Ultrasensitive electronic impedance based microfluidic assays for proteins and nucleic acids
Creators: name: Tayyab, Muhammad
Publisher: name: No Publisher Supplied
Year: 2023

description: Proteins are useful biomarkers for a wide range of applications such as cancer detection, discovery of vaccines, and determining exposure to viruses and pathogens. Similarly, fast and accurate detection of genetic material is a cornerstone for diagnostics with far reaching applications, including the identification of infectious diseases and patient biomarkers. In the research presented here, I address multiple challenges for the improvement in impedance-based detection of proteins and nucleic acids. I demonstrate that I can detect these macromolecules with increased accuracy digitally in a microfluidic cytometry format through the use of both biological and electronic signal enhancement and advanced data analysis techniques.A challenge associated with impedance-based sensing is to develop a sensitive readout system in a portable form factor without compromising performance. I start by developing a low-noise front-end analog circuit interface towards development of a portable readout system for the label-free sensing of proteins with a form factor of approximately 35 cm2. The electronic interface consists of a low-noise lock-in amplifier enabling reliable detection of changes in impedance as low as 0.1% and thus detection of proteins down to the picomolar level.I applied machine learning techniques along with a commercial off the shelf programmable fluid control system to realize a fully integrated system for the detection and quantification of proteins down to 10 pg/ml in a digital microfluidic impedance cytometry format via the conjugation of proteins to beads. The aim of this research is to present the proof of concept for a machine learning assisted label-free electrochemical impedance-based method for oral health monitoring through quantification of proteins in a custom microfluidic chip using multiple frequencies. This work represents a proof-of-concept that machine learning enabled assays with automated fluidic control may eliminate much of the challenges associated with developing a point of care (POC) based solution.For the nucleic acid detection, I present a novel pathogen detection method combining a one-pot isothermal generation of DNA nanoballs with their detection by electrical impedance. We modified Loop-mediated Isothermal amplification (LAMP) by utilizing compaction oligonucleotides that self-assemble the amplified target into nanoballs. The movement of individual nanoballs between two electrodes is detected by a change in impedance providing a quantized readout. To demonstrate the applicability of this technology, I apply it to the detection of SARS-CoV-2. I also demonstrate detection of multiple DNA and RNA pathogenic sequences such as Influenza, HIV, β-lactamase, and mycobacterium to show the adaptability of the novel method to any sequence of interest.In the novel detection of pathogenic sequences using DNA nanoballs, I optimized the detection method by tuning the electrical parameters for the detection by using beads as a reference. I present a method to achieve better detection fidelity and more by adapting advanced wireless communications technique to a digital microfluidic impedance cytometry format. Specifically, I leverage the Alamouti code, a technique used for space time diversity in communication systems as an inspiration, to design space time coded electrodes and corresponding signal combining techniques to achieve high detection fidelity through improved thresholding.Finally, I present a novel method for the digital genotyping of known diseases by harnessing the power of multifrequency impedance spectroscopy on DNA directly in solution. The method for characterizing DNA in solution is based on the gradual change in impedance due to the increasing concentration of DNA in solution around the sensing region. I have verified the method by using DNA at various concentrations. Furthermore, I have successfully demonstrated the applicability of the method on clinical samples for the detection of TTR variant DNA obtained from cardiac amyloidosis patients.