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A BIOSENSOR APPROACH FOR THE DETECTION OF ACTIVE VIRUS USING FTIR SPECTROSCOPY AND CELL CULTURE by Felipe T. Lee Montiel ______________________ A Dissertation Submitted to the Faculty of the DEPARTMENT OF AGRICULTURAL AND BIOSYSTEMS ENGINEERING In Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY In the Graduate College THE UNIVERSITY OF ARIZONA 2011 2 THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Felipe T. Lee Montiel entitled A BIOSENSOR APPROACH FOR THE DETECTION OF ACTIVE VIRUS USING FTIR SPECTROSCOPY AND CELL CULTURE and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy. _______________________________________________________________________ Date: 07/29/2011 Dr. Mark Riley _______________________________________________________________________ Date: 07/29/2011 Dr. Jeong-Yeol Yoon _______________________________________________________________________ Date: 07/29/2011 Dr. Lingling An _______________________________________________________________________ Date: 07/29/2011 Dr. Kelly Reynolds _______________________________________________________________________ Date: 07/29/2011 Dr. Joel Cuello Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement. ________________________________________________ Date: 07/29/2011 Dissertation Director: Dr. Mark Riley 3 STATEMENT BY AUTHOR This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author. Signed: Felipe T. Lee Montiel 4 ACKNOWLEDGEMENTS I would like to express my sincere gratitude to my supervisor Dr. Mark Riley for his mentoring and guidance. He has encouraged me to reach my full potential as a research scientist and to be an independent and critical thinker. He provided me with many academic and vocational opportunities on campus to widen my horizons. I also acknowledge my committee members, Dr. Jeong-Yeol Yoon, Dr. Lingling An, Dr. Kelly Reynolds and Dr. Joel Cuello, for their advice and time. These people have supported me through the transition from a new student with a new project, to a confident scientist. I would also like to thank everyone who helped with experiments, talking through ideas, my classmates and colleagues for making the campus an enjoyable and intellectually stimulating place to be. In particular the other members of the Riley lab, Dr. Werner Zimmt, Phat LeTran, Dominic, Brian, Jessi and Federico. Thanks to Bodil Cass for her support and encouragement. I love you Bo. Muchas gracias a toda mi familia en especial a mi madre, mi padre y mi hermano, por su amor y apoyo. Espero poder regresarles pronto un poco de lo mucho que me han dado, y también quiero decir que siento no haber estado cerca por el tiempo que he estado lejos de ellos luchando por mis sueños pero espero que les pueda agradecer mucho todo lo que he hecho por mi porque sin ellos nunca hubiera logrado esta meta. 5 DEDICATION I would like to dedicate this work to my father for his example of hard work everyday, to my mother for her love and advice. 6 TABLE OF CONTENTS LIST OF TABLES ................................................................................................... 9 LIST OF FIGURES ................................................................................................ 10 ABSTRACT ........................................................................................................... 11 INTRODUCTION .................................................................................................. 13 1. Background of Biosensors............................................................................... 13 1.1 Biosensors ................................................................................................. 13 1.2 Cell-Based Biosensors .............................................................................. 14 1.3 FTIR Spectroscopy in CBB ...................................................................... 16 1.4 Virus Detection with FTIR-CBB .............................................................. 17 2. Waterborne Viruses ......................................................................................... 19 2.1 Biology of Enterovirus Infection .............................................................. 20 2.2 Morphological and Biochemical Alterations in Enterovirus Infected Cells ......................................................................................................................... 21 2.3 Globalization, Outbreaks and Drinking Water Problems ......................... 24 2.4 Water monitoring ...................................................................................... 26 3. Current Enterovirus Detection Methods.......................................................... 28 3.1 Problems with Current Detection Methods............................................... 29 4. Research Objectives ........................................................................................ 30 4.1 Specific Aims ............................................................................................ 30 7 PRESENT STUDY ................................................................................................ 31 Overall Conclusions and Recommendations .................................................. 32 REFERENCES ....................................................................................................... 33 APPENDIX A: A NOVEL DETECTION SCHEME FOR POLIOVIRUS INFECTION USING FTIR SPECTROSCOPY AND CELL CULTURE ............ 37 Abstract ............................................................................................................... 38 Background ......................................................................................................... 40 Results ................................................................................................................. 44 Discussion ........................................................................................................... 49 Conclusions ......................................................................................................... 53 Methods ............................................................................................................... 54 References ........................................................................................................... 61 Figures ................................................................................................................. 65 Tables .................................................................................................................. 71 APPENDIX B: FTIR SPECTROSCOPY AND PCA TO DISCRIMINATE CELLS WITH DIFFERENT ENTEROVIRUS INFECTIONS .......................................... 75 Abstract ............................................................................................................... 76 Background ......................................................................................................... 78 Results ................................................................................................................. 82 Discussion ........................................................................................................... 85 Conclusions ......................................................................................................... 90 Acknowledgments ............................................................................................... 90 8 Materials and methods......................................................................................... 91 References ......................................................................................................... 109 APPENDIX C: REAL-TIME, CONTINUOUS MEASUREMENT OF CHANGES IN CELLULAR COMPONENTS FOLLOWING VIRAL INFECTION ............ 112 Abstract ............................................................................................................. 113 Introduction ....................................................................................................... 115 Results ............................................................................................................... 119 Conclusions ....................................................................................................... 127 Figures ............................................................................................................... 128 Materials and methods....................................................................................... 135 Acknowledgments ............................................................................................. 138 References ......................................................................................................... 139 9 LIST OF TABLES Table 1. Molecular and physiological changes of mammalian cells caused by poliovirus infection……………………………………………….…………………………… 23 10 LIST OF FIGURES Figure 1. Diseases contributing to the water, sanitation and hygiene related disease burden. Adapted from Safer water, better health : costs, benefits and sustainability of interventions to protect and promote health. WHO 2008 ......................................... 20 Figure 2. Attribution of disease burden from water, sanitation and hygiene to areas/sectors. Adapted from Safer water, better health : costs, benefits and sustainability of interventions to protect and promote health. WHO 2008 .............. 25 11 ABSTRACT Worldwide, 3.575 million people die each year from water-related diseases. The water and sanitation crisis claims more lives than any warfare and is predicted to be one of the biggest global challenges of this century. The rapid, accurate detection of viral pathogens from environmental samples is an ongoing and pertinent challenge in biological engineering. Currently employed methods are lacking in either efficiency or specificity. Here we explore a novel method for virus detection and concurrently use this method to learn more about the very early stages of the virus infection process. The method combines Fourier transform infrared (FTIR) spectroscopy, a method of visualizing molecules based on changes in vibration of particles, and mammalian cells as the biosensor. This method is used to detect and investigate viruses from the family picornaviridae, chosen due to their public health burden and their widespread presence in environmental samples, especially water sources. This family includes the Polioviruses, echoviruses and Coxsackieviruses, among others, many of which are human pathogens. The research outlined in this dissertation is aimed at developing and implementing a new cell-based biosensor that combines the advantages of FTIR spectroscopy with the ability of buffalo green monkey kidney (BGMK) cells to sense diverse stimuli, including infective enteroviruses. The goal of developing this biosensor is outlined in the first paper. The second paper focuses on the application of advanced statistical methods to analyze the spectra to discriminate different viral infections in BGMK cells. Finally, we designed a non-reactive metal biochamber to use with attenuated total reflectance-FTIR. 12 This allowed near-continuous acquisition of real-time spectral data for the study of biochemical changes in mammalian cells caused by poliovirus (PV1) infection. This system is capable of tracking changes in cell biochemistry in minute intervals for many hours at a time. This work demonstrates the feasibility of FTIR spectroscopy in combination with the broad sensitivity of mammalian cells for potential use in the detection of infective viruses from environmental samples. We envision this method being extended to high throughput, automated systems to screen for viruses or other toxins in drinking water systems and medical applications. Key words: Fourier transform infrared (FTIR) spectroscopy, cell-based biosensor, attenuated total reflectance, zinc selenide, buffalo green monkey kidney (BGMK) cells. 13 INTRODUCTION 1. Background of Biosensors 1.1 Biosensors Over the last two decades, biosensor research has been growing tremendously, with a special interest in environmental, medical, toxicological, drug discovery, single-cell analysis and defense applications. Biosensors are analytical devices that integrate a biological sensing element (for example, DNA, enzymes, antibodies, proteins or whole cells) that converts external stimuli into signals, combined with a transducer element (electrochemical or optical) for reading the signal and processing [1-3]. Some desirable feature of any new biosensor include the capacity for continuous monitoring, rapid response times, automation, reusability and portability (point-of-care) [1]. The concept of point-of-care (POC) testing has been well established especially for implementation in challenging environments around the world. Results can be obtained in a much shorter time when the testing facility is conveniently brought to the vicinity of the sample. There has been a major effort in recent years to develop POC molecular diagnostic devices for resource-limited regions where well-equipped centralized laboratories are not readily accessible [10]. POC testing is most useful for applications that have time-sensitive samples including medical/veterinary diagnostics, environmental monitoring, and defense services. 14 The following minimum requirements have been proposed in order for a device to be considered a real POC system: (A) minimal pretreatment to allow the direct introduction of the sample; (B) portability, that is, small in size, lightweight and autonomous power supply; (C) user-friendly such that it can be operated by people with minimal training; and (D) offer qualitative and quantitative results [10]. 1.2 Cell-Based Biosensors Cell-based biosensors (CBB) have two components: a sensitive element (cellular responses) and a transducer (in the case presented here, an FTIR spectroscopy device) that uses the cells’ remarkable ability to detect, transduce, and amplify very small changes in external stimuli. The combination of biologically relevant cellular detection interfaced with a powerful optical tool provides many advantages over previous biosensors. CBB can have broad sensitivity to pathogens and have the potential to provide rapid, sensitive, low-cost options for applications requiring the detection and monitoring of unknown agents. These characteristics have the potential to revolutionize toxicology, environmental contamination management and other medical areas [4]. The use of CBB outside of the laboratory, however, has been challenging due to many requirements including preparation of the sample, maintenance of the biological environment for the sensitive element, and integration of the electronics for data collection and analysis. Some aspects of biosensors limit their use outside of controlled laboratory conditions. For example, it is difficult to have a portable cell culture system 15 that keeps the cells alive and uncontaminated in the field. Nonetheless, long-term maintenance of cells on biochips has been achieved using enclosed fluid chips and endothelial cells in a portable bench top toxin sensor. This device monitored changes in the cells with Electric Cell-substrate Impedance Sensing following exposure to water samples [5]. An additional issue with CBB is that ‘noise’ can occur when other environmental stimuli present in the experimental system interfere with the cells in culture. The cellular responses to changes in temperature, pH or osmolarity can be difficult to distinguish from responses to the stimulus of interest. These confounding stimuli could create a false positive result if the unwanted response is similar to the response of interest, or a false negative if the response of interest is masked by the noise. The most effective CBB systems will aim to minimize this interference by providing the cells with a tightly controlled environment in the bioreactor growth chamber [6]. For example, DeBusschere et al. [7] designed a system that addresses several of these issues with an integrated silicon–polydimethylsiloxane cell-cartridge. Cell based biosensors can be of many types, such as optical, thermal, electrical or electrochemical, depending on the transducing mechanism used. In recent years, electrical biosensors are becoming a popular approach to be used in CBB. These biosensors measure current and/or voltage to sense changes in the cell condition, usually to detect cell binding. They are simple, inexpensive and generally energy efficient, giving 16 them perhaps the best potential for lab-on-a-chip adaptations. Electrical sensors can be further divided into amperometric (measuring changes in voltage or current with electrodes), or impedance (measuring electrical impedance) [8]. Impedance biosensors have been employed to measure changes in the cell cytoskeleton or cell morphology to indirectly track intracellular signaling and receptor-specific response profiles [9]. Spectroscopy using infrared light may be a good technique to use in biosensors. Infrared light is well resolved and has a long wavelength that penetrates into the cells further and with less unwanted scattering than other wavelengths. It can also be targeted towards specific regions of a sample using a microscope [12]. It has the potential to detect small changes from a small sample of cells without the need for expensive reagents. Infrared spectroscopy also has the potential to infer structural changes in particular biomolecules, even in living samples. These are detected by linking the frequency of absorbance regions to the different structural features using coordinates on the spectral region [13]. 1.3 FTIR Spectroscopy in CBB Fourier transform infrared (FTIR) spectroscopy is a nondestructive technique based on vibration spectroscopy that has shown much potential as a tool for use in microorganism identification [11]. It has the potential to inform about cellular biochemistry without destroying the sample. The spectroscopic study of biological cells and tissues is a dynamic area of research with the primary goal elucidating how viruses, bacteria and different types of cancer function and can be accurately detected. Consequently, FTIR 17 has the potential of providing qualitative understanding of the cellular biochemistry behind these illnesses. FTIR spectroscopy can be applied for pathogen detection and simultaneously to provide a spectrum of basic details about cell behavior in response to infection [4]. 1.4 Virus Detection with FTIR-CBB FTIR spectroscopy can be combined with cell culture for the development of a novel strategy for virus detection using live cells as biosensors. This approach utilizes mammalian cell culture and infrared light absorbance to monitor specific absorbance patterns produced following changes in cell components (such as lipids, proteins, nucleic acids and sugars) subsequent to the virus infection, effectively using the cells as biosensors [1, 14]. Mid-infrared spectroscopy can be used to observe changes in cell molecular machineries. It can track many aspects of the cell, such as nucleic acids, proteins, and lipids that have characteristic and well-defined IR-active vibrational modes. This type of data has broad applications in biomedicine, cellular biochemistry, disease monitoring, and observation of single-celled organisms [15-17]. It has previously been used to detect changes in cellular components following exposure to toxins [18] and to detect cell apoptosis [19]. Riley et al. [20] developed an infrared spectroscopic monitoring scheme using infrared transmissive optical fibers composed of TAS (Te, As, and Se)to detect an inhalation health hazard, Streptolysin O. Another application of FTIR has been to study herpes 18 infection kinetics, for example, Erukhimovitch et. al. [21] found that an IR peak attributed to carbohydrates disappeared completely in an exponential kinetics as a result of infection with herpes viruses HSV-I or VZV. This peak might be considered an important parameter in the study of the kinetics of the herpes virus infection and in the clinical detection of herpes virus infections [21, 22]. In addition, Vargas et. al. [23] used electrophoretic deposition on a Germanium ATR crystal to capture and detect poliovirus and a bacteriophage. The rapid and non-invasive manner of the cellular spectroscopy approach opens numerous possibilities for translation of this research to biomedical applications and biosensors. FTIR virus detection is faster than the currently standard methods required to detect viable viruses and improves on the shortcomings of the best available methods for human virus detection, which include cell culture and ICC/PCR (integrated cell culture / PCR). 19 2. Waterborne Viruses Viruses have the highest potential infectivity of all waterborne microorganisms, requiring exposure to only a small number of virions to cause infection. They often replicate in the gastrointestinal tract and spread through the feces of infected individuals in large numbers, and generally have the longest survival in the environment. They are not efficiently removed by conventional filtration and are more resistant to disinfectants than bacteria. Viruses are known to be the causative agent in 8% of drinking water outbreaks reported in recent years [24]. Waterborne viruses are particularly important due to their associative illnesses and effective transmission at low titers. Waterborne viruses include enteroviruses, hepatitis A virus (hepatitis, liver damage), noroviruses (diarrhea), astrovirus (diarrhea), adenovirus (diarrhea, respiratory disease, eye infections, heart disease), and rotavirus (diarrhea). Enteroviruses are small (approximately 30 nm), nonenveloped, single-stranded RNA viruses with an icosahedral capsid. They are among the most common viral infectious agents in humans worldwide [25, 26]. Figure 1 shows an estimated percentage of diarrheal diseases caused by enteroviruses worldwide. Serotypes of human enteroviruses have traditionally been classified into echoviruses, Coxsackieviruses group A and B, and polioviruses [25]. Clinical symptoms vary from mild febrile illness (diarrhea, fever) to more severe diseases, including meningitis, myocarditis, paralytic illness, and encephalitis. Each year, an estimated 10-15 million symptomatic enterovirus infections occur in the United States [27]. In addition, four of the human enteric viruses, 20 Coxsackievirus, echovirus, calicivirus, and adenovirus, have been included among the microorganisms of concern on the Environmental Protection Agency’s (EPA) Drinking Water Contaminant Candidate List (CCL) [28]. Diseases contributing to the water, sanitation and hygiene related disease burden )*+,,-./+0$1*2/+2/2$$ (mostly from enteroviruses) !"#$ &(#$ 3+0+,*+$ 456/275+0$5/8+6.1/$ *59/:7.52$ ;6-/,2$ '#$ %&#$ Figure 1. Diseases contributing to the water, sanitation and hygiene related disease burden. Modified from Safer water, better health: costs, benefits and sustainability of interventions to protect and promote health, adapted from W.H.O. 2008 [29]. 2.1 Biology of Enterovirus Infection Enteroviruses replicate primarily in the respiratory or gastrointestinal tracts but can advance to the circulatory system and thereby to other organs. The virus articles first bind to receptors on the cell surface. These specific receptors have been characterized previously; for example, poliovirus binds to CD155 in the immunoglobulin superfamily. 21 Echovirus 1 bind to Alpha-2-beta-1-integrin (VLA-2) and other echoviruses bind to Decay accelerating factor (DAF; CD55). Some Coxsackieviruses also use similar receptors to echoviruses, for example, Coxsackie A9 binds to Alpha-v-beta-a-integrin, some bind to DAF, whereas other Coxsackie A viruses use a rhino-virus receptor [30](and references therein). Poliovirus, Coxsackieviruses and echoviruses are cytophatic for most cell types and can cause changes in cell metabolism and appearance eventually leading to cell lysis. Changes can include detachment from the cell substrate and shrinking to a round cell morphology. Changes in cell internal structures can also be seen, including rearrangement of the cytoskeleton, clustering of the ribosomes, formation of membranous vesicles and of the nucleus into a crescent shape. Chromatin is also rearranged to localize at the edges of the nucleus. Host translation and transcription are usually inhibited during the infection process and the plasma membrane is made more permeable. 2.2 Morphological and Biochemical Alterations in Enterovirus Infected Cells The major morphological and biochemical steps in the poliovirus infection process are detailed in Table 1. Enteroviruses cause many changes to the cell nucleus. The cell’s translation is inhibited at 2 hours after infection. Subsequently, Polymerase I is inhibited within 2-3h, Pol II at 4h and pol III by 5h, greatly reducing the transcriptional ability of the cell. At 22 approximately 4 hours after infection the cell no longer synthesizes its own DNA and the chromatin is condensed into the nucleolus. Enteroviruses also affect the cell cytoskeleton components. Actin microfilaments, microtubules and intermediate filaments are considerably rearranged during infection and this affects cell shape, mobility and anchoring. Intermediate filaments accumulate at the nucleous and possibly serve as sites for viral RNA replication. The cell’s cytoplasmic membranes are also altered following infection to make many membranous vesicles. Most enterovirus infections end in cell lysis [31]. 23 Table 1. Molecular and physiological changes of mammalian cells caused by poliovirus infection. Hours post-infection 0-1 Microscopic observations Intact virus particles in the cytoplasm directly below the plasma membrane and in micropinocytotic vesicles Biochemical events Transcient increase in amino acid uptake, elevated Na+/K+ pump activity, decrease in membrane fluidity, parental capsid proteins and RNA in polysomes and lysosomes 1-2 Decrease in cell size, distortion of nucleus, wrinkling of nuclear membrane, chromosome condensation. Inhibition of host protein and RNA synthesis, release of host mRNA from cytoskeleton, beginning of viral protein synthesis, peak activity of Na+/K+ pump, exponential phase of RNA synthesis. 2-2.5 Membrane proliferation, appearance of large clusters of membrane bound polysomes in cytoplasmic periphery, structural arrangement of the cell cytoskeleton. Increase in chlorine incorporation, host cell mRNA inactive, viral protein synthesis and all newly formed viral proteins membrane associated, inactivation of Na+/K+ pump, increase in intracellular Na+, decrease in intracellular K+. 2.5-3 Several foci of vesicle formation Switch to linear phase of viral RNA synthesis: predominantly membrane associated vRNA synthesis, peak of RNA synthesis 2.5-3 Several foci of vesicle formation Switch to linear phase of viral RNA synthesis: predominantly membrane associated vRNA synthesis, peak of RNA synthesis 3-4 Perinuclear conglomeration of membrane enclosed vesicles into one large mass, nuclear extrusions, appearance of progeny virions Peak of virion assembly, increase in membrane permeability for monovalent cations, declining rates of protein and RNA synthesis. 4-6 Autophagic vacuoles, redistribution of lysosomal enzymes over mass of membrane bound vesicles Accumulation of ds RNA, depletion of metabolic precursors, release of lysosomal enzymes 6-8 Viral crystals in cell periphery lysis of host cell Modified from The Molecular Biology of Poliovirus [32]. 24 2.3 Globalization, Outbreaks and Drinking Water Problems Waterborne infectious diseases have continued to emerge due in part to (1) an increase in sensitive populations; (2) recognition of the importance of additional health effects; (3) globalization of commerce and travel including importation of food from countries with poor water quality; (4) natural evolution of microbes with increased virulence; (5) changes in drinking water treatment technologies; and (6) development of molecular methods for detection to improve identification of outbreaks and their sources. Changes in drinking water treatment technology or food supply production may also contribute, for example, an increase in the use of ultraviolet light for water treatment followed efforts to address concerns over protozoan contamination of water and chlorine resistance. However, increased reliance on UV light treatment has subsequently raised concerns over virus resistance [33, 34] . Most viruses are more environmentally stable than enteric bacteria and not cultivable. For this reason, it is completely unsafe to rely on bacteriological standards to assess the virological content of water. Nearly half of all documented waterborne outbreaks since 1971 remain uncategorized but many have characteristics consisten with virus infection patterns [35]. Waterborne disease outbreaks are considered to be primarily the result of technological failures or failure to treat the water [36]. Figure 2 shows the percentage of illness related to drinking water and sanitation in the world. Water treatment for human consumption 25 requires, at a minimum, reducing infectious viruses by 99.99% and protozoan parasites by 99.9% [24]. The US-EPA states an acceptable risk of one infection per 10,000 consumers annually. Attribution of disease burden from water, sanitation and hygiene to areas 22% 62% 16% Related behavior and other Ecosystem management Drinking-water and sanitation Figure 2. Attribution of disease burden from water, sanitation and hygiene to areas/sectors. Modified from Safer water, better health: costs, benefits and sustainability of interventions to protect and promote health, adapted from W.H.O. 2008 [29]. These studies emphasize the need to improve drinking water monitoring systems to better detect viral contamination, and the setting up of more efficient water treatments for virus inactivation once viruses are detected in the water supply. Future advances in biosensor technologies to detect the presence of human enteric viruses in a water source will be a valuable instrument in the prevention of waterborne diseases [37]. 26 2.4 Water monitoring Water monitoring is performed to protect human health against contamination of the water supply with pathogens. In the United States, the Environmental Protection Agency (EPA) and the Center for Disease Control and Prevention (CDC) regulate water quality. The EPA establishes drinking water standards including maximum contaminant levels and treatment techniques that states must meet through legislation. The EPA also issues advisories about drinking water contaminants. From 1971 to 2002 there have been 764 documented waterborne outbreaks associated with drinking water, with 8% caused by viruses. Reynolds et al. [24] estimated that 19.5 million people are estimated to be at risk from drinking water in the U.S. annually, indicating the possible health care impacts of a deficient drinking water monitoring. A recent study made by Craun et al. [38] showed that the annual number of reported drinking water outbreaks analyzed by water system type, in public water systems (community and noncommunity) was found to decrease during 1971 to 2006 . In contrast, the annual proportion of drinking water outbreaks associated with individual water distribution systems increased [38]. The reduction in total waterborne outbreaks is largely attributed to new regulations by the US-EPA, including the surface water treatment rule, and improving water treatment. In 2006, EPA signed the Ground Water Rule (GWR) with the purpose to reduce disease incidence associated with pathogens in drinking water. 27 One of the GWR requirements is compliance monitoring to ensure that treatment technology installed to treat drinking water reliably achieves at least 99.99% inactivation or removal of viruses. Although the current regulatory requirements do not appear to reduce the proportion of outbreaks associated with distribution systems [39]. Water treatment technologies need to increasingly address changes in water quality and quantity requirements along with changes in microbial populations caused by these used treatment technology, which could potentially produce a problem of detection [40]. 28 3. Current Enterovirus Detection Methods Current methods for enterovirus detection can be divided in techniques that determine only the total virus particle number (do not differentiate between the presence of inactive virus particles and viable virus) for example, immunological, nucleic acid-based, and techniques that are based in cell culture (infectivity based). The standard method for the detection of infective viruses uses mammalian cell culture. The principal drawback of this method is the requirement of complex analyses (visible monolayer cytopathic effects) that demand several days of laboratory time [41]. Polymerase chain reaction (PCR) methods for the detection of viruses have been developed, offering specificity, speed and cost advantages over cell culture methods [42, 43]. PCR methods alone do not, however, differentiate between the presence of physical (inactive) virus particles and viable (active) virus particles [44, 45]. Nonetheless, the development of molecular methods for pathogen detection and source tracking has aided in the monitoring of water supplies and identifying causative agents in outbreak situations. In-Situ Hybridization (ISH) can be used to detect nucleic acids inside living cells [46]. Yeh et al. 2009 [47] reviews some advantages of fluorescent probes in conjunction with fluorescence microscopy. This techniqu, allows the study of dynamic interactions of the viruses with different cellular structures in living cells that cannot be detected by other experiments. For example, Hwang et al. [48] used Fluorescence Resonance Energy Transfer (FRET) for rapid detection of enteroviral infection in vivo. General limitations of using ISH for pathogen detection include non-specific binding, high limits of detection 29 due to degradation of the probes, and complex methods [46]. 3.1 Problems With Current Detection Methods The current method in use for enterovirus detection use mammalian cell culture and require complex analyses (such as visible monolayer cytopathic effects) that require several days of laboratory time [41]. In contrast, PCR based methods offer specificity and speed but their major disadvantage is their inability to distinguish active from inactive virus particles leading to false positives on sample infectivity [42]. Cultural methods are often necessary for the determination of pathogen viability in water. Differentiation between a viral and bacterial etiology based on clinical symptoms is currently very difficult. Treating viral infections with antibiotics is ineffective and contributes to the development of antibiotic resistance, allergic reactions, toxicity and greater healthcare costs. Limiting the use of unnecessary antibiotics may help prevent the development of antibiotic resistance, reduce the number of patients with adverse effects of antibiotics, and substantially decrease healthcare costs [49]. Therefore, an alternative rapid, sensitive, and commercially available diagnostic tool for the detection of active viral particles and whether the infection is viral or bacterial in medical diagnostics and drinking water distribution systems is needed [50]. 30 4. Research Objectives The overall goal of this study was to evaluate the potential of Fourier transform infrared (FTIR) spectroscopy coupled with cell culture to detect the response of mammalian cell cultures to viral infection, understand early dynamics of infection, characterize the limitations as well as the possibility to discriminate between viral infections, and to develop a practical application of the method in drinking water quality monitoring and clinical analysis for a proper selection of early medical treatment. 4.1 Specific Aims 1. To develop techniques and evaluate the limits of performance (Appendix A). 2. To assess the ability to identify different types of viruses (Appendix B). 3. To design a continuous monitoring of infective viral particles (Appendix C). 31 PRESENT STUDY Appendix A describes the development of this novel method for virus detection using the well-studied poliovirus. It details the first-pass at virus detection using a standard protocol and then provides a revised, more effective protocol. This method can detect poliovirus at a titer as low as 17 plaque-forming units (PFU); is most effective at lower titers (101-103 PFU) and at an interval of 8 hours post infection. Changes in the carbohydrates, phosphates and lipids were observed relative to uninfected control cells and likely represent changes in the cell metabolic precursors viral RNA synthesis as the virus enters and begins to take over cellular functions and processes. Appendix B summarizes a successful discrimination between three different enteroviruses, poliovirus PV1, echovirus 30, and Coxsackievirus B5, based on the changes that they induce in mammalian cell components. The primary cellular components that provide distinct infrared spectral absorbance features include proteins (represented by the amide features at 1655 and 1544 cm-1) and the phosphodiester region between 900 – 1300 cm-1. The results of this study encourage the further pursuit of the development of FTIR microscopy for virus infection diagnosis. Appendix C summarizes the work done in the development of a continuous monitoring systems for active viral particles using attenuated total reflectance (ATR)-FTIR to continuously monitor the changes in the cells cause by a viral infection. Poliovirus was used as an model obtaining changes in the peak 915 cm-1, located between the 32 phosphodiester feature region (900 – 1300 cm-1), the peak height increased about 10% in duplicate experiments after 8 hours post infection, using a poliovirus titer of 106 plaque forming units per ml (PFU/ml). In response to poliovirus titer with a 104 PFU/ml, the feature at 967 assigned to DNA deoxyribose, increased by an average of 58.57%. These two biochemical changes could potentially be related to different stages of poliovirus infection. The combination of Appendixes A-C illustrate the progression of the engineered cellbased biosensor in the following manner: Appendix A provides insights of the detection limits when using BGMK cells to quantify enterovirus and also demonstrates that the assay perform best when used to detect lower viral titers. Appendix B shows the potential of this method to be used in the discrimination of enteroviruses and unexploited potential to be applied in the discrimination between bacterial and viral infection for clinical applications. Appendix C reviews the steps for the design of an ATR-FTIR cell-based biosensor for the continuous measurement of biochemical changes in vivo. 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Prilutsky D, Shneider E, Shefer A, Rogachev B, Lobel L, Last M, Marks RS: Differentiation between Viral and Bacterial Acute Infections Using Chemiluminescent Signatures of Circulating Phagocytes. Analytical Chemistry 2011, 83:4258-4265. 37 APPENDIX A A NOVEL DETECTION SCHEME FOR POLIOVIRUS INFECTION USING FTIR SPECTROSCOPY AND CELL CULTURE Felipe T Lee-Montiel1, Kelly A Reynolds2, Mark R Riley1§ 1 Agricultural and Biosystems Engineering, University of Arizona, Tucson, Arizona 85721 2 Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona 85724 § Corresponding author Email addresses: FTLM: [email protected] KAR: [email protected] MRR: [email protected] To be submitted to the Journal of Biological Engineering. 38 ABSTRACT Background In a globalized word, prevention of infectious diseases is a major challenge. Rapid detection of viable virus particles in water and other environmental samples is essential to public health risk assessment, homeland security and environmental protection. Current virus detection methods, especially assessing viral infectivity, are complex and timeconsuming, making point-of-care detection a challenge. Faster, more sensitive, more specific methods are needed to quantify potentially hazardous viral pathogens and to determine if suspected materials contain viable viral particles. Fourier transform infrared (FTIR) spectroscopy combined with cellular–based sensing, may offer a rapid way to detect specific viruses. This approach utilizes infrared light to monitor changes in molecular components of mammalian cells by measuring absorbance patterns produced following virus infection. In this work poliovirus (PV1) was used to evaluate the utility of FTIR spectroscopy with cell culture for rapid detection of infective virus particles. Results Buffalo green monkey kidney (BGMK) cells infected with different virus titers were studied at 1 – 12 hours post-infection (h.p.i.). A partial least squares (PLS) regression method was used to analyze and model cellular responses to different infection titers and times post-infection. Our model performs best at 8 h.p.i., resulting in an estimated root mean square error of cross validation (RMSECV) of 17 plaque forming units (PFU)/ml 39 when using low titers of infection of 10 and 100 PFU/ml. Higher titers were also tested up to 106 PFU/ml. Conclusions This new approach to poliovirus detection and quantification using FTIR spectroscopy and cell culture also illustrates the biochemistry behind the stages of viral infection and could potentially be extended to compare different biochemical cell responses to infection with different viruses. This virus detection method could be adapted to an automated plate reader scheme for use in areas such as water safety monitoring and medical diagnostics. Keywords: enterovirus, Fourier Transform Infrared (FTIR), zinc selenide (ZnSe), midinfrared, partial least squares, cell culture, buffalo green monkey kidney (BGMK) cells, virus detection, poliovirus (PV1). 40 BACKGROUND Increased population density and movement of people around the globe have generated a rise in the number of outbreaks of infectious diseases and the emergence of new infectious diseases [1]. Worldwide, 3.575 million people die each year from water-related diseases [2]. The water and sanitation crises claim more lives through disease than any warfare [2]. A key step in the prevention of outbreaks of communicable diseases is the early detection of virulent particles [3]. Rapid detection of active viral pathogens is of central importance for public health risk assessment and environmental protection. Waterborne viruses are particularly important for public safety monitoring due to their environmental stability and low infectious dose; a single virion is sufficient to initiate illness in previously unexposed, healthy adults [4]. Enteroviruses (family Picornaviridae) are a genus of waterborne viruses that infect humans and other mammals. They are a health problem worldwide, for example causing 10 to 15 million cases of symptomatic infection in humans annually in the United States [5]. Enteroviruses are single, positive-strand RNA viruses that include polioviruses, Coxsackieviruses and echoviruses, among others. Some enteric virus groups have emerged as waterborne pathogens because of their high levels of resistance to current water treatment processes, which include ultraviolet light inactivation and heat inactivation [6, 7]. Poliovirus was used here as a model virus because a large body of research data exists on the physical, chemical and biological properties of the virus, vaccination is available and its ease of cell culturing [8-10]. In addition, poliovirus 41 remains endemic in four countries. During 2002 the rejection of polio immunization led to a worrying resurgence of polio in some areas of Nigeria, followed by re-infection in 21 other countries; resurgence of the disease was also observed in India. Auxiliary vaccination actions were restarted and by 2007 most re-infected countries had become polio-free again. The goal of global polio eradication was re-set to 2010, but concerns continue to be expressed about the progress of this eradication program [11]. Current methods for enterovirus detection use mammalian cell culture and require complex analyses (visible monolayer cytopathic effects) that require several days of laboratory time [12]. Polymerase chain reaction (PCR) methods for the detection of viruses have been developed, offering specificity, speed and cost advantages over cell culture methods [13, 14]. PCR methods alone do not, however, differentiate between the presence of physical (inactive) virus particles and viable (active) virus particles [6, 15]. The major disadvantage of most current methods of virus detection is the inability to provide information about whether a viral particle can start an infection or not. Faster methods with increased sensitivity and specificity are needed to quantify active viral pathogens from medical and environmental samples. FTIR spectroscopy is a noninvasive measurement method that has previously been applied for identifying various biological components of cells by detecting changes in vibrations of molecules leading to changes in spectral patterns [12, 16]. It can be used as part of a sensitive method for the detection of specific cellular molecular changes [12, 17- 42 21]. Quantitative infrared absorption methods such as FTIR spectroscopy differ from ultraviolet/visible molecular spectroscopic methods because of the greater complexity of the spectra. FTIR spectroscopy has been applied in medicine, particularly to study the process of herpes virus infection [5] and in the diagnosis of cancers and other disorders [22-25]. In addition, FTIR spectroscopy has been used for the quantification of blood serum components such as glucose, protein, cholesterol and urea [26]. Cell based sensors detect changes in the physiological state of cells following exposure to an environmental stimulus. Changes in cell state can provide information about the stimulus; for example, cells have been used to sense toxins in water samples [27, 28]. Cell based sensors have recently been combine with spectroscopy and applied to viral detection [29, 30]. Cantera et al. [31] used an optical system that involved the use of molecular beacons as a way to detect infective virus particles, resulting in a detection limit of 1 PFU. Using live cells to assist in identifying and quantifying viruses in samples helps to bring the detection closer to an in vivo setting, allowing the natural and complex interactions between cell and virus to be part of the experimental setup. Here we present the development of a novel strategy for virus detection using a combination of FTIR spectroscopy and live BGMK cells as biosensors (Figure 1). The approach utilizes mammalian cell culture and infrared light absorbance to monitor specific absorbance patterns produced following changes in cell components (such as lipids, proteins, nucleic acids and sugars) subsequent to the virus infection, effectively 43 using the cells as biosensors [32, 33]. We also provide insights into the small-scale changes that host cells undergo upon interacting with a virus and discuss the remaining challenges to applying this method for virus detection in water samples. 44 RESULTS Optimal time for virus detection BGMK cells were infected with PV1 at different multiplicities of infection (m.o.i.) of 10 PFU (0 – 106 PFU/ml) and studied at 1, 1.5, 2, 4, 5, 6, 8 and 12 h.p.i. Virus infection regression models were developed to correlate changes in spectral features with time of infection. Changes in the spectra varied depending on the progress of the viral infection, with biochemical alterations appearing in poliovirus infected cells within 2 h.p.i. Example regression models for 1.5, 4, 6 and 8 h.p.i. are shown in Figure 2 and a summary of the regression model parameters are given in Table 1. We selected an infection time of 8 h for subsequent experiments based on the results here and the viral replication time [34]. Viral infection at 8 h.p.i. Spectra of cells at 8 h.p.i infected with different m.o.i. of PV1 are shown in Figure 3. The highlighted areas are the optimal regions of the spectra used in the virus detection model, as determined using interval Partial Least Square (iPLS) with an interval size of 10 cm-1 and a maximum of 8 latent variables. These 9 areas of the spectra correspond to the following wavenumbers: 660.90 – 667.26, 806.11 – 823.46, 844.68 – 862.03, 979.67 – 997.03, 1095.38 – 1112.74, 1191.80 – 1209.16, 1230.37 – 1267.02 and 1326.80 – 1344.16 cm-1. Changes in absorbance can be correlated with the development of poliovirus infection. 45 The region between 600 – 900 cm-1 corresponds to C2′ endo/anti (B-form helix) conformation, DNA and RNA molecules. 1000 to 1300 cm-1 relates to symmetric stretching mode of dianionic phosphate monoester in phosphorylated proteins and left handed helix DNA (Z form) [35]. Figure 4 shows a regression model of poliovirus at 8 h.p.i. that correlates changes in absorbance spectra with virus infection titer. This model has a RMSECV of 0.57 log. We achieved similar results using the whole spectral region or the nine regions selected by iPLS (data not shown). This FTIR spectroscopy with cell culture method can detect a viral titer of 101 – 102 PFU/ml with a RMSECV of 17 PFU/ml; at higher titers, 102 –104 PFU/ml, a RMSECV of 2009 PFU/ml for 8 h.p.i. was achieved. Effect of virus titer on characteristic spectra peak height at 8 h.p.i. The characteristic peaks of the BGMK cell line showed changes in relative absorbance following infection with different viral titers. These peaks correspond to different biomolecules. A graphical representation of the absorbance values of the eighteen characteristic peaks of each data set for the different infection titers (106, 105, 104, 103, 102 and 101 PFU/ml) for the 8 h.p.i. time point is given in Figure 5. A summary of the change in peak height upon virus infection and the corresponding biomolecules represented at the peak wavenumbers are given in Table 2. 46 Some trends are evident for the change in peak heights at different titers. For example, lower virus concentrations (102 and 103 PFU/ml) showed negative values relative to controls at 1399 – 2956 cm-1, indicating a decrease in the amount a specific biochemical component. In contrast, the mean absorbance of the cells with higher virus titers (104, 105 and 106 PFU/ml) only showed average increases in absorbance across all peaks relative to uninfected controls. The most significant differences were found for the peak at wavenumber 3293 cm-1 (Oneway ANOVA, d.f. 6, F = 7.63, p < 0.0001). The absorbance of the 101 PFU/ml sample was significantly higher than the absorbance of the 106 PFU/ml sample, whereas the 106 PFU/ml sample and uninfected control had significantly lower absorbance values than the 101 – 105 PFU/ml samples with an alpha value of 0.05 at this wavenumber (TukeyKramer HSD). Another significant difference was found for the peak at wavenumber 1043 cm-1 (Oneway ANOVA, d.f. 6, F = 2.47, p = 0.032). Pair-wise comparisons performed for each absorbance value for each titer (Tukey-Kramer HSD) showed that the absorbance of the 101 PFU/ml sample was significantly higher than the absorbance of the uninfected control using an alpha value of 0.05. The mean absorbance values for the different titers at the 700 cm-1 (ANOVA, d.f. 6, F = 2.37, p = 0.0383), 835 cm-1 (ANOVA, d.f. 6, F = 2.36, p = 0.0387) peaks also formed 47 significantly separate groups and the absorbance for the 1079 cm-1 (ANOVA, d.f. 6, F = 2.08, p = 0.0659), 1313 cm-1 (ANOVA, d.f. 6, F = 1.99, p = 0.0781), 2852cm-1 (ANOVA, d.f. 6, F = 1.89, p = 0.0942), 2924 cm-1 (ANOVA, d.f. 6, F = 2.16, p = 0.0575) titers showed trends. Microscopy of cell structure on crystal The cell architecture of the BGMK cells attached to a titanium oxide crystal can be seen in Figure 6. Actin, CD155 and cell nuclei are shown. Actin mediates a variety of cellular functions including internal and external cell movement and cell support. The organization of the actin cytoskeleton is highly regulated; the special distribution of actin is used as a measure of cell health. The average height of the cells on a titanium oxide crystal was approximately 6.87 µm. Cells were distributed evenly on the crystal with many focal adhesion points (data not shown). CD155 poliovirus receptors were observed to be close to the cell nuclei. CPE analysis The error associated with a standard CPE analysis was compared to the error of the new FTIR spectroscopy with cell culture method. The results of this assay are shown in Table 3. Standard error was calculated from duplicate experiments except where there were too many PFUs to count accurately. The average standard error was 12.0% of the mean estimated number of PFU/ml. For comparison, we saw an error of cross validation of 48 ~17% of the mean across titers using the FTIR with cell culture method. Note that these calculations assume a perfect serial dilution to obtain the starting known viral titers. 49 DISCUSSION Cells infected with PV1 showed consistent alterations in their infrared spectral features. The broad, undulating features observed relatively weakly in the spectra of entire cells were previously attributed to Mie scattering of the cellular nuclei [39, 40]. The overall variations in intensity in the initial method are most likely due to variations in the thickness of the cell, as well as the nucleus/cytoplasm (N/C) ratio. Multiple models were developed using a variety of pre-processing approaches, different spectral regions and different latent variables. For example, this FTIR spectroscopy with cell culture method could detect a viral titer of 101 –102 PFU/ml with a RMSECV of 17 PFU/ml in cells at 8 h.p.i. Appendix A illustrates the relationship between RMSECV and latent variables. At higher titers the accuracy decreased slightly, for example at 102-104 PFU/ml, a RMSECV of 2009 PFU/ml for 8 h.p.i. was achieved. Changes in cell absorbance spectra were correlated with specific cell components to better understand the progress of the poliovirus infection. The most significant changes were associated with –OH stretching, particularly at low virus titers. Another significant change associated with infection was a symmetric stretching vibration of PO2 due to RNA and DNA, which was most evident in the 101 PFU/ml virus titer. Broad cis-C-H out of plane bend and left handed helix of DNA also showed significant changes associated with PV1 infection. 50 In the first method cells were grown in a culture flask, trypsin was used to detach the cells, they were centrifuged and transferred to the ZnSe crystals. This method, however, resulted in models with a high level of error. The improved method of growing cells directly on the ZnSe crystals used a greatly simplified protocol, removing many steps where error and sample handling variation could occur. The direct cultivation method produced cleaner, more stable spectral patterns and had a higher reproducibility compared to the initial method. The modified method required less time and improved the quality of the spectra. The cleaner spectra results were likely due to reduced light scattering from more uniform thickness of the cell monolayer. This virus detection method was more accurate for lower virus titers. This could be because the lower titers cause less variation in the viral infection process of adsorption and penetration inside of each individual cell. For example, at low titers the viruses present may be invading the cells in synchrony, causing more equal changes in the cell components among cells. With many virus particles present there might be more than one virus infecting each cell, meaning that the stage of infection is not synchronized among cells. Cantera et al. [31, 36] have reported greater success for detection of lower virus titers of poliovirus after 12 h.p.i.. It may be that for lower virus infection it is necessary to wait longer until the cells reach some steady infection state so that the changes in cell components are saturated causing the overall signal pattern to stabilize. 51 Poliovirus replicates and lyses the cell in approximately 8h [34] and a previous study using molecular beacons also found this time period to be the best for viral quantification [31]. An infection period of eight hours was the most successful of the times tested here. Prior works support this capability, for example, eight hours of infection was found to be the best for detection of poliovirus with a method based on engineered BGMK cells expressing fluorescent proteins undergoing fluorescence resonance energy transfer (FRET) [36] and [37]. Perhaps this is the time point when the most dramatic changes in cell components are occurring. The poliovirus is known to have a replication time of eight hours under standard laboratory conditions in BGMK cells [36], meaning that the eight hour time point in our protocol may be just prior to virus release from infected cells. A CPE assay is the standard method for enterovirus detection and requires three to fourteen days to perform depending on the virus type. The poliovirus assay takes three to ten days in which the presence of a virus is marked by the death of animal cells in culture [3]. The cells are grown in a monolayer in a semi-solid media such that new virus particles infect surrounding cells. Cells killed by the virus form a plaque and the remaining living cells are stained with a dye. Another method, integrated cell culturePCR (ICC-PCR) is a fast molecular detection method that can identify low levels of viable virus [3]. A sample is applied to cell culture and then molecular methods are used to detect replicating virus in the cell culture. This method takes 1 to 3 days from infecting the cells, depending on the replication time of the virus [13]. 52 We present a relatively fast protocol for the detection of enterovirus. Cells need 24h to attach to the infrared transparent crystal, but with cells ready the sample can be added to the cells on the crystal and then left undisturbed for 8h for the virus to infect. The sample measuring process is then completed in a number of minutes. This method could conceivably be developed into an automated system. For example, a plate reader of ZnSe with wells for cell growth could be integrated with FTIR microspectroscopy. Software could be built to automatically compare known changes in cell components with a database to give an estimate of number of virus particles or to detect the type of virus present. 53 CONCLUSIONS Here we present a fast new method for the detection of low titers of viable poliovirus using mammalian cells as a biosensor combine with FTIR spectroscopy to detect changes in cell components. Virus titers from 101 to 106 PFU/ml were assayed over a range of infection times from 1h.p.i. to 12h.p.i., with detection most accurate at low titers from 101 to 104 PFU/ml. Prediction models of infected cells were best detected as early as 6 h.p.i., and having the most accurate prediction of virus titer at 8 h.p.i. The model gave a RMSECV of 17 viral particles. The major changes in cell components following virus infection were an increase in the peak 3293 cm-1 and negative trend in the absorbance for lipids region (3200 – 2800 cm-1) in the poliovirus infected cells with 102 – 103 PFU/ml. The cell culture method is still considered the standard for viral diagnosis as it has the advantages of detecting infectious viral particles and the ability to achieve low detection limits [6]. Combining the cell culture method with FTIR spectroscopy, we can enhance the cell culture method with the increased speed of FTIR spectroscopy. This new method has the potential to be extended for the detection of other viruses and adapted into a portable, automated system for detection of viruses from environmental samples. 54 METHODS Protocol development An overview of the method is presented in Figure 1. Our first experiments involved growing cells on the plastic surface of 24-well plates, removing the cells with an enzymatic treatment, pelleting the cells by centrifugation and then transferring them to ZnSe crystals to scan using FTIR spectroscopy. We then made several changes to the initial method to optimize both the laboratory steps and the spectra results by reducing the room for variation between experimental replicates. We present both methods for comparison. Cell culture BGMK cells were selected due to their high level of sensitivity to enteroviruses and their use in existing studies [38]. Cells were grown in T25 flasks to a confluent monolayer in Dulbecco’s Modified Eagle Medium (DMEM) with 584 mg/L L-glutamine (Cellgro) containing 0.1% NaHCO3, 10,000 units/ml penicillin, 10,000 µg/ml streptomycin (HyClone) and 10% (vol/vol) fetal bovine serum (FBS; HyClone) and buffered with 12 mM HEPES. All reagents were from Sigma Chemical Co. unless otherwise noted. Cells were maintained in culture at 37°C in a humidifying incubator with 5% carbon dioxide and passaged when confluent, approximately every two days. Cells were detached from the growth flask with 2.5% EDTA-trypsin, centrifuged at 18 g for 8 minutes, resuspended in media and transferred into a flask of fresh media. 55 Initial method For all viral exposure experiments, T25 flasks with 95% confluent cell monolayers were trypsinized, resuspended in media and seeded in 24-well plates. The 24-well plates were individually seeded with 125,000 cells per well. Cells were allowed to grow to 100% confluence by incubating for 48 h at 37°C and 5% CO2 before viral exposures. Different viral exposure times were administered during experiments for cells. Cells were exposed for different periods of time to monitor viral infection progression. Once cells were confluent different viral titers were made using DMEM with 10% NCS to expose the cell layers. The different viral titers were added in the eight central wells of a 24-well plate in the amount of 0.5 ml and 0.5 ml of media to give a total of 1 ml/well. A minimum of two control samples per experiment was inoculated with virus-free media. When viral exposure times were completed, cell layers were exposed to 70 µL of trypsin per well to remove cells from the wells for FTIR analysis. Once cells were removed, cells were pelleted by centrifugation at 18 x g for 8 minutes. Cells were washed by adding 1 ml of a 0.9% NaCl saline solution to remove any residual cell media and resuspended in 20 µL of the saline solution. Experiments were performed in a biosafety cabinet under laminar flow to maintain aseptic conditions. Revised method After the quantification of the cell pellet, individual ZnSe crystals were seeded with 150,000 cells with a final volume of 1 ml DMEM media per well in a 24-well plate. Cells 56 were incubated for 24 h to allow attachment to the crystal surface and infected with different virus titers (0.5 ml of media was removed and replaced with virus in suspension). ZnSe crystals were washed with soap, pure acetone and sterilized in 70% EtOH between uses. Experiments with different infection times were performed with the initial method. The 8 h.p.i. and 12 h.p.i. experiments were subsequently performed with the revised method. Virus titers and infection times Multiple serial dilutions were made from the initial stock concentration of 107 PFU/ml of purified vaccine strain poliovirus type 1 (PV1, LSc-2ab) using different time of infection experiments. Previous studies observed molecular and morphological changes in poliovirus infected cells within 2 h.p.i. [34]. This infection time was therefore used as a starting point for identification of the optimum incubation time for virus detection using FTIR spectroscopy. BGMK cells were infected with PV1 at different multiplicities of infection (m.o.i.) of 10 PFU (0 – 106 PFU/ml) and studied at 1, 1.5, 2, 4, 5, 6, 8 and 12 h.p.i. FTIR spectroscopy Infrared spectra of healthy cells and cells post-infection were collected in transmission mode on a ThermoNicolet Magna 560 FTIR spectrometer equipped with a liquidnitrogen cooled MCT-A detector, KBr beamsplitter and infrared light source. The 57 spectral collection parameters used were 128 co-added scans (requiring about 128 seconds) and a spectral resolution of 4 cm-1 with a 20% aperture opening. Preprocessing of spectra The advantage of preprocessing the data is the reduction of noise and other interferences in the data analysis. This preprocessing included computation of the first derivative of spectral intensity with respect to wavenumber, vector normalization and mean center. The derivative spectra exhibited nearly completely flat baselines and collapsed bandwidth. Spectral expansion emphasized the most diagnostic “mid-IR” region between 650 and 1700 cm−1 and eliminates a large spectral range (1800 – 2800 cm−1), which contains no vibrational spectroscopic information. Subsequently, spectra were vector normalized using amide I as the maximum absorbance equal to unity. This last step removed the variation in spectral intensity discussed above. Data analysis Collection of the spectra was performed using OMNIC 7.3 (Thermo Electron Corporation). The analysis of spectra from individual cells was carried out using the PLS toolbox. PLS is a well-established multivariate method ideally suited to analyze data with strongly collinear (correlated), noisy and numerous X-variables distinguishing small spectral variations in large data sets [41, 42]. Interval Partial Least Square (iPLS) was used to assist in the selection of narrower regions of the spectra that could be used to generate a more accurate virus prediction model. 58 OMNIC 7.3 was used to find the absorbance values of the eighteen characteristic peaks of each data set for the different infection titers (106, 105, 104, 103, 102 and 101 PFU/ml) for the 8 h.p.i. time point. Differences in peak height were analyzed using the statistical software JMP version 8.0. Significant differences were assessed among peak heights at each titer using a one-way ANOVA. Pair-wise comparisons were performed for each absorbance value for each titer using a Tukey-Kramer HSD test that corrects for multiple comparisons. Microscopy of cell structure on crystal An Actin Cytoskeleton and Focal Adhesion Staining Kit (Millipore) was used to visualize the cell architecture of the BGMK cells attached to the ZnSe crystal. Preliminary experiments were conducted growing cells on titanium oxide crystals. Actin was stained using TRITC conjugated Phalloidin. Nuclei were stained with DAPI. The average height of the cells on a titanium oxide crystal was determined using a z-axis stack of cell images. We also used the same kit to perform immunohistochemistry to observe the CD155 poliovirus receptor in the cell using FITC (Santa Cruz Biotechnology). Cytopathic effect assay A CPE assay was conducted as described previously [3]. Briefly, an initial sample with a known poliovirus titer of 107 PFU/ml was diluted in series. The CPE assay was performed on each dilution to estimate the virus concentration. 1 ml of 1% (wt/vol) carboxymethylcellulose (CMC; Sigma-Aldrich) in maintenance medium (with 2% FBS) 59 was overlaid onto the infected-cell monolayer. After 48 h of incubation at 37°C, plaques were visualized by staining with 0.8% (wt/vol) crystal violet (Sigma-Aldrich) in 3.7% (vol/vol) formaldehyde solution. 60 List of abbreviations (CPE): cytopathic effect; (FTIR): Fourier transform infrared; (BGMK): buffalo green monkey kidney; (PFU): plaque forming units; (DMEM) Dulbecco’s Modified Eagle Medium; (NCS): new calf serum; (PCR): polymerase chain reaction; (RNA): ribonucleic acid; (DNA): deoxyribonucleic acid; (RMSECV): root mean square error of cross validation; (PV1): poliovirus 1; (FBS): fetal bovine serum Competing interests The authors declare that they have no competing interests. Authors' contributions FLM conducted the experiments, performed the data analysis, participated in the design and coordination of experiments and drafted the manuscript. MR and KR conceived of the study and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript. 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Mohlenhoff B, Romeo M, Diem M, Wood BR: Mie-Type Scattering and NonBeer-Lambert Absorption Behavior of Human Cells in Infrared Microspectroscopy. Biophysical Journal 2005, 88:3635-3640. Liebmann B, Filzmoser P, Varmuza K: Robust and classical PLS regression compared. Journal of Chemometrics 2010, 24:111-120. Abdi H: Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdisciplinary Reviews: Computational Statistics 2010, 2:97-106. Rodriguez RA, Gundy PM, Gerba CP: Comparison of BGM and PLC/PRC/5 Cell Lines for Total Culturable Viral Assay of Treated Sewage. Applied Environmental Microbiology 2008, 74:2583-2587. 64 44. Cervera AE, Petersen N, Lantz AE, Larsen A, Gernaey KV: Application of nearinfrared spectroscopy for monitoring and control of cell culture and fermentation. Biotechnology Progress 2009, 25:1561-1581. 65 FIGURES 1. BGMK cells grown in cell culture 2. Cells seeded onto crystals ZnSe crystal ZnSe crystal 4. Cells infected with virus particles 3. Cells incubated for 24h to allow for cell adhesion Incubation time (h.p.i.) 5. Cells act as a biosensor 6. FTIR spectroscopy to detect changes in cells Detector Detector Absorbance Absorbance 7. Raw spectral data Wavenumber cm-1 Amines Lipids Wavenumber cm-1 9. Prediction of virus titer Predicted titer Absorbance 8. Changes in cell components Wavenumber cm-1 Actual titer Figure 1 - Schematic representation of viral detection method using cell culture and FTIR spectroscopy (not to scale) 66 (")$%"&"'"$ *"#$%"&"'"$ !"#$%"&"'"$ +"#$%"&"'"$ Figure 2. Poliovirus prediction models comparing the estimated PFU and predicted PFU at 1.5 – 8 h.p.i. The green lines indicate a 1:1 regression model. 67 Figure 3. Example FTIR spectra showing changes in absorbance when cells are infected with poliovirus Spectra in the wavelength region of 650 – 1750 cm-1 show the absorbance of BGMK cells infected with different PV1 titers 101 – 106 PFU/ml at 8 h.p.i. Uninfected cells served as a control. The nine highlighted regions illuminate the areas chosen by the PLS model as the most informative for detecting changes in cell components following virus infection. The different colors represent individual samples. 68 Figure 4 – Example regression analysis for cells infected with PV1 at 8 h.p.i. This model uses 7 latent variables. The regression uses a log scale and 0–103 PFU/ml in the 650 – 1600 cm-1 wavenumber region. 69 Figure 5 - Average peak absorbance values compared to uninfected control for different virus titers at 8 h.p.i. Error bars show standard error. Note that 1654 cm-1 was used to normalize the data and therefore shows no change. 70 Figure 6 - Confocal (Actin filaments and CD155 receptor) and bright field image of BGMK cells The left panel shows an immunofluorescence image of BGMK cell on a titanium dioxide crystal. Green color indicates CD155 receptor required for the poliovirus to infect the cells. The right panel shows a bright field image of a confluent monolayer of BGMK cells on a tissue culture flask. 71 TABLES Table 1 - Comparison of poliovirus prediction models using different infrared regions and virus titers Table shows root mean square error of calibration (RMSEC), root mean square error of cross validation (RMSECV) and correlation coefficient for each model. Method IR region used h.p.i Virus titers (PFU) 650 – 1650 cm-1 1.5 0, 102 – 104 4 0.910 0.4225 0.7093 650 – 1650 cm-1 4 0, 101, 103, 104 3 0.891 0.5225 0.8900 -1 650 – 1650 cm 6 0, 101, 102, 104 6 0.868 0.4914 0.8800 650 – 1650 cm-1 8 0 – 104 7 0.911 0.4290 0.8471 650 – 3600 cm-1 8 0 – 104 7 0.819 0.6480 0.9187 650 – 1650 cm-1 8 0 – 103 7 0.917 0.3298 0.5726 iPLS 9 regions 8 0 – 103 7 0.903 0.3577 0.5581 iPLS 9 regions 8 0,102 – 104 7 0.944 0.4019 0.6628 iPLS 9 regions 8 0 – 102 7 0.964 0.1640 0.3163 Initial Revised Latent variables R2 RMSEC RMSECV 72 Table 2 - Observed changes in relative peak absorbance values with corresponding biomolecules Peak wavenumber (cm-1) 700 Corresponding biomolecules Observed changes in cell absorbance following PV1 infection relative to uninfected control cells (Broad) cis-C-H out-of-plane bend General increase 835 – 840 C2′ endo/anti (B-form helix) conformation and left handed helix DNA (Z form) Gradual increase corresponding to increased PV1 titer 900 – 1350 Phosphodiester stretching bands (from absorbance due to collagen and glycogen) Decrease; trend is less clear at higher PV1 titers – 100 Symmetric stretching vibration PO2 due to RNA and DNA General increase 1040 1079/80 Sharp increase in BGMK cells infected with 101 PFU/ml titer 2 Symmetric phosphate stretching modes or ν(PO2 −) symmetric. Phosphate stretching modes followed by a decrease in the 10 PFU/ml infected cells then a gradual height incremental with the higher titers. originate from the phosphodiester groups in nucleic acids. 1153 Stretching vibrations of hydrogen bonding C-OH groups 1236/7 Amide III, stretching PO2−asymmetric (phosphate I) Decrease in the peak height for the cells infected with 102 and 103 PFU/ml; higher titers shown an increase 1312-1317 Collagen related, amide III band components of proteins General increase 1455/6 Asymmetric CH3 bending modes of the methyl groups of proteins General decrease 2852, 2873, 2924/5, 2956 Lipids region (CH2 symmetric, symmetric stretching vibration of CH3 of acyl chains, stretching C-H and asymmetric stretching vibration of CH3 of acyl chains respectively) 3084 Stretching N-H symmetric 3293 OH stretching (associated) Virus titer 102 PFU/ml shows a decrease Decrease for infection titers 102 – 103 PFU/ml General increase General increase; increase gradually less from 101 PFU/ml titers – 106 73 Table 3 - CPE Analysis for Poliovirus at a virus titer of 107 PFU/ml. Sample of Known Titer (PFU/ml) Dilution Factor Estimated PFU/ml S.E. 1000000 -6 7000000 2000000 100000 -5 550000 50000 100000 -4 380000 40000 10000 -4 35000 5000 10000 -3 39500 2500 1000 -3 4000 0 1000 -2 3300 100 10 -1 5 5 A standard CPE analysis was performed to compare the error of this method with the error of the new FTIR spectroscopy with cell culture method. An initial sample with a known poliovirus titer of 107 PFU/ml was diluted in series. A CPE assay was performed on each dilution to estimate the virus concentration. Standard error (S.E.) was calculated from duplicates. The dilution factor indicates which of the subsequent dilutions in the CPE protocol had a number of plaque forming units in the correct range to be counted. 74 SUPPLEMENTARY DATA RMSEC and RMSECV Losing information R2 RMSECV RMSEC !"#$%&'' Latent Variables Supplementary Figure 1 - Optimization of the number of latent variables by minimizing the RMSECV. Image adapted from [44]. 75 APPENDIX B FTIR SPECTROSCOPY AND PCA TO DISCRIMINATE CELLS WITH DIFFERENT ENTEROVIRUS INFECTIONS Felipe T Lee-Montiel1, Kelly A Reynolds2, Mark R Riley1§ 1 Agricultural and Biosystems Engineering, University of Arizona, Tucson, Arizona 85721 2 Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona 85724 § Corresponding author To be submitted to: Journal of Biotechnology and Bioengineering Email addresses: FTLM: [email protected] KAR: [email protected] MRR: [email protected] 76 ABSTRACT Background Fourier transform infrared (FTIR) spectroscopy was used to analyze buffalo green monkey kidney (BGMK) cells infected with different enteroviruses. FTIR spectra were compared among healthy cells and cells infected with one of poliovirus, Coxsackie virus or echovirus. Advanced statistical methods were applied to analyze the spectra for discriminating viral infections in BGMK cells. Partial Least Squares (PLS) toolbox (Eigenvector Research) was used to perform a principal component analysis (PCA), which allowed us to identify when spectral differences were correlated with different viral infection types. Results It was possible to discriminate and classify the absorbance spectra of BGMK cells with different viral infections using multivariate chemometric analysis. PCA plots are presented for the complete spectral range from 3600 to 650 cm-1 and for viruses at three different titers, 101, 102, and 103 PFU/ml. The partial least squares discriminant analysis (PLS-DA) using 3 latent variables (LV) for the complete data set had sensitivity and specificity values of 100% and 100% for poliovirus and echovirus, however Coxsackie had 95.9% and 94.3% respectively. Using 4 LV for the complete data set, the three viruses had sensitivity and specificity values of 100% and 100% respectively. A selected subset of the spectral range from 1614 – 650 cm-1 was found to have the most power for discriminating cells by virus infection type. A PLS-DA using this subset of the absorbance data and 4 LV gave rise to sensitivity and specificity values of 100% and 100% respectively. 77 Conclusions These results demonstrate the utility of FTIR spectroscopy combined with chemometrics to detect and discriminate virus species based on changes in the biochemical components of live cells with an active virus infection. This method has the potential to be applied to distinguish cells with other environmental stimuli. The relative speed of this method, clearly discriminating virus infections within 8 hours post infection (h.p.i.), indicates that it could be scaled to use in an automated plate reader to adapted for water safety monitoring or medical diagnostics. Keywords: enterovirus, Fourier transform infrared (FTIR) spectroscopy, zinc selenide (ZnSe), partial least square regression, virus detection, poliovirus (PV1), Coxsackievirus B5, echovirus 30, principal components analysis (PCA). 78 BACKGROUND Worldwide, waterborne viruses are of central importance in public health due to their stability in the environment and their easy transmission throughout water sources. Early stage detection and discrimination of infectious diseases leads to more effective medical treatments. Recent outbreaks of poliovirus and other enteroviruses [1, 2] further emphasize the importance of rapid virus diagnostics to aid in clinical treatment decisionmaking, monitoring of drinking water supplies and assessment of other environmental samples [3]. Molecular detection methods (nucleic acid-based) for infectious viruses determine only the total virus particle number and do not differentiate between the presence of physical virus particles and viable virus (infective particles). Unfortunately, current methods that asses virus infectivity require several days before a diagnosis can be made [4, 5]. As a result, the development of faster methods for the detection and identification of different viruses before the onset of symptoms is needed [6, 7]. Many current detection methods for rapid diagnosis of pathogens are based on molecular biology. Most of these tests are targeted at specific DNA sequences using PCR, enabling specific identification or broad spectrum pathogen detection [4]. The major advantages of molecular diagnostics are the high sensitivity, specificity, and potential speed. For example, PCR can detect bacteria directly from a sample in a short time [8] and can theoretically detect pathogen DNA from just a single copy. However, a significant disadvantage with PCR based methods is their inability to distinguish active from inactive virus particles leading to false positives on sample infectivity [9]. PCR methods 79 can also reach a limit of detection for rapidly evolving viruses and can be inhibited by environmental contaminants [10]. Here we present the use of FTIR to discriminate between enteroviruses of the Picornaviridae family. The Picornaviridae virus family contains small, positivestrand RNA viruses members including polioviruses, Coxsackie A viruses (CA), Coxsackie B viruses (CB), and echoviruses. Enteroviruses are a worldwide health problem causing 10 to 15 million cases of symptomatic infection in humans and produce significant costs associated with the 25,000 to 50,000 hospitalizations for "aseptic" meningitis annually in the United States [11]. Faster diagnosis of viral infection could consequently aid patients with suspected infections to choose appropriate treatment depending on the pathogen and virulence. Discrimination between bacterial or viral infection would reduce the incidence of antibiotic resistance, allowing more accurate pharmacological involvement with an overall reduction in health care costs [12]. Spectroscopic methods are being developed for use in the rapid detection of microorganisms. These methods include those that destroy the sample such as pyrolysis mass spectrometry and matrix-assisted laser desorption ionization time of flight mass spectrometry [13, 14]. Fourier transform infrared (FTIR) spectroscopy is a nondestructive technique based on vibration spectroscopy that has also shown much potential as a tool for use in microorganism identification [15]. 80 The potential use of FTIR spectroscopy to detect and identify changes in cells was proposed in the early 1990s [16] and increases in computing power have aided its development and application in biology and medicine [17-19]. It has been applied to prostate cancer detection [20, 21], to explore the biochemical changes caused by human papillomavirus [22] and used with partial least squares (PLS) analysis to investigate problems from Alzheimer disease to waste water management, among others [23]. Salman et al. [24] applied cluster analysis on FTIR microscopic signatures to differentiate normal cells from herpes-infected cells [25]. Similarly, Alam et al. [26] distinguished activated from inactivated murine macrophages using FTIR and principal components analysis (PCA). These prior studies provide a foundation for discriminating healthy from infected cells but were not able to differentiate types of infection [17]. FTIR spectroscopy uses infrared light to monitor small changes in the molecular structure of a sample, such as a collection of cells. It can detect changes in absorbance at different wavelengths that correspond to specific cell components such as lipids, amino acids and carbohydrates which in turn can be used for the identification, classification and diagnosis of virus infections [27]. Infrared spectral data provide many predictor variables (wavelengths) relative to the number of observations, leading to multicollineality. The data therefore requires chemometric analysis to extract useful information. PCA is a multivariate statistical method that collapses the many predictor variables into principle components, extracting 81 meaning from the data by combining variables into those that best describe the major data trends [28]. These combinations of variables can be used to describe and predict trends in the data and are often more robust than single variables. Here we investigated the effectiveness of FTIR spectroscopy with PCA for the discrimination of viruses in the family Picornaviridae. We used the partial least squares toolbox (Eigenvector Research) to perform PCA analysis on the infrared absorption spectra of BGMK cells infected with PV1, Coxsackievirus B5 and echovirus 30 and were able to demonstrate a capability for differentiating the type of viral infection from the information contained in the cell spectra alone. The potential of this method for virus identification is discussed. 82 RESULTS Studies were performed to evaluate the ability to differentiate between three types of viral infection and uninfected control within BGMK cell cultures. An individual virus type was applied at any one time. Analysis was performed on the entire data set after collection. Analysis of raw spectra of enterovirus infected cells Differences were observed in the raw spectral data of BGMK cells infected with poliovirus PV1, Coxsackievirus B5 and echovirus 30 for data combined from three different virus titers: 101, 102 and 103 PFU/ml. Figure 1a shows the complete data set for the vector-normalized spectra with approximately eight independent replicates per virus and per titer. Clear differences in absorbance spectra were observed, correlating with virus infection type, with the most distinct features present in the region from 1614 – 650 cm-1, but especially between 1399 – 1237 cm-1. The region of 3600 – 1750 cm-1 was less useful for distinguishing cells infected with the different viruses. Figure 1b shows the vector normalized mean absorbance spectra for cells infected with the three different viruses, demonstrating clear differences in the region between 1399 – 1237 cm-1. PCA of complete spectral range Several methods of data pre-processing were applied. The first derivative vector normalization showed better results than the second derivative vector normalization for the PCA. Figure 2 shows the results of this PCA analysis, which distinguished the absorbance spectra based on virus infection type for cells infected with three different 83 virus titers: 101, 102 and 103 PFU/ml for each virus type. The total percentage of variance captured by the PCA model was 93.49% using 3 principal components (PC) with the complete spectral range of 3600 – 650 cm-1. PCA using spectral subset 1614 – 650 cm-1 The PCA analysis resulted in a total percentage of variance captured by the PCA model of 84.20% using 3 PC and 91.01% using 4 PC for the vector-normalized spectral region between 1614 – 650 cm-1 for the three virus types at the three titers. Figure 3 shows the vector-normalized spectra for the enterovirus-infected cells in this wavenumber region. Figure 4 shows the results of the PCA scores plot for this region. A more specific subset within this region, between 1399 – 1237 cm-1, highlighted in Figure 3, resulted in a total percentage of variance captured by the PCA model of 98.44% using 3 PC and 99.23% using 4 PC. The vector normalized spectra and PCA scores plot for this more specific region are shown in the Appendix. PLS-DA The PLS-DA using 4 latent variables (LV) for the complete data set had sensitivity and specificity values of 100% and 100%, respectively. PLS-DAs using 4 LV for the selected subsets of the spectral range from 1614 – 650 cm-1 and from 1399 – 1237 cm-1 also had sensitivity and specificity values of 100% and 100%, respectively. The sensitivity and specificity values for both the complete spectral range and the large subset did not decrease when 3 LV were used. The sensitivity and specificity values for the smaller 84 subset dropped to 95.5% and 85.7% respectively when 3 LV were used. Table 1 shows the specificity and sensitivity values for the different models. Figure 5 shows the PLSDA plots for the full spectral region, the 1614 – 650 cm-1 and 1399 – 1237 cm-1 regions; the calculated threshold is shown as the horizontal red dashed line. The threshold is estimated using Bayer’s theorem and the available data in order to minimize total errors. The Bayesian threshold calculation assumes that the predicted y values follow a distribution similar to what will be observed for future samples. PCA using spectral subset 1614 – 650 cm-1 divided by virus titer Figure 6 shows the PCA score plots for the vector-normalized spectra in the 1614 – 650 cm-1 region divided by virus titer (101, 102, and 103 PFU/ml for Figure 5a, 5b and 5c respectively). This PCA also distinguished the absorbance spectra based on virus infection type. 85 DISCUSSION We have used advanced statistical methods to demonstrate clear discrimination of absorbance spectra obtained from enterovirus-infected cellular monolayers. This extends previous work to discriminate uninfected cells from cells infected with poliovirus using similar methods [29]. This method separated cells by virus infection type with strong statistical support using the entire FTIR spectral region or a subset. The results of this study attest to the efficacy of FTIR spectroscopy to discriminate three kinds of virusinfected cells at early stages of infection. It encourages the further pursuit of the development of FTIR microscopy for virus infection diagnosis. The primary cellular components that provide distinct infrared spectral absorbance features include proteins (represented by the amide features at 1655 and 1544 cm-1) and the phosphodiester region between 900 – 1300 cm-1. A viral infection would be expected to lead to changes in the cellular composition both due to replication of viral particles and to damage to the host cell. These would likely lead to distinct changes in the magnitude, position, or shape of infrared spectral features. It is possible that different viruses, each having their own mechanisms and rate of replication, may provide unique changes to these spectral features that would permit a diagnostic method to differentiate between types of viral infection. Being able to quantify active viral particles is the first step in this analysis and has been demonstrated by our group recently, which was able to identify polio infection over a range of titers from 101 to 106 PFU/ml [40]. An increasingly useful application would be to permit identification of the type of viral infection in addition to 86 the presence of an active infection so as to be able to implement appropriate treatment methodologies. We report here a clear and quantitative result. Differentiation between types of viral particles infecting a cell using the infrared spectra can be performed by visual inspection only when differences among spectra are large. Figure 1b demonstrates some of these differences, but using this alone is difficult to quantify the spectral differences. Detecting more subtle changes necessitates the use of chemometric methods such as PLS, PCA, and PLS-DA. These methods are described in more detail elsewhere [35]. In brief, PCA results are commonly displayed as a two dimensional plot of the values of different loading vectors. Samples with similar underlying features will tend to cluster into unique regions of this phase space. Differences in loadings are at least in part due to sample-to-sample variation that is typical of cellular studies. Figure 2 shows a clear demonstration of this clustering indicating that the spectral information alone can be used to classify a viral infection (at least from within this group of three evaluated here). This capability is somewhat surprising, especially with the degree to which clusters do not overlap across virus types. Providing less information into the analysis (by using a smaller spectral range) or reducing the number of LV’s applied greatly reduces classification success thus indicating an approximate level of information required for successful prediction. PLSDA, on the other hand, provides a model that predicts the class type for each sample. The results shown in Figure 6 provide a more simplified quantitative assessment of classification performance. 87 We found the spectral region from 1614 – 650 cm-1 to be the most useful for discriminating virus types at 8 h.p.i. This region incorporates the primary cellular components that interact with the different viruses including phosphates in DNA and RNA, amide II, amide III, collagen and glycogen. Further inspection reveals that the region between 1399 – 1237 cm-1 has the most useful spectral information. Peak assignments have been made for this region and indicate the most likely source is due to collagen and amide III. This type of data could be used in the future to develop specific vaccines for rapidly evolving viruses. Alternatively, these cell components could interact similarly to virus infections but at different h.p.i., such that we observe differences in this snapshot of infection taken at 8 h.p.i. A continuous spectral monitoring system of live cells infected with viruses would help to discriminate these two possibilities. The other regions of the spectra that were not as useful for virus infection type discrimination may still involve cell components that have substantial interactions with the viral particles, but not in a way specific to different picornaviruses. Testing of more virus types would help to explore the general interactions of viruses with the cell components monitored with FTIR spectroscopy. The cell components found in the spectral region with the most differences among virus infections are shown in Appendix A. Interestingly, the region with the clearest 88 differences between virus types corresponds to a collagen receptor. Collagen is a key component of the extracellular matrix. The alpha-2-beta-1 integrins mediate collagen formation in the extracellular matrix. Picornaviruses are known to interact with collagen receptors [30]. The function of the receptor could possibly be modified by the interaction with the virus. Changes in the extracellular matrix can also lead to changes inside the cell with gene expression and signal transduction, which could also be perceived by FTIR spectroscopy. The changes in collagen would then detected by the FTIR spectroscopy and could be different depending on the virus [31]. Other components in this spectral region include Stretching C-O, deformation C-H, deformation N-H. A similar spectral region was previously used to correlate FTIR spectra in the discrimination of different cancer cell lines [32]. Various biological markers, such as the phosphate level, the RNA/DNA, amide III and collagen, also displayed significant differences between the control and infected cells based on the analysis of the FTIR spectra. The data set provided here is comparatively small and so the high degree of success in classification may be due in part to low variability across the data set. As the calibration becomes larger, more variation is incorporated and performance classification typically improves. Even with this comparatively small data set, sufficient sample variation is present to calibrate such that 100% classification success is achieved. Reducing the information content reduces performance as anticipated. 89 The results shown here open new possibilities for classification of viral infections, which could be performed in an automated manner especially for assessment of safety of commercial products or municipal drinking water. This method using spectroscopic data does present some difficulties, including challenges in obtaining high spectral reproducibility. This could be due to biological or experimental variation causing sample variation and shifts in the baseline of the spectra. Data processing using corrections such as taking the first or second derivative of the data can reduce the baseline shift but loses spectral details. Another problem can be to determine which variables and therefore which cell components make the most significant contributions to the spectral changes; here mathematical tools aided this process. Here we focused on the region of the spectra with the most promising changes correlating with virus infection type. Note that this does not exclude the utility of other spectral regions and suggest that further analysis may pinpoint other regions to use in the discrimination of the above enteroviruses used. 90 CONCLUSIONS We demonstrated that FTIR cellular spectroscopy can discriminate between three types of enterovirus infection based on small alterations in the overall cellular molecular composition. This method could be very practical for use in a clinical diagnostic setting once automated, owing to the minimal need for sample processing and reagents. This method also provided specific, basic information about the different progress of viral infections. ACKNOWLEDGMENTS FLM was funded by a CONACYT fellowship. Dr. Kelly Reynolds generously provided purified vaccine strain poliovirus type 1 (PV1, LSc-2ab), Coxsackievirus B5 and Echovirus 30, at University of Arizona. Jonathan Sexton assisted with CPE analysis of the virus samples used in the development of this method. Brooke Beam help in the confocal microscopy. 91 MATERIALS AND METHODS Cell culture Cell culture was performed as described in [29]. Briefly, cells were grown in T25 flasks to a confluent monolayer in Dulbecco’s Modified Eagle Medium (DMEM) with 584 mg/L L-glutamine (Cellgro) containing 0.1% NaHCO3, 10,000 units/ml penicillin, 10,000 µg/ml streptomycin (HyClone) and 10% (vol/vol) fetal bovine serum (FBS; HyClone) and buffered with 12 mM HEPES at 37°C in a humidifying incubator with 5% carbon dioxide and passaged when confluent, approximately every two days. Sample preparation Zinc selenide crystals, which are highly transparent to IR radiation, were seeded with 150,000 BGMK cells with a final volume of 1 ml DMEM media per well in a 24-well plate. Cells were incubated for 24 h to allow attachment to the crystal surface. Cells were then infected with one of three different enteroviruses (or left as uninfected control) with at estimated concentration of 101, 102 or 103 PFU/ml by removing a volume of 0.5 ml of media and replacing it with virus in suspension, followed by an incubation period for 8 h.p.i. Uninfected control samples were included for each experiment day, but the data for these was not included in the PCA because our goal was to discriminate different virus infection types rather than uninfected from infected cells, as this has been achieved previously [29]. Samples were air-dried for 10 minutes under laminar flow then dried further by incubating overnight at room temperature. The dried cells were then examined then by FTIR spectroscopy. ZnSe crystals were washed with soap, pure acetone and 92 sterilized in 70% EtOH between uses. Virus type and titer replicates were conducted across many different experiment days to separate treatment from cell passage number. FTIR spectroscopy Infrared spectra of healthy cells and cells post-infection were collected in transmission mode on a Thermo-Nicolet Magna 560 FTIR spectrometer equipped with a liquidnitrogen cooled MCT-A detector, KBr beamsplitter and infrared light source. Spectral collection parameters used were 128 co-added scans (requiring about 128 seconds) and a spectral resolution of 4 cm-1 with a 20% aperture opening. A minimum of eight spectra samples of the cell infected with the different enteroviruses were collected from three different infection titer and the uninfected control to serve as multivariate biological fingerprints. Data processing Data processing was carried out immediately after spectral acquisition using OMNIC v.6.3 software. Classification of the enterovirus infected cells was achieved using partial least squares discriminant analysis that was performed using Partial Lease Squares (PLS) Toolbox version 6.2.1 (Eigen Vector Research Inc., Wenatchee, WA), operating in a MATLAB environment (v7.1, The Mathworks Inc., Natick, MA). PLS was used to preprocess the spectra by taking the first derivative and mean centering of the data set. Two spectral regions were used: 650 – 3600 cm-1 and 650 – 1614 cm-1. This resulted in 501 spectral data points for analysis in the smaller region. The spectra were then vector 93 normalized in Excel or Matlab, to correct for baseline shifts (scattering) [33]. The spectra were normalized using an arbitrarily chosen band, amide I (1654 cm-1). Each spectrum was assigned to one of three defined classes, poliovirus, echovirus 30 and Coxsackie B5. Two different pre-processing procedures were used on raw spectra and assessed for optimum discrimination; these steps were (1) vector normalization combined with first derivatization and (2) vector normalization combined with second derivatization (data not shown). Principal components analysis (PCA) Principal components analysis (PCA) of the baseline corrected and vector normalized mid-IR spectra data was first performed using data covering the range from 650 – 3600 cm-1 to preliminarily assess the separation of samples. Partial least square (PLS) toolbox (Eigenvector Research) was used to perform the analysis using two or three principal components based on the loadings and the percentage of variation captured by the model. Error ellipses in the PCA plots show 95 % confidence intervals. PLS-DA Partial least squares discriminant analysis (PLS-DA) was used to establish statistically significant differences between mid-infrared spectra for BGMK cells infected with poliovirus, echovirus 30 and Coxsackie B5. PLS-DA is a multivariate, full-spectrum calibration method. We specified the descriptor matrix as the spectral data of the cells infected with the three virus types, and used the PLS-DA to determine the best-fit 94 relationship that minimized the influence of spectral features that vary among replicate samples. This created a training dataset that could later be used as a classification model to test unknown samples [37, 38]. The PLS-DA model was built using cross validation (leave one out). This method used 90% of the data to make the model and tests the remaining 10% to assess the model, with a total of 10 iterations covering all of the samples as the test data. 95 FIGURES (a)! (b)! Figure 1 – Complete data set of absorbance of BGMK cells infected with three different enteroviruses a. The absorbance spectra of BGMK cells infected with either poliovirus PV1 (red), 96 Coxsackievirus B5 (green) or echovirus 30 (blue) at 8 h.p.i. Each spectra line represents a different sample. The spectral region ranges from 649.9 – 3600 cm-1. Data is shown for cells infected with three different virus titers: 101, 102 and 103 PFU/ml. b. Mean absorbance spectra of BGMK cells infected with either poliovirus PV1 (red), cocksaki virus B5 (green) or echovirus 30 (blue) at 8 h.p.i. The spectral region ranges from 649.9 – 3600 cm-1. Spectra represent the mean absorbance for cells infected with three different virus titers: 101, 102 and 103 PFU/ml. 97 Figure 2 – Principal components analysis for complete data set PCA score plot for the complete data set using three components, preprocessed with mean center and first derivative of the spectra. Each point represents one infrared spectrum. This PCA discriminates the absorbance of BGMK cells infected with either poliovirus PV1 (red), Coxsackievirus B5 (green) or echovirus 30 (blue) at 8 h.p.i. using the spectral region from 649.9 – 3600 cm-1. Data is shown for cells infected with three different virus titers: 101, 102 and 103 PFU/ml. 98 Figure 3 – Absorbance of BGMK cells infected with three different enteroviruses from 1614 – 650 cm-1 The absorbance spectra of BGMK cells infected with either poliovirus PV1 (red), coxsakievirus B5 (green) or echovirus 30 (blue) at 8 h.p.i. in a region selected to highlight where the most substantial differences between cells infected with the different viruses. Each spectra line represents a different sample. The spectral region ranges from 1614 – 650 cm-1. Data is shown for cells infected with three different virus titers: 101, 102 and 103 PFU/ml. 99 Figure 4 - Principal components analysis for BGMK cells infected with three different enteroviruses at 1614 – 650 cm-1 PCA score plot for the region where the most clear differences between cells infected with the three viruses were seen in the raw spectral data. Data was preprocessed with mean center and first derivative of the spectra, using three components. Each point represents one spectrum. This PCA discriminates the absorbance of BGMK cells infected with either poliovirus PV1 (red), Coxsackievirus B5 (green) or echovirus 30 (blue) at 8 h.p.i. using the spectral region from 937 – 1614 cm-1. Data is shown for cells infected with three different virus titers: 101, 102 and 103 PFU/ml. 100 Figure 4b- Principal components analysis for BGMK cells infected with three different enteroviruses at 1614 – 937 cm-1. PCA score plot for the region where the most clear differences between cells infected with the three viruses were seen in the raw spectral data. Data was preprocessed with mean center and first derivative of the spectra, using three components. Each point represents one spectrum. This PCA discriminates the absorbance of BGMK cells infected with either poliovirus PV1 (red), Coxsackievirus B5 (green) or echovirus 30 (blue) at 8 h.p.i. using the spectral region from 937 – 1614 cm-1. Data is shown for cells infected with three different virus titers: 101, 102 and 103 PFU/ml. 101 (a) (b) (c) Figure 5. PLS-DA class predictions for three different classes of enterovirus 102 In Figure 5, each data point represents a single spectrum. PLS Toolbox software generates an optimum threshold for sample classification that is plotted as the dashed line. Spectra that result in Y prediction values greater than the threshold value are classified as a different type of enterovirus. Figure 5a shows the prediction model for poliovirus, Figure 5b shows the prediction model for Coxsackievirus and Figure 5c shows the prediction model for echovirus. Red triangles represent poliovirus, green stars Coxsackievirus and blue squares echovirus. 103 Figure 6 - Principal component analyses for BGMK cells infected with three different enteroviruses at 1614 – 650 cm-1 at three virus titers a. PCA score plot for the region where the clearest differences between cells infected with the three viruses were seen in the raw spectral data at 101 PFU/ml. This PCA discriminates the absorbance of BGMK cells infected with either poliovirus PV1 (red), coxsakievirus B5 (green) or echovirus 30 (blue) at 8 h.p.i. using the spectral region from 1614 – 650 cm-1. 104 b. PCA score plot for the region where the clearest differences between cells infected with the three viruses were seen in the raw spectral data at 102 PFU/ml. This PCA discriminates the absorbance of BGMK cells infected with either poliovirus PV1 (red), coxsakievirus B5 (green) or echovirus 30 (blue) at 8 h.p.i. using the spectral region from 1614-650 cm-1. c. PCA score plot for the region where the clearest differences between cells infected with the three viruses were seen in the raw spectral data at 103 PFU/ml. This PCA discriminates the absorbance of BGMK cells infected with either poliovirus PV1 (red), coxsakievirus B5 (green) or echovirus 30 (blue) at 8 h.p.i. using the spectral region from 1614-650 cm-1. 105 TABLES Region Latent Variables Poliovirus Sensitivity Specificiy Coxsackievirus Echovirus Sensitivity Sensitivity Specificiy Specificiy 650 – 1650 2 1.000 1.000 0.909 0.943 1.000 0.952 650 – 1650 3 1.000 1.000 0.955 0.943 1.000 1.000 650 – 1650 4 1.000 1.000 1.000 1.000 1.000 1.000 1284 – 1400 2 1.000 1.000 0.818 0.514 0.933 0.881 1284 – 1400 3 1.000 1.000 0.955 0.829 1.000 1.000 1284 – 1400 4 1.000 1.000 1.000 1.000 1.000 1.000 Table 1. Shows the specificity and sensitivity values for the different PLS-DA models using two different regions of the spectra and 2 – 4 latent variables. 106 APPENDIX Appendix A - Absorbance of BGMK cells infected with three different enteroviruses from 1400 – 1284 cm-1 All spectra have been normalized at the amide I feature. 107 Appendix B – Principal components analysis using 3PC for the region 1400 – 1284 cm-1 108 1282 cm−1 1284 cm−1 1287 cm−1 13061312 cm−1 1317 cm−1 1327/8 cm−1 1328 cm−1 1330 cm−1 1337/8 cm−1 1339 cm−1 1340 cm−1 1358 /67cm−1 1368 cm−1 1369/70 cm−1 1370/1 cm−1 1370/1/3 cm−1 1373 cm−1 1380 cm−1 1390 cm−1 1395 cm−1 1396 cm−1 1398 cm−1 1399 cm−1 1400 cm−1 Amide III band components of proteins Collagen Amide III band components of proteins Collagen Deformation N-H Amide III band components of proteins Amide III band components of proteins Collagen Stretching C-N thymine, adenine Benzene ring mixed with the CH in-plane bending from the phenyl ring and the ethylene bridge CH2 wagging CH2 wagging Collagen In‐plane C-O stretching vibration combined with the ring stretch of phenyl CH2 wagging Collagen Stretching C-O, deformation C-H, deformation N-H δ(CH2), ν(CC) (polysaccharides, pectin) Stretching C-N cytosine, guanine Stretching C-O, deformation C-H, deformation N-H Deformation N-H, C-H Stretching C‐N cytosine, guanine δCH3 Stretching C-O, deformation C-H, deformation N-H Carbon particle Less characteristic, due to aliphatic side groups of the amino acid residues Symmetric CH3 bending of the methyl groups of proteins CH3 symmetric deformation Extremely weak peaks of DNA & RNA-arises mainly from the vibrational modes of methyl and methylene groups of proteins and lipids and amide groups Symmetric CH3 bending modes of the methyl groups of proteins δ[(CH3)] sym. δ[C(CH3)2] symmetric Symmetric stretching vibration of COO− group of fatty acids and amino acids δsCH3 of proteins Symmetric bending modes of methyl groups in skeletal proteins Specific absorption of proteins Symmetric stretch of methyl groups in proteins Appendix C - Biomolecules found in spectral region with more clear differences (1400 – 1284 cm-1) Table modified from [34] 109 REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 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Umetrics AB, Sweden 1999. 112 APPENDIX C REAL-TIME, CONTINUOUS MEASUREMENT OF CHANGES IN CELLULAR COMPONENTS FOLLOWING VIRAL INFECTION Felipe T Lee-Montiel1, Kelly A Reynolds2, Mark R Riley1§ 1 Agricultural and Biosystems Engineering, University of Arizona, Tucson, Arizona 85721 2 Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona 85724 § Corresponding author Email addresses: FTLM: [email protected] KAR: [email protected] MRR: [email protected] To be submitted to: Applied Spectroscopy 113 ABSTRACT Living cells are microscopic biosensors, constantly perceiving and responding to external stimuli. Cells can undergo dramatic changes in cellular components following an encounter with a toxin or pathogen. The precise details of these changes have been difficult to elucidate using existing methods. A continuous monitoring method for live cells is needed to understand some of the cellular changes in response to different stimuli. Here we present a technique for real-time, continuous measurement of alterations in cellular components following infection with poliovirus. A mammalian cell culture of Buffalo Green Monkey Kidney (BGMK) cells was grown on a Zinc Selenide trapezoid crystal and continuously monitored in situ via attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for approximately 12 h. The cells were grown in a specially designed batch-cell bioreactor that controlled physiological conditions. Infrared spectra were taken every 3 minutes after 24h from seeding the cells on the ZnSe crystal to allow cell adhesion. A feature at 915 cm-1 in between the phosphodiester region 900 – 1300 cm-1 increased about 10% in duplicate experiments after 8 hours post infection, using a poliovirus titer of 106 plaque forming units (PFU)/ml. In response to poliovirus titer 104 PFU/ml, the feature at 967 cm-1 assigned to DNA deoxyribose, increased by an average of 58.57%. These two biochemical changes could potentially be related to the stages of infection. Analogous optical biosensors are being applied in many research areas due to the advantages offered by their small size and ease of implementation. This reagent-free biosensor provides detailed information about cell 114 components and improves on previous methods with potential for use in medicine, toxicology and environmental sciences. 115 INTRODUCTION The ability to track changes in living cells under normal physiological conditions is a vital part of understanding cellular function and the effects of environmental stimuli, such as drugs, toxins and biologically active molecules. Early detection of infectious diseases within an individual often leads to an improved outcome by allowing more effective treatment. Unfortunately, most of the current disease detection methods involve the use of pathogen-specific macromolecules or host antibody production, each requiring days, if not weeks, of pathogen replication in the host before a diagnosis can be made. The mechanisms by which animal viruses, especially non-enveloped viruses, deliver their genomes are poorly understood [1]. This is due in part to technical difficulties involved in direct visualization of viral gene delivery and to uncertainties in distinguishing productive and non-productive pathways caused by the high particle-to-plaque forming unit ratio of most animal viruses. Furthermore, living organisms are extremely sensitive to changes in the environment; they can act as biosensors, detecting otherwise imperceptibly small-scale changes such as the presence of virus particles or trace amounts of toxins. Cell-based biosensors are being applied in medicine, drug discovery, toxicology and environmental monitoring [2]. They are generally constructed by interfacing cells to a transducer that converts cellular responses into signals detectable by electronic or optical devices [3]. These biosensors bridge the gap between in vitro, biochemical assays and in 116 vivo, whole organism experiments. They have the advantages of providing more complex information about biochemical pathways and they can place information gained in a biochemical assay into the context of the living cell. Cell-based sensors can detect phenotypic responses and explore functional information in the native cell environment, leading to a better understanding of the response of all the cellular components as a whole. The major advantages of these sensing arrays over conventional biosensors include rapid and inexpensive analyses, much smaller sample size requirement, high throughput and sensitivity [4]. A good example of a cell-based biosensor is label-free impedance electrical biosensors; these biosensors can continuously track biochemical and morphological changes of adherent cells providing quantitative data from in vivo experiments [5, 6] The ATR-FTIR system employs evanescence waves to measure changes in the surface of transparent infrared material. Optical biosensors that employ evanescent waves have seen widespread utility in both basic and applied research [7-11]. Interest in this field is rapidly growing as this technology helps to probe the activities of living cells, such as cell adhesion and spreading, toxicity, and proliferation [12-15]. Many cellular components emit signals in the mid-infrared region, 4000 – 650 cm-1. Midinfrared spectroscopy can be used to observe changes in these molecular machineries. It can be used to track many aspects of the cell, with broad applications in biomedicine, cellular biochemistry, disease monitoring, and observation of single-celled organisms 117 [16-18]. It has previously been used to detect changes in cellular components following exposure to toxins [19] and to detect cell apoptosis [20]. FTIR has been used to detect herpes infection [21, 22] and to monitor cell changes on infection with poliovirus [43]. Previous studies present a similar approach to detect changes in cell components, but do not apply the methods to virus detection [23, 24]. Miyamoto et al. [23] performed in situ observation of cell adhesion using infrared with cells grown on a crystal, however they used different ATR prism crystal materials, which have different physical and optical properties. Curtis et. al. [25] designed a portable automated bench-top mammalian cellbased toxicity sensor for drinking water applications that incorporates enclosed fluidic biochips containing endothelial cells monitored by Electric Cell-substrate Impedance Sensing technology. A desirable feature for any cell-based biosensor implementation is the capacity for continuous monitoring or the ability to be reused [26]. The goal of this study was to characterize the early infection steps that occur in cells caused by poliovirus infection. In the United States, waterborne disease outbreaks have been associated with treatment deficiencies in the water supply and distribution system contamination [27]. Close to 50% of all waterborne disease outbreaks are due to acute gastrointestinal illness caused by unknown agents [28]. The size and life cycle of viruses can make them difficult to sample and their disease symptoms may be unspecific, meaning viruses may be the most prevalent undiagnosed disease agent [29]. 118 We used an ATR ZnSe crystal covered with a monolayer of BGMK cells to achieve a non-invasive continuous monitoring of the virus infection process using ATR-FTIR. This system allowed us to access near-continuous spectral information (measurements were recorded every 2.6 minutes following infection) and to instantaneously track changes in cellular activity following a stimulus. We present the relative changes in lipids, carbohydrates, proteins, and other cell components caused by the viral infection process in live BGMK cells that characterize the early events in the poliovirus (PV1) infection. 119 RESULTS Cell adhesion to crystal The way in which cells attach in monolayer culture depends on the cell type and the characteristics of the surface. BGMK cells were confirmed to be biocompatible with the ATR ZnSe prism. The cell architecture of the BGMK cells attached to the ZnSe crystal can be seen in Figure 1a. Actin, vinculin and cell nuclei were stained to study cell adhesion of BGMK cells on the ZnSe crystal. The actin filaments are well distributed throughout the cell with many focal adhesion points, indicating healthy cells. The average height of the cells on the ZnSe crystal was approximately 4.42 µm. Figure 1b shows the cells under bright field microscopy, where they can be seen elongated and spread out over the crystal surface. Bioreactor design Figure 2 provides a schematic representation of the experimental setup. The first panel (Figure 2a) shows how the ATR-FTIR functions, indicating how the infrared light passes through the crystal creating an evanescent layer that encompasses the cells. The depth of penetration of the evanescent field is determined by the refractive index of the ATR crystal, the refractive index of the cells, the angle of the prism and the wavelength of the incident light [30]. Using a 45° angle in a ZnSe crystal achieves an evanescence field depth of penetration between 0.3 to 1.6 µm for the midinfrared region, sufficient to penetrate into the cell membrane. 120 A prototype metal plate biochamber for the cell-based biosensor was built entirely out of aluminum. Figure 2b shows the prototype drawn using Solidworks 2010 software. The water jacket surrounds the aluminum base, which is used to control the temperature of the cells on the crystal. The temperature was monitored and found to be stable at 35.6°C at the crystal surface using water at 40°C from the water bath driven through jacket. The chamber is coated in a layer of insulation tape to assist with temperature stability. The acrylic cover with a polydimethylsiloxane gasket is not shown in Figure 2b. Figure 2c shows the complete set up of the experiment with the FTIR machine and water bath. Tracking real-time changes in cells The ability of this system to track an immediate change in the cells was tested by adding trypsin to the cell layer to cause the cells to detach from the crystal surface. The spectra pre- and post-trypsinization is shown in Figure 3. A sharp decrease in the number of peaks and peak heights was observed upon addition of trypsin, corresponding to a decrease in cell components being detected by the FTIR spectroscopy. This also confirms that the cells were attached to the crystal prior to adding the trypsin. Monitoring changes in cell absorbance following virus infection The experimental system successfully recorded complete absorbance patterns from 3600 – 650 cm-1 at 2.6-minute intervals for 8 h.p.i. Figure 4 shows spectra collection of DNA feature (PO2 vibrations) spectrum from a single experiment run for 8 h.p.i. with a poliovirus titer of 106 PFU/ml. There were no baseline shifts in this experiment, allowing 121 tracking of feature heights without normalization. Spectra taken only at 30-minute intervals are shown for ease of viewing. Some interesting regions are shown in more detail with the 2.6-minute readings. The spectral region between 965 – 996 cm-1 which is related to DNA was one of the regions with most interesting changes. In experiments using 104 PFU/ml, changes in the feature monitored every 2.6 min has a total average increase of 58.57% after 8 h.p.i. Changes in absorbance features over time with 106 PFU/ml poliovirus infection Figure 5 shows the results of a duplicate experiment. The upper half of Figure 5 shows the change in the height of the detected absorbance features in the spectra of cells infected with 106 PFU/ml of poliovirus monitored every 2.6 minutes for 8 h.p.i. Changes include a jump in the feature absorbance height at 1084, 1239 and 1400 cm-1 for a 120minute window from approximately 160 minutes post infection. These features correspond to collagen, phosphate bands and methyl groups in skeletal protein. The peak related to amide I located at 1646 cm-1 decreased over time. The peaks corresponding to lipids, 2853, 2873, 2924 and 2955 cm-1, displayed a fluctuating absorbance pattern. In this lipid region are CH2 and CH3 symmetric and asymmetric vibrations, which increase with chain length. Changes in absorbance peaks over time with 104 PFU/ml poliovirus infection The lower half of Figure 5 shows the change in the height of the detected absorbance 122 features in the spectra of cells infected with 106 PFU/ml and 104 PFU/ml of poliovirus monitored every 2.6 minutes for 8 h.p.i. Figure 6 shows continuous monitoring of four biochemical features present in BGMK cells. DNA PO2 vibrations located at 1086 cm-1 increased over time, Amide I and Amide II protein bands located at 1645 cm-1 and 1545 cm-1 respectively decreased after 4 h.p.i, lipids (vibrations of CH2 in acyl chains) 2853 cm-1 showed a minimal decrease over 8 h.p.i. Figure 7 shows the changes in DNA PO2 vibrations feature located at 1086 cm-1; only 30-minute intervals are shown for ease of visualization. 123 DISCUSSION In the present study, ATR-FTIR was used to study the kinetics of development of infection with poliovirus PV1 virus. ATR-FTIR spectroscopy uses a multi-bounce infrared pattern that creates evanescence in the trapezoidal ZnSe crystal. This allowed scanning of the complete monolayer of cells simultaneously, obtaining more uniform information than by examining a single cell or a pinpoint sample of cells. Using the ATR method provides an overall view of the cell states within each experimental batch, overcoming the fluctuating processes in individual living cells [31]. A new ATR biochamber was designed and built for these experiments. This biochamber enabled control of the physiological conditions of the system to permit cell growth and create a realistic cellular environment. The standard, horizontal ATR plate made of stainless steel reacted with the cell culture media, causing corrosion. This has been observed previously and occurs because buffers with amino acids react with the stainless steel [32-34]. The new biochamber was constructed using 100% aluminum, which prevented further problems with corrosion. The chamber was held in place with four large screws and sealed with a silicone gasket. The silicone caused negligible interference with the spectra. One of the challenges for the design of this cell-based biosensor is the proper maintenance of the environment suitable for cell or tissue survival, which requires a humid environment. The presence of high water concentrations in the cell culture media 124 causes absorbance of mid-IR region. This is a major experimental challenge for the accurate IR absorption measurements from cell culture [35]. Interference from water is caused by its strong absorption bands in the region between 3600 – 3200 cm-1[31] . The commercially available ATR plate with a volume of 1.5 ml did not hold a sufficient volume of media to complete the experiment, thereby limiting nutrient availability for the cells. The increase in chamber volume to a capacity of >5 mL allowed the cells to survive longer; 2 ml of media was sufficient for an 8 h experiment. The acrylic lid also prevented evaporation of the media and thereby reduced the noise in the spectra. The cells adhered to the surface of the crystal with or without a functionalization layer. The only factor in this protocol that appears to affect cell adhesion to the crystal surface is hydrophobic residuals, which can be cleaned from the surface. Confocal microscopy was used to take 3D images of the cytoskeleton of the cells on the crystal surface to characterize the height and degree of cell adhesion across the surface. Cell adhesion plays an important role in many fundamental cellular processes such as cell morphology [43]. One of the distinctive features of adhesion to a rigid surface is the ability of forces to develop at the contact site. Such forces can be external, such as shear flow in blood vessels, or can be generated by the cells own contractile system. The main sites of cell adhesion to the substrate are focal adhesions (vinculin). These complex multimolecular assemblies link the extracellular matrix, via membrane-bound 125 receptors, to the cell’s cytoskeleton [44]. Focal adhesions are therefore also the sites at which forces are transmitted to the substrate [41]. The cells grown on a ZnSe surface had a height average of 5.43 µm; this height indicates that the cells spread evenly across the crystal surface. The BGMK cells also had many well-distributed focal adhesion points, showing a good attachment to the surface. Cells attached only to an underlying stiff surface differ in their spreading and cytoskeletal organization [43], for example, in cell types that grow preferentially on hard matrices, the tension will stimulate such a cell to extend about its periphery [45]. Previous experiments exhibited a cell height average of approximately 6.87 µm on titanium oxide crystals and 8.25 µm on glass. On very stiff substrates, stress fibers and strong focal adhesions predominate, suggesting equal contraction [42]. These observations by confocal microscopy show that the cells are biocompatible with the ZnSe. The cell height of 5.43 µm mean that only a maximum of 36% is probed by the penetration of the evanescence that reaches a range between 0.3 – 1.6 µm in the IR region between 650 – 4000 cm-1. The DNA feature at 1084 cm-1 corresponding to the phosphate vibrations increased in peak absorbance height over time. This could be related to the activity of the virus in the cell. The amide I and amide II peaks (1645 and 1545 cm-1) are associated with the amount of biomass. When the virus causes cell lysis, the cells will no longer be attached to the surface. The decrease in the absorbance of the amide peaks may be related to a 126 reduction in protein due to cells starting to lyse or change shape such that they are no longer in the evanescence field. The ability to study in vivo dynamic processes has numerous potential applications. This new method of continuous measurement of a live cell using the ATR biosensor is not constrained to viral detection. The most immediate application in this respect is to boost molecular and morphological data by measuring changes of biomolecules in single cells as a function of time following a stimulus (for example, a growth factor, nanoparticles, other viruses or toxins) [41, 42] or to compare different cell types (for example, cancerous and non-cancerous) following the same stimulus. It could be developed further for more complex samples (clinical blood samples or drinking water), although using the existing system the samples would first need to be filtered or centrifuged. This method has given an insight into the real-time changes that occur in cellular components upon virus infection. It will be interesting to perform future comparisons between different viruses and to track the steps of infection of each virus, as there is limited available information about infection kinetics with this timing and level of detail. For example, it is know that after 30 minutes the virus will release RNA, but the detailed biochemistry in between these steps could be elucidated with an ATR-FTIR biosensor. We see this experiment with poliovirus as a starting point to investigate other viruses with this method. 127 CONCLUSIONS Cell-based biosensors have promising potential in numerous applications ranging from pharmaceutical screening to environmental monitoring. Cellular responses of a monolayer of mammalian cells caused by the poliovirus were studied using ATR-FTIR spectroscopy. Changes in the IR spectra were correlated with some changes in the molecular cell components at different stages of the infection process. A custom designed biochamber allowed near-continuous acquisition of real-time spectral data for many hours following infection. The results of this study attest to the unexploited potential of ATR-FTIR spectroscopy for tracking molecular changes in cells following an encounter with an environmental stimulus. This type of biosensor could be used for continuous monitoring of viral agents or toxic chemicals in drinking water distribution systems for a rapid hazard assessment in potential public health threats. Potentially, the first use of this biosensor could be used as a presumptive test and not as a substitute for standard analytical tests. 128 FIGURES a b FIGURE 1. Microscopy of cells attached to ZnSe crystal a) Confocal image of BGMK cells adhered to a ZnSe ATR prism. Actin (red), Vinculin (green) and cell nuclei (blue) are shown. b) Bright microscopy image of BGMK cells adhered to a ZnSe ATR prism. Confocal image of cells on the ZnSe crystal showing the focal addition points, the actin filaments and the height of the cells. The height of the cells is linked to the evanescence wave of penetration of the ATR, which has an estimated depth of penetration between 0.3 129 – 1.6 µm allowing us continuous monitoring of the cellular processes. This parameter depends on the angle of crystal (45°) and the refractive index of the prism material. FIGURE 2 – Experimental setup a) Schematic of the ATR-FTIR biosensor. b) Schematic of ATR-FTIR biochamber design. c) Complete experimental setup. Absorbance 130 Wavenumbers cm-1 FIGURE 3. Continuous spectral collection of BGMK cells attached to a ZnSe crystal Cells were scanned every 2.6 minutes to track any change in their biochemistry. Scans 14 – 20 show the fast response of the biosensor to changes in the cell adhesion produced by trypsin, which detached the cells from the surface causing a reduction in absorbance. 131 DNA (band due to PO2 ! vibrations) 20min 8 hr FIGURE 4. Spectra collection of BGMKS cells infected with a poliovirus titer of 104 PFU/ml. DNA feature is shown between the region 1250 – 945 cm-1 Each series represents continuous collection of the spectra with 30 minutes intervals to track cellular changes cause by the virus infection. 132 PV1 106 PFU !#*" 8%%" 8''" !#&$" (&&" !#&" ('*" 7%$" !#%$" PV1 106 PFU !#&$" 7%$" 78%" !#&" %!)'" %!()" !#%$" %&*7" 78%" %!()" !#%" %)!!" !#%" %)$'" %&*7" %&'(" !#!$" %$)$#'" !#!$" %')$#7" %)!!" !" !" '!" %&!" %(!" &)!" *!!" *'!" )&!" )(!" +,-./01"231/",-40563-" %)$'" &($*" !" !" '!" %$)$#'" &(8*" %&!" %(!" &)!" *!!" *'!" )&!" )(!" &7&)#$'" +,-./01"231/",-40563-" PV1 104 PFU PV1 104 PFU !#*" !#)$" 87)#$" !#&$" (''#(" 78$#(" !#&" %!)8#%" %!()#8" !#%$" %&)%#7" !#%" !#!$" !" !" '!" %&!" %(!" &)!" *!!" *'!" )&!" )(!" +,-./01"231/",-40563-" (*(#(" !#*$" 7%)#%" !#*" 7'8#%" !#&$" %!()#8" !#&" %)!!" !#%$" %)$'" !#%" %$)$#'" !#!$" %'$" !" &($!#*" 8&%" !#)" %%%(#$" %&*!#*" %)!!" %)$'#7" %$)'#'" !" '!" %&!" %(!" &)!" *!!" *'!" )&!" +,-./01"231/",-40563-" Figure 5. BGMK peaks detected using OMINIC 7.3 software after an infection process Peaks heights were monitored every 2.6 min for 8 h.p.i. to study the early steps of poliovirus infection. %')$#7" 133 BGMK cells infected with PV1 10^6 peak height average 0.25 Absorbance 0.2 0.15 1084 1545.6 0.1 1645.9 2853 0.05 0 0 60 120 180 240 300 360 420 480 minutes post infection Figure 6. BGMK peak average height monitoring every 2.6 minutes for cells infected with Poliovirus 106 PFU/ml for 8 h.p.i. to study the early steps of poliovirus infection. 134 Peak assigned to DNA dexoyribose, increased in an average of 58.57%. 975.8 cm-1 967.1 cm-1 !#(" !#!)" !#!$7" !#!'" *+,-.+/012" " *+,-.+/012" !#!$" !#!&" !#!%" !#!(7" !#!(" !#!$" !#!!7" !" !" !" &!" ($!" ('!" $%!" )!!" )&!" %$!" %'!" 340562," !" &!" ($!" ('!" $%!" )!!" )&!" %$!" %'!" 340562," Figure 7. BGMK peak height monitoring every 2.6 minutes for 8 h.p.i. to study the early steps of poliovirus infection 135 MATERIALS AND METHODS Cell Culture Cell culture was performed as previously described [40] with the following modifications: 20 mM HEPES buffer was used instead of sodium bicarbonate and DMEM 1X media was used without phosphates (MP Biomedical) to avoid any phosphates interference. Virus Titers Multiple serial dilutions using DMEM 1X without phosphates (MP biomedical) were made from the initial stock concentration of 107 PFU/ml of purified vaccine strain poliovirus type 1 (PV1, LSc-2ab). These were used in experiments with varying infection times. FTIR Spectroscopy Infrared spectra of healthy cells and cells post-infection were collected in transmission mode on a ThermoNicolet Magna 560 FT-IR equipped with a liquid-nitrogen cooled MCT-A detector, KBr beamsplitter, and infrared light source. The spectral collection parameters used were 128 co-added scans (requiring 2.5 minutes) and a spectral resolution of 2cm-1 with a 68% aperture opening. A trapezoid ZnSe 45° angle, 80mm x 40mm x 10mm (Pike Technologies) was used for all experiments. 136 Biochamber Design To analyze the living culture in near real-time, we constructed a horizontal ATR biochamber of 100% aluminum around the ATR crystal. We increased the height of the channel to hold a total of 5ml of media. We also included an acrylic cover to prevent evaporation. The chamber was held together with a PDMS gasket between the acrylic top and the aluminum body with 4 screws to compress the gasket and seal the top. The sides of the chamber were further insulated by polyethylene tape foam. A schematic view of the environment control system is shown in Fig. 2c. The system consisted of a water bath (VWR) with a temperature controller and a pump to circulate hot water (41°C) in the biochamber (around the outside of the gasket). The temperature of the water bath allowed us to maintain the crystal at a constant 36°C. Experimental Setup After the cells in culture were confluent in the T25 flasks, cells were trypsinized and resuspended. Cells were quantified using a hemocytometer and approximately 106 cells in a total volume of 2ml of DMEM media with phosphates seeded into the biochamber directly onto the crystal. The biochamber was held for 24h in an incubator at 36°C to allow the cells to adhere to the crystal, forming a confluent monolayer. Cells were infected with 0.5ml of poliovirus samples at different titers and an additional 1.5ml of DMEM media without phosphates was added to the biochamber. The biochamber was used inside the FTIR sample compartment, connected to the water bath as described above. 137 The surface of the crystal was treated occasionally with 5% hydrogen peroxide for 1 minute to remove any hydrophobic residuals and make the surface more hydrophilic and favorable for cell adhesion. Between experiments, the crystal was rinsed with soapy water and distilled water, then sterilized using 70% ethanol. Continuous Data Measurement Collection of the spectra was performed using OMNIC 7.3 from Thermo Electron Corporation. We recorded spectra from the cells every two minutes following virus infection for several hours. Collection was automated using Macros/Basic OMINC software and converted to a single Excel file using a Matlab code. Data Analysis Absorbance was computed using the DMEM without phosphates spectrum as a background. Automatic water vapor and CO2 correction was implemented using this software. The analysis of spectra from the cell monolayer was carried out in Excel 2007 where we were looking to distinguish small variations in data that is strongly collinear and noisy and has many X-variables [37, 38]. Confocal Microscopy An Actin Cytoskeleton and Focal Adhesion Staining Kit (Millipore) was used to visualize the cell architecture of the BGMK cells attached to the ATR ZnSe prism. Actin filaments were stained using TRITC conjugated Phalloidin. Cell nuclei were stained with DAPI 138 and viculin adhesion points were stained with FITC. The average height of the cells on a ZnSe prism was determined using a z-axis stack of cell images. ACKNOWLEDGEMENTS FLM was funded by a CONACYT fellowship. 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