Department of Electronic and Electrical Engineering

UNIVERSITY COLLEGE LONDON TORRINGTON PLACE LONDON WC1E 7JE

Novel Nanosensors for Rapid Detection

of Antibiotic Resistance in Bacteria

MEng Project

Final Report

Jobie Budd

Michal Wojcicki

Supervisor: Prof Rachel McKendry

April 2016

DECLARATION

We have read and understood the College and Department’s statements and

guidelines concerning plagiarism.

We declare that all material described in this report is all our own work except

where explicitly and individually indicated in the text. This includes ideas

described in the text, figures and computer programs.

Name: ……………………………… Signature: ………………………

Name: ……………………………… Signature: ………………………

Name: ……………………………… Signature: ………………………

Date: ………………………………

Authors’ contributions ­ PROJECT WORK: Jobie Budd and Michal G. Wojcicki

General Overview of the work division: During the first half of the project duration time (Term 1) both authors worked closely together on all the laboratory work that was done, while during the second half (Term 2) of the project the work was divided into two separate approaches each researched by one group member individually (Jobie focusing on developing Lateral Flow Paper Tests; Michael focusing on characterising nanomechanical vibrations detection via AFM and developing Centrifuge­based Ab binding test).

Project work done by both authors together: Initial Characterisation of Bacteria Growth Bio­Layer Interferometry Characterisation of Antibody Binding

Project work done individually by Jobie Budd: CORE PROJECT: Developing Lateral Flow Paper Tests ­ all parts of the project Characterising CFU/mL Curve for Bacteria Growth ELISA Antibody Binding Test E. coli Susceptibility Tests ­ MIC and MBC, LIVE/DEAD stain

Project work done individually by Michal G. Wojcicki: CORE PROJECT: Sensing bacteria vibrations with AFM ­ all parts of the project CORE PROJECT: Developing Novel Centrifuge­Based Binding Test ­ all parts of the project Studying Tween Susceptibility of Bacteria Optimising Conjugation of Antibody to Gold Nanoparticles

Authors’ contributions ­ REPORT: Jobie Budd and Michal G. Wojcicki

Below a colour­coded Table of Contents of the report is presented with the red colour signifying the report chapters written by Jobie, the blue colour marking the chapters written by Michal and black colour meaning the chapters written by both authors together. 1. Introduction

2. Theory and Background

2.1. Antibiotic Mechanism of Action and Bacterial Resistance Mechanisms

2.2. Novel Nanosensors for AMR

2.2.1. Microfluidic and Nanoparticle­Based Rapid Bacterial Sensors

2.2.2. Nanomechanical Vibration­Based Sensors

3. Experimental Methods

3.1. Bacteria Characterisation Methods

3.1.1. Characterisation of E. coli Strains

3.1.2. Susceptibility Tests

3.2. Antibody Binding Assays

3.2.1. Bio­layer Interferometry Methods

3.2.2. ELISA Methods

3.2.3. Methods for Optimising Ab­AuNP Conjugation and Blocking

3.2.4. Novel Method for Testing Ab­Bacteria Binding via Centrifugation

3.3. Methods for Development of Rapid Lateral Flow Test of AMR

3.3.1. Buffer Optimisation for Lateral Flow Test ­ Tween Susceptibility Study

3.3.2. Lateral Flow Test Design & Preparation

3.3.3. Image Processing Quantitative Analysis

3.4. Methods for Sensing Nanomechanical Bacterial Vibrations with AFM

3.4.1. Atomic Force Microscopy Principles

3.4.2. AFM Setup, Calibration and Measurements

3.4.3. Signal Processing and Analysis with MATLAB

4. Results and Analysis

4.1. Determination of Bacterial Susceptibility

4.1.1. MIC and MBC

4.1.2. Live/Dead Stain as MIC Test

4.1.3. Conclusions for Bacterial Susceptibility

4.2. Development of Rapid Binding Assay for Bacterial Antibodies

4.2.1. Bio­layer Interferometry

4.2.2. ELISA

4.2.3. Novel Centrifuge­Based Binding Test

4.2.3.1. Testing Antibody Binding

4.2.3.2. Characterisation of Bacteria Centrifugation

4.2.3.3. Characterisation of Ab­AuNP Conjugates Centrifugation

4.2.3.4. Optimisation of Bacteria:Ab­AuNP Solutions Ratio

4.2.3.5. Conclusions for Centrifuge Test

4.2.4. Conclusions for Antibody Binding Assays

4.3. Development of Rapid Lateral Flow Test of AMR

4.3.1. Buffer Optimisation for Lateral Flow Tests (Tween T20 Study)

4.3.2. Separating Bacteria and Lysed Components using Nitrocellulose Membrane

4.3.3. Using LPS Release as a Quantitative Measure of Bacterial Lysis

4.3.4. Antibody Conjugated Nanoparticles as Specific Marker for Bacteria on Membrane

4.3.5. Optimisation for Development of Rapid Lateral Flow Test

4.3.6. Testing of Lateral Flow Susceptibility Test

4.3.7. Conclusions for Lateral Flow Bacterial Susceptibility Test

4.4. Nanomechanical Bacterial Vibrations for Point­of­Care AMR Sensor

4.4.1. Bacteria Vibrations

4.4.2. Coverage of Cantilever with Bacteria

4.4.3. Modifying Media

4.4.4. Frequency Spectra

4.4.5. Conclusions for Bacterial Vibrations

5. Conclusions

5.1. Detection of Antibody­Bacteria Binding with Centrifuge

5.1.1. Summary

5.1.2. Proposals for Future Work

5.2. Lateral Flow Test as a Rapid Test for AMR

5.2.1. Summary

5.2.2. Proposals for Future Work

5.3. Nanomechanical Bacterial Vibrations for AFM­Based AMR Sensor

5.3.1. Summary

5.3.2. Proposals for Future Work

6. References

7. Appendix

7.1. Appendix A: Supplementary Data

7.2. Appendix B: Evolution of Objectives

7.3. Appendix C: Acknowledgements

Novel Nanosensors for Rapid Detection of Antibiotic

Resistance in Bacteria

ABSTRACT

Antimicrobial Resistance among bacteria (AMR) poses an increasing threat to public health globally. Current susceptibility testing methods are costly and require several days with laboratory resources, leading to the general overprescription of antibiotics, which further induces AMR.

The bold aim of this Masters Project was to engineer innovative low cost, rapid antibiotic susceptibility tests for use at the point­of­care combating the spread of AMR. The project is aligned to i­sense, a major EPSRC programme which aims to create a new generation of early warning sensing systems for infectious diseases using mobile phones. Herein two complementary approaches were investigated: a novel lateral flow immunochromatographic optical using a smartphone camera reader and a nanomechanical sensor approach to detect recently discovered vibrations of living bacteria and the response to antibiotics within minutes.

This report is structured as follows: the first chapter describes the challenge of antibiotic resistance and the second chapter reviews recent research into novel methods of rapid AMR detection. Chapter 3 describes the experimental methods used in this project spanning from bacterial culture to Atomic Force Microscopy and the key results are shown in Chapter 4. The overall conclusions and proposed future work is described in Chapter 5.

The most significant findings of the project are as follows: We successfully showed that the lateral flow tests could distinguish between live E. coli cells from lysed dead cells based on porosity using a dead/alive stain. The feasibility of susceptibility testing with different antibiotic concentrations was demonstrated and the results benchmarked to gold­standard lab methods. The second approach based on nanomechanical vibrations was also able to detect live bacteria immobilised on the AFM cantilever and benchmarked to a recent Nature Nanotechnology publication. The work highlights the importance of surface coverage on the cantilever and the need for semi­automated data analysis. During the course of this work, a third, novel approach was introduced based on centrifugation of bacteria suspended in a solution containing antibodies bound to gold nanoparticles. This approach was able to specifically recognize surface antigens on different E. coli strains.

To conclude this early stage research demonstrates the feasibility of optical and mechanical sensors to rapidly detect antibiotic susceptibility. Future work should focus on improving the reproducibility, the specific capture of bacteria, data analysis and testing clinical samples. If successful, these sorts of innovative sensor technologies could dramatically improve the stewardship of antibiotics at the point­of­care, benefitting individual patients and populations.

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Table of Contents

1. Introduction 4

2. Theory and Background 5

2.1. Antibiotic Mechanism of Action and Bacterial Resistance Mechanisms 5

2.2. Novel Nanosensors for AMR 6

2.2.1. Microfluidic and Nanoparticle­Based Rapid Bacterial Sensors 6

2.2.2. Nanomechanical Vibration­Based Sensors 7

3. Experimental Methods 9

3.1. Bacteria Characterisation Methods 9

3.1.1. Characterisation of E. coli Strains 9

3.1.2. Susceptibility Tests 10

3.2. Antibody Binding Assays 11

3.2.1. Bio­layer Interferometry Methods 11

3.2.2. ELISA Methods 12

3.2.3. Methods for Optimising Ab­AuNP Conjugation and Blocking 12

3.2.4. Novel Method for Testing Ab­Bacteria Binding via Centrifugation 16

3.3. Methods for Development of Rapid Lateral Flow Test of AMR 26

3.3.1. Buffer Optimisation for Lateral Flow Test ­ Tween Susceptibility Study 26

3.3.2. Lateral Flow Test Design & Preparation 28

3.3.3. Image Processing Quantitative Analysis 29

3.4. Methods for Sensing Nanomechanical Bacterial Vibrations with AFM 30

3.4.1. Atomic Force Microscopy Principles 30

3.4.2. AFM Setup, Calibration and Measurements 31

3.4.3. Signal Processing and Analysis with MATLAB 35

4. Results and Analysis 37

4.1. Determination of Bacterial Susceptibility 37

4.1.1. MIC and MBC 37

4.1.2. Live/Dead Stain as MIC Test 39

4.1.3. Conclusions for Bacterial Susceptibility 40

4.2. Development of Rapid Binding Assay for Bacterial Antibodies 41

4.2.1. Bio­layer Interferometry 41

4.2.2. ELISA 43

4.2.3. Novel Centrifuge­Based Binding Test 44

4.2.3.1. Testing Antibody Binding 44

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4.2.3.2. Characterisation of Bacteria Centrifugation 46

4.2.3.3. Characterisation of Ab­AuNP Conjugates Centrifugation 48

4.2.3.4. Optimisation of Bacteria:Ab­AuNP Solutions Ratio 50

4.2.3.5. Conclusions for Centrifuge Test 52

4.2.4. Conclusions for Antibody Binding Assays 52

4.3. Development of Rapid Lateral Flow Test of AMR

4.3.1. Buffer Optimisation for Lateral Flow Tests (Tween T20 Study)

4.3.2. Separating Bacteria and Lysed Components using Nitrocellulose Membrane 58

4.3.3. Using LPS Release as a Quantitative Measure of Bacterial Lysis 61

4.3.4. Antibody Conjugated Nanoparticles as Specific Marker for Bacteria on Membrane 64

4.3.5. Optimisation for Development of Rapid Lateral Flow Test 68

4.3.6. Testing of Lateral Flow Susceptibility Test 70

4.3.7. Conclusions for Lateral Flow Bacterial Susceptibility Test 71

4.4. Nanomechanical Bacterial Vibrations for Point­of­Care AMR Sensor 72

4.4.1. Bacteria Vibrations 72

4.4.2. Coverage of Cantilever with Bacteria 73

4.4.3. Modifying Media 75

4.4.4. Frequency Spectra 78

4.4.5. Conclusions for Bacterial Vibrations 81

5. Conclusions 83

5.1. Detection of Antibody­Bacteria Binding with Centrifuge 83

5.1.1. Summary 83

5.1.2. Proposals for Future Work 83

5.2. Lateral Flow Test as a Rapid Test for AMR 85

5.2.1. Summary 85

5.2.2. Proposals for Future Work 85

5.3. Nanomechanical Bacterial Vibrations for AFM­Based AMR Sensor 87

5.3.1. Summary 87

5.3.2. Proposals for Future Work 87

6. References 89

7. Appendix 91

7.1. Appendix A: MATLAB Programs for Analysis of Bacteria Vibrations 91

7.2. Appendix B: Evolution of Objectives 95

7.3. Appendix C: Acknowledgements 95

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1. Introduction

The introduction of widespread use of antibiotic drugs in the 20th century was followed by a spread of antibiotic resistance (also referred to as “antimicrobial resistance” or AMR) among pathogenic microorganisms [1].

The misuse of antibiotics (the preventive over­prescription in case of infections not caused by bacteria, e.g. flu or not finishing the full treatment cycle by the patient) and a decline in development of new antibiotics have contributed to a recently accelerating spread of antimicrobial resistance amongst societies often originating and concentrating around hospital environments [1].

The over­prescription of antibiotics is a result of the ‘gold­standard’ bacterial susceptibility testing methods requiring skilled labour and laboratory facilities and, arguably most importantly, timely ­ the decision about treatment method is being made in the doctor’s office during a short patient’s visit while waiting for the results of the susceptibility test would delay the beginning of the treatment by up to few days.

This could be changed with a successful development of a rapid AMR tests available in the doctor’s practice or for patient self­testing.

The WHO reports more than 50% of reported E. coli infections in five of six world regions are resistant to 3rd generation cephalosporins (a class of B­Lactam antibiotics often used as a second­line treatment) or fluoroquinolones[1]. The high proportions of resistance to these treatments relies on the use of carbapenems, which are a last­resort treatment[1] and generally more expensive (as well as more scarce in low­resource settings)[1].

Rapid POC (Point of Care) bacterial diagnostic and susceptibility tests are urgently needed as preventive measure to address antibiotic resistance by limiting the over­prescription of antibiotics.

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2. Theory and Background

2.1. Antibiotic Mechanism of Action and Bacterial Resistance Mechanisms

Antibiotics can be generally defined as compounds toxic to bacteria, but not harming the host organism. They offer a wide range of mechanisms of action including compromising the ability of the bacterium to synthesise the cell wall, imposing stress on the cellular membrane or forcing depolymerisation of the cytoskeleton. In all cases they ultimately lead to the death of the pathogens either by directly killing them or disabling their ability to reproduce.[2]

Microorganisms gain the ability to resist the effects of antimicrobial drugs due to the natural selection processes. Such ability may also be based on a wide spectrum of resistance mechanisms from preventing the drug molecules from entering the cell to using complex, protein­based nanomachines pumping the antibiotic out through the cell’s membrane. Other methods may include production of enzymes capable of neutralizing the drug ­ an example of such enzyme is β­lactamase produced by resistant E. coli bacteria which inhibits the binding of ­lactam antibioticsβ

to the cell wall by breaking the antibiotics structure through hydrolysis.[2]

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2.2. Novel Nanosensors for AMR

2.2.1. Microfluidic and Nanoparticle­Based Rapid Bacterial Sensors

The specific detection of bacteria in point­of­care tests is a novel field, making use of recent developments in the biofunctionalization of nanomaterials and the increasing abundance of smartphones globally [3][4]. The potentials of telemedicine to provide more informed healthcare choices has called for the development of mobile, rapid and accessible diagnostic sensors [5]. Microfluidic diagnostic tests are already used widely as point­of­care devices, most often in the form of lateral flow pregnancy tests or more recently, home HIV testing [6]. Microfluidic tests can also provide a fast means of detection of bacteria in a sample (compared to conventional methods requiring incubation)[3], although often secondary analytes (such as pH changes caused by bacterial products [7]) are quantified, as the concentration of specific bacterial antigens are often too low for sensitive quantitative detection. The low concentration and relative complexity of bacterial cells in comparison to secondary antigens provide difficulties for the rapid specific detection of bacteria in a sample containing large numbers of other microbiology, necessary for the rapid diagnosis of infections in bodily fluids with a high cell count, such as blood[8]. This means that at the moment, bacteria­detecting microfluidic devices not not widely used for diagnostic purposes. The development of bacterial detection assays using novel nanomaterials such as quantum dots have been shown to specifically detect the lower concentrations required for diagnosis[9]. A functionalised glass capillary device was used to immobilise and detect whole E. coli cells in solution at concentrations as low as 5 CFU(Colony Forming Units)/ml[9]. Using fluorescent markers such as quantum dots often requires a fluorescent reader, making the device more expensive and less accessible. An alternative using a lateral flow test and visible chromatographic assay has been used to detect E. coli at similar concentrations, although passage of antigens through the microfluidic membrane first required cell lysis via lysis buffer[10]. Inspired by this method, a main aim of this project was to develop a microfluidic susceptibility test, based on the detection of the products of bacterial lysis ­ associating detection with lysis and antibiotic susceptibility.

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2.2.2. Nanomechanical Vibration­Based Sensors

2.2.2.1. Introduction

Recent publications [11­14] describe an alternative method for determining bacterial susceptibility to antimicrobial agents based on detecting nanomechanical vibrations of bacteria.

Evidence has been provided that live bacteria immobilised on an AFM cantilever introduce mechanical vibrations at nanometre range distinguishable from the background noise. It was further shown that such vibrations depend on the liquid buffer in which the experimental setup is immersed with nutrient media increasing the signal and unfavourable media (e.g. a solution containing antibiotic to which bacteria are susceptible) causing a temporary or permanent decay in vibrations.

This system can therefore be used to test bacterial susceptibility. If a particular strain of bacteria is resistant to the applied antimicrobial agent the nanomechanical signal will remain unaffected. The signal may also decay temporarily subsequently returning to original levels within a typical timescale of minutes ­ this is attributed to metabolic shock [11]. If the strain is susceptible to the tested antibiotic the signal will decay to the noise baseline and remain at that level permanently even after the buffer has been changed back to a favourable and nutritious medium free from any antimicrobial agents. This way an ultimate death of bacteria can be detected.

Considering that this response to the antibiotic typically can be detected within minutes, a great improvement on current tests [15], the above phenomenon could therefore be utilised for the development of a rapid, point­of­care AMR biosensor e.g. in a form of a device analysing patient’s urine sample in a doctor’s office. This would require overcoming certain constraints such as the high cost and the size of an AFM instruments, the skills and relatively long time (currently approx. 1­2 hours) required for sample preparation as well as introducing specificity of bacterial immobilisation from the patient’s sample.

2.2.2.2. Literature Review

Longo et. al. [11] provide evidence that live E. coli and S. aureus bacteria immobilised on the surface of an AFM cantilever via APTES linkers [(3­Aminopropyl)triethoxysilane molecules] introduce time­dependent, low frequency mechanical fluctuations with typical amplitude ≈10nm (compared to ≈0.1nm background noise of a bacteria­free cantilever) and a typical time­scale ranging from 5 milliseconds to 10 seconds (0.1 Hz to 200 Hz).

The group has also shown media­dependent variations in the signal which is larger in a nutritious Lysogeny Broth (LB) than it is in a ‘neutral’ phosphate buffer saline (PBS) and which further increases if the LB is glucose­enriched. Hence the study suggests that the origin of the vibrations is the metabolic activity of the bacterial cells, specifically their internal machinery including dynamic membrane structures such as ionic channels or porins (trans­membrane proteins allowing diffusion of large molecules) [16­17] or molecular motors such as ATPase whose activity also depends on the metabolic action [18­19].

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Finally, they demonstrated that introducing Ampicillin (antibiotic) to the medium results in a decay of vibrations of E. coli and that the signal remains at baseline level even after exchanging the medium back to antibiotic­free LB which implies the bacteria have been killed and indicates susceptibility to the treatment. On the other hand, when an Ampicillin­resistant strain was used the signal decayed less and ‘regenerated’ close to the original level within minutes while the bacteria were still in the Ampicillin­rich medium. Another demonstration of AMR presented a complete decay of vibrations in the Kanamycin (antibiotic) environment followed by the partial regeneration of the signal when the medium was exchanged back to LB (see Figure 1). These results portray a possible method to develop a nanomechanical vibration based AMR detector.

Figure 1: The effect of Kanamycin and Ampicillin antibiotics on the vibrations of E. coli bacteria immobilised on an AFM cantilever as demonstrated by Longo et. al. [11] TOP: The graph shows the flattened signal of vertical deflection of the cantilever in different media. Note: each signal section duration is 30 seconds. The time indicated on the x­axis is the beginning time of the recording of signal in each section. First “PBS” section represents bacteria­free cantilever in PBS and serves as a baseline. “B” is the immobilisation of bacteria (in situ), “K” is the introduction of Kanamycin and “A” is the introduction of Ampicillin into the system. The fluctuations can be seen for bacteria in PBS and for bacteria in LB. The signal decays after Kanamycin is introduced but regenerates in a following stage of LB wash indicating Kanamycin resistance. A further step of introducing Ampicillin kills the bacteria which is evident from the fact that the signal stays low after another LB wash. BOTTOM: The corresponding quantification of the fluctuations represented as variance of the signal. This shows the increase in vibrations in LB with respect to PBS as well as the partial regeneration of the signal after the first LB wash. Figure from [11].

In this report a method to reproduce and evaluate the published work using a different procedure and setup is presented. The experimental methods are described in chapter 3.4., the results presented in chapter 4.4. and the conclusions drawn in chapter 5.3. The work was aiming to verify the vibrations of bacteria and to ultimately improve the method by using antibodies instead of APTES linkers for specific capture of bacteria of interest from the sample bringing it one step closer to the proof­of­concept device. This gave rise to the work characterising the efficacy of an antibody specific against strains BL21 and K12 of E. coli bacteria used in the experiment as described in chapters 3.2. – Methods, 4.2. – Results and 5.1. – Conclusions.

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3. Experimental Methods

3.1. Bacteria Characterisation Methods

3.1.1. Characterisation of E. coli Strains E. coli laboratory strains BL21 and K12 were used in experiments. Both strains lack the LPS (Lipopolysaccharide) O­chain molecule, rendering them non­pathogenic but also ‘rough’ ­ more susceptible to cell wall compromising antibiotics and their surfaces more hydrophobic than ‘smooth’ wild strains [22]. Nonetheless, these lab strains can be treated as model organisms for pathogenic strains of E.coli and other gram­negative bacteria. In all cases unless otherwise stated, colonies were grown overnight in LB (Lysogeny Broth) at 37°C and 250 rpm. The viable cell count of bacterial cultures was quantified to give a measure of living bacteria (assuming each colony­forming­unit corresponds to one live cell) in solution from a spectrophotometer absorbance result. The optical absorbance of serial dilutions (from grown concentration) of both bacterial cultures were measured, and plotted against the mean growth (colonies counted) on agar at a dilution of 10­5, assuming a linear relationship between concentration and live cells. A count of live cells as opposed to the optical absorbance of cultures (consisting of cells both alive and dead) allows a better understanding of antibacterial susceptibility.

Figure[2] shows Colony Forming Units per ml of two E. coli strains as a function of Optical Absorbance, as measured by spectrophotometer.

The BL21 strain had been transformed with a plasmid to produce GFP (Green Fluorescent Protein)

and Beta­Lactamase, allowing detection via fluorescence intensity and providing resistance to ampicillin for selective growth, respectively.

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3.1.2. Susceptibility Tests

3.1.2.1.MIC and MBC

Initial determination of bacterial susceptibility used the standardised method described by [23]. Variations of this method constitute the current gold standard of susceptibility testing in medical environments. The MIC (Minimum Inhibitory Concentration) indicates the minimum antibiotic concentration required to inhibit the visible growth of an organism after overnight incubation, whereas MBC (Minimum Bactericidal Concentration) indicates the minimum antibiotic concentration required to prevent growth after culture in antibiotic­free media [23]. The standardised method for finding these values uses ‘visible growth’ as a cut­off point, however for reference this was determined by an optical absorbance over a standard deviation more than the control, as measured by spectrophotometer at a wavelength of 600nm. E. coli were diluted in LB to 5x10­5 CFU (as according to Figure[2]) and incubated overnight with a range of antibiotic concentrations (also diluted in LB) on a microtiter plate for MIC determination. 50μl of solutions showing no growth were plated on agar (see Appendix) and incubated overnight for MBC determination.

3.1.2.2. Live/Dead Stain

Live/Dead stains are a type of viability assay where fluorescent dyes are used to differentiate live and dead cells. SYTO9 and PI (Propidium Iodide) solutions were used as live and dead stains respectively. SYTO9 is a green fluorescent (emission peak at 498nm) nucleic acid stain which is cell­permeant. PI is a red fluorescent (emission peak at 636nm when in solution, 617nm when bound to nucleic acid) nucleic acid stain which is cell impermeant. PI shows a sufficiently large Stokes shift, which allows it to be excited simultaneously with the SYTO9 when in solution ( here they exhibit peak excitation wavelengths of 480nm and 493nm). When bound to nucleic acid, the PI shows a peak excitation wavelength of 535nm and increases in fluorescent emission intensity by a factor of ~20 [24]. The PI also binds more strongly to nucleic acid than the SYTO9 live stain by intercalating between DNA strands [24]. This means that the red fluorescence from cells with compromised membranes (where the PI can permeate) will be greater than the green fluorescence, and the fluorescence of bound stain molecules can be differentiated from background signal. The stains were obtained pre­mixed from ThermoFisher at 1.67nM:1.67nM and 1.67nM:18.3nM (SYTO9/PI) concentration ratios. 3μl of stain was added per 1ml bacterial, before mixing. After the stain was added, microtitre plates were covered by aluminium foil for 20 minutes, to avoid photobleaching during development.

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3.2. Antibody Binding Assays

3.2.1. Bio­Layer Interferometry Methods

Bio­Layer Interferometry uses optical techniques to determine the binding of biomolecules to a sensor surface. White light is propagated through a glass fibre with a polarised reflective layer at its end. Beyond this layer is the sensor surface, with a protein (usually a polyclonal antibody) used to bind another molecule of interest. A proportion of light is reflected by the reflective layer, whilst some is reflected by the immobilised molecule, and the interference of these two reflections are used to quantify binding.

Figure[3], an illustration of a single sensor of a Bio­Layer Interferometer [25]. Indirect detection is used when the binding of an monoclonal antibody­antigen pair is quantified ­ a monoclonal antibody is first immobilised by the polyclonal antibody attached to the sensor, before the antigen of interest is introduced. Typically the sensor will be immersed in a buffer between stages to remove poorly bound molecules. A ForteBio Octet [25] was used with anti­mouse sensors to test the binding of a mouse anti­LPS antibody to E. coli cells. Bio­Layer Interferometry typically measures the binding kinetics of single proteins, and the instrument used was not designed to test whole bacteria cells. Quantities of T20 great enough to lyse the cells (see Section[4.2.3]) was introduced before testing to release cell wall molecules for testing.

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3.2.2. ELISA Methods

An whole­cell bacterial ELISA (Enzyme­Linked Immunosorbent Assay) was developed from the assay described by Elder, B.L. et al [26]. The assay was modified to use HRP­conjugated secondary antibodies for indirect detection (instead of an isotopic marker). It was assumed that the negative charges of the bacterial outer membrane would provide electrostatic adhesion to the positively charged microtitre plate wells. Serial Dilutions of E. coli were suspended in carbonate buffer pH 9.6 and left overnight at 4°C to maximise adhesion to the wells of the microtitre plate. The monoclonal antibody of interest was added at 1 μg/ml (the minimum concentration determined effective for ELISA by the supplier). Before a secondary HRP­conjugated polyclonal anti­antibody was introduced at the same concentration to bind to the primary antibody. Wells were washed with PBS between stages to remove poorly or non­specifically bound antibodies. In all other cases a buffer of PBS and 2% milk powder was used to block nonspecific binding. The HRP was catalysed by adding TMB, where it was left to develop for 20 minutes. 0.2nM Sulfuric Acid was added to stop the enzyme reaction and the optical absorbance of wells was read at 600 nm by spectrophotometer.

Figure[4] shows the configuration of detection in the whole­cell ELISA. Whole E. coli cells adhere the the surface of the microtitre plate which then binds the anti­E. Coli antibody, itself binding the secondary HRP­conjugated antibody.

3.2.3. Methods for Optimising Ab­AuNP Conjugation and Blocking

Throughout the project Antibody (later referred to as “Ab”) to gold nanoparticle (later referred to as “AuNP” or “NP”) conjugates (Ab­AuNP) were used to test binding of the antibody to particular antigens or to detect the bacteria.

The conjugates’ quality in the tests performed can be expressed with the average number of Ab attached to each NP (since higher coverage improve the binding to bacteria). The unbound sites on NP are also undesirable since they allow for non­specific binding as well as the NPs sticking to each other which compromises the quality of tests e.g. by changing the colour of the solution.

Hence a series of tests was performed aiming to optimise the amount of antibody solution required relative to the nanoparticle solution in the process of creating the conjugates to ensure a high level of NP’s surface coverage with Ab.

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The effectiveness of binding was measured and quantified with a “salt test” – the addition of NaCl to the NP solution results in the NPs sticking to each other via the NaCl links which causes the solution to change the colour shifting from red towards the violet side of the electromagnetic spectrum (the NPs, originally at 20nm diameter range, form larger compounds upon clamping together hence changing the absorbance of electromagnetic spectrum). This happens only if there are unbound free sites available on the NPs therefore the effect was expected to be inversely correlated with the average number of Ab bound to the NP.

The experiments followed the protocol shown below:

a. For each tested Ab type (“LPS Ab” against LPS (lipopolysaccharides) on E. coli; “K,O Ab” against K­ and O­ epitopes on E. coli; “HIV Ab” against HIV type 1 and type 2) prepare the following concentrations of the antibody in the NP solution:

16, 8, 4, 2, 1, 0.5, 0.25 and 0 ug/mL.

b. BINDING: Leave the samples on thermoshaker for 20mins shaking at 650rpm at Troom.

c. CHECKING for unblocked sites: After 20mins take out 120uL from each of the samples and put into wells on the microplate. Also put 120uL of pure NP sol. into one more well as a reference.

Add 8uL of 10% NaCl solution into each well containing Ab­AuNPs.

LPS Ab+NP

+NaCl

K, O Ab+NP

+NaCl

HIV Ab+NP

+NaCl

NP

Mix properly with pipette.

Check SPECTRUM (here “SPECTRUM” means 395nm­805 nm wavelength range of electromagnetic radiation absorbance with 10nm step size sampling] of light absorbance with a “SpectraMAX” optical transmittance analysing instrument.

The results are presented in the Figure 5 below as the normalised signal intensity. The normalisation was done by using the signal of optical absorbance at 545 nm (the peak representing the NPs red colour in pure solutions) from untreated NP solution (with no Ab attached and no NaCl added) as the upper reference point (value I1) and using the signal from the untreated NP (no Ab) but with the addition of NaCl as the lower reference point (value I0). The signal for each sample (IS) was therefore normalised using the equation:

Inormalised = I −I1 0

I −IS 0

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Figure [5]: The result of binding the Ab to AuNPs using different concentrations of antibodies. The figure presents the signal as a normalised intensity of optical absorbance at 545nm corresponding to the pure NP solutions peak measured for 3 different types of antibody.

The results confirm the expected trend showing that the concentration of antibody in solution was in each case positively correlated with the amount of antibodies conjugated to the gold NPs as inferred from the signal remaining in the solutions after the salt test.

Depending on the quality of conjugates required the necessary concentration of Ab was chosen for each of the further test throughout the project.

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For instance, in case of the centrifuge binding test (see section 3.2.4.), a required quality threshold of 80% was set leading to the use of 8ug/mL concentrations of LPS Ab and K,O Ab and 16ug/mL concentration of HIV Ab.

In case of low NP binding sites coverage additional blocking with Bovine Serum Albumin (BSA) can be performed. The blocking efficiency was checked for the three types of antibody. The experimental procedure followed the protocol presented above with the additional step of adding 60uL of 0.1% BSA solution into each sample per each mL of the Ab­AuNP solution after the binding was allowed (step “b.” in the protocol) and leaving for 20mins on thermoshaker (650rpm, Troom) after which the regular procedure followed.

The results presented in Figure 6 demonstrate that the blocking step increased the conjugates quality (here meaning that less of the NP binding sites remained unbound) for K,O Ab and HIV Ab but not for LPS Ab. Indeed, in case of LPS antibody the signal decreased which may be attributed to the BSA particles ‘knocking out’ the LPS Ab bound to NPs.

This may be a sign of the LPS Ab binding being weaker than in case of the other two Ab tested (which allowed for it being ‘knocked out’ while the other Ab remained attached). Alternative explanation would assume the LPS initial binding being higher than the K,O Ab and HIV Ab (as could be inferred from the higher signal before blocking – 93% contrasted with 83% (K,O Ab) and 87% (HIV)) which again resulted in the effect of ‘knocking out’ being more significant in case of LPS Ab covered NPs.

In this test pure NP solution (not bound to any antibody) was used as a control. The signal for unblocked NPs was earlier defined as 0% in the normalisation procedure and for blocked NPs a signal increase was seen as expected demonstrating the blocking effect of BSA although pointing to its relatively low efficiency ­ only 55% of the normalised signal remained after the salt test in comparison to the signal from untreated NPs (without the addition of NaCl) defined earlier as 100%.

Figure [6]: The result of blocking Ab­AuNP conjugates with BSA. The figure presents the signal as a normalised intensity of optical absorbance at 545 nm corresponding to the pure NP solutions peak

15

measured for 3 different types of antibody as well as for the control NP solution with no Ab attached.

3.2.4. Novel Method for Testing Ab­Bacteria Binding via Centrifugation

As a consequence of the bio­layer interferometry failing to verify the effectiveness of the antibody binding to bacteria (see Results section), alternative methods were employed e.g. ELISA ­ a gold­standard, widely used but lengthy method.

In this chapter a novel method for testing the antibody­bacteria binding via the use of nanoparticle­antibody conjugates and centrifugation of their mixture with bacteria is introduced which aims to decrease the time of the binding verification as well as increase the reliability.

Protocols were designed for characterising each stage of the experiment.

3.2.4.1. Principles of the Method

Centrifugation is based on applying centripetal force to the sample (usually in an instrument rotating the samples holder at high speeds typically in a range of thousands of rotations per minute) which leads to a separation of the solutes based on their density. A gradient of solutes is created along the sample’s height (where the ‘bottom’ of the sample is defined as the direction in which the centripetal force acts onto the solutes) with the densest particles or objects at the bottom and the least dense on top.

The method introduced proposes the usage of gold nanoparticles (AuNP) of approx. 20nm diameter which appear red in solutions and are commonly used as markers e.g. on paper tests. The antibody (Ab) of interest (whose binding is to be verified) is linked to the AuNP forming a Ab­AuNP conjugates (i.e. a large 20nm in diameter gold nanoparticle with many small e.g. 3nm long antibodies attached to its surface). The free binding sites on the AuNP are then blocked (e.g. with BSA – Bovine Serum Albumin) to avoid non­specific attachment later in the test (See Figure

7).

16

Figure [7]: The schematic representation of a Ab­AuNP conjugate particle. Preparation of solution containing such conjugates is the first stage of the method. [Own work.]

Such prepared Ab­AuNP conjugates in a PBS buffer solution are then mixed with the solution containing bacteria of interest and left at appropriate conditions (as described in section 3.2.4.2.). In most cases bacteria solutions are yellow, beige, green or white with the opacity level dependant on the bacteria concentration. After mixing bacteria solution with the Ab­AuNP conjugates solution the sample changes colour to red due to the NPs present in the whole volume.

If the antibody is effective against the antigen (bacteria), the whole Ab­AuNP conjugates will stick to the epitope (part of bacteria recognisable by antibody) on the bacteria forming Epitope­Ab­AuNP links on the surface of bacteria. In other words, the bacteria cells will be covered with AuNPs attached to them via Ab linkers.

On the other hand, if the antibody does not recognise the bacterium, the Ab­AuNP conjugates will not bind to bacteria and merely mix with them in the solution (see Figure 8).

Figure [8]: Binding of Ab­AuNP conjugates to bacteria in case of effective antibody (left) contrasted with the non­effective antibody (right). [Own work.]

After time allowed for (potential) binding the samples are centrifuged at such speed, which forces the bacteria to the bottom of the sample. If the antibody was effective, the Ab­AuNP conjugates will also move to the bottom being stuck to the surface of the bacteria (Figure 9 Case A). If, however, the antibody was not effective, the conjugates will remain in the whole body of the sample (Figure 9 Case B.

17

Figure [9]: After centrifugation bacteria are spun down. If antibody binds, the Ab­AuNP conjugates are stuck at the bottom as well (LEFT). Otherwise they remain suspended in the whole sample volume (RIGHT). [Own work.]

The following step involves removing the supernatant solution from the sample with a pipette. In case of binding (A) the remaining precipitate will contain bacteria and the conjugates while in case of no binding (B) the nanoparticles will be removed with the supernatant.

The precipitates are then re­suspended in PBS.

If the sample preserves the red colour (caused by the presence of NPs) it is an indication of the binding and hence a positive test result.

If the red colour fades out or completely vanishes, it is an indication of a loss of nanoparticles and a negative result of the test (antibody does not recognize the bacteria). See Figure 10 for the schematic representation.

Figure [10]: Sample after re­suspension of precipitates in PBS. If the NPs remain in solution the sample will appear red. Otherwise the red colour will fade.

Along with the tested sample, control samples should be used e.g. containing antibody known not to bind to the bacteria (negative control) and/or antibody known to bind to bacteria (positive control). Other controls e.g. with free NPs (not bound to any antibody) can also be used to rule out nonspecific binding.

18

The colour of the samples after the test can be distinguished with naked eye, however other automated and objective means of colour determination can be used e.g. light spectroscopy measuring the transmittance of light at particular wavelengths.

3.2.4.2. Experimental Procedures

The sections below present protocols for the main binding test (3.2.4.2.1.) as well as the tests used for the characterisation and optimisation of different aspects and steps of the method (3.2.4.2.2.­4.).

3.2.4.2.1. Binding test protocol

A) 15mL of bacteria of interest were grown (e.g. E. coli strains BL21 or K12) in LB for 16 hours at 37 deg. C with shaking of samples at 250rpm.

B) Ab­AuNP conjugates solutions preparation (total volume min. required 1.5mL; prepared 2mL)

a. Prepare 4 samples in 2mL Eppendorfs (mix with pipette):

32uL NEW Ab + 1968uL AuNP sol (gives 8ug/mL concentration)

4uL OLD Ab + 1996uL AuNP sol (gives 8ug/mL concentration)

9uL HIV Ab + 1991uL AuNP sol (gives 16ug/mL concentration)

2000uL AuNP sol

b. BINDING: Leave samples on thermoshaker for 20mins, 650rpm, Troom

c. While waiting prepare 0.1% BSA solution (e.g. mix 10mg BSA in 10mL H2O)

d. BLOCKING: After 20mins add 120uL of 0.1% BSA solution into each of the 4 samples, leave for 20mins on thermoshaker (650rpm, Troom)

e. While waiting set centrifuge to 4°C

f. CHECKING for binding and blocking: After 20mins take out 120uL from each of the 4 samples and put into wells on the microplate. Also put 120uL of pure NP sol into 2 more wells.

Add 8uL of 10% NaCl solution into wells 1.­5.

NEW Ab+NP.

+BSA+NaCl

OLD Ab+NP

+BSA+NaCl

HIV Ab+NP

+BSA+NaCl

NP

+BSA

+NaCl

NP

+NaCl

NP

Mix properly with pipette.

Check SPECTRUM (here: “SPECTRUM” means 395nm­805nm with 10nm step size) of light absorbance with a “SpectraMAX” optical transmittance analysing instrument.

19

Expected:

well 6: RED (high peak around 525nm)

well 5: BLUE (peak shifted towards shorter wavelengths and smaller)

wells 1­4: as close to well 6 as possible

g. Centrifuge the 4 test samples at 15,000rcf, 20mins, 4°C

suck out supernatant (with pipette)

resuspend in 2mL PBS

C) BACTERIA binding to Ab­AuNP:

a. Clean bacteria in PBS 3 times, resuspend in PBS at twice original concentration “2X” (here X means “as grown”) (e.g. from 1mL original solution resuspend in 0.5mL PBS),

b. Check Optical Density (OD) at 600 nm and SPECTRUM at concentrations 2X (200uL bugs), 2X/3 (66uL bugs + 134 uL PBS), X/5 (20uL bugs + 180 uL PBS) and PBS only (200uL) control

c. Prepare 24 samples (in 1.5mL Eppendorfs) (each sample is 1 mL) in the following combinations:

Bacteria: BL21 K12

Ab: NEW OLD HIV None NEW OLD HIV None

2X

2X/3

X/5

For 2X row: 0.75mL bacteria solution + 0.25 mL Ab­AuNP solution

For 2X/3 row: 0.25mL bacteria solution + 0.5mL PBS + 0.25 mL Ab­AuNP solution

For X/5 row: 0.075mL bacteria solution + 0.675mL PBS + 0.25 mL Ab­AuNP solution

Mix with a pipette.

d. Leave for 15mins stationary

leave for 30mins on the thermoshaker 250 rpm, Troom

leave for 15mins stationary

e. Take pictures of samples in Eppendorfs (all should be red)

f. Put 200uL from each sample into well on ELISA plate. Check OD at 600 nm and SPECTRUM.

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g. Centrifuge samples at 5000 rpm, 2 min, Troom

h. suck out supernatant with pipette and store it in another Eppendorfs; resuspend precipitates in 1 mL PBS

i. Take pictures of samples in Eppendorfs

Where Ab­bacteria binding positive ­ red colour was expected to remain; where no binding – clear/yellow (bacteria colour).

Put 200uL from each sample into well on ELISA plate. Check OD at 600 nm and SPECTRUM.

Where Ab­bacteria binding is positive ­ peak expected around 545 nm.

3.2.4.2.2. Protocol for characterising centrifugation of bacteria:

A) Grow 7.5mL of K12 E. coli

B) Wash in PBS 3x (standard procedure) and re­suspend in PBS at half the original concentration (to imitate BACTsol:NPsol ratio 1:1) (Total: 15mL)

C) Prepare dilutions: original concentration X and X/3 (use each of those 2 concentrations for each sample)

a. Mix 3 mL of bacteria (X) with 9mL PBS to achieve (X/3) concentration (24mL)

b. Remaining is 12 mL of bacteria at (X) concentration

D) Check OD to ensure correct dilution and check CFU

E) Spin down for 2 mins at speeds [rpm]:

(10 types x 2 concentrations = 20 samples)

a. 100 (min)

b. 500

c. 1000

d. 2000

e. 3000

f. 4000

g. 5000

h. 7500

i. 10000

j. 14000 (max)

F) FOR EACH SAMPLE:

a. Observe precipitate (note down visibility of precipitates). Take photos if needed

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b. Suck out 2x 200µL supernatant and put into ELISA plate

c. Suck out and discard the remaining supernatant

d. Resuspend precipitate in 1 mL PBS, put 2x200uL into ELISA plate

e. Prepare controls: 3x PBS, 3x non­centrifuged bacteria

f. Measure OD of supernatants (for remaining bacteria) and re­suspended precipitates (for loss of bacteria)

G) Plot results and see threshold speed

3.2.4.2.3. Protocol for characterising centrifugation of Ab­AuNP conjugates

A) Prepare tested Ab­AuNP conjugates as in the regular procedure described before.

B) Set centrifuge to T=4°C

C) Centrifuge (standard 14,000rpm, 20 mins, T=4°C)

D) Re­suspend in PBS at half original concentration (to imitate BACTsol:NPsol ratio 1:1) mixing all in one container (e.g. re­suspend each precipitate in 1 mL then add these 3x1mL into 9 mL PBS)

E) Check SPECTRUM (395 – 805 nm, 10nm steps)

F) Prepare 10 identical samples, 1 mL each, in 1.5mL Eppendorfs

G) Spin down for 2 mins at speeds [rpm]:

a. 100 (min)

b. 500

c. 1000

d. 2000

e. 3000

f. 4000

g. 5000

h. 7500

i. 10000

j. 14000 (max)

H) FOR EACH SAMPLE:

a. Observe precipitate (note down visibility of precipitates). Take photos if needed.

b. Suck out 3x 200µL supernatant and put into ELISA plate

c. Suck out and discard the remaining supernatant

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d. Resuspend precipitate in 1 mL PBS, put 3x200uL into ELISA plate

e. Prepare controls: 3x PBS, 3x non­centrifuged NPs

f. Measure OD of supernatants (for remaining NPs) and re­suspended precipitates (for loss of NPs)

I) Plot results and see threshold

3.2.4.2.4. Protocol for optimising ratio of Bacteria solution to Ab­AuNP solution

A) Grow 15mL K12 overnight

B) Ab preparation

a. Prepare AuNP­Ab/BSA conjugates:

­ TEST samples: 1.8mL of NPs with LPS Ab:

mix 28.8uL LPS Ab + 1771.2uL AuNP sol (gives 8ug/mL concentration [93% sites covered])

­ CONTROL sample: 0.6mL of NPs with HIV Ab:

mix 2.7uL HIV Ab + 597.3uL AuNP sol (gives 16ug/mL conc)

­ CONTROL sample: 0.6mL of NPs

b. BINDING: Leave on thermoshaker for 20mins, 650 rpm, Troom

c. While waiting prepare 0.1% BSA solution (e.g. mix 10 mg BSA in 10 mL H2O)

d. BLOCKING: Add 120uL of 0.1% BSA solution into LPS sample

and 40uL into HIV Ab and NO Ab sample;

leave for 20mins on thermoshaker (650 rpm, Troom)

e. While waiting set centrifuge to 4°C

f. CHECKING for binding and blocking: After 20mins take out 120uL from each of the 3 samples and put into wells on the microplate. Also put 120uL of pure NP sol into 2 more wells.

Add 8uL of 10% NaCl solution into wells 1.­4. And 8uL of H2O

LPS Ab+NP.

+BSA+NaCl

HIV Ab+NP

+BSA+NaCl

NP+BSA+NaCl NP + NaCl NP + H2O

Mix properly with pipette.

Check SPECTRUM [here: “SPECTRUM” means 395nm­805 nm with 10nm step size].

Expected:

well 5: RED (high peak around 525 nm)

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well 4: BLUE (peak shifted towards shorter wavelengths and smaller)

wells 1­3: as close to well 5 as possible

g. Centrifuge the 3 test samples at 15,000rcf, 20mins, 4°C

suck out supernatant (with pipette)

resuspend in PBS at original concentrations

C) BACTERIA binding to Ab­AuNP:

a. Check OD at 600nm of bacteria as grown

b. Clean bacteria in PBS 3 times (in large bottle, large centrifuge)

c. While waiting calculate re­suspension ratios

d. Resuspend in PBS to (achieving e.g. 108 CFU/mL concentration) (required final volume 6mL so prepare at least 7mL)

e. Check OD at 600nm to ensure correct concentration

f. Prepare 8 samples (in 1.5mL Eppendorfs) (each sample is 1ml):

Antibody type: AuNP­Ab/BSA Volume [uL]: Bacteria Volume [uL]:

N/A (LPS) 0 1000

LPS 100 900

LPS 200 800

LPS 300 700

LPS 400 600

LPS 500 500

HIV 300 700

No Antibody 300 700

g. Mix properly with pipette.

h. Leave for 60mins on the thermoshaker: 250 rpm, Temp = 37 C.

i. Take pictures of samples in Eppendorfs (all should be red)

j. Put 200uL from each sample into wells on ELISA plate. Check SPECTRUM.

k. Put the 200uL (previously taken out) BACK into Eppendorfs.

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l. Centrifuge samples 5000 rpm, 2 min

m. suck out supernatant with pipette and KEEP IT in another Eppendorfs; resuspend precipitants in 1mL PBS

n. Take pictures of samples in Eppendorfs (precipitants re­suspended)

o. Put 200uL from each sample into wells in ELISA plate. Check SPECTRUM.

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3.3. Methods for Development of Rapid Lateral Flow Test of AMR

3.3.1. Buffer Optimisation for Lateral Flow Test ­ Tween Susceptibility study

Tween (T20) is a surfactant used throughout this project (at the concentrations of 1%) for improving the flow of solutes on the paper tests. However, since the chemical at sufficiently high concentrations becomes a detergent, its use for bacterial solutions was questionable. Therefore, the bactericidal properties of Tween were studied. Three separate tests were conducted to verify potential inhibition of growth of bacteria in T20 environment.

3.3.1.1. Test #1 ­ Inhibition of growth on agar plate

In Test #1 100 uL of K12 E. coli bacteria solution in PBS was spread on the surface of agar plate (solid growth medium). The plate was divided into 5 sectors and on each sector a 10uL drop of the following solution was placed:

­ PBS with 1% Tween

­ PBS with 50% EtOH

­ PBS with 2% Trigene (a common disinfectant)

­ PBS with 100 mg/L Kanamycin (antibiotic at concentration exceeding the MBC value)

­ PBS only

The plates were then incubated at 37 deg. C for 16 hours and inspected for the inhibition of growth on the areas covered with the droplets.

3.3.1.2. Test #2 ­ Growth on agar plate

Test #2 was somehow analogous to Test #1 but testing for growth rather than inhibition of growth. The five solutions prepared using K12 E. coli bacteria were:

­ Bacteria in PBS containing 1% Tween

­ Bacteria in PBS containing EtOH

­ Bacteria in PBS containing 2% Trigene

­ Bacteria in PBS containing 100 mg/L Kanamycin

­ Bacteria in PBS only

A 10uL droplet of each of the above solutions was placed in a respective sector on an agar plate (this time not pre­treated with bacteria solution). This plate was also incubated in 37 deg. C for 16 hours and inspected for growth on the areas covered with the solutions.

3.3.1.3. Test #3 ­ Growth in liquid medium

Test #3 involved growing bacteria in liquid growth medium (LB) with different concentrations of T20. Samples were prepared in test tubes for two strains of bacteria (K12 and BL21) in each case containing the following 6 concentrations of tween: 0, 0.1, 0.5, 1, 5 and 10%.

26

The samples were incubated at 37 deg. C for 18 hours and inspected for growth. The growth was quantified using optical absorbance spectroscopy for each sample and plotted against the tween concentration.

Results are presented in section 4.3.1.

27

3.3.2. Lateral Flow Test Design & Preparation

Lateral Flow tests are rapid immunoassays, used most widely for pregnancy tests. Lateral flow tests consist of a sample pad, (where a sample required to determine the presence of an antigen is added) a conjugation pad (where antigen­specific markers (usually antibody­conjugated colloidal gold nanoparticles) are released by the liquid in the sample to bind with the antigen), a nitrocellulose membrane (of desired pore size and flow rate) where the detection and control lines immobilise antigen­bound markers and non­bound markers respectively. The intensity of the detection line shows the presence of a detected antigen, whereas the the intensity of the control line shows that the marker has flowed to the end of the lateral flow strip and the test has worked correctly. When immersed in solution, liquid spontaneously travels up the lateral flow strip via capillary action, where the pores of the nitrocellulose membrane are sufficiently small enough to induce a flow driven by the surface tension of the solution and the electrostatically adhesive forces of the membrane.

Figure[11] shows the proposal for a whole­cell bacteria­detecting lateral flow test. [Own work] Lateral Flow Strips were prepared by hand in the lab, where a nitrocellulose membrane and absorbent pad were put onto a plastic adhesive backing. Strips were cut by guillotine, leading to some discrepancies in width due to human error. Test solutions used pre­conjugated antigens, and so only the nitrocellulose membrane and absorbent pad were used. Tests were immersed in solutions in the wells of a microtitre plate, so that flow could be attributed to capillary action through the nitrocellulose membrane and not turbulent flow from the propulsion of a pipette over its surface. Pore sizes within nitrocellulose membranes are not uniform and therefore different membranes are qualified by their flow rate ­ a property of the average pore size.

28

Figure[12] show lateral flow tests of varying width immersed in the wells of microtitre plate.

3.3.3. Image Processing Quantitative Analysis

In anticipation of the eventual development of an image­processing mobile application to determine the results of lateral flow immunoassays, the intensities of markers along lateral flow tests were compared via the Grey or RGB levels of photographs taken of them. ImageJ was used to take profile plots along a line in the centre of the lateral flow strips, averaging values along its width.

29

3.4. Methods for Sensing Nanomechanical Bacterial Vibrations with AFM

At this stage of the project – with a properly binding antibody (Ab35654 against LPS) identified, characterised and confirmed to be effective against the K12 E. coli – characterisation of the antimicrobial resistance detector based on nanomechanical vibrations of bacteria as described in chapter 2.2.2 was carried out. The aim was to first confirm the vibrations of bacteria, determine the dependence of the signal on the medium and then to improve the method by using the characterised antibody immobilised on the cantilever for the specific capture of the bacterial strain of interest.

3.4.1. Atomic Force Microscopy Principles

An Atomic Force Microscope is a versatile instrument widely used in nanotechnology labs. It is most commonly used to image surfaces of samples by ‘feeling’ the matter to determine the sample topography to sub­nanometre resolution. The core setup comprises a cantilever (typically a few hundred micrometres long ‘lever’) with a sharp tip on its underside. The sharp tip (radius ≥ 1 nm) interacts with the surface atoms due to electrostatic forces between their electrons – these forces result in static deflection of the cantilever ‘touching’ the surface (in the simplest contact mode). The deflection of the cantilever can be quantified in a variety of methods. Typically, an optical lever method [20] is used. In this case the laser beam is directed on the cantilever surface, reflected from it and consequently targeting a 4­quadrant photodetector (see Figure 13). The deflection of cantilever changes the angle of reflection of the laser beam and hence the position of the laser spot on the photodetector from which the deflection of cantilever movement can be calculated.

Figure [13]: A schematic representation of a cantilever deflection measurement method via reflection of the laser beam from a reflective area on the cantilever surface onto a 4­quadrant photodetector. Figure from: [20].

In this project the AFM instrument was used as a vibration detector. The cantilever tip was not used to image samples, instead the bacteria were immobilised on the whole cantilever surface

30

expected to introduce deflection on it as a results of their movement or metabolic activity as described in chapter 2.2.2.

3.4.2. AFM Setup, Calibration and Measurements

As the primary aim of this stage of the project was to recreate the results demonstrated in the to­date publications, the experimental procedure was designed based on methods published by Giovani Longo et.al. [11] and Sandor Kasas et.al. [12]. The group was visited at the Institut de Physique des Systèmes Biologiques at EPFL in Lausanne, Switzerland, in order to learn the details of their method in person.

Upon return to the LCN laboratory the following procedure was designed:

1) The AFM laser was left on for at least 12 hours prior to the beginning of the experiment to ensure saturation of the lasing action and a constant beam power.

2) K12 E. coli bacteria were grown in 10mL of liquid Lysogeny broth (LB) for around 17±2 hours at 37°C on a shaking stage at 250rpm reaching a typical density of approx. as discussed in chapter 3.1.1.

3) Immobilisation of bacteria on the AFM cantilever:

a. Bacteria were cleaned by centrifugation at 5000rpm for 2mins at Troom in PBS buffer three times and resuspended in PBS at 1 mL effectively increasing the “as grown” concentration 10­fold.

b. Cantilever chip was placed in a small Petri dish lined with a hydrophobic parafilm layer and stuck to its bottom.

c. A droplet (approx. 30µL) of 0.5% glutaraldehyde solution in ultrapure DI H2O was placed on the cantilever as shown in Figure 14 below and left for a 10­minute treatment of the surface. A fresh glutaraldehyde solution was prepared every time from a 25% stock solution. Glutaraldehyde [CH2(CH2CHO)2] is an organic compound with sterilising properties widely used in disinfecting medical equipment. It is also a fixative used in biochemical applications to stabilise bacteria and other cells specimens.

31

Figure [14]: Optical microscopy picture of two triangular cantilevers attached to the chip (part visible on the left­hand side) immersed in a droplet of 0.5% glutaraldehyde solution placed on the parafilm­lined Petri dish. The larger cantilever (top) of approx. 200 µm length was used in this experiment. The cantilevers appear blurred since they are covered with a drop of bacterial solution of low optical transmittance. Bottom illumination was used to see through the bacteria droplet.

a. After 10­minute treatment the cantilever was cleaned by running droplets of DI water over its surface and left for 5 minutes in room temperature for the water to evaporate.

b. A droplet of bacteria solution in PBS (approx. 30µL volume containing around bacteria01 5

cells) was placed on a cantilever and left for 30mins to allow for bacteria immobilisation.

c. After 30 minutes the cantilever was cleaned by dipping in a PBS­containing dish in order to remove loosely and improperly immobilised cells and floaters.

2) A dish with 2­4 mL of the medium (depending on the particular experiment) – typically LB or PBS – was placed on a stage.

3) The cantilever with bacteria immobilised was mounted onto the AFM head and immersed into the liquid medium. This time was noted as – time of immersion.t0

4) The coverage of the cantilever with bacteria was checked via optical microscopy (Figure 14 and 15).

The immobilisation density was calculated from the images by counting the number of cells per square area in several places on cantilever. The count results were then averaged0μm x 20μm2

and multiplied by a factor of 40 since the total surface of the cantilever (double­sided) was .6000μm1 2

The medium­range coverage of around cells per cantilever turned out to give best00 007 ± 4

results later in the project (see chapter 4.4.2.), hence this coverage density will be referred to as “medium” in the rest of this report (see Figure 15). This closely matches the coverage used by Longo et.al. reporting cells per cantilever.30 06 ± 7

32

Lower coverage typically around cells will be referred to as “low” (see Figure 15b) while00 01 ± 5

higher coverage typically around cells will be referred to as “high” (see Figure 15d and400 0002 ± 1

15e). In case of high coverage often a net­like structure of bacteria were occasionally observed inside the triangular cantilever corners as well as around its attachment to the chip (see Figure 15f). The formation of the net may be a result of cell walls of adjacent bacteria sticking together as a result of pre­treatment of cantilever with glutaraldehyde or as a result of the cell walls being compromised due to bacteria death resulting from a too­crowded environment.

Figure [15]: Coverage of cantilever with bacteria. Optical microscopy images of: a) empty cantilever (before immobilisation), b) low coverage, c) good coverage, d) high coverage, e) very high coverage, f) too high coverage with bacteria forming spider­web­like structures inside and outside the triangular cantilevers. (Difference in background and cantilever colour is a result of varied bright field intensity.)

1) The laser spot was adjusted on the cantilever in order to maximise the intensity of the reflected beam.

2) Calibration of the cantilever was performed before every measurement. Throughout the project 10 different cantilever chips were used:

a. Cantilever was brought close to the glass bottom of the dish using a Z­stepper motor after which a standard contact mode approach was performed using feedback to ensure the tip was not crashed.

33

b. Detector sensitivity calibration factor was determined by performing a force curve on glass. The deflection detection can be calculated from the linear region of the force curve.

c. Spring constant (metre­to­Newton calibration factor) was calculated from the curve fitted into the first resonance frequency peak of the cantilever.

Typical values of the calibration factors are presented in the Table 1 below.

Typical Value:

Calibration factor: 20.95 m/Vn

Resonance frequency: 3.53 Hzk

Q­factor: 1.669

A: 2.385 /m √Hz

Spring constant K: 0.0758 /mN

Vertical K: 78.18 N/mm

Table 1: Typical calibration results for an empty cantilever in PBS.

1) Cantilever was retracted to approx. 1mm above dish surface (remaining in liquid medium) to minimise the surface effects during the measurements.

2) Cantilever with laser beam shining onto it was left in the medium for 2 hours (measured from the time – time of immersion) to stabilise the temperature of the medium and the associatedt0

thermal drift.

It should be noted that the cantilevers were fabricated from silicon with a gold reflective layer on one side (for improved laser reflection). The laser beam was heats up the cantilever throughout the experiment which in turn resulted in a bimetal effect – the thermal expansion factor of gold is

higher ( [21]) than that that of silicon ( ) hence causing the whole4.2x10 m/mK1 −6 x10 m/mK3 −6

cantilever to bend with temperature. This drift stabilises over time and was removed from the signal via MATLAB signal processing (see chapter 3.4.3.).

3) After 2 hours the actual measurement was started with varying protocol. For instance, in one experiment the signal of bacteria in PBS was recorded for 1 hour after which 1 mL of PBS was added to the dish (as control), second recording was performed for 1 hour followed by the addition of 1mL of 16% glucose solution to the dish and a further recording of the signal of 1 hour.

34

The recording was done for the vertical deflection of the cantilever sampled with 20 kHz frequency.

4) After each experiment the cantilever chip was cleaned with DI water. The cantilever was considered re­usable only if in the experiment it was previously used for no bacteria or glucose were introduced to the system and if no visible damage to the cantilever was seen under optical microscope.

35

3.4.3. Signal Processing and Analysis with MATLAB

Raw data of the vertical deflection of the cantilever vs time of the experiment was collected at 20 kHz sampling frequency using the real time scan oscilloscope.

Several programs for the signal processing, analysis and plotting the graphs were written in a MATLAB programming environment. The core programs and functions used are presented in the Appendix: Supplementary Data section.

Below is the general overview of the typical processing of the recorded data:

1. The data were imported to MATLAB workspace using a ‘dlmread’ function.

2. The whole experiment was plotted (typically several hours in duration) in order to observe the drift and the precise moments of changing the experimental parameters (e.g. modifying medium, adjusting photodetector etc.) providing an overview of the experiment useful for the analysis that followed

3. Different length data fragments were studied ranging from 100 ms to 10 mins; eventually a standardised sample duration of 30 seconds was chosen and used for the further analysis of all experiments

4. The 30 second pieces were extracted from different moments of a measurement to ensure the fragments are an accurate representation of the longer signals at the particular stage of the experiment as well as to study the evolution of signal over time

5. The 30s signal fragments were then processed in the following way:

5.1. Whole signal was shifted vertically to be centred around the y=0 axis for the clarity of the representation of signal (the original raw data occupied inaccurate y­axis levels due to the constant drift throughout the experiment as discussed in the previous section)

5.2. A linear regression trend line was fitted into the data set

5.3. The signal was flattened by subtracting the linear fit from the raw data (again: to remove the drift affecting the signal during the 30sec measurement which for such short time­scale could be accurately approximated by a linear fit)

5.4. Corrections of the units were made based on the calibration factors of the cantilever (e.g. the spring constant) in order to express the signal in nanometres (as opposed to Newtons or Volts)

5.5. Variance of the signal was calculated using the ‘var()’ function

5.6. Discrete fast Fourier transform was performed using the ‘fft()’ function

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6. The time­domain signal was plotted using a standard set of methods with a ‘plot(x,y)’ function

7. The variance was plotted using ‘bar()’ function with the error bars calculated either by analysing different fragments of the same experiment or from a different experiment of the same type (e.g. Bacteria in LB)

8. The frequency­domain graphs were plotted using a ‘plot(x,y)’ function after taking the modulus of the signal and constructing a one­sided frequency axis. Different frequency ranges were analysed in search of the discrete signal components (as described in the results section) with the upper limits of 5 kHz, 200 Hz, 30 Hz, 20 Hz, 5 Hz and 1 Hz.

An alternative method for smoothing the data using a moving average of varied averaging range was tested but not used in the final analysis; this method was also used as a low­pass filter for time­domain plots, which also eventually did not reveal any significant information.

Polynomial, exponential and logarithmic fits were tested as means of removing the drift from the whole experiment duration sets, but were found to be inaccurate since the drift was highly affected by the change in experimental parameters or other disruptions (e.g. opening the AFM cupboard to inject different medium). These were corrected based on the overview of the whole experiment raw data. Finally, the drift turned out to be accurately approximated by linear fits within the short 30 sec fragments as described above nullifying the need for global drift correction.

It should be noted that the data files extracted from the experiment were very large by the current standards, as of year 2016, reaching 20 GB per file. This made them difficult to process and analyse due to the limited processing power and speed of the computers. E.g. importing a single file into MATLAB workspace in some cases took up to 2 hours per file. In case of recreating the experiments described in this project it would be highly advised to split the recording of data into shorter fragments e.g. of a few minutes duration (as opposed to few hours). Alternatively, a lower sampling frequency could be used since – as the result section demonstrates – the fluctuations are composed of very low frequency (below 20 Hz which can be contrasted to the 20,000 Hz of sampling frequency used). A decrease of sampling frequency to 2,000 Hz or even 200 Hz would drastically decrease the file sizes while still preserving all the relevant information, meeting the Nyquist criterion and leaving the long recording time.

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4. Results and Analysis

4.1. Determination of Bacterial Susceptibility

4.1.1. MIC and MBC The antibiotic susceptibility of K12 was studied using the standard method described in Section[3.1.1]. The susceptibility of E. coli was studied for two antibiotics of differing classes, Nafcillin­Ampicillin and Kanamycin (Beta­Lactam and Aminoglycoside antibiotic classes, respectively). Two antibiotics were compared so that the different mechanisms of action of the two antibiotics could be compared and used to describe the behaviour of alternative susceptibility testing methods. As the BL21 E. coli strain was modified with a plasmid providing Ampicillin resistance, only the susceptibility of the K12 strain was studied.

Figures [16,17] show bacterial growth as a function of antibiotic concentration. The antibiotic concentration is plotted against the Optical Absorbance of the inoculated medium and a control

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(LB). Figures[16,17] shows bacterial growth decreasing as antibiotic concentration increases, for both antibiotics. From Figures [16,17], the MIC value can be obtained by observing where the growth is within a standard deviation of the control. For Nafcillin­Ampicillin Inhibited growth, this can be seen to occur at 64mg/L, whereas for Kanamycin Inhibited growth, this can be seen to occur at 32mg/L. Susceptibility curves were approximated using third degree polynomials using the least­squares method. As this data was obtained using standardised methods, this curve will be used as a comparison to alternative methods described in this report. The MBC values were obtained by the method described in Section[3.1.2] as found to be 512mg/L and 1024mg/L respectively. The differences in Optical Absorbance for the control values in the two plots can be explained by the fact that the experiments were performed on different occasions, using different LB stock solutions (although the same recipe was used, factors such as age and autoclave method can produce variations in colour and optical attenuation). The differences in the susceptibility of the bacteria to the different antibiotics can be explained by their different mechanisms of action. The inhibitory effect of Kanamycin can be seen to act faster than Nafcillin­Ampicillin, due to its mechanism of suppressing ribosomal translocation. A higher Nafcillin­Ampicillin concentration is required to inhibit growth, but from the obtained MBC values, it can be said to have a more bactericidal effect, this is due to its mechanism of inhibiting cell wall development and causing cell lysis.

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4.1.2. Live/Dead Stain as MIC Test

A rapid test for bacterial susceptibility needs to be a viability assay that identifies the molecular processes of cell death. A Live/Dead stain (see Section[3.1.2]) was used to compare an alternative viability assay method to the standard in Section[4.1.1].

A live/dead stain was applied to bacterial solutions after being treated with antibiotic overnight. As peak excitation and emission frequencies were less than 15nm apart, solutions were excited at frequencies 20nm less than peak excitation frequency to avoid the reflections of the excitation signal being read as emission by the spectrophotometer.

Figures[18,19] compare the fluorescence of SYTO9 (green) and PI (red) over a range of antibiotic concentrations to the standard susceptibility curve (black) obtained in Figures[16,17]. SYTO9 live stain fluorescence (green) was obtained at an excitation frequency of 475nm and emission frequency of 500nm whereas PI dead stain (red) was obtained at an excitation frequency of 525nm and an emission frequency of 620nm.

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From Figures[18,19], SYTO9 fluorescence is shown to decrease with increasing antibiotic concentration whereas PI fluorescence is shown to increase. The rates of change for these values are comparable to the standard susceptibility curve obtained in Figures[16,17], however Figure[18] shows a further decrease in live stain fluorescence at the MBC. For Kanamycin induced growth, the dead stain seems to increase with more slowly with antibiotic concentration as the live stain increases. The disparities between the standard curve and the changes in fluorescence values, and the differences in rate of fluorescence change for the two antibiotics can be explained by their differing mechanisms of action. Nafcillin and Ampicillin attack cell wall synthesis during binary fission, leading to cell lysis and the exposure of nucleic acids to the PI stain (or inhibit SYTO9 fluorescence). At bactericidal concentrations (MBC), this would not only inhibit growth, but lyse existing cells in the solution and increase free nucleic acid further. However in Figure[19], red fluorescence does not increase at MBC as green fluorescence decreases. This could instead be due to a lower overall cell count in these samples at MBC. The faster inhibition of growth but slower bactericidal effect of Kanamycin can explain the differences in the rate of change of green and red fluorescence. As antibiotic concentration increases, there is less cell growth, but not necessarily more cell death.

4.1.3. Conclusions for Bacterial Susceptibility

Figures[18,19] show that an alternative method could be used to determine the MBC and differentiate between the effects of different antibiotics in less time (two days rather than three) than standard methods, when the behaviour of fluorescent or plasmonic particles change depending on cell lysis. However, in order to differentiate between the effect of antibiotics on a specific species and other microbiology in a sample, a specific target needs to be identified. Antibodies can provide specificity for detection when attached to the fluorescent or plasmonic particles. In order to develop a bacterial susceptibility test, antibodies against bacterial antigens would have to be assessed.

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4.2. Development of Rapid Binding Assay for Bacterial Antibodies In order for the specific detection of E. coli (differentiating E. coli antigens from other microbiology in a sample) in a rapid diagnostic test, the binding of an antibody to an E. coli antigen needs to be confirmed.

4.2.1. Bio­layer Interferometry Bio­Layer Interferometry (see section[3.2.1]) was the first method used to determine antibody binding as it could offer a result in the least time. A mouse anti­LPS AB was tested against lysed E. coli cells.

Figure[20] shows the level of molecular binding to the anti­mouse sensor of the Bio­Layer Interferometer over time. A reference value (sensor with no antibody bound) has been subtracted from all values shown. Values have also been aligned pre­antibody binding (~60 minutes) to compensate for discrepancies in sensor sensitivity. After calibration (where the sensors are immersed in a buffer and the optical signal detected is attributed a normalised value for binding) of the ForteBio sensors, they were immersed microtitre wells containing the LPS antibody at 1μg/ml (besides a well used as a control, containing the HIV antibody C1/2 at the same concentration), before being immersed in buffer and then a solution containing E. coli for an ‘association’ period before a washing and ‘dissociation’ period, both in buffer. Figure[20] shows no binding of LPS to the antibody during the ‘association’ period. Figure[20] also shows that the HIV antibody used as a control showed much higher affinity for the anti­mouse sensors than the LPS antibody. Given that Bio­Layer interferometry is not a conventional method of testing binding to bacteria, the antibody tested in Figure [20] was not dismissed. Given also that in a rapid test, using whole

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cells would be more favourable than using lysed cells, a more conventional method that did not involve lysis was used.

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4.2.2. ELISA

An ELISA (see Section[3.2.2]) was adapted from B. Elder et al. [26], using indirect detection via an HRP­conjugated anti­mouse antibody. Both K12 and BL21 E. coli were tested against the LPS antibody for comparison. Controls included microtitre wells with no primary antibody (LPS AB), no secondary antibody (HRP conjugated anti­mouse) and C1/2 as a primary antibody (the HIV AB used as a control in Figure[20]). Signals detected from these controls would indicate poor washing, blocking or non­specific interactions respectively.

Figure[21] shows the level of antibody binding to E. coli, detected by HRP conjugated anti­mouse AB. Figure[21] shows increased HRP detected with bacterial concentration in relation to controls, indicating increased antibody binding, for both E. coli strains. Bacterial concentrations were determined based on the their optical absorbance (see Figure[2]) before antibodies were added. BL21 exhibited more bound LPS antibodies per CFU than K12. The increased binding when using BL21 could be due to several factors: they exhibit a larger cell wall surface area due to a lack of flagella, exposing more binding sites. K12 strains also have a capsular K­antigen which could inhibit some binding of the LPS AB to the base of the LPS molecule. Figure[21] confirms the binding of the LPS AB to whole E. coli cells, but in order to determine whether they could be used to detection in a rapid test, it needs to be determined whether antibody conjugation to a nanoparticle marker would affect binding affinity.

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4.2.3. Novel Centrifuge­Based Binding Test

4.2.3.1. Testing Antibody Binding

Antibody Ab35654 against E. coli LPS was tested for binding to K12 and BL21 E. coli bacteria at 3 different concentrations of bacteria according to the protocol described in the Methods section. Three controls were used: first control ­ Antibody against different epitopes (K­ and O­) on E. coli bacteria (this antibody was expected to give negative results as found in earlier stages of the project and presented in previous sections of this report); second control ­ Antibody against HIV virus (type 1 and 2); third control – only gold nanoparticles, blocked with BSA but with no antibody attached.

The expectation was that the test will be positive for LPS antibody binding to K12 bacteria and possibly also to BL21 bacteria while all other combinations will give negative result and serve as controls.

Indeed, the red colour remained in the LPS­K12 combination but not in any other combination as seen in Figure 22 for K12 bacteria and in Figure 23 for BL21 bacteria.

Figure [22]: Results of the binding test for the highest concentration of K12 E. coli. The sample labelled “LPS Ab” is visibly red­coloured while the other 3 control samples are not. This shows that the LPS antibody binds to the K12 bacteria (as expected).

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Figure [23]: Results of the binding test for the highest concentration of BL21 E. coli. None of the samples are red­coloured showing that none of the tested antibodies binds to the BL21 bacteria.

Figure 24 presents the results as the optical absorbance spectra of the samples.

A distinguishable peak around 545 nm wavelength (resulting from nanoparticles remaining in the solution) was seen for the highest concentration of K12 bacteria. Lower concentrations of K12 bacteria also demonstrated the expected peak but significantly less prominent. In case of BL21 bacteria no significant peak was observed at highest or lowest concentration. The medium concentration showed a small signal.

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Figure [24]: The optical absorbance spectra of the samples. Left column – K12 bacteria, right column – BL21 bacteria. Shown for all 3 bacteria concentrations tested: highest (top), medium (centre) and lowest (bottom). The result is strongly positive for the LPS antibody (marked blue in all graphs) binding to the K12 bacteria, as expected, which is evident from the peak corresponding to the red nanoparticles colouring around 545 nm wavelength.

In conclusion the test was interpreted to be strongly positive for the LPS Ab binding to K12 bacteria (as expected) at highest concentration sample, weakly positive for the lower concentrations of K12 samples and negative for all controls as well as the BL21 bacteria.

In order to optimise the test a series of characterisation experiments were performed as described in the following sections. An attempt was made to find such centrifuging conditions which allow for spinning down maximum number of bacteria while leaving the highest possible amount of unbound Ab­AuNP conjugates in the top of the sample. This is presented in sections 4.2.3.2. and 4.2.3.3.

4.2.3.2. Characterisation of Bacteria Centrifugation

The effect of centrifuging bacteria at different speeds was studied for 3 different concentrations of K12 bacteria as described in the Methods section.

Bacteria in test tubes were centrifuged, the supernatants removed and the precipitants re­suspended. The density of bacteria was then studied both in the re­suspended samples and in the supernatant solutions using the optical absorbance spectroscopy.

The expectation was that there will be a positive correlation between centrifuging speed and the number of bacteria remaining in the sample after re­suspension and an inverted trend will occur for the supernatants.

Figure 25 shows the results of the experiment for the re­suspended precipitates and Figure 26 for the supernatants taken out from the samples.

The results confirm the expected trend. It is clear that speeds below 1000 rpm were not effective in spinning the bacteria down while speeds higher than that demonstrated increasing efficiency

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eventually saturating around 5000 rpm for the two lowest concentration while showing an increased efficiency for the highest concentration sample.

Figure [25]: The result of centrifugation of bacteria at different rotational speeds shown for three concentrations tested, measured by optical absorbance of the samples after re­suspending the precipitates in PBS. The optical absorbance is positively correlated with the concentration of bacteria in solution as demonstrated in the Methods section.

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Figure [26]: The result of centrifugation of bacteria at different rotational speeds shown for three concentrations tested, measured by optical absorbance of the supernatant solutions taken from the centrifuged samples. The optical absorbance is positively correlated with the concentration of bacteria in solution as demonstrated in the Methods section. The result is a confirmation of the trend seen in Figure 25.

In conclusion the threshold around 5000 rpm was determined for optimal centrifugation of bacteria.

4.2.3.3. Characterisation of Ab­AuNP Conjugates Centrifugation

A similar characterisation as for bacteria was done for Ab­AuNP conjugates using the HIV Ab. It was assumed that in this experiment the type of antibody used will not influence the results.

The expectation was again that the higher speeds will be more effective at spinning the conjugates down (which is undesirable as the binding test method requires the Ab­AuNP conjugates to remain suspended in the top volume of the sample).

I should also be noticed that the regular procedure for spinning down NPs (as described in section 3.2.3. involving centrifuging at 14,000 rpm (and showing high efficiency) is performed at 4°C while the test described in this section was done at room temperature to match the conditions of the

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binding test experiment as well as to support diffusion and effectively decrease the efficiency of spinning down the NPs as is desirable in this method.

The experiment followed the protocol described in the Methods section and the results are presented in Figure[27] for the re­suspended precipitants and in Figure[28]for the supernatants.

Again a clear trend was visible confirming the expectations – the number of conjugates remaining in the supernatant decreases with the increasing speed of centrifugation. The approximate threshold of 5000 rpm was revealed below which the majority of NPs remain in the top volume of the sample. Above this threshold increasingly more Ab­AuNP conjugates were spun down which would compromise the efficiency of the binding test.

Figure [27]: The result of centrifuging gold nanoparticles conjugated with HIV Ab and blocked with BSA at different rotational speeds shown for measured by optical absorbance of the samples after re­suspending the precipitates in PBS. The optical absorbance at 545 nm corresponds to the nanoparticles’ red peak as demonstrated in the previous stages of the project and serves as a mean of determining the amount of NPs in solution.

In conclusion, the threshold found matches the threshold found for bacteria which is a very fortunate result. Hence the optimum centrifugation speed for the binding test was found (5000 rpm) as it was demonstrated that such speed will force the majority of bacteria towards the bottom of the test tube while leaving the optimum amount of unbound Ab­AuNP in the top volume of the sample which is being removed in the test procedure.

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Figure [28]: The result of centrifuging gold nanoparticles conjugated with HIV Ab and blocked with BSA at different rotational speeds measured by optical absorbance of the supernatants taken from the centrifuged samples. The optical absorbance at 545nm corresponds to the nanoparticles’ red peak as demonstrated in the previous stages of the project and serves as a mean of determining the amount of NPs in solution. The result confirms the trend evident from Figure 27.

4.2.3.4. Optimisation of Bacteria:Ab­AuNP Solutions Ratio

The effect of varying Bacteria solution to Ab­AuNP solution ratio on the intensity of the red colour signifying binding was studied.

The experiment followed the procedure described in the Methods section testing the Ab­AuNP : Bacteria solutions ratio of 10%, 20%, 30% 40% and 50% were tested using the LPS antibody. Three controls were used: 0% NP solution (only bacteria), HIV Ab conjugated to NPs (at 30% ratio) as well as blocked NPs with no antibody attached (at 30% ratio).

As is evident from Figures 29 and 30 the amount of Ab­AuNP solution relative to bacteria solution is positively correlated with the intensity of the red colour after the binding test which increased the intensity of signal making the test more sensitive. Importantly, the red colour remaining was seen in all test samples containing LPS antibody including those at lowest ratio of only 10% and the difference was resolvable with a naked eye. All controls were demonstrated negative result with the sample containing NPs without Ab changing colour towards blue end of the spectrum

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signifying the NPs sticking to each other as described in section 3.2.3. which is a result of ineffective blocking.

Figure [29]: The effect of varying the relative volume of Ab­AuNP solution (VNP) to the volume of Bacteria solution (VBACT) on the signal intensity tested using the LPS antibody against K12 E. coli bacteria. The figure presents the photograph of the samples put into wells on an ELISA plate. The volume in each well is 200uL. Columns contain samples with varied NP sol. ratio including 3 controls: no NP solution (only bacteria) marked “0%”, HIV Antibody and NPs with no Ab. Rows present triplicates of the same samples. Note: the bottom well in the LPS Ab “30%” sample is empty due to an experimental mistake leading to the lack of required volume of the solution.

Figure [30]: The effect of varying the relative volume of Ab­AuNP solution (VNP) to the volume of Bacteria solution (VBACT) on the signal intensity tested using the LPS antibody against K12 E. coli bacteria. The figure presents the photograph of the samples as they appear in the centrifuge test tubes.

In conclusion, the increased ratio of NP solution relative to the bacteria solution at the range tested resulted in a stronger signal which is desirable for the binding test and increases the sensitivity of the test. It can be inferred that at this amounts of bacteria and NPs the binding sites

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on the bacteria are not saturated (not all are used for binding of Ab­AuNP) and hence an increased number of conjugates always results in an increased signal.

This is not a trivial conclusion since it should be noticed that increasing the NP solution ratio necessarily reduces the number of bacteria in the sample and at certain threshold the number of bacteria could reach too low level for the signal to be preserved (either because there would be not enough binding sites for the antibodies or because the total opacity of the solution would decrease eventually becoming clear for too low bacteria concentrations). This threshold was not achieved in the performed experiment.

Another important result shows that the signal was distinguishable even for the lowest ratio of antibody­nanoparticle conjugates relative to the bacteria solution. This allows for performing the test with lower amount of the Ab and NPs reducing the cost and waste of materials. Moreover, since the crucial factor for the test’s performance is the total number of bacteria in the test tube (for instance too few bacteria would not form a visible precipitate on the bottom of the test tube after centrifugation disabling the procedure which requires removal of supernatant with leaving the precipitate in the tube) the lower Ab­AuNP solution ration allows for the use of larger total volume and hence lower concentration of bacteria.

4.2.3.5. Conclusions for Centrifuge Test

The binding tests performed (repeated 3 times) and the three additional characterisation experiments allow to form the following conclusions:

The method described can verify whether the antibody does bind to the bacteria or not as shown with the LPS antibody binding to K12 bacteria and contracted to multiple controls.

The optimised centrifugation speed was found allowing to spin down the bacteria (with Ab­AuNPs that are bound to it) while leaving the unbound conjugates in the sample volume as required by the method.

It is possible to increase the signal intensity by using higher ratio of NP solution relative to the bacteria solution making the test results more evident.

At the same time even using the lowest ratio of NP solution which has been tested (10%) still results in a distinguishable signal intensity, resolvable with a naked eye. Using low Ab­AuNP solution ratio requires lower amounts of materials decreasing the cost of the method.

Presented binding test is capable of verifying the binding within approx. 2­3 hours which is an improvement over the commonly used ELISA test which typically requires 2 days to complete.

4.2.4. Conclusions for Antibody Binding Assays

Bio­layer interferometry failed to verify antibody binding to bacteria. Both ELISA and the newly developed centrifuge­based binding test did allow for the verification of binding.

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4.3. Development of Rapid Lateral Flow Test of AMR Lateral Flow Tests offer a method for cheap and rapid immunoassays (see Section 3.3.2). The development of a lateral flow bacterial susceptibility test would greatly decrease the required and time and resources compared to standard susceptibility testing. By collecting the tagged products of bacterial lysis at a capture line, the effects of antibiotics can be quantified for samples using a lateral flow test.

4.3.1. Buffer Optimisation for Lateral Flow Tests In order for optimised capillary flow of the solution carrying the antigen of interest, solutions for testing with paper lateral flow strips require suspension in a buffer containing a blocking protein (such as BSA) and a detergent (such as T20). However, T20 can also act as a lysing agent of bacteria and so its effect on E. coli was studied.

The results of Test #1 (see Methods section for the experimental procedure) of bacterial susceptibility to tween are shown in Figure 31.

The expectation was that a growth inhibition zone will be seen for the Kanamycin, Trigene and Ethanol sectors and not for the control PBS sector. The Tween sector was the test sample. Figure 31 demonstrates a growth inhibition zone only in the Kanamycin and Trigene sectors (as expected) but not in PBS (as expected), Ethanol (unexpected) or T20.

The lack of inhibition in Ethanol can be explained by the rapid evaporation of this solvent leaving no molecules inhibiting the growth during the 16 hour long incubation.

The lack of inhibition zone in the Tween sector is the core result of this test and indicates that the bacteria were unaffected and hence the T20 surfactant can be used in further tests safely without posing a risk of killing bacteria.

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Figure [31]: The growth inhibition test of K12 E. coli bacteria on an agar plate in the zones where droplets of potential disinfectants were placed. A well grown lawn of bacteria is visible on the whole surface of the agar plate with two areas of inhibition for the Kanamycin and Trigene sectors.

Results of Test #2 (see Methods section) are presented in figure 32 where bacteria were the plates were tested not for inhibition areas but for the direct growth in the droplets containing potential growth inhibitors.

The expectation was that the growth will be positive on the PBS control sector and also (after learning in test #1 that the Tween does not inhibit the growth) in the T20 sector. The growth was not expected in the Trigene and Kanamycin sectors and also not in the Ethanol sector since in this case the bacteria were incubated in the EtOH environment long enough for the alcohol to kill the bacteria so when the droplet was placed on the agar plate the bacteria were already dead and the rapid evaporation of Ethanol did not affect the result.

It is evident from Figure 32 that the results were just as expected with positive growth for T20 and PBS sectors and no growth for Kanamycin, Ethanol and Trigene sectors. Test #2 hence confirmed the finding of test #1 that the Tween at 1% concentration does not visibly inhibit the growth of bacteria and hence is not bactericidal.

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Figure [32]: The growth test of K12 E. coli bacteria on an agar plate in the droplets of potential bactericidal solutions were placed. A growth of bacteria is seen in the Tween region and the PBS region and not in the three regions where a solution known to have bactericidal effect was used.

Test #3 involved growing bacteria in a liquid medium (LB) in the presence of T20 at different concentrations as described in the Methods section. The T20 concentrations used were: 0, 0.1, 0.5, 1, 5 and 10%.

Figures 33 and 34 present the results for K12 and BL21 bacteria respectively. Both figures show a gradual decrease in the growth of bacteria with increasing Tween concentration. This does not nullify nor contradict the results of Tests #1 and #2 since the growth was still positive for the 1% concentration and, indeed, for all concentrations tested. The Test #3 however provides further insight and allows for the quantification of the effect of T20 on bacteria.

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Figure [33]: The effect of Tween in the liquid growth medium (LB) on the growth of K12 E. coli bacteria.

Figure [34]: The effect of Tween in the liquid growth medium (LB) on the growth of BL21 E. coli bacteria.

In conclusion, clearly the T20 environment is not bactericidal for K12 and BL21 E. coli bacteria at the tested range of concentrations (i.e. up to 10%) since no growth would be observed otherwise. The T20 might however induce a partial inhibition on the bacteria growth and this effect increases with the increasing tween concentration.

The findings of all 3 Tween susceptibility tests allows to conclude that it is safe to use T20 at 1% concentrations for the bacteria solutions.

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The use of T20 in a buffer for bacterial on lateral flow tests can be shown to not cause significant lysis. All lateral flow tests in Section 4.3 use a buffer of PBS with 2% BSA and 0.05% T20.

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4.3.2. Separating Bacteria and Lysed Components using Nitrocellulose Membrane

Lateral flow tests were examined as a means of bacterial antigen detection (see Section[3.3.2]). Live/Dead stains (see Section[3.1.2]) were used with K12 E. coli to test their flow through the nitrocellulose membrane. It was found that when using a membrane with flow rate 0.33mm/s, some PI stained components (dead) were separated from intact, SYTO9 stained cells (live). Given the size of E. coli K12 (~2μm) this could be attributed to whole cells being larger than average the average pore size of the membrane.

Figure[35] shows live/dead stained (see section[3.1.2]) K12 E. coli on lateral flow strips. Figure[35a] shows the differences in green and red fluorescence along the strip (taken from on image of the stip under a UV lamp for increased excitation), a measure of distance of flow of live cells and the components of dead cells along the nitrocellulose membrane. This pattern was consistent in all strips and each antibiotic concentration was repeated three times. The image in this figure has been increased in colour contrast, however the data plotted was obtained from an unmodified

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image. Figure[35b] shows an image of strips with K12 E. coli incubated with a decreasing (left to right) concentration of Nafcillin­Ampicillin under ambient light in the laboratory. Figure[35c] shows the base of the leftmost strip from Figure[35b] under a fluorescence microscope (x5) at blue (for live stain) and green (for dead stain) excitations. These images are composites of several photographs. Figure[35d] shows strips with solutions using an increased proportion of PI dead stain. The image on the left shows the difference between samples with no antibiotic and with Nafcillin­Ampicillin at 64mg/L. The image on the right shows a sample with no bacteria. Figure[35a] shows the red and green RGB intensity of a photograph taken of the nitrocellulose section of a lateral flow strip under a broad­spectrum UV lamp (for increased excitation (see Section[3.1.2]) and decreased background scattering). Whole cells and some PI stained components become trapped in the membrane (giving a yellow fluorescent emission due to the superposition of green and red fluorescence) at the level of the solution in the microtitre plate well, and some PI stained components travel along the strip via the capillary flow of the buffer (see Section[3.3.2]). Figure[35b] shows a photograph under ambient laboratory light where lateral flow strips have been immersed in 150μl solution for 15 minutes with E. coli K12 exposed to decreasing concentration of Nafcillin­Ampicillin and stained. The fluorescence of the SYTO9­stained live cells visibly decreases with increasing antibiotic concentration, although the PI stained components are less visible and their intensity remains consistent. Figure[35c] shows a photograph via fluorescent microscopy of the section immediately above the line of immersion of the leftmost strip in Figure[35b]. Live and dead stains have been excited separately using blue and green excitation filters respectively. Some PI stained components are shown to be trapped at a level equal to the live cells, whereas some also travel further along the strip. Figure[35d] shows tests with an increased PI:SYTO9 ratio to increase the visibility of PI stained components under ambient light. Tests here were immersed in 250μl solution for 60 minutes to increase the separation of live and dead components. Strips from left to right show stained bacterial solution with no antibiotic, Nafcillin­Ampicillin at 64mg/L and no bacteria. Where antibiotic was used, PI stained components are visible along the length of the strip. However, at increased concentrations of PI, fluorescence from the solution with no bacteria is also detected. Yellow sections at the level of the solution on the strip in Figure[35a] indicate that both live whole cells and cells with compromised membranes become trapped ­ the yellow fluorescence being the superposition of red and green fluorescence from the PI and SYTO9 stains. The red stripe detected further along the strip can be explained by ‘free’ nucleic acids, released from cells by lysis via antibiotic. Their smaller size allowing them to be carried along the capillary flow of the buffer along the lateral flow strip (Figure[35d] shows that they can travel along the length of the strip when sufficient solution is used and time allowed). Another explanation of the PI detected nearer the base of the strip is that prokaryotic nucleic acids can vary hugely in size: their singular circular chromosome is typically of the order of magnitude of the whole cell (~0.5­2μm ), whereas ribosomal RNA can be much smaller (~20nm). Smaller NAs (Nucleic Acids) would be expected to be released when cells are lysed, but larger NAs would become trapped with whole cells. From the decreasing green fluorescent intensity in Figure[35b] with increasing antibiotic concentration, it would be expected that the red fluorescent intensity further up the strip

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increases, however as NAs are internal to the cell membrane, they are only released when cell lysis occurs, which would happen on a larger scale only at bactericidal concentrations of antibiotic (those above MBC). The lack of visibility of the dead stain could also be explained by insufficient excitation by the more blue ambient light. However, when the PI concentration is increased in Figure[35d], the live stained areas appear orange, and the differences in green intensity are much less apparent. Figure [35d] also shows that high concentrations of PI also interact with proteins in the LB buffer used for growth, meaning that the visible red stain cannot be wholly attributed to the components of lysed cells. The LB (to allow for cell growth and division, necessary for the inhibiting and bactericidal action of antibiotics) could be removed via centrifugation and replaced with PBS to avoid interactions with proteins in the buffer, but this would also likely remove smaller nucleic acids in the solution made free by cell lysis. In order to develop a rapid test for bacterial susceptibility ­ the products of cell lysis and death must be quantified. This could be achieved by measuring the red intensity in a specific section of the lateral flow strip, although this has been shown to be susceptible to background noise in Figure[35d] and non­specific to bacteria. Figure[35] shows that the products of bacterial lysis can be separated from whole cells by the nitrocellulose membrane of the lateral flow test. In order to develop a specific test for bacterial antigens released by cell lysis, antibodies conjugated to a marker for detection are required (see Section[3.3.2]). Although it has been shown that the release of some nucleic acids are a function of cell lysis, NAs vary widely in their size, and do not behave consistently as a function of cell lysis. Being internal to the cell membrane, NAs are also released only by cell lysis and are not considered a marker of the more subtle inhibitory effects of antibiotic at concentrations lower than MBC.

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4.3.3. Using LPS Release as a Quantitative Measure of Bacterial Lysis

LPS release from whole bacterial cells has been been shown to be a function of antibiotic concentration, although studied in the context of avoiding increased toxin release with antibiotic treatment [27]. The experiment described by M. Evans, M. Pollack [27] to quantify LPS release was modified to use anti­LPS antibody conjugated AuNPs as a marker instead of an isotopic label, which are unstable and less suitable for point­of­care tests. The sensitivity of LPS release as a function of antibiotic susceptibility was testing using the LPS antibody­conjugated AuNPs described in Section[3.2.3]. These were bound to E. coli K12 cells before they were washed in PBS to remove excess nanoparticles (see Section[4.3.4]). Varying dilutions of antibiotic were introduced to the solution and it was incubated overnight. Solutions were then centrifuged and the supernatant removed. A centrifugal force was chosen from Section[4.2.3] to create a pellet of only whole E. coli cells. The optical absorbance at 520nm (the peak absorbance wavelength of the AuNPs, determined in Section[4.2.3]) was measured and subtracted from the same measurement of solutions before centrifugation. A smaller difference in optical absorbance at this wavelength signified more AuNPs in the supernatant, inferring a higher rate of separation of LPS from whole cells.

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Figures[36,37] show the release of LPS molecules from the surface of K12 E. coli as antibiotic concentration varies. This is compared to the standard susceptibility curves for the respective antibiotics, obtained in Figures[16,17]. The LPS release is quantified by the difference in Optical Absorbance of two samples of AuNP­conjugated E. coli, one uncentrifuged and another the supernatant of a centrifuged sample (containing only buffer and free products of lysed cells, including LPS). The closer to 0, the closer in Optical Absorbance the supernatant to that of the uncentrifuged sample, and the more LPS released from the whole cell as an effect of cell lysis and death from the antibiotic. Figures[36,37] compares the LPS release over a range of antibiotic concentrations for Nafcillin­Ampicillin and Kanamycin to the standard susceptibility curves obtained from Figure[16,17]. LPS release is shown to increase with antibiotic concentration at a rate similar to the inhibition of growth. Nafcillin­Ampicillin is shown to have a greater effect on LPS release. The differences in LPS release of the two antibiotics can be explained by their mechanism of

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action: Nafcillin­Ampicillin directly inhibits cell wall development (see Section[2.1]) and would therefore more directly affect the stability of LPS molecules as the main component of the outer membrane [22]. In order to develop a rapid susceptibility test, the released LPS, bound to AB­conjugated AuNPs would have to be quantified. In a lateral flow test, the bound LPS would have to flow to a capture line (see Section[3.3.2]), where they would be immobilised by antibodies and congregate for detection.

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4.3.4. Antibody Conjugated Nanoparticles as a Specific Marker for Bacteria on Membrane

In order for the conjugated AuNPs to reach the capture line, and to ensure whole E. coli cells are separated from the products of their lysis, a suitable buffer was required to disperse the AuNPs along the Nitrocellulose membrane. Buffers with PBS containing different combinations of BSA and T20 were compared.

Figure[38] shows a comparison of Gold Nanoparticle dispersal along the Lateral Flow Strip when different buffers are used. A low grey value (corresponding to low image intensity) shows poor dispersal. Consistent dispersal is required for the particles to reach the capture line and for it to have a consistent intensity, to be most effectively detected by an image processing program. Images at the legend have been modified to increase brightness and contrast, but images used for analysis were unmodified. Figure[38] shows that the buffer for optimum nanoparticle dispersal requires both BSA and T20. Both components are shown to have a strong effect compared to when solely PBS is used, and are most effective when used together. When PBS is used as a buffer, conjugated nanoparticles congregate near the bottom of the strip, become trapped by electrostatic forces with the nitrocellulose and each other. The use of BSA blocks the external proteins of the antibodies binding to the nitrocellulose and the use of T20 reduces the overall viscosity of the solution. Antibody­conjugated AuNPs with several different antibodies were bound to E. coli K12 and tested on Lateral Flow Strips to compare the contrast provided by binding (a measure of the density of binding sites) for optimised detection using image processing techniques.

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Figure[39] shows a comparison of Gold Nanoparticle dispersal along the Lateral Flow Strip when different buffers are used. A low grey value (corresponding to low image intensity) shows poor dispersal. Images at the legend have been modified to increase brightness and contrast, but images used for analysis were unmodified. Figure[39] shows that the LPS antibody gives the highest signal when used on Nitrocellulose Membrane. The O and K antigen­binding antibody shows a lesser signal and the C1/2 antibody (HIV antibody, used as control) shows little to no signal, comparative to when no AuNPs are used. The highest signal is shown when no antibody is used, and the AuNPs are directly attracted to the E. coli surface by electrostatic charge. This however is not specific detection (AuNPs could equally bind to other microbiology in a sample) and can be avoided by blocking the AuNPs with BSA (See Section[3.2.3]). 50ul of the E. coli used in Figure[39] was used to inoculate 1ml LB and grown overnight, showing similar growth to unbound E. coli (see Figure[2]) and therefore the bacteria in Figure[39] can be assumed live, whole cells. These whole cells become trapped by the nitrocellulose membrane similar to experiments in Section[4.3.2]. The detection of K12 and BL21 E. coli strains on nitrocellulose membrane was assessed by testing solutions that had been ‘washed’ (had their supernatant removed after centrifuging to remove unbound nanoparticles) an increasing amount of times.

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Figures[40,41] shows the Grey level along an image of Lateral Flow Tests for conjugated samples of K12 and BL21. The Grey Level is compared for the amount of washes of the sample, which here is a measure of the quality of antibody­bacteria binding and its detection via image processing. Images at the legend have been modified to increase brightness and contrast, but images used for analysis were unmodified. Figures[40,41] show that after washing, LPS AB conjugated AuNPs bind more strongly to whole K12 E. coli cells. This is contrary to the ELISA results in Figure[21] which showed greater binding of the LPS AB to BL21 E. coli, although consistent with the centrifuge binding experiments in Section[3.2.3]. This could be due to BL21 cells having a larger surface area and more binding sites (due to lack of flagella) but weaker binding affinity due to a smaller LPS molecule, where the binding has larger forces acting against it during centrifugation.

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LPS antibodies have been shown to detect live K12 cells, but in order to develop a rapid susceptibility test, its release also needs to be detected by a capture line on the nitrocellulose membrane.

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4.3.5. Optimisation for Development of Rapid Lateral Flow Test

A detection spot was made ~30mm from the base (Figure[41] shows whole cells are trapped ~10mm from the base, when 80μl of solution is used) of lateral flow tests by spotting 3μl of polyclonal anti­mouse antibodies at 1mg/ml. The anti­mouse antibody immobilises the nanoparticles by binding to the mouse anti­LPS antibodies that are electrostatically bound to its surface, meaning that they will be immobilised whether or not they have an LPS molecule attached. In order for the most sensitive LPS detection, the signal caused by particles not carrying an LPS molecule needs to be minimised. Solutions of AuNP­bound E. coli K12 were washed an increasing amount times, as in Figures[40,41], although here, the capture spot on the nitrocellulose strip was analysed.

Figure[42] shows the Grey level along an image of Lateral Flow Tests for conjugated samples of K12. The Grey Level is compared for the amount of washes of the sample, which here is a measure of conjugated AuNP capture. Images at the legend have been modified to increase brightness and contrast, but images used for analysis were unmodified. Figure[42] shows that at least three washes of the E. coli K12 are required to remove excess antibodies and reduce unwanted detection signal. This provides a hinderance to the speed of a rapid susceptibility test. Bacteria cells in a sample would either have to be washed, or an additional reference test, where no antibiotic is introduced would have to be used for comparison. An additional anti­LPS antibody could be used as a capture antibody, however it would have bind to a different part of the LPS molecule than the detection antibody. Given the reduced size of the LPS molecule in the E. coli strains tested (see Section[3.1.1]), this is difficult when using ‘rough’ lab strains. Had the E. coli LPS had an ‘O’ antigen (as those of pathogenic E. coli strains do), the anti­‘O&K’ antibody tested in Section[4.2.3] could

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have been used as a capture antibody. Continuing to use anti­mouse as a capture antibody, nitrocellulose membranes with differing pore sizes were compared for quality of nanoparticle capture to maximise signal intensity.

Figure[43] compares nitrocellulose membrane pore sizes and the intensity and definition on capture spots. The grey level is shown along images of Lateral Flow Tests for conjugated samples of K12. The grey level is compared for different pore sizes of the Nitrocellulose Membrane for the Lateral Flow Test, which here is a measure of the quality of detection when using image processing methods. Images at the legend have been modified to increase brightness and contrast, however, images used for analysis were unmodified. Grey values shown are the mean of three repeats. Figure[43] shows a more intense signal in the density of capture of nanoparticles (given by a lower grey level) for slower flow rates (smaller pore sizes). This could be due to a higher density of fibres for capture antibodies to attach to. Although the membrane of flow rate 0.33mm/s shows higher overall intensity, the membrane of flow rate 0.22mm/s shows better definition, suggesting that more nanoparticles are immobilised by the section of the capture spot first in contact with the molecules, suggesting less nanoparticles flow through the capture spot without being immobilised. This would be important for a capture line spotted with a commercial microspotter.

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4.3.6. Testing of Lateral Flow Susceptibility Test Gold nanoparticle­bound E. coli K12 were washed three times in PBS before being resuspended in LB and antibiotic. After incubation overnight, lateral flow strips of flow rate 0.22mm/s and an anti­mouse capture spot were immersed in 80μl of solution for twenty minutes.

Figure[44] compares the LPS detected by Lateral Flow susceptibility test when E. coli K12 is incubated with Nafcillin­Ampicillin and Kanamycin. The grey level is compared for different antibiotics, which here is a measure of AuNP­tagged LPS detection. Images at the legend have been modified to increase brightness and contrast, however, images used for analysis were unmodified. Figure[44] shows that more released LPS was detected by the capture spot when E. coli was incubated with high concentrations (>MBC) of Nafcillin­Ampicillin. When Kanamycin was used, the intensity of the capture spot was comparable to that of the control. This could be due to Nafcillin­Ampicillin inducing more LPS release (as shown in Figure[36,37]) and the higher MBC of Kanamycin (see Figure[17]). It should be noted that although consistent in triplicates, the intensity differences of the capture spots were small (compare the axis of Figures[42] and [44]) even for high concentrations of antibiotic, and therefore this method of detection can be said to have poor sensitivity.

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4.3.7. Conclusions for Lateral Flow Bacterial Susceptibility Tests

A Lateral Flow Bacterial Susceptibility test was proposed: it was shown that a nitrocellulose

membrane can separate whole bacterial cells and the products of their lysis. It was shown that the

release of both nucleic acids and LPS can be used to determine bacterial susceptibility, and that both markers can be used in paper microfluidic tests. In order to develop these findings in a rapid susceptibility test, the specificity and sensitivity of detection needs to be improved.

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4.4. Nanomechanical Bacterial Vibrations for Point­of­Care AMR Sensor

This chapter presents the results of detection and characterisation of bacterial vibrations using an AFM.

A fluctuating time­domain signal attributable to bacterial vibrations, distinguishable from background noise is demonstrated in section 4.4.1.; the dependence of vibrations on cantilever coverage with bacteria is presented in section 4.4.2.; and the effect of changing measurement medium to more or less favourable for bacteria is shown in section 4.4.3. Section 4.4.4. presents the results in frequency­domain.

For the experimental protocol of experiments see chapter 3.4.2.; and for the data analysis methods see chapter 3.4.3.

4.4.1. Bacteria vibrations

Figure 45 compares the signal measured in three separate experiments with cantilevers immersed in LB: empty cantilever (top), with live bacteria immobilised on the cantilever (centre) and with dead bacteria immobilised on cantilever (bottom). The dead bacteria were assumed killed as they were kept for 4 days in nutrient­free PBS medium at very high concentration (approx. 10^9 CFU/mL ) at 4°C.

The fluctuations were seen in case of the live bacteria but not for an empty cantilever nor for the dead bacteria providing evidence that they were indeed killed.

Figure [45]: Bacteria fluctuations in LB. TOP: Empty cantilever (with no bacteria immobilised) in LB. CENTRE: Live bacteria immobilised on cantilever at middle coverage; fragment extracted after 1 minute from the beginning of the recording; fluctuations remained for the whole duration of the experiment. BOTTOM: Bacteria were kept at in unfavourable conditions (nutrient­less medium

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(PBS), very high concentration (approx 10^9 CFU/mL ) and at 4°C) for 4 days and then immobilised on cantilever; the lack of fluctuations suggests they were dead. The graphs present data flattened with linear fit and shifted to centre around y=0 axis.

The fluctuations were quantified using variance calculated from the signal, which in case of live bacteria is one order of magnitude larger that the variance of the 2 other control signals (see Figure 46).

The difference between dead bacteria and the empty cantilever falls within the error uncertainty but may be a signature of a low percentage of bacteria still alive.

Figure [46]: Variance of the signal presented in Figure 45: empty cantilever noise signal (left), with live bacteria immobilised (centre) and with dead bacteria immobilised (right).

Error bars were calculated from other (30 sec) fragments extracted from the same stage of the experiment.

11 baseline measurements were performed and each of them resulted in a fluctuation­free signal. 19 measurements with live bacteria were performed and the presence of fluctuations was observed to be correlated with the coverage of the cantilever with bacteria as studied in the next section.

4.4.2. Coverage of Cantilever with Bacteria

The dependence of fluctuations on the cantilever coverage was characterised with four measurements of ‘low’ coverage, six measurements at ‘medium’ coverage and nine measurements at ‘high’ coverage (as defined in chapter 3.4.2.).

A representative selection of the aforementioned three immobilisation densities on cantilevers is shown in Figure 47 and quantified in Figure 48. All low coverage cases, when measured in LB, resulted in a low variance signal on average 14% higher than the baseline level.

The high coverage turned out to be problematic and often resulted in a formation of a bacteria web­like structure around the cantilever. All high coverage cases demonstrated signal of lower

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variance than the medium coverage signals with five cases presenting only baseline­like signal (which could be increased by modifying medium as discussed in the next section).

Figure [47]: Dependence of the signal on the bacteria immobilisation density on the cantilever. Shown are representative samples for low (top), medium (centre) and high (bottom) coverage.

The graphs present data flattened with linear fit and shifted to centre around y=0 axis.

Figure [48]: Variance of the signals presented in Figure 47.

Error bars were calculated from other (30 sec) fragments extracted from the same stage of the experiment.

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4.4.3. Modifying media

The effect of changing the medium to more nutritious for bacteria by adding glucose solution to the experimental dish containing LB was studied for all three representative levels of coverage. As seen from Figures 49 and 50 the addition of glucose increased the vibrations in all cases.

The low­coverage and the medium­coverage signals typically increased approx. 3­fold after addition of glucose; the high­coverage samples resulted in a large variance signal in all cases regardless whether the signal before addition of glucose was significant or baseline­like.

Figure [49]: The effect of adding glucose to the medium in an attempt to make it more nutritious for bacteria immobilised on cantilever at low (top row), medium (centre row) and high (bottom row) coverage density. Left column – before addition of glucose; right column – after addition of glucose. The graphs present data flattened with linear fit and shifted to centre around y=0 axis.

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Figure [50]: Variance of the signals presented in Figure 49 showing the effect of adding glucose to the medium with bacteria immobilised on cantilever at low (left), medium (centre) and high (right) coverage density. NOTE: The y­axis on the “HIGH coverage” graphs (right) is different from the other two graphs for the increased clarity and readability. Error bars were calculated from other (30 sec) fragments extracted from the same stage of the experiment and from different measurements in the same media conditions.

In contrast with enriching the medium with glucose, the bacteria were also studied in a nutrient­free environment of PBS. In one experiment an empty cantilever signal was measured in PBS (Figure 51 top); in the following experiment a medium­coverage number of bacteria were immobilised on the same cantilever (signal shown in Figure 51 centre) and finally a glucose solution was added to the solution in an attempt to turn the medium nutritious (Figure 51 bottom). The vibrations of bacteria on cantilever were significantly larger than the baseline signal both in amplitude (as evident from Figure 51) and in variance (3­fold increase as seen from Figure 52) although no fluctuations were seen at this time­scale.

Although the baseline noise in PBS and in LB was comparable, the signal from live bacteria in (non­nutritious) PBS was approx. three times lower than in (nutritious) LB as was expected.

Eventual addition of glucose increased the signal variance approx. 19­fold (see Figure 52) despite a 3­hour long incubation of cantilever in PBS showing the bacteria were still alive and suggesting that the medium turned nutritious enabling the onset of a metabolic action in the organisms as speculated from the published research (section 2.2.2.) [1].

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Figure [51]: Signal from bacteria in PBS (nutrient­free medium) before and after adding glucose.

TOP: Empty cantilever (control) in PBS. CENTRE: Bacteria immobilised on the cantilever at medium density in PBS before addition of glucose. BOTTOM: Signal after adding 1mL of 16% glucose

solution (dissolved in PBS) into the dish already containing 3mL of PBS resulting in a 4% solution. The graphs present data flattened with linear fit and shifted to centre around y=0 axis.

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Figure [52]: Variance of the signals presented in Figure 51 showing the signal of bacteria in PBS before and after addition of glucose. Error bars were calculated from other (30 sec) fragments extracted from the same stage of the experiment.

4.4.4. Frequency Spectra

The signals demonstrating fluctuations were also studied against the baseline signals in the frequency­domain.

As seen in Figure 53 the frequency spectrum ranging from 0 to 5kHz does not show any significant high­frequency peaks as expected based on the literature review. The increase of amplitude around 3.5kHz with respect to the lower and higher frequencies agrees with the cantilever resonance frequency measured to be during calibration.

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Figure [53]: Frequency domain spectrum ranging between 0Hz and 5,000Hz comparing the empty cantilever background noise (top) and the signal from live bacteria (bottom).

The frequency range from 0.1 Hz to 200 Hz was also studies since Longo et.al. suggested [1] these are the typical frequencies of operation for the bacteria’s internal machinery responsible for metabolism. As presented in Figure 54 no significant high­frequency components were found yet the amplitude began to be distinguishable larger at low frequencies.

Figure [54]: Frequency domain spectrum ranging between 0.1 Hz and 200 Hz comparing the empty cantilever background noise (top) and the signal from live bacteria (bottom).

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Therefore, only low frequency components between 0.1 Hz and 5 Hz were used for the further analysis of the signals providing a clear distinction between signals with and without vibrations as demonstrated in Figure 55 where live bacteria (centre) are contrasted with empty cantilever baseline (left) and dead bacteria (right).

Figure [55]: Frequency domain spectrum ranging between 0.1Hz and 5Hz comparing the empty cantilever background noise (left),the signal from live bacteria (centre) and signal from dead bacteria (right).

This finding was used to analyse the effect of glucose­enriched medium on the change in vibrations for all three levels of cantilever coverage. The results presented in Figure 56 indicate a clear distinction of the signals before (top row) and after (bottom row) the glucose was added to the LB medium. Still no specific peaks indicating discrete frequency components were observed, rather the whole low­frequency spectrum increases in amplitude when fluctuations increase.

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Figure [56]: The effect of adding glucose to the medium for bacteria immobilised on cantilever at low (left column), medium (centre column) and high (right column) coverage density in a frequency domain spectrum ranging between 0.1 Hz and 5 Hz. Top row – before addition of glucose; bottom row – after addition of glucose.

4.4.5. Conclusions for Bacterial Vibrations

In total over 30 different measurements of the signal from bacteria on cantilever (or empty cantilevers) were performed in different media (LB, PBS, glucose­enriched LB, glucose­enriched PBS) and in a variety of bacteria immobilisation levels.

The analysis of all collected data provides evidence for the following conclusions to be formed:

The live bacteria immobilised on an AFM cantilever immersed in LB have been found to exert vibrations onto it at the level distinguishable from the background noise of an empty cantilever.

The strength of the vibrations is correlated with the level of coverage of cantilever with bacteria. At low numbers around cells per cantilever the signal is too low for performing good quality experiment. Immobilisation of around cells was found to give best quality results (signal of the highest variance).

High immobilisation of around cells – perhaps surprisingly – demonstrated signals of lower variance than medium coverage as well as tended to form a web­like structures of bacteria sticking to each other. This may suggest that the bacteria become attached to each other at the expense of contact with the cantilever leading to them not transferring the mechanical signal onto its surface.

Too high levels of coverage should therefore be avoided.

The mechanism by which bacteria form these structures are not understood although the traces of glutaraldehyde (commonly used in microbiology for fixing the cells on the substrates) left at the

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stage of bacteria immobilisation procedure may be crosslinking the proteins on their surface effectively fixing them to each other.

Bacteria incubated in PBS for 2 hours exert significantly lower vibrations on the system (in PBS medium) than when immersed in LB.

Addition of glucose into LB or PBS media in both cases immediately (within 1 minute) increases the strength of the vibrations.

Since the density of nutrients available to bacteria has been shown to be positively correlated with the strength of the signal, the assumption about the origin of vibrations being a result of a metabolic action seems to gain new supporting evidence.

It was demonstrated that the vibrations can be detected and studied in frequency­domain, not only time­domain as was reported before [1­2].

No discrete frequency components responsible for the fluctuations were identified in the signals’ spectra – instead a wide range of frequency components between 0 and approx. 20 Hz have been found to rise in amplitude at the onset of vibrations. This provides evidence that the bacterial machinery creating the vibrations does not operate at a periodic basis.

Note: the lack of discrete frequency components cannot be the result of a periodic signals not being synchronised (as may be expected from the multitude of cells as well as multitude of machines inside each cell). This is because many signals of the same frequency even shifted in the time domain would still results in a single peak on in the frequency domain.

Neither can it be a result of a variation in the types of origins of vibrations since these would present a few separate discrete peaks not interfering with each other.

This further suggests that the origin of vibrations might be the metabolism rather than the flagellar movements since the flagella driving rotor operates at a quasi­periodic rate while the metabolic activity of cell’s other molecular machines is randomised by the signal transmitters (e.g. ligands in ligand­gated ion­channels etc.).

It has been demonstrated that the bacteria vibrations can be detected in a laboratory experimental system without a specialised liquid flow chamber and pumps (which were used in previously published research).

This also provides evidence that the bacteria signal observed is real and does not originate from the flow or exchange of liquids by the aforementioned hydraulic systems.

Similarly, the procedure used in this project did not require functionalization of the cantilever surface with APTES molecules – instead a simpler method of treating the cantilever with glutaraldehyde was successfully used and offered possibility of a wide range of immobilisation density levels from too low for accurate measurements to too high (as discussed above).

It should however be noticed that the limited amount of trials and presumably most importantly the lack of understanding of the origin of vibrations leaves the above conclusions open to verification. Further work suggestions together with a more detailed discussion of the sources of uncertainty are presented in section 5.3.

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5. Conclusions

5.1. Detection of Antibody­Bacteria Binding with Centrifuge

5.1.1. Summary

A novel method for verifying antibody binding to bacteria utilizing the gold nanoparticles markers and based on a centrifugation procedure was introduced and demonstrated to work. The results agreed with the expectations and were positive for the LPS Ab binding to K12 E. coli bacteria and negative for all the controls used which included 2 different strains of bacteria and 3 different antibodies as well as BSA­blocked NPs with no antibody attached.

The method was characterised finding the optimal speed of centrifugation and demonstrating the possibility to increase the signal intensity by increasing the Ab­AuNP conjugates solution volume relative to the bacteria solution volume.

The method was also shown to provide distinguishable results even at the lowest NP sol. ratio tested (10%) which allows for reduction of cost and material waste in the test. This result additionally allows for the use of lower concentrations of bacteria solutions as long as the total amount of bacteria remains high enough to form a visible precipitate in the test tube after centrifugation.

The test is significantly faster than the gold­standard ELISA method allowing for verification of the results within 2­3 hours as opposed to 2 days which is a standard time required by the ELISA test.

Since centrifuge is a versatile instrument commonly available in the scientific laboratories, the method provides a useful alternative for the ELISA test.

Arguably, the method could also be seen as simpler (as it does not involve chemical reactions and use of acids) as well as more reliable (since the ELISA method is typically prone to the samples in adjacent plate wells mixing with each other in the process of washing or exchange of solutions while the centrifuge test presented in this report ensures the samples being handled in a separate test tubes).

5.1.2. Proposals for Future Work

The method is novel and hence requires a lot of further research in order to fully characterise each stage of the test and optimise its performance.

The experiment varying the ratio of NP solution to the bacteria solution presented in this report should be repeated in a wider range of NP sol. ratios, since neither the upper or lower limit of this factor was found (while it was shown that the ratio does indeed play an important role in the test results). It may be expected that too high NP sol. ratio (and be necessity low bacteria sol. volume) will result in all of the bacteria binding sites being used and hence the increase in the number of NPs will no longer be correlated with an increased signal intensity. Additionally, at this side of the

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ratio spectrum, too low bacteria concentrations in the sample volume will turn the sample clear disabling reading of the test. On the other side of the ratio spectrum, too low NP sol. ratio must eventually lead to the decrease in signal intensity to levels indistinguishable from the control since the number of nanoparticles will be too low change the sample’s colour and be resolvable not only by naked eye but also by optical absorbance spectroscopy instruments.

The binding conditions could be varied with modifying the temperature (influencing the diffusion as well as antibody performance but also affecting the conjugates’ internal links energy possibly making them less robust), duration of the time allowed for binding as well as other conditions such as shaking of the samples.

In this project the method was only tested for one type of antibody (LPS) with the use of two other types (K, O Ab and HIV Ab) as controls and only two strains of bacteria (K12 and BL21 E. coli). The method should be tested for a wider range of other antibodies and other bacteria strains in order to confirm the findings and prove versatility of its application.

While this method was presented as a test verifying the antibody binding to bacteria, it could be investigated whether the similar principles can be used for the detection of bacteria in solutions. In such case the limit of detection should be found.

The test could be performed with dead bacteria cells to verify whether the conjugates would still bind to the lysed bacteria membrane fragments and also to characterise the centrifugation of lysed bacteria. If this turns out to vary from the whole live bacteria centrifugation characteristics e.g. by the lysed fragments requiring lower or higher spinning speed threshold, the method might offer potential for developing a rapid centrifuge­based AMR test in which e.g. the preservation of red colour would signify the bacteria being resistant to the drug introduced to the tube while the vanishing of the signal could be a sign of bacteria susceptibility to the antibiotic.

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5.2. Lateral Flow Test as a Rapid Test for AMR

5.2.1. Summary

Section[4.3] proposes a Lateral Flow Susceptibility Test for E. coli. that could be further developed to provide a rapid point­of­care test. Both the release of nucleic acids and LPS have been shown to increase with increasing antibiotic concentration at rates similar to standard methods.

5.2.2. Proposals for Future Work

It has been shown that the release of both nucleic acids and LPS can be used to determine bacterial susceptibility, and that both markers can be used in paper microfluidic tests. In order to develop these findings in a rapid susceptibility test, the specificity and sensitivity of detection needs to be improved. Since general nucleic acids have been shown to separate from whole cells during lysis, a fluorescent marker such as a quantum dots [9] could be used to make this test specific to bacterial nucleic acids. The use of fluorescent markers and a fluorescent reader as opposed to colloidal gold in ambient light improves detection by removing background scattering, and therefore sensitivity [9].

Figure[57] showing a proposal for a lateral flow susceptibility test with increased sensitivity. [Own work]

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Once improved, however, and the rapid susceptibility test has the potential to quantify bacterial susceptibility to antibiotics in much less time than standard methods. An image processing mobile application could be developed to read the capture line intensity from a lateral flow strip and compare to the standard susceptibility curve ­ allowing rapid susceptibility testing in low resource settings. The reduced time and cost, and lack of laboratory settings required could allow more thorough AMR testing, allowing individual cases of infection to be tested to provide the appropriate prescription of antibiotics, reducing the amplification of AMR. Applications could use edge detection algorithms to detect the lateral flow strip in a photograph (and plot its profile intensity and compare to the standard susceptibility curve) or use a bluetooth­enabled CMOS sensor, designed specifically to detect the desired marker.

Figure[58] shows a proposal for a mobile connected CMOS­reader. [Own work]

With increased sensitivity, lateral flow tests could also incorporate antibiotics into the sample pad, so that their instant antibacterial effect could be observed. This would be useful when testing a new antibiotic, especially fast­acting antibacterial peptides. Wax printing could be used to split tests into smaller microfluidic channels, some containing antibiotic and others not, where several detection lines could be used for a reference signal and more accurate detection via image processing. Channels could have different capture antibodies detecting different products of bacterial susceptibility, the detected combination of which could give clues to the mechanism of a desired antibiotic.

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5.3. Nanomechanical Bacterial Vibrations for AFM­Based AMR Sensor

This section briefly summarizes the conclusions discussed in more detail in chapter 4.4.5. It also provides a discussion on the overview of the AFM­related part of the project and finally presents suggestions for the future work.

5.3.1. Summary

Bacterial vibrations were detected (distinguishable from the background noise) for cells immobilised on an AFM cantilever and to show that modifying the immersion medium to more nutritious increases the signal strength while incubation in a nutrient­free medium causes a decay of vibrations.

It also demonstrated the necessity for the optimum bacteria coverage density of the cantilever since both too low and too high immobilisation levels result in a decreased signal strength as well as the general experiment reliability.

Moreover, it demonstrated that the fluctuations can be observed not only in time­domain (as suggested by literature review) but also in low­frequency­domain below approx. 20 Hz. The lack of discrete signal frequency components supports the presumption that the origin of the vibrations is linked to the metabolic action of the bacteria.

Notably, a simpler experimental methods were used to detect vibrations than in previously published work, not requiring hydraulic flow chamber system (making the method more versatile) or the use of APTES linker molecules which were here substituted with glutaraldehyde.

The termination of the project allocated time did not allow for combining the successful antibody characterisation work (able to specifically bind E. coli bacteria strain of interest) with the AFM­based bacteria vibrations work.

The detected bacterial vibrations and the signal dependence on the immersion medium presents promising evidence for the development of an AMR sensor based on this phenomenon and an AFM instrument.

5.3.2. Proposals for Future Work

Functionalization of the cantilever surface with an antibody would allow for specific capture of the bacteria of interest from the sample which may potentially contain other (perhaps non­pathogenic) strains of microorganisms e.g. in case of gut bacteria. A method for attaching antibodies to the cantilever could be found and samples containing a variety of organisms could be tested. If successful, this would lead to a step towards a more reliable proof­of­concept AMR­detector device development responsive only to the pathogen of interest.

With an antibody attached an attempt to immobilise the bacteria in­situ but without the flow chamber system could be done e.g. by simple injection of the bacteria into the dish. The effectiveness of such methods could be studied and the immobilisation quantified not only as a

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coverage density but also by the attachment strength (an issue not studied in this project). If the antibody link turns out to be much stronger than the non­specific attachment of bacteria e.g. via means of electrostatic attraction, then a washing intensity threshold should be found allowing for cleaning the cantilever of the non­specific or loosely immobilised bacteria while leaving the organisms of interest intact.

The nature of linkage between the cantilever and the bacteria could be studied in each case (APTES linking molecules, glutaraldehyde fixation, antibody­linked attachment, electrostatic attraction and any other methods introduced) which would allow for the understanding of how bacteria transfer the vibrations from the cell wall/body to the cantilever.

Similarly studying the process of bacteria ‘net’ formation (observed occasionally when glutaraldehyde was used) could provide insight into the nature of the links and vibrations transfer. In the light of the results presented it would be advised to avoid too high coverage of cantilever.

Studying molecular machines involved in metabolism (especially trans­membrane transport) would help to understand the origin fluctuations and design better experimental techniques and points of focus for the future work.

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6. References

[1] ­ WHO, 2014. Antimicrobial Resistance: Global Report on Surveillance

[2] ­ Benveniste, R. & Davies, J., 1973. Mechanisms of antibiotic resistance in bacteria. Annual review of biochemistry, 42, pp.471–506.

[3] ­ Rajendran, V.K., Bakthavathsalam, P. & Jaffar Ali, B.M., 2014. Smartphone based bacterial detection using biofunctionalized fluorescent nanoparticles. Microchimica Acta, 181(15­16), pp.1815–1821.

[4] ­ Martinez, A.W. et al., 2008. Simple telemedicine for developing regions: Camera phones and paper­based microfluidic devices for real­time, off­site diagnosis. Analytical Chemistry, 80(10), pp.3699–3707. [5] ­ Peeling, R.W., 2015. Diagnostics in a digital age: an opportunity to strengthen health systems and improve health outcomes. International health, 7(6), pp.384–9.

[6] ­ Yager, P. et al., 2006. Microfluidic diagnostic technologies for global public health. Nature, 442(7101), pp.412–418.

[7] ­ Tang, Y. et al., 2013. Rapid Antibiotic Susceptibility Testing in a Microfluidic pH Sensor. Anal. Chem., 85, pp.2787–2794.

[8] ­ Parolo, C. & Merkoci, A., 2013. Paper­based nanobiosensors for diagnostics. Chemical Society

Reviews, 42(2), pp.450–457. [9] ­ Zhu, H., Sikora, U. & Ozcan, A., 2012. Quantum dot enabled detection of Escherichia coli using a cell­phone. The Analyst, 137(11), pp.2541–4.

[10] ­ Hossain, S.M.Z. et al., 2012. Multiplexed paper test strip for quantitative bacterial detection.

Analytical and Bioanalytical Chemistry, 403(6), pp.1567–1576.

[11] – G. Longo, L. Alonso­Sarduy, L. Marques Rio, A. Bizzini, A. Trampuz, J. Notz, G. Dietler and S. Kasas; Rapid detection of bacterial resistance to antibiotics using AFM cantilevers as nanomechanical sensors; NATURE NANOTECHNOLOGY | VOL 8 | JULY 2013

[12] – Sandor Kasas, Francesco Simone Ruggeri, Carine Benadiba, Caroline Maillard, Petar Stupar, Hélène Tournu, Giovanni Dietler, and Giovanni Longo; Detecting nanoscale vibrations as signature of life; 378–381 | PNAS | January 13, 2015 | vol. 112 | no. 2

[13] – C. Lissandrello, F. Inci, M. Francom, M. R. Paul, U. Demirci, and K. L. Ekinci; Nanomechanical motion of Escherichia coli adhered to a surface; Applied Physics Letters 105, 113701 (2014);

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[14] – Rachel A. McKendry and Natascha Kappeler; Good vibrations for bad bacteria; NATURE NANOTECHNOLOGY | VOL 8 | JULY 2013

[15] ­ Reller, L.B., Weinstein, M., Jorgensen, J.H. and Ferraro, M.J., 2009. Antimicrobial susceptibility testing: a review of general principles and contemporary practices. Clinical infectious diseases, 49(11), pp.1749­1755.

[16] ­ Lenn, T., Leake, M. C. & Mullineaux, C. W. Clustering and dynamics of cytochrome bd­I complexes in the Escherichia coli plasma membrane in vivo. Mol. Microbiol. 70, 1397–1407 (2008).

[17] ­ Spector, J. Mobility of BtuB and OmpF in the Escherichia coli outer membrane: implications for dynamic formation of a translocon complex. Biophys. J. 99, 3880–3886 (2010).

[18] ­ Boiangiu, C. D. et al. Sodium ion pumps and hydrogen production in glutamate fermenting anaerobic bacteria. J. Mol. Microbiol. Biotechnol. 10, 105–119 (2005).

[19] ­ Dimroth, P., von Ballmoos, C. & Meier, T. Catalytic and mechanical cycles in F­ATP synthases—fourth in the cycles review series. EMBO Rep. 7, 276–282 (2006). [20] ­ Alice Pyne, Will Marks, Loren M. Picco, Peter G. Dunton, Arturas Ulcinas, Michele E. Barbour, Siân B. Jones, James Gimzewski, Mervyn J. Miles; High­speed atomic force microscopy of dental enamel dissolution in citric acid; Archives of Histology and Cytology Vol. 72 (2009) No. 4+5 P 209­215 [21] ­ http://www.engineeringtoolbox.com/linear­expansion­coefficients­d_95.html (retreived 03/04/16) [22] ­ Dong, H. et al., 2014. Structural basis for outer membrane lipopolysaccharide insertion. Nature, 511(7507), pp.52–6.

[23] ­ Andrews, J.M. & Andrews, J.M., 2001. Determination of minimum inhibitory concentrations. The Journal of antimicrobial chemotherapy, 48 Suppl 1, pp.5–16. [24] ­ Stiefel, P. et al., 2015. Critical aspects of using bacterial cell viability assays with the fluorophores SYTO9 and propidium iodide. BMC Microbiology, 15, p.36. [25] ­ Concepcion, J. et al., 2009. Label­free detection of biomolecular interactions using BioLayer interferometry for kinetic characterization. Combinatorial chemistry & high throughput screening, 12(8), pp.791–800. [26] ­Elder, B.L., Boraker, D.K. & Fives­Taylor, P.M., 1982. Whole­bacterial cell enzyme­linked immunosorbent assay for Streptococcus sanguis fimbrial antigens. Journal of clinical microbiology, 16(1), pp.141–144. [27] ­ Evans, M.E. & Pollack, M., 1993. Effect of antibiotic class and concentration on the release of lipopolysaccharide from Escherichia coli. The Journal of infectious diseases, 167(6), pp.1336–1343.

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7. Appendix

7.1. Appendix A: MATLAB Programs for Analysis of Bacteria Vibrations

Program for importing and processing the data

% IMPORTING fragment of data measurement beginningTime = 4200; % indicate the fragment's beginning time in seconds fragmentDuration = 30; % indicate fragment duration in seconds column = 2; % indicate column in the file in which the vertical deflection data were stored Fs = 20000; % measurement frequency was always 20kHz FirstLine = beginningTime*Fs; LastLine = FirstLine + fragmentDuration*Fs; calibFactor = 33.4; % indicate calibration factor % importing data vDefl = dlmread('file_name.out',' ',[6+FirstLine column 6+LastLine column]); vDefl = vDefl*1000/calibFactor; vDefl = vDefl*10^9; % change units from metres to nanometres time = 0:1/Fs:1/Fs*(length(vDefl)­1); time = time'; % shifting to centre of y­axis shift = vDefl(fragmentDuration*0.5*Fs); % take y­value for x­middle point and use as y­shift shifted = vDefl ­ shift; % creating linear regression model n = length(time); a1 = (n*sum(time.*shifted) ­ sum(time)*sum(shifted))/(n*sum(time.^2) ­ (sum(time))^2); a0 = mean(shifted) ­ a1*mean(time); y_model = a0 + a1.*time; % flattening using linear regression flatSig = shifted ­ y_model; %plotting plot(time, flatSig) grid on title('temporary graph') xlabel('time [seconds]') ylabel('Vertical Deflection [nm]') axis([0 fragmentDuration ­5 5]) set(gca, 'YTick', [­5 ­3 ­1 1 3 5]) % calculating variance variance = var(flatSig)

Program for plotting the time­domain signal

subplot(3,1,1) plot(time, flatSig10) % indicating the signal to be plotted from the workspace grid on title('Empty cantilever in PBS') xlabel('time [seconds]') ylabel('Vertical Deflection [nm]') axis([0 fragmentDuration ­7 7]) % setting axis range set(gca, 'YTick', [­7 ­5 ­3 ­1 1 3 5 7]) % setting axis ticks xlabh = get(gca,'XLabel'); set(xlabh,'Position',get(xlabh,'Position') ­ [­13 ­1.5 0]) % moving axis title position to the right hand side subplot(3,1,2)

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plot(time, flatSig11) grid on title('Bacteria in PBS') xlabel('time [seconds]') ylabel('Vertical Deflection [nm]') axis([0 fragmentDuration ­7 7]) set(gca, 'YTick', [­7 ­5 ­3 ­1 1 3 5 7]) xlabh = get(gca,'XLabel'); set(xlabh,'Position',get(xlabh,'Position') ­ [­13 ­1.5 0]) subplot(3,1,3) plot(time, flatSig11G) grid on title('Bacteria in PBS with 4% GLUCOSE') xlabel('time [seconds]') ylabel('Vertical Deflection [nm]') axis([0 fragmentDuration ­7 7]) set(gca, 'YTick', [­7 ­5 ­3 ­1 1 3 5 7]) xlabh = get(gca,'XLabel'); set(xlabh,'Position',get(xlabh,'Position') ­ [­13 ­1.5 0])

Program for performing the Fourier Transform and plotting the resulting spectrum

% indicating the parameters of the plot downfreq = 0.1; upfreq = 5; upval = 0.3; thickness = 2; Fs = 20000; subplot(1,3,1) % performing Fourier Transform: FFTsig = fft(flatSig4); % indicating the signal to be plotted from workspace L = length(FFTsig); % constructing axis P2 = abs(FFTsig/L); P1 = P2(1:L/2+1); P1(2:end­1) = 2*P1(2:end­1); f = Fs*(0:(L/2))/L; plot(f,P1, 'LineWidth', thickness) axis([downfreq upfreq 0 upval]) grid on title('Empty cantilever') xlabel('frequency [Hz]') ylabel('Amplitude (arb. units)') subplot(1,3,2) FFTsig = fft(flatSig5); L = length(FFTsig); P2 = abs(FFTsig/L); P1 = P2(1:L/2+1); P1(2:end­1) = 2*P1(2:end­1); f = Fs*(0:(L/2))/L; plot(f,P1, 'LineWidth', thickness) axis([downfreq upfreq 0 upval]) grid on title('LIVE bacteria') xlabel('frequency [Hz]') ylabel('Amplitude (arb. units)') subplot(1,3,3) FFTsig = fft(flatSig6);

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L = length(FFTsig); P2 = abs(FFTsig/L); P1 = P2(1:L/2+1); P1(2:end­1) = 2*P1(2:end­1); f = Fs*(0:(L/2))/L; plot(f,P1, 'LineWidth', thickness) axis([downfreq upfreq 0 upval]) grid on title('DEAD bacteria') xlabel('frequency [Hz]') ylabel('Amplitude (arb. units)')

Program for creating bar graphs and adjusting error bars

subplot(1,3,1) varianceBars = [variance91 variance91G]; uncert = varianceBars.*0.13; % indicating the calculated error bars as described in Method section hold on hb = bar(varianceBars, 'c'); % For each set of bars, find the centers of the bars, and write error bars pause(0.1); %pause allows the figure to be created % grid on title('LOW coverage') ylabel('variance [nm^2]') axis([0.5 2.5 0 2.5]) for ib = 1:numel(hb) %XData property is the tick labels/group centers; XOffset is the offset %of each distinct group xData = hb(ib).XData+hb(ib).XOffset; errorbar(xData,varianceBars(ib,:),uncert(ib,:),'k.') end x = 1:length(varianceBars); % y = varianceBars; bar(x,varianceBars,'c') for i1=1:numel(varianceBars) text(x(i1),varianceBars(i1),num2str(varianceBars(i1),'%0.4f'),... 'HorizontalAlignment','center',... 'VerticalAlignment','top') end set(gca,'XTick',x) set(gca,'XTickLabel','in LB','after adding GLUCOSE') subplot(1,3,2) varianceBars = [variance5 variance94G]; uncert = varianceBars.*0.1; hold on hb = bar(varianceBars, 'c'); % For each set of bars, find the centers of the bars, and write error bars pause(0.1); %pause allows the figure to be created % grid on title('MEDIUM coverage') ylabel('variance [nm^2]') for ib = 1:numel(hb) %XData property is the tick labels/group centers; XOffset is the offset %of each distinct group xData = hb(ib).XData+hb(ib).XOffset; errorbar(xData,varianceBars(ib,:),uncert(ib,:),'k.') end

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x = 1:length(varianceBars); % y = varianceBars; bar(x,varianceBars,'c') for i1=1:numel(varianceBars) text(x(i1),varianceBars(i1),num2str(varianceBars(i1),'%0.4f'),... 'HorizontalAlignment','center',... 'VerticalAlignment','top') end set(gca,'XTick',x) set(gca,'XTickLabel','in LB','after adding GLUCOSE') subplot(1,3,3) varianceBars = [variance7 variance93G]; uncert = varianceBars.*0.1; hold on hb = bar(varianceBars, 'c'); % For each set of bars, find the centers of the bars, and write error bars pause(0.1); %pause allows the figure to be created % grid on title('HIGH coverage') ylabel('variance [nm^2]') for ib = 1:numel(hb) %XData property is the tick labels/group centers; XOffset is the offset %of each distinct group xData = hb(ib).XData+hb(ib).XOffset; errorbar(xData,varianceBars(ib,:),uncert(ib,:),'k.') end x = 1:length(varianceBars); % y = varianceBars; bar(x,varianceBars,'c') for i1=1:numel(varianceBars) text(x(i1),varianceBars(i1),num2str(varianceBars(i1),'%0.4f'),... 'HorizontalAlignment','center',... 'VerticalAlignment','top') end set(gca,'XTick',x) set(gca,'XTickLabel','in LB','after adding GLUCOSE')

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7.2. Appendix B: Evolution of Objectives Deviations from the original work plan as given in the project proposal can be seen below. The binding of antibodies took longer to assess than anticipated as several techniques were tried [See Section 4.2]. Fluorescent­marker lateral flow tests were replaced with colloidal gold markers and the aim of bacterial detection was replaced with the detection of antimicrobial susceptibility, in order for more coherent group objectives.

Table showing original work plan as specified in the project proposal

Table showing actual work carried out in project

7.3. Appendix C: Acknowledgements

We would like to thank Prof. McKendry for providing us with the opportunity to pursue our interests and for her expertise and support throughout the project. We are very grateful to Ben Miller, Isabel Bennett, Dr. Alice Pyne, Dr. Natascha Kappeler and Dr. Claudio Parolo for their supervision and guidance. We are also thankful to Dr. Eleanor Gray for help in becoming familiar with the lab and immunoassay discussions, and to Candice Keane for Bio­Layer Interferometry discussions.

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