THE DEVELOPMENT OF NANOSENSORS FOR IN

VIVO DETECTION OF PHYSIOLOGICAL MOLECULES

Thesis Presented

By

Yi Luo

The Bouve’ Graduate School of Health Sciences

in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy in Pharmaceutical Sciences

May 2018

ii

TABLE OF CONTENTS

ABSTRACT ................................................................................................................................................. iii

ACKNOWLEGEMENT ............................................................................................................................... v

LIST OF TABLES ....................................................................................................................................... vi

LIST OF FIGURES .................................................................................................................................... vii

Chapter 1: Introduction and Dissertation Summary...................................................................................... 1

Chapter 2: Nanosensors for the Chemical Imaging of Acetylcholine Using Magnetic Resonance Imaging

.................................................................................................................................................................... 17

Chapter 3: Glucose-Sensitive Nanofiber Scaffolds with Improved Sensing Design for Physiological

Conditions ................................................................................................................................................... 61

Chapter 4: Conclusion and Future Direction .............................................................................................. 89

Reference .................................................................................................................................................... 92

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ABSTRACT

Nanosensors are an emerging tool for biomedical research and personalized medicine. By

incorporating a recognition moiety and a reporter on a nanoscale platform, the nanosensor has

displayed its ability to continuously monitor physiological molecules. To develop nanosensors for

in vivo applications, characteristics such as sensitivity, dynamic range, selectivity, reversibility,

biocompatibility, residency time, implantation, and clearance have to be considered. Choosing and

optimizing the recognition moiety is crucial for the fabrication of an in vivo nanosensor. Currently,

natural large molecules, such as proteins and nucleic acids, enzymes, and synthetic small

molecules have all been used as recognition moieties. In this thesis, we will demonstrate two

nanosensors using different recognition moieties to measure acetylcholine and glucose,

respectively. First, acetylcholine is a neurotransmitter associated with cognition, learning, and

memory. Previously, the detection of acetylcholine relied on microelectrode and microdialysis,

which suffer from invasiveness and limited spatial resolution respectively. To overcome these

issues, we developed a nanosensor to detect acetylcholine using magnetic resonance imaging

(MRI). Butyrylcholinesterase (BuChE) serving as the recognition moiety, and pH-sensitive

contrast agents serving as the reporter, are immobilized on the surface of a nanoparticle. Enzymatic

hydrolysis of acetylcholine created a pH-drop in the microenvironment close to the surface of the

nanoparticle, which was detected by the contrast agents leading to an increase in signal intensity.

Delivered to the brain of rats, this nanosensor detected drug-induced release of acetylcholine.

Second, we detected glucose as the first example of small molecule detection using an optode

format. The level of glucose is an important parameter to determine the dose of insulin therapy.

So far, the clinically used glucose monitors are all based on the enzymatic oxidation of glucose.

To extend the residency time of the glucose sensor, we developed a glucose-sensitive nanofiber

iv

based on a small molecule boronic acid as the recognition moiety. By incorporating boronic acids

containing an electro-withdrawing group, the nanofiber managed to continuously detect glucose

at physiological pH with an improved in vivo residency time.

v

ACKNOWLEGEMENT

This dissertation cannot be accomplished without the support and help from the following people

who I’d like to acknowledge. In Jan 2013, when I entered Dr. Clark’s lab, I was a student in the

master program and not sure about my future career. In the following years, my advisor Dr. Clark

guided me to the path to the degree of Ph.D. Not only she gave me the mentorship on the

development of nanosensor, but also trust and encouragement to help me overcome all the

difficulties and setbacks I encountered in the pursuit of science. I really appreciate all of the support

from her. I’m grateful to Dr. Mary Balaconis (Kate) and Eric Kim whom I learned a lot from when

we worked together on the projects presented in this dissertation. I want to thank all other past and

present members of our group including but not limited to Dr. Guoxin Rong, Wenjun Di, and

Jennifer Morales. I also want to thank Dr. Praveen Kulkarni and Dr. Chris Flask for their

instrumental help on MRI. Last but not least, I want to thank all my family members, including

my parents and my wife Dr. Yu Wang for their unconditional support and encouragement.

vi

LIST OF TABLES

Table 2.1. Relaxivity (r1) of contrast agents used in this study. ................................................... 40

Table 2.2. Relaxivity (r1) of ACh-MRNS in different pH used in this study. .............................. 40

vii

LIST OF FIGURES

Figure 1.1. Proposed general structure and sensing mechanism of nanosensor. ............................ 3

Figure 1.2. DNA-based nanosensor for optical detection of acetylcholine. ................................... 7

Figure 1.3. Sodium dependency of nanosensor. ........................................................................... 10

Figure 1.4. Boronic acid-based glucose nanosensor. .................................................................... 12

Figure 2.1. Schematic of the nanosensor structure and mechanism. ............................................ 32

Figure 2.2. Fabrication of the ACh-MRNS and pH-MRNS. ........................................................ 34

Figure 2.3. Structure and 1H NMR spectrum of pH-sensitive chelator. ....................................... 35

Figure 2.4 Characterization, in vitro calibration and selectivity of nanosensors. ......................... 37

Figure 2.5. TEM image of the ACh-MRNS using NanoVan stain. .............................................. 38

Figure 2.6. In vitro nanosensor to pH dependence. ...................................................................... 41

Figure 2.7. Characterization, in vitro calibration and selectivity of nanosensors. ........................ 43

Figure 2.8. pH change in the mixture of the nanosensor and acetylcholine. ................................ 45

Figure 2.9. Xylenol orange test. .................................................................................................... 46

Figure 2.10. Kinetics of BuChE. ................................................................................................... 47

Figure 2.11. Relative Signal intensity at different TR. ................................................................. 49

Figure 2.12. In vivo sensor contrast. ............................................................................................. 51

Figure 2.13. Histology. ................................................................................................................. 52

Figure 2.14. Acetylcholine detection in vivo. ............................................................................... 54

Figure 2.15. Diffusion of pH-MRNS in phantom brain................................................................ 57

Figure 3.1. Boronic acids incorporated into glucose-sensitive sensors. ....................................... 66

Figure 3.2. Response of glucose-sensitive macrosensors containing functionalized boronic acids

with increasing length of alkyl chains. ......................................................................................... 72

viii

Figure 3.3. Fluorescence decay of macrosensors with different boronic acids. ........................... 73

Figure 3.4. Response of macrosensors against different concentrations of glucose. ................... 75

Figure 3.5. Comparison of sensor response to two sugars, glucose and fructose. ....................... 76

Figure 3.6. Electrospun glucose-sensitive scaffolds. .................................................................... 79

Figure 3.7. Response of glucose-sensitive nanofibers containing different functionalized boronic

acids. ............................................................................................................................................. 80

Figure 3.8. In vivo comparison of glucose-sensitive nanoparticles and nanofiber scaffolds. ....... 81

Figure 3.9 Fluorescence measurements of glucose-sensitive nanoparticles and nanofiber

scaffolds over time in vivo. ........................................................................................................... 82

Figure 3.10. Response of glucose nanosensor embedded in alginate hydrogel. ........................... 86

Figure 3.11. The stability of hydrogel in PBS. ............................................................................. 88

1

Chapter 1: Introduction and Dissertation Summary

1.1 Introduction of in vivo application of nanosensors

Modern healthcare and biomedical research require imaging tools to measure critical physiological

analytes continuously in order to correlate them to biological activities for research, diagnosis and

personalized medicine. Nanosensors can be instrumental in revealing alterations of the

physiological analytes. After years of research, a wide variety of nanosensors have been developed

to detect health-related analytes in a well plate, cell culture, tissue and other biological systems.1-

3 Compared to molecular probes, such as fluorescent molecular indicators of ions and pH4-5, which

are also extensively used in biological systems for imaging and detection, nanosensors excel in the

capacity multiplexing and controllable modulation of physical and chemical properties.2

Functional moieties on nanosensor can be modified to detect other analytes. Size, surface potential,

and morphology of nanosensors can also be tuned to meet the metrics required for effective

biosensing. In vivo monitoring using nanosensors can measure physiological substances

continuously and detect complex activity and function in living organisms. Although some

nanosensors have been successfully applied in vitro, further modifications need to be made to

develop nanosensors for in vivo use. In vivo sensors are required to show desired sensitivity and

dynamic range to detect analytes, selectivity against potential interfering substances, fast response

time, and reversibility.6-7 Stability, residency time, biocompatibility, implantation, and clearance

also need to be considered in the development of a nanosensors for in vivo monitoring.8-9 For these

reasons, only a few types of nanosensors have been applied in live animals. In this dissertation, we

will focus on the development of nanosensors for in vivo detection of physiological molecules.

Nanosensors consist of a recognition moiety to selectively recognize analytes and a reporter to

transduce analyte-based biochemical changes into detectable electronic, optical and magnetic

2

signals. These elements are typically co-immobilized on a nano-platform, such as nanoparticles,

nanotubes, nanofibers, etc (Figure 1.1).1 Selection and modification of a recognition moiety in

this system are essential to determine if required sensitivity, dynamic range, and selectivity can be

achieved. The binding kinetics of the recognition element also impacts the reversibility and

response time of the sensor. Natural proteins, such as antibodies and receptors, can bind to some

physiological analytes selectively. If the binding can trigger a subsequent physical or chemical

change, these molecules can be potentially used as recognition moieties in nanosensors. An

enzymatic reaction is another suitable candidate mechanism to recognize analytes for its excellent

selectivity to substrates and high efficiency in generating detectable products. Also, organic

molecules have been synthesized to selectively bind to analytes. In this chapter, we will categorize

and summarize nanosensors for in vivo application by their mechanism of recognition: binding of

protein and nucleic acid, enzymatic reaction, and recognition by small synthetic molecules.

Recognition based on binding of large natural molecules

Natural large molecules, such as proteins and nucleic acids, selectively binds to analytes with their

unique 3D coformations. Antibodies have been widely used in in vitro assays, such as enzyme-

linked immunosorbent assays (ELISA), Western Blot, and Microchip assays. The other family of

large natural molecules, nucleic acids, can also bind to analytes via a process of evolution and

selection. In both cases, binding to the analytes needs to trigger a subsequent transduction to

detectable signal change. Although efficient and selective binding can be achieved using proteins

and nucleic acids without complex chemical synthesis or modification, the stability of the natural

molecules has restricted their application to sensing in living animals. Also, the high binding

affinity of antibodies make the recognition essentially irreversible and restrict itself as a

recognition moiety for continuous detection in vivo.10

3

Figure 1.1. Proposed general structure and sensing mechanism of nanosensor. The

nanosensor contains a recognition moiety and a reporter on a nanostructure. Binding to analytes

triggers a local chemical or physical change leading to a detectable signal.

4

Here we will briefly overview two types of nanosensors based on binding of proteins and nucleic

acids. As an example, to detect the cancer biomarker chorionic gonadotrophin (HCG), Kim et al.

covalently conjugated two monoclonal antibodies, anti-HCG-β95 and anti-HCG-β97, to iron

nanoparticles (CLIOs) separately to fabricate two types of sensors (CLIO-95 and CLIO-97).11

Once HCG was added to the blend of the two sensors, the antibodies bound to two different

epitopes crosslinking CLIOs, leading to a decrease in T2 signal in Magnetic Resonance Imaging

(MRI) study. This method can efficiently detect 0.5 – 5 µg/mL of HCG in solution. In a further

attempt, this group sealed the CLIO 95/97 blend in a medical device with a semi-permeable

membrane to prevent diffusion of nanoparticles in vivo and then implanted the device in a mouse

via a dorsal midline incision.12 When no tumor was induced, no T2 contrast was created by the

CLIOs in the device. When a tumor was induced in the mouse, the HCG released from tumor was

successfully sensed by the device resulting in a 15% drop in T2.

Single-stranded DNA and RNA show affinity to physiological analytes based on their 3-

dimensional conformation.13-14 A vast library of nucleic acids can be selected in a process termed

"systematic evolution of ligands by exponential enrichment," or SELEX to evolve aptamers with

high affinity to analytes.13 In this process, nucleic acids with random sequences are applied to

analytes, and only the successfully bound nucleic acid sequences are selected and reserved for the

next round of enrichment. After 8-15 cycles, the yielded nucleic acids can be used as a potential

recognition moiety in biosensors, resulting in a pico- to nanomolar affinity.

The massive diversity of nucleic acids and the process of SELEX lead to aptamers with a high

potential to detect targeted analytes. Although a broad range of in vitro applications have been

reported, the research on in vivo nanosensor containing aptamers is still limited. Yi et al. reported

the in vivo detection of adenosine triphosphate (ATP) by combining a two-photon (TP) dye, an

5

aptamer, and graphene oxide (GO).15 In this study, the TP dye was covalently conjugated to the

aptamer that selectively bound to ATP. Without the presence of ATP, the aptamer was adsorbed

on the surface of the GO via π-stacking and hydrogen bonds between nucleobases and the GO,16-

17 and the fluorescence from the tethered TP dye was quenched by the GO via Foster Resonance

Energy Transfer (FRET). When ATP was added, the aptamer was released from the GO when

bound to the analyte and “turned on” the fluorescence. The sensor demonstrated an increase in

fluorescence when 10 µM to 3 mM of ATP was added, and a lower limit of detection (LLOD) of

0.5 µM in cell culture. After the sensor was delivered to a zebrafish, a "turned on" fluorescence

indicated the presence of ATP in the animal at an imaging depth of 270 µm.

Recognition based on enzymatic reactions

Enzymatic reactions can also be used to sense physiological analytes. Compared to other

mechanisms, enzymatic reactions excel in high sensitivity and specificity towards analytes. Both

consumptions of substrates and creation of products can initiate chemical and physical changes

which can be further detected by reporters in nanosensors.

When the targeted analyte is the substrate of an enzymatic reaction, the enzyme can be

incorporated onto nanosensors as a recognition moiety. One commonly used example is the

monitoring of glucose by immobilizing glucose oxidase (GOx) or glucose dehydrogenase (GDH)

coupled with reporters. When glucose is present, the enzymes will oxidize glucose into glucono-

1,5-lactone. Glucose loses electrons in this redox reaction in the micro-environment close to the

surface of microelectrodes leading to a change in optical or electronic signal. This methodology

has been applied to fabricate research- and clinically-used electrodes18 and optical sensors19.

6

An acetylcholine nanosensor based on an enzymatic reaction has been successfully developed by

Walsh et al (Figure 1.2).20 In this study, a DNA-origami dendrimer was fabricated to form the

platform of this nanosensor. Both butyrylcholinesterase (BuChE) and the pH-sensitive dye

fluorescein were covalently conjugated to the DNA backbone. When acetylcholine was present,

the hydrolysis of the neurotransmitter catalyzed by the BuChE created a local pH drop protonating

fluorescein and triggering a decrease in fluorescence. This design enabled measuring micro- to

millimolar of acetylcholine in a well plate and successfully detected exogenously injected

acetylcholine in brain tissue. With a similar mechanism, a nanosensor for chemical imaging of

acetylcholine using MRI will be discussed in Chapter 2 in this thesis.

Cash et al. also reported a nanosensor using an enzymatic reaction to detect histamine in vivo.21 A

nanoparticle composed of a core of plasticized polyvinyl chloride (PVC) and a coat of amphiphilic

DSPE-PEG-lipids was fabricated as the nano-platform of the sensor. The oxygen sensitive

PtTPFPP was incorporated into the lipophilic core of the nanoparticle via sonication. The oxygen

sensitive nanoparticles were suspended in a buffered solution with free diamine oxidase (DAO).

When no histamine was present, the PtTPFPP was quenched by singlet oxygen in the solution.

When histamine was present, the enzymatic oxidation of histamine catalyzed by the DAO

consumed oxygen and unquenched the dye leading to an increase in phosphorescence. This design

can detect histamine in millimolar range with good reversibility. Combined with free DAO, an

increase in phosphorescence from subcutaneously administrated nanosensors was observed

starting at 5 mins post intraperitoneal injection of histamine. Other than incorporating enzyme as

a recognition moiety, an enzymatic reaction can be used to sense biomarkers in other ways.

Bhatia's group has been using substrate conjugated nanomaterial to detect the activity of protease

in vivo.22 Specifically, the substrate peptide of protease was conjugated to nanomaterial as a reco-

7

Figure 1.2. DNA-based nanosensor for optical detection of acetylcholine. Both the recognition

moiety butyrylcholinesterase and fluorescent reporter were conjugated to a DNA dendrimer. When

acetylcholine was hydrolyzed by the enzyme, the resulting local pH drop was detected by the pH-

sensitive fluorescein leading a decrease in fluorescence. Reprinted from permission from Ref 15

(Sci. Rep. 2015, 5, 14832. Copyright 2015, Macmillan Publishers Limited).

8

-gnition moiety. When the activity of protease was high, more peptide was cleaved by the

enzymatic hydrolysis. The hydrolyzed peptide coupled with a reporter was excreted in urine and

then analyzed to determine the relative activity of the protease. Although the final analysis was

carried out ex vivo, the enzymatic recognition happened in vivo. Using the same mechanism,

Kwong et al. reported a mass-encoded nanoworm sensor to detect the activity of protease

expressed from disease sites.23 The concentration of peptides cleaved by the protease was

determined by the analysis of urine using mass spectroscopy. They also showed that the

nanosensor could noninvasively monitor liver fibrosis and cancer in mice by assessing the level of

protease. A mathematical framework was also established to assess the activity of cancer-related

biomarkers using this mechanism.24

The same group further reported a nanoparticle-based sensor with a photolabile group blocking

the site of enzymatic cleavage.25 Only at the site of disease, a UV exposure photolyzed the blocking

molecule to unveil the peptide for the protease catalyzed cleavage. Again, more active protease

led to a higher amount of cleaved peptide in the urine of the animals. This method allowed specific

detection of the protease at the site of disease. Coupled with an immunoassay and microfluidics,

the subcutaneous delivery of the nanosensors, so-called “synthetic biomarkers”, provides a facile

point of care monitoring of thrombosis26-27 and noncommunicable diseases28, respectively.

Recognition based on binding of synthetic molecules

Synthetic organic or organometallics molecules can be screened and modified to recognize

analytes. Compared to natural proteins, it is relatively easier to synthesize and screen small

molecules to recognize the analyte. The binding affinity of the selected molecules can be further

optimized by chemical modifications. Up to now, a variety of synthetic molecules with desired

binding affinity and selectivity have already been reported or are even commercially available. Jin

9

et al. incorporated a sensing molecule, 4-amino-5-methylamino-2′,7′-difluorofluorescein (DAF-

FM) to a poly-(lactic-co-glycolic acid) (PLGA) nanoparticle to track nitric oxide in joint fluid in

vivo.29 In the presence of oxygen, DAF-FM reacted to nitric oxide inducing an increase in

fluorescent intensity. In another report, Zheng et al. immobilized an oxygen-sensitive iridium

complex to a poly(ε-caprolactone)-b-poly(N-vinylpyrrolidone) (PCL-PVP) nanoparticle to detect

hypoxia conditions in tumor tissue.30

Clark’s group has been fabricating optode-based nanosensors to detect critical physiological ions

(Na+, K+, Ca2+, etc.) in vivo using commercially available ionophores. The mechanism of this type

of nanosensor is based on a local protonation/deprotonation. The nanosensor is composed of a

hydrophobic core and an amphiphilic coating. Coated with negatively charged amphiphilic DSPE-

PEG-lipids, the nanosensor can maintain its stability in a buffered solution for days after

fabrication. All sensing components including pH-sensitive fluorescent chromoionophores, ion-

binding ionophores, plasticizer and additives are incorporated in the hydrophobic phase. When the

targeted ion is present, it will be selectively bound by the ionophore and then extracted into the

hydrophobic phase. The chromoionophore will then be protonated/deprotonated to balance the

charge to neutral leading to an alteration of fluorescent intensity. Using this mechanism, Dubach

et al. developed a nanosensor to detect sodium ion in its physiological range.31 In this study, a

higher concentration of sodium led to a lower fluorescent intensity. When this nanosensor was co-

injected with 0 to 500 mM sodium chloride solution to a mouse, a sodium dependency of

fluorescent intensity was observed (Figure 1.3).32 A similar mechanism was used by Cash et al.

to monitor therapeutic lithium in vivo.33 To achieve a high depth of imaging, a photoacoustic

chromoionophore was deprotonated triggered by the extraction of lithium ion. Injected into the

skin of a mouse, the nanosensor successfully detected intraperitoneal administrated lithium ions.

10

Figure 1.3. Sodium dependency of nanosensor. (A) Nanosensors responded to sodium in

aqueous buffer. (B) Nanosesnor responded to co-injected sodium chloride solution. Fluorescence

overlaid with brightfield is shown with seven different subcutaneous injections of sensors in

different sodium concentration. On the right is the corresponding sodium concentration inmM.

Reprinted from permission from Ref 27 (Integr. Biol. 2011, 3, 142-148. Copyright 2011, The

Royal Society of Chemistry.)

11

In addition, reported by Cash et al., an ionophore for histamine was used to track in vivo

concentration of histamine.34

Another optode-based nanosensor reported by Clark's group used small-molecule components to

detect glucose in vivo. Compared to the traditionally used enzyme-based sensing mechanism, this

design avoids local depletion of resources, such as oxygen when glucose oxidase is used, and the

dysfunction of the sensor caused by the degradation of biological components. Billingsley et al.

developed an optode sensor using a combination of alizarin dye and boronic acid to monitor

glucose concentration in vivo (Figure 1.4).35 Without glucose, the complex of alizarin and boronic

acid was fluorescent. When glucose is present, it competitively bound to boronic acid and left

alizarin free. The free alizarin loses its fluorescence. Thus, a high concentration of glucose was

indicated by a weak fluorescence. Implanted in the skin of mice, the optode sensor can detect

glucose administrated by oral gavage.36 To improve the implantation and in vivo residency time of

the optode sensor, glucose-sensitive nanoparticles were fabricated and then embedded in

commercially available hydrogels.37 A glucose-sensitive nanofiber to improve sensitivity and

residency time will be discussed in chapter 3 in this thesis. In this thesis, two recognition

mechanisms are applied to detect physiological molecules: cholinesterase catalyzed hydrolysis is

used in the design of nanosensor for acetylcholine, while boronic acid mediated small molecule

binding is used to sense glucose. The two projects will be discussed in detail in Chapter 2 and 3,

respectively.

12

Figure 1.4. Boronic acid-based glucose nanosensor. (A) Components and mechanism of boronic

acid-based glucose nanosensor. Reprinted from permission from Ref 32 (J. Diabetes. Sci. Technol.

2011, 5, 68-75. Copyright 2011, Diabetes Technology Society.) (B) Image of mouse injected at

four locations with glucose-sensitive nanosensors. Image was obtained with an IVIS-Spectrum

imaging system. Excitation and emission wavelengths were 500 and 600 nm, respectively.

Intensity bar displays the normalized fluorescence efficiency, which represents the fractional ratio

of fluorescent emitted photons per incident excitation photon. Residual background fluorescence

was attributed to remaining fur. (C) The representative response to oral gavage of the blood

glucose (red) and fluorescence of the glucose nanosensors (black). Mean ±SD for one mouse is

shown. Reprinted from permission from Ref 31 (Anal. Chem. 2010, 82, 3707-3713. Copyright

2010, American Chemical Society.)

13

1.2 Acetylcholine and its detection

Acetylcholine is a neurotransmitter expressed to transmit signals between neurons38-39 and in

neuromuscular junctions40. This neurotransmitter is released by cranial nerves, motor neurons, and

preganglionic neurons that exit the central nervous system (CNS). In the CNS, acetylcholine is

also found in the basal forebrain complex that projects to the hippocampus and neocortex, and the

pontomesencephalic cholinergic complex that projects to the dorsal thalamus and forebrain.41

Synthesis of acetylcholine from choline is catalyzed by choline acetyltransferase (ChAT) in

cholinergic neurons. Then the neurotransmitter is stored in synaptic vesicles, released from the

pre-synaptic membrane to bind to nicotinic or muscarinic receptors at either post-synaptic

membrane or axons.41 The nicotinic receptor is a ligand-gate ion channel. Once bound to

acetylcholine, the ion channel will open and allows sodium, potassium, and calcium to pass

through.42 On the other hand, five subtypes of muscarinic (M1-M5) are G-protein-coupled receptor

(GPCR).43 Binding of acetylcholine triggers a cascade of signal transduction involving Inositol

trisphosphate (IP3) or cyclic adenosine monophosphate (cAMP) as second messengers. After

signal transmission, acetylcholine will be hydrolyzed by cholinesterase into acetic acid and choline

which will be recycled by pre-synaptic neurons.44

In the CNS, acetylcholine is closely related to learning and memory.45-46 Behavioral pharmacology

reveals that scopolamine, a muscarinic antagonist, blocked sensory or attentional processes and

affected short-time memories in brains of rodents, monkeys, and humans.47 Also, a neurotoxin 192

IgG saporin created cholinergic lesions by decreasing activity of ChAT and impaired learning and

memory performance of rats.48 Deficiency of acetylcholine in brain is also associated with aging

and dementia especially Alzheimer’s disease (AD).49 Symptomized by memory loss, confusion

with time or place, decreased or poor judgement, etc., this disease is now the 6th leading cause of

14

death in the United States. According to the facts from Alzheimer’s Association, 5.7 million (2017)

Americans are now affected by this disease, and this number is expected to keep rising in next

decades.50

Research and clinical reports have unveiled that the decline of cholinergic function in basal

forebrain plays an important role in causing AD.49 Also, the formation of pathological beta-

amyloid (Aβ) is increased by stimulation of muscarinic acetylcholine receptors.51 There are reports

that Aβ interacts with nicotinic acetylcholine receptors.52 Thus, investigation of the relationship

between the concentration of acetylcholine and AD will render a crucial tool for research and

diagnosis of this disease.

Currently, functional positron emission tomography (PET), computed tomography (CT),

electroencephalogram (EEG) and magnetic resonance imaging (MRI) have been applied to explore

the alteration of brain function in animal models with the AD.53 More specific detection of

acetylcholine still relies on microelectrode54-55 and microdialysis.56-57 Though these methods

provide tools for direct measuring of acetylcholine, the invasive nature and limited spatial

resolution restrict their applications.

Chapter 2 will report our innovation to develop a nanosensor for in vivo detection of acetylcholine

using MRI. This nanosensor uses BuChE as the recognition moiety and a pH-sensitive MRI

contrast agent as the reporter. When acetylcholine is hydrolyzed by the enzyme, the local decrease

in pH will be sensed by the reporter and transduced to an increase in signal intensity in MR image.

This design enables selective imaging of acetylcholine in deep brain for the first time. Compared

to previously reported fluorescent nanoprobe for acetylcholine and CEST-based MR spectroscopic

imaging, the nanosensor we developed is advantageous for its high depth of imaging and

15

selectivity. More details of both in vitro and in vivo application of this nanosensor will be discussed

in detail in Chapter 2.

1.3 Glucose and its detection

Diabetes is a disease associated with the disorder of glucose metabolism. Currently, more than 29

million Americans (2016)58 and 388 million Chinese (2013)59 adults are diabetic, and much more

population have prediabetes. This epidemic has sparked interest in detecting and monitoring the

concentration of glucose which is an important reference to determine the dose of insulin

therapy.60-61 Although a lot of efforts have been made to detect glucose in tears62, sweat63 and other

body fluids64, the gold standard to measure glucose is still the finger-prick test. A series of point-

of-care glucose meters have been commercially available for patients to self-monitor blood

glucose.65 All of these devices still require blood samples for discrete measurement. Continuously

monitoring glucose can track the change of the blood glucose levels for hours and days and provide

more information on diet control, physical activity, and dose of medicine. Now there are four types

of continuous glucose monitors (CGM) available on market to invasively measure glucose in

interstitial fluid.66 All of these meters and monitors coat enzymes such as GOx or GDH on an

electrode to gain the desired sensitivity within the physiological range of glucose in the blood.

However, the degradation of biological elements in these monitors hinders the long-term

application of these devices. To minimize invasiveness, extend sensor residency time, and enhance

accuracy, the development of novel glucose sensors is still an area of interest.

The Clark lab has been using a non-biological small molecule boronic acid as a recognition moiety

to detect glucose. Coupled with a fluorescent dye alizarin as the reporter, a small molecule-based

nanosensor can be used to continuously measure glucose in vivo with minimal invasiveness.

Compared to enzyme-based glucose sensors, the application of small molecule boronic acids

16

avoids the potential degradation of biological elements in vivo. A nano-optode based sensor was

firstly reported in 2010 showing desired dynamic range, sensitivity, and selectivity.35 In vivo

experiment demonstrated this sensor can detect glucose administrated by oral gavage in mice. The

result was consistent with the gold-standard blood test. Later, the nanoparticle-based sensor was

embedded into commercially available hydrogels to extend its residency time at injection sites.37

In chapter 3 of this thesis, we will illustrate a new glucose sensitive nanofiber. The nanofiber was

fabricated to prolong the in vivo liftetime of the boronic acid-based nanosensors for the purpose of

coniuously monitoring glucose. We synthesized and screened boronic acids with lower pKa to

improve its sensitivity at physiological pH. Then we incorporated the selected boronic acids to a

nanofiber using electrospinning. The final sensor had an increase residency time in vivo.

17

Chapter 2: Nanosensors for the Chemical Imaging of Acetylcholine

Using Magnetic Resonance Imaging

2.1 Introduction

Imaging tools that enable real-time visualization of molecular neural events, namely

neurotransmitter release, are highly valuable for understanding the basis of brain function and

disease. Cellular-level methods such as electrophysiology67 and optical imaging68 offer recordings

of neural activity with precision and high specificity, however, are often limited to sampling a

relatively small area of the mammalian brain. Conversely, among modern imaging techniques,

magnetic resonance imaging (MRI)69 is a powerful tool that provides advantages for in vivo

analysis as it can be applied noninvasively, with unlimited tissue penetration and mapping

capabilities of the whole-brain. Despite these advantages, resolving target-specific detection of

neurotransmitters with molecular specificity for functional neuroimaging is yet to be fully

established, and only a limited number of successful studies have been implemented for in vivo

monitoring in the brain. This is in part due to a low concentration of neurotransmitters in the brain,

as well as the low intrinsic sensitivity and resolving power of the MRI. Consequently, synthesizing

a highly sensitive, stable and non-toxic probe is always a great challenge for effective application

of molecular neuroimaging in the brain. Currently, a number of MRI molecular contrast agents

have been explored for imaging of neurotransmitters in the brain. For example, detection of

dopamine70-71 and serotonin72 have been developed from engineered forms of flavocytochrome

P450-BM3 with a detectable T1 signal. A contrast agent to detect glutamate73 has also been

developed based on the displacement of mGluR5 receptor as well as agents based on a crown ether

cation-binding motif chelating a gadolinium (Gd) to target glutamate, GABA, and glycine albeit

18

with millimolar affinity.74 An additional approach, using the chemical exchange saturation transfer

(CEST) effect, has been demonstrated to detect glutamate in human subjects.75-76 While there is a

growing repertoire of neurotransmitter-sensitive MRI probes, there remains considerable

opportunities for the development of imaging agents that can respond to neural activity with high

chemical specificity and sensitivity in order to overcome the inherently low signal-to-noise ratio

of MRI.

In particular, the development of nanostructured sensors as MRI contrast agents is a promising

direction for the detection and activity-dependent in vivo monitoring of neurotransmitters in the

brain. Unlike traditional molecular organometallic chelates, the nanostructured probes offer

advantages of flexibility and modularity to modify their physicochemical properties and

functionalities.2 To date, MRI contrast agents have been packaged in nanoparticles in order to

amplify magnetic relaxivity (r1),77 improve penetration and retention in tumor,78 monitoring of

enzymatic activity79 and for theranostic applications.80 However, the development of nano-scale

MRI contrast agents for the chemical imaging of neurotransmitters has yet been realized.

In the present study, we developed a nanoparticle sensing platform for the imaging

neurotransmitter acetylcholine in the living brain tissue using MRI. As an important molecular

messenger, acetylcholine is involved in regulating chemical communication between cells in the

brain. In particular, the cholinergic system is one of the most important modulatory

neurotransmitter systems in the brain, in which both synaptic38 and volume39 transmission govern

activities that depend on selective attention,81 formation of working memories82 and cognitive

behavior.83 Additionally, perturbations of the cholinergic system are implicated in schizophrenia,84

depression85 and Alzheimer’s disease.86 Previously, direct measurement of choline using 1H MR

spectroscopy and its application in malignant breast tumor was reported.87-88 Our method differs

19

by detecting acetylcholine directly, since it is unlikely that choline could be used a surrogate in

the brain.89

The design of the nanosensor involves co-immobilizing the enzyme butyrylcholinesterase (BuChE)

and pH-sensitive gadolinium contrast agents on a nanoparticle to create a pH drop triggered by the

enzymatic hydrolysis of acetylcholine which detected by the proximate contrast agents within the

nanoparticle microenvironment leading to an increase in T1 relaxation rate (1/T1). The nanosensor

platform presented here prevents the sensing components from diffusing away in vivo and provides

a required proximity between the contrast agent and BuChE. As such, this design is well-suited

for real-time imaging of acetylcholine in the living brain, in addition to filling a need in the field

of MRI by providing chemical specificity to neuroimaging.

Here, we show: (1) synthesis and characterization of the nanosensor for detection of acetylcholine

(ACh-MRNS); (2) response of the nanosensor to increase in acetylcholine levels with suitable

sensitivity and selectivity in vitro; and (3) in vivo detection of endogenous release of acetylcholine

in the rat medial prefrontal cortex (mPFC), which is known to receive dense cholinergic inputs

from the basal forebrain and the hippocampal formation90 stimulated by systemic administration

of clozapine.

2.2 Materials and Methods

Materials

2-hydroxyl-5-nitrobenzyl bromide, acetic anhydride, bis-(2-ethylhexyl)sebacate (DOS),

butyrylcholinesterase from equine serum (EC 3.1.1.8) (BuChE), clozapine, 5,5′-Dithiobis(2-

nitrobenzoic acid) (DTNB), dopamine hydrochloride, fluorescein sodium salt, gadolinium(III)

nitrate hexahydrate, γ-aminobutyric acid (GABA), glutamic acid, glycine, methanol, N-(3-

dimethylaminopropyl)-N’-ethylcarbodiimide hydrochloride (EDC), N,N’-dimethylaminopyridine

20

(DMAP), N,N’-dimethylformamide (DMF), N-hydroxysuccinimide (NHS), N,N,N’,N’-

tetramethyl-O-(N-succinimidyl)uroniumtetrafluoroborate (TSTU), potassium carbonate (K2CO3),

triethylamine (TEA) trifluoro acetic acid (TFA) and xylenol orange tetrasodium salt were

purchased from Sigma Aldrich (St Louis, MO, USA). 15-azido-4,7,10,13-tetraoxa pentadecanoic

acid was purchased from Alfa Aesar. DO3A tert-butyl ester (t-BOC DO3A) was purchased from

Macrocyclics (Plano, TX, USA). Phosphate Buffered Saline (PBS) (1×, pH 7.4) and sterilized

0.9% saline solution were purchased from Invitrogen (Carlsbad, CA, USA). Hydrochloric acid

(1.0 N) and sodium bicarbonate were purchased from Fisher Scientific (Fair Lawn, NJ, USA). 1,2-

distearoyl-sn-glycero-3-phosphoethanolamine-N-[amino(polyethylene glycol)-2000] (ammonium

salt) (DSPE-PEG-amine), 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-

[dibenzocyclooctyl (polyethylene glycol)-2000] (ammonium salt) (DSPE-PEG-DBCO), 1,2-

distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycol)-550]

(ammonium salt) (DSPE-PEG-methoxy) and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-

N-[poly(ethylene glycol)2000-N'-carboxyfluorescein] (ammonium salt) (DSPE-PEG-fluorescein)

were purchased from Avanti Polar Lipids, Inc. (Alabaster, AL, USA), and octadecyl Rhodamine

chloride (R18) was purchased from Thermo Fisher Scientific (Waltham, MA, USA).

Synthesis of pH sensitive contrast agents

t-BOC DO3A (200 mg, 0.4 mmol) and 2-hydroxyl-5-nitrolbenzyl bromide (360 mg, 2.0 mmol)

were dissolved and stirred in a mixture of 2 mL DCM and 2 mL DMF for 1 h at room temperature.

After addition of 600 mg K2CO3, the resulting suspension was stirred overnight. Supernatant was

then collected after centrifugation, reduced by vacuum, and 3 mL TFA was added dropwise at 0

oC, then stirred overnight after it was allowed to warm to room temperature. TFA was removed by

21

vacuum and the residue was purified by flash chromatography to yield product (34% after two

steps). 1H NMR spectrum of product was compared91 to verify success of synthesis.

EDC (7.6 mg, 0.04 mmol) and NHS (4.6 mg, 0.04 mmol) in 200 µL 0.1× PBS, pH 6 was added to

pH-sensitive chelates (20 mg, 0.04 mmol) in 3 mL 0.1× PBS (pH 6) in 10 aliquots. The solution

was stirred for 30 min and then added to a solution of DSPE-PEG-amine (6 mg, 0.002 mmol) in 3

mL 0.1× PBS (pH 7.4). After the pH was adjusted to 7.4, the reaction mixture was stirred overnight

at room temperature. Gd(NO3)3·6H2O (36 mg, 0.08 mmol) in 200 µL DI water was added to the

resulting solution in 5 aliquots, and the pH of the reaction mixture was maintained between 4 and

6 during the addition. The resulting solution was stirred overnight at 40 oC, then diluted to 24 mL

with DI water and stored at 4 oC.

Fabrication of pH-sensitive nanoparticles

DSPE-PEG-DBCO (0.2 mg, 0.067 µmol) in 20 µL chloroform was dried in a glass vial before

addition of 4 mL stock solution of DSPE-PEG attached pH sensitive contrast agent (0.33 µmol).

The mixture was sonicated at 20% amplitude for 30 s to re-dissolve dried DSPE-PEG-DBCO using

a Branson digital sonifier (Danbury, CT). Following addition of a mixture of 3 mg PVC and 6 mg

DOS in 50 µL THF and 80 µL DCM, the solution was sonicated at 20% amplitude for 3 min. The

resulting nanoparticle suspension was filtered using Acrodisc syringe filter with 0.45 µm

membrane (Pall Cooperation, Ann Arbor, MI, USA), washed by DI water (1 mL × 5) in 100 kD

molecular weight cut off (MWCO) Amicon Ultra centrifugal filters (EMD Millipore, Billerica,

MA, USA), and concentrated to 0.1 mL in DI water by the same filters.

The concentrated suspension was treated with 1.5 µL TEA, vortexed for 3 h, then treated with 1

µL acetic anhydride, and vortexed overnight. The resulting nanoparticle suspension was washed

22

(1 mL × 5) in 100 kD MWCO spin-filters as mentioned above and then diluted to 0.5 mL with

PBS, pH 8.

Conjugation of BuChE to nanoparticles

Solution of 15-azido-4,7,10,13-tetraoxa-pentadecanoic acid (30 nmol in 3 µL DMF), TSTU (1.8

mg, 6.0 µmol) and DMAP (1.4 mg, 11 µmol) were all dissolved in 8 µL DMF. The solution was

vortexed for 1 h, and added to solution of BuChE (0.3 nmol in 292 µL PBS, pH 8). The mixture

was vortexed for another hour, washed (1 mL × 5) and diluted to 0.5 mL with PBS, pH 8 in 100

kD MWCO spin-filters as mentioned above. The modified enzyme solution and 0.5 mL

nanoparticle suspension were combined and incubated for 72 h at 4 oC. The suspension was

concentrated to 30 µL with 100 kD MWCO spin-filters as mentioned above before in vitro

calibration.

Particle sizing, zeta-potential and concentration of nanoparticles measurements

The conjugated nanoparticles above were characterized for measurement of particle size and zeta-

potential by dynamic light scattering (DLS) using a 90 Plus particle size analyzer (Brookhaven

Instruments Corporation). The concentration of nanoparticles was measured using nCS1TM

nanoparticle analyzer (Spectradyne, Torrance, CA).

Inductively coupled plasma mass spectrometry (ICP-MS)

To identify the relaxivity (r1) of nanoparticles, we used a Bruker Aurora M90 inductively coupled

plasma-mass spectrometer (Bruker Scientific Instruments, Billerica, MA, USA) to determine the

amount of Gd(III) bound to the surface of nanoparticles. Standard solutions were prepared by

dissolving Gd(NO3)3 in DI water with different concentrations (0, 0.5, 5, 20, 50 µg/L). The

concentrated nanoparticle suspension from previous step was diluted by DI water in ratios of 0.2,

23

2, 8, and 20 µL/L, and compared with the standard solution to identify the exact amount of bound

Gd(III).

Electron microscopy

Four microliters of sample was pipetted onto a C-Flat (Protochips) holey carbon film then plunge

frozen using a Gatan Cp3 CryoPlunge unit. Prepared grids were stored under liquid nitrogen until

loaded for imaging in an FEI Arctica CryoFEG-TEM with autoloader. Images were collected

using low-dose techniques at 200 kV. Images were analyzed using ImageJ software92 to measure

diameter of nanoparticles.

To stain lipid coating of the nanoparticles, diluted nanosensors (5 µL) were placed on a 300 mesh

carbon film coated copper grid (Electron Microscopy Sciences) for 1 min. The excessive liquid

was removed by a piece of filter paper. The remaining sample on carbon film was stained using 5

µL of methylamine vanadate (Nanovan) for 1 min, and then the excessive liquid was blotted by a

filter paper. After two rounds of staining, the images were acquired at 200 kV accelerating voltage

using FEI Arctica CryoFEG-TEM.

Relaxivity test in low magnetic field

ACh-MRNS corresponding to 0.43, 0.21, 0.12. 0.11 and 0.043 mM Gd(III) was suspended in 500

µL 1× PBS in NMR tubes and then analyzed by a 1.5 T Bruker Minispec mq60 NMR analyzer (60

MHz, Bruker Inc., Billerica, MA) at 37 oC to yield T1 of each sample. 1/T1 was plotted as a function

of concentration of Gd(III) and the slope of the plotted curve is r1.

pH calibration

We added 2 µL of a nanosensor suspension to 198 µL PBS, pH 6, 6.5, 7, 7.4 and 8 in well plates.

The well plate was scanned in a 7 T Bruker Biospec MRI scanner for small animals (Bruker

24

Scientific Instrument, Billerica, MA, USA). A T1-weighted Rapid Acquisition with Relaxation

Enhancement with Variable TR (RARE-VTR) sequence (1 slice; 1.0 mm; TE = 12.5 ms, TR = 70,

291, 576, 976, 1651, and 5000 ms, FOV = 40 mm × 40 mm; data matrix 64 × 64) was used to

generate a T1 map in about 9.5 min. The signal intensities at different TRs were also collected

using Matlab code.

In vitro calibration

Sensor calibration was performed in a Bruker coil with an inner diameter of 7.5 cm. A Tripilot

scan was initially conducted followed by addition of 2 µL of a nanosensor suspension to 198 µL

solution of acetylcholine (0, 50, 100, 250, 500 and 1000 µM) in 1× PBS, pH 7.4 in well plates.

The well plate was scanned in a 7 T Bruker Biospec MRI scanner for small animals (Bruker

Scientific Instrument, Billerica, MA, USA). A T1-weighted Rapid Acquisition with Relaxation

Enhancement with Variable TR (RARE-VTR) sequence (1 slice; 1.0 mm; TE = 12.5 ms, TR = 70,

291, 576, 976, 1651, and 5000 ms, FOV = 40 mm × 40 mm; data matrix 64 × 64) was used to

generate a T1 map in about 9.5 min. The signal intensities at different TRs were also collected

using Matlab code. To verify that the pH change is a local effect, we also suspended 2 µL pH

sensitive nanoparticles without conjugation of enzyme (pH-MRNS) and 8.7 units free BuChE in

198 µL solution of acetylcholine (0, 10, 50, 100, and 500 µM) in PBS, pH 7.4 in well plates. The

same sequence was used for the T1 map.

In vitro calibration at pH 7.2, 7.4 and 7.8

ACh-MRNS corresponding to 0.04 mM Gd(III) was suspended in 500 µL 1× PBS, pH 7.2, 7.4 and

7.8 with a concentration of acetylcholine varying from 0 to 100 µM in NMR tubes and then

analyzed by a 1.5 T Bruker Minispec mq60 NMR analyzer (60 MHz, Bruker Inc., Billerica, MA)

at 37 oC to yield T1 of each sample.

25

Measurements of overall pH change in the mixture of nanosensors and acetylcholine

Fluorescein sodium (5 µg/mL) was mixed with ACh-MRNS (0.2 mM Gd(III)) and pH-MRNS (0.2

mM Gd(III) with 8.7 units of BuChE in 100 µL 1× PBS, pH 7.4, respectively. Then 100 µL of

acetylcholine solution with concentrations varying from 0 to 1 mM in 1× PBS, pH 7.4 was added

to the mixture. The mixture was excited at 460 nm and read the emission at 520 nm using a

SpectraMax Gemini EM plate reader.

Xylenol orange test

The test was performed following the procedure reported in the previous literature.93 Specifically,

the ACh-MRNS corresponding to 0.1 mM conjugated Gd(III) was incubated with concentrations

of acetylcholine varying from 0 to 5 mM in 100 µL HEPES buffer, pH 7.4 for 10 min and then

was added with 100 µL of 0.6% (mg/mL) xylenol orange in 50 mM acetic acid buffer, pH 5.4. The

absorbance at 573 nm and 433 nm was read using a SpectraMax Gemini EM plate reader. No

significance change in A573/A433 was observed. We also plotted a work curve of the test: 100 µL

of 0.6% (w/v) xylenol orange in 50 mM acetic acid buffer, pH 5.4 was added to 100 µL of

Gd3(NO3)3 solution in HEPES buffer, pH 7.4 to make the final concentrations of Gd3+ varying

from 0 to 100 µM. The absorbance at 573 nm and 433 nm was read using a SpectraMax Gemini

EM plate reader.

Enzymatic kinetics studies

DSPE-PEG-methoxy was used to replace pH-sensitive contrast agents conjugated DSPE-PEG-

amine to coat nanoparticles to avoid interference from pH-sensitive contrast agent’s absorbance at

400 nm in this study. Ellman’s assay was used to test the catalytic kinetics of conjugated and free

BuChE. 8.7 units of BuChE in each form was suspended in 1 mM DTNB in 100 µL PBS, pH 7.4

26

in a 96 well plate. Acetylthiocholine in 100 µL PBS, pH 7.4, was added to the mixture to make

final concentration of acetylthiocholine 0, 5, 10, 50 and 100 µM. The absorbance at 412 nm was

recorded right after the addition every 5 s for 2 min using a SpectraMax Gemini EM plate reader.

To study the overall change of pH led by the enzymatic hydrolysis, we suspended 2 µL

nanoparticles coated by DSPE-PEG-fluorescein and 8.7 units of azide modified BuChE in 100 µL

1× PBS, pH 7.4. Acetylcholine in 100 µL 1× M PBS, pH 7.4, was added to the mixture to make

final concentration of acetylthiocholine 0, 5, 10, 50, 100, 500 and 5000 µM. The fluorescent signal

(excitation: 490 nm, emission: 520 nm, cut-off: 515 nm) was recorded right after the addition every

10 s for 10 min using a SpectraMax Gemini EM plate reader.

Selectivity studies

2 µL of concentrated nanosensors were suspended in 198 µL of either glutamate (5 mM), dopamine

(5 mM), GABA (5 mM), or glycine (5 mM) in PBS, pH 7.4, and scanned with the same coil and

sequence used for in vitro calibration. The resulting 1/T1 was compared with the 1/T1 of

nanosensors in PBS, pH 7.4 and acetylcholine solution (0.1 mM in 1× PBS, pH 7.4).

Animal care and stereotaxic surgery

Adult male Sprague-Dawley rats (230-300 g) were obtained from Charles River Laboratories

(Wilmington, MA). The rats were maintained on a 12:12 h light:dark cycle and allowed access to

food and water ad libitum. All procedures were approved by the Northeastern University

Institutional Animal Care and Use Committee and were in accordance with the National Institutes

of Health guidelines.

Three days prior to MRI experiments, unilateral implantation of 26-gauge plastic guide cannula

(Plastics One) aimed at the medial prefrontal cortex (mPFC) was performed on animals using a

27

stereotaxic device (Kopf Instruments) under isoflurane anesthesia. A small incision was made to

expose the dorsal surface of the skull and wiped clean to reveal the position of lambda and bregma

landmarks. A small hole was drilled into the skull at the coordinate position necessary to gain

access to the prefrontal cortex (bregma: +2.8 mm anterior, +0.8 mm lateral, +4.0 mm below the

surface of the skull). The cannula was placed in the brain and anchored using plastic screws and

dental acrylic. The head-wound was then sutured closed and topical antibiotic ointment was

applied to the wound area. Buprenorphine (0.5 mg/Kg) was administrated to reduce pain.

In vivo nanosensor injection and MRI

Animals were first anesthetized with 1-2% isoflurane and placed in a plastic positioning device

and a head holder built-in with quadrature transmit/receive volume coil. Infusion of nanosensors

into the mPFC was performed by lowering and placing the internal cannula attached to a 10 µL

Hamilton syringe via polyethylene tubing filled with nanosensors through the guide cannula,

delivering a final volume of 2 µL of nanosensors. The air was first removed prior to nanosensor

delivery. The internal-injector cannula protruded 1 mm beyond the guide cannula toward the

mPFC.

After delivery, MRI experiments were conducted using a 7 T Bruker Biospec 300 MHz MRI

scanner for small animals (same as above for in vitro studies). The design of the positioning device

and head holder coil provided complete coverage of the brain from olfactory bulbs to brain stem

with excellent B1 field homogeneity. At the beginning of each imaging session, a high-resolution

anatomical data set was collected using the RARE-VTR sequence (25 slices; 1.0 mm; TE = 12.5

ms, TR = 513, 800, 1400, 2200, and 6000 ms, FOV = 40 mm x 40 mm, data matrix 128 x 128) to

assess time-lapse nanosensor response, followed by acquisition of same sequence at multiple time-

points (0, 23, 46 min post-nanosensor injection). Each scan took about 23 minutes. For detection

28

of drug-evoked cholinergic transients, subcutaneous injection of clozapine (20 mg/Kg) dissolved

in PBS, pH 6.5 into the back of rat was administered at the time of nanosensor injection, i.e., just

prior to T1 scan at 0 min time point. MRI signal was quantified in regions of interest (ROIs)

covering the injection sites. Each ROI volume was defined by a cylinder with a diameter of 1.2

mm in the coronal plane and a thickness of 1 mm along the rostrocaudal axis at the site of injection,

centered on the tip of the internal cannula, and computing signal amplitudes normalized with

respect to identical control ROIs placed on respective coordinates on the contralateral side of the

brain without sensor delivery.

Histological analysis

To verify cannula placement following MRI contrast agent injection experiments, animals were

anesthetized with carbon-dioxide and transcardially perfused with a solution of PBS (pH 7.4) with

1% sodium nitrite, followed by 4% wt/vol paraformaldehyde in 0.1 M phosphate buffer (pH 7.4).

Brains were removed, postfixed for 90 min in perfusion fixative, and cryoprotected in a series of

20% and 30% sucrose in 0.1 M phosphate buffer each overnight at 4 oC. Coronal sections of 40

µm thickness across a range extending ~2 mm anterior and posterior to the cannula insertion site

was sectioned on a cryostat (Microm HM 550). Standard protocols were used for choline

acetyltransferase (ChAT) immunohistochemical and cresyl violet (Nissl) histological staining.

Briefly, for ChAT staining, free-floating sections in well-plates were first incubated in 1%

H2O2/50% methanol solution for 10 min, followed by serum-blocking buffer for 60 min at room

temperature. Sheep polyclonal anti-ChAT antibody (ab18736, Abcam) diluted 1:1,000 in

immunobuffer containing 1% normal rabbit serum (16120107, Thermo Fisher) in PBS-0.2%

Triton-X100 (PBS-T) was applied and incubated overnight at 4 oC. The sections were then

incubated with horse-radish peroxidase (HRP)-conjugated rabbit anti-sheep IgG secondary

29

antibody (818620, Thermo Fisher) diluted 1:1,000 in immunobuffer for 2 h at room temperature,

and developed in 3,3-diaminobenzidine tetrahydrochloride (DAB) solution (34002, Thermo

Fisher). The sections were washed between each step (3× 5 min) in PBS-T. The sections were then

mounted onto 0.5% gelatin/0.05% chrom alum coated glass slides, allowed to air-dry, and

dehydrated through a series of alcohols (75%, 85%, 95%, 100% twice 5 min each), cleared with

xylene, and coverslipped with Permount (Fischer Scientific, Pittsburgh, PA). The sections were

viewed with Olympus BX51 light microscope. Sections processed to determine non-specific

staining by following the same procedures, but with omission of the primary antibody, showed no

immunohistochemical labeling.

Diffusion of the fluorescent nanosensors

The fluorescent nanosensors were fabricated as the pH-MRNS except for 0.001 mg R18 was

incorporated to the mixture of PVC, DOS and THF. 2 µL of nanosensors containing 47 µM of Gd

was delivered into a phantom made of 0.6% agarose. The follow-up imaging was performed on

IVIS Lumina II (Perkin Elmer) small animal imager in fluorescence mode with a 535 nm excitation

filter and DSRed emission filter at 0, 2, 5, 10, 15, 20, 30, 40, 50 and 60 min post injection for 1 h.

To obtain quantitative information about nanosensor diffusion, circular ROI was positioned

centered near the tip of injection site and fluorescence signal amplitudes per unit area (mm2),

normalized with respect to signal intensity at t = 0 was acquired using Image J software.92 Relative

intensity from three sets of identical ROIs were obtained and averaged for the each time point.

Acetylcholine signal analysis and group sizes

Data for final analysis was extracted from 19 animals divided into three main groups: ACh-MRNS

(N = 6); control (N = 6), and pH-MRNS (N = 3) groups. Only animals with correctly placed cannula

tips into the mPFC, as judged after surgery from MRI scans and histological analyses, were

30

included in the data analysis. Based on this criteria, four animals with incorrectly placed cannula

were excluded from the final analysis. No randomization was conducted to determine allocation

of different animal groups for the MRI scan procedure. Instead, animals were split to both

experimental and control groups in each procedure. Prior to ROI image analyses, individual cases

were assigned with a random number to ensure analyses were conducted in a blinded fashion.

Image and data analysis

All data analysis and image processing was performed with Bruker Paravision 5.1 software

(Billerica, MA, USA), Matlab (Mathworks, Natick, MA), and itk-SNAP.94 Images were

reconstructed and analyzed using custom routines running in Matlab. Relaxivities were calculated

from T1 obtained from itk-SNAP and concentration of Gd(III). Graphs and illustrations were

compiled using Origin (OriginLab, Northampton, MA, USA) and Illustrator (Adobe, San Jose,

CA, USA), respectively.

Statistical analysis

All the relaxivities, T1, sizes, and zeta-potentials collected from the in vitro experiments are

average of three separate studies using different batches of ACh-MRNS or pH-MRNS. The T1

collected from the in vivo experiments are average of T1 collected from 6 rats in Control group, 6

rats in Experiment group and 3 rats in pH-MRNS group using different batches of ACh-MRNS or

pH-MRNS.

The differences between groups for the selectivity study was calculated using one-way analysis of

variance (ANOVA) and adjusted using Tukey’s HSD for multiple comparisons. Kolmogorov-

Smirnov normality test was used to test for normality. Differences between each group obtained

from in vivo data were calculated using Student’s t- test. The ɑ level for all statistical analyses was

31

set at 0.05. Sample size was chosen based on previous reports on MRI method development70 and

drug concentrations.95 When P < 0.05, the difference was considered as significant.

Errors (Table 2.1, 2.2, Figures 2.7B and C, 2.12B, 5C and D, Figure 2.14) were propagated from

standard deviation (S.D.) of T1 (σT1) or normalized T1 (σ(normalized T1) using formula derived

from previous reports.96

The equation used for Table 1 and 2 is defined as:

1 1( ). .

[ ]

T average of TS D

Gd

=

where, [Gd]: concentration of Gadolinium in mM.

The equation used for Figures 2.7B, C, and 2.12B is defined as:

2

1 1. . ( )S D T average of T −=

The equation used for Figures 2.14C, D, and 2.6 defined as:

2

1 1. . ( )( )S D normalized T average of normalized T −=

where, normalized T1: T1 of each animal normalized to 0 min post nanosensor injection.

2.3 Results and Discussion

Nanosensor mechanism and particle characterization

In our nanosensor design we fabricated highly-plasticized polymer nanoparticles as a platform and

functionalized the surface to co-immobilize both transduction and signaling moieties (Figure 2.1).

The mechanism is based on the enzymatic hydrolysis of acetylcholine by BuChE into choline and

acetic acid, and the resulting reduction in local pH alters the water coordination97 of the pH-

sensitive contrast agent leading to an increase in r1 and T1 relaxation rate (1/T1).

32

Figure 2.1. Schematic of the nanosensor structure and mechanism. (A) The pH-MRNS. Only

pH-sensitive contrast agents were covalently conjugated to the DSPE-PEG lipids and coated on

the surface of the lipophilic core. Without co-immobilized BuChE, acetylcholine will not be

hydrolyzed to alter local pH. (B) The ACh-MRNS. Both pH-sensitive contrast agents and BuChE

were covalently conjugated to the DSPE-PEG lipids and coated on the surface of the lipophilic

core. The The BuChE catalyzes the hydrolysis of acetylcholine to choline and acetic acid, and the

resulting drop in local pH triggers a conformational switch of the contrast agent: There is one more

water molecule coordinated to one Gd(III) chelate in acidic conditions compared to its structure in

basic conditions, which leads to increased T1 relaxation rate (1/T1) of the contrast agent.

33

The close proximity of the enzymes and pH-sensitive contrast agents on a nanoscale platform

ensures MR-signal changes are generated by the contrast agent at the origin of the pH changes

created by the enzymatic hydrolysis of acetylcholine. The H+ will be generated in the

microenvironment close to the surface of the ACh-MRNS during the enzymatic hydrolysis and

detected by the pH-sensitive contrast agent nearby. The localized pH changes result from a

gradient as acetylcholine is consumed and H+ ions are released at a fast rate. The signal is

reversible, dependent on acetylcholine concentration, and achieves a limit of detection (LOD) and

sensitivity in the physiological concentration of acetylcholine (nanomolar to micromolar in the

extracellular space of the brain).39

We characterized two types of nanosensors intended for the in vivo study: (1) ACh-MRNS and (2)

a pH-sensitive sensor (pH-MRNS) (Figure 2.1 and Figure 2.2). The ACh-MRNS contained both

the pH-sensitive contrast agents and BuChE conjugated to nanoparticles for specific detection of

acetylcholine; and the pH-MRNS was conjugated with pH-sensitive contrast agents only, i.e.,

without enzyme. The pH-MRNS served as a control due to potential interference from systemic

changes in pH. The structure of nanoparticle is similar to the optode based nanosensors previously

reported by our lab.98-99 The nanoparticle is composed of a core of highly-plasticized polyvinyl

chloride (PVC) and a coat of amphiphilic DSPE-PEG-lipid. The high molecular weight PVC

(Sigma catalog No.: 81392; Mw ~90,000) was dissolved in bis-(2-ethylhexyl)sebacate (DOS) to

form a highly lipophilic nanoparticle. The surface of the particles is derivatized through the use of

DSPE-PEG lipids as a biocompatible coating on the lipophilic surface (Figure 2.2). Separately,

pH-sensitive contrast agents (Compound 1) and BuChE (Compound 2) were covalently linked

sequentially to corresponding DSPE-PEG-lipid derivatives (see Materials and methods).

34

Figure 2.2. Fabrication of the ACh-MRNS and pH-MRNS. To fabricate the pH-MRNS,

contrast agents conjugated DSPE-PEG lipids and DSPE-PEG-DBCO were coated on the lipophilic

core of the nanoparticle. The pH-MRNS react with azide modified BuChE to form the ACh-

MRNS.

35

Figure 2.3. Structure and 1H NMR spectrum of pH-sensitive chelator. Structure (inset) and

the spectrum was obtained from a Varian Inova 500 MHz NMR spectrometer. 1H NMR (500 MHz,

D2O): δ = 8.35 ppm (s, 1H), 8.22 ppm (m, 1H), 7.01 ppm (m, 1H), 4.97 ppm (m, 1H), 3.66 ppm

(s, 2H), 3.36 ppm (m, 16H), 3.05 ppm (m, 6H).

36

We synthesized the transduction element, Gd(NP-DO3A), a pH-sensitive analogue of the

clinically-used Gd(DOTA) based on the procedure by Woods et al.,91 (Figure 2.3). In this

structure, paramagnetic Gd(III) is chelated within a twelve-member ring containing a p-

nitrophenol. Under basic conditions, the probe is coordinated to a single water molecule, but under

acidic conditions, two water molecules will coordinate to Gd(III), leading to increase in 1/T1

(Figure 2.1).

The size and surface charge of fabricated nanoparticles were characterized by dynamic light

scattering (DLS) and zeta-potential in 1× PBS, pH 7.4, respectively. We measured the average size

of three batches of the ACh-MRNS and pH-MRNS using DLS. The average hydrodynamic

diameter of ACh-MRNS was 96 ± 26 nm (mean ± standard deviation) and the size of the pH-

MRNS was 77 ± 23 nm. Figure 2.4A and 2.4B display the size distribution of a single nanosensor

batch, which is representative of a typical batch. The surface charge of the ACh-MRNS and pH-

MRNS was -41 ± 1.9 mV and -29 ± 3.4 mV, respectively. The negative charge of BuChE may

contribute to the difference in zeta-potential.100 We also performed transmission electron

microscopy (TEM) using one batch of the ACh-MRNS as an example, and the generated image in

Figure 2.4C indicated spherical ACh-MRNS with a size of 118 ± 25 nm and distribution shown

in Figure 2.4D. The DSPE-PEG lipid coat was also observed in the TEM (Figure 2.5). The

concentration of nanoparticles (2.62 ± 0.13 × 1014 particles/mL) was also identified using an

nCS1TM nanoparticle analyzer (Spectradyne, Torrance, CA). By combining the concentration and

information from the amount of Gd bound on the surface of the nanoparticle quantified by

Inductively Coupled Mass Spectrometry (ICP-MS), we estimated the density of Gd(III) on the

surface of the nanoparticle to be 0.99 ± 0.27 atoms of Gd/nm2.

37

Figure 2.4 Characterization, in vitro calibration and selectivity of nanosensors. Dynamic light

scattering (DLS) analysis showing size distribution of nanosensors, (A) ACh-MRNS, and (B) pH-

MRNS. (C) TEM image of the ACh-MRNS. (D) Distribution of sizes of the ACh-MRNS from the

TEM image.

38

Figure 2.5. TEM image of the ACh-MRNS using NanoVan stain. The coated lipid layer was

positively stained and shown in bright at the surface of the nanoparticle.

39

The r1 of ACh-MRNS and pH-MRNS was determined as 6.36 and 6.91 mM-1 s-1 (pH 7.4),

respectively, from the concentration of Gd(III) and the T1 collected from a 7 T Bruker Biospec

small animal MRI scanner (Bruker Inc., Billerica, MA). In addition, the r1 of ACh-MRNS was

also measured in the low field with a 1.5 T Bruker Minispec mq60 NMR analyzer (Bruker Inc.,

Billerica, MA) as 12.1 mM-1 s-1 (pH 7.4) at 37 oC. The r1 of both ACh-MRNS and pH-MRNS were

approximately twice the value obtained from the clinically used DOTA-Gd101 and the free Gd(NP-

DO3A) (Table 2.1), indicating suitable contrast for subsequent studies. Compared to free

molecules, the density of bound contrast agents and the slower tumbling rate of the particles may

restrict their internal and overall motion leading to a longer rotational correlation time of the water-

bound contrast agent (τR), which would theoretically account for the increased r1 at the same pH.102-

103

Sensor pH dependence

To evaluate the pH-dependence of the r1 and the corresponding 1/T1, we suspended ACh-MRNS

in PBS at pH 6, 6.5, 7, 7.4 and 8, and scanned using MRI. When the pH decreased from 8 to 6, the

signal intensity (1/T1) increased by 24% (Figure 2.6A). The corresponding r1 of ACh-MRNS at

different pH are indicated in Table 2.2. The extracellular pH in the brain is approximately 7.3.104

Within the physiological pH range 7.2 to 7.8, the 1/T1 of the ACh-MRNS changed by less than 5%

(Figure 2.6A). The moderate response in this range means that the ACh-MRNS are minimally

affected by global pH fluctuations in the brain. To further assess the effect of pH on the response

of ACh-MRNS’s, we calibrated the acetylcholine response of the sensor in a background of varied

pH using a 1.5 T Bruker Minispec mq60 NMR analyzer (Bruker Inc., Billerica, MA). We did not

observe significant differences in the calibration against acetylcholine at pH 7.2, 7.4 and 7.8

(Figure 2.6B). According to the literature,105 BuChE is active between pH 6 to 8.

40

Table 2.1. Relaxivity (r1) of contrast agents used in this study.

Contrast agent r1 (mM-1 s-1) at pH 7.4

DOTA-Gd 3.2191

Gd(DO3A-NP) 3.15 ± 0.47

ACh-MRNS 6.36 ± 0.44

pH-MRNS 6.91 ± 0.65

*Errors were calculated from S.D. of T1 of three independent tests using error propagation.

Table 2.2. Relaxivity (r1) of ACh-MRNS in different pH used in this study.

pH r1 (mM-1 s-1)

8 6.14 ± 0.57

7.4 6.36 ± 0.44

7 7.13 ± 0.32

6.5 7.87 ± 0.35

6 8.55 ± 0.56

*Errors were calculated from S.D. of T1 of three independent tests using error propagation.

41

Figure 2.6. In vitro nanosensor to pH dependence. (A) Response of nanosensors to varying pH

was examined in 1× PBS at pH 6, 6.5, 7, 7.5 and 8. (B) The ACh-MRNS were exposed to solutions

of 0, 5, 10, 2, 30, 40, 50 and 100 µM of acetylcholine in 1× PBS, pH 7.2, 7.4 and 7.6. The 1/T1

was calculated from an average T1 of three independent measurements. Error bars were calculated

from S.D. of T1 using error propagation (error bars are small in (B) to be observed).

42

When acetylcholine is hydrolyzed, the enzyme will remain active until the pH drops below 6, at

which point the activity will be reduced. Thus, we believe that the capability of the ACh-MRNS

to measure acetylcholine in this range is not affected by endogenous changes in pH. It will be

important to continue to use the pH-MRNS as a control to further mitigates the risk of interference

from physiological pH changes.

Sensor calibration

We calibrated the nanosensors by suspending nanoparticles corresponding to 0.079 mM

conjugated Gd(III) with concentrations of acetylcholine varying from 0 to 100 µM in 1× PBS, pH

7.4. The ionic strength of this buffer is 162.7 mM which is consistent with the ionic strength of

artificial cerebrospinal fluid (ACSF) (152.8 mM). Since the sensors were delivered to the CSF

instead of blood, we did not incorporate serum proteins to our measurements. A clear gradient in

brightness was observed from the T1-weighted MR image (Figure 2.7A): the higher concentrations

of acetylcholine led to increasingly brighter images, which represents a higher 1/T1. A calibration

curve of 1/T1 as a function of concentration of acetylcholine (Figure 2.8B), showed an

enhancement of 1/T1 by more than 10% when the concentration of acetylcholine increased from 0

to 6 µM, and 20% when the concentration increased to 10 µM (blue line). Using the sigmoidal fit

of the calibration curve to calculate the analytical properties of the ACh-MRNS, (Figure 2.7B) we

found a lower limit of detection (LLOD) of 2.64 µM and a sensitivity of ±4 µM (calculated from

the fitted curve and error bar in Figure 2.7B) at an acetylcholine concentration of 10 µM. In

comparison, a control study using an equivalent amount of pH-MRNS with the same concentration

of Gd(III) and free BuChE (not conjugated to the particle) led to no significant change in 1/T1

when the concentration of acetylcholine was increased from 0 to 500 µM (grey). This result

indicates that the amount of free enzyme was not sufficient to create T1 contrast by the pH-MRNS.

43

Figure 2.7. Characterization, in vitro calibration and selectivity of nanosensors. (A) Higher

concentrations of acetylcholine led to brighter MR images. (B) 1/T1 of ACh-MRNS was enhanced

when higher concentrations of acetylcholine were present. The ACh-MRNS (blue) were exposed

to solutions of 0, 1, 2, 6, 10, 30 and 100 µM of acetylcholine. The pH-MRNS and free BuChE

(grey) were exposed to solution of 0, 10, 50, 100 and 500 µM of acetylcholine. (C) The selectivity

towards acetylcholine of ACh-MRNS. The nanosensors were exposed to PBS buffer or to solutions

of acetylcholine (0.1 mM), glutamate (5 mM), dopamine (5 mM), GABA (5 mM) and glycine (5

mM). 1/T1 in solutions of acetylcholine was significantly higher than other groups (one-way

ANOVA, *P < 0.005, for N = 3). The 1/T1 in panel (B) and (C) were calculated from an average

T1 of three independent measurements. Error bars in panel (B) and (C) were calculated from

standard deviation (S.D.) of T1 using error propagation.

44

Our design theory was that the nanoscale particle is a necessary component of the sensing

mechanism. In short, adding the individual components without immobilization on the scaffold

would not be sufficient for two reasons: First, the sensing components would diffuse away from

each other in vivo and would not remain in proximity for sensing. Second, the enzyme and pH

indicator are preconcentrated on the sensor, which sets up a localized pH environment that would

not be seen if the enzyme was not attached to the particle. In order to validate the pH effect

confined to a localized microenvironment, we measured the pH in the mixture of 0 to 5 mM of

acetylcholine with the ACh-MRNS (Figure 2.8A) and pH-MRNS (with free BuChE) (Figure

2.8B), respectively, using fluorescein, a commonly used pH indicator. The results showed that no

detectable pH changes of the bulk buffered system were detected until the level reached 5 mM of

acetylcholine. Thus, we extrapolate these findings to the enhancement of 1/T1 by locally generated

protons (H+) from the enzymatic hydrolysis of acetylcholine in the microenvironment at the

surface of the ACh-MRNS. Similar localized pH effects have been observed in biosensors and

microelectrodes, as reported by our lab and other groups.20, 106-108 Also, previous report suggested

that within the hydrophilic microenvironment close to the surface of an electrode, the pH can be

inhomgeneously distributed depending on the local chemical environment.109-111 This discovery

suggested the local pH-drop created within the microenvironment can be detected by the contrast

agents in the vicinity. To confirm that the change in 1/T1 was not initiated by free Gd3+, a xylenol

orange test showed that the A573/A433 did not increase when higher concentrations of

acetylcholine were hydrolyzed by the ACh-MRNS and no free Gd3+ was generated in this process

(Figure 2.9). Thus, the increase of 1/T1 was not caused by the alteration of free Gd3+.

To determine the reaction time of the enzymatic hydrolysis, we performed an Ellman’s assay

which indicated that the conjugated BuChE consumed 5-100 µM of acetylcholine between 40 s

45

Figure 2.8. pH change in the mixture of the nanosensor and acetylcholine. Changes of

fluorescence at 520 nm led by the hydrolysis of acetylcholine with concentrations varying from 0

to 5000 µM by (A) ACh-MRNS and (B) pH-MRNS and unconjugated BuChE using fluorescein

as a pH indicator. Error bars were calculated from S.D. of three separate tests of fluorescence using

error propagation.

46

Figure 2.9. Xylenol orange test. (A) Xylenol orange test of the mixture of the ACh-MRNS and

acetylcholine. The A573/A433 didn’t increase when higher concentrations of acetylcholine were

present, indicating that increasing acetylcholine is not removing Gd from the sensor. (B) For

reference, xylenol orange test of a solution of increasing Gd(NO3)3 levels. The A573/A433 increased

when higher concentrations of Gd(NO3)3 were present. The value of A573/A433 is an average of

three independent measurements. Error bars were calculated from the standard deviation (S.D.) of

recorded absorbance at 573 nm and 433 nm, respectively, using error propagation (error bars are

small to be observed).

47

Figure 2.10. Kinetics of BuChE. Changes of absorbance at 412 nm led by hydrolysis of 0, 5, 10,

50, and 100 µM of acetylthiocholine by (A) free BuChE and (B) conjugated BuChE in 2 min after

the start of the reaction in the Ellman’s assay was plotted. Error bars were S.D. of three separate

tests of absorbance.

48

and 2 min (Figure 2.10A and B). For MRI measurements, though, our acquisition times are longer

than this two-minute timeframe. For the acquisition, we used a Rapid Acquisition with Relaxation

Enhancement with Variable TR (RARE-VTR) sequence to obtain the T1 in a total time of 9.5 min.

This sequence was chosen as it minimizes distortion during in vivo imaging.112 During the scan,

signal intensities at six different TRs were collected sequentially. The relative changes of signal

intensity elicited by increasing acetylcholine levels decreased from 20% at the first TR (0-4.3 s)

to 0 at last TR (236.3-570.1 s) scan sequence (Figure 2.11). These results indicate that the in vitro

calibration mainly represented the response of ACh-MRNS to acetylcholine in the first 2 min of

the scan during which the enzymatic hydrolysis of acetylcholine took place. Thus, although the

nine-minute scan is essential for obtaining a reliable T1, the final value correctly reflects the shorter

timeframe of acetylcholine hydrolysis.

Sensor selectivity

Since ACh-MRNS is enzyme-based, we expect high specificity against other neurotransmitters

(Figure 2.7C). To verify the selectivity, we suspended nanoparticles (corresponding to 0.079 mM

conjugated Gd) in 200 µL solutions of either PBS buffer, acetylcholine, glutamate, dopamine,

GABA or glycine. By measuring 1/T1, we found that only acetylcholine solution led to a more than

20% increase compared to PBS (P = 0.0001, F-value = 21.68, df = 17; ANOVA with Tukey’s

post-hoc test), and none of the potential interfering neurotransmitters elicited any significant

difference (P > 0.5). These results are consistent with the assumption that BuChE selectively

hydrolyzes acetylcholine, and supports our nanosensors to selectively respond to acetylcholine.

Acetylcholine detection in the rat brain in vivo

We investigated the ability of ACh-MRNS to detect endogenous acetylcholine release in the rat

medial prefrontal cortex (mPFC).

49

Figure 2.11. Relative Signal intensity at different TR. Signal intensities in in vitro calibration

at TR = 70, 291, 576, 976, 1651, and 5000 ms were collected sequentially in 4.3, 19.1, 38.1, 64.8,

110.0 and 333.8 s, and normalized to the corresponding signal intensity elicited by 0 µM of

acetylcholine, respectively. The normalized data was plotted and fitted in sigmoidal curves. Error

bars were calculated from S.D. of T1 using error propagation.

50

First, to assess the amount contrast provided by ACh-MRNS in the living brain tissue, 2 µL

solution (47 µM of Gd(III)) of the nanosensors were injected into the mPFC through implanted

cannula of anesthetized rat subjects (Figure 2.12). An increase of 60% in 1/T1 was produced

(Figure 2.12B) and a strong T1-weighted contrast was observed at the site of injection (Figure

2.12C). Placement of the cannula in the mPFC was verified during the anatomical MRI scans and

after surgery from histological analysis (Figure 2.13).

Next, time-course changes in acetylcholine-dependent 1/T1 were acquired by stimulating the

release of acetylcholine in the mPFC using a pharmacological agent, clozapine. Clozapine, an

atypical antipsychotic drug, has been shown to induce 2-3 fold increase in acetylcholine

concentration from the basal level, which peaks after 30 min, and is sustained for over one hour in

the rat mPFC.95 Briefly, the procedure for in vivo imaging included three consecutive scans after

nanosensor delivery on a 7 T MRI scanner at an interval of every 23 min (MRI scan procedure: 0,

23, 46 min post nanosensor injection; Figure 2.14A). The experimental group (N = 6) consisted

of ACh-MRNS infused through the cannula and a concurrent subcutaneous injection of clozapine

into the hind of the animal. For the control group (N = 6), ACh-MRNS were delivered without

clozapine treatment. T1 was quantified in regions of interest (ROIs) covering the injection sites.

Each ROI volume was defined by a cylinder with a diameter of 1.2 mm and a thickness of 1 mm

centered at the injection site, and T1 was measured and normalized with respect to control ROIs

identically placed on the contralateral side of the brain with no sensor delivery. Examination of

the T1 and 1/T1 time courses in Figures 2.14B-D clearly displayed a difference in 1/T1 after 30 min

post sensor delivery in the experimental group (blue) compared to the control group (green). For

both groups, a slight decrease in 1/T1 was observed at the 23min time period.

51

Figure 2.12. In vivo sensor contrast. (A) Schematic diagram of placement of cannula and ACh-

MRNS infusion (blue) aimed at rat mPFC, and MRI data acquired from sensor encompassing slices.

(B) ROI-averaged MR signal intensity showing increase in 1/T1 for >60% in the post-nanosensor

injection in comparison to pre-injection slice. (C) Coronal MR image brain slice (bregma: +2.8

mm) of pre- (left) and post-nanosensor injection (right), with circle ROI (blue) defined for analysis.

Arrowhead (white) indicates the position of cannula, and the circle (blue) defines the ROI for

analysis. Error bars were calculated from S.D. of T1 using error propagation. Diagram adapted

from Paxinos and Watson 113.

52

Figure 2.13. Histology. Representative photomicrographs showing placement of cannula in the

rat cortex. Coronal rat brain sections in the bottom left panel shows choline acetyltransferase

(ChAT) immunoreactivity and cresyl violet (Nissl) histological staining at the level of mPFC.

Diagram adapted from Paxinos and Watson. 113

53

A markedly evident 1/T1, however, were observed at 46 min time point with a significant

difference of more than 13% 1/T1 in the experimental group compared to the controls (P = 0.018,

Student’s t-test). With microdialysis, Ichikawa et al. uncovered that clozapine elicited a 2.5-times

increase in the concentration of acetylcholine in the mPFC half hour after the drug administration.

.114 The peak time recorded by the microdialysis is consistent with the result from the ACh-MRNS.

In addition, the pattern of 1/T1 decrease over time in the control groups were consistent with the

effects of particle diffusion, as indicated by a similar ~19% decrease in 1/T1 by observing the time

course of similarly injected fluorescent nanoparticles in the 0.6% agarose phantoms (Figure 2.15).

The particle diffusion was significantly slower than a molecular dye (data not shown). Previous

reports show that nanoparticles can be cleared by microglia/macrophages in the CNS.115-117 The

rate of sensor diffusion and phagocytosis is a potential factor for in vivo measurements, and we

reason that the ACh-MRNS diffused at the site of injection would lead to a decrease in 1/T1,

however, this effect was offset by the response of ACh-MRNS against endogenously released

acetylcholine in the experiment group as observed by the significant difference detected in the scan

at 46 min compared to controls.

To ascertain whether global pH changes in the brain were induced by clozapine administration and

interfered with the sensor response, identical scanning procedures were performed on another

cohorts (N = 3). In this group, the pH-MRNS (grey, Figure 2.14C) were delivered with a

concurrent clozapine injection, procedurally identical to the experimental group above. A similar

1/T1 was observed after injection of nanosensors (0 and 23 min post-injection period). However,

at a subsequent time point at 46 min post-injection, a difference of >15% 1/T1 was observed in

comparison to the experimental group (P = 0.005) which indicates that clozapine does not cause

significant acidic conditions in the brain.

54

Figure 2.14. Acetylcholine detection in vivo. (A) Experimental procedure: Delivery of

nanosensors (ACh-MRNS or pH-MRNS) through cannula followed by subcutaneous

administration of drug (clozapine) and then three consecutive MR scans 23 min apart denoted, t =

0, 23, 46 min. (B) Coronal brain slices showing time-courses of acetylcholine detection. In the

experimental group (top panels, N = 6), ACh-MRNS were injected through the cannula with

clozapine administration, while the control group (bottom panels, N = 6) comprised ACh-MRNS

55

delivery without clozapine. For the purpose of display, the heatmap of the top layer (T1-registered

map) was generated between 600 and 1600 ms and overlaid onto the image. (C) Distinct

acetylcholine signal changes accompanied by a difference of 13% in 1/T1 (P = 0.018; Student’s t-

test) between experimental (blue) and control groups (green) was observed at 46 min, indicating

distinct detection of acetylcholine driven by local enzymatic hydrolysis by the ACh-MRNS

induced by clozapine, as shown by higher 1/T1 in the experimental group. Identical MR scanning

procedures were conducted in a new cohort (pH-MRNS group, grey, N = 3) to study the localized

effects of enzymatic hydrolysis of acetylcholine to trigger changes in 1/T1. In this group, pH-

MRNS (nanosensors with pH-sensitive Gd and without conjugated enzymes) were delivered along

with clozapine administration (grey). After 46 min, 1/T1 also showed a difference of >15% (P =

0.005) in comparison to the experimental group, showing global pH changes were not detected

and induced by clozapine administration and confirming the validity of specific detection of

acetylcholine driven by local enzymatic hydrolysis by the ACh-MRNS. (D) The individual data

points of relative 1/T1 in the ACh-MRNS (blue, N = 6), control (green, N = 6) and pH-MRNS

(grey, N = 3) group at 0, 23, 46 min post-injection were plotted. Error bars were calculated from

S.D. of normalized T1 using error propagation.

56

The individual data points of relative 1/T1 in this group is available in Figure 2.14D. This result

suggests that ACh-MRNS reliably detect acetylcholine levels as governed by the predicted

mechanism of the nanosensor, and not due to a global change in pH in the brain. The individual

difference in activation of microglia and rate of diffusion between each animal may explain the

variance in the control and pH-MRNS cohort.

Discussion

The use of MR-active nanosensors to image acetylcholine is particularly attractive for several

reasons. First, the nanoscale platform incorporates all sensing components together, and hence the

close proximity between cholinesterase and pH-sensitive contrast agent creates a localized effect

and facilitates specific detection of acetylcholine. Also, the nanosensors are based on a modular

design that can be extended to the detection of other neurotransmitters and physiological analytes,

by simple substitution of enzymes or functional contrast agents.

In our studies, a 1/T1 of ~13% at 46 min indicates a micromolar increase in the acetylcholine levels,

as estimated according to the in vitro calibration. This finding was comparable to other groups’

attempts to measure acetylcholine in the brain, although there is no direct method of comparison

in the literature. As cited above, our studies reflect the temporal increase in acetylcholine in

response to clozapine stimulation, as had been observed previously via microdialysis.95 Other

examples include those that used coated microelectrodes, nicotine118 or KCl89 as local stimuli to

trigger an increase of acetylcholine of up to 25 µM.55, 119 However, we emphasize the difficulty of

direct comparison between methods of analysis and pharmacological stimulation. Our future

studies will focus on in vivo calibration and rigorous comparison as an assessment of the

advantages and disadvantages of various methodologies.

57

Figure 2.15. Diffusion of pH-MRNS in phantom brain. Rhodamine 18 incorporated pH-MRNS

diffusion profile in 0.6% agarose phantom imaged using IVIS fluorescence imager at 0, 2, 5, 10,

15, 20, 30, 40, 50 and 60 min post-injection. The relative intensity per area data at each injection

site was collected using Image J, and normalized to the relative intensity per area at t = 0 min.

Error bars indicate S.D. derived from three ROI intensity measurements at each time point

indicated by the blue circle (shown on top right phantom).

58

In our in vivo study, we expect that nanoparticles diffuse similarly in both Experimental and

Control groups, hence a sustained 1/T1 signal is attributed to the molecular changes in the brain

rather than other natural variation occurring in the brain, such as pH changes, anesthetic effect,

temperature differences, as the two groups differ only by administration of the drug otherwise all

other experimental conditions were identical. We did observe larger differences in the Control

group (Figure 2.14D), however, the variation becomes more pronounced at later scanning stages,

i.e., 46 min, than earlier scan time points. This is observable in both Control and pH-MRNS groups,

but evidently less in the ACh-MRNS group. An alternative faster pulse sequence paired with

higher-resolution imaging could be performed to improve temporal resolution and increase the

fidelity of acetylcholine detection.120-121 The current methodology demonstrates relative changes

in acetylcholine levels, and would require in-situ calibration to be performed before quantitative

results could be achieved.

Due to the difficulty of delivering our nanosensors noninvasively through the blood-brain barrier

(BBB),122 which remains a key challenge for molecular neuroimaging applications in live animals,

we have implemented a cannula placement for delivery of nanosensors aimed at the mPFC. For

ongoing applications in animal studies, disruption of BBB using hyperosmotic shock or ultrasound

methods which have been used to deliver small molecules123 and nanoparticles124 into the brain,

will be explored to improve probe delivery. Also, non-disruptive methods to deliver nanoparticles

to the CNS have been established in pre-clinical studies. Polymeric nanoparticles are coated with

artificial amphiphilic polymers or protein-based antibodies, peptides, or receptors to overcome the

BBB via transcytosis for therapy against stroke, Alzheimer’s disease, or Parkinson’s disease.125-

126 Thanks to the modular feature of the ACh-MRNS, modification of the coatings with targeting

protein will also be considered as a strategy to deliver the nanosensors. As with all measurement

59

methods, the risk of distorting the biological environment by the removal of analyte for detection

is a real possibility. In these studies, due to the size of the sensors, we believe they are located in

the extrasynaptic microdomain. Thus, the volume transmission i.e., “spillover” acetylcholine are

the species that are predominantly detected, rather than acetylcholine molecules directly involved

in the synaptic cleft. Hence, the acetylcholine in the synaptic cleft will be readily available for

recycling process, reducing the buffering effect by the sensors. In the future, as we strive to reduce

the size of the sensors and target them to the synaptic cleft, the possible consumption of analyte

may become a greater issue. Lastly, enhanced sensitivity for detection of acetylcholine can be

achieved by exploring a more active enzyme such as acetylcholinesterase.127 These steps will

facilitate the application of MRI and nanosensors for chemical imaging of neurotransmitters

fundamental to the understanding of brain function and disease.

2.4 Summary

In summary, we have developed and characterized a neurotransmitter-sensitive MR-active

nanosensor for the detection of acetylcholine in the brain. Acetylcholine is a neurotransmitter

known to play a prominent role in mammalian social behaviors and neural processes that govern

cognition and memory. As such, there is a considerable interest for imaging this molecule. In this

study, we firstly demonstrated the ACh-MRNS was capable of measuring acetylcholine in low

micromolar concentration by co-immobilizing cholinesterase and pH-sensitive contrast agents on

a nanoparticle. And we further proved that the ACh-MRNS can detect clozapine induced

endogenous release of acetylcholine in the rat brain. The ACh-MRNS we report here is the first

nanosensor for the detection of acetylcholine using MRI in living brain, as characterized and

implemented both in vitro and in vivo. Also, the modular design of sensors offers a sensing

platform which can be extended to integrate different components for detection of other

60

neurotransmitters and physiological analytes, by substituting the enzymes or functional contrast

agents to achieve better specificity and sensitivity.

2.5 Acknowledgement

This work was supported by the National Institutes of Health through Grant R01NS08164. We

thank K. Bardon, P. Larese-Casanova, C. Marks for technical assistance in flash column

chromatography, ICP-MS, and TEM, respectively. We also thank C. Ferris and P. Kulkarni for

help in MRI setup and pulse sequences.

61

Chapter 3: Glucose-Sensitive Nanofiber Scaffolds with Improved

Sensing Design for Physiological Conditions

3.1 Introduction

Continuously monitoring physiological analytes such as electrolytes and glucose may

revolutionize disease diagnosis and management by enabling patients and physicians to accurately

track an individual’s analyte levels and fluctuation patterns. Implantable nanosensors offer a

promising platform for physiologic monitoring because their small size makes implantation

minimally-invasive, and the small suite of biocompatible polymers already FDA-approved for

implant coatings and catheters provide a safe starting point for material selection. Optode-based

nanosensors are robust tools for continuous and reversible physiological analyte measurements,

and several designs have successfully monitored glucose, histamine, and sodium in vivo.21, 32, 35 In

optode-based nanosensors, reviewed extensively elsewhere128-130, a hydrophobic plasticized

polymer matrix provides support for hydrophobic analyte recognition elements and hydrophobic

reporters. When the recognition element binds to its target analyte, the binding event causes a

change in the local environment (e.g.; pH change, charge movement, oxygen consumption) and

the reporter’s optical properties change concomitantly. Nanosensors designed around optodes are

essentially nanoparticles that incorporate the recognition and reporting chemistries. The

components are contained within the hydrophobic nanoparticle and the resulting nanosensors’

analytical properties can be tuned by changing the relative ratio of sensing components within the

nanoparticle.

Previous works evaluating optode-based nanosensors for bio-analyte monitoring have used

platforms such as a sliver sensor, which contained individual sensing capsules on a cellulose

62

acetate support,131-132 Others, such as McShane, have encapsulated sensing and reporting

chemistries for glucose contained within alginate microspheres and subcutaneously injected those

microspheres into rats for glucose monitoring.133 The eventual clinical utility of any nanosensor

will depend on the nanosensors’ sensitivity, selectivity, biocompatibility, reversibility, response

time, appropriate residency and clearance time.134 To date, no implantable nanosensor system

meets the clinical requirements for all of those factors.

Any sensor will have a recognition element and a reporting element, and personal glucometers

often use the enzyme glucose oxidase and then use electrochemistry to detect the enzyme’s activity

in response to blood glucose from a finger prick. Alternatively, non-enzymatic recognition

elements such as concanavalin A, a lectin that specifically and reversibly binds to polysaccharides

via hydrogen bonds and van der Waals interaction135, or boronic acids, which reversibly bind to

diols through boronate ester formation136-137 can detect glucose. Boronate ester formation increases

through the addition of electron withdrawing groups to the boronic acid, strengthening diol

binding.138-141 Asher and coworkers used this approach by incorporated a fluoro- electron-

withdrawing group onto their boronic acid derivative and were able to monitor glucose at pH 7.4

with their photonic crystal glucose sensing material.139, 142 Using carbon nanotube-based sensors,

Strano and coworkers also showed that boronic acids with electron-withdrawing groups such as

chloro- and cyano- groups were optimal for their sensing design.143 Thus, we hypothesize that the

sensitivity of boronic acids to glucose at physiological pH can be tuned by increasing or decreasing

the electro-withdrawing ability of functional groups on a boronic acid derivative. We aim to design

optode-based nanosensors that respond to physiologic glucose concentrations by synthesizing

hydrophobic boronic acids with electron-withdrawing groups and fabricating nanosensors with

those boronic acids. The reporter in this design is alizarin, which has a diol group, allowing it to

63

compete with glucose for reversible binding to boronic acids. When bound to a boronic acid, it

fluoresces very strongly, but its fluorescence decreases when displaced from the boronic acid by

glucose. Higher lever of glucose will lead to less binding between boronic acid and alizarin, and

cause larger drop of fluorescence. Based on this mechanism, concentration of glucose can be

measured by the decrease of fluorescence.

A variety of nanosensors have been developed for in vivo glucose monitoring, but many of them

have a limited residence time at the site of injection.35 Despite their short residency time, the in

vivo experiments showed that fluorescent glucose-responsive nanosensors are able to track

changes in glucose levels for up to one hour.35 Similar results were observed with sodium-sensitive

nanosensors, and short in vivo residency times were attributed to particle migration away from and

cellular uptake at the injection site.32 Various approaches have been used to overcome these issues

by immobilizing nanosensors within gels144 or producing high aspect-ratio sensor geometry.145

Gel immobilization improved sensor residence time at the injection site over the course of one

hour, but did not provide a long-term solution to sensor migration because nanosensors are small

enough to diffuse out of the gels.144 Our group previously demonstrated that encapsulating

nanosensors into worm-like geometries with chemical vapor deposition prevented the signal loss

associated with diffusion away from the injection site,145 though the chemical vapor deposition

fabrication methods used in that study have low batch yields. Electrospinning is a high-yield

process that can fabricate continuous polymer nanofibers of optode material. With nanofiber

geometries, implanted nanosensors may achieve a residency time in conjunction with a high

throughput and scalable production technique while retaining advantages of nano-scale sensors.146

Although other groups have utilized electrospinning to fabricate sensors for detecting silver,147

64

mercury,148 nitroaromatics,149 and glucose,150-151 none have shown that their sensor designs

function in vivo.

In this work, to improve stability of our previously presented1 glucose-sensitive nanosensors, we

fabricated nanofiber scaffolds from plasticized polycaprolactone and incorporated the best of the

boronic acid derivatives with alizarin to show that this sensor platform exhibits extended in vivo

sensor residency time.

In addition, we functionalized 4-carboxy-3-fluorophenyl boronic acid with hydrophobic alkyl side

chains of varying lengths to increase the nanosensors’ stability to leaching and sensitivity to

glucose, as compared to previous formulations.1

3.2 Material and Methods

Materials: Carboxylated poly(vinyl chloride) (>97% GC) (PVC-COOH), bis-(2-

ethylhexyl)sebacate (DOS), polycaprolactone (Mn 70,000-90,000) (PCL),

tridodecylmethylammonium chloride (TDMAC), alizarin, 4-carboxy-3-fluorophenylboronic acid

(1), 3-fluoro-4-methoxycarbonylphenylboronic acid (2a), D-(+)-glucose, tetrahydrofuran (≥

99.9%) (THF), dicyclohexylcarbodiimide solution (60% w/v in xylene) (DCC), N-

hydroxysuccinimide (NHS), aniline(≥ 99.5%), 1-propanol (anhydrous, 99.7%), 1-butanol (HPLC,

99.7%), 1-hexanol (98%), cyclohexanol (99%),sodium sulfate (anhydrous,≥ 99.9%), sodium

chloride, ethyl acetate (anhydrous, 99.8%), hexane (anhydrous, 99.5%), N, N’-dimethylformamide

(DMF) and N, N’-dimethylaminopyridine (DMAP) were purchased from Sigma Aldrich (St Louis,

MO, USA). Octylboronic acid (>97%) and Citroflex A-6 were acquired from Synthonix (Wake

Forest, NC, USA) and Vertellus (Indianapolis, IN, USA), respectively. Phosphate Buffered Saline

(PBS) (1x, pH = 7.4) was purchased as a solution from Invitrogen (Carlsbad, CA, USA).

65

Hydrochloric acid (1.0 N) and sodium bicarbonate were purchased from Fisher Scientific (Fair

Lawn, NJ, USA). 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene

glycol)-550] (ammonium salt) (DSPE-mPEG550) was purchased from Avanti Polar Lipids, Inc.

SKH1-E mice were acquired from Charles River Laboratories International Inc. (Wilmington,

MA).

Boronic Acid Synthesis. To control response, we systematically functionalized BA1 with alkyl

chains of various lengths (Figure 3.1). The synthesis protocol has been previously developed by

Steglich and coworkers. 152 Specifically, 200 mg BA1 (1.09 mmol, 1 Eq.) was mixed with 40 mg

DMAP (0.33 mmol, 0.3 Eq.) and alcohol 2 (3.27 mmol, 3 Eq.) in 4 mL DMF. DCC solution in

xylene (60% w/v) (1.09 mmol, 1 Eq.) (220 µL) was added dropwise to the reaction mixture at 0o

C, which was then warmed to room temperature and stirred overnight. The urea precipitate was

removed by centrifugation and then the supernatant was extracted with 20 mL ethyl acetate and

0.5 M HCl aqueous solution. This process was repeated three times. The product was washed

with saturated NaHCO3 aqueous solution and then brine (saturated sodium chloride solution). The

organic phase was dried over Na2SO4 and further purified by flash column chromatography. The

product was characterized by 1H NMR recorded on a Varian Inova 500 MHz NMR spectrometer.

1H NMR data is available in the supplementary information.

Optode Composition. Macrosensors, nanofiber scaffolds, and nanoparticle-based sensors were

formed from optode cocktails containing all sensing components. Macrosensors were made from

the following components: 30 mg PCL, 60 μl Citroflex A6, 82.3 µmol of a boronic acid (BA)

derivative (BA2b – BA2c), 2.0 mg (2.49 µmol) TDMAC, and 1.0 mg (4.16 µmol) alizarin. These

materials were placed into a glass vial and then dissolved in 500 μl THF. The boronic acids

incorporated into these formulations were BA1, and BA2a-c.

66

Figure 3.1. Boronic acids incorporated into glucose-sensitive sensors. (A) Structures and (B)

synthesis of boronic acids with different alkyl chain lengths and ring structures.

67

For production of electrospun scaffolds, the general optode cocktail was made with a solution of

12% (weight/volume) of PCL in Citroflex A-6 and THF. Of this weight percentage, 10% was

Citroflex A-6. Specifically, the optode formulation was: 216 mg PCL, 24.0 μL Citroflex A-6, 2.0

mg (2.49 µmol) TDMAC, 1.0 mg (4.16 µmol) alizarin, and 82.3 µmol boronic acid in 2 ml THF.

Three boronic acids, 2a, 2b and 2c, were tested in electrospun scaffolds. Nanoparticle-based

sensors were fabricated with an optode formulation previously described and include: 30 mg high

molecular weight PVC-COOH, 60 μl DOS, 2.0 mg octylboronic acid, 4.0 mg TDMAC, and 1.0

mg alizarin.35 These materials were transferred into a glass vial and then dissolved in 500 μl THF.

Response of Macrosensors to Glucose. Prior to miniaturization to the nanoscale, each new BA

was assessed as a glucose-sensitive macrosensor. The method for testing macrosensor responses

has been described previously.35 Briefly, macrosensors are formed by pipetting 2 µL of optode

onto glass discs adhered to the bottom of an optical bottom 96-well plate. The optodes were then

allowed to dry at least 15 minutes forming thin film macrosensors. A Spectramax Gemini EM

micro plate fluorometer (Molecular Devices, Sunnyvale, CA, USA) acquired fluorescence data

(ex/em: 460/570 nm). After forming macrosensors, each macrosensor was hydrated in 200 µL

PBS (pH=7.4) for 45 minutes. This process was repeated 4 times until the fluorescence intensity

stabilized. After the macrosensors were hydrated, the PBS solution was removed from all wells

and 200 μl of 0.1 M glucose in PBS was pipetted into half of the wells to determine macrosensor

response to glucose. The remaining wells acted as controls and contained fresh, glucose-free PBS.

Changes in fluorescence response were monitored for 60 minutes at a sampling rate of 5 minutes.

The fluorescence intensity of each sensor was normalized to time zero and then the mean was

taken for both the experimental and control groups. The average of the experimental group was

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subtracted from the control group and multiplied by 100 to obtain a percent change. The error of

percent change was calculated using error propagation.

Fabrication of Fibrous Scaffolds: Electrospinning was performed on a Nanospinner NE 200

(Inovenso, Istanbul, Turkey) equipped with a syringe pump. The optode solution was spun at a

distance of 10 cm from the collector with a rate of 3 ml/hr and at an applied voltage of 15 kV. The

fibers were spun onto either aluminum foil or silanized glass discs attached to aluminum foil for

imaging and testing scaffold response.

Nanofiber Scaffold Responses to Glucose: To determine scaffold response to glucose, scaffolds

spun onto glass discs were removed from the aluminum foil using a 6 mm biopsy punch (Miltex,

Inc., Plainsboro, NJ, USA) and placed in a 96-well optical bottom well plate. PBS (200 µL) was

added to each well and the sensors were hydrated in PBS overnight to stabilize the fluorescence

intensity. All fluorescence measurements (ex/em: 460/570 nm) were acquired using a SpectraMax

Gemini EM plate reader. After hydration, the PBS was replaced with 200 µL of fresh PBS (pH

7.4) as a control or 0.1 M glucose in PBS (pH 7.4). The fluorescent responses were measured for

60 minutes at 5-minute intervals. Fluorescence measurements were normalized to the first time

point and averaged for each experimental group. The average response of the experimental group

was subtracted from the control group and then plotted over time. Error was determined using

error propagation.

Fluorescence Imaging: Images of scaffolds were acquired on a Zeiss Confocal Microscope

(Thornwood, NY) using a 488 nm laser and 10x air objective (PlanApo, NA = 0.17).The laser

intensity was set to 1% (10 mW full power)

69

SEM Acquisition: Images of scaffolds were acquired on a Hitachi S4800 with a 5 kV accelerating

voltage. Samples were not sputter coated. Fiber diameters were measured using Quartz

PCI (Quartz Imaging Corp.) software. Magnification – 10X, NA – 0.45 in

Fabrication of Nanoparticle-based Sensors: The fabrication of nanoparticle-based sensors is

described previously. 35 Briefly, optode was dried overnight on a glass plate, and then transferred

into a scintillation vial. Then 5 ml of PBS (pH=7.4) and 5 mg of DSPE-mPEG (550) in 500 µL of

chloroform was added. The mixture was sonicated for 3 minutes at 40% amplitude using a

Branson digital sonifier (Danbury, CT). The nanosensor solution was pipetted out from vial

leaving residual optode.

In Vivo Studies: Animal procedures were approved by Northeastern University’s Institutional

Animal Care and Use Committee. To determine whether nanofiber scaffolds minimized sensor

diffusion in vivo, glucose nanosensors and scaffolds were prepared as above. Scaffolds were cut

into circular pieces using a 6 mm diameter biopsy punch and sterilized by soaking in 70% ethanol

and then sterile PBS (pH=7.4). SKH1-E mice were anesthetized and then injected with 20 µL of

either nanosensors or scaffolds along their back. To determine the injection volume, the amount

of sensor material in a 6 mm diameter scaffold was estimated and then approximated to the same

amount of material in the nanosensor formulation. Nanosensors were injected with 31G insulin

syringes (BD Biosciences, Franklin Lakes, NJ). Scaffolds were injected using an indwelling

needle assembly.153 The assembly consisted of a 20 G outer needle and a 25 G inner needle with

a blunted tip that acted as the plunger. 3M Vetbond ™ tissue adhesive (3M Animal Care Products,

St. Paul, MN) was then applied to the injection site. Imaging was performed on an IVIS Lumina

II (Perkin Elmer) small animal imager in fluorescence mode with a 465/30 excitation filter and

580/20 emission filter. Mice were imaged every 5 minutes for 1 hour and then at 3 hours post-

70

injection. Fluorescence measurements were analyzed by selecting a region of interest around each

injection spot to obtain the total radiant efficiency of the area. The background-subtracted total

radiant efficiency from each region of interest containing either scaffolds or nanosensors was

measured at each time point and then normalized to the total radiant efficiency at time 0. The

normalized values were then averaged across three mice for both the scaffolds and nanosensors.

To account for sensor degradation over time, scaffolds and nanosensors were prepared as above

and placed into a 96-well plate with a total volume of 200 µL of either PBS or PBS and nanosensors.

Their total radiant efficiency was tracked using the same imaging parameters and data analysis as

the in vivo studies.

3.3 Results and Discussion

Boronic Acid (BA) Selection. The clinical utility of glucose-responsive nanosensors depends on

their ability to exhibit proper dynamic range and sensitivity.7 In the sensors presented here, the

boronic acid sensing moiety governs the sensor response to glucose. The sensors respond to

glucose by a competitive binding interaction between boronic acids and diols on either alizarin or

glucose. In the absence of glucose, the boronic acid binds to the diol on alizarin, statically

quenching its fluorescence. As local glucose concentrations increase, those molecules displace the

alizarin, allowing it to fluoresce.

We derived phenylboronic acids containing fluoro- and carboxyl- groups that withdraw electrons

in order to improve sensor response compared to octylboronic acid, which was used previously.1

Comparing boronic acid used in this paper to cotylboronic acid, the fluorescence was enhanced by

10%. In addition to acting as an electron-withdrawing group, carboxyls provide a site for the alkyl

chain additions performed herein and other chemical modifications.

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The initial screen for glucose-responsiveness showed that macrosensors with 4-carboxy-3-

fluorophenyl boronic acid (BA1) increased fluorescence 13% from baseline in response to 100

mM glucose (Figure 3.2). This compound’s reactivity derives from having both fluoro- and

carboxyl groups withdrawing electrons from the boronic acid group, however this increases the

compound’s polarity. Consequently, BA1 readily leached from the hydrophobic sensor platform

over time (Figure 3.3), which leads to signal degradation and loss of sensitivity to glucose. We

then produced a new set of boronic acid molecules with varying polarities by systematically

converting the carboxyl group into esters with various alkyl chain lengths to find responsive and

stable sensors.

Adding a methyl ester to BA1 produced BA2a, which leached out of the macrosensors

significantly less than BA1, and longer alkyl chains (BA2b &BA2c) produced no significant

reduction in leaching compared to the methyl ester (Figure 3.3). Improvement in stability when

replacing the carboxylate ligand to an ester suggests that leaching of boronic acid may play in an

important role. Increasing the alkyl length decreased the resulting boronic acid’s reactivity; the

magnitude of macrosensor responses to glucose when formulated with BA2a, BA2b, and BA2c

were all less than compared to macrosensors made with BA1. Macrosensors with BA2a were still

relatively sensitive at physiological pH, exhibiting a 10% increase in fluorescence in response to

100 mM glucose. By contrast, macrosensors made with BA2b and BA2c only increased by 3%

and less than 1%, respectively (Figure 3.2).

The nanosensors’ competitive binding mechanism depends on the boronic acid diffusing within

the hydrophobic matrix and interacting with glucose molecules at the sensor-environment interface.

The result that longer alkyl chains reduced the magnitude of sensor responses suggests that long

alkyl chains inhibited boronic acid diffusion within the polymer matrix.

72

Figure 3.2. Response of glucose-sensitive macrosensors containing functionalized boronic

acids with increasing length of alkyl chains. The macrosensors contain Boronic Acids 1 (Ncontrol

= 7, Nglucose = 8), 2a (Ncontrol = 7, Nglucose = 7), 2b (Ncontrol = 7, Nglucose = 7), or 2c (Ncontrol = 8, Nglucose

= 8). Macrosensors were exposed to either PBS as a control or 100 mM glucose in PBS for 60

minutes. The percent change in fluorescence response was calculated as the average normalized

difference between the control and glucose groups. Error bars were calculated using error

propagation.

73

Figure 3.3. Fluorescence decay of macrosensors with different boronic acids. The

macrosensors contained Boronic Acids 1 (N = 7), 2a (N = 7), 2b (N = 7) or 2c (N = 8) and were

exposed to PBS for 60 minutes. Fluorescence intensities were normalized to time 0 and error bars

represent standard deviations.

74

While leaching is much less problematic for those derivatives such as BA2c, the increased

hydrophobicity may impart too high of an affinity to polymer matrix, causing sluggish diffusion

and small sensor responses.

Analysis of the calibration curve displays 10 fold change between 30 mM and 100 mM glucose

(Figure 3.4). To examine selectivity of the formulation as compared to other sugars fructose was

tested in vitro. The signal intensity of the 1 mM fructose solution was about half that of the 100

mM glucose after 1 h of incubation (Figure 3.5). We chose to compare 1 mM fructose because it

is present in the body at a concentration of about 8 µM30, so we tested it at 100-fold excess, as we

did with the glucose. Considering the difference in the comparative concentrations of these sugars

in the body, the interference is at an acceptable, if not optimal, level.

Our previous glucose-sensitive nanosensors included octylboronic acid, a hydrophobic aliphatic

derivative, as the sensing moiety,1,31 because it was stable in the hydrophobic nanosensor core.

14,32 Despite its stability, nanosensors with octylboronic acid were not sufficiently sensitive to

glucose. From studies on optodes, we discovered that 4-carboxyl-3-fluoroboronic acid 1 and its

derivatives are more sensitive to glucose due to their fluoro- and carboxyl groups. With the results

showing that BA2a leaches significantly less than BA1 and is must more responsive to glucose

than BA2b and BA2c, BA2a was selected as the lead candidate for nano-scale sensor fabrication.

Glucose-Sensitive Nanofibers. In addition to improvements in nanosensor sensitivity, nanosensor

systems need new design strategies for increasing residency time at the implantation site, ideally

with minimally-invasive delivery methods. Glucose nanosensors with BA2a were electrospun to

produce nanosensors with nanofiber architectures, requiring a plasticizer content of 10%.

For comparison, spherical nanosensors were also made using the fabrication method described in

the Materials and Methods section.

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Figure 3.4. Response of macrosensors against different concentrations of glucose. The

macrosensors contained Boronic Acids 1a were exposed to 0 mM(N = 4), 30 mM (N = 4), 50 mM

(N = 4), 80 mM (N = 4) or 100 mM (N = 4) glucose solution in PBS for 60 minutes. The percent

change in fluorescence was calculated as the average normalized difference between the control

and glucose groups. Error bars were calculated using error propagation.

76

Figure 3.5. Comparison of sensor response to two sugars, glucose and fructose. Macrosensors

containing Boronic Acid 1a were exposed to PBS as a control ( N = 4), 100 mM glucose (N = 4)

or 1 mM fructose (N = 4) solution in PBS a for 60 minutes. The percent change in fluorescence

response was calculated as the average normalized difference between the control and glucose

groups. Error bars were calculated using error propagation.

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Electrospinning optodes with 70 – 90 kDa PCL successfully produced continuous polymer

nanofibers, as confirmed with SEM images for high resolution fiber measurements and with

confocal images to show homogenous fluorescence from the alizarin within the fibers (Figure 3.6).

Measurements from the SEM images indicate that fiber diameters were 374 ± 142 nm and were

continuous without beading or wetting. Optode-based sensors are typically highly plasticized to

aid the mobility of sensor components and analytes within the sensor.33 Nanofibers that were

electrospun with PCL and 30% or 60% plasticizer increased the glucose-sensitivity by 6%, as

expected. However, even the 30% plasticized scaffolds showed signs of electrospinning instability

with discontinuous fibers and areas of pools of plasticizer (data not shown). Therefore, in order

to maintain the nanofibrous structure, we used 10% plasticizer content at the trade-off of sensor

response.

Glucose-sensitive nanofibers with BA2a, 2b and 2c responded 2% less, 2% more and 1% less than

their macrosensor counterparts respectively. Boronic acids with longer alkyl chains decreased the

sensitivity to glucose. To test the electrospun nanosensor response times, fluorescence intensity

was monitored over one hour after placing scaffolds in 100 mM glucose in PBS. Sensors

containing BA2b reached 95% of their maximum response within 12 minutes, but sensors

containing BA2a did not level off within an hour (Figure 3.7). The slow response times are likely

due to the low plasticizer content as well as the static flow conditions for the experimental

configuration. Low plasticizer content would restrict components from diffusing to the sensor-

environment interface. An experiment conducted in a flow cell would have enhanced the rate of

solution diffusion throughout the porous scaffold and decreased the response time. Despite these

slow response times, it is important to note that physiologic glucose levels change over the course

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of tens of minutes,34 meaning that the BA2b formulation in nanofiber form responds sufficiently

fast to capture these changes.7

Higher molecular weight PCL or other polymers can support higher plasticizer percentage; for

example, electrospun nanofibers fabricated with ethyl cellulose were able to support up to 40%

plasticizer.23 Such strategies offer additional ways to improve the sensitivity and response times

of future nanosensor designs.

In Vivo Residency time Studies. In previous in vivo studies, nanoparticle-based sensors diffused

away from the implantation site within one hour. To show that nanofiber nanosensors improve

residency times at the implantation site, either spherical nanosensors1 or nanofiber nanosensors

were implanted subdermally (Figure 3.8) and their signal loss was directly compared to their in

vitro signal loss. Similar to previous experiments, the spherical nanosensors lost radiant efficiency

at the injection site significantly greater than the signal loss observed in vitro. In vitro signal loss

is attributed to boronic acid leaching from the hydrophobic core, and the difference between in

vivo and in vitro signal loss is attributed to nanosensor diffusion away from the implantation site.

By contrast, nanofiber scaffolds exhibited very closely matched signal loss between the in vivo

and in vitro experiments after one hour, and they were nearly equal after three hours (Figure 3.9).

The spherical nanosensors experienced a ~30% difference in total radiant efficiency loss when

compared to the in vitro control, whereas the decay constants for nanofiber scaffolds differed only

by 6%. Several factors accelerated the signal loss for spherical nanosensors in vivo compared to in

vitro, most notably sensor diffusion, cellular uptake, and the potential for facilitated transport of

components (either alizarin or boronic acid) out of the nanosensors due to amphiphilic serum

components in the in vivo environment.

79

Figure 3.6. Electrospun glucose-sensitive scaffolds. (A) Confocal image, (B) SEM image and

(C) size distribution of glucose-sensitive nanofibers. The average fiber diameter was 374 ± 142

nm (n=49). The width of histogram columns represents 50 nm.

80

Figure 3.7. Response of glucose-sensitive nanofibers containing different functionalized

boronic acids. Glucose-sensitive nanofibers contained fluorinated boronic acid derivatives 2a

(Ncontrol = 6, Nglucose = 8), 2b (Ncontrol = 5, Nglucose = 7), and 2c (Ncontrol = 7, Nglucose = 8). Increasing

alkyl chain lengths on fluorinated boronic acid derivatives effected the response of glucose-

sensitive nanofibers. Nanofibers were exposed to either PBS as a control or 100 mM glucose in

PBS for 60 minutes. The percent change in fluorescence response was calculated as the average

normalized difference between the control and glucose groups. Error bars were calculated using

error propagation.

81

Figure 3.8. In vivo comparison of glucose-sensitive nanoparticles and nanofiber scaffolds.

Mice were injected with glucose-sensitive nanoparticles and nanofiber scaffolds along the back

and then imaged with a fluorescent small animal imager for one hour and then at 3 hours post-

injection. Shown here are the fluorescent images from one mouse over this time frame.

82

Figure 3.9 Fluorescence measurements of glucose-sensitive nanoparticles and nanofiber

scaffolds over time in vivo. The average normalized total radiant efficiency of glucose-sensitive

(A) nanoparticles and (B) nanofiber scaffolds both in vivo (○) and in vitro control (■) were plotted

over time. The normalized in vivo average for nanoparticles and nanofiber scaffolds was

calculated across 3 different mice with Nnanoparticles= 8 and Nnanofiber scaffolds= 6 injection spots.

Similarly, the normalized in vitro average was calculated from Nnanoparticles= 8 and Nnanofiber scaffolds=

7. Error bars represent standard deviations.

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Since the in vivo residency time of the nanofiber scaffold compared to the nanoparticles was

increased almost to the levels observed with the nanofibers in vitro, we could conclude that the

new sensor geometry maintained sensor residency at the injection site and would allow for longer

monitoring times.

3.4 Summary

In this study, we developed optode-based glucose nanosensors that were more sensitive to glucose

and more stable at the site of in vivo implantation.20 The initial macrosensor screen showed that

electron-withdrawing groups on BA1 and its derivatives facilitated a response to glucose under

physiological conditions, which is a major improvement over previous hydrophobic boronic acid

derivatives. Using the most responsive hydrophobic boronic acid derivative, BA2a, nanosensors

were electrospun into nanofibers and the nanofiber format was significantly more stable in vivo

than spherical nanosensors. Future work will focus on further increasing sensitivity and stability

by red-shifting the reporters and adding a reference signal for quantitative measurements.

3.5 Acknowledgments

This work was supported by the National Institute of Health under award number 5RO1GM084366

and Northeastern University’s internal funding Tier 1 support. Additionally, we thank Chris

Skipwith for his help in obtaining SEM images, Roger Kautz for his help in obtaining NMR spectra,

and Ganesh Thakur for his help with flash column chromatography.

3.6 Appendix: Glucose nanosensors emebedded in an alginate hydrogel

Previously, our group incorporated glucose-sensitive nanoparticles into commercially available

hydrogels containing thiol-modified hyaluronate and gelatin.37 Acrylate was used to crosslink the

thiol groups in the polymer. Incorporating glucose nanosensors to this polymer prolonged the in

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vivo residency time of the nanosensor from less than 1 hour to 3 hours but decreased its sensitivity

towards glucose as well. To improve the sensitivity, response time, and residency time of the

glucose nanosensor, we incorporated the optimized recognition moiety, BA2a, into a glucose-

sensitive nanoparticle and then embedded the nanoparticle into a matrix of alginate hydrogel this

time. Alginic acid is a polysaccharide containing guluronic acid and mannuronic acid monomers.

Calcium ions can crosslink the polymers by the chelation with the carboxylates on guluronic

groups forming a hydrogel structure in aqueous buffer.154 The alginate hydrogel has been used

extensively applied in oral especially enteric drug delivery for its biocompatible feature and

responsiveness to pH.155 We proposed that the hydrogel structure will protect embedded

nanosensors from diffusion and clearance at the injection site to improve the in vivo residency time.

On the other hand, the cavities created by un-crosslinked monomers and hydrophilic environment

within the hydrogel facilitate glucose molecules to approach the sensors.

The glucose-sensitive nanoparticle was fabricated as shown in the Material and methods section

in this chapter. BA2a was used to prepare the optode. We followed procedure reported by Dong

et al. to fabricate hydrogels.156 Specifically, 0.5 mL nanoparticles suspended in DI water was

mixed with 0.5 mL HEPES buffer containing 4% sodium alginate and 4% gelatin. For the study

conducted using plate reader, 50 µL mixture in a well in 96 well plate was treated with 50 µL 10%

CaCl2 in DI water and then incubated for 10 mins. After the gelation was completed, the liquid in

the well was removed using a pipette, and the gel was washed three times with PBS before 100 µL

PBS was added to each well. For the study conducted using IVIS, 200 µL mixture in a PetriDish

was treated with 200 µL 10% CaCl2 and then incubated for 10 mins. After the gelation was

completed, the liquid in the well was removed using a pipette, and the gel was washed three times

85

with PBS before 3 mL PBS was added to each dish. The gelatin was used to improve the mechanic

property of the gel.

We firstly tested the response of the embedded sensor to glucose. Hydrogels containing glucose-

sensitive nanoparticles were soaked in PBS for 2 hours before the test in a SpectraMax Gemini

EM plate reader. After 50 mM glucose was added to the solution, the relative difference between

the experiment group and control group increased by more than 10% in 30 mins (Figure 3.10).

The difference became distinguishable in first couple minutes. Comparing to glucose-sensitive

nanofiber, this design improved sensitivity and response time of the detection. We reason that the

hydrophilic cavities in the hydrogel matrix facilitate the glucose to approach the sensors leading

to the improvements.

We also examined the stability of the hydrogel-embedded nanosensor. Since the crosslink of the

alginate hydrogel depends on the chelation of Ca2+ by carboxylates, we propose that if the

nanoparticle can be coated with carboxylates which may participate in chelation, the stability of

the nanosensor within the matrix can be enhanced. In this study, we used two types of amphiphilic

PEG-lipids to coat nanoparticles: the DSPE-mPEG550 (with a methoxy group at the hydrophilic

end), and DSPE-PEG-carboxylate (with a carboxylate at the hydrophilic end). The study was

conducted using IVIS with a 465/30 excitation filter and 580/20 emission filter. The hydrogels

were soaked in PBS in PetriDish and measured at different time points. Fluorescence

measurements were analyzed by selecting a region of interest around each injection spot to obtain

the total radiant efficiency of the area. The background-subtracted total radiant efficiency from

each region of interest containing either hydrogels was measured at each time point and then

normalized to the total radiant efficiency at time 0. In first two hours, the fluorescence from

hydrogels containing both types of nanoparticles dropped.

86

Figure 3.10. Response of glucose nanosensor embedded in alginate hydrogel. Hydrogels

containing glucose-sensitive nanoparticles were exposed to either PBS as a control (N = 5) or 50

mM glucose (N = 6) in PBS for 30 minutes. The percent change in fluorescence response was

calculated as the average normalized difference between the control and glucose groups. Error

bars were calculated using error propagation.

87

The total radiant efficiency from the nanoparticle coated by DSPE-mPEG550 dropped by 12% and

the one coated by DSPE-PEG-carboxylate dropped by 7% (Figure 3.11A). In 8 days after the

gelation, the fluorescence from the nanoparticle using DSPE-mPEG550 dropped by 20%, while

the one coated by DSPE-PEG-carboxylate dropped by less than 10% (Figure 3.11B). This result

indicated that the coating of the nanoparticle played a role in the stability of nanosensors within

the hydrogel matrix. The possible chelation of Ca2+ by carboxylates on nanoparticles and alginates

may contribute to this change.

In future, more studies need to be carried out to further exploit this design of using alginate

hydrogels as a matrix. The ratio of nanoparticles, alginate, and gelatin can be trialed and screened.

On the other hand, the condition of crosslink, such as the concentration of Ca2+ and the time for

crosslink can also be optimized to yield a glucose nanosensor with improved sensitivity, response

time and physical strength suitable for in vivo application. Then, the hydrogel can be applied in

vivo for continuous detection of glucose

88

Figure 3.11. The stability of hydrogel in PBS. The alginate hydrogels containing DSPE-

mPEG550 coated (N = 3) and DSPE-PEG-Carboxylate coated (N = 3) nanosensors were exposed

to PBS. (A) The normalized decay of total radiant efficiency in first 2 hours after fabrication. (B)

The normalized dacay of total radiant efficiency in 8 days after fabrication. Error bars represent

S.D.

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Chapter 4: Conclusion and Future Direction

The development of personalized medicine, health monitoring devices, and early-stage diagnosis

requires more accurate, precise, and fast biological and chemical nanosensors to detect

physiological substances in vivo. Selection of the recognition moiety plays a significant role in

achieving the desired sensitivity, dynamic range, selectivity, and reversibility of the nanosensor.

In this thesis, we have demonstrated two nanosensors using different recognition mechanisms: 1)

ACh-MRNS using enzymatic hydrolysis to recognize acetylcholine, and 2) glucose sensitive

nanofibers utilizing boronic acids to detect analytes. In both cases, we co-immobilized the

recognition moiety as well as a reporter to create local physical and chemical changes leading to

an alteration in detectable signal intensities. In vitro calibration demonstrated that the dynamic

range, limit of detection, sensitivity and selectivity of ACh-MRNS are suitable for measuring

acetylcholine in brain; Delivered to the brain of rats, the ACh-MRNS detected drug-induced

endogenously released acetylcholine. To improve the sensitivity of the glucose-sensitive nanofiber,

we screened purchased and newly synthesized boronic acids to optimize the binding affinity

between the recognition moiety and glucose. The nanofiber with the optimized boronic acid

responded to glucose at physiological pH with an improved in vivo residency time.

The recognition moieties used in this dissertation can be further modified to improve the

nanosensors for in vivo application. In the ACh-MRNS, BuChE can be replaced by

acetylcholinesterase (AChE) to achieve a better sensitivity since the AChE is 2.5 times more active

than the BuChE to catalyze the hydrolysis of acetylcholine. This modification needs to be paired

with a faster MRI pulse sequence to improve temporal resolution and increase the fidelity of

acetylcholine detection. On the other hand, boronic acids with a higher affinity to glucose can also

be explored. For example, cyano and nitro groups can substitute fluoride or carboxylate to render

90

a stronger electron withdrawing effect to increase the affinity between glucose and boronic acids.

Other than recognition moieties, nanoplatforms and reporters can also be optimized to improve the

in vivo use of nanosensors. Polyamindoamine (PAMAM) dendrimer can be used as the nano-

backbone for the ACh-MRNS: The small size of PAMAM allows the ACh-MRNS to diffuse into

the synapse where a higher concentration of acetylcholine is released and the synaptic transmission

takes place. To improve in vivo detection of glucose, we are still looking for new matrix material

such as a hydrogel to contain all sensing components as well as facilitate diffusion of glucose to

overcome kinetic barriers created within the matrix. Embedding nanoparticle-based sensors in

biocompatible hydrogel may be a route to meet both requirements of sensitivity and residency time.

In the future, different types of hydrogels made from peptides and organic polymers can be further

interrogated to find the best matrix.

Nanoscale biosensors have displayed the capability to detect physiological molecules with the

desired dynamic range, sensitivity, reversibility, and selectivity. However, to apply nanosensors

to in vivo measurement, more studies, including optimization of recognition moieties, are still an

ongoing challenge. The work demonstrated in this dissertation used two different types of

recognition moieties to detect two critical physiological molecules: acetylcholine and glucose. The

Ach-MRNS is the first nanosensor to selectively image and monitor acetylcholine in vivo. This

sensor can be used to uncover the neural activity in brain and mechanisms of acetylcholine-related

diseases. The glucose-sensitive nanofiber provides a candidate for continuously monitoring the

level of glucose in vivo. This design prolonged the residency time of the sensor making the long-

term self-monitoring by patients with diabetes possible. In the future, more sensitive recognition

moiety for in vivo application can be synthesized, screened and incorporated to improve the

nanosensors for in vivo application. Other components in the nanosensors, such as the

91

nanoplatform and reporter, can also be modified to meet the needs of the in vivo application. With

the groundwork complete, the nanosensors for acetylcholine, glucose and other physiological

molecules will pave a way for future biological and preclinical studies.

92

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