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APTAMERS FOR PARALLEL MEASUREMENTS OF
CELLULAR MOLECULES

presented by
Shengnan Xie

has been accepted towards fulfillment
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APTAMERS FOR PARALLEL MEASUREMENTS OF CELLULAR MOLECULES
By

Shengnan Xie

A DISSERTATION

Submitted to
Michigan State University
in partial fiilfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Chemical Engineering

2010

Abstract

APTAMERS FOR PARALLEL MEASUREMENTS OF CELLULAR MOLECULES
By
Shengnan Xie

Measuring the levels of the various molecules present in cells at any time is
essential for the thorough and systematic understanding of complex biological processes.
Thus, continuing innovation in analytical techniques for making these measurements,
especially in parallel, can yield significant improvement in our understanding of
biological systems. Moreover, successful analytical approaches often move from being
applied purely for laboratory studies to use in clinical diagnoses, where an even greater
impact is possible. Among the different types of biological analytes, much current
research is focused on developing techniques to enable quantitative and parallel analyses
of non-nucleic acid molecules such as proteins and small molecules.

Affinity-based analytical techniques have proven useful for nucleic acid, protein,
and small molecule measurements. Aptamers, being nucleic acids that can bind non-
nucleic acid molecules with high affinity and specificity, are an emerging class of affinity
reagents that have potential advantages for analytical/biosensing applications. The aim of
this dissertation was to use aptamers to develop analytical methods that leverage
established nucleic acid analytical technologies for the analysis of proteins and small
molecules. This work focused on two technologies for the analysis of small molecules
and proteins, respectively.

One advantage of aptamers is the opportunity to couple sensing and readout of the

analytes. This is particularly true with a unique class of aptamer-based sensing molecules,

termed structure-switching aptamers (SSAS), wherein the binding of the target molecule
results in a conformational change of the molecule. Unfortunately, the parameters that
determine the fidelity of the signal from SSA biosensors are generally unknown. In the
first study, an SSA-based biosensing assay was developed to detect a model small
molecule, theophylline, in solution. Through both computational and experimental
analysis, it was found that the biosensor fidelity is primarily controlled by the relative
stability of the two conformations of the SSA.

‘ Based on this result, a protein biosensor was developed using a dual-aptamer
approach. It was predicted that this approach would have improved dynamic range and
sensitivity, relative to the SSA-based technique. The dual-aptamer biosensor relied on
recognition of the target protein by two unique aptamers targeting different epitopes on
the protein. Results showed that the technique achieved simultaneous and quantitative
detection of proteins with high specificity. Moreover, the empirical biosensing
performance agreed with expectations based on theoretical calculations.

To demonstrate the applicability of the dual-aptamer sensing technique for

proteomics research, in vitro selection was performed to generate new aptamers.

Aptamers for acute phase proteins fibrinogen and C-reactive protein (CRP) were selected

and characterized to have nanomolar affinity for the proteins. Furthermore, two
fibrinogen aptamers selected from two different pools bound the protein in a non-
competitive fashion. The selected aptamers will serve as affinity reagents for developing
multiplex protein analytical technique for these APPS. The results also suggested that
identifying multiple, non-competitive aptamers for a target protein may be a general

phenomenon that can be used to generate pairs of aptamers for our biosensing approach.

V j

 

 

To my family

iv

ACKNOWLEDGEMENTS

This dissertation could not have been completed without the help and guidance of
my advisor, Prof. S. Patrick Walton. He has provided me with invaluable academic
independence yet made sure I am on the right track. I want to thank him for his brilliant
ideas, insightfiil suggestions, and constant encouragement, all of which helped inspired
my research. I’m grateful for having the opportunity to have worked with Prof. Walton,
especially at the beginning of his professional career which I am sure will be very fruitfiil.
I thank him for being such a great advisor.

I also would like to thank my committee members Prof. Chan, Prof. Worden and
Prof. DeWitt. The discussions with all of them have always been productive and helpful.
Prof. Chan has kept a close relationship to our research group and provided me with
many creative ideas and invaluable suggestions. Prof. Worden helped to build an
academic foundation for my biochemical engineering background. I have always been
grateful for his suggestions and encouragement throughout my Ph.D. study. The
insightful suggestions of Prof. DeWitt have inspired the conception of a large portion of
my review paper in proteomics among many other works in this dissertation. I want to
express my special appreciation to all of them.

My work would not have been possible without the assistance of Prof. Andrew
Ellington at University of Texas-Austin (UT-Austin), who gave me the opportunity to
learn selection in his lab. I want to give special thanks to Brad Hall at (UT-Austin) for his
hosting and training and Xi Chen for tremendously enlightening discussions. My UT-
Austin experiences had a powerful and significant impact on my research for which I am

truly appreciative.

I also want to thank former and present members of the Cellular and
Biomolecular Laboratory. Special thanks go to Joe Gredell and Hemant Kini, who made
my transition from coursework to laboratory work much smoother; I was happy to form
good friendships with both of them which I am sure will last through our professional
careers. In addition, I want to thank Mark Swartzlander for his assistance with

experiments. I am also thankful to Zheng Li and Lufang Sheng, who welcomed me on

tar-m

my first arrival to MSU and offered me so much care and help throughout; for that I am

always grateful. I would also like to express my gratitude to Linxia Zhang, Xuerui Yang,

 

Yifei Wu, Xuewei Wang, Hyun Ju Cho, Li Liu, Betul Bilgin, Hirosha Geekiyanage,
Amanda Portis and Ming Wu for many many helpful interactions. I will always cherish
the days I work with them.

I was fortunate to meet many other friends outside of lab, who have been a
constant help and encouragement for me. Special thanks to Jun Sun, who is so
knowledgeable and intelligent. I thank him for all the discussions, suggestions and
techniques. A special note of thanks goes out to Susan Farhat-for sharing the journey
from the beginning to the end-and to Cecelia Chen, for all the years of growing pains,
with tears and laughter. I also want to thank Xi Hong, Yaoyan Xi, and Weipeng Liu for
their friendships.

I want to express my deep gratitude to my parents who have given me endless
unconditional love. Without them I would never have become the person I am today.
Finally, I want to thank my beloved fiance F ei Ren for his patience, support and

encouragement. Thank you all for always being there for me.

Thank you.

vi

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................. ix
LIST OF FIGURES ............................................................................................................ x
LIST OF ABBREVIATIONS ........................................................................................... xii
CHAPTER 1 INTRODUCTION ........................................................................................ 1
1.1 Significance ............................................................................................................... 2
1.2 Current technologies for cellular molecule measurements ....................................... 2
1.2.1 Nucleic acid analytical techniques ..................................................................... 2
1.2.2 Protein analytical technologies .......................................................................... 4
1.2.2.1 Planar antibody arrays ................................................................................. 8

1.2.2.2 Bead-based antibody arrays ........................................................................ 9

1.2.2.3 Antibody conjugated-DNA barcoding ...................................................... 12

1.2.3 Small molecule analytical technologies ........................................................... 15

1.3 Aptamers ................................................................................................................. 16
1.3.1 What are aptamers? .......................................................................................... 16
1.3.2 Advantages of aptamers ................................................................................... 18
1.3.3 Aptamer applications ....................................................................................... 19
1.3.4 Aptamers as in vitro biosensors ....................................................................... 19
1.3.4.1 Binding-based aptamer biosensors ........................................................... 19

1.3.4.2 Structure-switching aptamers .................................................................... 21

1.4 The scope of this study ........................................................................................... 24
References ..................................................................................................................... 25

CHAPTER 2 APPLICATION AND ANALYSIS OF STRUCTURE-SWITCHING

APTAMERS FOR SMALL MOLECULE QUANTIFICATION .................................... 41
2.1 Abstract ................................................................................................................... 42
2.2 Introduction ............................................................................................................. 43
2.3 Experimental section ............................................................................................... 46

2.3.1 Synthesis of the MBS ...................................................................................... 46
2.3.2 Binding reactions and theophylline sensing experiments ................................ 47
2.3.3 Binding affinity and theoretical signal-to-noise ratio calculation .................... 49
2.3.4 Reverse transcription and quantitative PCR .................................................... 50
2.4 Results and discussion ............................................................................................ 51
2.4.1 Application of theophylline-responsive MBS ................................................. 51
2.4.2 Analysis of MBS performance ......................................................................... 70
2.4.3 Thermodynamic constructs for predicting MBS function ............................... 75
2.5 Conclusions ............................................................................................................. 81
References ..................................................................................................................... 83
vii

_,. 1"

 

CHAPTER 3 DEVELOPMENT OF A DUAL APTAMER-BASED MULTIPLEX

PROTEIN BIOSENSOR .................................................................................................. 89
3.1 Abstract .................................................... ' ............................................................... 9 O
3.2 Introduction ............................................................................................................. 91
3.3 Experimental section ............................................................................................... 93

3.3.1 Proteins and aptamer sequences ....................................................................... 93
3.3.2 Dual aptamer sensing assays ............................................................................ 95
3.3.3 Aptamer affinity characterization .................................................................... 96
3.3.4 PCR and qPCR ................................................................................................. 96
3.3.5 Detection in bovine and human sera ................................................................ 97
3.4 Results and discussion ............................................................................................ 98
3.5 Conclusions ........................................................................................................... 124
References ................................................................................................................... 13 1

CHAPTER 4 IN VI T R0 SELECTION OF APTAMERS TO ACUTE PHASE

PROTEINS ..................................................................................................................... 137
4.1 Abstract ................................................................................................................. 137
4.2 Introduction ........................................................................................................... 138
4.3 Experimental section ............................................................................................. 139

4.3.1 Synthesis of randomized RNA pool .............................................................. 139
4.3.2 In vitro selection ............................................................................................ 141
4.3.3 Cloning and sequencing ................................................................................. 142
4.3.4 Affinity characterization by filter binding assays .......................................... 143
4.3.5 Competition assays ........................................................................................ 144
4.4 Results and discussion .......................................................................................... 144
4.5 Conclusions ........................................................................................................... 161
References ................................................................................................................... 162

CHAPTER 5 CONCLUSIONS AND FUTURE WORK ............................................... 163

References ................................................................................................................... 166

viii

 

LIST OF TABLES

Table 1.1 Comparison of conventional and affinity-based proteomics techniques. ........... 7
Table 2.1: Thermodynamic Predictions for MBS design modifications. ......................... 78
Table 3.1: KB measured for each aptamer for its target protein. .................................... 111
Table 3.2: C; values used to generate Figure 3.4. ........................................................... 112
Table 4.1: Summary of in vitro selection for the RSO-fibrinogen pool. ......................... 146
Table 4.2: Summary of in vitro selection for the R70-fibrinogen pool. ......................... 147
Table 4.3: Summary of in vitro selection for the R60-CRP pool. .................................. 148

Table 4.4: The sequences of the random regions from selected clones of round 6 of the
RSO-fibrinogen selection pool ......................................................................................... 152

Table 4.5: The sequences of the random regions from selected clones of round 10 of the
R70-fibrinogen selection pool ......................................................................................... 153

Table 4.6: The sequences of the random regions from selected clones of round 10 of the
R60-CRP selection pool .................................................................................................. 154

ix

 

LIST OF FIGURES

Figure 1.1: Overview of the in vitro selection (SELEX) process. .................................... 17
Figure 2.1: MBS design and detection strategy. ............................................................... 53
Figure 2.2: Impact of theophylline on MBS-MBC hybridization. ................................... 54
Figure 2.3: MBS-MBC binding affinity characterization ................................................. 57
Figure 2.4: Validation of the fidelity of magnetic bead separation. ................................. 61
Figure 2.5: Concentrations for optimal S/N, S-N and aggregate score. ........................... 62
Figure 2.6: Theophylline and caffeine standard curves at optimal MBS and MBC
concentration. .................................................................................................................... 67
Figure 2.7: Validation of qPCR assay for MBS detection ................................................ 68
Figure 2.8: Quantification of theophylline concentration ................................................. 69
Figure 2.9: Theoretical analysis of S/N, S-N and aggregate score. .................................. 71
Figure 2.10: Thermodynamic constructs for MBS-MBC binding in the absence and
presence of theophylline. .................................................................................................. 77
Figure 3.1: Sensing response with respect to detecting aptamer concentration. ............ 100
Figure 3.2: Dual-aptamer detection schematic. .............................................................. 104
Figure 3.3: Validation of qPCR amplification of sensing aptamers. .............................. 106
Figure 3.4: Concentrations of sensing aptamers for optimal signal dynamic range. ...... 108
Figure 3.5: Affinity characterization of thrombin and PDGF-BB aptamers. ................. 109
Figure 3.6: Standard curves for singleplex detection of individual proteins using qPCR].16
Figure 3.7: Multiplex detection of proteins using electrophoresis as the readout method.18
Figure 3.8: Multiplex detection using qPCR as the readout method. ............................. 119
Figure 3.9: Specificity of detection in the presence of competing protein. .................... 120

Figure 3.10: Multiplex detection of proteins in fetal bovine serum and human serum. 125

 

Figure 3.11: Correlation of various analyses. ................................................................. 129

Figure 4.1: Affinity improvement of selected pools. ...................................................... 150
Figure 4.2: Affinity characterization of selected clones. ................................................ 157
Figure 4.3: Competition assays for fibrinogen aptamers. ............................................... 159

xi

 

 

LIST OF ABBREVIATIONS

2-DE - two-dimensional gel electrophoresis

AF P - a-fetoprotein

APP - acute phase proteins

ATP - adenosine triphosphate

BrdU - bromodeoxyuridine

cAMP - cyclic-adenosine monophosphate

CE-LIF - capillary electrophoresis with laser-induced fluorescence
CIAP - Calf Intestinal Alkaline Phosphatase

CRP - C-reactive protein

Ct - threshold cycle

DTT - dithiothreitol

EGFR - epidermal grth factor receptor

ELISA - enzyme-linked immunosorbent assay

ELONA - enzyme-linked oligonucleotide assay

EMSA - electrophoretic mobility shift assay

Erk - extracellular regulated kinase

FBS - fetal bovine serum

GBM - glioblastoma multiform

HCG - human chorionic gonadotropin

HIT - high-throughput immunophenotyping using transcription

hVEGF - human vascular endothelial grth factor

xii

 

MB - molecular barcode

MBC - molecular barcode complement

MBS - molecular barcode switch

MS - mass spectrometry

PDGF - platelet-derived grth factor

PLA - proximity ligation assay

PSA - prostate specific antigen

QD - quantum dot

qPCR - quantitative Polymerase Chain Reaction
RCA - rolling circle amplification

RT - reverse transcription

S/N - signal-to-noise ratio

SELEX - Systematic Evolution of Ligands by EXponential enrichment
S-N - signal-to-noise difference

SSA - structure-switching aptamer

TPP - thyamine pyrophosphate

UTR - untranslated region

VEGF - vascular endothelial growth factor

xiii

CHAPTER 1 INTRODUCTION

Portions reproduced from: Xie, S., Moya, C., Bilgin, B., Jayaraman, A. and Walton, S. P.,
“Emerging affinity-based techniques in proteomics”, Expert review of proteomics, 6:573-

583 (2009) with permission of Expert Reviews Ltd.

1.1 Significance

The advancement of both basic and applied biological research relies on the improvement
of analytical techniques for measuring biomolecules, especially in a multiplex fashion.
With the completion of human genome project, large amounts of gene sequence
information became available. Now, the task is to concretely establish the functions of
each of the gene products produced from these sequences. This will require continued
advancement in technology and will lead to a more complete understanding of biological
processes. In turn, it should accelerate application of this understanding in clinical

applications and research.

1.2 Current technologies for cellular molecule measurements

Nucleic acid analyses, both in a single and multiplex fashion, are very mature at this
point. However, a fiill description of cellular function also requires the measurement of
downstream products of gene expression, such as proteins and metabolites/small
molecules. Current research on technology development focuses on developing reliable
and flexible techniques for analyzing proteins and small molecules, especially in a high-
throughput fashion. In this section, the state of the art technologies will be reviewed, with

an emphasis in multiplex detection techniques.

1.2.1 Nucleic acid analytical techniques

Analytical technologies for DNAs and RNAs were established early, primarily based on

Watson-Crick base pairing of nucleic acids, with, for instance, quantitative polymerase

 

chain reaction (qPCR) now common practice for nucleic acid analyses [1-5]. The
invention of microarrays allowed large quantities of nucleic acids to be simultaneously
monitored on a single chip, which resulted in a revolution relative to the historical “one
gene-one experiment” method [6]. Microarrays are fabricated with DNAs of known
sequence arranged on solid substrates. Typically, fluorescently-labeled sample nucleic

acids are hybridized to the complementary sequences immobilized on the substrate,
yielding a fluorescent signal from the bound material [7]. By calculating the signal

intensity, concentrations of the nucleic acid samples can be quantified. DNA sequences
can be either spotted or synthesized onto the substrates. Spotted microarrays are produced
by physical deposition of DNA, either cDNAs or oligonucleotides, onto the substrate [8],
while oligonucleotide arrays can also be synthesized nucleotide by nucleotide in situ

[9,10].

Since their invention, microarrays have been widely used for monitoring gene expression
[11,12], characterizing DNA polymorphisms [13], and detecting genome-wide
chromosomal alterations in cancer and development [10]. DNA microarrays are now
standard practice for parallel nucleic acid analyses, and they are also finding novel
applications in parallel analyses of other biomolecules (discussed in Section 1.2.2.3,

below).

The advancement of high-throughput DNA sequencing technologies has also enabled
their applications in nucleic acids analyses [14,15]. For example, the 454 sequencing

technology allows for simultaneous sequencing of genomic DNA or a large number of

clones with significant reduction in experimental cost and time. This technology has
started to be used for gene expression analysis [16] and identifying DNA methylation
patterns in cancer patients [17]. It has also been applied for comprehensive analysis of
small RNAS, including their discovery and differential expression profile characterization

[18,19].

1.2.2 Protein analytical technologies

Proteins lack the natural complementarity of nucleic acids and so must be analyzed by a
variety of techniques that leverage their unique physical and chemical properties.
Individual proteins can be measured by techniques such as electrophoresis,
chromatography, spectroscopy, and immunoassays [20]. However, high-throughput
analytical techniques for proteins still do not approach the scale of current genomics

methods.

Although techniques for separating proteins such as polyacrylamide gel electrophoresis
and chromatography have been in practice for a long time, ‘large-scale’ protein
separation was first described by O’Farrell in 1975 [21]. The technique, two-dimensional
gel electrophoresis (2-DE), separates proteins in a polyacrylamide gel in two orthogonal
dimensions. Proteins are first separated based on their isoelectric point followed by
molecular weight/size-based separation. 2-DE has been applied to a diverse range of
samples [22-26]. While simple to use and without complex instrumentation, 2-DE has
some significant drawbacks. First, not all types of proteins (e. g., membrane proteins,

hydrophobic proteins, proteins greater than 150 kDa) are resolved equally well in gel

electrophoresis [27]. Second, the loading capacity of gels is limited, which in turn limits
the amount of protein that can be separated and the depth of proteome coverage that can
be obtained. This is especially important when working with complex biological fluids
such as serum where the range of protein concentrations spans at least five orders of
magnitude [28,29]. Although approaches such as sample fractionation, use of ‘zoom gels’
(i.e., narrow pI range isoelectric focusing gels), and more sensitive staining methods (e. g.,
fluorescence-based stains) have been developed to overcome these drawbacks, 2-DE is
currently used primarily for initially characterizing proteomes prior to more thorough

analysis by other methods.

The development of mass spectrometry (MS) methods has addressed some of the
drawbacks of gel-based proteomics methods mentioned above, especially in handling
complex protein samples and increasing the depth of proteome coverage. Proteins are
first separated by gel electrophoresis or by liquid chromatography, prior to proteolytic
fragmentation and identification by MS [30]. Alternatively, the entire protein sample can
be digested and the generated peptides separated and analyzed [31]. This “shotgun
approach” has the advantage of utilizing multiple liquid chromatography techniques (e.g.,
size exclusion followed by strong-cation exchange chromatography) to separate the
complex peptide mixture before MS analysis. However as the number of generated
peptides exceeds 2 million [32], the ability to separate ions with very similar mass-to-
charge ratios and instrument sensitivity are limiting factors. Therefore, complementary,

affinity-based technologies are actively under development for multiplex protein analyses.

Emerging techniques using affinity reagents for the analysis of focused sets of proteins
have the potential for improved sensitivity, less effort, and more broadly focused than
2DE/MS techniques (see Table 1.1 for a general comparison of 2-DE/MS-based and
affinity-based methods). These methods are highly tunable to meet the needs of the
investigator and the system of interest. However, they are only applicable for known
protein targets and, furthermore, these targets need to have well characterized, specific
affinity reagents. To date, the use of antibodies has predominated affinity-based protein
analytical methods. Nonetheless, the interactions between proteins and their non-
proteinaceous binding partners such as transcription factor binding sequences [33] and
aptamers derived from in vitro selection (chapter 1.3), have been screened in various
array formats. In this section, the state of the art in antibody-based proteomic strategies
will be discussed, focusing primarily on new techniques that are using antibodies in

creative ways to circumvent the limitations of existing technologies.

Table 1.1 Comparison of conventional and affinity-based proteomics techniques.

 

 

 

 

 

 

 

 

 

Propefl 2DE/MS-based Affinig-based
Applications Identification, expression, post Expression, interactions, function,
translational modifications post translational modifications
Specificity Tunable based on separation Depends on specificity of affinity
method used reagents
Sensitivity Difficulty with low abundance Nano- to femtomolar protein
proteins in mixtures concentration
Breadth of Can approach all proteins in a Focused sets of proteins defined
coverage sample llyresearcher
Instrumentation Significant Varies broadly depending on
Requirements technique
Pretreatment Typically requires removal of Often no pretreatment required

 

high abundance proteins

 

 

 

 

1.2.2.1 Planar antibody arrays

Planar antibody arrays were the earliest form of proteomic arrays for profiling protein
expression levels. Compared to 2-DE/MS methods, antibody arrays can, in some cases,
perform more rapid and sensitive multiplex analyses of the non-fractionated proteome
[34]. Antibody arrays can generally be classified into three categories: i) immobilized
antibodies to capture labeled proteins from the solution [35]; ii) immobilized proteins
detected by labeled antibodies [36,37], and iii) dual antibody sandwich assays [3 8-40].
Protein arrays have been widely used for cytokine profiling in a variety of studies,
including providing comprehensive information for inflammation and aging studies
[41,42]. They have also been actively used in biomarker identification for a variety of
cancers, including prostate [43], breast [44], hepatic [45], and pancreatic [46]. Analysis
of secreted proteins to establish disease biomarker profiles continues to be a primary use

of protein arrays.

Antibody microarrays have also been applied to study intracellular signaling. Antibodies
to epidermal grth factor receptor (EGFR) and ErbB2/Her2 and their tyrosine
phosphorylated forms were used to monitor the concentrations and activities of receptor
tyrosine kinases in a ratiometric microarray format [47]. A label-free sandwich approach
using near infrared spectroscopy was applied to study extracellular regulated kinase (Erk)

1/2 and Stat3 signaling in cell lines and primary cells over time [48].

Unique methods have been explored to improve the sensitivity and performance of

antibody-based methods. One such method, rolling circle amplification (RCA) was used

to maximize the signal response from protein recognition in a multiplexed assay [49].
After protein capture by monoclonal antibodies on the microarray, secondary,
biotinylated polyclonal antibodies for the proteins were added. Finally, the secondary
antibodies were bound by anti-biotin antibodies conjugated with DNA primers.
Following addition of a circular DNA template, polymerase, and nucleotides, RCA
begins and produces long ssDNA strands that were then detected with fluorescently-
labeled, complementary oligonucleotides. This technique was used for the Simultaneous
measurement of 75 cytokines with femto- to picomolar sensitivity. RCA enabled
detection of cytokine levels 1000-fold below the sensitivity limit of direct fluorescent
detection without amplification. In addition, because of the ligation of the readout to the
antibody binding, signal diffusion, often an issue with enzyme catalyzed

chemiluminescent readouts, was largely avoided.

1.2.2.2 Bead-based antibody arrays

Solution-phase, multiplexed immunoassays have been developed on microbeads, enabled
by flow cytometric detection of the color-coded beads used to immobilize the antibodies
[50]. Each bead is then detected by its unique optical signature. The beads can also be
conjugated with other capture molecules such as antigens, receptors, or enzyme
substrates. A commercially available platform has been developed using beads labeled
with two fluorescent dyes each with 10 different intensities (Luminex Corporation,
Austin, TX) [51], which limits analyses to a maximum of 100 unique analytes. However,
as the limitation is based on head encoding, rather than sensitivity or specificity, the

potential to expand the technique to greater parallelism, certainly exists.

Compared with planar protein arrays, bead-based assays have advantages such as solution
phase processing and detection, increased flexibility, and Simplified automation [52].
Unlike planar arrays, which are relatively inflexible after synthesis, bead-based arrays
can be readily customized through inclusion of different sets of existing or user-
customized beads [51]. Because they are typically based on two-molecule detection (e. g.,
detection of the bead color and fluorescence from the analyte), bead-based assays often
do not require a wash step to reduce non-specific background, which maximizes the

sensitivity of the assay while simultaneously simplifying sample processing.

Bead-based assays have been applied for a variety of systems, including detection of
cytokines [53-58], auto-antibodies [59,60], and biological warfare agents [61]. In one
interesting application, the bead system was used for fast analysis of binding specificities
of monoclonal antibodies [62]. Protein epitope signature tags from which antibodies were
generated were immobilized and mixed. The binding specificity of 84 antibodies was
assayed by incubating each antibody with the bead mixture followed by detection with
fluorescently labeled secondary antibodies. Results from the bead technique were
validated with planar microarrays [62]. The authors were also able to use their validated
antibodies with the bead system to measure 20 serum proteins from 200 clinical serum

samples in under 2 days [63].

Recently, bead-based profiling was applied for measuring the phosphorylation activity of

62 of 90 tyrosine kinases present in various cancer cell lines [64]. Tyrosine kinase

10

specific antibodies were coupled to the beads for immobilization of the enzymes. A
biotinylated, antiphosphotyrosine antibody was then used for detection of the
phosphorylation status of the enzymes from cell lysates. Kinases with similar
phosphorylation patterns across multiple cancer cell lines were identified [64].
Importantly, the frequent activation of SRC in glioblastoma was confirmed in follow up
experiments with primary glioblastoma multiform (GBM) patient samples. SRC was also
indicated as a potential therapeutic target for GBM. This multiplexed method
significantly reduced the required cell number and cost per assay and proved to have

specificity and sensitivity equivalent to western blotting.

Quantum dots (QDs) have been explored to address the limit on the number of possible
fluorescent bead encodings. QDs are inorganic crystals composed of a cadmium selenide
core and a zinc sulfide shell. QDs typically have higher brightness, orders of magnitude
higher resistance to photobleaching, and narrower emission spectra, compared to organic
fluorophores [65]. Moreover, the properties of QDs result from their chemical
composition and physical structure, allowing facile manipulation of their characteristics
to avoid spectrum overlap and increase their potential for multiplex applications [65].
Polymeric microbeads coded with QDs were developed by infusing QDs into preformed
beads in organic solvents [66]. The QD encoded beads were used in a multiplexed
detection of autoantibodies of TopoI and sm-antigen in serum samples of systemic
sclerosis patients [67]. QDs were also used directly as fluorescent labels in
immunophenotyping [68]. A l7-color staining panel composed of 7 ODS and 10 other

fluorophore labeled antibodies and pMHCI antigens was used to probe surface marker

11

expression of antigen specific T-cell populations within an HIV-seropositive individual
by polychromatic flow cytometry [68]. It was found that every antigen specific subset of
cells had a unique phenotypic pattern, providing an example of the potential of QD-based

readouts in proteomic applications.

1.2.2.3 Antibody conjugated-DNA barcoding

An alternative to direct readout of protein detection is through the translation of protein
Signal to the nucleic acid domain through strategies that can collectively be described as
antibody conjugated-DNA barcoding. These methods have the advantage that the
terminal readout is the quantity of the nucleic acid, rather than the quantity of protein,
therefore the readouts can take advantage of convenient, parallel, sensitive, and relatively
ubiquitous methods for nucleic acid detection (as discussed in Chapter 1.2.1 above). One
manner by which this is accomplished is to assign antibodies with a unique DNA label.
An early application of a barcoding strategy was in the use of gold nanoparticles
functionalized with an antibody and multiple copies of a biobarcode oligonucleotide [69].
The particles were then applied in a sandwich-like format with magnetic particles
conjugated with another monoclonal antibody for the same protein target. Different
oligonucleotides were used as identifiers for different target proteins, and, after magnetic
separation, the biobarcodes were eluted from the nanoparticles with dithiothreitol (DTT)
and measured using a DNA microarray. This method was used in a multiplex assay to
measure three established cancer markers: prostate specific antigen (PSA, a prostate
cancer marker), human chorionic gonadotropin (HCG, a testicular cancer marker), and 0t-

fetoprotein (AF P, a hepatocellular carcinoma marker) at 170 M concentration in diluted

12

serum. The extraordinary sensitivity was resulted from the release of multiple biobarcode
oligonucleotides for each protein molecule detected. It is possible that even further
sensitivity might be possible using PCR to amplify the released oligonucleotides prior to

detection.

Antibodies directly conjugated with DNA tags have also been used to measure six
proteins simultaneously in what is termed a proximity ligation assay (PLA) [70]. Each
protein analyte was probed by a pair of antibodies each modified with an oligonucleotide
probe. Upon binding of both antibodies to the target, the probes were in close proximity
and were ligated together in the presence of template strands complementary to the two
probes. Ligated products were amplified with specific primers to each probe and detected
with real time PCR [70]. This assay can sensitively detect proteins at femtomolar
concentrations in 1 pl samples (roughly 1000-10000 total molecules of protein per
sample). The high sensitivity and low background of this assay was due to the
amplification capacity of PCR and the dual binding events required for generating a
positive signal, similar to the biobarcode assay described above. However, in this study,
the unique barcode sequences were not utilized for discrimination among the protein

signals in parallel.

DNA barcoding has been combined with microfluidics for rapid, flexible construction of
a tool for plasma protein analysis. Rather than depositing the antibodies directly onto an
array, antibodies were conjugated with multiple copies of a ssDNA barcode and were

then hybridized to an array fiinctionalized with complementary ssDNA strands [71]. The

13

ligated DNA barcodes ensured that the antibodies were separated so that any region of
the array was specific for a single antigen. The array was then applied in a sandwich
format with detection antibodies. Coupling the array to a microfluidic system, direct on-

chip separation of plasma from blood obviated the need for prior centrifugation, enabling

measurement of 12 protein concentrations simultaneously from a 10 [IL sample [71].

In the high-throughput immunophenotyping using transcription (HIT) technique, each
DNA tag used to label the antibody contained a T7 promoter and was amplified by the T7
RNA polymerase [72]. The labeled RNA tags were then purified and hybridized to a
DNA microarray for quantification. Using two 48-plex HIT reactions, 90 surface markers
on human naive T helper cells were profiled. Although the assay format was limited to
measuring the surface proteins on pure populations of cells, the concept of using existing
DNA microarrays for new purposes suggests an efficient means for the rapid

development of new proteomics platforms.

Although antibodies are currently the most commonly used affinity reagent, the majority
of human proteins still lack a specific antibody. Moreover, many proteins have a large
number of antibodies and their qualities and performances are not easily defined.
Moreover, the costs are also extraordinarily high for proteomics analysis [73]. Therefore,
other affinity reagents such as aptamers are being explored as alternative and

complementary affinity reagents for protein analytical applications (Chapter 1.3).

14

1.2.3 Small molecule analytical technologies

Metabolites/small molecules are another essential set of cellular molecules. The
systematic study of small molecule profiles resulting from specific cellular processes is
usually termed metabolomics. Together with transcriptomic (mRNA) and proteomic
(protein) analyses, metabolomic analyses provide a comprehensive description of cellular
physiology. The quantification of disease-associated metabolites provides another means

of biomarkers that may serve as guidance for development of therapeutics [74].

While certain metabolites and hormones can be detected using immunoassays, and some
small molecules such as glucose and alcohols can be measured by enzymatic assays [75],
metabolomics analyses in general are the most complex among parallel biomolecule
analysis [76]. Identification of individual small molecules within a complex samples
requires techniques that can separate the various analytes using methods such as gas or
high performance liquid chromatography, followed by mass spectrometry [75,77].
Metabolomics was used to identify metabolite biomarkers for prostate cancer [78]. 1126
metabolites across 262 clinical samples related to prostate cancer were profiled.
Sarcosine was identified as a marker metabolite whose concentration was highly elevated
during prostate cancer progression to metastasis and whose inhibition was also suggested

to attenuate prostate cancer progression [78].

Nonetheless, the metabolome is so complex that no single method can effectively analyze

all of the unique molecules within most biological samples. Analysis of data from these

methods can also be complicated due to the difficulty in resolving constituents with

15

chemical or structural similarity [79]. The combination of current and emerging
technologies is contributing a more comprehensive description of the entire cellular

metabolome.

1.3 Aptamers

1.3.1 What are aptamers?

Aptamers are single-stranded nucleic acids that can bind specific target molecules with
high affinity and specificity. Two groups published the method to generate aptamers
almost simultaneously [80,81]. High-affinity aptamers can be isolated by an in vitro
selection process named SELEX (Systematic Evolution of Ligands by EXponential
enrichment) (Figure 1.1). The selection starts from a large library (1014-1015) of
randomized RNA or single stranded DNA sequences. The pool is mixed with the target
molecules and sequences that bind with the target are separated from the unbound ones.
The recovered sequences are amplified by Polymerase Chain Reaction (PCR). This
enriched pool is then subjected to another round of selection. This process is repeated for
several rounds until the pool primarily consists of aptamers of high affinity to the target
molecules. To date, aptamers have been generated against a variety of molecules
including amino acids [82], peptides [83], proteins [84,85], small molecules [86,87],
metal ions [88], oligosacchan'des [89,90], and lipids [91,92]. Therefore, SELEX can be
used to isolate oligonucleotide sequences with the capacity to recognize virtually any

target biomolecule with high affinity and specificity.

16

.p

  
 

. . . a. a, Randomized
Amplification ea .,.9‘a~9& sequence pool
Isolation of high a... ‘9
.. ¢<—-
affinity sequences /
.9 In vitro selection by
a~ O target binding
Elution of bound 3. ~
‘ a 6. Removal of unbound
sequences 6‘ ‘9 sequences

Figure 1.1: Overview of the in vitro selection (SELEX) process. A library of single
stranded DNA or RNA is incubated with target molecules. Unbound sequences are
separated and removed. The target-bound sequences are eluted from the target and
amplified to start the next round of selection. This process is repeated until high affinity

aptamers are enriched and isolated for characterization.

17

Interestingly, aptamers have also been discovered in the mRNA of natural organisms and
they typically reside in the 5’ untranslated regions (UTRs) [93]. These aptamers couple in
vivo sensing of small molecules and response domains to participate in the response to
stimuli. Because their response depends on conformational changes in the molecule, they
were termed riboswitches [93]. Since their initial discovery, bioinformatics tools have
identified 20 distinct classes of metabolite binding aptamers, primarily in eubacteria, with
the thyamine pyrophosphate (TPP) riboswitch class also discovered in eukaryotes [94].
Riboswitches are suggested to play significant roles in regulating the expression of genes

that are vital to metabolism [93,95-101]

1.3.2 Advantages of aptamers

In comparison to antibodies (proteinaceous affinity molecules), aptamers offer a number
of advantages. First, aptamers can be reliably synthesized from known sequences using
chemical or enzymatic procedures [102,103]. Second, because aptamers are generated by
in vitro selection, aptamers targeting any analyte, including those too toxic to allow I
generation of antibodies, could potentially be generated. Third, the specificity and affinity
of aptamers is tunable through manipulation of the selection conditions, such as by
incorporating negative selection against similar analytes for an increased target
specificity [86]. The high specificity that can be achieved is especially advantageous for
multiplex sensing applications. Aptamers can also be generated with chemically-modified
nucleotides to enhance their properties for various applications, such as stability to

nucleases for measurements in biological fluids [104-106]. Moreover, aptamers can be

18

readily engineered for various applications using designed selection or through post hoc

manipulations [107-1 14]

1.3.3 Aptamer applications

Aptamers have been used in various biotechnology and biomedicine applications. One
therapeutic aptamer, Macugen, which binds vascular endothelial growth factor (VEGF),
is already available in the market [115], and, to date, six more aptamer drugs are in
clinical trials [115]. Non-natural riboswitches have also been produced for controlling
transcription, translation, and mRNA stability [116,117]. Moreover, aptamers can be

immobilized in chromatography columns for affinity purification [118,119].

1.3.4 Aptamers as in vitro biosensors

Aptamers are being actively applied as biosensing elements for in vitro diagnostics.
Aptamer based biosensors have been developed for a variety of analytes such as small
molecules, metal ions and proteins [120]. While the readout mechanisms vary, aptamer-
based biosensing methods are becoming invaluable components of current biomolecule

analytical technologies.

1.3.4.1 Binding-based aptamer biosensors

Aptamers have been used in a variety of applications for which antibodies are typically
applied. In a so-called enzyme-linked oligonucleotide assay (ELONA), an aptamer was
used in place of the secondary antibody in a sandwich-type assay for the detection of

human vascular endothelial grth factor (hVEGF) [121]. The assay showed a detection

l9

sensitivity of 25 pg/ml (0.55 pM), and a good correlation to the enzyme-linked
immunosorbent assay (ELISA) results. A similar approach was used in the detection of
thrombin with a detection limit of 1 nM [122]. A quartz crystal biosensor was designed
for IgE with a detection limit of 0.5 nM [123]. The same target was analyzed with
capillary electrophoresis with laser-induced fluorescence detection (CE-LIF) with a
detection limit of 46 pM [124]. Aptamers were also used in designing biosensors using
fluorescence anisotropy [125], electrochemical detection [126], micro-fabricated

cantilever [127], and surface plasma resonance [128].

While the majority of techniques are in the singleplex format, several groups have since
developed aptamer arrays to measure multiple proteins simultaneously [129-131]. A four-
element RNA aptamer array was developed in which biotinylated RNA aptamers were
deposited onto streptavidin coated slides using contact printing. Analyte proteins were
labeled with Cy3 or Cy5 fluorescent dyes and detected in a cell lysate background [130].
A dual protein detection scheme using an electrochemical biosensor was recently
demonstrated [132]. A flow cell system enabling real time detection of multiple aptamer

binding events was also developed on a bead array format [133].

In native aptamer (and antibody) assays for measuring proteins, non-specific interactions
are reduced through washes or other means. However, in doing so, specific signal is also
reduced. In an effort to maintain sensitivity with minimal background, photoaptamers
were developed [134]. Photoaptamers are selected containing bromodeoxyuridine (BrdU)

modifications that readily crosslink to bound proteins upon UV exposure. These were

20

 

applied in an aptamer array that measured 17 proteins simultaneously from serum
samples [129]. Crosslinked proteins were detected by universal protein stain or specific
secondary antibodies. Because of the low background, the detection limits of about one
third of the analytes were below picomolar concentration, with several analytes

exhibiting an impressive detection limit below 10 M.

1.3.4.2 Structure-switching aptamers

A unique class of aptamer-based sensors has been termed structure—switching aptamers
(SSAS), wherein the binding of the target molecule results in a conformational change of
the SSA. As engineering protein biosensors is difficult because it generally requires
knowledge of protein tertiary structures, the ability to design SSAs solely based on their
primary (sequence) and secondary (intramolecular hybridization) structures is essentially
unique to nucleic acids. The sensing can be simultaneously measured by detection of the
conformational rearrangement, in some cases without requiring the addition of secondary

reagents. Therefore, SSAs are of particular interest for biosensing applications [135-147].

The most straightforward method to construct an SSA is to utilize the aptamer
rearrangement upon target binding. For instance, methods were developed such that a
single modification with a fluorophore was made to the aptamer on a position that
underwent significant structural rearrangement upon target binding. The change in the
environment around the fluorophore would result in altered signal. The fluorescence
signal then was directly dependent on the degree of target binding [113,148]. Aptamer

beacons, wherein the spacing between a fluorophore and a quencher changes upon target

21

binding, have also been applied [149-151]. Electrochemical detection was also used in
which the free end of the immobilized aptamer was labeled with a redox moiety, whose
distance to the sensor surface changed upon target binding, thereby altering the electron
transfer efficiency [152,153]. However, these designs usually result in a high background
or limited signal response, due to the relative flexibility of aptamer conformations in the

absence of the target.

To minimize background, target induced strand displacement architectures were
developed in which a complementary strand was incorporated to hybridize with the
designed aptamer construct. In one fluorescence approach, fluorophore-labeled aptamers
were hybridized with a quencher labeled antisense strand in the absence of target [154].
Upon target binding to the aptamer strand, the duplex strand was disrupted and
fluorescence was observed. Such signaling SSAS were created by both rational design
and in vitro selection approaches with the detection limit for adenosine triphosphate
(ATP) at micromolar concentrations and thrombin at nanomolar concentrations
[144,154,155]. As above, an electrochemical approach has also been applied, showing
detection of thrombin at nanomolar concentrations [138]. While the readout strategies
vary, SSA-based biosensors have to date been used primarily for single analytes. In
addition, the parameters controlling the sensitivity and the signal-to-noise ratios for this

type of biosensor were poorly understood.

Structural rearrangement upon aptamer-target binding was also used in designing

allosteric ribozyme biosensors. Allosteric ribozymes are nucleic acid enzymes composed

22

of an aptamer domain and a ribozyme domain. Target binding to the aptamer alters the
conformation of the construct, leading to a change in ribozyme catalytic activity.
Allosteric ribozymes have been designed for self-cleavage [156-158] and ligation [159].
Modular rational design, in vitro selection, and computational strategies have all been
used to generate allosteric ribozymes, and their altered activity upon target binding can be

transduced to an observable signal for use in biosensing [160,161].

The most prominent example of such class of biosensors is a small molecule array
fabricated by depositing seven allosteric ribozymes onto a gold surface [162]. By
measuring the fraction of radiolabeled ribozymes that underwent self-cleavage in the
presence of different small molecules in a complex cellular environment, the analyte
composition and concentration can be determined. The array was used to determine the
concentrations of 3’-5’ cyclic-adenosine monophosphate (cAMP), a secreted product in
bacterial cultures. An aptazyme ligation assay was also developed by immobilizing a
biotinylated substrate in a 96-well format and measuring the fraction of radioactive
aptazyme ligated to the substrate upon target binding [163]. This assay was used to detect
a variety of analytes, such as metabolites, peptides and proteins, at nanomolar
concentrations with good specificity. These methods demonstrated the possibility of
utilizing an ensemble of aptamers for multiplex sensing applications and corroborated
that aptamers retain their specificity in complex, mixed analyte environments. Improved
readout methods using fluorescence have also been developed [164,165]. However, their

use in multiplexed applications has yet to be demonstrated.

23

1.4 The scope of this study

In this study, we leverage the use of nucleic acid quantification techniques for designing
aptamer-based, multiplex analytical technologies for proteins and small molecules. In
chapter 2, the development and analysis of an SSA biosensor were discussed, for the
detection of a model small molecule theophylline. Quantitative theophylline detection.
over near three orders of magnitude was achieved with convenient nucleic acid
quantification techniques. We investigated the biosensor performance by both
computational and experimental approaches and found that the relative stability of the
two SSA conformations is the factor limiting the S/N. It was flirther proposed that the

direct binding approach would provide a larger dose response relative to the SSA design.

Based on the prediction, in chapter 3, a dual aptamer-based multiplex protein sensor was
developed. Results demonstrated that the technique achieved simultaneous and
quantitative detection of thrombin and platelet-derived growth factor-BB (PDGF-BB) at
pico- to nanomolar concentrations with high specificity both in buffered solutions and in
sera. The biosensing performance derived experimentally confirmed the theoretical

prediction.

In chapter 4, we describe the in vitro selection of aptamers for the acute phase proteins,

C- reactive protein (CRP) and fibrinogen. Two fibrinogen aptamers that bind the protein
in a non-competitive fashion were generated. The selected aptamers are designed for use
in the biosensing assays we have developed. Our approach provides a universal platform

for generating dual-aptamers and designing robust multiplex protein biosensors.

24

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40

CHAPTER 2 APPLICATION AND ANALYSIS OF
STRUCTURE-SWITCHING APTAMERS FOR
SMALL MOLECULE QUANTIFICATION

Portions reproduced from: Xie, S. and Walton, S. P., “Application and analysis of
structure-switching aptamers for small molecule quantification”, Analytica Chimica Acta,

638:213-219 (2009) with permissions from Elsevier.

41

2.1 Abstract

Modern tools for the analysis of cellular function aim for the quantitative measurement of
all members of a given class of biological molecules. Of the analyte classes, nucleic acid
measurements are typically the most tractable, both on an individual analyte basis and in
parallel. Thus, tools are being sought to enable measurement of other cellular molecules
using nucleic acid biosensors. Of the variety of potential nucleic acid biosensor strategies,
structure-switching aptamers (SSAS) present a unique opportunity to couple sensing and
readout of the target molecule. However, little has been characterized about the
parameters that determine the fidelity of the signal from SSA biosensors. In this study, a
small molecule biosensor based on an SSA was engineered to detect the model small
molecule, theophylline, in solution. Quantitative theophylline detection over nearly three
orders of magnitude was achieved by scintillation counting and quantitative PCR. Further
analysis showed that the biosensor fidelity is primarily controlled by the relative stability

of the two conformations of the SSA.

42

2.2 Introduction

Evolution in the analysis of biological function occurs in concert with the development
and application of new analytical techniques. Quantification techniques for DNA or RNA
molecules were established early, based on Watson-Crick base pairing of nucleic acids,
with, for instance, quantitative polymerase chain reaction (qPCR) now common practice
for nucleic acid analyses [1-5]. Sensing techniques for non-nucleic acid molecules such
as proteins and small molecules, especially parallel techniques, are markedly less mature.
The importance of small molecule analyses is illustrated by current efforts in
metabolomics, as understanding the profile of small molecules generated by metabolism
provides a comprehensive picture of the effects of upstream transcriptional and
translational changes. Moreover, a great number of drugs are small molecules, so specific
and accurate measurement of their levels is essential to therapeutic monitoring of disease
treatment. Currently, small molecule measurements are primarily performed using
spectroscopic analyses combined with chromatography [6,7]. Analysis of data from these
methods can be limited due to the difficulty in resolving constituents with chemical or
structural similarity. Thus, more flexible methods for small molecule analyses are being

sought

In recent years, aptamers have shown promise in small molecule and protein biosensing
applications [8-14]. Aptamers are nucleic acid affinity molecules with exceptional
specificity for a target for which they were specifically selected [15,16]. In comparison to
antibodies, aptamers offer a number of advantages for sensing applications. First, the

sequences for a large number of aptamers have been reported [17,18], and these

43

sequences can be easily synthesized chemically or enzymatically with high
reproducibility and purity. Moreover, because aptamers are generated by in vitro
selection, in theory, aptamers targeting any analyte could be generated. Second, the
specificity and affinity of aptamers is tunable through manipulation of the selection
conditions, such as by incorporating negative selection against similar analytes that
would confound accurate detection [19]. The degree of specificity that can be achieved is
especially advantageous for multiplex sensing applications. Third, aptamers can be
generated with chemically-modified nucleotides to enhance their properties for various
applications, such as stability to nucleases for measurements in biological fluids [20,21].
Finally, aptamers can be readily engineered for specific signaling function using designed

selection or through post hoc manipulations [22,23].

A unique class of aptamer-based sensors has been termed structure-switching aptamers
(SSAS), wherein the binding of the target molecule results in a conformational change of
the SSA. Given their simultaneous sensing and readout, SSAS are of particular interest
for biosensing applications [24-31]. A common mode for the structure-switching
behavior is based upon helix-rearrangement coupled with strand displacement [29,32,33].
The ability to design SSA sequences a priori to utilize this rearrangement is essentially
unique to nucleic acids. This enables flexible and facile design of SSA biosensors with
coupled readouts, such as to mimic molecular beacons [34—36] or to engineer cellular

responses [37,38].

44

The utility of SSA-based sensing techniques, as with all analytical methods, is defined
largely by the signal-to-noise ratio of the technique. Both rational design strategies and
high-throughput screening techniques have been investigated to generate SSAS with high
signal-to-noise ratio [3 8,39]. However, little has been characterized about the parameters
impacting the signal fidelity of an SSA. Thus, there is a clear need to identify these
parameters and use them as theoretical guidance in generating SSA biosensors with
optimal function. In this work, a modular-designed SSA that acts by strand displacement
was applied to quantify the concentration of a model small molecule, theophylline. This
SSA was designed for future parallel analyses through the inclusion of a molecular
barcode (MB) [40] and was therefore termed a molecular barcode switch (MBS). These
MBS have been shown to provide quantitative accuracy in parallel nucleic acid analyses
[41,42]. An oligonucleotide complementary to the MB domain of the MBS (molecular
barcode complement, MBC) was used to probe the conformational change upon target
binding and to utilize the change for the concentration readout. It was observed that the
affinity between the MBS and the complementary strand relative to the internal stability
of the MBS structure was a principal factor limiting the achievable signal-to-noise ratio.
Using the designed construct, theophylline concentrations as low as 0.5 [AM can be
reproducibly detected in solution. The observed lower detection limit for theophylline
exceeded that of caffeine by over three orders of magnitude, confirming analytical
specificity. As previous studies have coupled PCR amplification with aptamer-based
protein measurement [39,43-45], here we also report the applicability of PCR for small

molecule quantification.

45

2.3 Experimental section

2.3.1 Synthesis of the MBS

A double-stranded DNA was prepared by Klenow extension from a synthesized single-

stranded DNA template and the reverse primer. The sequence of the single-stranded
DNA template was (5'-
TAATACGACTCACTATAA CCT AA CT CGAA GT GAA GCCCACTTCGAGTTAGGTGA

TACCAGCATCGTCTTGATGCCCTTGGCAGCACCTAACT-3', underlined: T7

promoter; italicized: MB sequence; bold: reverse primer binding site) and the reverse

primer was (5'-AGTTAGGTGCTGCCAAGGGC-3'). The double-stranded DNA was in

vitro transcribed using Ampliscribe T7 High Yield Transcription Kit (Epicentre, Madison,

WI). Reactions were incubated at 42 0C for 2 hrs, and RNA transcripts were purified on

10% polyacrylamide gels, eluted, ethanol precipitated, and resuspended in TE (10 mM

Tris, pH 7.0, 1 mM EDTA). The 20-nt MBC has a sequence of 5'-

GGCTTCACTTCGAGTTAGGT-Biotin-3'. All oligonucleotides were synthesized and

purified by IDT(Cora1ville, IA). Assays with varying length of MBC showed no
significant binding to the MBS when the MBC was shorter than 14-nt. Longer MBC
sequences tended to cause higher background binding. Therefore, an MBC of 20-nt in
length was chosen to generate the maximum signal-to-noise ratio (S/N). For subsequent

separation using streptavidin-coated magnetic beads (see below), the MBC was

biotinylated at the 3'end. For reverse transcription-qPCR (RT-qPCR), the forward primer

46

sequence was (5'-TAGGTGATACCAGCATCGTC-3'). Several sets of primers were

tested to produce the least mispriming products (data not shown). To avoid mispriming
products due to the self-complementary sequence of the MBS, primers were chosen to be

outside the range of the MB domain, resulting in a 45 bp product.

2.3.2 Binding reactions and theophylline sensing experiments
Binding reactions were performed in a buffer of 50 mM Tris-HCl (pH 8.0), 100 mM

NaCl, and 5 mM MgC12. Varying concentrations of theophylline (Sigma Aldrich, St.

Louis, M0) were added to thermally annealed MBSS and incubated at room temperature
for 30 min. MBCs were then added and incubated at room temperature. The total binding

reaction volume was 30 uL. The MBC incubation time was 10 min for theophylline

sensing applications and 20 hrs for deriving equilibrium dissociation constants (KD).

Separation of MBS-MBC complexes was achieved by either electrophoretic mobility

shift assay (EMSA) or streptavidin-coated magnetic beads. For EMSA, the MBCs were

5'-labeled with 33P-y-ATP using T4 polynucleotide kinase (Ambion, Austin, TX)

followed by purification by Quick Spin Columns (Roche, Indianapolis, IN). Prior to gel
loading, glycerol (final concentration 10% v/v) was added to each binding reaction. 15
pL were then loaded for electrophoresis. Electrophoresis was performed at 90 V in 10%

polyacrylamide gels in 1X TBE Extended Range Buffer (Bio-Rad, Hercules, CA) for 1 hr
at room temperature. Gels were then dried on Whatrnan filter paper at 80 0C for 45 min

under vacuum. Dried gels were exposed to phosphorimaging screens for 16 hrs followed

47

by quantification on a Storm 860 (GE Healthcare/Amersham Biosciences, Piscataway,
NJ). Band intensities were quantified, and the fraction bound was calculated as theband
intensity of the MBS-MBC complex divided by the total intensity of the bands for the

complex and the free MBCs.

For magnetic bead separations, binding reactions were performed with 5'-33P labeled

MBSS when using scintillation counting as the read out and with unlabeled strands when
using RT-qPCR. Binding reactions were added to streptavidin-coated magnetic beads
(Dynal; Oslo, Norway), and a magnet was applied to retain the beads while the
supernatant was removed. The final concentration of beads was 20 ug/uL. 30 [L binding
reactions were then added to 5 uL of bead solution and incubated at room temperature for
5 min. Kinetic experiments showed 5 min was sufficient to yield 95-99% binding of
biotinylated MBC to the beads (data not shown). A magnet was then applied to retain the
beads while the supernatant was removed. The beads were then washed three times with

30 pL 1x binding buffer. Retained MBSS were eluted from the magnetic beads in 30 pL
nuclease-free water at 650C for 5 min. The magnet was applied to retain the beads, and

the supernatant was collected for scintillation counting or RT-qPCR quantification. All
scintillation counts were background corrected by subtracting counts of scintillation vials
containing no radioactivity. Scintillation counting validation experiments were performed
using 333 nM MBC and varying MBS concentrations. The MBS and MBC were mixed
for 1 hr at room temperature followed by a 5-min incubation with the magnetic beads.
The theophylline detection limit for scintillation counting was defined to be signal at least

three standard deviations above background. By linear interpolation of data near the

48

lower detection limit, a minimum detectable theophylline concentration of 0.5 uM was

obtained.

Experiments for determining optimal sensing conditions were performed in binding
reactions with varying concentration pairs of MBC (0.1, 1.0, 10, 50, and 100 nM) and
MBS (0.1, 1.0, 10, and 50 nM) in the presence (2 mM) or absence of theophylline.
Magnetic beads were used to separate the bound MB 83, followed by scintillation
counting as described. MBS fractional binding in the presence of 2 mM and 0 mM
theophylline were defined as signal and noise, respectively. Signal-to-noise ratios (S/N)
and their differences (S-N) were calculated from the measured data for each
concentration pair. Contour plots were generated by Sigmaplot. The aggregate score was
obtained by scaling the ratios and differences as a fraction of the range from the
minimum to the maximum and then summing the two scaled values. The resulting sums

were then sealed from 0 to l to obtain the displayed results.

2.3.3 Binding affinity and theoretical signal-to-noise ratio calculation

For experiments to determine dissociation constants, the KD values were calculated from

curve fits using Origin software based on:

[M3510
KD + [M19510 ‘2'”

 

f:

where f is the fraction bound of MBC as calculated from the quantified gel images. This
fit requires that the concentration of free MBS is nearly constant at its initial value

throughout the experiment. MBC concentrations were minimized to maximize the

49

validity of this assumption [46]. Data from at least three experimental runs were fit

simultaneously to generate a single parameter fit to all the data.

Theoretical fractional MBS binding at each MBS and MBC concentration pair was

calculated as follows:

 

= ([MBS]O + [MBC]O + KD) — \[([MBS]0 + [MBC]O + K D)? — 4[MBS]0[MBC]O (2.2)
2[MBS]0

 

f

where f is the MBS fraction bound and K1) is the dissociation constant of MBS-MBC
binding. This arises from the equilibrium reaction equation wherein one free MBS and
one free MBC combine to form one complex. SM and S-N were then calculated by

solving (2.2) using the appropriate KD, [MBS]0, and [MBC]0 values for each condition.

2.3.4 Reverse transcription and quantitative PCR

Following binding and elution from the magnetic beads as described above, MBSS were
reverse-transcribed using the reverse primer and Superscript III (Invitrogen, Carlsbad,
California) per the manufacturer’s instructions. The cDNA was amplified by qPCR using
iQ SYBR Green Supermix (Bio-Rad, Hercules, CA) in a MyiQ Real Time PCR detection

system (Bio-Rad, Hercules, CA). The purity of the resulting qPCR products was

confirmed by gel electrophoresis. Threshold cycle (C0 values for the qPCR standard

curve were generated with ten-fold dilutions of initial MBS template. Initial template

concentrations ranged from 485 aM to 485 pM, in 50 uL qPCR reactions.

50

2.4 Results and discussion

2.4.1 Application of theophylline-responsive MBS

The goal of any analytical strategy is precise, accurate, and quantitative measurement of
the analyte(s) of interest. SSAS have been proposed as a basis for solution-phase and
solid-substrate mediated single and parallel analyses [24,25,34,47]. Here, a theophylline-
sensitive SSA was constructed where the quantity of aptamer that undergoes a
conformational change provided the readout of the theophylline concentration (Figure
2.1). The SSA consists of two domains, the aptamer domain and the readout domain. This
modular design approach is well-established [37,3 8] and provides a basis for convenient

multiplex detection of SSAS by careful design of the readout domain.

In the described detection strategy, the readout is based on the enhanced binding affinity
of MBCs to MBSS bound to their target relative to MBSS without bound target (Figure
2.1). This is due to the structural rearrangement of the aptamer domain upon target
binding. Upon rearrangement, intermolecular hybridization in the MB domain can
compete more easily with native intramolecular hybridization. Streptavidin-coated
magnetic beads loaded with MBC sequences are used to isolate MBSS with bound
analyte for subsequent quantification by qPCR, microarray, or other convenient nucleic
acid analytical strategy. The technique thus provides an indirect but still quantitative
measurement of the theophylline concentration. Using gel-shift experiments (Figures 2.2
(a)), it was verified that the binding of MBC by MBS is dependent on theophylline
concentration. Maximal binding of the MBC occurred at 200 uM - 2 mM concentration

of theophylline (Figure 2.2 (b)), thus subsequent experiments where saturating MBS-

51

theophylline interactions were desired were performed at 2 mM theophylline. Binding
with 2 mM caffeine showed fractional MBC binding equal to that with 2 uM
theophylline (Figure 2.2 (a), lane 10), which is consistent with the difference in the
affinity of the aptamer for these two species [19]. Thus, the specificity of the aptamer for
theophylline over caffeine was maintained in the MBS design, further support for use of
the modular MBS construction strategy. It is worthwhile to note that even in the absence
of theophylline, the fiactional MBC hybridization with the MBS was 0.25 (Figure 2.2 (a),
lane 3). This suggests that caution must be taken in the development of SSA-based

analyses to ensure minimal background.

52

  

 

5’
\“
"HHHHHHU

5' C7, Target binding C chl'mmmmmms' 3|

  

 

Magnet

Figure 2.1: MBS design and detection strategy. In the native conformation of the MBS,

the MB sequence forms a 15 bp intramolecular stern. Upon theophylline binding with the

aptamer domain (light gray), competition by the free 3'-end of the MBS becomes more

favored, increasing the exposure of the MB domain (dark gray) for hybridization with an
MBC. Using biotinylated MBCS and streptavidin-coated magnetic beads, the MBSS with
available MB domains can be selectively removed from the solution and quantified by

standard nucleic acid analytical techniques.

53

(a)
Theophylline Ell Tl

 

HHHHHHH.‘

.1

anemone-onw- -- ----q_-n
12 3 4 5 6 7 8 910

Figure 2.2: Impact of theophylline on MBS-MBC hybridization. (a) Theophylline at

 

concentrations of 0-2 mM (lanes 3-9) was incubated with MBSS followed by L
hybridization with radiolabeled MBCS. Signal from the upper band indicates an MB S-

MBC complex. Complexes with and without theophylline cannot be distinguished. Lane

1: MBC alone. Lane 2: 5 nM MBC with 2 mM theophylline. Lane 3-9: 5 nM MBC, 16

nM MBS and 0, 20 nM, 200 nM, 2 nM, 20 pM, 200 pM, and 2 mM theophylline. Lane

10: 5 nM MBC, 16 nM MBS and 2 mM caffeine. Image contrast was adjusted to improve

image clarity but band intensities used for fraction bound calculations were not altered. (b)

Quantification of band intensity from (a). Quantification was followed by calculation of

the fraction of signal in the upper band relative to the total of the upper and lower bands.

54

(b)

MBC fraction bound

0.8-
0.71
0.6:
0.5;
0.41
0.3-.

 

0.2'—r-7’

Figure 2.2 continued.

W
0.00 10‘1 10° 101 1o2 103

Theophylline concentration (nM)

55

To assess further if structural rearrangement of the MBS upon theophylline binding was
responsible for the change in MBS-MBC binding affinity, gel shift assays were

conducted in the absence and presence of 2 mM theophylline. Initially, a wide range of

MBS concentrations were chosen to estimate roughly the KB of MBS-MBC complex

formation. Subsequent experiments focused on the range surrounding the concentration

resulting in 50% binding for a more accurate evaluation of the K1). As expected, with

increasing MBS concentration, MBC fractional binding increases (Figures 2.3), yielding

KD values for MBS-MBC hybridization in the absence and presence of theOphylline of

9.9 nM and 1.0 nM, respectively. This change in affinity reflects the change in the
relative stability of the two MBS conformations due to theophylline binding. The basal

level of binding between the MBS and MBC that occurred in the absence of theophylline

was therefore predefined by their KD and the concentration of the two strands. It should

also be noted that even at saturating theophylline concentration, the MB domain was not
necessarily completely exposed to hybridize with its complement. This is reflected in a
much lower affinity between the strands than might be expected for hybridization of
sequences of this length [46]. Nonetheless, the significantly enhanced binding of the
MBC indicates increased accessibility of the MB sequence on the MBS, indicative of the

expected structural rearrangement.

56

(a)

 

MBS (nM) 0 0.2 0.6 1.2 2.4 5 10 20 40 80

(b)

-,,._..-, met than! “of“ Him-l H

 

MBS (nM) 0 0.4 1.2 2.4 4.8 10 20 40 80 160
Figure 2.3: MBS-MBC binding affinity characterization. EMSAs were used to
measure the KD for the complex in the absence (a) and in the presence (b) of 2 mM

theophylline. 1-2 fmol of MBC (final concentration of 33 pM-67 pM) was incubated with
increasing concentrations of MBS. (c) Quantification of MBS-MBC binding affinity. The
fraction of MBC bound was calculated. Data from at least three gels for each condition

were plotted simultaneously and fit with a single parameter. In the absence and presence

of theophylline, the measured KD values were 9.9 :l: 1.0 nM (R2=0.884) and 1.0 i 0.1 nM

(R2=0.957), respectively.

57

(C)

MBC fraction bound

1.0- O

0.8

l

0.64

0.4-

 

Figure 2.3 continued.

l- - No theophylline

    
 

data/Curve fit 0
/-—2 mM theophylline
data/Curve fit O
6
O
o 0 g
0 I
I
’ e
. o
0 Co

 
  
 

I 00

0% /

aaefief

10'1 10° 101 1o2 103
MBS concentration (nM)

 

58

It was now desired to test the possibility of accurate solution phase analyte detection
using the MBS rearrangement. The analytical method as described (Figure 2.1) requires
that the quantity of retained MBSS following separation by magnetic beads be
quantitatively reliable over a broad dynamic range. Given that MBSS are both the
detectors and the readout in this scheme, two methods can be used for quantification that
are convenient with nucleic acids, scintillation counting and qPCR. Using radiolabeled
MBSS, the quantitative reliability of the magnetic bead separation over the concentration
range of interest was first confirmed (Figure 2.4). Across a four order-of-magnitude span,
the separation and detection of labeled MBSS was quantitatively accurate as compared to
the initial input of labeled MBS. Thus, a standard curve built from the signal obtained
from the separated and washed MBSS does provide accurate and sensitive quantification

of the initial quantity of MBSS.

Using scintillation counting, the conditions for optimal sensing of theophylline with this
MBS-MBC design were determined. The signal was defined as the fractional binding of
MBS to MBC in the presence of saturating concentration of theophylline (2 mM) and
noise as the fractional binding in the absence of theophylline. By varying the
concentrations of MBS and MBC, changes in S/N (Figure 2.5 (a)) were determined
experimentally. The highest measured S/N was found to be 2.9 at MBS and MBC
concentrations of 10 nM. Synthetic riboswitches that acts as theophylline sensors in vivo
[3 8] or in in vitro translation [48] systems showed higher S/N values than we obtained in
our in vitro system. One explanation could be that, in these systems, enzymatic events,

such as translation, provide the readout for sensor function. The S/N can therefore be

59

improved, for instance, by the accumulation of proteins that occurs following each
signaling event. The relatively-lower frequency of events in the absence of signal also
limits the noise from these readouts, further increasing the S/N. Nonetheless, our system
provides i)_the sensitivity and precision required for accurate measurement of
theophylline at concentrations spanning the medically-relevant regime and ii) a basis for

exploring the important parameters of SSA-based biosensor design.

The signal-to-noise differences (S-N, Figure 2.5 (b)) at varying concentrations of MBS
and MBC were also determined. The largest value was achieved at 1 nM MBS and 50
nM MBC. Using these conditions results in a 2.1-fold increase in signal relative to 10 nM
MBS and 10 nM MBC, but it comes at the cost of a 3.8-fold increase in the noise,
resulting in a lower S/N. As S/N measures the relative discrimination of the two signals
and S-N measures the difference in their absolute levels, both metrics are valuable in
choosing the conditions for optimal sensing. We therefore summed the scaled values of
these two parameters to define an aggregate score for sensing (Figure 2.5 (c)). The
maximal aggregate score was, as with S/N, obtained at MBS and MBC concentrations of
10 nM. Thus, subsequent experiments to examine the quantitative capacity and sensitivity
for theophylline sensing by the technique were performed at these MBS and MBC

concentrations.

60

0 Initial input of MBS
106 I Recovered MBS

Scintillation Counts

10“ 10'3 10'2 10'1 10° 101 102
Radiolabeled MBS (nM)

Figure 2.4: Validation of the fidelity of magnetic bead separation. The measured
scintillation counts from a serial dilution of MBS concentration counted directly (circles)
and after binding by MBCS and elution from magnetic beads (squares) are shown. The
signal from the separated MBSS is consistently ~30 % of the signal of the directly-

counted MBSS.

61

(a)

 

log [MBC (nM)]

 

 

 

 

-1.0 -0.5 0.0 0.5 1 .0 1 .5

log [MBS (nM)]

Figure 2.5: Concentrations for optimal S/N, S-N and aggregate score. The fractional
MBS binding was calculated as counts from the eluted MBSS normalized to the total
counts recovered from the supernatant, washes, and elution. The fractional MBS binding
in the presence of 2 mM (signal) and 0 mM (noise) theophylline was measured from
which (a) signal-to-noise ratio (S/N), (b) Signal-to-noise difference (S-N) and (c)
aggregate score were calculated as described in the experimental section (Chapter 2.3.3).
Using MBS and MBC concentrations of 10 nM provided the maximal experimental S/N

and aggregate score.

62

Figure 2.5 continued.

(1))

 

 

log [MBC (nM)]

 

 

 

—1.0 -0.5 0.0 0.5 1.0 1.5
log [MBS (nM)]

63

(C)

log [MBC (nM)]

Figure 2.5 continued.

0.0 0.5 1.0
log [MBR (nM)]

 

1.5

 

 

 

 

With the analytical process validated, theophylline concentrations were measured using
the described technique. Radiolabeled MBS was incubated in the presence of serial
dilutions of theophylline ranging from 0.2 [AM to 2 mM. MBC was then added for 10 min,
the incubation time that provided the best resolution of bound and free MBS (data not
shown). Hence, the interaction of the MBS and MBC is kinetically-limited, rather than
equilibrium-limited, as at equilibrium the 20-nt MBC would outcompete the 15-nt cis
regulator strand and raise the background (lower S/N) relative to the time frame chosen
empirically for these experiments. Accurate quantification of theophylline concentration
from 0.5 [AM to 200 1.1M could be consistently achieved (Figure 2.6). This detection limit
is lower than other current sensing techniques for theophylline [49-51]. The dynamic
range is also greater than other recently-described aptamer-based theophylline sensing
techniques [33,52], and easily spans the range of clinically-relevant theophylline
concentrations (55-111 nM) [53]. Furthermore, in our experiments, only 3 pL samples
were used, less than the minimum volumes required for many theophylline measurement
techniques [54]. As caffeine can convolute the signal for theophylline sensors, the
function of the technique in response to caffeine was verified, and it was found that the
specificity of separation and detection was maintained at lower than 1 mM analyte
concentrations (Figure 2.6). Only 2 mM caffeine had measurable signal significantly
above background. This concentration is one order-of-magnitude higher than the
analytically important plasma caffeine range (25 to 100 nM) [52]. Therefore, the
designed theophylline sensing technique can selectively discriminate highly structurally-

similar molecules at appropriate and useful concentrations.

65

The advantages of radioactive readouts are their sensitivity and dynamic range. In many
circumstances, however, use of radioactivity is undesirable. With the designed MBS
sensors, it was important to determine if qPCR could be used as an amplifiable readout of
theophylline concentration. PCR-based readouts are particularly useful for parallel
applications, as demonstrated previously with MBS [41,42]. The fidelity of qPCR from
MBS templates was first confirmed (Figure 2.7), showing a linear response over six
orders of magnitude of template concentration. Theophylline concentrations were then
measured using qPCR as the readout (Figure 2.8). Theophylline was incubated with
unlabeled MBS and biotinylated MBC. The complexes were separated with streptavidin

magnetic beads, and eluted MBSS were reverse transcribed and amplified by qPCR. As

expected, the results showed decreasing Ct values with increasing theophylline

concentration. The detection limit determined by student t-test is 1 pM, and the dynamic

(125-10.8))

range is from 1 uM to 20 nM. The maximal signal-to-noise ratio was ~3.2 (2

similar to what was found with the scintillation counting readout_(Figure 2.6), and no

significant response was observed for caffeine over the same concentration span.

66

31'
a.

 

012- O Theophylline
I Caffeine

0.10:
- t

0.08- _.

0.06- ; é *

0.04-
i

it -+ .1

O_02 -.—.—/,I... . . . .......

0000101 10° 101 102 103
Analyte concentrations (pM)

*-

 

 

 

1

 

Fraction eluted

 

 

Figure 2.6: Theophylline and caffeine standard curves at optimal MBS and MBC
concentration. Using 10 nM MBC and 10 nM MBS, the standard curves for theophylline
(circles) and caffeine (squares) show a dynamic range for theophylline of over two orders
of magnitude, while maintaining nearly four order-of-magnitude discrimination against
the structurally-similar caffeine ("‘ denotes signal significantly higher than background, p
< 0.05). For simplicity, the fractional MBS binding at each analyte concentration was
calculated as counts of eluted MBSS relative to counts of total MBS input. Relative
values were equivalent to those normalized to the total counts recovered from the sum of

supernatant, wash and elution from beads.

67

Ct=-3.43log[MBS]-24.18
R2=0.987

    

 

WWW—WWW"
10'16 10‘15 10'” 10‘13 10‘12 10'11 101° 10'9
MBS template (M)
Figure 2.7 : Validation of qPCR assay for MBS detection. Serial dilutions of MBS
were reverse transcribed and quantified by qPCR. The curve fit shows a slope of -3.4
(R2=0.987), indicating nearly 100% amplification efficiency across the tested dynamic
range, covering six orders of magnitude from 24.2 zmol to 24.2 fmol of template. For

most points, the error bars lie within the plot symbols.

68

14-

 

O Theophylline
I Caffeine

I + I -
. i + .
11- ll} ; ..

10-—.—./,L.—.-mm.—.—.-m... WW
0.0 10'1 10° 101 102 103
Analyte concentrations (pM)

 

 

 

13-

a.

 

 

 

Figure 2.8: Quantification of theophylline concentration. After binding and magnetic
bead separation, RT-qPCR was performed on eluted MBSS. Concentration sensitivity for
theophylline (circles) was demonstrated across over two orders of magnitude in
concentration spanning the clinically-relevant theophylline concentration range (*
denotes signal significantly higher than background, p < 0.05). No significant response

was seen for caffeine (squares) over the same concentration span.

69

2.4.2 Analysis of MBS performance

Having determined the maximal S/N experimentally using two methods, it was of interest
to analyze further the variables limiting the resolution of our SSA-based method. Using
the measured values for the binding affinities in the presence and absence of theophylline,
the theoretical equilibrium behavior of the system was calculated. Assuming that, when
theophylline is present, it is fully-equilibrated with the MBS, the fractional binding of

MBS to the MBC was calculated at specified initial concentrations of MBS and MBC.

S/N, S-N, and the aggregate score were then calculated using a KD of 1 nM (saturating

theophylline condition) and a KB of 9.9 nM (no theophylline condition) (Figures 2.9).

The ratio (Figure 2.9 (a)) reaches a maximum approaching 10 at low MBS and MBC

concentrations. This ratio depends exactly on the 10-fold change in affinity between the

two conditions for our MBS design; i.e., KD becomes the dominant term in equations

(2.1) and (2.2). At limiting concentrations of both MBC and MBS, fractional binding

scales exactly as the inverse of KD. However, this resolution could only be achieved with

superb analytical sensitivity, due to the extraordinarily small quantities of analytes at

such concentrations (e. g., only 105 MBS molecules would be contained in a 3 uL sample

at a concentration of 10.4 pM).

7O

(a)

S/ N

 

OQVQUIAQN

is.)
1

log [MBC (nM)]

 

 

 

 

 

 

-4 -3 -2 -1 0 1 2 3

log [MBS (nM)]

Figure 2.9: Theoretical analysis of S/N, S-N and aggregate score. Using the KD values

for MBS-MBC complex formation in the presence of saturating levels of theophylline
and in the absence of theophylline, the equilibrium MBS fraction binding at varying MBS
and MBC concentrations was calculated using equation (2.2). S/N values (a), S-N (b) and
aggregate score (c) were then calculated as in Figure 2.5. (d) The scaled aggregate score

at 10 nM MBS shows a maximum at 10 nM MBC, as in the experimental results.

71

0))

log [MBC (nM)]

-3

Figure 2.9 continued.

 

-2 -1 0 1
log [MBS (nM)]

72

 

 

 

 

(C)

log [M BC (nM)]

Scaled aggregate score

0.7-
0.6-
0.5:
0.4-
0.3:
0.2!

0.1-

 

 

0.0 -

Figure 2.9 continued.

Score

 

- 0.2
_ 0.4
- 0.6
— 0.8
-fj 1.0

 

 

 

-4 -3 -2 -1 O 1 2 3

log [MBS (nM)]

0.1 0.3 1 10 30 100

3
MBC (nM)

73

As the concentrations increase, especially above the K1) values, the concentrations

dominate in equation (2.1) and (2.2), minimizing the impact of the affinity difference and
causing the ratio to approach 1 (Figure 2.9 (a)). At 10 nM MBS and MBC, the theoretical
S/N value is 1.9, in good agreement with the experimentally-derived value of 2.9. The
discrepancy likely arises from the fact that our experimental data were not taken at the
equilibrium condition. Because intermolecular hybridization affinity can scale with
association rate [46], short time points would therefore improve the ratio relative to the
equilibrium condition. The correlation of the experimental and theoretical data across the
concentration ranges for low concentrations of MBC and MBS is not as strong. This is
likely due to technical differences between the calculations and the experiments. The
theoretical plots do not account for the lower detection limits for scintillation counting. In
addition, to maximize the true signal eluted from the beads, the complexes were washed,
which immediately shifts the equilibrium to reduce the number of bound MBSS that are
recovered. On a fractional basis, the loss of bound MBSS during the wash steps will
reduce the signal recovered at low MBS and MBC concentrations far more dramatically
than at high concentrations. Hence, both signals approach the detection background. This
is likely the reason the experimental ratios decline at lower than 10 nM concentrations.
While the S/N achieved experimentally was sufficient to provide sensitive and accurate
theophylline detection, the theoretical analysis suggests that improvements to the

resolution of the technique may still be achievable.

Examining the theoretical difference, it peaks at MBC concentrations between the KB

values, at limiting MBS concentrations (Figure 2.9 (b)). This is expected due to the

74

maximum absolute distance between the binding curves at these conditions (similar to the
separation between the curves along the vertical line at [MBS] z 3 nM on Figure 2.3 (c)).
The experimental S-N showed a peak value at MBC and MBS concentrations (50 nM and
1 nM, respectively) higher than those of the theoretical peak, but the peak experimental
S-N value of 0.18 is in line with the theoretical value of 0.2 at these concentrations. For
the aggregate score, the experimental peak score also occurred at MBS and MBC
concentrations both above those of the theoretical peak (compare Figures 2.5 (c) and 2.9
(c)). However, looking only at 10 nM concentration of MBS, we see the maximum
aggregate score at 10 nM MBC (Figure 2.9 (d)), to some degree supporting our
experimental results. For the experimental conditions, the theoretical aggregate score was
~60% of the global theoretical maximum, another indication that technical improvements

to our analytical strategy may be possible.

2.4.3 Thermodynamic constructs for predicting MBS function

Typically, to design an SSA biosensor, multiple designs are tested to identify which has
the most desirable properties by some metric, such as S/N [24]. Our analyses argue
strongly that the experimental behavior of the MBS can be reliably captured by a
relatively simple theoretical model in which only the affinities of the SSA for its

complement in the presence and absence of target are required. The analysis described

above relies on the experimentally derived KD values to predict the expected S/N. As

minimum free energy structures have proven useful in unimolecular nucleic acid sensor
design [55] and interaction free energies between molecules tend to be at least relatively

correct as compared to experimental data [46], we also preliminarily investigated whether

75

a thermodynamic construct using minimum free energy structure predictions, based on

our previous work [56], would allow us to predict KD values as another means of

evaluating alternative designs for the MBS and MBC that would provide higher S/N

(Figure 2.10 and Table 2.1).

Improvements in S/N should result from increases in the affinity of the MBS for the
MBC in the presence of analyte relative to the affinity in the absence of analyte. To avoid
testing every possible MBS and MBC design experimentally, a thermodynamic construct
was defined to calculate the predicted affinities of different MBC and MBS designs. In
solutions containing theophylline, the MBS will exist largely in the minimum free energy
conformation available to the MBS alone (Figure 2.10, construct 1) or that available to
the MBS bound to theophylline (Figure 2.10, construct 2). Changes in the experimental

SfN should then mimic changes in the relative affinities calculated for the two constructs.

76

l AGOUNI

J” '
(:munmmu .

5' 3'
W 53:, _; .
11111.\y3' +
51
0
[AG W2

MBSUN + mew, AG,

AGOOHI

 

 

 

1AG°051
AGOTOI
K D1
0— AGOTog
KD2
iAG°osz

HybridUN

| AG°RE1

lllllllllllllllfllll q.
0 J

r”.

.
“.I

5'

 

 

0H2

o——— 3'

IAG°RE2
HybridUN

Figure 2.10: Thermodynamic constructs for MBS-MBC binding in the absence and

presence of theophylline. The ratios of the predicted KD values are representative of the

expected S/N in the experimental technique.

77

Table 2.1: Thermodynamic Predictions for MBS design modifications.

 

 

 

0 _
Modifications to MBS AGOUNI AGOUNZ fAG TO; KDl /KD2
AG UNl'AG UN2
Initial sequence 3 1.8 3 1.1 0.7 3 .21
Stabilize MB by adding 4 bp 36.1 35.4 0.7 3.21
Stabilize MB by adding 5 bp 39.2 38.4 0.8 3.79
Stabilize MB by adding 10 bp 49.1 48.4 0.7 3.21
Destabrlrzrng 1:1: by deleting 1 28.5 27.8 0.7 3.21
Destabrlrzrng 1:1: by deleting 5 22.1 21.4 0.7 3.21
Destabrlrzrng :Eeby mutating 1 25.8 25.1 0.7 3.21
Lengthening the aptamer 3 stem 31.8 31.5 0.3 1.65
by 2 bases
Shortening the aptamer 3 stem 31.8 31.4 0.4 1.95
by 5 bases
Extending the 5' MB by G 32.2 31.1 1.1 6.25
Extending the 5' MB ELGGG 33.5 31.1 2.4 54.60

 

78

Using mfold to perform the structural calculations (in a similar fashion to our earlier
work) [56], the free energy for MBS-MBC hybridization was calculated in the absence

and presence of theophylline as follows:

AGOTO = AG°UN + AGOOS + AGOQH + AGORE

where: AGOTQ = the overall free energy change due to the structural rearrangement

upon MBC hybridization

AGOUN = the cost to unfold the native MBS structure
AGOOS = the cost to unfold the native MBC structure
AGOOH = the energy released upon intermolecular MBS-MBC hybridization

AGORE =the energy released upon rearrangement of the complex to the

available conformation with the lowest free energy

Predicted KD values for the interactions can then be calculated from:
AGOTO = RTanD

For a given MBS-MBC pair, AGOOS, AGOOH, and AGORE are equal, therefore

manipulations like varying the length or sequence of the MBC does not alter the
predicted relative affinities of the constructs and is not expected to improve the S/N in

practice. The relative affinities, and, hence, the expected S/N, are then defined only by

79

the change in the cost of unfolding the construct with theophylline bound (for the

construct used in developing the biosensor, AGOUN1 = 31.1 kcal/mole) vs. that without
theophylline (AGOUNZ = 31.8 kcal/mole). For our MBS-MBC pair, the resulting

predicted KD values, 0.76 nM and 2.4 nM, respectively, are in reasonable agreement with

our measured values of 1.0 nM and 9.9 nM, supporting this analysis as a valid method for

screening MBS and MBC manipulations.

Though we did not expect, based on our analysis, that changing the MBC length would
alter the S/N in an equilibrium circumstance, we also tested this experimentally. As stated
in the experimental section (Chapter 2.3.1), we found that using MBC sequences shorter

than 14 nt did not provide adequate signal, as might be expected from the higher (less

negative) AGOT values due to higher AGOOH values. Sequences longer than 20 nt had
0

high background binding due to the relatively stronger intermolecular hybridization

(more negative AGOOH values) with increasing MBC length as compared to the

intramolecular structural stability (AGOUN values). Ultimately, a 20 nt MBC sequence

was chosen, because the detection range provided by this sequence effectively balances

the need for adequate signal while minimizing background,
Computationally, we also modified other domains of the MBS and predicted the change

in predicted affinity (Table 2.1). When the aptamer stem, which is involved in both

theophylline binding and barcode displacement, was modified, the results showed that

80

lengthening the aptamer stem increased the cost of unfolding the 3'-tail of the

theophylline bound conformation, reducing the predicted affinity. Shortening the stem
length reduced the displacement of the MB portion of the construct, again decreasing the
predicted affinity. As such, neither case was anticipated to improve MBS function

experimentally.

However, we did observe that stabilizing the MB stem by extending the 5'-end of the MB

sequence can improve the relative affinity ratio. We synthesized an MBS with 3GS at the

5'-end, which is predicted to improve the relative affinity ratio by nearly 20-fold. A

theophylline dose response assayed by electrophoretic mobility shift at equilibrium
showed a S/N of approximately 9, an improvement over our initial design, in line with
the calculated results. However, the improvement was due to a reduction of noise rather
than an increase in signal. Presumably, the increased structural stability of the MBS
reduced the association kinetics for the MBS-MBC pair. Therefore, the non-equilibrium
readout using the magnetic beads did not have sufficient signal using this construct to get
a reproducible signal. This then becomes a readout problem rather than a sensor design
problem. The construct described allowed us to best demonstrate our technique with our

current protocols.

2.5 Conclusions

A theophylline quantification technique was designed using an SSA in combination with

scintillation counting or qPCR. In this manner, the SSA converted the analyte signal into

81

a nucleic acid signal for subsequent amplification and quantification. Analytical accuracy
and specificity were readily achieved by this technique. Analysis of the controlling
parameters of the system indicated that fiirther performance enhancements may be

achievable through careful SSA design and protocol optimization.

82

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88

CHAPTER 3 DEVELOPMENT OF A DUAL
APTAMER—BASED MULTIPLEX PROTEIN
BIOSENSOR

Portions reproduced from: Xie, S. and Walton, S. P., “Development of a dual aptamer-
based multiplex protein biosensor”, Biosensors and Bioelectronics (2010),

doi:10.1016/j.bios.2010.04.034 with permissions from Elsevier.

89

3.1 Abstract

Parallel biosensors for proteins are becoming more essential for the thorough and
systematic investigation of complex biological processes. These tools also enable
improved clinical diagnoses relative to single protein analyses due to their greater
information content. If implemented correctly, affinity-based techniques can provide
unique advantages in terms of sensitivity and flexibility. Aptamers are increasingly being
used as the affinity reagents of choice for protein biosensing applications. Here, we
describe the development and characterization of an aptamer-based method for parallel
protein analyses that relies on recognition of the target protein by two unique aptamers
targeting different epitopes on the protein. It was predicted that this approach would have
improved dynamic range and sensitivity, relative to the SSA-based technique. Our results
show that the technique achieved simultaneous and quantitative detection of thrombin
and platelet-derived growth factor-BB (PDGF-BB) with high specificity both in buffered
solutions and in serum samples. Moreover, the empirical biosensing performance agreed

with expectations based on theoretical calculations.

90

 

3.2 Introduction

Improvements in clinical, laboratory, and biodefense applications rely on the continuing
development of biosensing techniques for various biomolecules. In particular,
considerable current work is focused on novel tools for parallel biosensors. While nucleic
acid detection tools are relatively mature at this point, further evolution in the area of

protein biosensing technologies, especially in a multiplex fashion, is required.

While classical parallel protein analytical techniques, such as two-dimensional gel
electrophoresis followed by mass spectroscopy (2DE/MS) [1], will continue to be
valuable tools for discovery-oriented protein measurements, affinity-based techniques
have emerged as popular approaches for the detection of single and focused sets of
proteins [2]. Antibody-based multiplex sensing assays have been developed in various
planar and bead array formats and are becoming accepted methods for protein expression
and interaction studies [3-5]. However, the function of these arrays may be impaired by
the high local concentrations of surface-bound antibodies and non-specific interactions
between the substrate and the antibodies or analytes [6]. Additional limitations of
antibody-based proteomics methods, in general, include batch-to-batch variation in
antibody quality and the cost of antibody generation and purification [7]. Therefore,
novel approaches are being sought that can complement or expand existing antibody-

based techniques.

91

Aptamers are gaining popularity as alternative affinity reagents for biosensing
applications [8-16]. Aptamers are single-stranded nucleic acids that bind other molecules
with high specificity and affinity [17,18] and may provide unique advantages for
multiplex analytical applications [6,19,20]. Aptamers are selected in vitro and in theory
can be selected to bind almost any target with automated and high-throughput techniques
for aptamer selection having been demonstrated [21]. Furthermore, aptamers are nucleic a“
acids rather than proteins and therefore can be easily synthesized commercially or
enzymatically with high reproducibility and minimal batch-to-batch variation [22,23],

which is valuable for the development of reproducible biosensors.

 

The majority of current aptamer-based biosensor designs are specific for a single analyte
[14,24-32]. While chip-based aptamer arrays for multiplexed protein measurements are
currently in development both by academic and commercial groups [19,20,33-3 7],
Aptamer-based sensing combined with capillary electrophoresis has been used to
measure four proteins simultaneously [3 8]. Nonetheless, the development of additional
solution-phase and bead-based assays is still valuable, as they can potentially be
customized for specific sets of target proteins and may have sensitivity advantages over

current techniques.

In this work, we describe proof-of-concept development of a solution-phase biosensor for
multiplex protein measurements that relies on dual-aptamer analyte recognition. We have

shown previously that the detection dynamic range based on aptamer conformational

92

rearrangement can be limited [16]. We hypothesized that altering the sensing design to
use increased aptamer concentrations, two specific recognition events, and an affinity-
based separation would have improved sensitivity, specificity, and dynamic range
compared to the structure-switching aptamer approach. Applying our new technique to
measure the concentrations of thrombin and platelet-derived grth factor (PDGF-BB),
we found a reproducible, linear dynamic response to analyte concentrations over a three
order-of-magnitude range of concentration. For both proteins, accurate and reproducible
measurements were obtained using multiple readouts in both buffered solutions and in

complex mixtures.

3.3 Experimental section

3.3.1 Proteins and aptamer sequences

Thrombin was purchased from Haematologic Technologies (Essex Junction, VT). PDGF-
BB was purchased from R & D Systems (Minneapolis, MN). Haptoglobin, fibrinogen, C-

reactive protein and lysozyme were from Sigma Aldrich (St Louis, MO). The sequences

for the thrombin sensing and capture aptamers were 5'-

GATGTCCACGAGGTCTCTA TTGCA TT C GCA C TT C C GA ITTTTTCAGTCCGTGGTA

GGGCAGGTTGGGGTGACTCGTACGCTGCAGGTCGAC-3' (MB-T, 91 nt, italicized:

MB sequence) and 5'-
GTGACTACTGGT‘TGGTGAGGTTGGGTAGTCACAAATTTTTTTTTT-Bio-3' (Bio-

T), respectively. The sequences for the PDGF-BB sensing and capture aptamers were 5'-

93

GATGTCCACGAGGTCTCTGCGGCA T CAA CAA T C T C GAA TTTTTTACTCAGGGCA

CTGCAAGCAATTGTGGTCCCAATGGGCTGAGTATCGTACGCTGCAGGTCGAC-
3' (MB-P, 106 nt, italicized: MB sequence) and 5'-
TACTCAGGGCACTGCAAGCAATTGTGGTCCCAATGGGCTGAGTATT'ITTT-Bio-
3' (Bio-P), respectively. Each of these aptamers was derived from sequences available in

the literature [32, 39-41]. Aptamer-protein affinities were measured by filter binding

assays. Capture aptamers were synthesized with 3’-biotins spaced from the aptamer

sequence by oligo(dT) sequences. Primer sequences specific for MB-T were 5'-
ATTGCATTCGCACTTCCGAT-3' 05',» and 5'-AGTCACCCCAACCTGCCCTA-3'
(T3'p). Primer sequences specific for MB-P were 5'-GCGGCATCAACAATCTCGAA-3'
(P5'p) and 5'-ATACTCAGCCCATTGGGACC-3' (P3'p). Aptamer specific primers were
used for single-protein and multiplex qPCR readouts. The 5' and 3' primers were chosen

to be specific to the MBS and to the aptamer 3' ends, respectively. Using the

hybridization server on mfold [42], both sets of template specific primers were confirmed

not to cross-prime the other sensing aptamer template (data not shown). Universal primer

sequences for both sensing aptamers were 5'-GATGTCCACGAGGTCTCT-3' (U5'p) and

5'-GTCGACCTGCAGCGTACG-3' (U3'p). Universal primers were used for multiplex

experiments using electrophoresis readouts. All oligonucleotides were synthesized and

purified from Integrated DNA Technologies, Inc (Coralville, IA).

94

3.3.2 Dual aptamer sensing assays

Prior to use, aptamers were annealed at 95°C for 3 min and cooled to room temperature

over a period of 30 min. Proteins and annealed aptamers were mixed in binding buffer

(50 mM Tris-HCl (pH 7.5), 100 mM NaCl, 1 mM MgC12, 0.1% BSA, and 5 11ng

poly(dI-dC)) in a total volume of 30 pL. Binding reactions were incubated at room
temperature for 30 min followed by the addition of streptavidin coated magnetic beads
(Dynal/Invitrogen, Oslo, Norway) for an additional 30 min at room temperature. A
magnet was then applied to retain the beads, and the supernatant was removed. The beads

were then washed three times with 100 pL of wash buffer (binding buffer plus 0.02%

 

Tween 20). Retained sensing aptamers were eluted from the magnetic beads in 100 pL of
water at 95°C for 5 min. Some or all of the recovered sensing aptamers were amplified by

PCR.

Experiments determining the optimal concentration of thrombin or PDGF-BB sensing
aptamer were performed in binding reactions with varying concentrations of sensing
aptamer (5, 50, 250, 500, 1000 and 2500 nM) and 500 nM of capture aptamer in the
presence and absence of 500 nM protein. After binding and separation as described above,

sensing aptamers were eluted and amplified by qPCR. The metric for optimal sensing

was ACt where ACt = (Ct value in the absence of protein) - (Ct value in the presence of

protein). Higher ACt values indicate better recovery of sensing aptamers with minimal

background.

95

3.3.3 Aptamer affinity characterization

Affinities of aptamers to the target proteins were characterized with a filter binding assay.

Aptamers were 5'-labeled with 32P-y-ATP using T4 polynucleotide kinase (Ambion,

Austin, TX) followed by purification by Quick Spin Columns (Roche, Indianapolis, IN).
Aptamers were added to serial dilutions of target proteins in binding buffer at 1 nM final
concentration and incubated at room temperature for 30 min. The solutions were passed
through a dot blot apparatus with a nitrocellulose membrane on top of a nylon membrane.
The membranes were exposed to phosphor screens overnight, and the amount of
radioactivity of each dot was quantified by Storm 860 (Amersham Biosciences/GE
Healthcare, Piscataway, NJ). Fractional binding at each protein concentration was

calculated as the dot intensity on the nitrocellulose membrane divided by the total dot

intensity of the two membranes. The dissociation constant (KD) of the aptamer protein

interaction was determined by curve fitting using equation (3.1)

P0
f- K1) +Po (3.1)

where f is the fraction bound as calculated from the quantified dot blot image. P0 is the

initial concentration of the protein added to each binding reaction.

3.3.4 PCR and qPCR
For electrophoresis readout experiments, eluted sensing aptamers were added with

universal primers U5'p and U3'p at 500 nM each and amplified for 20 cycles with iQ
Supermix (Bio-Rad, Hercules, CA) in 50 uL reactions. The PCR program was 94°C for

96

 

45 s, 54°C for 45 s and 72°C for 1 min. PCR products were analyzed by electrophoresis

(5 [LL mixed with 2 uL of loading buffer and electrophoresed at 100 V for 1 hr in 10 %
acrylamide gels). Gels were stained with SYBR Gold nucleic acid gel stain (Invitrogen,
Carlsbad, CA) and visualized in a Molecular Imager ChemiDoc XRS System (Bio-Rad,

Hercules, CA).

 

F
For qPCR readout experiments, primers specific for sensing aptamers (e. g. TS'p and T3'p
for MB-T; P5'p and P3'p for MB-P) were added at 500 nM each and amplified for 40

:1
cycles with iQ SYBR Green Supermix (Bio-Rad, Hercules, CA) in 50 [IL reactions using I1:

the same PCR program as above. Threshold cycle numbers (Ct) for deriving sensing
aptamer qPCR standard curves were generated from PCRs containing MB-T or MB-P
template serially diluted fiom 3 X 108 to 3000 or 300 initial template molecules. In all

standard curves and the correlation analysis, error bars represent at least duplicate
experiments with each experiment assayed twice by PCR (four total PCRs). Signal-to-

noise ratio was calculated as the fold difference in signal obtained from the maximal

signal relative to the background. For qPCR data, this is 2 raised to the power (Ct value

for background - Ct value for maximal sensitive concentration)

3.3.5 Detection in bovine and human sera

Binding reactions were performed as described above except in undiluted fetal bovine

serum (FBS) (GIBCO, Invitrogen, Carlsbad, CA) or human serum (Sigma, St. Louis, MO)

97

with thrombin and PDGF-BB spiked in. After separation and elution, the recovered

aptamers were amplified with sensing aptamer specific primers to derive Ct values.

3.4 Results and discussion

In developing aptamer-based biosensors, or any affinity-based biosensor, sensing of the
analyte depends on the complex formation between the analyte and the detector molecule,

in our case an aptamer. Because the formation of the l-to-l complex is an equilibrium

process, the degree to which complex forms is controlled by the affinity (e.g., the K9) of

 

the interaction and the concentrations of the analyte and aptamer. For significant complex

formation to occur, either the analyte or aptamer concentration must be in the range of or
above the K9 of the interaction. Thus, a critical decision must be made regarding the
detecting aptamer concentration (Figure 3.1).The formation of the aptamer-analyte
complex is an equilibrium reaction (equation (3.2)) with the dissociation constant KD

(equation (3.3)).

 

 

Apt + Ana 4 KB > Apt-Ana (3.2)

 

= [Apt][Ana]

[Apt-Ana] (33)

The concentration of the bound complex can be calculated by solving the quadratic
equation (3 .4), by expressing the concentrations of the free aptamer and analyte as their

initial concentrations minus the concentration of the bound complex.

98

 

[Apt - Ana] =%[([Apt]0 + [Ana]0 + KD)—\/([Apt]0 + [Ana]0 + KD )2 —4[Apt]0[Ana]0]

(3.4)

where [Apt - Ana] is the concentration of the aptamer-analyte bound complexes. [Apt]0

and [Ana]0 are the initial concentration of the aptamer and analyte added to each binding

reaction.

 

99

 

 

 

 

 

 

(a)
1000

g; 100 -

>< 500
9

g- 80 -

o 100

o 60
'g /

3 l“-
.8 4o - 50
q— .

O 4

c
.9 20 -

g \ 10 L?

C 0 _ 1

g . . 1 ' \ . 0.1
8 0 20 40 60 80 100

[Analyte] (nM)

Figure 3.1: Sensing response with respect to detecting aptamer concentration. (a)
Shown is the concentration of analyte-aptamer complexes at varying analyte and aptamer

concentrations. (b) Shown is the fraction of analyte bound at varying analyte and aptamer

concentrations. Simulations were performed using equation (3.4), assuming KD=10 nM

and [Analyte]0 from 1 to 100 nM. [Aptamer]0 values used in the simulation are listed

next to the corresponding curves. Solid arrows indicate increasing detecting aptamer

concentrations.

100

Figure 3.1 continued.

 

 

 

(b)
q 1000 I
1.0 500 -

'0 10.

r:

:3 0.8- 50

O

.0

(D

“S, 0.6-

2 10

cu

q_ 0.4“

O

8

:7:- 0.2-

o

E 1

L1.

0.0“ 0.1_ ]

 

 

 

F I ' I ' I ' I ' I

0 20 40 60 80 '100'
[Analyte] (nM)

101

Using an aptamer concentration below the analyte concentration (limiting aptamer), the
concentration of aptamer-analyte complexes cannot exceed the sensing aptamer
concentration. The dynamic range of the technique is limited, and the total signal
recovered saturates at the limiting aptamer concentration (Figure 3.1 (a)). In contrast,
when the sensing aptamer concentration is in excess relative to the analyte concentration,
the concentration of analyte-aptamer complexes increases linearly with analyte T
concentration over the entire range of analyte concentrations. The fraction of the analyte
that is bound for the limiting aptamer case decreases as the concentration of analyte

increases, while it remains close to 1 when the aptamer is in excess (Figure 3.1(b)). Thus,

 

the relative information content of the excess aptamer approach (fraction of the analyte
bound with excess aptamer relative to the fraction bound with limiting aptamer) far
exceeds that of the limiting aptamer approach. Therefore, using excess sensing aptamer
provides the potential for greater dynamic range (a) and higher sensitivity (b) relative to
limiting aptamer approaches. Both the dynamic range and total signal are maximized

over the entire analyte concentration range (Figure 3.1).

That said, in techniques where the aptamers are labeled for detection, for instance by
fluorescence, the feasibility of using excess aptamer is usually reduced by the presence of
a high concentration of unbound aptamer whose signal masks that of the analyte-aptamer
complexes. The dynamic range and sensitivity advantages of the excess aptamer design
must then be weighed against the need for a stringent separation, wherein the aptamer-
analyte complexes (the signal) are potentially less than 1% of the total aptamers present

(the background) (Figure 3.1). As discussed above, the necessary separation can be

102

achieved by methods such as microarrays or electrophoresis [3 6,3 8]. Here, we describe a
method where we have combined aptamer-based separation and aptamer-based
detection, using excess aptamers, to design a parallel protein analytical technique (Figure.
3.2) with specificity for the target protein and the potential for excellent sensitivity and

broad dynamic range.

 

F...
In our technique, one aptamer, termed the capture aptamer, is biotinylated to immobilize .
the protein onto streptavidin-coated magnetic beads, while the other aptamer, termed the

sensing aptamer, is used for quantification of the protein concentration by PCR,

microarray, or any other nucleic acid readout. Detection of the sensing aptamer rather #-

than the protein directly leverages the simplicity of multiplex PCR [43,44], through the
incorporation of universal primer sequences on all sensing aptamers. The use of PCR also
provides straightforward signal amplification, a unique advantage of nucleic acid
analytical approaches [4]. In addition, we have incorporated molecular barcodes (MBS)
into our sensing aptamers which permits facile parallel detection of the sensing aptamers
by sequence alone, as has been shown using oligonucleotide microarrays [45-47].
Readout of the concentration of the MB sequence then serves as an indirect readout of the
concentration of the protein target. In this study, we demonstrate proof—of-concept use of

our technique for multiplex detection of thrombin and PDGF-BB (Figure 3.2).

103

U5' p M81 Apt1A U3’p

..........

we" MB-T

‘ .......... .—

 

\
\
[c
U]
['0
a
m
1N
>
39.
N
>
C
u:
"U

..........

 

----------

<7 peR
‘
Oligo(dT) Apt 28 .m—

‘ .2: Bio-P _=.=_
\\\\\

— Bio-T
Oligo(dT) Apt 1 B

 

  
  

Figure 3.2: Dual-aptamer detection schematic. Thrombin and PDGF-BB are
represented by triangles and double-ellipses, respectively. The sensing aptamers (MB-T,

MB-P) containing thrombin aptamer (Apt 1A) and PDGF-BB aptamer (Apt 2A) are each

labeled with an MB tag (MBl and MBZ) and flanked by a pair of universal primers (5'p

and 3'p). Universal primers are labeled as solid arrows and aptamer specific primers are

labeled as dashed arrows. Capture aptamers (Bio-T and Bio-P) containing thrombin
aptamer (Apt 1B) and PDGF-BB aptamer (Apt ZB) are biotinylated to be attached to
streptavidin-coated magnetic beads. Oligo(dT)s are used as the spacer between the
aptamer sequences and the beads. After separation, eluted sensing aptamers are amplified

by multiplex PCR and quantified.

104

The fidelity of the sensing aptamers as qPCR templates were first confirmed using serial
dilutions of sensing aptamers and template specific primers (similar results were obtained
using universal primers) (Figure 3.3). PCR for both templates showed the expected log-
linear response over at least a five order-of-magnitude dynamic range (Figure 3.3).
Responses plateaued outside of the regions shown on the plot (data not shown). The

quantitative response of sensing aptamer concentration relative to protein concentration

was then examined. To identifyI the optimal conditions for protein sensing, ACt values

(defined as the difference in Ct value in the presence and absence of 500 nM protein)

were measured at various sensing aptamer conditions for both proteins (Figure 3.4).
Capture aptamer concentrations were fixed at 500 nM, which should bind 46% of the
thrombin and 81% of PDGF-BB based on the measurement of aptamer-protein affinities

(Figure 3.5 and Table 3.1). At varying sensing aptamer concentrations, binding reactions

were set up, the beads were washed, and the retained sensing aptamers were eluted. Ct
values were derived experimentally, and ACt values were calculated for each protein. A
maximum ACt of 16.1 was observed for thrombin at 250 nM sensing aptamer (MB-T)

concentration. Similarly, a maximum ACt of 7.5 was observed at 50 nM sensing aptamer

(MB-P) concentrations for PDGF-BB. In each case, higher concentrations of sensing
aptamer resulted in higher non-specific retention of sensing aptamers in the absence of
protein (Table 3.2), while lower concentrations had reduced sensitivity in the presence of
protein. The larger maximal AC, observed for thrombin versus PDGF-BB is a result of

higher background using the PDGF-BB aptamers (Table 3.2).

105

(a)
30 4

Ct=38.5-3.3 log (MB-T molecules)
R’=0.998

15-

 

 

 

10- u it!!!" 1 uuun
103 104 105 10° 107 10°
Number of MB-T molecules

Figure 3.3: Validation of qPCR amplification of sensing aptamers. Serial dilutions of

MB-T (a) and MB-P (b) were amplified by qPCR with template-specific primers. The

curve fit for both template showed a slope of -3.3 (R2=0.998), indicating an amplification

efficiency of nearly 100% over the tested dynamic range, covering five orders of
magnitude from 5 zmol to 0.5 frnol of MB-T in (a), and six orders of magnitude from 0.5

zmol to 0.5 fmol of MB-P in (b).

106

 

(b)

0*.

25-
20-
15-

10-

30-

  
   

 

Figure 3.3 continued.

Ct=37.3-3.3log(MB-P molecules)

R2=0.998

102 103 104 10° 10° 107 108

Number of MB-P molecules

107

 

 

20 — m Thrombin
E PDGF-BB

 

 

 

 

 

g\\\\\\\\\\\\\\\\\\\\\\\\\\es-

 

RW-
W

 

 

 

 

 

 

0 .r.// . j

5 50 0 5 0 1000 2500
[Sensing aptamer] (nM)

 

N
01
O

Figure 3.4: Concentrations of sensing aptamers for optimal signal dynamic range.
ACt values were derived as described in the experimental section (Chapter 3.3.2) for

thrombin (hatched columns) and PDGF-BB (open columns). Subsequent studies with the

technique used concentrations of MB-T and MB-P of 250 nM and 50 nM, respectively, to

maximize AC,[ and minimize reagent consumption.

108

(a)

 

 

 

 

1,0- E1 / MB-Tdata/Curvefit
. O /- - Bio-T data/ Curve fit
0.8-
'o
S
g 0.6-
r:
.9
*5 0.44
‘3
LI.
0.2-
[:1
'3 ea
00. 0 ...5’1 - - ......2. .......3. .....-4
10 10 10 10 10

[Thrombin] (nM)

Figure 3.5: Affinity characterization of thrombin and PDGF-BB aptamers.

Radiolabeled aptamers were added to serially diluted thrombin (a) or PDGF-BB (b) and

separated with dot blot assays. KD values were derived with non-linear least square

fitting as described in the experimental section (Chapter 3.3.3).

109

 

(b)

1.0-

0.8-

0.6-

0.4+

Fraction bound

0.2-

0.0-

Figure 3.5 continued.

I l- - MB-P data/Curve fit
0 /-—- Bio-P data/Curve fit

 

 

[PDGF-BB] (nM)

110

Table 3.1: KD measured for each aptamer for its target protein.

 

Aptamer MB-T Bio-T MB-P Bio-P
KD(nM) 140.1 310.5 34.7 21.6

 

(The MB-T and Bio-T sequences contain unique aptamer domains while MB-P and Bio-P

have the same aptamer domain.)

111

Table 3.2: Ct values used to generate Figure 3.4.

 

MBJI‘ Background Ct Ct at 500 nM protein A Ct Ave STDEV

 

 

 

 

 

 

 

 

 

 

(nM)R1R2R3R4R1R2R3R4R1R2R3R4CtCt

5 27.7 30.9 30.7 28.5 22.9 22.7 22.1 20.8 4.8 8.2 8.6 7.7 7.3 1.7
50 26.5 29.5 29.1 27.8 13.9 14.8 12.8 12.8 12.5 14.7 16.3 15.0 14.6 1.6
100 23.4 23.9 25.6 26.6 12.4 13.0 11.5 10.9 11.0 10.9 14.1 15.8 12.9 2.4
250 26.1 26.3 27.6 25.6 10.1 12.0 9.4 9.8 16.0 14.3 18.2 15.8 16.1 1.6
500 25.1 25.0 25.6 23.5 11.0 10.5 10.2 9.1 14.1 14.5 15.4 14.4 14.6 0.6

1000 24.9 25.5 23.9 24.0 9.8 11.3 9.1 9.3 15.2 14.3 14.8 14.7 14.7 0.4
2500 23.9 24.9 23.9 24.8 10.7 11.0 10.2 9.4 13.3 13.9 13.7 15.4 14.1 0.9

 

 

 

 

 

 

 

 

MB-P Background Ct Ct at 500 nM protein A Ct Ave STDEV

 

 

 

 

 

 

 

 

 

 

JnM)R1R2R3R4RlR2R3R4R1R2R3R4CtCt

5 24.8 25.6 23.7 24.8 18.8 20.3 16.1 16.6 6.0 5.4 7.6 8.2 6.8 1.4
50 20.1 21.2 19.4 20.6 13.0 14.7 11.6 12.0 7.1 6.5 7.8 8.6 7.5 0.9
100 18.4 19.9 18.3 19.9 13.5 13.9 12.4 11.4 4.9 6.0 5.9 8.5 6.3 1.5
250 16.4 17.9 16.5 17.8 11.6 13.3 9.7 11.1 4.8 4.5 6.8 6.7 5.7 1.2
500 14.9 16.4 14.6 16.2 10.8 12.6 9.4 9.7 4.1 3.8 5.2 6.5 4.9 1.2

1000 14.8 15.8 14.1 15.2 11.0 12.7 9.6 9.9 3.8 3.2 4.6 5.3 4.2 0.9
2500 14.3 14.7 13.6 13.6 11.8 12.3 10.8 9.9 2.5 2.3 2.8 3.7 2.8 0.6

 

 

 

 

 

 

 

 

(Concentrations highlighted in bold were determined to be the optimum.)

112

The optimal concentrations derived above (as selected from the concentrations with the

maximum ACt value) were then used to generate standard curves for single protein

measurements (Figure 3.6). Serial dilutions of thrombin and PDGF-BB were added to
binding reactions containing capture and sensing aptamers. After separation, the eluted
sensing aptamers were quantified by qPCR. The technique showed sensitivity down to 1
nM protein concentration for thrombin and 10 nM for PDGF-BB. Sensitive detection
extended over at least a two order-of-magnitude dynamic range. A linear dose response

was observed over the sensing dynamic range, resulting a signal-to—noise ratio of ~ 24000

(25.4-10.9) (19.3-12.9)

(2 ) for thrombin, and ~84 (2 ) for PDGF-BB, an improvement over

single aptamer approaches [48-51] as well as over a related surface-based aptamer
approach [14]. While maximizing dynamic range is valuable for any sensing technique,
other factors, such as specificity for the analyte of interest, must be examined in

technique development.

We then examined whether the concentration sensitivity would be maintained using the
assay in multiplex format (Figure 3.7). After addition of either or both proteins, binding,
and washing, sensing aptamers were eluted and amplified by multiplex PCR using the
universal primers. PCR products were separated by electrophoresis and visualized. When
thrombin was added alone, a band corresponding to the 91 bp MB-T amplification
product was observed (Figure 3.7, lane 2). Likewise, when PDGF-BB was added alone, a
band representing the 106 bp MB-P was observed (Figure 3.7, lane 3). Upon addition of
both proteins, both bands appeared and band intensity increased dose-dependently with

increasing protein concentrations (Fig 3.7, lanes 4-6). No signal was returned from either

113

 

aptamer pair in the presence of haptoglobin, fibrinogen, C-reactive protein, or lysozyme,
further demonstrating good specificity for detection of only the specific protein targets

(Figure 3.7, lanes 7-10).

Dose-response curves for multiplex detection of both proteins were derived with serial
dilutions of both proteins ranging from 333 pM to 333 nM (Figure 3.8). Eluted sensing
aptamers were quantified with qPCR using MB-T or MB-P specific primers. In the
multiplex format, the detection limits were 333 pM for thrombin and 3.3 nM for PDGF-
BB, respectively, essentially in agreement with the single protein measurements. In both
cases, we expect technical improvements in binding, washing, and elution protocols
could extend the dynamic range both higher and lower in concentration. That said, the
current dynamic range exceeds that of a recently described fluorescence-based method by
an order of magnitude [52], while the sensitivity approaches that of a capillary

electrophoresis based technique [38].

Given that our initial multiplexing experiments were performed with both proteins at
identical concentration in all tests, we examined the concentration sensitivity in the
presence of high and low concentrations of the competing proteins (Figure 3.9). Serial
dilutions of one protein were measured in multiplex in the presence and absence of 500
nM (Figure 3.9 (a, c)) or 333 nM (Figure 3.9 (b, d)) of the other protein. Afier separation,
eluted aptamers were amplified by PCR and assayed by electrophoresis (Figure 3.9 (a, c)).

In all cases and concentrations, the signal returned from each aptamer pair was essentially

114

identical to that detected in the single-protein case (compare corresponding Ct values

from Figure 3.6 and 3.9 (b, d)).

115

 

(a)
28 -

20-

flat-

16-

12-

la-

 

 

0 10° 101 102 1o3
[Thrombin] (nM)

Figure 3.6: Standard curves for singleplex detection of individual proteins using
qPCR. (a) The standard curve for thrombin showed a three order-of-magnitude dynamic
range. This was generated using 500 nM Bio-T and 250 nM MB-T. (b) The PDGF-BB

standard curve showing over a two order-of—magnitude dynamic range was derived using

500 nM Bio-P and 50 nM MB-P. ("‘ denotes Ct values significantly lower than

background by student's t-test, (p < 0.01)). Lower detection limits were 1 nM and 10 nM

for thrombin and PDGF-BB, respectively.

116

 

Figure 3.6 continued.

 

 

 

(b)
22-
202 q»
181 q) (I) <l> g
o“ 16: q:
141
123
0 10° 101
[PDGF-BB] (nM)

117

1 2 3 4 5 6 7 8 9 10 MB-Pspecificband

- _|

MB-T specific band

 

a... ., ', ... w } Universal primers

 

 

 

Figure 3.7: Multiplex detection of proteins using electrophoresis as the readout

method. Control or target protein samples were added to binding buffer containing 250

 

nM MB-T, 500 nM Bio-T, 50 nM MB-P and 500 nM Bio-P. After binding and separation,
eluted sensing aptamers were amplified with universal primers for 20 cycles. PCR
products were separated by acrylamide gel and detected by gel staining. Higher bands are
the products from amplification of the PDGF-BB and thrombin sensing aptamers.
Remaining primers run to the bottom of the gel. Lane 1: Buffer; Lane 2: 333 nM
thrombin; Lane 3: 333 nM PDGF-BB; Lanes 4-6: both thrombin and PDGF-BB at 33,

100 and 333 nM from left to right; Lanes 7-10: haptoglobin, fibrinogen, C-reactive

protein and lysozyme each at 333 nM. Image contrast was adjusted to reduce background.

118

 

 

 

 

 

 

 

I Thrombin
24- i -* * o PDGF-BB
. g * *
20_ (13 (I) <13 <3 5
.. # # * _
0 16- “E q;
a 5
#
12-
j luv-r
#
8 . ”I; . ......r .. ....... . .......

0 1o0 101 102 103
[Proteins] (nM)

Figure 3.8: Multiplex detection using qPCR as the readout method. Both sets of
aptamer pairs were added with serial dilutions of protein mixtures containing both

thrombin and PDGF-BB. After binding, separation, and elution, eluted sensing aptamers

were amplified with qPCR using template specific primers. (* and # denote Ct values

significantly lower than background for thrombin and PDGF-BB, respectively (p < 0.01)).

Lower detection limits were 330 pM and 3.3 nM for thrombin and PDGF-BB,

respectively.

119

(a)

MB-P specific band

J

 

 

 

 

PDGF-BB(nM) - 100 200 500 - 100 200 500 MB‘TSPeCifiC band
Thrombin(nM) - - - - 500 500 500 500

Figure 3.9: Specificity of detection in the presence of competing protein. Both pairs
of aptamers were added to serial dilutions of PDGF-BB in the absence or presence of
thrombin (a-b) or to serial dilutions of thrombin in the absence or presence of PDGF-BB
(c-d). After separation, eluted sensing aptamers were amplified with universal primers (a,
c) or sensing aptamer specific primers (b, d) and read out using either electrophoresis (a,
c) or qPCR (b, d). For (a) and (c), all four aptamers were used at 500 nM concentration to
assess how much cross reactivity occurred at high aptamer concentrations. Little cross-
reactivity was seen in either case. For (b) and (d), the optimal sensing aptamer

concentrations (from Figure 3.3) were used.

120

 

Figure 3.9 continued.

0))

 

 

 

 

 

 

O PDGF-BB at 0 nM Thrombin
o PDGF-BB at 333 nM Thrombin
28 - [:1 Thrombin at 0 nM Thrombin
W l Thrombin at 333 nM Thrombin
24- m [I]
.
20' 9 é
o“ ' i
16-
- Q
12‘ I I 0 C
8-L.—,—/,I.... 0 . ........1 . ..., zfi ......., 3
0 1o 10 10 1o

[PDGF-BB] (nM)

121

Figure 3.9 continued.

 

 

 

 

 

 

 

 

 

 

(c)
MB—P specific band
J
”- — - — = = ELI
PDGF-BB(nM) - - - - 500 500 500 500 MET Specific band
Thrombin(nM) - 100 200 500 - 100 200 500
(d)
28- Cl Thrombin at 0 nM PDGF-BB
I Thrombin at 333 nM PDGF-BB
‘ O PDGF-BB at 0 nM PDGF-BB
24- E o PDGF-BB at 333 nM PDGF-BB
20- (D E ‘3 q)
0H
. a
o 1
124 g
BL—H,t..., . ........ . .
0 10° 101 1o2 103
[Thrombin] (nM)

122

Standard curves were also derived in complex biological fluids, fetal bovine serum (FBS)
and human serum (Figure 3.10). Results showed that both proteins can still be detected in
undiluted serum at low nanomolar concentrations. It was important to confirm that the
results in the complex environment agreed with the standard curves derived for the
individual proteins in buffered solution (Figure 3.11). For thrombin, the multiplex data
derived in buffer, FBS and human serum all agreed well with the singleplex data,
showing that competition of the multiplex measurement did not impact the accuracy of
our method (Figure 3.11 (a)). For PDGF-BB, the majority of the data derived from
multiplex measurements underestimates the PDGF-BB concentration by about two-fold
as compared to the singleplex measurement (Figure 3.11 (b)). This implies a greater
effect of the binding environment on PDGF-BB detection as compared to thrombin,
perhaps due to greater non-specific binding of the sensing aptamer. Nonetheless, the

sensitivity and accuracy of concentration detection for the protein are largely maintained.

It could be argued that our method requires more processing as compared to other
aptamer-based protein sensing methods [3 8]. Moreover, detection with our technique
does not provide the sensitivity of more traditional methods such as ELISA [53].
However, we feel that our approach has potential utility that makes it worthwhile to
continue optimization of the approach. In particular, our method is designed specifically
for parallel analyses due to the requirement for multiple specific interactions, the affinity-
based separation, and incorporation of the MBs. Thus, our technique avoids any issues
that might arise using other analytical approaches, such as the inability to separate

proteins of similar size, charge, or hydrophilicity. Currently our sample size (3 uL) is far

123

 

lower than that required for most other methods [3 8,53,54]. Finally, as we continue to
lower our background signal through technical improvements, we will also more fully
leverage the parallel signal amplification provided by multiplex PCR, giving us the

potential for sensitivities far in excess of current methods.

3.5 Conclusions

In this study, a novel technique for parallel protein measurement using dual-aptamer
recognition was developed. Simultaneous and quantitative detection of thrombin and
platelet~derived growth factor (PDGF-BB) was achieved with good specificity and
reproducibility in both buffered solutions and a serum background. We have
demonstrated that the technique is flexible and has the potential for straightforward
customization to sets of proteins, depending on the availability of aptamer pairs for each
protein and their specificity and compatibility. The sensitivity and dynamic response of
the approach warrants further selection and characterization of aptamer pairs and their

use in the described approach.

124

 

(a)
22 -

20-

I11

I31-

18-

16~

x.

14-

 

 

12-—.—.//fi ...... .. ...... . ....... . . .......
0 10° 101 1o2 10
[Thrombin] (nM)

Figure 3.10: Multiplex detection of proteins in fetal bovine serum and human serum.
Both pairs of aptamers were added at the optimal concentrations to fetal bovine serum (a-
b) and human serum (c-d) spiked with varying concentrations of both thrombin and

PDGF-BB. Afier separation and elution, the eluted sensing aptamers were amplified with
qPCR using template-specific primers. (* denotes Ct values significantly lower than

background, (p < 0.01))

125

(b)

22-

20~
18-
16-

14-

 

12

‘ [III 1 U UVTYV'

0 10° 101 102 103

Figure 3.10 continued.

WM]

 

Few

II
I I IIII'

[PDGF-BB] (nM)

126

Figure 3.10 continued.

 

 

(C)
24-
22- l
l l . *
. q- *
0“ 18: I *
16 '1'
‘ *
14- {I
12'—"_V’rl ' "H"! ' ...”... - . ......l . - ......I
0 10° 101 1o2 103
[Thrombin](nM)

127

 

(d)
22 -

20
18-
15-

14-

 

Figure 3.10 continued.

@311 <1>

<13

I-G-lx-
r—G—lx.
Feu-

12-—.—./,.4........ . .. .. ........

 

0 10° 101 102 1o3
[PDGF-BB] (nM)

128

 

 

 

 

 

28
c D BUfier (333 pM)
.2 A FBS
"' 24‘ (33nM
g 0 Human serum - CD
... .
a:
1: 20— <33nM $3 (0)
x (1 nM)
—g—_ E (10nM)
E 16- (333 nM)
3 .
E HIH
: 12_ (100 nM)
010-0
8 . . . . . . . . .
8 12 16 20 24 28

Ct in singleplex detection

Figure 3.11: Correlation of various analyses. Multiplex Ct values derived in buffer or

serum samples were plotted against the Ct value derived from singleplex experiments in

buffer for thrombin (a) and PDGF-BB (b).

129

(b)

Ct in multiplex detection

Figure 3.11 continued.

 

 

 

 

22
(333pM)
(3.3nM
2o-
(33nM) (O)
18- (1 nM)
(10nM)
16-
‘(333”) D Buffer
14- A FBS
10 M
(0") 0 Human serum
12 ' I 1 ' I ' I '
12 14 16 18 20

Ct in singleplex detection

130

22

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136

CHAPTER 4 IN VI T R0 SELECTION OF APTAMERS
TO ACUTE PHASE PROTEINS

4.1 Abstract

Acute phase proteins (APPS) are secreted as part of the body’s response to inflammation,
and their concentrations can be greatly altered in response to a variety of abnormities. C-
reactive protein (CRP) and fibrinogen are two widely studied APPS whose elevated
concentrations are associated with an array of diseases. Thus, analytical techniques for
quantifying these proteins are important for clinical diagnostics. In this study, RNA
aptamers specific for CRP and fibrinogen were generated by in vitro selection. Two
random RNA pools, R50 and R70, containing 50 and 70-nt random sequences
respectively were used for fibrinogen aptamer selection, and a R60 pool containing a 60-
nt random region was used for in the CRP aptamer selection. The selected aptamers were
characterized by sequencing. Results showed one aptamer from each pool was selected to
have nanomolar affinity for its target protein. In addition, two fibrinogen aptamers
selected from two different random pools bind on the protein in non-competitive fashion.
These aptamers will serve as affinity reagents for use in the multiplex protein analytical

technique we have developed.

137

 

4.2 Introduction

Acute phase proteins (APP) are part of bodies’ response to inflammatory signals and their
circulating levels can be greatly altered in response to inflammation. As growing
evidence has suggested that inflammation plays a strong role in the development of
vascular diseases and cancers, APPS have began to be studied for their predictive roles in
such disorders [1,2]. Among these important APPS are C-reactive protein (CRP) and

fibrinogen.

CRP is elevated during the body’s non-specific response to inflammation [3]. Recent
research has shown that elevated CRP concentrations are also associated with increased
risk of cardiovascular disease and reduced survival in metastatic cancer [1,4]; therefore,
CRP was identified as a useful biological marker that can be used to guide treatment
decisions [5,6]. Fibrinogen plays a key role in the coagulation cascade [7]. During the
cascade, fibrinogen is converted to fibrin and insoluble fibrin aggregates to block the
damaged blood vessel and prevent fiirther bleeding [7]. Elevated fibrinogen levels have
also been associated with a number of diseases including stroke, cancer, and diabetes [8-
10]. Therefore, quantitative techniques for measuring these proteins are proving useful in

the diagnosis of a variety of diseases.

As aptamers do not exist for these proteins, we sought to select high-affinity aptamers for
CRP and fibrinogen. Aptamer sequences with nanomolar affinities were selected from
three pools with different lengths of random sequence. The selected aptamers will be

useful in designing analytical assays for these proteins. Moreover, competition

138

 

f 5-1.1?

experiments showed that the selected fibrinogen aptamers bind the protein in a non-
competitive fashion, making them well suited for use in the dual-aptamer analytical

approach we developed (Chapter 3).

4.3 Experimental section
4.3.1 Synthesis of randomized RNA pool

The template used for preparing the single stranded RNA pool was 5' - TAA TAC GAC

TCA CTA TAG GGG ATG TCC ACG AGG TCT CTGA T GC GGC AC T A T C GC T
CA T <N50>C GTA CGC TGC AGG TCG AC - 3' for R50, and 5' - TAA TAC GAC

TCA CTA TAG GGG ATG TCC ACG AGG TCT CTCCT GCG T GCAAA T C T AAC

AA<N60>C GTA CGC TGC AGG TCG AC - 3' for R60 (Underlined: T7 promoter,
italicized: MB sequence). The primers for PCR amplifications were 5'-
TAATACGACTCACTATAGGGGATGTCCACGAGGTCTCT-3' (p46) and 5'-
GTCGACCTGCAGCGTACG-3' (p47). p47 was also used for Klenow fill-in reaction

and reverse transcription. The template for preparing R70 RNA pool was 5'-

TTCTAATACG ACTCACTATA GGATGCGTAG GCGTGTAACT

TG<N70>CACATTACAA GCTCGCCAGT-3' (Underlined: T7 promoter). The primers

for PCR amplification of R70 pool were 5'-

139

 

TTCTAATACGACTCACTATAGGATGCGTAGGCGTGTAACTTG-3' (R70-5'p) and

5'-ACTGGCGAGCTTGTAATGTG-3' (R70-3'p).

Double stranded DNAS were prepared by annealing single stranded oligo templates and
3' primers at 95°C for 5 min in Klenow annealing buffer (100 mM Tris-HCl, pH 8.0 and
100 mM MgC12) followed by cooling on ice for 5 min. Klenow fill-in reactions were

performed by adding dNTP (0.8 mM each) and Exo- Klenow fragment (N EB, Ipswich,

MA) to the annealed reactions and incubating at 37 °C for 1 hr.

 

Phenol/Chloroform extraction was performed to purify the enzymatic reactions. The
reactions were added with l/ 10 volume of 3M sodium acetate and extracted with equal
volume of phenol/chloroform followed with an equal volume of chloroform. The aqueous

phase was recovered and added with 2.5 volumes of 100% ethanol to precipitate the

DNA. The reactions were frozen at -80 c’C for 15 min and centrifuged at 4 °C for 15 min.

The pellets were dried and resuspended in TB 8.0.

Double stranded DNAs were in vitro transcribed using Ampliscribe T7 in vitro

transcription kits (Epicentre, Madison, Wisconsin) at 42 CC for 2 hrs. After incubation,

DNase was added to remove the DNA templates at 37 °C for 30 min. Transcribed RN As

were loaded onto 10% polyacrylamide urea gels and electrophoresed at 100 V for 1 hr.

140

The RNAS were purified by UV shadowing, and gel fiagments were soaked in TE buffer

(pH 7.0) overnight at room temperature and ethanol precipitated.

4.3.2 In vitro selection

A negative selection in the absence of protein was performed in each round before a

positive selection. Purified RNA pools in 1x binding buffer (20 mM HEPES, pH 7.5, 150

mM NaCl, 5 mM MgC12) were heated to 65 °C for 5 min and cool to room temperature

over a 30-min period. The 0.45 mm HAWP filters (Millipore, Bedford, MA) were
assembled into the filter holder and pre-wetted with 100-uL of 1x binding buffer.
Binding reactions were then loaded onto the filters to exclude filter binding species.
Liquid flow-through was collected and added with target proteins for the positive
selection. The reactions were incubated at room temperature for 30 min and subjected to

filter separation. The filters were washed 3 times with 100 pL 1x binding buffer and
eluted twice each with 200 uL elution buffer (25 mM EDTA and 5 M Urea) at 95 °C for

5 min.

Eluted RNAS were ethanol precipitated by adding 1/10 volume of 3 M NaOAc, 3 uL
glycogen and 2.5 volumes of 100% ethanol, and frozen at -80 °C for 15 min. The RNAs
were centrifiiged at 13000 rpm at 4 °C for 30 min. The supernatant was decanted and the

pellets were washed slowly with 100 uL of 70% ethanol. The reactions were chilled on
ice for 5 min and centrifuged for 5 min. The liquid was removed and the pellets were

dried in a Speed-vac and resuspended in nuclease-free water.

141

 

Selected RNA were reverse transcribed with Superscript II (Invitrogen, Carlsbad, CA) at

42 °C for 50 min and heat inactivated at 70°C for 15 minutes. Reverse transcription

reactions of l-uL aliquots were PCR amplified with Taq polymerase in 10x PCR buffer
(100 mM Tris-HCl, pH 8.3, 500 mM KCl, 20 mM MgC12) in 100 uL reactions. Every 2-3
cycles, a 5-uL aliquot was sampled and analyzed by Electrophoretic Mobility Shift

Assays (EMSA) to decide the optimum cycle number. An 800-uL large scale PCR was

assembled with 8-uL reverse transcription reaction and cycled for the optimum cycle

 

number. PCR products were pooled and ethanol precipitated as described and
resuspended in 30 uL nuclease-free water. PCR products were in vitro transcribed and

the RNAS were purified and subjected to another round of selection.

4.3.3 Cloning and sequencing

After selection, the pools were cloned using the TOPO TA cloning kit (Invitrogen,
Carlsbad, CA). Fresh PCR products were incubated with TOPO vectors for 30 min on

ice. Ligated vectors were transformed into One Shot competent E. coli cells (Invitrogen,

Carlsbad, CA). The cells were grown on X-gal coated agar plates at 37 °C overnight.

White colonies were selected and grown in liquid culture overnight. The cultures were
harvested and the plasmids were extracted by Plasmid Miniprep kits (Qiagen, Valencia,

CA). The plasmids were sequenced by standard dideoxy-sequencing methods.

142

Selected clones were PCR amplified by p46 and p47. PCR products were in vitro
transcribed and purified as described. Affinities of the derived RNA sequences were

characterized by filter binding assays.

4.3.4 Affinity characterization by filter binding assays

Affinities of selected RNA pools were characterized by filter binding assays. The RNAS

were dephosphorylated by Calf Intestinal Alkaline Phosphatase (CIAP) (Ambion, Austin,

TX) at 37 °C for 1 hr, 5 ’ end-labeled with 32P-y-ATP using T4 polynucleotide kinase

 

(Ambion, Austin, TX) and purified by Quick Spin Columns (Roche, Indianapolis, IN).
To monitor the pool affinity improvement progression, labeled RNAS were incubated
with 500 nM of proteins in 1x binding buffer at room temperature for 30 min. To
determine the dissociation constants, labeled RNA was incubated with increasing
concentrations of proteins in 1x binding buffer for 30 min at room temperature. The
reaction products were passed through a vacuum manifold (Schleicher & Schuell)
equipped with a nitrocellulose membrane (Whatrnan, Piscataway, NJ) over a piece of
hybond nylon membrane (Amersham, Piscataway, NJ). After the separation, both
membranes were exposed to phosphorimaging screen followed by quantification of the
signal intensity. The fiactional binding of the pool or clone to the proteins were

calculated as the signal retained on the nitrocellulose membrane divided by the total

signals of the two membranes. The dissociation constant (KD) of the aptamer protein

interaction was determined by curve fitting using equation (4.1)

143

f: CmaxPO
KD +I’O (4.1)

where f is the fraction bound as calculated from the quantified dot blot image. Cmax is the

extrapolated maximal retention of RNA-protein complex on the filter. P0 is the initial

concentration of the protein added to each binding reaction.

4.3.5 Competition assays

Competitions between the random pools R50, R70 and R70-3 for R50-9 were performed

by mixing 100 nM fibrinogen with 1 uM each unlabeled RNA in binding buffer followed

by addition of 2 nM 32P end-labeled R50-9. Binding assays were incubated for 30 min at

room temperature. Competition assays between the random pools R50, R70 and R50-9
for R70-3 were performed under the same conditions. The separation between protein
bound and free RNA were performed on a vacuum manifold and the fractional binding

was calculated as described.

4.4 Results and discussion

Fibrinogen and CRP from human plasma were used in the aptamer selection. In vitro
selection for fibrinogen was initiated from two RNA pools with different randomized
sequence lengths. The R50 pool contained a 50-nt randomized region and was labeled
with an MB sequence for firture multiplex analytical applications. Another pool, R70,
contained a 70-nt randomized sequence and was used in a separate selection for

fibrinogen aptamers to compare the outcome sequences selected for the same protein

144

 

using different pools. The RNA pool designed for CRP, R60, contained 60 randomized

positions and was also labeled with a MB sequence.

In each round of selection, RNA pools were incubated with the proteins and bound

sequences were separated from the unbound ones by nitrocellulose filter membranes.

 

Protein-bound RNAS were retained on the filters, eluted, and amplified for the subsequent n
round of selection. A negative selection was incorporated in each round to minimize the ‘

enrichment of filter binding species. As the selection progressed to higher rounds, the

 

optimum cycle numbers determined for PCR cycle course decreased (data not shown). To H
increase the selection stringency, the amount of protein used was reduced as the selection

progressed to higher rounds (Tables 4.1, 4.2 and 4.3).

145

 

Table 4.1: Summary of in vitro selection for the R50-fibrinogen pool.

 

Round of Input RNA Input protein [RNA]: % bound % bound

 

selection (nM) (nM) [Protein] to protein to filter

1 2000 320 6.25:1 0.48 0.26

2 2000 320 6.25:2

3 2000 320 6.25:3

4 2000 320 6.25:4 .

5 2000 320 6.25:5 14.04 0.26
6 800 32 25:1 15 0.71
7 800 32 25:1 10.57 0.98
8 800 32 25:1 10.55 1.32

 

(Round highlighted in bold was selected for further characterization.)

 

146

 

Table 4.2: Summary of in vitro selection for the R70-fibrinogen pool.

 

Round of Input RNA Input protein [RNA]: % bound % bound

 

selection (nM) (nM) [Proteig to protein to filter

1 2000 320 6.25:1 0.42 0.12
2 2000 320 6.25:2

3 2000 320 6.25:3

4 2000 320 6.25:4

5 2000 320 6.25:5

6 2000 80 25:1

7 2000 32 62.5:1 2.79 0.21
8 800 32 25:1 2.51 0.18
9 800 32 25:1 2.42 0.22
10 800 32 25:] 2.65 0.22

 

(Round highlighted in bold was selected for further characterization.)

147

Table 4.3: Summary of in vitro selection for the R60-CRP pool.

 

 

Round of Input RNA Input protein [RNA]: % bound % bound
selection (nM) (nM) [Protein] to protein to filter
1 2000 2000 1:1 0.6 0.27
2 2000 2000 1:1
3 2000 2000 1:1
4 2000 2000 1:1
5 2000 2000 1:1
6 2000 500 4:1
7 2000 200 10:1 26.65 0.61
8 800 32 25:1 15.3 0.76
9 800 32 25:1 34.69 2.6
10 800 32 25:1 37.09 4.21

 

(Round highlighted in bold was selected for further characterization.)

148

 

 

 

The progression of the selection was also monitored by filter binding assays (Figure 4.1).
For the R50-fibrinogen selection, the fractional binding of the RNA pool in the presence
of 500 nM of fibrinogen increased to 15% at round 6 and started to decrease with further
selection, while the filter binding fraction started to increase to 1.32% at round 8 (Table
4.1 and Figure 4.1 (a)). The results indicated further selection was unable to increase the
pool binding affinity and non-specific filter binders were enriched instead. Therefore,
round 6 from R50 pool was chosen for further characterization. Filter binding assays
performed with the R70-fibrinogen pool selected against the same protein target showed
a plateau of affinities from round 7 to round 10, though with a smaller fractional binding
of ~ 2.5% (Table 4.2 and Figure 4.1 (b)). The fractional binding of the selected R60 pool
for CRP decreased at round 8 and then increased gradually and ultimately yielded 37.9%

binding at round 10 with a background binding of 4.21% (Table 4.3 and Figure 4.1 (c)).

Round 6 of R50-fibrinogen and Round 10 of R70-fibrinogen and R60-CRP (highlighted
in Tables 4.1-4.3) were cloned and sequenced. 15 clones were selected from the R50-
fibrinogen selection pool (Table 4.4). R50-20 and R50-9 were the most abundant clones,
each represented 6 times. Therefore, they were selected for further affinity
characterization by filter binding assays. 13 clones were selected for sequencing from the
R70-fibrinogen selection pool, with two primary aptamer families represented by the
clones R70-1 and R70-3. Both of these were selected for affinity characterization (Table
4.5). 17 clones were selected from the R60-CRP selection pool, and the most abundant

clones R60-5 and R60-7 were chosen for affinity characterization (Table 4.6).

149

 

 

(a)

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4

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R50 fractional binding

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-Flb [+Fib -FIb1+Fib' -F|b Maul +11) +F'bl Hal +Flb

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Rd1iRd5;Rd6 Rd7-Rd8l

Figure 4.1: Affinity improvement of selected pools. Selected pools were radiolabeled
and incubated with target proteins. The mixtures were then passed through a vacuum
manifold. The fractional binding in the presence (+) and absence (-) of proteins were

plotted for R50-fibrinogen (a) and R70-fibrinogen (b) and R60-CRP (c)

150

 

 

Figure 4.1 Continued.

 

    

  

 

 

 

 

 

 

 

 

(b)
0.04 -
a .
.E l
'2 0.03 ~
__ 1
To . .
c 0.02 i .
.2 , . :1
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to- 0.01 i . 1
2 i '
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0 LET-TEL . T ,l , 1 ET ,
1-Hb5+Fm,-Hbl+Fmi-Hbl+Fm‘-Hb +le-Hbl+Fm
l l i
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R60 fractional binding

  

 

 

 

 

 

 

0.1»: _, 1,.-
l ‘0 ’ a F :5
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1-CRP1+CRP{-CRP!+CRP -CRPE+CRP -CRR+CRPl-CRPl+CRP1
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Rd1 5 Rd7 1 Rd8 1 Rd9 Rd10

i

151

 

 

Table 4.4: The sequences of the random regions from selected clones of round 6 of the
R50-fibrinogen selection pool.

 

Clone ID Random region sequence Repeats

R50-20 tggtcggagggaggggagagggtgagatgcctttgggagactagtc 6
R50-9 gacttagggcaccaggaactttagcatttgggttaaatgtcatgggagtg
R50-5 cgcggtatgtcagaaacatcagagggcctgcttgccgtttgagaacgact
R50-6 gcaatgagcattttggtcgcactggctgggtctcaggctgcagggcactt
R50-8 ccgcggcaaatgccgcgattgagctgtagatggtcgtacaggctgggtct

y‘Ht—ia

(Clones highlighted in bold were selected for affinity characterization)

 

152

Table 4.5: The sequences of the random regions from selected clones of round 10 of the
R70-fibrinogen selection pool.

 

 

Clone ID Random region sequence Repeats
ggaattcaaaacccagatcaaaaaaattactcaatcgtcagattatgctttg
R70-l ttctttgcgttgaactgc 10
gctctcaaatttgattactaactcgctagttagtcttacaaagttcatccaag
R70-3 cagt_cg_c£t1gggtca 3

 

 

(Clones highlighted in bold were selected for affinity characterization)

153

 

 

Table 4.6: The sequences of the random regions from selected clones of round 10 of the

R60-CRP selection pool.

 

Clone ID Random region sequence

R60-5 cttatgcagaggttcgtactaagggtagagggagagtggaatgtattgcacccgttggta

R60-7 aaagatttctacggattaggctgtaggcggtactaatggttggtgtagggagtgtagcgc
R60-19 tagatcgtacggtctcgtaagaaatggagggtagggtattaaagggcagaaaatagaaac

R60-2 tagtatgatcgatttagcagcgaagtgacggttaagtgggtgggaataaaggagggtagc
R60'6 ccggtatacagttcacaggtcgtgggtgggtggaggaaagtgaatgagatgcggattgtg
R60-3 tttcageattaggccgttggcgtactcttgggatggaggggcaatttagggggagtggtg
R60-4 tggtgtcgaatggtggtgtagggaatggtaccattaaagttgattgctcttaacatgtag
R60-1 3 ggatcgtggtggaagttactggatgttggagagcgcggttttgaattgtctaacattagc

(Clones highlighted in bold were selected for affinity characterization)

154

Repeats

7

4
1
1
1
1
1
l

I “I“..i J'T' .1

 

The selected clones were PCR amplified and the PCR products were in vitro transcribed.
The resulting RNAS were purified and radiolabeled for use in the filter binding assays.
Filter binding assays were performed with limiting amount of RNA and increasing
concentrations of respective proteins for all six selected sequences (Figure 4.2). R50-20
showed constant background binding at varying protein concentrations and was

determined to be a filter binder. R50-9 showed a fibrinogen dose response with
increasing protein concentrations from 0 to 1 nM. The dissociation constant KD of R50-9

was determined by non-linear least square fitting (as described in the experimental

section (Chapter 4.3.4» to be 91.6 nM with a R2: 0.93 (Figure 4.2 (b)).

 

For the selection with R70 pool, both R70-1 and R70-3 showed fibrinogen-responsive

binding. R70-3 was determined to have a KD=416 nM with R2= 0.99 (Figure 4.2 (b)).

The affinity of R70-1 was too weak (>1 pM) and was not selected for further analysis.
The affinity of R50-9 was higher than that of R70-3 and was consistent with the pool
affinity characterization (compare 4.1 (a) and (b)). Through selection, the R70 pool
seemed to have converged so much that only two sequences dominated the final pool and
this could also explain the affinity saturation at higher rounds (Figure 4.1 (b)). Saturation
of the pool at a lower binding affinity was perhaps due to the R70-fibrinogen pool

aptamers targeting the protein on a surface less favorable for aptamer binding

For the selection with the R60-CRP pool, clone R60-5 showed a CRP dose response in

the filter-binding assays (Figure 4.2 (a)). The affinity was determined to be 109.0 nM

155

 

with R2=0.97 (Figure 4.2 (c)). The other sequence, R60-7, was determined to be a filter

binder. It is worthwhile to note that filter binders were still selected, even with negative
selection during the SELEX process. This may be due to imperfect binding and
separation using the filters, even in the absence of protein. Thus, the sheer number of
filter binding sequences relative to specific binding sequences would give them an

advantage throughout the process.

The two aptamers selected against fibrinogen, R50-9 and R70-3, showed no sequence
homology. It was therefore of interest to investigate further their interaction with their
target protein fibrinogen. A competition assay was performed for R50-9 and R70-3
(Figure 4.3). Radiolabeled R50-9 or R70-3 was incubated with 100 nM of fibrinogen and
added with unlabeled R50-9, R70-3, round 0 of R50 randomized pool and round 0 of R70
randomized pool. The fractional binding in the presence of different competitors was
determined (Figure 4.3). It was observed that R50-9 did not compete for binding with
R70-3 or any of the randomized sequences (Figure 4.3 (a—b)). This was true for either
R50-9 or R70-3 being labeled. These results demonstrated that R50-9 and R70-3 bind in
a non-competitive fashion on fibrinogen, and they likely bind at distinct epitopes on the

protein.

156

 

 

(a)

______ [Protein]

  

Figure 4.2: Affinity characterization of selected clones. The RNA sequences from
selected clones were synthesized, radiolabeled, and incubated with increasing
concentrations of protein. (a) Nitrocellulose membrane image obtained for R50-20, R50-
9, R60-5, R60-7, R70-l and R70-3 (row 1 through row 6), protein concentrations
increased from left to right. (b) Aptamer R50-9 and R70-3 fractional binding at
increasing fibrinogen concentrations. (c) Aptamer 60-5 fractional binding at increasing
CRP concentrations. Curve fits were derived with non-linear least square fitting as

described in the experimental section (Chapter 4.3.4).

157

Figure 4.2 Continued.

 

 

 

   
   

 

 

 

(b)
I
.E R50-9 data/Curve fit
12 . u ----------
:5 0.4, R70-3 data/Curve fit xA
Tu
C ‘.
.2
‘5 0.2-
E
u.
0.0-
100 101 102 103
[Firbrinogen] (nM)
(c)
0.8-
.8 . /-— .
'g 0.6- R60-5 data/ Curve fit
3
2 0.4-
.2
H
8
,3 0.2-
O
8
0.0- 0
100 101 102 103
[CRP] (nM)

158

 

(a)
Competitors: R50-9 R70-3 R50-R0 R70-R0

R50-9 labeled . fr

   

 

(b)

0.8-

R50-9 fractional binding

 

 

0-
R50-9 R70-3 R50-R0 R70-R0
Competitors

Figure 4.3: Competition assays for fibrinogen aptamers. (a) Aptamer R50-9 was
radiolabeled, incubated with 100 nM fibrinogen, and added with unlabeled R50-9, R70-3,
round 0 of the R50 randomized pool, or round 0 of the R70 randomized pool. Binding
reactions were separated by filter binding, and fractional binding in the presence of each
competitor was quantified (b). (c) Aptamer R70-3 was radiolabeled, incubated with 100
nM fibrinogen and competed with unlabeled R70-3, R50-9, round 0 of the R50
randomized pool, or round 0 of the R70 randomized pool. Binding reactions were
separated by filter binding, and fractional binding in the presence of each competitor was

quantified (d).

159

(C)

(d)

Competitors:

R70-3 labeled

R70 fractional binding

0.20 1

0.16-

0.12-

0.08-

0.04 -

 

Figure 4.3 continued.

 

R70-3

R70-3 R50-9 R5

"4"“?- 1-

R50-9 R50-R0
Competitors

160

O-RO R70-R0

‘1.

 

R70-R0

4.5 Conclusions

In conclusion, RNA aptamers were generated for potential cancer biomarker proteins,
fibrinogen and CRP, from pools with varying lengths of random sequence. Two

fibrinogen aptamers R50-9 from the R50 pool and R70-3 from the R70 pool were
selected. R50-9 and R70-3 were characterized to bind fibrinogen with KD of 91.6 and
416 nM, respectively. The CRP aptamer R60-5 selected from the R60 pool has an affinity
of 109.0 nM. Moreover, fibrinogen aptamers generated from two randomized pools

showed no homology, and competition assays demonstrated that the aptamers bind

fibrinogen in a non-competitive fashion. These aptamers can serve as biosensing

 

elements for designing biosensing assays for measuring these proteins.

161

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CHAPTER 5 CONCLUSIONS AND FUTURE WORK

The motivation for this work was to provide new technologies for the analysis of proteins
and small molecules in parallel. The approach we chose was to leverage existing nucleic
acid analytical technologies such as PCR and standardized oligonucleotide microarrays
for proteomic and metabolomic analyses. To do this, we chose to use aptamers for
affinity capture of the analytes and readout of the signal. Each aptamer was designed in

advance for parallel applications through the incorporation of unique MB sequences.

To this end, an SSA was first designed to develop a theophylline biosensor in
combination with scintillation counting and qPCR. The SSA converted the analyte signal
into a nucleic acid signal for subsequence amplification and quantification. Analysis of
the controlling parameters of the system indicated that further performance enhancements
may be achievable through careful SSA design and protocol optimization. The theoretical
framework for analyzing SSA biosensing performance can be easily adapted to other
SSA-based in vitro biosensor designs, as these usually require multiple constructs to be
tested experimentally to identify the best biosensors, based on some metric such as S/N.
This work can also guide the design of non-natural riboswitches used for in vivo sensing
and gene expression control. This work further provides a foundation for future parallel
measurements of small molecules using SSAS. It is expected that SSAS composed of
aptamers targeting other analytes can be labeled with other MBS, and the PCR
amplification products can be analyzed simultaneously by microarray or other parallel

readout.

163

 

Furthermore, a novel parallel protein measurement technique was developed using dual-
aptamer recognition. It was demonstrated that the technique is flexible and has the
potential for straightforward customization to sets of proteins. The sensitivity and
dynamic response of the approach warrants further selection and characterization of
aptamer pairs and their use in the described approach. Continued development of this
technology in combination with a microarray-based readout will provide a valuable
platform for screening of biomarker proteins in a robust, sensitive, and multiplexed
fashion. This platform will greatly facilitate the development of diagnostics for disease

detection and treatment decisions.

The dual-aptamer based biosensor is also uniquely suited for studying protein-protein
interactions. Similar to the design rationale for PDGF-BB analysis, aptamers targeting
each component of a pair of interacting proteins can be used to quantitatively measure the
existence and persistence of their interaction. By specifically capturing a Single protein
and adding sensing aptamers for a variety of potential binding partners, the composition
of protein complexes that form could be identified from the recovered aptamers, with the
strength of the interaction described by the number of recovered sensing aptamers. This
technique can provide a systematic analysis of protein interactions involved in cell
signaling and uncover new connections in cellular pathways. Moreover, as aptamers can
be selected to bind specifically with post-translationally modified (e.g., phosphorylated)
proteins [1], it is straightforward to envision how the dual-aptamer approach would be

extended to the analysis of the relative quantities of modified and unmodified protein.

164

 

This technology would add another layer of information to proteomic research, which is

important for the complete understanding of cellular activity.

We have demonstrated that two aptamers can be selected that bind on distinct epitopes on
a protein. This suggests that our multiple affinity detection approach would also be useful
for detecting still larger targets such as cells, spores and bacteria, as these targets also
have multiple surface protein markers. Pairs of aptamers selected against different

epitopes on these targets would be useful in a variety of cell biology, biodefense, and

 

biosensing applications.

The current experiments were performed in idealized laboratory conditions. A key future
direction would be the continued optimization of processing and development of the
mechanical details of the assay. For instance, the dual-aptamer based assays are currently
performed in batch reactions but could be applied in a continuous format, such as on a
microfluidic device. With continued improvement of techniques for high-throughput
aptamer selection, this technique can mature to an automatic, durable, portable and
economic biosensor in high-throughput format. Ultimately, the flexibility, sensitivity, and
specificity of the multiplexed analytical technologies developed in this dissertation could
not only enable convenient and more accurate monitoring of biological systems with
reduced experimental costs and time, but also could facilitate development of a larger

variety of sensors as point-of-care analytical tools.

165

References

1. Li MY, Lin N, Huang Z et al. Selecting aptamers for a glycoprotein through the
incorporation of the boronic acid moiety. Journal of the American Chemical
Society, 130(38), 12636-12638 (2008).

 

 

 

166