It is found that effects of peptide sequence properties on IgG binding with HealthTell array are quiet similar to what was observed with &beta-galactosidase on LC Science array surface. In summary, the approach presented in this dissertation can provide binding information for both structured and unstructured interactions taking place at complex surfaces and has the potential to help develop surfaces covered with specific short peptide sequences with relatively specific protein interaction profiles.

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Date Created
  • 2014

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Cancer autoantibody biomarker discovery and validation using nucleic acid programmable protein array

Description

Currently in the US, many patients with cancer do not benefit from the population-based screening, due to challenges associated with the existing cancer screening scheme. Blood-based diagnostic assays have the

Currently in the US, many patients with cancer do not benefit from the population-based screening, due to challenges associated with the existing cancer screening scheme. Blood-based diagnostic assays have the potential to detect diseases in a non-invasive way. Proteins released from small early tumors may only be present intermittently and get diluted to tiny concentrations in the blood, making them difficult to use as biomarkers. However, they can induce autoantibody (AAb) responses, which can amplify the signal and persist in the blood even if the antigen is gone. Circulating autoantibodies is a promising class of molecules that have potential to serve as early detection biomarkers for cancers. This Ph.D thesis aims to screen for autoantibody biomarkers for the early detection of two deadly cancer, basal-like breast cancer and lung adenocarcinoma. First, a method was developed to display proteins in both native and denatured conformation on protein array. This method adopted a novel protein tag technology, called HaloTag, to covalently immobilize proteins on glass slide surface. The covalent attachment allowed these proteins to endure harsh treatment without getting dissociated from slide surface, which enabled the profiling of antibody responses against both conformational and linear epitopes. Next, a plasma screening protocol was optimized to significantly increase signal to noise ratio of protein array based AAb detection. Following this, the AAb responses in basal-like breast cancer were explored using nucleic acid programmable protein arrays (NAPPA) containing 10,000 full-length human proteins in 45 cases and 45 controls. After verification in a large sample set (145 basal-like breast cancer cases / 145 controls / 70 non-basal breast cancer) by ELISA, a 13-AAb classifier was developed to differentiate patients from controls with a sensitivity of 33% at 98% specificity. Similar approach was also applied to the lung cancer study to identify AAbs that distinguished lung cancer patients from computed-tomography positive benign pulmonary nodules (137 lung cancer cases, 127 smoker controls, 170 benign controls). In this study, two panels of AAbs were discovered that showed promising sensitivity and specificity. Six out of eight AAb targets were also found to have elevated mRNA level in lung adenocarcinoma patients using TCGA data. These projects as a whole provide novel insights on the association between AAbs and cancer, as well as general B cell antigenicity against self-proteins.

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Date Created
  • 2015

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Characterization and analysis of a novel platform for profiling the antibody response

Description

Immunosignaturing is a new immunodiagnostic technology that uses random-sequence peptide microarrays to profile the humoral immune response. Though the peptides have little sequence homology to any known protein, binding of

Immunosignaturing is a new immunodiagnostic technology that uses random-sequence peptide microarrays to profile the humoral immune response. Though the peptides have little sequence homology to any known protein, binding of serum antibodies may be detected, and the pattern correlated to disease states. The aim of my dissertation is to analyze the factors affecting the binding patterns using monoclonal antibodies and determine how much information may be extracted from the sequences. Specifically, I examined the effects of antibody concentration, competition, peptide density, and antibody valence. Peptide binding could be detected at the low concentrations relevant to immunosignaturing, and a monoclonal's signature could even be detected in the presences of 100 fold excess naive IgG. I also found that peptide density was important, but this effect was not due to bivalent binding. Next, I examined in more detail how a polyreactive antibody binds to the random sequence peptides compared to protein sequence derived peptides, and found that it bound to many peptides from both sets, but with low apparent affinity. An in depth look at how the peptide physicochemical properties and sequence complexity revealed that there were some correlations with properties, but they were generally small and varied greatly between antibodies. However, on a limited diversity but larger peptide library, I found that sequence complexity was important for antibody binding. The redundancy on that library did enable the identification of specific sub-sequences recognized by an antibody. The current immunosignaturing platform has little repetition of sub-sequences, so I evaluated several methods to infer antibody epitopes. I found two methods that had modest prediction accuracy, and I developed a software application called GuiTope to facilitate the epitope prediction analysis. None of the methods had sufficient accuracy to identify an unknown antigen from a database. In conclusion, the characteristics of the immunosignaturing platform observed through monoclonal antibody experiments demonstrate its promise as a new diagnostic technology. However, a major limitation is the difficulty in connecting the signature back to the original antigen, though larger peptide libraries could facilitate these predictions.

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Created

Date Created
  • 2011

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Foundational studies for array-based electrophoretic exclusion of proteins

Description

Disease prevention and personalized treatment will be impacted by the continued integration of protein biomarkers into medical practice. While there are already numerous biomarkers used clinically, the detection of protein

Disease prevention and personalized treatment will be impacted by the continued integration of protein biomarkers into medical practice. While there are already numerous biomarkers used clinically, the detection of protein biomarkers among complex matrices remains a challenging problem. One very important strategy for improvements in clinical application of biomarkers is separation/preconcentration, impacting the reliability, efficiency and early detection. Electrophoretic exclusion can be used to separate, purify, and concentrate biomarkers. This counterflow gradient technique exploits hydrodynamic flow and electrophoretic forces to exclude, enrich, and separate analytes. The development of this technique has evolved onto an array-based microfluidic platform which offers a greater range of geometries/configurations for optimization and expanded capabilities and applications. Toward this end of expanded capabilities, fundamental studies of subtle changes to the entrance flow and electric field configurations are investigated. Three closely related microfluidic interfaces are modeled, fabricated and tested. A charged fluorescent dye is used as a sensitive and accurate probe to test the concentration variation at these interfaces. Models and experiments focus on visualizing the concentration profile in areas of high temporal dynamics, and show strong qualitative agreement, which suggests the theoretical assessment capabilities can be used to faithfully design novel and more efficient interfaces. Microfluidic electrophoretic separation technique can be combined with electron microscopy as a protein concentration/purification step aiding in sample preparation. The integrated system with grids embedded into the microdevice reduces the amount of time required for sample preparation to less than five minutes. Spatially separated and preconcentrated proteins are transferred directly from an upstream reservoir onto grids. Dilute concentration as low as 0.005 mg/mL can be manipulated to achieve meaningful results. Selective concentration of one protein from a mixture of two proteins is also demonstrated. Electrophoretic exclusion is also used for biomarker applications. Experiments using a single biomarker are conducted to assess the ability of the microdevice for enrichment in central reservoirs. A mixture of two protein biomarkers are performed to evaluate the proficiency of the device for separations capability. Moreover, a battery is able to power the microdevice, which facilitates the future application as a portable device.

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Date Created
  • 2019

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Dense non-natural sequence peptide microarrays for epitope mapping and diagnostics

Description

The healthcare system in this country is currently unacceptable. New technologies may contribute to reducing cost and improving outcomes. Early diagnosis and treatment represents the least risky option for addressing

The healthcare system in this country is currently unacceptable. New technologies may contribute to reducing cost and improving outcomes. Early diagnosis and treatment represents the least risky option for addressing this issue. Such a technology needs to be inexpensive, highly sensitive, highly specific, and amenable to adoption in a clinic. This thesis explores an immunodiagnostic technology based on highly scalable, non-natural sequence peptide microarrays designed to profile the humoral immune response and address the healthcare problem. The primary aim of this thesis is to explore the ability of these arrays to map continuous (linear) epitopes. I discovered that using a technique termed subsequence analysis where epitopes could be decisively mapped to an eliciting protein with high success rate. This led to the discovery of novel linear epitopes from Plasmodium falciparum (Malaria) and Treponema palladium (Syphilis), as well as validation of previously discovered epitopes in Dengue and monoclonal antibodies. Next, I developed and tested a classification scheme based on Support Vector Machines for development of a Dengue Fever diagnostic, achieving higher sensitivity and specificity than current FDA approved techniques. The software underlying this method is available for download under the BSD license. Following this, I developed a kinetic model for immunosignatures and tested it against existing data driven by previously unexplained phenomena. This model provides a framework and informs ways to optimize the platform for maximum stability and efficiency. I also explored the role of sequence composition in explaining an immunosignature binding profile, determining a strong role for charged residues that seems to have some predictive ability for disease. Finally, I developed a database, software and indexing strategy based on Apache Lucene for searching motif patterns (regular expressions) in large biological databases. These projects as a whole have advanced knowledge of how to approach high throughput immunodiagnostics and provide an example of how technology can be fused with biology in order to affect scientific and health outcomes.

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Created

Date Created
  • 2014