Label-Free Characterization of Single Proteins Using Synthetic Nanopores

Molecular diagnosis has proven to be a powerful tool for early detection of neurodegenerative disease, but research in this field is still relatively nascent. In Alzheimer's Disease specifically, levels of microtubule associated protein tau and amyloid beta in cerebrospinal fluid are becoming reliable pathological indicators. The current gold standard for detecting these biomarkers is an enzyme-linked immunosorbent assay, and while this method has a limit of detection on the order of picograms per mL, it lacks the ability to provide information about aggregation extent and structure on a per-protein basis. From a disease standpoint, neurological pathologies are often extremely complex in their biological manifestation, and precise mechanisms for many of these diseases are still being discovered and revised. A thorough understanding of in situ structure and properties of neurological disease-related proteins would likely help clarify some of these complicated mechanisms. Resistive-pulse methods may be useful in this effort, as they can determine specific biomarker concentrations and can also unveil multiple physical qualities of single proteins or protein aggregates in an aqueous sample. The latter capability is critical, and could allow for both earlier diagnoses and a stronger mechanistic understanding of neurological disease progression. The work presented in this dissertation, therefore, represents broad efforts toward developing a nanopore-based system able to characterize amyloids and protein complexes related to neurodegenerative disease. These efforts range from upstream fabrication and characterization of nanopores in synthetic substrates to downstream techniques for optimizing the accuracy and efficiency of analyses on resistive pulses. Single proteins rotating and translating while tethered to the surface of a nanopore provide rich information during transit through the pore that makes it possible to determine their ellipsoidal shape, volume, dipole moment, charge, and rotational diffusion coefficient in a time frame of just a few hundred microseconds. This five-dimensional protein fingerprint, however, requires chemical modification of each protein and is thus not ideal for studying protein dynamics or transient protein complexes, both of which are relevant when characterizing amyloids. Transitioning to low-noise nanopore substrates and high-bandwidth recordings enables label-free identification and quantification of unperturbed, natively-folded proteins and protein complexes in solution -- no chemical tags, tethers, or fluorescent labels are needed. Such a transition is nontrivial; proteins passing uninhibited through the strong electric field inside of a nanopore rotate and translocate rapidly, posing a challenge to time-resolve their various orientations adequately while circumventing adhesion to nanopore walls. Furthermore, during their translocation through the nanopore, untethered, native proteins diffuse laterally, generating asymmetric disturbances of the electric field and larger-than-expected resistive pulse magnitudes. Known as off-axis effects, these latter phenomena add a noise-like element to the electrical recordings. We evaluate, both computationally and experimentally, the influence of such label-free complications on resulting parameter estimates, and place these results in the context of developing future iterations of nanopore-based protein sensors. In light of the spectacular recent success of nanopore-based nucleic acid sequencing, it is likely that the next frontier for nanopore-based analysis is the characterization of single proteins and, in particular, the characterization of protein aggregates such as amyloids. The experiments and results presented here enable future particle-by-particle analysis of amyloids with nanopores to rapidly reconstruct their heterogeneity in size and shape, both of which are correlated with the neurotoxicity of amyloid samples and are being investigated as biomarkers for neurodegenerative diseases.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/151730/1/jaredsh_1.pdfDescription of jaredsh_1.pdf : Restricted to UM users only

Bioinspired, nanoscale approaches in contemporary bioanalytics (Review)

The genesis for this topical review stems from the interdisciplinary Biointerfaces International conference 2016 (BI 2016) in Zurich, Switzerland, wherein the need for advances in analytical tools was both expressed and addressed. Pushing the limits of detection for characterizing individual components, such as single proteins, single drug-delivery vehicles, or probing single living cells in a more natural environment, will contribute to the understanding of the complex biomolecular systems central to a number of applications including medical diagnostics, tissue engineering, and drug screening and delivery. Accordingly, the authors begin with an overview of single nanoparticle analytics highlighting two emerging techniques and how they compare with existing techniques. The first is based on single particle tracking of nanoparticles tethered to a mobile supported lipid bilayer, enabling the simultaneous characterization of both size and composition of individual nanoparticles. The second technique is based on probing variations in the ionic conduction across nanoscale apertures for detection of not only nanoparticles but also membrane-tethered proteins, thereby allowing a multiparameter characterization of individual nanoscopic objects, addressing their size, shape, charge, and dipole moment. Subsequently, the authors lead into an example of an area of application that stands to benefit from such advances in bioanalytics, namely, the development of biomimetic lipid- and polymer-based assemblies as stimuli-responsive artificial organelles and nanocarriers designed to optimize delivery of next generation high-molecular-weight biological drugs. This in turn motivates the need for additional advanced techniques for investigating the cellular response to drug delivery, and so the review returns again to bioanalytics, in this case single-cell analysis, while highlighting a technique capable of probing and manipulating the content of individual living cells via fluidic force microscopy. In presenting a concerted movement in the field of bioinspired bioanalytics, positioned in the context of drug delivery, while also noting the critical role of surface modifications, it is the authors’ aim to evaluate progress in the field of single component bioanalytics and to emphasize the impact of initiating and maintaining a fruitful dialogue among scientists, together with clinicians and industry, to guide future directions in this area and to steer innovation to successful translation

Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project):study design and rationale

Background: Since the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates.Methods: The project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM.Expected results: This study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini (“data node”), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda.Discussion: The project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning

Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project):study design and rationale

Background: Since the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates.Methods: The project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM.Expected results: This study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini (“data node”), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda.Discussion: The project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning

Effect of powder metallurgy synthesis parameters for pure aluminium on resultant mechanical properties

In this work, pure aluminium powders of different average particle size were compacted, sintered into discs and tested for mechanical strength at different strain rates. The effects of average particle size (15, 19, and 35 μm), sintering rate (5 and 20 °C/min) and sample indentation test speed (0.5, 0.7, and 1.0 mm/min) were examined. A compaction pressure of 332 MPa with a holding time of six minutes was used to produce the green compacted discs. The consolidated green specimens were sintered with a holding time of 4 h, a temperature of 600 °C in an argon atmosphere. The resulting sintered samples contained higher than 85% density. The mechanical properties and microstructure were characterized using indentation strength measurement tests and SEM analysis respectively. After sintering, the aluminium grain structure was observed to be of uniform size within the fractured samples. The indentation test measurements showed that for the same sintering rate, the 35 μm powder particle size provided the highest radial and tangential strength while the 15 μm powder provided the lowest strengths. Another important finding from this work was the increase in sintered sample strength which was achieved using the lower sinter heating rate, 5 °C/min. This resulted in a tangential stress value of 365 MPa which was significantly higher than achieved, 244 MPa, using the faster sintering heating rate, 20 °C/min

Dissolvable Bridges for Manipulating Fluid Volumes in Paper Networks

A capability that is key to increasing the performance of paper microfluidic devices is control of fluid transport in the devices. We present dissolvable bridges as a novel method of manipulating fluid volumes within paper-based devices. We demonstrate and characterize the operation of the bridges, including tunability of the volumes passed from 10 μL to 80 μL, using parameters such as geometry and composition. We further demonstrate the utility of dissolvable bridges in the important context of automated delivery of different volumes of a fluid from a common source to multiple locations in a device for simple device loading and activation

Investigation of Reagent Delivery Formats in a Multivalent Malaria Sandwich Immunoassay and Implications for Assay Performance

Conventional lateral flow tests (LFTs), the current standard bioassay format used in low-resource point-of-care (POC) settings, have limitations that have held back their application in the testing of low concentration analytes requiring high sensitivity and low limits of detection. LFTs use a premix format for a rapid one-step delivery of premixed sample and labeled antibody to the detection region. We have compared the signal characteristics of two types of reagent delivery formats in a model system of a sandwich immunoassay for malarial protein detection. The premix format produced a uniform binding profile within the detection region. In contrast, decoupling the delivery of sample and labeled antibody to the detection region in a sequential format produced a nonuniform binding profile in which the majority of the signal was localized to the upstream edge of the detection region. The assay response was characterized in both the sequential and premix formats. The sequential format had a 4- to 10-fold lower limit of detection than the premix format, depending on assay conjugate concentration. A mathematical model of the assay quantitatively reproduced the experimental binding profiles for a set of rate constants that were consistent with surface plasmon resonance measurements and absorbance measurements of the experimental multivalent malaria system