Pathogenic bacterial infections are a serious threat to public health, claiming millions of lives every year. In order to contain the spread of infectious diseases sensitive and timely diagnosis of pathogenic bacteria is of significant importance. The rapid detection of low abundance analytes is still challenging in the most common bacteria detection techniques including, culture and colony counting, Enzyme-linked Immunosorbent Assay (ELISA) and Polymerase Chain Reaction (PCR). Conventional bacteria detection techniques suffer from limitations such as low sensitivity, cost, long procedural time and requiring complex lab equipment. Thus, there is a critical need for rapid, sensitive and low-cost bacterial detection platform in various applications ranging from water and food safety to medical diagnosis. The quest to overcome these limitations have sparked significant interest in innovative biosensor development, with considerable emphasis on optical techniques. Among optical biosensors, label-free methods are highly desirable over label-based alternatives for eliminating the additional cost and sample processing required for labeling. Also, techniques for whole-cell bacteria detection are preferred to detection of pathogenic molecular components detection due to the requirement for extracting and isolating the desired bacterial components such as nucleic acids or proteins. Overall, label-free whole-cell detection of pathogenic bacteria has a significant advantage of simplicity in sample preparation that translates to time and cost reduction.
An additional benefit of detecting whole-cell bacteria without labels, thus in their natural environment, is the ability of monitoring the growth and replication of individual pathogens with a potential application in antimicrobial susceptibility determination. Despite the significant advantages of antibiotics as one of our most powerful tools for fighting infections, their extensive misuse and overuse over the years, have resulted in the emergence of antibiotic resistant bacteria as the global health crisis of our time. The current gold-standard technique for antibiotic susceptibility testing (AST) used in clinics, is culture-based disk diffusion assays. The time-consuming diagnosis method of the common clinical susceptibility testing, which is an inherent limitation of culture-based techniques, have necessitated the need for an alternative AST analysis platform. A clinical diagnosis test that could perform rapid pathogenic bacteria identification and determine its susceptibility to a panel of selected antibiotics, would greatly reduce the hospital stay time for patients with bacterial infection, therefore decreasing mortality and morbidity rate. In addition, it will have a great economic impact on the global healthcare system by advising optimal antibiotic use and maintaining the value of existing drugs.
In this dissertation, we describe the design and development of a rapid, sensitive, and multiplexed biosensor platform that can both identify pathogenic bacteria and perform image-based AST on a single reader instrument. The simple and low-cost design of our biosensing platform makes it a perfect candidate as a point-of-care (POC) diagnostic tool in clinical setting. The biosensor presented in this dissertation is based on interferometric enhancement of the visibility of individual biological particles, such as viruses and bacteria, afforded by Single Particle Interferometric Reflectance Imaging Sensing (SP-IRIS), previously developed in our group. The integration of SP-IRIS with microfluidic flow cells provides kinetic measurements capability, by enabling in-liquid imaging of the sensor surface in real-time, therefore making it a promising diagnostic platform. Here, we build upon the SP-IRIS platform and utilize it for pathogenic bacteria identification and image-based AST analysis. To validate our biosensor's functionality, we demonstrate E. coli detection and characterization in end-point and real-time measurement modality through particle detection and tracking analysis of the acquired images from sensor surface. In addition, we perform rapid image-based AST analysis for E. coli bacteria against two antibiotics, ampicillin and gentamicin, by monitoring single cell morphological variations and tracking their growth rate under various antibiotic challenges.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/42614 |
| Date | 15 May 2021 |
| Creators | Zaraee, Negin |
| Contributors | Unlu, M. Selim |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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