STUDIES INVOLVING THE EFFECT OF ORGANIC SOLVENTS ON
LIPOSOME-BASED LATERAL-FLOW AND SILICA-COATED MAGNETIC
BEAD ASSAYS
A Thesis
Presented to the Faculty of the Graduate School
of Cornell University
in Partial Fulfillment of the Requirements for the Degree of
Master of Engineering
by
Daniel Tse Wen Lee
May 2005
© 2005 Daniel Tse Wen Lee
ii
ABSTRACT
The ubiquitous threat of Escherichia coli (E. coli) O157:H7 as an enterohemorrhagic
pathogen to humans has challenged scientists worldwide to develop a biosensor that
would accurately and efficiently detect the bacteria in the environment. This research
involved modifications made to the liposome-based RNA biosensor developed in
Baeumner et al., 2003. The goal was to investigate the effect of alcohols on the lateralflow assay with the ultimate objective of developing a silica-coated magnetic bead
biosensor based on integrated RNA isolation and detection. Both isolation and
detection are carried out in the presence of 40 % ethanol, which immobilizes RNA
onto silica. Detection is done through hybridization between the target RNA, reporter
probe and dye-encapsulating liposome. If successful, this biosensor would be able to
isolate E. coli O157:H7 mRNA for the heat shock gene and detect for viable bacteria
in one step. The project was thus divided into two segments: The effect of ethanol on
the lateral-flow biosensor assay was first investigated, after which the principle of
bead assay was studied. Interestingly, it was found that 33 % ethanol in the
hybridization buffer and 20 % ethanol in the running buffer created conditions in the
lateral-flow assay that increased the overall signal to noise ratio from 1.46 to 3.66, or
2.5 times, when 50 fmol target sequence was used. Further investigations with other
target analytes and over a broader range of target concentrations are needed, and will
likely lead to an improved standard lateral-flow assay with significantly lowered limits
of detection. Secondly, the principle of the integrated RNA isolation and detection
bead-assay was proven, and the amount of background RNA and silica-coated
iii
magnetic beads optimized. However, the signals obtained were affected negatively by
very high background noise and high standard deviations. A great deal of research into
this type of assay is still needed in order to transform it into a viable assay.
iv
BIOGRAPHICAL SKETCH
Daniel Lee is a citizen of Singapore. He graduated from Raffles Junior College,
Singapore in 1999 and received his Bachelor of Science degree in Biological and
Environmental Engineering from Cornell University in May 2004. He began the
Master of Engineering Program in the field of Agricultural and Biological Engineering
in August 2004. Daniel will be returning to Singapore upon completing the program to
work for the Republic of Singapore Navy.
v
ACKNOWLEDGEMENTS
I would like to extend my gratitude to my advisor Dr. Antje Baeumner, for her
invaluable guidance and direction, and all the members of Dr. Antje Baeumner’s
Biosensors and Bioanalytical Microsystems Laboratory for their assistance throughout
this research. Special mention goes out to Sam Nugen, Christina Siryk, Katie
Edwards, John Connelly and Barbara Leonard for answering my incessant questions. I
would also like to thank my family and friends for their continuous support and
encouragement.
vi
TABLE OF CONTENTS
Abstract
iii
Biographical Sketch
v
Acknowledgements
vi
List of Tables
ix
List of Figures
x
Chapters:
1. Introduction
1
1.1. Escherichia Coli O157:H7
2
1.2. Biosensors
5
1.2.1. Biorecognition Molecules and Immobilization
7
1.2.2. Transducers
14
1.3. Biosensor Assay Design
16
1.3.1. Lateral-Flow Biosensor
17
1.3.2. Silica-magnetic Beads for RNA Isolation and Detection
19
2. Materials and Methods
19
2.1. Materials and Reagents
20
2.1.1. Synthetic Target Sequence
20
2.1.2. Universal Liposomes
21
2.2. Universal Membrane Preparation
21
2.3. Background RNA Isolation
22
2.4. Lateral-Flow Assay Format
23
vii
2.5. Silica-coated Magnetic Bead Assay Format
3. Results and Discussion
25
29
3.1. Varying Ethanol and Methanol Concentrations in Hybridization
Mixture for the Lateral-flow Assay
29
3.2. Reducing Synthetic Target from 500 to 50 fmol for the Lateral-flow
Assay
32
3.3. Using A Different Running Buffer
38
3.4. Optimizing the Ethanol Concentrations in the Hybridization Mixture
and Running Buffer for the Lateral-flow Assay
41
3.5
Investigation of Silica-coated Magnetic Bead Liposome-RNA
Hybridization
48
3.5.1 Varying the Addition of Background RNA
48
3.5.2 Varying the Amount of Background RNA
51
3.5.3 Varying the Amount of Silica-coated Magnetic Beads
54
4. Conclusion
57
References
59
viii
LIST OF TABLES
Table 1. DNA sequences of oligonucleotides used in this research.
21
Table 2. Reflectometer readings for varying ethanol and methanol
concentrations in the hybridization mixture
31
Table 3. Reflectometer readings for 0, 20 and 40 % ethanol in the
hybridization mixture, using 0, 50 and 500 fmol target.
34
Table 4. Reflectometer readings for 0, 20 and 40 % methanol in the
hybridization mixture, using 0, 50 and 500 fmol target.
35
Table 5. Reflectometer readings for 0, 20 and 40 % ethanol in the running
buffer, using 0, 50 and 500 fmol target and 0 % ethanol in the
hybridization mixture.
43
Table 6. Reflectometer readings for 0, 20 and 40 % ethanol in the running
buffer, using 0, 50 and 500 fmol target and 33 % ethanol in the
hybridization mixture.
44
Table 7. Fluorometer readings for two different methods of background
RNA addition, using 0, 250 and 500 fmol target in the
hybridization mixture.
50
Table 8. Fluorometer readings for 0, 1, 2 and 3 μL of background RNA
with 0, 250 and 500 fmol target in the hybridization mixture.
52
Table 9. Fluorometer readings for 1, 2, 3 and 4 μL of silica beads with
0, 250 and 500 fmol target in the hybridization mixture.
55
ix
LIST OF FIGURES
Figure 1. General layout of a biosensor.
6
Figure 2. Specificity of biosensors.
7
Figure 3. NASBA amplification pathway.
11
Figure 4. The lateral-flow assay.
18
Figure 5. The silica-magnetic bead assay.
19
Figure 6. Membranes showing effect of increasing ethanol and methanol
concentrations in the hybridization mixture.
30
Figure 7. Graphs showing intensity of signal against varying ethanol and
methanol concentrations in the hybridization mixture.
31
Figure 8. Signal to noise ratios for 50 and 500 fmol of target for 0, 20 and
40 % ethanol in the hybridization mixture.
36
Figure 9. Signal to noise ratios for 50 and 500 fmol of target for 0, 20 and
40 % methanol in the hybridization mixture.
36
Figure 10. Zoom up of actual membranes using 50 fmol of target for 40 %
ethanol and 20 % methanol in the hybridization mixture.
38
Figure 11. Actual membranes using running buffers RB 1, RB 2 and RB 3
for 0 and 500 fmol target.
40
Figure 12. Comparing the signal to noise ratios between 0 and 33 % ethanol
in the hybridization mixture, when 50 fmol of target with 0, 20
and 40 % ethanol in the running buffer was used.
45
Figure 13. Background noise for 0 and 33 % ethanol in the hybridization
mixture and 0, 20 and 40 % ethanol in the running buffer.
45
Figure 14. Actual membranes using 0, 20 and 40 % ethanol in the running
buffer, and 0, 50 and 500 fmol of target with 0 and 33 %
ethanol in the hybridization mixture.
46
Figure 15. Mean fluorescent signals for 0, 250 and 500 fmol of target
using the two manners of background RNA addition.
50
x
Figure 16. Mean fluorescent signals for 0, 1, 2 and 3 μL of background
RNA with 0, 250 and 500 fmol of target in the hybridization
mixture.
53
Figure 17. Mean fluorescent signals for 1, 2, 3 and 4 μL of silica beads with
0, 250 and 500 fmol of target in the hybridization mixture
55
xi
1. INTRODUCTION
The discovery of Escherichia coli (E. coli) in 1885 by German physician Theodor
Escherich began an extensive study into one of the most researched microorganism we
know of today. Classified under the Enterobacteriaceae family, E. coli is a rod-shaped,
facultative anaerobe that stains gram-negative and is typically 1 – 3 μm in length.
Most E. coli bacteria have peritrichous flagella which give motility, and
polysaccharide capsules which provide protection against desiccation and phagocytic
attacks. Therefore, although more well-known to colonize the lower intestines of
warm-blooded animals, E. coli is able to survive and disseminate pervasively when
released into the natural environment, and is very much an important component of
the biosphere (Blattner et al., 1997).
Most of the 2561 different strains of E. coli – as listed by the National Institute of
Genetics in Japan – are known to be harmless, with some strains even considered to be
beneficial. E. coli bacteria make up approximately 0.1 % of the intestinal microflora in
adult humans (Tannock, 1995) and contribute to normal health and development. The
physical presence of the bacteria and the bactericidal colicins it produces prevent other
possibly harmful types of bacteria from competing for space, and intestinal E. coli is
also known to synthesize vitamins B12 and K in amounts that are large enough to be
valuable should the diet become deficient (Foulke, 1994). In animals, it has been
demonstrated that an E. coli strain from rat feces successfully reversed the vitamin K
deficiency symptoms in gnotobiotic rats (Bently and Meganathan, 1982).
1
There are also five enterovirulent classes of E. coli that can cause inflammation of the
human gastrointestinal tract (Todar, 2002). Enterotoxigenic E. coli colonizes the tract
by means of a fimbrial adhesin and is non-invasive, producing heat-stable Shiga toxins
which cause an increase of fluid and electrolyte secretion, leading to diarrhea.
Enteroinvasive E. coli penetrates and multiplies within epithelial cells of the colon
causing widespread destruction, giving rise to a dysentery-like diarrhea with fever.
Enteropathogenic E. coli adheres to the intestinal mucosa by means of a non-fimbrial
adhesin, rearranging the ultrastructure of intestinal cells and interfering with normal
cellular signal transduction. Enteroaggregative E. coli is so distinguished for its ability
to attach to tissue cells in an aggregative manner, causing non-bloody diarrhea without
invading or causing inflammation. Finally, enterohemorrhagic E. coli produces the
Shiga toxin which causes a diarrheal syndrome characterized by copious bloody
discharge and no fever. Other complications include a type of acute kidney failure
known as hemolytic uremic syndrome (HUS) which could be fatal. This fifth class of
enterovirulent E. coli is mainly represented by E. coli O157:H7, which will be
discussed in further detail.
1.1. Escherichia coli O157:H7
The Centers for Disease Control and Prevention (CDC) first isolated E. coli O157:H7
in 1975, and in 1982 identified it as the cause of severe bloody diarrhea traced to
contaminated ground beef patties during two illness outbreaks in Oregon and
Michigan (Foulke, 1994). However, it was only in 1993, after a large multistate
outbreak linked to undercooked hamburgers from a fast-food chain (Bell et al., 1994),
2
that E. coli O157:H7 infection was acknowledged to be a major cause for concern in
the United States. Since then, an estimated 73,480 illnesses due to E. coli O157:H7
infection have been reported each year in the United States, leading to an estimated
2,168 hospitalizations and 61 deaths annually (Rangel et al., 2005).
The morbidity and mortality associated with E. coli O157:H7 infection existed not
only in the United States, but also all over the world in countries such as Slovakia
(Liptakova et al., 2004), Japan (Ozeki et al., 2003) and Swaziland (Effler et al., 2001).
Modes of bacterial transmission vary from case to case, but most have been traced
back to farm animals, be it through consumption or contact. Cattle is as a major
reservoir of E. coli O157:H7 (Elder et al., 2000) and the bacteria has also been found
in the intestines of pigs, goats, sheep and chicken (Cobeljic et al., 2005). During
slaughter and processing, the meat of the animals may become contaminated with the
bacteria, and if this meat is not sufficiently cooked before eating, the consumer has a
high likelihood of becoming infected. Unpasteurized milk that has been contaminated
is also a source of E. coli O157:H7 (Wang et al., 1997).
Another transmission method of E. coli O157:H7 is the consumption of agricultural
products which came into contact with contaminated animal feces sometime during
production. Examples include lettuce (Hilborn et al., 1999), alfalfa sprouts (Breuer et
al., 2001) and apple juice (Cody et al., 1999). In recent years, new routes of
transmission have emerged, like contact with animals during farm visits and a wide
variety of environment-related exposures (Caprioli et al., 2005). In October and
3
November, 2004, an E. coli O157:H7 outbreak of 108 cases occurred in North
Carolina, with the source determined to be petting zoos at the North Carolina State
Fair (NC DHHS Press Release, 2004).
When E. coli O157:H7 has entered the human gastrointestinal tract, it targets follicleassociated epithelium in the distal small intestine. The H7 flagellum of the bacteria
induces production of chemokines such as interleukin 8, and neutrophilic infiltration
of the intestinal mucosa, which in turn may enhance uptake of the Shiga toxin the
bacteria produces across the intestinal epithelium (Moxley, 2004). Upon infection,
syndromes caused by E. coli O157:H7 include diarrhea, hemorrhagic colitis, and HUS
(Tarr, 1995). It has been observed that among Shiga-toxin-producing E. coli, O157:H7
has the strongest association worldwide with HUS (Tarr et al., 2005). HUS develops
in 2-7 % of E. coli O157:H7 illnesses, when the Shiga toxin enters the patient’s blood
stream and travels to the smaller arteries that supply the kidneys, and damages the
vessels (Foulke, 1994). Children under 5 years of age and the elderly are most at risk,
and HUS is fatal in about 3-5 % of the cases.
With such a dangerous and easily transmittable pathogen existing in our world today,
it is expected that there is an increasing number of methods being developed to detect
the E. coli O157:H7 pathogen. In 1997, a sample treatment method based on buoyant
density centrifugation which separates bacteria from food, and analysis by PCR aimed
at verocytotoxin and intimin-encoding eae genes was developed. This method gave E.
coli O157:H7 detection limits of 0.5 cfu/g-beef, 5 cfu/g-minced-beef, and 3000
4
cfu/ml-lettuce (Lindqvist, 1997). Another detection kit is available from Innovative
Biosensors, which comprises of a sensor that uses engineered B lymphocytes to emit
light within seconds of exposure to specific bacteria and viruses. The E. coli O157:H7
detection limit for this kit is 500 cfu/ml-lettuce, and the bacteria can be detected in less
than five minutes (Rider et al., 2003). More recently, a composite self-excited
millimeter-sized lead zirconate titanate glass cantilever with affinity purified
monoclonal antibody specific to E. coli O157:H7 immobilized at the cantilever glass
tip was fabricated. This gives a detection limit of 700 cells/mL. At the clinical level,
the CDC recommends diagnosis of E. coli O157:H7 infection by detecting the bacteria
in the stool, with testing done on sorbitol-MacConkey agar.
However, there is still a pressing need to develop more accurate, cheaper and faster
methods of E. coli O157:H7 detection in order to respond more quickly to suspected
cases of infection, as well as to identify and take measures against potential E. coli
O157:H7 reservoirs. For many years, laboratory detection of food-borne pathogens
has relied on direct culture isolation which has the limitation of being time consuming
and labor intensive (Khan et al., 2003). What needs then to be developed are rapid and
automated analytical methods to detect E. coli O157:H7, with low limits of detection
and high specificity, and the use of biosensors looks to be one promising solution.
1.2. Biosensors
A biosensor is a device for the detection of an analyte that combines a biological
component with a physiochemical detector component, and consists of two main parts.
5
The first is the biorecognition element which is a biological molecule that recognizes
the target analyte, while the second is the transducer which converts the recognition
event into a measurable signal. This integration allows a no-hassle continuous flow
analysis of the sample, since the user just needs to quantify the signal obtained without
having to take any intermediate steps. Figure 1 shows the simplicity of biosensors,
while Figure 2 demonstrates the specificity of biosensors.
Figure 1. General layout of a biosensor. The analyte interacts with the biorecognition
element, which is required to be immobilized in the vicinity of the transducer, either
by physical entrapment or chemical attachment. The recognition event is recorded by
the transducer, which leads to the generation of a measurable signal. The signal can be
merely qualitative, but a quantitative signal proportional to the amount of analyte
present in the sample is usually preferred.
Biosensors have a wide variety of applications, including fermentation control,
pharmaceutical analysis, clinical diagnosis, military monitoring, pollution control, and
of course, microbial detection. In designing a biosensor, the following considerations
need to be taken: selection of a suitable biorecognition molecule, selection of a
suitable immobilization method, selection of a suitable transducer, and packaging of
the biosensor. These variables need to be optimized to obtain a biosensor that will not
only have high accuracy, repeatability, sensitivity, resolution, response speed and
6
reliability, but must also be small (portable), inexpensive and easily mass produced.
Ideally, the biosensor must perform well outside the laboratory, so that it can be taken
directly to the sample source where analysis of the target analyte can take place in situ.
Figure 2. Schematic showing the specificity of biosensors. Only when the target
analyte is present in the sample will there be a measurable signal generated.
Background noise is a common occurrence, which must always be determined and
subtracted from all readings to give a more accurate analysis.
1.2.1. Biorecognition Molecules and Immobilization
There are many different types of biorecognition elements that can be used in a
biosensor, e.g. enzymes, receptors, channels, antibodies, oligonucleotides and whole
cells or tissues (Foultier et al., 2005). Enzymes are the most commonly used sensing
agents in catalytic biosensors (Davis et al., 1995), which is not surprising due to their
three dimensional structure that fits only a particular substrate. Compared to cell-based
biosensors, enzyme biosensors respond faster due to shorter diffusion paths without
the presence of cell walls. There has also been a lot of progress made on enzyme
immobilization techniques. One example is the low cost immobilization on gelatin by
means of the cross-linking reactions between the free amino groups of gelatin and the
7
enzyme molecule, through cross linkers, to form a covalent linkage (Srivastava et al.,
2001). However, it is expensive to isolate enzymes and keep them stable when
isolated, and many enzymes require cofactors before they are functional.
Using microbes as biorecognition elements has its advantages. They are present
ubiquitously, are able to metabolize a wide range of chemical compounds, have a great
capacity to adapt to adverse conditions, and have potential in developing the ability to
degrade new molecules with time (D’Souza, 2001). Microbes can also be genetically
modified through mutation or recombinant DNA technology and serve as an
economical source of intracellular enzymes. Keeping the enzymes inside the cell
avoids the lengthy and expensive operations of enzyme purification, preserves the
enzyme in its natural environment, and protects it from inactivation by external
toxicants such as heavy metals (D’Souza, 2001). On the other hand, microbial
biosensors present a slow response and low selectivity due to a variety of metabolic
processes occurring in a living cell (Mello and Kubota, 2002).
Immunosensors act on the principle that the immune response of a microorganism to
contaminants will produce antibodies. The potential use of immunosensors is due to
their general applicability and to the specificity and selectivity of the antigen-antibody
reaction (Mello and Kubota, 2002). Unfortunately, most immunosensors to date have a
major disadvantage, in that they need extra reagents such as enzymes, fluorescent
molecules and radioactive isotopes to report the affinity reaction. In view of this, there
have been recent efforts to produce immunosensors with no added labels, such as the
8
ELISHA project, which is researching on fluoroquinoline antibiotics, cancer markers
such as prostatic specific antigen (PSA) and prion peptides (Grant et al., 2003). As of
April 2005, the ELISHA project has successfully demonstrated a labeless format
immunosensor, with a calibration curve for PSA, the biomarker used to diagnose
prostate cancer. PSA was electropolymerized to give a reactive conducting polymer,
which was able to form a photo-activated immobilization matrix that was interrogated
using impedance spectroscopy to generate the calibration curve.
DNA based biosensors hold an enormous potential for environmental monitoring
(Mascini, 2001). Due to the unique complementary structure of DNA as described by
Watson and Crick in 1953, single stranded DNA molecules are able to reanneal and
form hydrogen bonds with matching DNA strands in a process referred to as
hybridization. If the target analyte is chosen to be a sequence of single stranded DNA
that is already known, the complementary sequence, or probe, can be immobilized on
the biosensor to “catch” the analyte. Since no two strains of bacteria have identical
DNA fingerprints, a DNA based biosensor is able to single out any strain, presuming
that its genome has been determined, and the sequence of analyte chosen is highly
specific to the strain. The only problem left to be dealt with then is how to generate the
single-stranded DNA analyte from the rest of the organism’s genetic material.
One commonly used method of target amplification is the polymerase chain reaction
(PCR), which has three main steps, namely denaturation, annealing and extension.
During denaturation at high temperatures, the double-stranded DNA is split apart,
9
forming two complimentary single strands of DNA. Primers can then attach
themselves in complementary fashion to the DNA template strand, targeting the
analyte sequence, before DNA polymerase works to incorporate nucleotides into the
nascent DNA strand. Thermus aquaticus (Taq) polymerase is typically used in PCR
because of its thermostability at high denaturation temperatures. After the three steps
are complete, the cycle is repeated again, for about 30 cycles. This will generate about
109 target analytes from one double-stranded DNA.
The main disadvantage of PCR in detecting microorganisms such as E. coli O157:H7
is that it does not distinguish between living and dead organisms (Lindahl, 1993). This
is unfortunate since viability is an important aspect when measuring for pathogenic
organisms (Baeumner et al., 2003). Messenger ribonucleic acids (mRNA), the vehicle
of transcription, is turned over rapidly in living bacterial cells, with most having a
half-life of only a few minutes (Sheridan et al., 1998). Using mRNA as the target
analyte in a biosensor would therefore be a good indicator of pathogenic viability in
the sample.
mRNA can be amplified using the nucleic acid sequence-based amplification
(NASBA) technique, which is a continuous, isothermal enzyme-based method for the
amplification of nucleic acid. A NASBA reaction is based on the simultaneous activity
of avian myeloblastosis virus (AMV) reverse transcriptase (RT), RNase H and T7
RNA polymerase with two oligonucleotide primers (Compton, 1991). Because the
NASBA reaction is maintained at 41°C, specific amplification of single stranded RNA
10
is possible if denaturation of double stranded DNA is prevented in the sample
preparation procedure. It is thus possible to pickup mRNA in a double stranded DNA
background without getting false positive results caused by genomic double stranded
DNA (Polstra et al., 2002). Figure 3 outlines the main steps of the NASBA reaction.
Figure 3. The NASBA amplification pathway with the P1 (anti-sense) – P2 (sense)
oligonucleotide primer set. The straight lines represent DNA while the wavy lines
represent RNA. The overhang on P1 encodes the promoter sequence for the T7 RNA
polymerase. The final product is single-stranded RNA that is anti-sense to the one
original strand (Cook, 2003).
The NASBA reaction is initiated by the annealing of the primer P1 to the target RNA
present in the sample. The 3’ end of P1 is complementary to the target, while the 5’
end (the overhang in the figure) encodes the T7 RNA polymerase promoter. After
annealing, the reverse transcriptase activity of AMV-RT is engaged and a
complementary DNA (cDNA) copy of the RNA target is produced. The RNA portion
of the resulting hybrid molecule is hydrolyzed through the action of RNase H. This
11
permits P2, which is complementary to an upstream portion of the RNA target, to
anneal to the cDNA strand. This permits the DNA-dependent DNA polymerase
activity of AMV-RT to be engaged again, producing a double stranded cDNA copy of
the original RNA target with a fully functional T7 RNA polymerase promoter at one
end. This promoter is then recognized by the T7 RNA polymerase, which finally
produces anti-sense, single stranded RNA transcripts corresponding to the original
RNA target.
A different method of mRNA amplification makes use of the heat-shock response.
When cultured cells or whole organisms are exposed to elevated temperatures, they
respond by synthesizing a small number of highly conserved proteins, the heat-shock
proteins. This response is universal (Lindquist, 1986). The implication of this is
obvious but thrilling: a biosensor that is modeled for detecting the heat-shock response
can be used for virtually any organism currently known to man. In E. coli, the heatshock response is expressed at a temperature of 42 °C, during which the heat-shock
mRNA will increase greatly in number, and can thus be easily detected.
The immobilization of the nucleic acid probe onto the biosensor should lead to a welldefined probe orientation that makes it readily accessible to the target analyte
(Foultier, 2005). Various procedures can be done to attach the probe, for instance (i)
the use of thiolated DNA for self assembly onto gold electrodes or gold-coated
piezoelectric crystals; (ii) covalent linkage to a gold surface via functional alkanethiolbased monolayers; (iii) the use of biotinylated DNA for complex formation with a
12
surface-confined avidin or streptavidin; (iv) covalent coupling to functional groups on
carbon electrodes; and (v) a simple adsorption onto carbon surfaces (Wang, 2000).
One of the immobilization procedures involves the forming of the biotin-streptavidin
complex. This system has one of the largest free energies of association observed for
noncovalent biological interaction between a protein and small ligand in aqueous
solution, with a Ka value of 1015 M-1. The large equilibrium binding minimum and the
high barrier to dissociation is constructed by an extensive set of hydrogen bonding and
van der Waals interactions, as well as two surface binding loops that become ordered
when biotin is bound (Stayton et al., 1999). The bond formation between streptavidin
and biotin is thus rapid and essentially non-reversible, and is unaffected by most
extremes of pH, organic solvents, and denaturing reagents. Biotinylation, which is the
simple method of tagging the probe with a biotin sequence, will allow the probe to
strongly bind to any streptavidin-coated surface. Use of this intermediary is beneficial
as it moves the capture probe away from the biosensor surface, preventing steric
hindrance (Valk et al., 2003). At the other side, streptavidin is known to covalently
bind to polyethersulfone membranes by adsorption under suitable conditions
(Baeumner et al., 2004).
The binding of DNA in the presence of chaotropic agents NaI or NaClO4 to silica is
well known, and both DNA and RNA have been purified using this principle (Boom et
al., 1990). Several nucleic acid purification kits available in the market such as
Qiagen’s RNeasy Mini Kit and Machery-Nagel’s NucleoSpin® RNA II utilizes the
13
binding of nucleic acids to silica in the presence of ethanol to separate nucleic acids
from the other components of the cell. Even though repulsion does occur between the
negatively-charged nucleic acid and the negatively-charged silica surface, the presence
of high concentrations of chaotropic salts or ethanol creates a dehydration effect,
which drives the unsolvated nucleic acids to stick on the silica surface for entropic
considerations (Balludar et al., 1997). Therefore, if the appropriate concentration of
ethanol was to be present in the biosensor at all times, the target analyte will stay
attached to the silica, and is essentially immobilized.
1.2.2. Transducers
As with biorecognition molecules, there are many possible transducers that can be
used in a biosensor, e.g. optical, electrochemical and piezoelectric transducers (Junhui
et al., 1997). Some optical transducers make use of fluorescence together with the total
internal reflection in optical fibers to give a measurable detection signal. The
fluorescent DNA stain ethidium bromide is a commonly used dye for the detection of
DNA (Monaco and Hausheer, 1993). The ethidium cation strongly associates with
double stranded DNA by intercalation into the base stacking region, allowing the
amount of target present to be determined by measuring the fluorescence intensity
using UV-visible spectrometry (Junhui et al., 1997). A different optical transduction
based on evanescent wave devices offers real-time label-free optical detection of DNA
hybridization. These biosensors rely on monitoring changes in surface optical
properties resulting from the surface binding reaction, combining the simplicity of
surface plasmon resonance with the sensitivity of wave guiding devices (Wang, 2000).
14
Electrochemical biosensors are chemically modified electrodes, on which the
electronic conducting, semiconducting or ionic conducting material is coated with a
biofilm (Thévenot et al., 1999). These biosensors use DNA probe molecules attached
to an electrically active surface and measure current or resistance changes caused by
hybridization of target DNA (Vercoutere and Akeson, 2002). Recently, a new type of
electrochemical biosensor based on oligonucleotide-functionalized polypyrrole was
developed (Korri-Youssoufi and Yassar, 2001). In the presence of a noncomplementary oligonucleotide, the cyclic voltammogram of the polypyrrole remains
unmodified, whereas a significant modification of the voltammogram is observed
upon addition of a complementary oligonucleotide "target" to the electrolytic medium,
which can be quantitatively determined by amperometric methods. The detection limit
of this electrochemical biosensor is about 10-11 mol without any signal processing,
which is considerably low.
The third group of piezoelectric transducers is based on the principle that resonant
frequency of an oscillating piezoelectric crystal can be affected by a change in mass at
the crystal surface (Kumar, 2000). Quartz crystal microbalance (QCM) biosensors
consist of an oscillating crystal with the DNA probe immobilized on its surface. The
increased mass, associated with the hybridization reaction, results in a decrease of the
oscillating frequency (Wang, 2000). In general, piezoelectric transducers offer the
advantages of a solid-state construction, chemical inertness, durability, and ultimately
the possibility of low cost mass production. However, the detection limit of mass
bound to the electrode surface is only about 10-10 to 10-11 g, and each crystal has to be
15
calibrated since its frequency depends on the crystal geometry and the uniformity of
the probe immobilized on the surface (Rogers and Mascini, 2004).
In Dr. Antje Baeumner’s Biosensors and Bioanalytical Microsystems Laboratory, the
main bulk of research involves the use of liposomes as a form of signal generation and
amplification for optical and electrochemical transducers. Liposomes are closed
spherical vesicles with a membrane composed of a phospholipid bilayer surrounding
an aqueous cavity that can encapsulate a wide variety of molecules (Park, 2004). In
biosensors, liposomes can be used in many ways, e.g. encapsulating enzymes to
protect against denaturation and proteolytic enzymes while retaining full functionality
of the enzymes (Nasseau, 2001). Our focus, however, is on the dye or electrochemical
marker-encapsulating ability of liposomes. One liposome can contain hundreds of
thousands of markers, which amplifies the signal considerably for easier detection. It
is possible to attach oligonucleotide tags to the outer surface of liposomes, so if the
complementary sequence of the target analyte – otherwise known as the reporter probe
– is attached to the liposome, the liposome will bind to the single stranded target
mRNA. The amount of target mRNA is then directly proportional to the amount of
dye that remains bound to the target.
1.3. Biosensor Assay Design
The goal of this research was to study the effect of organic solvents on two separate
biosensor technologies, with the eventual intention of developing a successful
biosensor based on RNA isolation directly integrated with the detection. The two
16
technologies examined were (1) a lateral-flow assay for the determination of assay
conditions on the RNA/DNA hybridization and (2) a silica-coated magnetic bead
assay for the isolation of RNA from complex mixtures. As discussed in chapter 1.2.1,
the use of ethanol with silica surfaces to isolate RNA has been extensively developed,
with ethanol shown to have minimal effect on the integrity of RNA under optimal
conditions. However, it is unknown if hybridization of single stranded nucleic acids
and detection of RNA using the above technologies can still occur unhindered in the
presence of organic solvents. One potential problem that could occur is the rupturing
of marker-encapsulating liposomes by the solvents, since extraction of liposome
contents with ethanol and methanol is a typical practice (Lutsiak et al., 2002).
Fortunately, this effect varies with the exact components and conditions, so the
liposomes used in this research may not necessarily be affected by organic solvent
presence. In addition, the polyethersulfone membranes used in the lateral-flow assay
are chemically resistant to methanol and ethanol (Mitsui Chemicals, Inc., 2004), and
thus are expected to retain their adsorption properties if these organic solvents are
present in the assay.
1.3.1. Lateral-Flow Biosensor
In the lateral-flow biosensor, a DNA/RNA sandwich hybridization reaction is detected
using dye-encapsulating and reporter-probe tagged liposomes. The biotinylated
capture probe is immobilized via streptavidin onto a polyethersulfone membrane
(Figure 4). In this assay, the effect of ethanol and methanol on the DNA/RNA binding
and the liposome amplification was investigated. This assay has been developed
17
successfully previously for a number of different analytes, including E. coli
(Baeumner et al., 2003). In brief, a hybridization mixture containing sample (which
may or may not have the target mRNA), reporter probes, capture probes, dyeencapsulated liposomes and a buffer is allowed to migrate along the membrane strip.
The membrane strip has a capture zone containing streptavidin, such that when the
hybridization mixture passes through the capture zone, self-assembly of the
“sandwich” occurs if target mRNA is present. The hybridization mixture continues to
migrate, leaving behind only the liposomes that have been bound in the sandwich. The
intensity of the dye coloration at the capture zone can then be measured with a
reflectometer to determine the amount of target mRNA that was present in the sample.
Figure 4. The lateral-flow assay. The hybridization mixture migrates up the membrane
and self-assembly occurs in the capture zone. The magnified portion shows an
immobilized nucleic acid sandwich. Figure not drawn to scale.
18
1.3.2. Silica-magnetic Beads for RNA Isolation and Detection
In this part of the project, the immobilization of RNA on silica-coated magnetic beads
was investigated with the focus on leaving the RNA immobilized after isolation for
direct liposome hybridization and detection. Therefore, RNA is first isolated from cell
extract and then subjected to a modified protocol of the liposome assay: A
hybridization mixture of sample, silica beads, reporter probes, dye-encapsulated
liposomes and an ethanol-containing buffer is placed in a well and incubated, after
which the beads are washed several times with ethanol-containing buffer to remove
the unbound liposomes. The magnet keeps the beads immobilized, while the presence
of ethanol keeps the mRNA attached to the silica beads. In this case there is likely to
be other RNA coating the beads, but the reporter probe-liposome complex will only
bind to the target mRNA. The amount of mRNA present can then be determined by
measuring the amount of dye left with a fluorometer. This silica-coated magnetic bead
assay is shown in Figure 5.
Figure 5. The silica-coated magnetic bead assay. Hybridization occurs in the presence
of ethanol, which keeps the mRNA bound to the silica bead. The bead is in turn
immobilized by the magnet. The figure is not drawn to scale.
19
2. MATERIALS AND METHODS
2.1. Materials and Reagents
General laboratory chemicals, organic solvents and buffer reagents were purchased
from Sigma-Aldrich Corp., St. Louis, MO, USA and VWR Scientific Products, West
Chester, PA, USA. Polyethersulfone membranes were purchased from Pall/Gelman
Company, Port Washington, NY, USA. Superparamagnetic silica beads were
purchased from chemicell GmbH, Berlin, Germany. Lipids were purchased from
Avanti Polar Lipids, Inc., Alabaster, AL, USA. Streptavidin and sulforhodamine B
were purchased from Molecular Probes, Inc., Eugene, OR, USA. The RNeasy Mini
Kit was purchased from Qiagen, Inc., Valencia, CA, USA.
2.1.1. Synthetic Target Sequence
The target analyte used in this research is a synthetic sequence of a portion of the E.
coli O157:H7 heat shock gene, labeled C456-R600. Table 1 above shows the DNA
sequence of C456-R600 and its corresponding reporter and capture probes. These
single stranded oligonucleotides were purchased from Operon Biotechnologies, Inc.,
Huntsville, AL, USA, and stored in 50 mM phosphate buffer with 1 mM EDTA, pH
7.8.
20
Table 1. DNA sequences of oligonucleotides used in this research. C456-R600 is the
target analyte for the biosensor assays. The gggggTgggggTgggggTgg sequence of the
reporter probe anneals to the universal liposome, while the capture probe binds to
streptavidin with a biotin sequence attached to its 5’ end (not shown in table).
Name
DNA sequence (5’ – 3’ orientation)
C456-R600
TAC Tgg ATg ATg ggC gTC TgC TTg gTg C
Reporter probe
CAg ACg CCC ATC ATC CAG TAg ggg gTg ggg gTg ggg gTg g
Capture probe
gCA CCA Ag
2.1.2. Universal Liposomes
Liposomes entrapping 150 mM sulforhodamine B with a diameter of 0.6 m and an
osmolality of 612 mmol/kg were provided by Barbara Leonard from Dr. Antje
Baeumner’s Biosensors and Bioanalytical Microsystems Laboratory. These liposomes
had 5’ – 3’ sequences of CCA CCC CCA CCC CCA CCC CC attached to the surface
for hybridization with the reporter probe.
2.2. Universal Membrane Preparation
The polyethersulfone membranes were first cut into strips of 80 × 4.5 mm, then 1 μL
of 20 pmol/μL of streptavidin in 0.4 M sodium carbonate buffer with 5 % methanol,
pH 9 was spotted on each of the membranes. This spot defined the capture zone and
was positioned approximately 20 mm from one end of the strip. The membrane strips
were then dried for 1.5 hours in a vacuum oven at 15 psi and 52 °C. Following that,
the membrane strips were immersed in a blocking reagent of 0.015 % casein and 0.2
% polyvinylpyrrolidone in Tris buffer saline (TBS), which contained 0.02 M Trizma
base, 0.15 M sodium chloride and 0.01 % sodium azide, pH 7. This immersion took
21
place at room temperature for 30 minutes on a shaker, after which the membranes
were taken out, blotted dry and finally dried for 3 hours in a vacuum oven at 15 psi
and 30 °C. The membranes were vacuum-packed and stored at 4 °C.
2.3. Background RNA Isolation
Background RNA used in the silica-coated magnetic bead assay was isolated from an
E. coli K12 strain using the Qiagen RNeasy Mini Kit. Colonies of the bacteria were
grown at 37 °C on agar plates and stored at 4 °C. 12 hours before an extraction, 4 mL
of Lennox Broth (LB) was innoculated with the E. coli and placed in a shaker at 37
°C, 175 RPM. At the start of extraction, 1 mL of the innoculated LB was placed into a
microcentrifuge tube. This was centrifuged at 6500 RPM for 5 min to concentrate the
bacteria cells into a pellet, after which the supernatant was decanted. 100 L of TE
buffer (10 mM Tris-Cl, 1 mM EDTA, pH 8.0) containing 40 g of lysozyme was
added and the tube was vortexed for 5 min to allow the lysozyme to break open the
bacteria cells. Next, 350 L of Buffer RLT containing -mercaptoethanol was added
before the tube was vortexed again. -mercaptoethanol is an antioxidant which
stabilizes RNA, and Buffer RLT also contains highly denaturing guanidine
isothiocyanate (GITC) which immediately inactivates RNases. 250 L of 100 %
ethanol was then added to allow RNA to bind to the silica-gel membrane in the
RNeasy Mini column.
After allowing the mixture to flow through the RNeasy Mini column by centrifuging
for 15 seconds at 10000 RPM, the eluent was discarded. The column was then washed
22
three times, using 700 L of Buffer RW1 the first time, and 500 L of Buffer RPE for
the next two times. The first two washes were done by centrifuging for 15 seconds at
10000 RPM, while the third was for 2 min at 10000 RPM, and the eluent discarded
each time. Buffer RW1 and Buffer RPE contains ethanol which keeps RNA bound to
the silica-gel membrane while washing away impurities. Finally, 50 L of RNase-free
water was passed through the column by centrifuging at 10000 RPM for 1 min, and
the RNA-containing eluent collected. The concentration of RNA was evaluated using
a DU520 Beckman spectrophotometer at wavelengths of 260 and 280 nm and the
absorbance for a mixture of 10 μL of RNA-containing eluent and 90 μL of RNase-free
water was measured and recorded. The RNA was stored at -80 °C.
The concentration and purity of the RNA can be determined with the absorbance at
260 nm (A260) and at 280 nm(A280) using the following equations:
(a) concentration of RNA = (40 × A260 × dilution factor of 10) μg/mL
(b) purity of RNA =
A 260
A 280
Note: Pure RNA would give a purity of 1.8 – 2.0.
2.4. Lateral-flow Assay Format
Experiments conducted using the lateral-flow assay involved varying ethanol and
methanol concentrations in the hybridization mixture and the running buffer to
determine their effects. Specific information about the components used in each
experiment are given in Chapter 3, but the general assay format is outlined here. The
23
hybridization mixture used in successful assays during pre-analysis of the target
sequence and probes had the following components:

1 μL of 1 pmol/μL capture probe

1 μL of 2 pmol/μL reporter probe

1 μL of 0.5 pmol/μL target sequence

2 μL of liposomes, A532 nm = 0.380

5 μL of master mix (20 % formamide, 4× SSC, 0.2 % Ficoll, 0.2 M sucrose)
In order to accommodate up to 40 % of alcohol in the hybridization mixture, the
concentrations of the different components had to be adjusted to the following:

1.5 μL of the following mixture:
o 1 μL of 3 pmol/μL capture probe
o 1 μL of 6 pmol/μL reporter probe
o 1 μL of 1500 fmol/μL synthetic target sequence

2 μL of liposomes, A532 nm = 0.380

2.5 μL of 2× master mix (40 % formamide, 8× SSC, 0.4 % Ficoll, 0.4 M
sucrose)

4 μL of 100 % alcohol or dH2O
The hybridization mixtures were incubated for 20 min at 41 °C, allowing the sandwich
hybridization between the capture probe, synthetic target, reporter probe and dyeencapsulating liposomes to occur. After that, the mixtures were placed in individual 10
× 75 mm culture tubes and a membrane strip inserted into each tube, initiating
adsorption of the hybridization mixture on the membrane. 35 μL of running buffer was
then added to allow the hybridization mixture to fully migrate up the membrane.
24
Similar to the hybridization mixture, the running buffer had to be adjusted to
accommodate the addition of ethanol. Four different running buffer compositions were
used in this research:

RB 1 (40 % formamide, 8× SSC, 0.2 % Ficoll, 0.2 M sucrose)

RB 2 (20 % formamide, 4× SSC, 0.2 % Ficoll, 0.2 M sucrose)

RB 3 (40 % ethanol, 20 % formamide, 4× SSC, 0.2 % Ficoll, 0.2 M sucrose)

RB 4 (20 % ethanol, 20 % formamide, 4× SSC, 0.2 % Ficoll, 0.2 M sucrose)
A running buffer containing 40 % ethanol, 40 % formamide, 8× SSC, 0.2 % Ficoll, 0.2
M sucrose was attempted to be made, but the salts would not dissolve completely. RB
3 also had to be prepared fresh before each experiment as crystallization would occur
in the solution if left to stand.
After migration of the hybridization mixture up the membrane had been completed,
the capture zone of the membrane strip was analyzed with a BR-10 reflectometer
purchased from ESECO, Cushing, OK. The reflectometer measures the intensity of the
color in the capture zone, which is directly proportional to the amount of synthetic
target detected in the sample. For an internal negative control, a reading was also
taken just below the capture zone.
2.5. Silica-coated Magnetic Bead Assay Format
In this research, the experiments involving the silica-coated magnetic bead assay was
limited to target detection. Preparation of the hybridization mixture involved three
main steps, done in individual wells of a clear, round-bottom, 96-well Corning
25
reaction plate (#3795) from Corning, NY. In the first step, the synthetic target was
incubated with the beads for 5 min at room temperature in the presence of 40 %
ethanol to allow the target RNA to bind with the beads. In the second step, background
RNA extracted from E. coli was added to the mixture to cover up the remaining free
surfaces of the beads. It was hypothesized that this would prevent the universal
liposomes and reporter probes from binding to the silica in addition to binding with
the synthetic target. This mixture was incubated for 5 min at room temperature. The
third step involved the addition of liposomes, reporter probes and master mix. The
resulting hybridization mixture was incubated for 20 min at 41 °C to allow the
sandwich hybridization between the synthetic target, reporter probe and dyeencapsulating liposome to occur. In general, the mixtures were mixed by pipetting 10
times, and the components used in each step were:

Step 1 (7.5 μL liquid volume)
o 1 – 4 μL of 60 % beads
o 1.9 – 3.1 μL of dH2O
o 3 μL of 100 % ethanol
o 1 μL of synthetic target

Step 2 (additional 7.5 μL liquid volume)
o 0 – 4 μL of background RNA
o 1.5 – 4.5 μL of dH2O
o 3 μL of 100 % ethanol

Step 3 (additional 15 μL liquid volume)
o 1 μL of liposomes
26
o 0.5 μL of 4 pmol/μL reporter probe
o 7.5 μL of 2× master mix (40 % formamide, 8× SSC, 0.4 % Ficoll, 0.4
M sucrose)
o 6 μL of 100 % ethanol
Subsequently, the reaction plate was placed onto a ring magnet plate (SPRIPlate 96R
from Agencourt, Beverly, MA) for 5 min to separate the beads from the solution. The
solution was then aspirated and discarded while the reaction plate was still on the ring
magnet plate. This was followed by two washes with running buffer containing
ethanol to remove unbound liposomes, each carried out in the following steps:

Separate reaction plate from ring magnet plate

Add 40 μL of RB 3 (40 % ethanol, 20 % formamide, 4× SSC, 0.2 % Ficoll, 0.2
M sucrose) to each well, mixing by pipetting 10 times

Place reaction plate onto ring magnet plate for 5 min

Aspirate and discard solution
The reaction plate was once more separated from the ring magnet plate, and 30 μL of
dH2O was added to each well, mixing by pipetting 10 times. The reaction plate was
placed back onto the ring magnet plate for 5 min, before the solution from each well
was aspirated and transferred to a black, flat-bottom, 96-well Corning microplate
(#3915) from Corning, NY. 30 μL of 60 mM n-Octyl-Beta-D-Glucopyranoside (OG)
was then added to each well to break open the liposomes, freeing the Sulforhodamine
B dye, and the wells were immediately covered in aluminium foil to prevent light from
entering. Finally, the samples were analyzed using a Packard Bioscience fluorometer
27
(#426000) at 540 nm excitation and 580 nm emission. The Fusion Instrument Control
version 3.5 software was used and additional fluorometer settings were as follows:

well read time: 1 second

number of times to read each well: 3

expected sample activity: high level

light source intensity: 1

top fluorescence
The reading obtained from the fluorometer is directly proportional to the amount of
Sulforhodamine B dye present in each well, which gives an indication of the amount
of synthetic target detected in the assay. A standard curve was plotted each time the
fluorometer was used to quantify the amounts of Sulforhodamine B present.
28
3. RESULTS AND DISCUSSION
3.1. Varying Ethanol and Methanol Concentrations in Hybridization Mixture for
the Lateral-flow Assay
As discussed in Chapter 1.2.1, nucleic acids will bind to silica beads in the presence of
ethanol. However, it is not known what effect ethanol would have in the hybridization
process between the target analyte, reporter probe and dye-encapsulated liposome.
Therefore, the first step of the project was to observe the effect of different
concentrations of ethanol on the lateral-flow biosensor, with changes made to the
hybridization mixture. Methanol was also investigated as an alternative organic
solvent. The hybridization mixture was adjusted to include between 0 to 41.7 % of
ethanol or methanol. The assay format was as follows:

Hybridization mixture components:
o 1 μL of 3 pmol/μL capture probe
o 1 μL of 6 pmol/μL reporter probe
o 1 μL of 1.5 pmol/μL synthetic target
o 6 μL of liposomes, A532 nm = 0.351
o 7.5 μL of 2× master mix (40 % formamide, 8× SSC, 0.4 % Ficoll, 0.4
M sucrose)
o 0, 1, 2, 3 ... 10, 11, 12.5 μL of 100 % ethanol or methanol
o topped up to 30 μL with dH2O

Incubate 10 μL of hybridization mixture at 41°C for 20 minutes

Place universal membrane strip into mixture to initiate adsorption
29

Add 35 μL of running buffer RB 1 (40 % formamide, 8× SSC, 0.2 % Ficoll,
0.2 M sucrose)
The results of the actual membrane strips are shown in Figure 6, while the
reflectometer signals are reported in Table 2 and plotted in Figure 7.
Figure 6. Membranes showing effect of increasing ethanol (left) and methanol (right)
concentrations ranging from 0 - 41.7 % (top-down direction) in the hybridization
mixture. The control did not contain any ethanol or synthetic target. Table 2 gives
details about the amounts of ethanol or methanol used for each membrane.
30
Table 2. Reflectometer readings for varying ethanol and methanol concentrations in
the hybridization mixture. 500 fmol synthetic target was analyzed using the lateralflow assay with solvent concentrations ranging from 0 – 41.7 % under otherwise
optimal assay conditions.
Label Alcohol % Ethanol Reflectometer Reading Methanol Reflectometer Reading
control
0
1
2
3
4
5
6
7
8
9
10
11
12.5
0
0.0
3.3
6.7
10.0
13.3
16.7
20.0
23.3
26.7
30.0
33.3
36.7
41.7
15
55
60
72
51
56
65
74
65
77
78
94
86
65
19
61
77
70
72
78
83
84
87
99
99
110
101
91
Reflectometer Reading vs Alcohol %
Reflectometer Reading
120
100
80
Methanol
60
Ethanol
40
20
0
0.0
10.0
20.0
30.0
40.0
50.0
Alcohol %
Figure 7. Graphs showing intensity of signal against varying ethanol and methanol
concentrations in the hybridization mixture. 500 fmol synthetic target was analyzed
using the lateral-flow assay with solvent concentrations ranging from 0 – 41.7 %
under otherwise optimal assay conditions.
31
From Figure 6, Figure 7 and Table 2, it can be seen that the presence of ethanol and
methanol in the hybridization mixture does not prevent detection of the target
sequence using the lateral-flow biosensor. This indicates that alcohols do not hinder
the hybridization process, at least for concentrations up to 41.7 %. In fact, increasing
alcohol concentrations seem to increase the intensity of the signal obtained, peaking at
33.3 % for both alcohols, with methanol providing in general higher signals than
ethanol additions. For example, the signal at 33.3 % for ethanol was 70.9 % higher
than with no ethanol, and at 33.3 % methanol was 80.3 % higher than without
methanol.
Whereas the signal in the capture zone is evenly circular at low alcohol
concentrations, the signal at higher concentrations becomes more linear, concentrating
at the base of the capture zone. Since nucleic acids are known to precipitate at high
ethanol concentrations in certain conditions (Shapiro, 1981), it is postulated that the
higher alcohol concentrations are causing the nucleic acids to aggregate, slowing
down the speed of migration up the membrane, such that most of the capture probes
are caught by the streptavidin at the base of the capture zone. This observation
suggests the potential use of alcohol to lower detection limits for the lateral-flow
biosensor, particularly methanol which gives stronger signals than ethanol.
3.2. Reducing Synthetic Target from 500 to 50 fmol for the Lateral-flow Assay
The next step was to investigate the effect of alcohol on the detection of a smaller
amount of target sequence. 0, 50 and 500 fmol of target sequence were used, against 0,
32
20 and 40 % of ethanol or methanol in the hybridization mixture. The assay format
was as follows:

Hybridization mixture components:
o 1.5 μL of the following mixture:

1 μL of 3 pmol/μL capture probe

1 μL of 6 pmol/μL reporter probe

1 μL of 0, 150 or 1500 fmol/μL synthetic target

1.5 μL of dH2O
o 2 μL of liposomes, A532 nm = 0.408
o 2.5 μL of 2× master mix (40 % formamide, 8× SSC, 0.4 % Ficoll, 0.4
M sucrose)
o 0, 2 or 4 μL of 100 % ethanol or methanol
o topped up to 10 μL with dH2O

Incubate hybridization mixture at 41°C for 20 minutes

Place universal membrane strip into mixture to initiate adsorption

Add 35 μL of running buffer RB 1 (40 % formamide, 8× SSC, 0.2 % Ficoll,
0.2 M sucrose)
The results of the reflectometer signals are reported in Tables 3 and 4, and plotted
in Figures 8 and 9, for ethanol and methanol studies, respectively.
33
Table 3. Reflectometer readings for 0, 20 and 40 % ethanol concentrations in the
hybridization mixture, using 0, 50 and 500 fmol synthetic target. The signal to noise
ratio was calculated by dividing the capture zone reading by the average capture zone
reading of the negative control. The means and standard deviations given are for the
signal to noise ratios.
Ethanol Target
(%) (fmol)
0
0
50
500
20
0
50
500
40
0
50
500
Reflectometer Reading
Before
Capture Zone
Capture Zone
19
19
20
19
21
21
17
25
20
23
19
25
18
81
24
70
19
79
26
26
21
23
22
20
22
31
28
35
21
27
34
106
21
82
31
93
31
32
27
29
28
27
35
39
36
47
19
25
24
32
25
51
37
78
34
Visual Signal To Mean Standard
Noise Ratio
Deviation
+/+/+/+ ++
+ ++
+ ++
+
+
+
+ ++
+ ++
+ ++
+
+
+
+ ++
+ ++
+ ++
-
-
-
1.27
1.17
1.27
4.12
3.56
4.02
-
1.24
0.06
3.90
0.30
-
-
1.35
1.52
1.17
4.61
3.57
4.04
-
1.35
0.17
4.07
0.52
-
-
1.33
1.60
0.85
1.09
1.74
2.66
1.26
0.38
1.83
0.79
Table 4. Reflectometer readings for 0, 20 and 40 % methanol concentrations in the
hybridization mixture, using 0, 50 and 500 fmol synthetic target. The signal to noise
ratio was calculated by dividing the capture zone reading by the average capture zone
reading of the negative control. The means and standard deviations given are for the
signal to noise ratios.
Ethanol Target
(%) (fmol)
0
0
50
500
20
0
50
500
40
0
50
500
Reflectometer Reading
Before
Capture Zone
Capture Zone
25
23
20
21
18
17
26
29
22
28
23
30
25
96
16
76
24
87
24
20
20
19
25
25
23
33
22
35
22
31
23
129
27
132
22
104
27
27
25
24
27
31
28
35
25
28
22
64
30
103
26
67
35
Visual Signal To Mean Standard
Noise Ratio
Deviation
+/+/+ ++
+ ++
+ ++
+
+
+
+ ++
+ ++
+ ++
+
+/+
+ ++
+ ++
+ ++
-
-
-
1.43
1.38
1.48
4.72
3.74
4.28
-
1.43
0.05
4.25
0.49
-
-
1.55
1.64
1.45
6.05
6.19
4.88
-
1.55
0.09
5.70
0.72
-
-
1.22
1.37
1.10
2.51
4.04
2.63
1.23
0.14
3.06
0.85
Signal To Noise Ratio
Varying Ethanol % in Hybridization Mixture
5.00
4.00
0% Ethanol
3.00
20% Ethanol
2.00
40% Ethanol
1.00
0.00
50
500
Synthetic Target (fmol)
Figure 8. Bar graphs showing the signal to noise ratios for 50 and 500 fmol of
synthetic target for 0, 20, 40 % ethanol concentrations in the hybridization mixture.
The error bars are also given to show the spread of the readings. The graphs indicate
that adding 20 % and 40 % ethanol does not reduce the signal for low amounts of
synthetic target (50 fmol). Also, the discrepancy between 20 % and 40 % ethanol seen
for 500 fmol is greatly reduced when synthetic target is 50 fmol.
Signal To Noise Ratio
Varying Methanol % in Hybridization Mixture
7.00
6.00
5.00
0% Methanol
4.00
20% Methanol
3.00
40% Methanol
2.00
1.00
0.00
50
500
Synthetic Target (fmol)
Figure 9. Bar graphs showing the signal to noise ratios for 50 and 500 fmol of
synthetic target for 0, 20, 40 % methanol concentrations in the hybridization mixture.
The error bars are also given to show the spread of the readings. The graphs indicate
that reducing the target from 500 to 50 fmol does not give unexpected results, i.e. the
relative signals between 0, 20, 40 % remain approximately the same.
36
The analysis of the reflectometer readings indicate that for both ethanol and methanol,
reducing the amount of synthetic target from 500 fmol to 50 fmol still gives signals for
0, 20 and 40 % alcohol concentration in the hybridization mixture. In addition, in the
case of the 50 fmol signals, higher signal to noise ratios are obtained for the 20 %
alcohol concentrations which correlates well with the data shown in Figure 7. Thus, it
can be proposed, that a 33.3 % ethanol or methanol concentration would also be
optimal for low concentrations. In the case of ethanol, measuring a smaller amount of
synthetic target also reduces significantly the discrepancy between using 20 % and 40
% ethanol.
Readings taken by the reflectometer suggest that methanol gives better signals than
ethanol for 50 fmol of target sequence. For example, the signal to noise ratio for 40 %
ethanol is 1.26, whereas the signal to noise ratio for 20 % methanol is 1.55. However,
these results do not agree with observations taken by visually assessing the actual
membrane strips that were used in the experiment. Looking at Figure 10 below, it can
be seen that using 40 % ethanol in the hybridization gives clear distinct signal lines in
the capture zone, while practically no signal can be observed for 20 % methanol. This
disagreement between the reflectometer readings and the visual assessment is possibly
due to the reflectometer measuring the total color intensity of a circular area, rather
than the color intensity at a line or a point. This means that the reading taken for a dark
distinct line may be lower than a faint circular smear, when in fact the distinct line will
give a more obvious positive detection signal. Due to this limitation of the
reflectometer, the visual assessment of the actual membranes hold more weight, and
37
thus the conclusion is that at lower amounts of target RNA, addition of ethanol to the
hybridization mixture gives a stronger signal than if methanol was added and if no
alcohol was added.
Figure 10. Zoom up of actual membranes using 50 fmol of synthetic target for 40 %
ethanol (left) and 20 % methanol (right) concentrations in the hybridization mixture.
The arrows indicate the capture zones of the membranes. Visually, the left membranes
give better signals than the right, but the mean signal to noise ratio derived from
reflectometer readings is 1.26 for the left membranes and 1.55 for the right
membranes.
3.3. Using a Different Running Buffer
Now that it had been established that adding 40 % ethanol in the hybridization mixture
will still result in successful hybridization giving rise to a positive signal, the next step
was simply to dissolve the working running buffer RB 1 into 40 % ethanol and move
on to the silica-coated magnetic bead assay. Unfortunately, the running buffer
containing 40 % ethanol, 40 % formamide, 8× SSC, 0.2 % Ficoll, 0.2 M sucrose could
not be made as the salts were unable to dissolve completely. As a result, the running
buffer had to be adjusted to reduce the amount of salts present, and the new buffer
tested with the lateral-flow assay to ensure that hybridization will not be significantly
38
affected. Two running buffers were tested against the original RB1 buffer: RB2 (20 %
formamide, 4× SSC, 0.2 % Ficoll, 0.2 M sucrose) and RB3 (40 % ethanol, 20 %
formamide, 4× SSC, 0.2 % Ficoll, 0.2 M sucrose). The assay format was as follows:

Hybridization mixture components:
o 1.5 μL of the following mixture:

1 μL of 3 pmol/μL capture probe

1 μL of 6 pmol/μL reporter probe

1 μL of 0 or 1500 fmol/μL synthetic target

1.5 μL of dH2O
o 2 μL of liposomes, A532 nm = 0.408
o 2.5 μL of 2× master mix (40 % formamide, 8× SSC, 0.4 % Ficoll, 0.4
M sucrose)
o 4 μL of 100 % ethanol

Incubate hybridization mixture at 41°C for 20 minutes

Place universal membrane strip into mixture to initiate adsorption

Add 35 μL of RB 1, RB 2 or RB 3
Figure 11 below shows the effect of reducing the formamide and SSC concentrations
of the running buffer. Reducing the formamide from 40 to 20 % and SSC from 8× to
4×, i.e. changing the running buffer from RB 1 to RB 2, did not prevent the assay from
giving a positive result when target was present. However, when 40 % of ethanol was
added to RB 2, the new running buffer RB 3 was unable to give a positive result even
when synthetic target was present. One possible explanation for this is that the
39
hybridization mixture was unable to fully migrate up the membrane strip due to the
high concentration of ethanol (40 % ethanol in both the hybridization mixture and
running buffer). The adsorption properties of the nucleic acids onto the membrane
strip could have been changed by the ethanol, resulting in a false negative signal.
However, visual assessment of the membranes suggest that the ethanol present in RB
2 seemed to “clean” the background of the membrane strips. This would have the
potential to reduce background noise, but at a lower concentration of ethanol than 40
% in the running buffer assuredly.
RB 1, 0 fmol
RB 1, 500 fmol
RB 3, 0 fmol
RB 3, 500 fmol
RB 2, 0 fmol
RB 2, 500 fmol
Figure 11. Actual membranes using running buffers RB 1, RB 2 and RB 3 for 0 fmol
and 500 fmol synthetic target. RB 1 and RB 3 gave positive signals when target was
present, but RB 2 gave a negative signal even when target was present.
40
3.4. Optimizing the Ethanol Concentrations in the Hybridization Mixture and
Running Buffer for the Lateral-flow Assay
The observations made in chapters 3.1, 3.2 and 3.3 suggested the possibility of an
optimal combination of ethanol concentrations in the hybridization mixture and
running buffer that would result in a much improved signal over not using ethanol in
the assay. Although it was found that methanol in the hybridization mixture generally
gave higher signals than ethanol in chapter 3.1, this seemed to be applicable only for
larger amounts of target (500 fmol), as discussed in chapter 3.2. Therefore, it was
decided that ethanol would be used instead of methanol, since it is more crucial to
detect for lesser amounts of target. Moreover, the use of ethanol in an RNA extraction
procedure makes it more useful in an RNA biosensor.
From chapter 3.1, it was determined that 33 % of ethanol should be in the
hybridization mixture to give the highest signal. For the running buffer, chapter 3.3
indicated the potential of using ethanol to lower the background noise, thereby
enhancing the signal in the capture zone. However, 40 % was too high a concentration.
It was then hypothesized that 20 % ethanol in the running buffer would reduce
background noise sufficiently without giving a false negative signal.
Two sets of experiments were thus conducted to test this postulation. The first used 0,
20 and 40 % ethanol in the running buffer with 0, 50 and 500 fmol synthetic target and
no ethanol in the hybridization mixture. The second also used 0, 20 and 40 % ethanol
41
in the running buffer, but had 33 % ethanol in the hybridization mixture. The assay
format was as follows:

Hybridization mixture components:
o 2.2 μL of the following mixture:

1 μL of 3 pmol/μL capture probe

1 μL of 6 pmol/μL reporter probe

1 μL of 0, 150 or 1500 fmol/μL synthetic target

3.6 μL of dH2O
o 2 μL of liposomes, A532 nm = 0.408
o 2.5 μL of 2× master mix (40 % formamide, 8× SSC, 0.4 % Ficoll, 0.4
M sucrose)
o 3.3 μL of 100 % ethanol or dH2O

Incubate hybridization mixture at 41°C for 20 minutes

Place universal membrane strip into mixture to initiate adsorption

Add 35 μL of RB 2, RB 3 or RB 4
The results of the reflectometer signal readings are reported in Tables 5 and 6, and
plotted in Figures 12 and 13. The actual membranes are shown in Figure 14.
42
Table 5. Reflectometer readings for 0, 20 and 40 % ethanol concentrations in the
running buffer, using 0, 50 and 500 fmol synthetic target and 0 % ethanol in the
hybridization mixture. The signal to noise ratio was calculated by dividing the capture
zone reading by the average capture zone reading of the negative control. The means
and standard deviations given are for the signal to noise ratios.
Ethanol Target
(%) (fmol)
0
0
50
500
20
0
50
500
40
0
50
500
Reflectometer Reading
Before
Capture Zone
Capture Zone
21
21
18
19
16
17
25
33
19
30
16
20
20
108
20
85
10
64
13
13
11
11
9
9
20
37
14
28
12
27
14
102
10
105
20
119
5
7
6
7
3
3
1
4
1
3
1
7
6
41
6
38
3
42
43
Visual Signal To Mean Standard
Noise Ratio
Deviation
+/+
+/+ ++
+ ++
+ ++
+
+
+
+ ++
+ ++
+ ++
+/+/+/+ +
+ +
+ +
-
-
-
1.74
1.58
1.05
5.68
4.47
3.37
-
1.46
0.36
4.51
1.16
-
-
3.36
2.55
2.45
9.27
9.55
10.82
-
2.79
0.50
9.88
0.82
-
-
0.71
0.53
1.24
7.24
6.71
7.41
0.82
0.37
7.12
0.37
Table 6. Reflectometer readings for 0, 20 and 40 % ethanol concentrations in the
running buffer, using 0, 50 and 500 fmol synthetic target and 33 % ethanol in the
hybridization mixture. The signal to noise ratio was calculated by dividing the capture
zone reading by the average capture zone reading of the negative control. The means
and standard deviations given are for the signal to noise ratios.
Ethanol Target
(%) (fmol)
0
0
50
500
20
0
50
500
40
0
50
500
Reflectometer Reading
Before
Capture Zone
Capture Zone
50
48
51
49
46
45
45
73
46
85
43
81
46
137
43
146
37
150
19
19
20
21
19
18
18
77
15
63
14
72
15
122
17
116
15
111
15
18
15
17
14
12
18
38
14
40
14
61
17
65
14
63
16
66
44
Visual Signal To Mean Standard
Noise Ratio
Deviation
+
+
+
+ ++
+ ++
+ ++
+
+
+
+ ++
+ ++
+ ++
+/+
+
+ +
+ +
+ +
-
-
-
1.54
1.80
1.71
2.89
3.08
3.17
-
1.68
0.13
3.05
0.14
-
-
3.98
3.26
3.72
6.31
6.00
5.74
-
3.66
0.37
6.02
0.28
-
-
2.43
2.55
3.89
4.15
4.02
4.21
2.96
0.81
4.13
0.10
Signal To Noise Ratio
Comparison Between 0 % and 33 % Ethanol in
Hybridization Mixture for 50 fmol Target
5
4
0% Ethanol in RB
3
20% Ethanol in RB
2
40% Ethanol in RB
1
0
0
33
Ethanol % In Hybridization Mixture
Figure 12. Bar graphs comparing the signal to noise ratios between 0 and 33 % ethanol
in the hybridization mixture, when 50 fmol of target with 0, 20, 40 % ethanol in the
running buffer (RB) was used. The error bars are also given to show the spread of the
readings. The graphs clearly show that using 33 % ethanol in the hybridization
mixture and 20 % ethanol in the running buffer gave the best signal to noise ratio.
Also, the change from 0 to 33 % ethanol in the hybridization significantly increased
the signal for 40 % ethanol in the running buffer.
Reflectometer Reading
Background Noise for Varying Ethanol % in Hybridization
Mixture and Running Buffer
50
40
0% Ethanol in RB
20% Ethanol in RB
40% Ethanol in RB
30
20
10
0
0
33
Ethanol % In Hybridization Mixture
Figure 13. Bar graphs comparing the background noise for 0 and 33 % ethanol in the
hybridization mixture and 0, 20, 40 % ethanol in the running buffer (RB). There is a
clear indication that increasing the ethanol concentration in the running buffer reduced
the background noise.
45
Figure 14. Actual membranes using 0, 20, 40 % ethanol in the running buffer, and 0,
50, 500 fmol of synthetic target and 0 % (left) and 33 % (right) ethanol in the
hybridization mixture. It can be seen that 33 % ethanol in the hybridization mixture
does increase the signal significantly for 50 fmol of target, and that 20 % ethanol in
the running buffer does reduce background noise significantly. Among the strips with
50 fmol target, the combination of 33 % ethanol in the hybridization mixture and 20 %
ethanol in the running buffer gives the best signal.
The results show that the hypothesis postulated earlier on in this chapter was correct,
i.e. 33 % ethanol in the hybridization mixture combined with 20 % ethanol in the
running buffer indeed gave an improved signal to noise ratio over not using ethanol in
46
the assay. In fact, two main observations were made from the data collected. The first
was that adding 20 % ethanol in the running buffer reduced the background noise
significantly as compared to having 0 % ethanol in the running buffer, but did not
“clean” the membrane strips too much that the signal was reduced like in the cases
where 40 % ethanol in the running buffer was used. In the case of 33 % ethanol in the
hybridization mixture, addition of 20 % ethanol in the running buffer resulted in the
background noise dropping from 47.3 to 19.3. The relationship between background
noise and ethanol % in the running buffer can be seen in Figure 13.
The second observation was that adding 33 % ethanol in the hybridization mixture
indeed gave a big improvement to the signal in the capture zone, especially for 50
fmol of target. When 50 fmol of target was used with 20 % ethanol in the running
buffer, the signal to noise ratio for 33 % ethanol in the hybridization mixture was 3.66
compared to 2.79 when there was no ethanol. This improvement was not as obvious
for 500 fmol of target, but this could be due to the signal being at a saturation point at
such a high amount of target. Furthermore, we are more concerned with reducing the
lower detection limit of the later-flow assay, so the improvement at 50 fmol of target
is a valuable find. As discussed in chapter 3.1, this signal enhancement is suspected to
be due to nucleic acid aggregation in 33 % ethanol. Another possibility could be
liposome aggregation but that has to be explored further. Whatever the actual reason,
the experimental results clearly show that adding 33 % ethanol to the hybridization
mixture and 20 % ethanol to the running buffer would significantly increase the
detection capabilities of the lateral-flow biosensor.
47
3.5. Investigation of Silica-coated Magnetic Bead Liposome-RNA Hybridization
In chapters 3.1 and 3.2, it was determined that 40 % ethanol in the hybridization
mixture did not hinder the hybridization process between the synthetic target, reporter
probe and universal liposome completely. The next few experiments were therefore
set up in an attempt to test the viability of an assay in which liposomes (attached to
reporter probes) would directly hybridize to RNA isolated on silica-coated magnetic
beads under ethanol binding conditions. The key issue was that if the universal
liposomes and/or the liposome-reporter probe complexes – with their single stranded
nucleic acids – remained bound to the silica beads in the presence of ethanol, a false
positive would be obtained even if target were absent. The proposed solution was to
add background RNA to the synthetic target, in the hope that the background RNA
would completely cover up the silica surface of the beads, preventing reporter probes
and liposomes from binding to the silica. Three separate experiments were thus carried
out to determine the validity of this hypothesis.
3.5.1. Varying the Addition of Background RNA
The first experiment was done to determine if the manner of adding background RNA
to the synthetic target would have any effect on the assay. In the protocol detailed in
chapter 2.6, the background RNA is added after the synthetic target is allowed to bind
to the silica beads. An alternative to that would be adding a mixture of target RNA and
synthetic target to the beads, allowing them to bind to the beads at the same time. 0,
250 and 500 fmol of target were used to investigate the relationship between target
48
amount and signal for both cases. The assay format given in chapter 2.6 was adjusted
as follows:

Protocol (step1 followed by step 2)
o Step 1

2 μL of 60 % beads

2.7 μL of dH2O

3 μL of 100 % ethanol

1 μL of 0, 250 or 500 fmol/μL synthetic target
o Step 2


2 μL of background RNA

2.5 μL of dH2O

3 μL of 100 % ethanol
Alternative (step 1 combined with step 2)
o Step 1 and 2

5.2 μL of dH2O

6 μL of 100 % ethanol

1 μL of 0, 250 or 500 fmol/μL synthetic target

2 μL of background RNA (concentration = 50.8 μg/mL, purity
= 1.81)


2 μL of 60 % beads added last
The rest of the assay format remained the same
The results given as fluorometer readings are summarized in Table 7 and plotted in
Figure 15.
49
Table 7. Fluorometer readings for the two methods of background RNA addition,
using 0, 250 and 500 fmol synthetic target in the hybridization mixture. For ‘Step 1
followed by step 2’ the background RNA was added only after the synthetic target was
allowed to bind with the silica beads for 5 min. For ‘Step 1 with step 2’ the
background RNA and synthetic target were mixed before incubating with the silica
beads. The average and standard deviations given are for the fluorometer readings.
The signal to noise ratio was calculated by dividing the average fluorometer reading
over the average fluorometer reading of the negative control.
Method
Target
Fluorometer Reading (RFU) Average Standard Signal To
(fmol) Sample Sample Sample Sample
Deviation Noise Ratio
1
2
3
4
Step 1
0
527
657
401
746
583
151
followed 250
629
765
847
711
738
92
1.266
by step 2 500
987
934
830
887
910
67
1.561
Step 1
0
731
875
923
648
794
127
with
250
525
873
643
1175
804
286
1.012
step 2
500
858
727
787
871
811
67
1.021
Fluorometer Reading (RFU)
Varying the Addition of Background RNA
1200
1000
800
0 fmol
600
250 fmol
400
500 fmol
200
0
Step 1 followed by step 2
Step 1 with Step 2
Manner of Addition
Figure 15. Bar graphs showing the mean fluorescent signals for 0, 250, 500 fmol of
synthetic target using the two manners of background RNA addition. The error bars
are also given to show the spread of the readings taken with 4 samples. For ‘Step 1
followed by step 2’ the background RNA was added only after the synthetic target was
allowed to bind with the silica beads for 5 min. For ‘Step 1 with step 2’ the
background RNA and synthetic target were mixed before incubating with the silica
beads.
50
Table 7 and Figure 15 suggest that it was essential for the background RNA to be
added to the hybridization mixture only after the synthetic target had been allowed to
bind to the silica beads. This gave fluorescent readings that were proportional to the
amount of synthetic target present in the hybridization mixture, which was expected if
the assay were viable. When background RNA had been mixed with the synthetic
target before silica beads were added, the fluorescent readings were relatively similar
for varying synthetic target amounts present, which made the assay a poor indicator of
the true amount of synthetic target present. As such, ‘Step 1 followed by step 2” was
used for the rest of the experiments involving the silica-coated magnetic bead assay.
However, it must be noted that the error bars were reasonably large, and thus no
conclusion could yet be made about the functionality of the assay.
3.5.2 Varying the Amount of Background RNA
The second experiment was done to determine if the amount of background RNA used
affected the assay in accurately detecting the amount of synthetic target present. The
assay format given in chapter 2.6 was adjusted as follows:

Step 1
o 2 μL of 60 % beads
o 2.7 μL of dH2O
o 3 μL of 100 % ethanol
o 1 μL of 0, 250 or 500 fmol/μL synthetic target

Step 2
o 3 μL of 100 % ethanol
51
o 0, 1, 2 or 3 μL of background RNA (concentration = 50.8 μg/mL,
purity = 1.81)
o 4.5, 3.5, 2.5 or 1.5 μL of dH2O

The rest of the assay format remained the same
The results given as fluorometer readings are summarized in Table 8 and plotted in
Figure 16.
Table 8. Fluorometer readings for 0, 1, 2, 3 μL of background RNA with 0, 250 and
500 fmol synthetic target in the hybridization mixture. These volumes of background
RNA correspond to 0, 50.8, 101.6, 152.4 ng of RNA respectively. The average and
standard deviations given are for the fluorometer readings. The signal to noise ratio
was calculated by dividing the average fluorometer reading over the average
fluorometer reading of the negative control.
Backgrnd Target
Fluorometer Reading (RFU) Average Standard Signal To
RNA (μL) (fmol) Sample Sample Sample Sample
Deviation Noise Ratio
1
2
3
4
0
0
1348 1433 1288 1213
1321
93
250
1006 1262 1014 1111
1098
119
0.832
500
1139 1223 1354 1499
1304
157
0.987
1
0
806
776
773
888
811
54
250
866
773
738
777
789
55
0.973
500
743
832
633
876
771
107
0.951
2
0
590
767
561
669
647
92
250
752
736
895
864
812
80
1.255
500
1197 1141
686
1116
1035
235
1.600
3
0
987
923
803
1008
930
92
250
1127
765
753
950
899
177
0.966
500
788
1090
813
951
911
139
0.979
52
Fluorometer Reading (RFU)
Varying the Amount of Background RNA
1600
1400
1200
1000
800
600
400
200
0
0 fmol
250 fmol
500 fmol
0
1
2
3
Background RNA (L)
Figure 16. Bar graphs showing the mean fluorescent signals for 0, 1, 2, 3 μL of
background RNA with 0, 250, 500 fmol of synthetic target in the hybridization
mixture. These volumes of background RNA correspond to 0, 50.8, 101.6, 152.4 ng of
RNA respectively. The error bars are also given to show the spread of the readings
taken with 4 samples. Higher fluorometer readings were obtained when no
background RNA was used, and when background RNA was used, only 2 μL of RNA
gave a proportional dose response.
Table 8 and Figure 16 suggest that the assay was only able to distinguish between the
different amounts of synthetic target that was present in the hybridization mixture
when 2 μL of background RNA, corresponding to 101.6 ng of RNA, was used.
However, the large standard deviations, high background noise, and low signal to
noise ratios of 1.255 and 1.600 for 250 and 500 fmol of target respectively, throw
much doubt on the functionality of this assay. For 1 and 3 μL of background RNA, the
fluorometer readings did not change significantly when there was a change in the
amount of synthetic target. When no background RNA was used, the fluorometer
readings were generally higher. This was expected since the absence of background
RNA would mean there was free silica surface for reporter probes and universal
liposomes to bind directly to the beads. Therefore, the background RNA was indeed
53
successful in reducing the amount of direct binding between the silica beads and the
liposomes.
3.5.3 Varying the Amount of Silica-coated Magnetic Beads
The third experiment was done to determine if the amount of beads used affected the
assay in accurately detecting the amount of synthetic target present. The assay format
given in chapter 2.6 was adjusted as follows:

Step 1
o 1, 2, 3 or 4 μL of 60 % beads
o 3.1, 2.7, 2.3 or 1.9 μL of dH2O
o 3 μL of 100 % ethanol
o 1 μL of 0, 250 or 500 fmol/μL synthetic target

Step 2
o 2 μL of background RNA (concentration = 50.8 μg/mL, purity = 1.81)
o 2.5 μL of dH2O
o 3 μL of 100 % ethanol

The rest of the assay format remained the same
The results given as fluorometer readings are summarized in Table 9 and plotted in
Figure 17.
54
Table 9. Fluorometer readings for 1, 2, 3, 4 μL of silica beads with 0, 250, 500 fmol
synthetic target in the hybridization mixture. These volumes of beads correspond to
0.6, 1.2, 1.8, 2.4 μg respectively. The average and standard deviations given are for
fluorometer readings. Signal to noise ratio was calculated by dividing the average
fluorometer reading over the average fluorometer reading of the negative control.
Beads
(μL)
Target
Fluorometer Reading (RFU) Average Standard Signal To
(fmol) Sample Sample Sample Sample
Deviation Noise Ratio
1
2
3
4
0
541
817
847
601
702
153
250
784
761
936
507
747
178
1.065
500
501
832
851
785
742
163
1.058
0
527
657
401
746
583
151
250
629
765
847
711
738
92
1.266
500
987
934
830
887
910
67
1.561
0
452
343
557
673
506
141
250
485
541
657
715
600
105
1.184
500
490
813
672
700
669
134
1.321
0
452
509
833
443
559
185
250
600
605
628
749
646
70
1.154
500
855
529
585
443
603
178
1.078
1
2
3
4
Fluorometer Reading (RFU)
Varying the Amount of Silica-coated Magnetic Beads
1200
1000
800
0 fmol
600
250 fmol
400
500 fmol
200
0
1
2
3
4
Silica Beads (L)
Figure 17. Bar graphs showing the mean fluorescent signals for 1, 2, 3, 4 μL of silica
beads with 0, 250, 500 fmol of synthetic target in the hybridization mixture. These
volumes of beads correspond to 0.6, 1.2, 1.8, 2.4 μg respectively. The error bars are
also given to show the spread of the readings taken with 4 samples. Only 2 μL and 3
μL of beads appeared to give a proportional does response.
55
Table 9 and Figure 17 confirm the suspicion in chapter 3.5.2 that the silica-coated
magnetic bead assay used in this research would not be a viable method for target
RNA detection. While using 2 and 3 μL of beads resulted in proportional changes of
signal with respect to the target concentration, the differences were not very high, i.e.
an increase in signal between 0 and 500 fmol of only 56.1 % and 32.1 % for 2 and 3
μL of beads respectively. In addition, large error bars suggest a randomness and
inaccuracy inherent in the assay that would not give consistent results.
56
4. CONCLUSION
Investigations toward a simplified and novel on-bead RNA-liposome biosensor, based
on nucleic acid binding to silica in 40 % ethanol, have shown that in general, the
principle works, but much improvement is needed in order to change this into a viable
assay. While optimization of the amount of background RNA and silica-coated
magnetic beads resulted in clear dose-response curves when analyzing 0 – 500 fmol of
target RNA, high standard deviations and background noise indicated a poor
performance of this strategy. Variability introduced during the conduct of the
experiments could have been reduced if more efficient experimental techniques had
been applied, such as the use of multi channel pipettes to load and aspirate samples in
the wells. It was also observed during pipette-mixing to wash the beads, that
unnecessary abrasion between the pipette tip and the beads was occurring, which
could be eliminated by using a system involving continuous but gentle flow of
washing buffer.
In addition, alternative methods could be investigated to ensure that only target RNA
is quantified in the assay. For example, instead of the liposome-reporter probe
complex, RNA-binding proteins that are specific to the target and do not bind to the
silica could be used as the transducer. Another approach is to find a way to quench the
exposed silica after adding target sequence to the beads, perhaps by chemically
reacting negatively charged silica with high-valent cations. This would prevent direct
binding of the liposomes and/or reporter probes onto the silica.
57
Amazing and interesting results were obtained in the ethanol and methanol study of
the lateral-flow assay, i.e. a novel method of enhancing the signal in the lateral-flow
assay was discovered during this research. It was found that adding ethanol in the
hybridization mixture would increase the intensity of the signal at the capture zone by
up to 100 %, with 33 % ethanol being the optimal concentration. Methanol was
investigated as well but found to be less effective at low amounts (50 fmol) of
synthetic target. In addition, ethanol present in the running buffer had the ability to
reduce the background noise on the membrane, improving the signal even further. 40
% ethanol in the running buffer proved to be too high a concentration, but 20 %
ethanol gave very well-developed and distinct signals. A combination of 33 % ethanol
in the hybridization mixture and 20 % ethanol in the running buffer was established to
give the greatest improvement of the signal compared to if no alcohol had been used in
the assay, with signal to noise ratio improving from 1.46 to 3.66. This would no doubt
reduce the lower limit of detection for the lateral-biosensor assay.
Recommendations to improve the lateral-flow assay include using the Linomat to
apply streptavidin onto the membranes, since the capture zone will be linear instead of
a circular area, allowing for more accurate comparisons between membranes. Other
organic solvents could be explored to evaluate their signal-enhancing abilities,
suggestions include isopropanol and polyethylene glycol. Also, if nucleic acid
aggregation is indeed the cause of signal improvement, other dehydrating agents such
as lithium chloride, cobalt-hexamine and sodium perchlorate could be tested for their
effectiveness.
58
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