Advances in Microfluidics and Lab-on-a-Chip Technologies

Advances in molecular biology are enabling rapid and efficient analyses for effective intervention in domains such as biology research, infectious disease management, food safety, and biodefense. The emergence of microfluidics and nanotechnologies has enabled both new capabilities and instrument sizes practical for point-of-care. It has also introduced new functionality, enhanced sensitivity, and reduced the time and cost involved in conventional molecular diagnostic techniques. This chapter reviews the application of microfluidics for molecular diagnostics methods such as nucleic acid amplification, next-generation sequencing, high resolution melting analysis, cytogenetics, protein detection and analysis, and cell sorting. We also review microfluidic sample preparation platforms applied to molecular diagnostics and targeted to sample-in, answer-out capabilities

DEVELOPMENT OF STREAMLINED PLATFORM FOR BACTERIAL IDENTIFICATION AND ANTIBIOTIC SUSCEPTIBILITY TESTING

Infectious diseases spread by pathogenic bacteria continue to pose a significant threat to global public health. The severity of this threat is further exacerbated by the emergence and proliferation of drug-resistant bacteria. Effective and targeted treatment of infectious diseases necessitates knowing the identity of the responsible pathogen (or multiple pathogens, in the case of polymicrobial infections) and its antimicrobial susceptibility profile. Current clinical methods used to determine bacterial identity and its antimicrobial susceptibility rely on bulk bacterial culture, which can take from several days to weeks to complete. The long duration needed for definitive diagnosis results in common use of broad-spectrum antibiotics, which subsequently promotes the development and spread of antimicrobial resistance. Thus, rapid diagnostics are essential for delivering effective and targeted antimicrobial treatments and improving patient care. Specifically, there remains a critical need for finding the identity of the infecting bacteria and its antimicrobial susceptibility profile from patient samples rapidly, such that healthcare providers could initiate effective treatments in a timely manner. In this thesis, we present a rapid bacterial diagnostic approach that is capable of automated bacterial identification (ID) and antimicrobial susceptibility testing (AST) in heterogeneous samples by using molecular-based techniques, i.e. polymerase chain reaction (PCR) and high-resolution melt (HRM) analysis. A machine learning algorithm is employed to automatically identify bacterial species based on melt profiles of amplicons generated through universal PCR on 16S rRNA gene. For AST, we introduce “pheno-molecular” approach, which combines phenotypic growth (i.e., in vitro bacteria culture with antibiotics) with molecular-based detection method. PCR is utilized to quantify nucleic acids after brief incubation, which correlates to phenotypic growth responses of individual pathogens in samples. By comparing bacterial growth in the presence or absence of antibiotics after brief cultivation, susceptibility profile of each bacterial species can be revealed. We start with an introduction to diagnosing bacterial infections, including the current gold standard of diagnostics and discuss on some existing alternative methods that try to address these shortcomings. We also describe the fundamentals of molecular techniques that we employ, universal PCR and HRM, and their utilities in detail (Chapter 1). Then, we aim to develop an automated and supervised melt curve-based classification by exploring various well-known machine learning techniques (Chapter 2). Next, we develop a universal amplification reaction of long amplicon PCR covering 16S gene loci for HRM-based bacterial species identification. Nested support vector machine is described in detail as it pertains to automatically identify bacterial species based on our melt curve database of 37 clinically-relevant bacterial species (Chapter 3). We then further expand the assay’s application to perform both ID and AST in a streamlined process by including brief bacterial culture prior to PCR amplification (Chapter 4). Finally, we utilize limiting dilutions and a microfluidic platform to enable single molecule analysis, which allows for individual bacterial species investigation and also improves assay turnaround time (Chapter 5)

Rapid electrochemical detection of coronavirus SARS-CoV-2

Coronavirus disease 2019 (COVID-19) is a highly contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Diagnosis of COVID-19 depends on quantitative reverse transcription PCR (qRT-PCR), which is time-consuming and requires expensive instrumentation. Here, we report an ultrasensitive electrochemical biosensor based on isothermal rolling circle amplification (RCA) for rapid detection of SARS-CoV-2. The assay involves the hybridization of the RCA amplicons with probes that were functionalized with redox active labels that are detectable by an electrochemical biosensor. The one-step sandwich hybridization assay could detect as low as 1 copy/μL of N and S genes, in less than 2 h. Sensor evaluation with 106 clinical samples, including 41 SARS-CoV-2 positive and 9 samples positive for other respiratory viruses, gave a 100% concordance result with qRT-PCR, with complete correlation between the biosensor current signals and quantitation cycle (Cq) values. In summary, this biosensor could be used as an on-site, real-time diagnostic test for COVID-19.</p

Nested Machine Learning Facilitates Increased Sequence Content for Large-Scale Automated High Resolution Melt Genotyping.

High Resolution Melt (HRM) is a versatile and rapid post-PCR DNA analysis technique primarily used to differentiate sequence variants among only a few short amplicons. We recently developed a one-vs-one support vector machine algorithm (OVO SVM) that enables the use of HRM for identifying numerous short amplicon sequences automatically and reliably. Herein, we set out to maximize the discriminating power of HRM + SVM for a single genetic locus by testing longer amplicons harboring significantly more sequence information. Using universal primers that amplify the hypervariable bacterial 16 S rRNA gene as a model system, we found that long amplicons yield more complex HRM curve shapes. We developed a novel nested OVO SVM approach to take advantage of this feature and achieved 100% accuracy in the identification of 37 clinically relevant bacteria in Leave-One-Out-Cross-Validation. A subset of organisms were independently tested. Those from pure culture were identified with high accuracy, while those tested directly from clinical blood bottles displayed more technical variability and reduced accuracy. Our findings demonstrate that long sequences can be accurately and automatically profiled by HRM with a novel nested SVM approach and suggest that clinical sample testing is feasible with further optimization

Classification results with varied parameters.

<p>A) The KNN classifiers were tested by varying number of neighbors, k from 1 to 7. The plot shows average accuracy for each k. k = 1 and k = 2 resulted in the best performance. B) PCA-LDA classification result with varied number of eigenvectors. Our PCA-LDA classifiers were tested for dimensionality reduction varied from one through seven different eigenvectors. The plot shows the highest accuracy when using six eigenvectors.</p