A Multichannel DNA SoC for Rapid Point-of-Care Gene Detection

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Single-Molecule Detection of Unique Genome Signatures: Applications in Molecular Diagnostics and Homeland Security

Single-molecule detection (SMD) offers an attractive approach for identifying the presence of certain markers that can be used for in vitro molecular diagnostics in a near real-time format. The ability to eliminate sample processing steps afforded by the ultra-high sensitivity associated with SMD yields an increased sampling pipeline. When SMD and microfluidics are used in conjunction with nucleic acid-based assays such as the ligase detection reaction coupled with single-pair fluorescent resonance energy transfer (LDR-spFRET), complete molecular profiling and screening of certain cancers, pathogenic bacteria, and other biomarkers becomes possible at remarkable speeds and sensitivities with high specificity. The merging of these technologies and techniques into two different novel instrument formats has been investigated. (1) The use of a charge-coupled device (CCD) in time-delayed integration (TDI) mode as a means for increasing the throughput of any single molecule measurement by simultaneously tracking and detecting single-molecules in multiple microfluidic channels was demonstrated. The CCD/TDI approach allowed increasing the sample throughput by a factor of 8 compared to a single-assay SMD experiment. A sampling throughput of 276 molecules s-1 per channel and 2208 molecules s-1 for an eight channel microfluidic system was achieved. A cyclic olefin copolymer (COC) waveguide was designed and fabricated in a pre-cast poly(dimethylsiloxane) stencil to increase the SNR by controlling the excitation geometry. The waveguide showed an attenuation of 0.67 dB/cm and the launch angle was optimized to increase the depth of penetration of the evanescent wave. (2) A compact SMD (cSMD) instrument was designed and built for the reporting of molecular signatures associated with bacteria. The optical waveguides were poised within the fluidic chip at orientation of 90° with respect to each other for the interrogation of single-molecule events. Molecular beacons (MB) were designed to probe bacteria for the classification of Gram +. MBs were mixed with bacterial cells and pumped though the cSMD which allowed S. aureus to be classified with 2,000 cells in 1 min. Finally, the integration of the LDR-spFRET assay on the cSMD was explored with the future direction of designing a molecular screening approach for stroke diagnostics

Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is the most common type of leukemia. The Cancer Genome Atlas Research Network has demonstrated the increasing genomic complexity of acute myeloid leukemia (AML). In addition, the network has facilitated our understanding of the molecular events leading to this deadly form of malignancy for which the prognosis has not improved over past decades. AML is a highly heterogeneous disease, and cytogenetics and molecular analysis of the various chromosome aberrations including deletions, duplications, aneuploidy, balanced reciprocal translocations and fusion of transcription factor genes and tyrosine kinases has led to better understanding and identification of subgroups of AML with different prognoses. Furthermore, molecular classification based on mRNA expression profiling has facilitated identification of novel subclasses and defined high-, poor-risk AML based on specific molecular signatures. However, despite increased understanding of AML genetics, the outcome for AML patients whose number is likely to rise as the population ages, has not changed significantly. Until it does, further investigation of the genomic complexity of the disease and advances in drug development are needed. In this review, leading AML clinicians and research investigators provide an up-to-date understanding of the molecular biology of the disease addressing advances in diagnosis, classification, prognostication and therapeutic strategies that may have significant promise and impact on overall patient survival

Nanomaterials for Healthcare Biosensing Applications

In recent years, an increasing number of nanomaterials have been explored for their applications in biomedical diagnostics, making their applications in healthcare biosensing a rapidly evolving field. Nanomaterials introduce versatility to the sensing platforms and may even allow mobility between different detection mechanisms. The prospect of a combination of different nanomaterials allows an exploitation of their synergistic additive and novel properties for sensor development. This paper covers more than 290 research works since 2015, elaborating the diverse roles played by various nanomaterials in the biosensing field. Hence, we provide a comprehensive review of the healthcare sensing applications of nanomaterials, covering carbon allotrope-based, inorganic, and organic nanomaterials. These sensing systems are able to detect a wide variety of clinically relevant molecules, like nucleic acids, viruses, bacteria, cancer antigens, pharmaceuticals and narcotic drugs, toxins, contaminants, as well as entire cells in various sensing media, ranging from buffers to more complex environments such as urine, blood or sputum. Thus, the latest advancements reviewed in this paper hold tremendous potential for the application of nanomaterials in the early screening of diseases and point-of-care testing

A Platform for Combined DNA and Protein Microarrays Based on Total Internal Reflection Fluorescence

We have developed a novel microarray technology based on total internal reflection fluorescence (TIRF) in combination with DNA and protein bioassays immobilized at the TIRF surface. Unlike conventional microarrays that exhibit reduced signal-to-background ratio, require several stages of incubation, rinsing and stringency control, and measure only end-point results, our TIRF microarray technology provides several orders of magnitude better signal-to-background ratio, performs analysis rapidly in one step, and measures the entire course of association and dissociation kinetics between target DNA and protein molecules and the bioassays. In many practical cases detection of only DNA or protein markers alone does not provide the necessary accuracy for diagnosing a disease or detecting a pathogen. Here we describe TIRF microarrays that detect DNA and protein markers simultaneously, which reduces the probabilities of false responses. Supersensitive and multiplexed TIRF DNA and protein microarray technology may provide a platform for accurate diagnosis or enhanced research studies. Our TIRF microarray system can be mounted on upright or inverted microscopes or interfaced directly with CCD cameras equipped with a single objective, facilitating the development of portable devices. As proof-of-concept we applied TIRF microarrays for detecting molecular markers from Bacillus anthracis, the pathogen responsible for anthrax

Advances in Rapid Detection and Antimicrobial Susceptibility Tests: A Review

The rise of antibiotic resistance is an emerging problem of the millennium. Clinical microbiology plays an important role in combating the problem by facilitating diagnostics and therapeutics thus managing infection in patients. Diagnostic failures are a major limiting factor during bacterial infection that causes inappropriate use of antibiotics, delay in start up of treatment and decrease in the survival rate during septic conditions. Thus rapid and reliable detection is highly relevant during such bacterial infections and also at the time of disease outbreak as many such pathogens can be used as biothreat agents or bioweapons affecting human health and posing risk to national security. This review highlights the importance of various methods for fast pathogen detection and antimicrobial susceptibility determination. These methods have the potential to provide very precise and rapid ways for bacterial screening and identifying the correct antibiotics to cure infectio

Molecular Approaches for Analyzing Organismal and Environmental Interactions

Our planet is undergoing rapid change due to the expanding human population and climate change, which leads to extreme weather events and habitat loss. It is more important than ever to develop methods which can monitor the impact we are having on the biodiversity of our planet. To influence policy changes in wildlife and resource management practices we need to provide measurable evidence of how we are affecting animal health and fitness and the ecosystems needed for their survival. We also need to pool our resources and work in interdisciplinary teams to find common threads which can help preserve biodiversity and vital habitats. This dissertation showcases how improved molecular biology assays and data analysis approaches can help monitor the fitness of animal populations within changing ecosystems. Chapter 1 details the development of a universal telomere assay for vertebrates. Recent work has shown the utility of telomere assays in tracking animal health. Telomere lengths can predict extinction events in animal populations, life span, and fitness consequences of anthropogenic activity. Telomere length assays are an improvement over other methods of measuring animal stress, such as cortisol levels, since they are stable during capture and sampling of animals. This dissertation provides a telomere length assay which can be used for any vertebrate. The assay was developed using a quantitative polymerase chain reaction platform which requires low DNA input and is rapid. This dissertation also demonstrates how this assay improves on current telomere assays developed for mice and can be used in a vertebrate not previously assayed for telomere lengths, the American kestrel. This work has the potential to propel research in vertebrate systems forward as it alleviates the need to develop new reference primers for each species of interest. This improved assay has shown promise in studies in mouse cell line studies, American kestrels, golden eagles, five species of passerine birds, osprey, northern goshawks and bighorn sheep. Chapter 2 presents a machine learning analysis, using a topic model approach, to integrate big data from remote sensing, leaf area index surveys, metabolomics and metagenomics to analyze community composition in cross-disciplinary datasets. Topic models were applied to understand community organization across a range of distinct, but connected, biological scales within the sagebrush steppe. The sagebrush steppe is home to several threatened species, including the pygmy rabbit (Brachylagus idahoensis) and sage-grouse (Centrocercus urophasianus). It covers vast swaths of the western United States and is subject to habitat fragmentation and land use conversion for both farming and rangeland use. It is also threatened by increases in fire events which can dramatically alter the landscape. Restoration efforts have been hampered by a lack of resources and often by inadequate collaboration between stakeholders and scientists. This work brought together scientists from four disciplines: remote sensing, field ecology, metabolomics and metagenomics, to provide a framework for how studies can be designed and analyzed that integrate patterns of biodiversity from multiple scales, from the molecular to the landscape scale. A topic model approach was used which groups features (chemicals, bacterial and plant taxa, and light spectrum) into “communities” which in turn can be analyzed for their presence within individual samples and time points. Within the landscape, I found communities which contain encroaching plant species, such as juniper (Juniperus spp.) and cheatgrass (Bromus tectorum). Within plants, I found chemicals which are known toxins to herbivores. Within herbivores, I identified differences in bacterial taxonomical communities associated with changes in diet. This work will help to inform restoration efforts and provide a road map for designing interdisciplinary studies