Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Luka, George
Doctor of Philosophy - PhD
University of British Columbia
Cryptosporidium, a critical waterborne protozoan pathogen found in water resources, is one of the main causes of serious illnesses and death worldwide, costing millions of dollars annually for its detection, identification and treatments. Early diagnostic techniques for detecting Cryptosporidium were established based on the existing tools such as microscopes immunoassays, and molecular techniques in specialized laboratories. However, these techniques are associated with certain limitations such as long processing times and the requirement for expensive equipments, labelling and costly reagents. The need for on-site and real-time detection in water resources and remote areas revealed the urgent need to improve the existing techniques and develop low-cost, sensitive, selective, portable, PCR/label-free and rapid detection tools for Cryptosporidium. This led to the involvement of on-chip, biosensing and nanotechnology to develop sophisticated tools to detect Cryptosporidium. In essence, on-chip, biosensing, nanotechnology, image processing, three-dimensional (3D) printing and portable diagnostic devices have now revolutionized various research fields and proved to have a great potential for point-of-care (POC) applications. However, minimal studies have been performed to incorporate these technologies in the detection of Cryptosporidium. A simple, label/PCR-free, cost-effective and portable sensing platform for detecting Cryptosporidium is yet available. In this thesis, the state-of-art on-chip-based biosensing platforms (capacitive, electrochemical and colorimetric) and POC devices are developed for sensitive, selective, rapid, portable and label/PCR-free detection of Cryptosporidium. For the capacitive and electrochemical sensors, different sensor structures are designed, fabricated, modified and tested for their selectivity, cross-reactivity, and detection limit. For the colorimetric assay, an on-chip-based PCR/label-free colorimetric assay is developed based on the aggregation of gold nanoparticles for the detection of Cryptosporidium RNA as well as providing information about Cryptosporidium species present in the water sample. A point-of-care, potable smartphone integrated on-chip system in a 3D printed holder assembly is also developed and used for color change measurements. The detection limits for the capacitive, electrochemical and colorimetric measurements are found to be 40 cells/5µl, 20 cells/5µl, and 5 µM, respectively. These results confirm that the developed on-chip biosensing platforms and portable smartphone integrated on-chip system have great potentials for the near real-time and on-site detection of Cryptosporidium oocysts.
ON-CHIP-BASED BIOSENSORS AND POINT-OF-CARE DEVICES FOR LABEL/PCR-FREE DETECTION OF CRYPTOSPORIDIUM by George Luka B.Sc., Alexandria University, Egypt, 2003 M.Sc., Bangor University, UK, 2007 MPHIL, Bangor University, UK, 2012 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) February 2021 © George Luka, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: On-chip-based biosensors and point-of-care devices for the label/PCR-free detection of Cryptosporidium submitted by George Luka in partial fulfillment of the requirements of the degree of Doctor of Philosophy. Dr. Mina Hoorfar (Supervisor), School of Engineering, The University of British Columbia Supervisor Dr. Homayoun Najjaran (Co-Supervisor), School of Engineering, The University of British Columbia Co-Supervisor Dr. Jonathan Holzman, School of Engineering, The University of British Columbia Supervisory Committee Member Dr. Keekyoung Kim, Department of Mechanical and Manufacturing Engineering, The University of Calgary Supervisory Committee Member Dr. Kasun Hewage, School of Engineering, The University of British Columbia University Examiner Dr. Khashayar Khoshmanesh, Department of Electrical and Biomedical Engineering, RMIT University External Examiner iii Abstract Cryptosporidium, a critical waterborne protozoan pathogen found in water resources, is one of the main causes of serious illnesses and death worldwide, costing millions of dollars annually for its detection, identification and treatments. Early diagnostic techniques for detecting Cryptosporidium were established based on the existing tools such as microscopes immunoassays, and molecular techniques in specialized laboratories. However, these techniques are associated with certain limitations such as long processing times and the requirement for expensive equipments, labelling and costly reagents. The need for on-site and real-time detection in water resources and remote areas revealed the urgent need to improve the existing techniques and develop low-cost, sensitive, selective, portable, PCR/label-free and rapid detection tools for Cryptosporidium. This led to the involvement of on-chip, biosensing and nanotechnology to develop sophisticated tools to detect Cryptosporidium. In essence, on-chip, biosensing, nanotechnology, image processing, three-dimensional (3D) printing and portable diagnostic devices have now revolutionized various research fields and proved to have a great potential for point-of-care (POC) applications. However, minimal studies have been performed to incorporate these technologies in the detection of Cryptosporidium. A simple, label/PCR-free, cost-effective and portable sensing platform for detecting Cryptosporidium is yet available. In this thesis, the state-of-art on-chip-based biosensing platforms (capacitive, electrochemical and colorimetric) and POC devices are developed for sensitive, selective, rapid, portable and label/PCR-free detection of Cryptosporidium. For the capacitive and electrochemical sensors, different sensor structures are designed, fabricated, modified and tested for their selectivity, cross-reactivity, and detection limit. For the colorimetric assay, an on-chip-based PCR/label-free colorimetric assay is developed based on the aggregation of gold iv nanoparticles for the detection of Cryptosporidium RNA as well as providing information about Cryptosporidium species present in the water sample. A point-of-care, potable smartphone integrated on-chip system in a 3D printed holder assembly is also developed and used for color change measurements. The detection limits for the capacitive, electrochemical and colorimetric measurements are found to be 40 cells/5µl, 20 cells/5µl, and 5 µM, respectively. These results confirm that the developed on-chip biosensing platforms and portable smartphone integrated on-chip system have great potentials for the near real-time and on-site detection of Cryptosporidium oocysts. v Lay Summary In this thesis, the state-of-art on-chip biosensing platforms and POC potable smartphone integrated on-chip system in a 3D printed holder assembly are developed for the label/PCR-free detection of Cryptosporidium. The developed sensing platforms have improved portability, sensitivity and selectivity, reduced the cost, and combined label/PCR-free detection of Cryptosporidium. The sensing platforms' development involves the immobilization of different molecular biological elements, including antibodies and DNA, onto the fabricated sensor chips and gold nanoparticles, respectively. The antibodies and DNA can selectively capture Cryptosporidium oocyst and its RNA, respectively, causing a measurable signal proportional to the analyte concentration. A prototype of the antibodies-modified on-chip sensing electrodes is then tested for the capacitive and electrochemical label-free detection of Cryptosporidium oocysts. For the colorimetric method, DNA-modified gold-nanoparticles are used for the label/PCR-free detection of Cryptosporidium RNA. A 3D portable holder assembly integrated with a smartphone was developed to measure the colorimetric signal. vi Preface The research presented in this thesis is the original work performed by the author. This thesis was supervised by Dr. Mina Hoorfar and co-supervised by Dr. Homayoun Najjaran at the Advanced Thermo-Fluidic Laboratory (ATFL) and the Advanced Control and Intelligent Systems Laboratory (ACIS) in the School of Engineering, University of British Columbia. Parts of this thesis have been published in peer-reviewed journals and presented in conference proceedings. Details of the publications and the author’s contributions to them are explained below: a. Refereed journal publications 1. Luka, G., Samiei, E., Tasnim, N., Dalili, A., Najjaran, H., Hoorfar, M., Comprehensive review of conventional and state-of-the-art detection methods of Cryptosporidium, Water Research, 2020 (Submitted). Impact Factor: 9.130 Lead author: I originated the idea, wrote the manuscript, prepared figures and tables, and reviewed drafts of the manuscript. 2. Luka, G., N., Najjaran, Hoorfar, M., On-chip-based-electrochemical biosensor for the label-free detection of Cryptosporidium, Biosensors and Bioelectronics, 2020 (Submitted). Impact Factor: 10.257 Lead author: I originated the idea and designed the experiments, performed the experiments, analyzed the data, selected reagents/materials/analysis tools, wrote the manuscript, prepared figures and tables, and reviewed drafts of the paper. 3. Luka, G., N., Najjaran, Hoorfar, M., Portable on-chip colorimetric biosensing platform integrated with a smartphone in a 3D-printed holder assembly for the label/PCR-free detection of Cryptosporidium RNA based on gold nanoparticles, ACS Nano, 2020 (Submitted). Impact Factor: 14.588 vii Lead author: I originated the idea and designed the experiments, performed the experiments, analyzed the data, selected reagents/materials/analysis tools, wrote the manuscript, prepared figures and tables, and reviewed drafts of the paper. 4. Luka, G., Samiei, E., Dehghani, S., Johnson, T., Najjaran, H., Hoorfar, M. Label-Free Capacitive Biosensor for Detection of Cryptosporidium. Sensors, 2019, 19 (258):1-9. https://doi:10.3390/s19020258. Impact Factor: 3.275 Lead author: I conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the papers, prepared figures and tables, reviewed drafts of the papers. 5. Fordwour, O. B.; Luka, G.; Hoorfar, M.; Wolthers, K. R. Kinetic Characterization of Acetone Monooxygenase from Gordonia Sp. Strain TY-5. AMB Express. 2018, 8 (1), 181. https://doi.org/10.1186/s13568-018-0709-x. Impact Factor: 2.623 Co-author: I conceived and designed parts of the experiments, performed parts of the experiments, reviewed drafts of the paper. 6. Luka, G.; Nowak, E.; Kawchuk, J.; Hoorfar, M.; Najjaran, H. Portable Device for the Detection of Colorimetric Assays. R. Soc. Open Sci, 2017, 4 (11). https://doi.org/10.1098/rsos.171025. Impact Factor: 2.515 Lead author: I originated the idea and designed the experiments, performed the experiments, analyzed the data, selected reagents/materials/analysis tools, wrote the manuscript, prepared figures and tables, and reviewed drafts of the manuscript. 7. Samiei, E.; Luka, G.; Najjaran, H.; Hoorfar, M. Integration of Biosensors into Digital Microfluidics: Impact of Hydrophilic Surface of Biosensors on Droplet Manipulation. Biosens. Bioelectron. 2016, 81, 480–486. https://doi.org/10.1016/j.bios.2016.03.035. Impact Factor: 10.257 Co-author: I conceived and designed the experiments, performed major parts of the experiments, analyzed the data, wrote major parts of the manuscript, reviewed drafts of the article. viii 8. Luka, G.; Ahmadi, A.; Najjaran, H.; Alocilja, E.; DeRosa, M.; Wolthers, K.; Malki, A.; Aziz, H.; Althani, A.; Hoorfar, M. Microfluidics Integrated Biosensors: A Leading Technology towards Lab-on-a-Chip and Sensing Applications. Sensors. 2015, 15 (12), 30011–30031. https://doi.org/10.3390/s151229783. Impact Factor: 3.275 Lead author: I wrote the manuscript, prepared figures and tables, and reviewed drafts of the paper. b. Conference proceedings and presentations 1. Luka, G.; Samiei, E.; Najjaran, H.; Hoorfar, M. Digital Microfluidic based fluorescent biosensor for the multiplex and rapid detection of Cryptosporidium, The 20th International Conference on Miniaturized Systems for Chemistry and Life Sciences (MicroTAS), October 9 – 13, 2016, Dublin, Ireland. Lead author: I originated the idea and designed the experiments, performed the experiments, analyzed the data, selected reagents/materials/analysis tools, wrote the manuscript, prepared figures and tables, and reviewed drafts of the paper. 2. Luka, G.; Samiei, E.; Thomas, J; Najjaran, H.; Hoorfar, M. Label-free interdigitated capacitive biosensor for the detection of Cryptosporidium, Miniaturized systems for chemistry and life sciences (MicroTas), October 9 – 13, 2016, Dublin, Ireland. Lead author: I conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the papers, prepared figures and tables, reviewed drafts of the article. 3. Luka, G.; Nowak, E.; Yesilcimen, K.; Hoorfar, M.; Najjaran, H. Ultra-portable colorimetric-based platform for on-site and point-of-care analysis, Proceedings of The Canadian Society for Mechanical Engineering International Congress, June 26, 2016, Kelowna, Canada. Lead author: I conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and tables, reviewed drafts of the article. ix 4. Luka, G.; Urmi, N.; Roberts, DG.; Hoorfar, M. Label-free interdigitated capacitive biosensor for the detection of Cryptosporidium, IC-IMPACTS nanotechnology summer institute, May 2016, University of Alberta, Alberta, Canada. (Won first place and best speaker prize in the competition). Lead speaker: I designed the presentation, prepared figures and tables, reviewed drafts of the presentation and poster. 5. Luka, G.; Den Berg, AV.; Roberts, DG.; Najjaran, H.; Hoorfar, M. Free gold nanoparticles-based colorimetric assay for direct detection of Cryptosporidium using a smartphone, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), August 25-29, 2015, Milan, Italy. Lead author: I conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the papers, prepared figures and tables, reviewed drafts of the paper. 6. Luka, G.; Hoorfar, M. Gold Nanoparticle-based Colorimetric Assay for Direct Detection of Cryptosporidium, IC-IMPACTS nanotechnology summer institute, July 2015, University of Toronto, Toronto, Canada. Lead speaker: I designed the presentation, prepared figures and tables, reviewed drafts of the presentation and poster. 7. Luka, G.; Hoorfar, Direct Cryptosporidium detection for developed and developing nations, IC-IMPACTS annual general meeting and research conference, March 2015, University of British Columbia, Vancouver, Canada. Lead speaker: I designed the presentation, prepared figures and tables, reviewed drafts of the presentation and poster. x Table of Contents Abstract ............................................................................................................................... iii Lay Summary........................................................................................................................v Preface ................................................................................................................................. vi Table of Contents ..................................................................................................................x List of Tables ..................................................................................................................... xiv List of Figures .....................................................................................................................xv List of Symbols and Abbreviations ................................................................................ xxii Acknowledgements ......................................................................................................... xxiv Dedication ..........................................................................................................................xxv Chapter 1: Introduction ...................................................................................................... 1 1.1 Introduction ...............................................................................................................1 1.2 Conventional detection methods for Cryptosporidium in water samples .................7 1.2.1 Detection based on fluorescence microscopy .................................................... 8 1.2.1.1 EPA-1623.1 method ................................................................................... 8 1.2.2 Detection based on immunological techniques ................................................ 11 1.2.2.1 Enzyme-linked immunosorbent assay (ELISA) ....................................... 11 1.2.2.2 Commercial immunological diagnostic kits ............................................. 13 1.2.3 Detection based on molecular methods ............................................................ 13 1.2.3.1 Polymerase chain reaction (PCR) ............................................................ 13 1.2.3.2 Loop-Mediated Isothermal Amplification (LAMP) ................................. 15 1.2.3.3 Commercial molecular diagnostic kits ..................................................... 17 1.3 Motivation ...............................................................................................................19 1.4 Objectives ................................................................................................................20 Chapter 2: State-of-the-art methods for detection of Cryptosporidium ......................... 27 2.1 Overview .................................................................................................................27 2.2 Nanotechnology ......................................................................................................27 2.3 On-chip-based biosensors .......................................................................................28 2.3.1 Capacitance ...................................................................................................... 31 2.3.2 Electrochemical ................................................................................................ 32 2.3.3 Colorimetric ..................................................................................................... 37 xi 2.3.4 Fibre optics ....................................................................................................... 40 2.3.5 Surface plasmon resonance .............................................................................. 42 Chapter 3: Theoretical Background ................................................................................. 46 3.1 Overview .................................................................................................................46 3.2 Antibodies ...............................................................................................................46 3.3 Deoxyribonucleic acid (DNA) ................................................................................48 3.4 Surface modification ...............................................................................................49 3.4.1 Ideal surface modification ................................................................................ 50 3.4.2 Immobilization methods ................................................................................... 50 3.4.2.1 Covalent bonding ..................................................................................... 51 3.5 Antibody-based biosensor (immunosensors) ..........................................................53 3.5.1 Capacitive-based immunosensors .................................................................... 54 3.5.2 Electrochemical impedance spectroscopy-based immunosensors ................... 56 3.6 DNA-based biosensors ............................................................................................57 3.6.1 Colorimetric-based DNA biosensors ............................................................... 59 Chapter 4: On-Chip Sensing Platforms and Portable Devices Fabrication ................. 61 4.1 Overview .................................................................................................................61 4.2 On-chip fabrication steps ........................................................................................62 4.2.1 Substrate preparation ........................................................................................ 62 4.2.2 Conductive layer deposition ............................................................................. 63 4.2.3 Sensing electrodes patterning ........................................................................... 63 4.2.4 Hydrophobic layer deposition .......................................................................... 65 4.2.5 Lift-off procedure ............................................................................................. 65 4.2.6 Portable diagnostic devices fabrication ............................................................ 66 Chapter 5: On-Chip-based Capacitive Biosensor ........................................................... 67 5.1 Overview .................................................................................................................67 5.2 Materials ..................................................................................................................68 5.3 Experimental Procedure ..........................................................................................68 5.3.1 Sensor fabrication ............................................................................................. 68 5.3.2 Antibody immobilization ................................................................................. 69 5.3.3 Electrochemical measurements of SAM and antibody immobilization ........... 70 xii 5.3.4 Contact angle measurements ............................................................................ 71 5.3.5 Sample preparation and measurement ............................................................. 71 5.3.6 FITC Measurements ......................................................................................... 73 5.3.7 Calibration curve and limit of detection (LOD) ............................................... 74 5.4 Results and Discussion ............................................................................................74 5.4.1 Preparation of the biosensing layer .................................................................. 74 5.4.2 Surface characterization ................................................................................... 75 5.4.3 Capacitance detection of Cryptosporidium oocysts ......................................... 76 5.5 Summary .................................................................................................................78 Chapter 6: On-Chip-based Electrochemical Biosensor .................................................. 79 6.1 Overview .................................................................................................................79 6.2 Materials ..................................................................................................................80 6.3 Experimental procedure ..........................................................................................80 6.3.1 Sensor fabrication ............................................................................................. 80 6.3.2 On-chip immunosensor preparation ................................................................. 81 6.3.3 Electrochemical measurements of SAM and antibody immobilization ........... 82 6.3.4 Contact angle measurements ............................................................................ 84 6.3.5 Sample preparation and EIS measurement ...................................................... 84 6.3.6 Calibration curve and limit of detection (LOD) ............................................... 86 6.4 Results and discussion .............................................................................................86 6.4.1 Preparation of the biosensing layer .................................................................. 86 6.4.2 Optimization of the capture antibody concentration and incubation time ....... 88 6.4.3 Surface characterization ................................................................................... 89 6.4.4 Electrochemical detection of Cryptosporidium oocysts .................................. 89 6.4.5 Selectivity (cross-reactivity) of the sensing platform ...................................... 92 6.5 Summary .................................................................................................................92 Chapter 7: Portable On-Chip-based Colorimetric Biosensor ........................................ 94 7.1 Overview .................................................................................................................94 7.2 Materials ..................................................................................................................95 7.3 Experimental methods .............................................................................................96 7.3.1 Sensor fabrication ............................................................................................. 96 xiii 7.3.2 Holder assembly fabrication ............................................................................ 96 7.3.3 Conjugation of the thiolated oligonucleotide to gold nanoparticles ................ 97 7.3.4 Sample preparation and colorimetric measurements ....................................... 99 7.3.5 Calibration curve, limit of detection (LOD), and selectivity ......................... 100 7.4 Results and discussion ...........................................................................................100 7.4.1 Evaluation of AuNPs conjugation using UV-vis spectroscopy ..................... 100 7.4.2 Detection of Cryptosporidium RNA using UV-vis spectroscopy .................. 101 7.4.3 Detection of Cryptosporidium RNA using on-chip biosensor ....................... 102 7.4.4 Selectivity of colorimetric assay towards Cryptosporisum RNA .................. 103 7.4.5 Data validation ............................................................................................... 105 7.5 Summary ...............................................................................................................106 Chapter 8: Conclusions and Future Work .................................................................... 108 8.1 Summary of results ...............................................................................................108 8.1.1 On-chip-based capacitive biosensor ............................................................... 109 8.1.2 On-chip-based electrochemical biosensor ...................................................... 109 8.1.3 On-chip-based colorimetric biosensor ........................................................... 110 8.2 Limitations of the developed sensing platforms ...................................................113 8.2.1 On-chip-based capacitive and electrochemical sensors ................................. 113 8.2.2 On-chip-based colorimetric sensor ................................................................ 113 8.3 Contributions to the field ......................................................................................114 8.4 Future work ...........................................................................................................115 References .......................................................................................................................117 xiv List of Tables Table 1.1 The difference in sizes and shapes of some of the currently recognized species of Cryptosporidium oocyst. ............................................................................................................ 4 Table 1.2 Fluorescence microscopy-based techniques used for detecting Cryptosporidium oocysts in water samples (reproduced from [13] with permission from MDPI). .................... 11 Table 1.3 Commercial immunological assays for detecting Cryptosporidium antigens. ........ 13 Table 1.4 PCR versus microscopy for detecting Cryptosporidium. ......................................... 14 Table 1.5 Some of the molecular approaches used for detecting Cryptosporidium oocysts in water samples. .......................................................................................................................... 15 Table 1.6 Commercially available molecular kits for the detection of Cryptosporidium based on multiplex PCR. .................................................................................................................... 18 Table 1.7 Comparative evaluation of microscopy, immunological, and molecular techniques used for the detection of Cryptosporidium. .............................................................................. 18 Table 7.1 Sequences of the oligonucleotide probes with a specific Cryptosporidium 42-nt RNA and non-complementary RNA used in this study. ................................................................... 99 Table 7.2 Application of the developed sensing platform for the detection of Cryptosporidium RNA samples spiked with different concentrations of Cryptosporidium RNA. .................... 105 Table 8.1 Comparison of the sensing platforms previously reported in the literature and those developed in this thesis for the detection of Cryptosporidium. ............................................. 112 xv List of Figures Figure 1.1 A schematic of the global distribution of waterborne protozoan outbreaks. a) from 1954 to 2004 and b) from 2004 to 2010 (adapted from [6,7]). .................................................. 2 Figure 1.2 Transmission pathways of Cryptosporidium spp. .................................................... 3 Figure 1.3 The motivation behind the development of on-chip-based biosensors and portable diagnostic devices for detecting Cryptosporidium. .................................................................... 7 Figure 1.4 The steps of USEPA 1623.1 involved in the detection of Cryptosporidium (Reproduced from ref. [67] with permission from Elsevier). .................................................. 10 Figure 1.5 Different approaches of ELISA technique.............................................................. 13 Figure 2.1 Schematic showing different components of a biosensor (Reproduced from ref. [166] with permission from Elsevier). ............................................................................................... 29 Figure 2.2 Capacitance measurement for the detection of Cryptosporidium. a) DMF integrated capacitive biosensor; b) Measured capacitive signals for 5 concentrations of Cryptosporidium oocysts ranging from 15 cells/µl to 240 cells/µl in spiked water samples, and c) the calibration curve for the results presented for 8 replications (Reproduced from ref. [178] with permission from Elsevier). .......................................................................................................................... 32 Figure 2.3 The electrochemical impedance spectroscopy-based biosensor. a) Illustration of the PCB-based biochip; b) SEM of the electrodes; c) Sideview of the biochip; d) Layout showing the electrodes, and e) Fluorescence stained cells on the biochip surface (Reproduced from ref. [177] with permission from Elsevier). ..................................................................................... 33 Figure 2.4 Illustration showing the electrochemical detection protocol (Reproduced from ref. [132] with permission from PLOS ONE). ............................................................................... 35 Figure 2.5 Schematic of a gold IDUA fabricated on a PMMA substrate (Reproduced from ref. [183] with permission from Elsevier). ..................................................................................... 36 Figure 2.6 Illustration showing the sandwich hybridization assay and binding between the liposomes (yellow) and magnetic beads (brown) (Reproduced from ref. [183] with permission from Elsevier). ......................................................................................................................... 36 xvi Figure 2.7 The set-up for a strip-based biosensor. a) Illustration showing the covalently attached oligonucleotides to the outer surface of the liposomal nanovesicles as well as the detection zone of the polyethersulfone membranes coated with streptavidin and b) Illustration showing the positioning of reflectometer readings with the bottom and top parts of the membrane to show the direction of the flow. The migration of the hybridization mix begins towards the top part via capillary action once the bottom part of the membrane is inserted into the glass tube. Zone 1 represents the capture zone, where a signal will occur. Zone 2 is where the reference measurement was taken (Reproduced from ref. [185] with permission from American Chemical Society). ................................................................................................................................... 39 Figure 2.8 The microfluidic integrated optic biosensor. a) The experimental setup showing the light source at the lower left, the microfluidic chip at the right side, and the portable spectrometer at the top left and b) Schematic illustrating the microfluidic-based optical biosensor (Reproduced from ref. [188] with permission from Royal Society of Chemistry). 42 Figure 2.9 Illustration of the SPR-based biosensor developed by Kang et al. [192] for the detection of Cryptosporidium oocysts. a) Schematic diagram showing the 3 immobilization steps of the antibodies onto the biosensing chip and b) SPR signal using different sample matrices spiked with Cryptosporidium oocysts and other pathogens, where: Detection 1: HBS-EP buffer spiked with C. parvum oocysts; Detection 2: HBS-EP buffer spiked with Chlamydomonas reinhardtii, Bacillus stearothermophilus spore, Escherichia coli, and C. parvum oocysts; Detection 3: tap water spiked with C. parvum oocysts; Detection 4: reservoir water spiked with C. parvum oocysts. The concentration of all pathogens was 1x105 cells/ml in all samples (Reproduced from ref. [192] with permission from Elsevier). ............................. 44 Figure 3.1 Schematic showing the interaction between the antibody and antigen (Created with BioRender.com). ...................................................................................................................... 47 Figure 3.2 Schematic showing the structure of IgG antibody isotype. (Created with BioRender.com). ...................................................................................................................... 48 Figure 3.3 The chemical structure of DNA (Created with BioRender.com). .......................... 49 Figure 3.4 Schematic of the different standard methods of immobilization. a) covalent bonding; b) adsorption; c) encapsulation, and d) entrapment. ................................................................ 50 xvii Figure 3.5 Schematic representation of SAM. ......................................................................... 52 Figure 3.6 Schematic representation of non-specific adsorption on two different electrodes. a) non-protected surface (no blocking agent) and b) protected surface (modified with a blocking agent). ....................................................................................................................................... 52 Figure 3.7 Schematic representation of immunosensors. ......................................................... 54 Figure 3.8 Schematic of a capacitive biosensor, C1 is the capacitance of the SAM, C2 is the capacitance of the molecular recognition element, and C3 is the capacitance of the analyte. . 55 Figure 3.9 Schematic representation of EIS measurement. a) sensing surface modified with antibodies. A redox-active compound ([Fe(CN)6]3-/4- has to reach the electrodes’ surface to exchange electrons. However, modifying the surface with antibodies partially blocked the electron transfer, measured by RCT and b) An additional layer is formed on the electrode surface due to the interaction between the immobilized antibodies and the target which increases the blocking effect and thus RCT. .................................................................................................... 56 Figure 3.10 Schematic representation of DNA-based biosensor based on ssDNA-DNA hybridization. ........................................................................................................................... 58 Figure 3.11 Color change associated with the aggregation of gold nanoparticles. a) before the addition of NaCl (colloidal gold nanoparticles (red color)) and b) after the addition of NaCl (blue to purple color (aggregated gold nanoparticles)). ........................................................... 60 Figure 3.12 Schematic illustration of the colorimetric detection of DNA based on the aggregation of gold nanoparticles. ........................................................................................... 60 Figure 4.1 Schematic of the photolithography process for patterning the sensing electrodes: i) sputtering the glass substrate with chromium and gold layers; ii) spin coating the glass/chromium/gold slide with the positive photoresist S1805; iii) exposing the photoresist layer, along with the mask patterned with the electrodes design, to the UV light; iv) developing the electrodes pattern on the photoresist layer using MF-319 developer; v) etching the gold and chromium layers with gold and chromium etchants, respectively, and vi) removing the remaining photoresist using acetone. ....................................................................................... 64 Figure 4.2 Schematic of the 3 on-chip-based biosensors used in this research. a) capacitive-based biosensor; b) electrochemical-based biosensor, and c) colorimetric-based biosensor. .. 65 xviii Figure 5.1 The fabricated interdigitated capacitive-based biosensor with a layout of the IDE sensing electrodes and the schematic of the contact probe (Reproduced from ref. [13] with permission from MDPI). .......................................................................................................... 69 Figure 5.2 The process used for coating the surface of IDE with SAM and the immobilization of anti-Cryptosporidium antibodies (Reproduced from ref. [13] with permission from MDPI). .................................................................................................................................................. 70 Figure 5.3 Schematic illustrating the experimental setup for contact angle measurement. ..... 71 Figure 5.4 Diagram illustrating the detection process: a) incubation of the anti-Cryptosporidium antibodies by coating the sensing electrodes with water samples containing different concentrations of Cryptosporidium; b) washing with phosphate-buffered saline (PBS) of pH 7 to remove any unbound and non-specific molecules; c) drying the interdigitated electrodes, and d) capacitance measurements over a specific frequency range (Reproduced from ref. [13] with permission from MDPI). .......................................................................................................... 73 Figure 5.5 Cyclic voltammetry (CV) for the case of bare Au electrode (blue curve); added protein G/thiol (red curve); immobilized anti-Cryptosporidium antibodies (gray curve), and added bovine serum albumin (BSA) (yellow curve) (Reproduced from ref. [13] with permission from MDPI). ............................................................................................................................. 75 Figure 5.6 Results of the relative capacitance measurement for various concentrations of Cryptosporidium. a) Relative capacitance change measurements for different concentrations of Cryptosporidium for samples ranging from 15 cells/mm2 to 153 cells/mm2 and b) The calibration curve generated from capacitance measurements (Reproduced from ref. [13] with permission from MDPI). .......................................................................................................... 77 Figure 5.7 Fluorescence microscopic images illustrate 20 cells of Cryptosporidium bound to the immobilized antibodies on a 1 mm2 electrode surface area using direct fluorescein isothiocyanate (FITC)-conjugated anti-Cryptosporidium oocysts monoclonal antibodies (Reproduced from ref. [13] with permission from MDPI)....................................................... 77 Figure 6.1 The fabricated electrochemical-based biosensor with layout of the electrochemical sensing electrodes (reference electrode (RE), working electrode (WE), and auxiliary electrode (AE). ......................................................................................................................................... 81 xix Figure 6.2 The step-wise process of coating the surface of the microfabricated Au WE (2 mm) with SAM and the immobilization of anti-Cryptosporidium antibodies onto the microfabricated Au WE. Clean Au WE were initially functionalized by incubating it with thiolated protein G for 48 h at 4 °C. The sensors-SAM were then incubated with 3 µL of 100 µg/mL specific anti-Cryptosporidium antibodies (IgG3) at 4 °C for 24 h. Lastly, the modified sensors were blocked by incubation with 100 mM BSA at 4 °C for 2 h..................................................................... 82 Figure 6.3 Characterizations of the gold surface for the development and modification of the sensing platform Cyclic Voltammetry (CV), Square Wave Voltammetry (SWV), and Electrochemical Impedance Spectroscopy (EIS). .................................................................... 83 Figure 6.4 Schematic illustrating the experimental setup for contact angle measurement. ..... 84 Figure 6.5 A schematic illustration of sample preparation and electrochemical measurements. In the absence of Cryptosporidium oocysts, the [Fe(CN)6]3-/4- redox probe permeates the monolayer and reach the surface of the WE. The redox probe, [Fe(CN)6]3-/4-, at the surface can be either oxidized or reduced resulting in a charge transfer (current) to or from the surface. However, the presence of Cryptosporidium oocysts in the sample results in increase in the sensor film resistance and electron charge-transfer permittivity, measured by electrochemical impedance spectroscopy. .......................................................................................................... 85 Figure 6.6 Characterizations of the on-chip label-free immunosensor for the detection of Cryptosporidium. a) CV; b) SWV, and c) EIS for the bare Au electrode, added protein G/thiol, immobilized anti-Cryptosporidium antibodies, and added bovine serum albumin (BSA). The measurements were conducted using the microfabricated Au working electrode, microfabricated Au auxiliary electrode and an external Ag/AgCl reference electrode. All electrochemical measurements were started at the OCP and were performed in the presence of an electrolyte, which consisted of a 5 mM [Fe(CN)6]3-/4- aqueous solution with 1 M NaClO4 as the supporting electrolyte. The CV measurements started with a 30 s quiet time followed by a positive scan polarity, which swept between the potential range of −200 mV to +600 mV (against Ag/AgCl) at a scan rate of 50 mV/s mV/s. SWV measurements were conducted in the potential range of −100 mV to 600 mV with an amplitude of 25 mV and a 2 s quiet time. EIS measurements were performed in the frequency range of 0.1 Hz – 100 kHz with a 5 mV amplitude and started with a 30 s quiet time. ........................................................................... 87 xx Figure 6.7 Optimization of the antibody’s concentrations and binding time. a) RCT value for different concentrations of anti-Cryptosporidium antibodies and b) RCT values for the binding time of anti-Cryptosporidium antibodies on the sensor surface. .............................................. 89 Figure 6.8 Responses of microfabricated immunosensor to Cryptosporidium oocysts. a) and c) EIS measurements and calibration curve for different concentration of oocysts (20, 30, 50, 100, 200, and 300 cells/5µl) in the presence of antibodies; b) and d) EIS measurements and calibration curve in the absence of antibodies for the same number of oocysts. ..................... 91 Figure 6.9 Responses of microfabricated immunosensor to Cryptosporidium oocysts. a) and b) EIS measurements and calibration curve for different concentration of E. Coli (1000, 2000, 3000, and 4000 CFU.ml-1). ...................................................................................................... 92 Figure 7.1 Schematic illustration of label/PCR-free sensing platform. ................................... 95 Figure 7.2 The fabricated chip and 3D portable holder assembly integrated with a smartphone. a) the major components of the detection system and b) assembled detection system. ........... 97 Figure 7.3 UV-vis absorption spectra of gold nanoparticles before and after conjugation. .. 101 Figure 7.4 Detection of Cryptosporidium RNA using UV-visible spectroscopy. a) UV-vis absorbance spectra of conjugated AuNPs after adding Cryptosporidium RNA at different concentrations (5, 25, 60, 100, 200 and 300 µM). Inset Images shows the color of the modified AuNPs in the presence of different concentrations of Cryptosporidium RNA and b) calibration curve plot of the A630/A523 ratio versus different concentrations of Cryptosporidium RNA. Inset: plot shows the linear range for different concentrations of Cryptosporidium RNA. ............. 102 Figure 7.5 Detection of Cryptosporidium RNA using the developed sensing platform. a) calibration curve plot of the saturation value (S) of the HSV coordinates of the recorded images versus different concentrations of Cryptosporidium RNA and b) the linear range for different concentrations of Cryptosporidium RNA. ............................................................................. 103 Figure 7.6 Selectivity results were obtained by a) UV-vis absorption spectra and b) the developed sensing platform in the presence of the analyte of interest and the random sequence RNA. All measurements were performed using the same conditions and a final concentration of 200 µM of the target analyte and the random sequence. ................................................... 104 xxi Figure 7.7 A linear relationship between the concentrations of Cryptosporidium RNA in the prepared samples and those obtained by the developed sensing platform. ............................ 106 xxii List of Symbols and Abbreviations WHO World Health Organization DALYs Disability Adjusted Life Years USA United States of America AIDS Immune Deficiency Syndrome GEMS Global Enteric Multicenter Study MSD Moderate-to-Severe Diarrhea LSD Less-Severe Diarrhea ECDC European Centre for Disease Control CDC Centre for Disease Control NNDSS Australian National Notifiable Diseases Surveillance System USEPA USA Environmental Protection Agency IMS Immunomagnetic Separation IgM Immunoglobulin-M mAb Monoclonal Antibody IgG Immunoglobulin-G FITC Fluorescein Isothiocyanate ELISA Enzyme-Linked Immunosorbent Assay PCR Polymerase Chain Reaction RIA Radioimmunoassay HRP Horseradish Peroxidase ALP Alkaline Phosphatase USFDA US Food and Drug Administration qPCR Quantitative PCR COWP Cryptosporidium Oocyst Wall Protein POC Point-Of-Care UV Ultraviolet PDMS Polydimethylsiloxane PBS Phosphate Buffer Solution SELEX Systematic Evolution of Ligands by Exponential Enrichment xxiii ssDNA Single-Stranded DNA SWV Square Wave Voltammetry IDUA Interdigitated Ultramicroelectrode Array IDGE Interdigitated Gold Electrodes PMMA Polymethyl Methacrylate DMF Digital Microfluidics SPR Surface Plasmon Resonance EIS Electrochemical impedance SAM Self-Assembled Monolayer DNA Deoxyribonucleic Acid IDE Interdigitated Electrodes dsDNA Double-Stranded DNA 3D Three-Dimensional LED Light-Emitting Diode AuNPs Gold Nanoparticles LSPR Localized Surface Plasmon Resonance CV Cyclic Voltammetry SLOT Short-Open-Load-Through RF Reference Electrode AE Auxiliary Electrode WE Working Electrode xxiv Acknowledgements I would like to express my sincere gratitude and appreciation to my supervisor, Dr. Mina Hoorfar and co-supervisor, Dr. Homayoun Najjaran, for the invaluable support, great guidance, encouragement, and patience throughout my studies. This milestone would not have been possible without their immense knowledge and motivation. I am also grateful to my advisory committee, Dr. Jonathan Holzman and Dr. Keekyoung Kim, for their support and kind advisory throughout the years, thank you. I would also like to thank those who helped me with their greatest knowledge and provided me with their resources, especially Dr. Thomas Johnson, Dr. Deborah Roberts and Dr. Kirsten R. Wolthers, who kindly dedicated plenty of their time to advise me. I also thank my lab mates and all the undergraduate students who did their honours research or volunteered in the ATFL and ACIS labs. I wish to thank all professors, staff and graduate students in the Departments of Engineering and Chemistry for making my stay an enjoyable one. I am very thankful to all of my friends who shared part of their lives with me and created unforgettable memories. Finally, I would like to thank my dad, Mr. Said Luka, my mom Mrs. Nadia Awad, and my brothers, Victor and Remon, for their immense support through the good and the bad times and all their support through the years. xxv Dedication This thesis is dedicated to my parents and my brothers for their endless love and support. 1 Chapter 1: Introduction1 1.1 Introduction According to a recent estimation published by the World Health Organization (WHO) and the United Nations Children’s Fund, about 768 million people worldwide still depend on water sources contaminated with high levels of pathogens [1]. Untreated water leads to 1.3 million deaths annually, especially in developing countries. In developed countries, despite all the improvements that have been made in water treatment technologies [2], modern treatment processes remain suboptimal and susceptible to failures leading to major waterborne disease outbreaks [3]. Waterborne infectious diseases caused by water-associated pathogens are a major cause of illness and death globally. These diseases are the leading cause of 4 billion diarrhea cases leading to 62.5 million Disability Adjusted Life Years (DALYs) worldwide and 1.6 million deaths annually [4]. Although bacterial pathogens are considered one of the major causes of waterborne diseases in developing countries, most waterborne outbreaks in the United States of America (USA) and Canada are related to protozoa [5]. The global distribution of waterborne protozoan outbreaks from 1954 to 2001 (325 cases) and from 2004 to 2010 (191 cases) are shown in Figure 1.1 a) and 1.1 b), respectively [6,7]. According to the statistics reported by Karanis et al. in 2007 [6] and Baldursson et al. in 2011 [7], more than 50% of these outbreaks were caused by Cryptosporidium. 1Parts of this chapter have been published in Samiei, E.; Luka, G.; Najjaran, H.; Hoorfar, M. Integration of Biosensors into Digital Microfluidics: Impact of Hydrophilic Surface of Biosensors on Droplet Manipulation. Biosens. Bioelectron., 2016, 81, 480–486. https://doi.org/10.1016/j.bios.2016.03.035, and Luka, G.; Ahmadi, A.; Najjaran, H.; Alocilja, E.; DeRosa, M.; Wolthers, K.; Malki, A.; Aziz, H.; Althani, A.; Hoorfar, M. Microfluidics Integrated Biosensors: A Leading Technology towards Lab-on-a-Chip and Sensing Applications. Sensors, 2015, 15 (12), 30011–30031. https://doi.org/10.3390/s151229783 2 Figure 1.1 A schematic of the global distribution of waterborne protozoan outbreaks. a) from 1954 to 2004 and b) from 2004 to 2010 (adapted from [6,7]). Cryptosporidium, a protozoan parasite, was first discovered by Tyzzer in 1907 [8], and it was first diagnosed in humans in 1976 [9]. In the past 25 years, Cryptosporidium has emerged as a concerning pathogen for humans and animals [10]. Delivering safe drinking water has become challenging because of Cryptosporidium water contamination [11,12]. The oocyst has a high resistance to typical chemical disinfection treatments (such as chlorination), as it is protected by a thick outer wall, making it persistent in the environment for up to 16 months and facilitating its transmission through contaminated water and food [13,14]. The life cycle of Cryptosporidium involves both asexual and sexual reproduction and requires an individual host (monoxenous) to be completed [15]. Transmission of Cryptosporidium happens via its environmentally robust oocyst during the oocyst stage outside the host, which is the infectious element in the life cycle [16]. The transmission of Cryptosporidium spp. via direct routes (animal to person and person to person) [17,18] and indirect routes (through contaminated water (e.g., drinking and 3 recreational waters) and food) [18,19] are explained in Figure 1.2. The most commonly recognized environmental routes of transmission are water and food [20]. Figure 1.2 Transmission pathways of Cryptosporidium spp. Among 24 recreational water disease outbreaks [21] that occurred during 2009 and 2010, Cryptosporidium spp. was one of the major pathogens found in water, agricultural runoff and sewage discharge into surface waters [22,23]. Their presence in rivers can also contaminate the marine environment [24]. The small size (3 – 7 µm) and the low sedimentation rate (0.5 µm/s) 4 of Cryptosporidium oocysts facilitate their transmission in the environment [25]. The shapes and sizes of Cryptosporidium oocyst from different species are shown in Table 1.1. Table 1.1 The difference in sizes and shapes of some of the currently recognized species of Cryptosporidium oocyst. Species Primary host Secondary host Infection site Dimensions of oocyst (µm) Ref C. parvum Livestock, cattle, humans Mice, deer, pigs Small intestine 4.22~5.69 × 3.62~5.25 [26] C. andersoni Bactrian camel, Cattle Sheep Abomasum 6.69~8.41 × 4.84~6.18 [27] C. bovis Cattle Sheep Small intestine 4.09~5.44 × 3.96~5.14 [26][28] C. hominis Humans Sheep, Dugong Small intestine 4.61~6.21 × 4.24~5.84 [29] C. serpentis Snakes, lizards None Stomach 5.73~6.49 × 4.96~5.38 [30] The current classification of Cryptosporidium oocyst relies on the identification of the host, site of infection, and morphological differences [31]. There are 20 known Cryptosporidium species and more than 44 genotypes with a significant difference in their molecular signature. C. parvum, C. hominis, C. felis, C. meleagridis, C. canis, C. suis, C. muris, and C. andersoni are the most common Cryptosporidium species that infect humans [32,33]. However, C. parvum and C. hominis are the most commonly detected species. Among those two species, C. parvum is the well-known species of causing death in children, immunocompromised individuals and diarrhea in adults, with the consequent economic loss [34,35]. Furthermore, C. parvum is a major source of environmental contamination and it has infected more than 152 mammalian species, including, dugongs and sea lions [36,37]. This species is considered a severe medical, environmental and veterinary concern [36]. Cryptosporidium infects the gastrointestinal tract of various animal species and humans [18], causing severe gastroenteritis and diarrheal disease known as cryptosporidiosis [38]. Currently, no effective medications have been verified for treating cryptosporidiosis infections [39]. Common symptoms of cryptosporidiosis are watery diarrhea, vomiting, abdominal pain, 5 loss of appetite, fever, and nausea [40]. In healthy individuals, these symptoms can last up to 1 month with a continuous excretion of oocysts for several weeks after symptoms have been resolved [41]. Additionally, according to a study of 53 Cryptosporidium infected cases conducted by Insulander et al. [42], these symptoms persisted in 2 - 8 patients after the initial infection for up to three years. Since the beginning of the 1980s, there has been a growing interest in the study of cryptosporidiosis [10] due to the increasing number of immune deficiency syndrome (AIDS) patients who had experienced cryptosporidiosis [43]. Recently, the Global Enteric Multicenter Study (GEMS) has classified Cryptosporidium as a major cause of diarrhea in infants and youth in developing countries [44]. According to GEMS, Cryptosporidium causes from 5 to 15% of moderate-to-severe diarrhea (MSD) in children. Furthermore, Cryptosporidium remains a major source of diarrhea in 12 - 23 months old infants. As a result, Cryptosporidium was ranked third after Rotavirus and Shigella in all study sites [44]. Globally, there are 760 000 death cases in children, and approximately 1.7 billion diarrheal disease cases are reported every year [45]. Cryptosporidium was significantly associated with less-severe diarrhea [LSD] and MSD below the age of 24 months. Among the positive MSD cases, C. parvum was detected in 9.9% [46]. The high death rate in children is due to the immaturity of the immunological system, breastfeeding transition, and contact with contaminated food and water [47]. The various outbreaks of cryptosporidiosis through contaminated potable water from public water supplies and the growing number of immune-compromised individuals have resulted in a greater emphasis on studying and detecting Cryptosporidium [11]. Despite the presence of advanced control systems such as the European Centre for Disease Control (ECDC), the US Centre for Disease Control (CDC), and the 6 Australian National Notifiable Diseases Surveillance System (NNDSS) in developed countries, these systems still fail to detect and thereby underestimate the occurrence of Cryptosporidium outbreaks [48]. This lack of effective surveillance can be primarily attributed to the lack of sensitive detection tools. Most of the existing analytical tools for detecting Cryptosporidium are time-consuming, expensive, insensitive, labor-intensive, require advanced laboratories and highly-skilled personnel and unsuitable for on-site detection. These limitations are especially challenging in developing countries due to 1) lack of resources; 2) poor communication and transportation, and 3) absence of highly-trained personnel, advanced laboratories and equipment [49]. All these limitations make the current methods a challenge to implement for routine (daily) monitoring. Recently, a series of efforts have been made to replace the current methods and develop robust, sensitive, selective, portable and user-friendly on-chip biosensors and portable diagnostic devices for the on-site detection of Cryptosporidium [50,51]. Despite the promising advantages of these detection systems, more efforts are needed to further improve their detection limit and analysis time, ease of fabrication, portability, cross-reactivity, sensitivity, selectivity and throughput. Hence, the detection of Cryptosporidium remains a significant challenge. Therefore, a near-real-time (turnaround time < 24 hrs.), portable, automated, fast, user-friendly and simple in-house on-chip biosensors and portable devices are still needed for regular and rapid detection of Cryptosporidium species in environmental samples. Furthermore, these sensors must have the capability of providing information about the different species of Cryptosporidium present in the water sample. As a result, there has been a significant emphasis on improving the current conventional and on-chip-based biosensors for detecting Cryptosporidium [11][35]. 7 Because of the challenges caused by Cryptosporidium to the water sectors, public health and veterinary, this chapter is replete with in-depth discussions of the different species of Cryptosporidium, the diseases caused by them, their global importance and the statistics of their outbreaks. Due to its virulence and disease burden, a significant research effort has been dedicated to developing techniques to detect this pathogen. These techniques, from the conventional, to the existing commercial kits, along with their advantages and disadvantages, are chronologically reviewed in this chapter. These techniques are categorized into three major streams: conventional methods, immunological methods, and molecular methods. Each of these streams is thoroughly evaluated in terms of efficiency, sensitivity, selectivity, cross-reactivity, cost-effectiveness, equipments involved, throughput and their capability of performing label-free and on-site analysis. The advantages and disadvantages of these three techniques were then summarized (see Table 1.7). Lastly, the motivation and objectives of this research are described. Figure 1.3 The motivation behind the development of on-chip-based biosensors and portable diagnostic devices for detecting Cryptosporidium. 1.2 Conventional detection methods for Cryptosporidium in water samples Detecting Cryptosporidium in environmental samples such as water is crucial for preventing outbreaks and controlling waterborne parasitic infections in water. The first recorded outbreak of Cryptosporidium was in the 1980s [52]. Since then, many approaches and methods were 8 developed and tested to recover and detect Cryptosporidium. These methods were modified from those originally developed to detect Giardia oocysts in environmental samples [53]. All standard detection and recovery methods involved 3 main steps: 1) Cryptosporidium oocysts recovery from large-volume samples by filtration; 2) purification, and 3) immunofluorescence staining of the oocysts and detection using epifluorescent microscopy [54,55]. Conventional detection methods for Cryptosporidium spp. based on immunofluorescence assay, immunological and molecular techniques are explained in the following sections in detail. 1.2.1 Detection based on fluorescence microscopy 1.2.1.1 EPA-1623.1 method The US Environmental Protection Agency (USEPA) initiated an extensive research effort to develop new and advanced methods for detecting Cryptosporidium in 1996 [56,57]. After consultations with experts in the field and extensive discussions with commercial laboratories, the USA Engineering and Analysis Division within the Office of Science and Technology drafted “method 1622” to detect Cryptosporidium. This method was further confirmed in 1998 after several laboratory trials [58]. In 1998, the method was changed to “1623” and was approved by the USA EPA in 1999 and was published as “EPA-1623.1” in 2012 after extensive revisions and several field trials. The method was based on the following steps: 1) water sample filtration; 2) immunomagnetic separation (IMS); 3) fluorescence staining, and 4) detection using immunofluorescence microscopy [56,57]. According to the “Safe drinking water act amendments” of USEPA, EPA 1623.1 is the only currently approved technique for the detection for detecting Cryptosporidium oocysts in drinking water [59]. The first step in the EPA 1623.1 method, filtration, can be performed directly in the field by using a portable pump and a filter to separate Cryptosporidium from other undesired 9 substances in the water. Based on the kind and the pore size of the filter used in the filtration process, a wide range of recovery efficiency (between 0 - 93%) can be achieved [60]. The Envirochek capsule (membrane filter) is used in the EPA method because of its capability of processing up to 40 L of water without clogging [61], and it is easy to use in both laboratory and in-field [62]. Cryptosporidium is retained in the filter membrane along with other contaminants. The whole membrane is then transferred to the laboratory, and an elution buffer is used to elute the oocyst through a mechanical wash process. A significant limitation of this method is that undesired soil and water particles of similar size to Cryptosporidium oocysts (such as bacteria, free-living protozoans, algae organic matter and colloidal particles) are usually accumulated along with the recovered oocyst. This makes the detection of Cryptosporidium challenging and increases the number of false-positives [63]. To overcome this issue, a Dynabeads® anti-Cryptosporidium immunomagnetic separation (IMS) is usually used to separate the oocysts from other undesired substances [64]. The IMS kit consists of super-paramagnetic beads, functionalized with anti-Cryptosporidium antibodies [65]. Those functionalized beads are used to capture Cryptosporidium and using a magnet or magnetic field, those captured cells can be collected. IMS increases the separation efficiency of Cryptosporidium oocysts from other substances in the sample and reduces the number of false-positive [59][66]. The EPA's recovery efficiency using the IMS method ranges from 21 to 100% [61]. Following the separation step, the oocysts are stained by Cryptosporidium-specific antibodies conjugated with fluorescein isothiocyanate (FITC). The stained oocysts are then visualized using a fluorescence microscope [61]. The steps of USEPA 1623.1 for the Cryptosporidium detection process are shown in Figure 1.4. Different antibody capturing 10 probes such as Immunoglobulin-M (IgM) monoclonal antibody (mAb) and Immunoglobulin-G (IgG) mAb have been tested and used in the EPA 1623 method. Figure 1.4 The steps of USEPA 1623.1 involved in the detection of Cryptosporidium (Reproduced from ref. [67] with permission from Elsevier). Both EPA 1622 and 1623 methods have shown fewer false-positive results; however, the existence of a high concentration of dissolved iron in the samples has been shown to have an inhibitory effect on the IMS and immuno-fluorescence assay components of the EPA methods [68]. According to Yakub and Stadterman-Knauer [68], the interaction between oocyst surface and dissolved iron causes significant errors. Nevertheless, the EPA-1623.1 is the current validated and approved gold standard method employed by the USA EPA to detect Cryptosporidium. This method, however, is expensive ($700 per sample) [69], time-consuming, needs extensive sample preparation (e.g. IMS and labelling), requires skilled personnel and advanced laboratories and equipments (e.g. fluorescence microscope for data interpretation), which make this method unsuitable for in-field detection [59][69]. Moreover, it has limited selectivity and sensitivity due to: 1) its cross-reactivity with other pathogens such Giardia spp. and 2) the production of background noise, false-positive and false-negative results [13]. 11 Therefore, the EPA method is only applicable to confirm the occurrence of an outbreak as it is not timely enough to be applied for regular surveillance across a wide range of water resources, i.e., it is not sufficient to prevent Cryptosporidium spread [59][70]. A summary of the EPA-1623.1 method is listed in Table 1.2 Table 1.2 Fluorescence microscopy-based techniques used for detecting Cryptosporidium oocysts in water samples (reproduced from [13] with permission from MDPI). Technique Concentration Filtration & Capacity Purification Detection Recovery (%) Method 1622/1623: USEPA, 1999a, USEPA, 1999b (USA) Centrifuged 1100× g 15 min Membrane filter (Envirochek™ HV) 10–1000 L Dynal IMS Fluorescence microscopy 12 – 93 (21 – 100) 1.2.2 Detection based on immunological techniques 1.2.2.1 Enzyme-linked immunosorbent assay (ELISA) Recently, immunological techniques for detecting Cryptosporidium have been studied to overcome the limitations associated with microscopy-based methods [35]. Immunological assays are based on a specific antigen-antibody reaction. They involve the use of either immobilized antigens or antibodies for detection [71]. The first immunoassay using radioisotopes labels, known as radioimmunoassay (RIA), was developed by Berson and Yalow [72] for detecting insulin in endogenous plasma [72]. Berson and Yalow were awarded the Nobel Prize in Medicine and Physiology in 1977 for such a development. Since then, immunological techniques have been widely used for drug screening, diagnosis, and the quality control of many commercial detection kits. However, since the study conducted by Avrameas et al. in 1969 [73], some modifications have been made to the original RIA developed by Berson and Yalow, where enzyme labels replaced radioisotopes due to safety concerns. In the study, Avrameas has linked antibodies or antigens to enzymes using glutaraldehyde. The modified immunoassay was referred to as enzyme immunoassay/enzyme-linked immunosorbent assay 12 (ELISA). The reported sensitivity and specificity of these techniques were in the range of 93 – 100% [74]. In ELISA, the specificity and selectivity of immobilized antibodies are combined with the sensitivity of the enzymatic assay to measure the target analyte concentration. The common enzymes used in ELISA are alkaline phosphatase (ALP) [75], horseradish peroxidase (HRP) [76], and β-galactosidase [77]. Generally, in ELISA, the antigen in the fluid phase is immobilized onto a solid phase. Subsequently, the interaction between the antigen and the specific antibody is detected by an enzyme-labelled secondary antibody. The enzymatic reaction results in a colored product, indicating the presence of the antigen in question. Finally, the formed colored products are detected by the naked eye for quantitative analysis or using optical instruments for quantitative analysis (Figure 1.5) [78]. ELISA can be classified into 3 main types, including 1) Direct ELISA; 2) Indirect ELISA, and 3) Sandwich ELISA [79] (Figure 1.5) Despite the high specificity and sensitivity of ELISA, this technique is costly, time-consuming, can lead to false-positive and false-negative analysis, need advanced laboratories, bulky equipment (for visual determination) and trained personnel. The leading causes of false-negative and false-positive results are: 1) long analysis time; 2) inconsistency between readers and test operators; 3) wrong preservation procedures; 4) use of expired test kits; 5) cross-reactions with closely related parasitic antigens, and 6) the use of improper storage temperature [80,81]. All these drawbacks make this technique unsuitable for on-site detection [78] 13 Figure 1.5 Different approaches of ELISA technique. 1.2.2.2 Commercial immunological diagnostic kits The US Food and Drug Administration (USFDA) has recently developed and approved many immunological detection tests for the detection of Cryptosporidium. However, most of these kits are time-consuming and involve using expensive fluorescence-labelled secondary antibodies as a reporter and fluorescence microscopes for data interpretation [82]. Commercial immunological assays for detecting Cryptosporidium oocyst antigens are listed in Table 1.3. Table 1.3 Commercial immunological assays for detecting Cryptosporidium antigens. Assay Company Sensitivity % Specificity % Ref Crypto-Strip Coris BioConcept 47.2 100 [83] RIDA1QUICK R-biopharm Diagnostic 62.4 98 [83] RIDA1QUICK Combi R-biopharm Diagnostic 100 100 [84] Remel-Xpect Remel Inc. 68 100 [83] ImmunocardSTAT C/ G Meridian Bioscience Inc. 5.5 – 70 96 – 100 [83] ImmunoCard STAT CGE Meridian Bioscience 100 45 - 100 [84] Cryptosporidium and Giardia Duo-Strip Coris BioConcept 91.7 100 [84] 1.2.3 Detection based on molecular methods 1.2.3.1 Polymerase chain reaction (PCR) Limitations of microscopic and immunological techniques resulted in the development of DNA-based methods, such as polymerase chain reaction (PCR), to detect Cryptosporidium 14 [35][82]. PCR was first used for the C. parvum in 1991 [85]. Since then, many PCR-based methods have been developed and used to detect the different species and subtypes of Cryptosporidium. Compared to the microscopic and immunological techniques, molecular techniques are more sensitive and specific [86,87]. Table 1.4 shows a comparison of microscopy techniques versus the PCR technique for detecting Cryptosporidium oocysts in terms of sensitivity, specificity and strain identification. Table 1.4 PCR versus microscopy for detecting Cryptosporidium. Technique Number of samples tested Number of positives Sensitivity (%) Specificity (%) Strain identification Ref. Microscopy 511 29 83.7 98.9 No [88] PCR 511 36 100 100 Yes [88] Furthermore, they can detect multiple targets in one multiplex assay using quantitative polymerase chain reaction (qPCR) [89]. qPCR can detect a low number of Cryptosporidium in the sample and does not require antibody binding or visual determination; hence, it is considered an effective method for detecting Cryptosporidium [90]. The PCR detection process involves: 1) breaking open the oocysts by boiling, freeze-thawing, or use of beads to extract the genomic DNA [91]; 2) mixing the extracted DNA with a PCR mixture that contains primers to target specific regions of the extracted DNA [92,93]; 3) amplifying the DNA target sequence [94]; 4) purifying the amplified product [92], and lastly, 5) separating and visualizing the amplified product using gel-electrophoresis and fluorescent dyes [95,96]. One of the significant advantages of PCR is the flexibility of primer design [97]. Some of the molecular methods used for detecting and quantifying Cryptosporidium oocysts in water samples are listed in Table 1.5. 15 Table 1.5 Some of the molecular approaches used for detecting Cryptosporidium oocysts in water samples. Target Detection method Detection limit (oocyst) Ref. C. parvum heat shock protein (hsp70) RT-PCR 1 [98] C. parvum (hsp70 mRNA) RT-PCR 10 [99] Several types of molecular methods have been used for the determination of C. parvum and C. hominis. One such example is using molecular techniques for detecting the Cryptosporidium oocyst wall protein (COWP) gene, which has been used to identify different species of Cryptosporidium. On many occasions, COWP has resulted in determining C. parvum in new animal hosts [100]. However, the COWP-based tools have the limitation of being non-specific and cannot differentiate between C. parvum and C. hominis subtype DNA sequences (> 96% similarity). Therefore, sequencing and amplification of the gp60 gene have been used for further subtyping within species [101]. The gp60 gene has tandem repeats of the serine-coding trinucleotides TCG, TCA or TCT at its 5’ end. In addition to the variations in the number of repeats of the trinucleotide, the gene has vast differences in its non-repeats’ regions. These differences are used for categorizing C. parvum and C. hominis subtype families. Furthermore, the gp60 protein is a single polymorphic marker identified so far in the genomic DNA of Cryptosporidium [102,103]. Unlike other subtyping targets, gp60 localizes in the sporozoite surface, making it an easy target in the subtyping process [104]. 1.2.3.2 Loop-Mediated Isothermal Amplification (LAMP) Recently, Loop-Mediated Isothermal Amplification (LAMP) has been recognized as a specific, sensitive, rapid and efficient emerging technique for Cryptosporidium detection. LAMP can amplify a target nucleic acid from partially processed samples under isothermal conditions [105]. This method was first introduced by Notomi et al.[106], and employs Bst polymerase for 16 autocycling strand displacement DNA synthesis as well as a set of four primers, specially designed to recognize a total of six distinct sequences on the target DNA. Advantages of LAMP over other detection methods for detection of Cryptosporidium include: 1) the ability to detect Cryptosporidium at relatively low concentration in water samples and 2) the capability of isolating and synthesizing a large amount of DNA a sample containing numerous biological contaminants [107]. Also, LAMP is insensitive to commonly occurring PCR-inhibitors in environmental samples and has a shorter analysis time compared to PCR [106,107]. In a study conducted by Mahmoudi et al. [108], comparing different methods for the detection of Cryptosporidium, LAMP showed higher sensitivity compared to immunofluorescence assays (IFA) and PCR in water samples. Another study conducted by Koloren et al. [109] proved that the detection limit achieved by using the LAMP technique for the detection of C. parvum DNA was 1.8 fg, while the detection limit using nPCR was 100 fg. [110]. A few LAMP commercial kits are now available and are being used for detecting Cryptosporidium oocysts in environmental samples such as Loopamp®, Eiken Chemical Co, Japan. Although LAMP is an effective, rapid, and sensitive diagnostic method, the limited availability of sequences of Cryptosporidium species for the primer design limits the use of this technique [111]. Despite their advantages, molecular techniques have not yet been broadly implemented in diagnostic laboratories. In the developed countries, the increasing demand for highly trained personnel, the need for less time-consuming assays, and the low specificity and sensitivity of the traditional techniques have resulted in a growing demand for advanced laboratories to adopt molecular technologies replacing the microscopy methods [112]. These demands are even more significant in developing countries as there is an urgent need for more specific and sensitive techniques that can provide active surveillance and prevent transmission. Another major 17 disadvantage is that all PCR-based detection methods are only capable of identifying predefined targets. Therefore, despite the high sensitivity and specificity of the molecular techniques, they are not suitable for on-site detection and point-of-care (POC) applications [113]. Furthermore, issues associated with molecular techniques such as the time-consuming sample pre-treatment (e.g., DNA extraction, purification, amplification) and PCR interference with inhibitors in the sample must be addressed. A sensitive PCR-based technique that can overcome inhibitors is required to identify Cryptosporidium oocysts from undesired substances and species in the sample. 1.2.3.3 Commercial molecular diagnostic kits As mentioned before, molecular techniques have improved the detection of Cryptosporidium as they are sensitive, specific, have high throughput, and are amenable to automation [89]. Most of these techniques have recently become available to identify and detect Cryptosporidium (Table 1.6). However, a significant disadvantage of using molecular-based diagnostic kits (kits), especially in developing countries, is the high cost of assays and labor. Molecular kits require large and potentially expensive (more than US$ 40,000) technical instruments for analysis [114]. Moreover, any changes to the existing primers or the designing of new primers require further evaluation of the entire assay, including its sensitivity and specificity. This re-evaluation is costly, challenging, and time-consuming [115]. A comparison between the advantages and disadvantages of microscopy, immunological, and molecular methods is listed in Table 1.7. 18 Table 1.6 Commercially available molecular kits for the detection of Cryptosporidium based on multiplex PCR. Kit Sensitivity % Specificity % Advantages Disadvantages Ref BD Max parasitic Panel (EPP) 95.5 99.6 - No cross-reaction with other parasites - Can be used to detect preserved or non-preserved samples - Time-consuming - Expensive [116][117] Luminex TAG1 Gastrointestinal Pathogen Panel (GPP) 95 – 100 100 - Sensitive - Can detect mixed samples - Very expensive - Has a high potential for contamination [118][119] [120][121] Biofire FilmArrayTM 100 99.6 - 100 - Ability to detect emerging Cryptosporidium in human - Sensitive (23.8 – 80 %) to Aeromonas [114] Table 1.7 Comparative evaluation of microscopy, immunological, and molecular techniques used for the detection of Cryptosporidium. Technique Advantage Disadvantage Ref Microscopy - Inexpensive technology - Widely available - Low sensitivity (about 70–80% with modified acid-fast stain - Requiring special stains and skilled technicians - Time-consuming and potential false-positive results [122–124] Immunological Techniques - Good sensitivity (70 –100 %). - Commercially available - Excellent for screening a large number of samples. - The ability for automation. - Costly for resource-limited countries - Time-consuming. - Involving many preparation steps. - Require expensive labels and instrumentations. - Technically demanding. - Require skilled personnel. Not suitable for on-site detection. [125,126] Molecular Techniques - Excellent accuracy and sensitivity. - Species specification. - - Require expensive and complicated instrumentation. - Time-consuming. - Technically demanding. - Require skilled technicians for DNA extraction and amplification. - Interference with PCR inhibitors in environmental samples. - Any change in the exciting primers could lead to false-positive and false-negative results. [127–129] 19 1.3 Motivation Cryptosporidium represents a major threat not only to public health but also to the environment and economy. Hence, effective monitoring techniques for detecting Cryptosporidium are crucial for protecting public health, the environment, and the economy. Currently, detection of Cryptosporidium still relies on conventional methods such as fluorescence microscopy. These expensive, complicated methods require highly trained individuals and advanced laboratories, which make them unsuitable for POC applications. Immunological and molecular techniques have proven to be suitable for specific detection of Cryptosporidium at low concentrations. However, these techniques are time-consuming, expensive, need trained personnel, expensive labels and advanced laboratories. Additionally, molecular techniques require extensive sample pre-treatment processes (e.g., oocysts lysis, DNA extraction, purification, amplification) and suffer from DNA amplification failure due to the interference with PCR inhibitors in the sample or primer failures. New robust, versatile and label/PCR-free methods are urgently needed to detect Cryptosporidium to address the shortcomings of the currently-used methods. These new methods must be label/PCR-free, simple, user-friendly, portable, have multiplex analysis capability and allow for POC detection. Furthermore, these new methods must be able to provide information about the species of the pathogen in question in the sample. On-chip-based biosensors and portable analytical devices have now revolutionized numerous research fields and are shown to have a great potential for POC applications. Furthermore, these techniques lower the power, sample and reagents consumption and hence, reduces the risk of contamination by the analyte of interest such as Cryptosporidium in this research. It has also eliminated human error significantly and the need for bulky instruments such as those required in conventional laboratory-based techniques. Moreover, the small size of these systems allows for their 20 integration into larger systems (e.g., purification systems). Hence, the potential of these techniques can undoubtedly be extended to the detection of Cryptosporidium. However, minimal studies have been performed to incorporate on-chip-based biosensing technology and potable analytical devices to detect Cryptosporidium. A few on-chip-based biosensors were developed for the detection of Cryptosporidium oocysts and DNA. However, these sensors still have limitations in terms of sensitivity, detection limit, use of expensive labels, and the involvement of lengthy labelling steps. A simple, automated, cost-effective, label/PCR-free, and portable sensing platform for the on-site detection of Cryptosporidium, are still unavailable. Therefore, the development of new techniques to further improve and overcome such limitations will be beneficial for rapid, high throughput and on-site detection of Cryptosporidium. Amongst the current sensing techniques, capacitance, electrochemical impedance spectroscopy and colorimetric stand out as cost-effective, rapid, non-invasive and label-free methods. Furthermore, they can be effortlessly integrated into the on-chip, allowing for high-throughput screening, on-site detection and POC applications [130]. Hence, these techniques are chosen as the core sensing techniques in this thesis. 1.4 Objectives This research aims to develop on-chip-based biosensors and POC devices for the sensitive, selective, portable, cost-effective, label/PCR-free and on-site detection of Cryptosporidium. Different structures were designed and fabricated for each of those sensors. Such platforms consist of 3 main elements: 1) fabricated sensor chips and 3D printed portable diagnostic devices; 2) biological recognition elements (antibodies and DNA), and 3) the analyte 21 (Cryptosporidium oocysts and RNA). To achieve this goal, a series of experiments and studies are performed to achieve the highest performance of the sensing platforms. First, a high-throughput fabrication method based on lithography and 3D printing is designed and characterized to develop the sensing platforms. The fabricated sensing platforms were then modified with a specific and selective molecular recognition element such as antibodies and DNA oligonucleotides to detect Cryptosporidium oocysts and RNA, respectively. Molecular recognition elements were used to enhance the developed sensing platforms' selectivity and specificity towards the target analyte and reduce the interference from undesired contaminants in the sample. The modification of the sensing platforms with the molecular recognition elements was carried out via a covalent bond between thiols and the gold surface. The binding recognition event between the modified sensing surface and the target analyte results in a quantifiable change, which is proportional to the concentration of the target analyte. Capacitive, electrochemical sensing of Cryptosporidium oocysts and colorimetric sensing of Cryptosporidium RNA is then carried out using the modified sensing platforms. Protocols and materials used are adjusted and optimized to enhance portability, selectivity, detection limit, reproducibility, of the developed sensors as well as promoting their capability to conduct label/PCR-free and on-site analysis. The procedures are optimized through a series of capacitive, electrochemical, colorimetric, UV–vis spectroscopy and contact angle studies. The sensing platforms were then evaluated and validated for the detection of Cryptosporidium. The results obtained by the capacitive measurements show that the sensor is sensitive, selective and can distinguish between the different concentrations of Cryptosporidium as well as providing label-free detection. The detection limit achieved by the capacitive sensor was 40 cells/5 µl. 22 To further improve the detection limit, I designed and fabricated a new on-chip biosensing platform with a new structure (three-electrode setup) operating based on measuring the electrochemical response due to the biological recognition event. Fabrication and measurements were conducted under the same conditions used with the capacitive sensor. This sensing platform has also provided a selective and label free-analysis. Additionally, it has improved the detection limit by 50% (20 cells/5 µl) and proved to be more sensitive than the capacitive sensor. This could be attributed to the fewer analysis components involved in the electrochemical sensing as well as using a redox couple which enhances the sensitivity. Unlike other sensors reported in the literature for the detection of Cryptosporidium, the capacitive and electrochemical sensing platforms developed here do not rely on complicated fabrication nor expensive substates [131] or gold nanoparticles (labels) [132] to enhance the sensitivity. Furthermore, they have improved the limit of detection and selectivity and provided label-free and near-real-time analysis. From an environmental and public health perspective, it is crucial to differentiate different Cryptosporidium species following their detection to allow appropriate risk assessments. For this purpose, I developed an on-chip label/PCR-free colorimetric assay to detect Cryptosporidium and provide information about their species via their specific RNA. The sensing assay is based on modifying gold nanoparticles (AuNPs) with specific single-stranded DNA (ssDNA) complementary to adjacent sequences on Cryptosporidium RNA. The hybridization between the RNA and the immobilized ssDNA on the AuNPs results in the aggregation of AuNPs leading to a visible color change which is measured and correlated to the RNA concentration in the sample. Compared to DNA, RNA is shorter and allows for the direct detection of a single mismatch or mutation without amplification (PCR). Hence, RNA was used 23 as the target analyte in this project [133]. To enhance the portability of the sensing platform and its accessibility in terms of cost, I designed a 3D-printed portable holder assembly integrated with a smartphone to measure the color change in presence of Cryptosporidium RNA. The results showed that the sensing platform can directly detect Cryptosporidium-specific RNA as well as identifying its species. Compared to other DNA and RNA-based colorimetric sensors, the sensor developed here provided high sensitivity and selectivity with a detection limit of 5 µM. Furthermore, it eliminated the need for expensive labels, labelling steps, PCR amplification, and the need for bulky instruments. Moreover, using a smartphone as the detector has enhanced portability and reduced the need for high power and expensive laboratory instruments. This thesis's main novelty is the integration of a series of technologies, allowing for a fully functional on-chip sensing device with multiplex detection capability. These fully functional on-chip sensing systems have a great potential to be integrated into a device with the capability of capturing, separating and concentrating Cryptosporidium. Such a device has been previously developed in the ATFL laboratory [134]. This integration will allow for the personalized and POC detection of Cryptosporidium. Furthermore, these sensing platforms can be extended to detect other biomarkers in diverse environmental and biomedical applications. The specific objectives of this thesis are outlined as: Objective 1 – To devise and develop an on-chip-based capacitive biosensor for the label-free detection of Cryptosporidium. The device's prototype is now ready, and it is featured in several newspapers and the university website. This work has been published in Sensors ‘’ Label-Free Capacitive Biosensor for Detection of Cryptosporidium’’ (George Luka, Ehsan Samiei, 24 Soroush Dehghani, Thomas Johnson, Homayoun Najjaran and Mina Hoorfar). To realize this objective, the following tasks are defined: a) Design and fabricate the on-chip-based capacitive sensor. b) Modify (immobilization) the fabricated sensing chip (interdigitated gold electrodes (IDGE)) with specific anti-Cryptosporidium monoclonal antibodies (IgG3, selective capture probe). c) Confirm self-assembled monolayer (SAM) formation and antibodies immobilization on gold IDE via electrochemical measurements. d) Sample preparation and capacitive measurement calibration of the developed sensing chip using spiked samples. In this step, the detection limit and specificity of the sensor were determined. e) Confirm and validate the number of Cryptosporidium oocysts captured on the electrode surface using an immunofluorescence assay and relate it to the capacitive measurements. f) Compare the developed sensing method with other regular techniques such as immunofluorescence assays. Objective 2 – To devise and develop an on-chip-based electrochemical biosensor for the label-free detection of Cryptosporidium. The device's prototype is now ready, and the results have been submitted to the journal of Biosensors and Bioelectronics. To realize this objective, the following tasks are defined: a) Design and fabricate the on-chip-based electrochemical sensor. b) Modify the fabricated sensing chip with the capturing probe (IgG3). 25 c) Confirm SAM formation and antibodies immobilization via electrochemical measurements. d) Sample preparation and EIS measurement calibration using spiked samples. In this step, the detection limit and specificity of the sensor were determined. a) Test the sensor with E. coli to confirm the selectivity of the sensing platform. b) Compare the detection limit of the electrochemical measurements with the detection limit of the capacitive measurements. Objective 3 – To develop and optimize a and label/PCR-free colorimetric assay (protocol) for detection of Cryptosporidium specific RNA as well as an in-house 3D printed portable holder assembly integrated with a smartphone for measuring the colorimetric signal. The colorimetric assay and the prototype for the device are now ready, and the results have been submitted to the journal of ACS Nano. To realize this objective, the following tasks are defined: a) Design and fabricate the on-chip-based colorimetric sensor. b) Design and 3D print the holder assembly to house the chip and the smartphone. c) Design the specific DNA oligonucleotides sequence and the complementary RNA. d) Modify the gold nanoparticles by the specific thiolated-DNA oligonucleotides (capturing probe), which have a high binding affinity to Cryptosporidium-specific RNA. e) Optimize the gold nanoparticles/DNA probe for the Cryptosporisum RNA detection. f) Confirm immobilization using UV–vis spectroscopy and colorimetric measurements using a smartphone. g) RNA sample preparation and colorimetric measurement calibration using spiked samples. In this step, the detection limit and specificity of the assay were determined. 26 The measurements were carried out in a cuvette using UV–vis spectroscopy and on-chip using the 3D printed holder assembly integrated with a smartphone. h) Data validation for which additional experiments were carried out to examine the reliability reproducibility, and precision of the measurements using the smartphone. i) RNA measurements comparison between the data produced by the developed sensing platform and conventional methods such as UV–vis spectroscopy. j) Test the assay with non-complementary (non-specific) RNA to confirm the selectivity of the assay. 27 Chapter 2: State-of-the-art methods for detection of Cryptosporidium2 2.1 Overview Contamination of recreational (e.g., swimming pools) and drinking water with Cryptosporidium is still a major threat to animals, the environment and the population. Hence, there is an urgent need for effective and efficient detection methods for detecting Cryptosporidium and providing information about its species. Regular methods for detecting Cryptosporisum rely on pre-concentration, extensive sample preparation, labelling and the use of advanced equipment for detection. These procedures require a long time ranging from 7 days to up to 2 weeks. Recently, great research efforts have been made to develop state-of-the-art methods to provide portable, real-time and on-site detection. This chapter will chronologically discuss the recent developments of the state-of-the-art methods for detecting Cryptosporidium, along with their advantages and disadvantages. These techniques are categorized into two major streams, including nanotechnology and on-chip-based biosensors. These two main categories are thoroughly explained to assess the future of those techniques for the on-site detection of Cryptosporidium. 2.2 Nanotechnology Recently, significant research efforts have been made to use nanotechnology in developing new sensors for detecting Cryptosporidium. The aim is to improve the specificity, selectivity and sensitivity while overcoming the drawbacks of conventional, immunological and molecular 2Parts of this chapter have been published in Samiei, E.; Luka, G.; Najjaran, H.; Hoorfar, M. Integration of Biosensors into Digital Microfluidics: Impact of Hydrophilic Surface of Biosensors on Droplet Manipulation. Biosens. Bioelectron., 2016, 81, 480–486. https://doi.org/10.1016/j.bios.2016.03.035, and Luka, G.; Ahmadi, A.; Najjaran, H.; Alocilja, E.; DeRosa, M.; Wolthers, K.; Malki, A.; Aziz, H.; Althani, A.; Hoorfar, M. Microfluidics Integrated Biosensors: A Leading Technology towards Lab-on-a-Chip and Sensing Applications. Sensors, 2015, 15 (12), 30011–30031. https://doi.org/10.3390/s151229783. 28 detection methods [135,136]. As mentioned previously, one of the limitations of immunofluorescence microscopy is the interference with auto-fluorescence algae and inert particles in the sample, which results in the generation of false-positive results. Furthermore, these fluorophores suffer from photo-degradation over time [133]. To overcome these drawbacks, semiconductor quantum dots have been used [137] as fluorescent labels to detect C. parvum oocysts in water samples. The use of semiconductor quantum dots has shown consistency and reliability. Although there is no significant difference in the results generated by FITC and semiconductor quantum dots [138], quantum dots are more photo-stable under continuous Ultraviolet (UV) excitation for up to 5 mins, while the photostability of FITC can decrease by 19.5% under the same conditions [137]. Moreover, compared to organic fluorophores, quantum dots have 50% less interference with other fluorescence materials in water samples [137]. Oligonucleotide gold nanoparticles have also been used in the molecular detection of Cryptosporidium without the need for DNA amplification [139]. For example, Chinnasamy et al. [140] used gold nanoparticles to develop an electrochemical immunosensor based on a sandwich enzyme-linked assay for detecting C. parvum in water samples. Gold nanoparticles were also used by Chinnasamy et al. [141] to develop a rapid immuno-dot blot assay using an advanced dual labelled gold nanoparticle probe for the detection of C. parvum. This assay improved the sensitivity of detection by 500 times compared to regular ELISA. Furthermore, the results showed good coherence with PCR validation. 2.3 On-chip-based biosensors Biosensors are promising analytical detection tools with a significant potential to provide accurate, rapid, and inexpensive quantification and detection of Cryptosporidium. They are 29 used to identify a particular analyte's presence or measure its concentration and kinetics in a given sample [142]. A typical biosensor comprises of a recognition element (such as antibodies [143], nucleic acids [144], enzymes [145,146], aptamers [147] or whole cells [148]), transducer, and detector [149] (Figure 2.1). The recognition element dictates the biosensor's specificity and selectivity [150], while the transducer transforms the response produced from a biorecognition event to a measurable signal [151]. The signal is then detected using a suitable detector [152]. Biosensors offer several advantages over the regular lab-based detection methods [153] such as reliability [154], accuracy [155], cost-effectiveness [156], selectivity [157], sensitivity [158] and real-time analysis [159]. Furthermore, biosensors can play a vital role in detecting proteins [160], cancer cells [161], enzymes [162], protein interaction [163], bacterial DNA [164], and pathogens [165]. Biosensors can be classified into different categories based on the type of molecular recognition element and the transducer. Figure 2.1 Schematic showing different components of a biosensor (Reproduced from ref. [166] with permission from Elsevier). Micrototal analysis system (μTAS), which was later named lab-on-a-chip (LOC), was first introduced by Manz et al. [167] in 1990. The concept of on-chip technology involves the miniaturizing of chemical assays and analytical instruments for improving portability and 30 performance. Since the development of on-chip technology, significant research efforts have been made to merge biosensors into this technology for detection and point of care applications [168]. On-chip biosensors offer alternative miniaturized diagnostic tools, with advantages such as reducing the volume of reagents, samples, waste production and hence reducing the risk of contamination. Besides, this technology reduces energy and power consumption [169,170]. Moreover, these systems increase the sensitivity, specificity and are cost-effective compared to the conventional analytical methods. For example, due to these systems' small size, a full analysis can be performed on a single chip [171]. Furthermore, this integrated technology enhances analytical performance and provides real-time detection, high throughput, rapid analysis, and portability [172,173]. All these advantages are suitable and adaptable for point-of-care (POC) applications [174]. Hence, on-chip biosensors are considered powerful diagnostic tools and provide a leap forward in developing POC and home-testing diagnostic tools for the detection of Cryptosporidium, which will benefit both developed and developing countries. Most of the developed on-chip diagnostic devices for the detection of Cryptosporidium are mainly used for environmental analysis in contaminated water. These on-chip devices have been used for DNA amplification [175] and immunological capturing of microorganisms [176]. These diagnostic tools have not only the potential to be used for detecting Cryptosporidium oocysts in environmental samples, but they could also be used for clinical samples. In the following subsections, examples of on-chip-based biosensors based on the transduction process will be highlighted. 31 2.3.1 Capacitance The electrical impedance of materials under a specific potential is related to their electrical properties and geometrical configurations. Alterations in a system's geometry or material composition will result in a change in its electrical impedance. This concept has been employed as a sensing mechanism for different applications, including micromachines and biosensors [177]. In the systems actuated with alternating voltages, the system's capacitance could also be derived from the complex impedance of the system. Samiei et al. [178] developed an on-chip interdigitated biosensor to detect Cryptosporidium oocysts based on the change in the capacitance of the sensing layer due to the presence of oocysts in the sample. The sensing surface was functionalized with anti-Cryptosporidium monoclonal antibodies using protein G linker. This biosensor was integrated into a digital microfluidic (DMF) device, which was used for the manipulation of the sample droplets. Sample droplets (5 µl) containing different concentrations of Cryptosporidium were transported using the DMF system to the location where the capacitive biosensor was designed on the chip and incubated for 30 min. After incubation, the sensing surface was washed with a washing buffer to remove the non-captured oocysts. The plate containing the biosensor pattern was then detached and dried for the detection/sensing step. Sensing was performed using a network analyzer (N5241A, Agilent Technologies), and the capacitance was calculated from the measured signals. The measurements were carried out with the sensors before and after the incubation process, and the change in the capacitance was calculated. Figure 2.2 illustrates a) the on-chip-based biosensor; b) the measured signals, and c) the calibration curve based on the number of oocysts spiked in the unit sample volume. This proof-of-concept device demonstrated an enormous 32 potential of capacitance as a mechanism for biosensing for Cryptosporidium detection. However, no detection limit was reported. Figure 2.2 Capacitance measurement for the detection of Cryptosporidium. a) DMF integrated capacitive biosensor; b) Measured capacitive signals for 5 concentrations of Cryptosporidium oocysts ranging from 15 cells/µl to 240 cells/µl in spiked water samples, and c) the calibration curve for the results presented for 8 replications (Reproduced from ref. [178] with permission from Elsevier). 2.3.2 Electrochemical Electrochemical biosensors have widely been employed for the detection of analytes and cells. These biosensors usually involve a three electrodes setup and function based on the potential difference between them due to their intrinsic potential [179]. The construction of these biosensors involves immobilizing a specific molecular recognition element such as antibodies, aptamers, DNA [168] or oxidoreductase enzymes [146] on the sensing electrode surface. The interaction between these biological recognition elements and the analyte of interest results in an electrochemical change. This change can be measured using the three-electrode system and processed as the output signal [180]. These electrochemical biosensors have been used for the detection of a variety of waterborne pathogens, including Cryptosporidium [181]. Houssin et al. [177] developed a label-free EIS chip-based biosensing platform for the detection of Cryptosporidium in water samples. The chip consisted of four sensors on a Pyrex substrate. Each sensor consisted of 4 mm wide interdigitated electrodes fabricated by standard lithography. The sensing platform also consisted of four wells created in a PDMS layer and 33 aligned with the sensor array (Figure 2.3). The system relied on measuring the change of conductivity upon the release of ions from Cryptosporidium. The reported detection limit was lower than 10 cells/μl. In this research, they performed the experiment in a phosphate buffer saline (PBS) solution, which is a conductive buffer. The buffer conductivity interfered with the measurements, leading to inaccurate results. Thus, the authors stated that for effective detection, a low conductive medium is required, such as filtered and treated water samples. Furthermore, the sensor exhibited low selectivity, which is considered one of the most critical limitations of the sensor. Therefore, the study suggested that for better selectivity, IMS is still required, which is an expensive and time-consuming technique. Furthermore, it is not suitable for real-time detection. Figure 2.3 The electrochemical impedance spectroscopy-based biosensor. a) Illustration of the PCB-based biochip; b) SEM of the electrodes; c) Sideview of the biochip; d) Layout showing the electrodes, and e) Fluorescence stained cells on the biochip surface (Reproduced from ref. [177] with permission from Elsevier). 34 Thiruppathiraja et al. [182] developed an electrochemical biosensor for detecting Cryptosporidium in water samples based on sandwich enzyme-linked immunosensing. The sandwich assay configuration consisted of gold nanoparticles, the primary antibodies, the oocysts, and the secondary antibodies conjugated with gold nanoparticles. The dual conjugated gold nanoparticles enhanced the measured electrochemical signal resulting in a detection limit of 3 oocysts/ml. The results generated by this study illustrated the biosensor's enhanced sensitivity with a significantly low detection limit. The drawback of their technique is the need for labelling, which is not suitable for label-free and on-site detection. Iqbal et al. [132] developed a screen-printed aptamer-based electrochemical biosensing platform to detect Cryptosporidium oocysts in fruits. In the study, DNA aptamers specific to the wall protein of Cryptosporidium parvum oocyst were developed by the group and used for detecting the oocyst in the sample. The aptamers were developed using the Systematic Evolution of Ligands by Exponential enrichment method (SELEX). The group studied the affinity of different 14 aptamer clones for C. parvum oocysts. The aptamers were used to construct a simple electrochemical biosensor for detecting Cryptosporidium. A screen-printed carbon electrode modified with gold nanoparticles was used as the sensing platform (transducer). A thiolated single-stranded DNA (ssDNA) primer was used as the linker between the sensing surface and the developed aptamer (capture probe) (Figure 2.4). Square wave voltammetry (SWV) was used to detect the binding recognition event. The sensor showed a detection limit of 100 oocysts/30 µl. One point to emphasize regarding this study is the cost/labor associated with the development of the aptamers (costly), their commercial availability and the use of gold nanoparticles to enhance the sensitivity. 35 Figure 2.4 Illustration showing the electrochemical detection protocol (Reproduced from ref. [132] with permission from PLOS ONE). Another chip-based electrochemical biosensor with an integrated mixer for detecting Cryptosporidium oocyst nucleic acids was developed by Nugen et al. [183]. Their study discussed the development and microfabrication of an on-chip-based electrochemical biosensor using polymer substrates, enabling the disposability and cost-effectiveness of the sensing platform. As a proof of concept, amplified hsp70 mRNA from the C. parvum was detected in the study. The electrochemical sensing platform containing an array of interdigitated ultramicroelectrodes with a width of 10-µm and a gap size of 5 µm were fabricated directly on a polymethyl methacrylate (PMMA) surface using standard photolithography techniques (Figure 2.5). The analyte of interest (hsp70 mRNA) was isolated from the oocysts and amplified before detection. The detection was conducted using a sandwich hybridization assay, consisting of liposomes modified with a reporter probe and superparamagnetic beads coated with a capture probe. A magnet was placed over the fabricated channel to capture the bead/liposome complex (Figure 2.6). Potassium ferro/ferrihexacyanide (electrochemical reporter) was entrapped inside the liposomes to allow for amperometric detection of the amplicon on the interdigitated 36 ultramicroelectrode array (IDUA). The detection of the target analyte was carried out by measuring the current between the IDUA and the concentration of the released potassium ferro/ferrihexacyanide from the liposomes. The system was able to detect the amplified mRNA as low as a single oocyst. However, the system relied on using expensive labels, a magnet to capture the bead/liposome complex and time-consuming labelling and preparation steps, including amplification of mRNA before detection. All these drawbacks hinder the possibility of using the sensor for on-site and label-free detection. Figure 2.5 Schematic of a gold IDUA fabricated on a PMMA substrate (Reproduced from ref. [183] with permission from Elsevier). Figure 2.6 Illustration showing the sandwich hybridization assay and binding between the liposomes (yellow) and magnetic beads (brown) (Reproduced from ref. [183] with permission from Elsevier). 37 2.3.3 Colorimetric In biosensors, colorimetric detection techniques involve measuring the produced colored product's concentration resulting from the reaction between the biological recognition element and analyte of interest. The produced colored product can be qualitatively spotted by a naked eye or quantitively (concentration) detected and measured using an optical sensing instrument. The measured color change is then correlated to the concentration of the analyte in question [168]. Esch et al. [184] developed a single-use competitive lateral flow (chip) based bioassay using oligonucleotide-tagged liposomes to detect Cryptosporidium. The assay involved the extraction and amplification of mRNA coding for the C. parvum heat-shock protein, hsp70. The produced amplicons were then hybridized with specific DNA oligonucleotides conjugated with dye-entrapping liposomes (reporter probes) and biotin. The assay mixture was then allowed to migrate upward on a membrane. The nitrocellulose strip consisted of two zones: 1) zone 1 (oligonucleotide zone), which consisted of an immobilized complementary probe to the reporter probe and 2) zone 2, which consisted of anti-biotin antibody (detection zone). The detection relied on a competitive assay format, in the presence of the target RNA, a hybridization occurs and the liposomes bind to the detection zone resulting in a visible color. Visual detection and quantification were performed using an Epson scanner to quantify the presence of liposomes in either zone. The reported detection limit was 80 fmol amplicon/sample. However, the sensor relied on using labels, and time-consuming preparation steps, including amplification of mRNA before detection. All these drawbacks make the sensor unsuitable for on-site and PCR/label-free detection. 38 Baeumner et al. [185] developed a quantitative lateral flow colorimetric assay for the specific detection of Cryptosporidium nucleic acid sequences. The assay was designed similarly to the assay developed by Esch et al. [184]. The sensing platform consisted of a dye-entrapping liposomal nanovesicle and a membrane. A generic oligonucleotide sequence was covalently attached to the outer surface of the liposomal nanovesicle. The reporter probe consisted of a 3′ end with a complementary sequence to the generic liposomal oligonucleotide and a 5′ end, which was complementary to the target nucleic acid sequence. The detection zone of the polyethersulfone membranes was coated with streptavidin. The capture probes were conjugated with biotin at the 5′ end, which was complementary to another sequence in the target nucleic acid sequence. Upon the addition of the target sequence, a sandwich complex is formed by incubating the assay components in a hybridization buffer. The hybridized mixture was then incubated in a test tube at 41 ºC for 20 min (Figure 2.7 a)). Subsequently, the hybridized mixture was allowed to migrate upwards on the polyethersulfone membrane. Only hybridized sequences were captured in the detection zone using the streptavidin-biotin binding strategy (Figure 2.7 b)). The captured liposomal nanovesicles were detected and quantified using a portable reflectometer and correlated to the analyte concentration in the sample. The developed assay showed an upper and lower detection limit of 1 fmol and 1000 fmol, respectively. However, the sensor suffered from the same limitations as the sensor developed by Esch et al. [184]. 39 Figure 2.7 The set-up for a strip-based biosensor. a) Illustration showing the covalently attached oligonucleotides to the outer surface of the liposomal nanovesicles as well as the detection zone of the polyethersulfone membranes coated with streptavidin and b) Illustration showing the positioning of reflectometer readings with the bottom and top parts of the membrane to show the direction of the flow. The migration of the hybridization mix begins towards the top part via capillary action once the bottom part of the membrane is inserted into the glass tube. Zone 1 represents the capture zone, where a signal will occur. Zone 2 is where the reference measurement was taken (Reproduced from ref. [185] with permission from American Chemical Society). Connelly et al. [186] have developed a portable strip-based colorimetric method with a single Cryptosporidium oocyst detection capability. The assay format was also similar to the assay designed by Esch et al. [184]. In the assay, the isolation of Cryptosporidium oocysts from the water was performed using IMS. The pathogen mRNA (hsp70 mRNA) was extracted using oligo-dT magnetic beads and amplified. Finally, the amplified target sequence was detected using lateral flow assay. The production of hsp70 mRNA was stimulated using a brief heat shock. The authors claimed that the developed biosensing platform could detect a single oocyst in a sample volume of 10 μL using a flow cytometer. However, the sensor was challenged by the existence of other substances and microorganisms in the sample. 40 Despite the high sensitivity and the portability of the strip-based colorimetric assays explained in the previous examples, they need extensive and time-consuming sample preparation (such as heat shock, lysis, mRNA isolation (IMS), amplification, pre-concentration), and the use of expensive labels. All these drawbacks make those systems unsuitable for on-site and label-free detection. 2.3.4 Fibre optics Optical fibre is employed in optical detection systems as a sensing element to transduce the biological recognition event into optical readout. They are used in a wide range of applications due to the prevalence of optical devices used in the majority of conventional detection methods. This consequently led to the development of miniaturized and case-specific devices for sensing applications [187,188]. Kramer et al. [187] developed an automated disposable on-chip-based biosensor using immunofluorescence assay and fibre-optic for the detection of Cryptosporidium. In the assay, anti-Cryptosporidium antibodies were immobilized on the tip of the optical fibre, and fluorescently-labelled anti-Cryptosporidium antibodies were used as the reporter in a sandwich format assay. A laser diode passing through the optical fibre inserted in a microfluidic disposable polystyrene chip was used to excite the fluorophore reporter. The emitted light from the fluorophore (reporter) was then coupled into the fibre optic and measured using a photodiode. The miniaturized biosensor had a detection limit of 105 oocysts/mL. Remarkably, they demonstrated that boiling the Cryptosporidium oocysts before detection has resulted in increasing the sensitivity by 10-fold. Despite the portability and sensitivity of the developed sensing platform, it has several significant drawbacks. These drawbacks involve the time-consuming sample pre-concentration, antibody labelling, and the need for expensive and bulky 41 equipment such as microscopes for data interpretation. These drawbacks limited the sensor applicability for real-time and label-free detection. Another microfluidic-integrated optical biosensor for the detection of Cryptosporidium was developed by Angus et al. [188]. This microfluidic system was capable of detecting a single oocyst from field water samples. The system, which is shown in Figure 2.8, includes a microfluidic system with two inlets and one output channel. The two inputs introduce the water sample and microbead conjugated antibodies, and after these two streams are merged, the flow continues through a single channel. The accumulation of the microbeads around the oocysts changed the optical properties of the oocyst, which were then optically detectable. An optical waveguide was integrated into the device, which was filled with silicone oil. Through an LED light source, a light beam was shone through the waveguide, and the scattered light was guided to a miniature spectrometer using a second waveguide. Despite the sensitivity of the developed sensing platform, the system requires labels such as microbeads. Furthermore, the detection required breaking off the surface antigens from the oocysts to enable the immunoagglutination of the microbeads. These disadvantages reduce the applicability of the sensing platform for on-site detection. 42 Figure 2.8 The microfluidic integrated optic biosensor. a) The experimental setup showing the light source at the lower left, the microfluidic chip at the right side, and the portable spectrometer at the top left and b) Schematic illustrating the microfluidic-based optical biosensor (Reproduced from ref. [188] with permission from Royal Society of Chemistry). 2.3.5 Surface plasmon resonance Surface Plasmon Resonance (SPR) is a phenomenon, which is based on measuring the refractive index change at the interface between a dielectric material and a planar metal surface [189]. In biosensors, SPR is used to measure the change of the incident and reflected light due to the biological recognition event [190]. A few studies reported the use of SPR as a detection method for Cryptosporidium. Kang et al. [191] developed a modified real-time SPR-based biosensor for detecting Cryptosporidium oocyst in a flowing buffer system. In the study, 43 streptavidin-biotin was used as a linker for immobilizing antibodies onto the surface. The reported limit of detection was mainly dependent on the nature of the biological recognition element used in the experiment. When streptavidin-biotin was used for antibody immobilization onto the sensor surface, the detection limit was as high as 1 x 106 oocysts/mL. This was because of the low capture efficiency of the immobilized antibodies. To improve the detection limit down to 100 oocysts/mL, the Cryptosporidium oocysts were labelled with biotin which increased the affinity between the immobilized streptavidin and biotin. However, the sample processing required centrifugation, which made it challenging to integrate this detection method into a continuous flow system, thus, limiting the portability of the device. Additionally, to improve the detection limit a labelling step was required. The sensor also required highly concentrated samples which led to the addition of a pre-concentration step. These drawbacks made the system expensive and not suitable for on-site detection. The same group in 2008 [192], using the same immobilization strategy (Figure 2.9 a)), studied the sensing platform response to Cryptosporidium in different water and buffer solutions. Various concentrations of Cryptosporidium oocysts along with other pathogens were added to tap water, reservoir water and buffer samples. In the study, an unspiked buffer was used as a control sample (Figure 2.9 b)). Using buffer as a control sample is one of the drawbacks of this system as it does not provide any evidence that non-specific binding of other contaminants does not occur using different matrices (Figure 2.9 c)). Also, the refractive index would be different for every different medium. Variations in the refractive index of different sample matrices with varying levels of turbidity will result in different responses and results for each different sample. These drawbacks limited the applicability of using the sensor for the detection of Cryptosporidium oocysts in real samples. 44 Figure 2.9 Illustration of the SPR-based biosensor developed by Kang et al. [192] for the detection of Cryptosporidium oocysts. a) Schematic diagram showing the 3 immobilization steps of the antibodies onto the biosensing chip and b) SPR signal using different sample matrices spiked with Cryptosporidium oocysts and other pathogens, where: Detection 1: HBS-EP buffer spiked with C. parvum oocysts; Detection 2: HBS-EP buffer spiked with Chlamydomonas reinhardtii, Bacillus stearothermophilus spore, Escherichia coli, and C. parvum oocysts; Detection 3: tap water spiked with C. parvum oocysts; Detection 4: reservoir water spiked with C. parvum oocysts. The concentration of all pathogens was 1x105 cells/ml in all samples (Reproduced from ref. [192] with permission from Elsevier). Despite the significant advancements that have been made in the example discussed above, there is still a need for further improvement of their functionality, fabrication, scalability, sensitivity, cost-effectiveness and portability. Furthermore, the development of standard operating protocols is still needed to achieve high reproducibility. Such improvements will allow for real-time and on-site detection as well as the development of high-throughput POC sensing platforms. Finally, for the development of successful POC diagnostic tools with the most significant impact in remote areas and resource-limited settings, these systems must be fully integrated and affordable with minimum requirements for highly trained personnel and with the capability of running with no external power [193,194]. 45 The following chapters will discuss the development of fully integrated on-chip biosensing platforms for the label/PCR detection of Cryptosporidium oocysts and RNA with the capability of overcoming the challenges and limitations associated with the existing on-chip-based biosensors. 46 Chapter 3: Theoretical Background3 3.1 Overview This chapter summarizes the theories behind the biological recognition elements, methods used for the surface functionalization (immobilization), and the sensing techniques used to construct the biosensors in this research. These theories explain the chemistry and biology behind the immobilization of the molecular recognition elements, capacitive, electrochemical and colorimetric sensing. In this thesis, on-chip-based capacitive, electrochemical and colorimetric biosensors were constructed for detecting Cryptosporidium. Specific anti-Cryptosporidium antibodies were used as the recognition element to construct the on-chip-based capacitive and electrochemical biosensors to detect Cryptosporidium oocysts. Specific DNA oligonucleotides were used as the biological recognition element to construct the on-chip-based colorimetric sensor to detect Cryptosporidium-specific RNA. 3.2 Antibodies Antibodies are Y-shaped proteins that are generated by the immune system (white blood cells, B-cells) which can neutralize or tag a specific antigen such as a virus or bacterium. They have a region called paratope that specifically binds to the epitope region of an antigen [195] (Figure 3.1). Recently, antibodies, also known as (Ig), have been applied widely as recognition elements in biosensors [196]. The use of antibodies as recognition elements has attracted 3Parts of this chapter have been published in Luka, G.; Ahmadi, A.; Najjaran, H.; Alocilja, E.; DeRosa, M.; Wolthers, K.; Malki, A.; Aziz, H.; Althani, A.; Hoorfar, M. Microfluidics Integrated Biosensors: A Leading Technology towards Lab-on-a-Chip and Sensing Applications. Sensors, 2015, 15 (12), 30011–30031. https://doi.org/10.3390/s151229783. 47 attention, especially after establishing the monoclonal antibody (Mab) technology by Kohler and Milstein [197]. The major advantages of the antibody-based biosensor are their specificity, selectively and the fact that the immunogen (i.e., the analyte of interest) does not need to be purified before detection (direct detection) [196][198]. Recently, recombinant antibodies have been successfully created by genetically modifying antigen-binding sites (Fab fragment) of common antibodies [198]. In placental mammals, antibodies exist in five primary types (isotypes) for different immune responses. These types are IgG, IgA, IgG, IgE, and IgD. Each type has different biological properties. In this research, only IgG (Figure 3.2) was chosen as the biological recognition element for constructing the capacitive and electrochemical biosensors for its specificity to Cryptosporidium oocysts and commercial availability. Figure 3.1 Schematic showing the interaction between the antibody and antigen (Created with BioRender.com). 48 Figure 3.2 Schematic showing the structure of IgG antibody isotype. (Created with BioRender.com). 3.3 Deoxyribonucleic acid (DNA) DNA is a nucleic acid consists of two long polynucleotide chains that spiral around each other, forming the DNA double helix structure [199]. The primary function of DNA molecules is to carry the genetic information needed for the reproduction, development, growth of all known organisms and DNA-viruses. The two long polynucleotides consist of nucleotides (simpler monomers), containing a nitrogenous base (thymine [T], adenine [A], cytosine [C], or guanine [G]), a sugar (deoxyribose) and one or more phosphate group. These nucleotides are linked together by a phospho-diester linkage (covalent bond) between the phosphate group of one nucleotide and the sugar the next, resulting in the formation of a phosphodiester backbone. The two polynucleotides' nitrogenous bases are joined together and stabilized by the two hydrogen bonds between A and T and three hydrogen bonds between G and C, forming a double-stranded DNA (dsDNA). These two strands are complementary to each other and are anti-parallel, as they run in opposite directions to each other [200] (Figure 3.3). 49 Figure 3.3 The chemical structure of DNA (Created with BioRender.com). Therefore, only ssDNA strands that are complementary to each other can hybridize to form a dsDNA. The on-chip-base colorimetric biosensor was constructed using this natural phenomenon (hybridization) between complementary ssDNA to capture a target RNA strand in a spiked water sample. 3.4 Surface modification This section will discuss the different techniques used in this thesis for surface modification. It will also discuss the importance of surface modification and highlight the advantages and disadvantages of the most common surface modification techniques. Furthermore, a brief overview of the surface chemistry required for immobilization and the binding process between the biological recognition element and the surface will be explained. Finally, an introduction to immobilization based on covalent bonding will be elucidated. 50 3.4.1 Ideal surface modification A biosensing platform's sensitivity and selectivity are evaluated by its ability to detect a specific analyte, among other many undesired substances in the sample mixture [201] (see Figure 2.1). Specific surface modification crucially influences the chemical properties of the sensing platform, and thus its capability of only allowing a specific binding between the biological recognition element and the surface while blocking the non-specific ones [202]. 3.4.2 Immobilization methods Immobilization in biosensors can be defined as the firm attachment of the biological recognition element (antibodies and DNA in this thesis) to a physicochemical transducer. This is to prevent their loss or mobility in the solution. However, these immobilized biological recognition elements should remain unobstructed and unmodified to retain their functionality. Several techniques, ranging from covalent bonding, adsorption, encapsulation to entrapment, have been investigated and used successfully for immobilizing different types of biological recognition elements over the years (Figure 3.4) [203,204]. Figure 3.4 Schematic of the different standard methods of immobilization. a) covalent bonding; b) adsorption; c) encapsulation, and d) entrapment. 51 Each technique has advantages and limitations based on the nature of the substrate material and properties. Furthermore, some of these techniques allow the immobilization of the biological recognition element only on a particular substrate such as glass and gold. Among these techniques, covalent bonding is the most common and preferable due to its irreversibility and stability. Furthermore, this method assures the orientation of the biological recognition element, thus increasing the accessibility and availability of the biological recognition element on the surface [205]. Hence, covalent bonding was used as the immobilization method in this research. 3.4.2.1 Covalent bonding This immobilization method involves forming a covalent bond between the biological recognition element and the support material (sensing platform). The strength of covalent bonding is strong, and hence the leakage possibility of the molecular recognition element is low [206]. This technique requires functional groups such as thiol on the sensor surface to enable the formation of a covalent bond between the molecular recognition element and the sensor surface [207]. Therefore, the surface modification must ensure the interaction between the biological recognition element and the analyte of interest remains intact and unhindered. One of the most straightforward surface modification techniques is the self-assembled monolayer (SAM) [208]. In the SAM procedure, three functional blocks or functional groups are formed: 1) a strong covalent bond between the thiol functional group and the gold surface; 2) carbon backbone or protein (e.g., protein G) which enhance the self-organization, and 3) an active group in which the biological recognition element (e.g., antibodies or DNA) can be bound. The gold surface modification with SAM can be achieved by incubating or dispensing the linker solution on the gold surface for 24 hours. The SAM molecules will then self-align themselves 52 on the surface and form a homogenous layer. Following this step, the sensor is ready to detect the analyte of interest [209] (Figure 3.5). Figure 3.5 Schematic representation of SAM. As mentioned above, ideal surface modification increases the immobilized biological recognition element's selectivity and sensitivity towards the analyte of interest. However, it is necessary to reduce the interference from undesired substances in the complex mixture. Hence, modifying the surface with a blocking agent such as bovine serum albumin (inert protein) is crucial to protect the surface from non-specific adsorption [210,211] (Figure 3.6). Figure 3.6 Schematic representation of non-specific adsorption on two different electrodes. a) non-protected surface (no blocking agent) and b) protected surface (modified with a blocking agent). 53 3.5 Antibody-based biosensor (immunosensors) Immunoassays are biochemical tests used to detect the presence or the concentration of specific analytes using antibodies that have high specificity, selectivity and affinity toward the analyte [212]. Biosensors-based on using immunoassays are called immunosensors [213] (Figure 3.7). Over the past decade, immunosensors have extensively been used in the detection of many analytes, including DNA [214], proteins [215], hormones [216], bacteria [217] and pathogens [218]. There are two classes of immunoassays: 1) indirect immunoassay and 2) direct immunoassay [78]. In the indirect immunoassay, the analyte is detected once an additional label such as enzymes or fluorescence labels has bound to the immobilized analyte of interest. The most common technique used in indirect immunoassays is ELISA (see Section 1.2.2 and Figure 1.5). Despite the sensitivity of indirect immunoassays, they are expensive, time-consuming and require expensive labels, limiting the applicability of these assays for real-time measurements [78]. On the other hand, direct immunoassays allow for the direct detection of the immune complex formation without the need for expensive labels [78]. Common examples of these types of biosensors are the capacitive and EIS biosensors. These two types of transduction have been of great interest to biosensors working based on label-free detection due to their relative simplicity, sensitivity and ease of integration into the on-chip technology. Furthermore, they have shown many advantages, including ease of detection, high sensitivity, low power consumption, size flexibility, and simplicity [219,220]. These advantages are crucial for miniaturization, which is essential to applications requiring on-site and in-field detection. Therefore, capacitive and EIS were used in this research as detection methods. Most of these 54 techniques are used to detect the change of the surface properties resulting from the biological event. Figure 3.7 Schematic representation of immunosensors. 3.5.1 Capacitive-based immunosensors Capacitive immunosensors are a class of impedance biosensors. These sensors are used to measure the change in the complex's relative permittivity due to the attachment of the target analyte to the surface. A simple equation relating the capacitance and the relative permittivity of a parallel plate capacitor is as follows [221]: 𝐶 = 𝜀0 × 𝜀𝑟 × 𝐴 × 𝑑−1 (3.1) where ɛ0 and ɛr represent the vacuum permittivity (8.85 × 10−12 F/m) and the relative permittivity of a dielectric material (between the electrodes), respectively; A is the surface area of the electrode plates, and d is the distance between the two plates. Equation (2.1) shows a direct relationship between the capacitance and the distance between the plates [13]. Therefore, for enhancing the capacitance measurements, the distance between the electrodes needs to be decreased [221]. The measured capacitance always depends on two factors: (1) the dielectric property of the medium and (2) the sensor's geometry, which is constant. In the last two decades, there has been a significant interest in using interdigitated electrodes (IDE) in the fabrication of capacitance-based biosensors for the development of 55 label-free sensing platforms [222–224]. Interdigitated electrodes (used to decrease the distance between metal electrodes) has a coplanar configuration that allows for performing the detection processes [225,226]. The interdigitated configuration is usually fabricated using lithography, which is a suitable technique for the development of an inexpensive, miniaturized and sensitive sensing platform. This configuration usually consists of electrode fingers with a specific dimension and distance between the fingers [227]. The width and distance vary from nano to micron-scale size, depending on the nature of the application [228,229]. In capacitive immunosensors, a biological element is usually immobilized directly on the electrodes' surface (with or without linkers) to selectively capture the analyte of interest [229]. A washing step for removing unbound targets is needed before recording the signal produced by the interaction between the analyte and the biological element. The washing step also minimizes the probability of receiving false-positive and false-negative signals [230]. A detectable change in the dielectric properties of the system happens upon the recognition between the immobilized biological element and the target (Figure 3.8). The change in the dielectric properties is directly proportional to the analyte concentration in the sample. Figure 3.8 Schematic of a capacitive biosensor, C1 is the capacitance of the SAM, C2 is the capacitance of the molecular recognition element, and C3 is the capacitance of the analyte. 56 3.5.2 Electrochemical impedance spectroscopy-based immunosensors Electrochemical Impedance Spectroscopy (EIS) is a powerful label-free technique that can be used to analyze the complex electrical resistance, such as impedance of a system and measuring its bulk and surface electrical properties [231]. In biosensors, EIS is particularly well-suited to the detection of recognition event occurs on the electrode surface. Furthermore, it is a valuable method for characterizing surface modifications, such as immobilization of molecular recognition elements onto the sensor surface [232]. As mentioned before, in all biosensors, the electrode is usually modified with a specific molecular recognition element. In electrochemical systems, EIS measurements with such modified electrodes involve the presence of redox-active species in the solution such as [Fe(CN)6]3-/4-. The purpose of adding a redox-active compound is to measure the ability of this compound to become reduced or oxidized at the electrode surface [233] (Figure 3.9). Figure 3.9 Schematic representation of EIS measurement. a) sensing surface modified with antibodies. A redox-active compound ([Fe(CN)6]3-/4- has to reach the electrodes’ surface to exchange electrons. However, modifying the surface with antibodies partially blocked the electron transfer, measured by RCT and b) An additional layer is formed on the electrode surface due to the interaction between the immobilized antibodies and the target which increases the blocking effect and thus RCT. 57 The redox-active compound's addition results in a distinct charge transfer resistance RCT. If this redox-active compound is removed or blocked by the layers added to the electrode surface, a change in impedance is observed, and the RCT value increases. Thus, any surface modification or the biological recognition event can be detected by the change in the RCT value [233]. RCT can be calculated from the following equation: 𝑅𝐶𝑇 = 𝑅𝑇𝐹2 𝐴𝜆𝐾 (3.2) where R represents the molar gas constant, T represents the temperature, F represents the Faraday constant, λ represents the electrochemical reaction kinetic rate constant, and K is the electroactive species concentration. 3.6 DNA-based biosensors Recently, there is a vast amount of genetic information established by extensive genome sequencing. The availability of this genetic information has increased the need for fast, simple, cost-effective and miniaturized sensing tools to fulfil the growing demand for molecular diagnostic tools such as DNA detection. Molecular diagnostic enabled the development of numerous amplification-based (e.g., PCR) and hybridization methods for detecting a wide range of diseases (e.g., inherited disease) and human pathogens through their specific DNA/RNA sequences [234]. The amplified DNA can then be detected by conventional techniques such as gel electrophoresis. However, gel electrophoresis is lengthy and requires toxic fluorescence dyes such as ethidium bromide and expensive UV instruments for visualization [235]. Furthermore, this technique is unsuitable for quantitative analysis as the relative amounts of genetic material rely on the band's thickness (darkness) spots on the gel [236]. Moreover, the gel can melt during the electrophoresis process, which decreases the accuracy of the analysis [237]. Due to these limitations, it is necessary to develop an alternative portable approach that 58 can be used for the simple and rapid identification of DNA. Such approaches are known as DNA-based biosensors. DNA-based biosensors are constructed by immobilizing a specific single-stranded DNA (ssDNA) on a supporting surface capable of detecting a complementary DNA or RNA sequence [238]. In biosensing, the hybridization happens typically between a known sequence of the ssDNA and the unknown complementary ssDNA or RNA (target). The hybridization process occurs directly on the surface of the electrode (unlike conventional hybridization assays) [238]. In essence, DNA-based biosensors enable faster and cheaper results than the traditional amplification and hybridizing assays (e.g., PCR and gel electrophoresis) while maintaining high selectivity and sensitivity. The double-stranded DNA (dsDNA) or DNA-RNA complex formation can be detected through the change in the signal due to the binding between the two strands or by a hybridization indicator [239] (Figure 3.10). Figure 3.10 Schematic representation of DNA-based biosensor based on ssDNA-DNA hybridization. A high-performance DNA-based biosensor should be able to distinguish as few as a single base-pair mismatch between different DNA/RNA sequences [240]. The strength and specificity of hybridization depend mainly on the complementarity degree between the immobilized probe and the target sequence. The stability and sensitivity of the sensor reach their maximum in the case of 100% complementarity. This can be easily accomplished by a proper design and 59 selection of the probe sequence [241]. Unlike enzyme-based biosensors, in DNA-based biosensors, the DNA capture probes are easily synthesizable and stable. Furthermore, DNA-based biosensors are capable of distinguishing pathogen strains, allowing earlier diagnosis [239]. 3.6.1 Colorimetric-based DNA biosensors Recently, colorimetric detection based on inorganic nanomaterials has attracted great attention due to their simplicity, cost-effectiveness, and distinct color variation associated with their morphology or size change [242]. Moreover, colorimetric assays can be visualized with the naked eye without the need for sophisticated instruments or tedious training [243]. Gold nanoparticles (AuNP) are of particular interest because of gold nanoparticles' unique features, including their catalytic properties and controllable sizes [244]. More importantly, they are easily synthesizable and modified with several thiolated molecular recognition elements. Furthermore, the unique localized surface plasmon resonance (LSPR) properties of AuNPs allow them to have different colors based on their size. For example, AuNPs with 20 nm have a red color, and their LSPR can be measured at a wavelength of 520 nm [245]. Colloidal AuNPs are negatively charged and hence repel each other. The repulsion between the particles allows them to stay colloidal and maintain their red color. However, when salt such as NaCl is added, these negative charges are neutralized (masked), resulting in aggregation of AuNPs (Figure 3.11 a)). Aggregation results in a decrease in the distance between the particles and an increase in their size. This results in a change in color from red to blue due to a large shift of the LSPR peak (absorption at a longer wavelength (630 nm)) [246] (Figure 3.11 b)). 60 Figure 3.11 Color change associated with the aggregation of gold nanoparticles. a) before the addition of NaCl (colloidal gold nanoparticles (red color)) and b) after the addition of NaCl (blue to purple color (aggregated gold nanoparticles)). In DNA biosensing, gold nanoparticles are usually modified with a biological recognition element such as a complementary oligonucleotide specific and selective to the target DNA. The hybridization between the modified AuNPs and the target DNA or RNA in the sample results in a color change (red to blue or purple) (Figure 3.12). The color change is proportional to the concertation of the analyte in question and can be qualitatively detected by the naked eye [247]. Due to these features, AuNP has led to a new generation of biosensors with high stability, selectivity, cost-effectiveness and sensitivity. Figure 3.12 Schematic illustration of the colorimetric detection of DNA based on the aggregation of gold nanoparticles. 61 Chapter 4: On-Chip Sensing Platforms and Portable Devices Fabrication4 4.1 Overview In this chapter, the experimental setup, equipment, the protocols used to perform the experiments, and the methods used to fabricate the on-chip-based biosensors and the portable diagnostic device are explained. The studies performed in this thesis are based on the fabrication and development of on-chip-based biosensors and a portable diagnostic device for the detection of Cryptosporidium. Different chip sensor structures are fabricated for the on-chip capacitive and electrochemical sensing protocol since every sensing method needs a specific design in terms of geometry, connection, and equipment to perform the analysis. For the colorimetric sensing protocol, a high-resolution smartphone camera was used for detection and data processing. A low-cost 3D printed holder assembly was fabricated to house the smartphone and the chip in one compact unit. For the capacitive sensing, immobilization, optimization and surface characterization were carried out through capacitive and contact angle measurements. For electrochemical sensing, immobilization and surface characterization were performed through electrochemical and contact angle measurements. In the case of the colorimetric assay, immobilization and surface characterization was performed using UV–vis spectroscopy and a smartphone, separately. Finally, capacitive and electrochemical biosensing was performed by preparing the reagents and samples, dispensing them onto the chip. Then the chip was moved and connected to the appropriate sensing device. 4Parts of this chapter have been published in Samiei, E.; Luka, G.; Najjaran, H.; Hoorfar, M. Integration of Biosensors into Digital Microfluidics: Impact of Hydrophilic Surface of Biosensors on Droplet Manipulation. Biosens. Bioelectron., 2016, 81, 480–486. https://doi.org/10.1016/j.bios.2016.03.035 and Luka, G.; Nowak, E.; Kawchuk, J.; Hoorfar, M.; Najjaran, H. Portable Device for the Detection of Colorimetric Assays. R. Soc. Open Sci, 2017, 4 (11). https://doi.org/10.1098/rsos.171025. 62 For the colorimetric assay, the detection was performed by measuring the color change in the presence of different concentrations of Cryptosporidium RNA using both UV-vis spectroscopy and the developed sensing platform. 4.2 On-chip fabrication steps All chips were fabricated on 38 𝑚𝑚 × 75 𝑚𝑚 or 50 𝑚𝑚 × 75 𝑚𝑚 glass slides (Corning Inc.). Glass substrates were chosen due to their smoothness, insulation and transparency. The fabrication steps included substrate preparation, conductive layer deposition, electrode patterning, surface functionalization with anti-Cryptosporidium antibodies. Substrate preparation included cleaning and improving the adhesion. Conductive layer deposition was conducted by sputtering conductive materials such as gold on the cleaned chip. Standard photolithography was used for electrode patterning. Each fabrication step is outlined in the following sections. 4.2.1 Substrate preparation Substrate preparation is the most crucial step in the fabrication steps of the sensing chip, as the presence of undesired micro-particles (such as dust and dirt) on the glass slide can significantly decrease the quality of the patterned microelectrodes. To avoid contamination of the chip and for high resolution, all fabrication steps were carried out in a class 100 cleanroom facility. All substrates were cleaned with a particle-free cloth soaked in acetone (Sigma Aldrich Co.) and soaked in acetone (Sigma Aldrich Co.) for 10 min. Following the soaking step, all slides were then thoroughly rinsed with cleanroom grade acetone for 15 s. Finally, the substrates were then rinsed for 10 s with ethanol, isopropanol (Sigma Aldrich Co.), ultrapure water, and dried with air at room temperature. To remove all washing materials, the substrates were baked on a hotplate for 15 min at 200 ºC. Before the conductive layer deposition step, all substrates were 63 treated using oxygen plasma for 1 min (PM313, Plasma Etch Inc.) to increase the adhesion of the conductive layer to the substrate. 4.2.2 Conductive layer deposition For all biosensing chips, chromium-gold was used for patterning the sensing electrodes on the prepared glass substrates using a magnetron sputtering system (Angstrom Sciences Inc.). Gold was chosen over other materials as it does not oxidize easily. However, the adhesion of gold to glass substrates is low. Thus, before the deposition of the gold layer onto the glass slide, a thin chromium layer was first sputtered on the glass substrate to enhance the surface adhesion of the gold layer to the substrate. Then, gold was sputtered on the chromium layer. For all on-chip-based biosensing platforms, a chromium layer (50 nm) was sputtered in an argon atmosphere on the clean glass substrate. Subsequently, a gold layer (250 nm) was sputtered on the glass/chromium substrate. 4.2.3 Sensing electrodes patterning All sensing electrodes were patterned on the chromium/gold-coated glass substrates using standard photolithography. The photolithography process was carried out as follows: the glass substrate coated with copper/gold layers was first cleaned with ethanol and ultrapure water and dried with air. A layer of a positive photoresist S1805 (MicroChem Corp.) was then spun coated on the sputtered copper layer for 60 s at a speed of 3000 rpm and post-baked for 2 min at 100 ºC. Following this step, the spin-coated substrate was aligned with a mask printed with the sensing electrodes pattern. All masks were designed using AutoCAD and printed by CAD/Art Services, Inc. Next, the slide and mask were exposed to a uniform ultra-violet (UV)-light (365 nm wavelength). The exposure time to pattern the S1805 photoresist was 7 s. Following this step, the patterned substrate was immersed in a glass container filled with MF-319 developer 64 (MicroChem Corp.) for 30 s or until the pattern became visible and clear. MF-319 developer was used as it can dissolve and remove the unexposed parts of the positive photoresist. Finally, the slides were removed from the developer container, rinsed with ultrapure water chromium, and the electrodes were patterned using chromium and gold etchants (Transene), respectively. Any remaining photoresist was removed by rinsing the slides thoroughly with acetone followed by ultrapure water and blown dry. The photolithography process is explained in Figure 4.1, and a schematic of the three on-chip-based biosensors used in this thesis is shown in Figure 4.2. Figure 4.1 Schematic of the photolithography process for patterning the sensing electrodes: i) sputtering the glass substrate with chromium and gold layers; ii) spin coating the glass/chromium/gold slide with the positive photoresist S1805; iii) exposing the photoresist layer, along with the mask patterned with the electrodes design, to the UV light; iv) developing the electrodes pattern on the photoresist layer using MF-319 developer; v) etching the gold and chromium layers with gold and chromium etchants, respectively, and vi) removing the remaining photoresist using acetone. 65 Figure 4.2 Schematic of the 3 on-chip-based biosensors used in this research. a) capacitive-based biosensor; b) electrochemical-based biosensor, and c) colorimetric-based biosensor. 4.2.4 Hydrophobic layer deposition For the on-chip-based colorimetric detection of Cryptosporidium RNA, a hydrophobic layer was deposited on the glass/chromium/ gold substrate. A lift-off process was carried out to create a hydrophobic circular barrier around the detection spots. A fluoropolymer-based material, cytop (PFC 1603V, Cytonix Co.), was used as the hydrophobic material. The polymer concentration was 3 % wt/v, and a spin coater was used at a speed of 3500 rpm for 60 s to deposit the hydrophobic material onto the substrate. Finally, the sensing chip was baked for 30 min at 150 ºC. 4.2.5 Lift-off procedure To create hydrophobic circular barriers around the detection spot, a circular part of the hydrophobic layer coated on the surface of the sensing platform was removed. To increase the hydrophilicity of the hydrophobic surface, the hydrophobic layer-coated sensing platform was first treated with oxygen plasma for 5 s. Next, a standard photolithography procedure was followed to deposit a layer of a negative photoresist S1813 (MicroChem Corp.). The photoresist 66 was patterned, as explained in Section 4.2.3. The pattern was designed to protect the entire hydrophobic surface except for the circular detection spot. Following this step, the sensing platform was exposed to UV light, as explained in Section 4.2.3 and treated with oxygen plasma for 2 min to remove the hydrophobic layer on the circular detection spot. This process has led to circular hydrophobic barriers around the detection spot to enable controlling the sample in this area. 4.2.6 Portable diagnostic devices fabrication For the colorimetric assay, a high-resolution smartphone camera was used to collect a picture of the sample and detect the color change. A smartphone was used to eliminate the need for bulky and expensive instruments for analysis. An open-source app (Color Grab, Loomatix) was used to analyze the captured images in real-time by identifying the hue–saturation–value (HSV) coordinates of the image. The device sensing platform was purposefully designed to be cost-effective yet versatile. The results obtained by the developed sensing platform have been compared to those obtained from a conventional method in terms of sensitivity, selectivity, accuracy, reproducibility and reliability. A holder assembly was printed using a 3D printer (Objet500 Connex, Stratasys) with a resolution of 14 μm. The 3D printed holder assembly was designed to have the necessary components for the portable analysis, including a light-emitting diode (LED), a chip frame and a smartphone holder. 67 Chapter 5: On-Chip-based Capacitive Biosensor5 5.1 Overview In this chapter, an on-chip interdigitated-based capacitive biosensor has been introduced for the label-free detection of Cryptosporidium oocysts in water samples. Specific anti-Cryptosporidium monoclonal antibodies (IgG3) were covalently immobilized onto interdigitated gold electrodes as the capture probes, and bovine serum albumin was used to avoid non-specific adsorption. The immobilization of the antibodies was confirmed by cyclic voltammetry, capacitance, and contact angle measurements. The detection was achieved by measuring the relative change in the capacitive/dielectric properties due to the formation of the Cryptosporidium-antibody complex. The biosensor has been tested for different concentrations of Cryptosporidium. The results show that the biosensor developed can accurately distinguish different numbers of captured cells and densities on the biosensor's surface. The number of Cryptosporidium oocysts captured on the electrode surface was confirmed using a fluorescein isothiocyanate (FITC) immunofluorescence assay. The response from the developed biosensor has been mainly dependent on the concentration of Cryptosporidium under optimized conditions. The biosensor showed a linear detection range between 15 and 153 cells/mm2 and a detection limit of 40 cells/mm2. Compared to similar sensors reported in the literature [131], the sensor presented here does not need complicated fabrication or expensive substrates such as nanocrystalline diamond (NCD) to increase sensitivity. 5Parts of this chapter have been published in Luka, G., Samiei, E., Dehghani, S., Johnson, T., Najjaran, H., Hoorfar, M. Label-Free Capacitive Biosensor for Detection of Cryptosporidium. Sensors, 2019, 19 (258):1-9. https://doi:10.3390/s19020258 and Samiei, E.; Luka, G.; Najjaran, H.; Hoorfar, M. Integration of Biosensors into Digital Microfluidics: Impact of Hydrophilic Surface of Biosensors on Droplet Manipulation. Biosens. Bioelectron., 2016, 81, 480–486. https://doi.org/10.1016/j.bios.2016.03.035. 68 The sensing platform has also shown an improvement in the efficiency and limit of detection of Cryptosporidium compared to other staining and immunoassay-based detection methods [74][248]. The on-chip capacitive biosensor developed has a great potential for detecting Cryptosporidium in environmental water samples. Furthermore, under optimized conditions, this label-free biosensor can be extended to detect other biomarkers for biomedical and environmental analyses. 5.2 Materials Purified Cryptosporidium parvum oocysts at the concentration of 106 oocysts/mL and FITC-labeled anti-Cryptosporidium parvum oocyst antibody (Crypt-a-Glo) were purchased from Waterborne, Inc., New Orleans, LA, USA. Cryptosporidium oocysts were kept at 4 °C in 0.1 M phosphate-buffered saline (PBS), pH 7.4. A specific antibody for the Cryptosporidium oocyst, i.e., IgG3 subclass monoclonal mouse, was purchased from ABD Serotec (Currently Biorad, Burlington, ON, Canada). Recombinant protein G/thiol was purchased from protein MOD (Madison, WI, USA). Sodium phosphate monobasic monohydrate, sodium phosphate dibasic and bovine serum albumin (BSA) were purchased from Sigma (Sigma-Aldrich, St. Louis, MO, USA). All other reagents and solvents were of analytical grade, and ultrapure water was used throughout the experiments. 5.3 Experimental Procedure 5.3.1 Sensor fabrication The fabrication of the interdigitated electrodes (IDE) was completed, as explained in the following steps in Section 4.2.3. A glass slide was cleaned using piranha solution followed by 10 min oxygen plasma treatment. Chromium and gold layers were sputtered (Angstrom Engineering) in an argon atmosphere on the glass surface with thicknesses of 50 nm and 250 69 nm, respectively. The electrodes were patterned using the standard lithography process and a mask with the IDE pattern [13][178]. Each sensor has 18 pairs of the interdigitated electrodes, and each electrode has a width of 30 µm, a length of 500 µm, and a gap spacing of 30 µm. Each one of the fabricated sensing electrodes works as a single biosensing platform. The interface to the electrodes is made through a coplanar waveguide probe with a pitch of 100 µm, which makes contact at the location (Figure 5.1). Figure 5.1 The fabricated interdigitated capacitive-based biosensor with a layout of the IDE sensing electrodes and the schematic of the contact probe (Reproduced from ref. [13] with permission from MDPI). 5.3.2 Antibody immobilization The interdigitated gold electrodes were treated with plasma oxygen and were coated with a self-assembled monolayer (SAM) by adding recombinant protein G/thiol. The sensor was then incubated at 4 °C for 48 h. To wash away the unbound recombinant protein G/thiol, the platform was rinsed with ultrapure water followed by phosphate-buffered saline (PBS). The sensors-SAM were then incubated with 3 µL of 100 µg/mL specific anti-Cryptosporidium antibodies (IgG3) at 4 ºC for 24 h, followed by a washing step, as mentioned in the previous step. The gold interdigitated electrodes were incubated with 100 mM BSA for 2 h at 4 ºC to block the surface. The sensors were then washed several times with ultrapure water, followed by phosphate-buffered saline (PBS) (Figure 5.2). 70 Figure 5.2 The process used for coating the surface of IDE with SAM and the immobilization of anti-Cryptosporidium antibodies (Reproduced from ref. [13] with permission from MDPI). 5.3.3 Electrochemical measurements of SAM and antibody immobilization The formation of SAM and antibodies immobilization on gold IDE was checked using an electrochemical cell equipped with a three-electrode system. The fabricated gold IDE was used as the working electrode. An external platinum wire and an Ag/AgCl/3.0 M KCl electrode were used as the counter and the reference electrodes. A salt bridge, filled with 1 M KNO3 aqueous solution and agar, was used to reduce chloride ion diffusion into the electrolyte solution. Cyclic voltammetry (CV) was recorded for the bare gold, SAM, and antibodies using a VersaSTAT 4 potentiostat (Princeton Applied Research) in a potential range from −300 to +500 mV (against Ag/AgCl) at a scanning rate of 50 mV/s. The electrolyte used for the CV measurement consisted of a 5 mM [Fe(CN)6]3-/4- aqueous solution with 1 M NaClO4 as the supporting electrolyte. An open circuit potential was used in all electrochemical measurements to be consistent. All fabricated working electrodes were cleaned before making measurements by incubation for 30 s in piranha solution (H2SO4-H2O2 3:1 (v/v)) and rinsed with ultrapure water. All electrodes were further cleaned electrochemically by cycling them in a 0.5 M KOH (basic) solution in the range of −2 to 0 V versus the reference electrode (Ag/AgCl), followed by cycling them in a 0.5 M H2SO4 (acidic) solution in the range of 0 to +1.5 V. 71 5.3.4 Contact angle measurements Contact angles were determined to further verify the immobilization of the antibodies on the surface. The measurement was performed similarly to the method explained in [178], showing that the antibodies' presence on the surface changes the sensor's surface properties from hydrophilic to superhydrophilic. Two substrates were prepared: one with the chromium and gold layers and the other with the immobilized antibodies on the chromium and gold layers. A water droplet was dispensed on the surface of the substrates using a syringe pump. The water was dispensed through a needle with a hydrophobic outer surface. The needle's surface was coated with a hydrophobic material to avoid the effect of the needle on the contact angle on the surface. The contact angle was measured from the side view images taken in the experiment [178] (Figure 5.3). Figure 5.3 Schematic illustrating the experimental setup for contact angle measurement. 5.3.5 Sample preparation and measurement Series of dilutions of Cryptosporidium samples were prepared in PBS as the diluent. Typical dilutions were 250, 200, 150, 100, 50, and 0 oocysts in a final volume of 5 µL, as explained in [13]. Different concentrations in a volume of 5 µL were incubated for 20 min at room 72 temperature (23 ºC) with the sensing electrodes functionalized with specific anti-Cryptosporidium antibodies (ABD Serotec, Burlington, ND Canada) (Currently Biorad). All electrodes were washed with PBS, then with ultrapure water to remove any unbound and loosely bound Cryptosporidium from the electrode surface (Figure 5.4). The samples were dried in air for 30 min, and measurements were performed. Capacitance measurements were obtained before and after the incubation of Cryptosporidium using a network analyzer (N5241A, Agilent Technologies, Santa Clara, CA, USA) connected to a coplanar waveguide probe, which makes a calibrated impedance measurement referenced to the contact interface of the electrodes. S-parameter measurements [249] were performed versus a frequency range of 5.5 GHz to 8.5 GHz and converted to equivalent circuit capacitance values. The network analyzer was calibrated using the short-open-load-through method (SOLT), and the S-parameter values were recorded at two different stages: 1) after SAM formation with immobilized anti-Cryptosporidium antibodies and 2) after incubating the sensor with various concentrations of Cryptosporidium. All measurements were repeated three times to conduct a statistical error analysis and to confirm the detection limit. The changes in the capacitance and dielectric properties between negative control and after capturing Cryptosporidium were compared. 73 Figure 5.4 Diagram illustrating the detection process: a) incubation of the anti-Cryptosporidium antibodies by coating the sensing electrodes with water samples containing different concentrations of Cryptosporidium; b) washing with phosphate-buffered saline (PBS) of pH 7 to remove any unbound and non-specific molecules; c) drying the interdigitated electrodes, and d) capacitance measurements over a specific frequency range (Reproduced from ref. [13] with permission from MDPI). The following equation was used to calculate the relative capacitance for each concentration (from the generated data) as a function of the frequency for four different cell densities ranging from 15 to 152 cells/mm2 of the captured Cryptosporidium oocysts on the sensing surface. ∆ 𝐶 (%) =𝐶−𝐶0𝐶0× 100 (5.1) In the above relation, Co is the capacitance before binding of Cryptosporidium to the immobilized antibodies, and C is the capacitance after binding of a specific concentration of Cryptosporidium. 5.3.6 FITC Measurements The number of Cryptosporidium oocysts captured on the electrode surface was confirmed using an immunofluorescence assay. Staining was carried out according to the manufacturer: a) all electrodes were stained with fluorescein isothiocyanate (FTIC)-conjugated anti-Cryptosporidium oocysts monoclonal antibodies (mAb) (Crypto-a-Glo kit, Waterborne, Inc, New Orleans, LA, USA), b) all electrodes were incubated in a humid chamber at room temperature for 30 min, c) the electrodes were rinsed by adding the 50 – 100 µL washing buffer 74 and left for 1 min at room temperature (23 ºC), d) the electrode surfaces were tilted slightly to remove the washing buffer, and e) an absorbent material was placed at the edge of the electrodes to absorb the excess fluid on the surface. Electrodes were imaged with a 20× epifluorescence microscope (Zeiss AxioVision, Thornwood, NY, USA), and the number of oocysts was counted. 5.3.7 Calibration curve and limit of detection (LOD) To assess the applicability of the sensing platform for detecting Cryptosporidium, the calibration curve was constructed by plotting ΔC/Co% versus the number of Cryptosporidium cells. The limit of detection (LOD) was calculated using the following expression: LOD = 3SDb/m, where SDb is the standard deviation of the blank and m is the slope of the calibration curve [250,251]. 5.4 Results and Discussion 5.4.1 Preparation of the biosensing layer Cyclic voltammetry (CV) and contact angle measurement were conducted step by step to confirm the formation of the SAM and the immobilization of the antibodies. The CV measurements for the different stages of the electrode modification with protein G/thiol and antibodies are shown in Figure 5.5. 75 Figure 5.5 Cyclic voltammetry (CV) for the case of bare Au electrode (blue curve); added protein G/thiol (red curve); immobilized anti-Cryptosporidium antibodies (gray curve), and added bovine serum albumin (BSA) (yellow curve) (Reproduced from ref. [13] with permission from MDPI). A redox peak pair was observed for the bare IDE (blue curve). The current decreased after modifying the electrodes with protein G/thiol (red curve), after immobilization of antibodies (gray curve), and after blocking with BSA (yellow curve). The generated results from the CV analysis confirmed the formation of SAM, immobilization of the antibodies and the surface blocking step. Functionalization of the surface by antibodies was also confirmed by contact angle measurement. For this purpose, advancing and receding contact angles were measured, which shows the surface's level of wettability. In essence, the immobilization of biological species on the surface significantly increases the surface's wettability [178]. 5.4.2 Surface characterization Advancing and receding contact angles were measured as 90º and 46º (±1º) for the gold surface, respectively, and 57º and 4º for the gold surface with immobilized antibodies. The measured contact angles show the gold surface has hydrophilic properties with low receding contact angles. In contrast, the surface-functionalized with antibodies is superhydrophilic with a minimal receding contact angle. 76 5.4.3 Capacitance detection of Cryptosporidium oocysts Capacitance measurements were obtained first for the modified IDE with the immobilized anti-Cryptosporidium antibodies (Co) and then after capturing the Cryptosporidium oocysts (C). These values are correlated to the number of Cryptosporidium oocysts bound to the immobilized antibodies. Figure 5.6 shows the change in the capacitance values (ΔC/Co%) as a function of the frequency for six different cell densities (from 15 to 152 cells/mm2) of captured Cryptosporidium oocysts on the sensing surface (Figure 5.6 a)). A distinct change in capacitance was measured for different concentrations of Cryptosporidium over the frequency range of 5.5 GHz to 8.5 GHz. The statistical error, average values, and the lowest detection limit were calculated. The results are shown in Figure 5.6 a), and an increase in the measured relative capacitance (ΔC) as a function of Cryptosporidium concentration is observed. The maximum change in the response due to different cell densities was observed at 7.5 GHz (Figure 5.6 a)). Hence, for each number of the captured oocysts, the maximum value of the relative change in the capacitance at 7.5 GHz was used to generate the calibration curve (Figure 5.6 b)). The curve is linear over a range of 15 to 153 cells/mm2, showing the platform's effectiveness as a cost-effective, label-free and sensitive tool. This curve also shows the detection limit is approximately 40 cells/mm2 and, below this limit, the measurement error increases significantly. 77 Figure 5.6 Results of the relative capacitance measurement for various concentrations of Cryptosporidium. a) Relative capacitance change measurements for different concentrations of Cryptosporidium for samples ranging from 15 cells/mm2 to 153 cells/mm2 and b) The calibration curve generated from capacitance measurements (Reproduced from ref. [13] with permission from MDPI). After the capacitance measurement step, specific immunofluorescence assays for Cryptosporidium were used to confirm the number of Cryptosporidium oocyst captured and detected by the developed sensor, as explained in Section 5.3.6. Fluorescence microscopic images were taken, and the number of Cryptosporidium bound to the antibodies immobilized onto the IDE surface were counted and correlated to the capacitance measurements. The fluorescence results confirm the selectivity of the sensor, with anti-Cryptosporidium antibodies on the surface, towards Cryptosporidium (Figure 5.7). Figure 5.7 Fluorescence microscopic images illustrate 20 cells of Cryptosporidium bound to the immobilized antibodies on a 1 mm2 electrode surface area using direct fluorescein isothiocyanate (FITC)-conjugated anti-Cryptosporidium oocysts monoclonal antibodies (Reproduced from ref. [13] with permission from MDPI). 78 5.5 Summary This chapter reports an easy to fabricate, and cost-effective on-chip-based interdigitated capacitive biosensor for the label-free detection of Cryptosporidium. The sensitivity and detection limits of the sensing platform were measured. The results showed a linear detection range for Cryptosporidium concentrations with a detection limit of 40 cells/mm2. The developed sensing platform demonstrates the potential of implementing a biosensing platform, reducing the need for trained technicians and specialized laboratories. Additionally, the sensor developed here does not need complicated fabrication or expensive substrates such as nanocrystalline diamond (NCD) to increase sensitivity [131]. Furthermore, sample pretreatments such as sonication and UV are not required while using this platform [252–254], which has high flexibility. In the future, the developed sensing platform will be tested with real samples to confirm the applicability of the technology as a replacement for the staining and the fluorescence detection part in the EPA 1623 method. The label-free biosensor developed in this research can be extended to detect other analytes in a variety of biomedical and environmental analyses through the emerging biosensing technology. This technology also has a great potential to be integrated into microfluidic devices for portable, personalized and point-of-care (POC) applications. 79 Chapter 6: On-Chip-based Electrochemical Biosensor6 6.1 Overview In this chapter, a simple, easy to fabricate, and cost-effective, on-chip-based electrochemical biosensor has been developed for the label-free detection of Cryptosporidium oocysts in water samples. Specific anti-Cryptosporidium monoclonal antibodies (IgG3) were covalently immobilized via protein G/thiol onto the fabricated gold (Au) electrodes as the capture probes. Bovine serum albumin was used to block the surface and avoid non-specific adsorption. Electrochemical techniques such as cyclic voltammetry (CV), square wave voltammetry (SWV), and electrochemical impedance spectroscopy (EIS) were used to confirm the successful immobilization of the IgG3 onto the sensor surface. The electrochemical sensor was fabricated using the standard lithography process and a mask with the 3-electrode design. The EIS was employed for the detection of Cryptosporidium in the range of 0 to 300 oocysts. The sensor showed a detection limit of approximately 20 oocysts/5µl and a wide linear response in the range between 20 and 200 oocysts/5µl. The change in the sensor electron charge-transfer and surface film resistance was measured in the presence and absence of the target analyte. Compared to other sensors reported in the past, the sensor developed here does not need expensive substrates or complicated fabrication processes (such as gold nanoparticle-modified screen-printed carbon electrode [132]) to enhance sensitivity. Thus, the proposed on-chip sensing platform has a great potential to be used as an inexpensive, flexible, portable, and reliable sensing platform for early and immediate on-site detection of Cryptosporidium in water 6Parts of this chapter have been submitted to Biosensors and Bioelectronics: Luka, G., N., Najjaran, Hoorfar, M., On-chip-based-electrochemical biosensor for the label-free detection of Cryptosporidium, Biosensors and Bioelectronics, 2020. 80 samples. Furthermore, the developed sensing platform can be modified with a wide variety of other biological recognition elements and used to detect other pathogens, viruses and biomarkers for environmental, security and defense and biomedical applications. 6.2 Materials All materials were purchased and used, as described in Section 5.2. All other reagents and solvents were of analytical grade, and ultrapure water was used throughout the experiments. 6.3 Experimental procedure 6.3.1 Sensor fabrication The fabrication of the chip-based electrochemical immunosensor was performed using the steps explained in Section 4.2.3. A piranha solution was used to clean all glass slides, followed by an oxygen plasma treatment for 10 min. Chromium and gold layers with thicknesses of 50 nm and 250 nm, respectively, were sputtered (Angstrom Engineering) in an argon atmosphere on the glass surface. The sensing platform was patterned using the standard lithography process and a mask with the electrode pattern. Each sensor consists of a working electrode (WE with 2 mm in diameter) and an auxiliary electrode (AE) (Figure 6.1). An external Ag/AgCl reference electrode was used for the measurement. 81 Figure 6.1 The fabricated electrochemical-based biosensor with layout of the electrochemical sensing electrodes (reference electrode (RE), working electrode (WE), and auxiliary electrode (AE). 6.3.2 On-chip immunosensor preparation Before surface modification, the fabricated electrodes were immersed in the piranha solution (H2SO4-H2O2 3:1 (v/v)) for 30 s and rinsed with ultrapure water. All electrodes were subsequently cleaned electrochemically by cycling them in a 0.5 M KOH (basic) solution in the range of −2 V to 0 V versus the reference electrode (Ag/AgCl), followed by cycling them in a 0.5 M H2SO4 (acidic) solution in the range of 0 to + 1.5 V, separately. Plasma oxygen was then used to treat the fabricated gold electrodes. For the formation of SAM, the cleaned electrodes were immediately incubated with recombinant protein G/thiol at 4 ºC for 48 h. The recombinant protein G/thiol monolayer was used to link the Au microfabricated gold WE and the anti-Cryptosporidium antibodies. The modified sensing electrodes were then thoroughly rinsed with ultrapure water followed by PBS (to wash away unbound recombinant protein/G/thiol) and blown dry by N2 (g). The sensor-SAM was incubated with 3 µL of 100 µg/mL specific anti-82 Cryptosporidium antibodies (IgG3) at 4 °C for 24 h. The sensors-SAM-anti-Cryptosporidium antibodies were rinsed again with PBS (to wash away unbound antibodies) and blown dry. The modified microfabricated Au electrode surfaces were then blocked by incubation with 100 mM BSA at 4 °C for 2 h. Lastly, the electrodes were washed several times with PBS to remove excess BSA [13][178] and blown dry (Figure 6.2). The prepared electrodes (Au‐thiolated protein G/Abs/BSA) were stored in dry conditions at 4 ºC. Figure 6.2 The step-wise process of coating the surface of the microfabricated Au WE (2 mm) with SAM and the immobilization of anti-Cryptosporidium antibodies onto the microfabricated Au WE. Clean Au WE were initially functionalized by incubating it with thiolated protein G for 48 h at 4 °C. The sensors-SAM were then incubated with 3 µL of 100 µg/mL specific anti-Cryptosporidium antibodies (IgG3) at 4 °C for 24 h. Lastly, the modified sensors were blocked by incubation with 100 mM BSA at 4 °C for 2 h. 6.3.3 Electrochemical measurements of SAM and antibody immobilization The modification of the on-chip fabricated electrodes with SAM and antibody immobilization was checked using different electrochemical methods (CV, SWV, and EIS). In a typical electrochemical experimental setup, the modified anti-Cryptosporidium antibody microfabricated Au electrode (2 mm) and AE were used as the working electrode and auxiliary electrode, respectively. An external Ag/AgCl/3.0 M KCl electrode was used as the reference electrodes. A salt bridge, filled with agar and 1 M KNO3 aqueous solution, was used to reduce 83 chloride ion diffusion into the electrolyte solution. Electrochemical measurements were recorded using the VersaSTAT 4 electrochemical station (Princeton Applied Research) in a three‐electrode setup. All electrochemical measurements were performed in the presence of an electrolyte consisted of a 5 mM [Fe(CN)6]3-/4- aqueous solution with 1 M NaClO4 (as the supporting electrolyte) (Figure 6.3). The CV measurements started with a 30s quiet time followed by a positive scan polarity, which swept between the potential range of −200 mV to +600 mV (against Ag/AgCl) at a scan rate of 50 mV/s. The SWV measurements were conducted in the potential range of -100 mV to 600 mV with an amplitude of 25 mV and a 2 s quiet time. The EIS measurement was performed in the frequency range of 0.1 Hz–100 kHz with a 5-mV amplitude and started with a 30 s quiet time. Figure 6.3 Characterizations of the gold surface for the development and modification of the sensing platform Cyclic Voltammetry (CV), Square Wave Voltammetry (SWV), and Electrochemical Impedance Spectroscopy (EIS). 84 6.3.4 Contact angle measurements Contact angle measurements were conducted to further verify the formation and the immobilization of the antibodies onto the electrodes. In essence, the contact angle measurement was performed (as explained previously in Section 5.3.4) to ensure the SAM and antibodies' presence on the surface based on the changes in the surface properties from hydrophilic to superhydrophilic (Figure 6.4). Figure 6.4 Schematic illustrating the experimental setup for contact angle measurement. 6.3.5 Sample preparation and EIS measurement A series of dilutions of Cryptosporidium samples were prepared in the PBS buffer. Typical dilutions were 300, 200, 100, 50, 30, 20, and 0 oocysts in a final volume of 5 µL. The modified SAM/anti-Cryptosporidium antibodies microfabricated Au WE were incubated with different concentrations of Cryptosporidium in a final volume of 5 µL for 20 min at room temperature (23 ºC). Subsequently, all electrodes were washed with PBS to remove any loosely bound and unbound Cryptosporidium from the WE surface. The samples were blown dry in air for 30 min, followed by measurements. The EIS measurements were carried out using the VersaSTAT 4 electrochemical station in the three‐electrode setup before and after the incubation with 85 different concentrations of Cryptosporidium oocysts. All measurements were repeated three times to confirm the detection limit and conduct a statistical error analysis (Figure 6.5). The blank electrodes (Au/thiolated protein G/BSA) were prepared using the same procedure, shown in Figure 6.3, excluding the antibody step. Figure 6.5 A schematic illustration of sample preparation and electrochemical measurements. In the absence of Cryptosporidium oocysts, the [Fe(CN)6]3-/4- redox probe permeates the monolayer and reach the surface of the WE. The redox probe, [Fe(CN)6]3-/4-, at the surface can be either oxidized or reduced resulting in a charge transfer (current) to or from the surface. However, the presence of Cryptosporidium oocysts in the sample results in increase in the sensor film resistance and electron charge-transfer permittivity, measured by electrochemical impedance spectroscopy. The results were evaluated using the Randles equivalent circuit in ZView software. The equivalent circuit consisted of the double-layer capacitance at the electrode surface (CDL), the solution resistance (RS), Warburg impedance (ZW) and charge transfer resistance (RCT). The real (Z′) and imaginary (Z′′) parts of the impedance were plotted in Nyquist diagrams. The charge transfer resistance occurs in the kinetically-controlled high-frequency region; whereas, the Warburg impedance was observed in the diffusion‐controlled low-frequency region [255]. The binding of Cryptosporidium oocyst to the modified Au electrode results in increasing the value of RCT, which appears as a larger semicircle in the plot. The changes in the RCT between blank 86 and after capturing Cryptosporidium were compared. The equation of % ∆𝑅𝐶𝑇 = (𝑅𝐶𝑇 (with 𝐶𝑟𝑦𝑝𝑡𝑜𝑠𝑝𝑜𝑟𝑖𝑑𝑖𝑢𝑚) − 𝑅𝐶𝑇 (blank))/𝑅𝐶𝑇 (blank) × 100 was used to calculate the relative change of the charge transfer resistance (ΔRCT (%)) as a function of the Cryptosporidium concentration (the six different cell concentrations used here range from 20 to 300 cells/5µl). 6.3.6 Calibration curve and limit of detection (LOD) To assess the applicability of the anti-Cryptosporidium antibodies-modified microfabricated sensor for detecting Cryptosporidium, the calibration curve was constructed by plotting RCT versus the number of Cryptosporidium cells. The limit of detection (LOD) was calculated as described in Section 5.3.7. 6.4 Results and discussion 6.4.1 Preparation of the biosensing layer The CV and SWV result in Figure 6.6 a) and b) show that there is a decrease in the charge transfer (and hence a decrease in the current and an increase in the impedance) after modifying the electrode surface with protein G/thiol, after immobilization of antibodies, and after blocking with BSA. The current decrease confirms the formation of SAM, immobilization of the antibodies, and the surface blocking step. This is due to the increase in the surface monolayer thickness after each modification step, which increases the separation between the oxidation and reduction peaks of the [Fe(CN)6]3-/4-. Furthermore, the current decrease confirms an increase in the film resistance and electron charge-transfer permittivity, further confirmed using EIS, as shown in Figure 6.6 (c). The data clearly show a significant increase in the RCT values form 20.42 ± 0.5 Ω (bare gold) to 3187 ± 3.2 Ω (anti-Cryptosporidium antibodies modified surface). The increase in RCT is due to the formation of an electrically inactive layer (causing 87 the isolation and consequently preventing the charge transfer to the electrode) after each modification step. Figure 6.6 Characterizations of the on-chip label-free immunosensor for the detection of Cryptosporidium. a) CV; b) SWV, and c) EIS for the bare Au electrode, added protein G/thiol, immobilized anti-Cryptosporidium antibodies, and added bovine serum albumin (BSA). The measurements were conducted using the microfabricated Au working electrode, microfabricated Au auxiliary electrode and an external Ag/AgCl reference electrode. All electrochemical measurements were started at the OCP and were performed in the presence of an electrolyte, which consisted of a 5 mM [Fe(CN)6]3-/4- aqueous solution with 1 M NaClO4 as the supporting electrolyte. The CV measurements started with a 30 s quiet time followed by a positive scan polarity, which swept between the potential range of −200 mV to +600 mV (against Ag/AgCl) at a scan rate of 50 mV/s mV/s. SWV measurements were conducted in the potential range of −100 mV to 600 mV with an amplitude of 25 mV and a 2 s quiet time. EIS measurements were performed in the frequency range of 0.1 Hz – 100 kHz with a 5 mV amplitude and started with a 30 s quiet time. 88 6.4.2 Optimization of the capture antibody concentration and incubation time During fabricating and preparation of the immunosensor, it was found the concentration of the biological recognition element (antibodies) and incubation time on the surface was of great importance for optimizing the analytical performance of the sensor. Such optimization will increase the amount of the molecular recognition element available on the surface for capturing the Cryptosporidium oocysts. For this purpose, different concentrations of antibodies (20, 30, 50, 100, 200 and 300 µ ml-1) were immobilized on the sensor surface to determine the optimum concentration required to achieve the highest performance. The RCT changes in Figure 6.7 a) confirms that immobilization capacity is a function of the dilution: the RCT was increased to a maximum value of 100 µg/mL and then slightly increased with a concentration > 100 µg/mL. The results suggested that the optimum analytical performance of the sensor happens using 100 µg/mL of antibodies (chosen here for all experiments). The incubation time of the molecular recognition element on the surface is also a crucial parameter that influences the impedance measurements (RCT), and hence the sensor's analytical performance. Figure 6.7 b) shows the influence of the incubation time on increasing the amount of antibodies available for capturing the target pathogen. It is clear that RCT increases as the antibody’s incubation time increases, reaching a plateau after 24 h. Thus, 24 h was used in all subsequent experiments as the optimal incubation time of the antibodies. 89 Figure 6.7 Optimization of the antibody’s concentrations and binding time. a) RCT value for different concentrations of anti-Cryptosporidium antibodies and b) RCT values for the binding time of anti-Cryptosporidium antibodies on the sensor surface. 6.4.3 Surface characterization To further confirm the surface's functionalization by SAM and antibodies, contact angle measurement was employed (see Figure 6.4). Contact angle measurements, measuring the advancing and receding contact angles, reveal the surface's level of wettability. In essence, the formation of SAM and the immobilization of antibodies results in a significant increase in the surface's wettability [13][178]. The measured advancing and receding contact angles for the bare gold were 88º and 42º (±1°), respectively, and 53º and 3º for the SAM-antibodies modified surface. The decrease in the advancing and receding contact angle values confirms that the modified surface is extremely hydrophilic compared to the bare gold surface. Furthermore, the results confirm the formation of SAM-antibodies film on the Au surface. 6.4.4 Electrochemical detection of Cryptosporidium oocysts After confirming surface modification through CV, SWV, and EIS, the interaction of the anti-Cryptosporidium antibodies modified sensor with Cryptosporidium was studied, and the binding of Cryptosporidium oocysts was monitored electrochemically in the presence of 5 mM [Fe(CN)6]3-/4- (as described in Sections 6.3.5). Briefly, EIS measurements were obtained first 90 for the anti-Cryptosporidium antibodies modified microfabricated Au electrode (referred to as blank), and then after incubating the sensor with different concentrations of Cryptosporidium oocysts (referred to as test). The measured values are correlated to the number of the Cryptosporidium oocysts bound to the modified anti-Cryptosporidium antibodies microfabricated Au sensing platform. EIS measurements were acquired for 5 different cell densities (from 20 to 300 cells/5µl). The real (Z′) and imaginary (Z′′) parts of impedance were plotted in Nyquist diagrams (Figure 6.8 a)) and analyzed by the Randles’ equivalent circuit, which best models the EIS spectra using the ZView software. In essence, EIS measures resistance, which increases due to a decrease in the charge transfer and vice versa. Figure 6.8 a) shows a dramatic increase in the impedance and conversely a decrease in the charge transfer due to the binding of Cryptosporidium oocysts to the immobilized anti-Cryptosporidium antibodies. Such changes are directly related to the Cryptosporidium concentrations; in a very high concentration of Cryptosporidium oocysts (> 200 oocysts); however, the RCT increase is not as significant as other concentrations. This could be due to the saturation of the sensor. The average values, statistical error, and the lowest detection limit were also calculated. The maximum change in the response due to the presence of different cell densities was observed. The maximum value of the relative change in RCT for each number of the captured oocysts was used to generate the calibration curve (Figure 6.8 b)). The curve shows a linear range of up to 200 cells/5µl and a LOD of approximately 20 cells/5µl. The results confirm the effectiveness of the on-chip microfabricated sensing platform as a label-free, cost-effective and sensitive sensing tool. To assess the extent of non‐specific binding, a blank sensor was modified similarly to the test sensor (as explained in Section 6.3.2, excluding the step taken for the antibodies 91 immobilization). The real (Z′) and imaginary (Z′′) parts of impedance were plotted in Nyquist diagrams (Figure 6.8 b)) and analyzed by the Randles’ equivalent circuit. Figure 6.8 d) shows a negligible binding of Cryptosporidium, e.g., the specific binding of Cryptosporidium oocyst in the concentration of 20 cells/5µl showed a much higher signal than that obtained for the same concertation in the absence of anti-Cryptosporidium particular antibodies. This proves that the possibility of non-specific binding is not significant. Hence, the developed sensor has a high specificity and selectivity towards Cryptosporidium. Figure 6.8 Responses of microfabricated immunosensor to Cryptosporidium oocysts. a) and c) EIS measurements and calibration curve for different concentration of oocysts (20, 30, 50, 100, 200, and 300 cells/5µl) in the presence of antibodies; b) and d) EIS measurements and calibration curve in the absence of antibodies for the same number of oocysts. 92 6.4.5 Selectivity (cross-reactivity) of the sensing platform For testing the cross-reactivity, the modified sensor with anti-Cryptosporidium antibodies was incubated with four different concentrations of E. coli (1000, 2000, 3000, and 4000 CFU.ml-1). As shown in Figure 6.9 a), there is a negligible increase in the impedance and the ∆RCT (Figure 6.9 b)). This increase confirms that there is low interaction with E. coli (which could be due to non-specific adsorption) and the high selectivity of the sensor towards Cryptosporidium. Figure 6.9 Responses of microfabricated immunosensor to Cryptosporidium oocysts. a) and b) EIS measurements and calibration curve for different concentration of E. Coli (1000, 2000, 3000, and 4000 CFU.ml-1). 6.5 Summary A simple, easy to fabricate, and cost-effective label-free on-chip-based EIS-based immunosensor was developed for the sensitive and rapid detection of Cryptosporidium. The specific anti-Cryptosporidium-antibodies were immobilized on the microfabricated sensor via thiolated protein G. The EIS measurements were conducted in the presence of the antibodies (test) and the absence of antibodies (blank). The detection limits, sensitivity and selectivity of the sensing platform were obtained. The results showed a linear detection range for Cryptosporidium concentrations of 20 cells/5µl to 200 cells/µl with a detection limit of 20 cells/5µl. The specificity and selectivity tests proved the high selectivity of the developed 93 sensing platform toward Cryptosporidium, demonstrating the applicability of the developed microfabricated sensor for detecting Cryptosporidium. Compared to the capacitive sensor developed and described in Chapter 5, this sensor has improved the detection limit by 50 %. Furthermore, the sensor developed here does not need expensive substrates or modifying the sensor with nanomaterials as reported in [132] to enhance sensitivity. Moreover, the developed sensor in this research has a great potential to be integrated into a device with the capability of capturing, separating and concentrating Cryptosporidium. Such a device has been previously developed in the past [134]. This integration will reduce the cost, enhance portability, allow for real-time analysis and eliminate the need for trained technicians and specialized laboratories. In future work, the developed sensor will be tested with real water samples to confirm the applicability of the sensor to replace the fluorescence-based microscopy detection part in the EPA 1623 method. Furthermore, these sensing platforms can be extended for the detection of other analytes for environmental and biomedical applications. 94 Chapter 7: Portable On-Chip-based Colorimetric Biosensor7 7.1 Overview This chapter reports the development of a simple, cost-effective, easy to fabricate, sensitive and portable on-chip-based colorimetric biosensor for the direct detection of Cryptosporidium RNA. The colorimetric sensing mechanism is based on two sets of modified AuNPs with specific oligonucleotides complementary to adjacent sequences on Cryptosporidium RNA. Upon adding Cryptosporidium RNA into the modified AuNPs solution, hybridization between two complementary sets of oligonucleotides immobilized on the AuNPs and the RNA occurs, forming a network of oligonucleotide-AuNPs. The aggregation of AuNPs results in a colorimetric shift due to the change in LSPR due to a reduction in the distance between the AuNPs and increasing the size of the AuNPs. The outcome is a visible color change of the AuNPs from red to blue, observed and recorded within 5 minutes (Figure 7.1). The colorimetric assay was performed on a fabricated chip integrated with a 3D-printed holder assembly to facilitate its applicability for on-site detection and POC applications. A smartphone in a 3D-printed holder assembly was used to measure the color change due to the presence of the target analyte (Cryptosporidium RNA). A smartphone was chosen based on its lightweight, portability, and its data transmission and on-site image analysis capability All these advantages are suitable for in-filed detection and POC applications. The sensing platform in this research has provided an exclusive sensitivity, selectivity and portability towards the detection of Cryptosporidium RNA. The proposed colorimetric 7Parts of this chapter have been submitted to ACS Nano: Luka, G., N., Najjaran, Hoorfar, M., Portable on-chip colorimetric biosensing platform integrated with a smartphone in a 3D-printed holder assembly for the label/PCR-free detection of Cryptosporidium RNA based on gold nanoparticles, ACS Nano, 2020. 95 sensing platform was employed to detect Cryptosporidium RNA in the range of 5 µM to 300 µM. A detection limit of 5 µM of Cryptosporidium RNA spiked in water samples can be detected via the developed on-chip colorimetric assay in 30 minutes, reducing the turnaround detection time following RNA extraction from contaminated sites. The results generated by the developed sensing platform were in good agreement with those generated by the conventional spectroscopic techniques. All these confirm the developed sensing platform combined with the 3D printed holder assembly integrated with a smartphone could directly detect different concentrations of Cryptosporidium RNA in a spiked sample without DNA amplification. Figure 7.1 Schematic illustration of label/PCR-free sensing platform. 7.2 Materials Gold nanoparticles (AuNPs) with a diameter of 20 nm were purchased from Cytodiagnostics (Canada). According to the manufacturer, AuNPs were synthesized by citrate reduction, and thus they have negatively charged citrate ions adsorbed on their surface, which enhance their stabilization via electrostatic repulsion. Two sets of thiolated-oligonucleotides capturing probes 96 and Cryptosporidium RNA were designed and purchased from Integrated DNA Technologies (IDT, Canada) (Table 7.1). Sodium phosphate monobasic monohydrate, sodium phosphate dibasic, DL-Dithiothreitol (DTT), sodium azide (NaN3), dextran sulphate, formamide, sodium chloride and sodium dodecyl sulphate were purchased from Sigma-Aldrich (Canada). Illustra NAP 5 columns were purchased from VWR (Canada). All other reagents and solvents were of the analytical grade and purchased from Sigma Aldrich. Ultrapure water was used throughout the experiments. 7.3 Experimental methods 7.3.1 Sensor fabrication The fabrication of the chip-based colorimetric biosensor was performed as previously described in Section 4.2.3. All glass slides were cleaned using piranha solution followed by an oxygen plasma treatment for 10 min. Chromium and gold layers with thicknesses of 50 nm and 250 nm, respectively, were sputtered (Angstrom Engineering) on the cleaned glass slides in an argon atmosphere. Sensing transparent circular zones with a diameter of 3 mm were patterned using the standard lithography process, as described in [178]. A lift-off procedure was followed as described in Section 4.2.5 to create hydrophobic circular barriers around the detection zone. This process has led to circular hydrophobic barriers around the detection spot to control the sample in the sensing area. 7.3.2 Holder assembly fabrication The 3D portable holder assembly integrated with a smartphone is shown in Figure 7.2. A smartphone camera is used to collect image samples. An open-source application (Color Grab, Loomatix) from the android store is used to analyze the collected images and display the analyte concentration to the user. A battery built in the smartphone is used to supply power. This allows 97 the smartphone to be charged using an inexpensive micro-USB connector, enabling the smartphone to operate on the battery power for several hours [194]. The smartphone touch screen is used to control the detection process. Furthermore, the smartphone's built-in Wi-Fi chip allows a 2.4 GHz wireless network to transfer data for further analysis on an external computer or external storage when required. A three-dimensional-printed holder assembly was designed and fabricated as described in Section 4.2.6 to house all the chips and the smartphone inside a compact unit. Figure 7.2 The fabricated chip and 3D portable holder assembly integrated with a smartphone. a) the major components of the detection system and b) assembled detection system. 7.3.3 Conjugation of the thiolated oligonucleotide to gold nanoparticles The conjugation of the two sets of thiolated oligonucleotides (Table 7.1) to gold nanoparticles was carried out via two parts involving: 1) the reduction of the two sets of thiolated oligonucleotides and 2) the conjugation of the reduced oligonucleotides to the gold nanoparticles. In the first part, the two sets of lyophilized oligonucleotides were dissolved to a final concentration of 500 µM in ultrapure water. A 50-µl dissolved oligonucleotide was then mixed with 450 µl of 0.15 M sodium phosphate buffer supplemented with 0.1 M DTT. 98 Following this step, the mixture was incubated at room temperature for 2 hours to reduce the oligonucleotides. Subsequently, a NAP 5 column operated in water was used to separate the reduced oligonucleotides from trityl-SH and DTT. The final elute from the NAP 5 column was 1 ml in water with an approximate concentration of 25 μM. The final concentration of the elute was measured with UV-VIS spectroscopy by measuring the absorbance at 260 nm. In the second part, a 1 ml solution of unmodified gold nanoparticles was transferred to a 1.5 ml Eppendorf tube. Then, 24 µl of the reduced thiolated oligonucleotides (25 μM in water, equivalent to 0.025 nmol/μl) was added to the gold nanoparticles and incubated for 1 hour at room temperature. Following this step, 0.1 M sodium phosphate buffer (pH 7.0) was added to the mixture in a stepwise manner (in 10 μl increment with 5 minutes incubation time in between) until a final concentration of 10 mM sodium phosphate (pH 7.0) was achieved. Subsequently, 1M NaCl was added in a similar stepwise manner until a final concentration of 100 mM was reached. The mixture was then incubated at 4C for 16 hours. The above steps minimize the aggregation of the gold nanoparticles by immobilizing the thiolated oligonucleotides properly onto the gold nanoparticles surface, which improves the overall stability and quality of the conjugation process. The conjugated gold nanoparticles were then centrifuged at 17,000 × g for 30 minutes to pellet the oligonucleotide gold conjugate. Lastly, the supernatant was removed, and the oligonucleotide gold conjugate was resuspended in 200 μl of 10 mM sodium phosphate buffer, 100 mM NaCl and 0.01% (w/v) NaN3. 99 Table 7.1 Sequences of the oligonucleotide probes with a specific Cryptosporidium 42-nt RNA and non-complementary RNA used in this study. Oligonucleotide Sequence Oligonucleotide probe A 5’/5ThioMC6-D/TTTTTTTTTTA15ATTGTTATTTCTTGTCACTAC-3’ Oligonucleotide probe B 5’/5ThioMC6-D/T9TA14ATACAAAACCAAAAAGTCCTGT-3’ Cryptosporidium 42-nt RNA Target 5′-GUAGUGACAAGAAAUAACAAUACAGGACUUUUUGGUUUUGUA-3′ Non- complementary RNA Target 5′-CAUCACUGUUCUUUAUUGUUAUGUCCUGAAAAACCAAAACAU-3′ 7.3.4 Sample preparation and colorimetric measurements For the colorimetric detection of Cryptosporidium RNA using the developed sensing platform, 3 µl of AuNPs modified with probe A (25 μM) and 3 µl of AuNPs modified with probe B (25 μM) were first added to the sensing zone on the fabricated chip. Next, 5 µl of different concentrations of Cryptosporidium RNA (5, 25, 60, 100, 200 and 300 µM) and 4 µl of hybridization buffer (a stock solution of 16% dextran sulphate and 20% formamide) were added to the modified AuNPs. The chip was then incubated at 70 ºC for 15 min, followed by incubation at 37 ºC for 20 min. Lastly, the chip was inserted in the holder assembly at room temperature. Cryptosporidium RNA in the sample results in a detectable color change after 5 min in the detection zone patterned on a chip, which can be visualized by the naked eye. For quantitative analysis, the color change is captured by a smartphone camera (Samsung Galaxy Note 10) integrated with a 3D-printed holder assembly under controlled light conditions. A region of interest (ROI) in the captured image was selected and fixed for consistency measurements. An open-source application (Color Grab, Loomatix) from the android application store was installed on the smartphone and used for converting the ROI from the red, green, blue (RGB) color space to the Hue, Saturation, Value (HSV) color space. This previous step was performed to achieve a linear relationship between the color components and the color change. Image analysis was performed by calculating the saturation value of the recorded images' HSV coordinates and correlated to the sample's analyte concentration. 100 For the UV-vis measurements, the same concentrations were used in a 1 ml volume sample. All measurements were performed before and after the incubation with different Cryptosporidium RNA concentrations and were repeated three times to confirm the detection limit and conduct a statistical error analysis. 7.3.5 Calibration curve, limit of detection (LOD), and selectivity To assess the developed on-chip colorimetric sensor's applicability, a calibration curve was obtained by plotting A630/A523 nm absorption ratio and Saturation values of the HSV coordinates of the recorded images versus the RNA concentration using the UV-vis and the smartphone, respectively. The limit of detection (LOD) was calculated as described in Section 5.3.7. To evaluate in terms of cross-reactivity, the developed sensing platform's selectivity, aqueous solutions spiked with a non-complementary RNA consisting of 42 nucleotides (random sequence RNA) were tested and used as the control (see Table 7.1). 7.4 Results and discussion 7.4.1 Evaluation of AuNPs conjugation using UV-vis spectroscopy To confirm the conjugation of AuNPs with the oligonucleotides, UV-vis spectra were recorded for of AuNPs colloid (20 nm) before and after conjugation (Figure 7.3). Gold nanoparticles colloid (with a diameter of 20 nm) exhibits an extinction maximum at wavelength 520 (Figure 7.3). After conjugating the AuNPs with oligonucleotides, a slight shift of 3 nm in the absorption wavelength was observed. This slight shift confirms the conjugation of AuNPs. 101 Figure 7.3 UV-vis absorption spectra of gold nanoparticles before and after conjugation. 7.4.2 Detection of Cryptosporidium RNA using UV-vis spectroscopy The colorimetric assay was first tested with different concentrations of Cryptosporidium RNA, as described in Section 7.3.4, and verified using UV-vis spectroscopy. The UV-vis spectroscopy results in Figure 7.4 a) show that the presence of different analyte concentrations in the sample resulting in a decrease in the absorption peak at 523 nm. In comparison, there is a gradual increase in the absorption peak at 630 nm. Hence, the A630/A523 ratio was used in this study to investigate the aggregation state of the modified AuNPs. The results in Figure 7.4 b) show that increasing the target analyte's concentration from 5 µM to 300 µM has led to a gradual increase in A630/A523 ratio, and a noticeable color change of AuNPs from red to blue was observed. Furthermore, the color change can be visualized at high concentrations of the target analyte (> 25 µM) (see the inset in Figure 7.4 a)). The A630/A523 ratio plotted versus the RNA 102 concentration in the sample exhibited an excellent linear range between 5 µM and 100 µM with a detection limit of approximately 5 µM. The detection limit (calculated as described in Section 5.3.7) was determined from the calibration curve created by plotting the 630/520 nm absorption ratio, demonstrating the degree of AuNPs aggregation versus the analyte concertation. Although the apparent detection limit visualized by the naked eye was 25 µM (see the inset in Figure 67.4 a)), quantitative analysis needs to be performed relative to the blank color. Otherwise, misanalysing under different environmental conditions can occur. Figure 7.4 Detection of Cryptosporidium RNA using UV-visible spectroscopy. a) UV-vis absorbance spectra of conjugated AuNPs after adding Cryptosporidium RNA at different concentrations (5, 25, 60, 100, 200 and 300 µM). Inset Images shows the color of the modified AuNPs in the presence of different concentrations of Cryptosporidium RNA and b) calibration curve plot of the A630/A523 ratio versus different concentrations of Cryptosporidium RNA. Inset: plot shows the linear range for different concentrations of Cryptosporidium RNA. 7.4.3 Detection of Cryptosporidium RNA using on-chip biosensor The sensor response from different concentrations of Cryptosporidium RNA is presented in Figure 7.5. The colorimetric results show that increasing the analyte concentration from 5 µM to 300 µM results in a decrease in the saturation value (S) of the HSV coordinates of the recorded images and a distinct color change (see the inset in Figure 7.5 a)). We found that the S value is more reliable and accurate than the H and V values for monitoring the sample's RNA 103 concentration. The sensor showed a wide linear response between 5 µM and 100 µM with a detection limit of approximately 5 µM (see Figure 7.5 b)). The results obtained by the developed sensing platform show a good agreement with the results obtained by the conventional method (UV-vis spectroscopy). However, the sensing platform results are more easily differentiable (see the inset in Figure 7.5 a)). Additionally, the sample volume used with the on-chip method was 15 µl, making the proposed sensing platform safer and less expensive than the UV-vis spectroscopy that requires a larger sample volume (e.g., 1 ml). Furthermore, small volumes result in a shorter analysis. In essence, the total analysis time can be reduced from hours to 30 min using the developed sensing platform. Moreover, multiple samples from different contaminated sites can be analyzed at once, which will further shorten the analysis time and result in a large-scale screening process. Figure 7.5 Detection of Cryptosporidium RNA using the developed sensing platform. a) calibration curve plot of the saturation value (S) of the HSV coordinates of the recorded images versus different concentrations of Cryptosporidium RNA and b) the linear range for different concentrations of Cryptosporidium RNA. 7.4.4 Selectivity of colorimetric assay towards Cryptosporisum RNA To investigate the selectivity of the developed colorimetric assay, the degree of the color change of the modified AuNPs towards the analyte of interest and a non-complementary sequence RNA 104 consists of 42 nucleotides (random sequence RNA) were recorded by both UV-vis spectroscopy (Figure 7.6 a)) and the developed sensing platform (Figure 7.6 b)). In this experiment, the concertation of the target analyte and the random sequence was 200 µM. A high concentration of Cryptosporidium RNA and the random sequence RNA was chosen to investigate the effect of other random bacterial sequences RNA in high-contaminated samples. The results show that a high concentration of a random sequence RNA does not induce any noticeable color change (minor changes of A630/A523 ratio in Figure 7.6 a)) to the modified AuNPs. In contrast, with the addition of 200 µM of Cryptosporidium RNA, a significant increase of A630/A523 was observed. In the case of the developed sensing platform (see Figure 7.6 b))., a considerable decrease in the S value from 175 (analyte of interest) to 53 (random RNA sequence), which approximately equal to the S value of the blank. The results confirm the high selectivity of the developed colorimetric assay and the sensing platform to detect Cryptosporidium RNA in complex samples. Figure 7.6 Selectivity results were obtained by a) UV-vis absorption spectra and b) the developed sensing platform in the presence of the analyte of interest and the random sequence RNA. All measurements were performed using the same conditions and a final concentration of 200 µM of the target analyte and the random sequence. 105 7.4.5 Data validation Additional experiments were performed to investigate the repeatability, practicality, accuracy and reliability of the developed sensing platform. Five samples with different concentrations (5, 10, 15, 20 and 25 µM) were prepared as described in Section 7.3.4. All measurements were performed under the same conditions as those performed for the calibration measurements. Table 7.2 summarizes a comparison between the prepared sample concentrations and those obtained by the developed sensing platform. Table 7.2 Application of the developed sensing platform for the detection of Cryptosporidium RNA samples spiked with different concentrations of Cryptosporidium RNA. RNA concentration in the sample (µM) RNA concentration measured by the developed sensing platform (µM) Error (%) 5.0 6.0 20.0 10.0 10.3 3.0 15.0 15.5 3.3 20.0 20.7 3.5 25.0 24.3 2.8 In general, the obtained results are in good agreement, with maximum errors occurring at the lowest concentration (5 µM). For a better demonstration of the results, the values listed in Table 7.2 were plotted (see Figure 7.7). The results are shown in this figure prove that the developed sensing platform can accurately and reliably be used to detect Cryptosporidium's rapid and on-site detection. 106 Figure 7.7 A linear relationship between the concentrations of Cryptosporidium RNA in the prepared samples and those obtained by the developed sensing platform. 7.5 Summary A cost-effective and simple colorimetric assay combining the chemical and photoelectric properties of AuNPs on a chip with 3D-printed holder assembly integrated with a smartphone was developed for the sensitive, label-free and on-site detection of Cryptosporidium specific RNA. This sensing platform eliminated the need for PCR amplification, well-trained personnel and bulky instruments. Two sets of AuNPs modified with oligonucleotides capturing probes specific and complementary to adjacent sequences on Cryptosporidium rRNA were used to enhance the specificity and selectivity towards the analyte of interest to reduce the inference from undesired substances in the sample. After assessing the colorimetric assay using UV-vis spectroscopy, the assay was tested using the developed sensing platform to confirm the applicability, reproducibility and reliability of the sensing platform for the quantitative detection of Cryptosporidium. Under optimized conditions, results obtained from the UV-vis spectroscopy and the developed sensing platform showed a linear detection range between 5 and 100 µM with a 107 detection limit of 5 µM. The detection limit obtained by the developed sensing platform was similar to the detection limit obtained by UV-vis spectroscopy without using bulky and advanced laboratory equipment. The total turnaround analysis time using the developed sensing platform was reduced from hours to 30 mins. Even when a high concentration of random sequences RNA in spiked samples were tested with the developed sensing assay, the developed sensing platform showed high selectivity and specificity towards Cryptosporidium RNA. Furthermore, the sample volume used for measurements using the developed sensing platform was 15 µl, which reduces the sample volume and cost required for a single measurement required for analysis compared to the conventional method. In addition to the direct measurement of Cryptosporidium RNA without amplification, this sensing platform can enable a simple way to screen multiple samples for Cryptosporidium from different contaminated sites and quantitatively determine its concentration in the sample. 108 Chapter 8: Conclusions and Future Work 8.1 Summary of results EPA 1623 as the currently approved method for the detection of Cryptosporidium relied on immunomagnetic separation, expensive labels and equipments for data analysis. This technique is expensive, time-consuming and unsuitable for on-site detection. Only a handful exists for the on-site detection of Cryptosporidium oocysts in water samples, which is very critical in the fast-decision-making process and in preventing future outbreaks. A few on-chip-based biosensors for detecting Cryptosporidium oocysts and DNA have been developed, comprehending the importance of the two techniques in developing sensing platforms that can be used for on-site detection. Despite the innovative approach of integrating those two techniques in the detection of Cryptosporidium, most of the developed on-chip sensors for the detection of Cryptosporidium suffer from low sensitivity, selectivity and detection limit and the involvement of labelling and complicated fabrication steps. Such drawbacks hinder their applicability for real-time and on-site detection. Hence, new on-chip platforms with high sensitivity, selectivity and suitable for on-site detection are required. The current detection methods for detecting Cryptosporidium (such as the EPA-1623.1 method) require large sample volumes, immuno-magnetic separation, long processing times, highly trained personnel, expensive labels and expensive laboratory equipment for data interpretation. These drawbacks hinder the range of applicability of the methods for on-site detection and fast-decision making. In this thesis, several on-chip-based biosensors (capacitive, electrochemical and colorimetric) have been developed to overcome the limitations of the current detection methods of Cryptosporidium. For the on-chip-based colorimetric biosensor, a novel 3D portable holder assembly integrated with a smartphone was developed and used to 109 measure the colorimetric signal. The sensing platforms developed in this research have been thoroughly characterized and explained in Chapters 5 to 7. The successful application of these sensors and their selectivity, sensitivity and detection limit have been explained in Chapters 5 to 7. A summary concluding each of the sensors developed in this thesis is explained here. 8.1.1 On-chip-based capacitive biosensor In this part of the thesis, an on-chip-based interdigitated biosensor was designed, fabricated and developed for the label-free detection of Cryptosporidium oocysts. The sensing electrodes were modified with specific anti-Cryptosporidium antibodies to enhance the selectivity of the sensor. Modification of the modified sensor was tested using various techniques such as electrochemical, capacitance and contact angle measurements. The detection was performed based on measuring the relative change in the capacitive/dielectric properties due to the formation of the Cryptosporidium-antibody complex. A fluorescein isothiocyanate (FITC) immunofluorescence assay was used to confirm the number of captured oocysts on the sensor surface. The results showed that Cryptosporidium oocysts with different concentrations could be distinguished and detected using the developed sensor. The biosensor showed a linear detection range between 15 and 153 cells/mm2 and a detection limit of 40 cells/mm2. Compared to other capacitive interdigitated reported in the literature, the sensor developed in this research showed high sensitivity without the need for complicated fabrication or expensive substrates such as nanocrystalline diamond (NCD) [131]. 8.1.2 On-chip-based electrochemical biosensor With the integration of biosensing and on-chip technologies, in this part of the thesis, a simple, easy to fabricate, and cost-effective on-chip-based electrochemical biosensor was designed, developed and used for the detection of Cryptosporidium. The sensor was modified and 110 characterized similarly to the capacitive biosensor. Electrochemical Impedance Spectroscopy (EIS) method was used to detect and quantify the captured oocysts on the sensing electrode. The detection was based on measuring the change in the sensor film resistance and electron charge transfer due to Cryptosporidium-antibody complex formation. The results showed that the sensor has high sensitivity, selectivity towards the detection of Cryptosporidium oocysts. EIS was employed to detect C. parvum in the range of 0 to 300 oocysts. The detection limit achieved was approximately 20 oocysts/5µl with a linear response between 20 and 200 oocysts/5µl. The proposed chip-based biosensor has comparable sensitivity, efficiency and limit of detection to other biosensors reported in the literature [132]. However, the sensor does not need complicated fabrication or modification with gold nanoparticles to enhance the sensitivity, as reported previously in the literature [132]. 8.1.3 On-chip-based colorimetric biosensor In this part of the thesis, a portable on-chip colorimetric biosensor was developed for the on-site and label/PCR-free detection of Cryptosporidium RNA. Two sets of AuNPs with a diameter of 20 nm were modified with oligonucleotides (probe A and probe B) specific and complementary to adjacent sequences on Cryptosporidium RNA. Oligonucleotides were designed to be complementary to the RNA sequence to enhance the sensing platform's selectivity and sensitivity. In the presence of the target RNA in the sample, hybridization between the oligonucleotides and the RNA occurs, resulting in a network of oligonucleotide-AuNPs (aggregation of AuNPs). The aggregation results in a colorimetric shift due to the change in SPR due to reducing the distance between the AuNPs and increasing the size of the AuNPs. The colorimetric assay was performed on a fabricated chip to enhance the portability of the sensing platform. A high-resolution smartphone camera was used to collect images of 111 the color change, and an open-source android application was used for image analysis. A 3D-printed holder assembly was designed and fabricated to house the chip and the smartphone in one compact unit. The colorimetric sensing platform was tested in terms of applicability, portability, reproducibility, cross-reactivity, selectivity and sensitivity. The results obtained by the developed sensing platform were in good agreement with those obtained by the conventional method (UV-vis spectroscopy). Both results obtained by the UV-vis spectroscopy and the developed sensing platform showed a linear response range between 5 and 100 µM and a detection limit of 5 µM. These results confirm that the biosensing platform is accurate and reliable for the rapid and on-site detection of Cryptosporidium RNA without the need for bulky and advanced laboratory equipment. Also, the time needed for analysis using the developed sensing platform was 5 min, from when the sample was mixed until the image was captured. The total turnaround analysis time using the sensing platform was reduced from hours to 30 mins. In terms of selectivity, the developed sensing platform showed high selectivity towards Cryptosporidium RNA even in high concentrations of random sequences RNA. Compared to the conventional method, the sample volume needed for analysis using the developed sensing platform was reduced from 1 ml to 15 µl. This has reduced the cost required for a single measurement. Finally, the results confirmed the developed sensing platform's capability for the direct detection of Cryptosporidium RNA without the need for expensive labels or DNA amplification. Moreover, this sensor is capable of identifying different species of Cryptosporidium in the sample. The designed holder assembly integrated with the smartphone enhanced the portability of the sensing platform and its accessibility in terms of cost, high power and expensive laboratory instrument requirements. Table 8.1 shows a comparison of the sensing 112 platforms developed in this research versus those previously reported in the literature for the detection of Cryptosporidium in terms of principle, detection limit, label/PCR-free analysis, fabrication, speed, sample volume, cost, reproducibility and on-site detection. Table 8.1 Comparison of the sensing platforms previously reported in the literature and those developed in this thesis for the detection of Cryptosporidium. Transducer Reported in the literature This Thesis Electrical Principle - Change in the capacitance of the transducer due to the presence of oocysts [178] - Antibody-based - The biosensor is integrated into a digital microfluidic (DMF) device - Change in the capacitance of the transducer due to the presence of oocysts [13] - Antibody-based - On-chip-based Detection limit - n/a - 40 cells/ 5 µl Label/PCR-free - Yes - Yes Fabrication - Complicated - Easy Analysis time - > 24 h - > 24 h Sample volume - 5 µl - 5 µl Chip cost - > 1 US dollar - > 1 US dollar Reproducibility - Yes - Yes On-site detection - Yes - Yes Electrochemical Principle - Square wave voltammetry (change in current due to the presence of oocysts) [132] - Aptamer-based - Gold nanoparticles modified screen-printed electrode as the sensing platform - Electrochemical impedance spectroscopy (change in resistance-charge transfer due to the presence of oocysts) - Antibody-based - On-chip-based Detection limit - 4 cells / 1 µl - 4 cells / 4 µl Label/PCR-free - No - Yes Fabrication - Complicated (use of gold nanoparticles to enhance sensitivity) - Easy fabrication (no modification) Analysis time - n/a - > 24 h Sample volume - 100 µl - 5 µl Chip cost - n/a (use of expensive and non-commercial aptamers) - > 1 US dollar Reproducibility - n/a - Yes On-site detection - No - Yes Colorimetric Principle - Competitive lateral flow (chip) based bioassay based on dye-entrapping liposomal nanovesicle [185] - Using complementary oligonucleotides to a specific - On-chip-based on gold DNA-RNA hybridization and gold nanoparticles aggregations - A 3D portable holder assembly integrated with a smartphone is developed to 113 Cryptosporidium DNA sequence measure the colorimetric signal Detection limit - 1 fM - 5 µM Label/PCR-free - No (require labels and DNA amplification to enhance sensitivity) - Yes (no labels or DNA amplification) Fabrication - Complicated - Easy Analysis time - 30 min - 30 min Sample volume - n/a - 5 µl Chip cost - n/a - > 1 US dollar Reproducibility - n/a - Yes On-site detection - No - Yes 8.2 Limitations of the developed sensing platforms 8.2.1 On-chip-based capacitive and electrochemical sensors A limitation of the capacitive and electrochemical sensing platforms developed in this thesis is the scarcity of non-specific adsorption. To overcome this challenge, other non-ionic polymers or blocking agents should be tried. 8.2.2 On-chip-based colorimetric sensor The detection limit achieved was high (5 µM), compared to the one reported in [185], which is a limitation of this sensing platform. However, unlike our sensing platform, in [185] DNA amplification was conducted to enhance the sensitivity. This has resulted in increasing the cost, analysis time and hindered portability and thus on-site detection. In our sensing platform DNA oligonucleotides were used and the capture probe for the detection of RNA. This has led to increasing the number of base-pairs mismatch between DNA and RNA and thus incomplete hybridization, which results in less sensitivity and detection limit. The use of RNA oligoprobes (capture probe) is expected to enhance the detection limit and sensitivity by 10 orders of magnitude. This is due to the high degree of stability of RNA/RNA hybrid duplexes compared to RNA/DNA ones [256]. 114 8.3 Contributions to the field The detection of Cryptosporidium in the water sample is significantly important due to the challenges caused by this pathogen to the water sectors, public health and veterinary. The existing techniques and sensing platforms for the detection of Cryptosporidium are expensive, time-consuming, require expensive labels, and involve complicated fabrication processes. Furthermore, they are not suitable for the label/PCR-free and on-site detection of Cryptosporidium, considering them as a low throughput detection technique. Therefore, a simple, automated, cost-effective, label/PCR-free, and portable sensing platform for the on-site detection of Cryptosporidium, are still unavailable., Each of the developed sensing platforms in this research intends to overcome those challenges. Furthermore, they can significantly contribute to on-chip biosensing to detect Cryptosporidium by providing a significant step towards developing fast, real-time, and inexpensive sensing tools for early warning and immediate on-site detection of C. parvum oocysts in water samples. The following points summarize the contribution, potential features and novelty of the developed sensing platforms: - The sensing platforms developed in this thesis are a high-throughput model that can be integrated into a device with the capability of capturing, separating and concentrating Cryptosporidium [134] for the rapid and on-site detection of Cryptosporidium oocysts. - Compared to the currently approved methods for the detection of Cryptosporidium, the sensing platforms developed in this research have improved the portability, selectivity, sensitivity and cost-effectiveness. - The fabrication process of the sensing platforms developed in this research was very easy and simple compared to those developed preciously and reported in the literature. 115 - The on-chip colorimetric-based biosensor developed in this research was used for the direct detection of Cryptosporidium-specific RNA as well as providing information about its species. The detection was carried out without the need for a complicated DNA amplification process. This is a great step towards the direct detection and label/PCR-free detection of Cryptosporidium. A 3D holder assembly integrated with a smartphone was fabricated and used for the colorimetric measurements. The low power rating of the developed on-chip colorimetric biosensor and using a smartphone for image analysis has replaced the need for expensive benchtop instruments and enhanced portability of the sensing platform. Moreover, it has enhanced its applicability for on-site detection. - All sensing platforms developed in this thesis have a great potential to be used for on-site detection of Cryptosporidium. - The high sensitivity, selectivity and reproducibility of the developed sensing platforms suggest that these platforms can be successfully modified and used for the detection of other analytes for environmental and biomedical applications. - The developed sensing platforms showed high sensitivity and selectivity, which increase their applicability for the detection of a variety of other analytes and biomarkers. - This thesis's main novelty is the integration of a series of technologies, allowing for a fully functional on-chip sensing device suitable for POC applications and on-site detection. 8.4 Future work The sensing platforms developed in this thesis are highly versatile, portable, specific and selective for the detection of Cryptosporidium and can be used for the detection of many other 116 analytes. These sensors were devolved based on the detection of Cryptosporidium in spiked samples. This established the selectivity, cross-reactivity, sensitivity and detection limit of the platforms. As the future direction, it is suggested to test the sensors with real samples to verify the reliability of the sensing platform and their ease of handling and operation by users. Also, the effect of disinfectants, such as chlorine, on the developed sensing platforms' performance should be evaluated. Another suggestion is to determine the optimum operating conditions (such as pH and temperature) for higher performance of the sensing platforms. This task will determine the applicability of the developed sensing platforms in different media and at different temperatures. Also, to reduce the analyte's nonspecific adsorption and enhance reproducibility, other blocking agents should be tested instead of BSA. 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Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
On-chip-based biosensors and point-of-care devices for label/PCR-free detection of Cryptosporidium
Text