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MOLECULAR ANALYSIS OF HYALURONIC ACID (HA) USING SOLID-STATE
NANOPORES
BY
FELIPE RIVAS DUARTE
A Thesis Submitted to the Graduate Faculty of
VIRGINIA TECH-WAKE FOREST SCHOOL OF BIOMEDICAL ENGINEERING
in Partial Fulfilment of the Requirements
for the Degree of
MASTER OF SCIENCE
Biomedical Engineering
August, 2017
Winston-Salem, North Carolina
Approved By:
Elaheh Rahbar, Ph.D., Advisor/Chair
Adam R. Hall, Ph.D., Co-Advisor
Aleksander Skardal, Ph.D.
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DEDICATION & ACKNOWLEDGEMENTS
I would like to thank my advisor Dr. Rahbar and Co-advisor Dr. Hall for welcoming me
into their labs, and giving me the opportunity to work with them for the past two years. I am very
grateful for their invaluable help throughout this process, their mentorship and fostering an
environment where I was able to grow professionally. I appreciate their patience and willingness
to meet with me on short notice despite their busy schedules, always making time for me when I
needed advice and guidance. Overall, thank you for showing me by example what it means to think
critically as a scientist and being part of my professional and personal development. I look forward
to continuing my academic and professional career under both of their mentorships.
I would also like to thank my committee member Dr. Skardal for his advice and asking
difficult questions striving that I think critically, on the problems I was facing. I would also like to
thank our project collaborators Dr. Paul DeAngelis and Dr. Heidi Reesink, for their support (with
providing samples and materials), insightful feedback and sharing their expertise on their subject
matter, helping to move the project forward.
I would also like to personally thank my lab teammates (Shiny, Fanny, Osama Charlotte
and Kelli) for making the experience in the lab very enjoyable. It has been a true pleasure to work
and learn alongside all of you. Special thanks to Osama Zahid, for helping me with everything,
from the moment I started working in the lab, sharing his knowledge and expertise and being the
person that I could count on for help in the lab.
On a personal note, I would like to thank my parents and sisters for being my network of
support when I needed it the most. All of you have contributed to allow me, to get to the place that
I am today, and I could not be more immensely grateful. Despite the distance your warmth, words
of encouragement and love always felt present which kept me motivated. Bree Bonnema, my
girlfriend, deserves very special thanks for being with me on my side through the good and
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challenging moments that I have faced. You have always encouraged me to pursue my passions
and dreams and I am very grateful for this. Thank you for brightening my days, being an inspiration
to me and most importantly my best friend.
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TABLE OF CONTENTS
LIST OF FIGURES AND TABLES ............................................................................... vi
LIST OF ABBREVIATIONS ....................................................................................... viii
ABSTRACT ...................................................................................................................... ix
1. CHAPTER1- INTRODUCTION .................................................................................1
1.1. HYALURONIC ACID .................................................................................................... 1
1.1.1. Chemical Structure ............................................................................................... 2
1.1.2. Physical Structure ................................................................................................. 3
1.1.3. Biological Role ....................................................................................................... 4
1.1.4. HA’s role in Disease States and Potential Use as a Biomarker ......................... 9
1.2. HYALURONIC ACID SYNTHESIS AND QUANTIFICATION ............................ 16
1.2.1. Laboratory Synthesis .......................................................................................... 16
1.2.2. HA Quantification Methods ............................................................................... 17
1.3. NANOPORES: .............................................................................................................. 20
1.3.1. What are Nanopores?.......................................................................................... 20
1.3.2. Types of Nanopores ............................................................................................. 21
1.3.3. Application of Nanopores in Single Molecule Detection .................................. 23
1.4. RESEARCH MOTIVATION ....................................................................................... 24
2. CHAPTER 2-MOLECULAR ANALYSIS OF HA USING SS-NANOPORE .......26
2.1. INTRODUCTION ......................................................................................................... 26
2.1.1. Solid State-Nanopore Platform .......................................................................... 26
2.1.2. Research Goals .................................................................................................... 30
2.2. METHODS .................................................................................................................... 30
2.2.1. HA Loading Experimental Conditions .............................................................. 30
2.3. RESULTS ....................................................................................................................... 35
2.3.1. Polydisperse HA .................................................................................................. 36
2.3.2. Sheared Polydisperse HA .................................................................................... 39
2.3.3. Monodisperse HA Characterization .................................................................. 42
2.4. DISCUSSION ................................................................................................................ 46
2.5. LIMITATIONS ............................................................................................................. 54
2.6. CONCLUSIONS ........................................................................................................... 56
3. CHAPTER 3- ISOLATION OF HA FROM BIOLOGICAL SAMPLES ..............57
v
3.1. INTRODUCTION ......................................................................................................... 57
3.2. METHODS .................................................................................................................... 59
3.2.1. HA Isolation/Extraction Methods ...................................................................... 60
3.2.2. Gel Electrophoresis Verification of Extracted HA ........................................... 67
3.3. RESULTS ....................................................................................................................... 67
3.2.3. Evaluation of HA Extracted from PLASMA .................................................... 67
3.2.4. Evaluation of HA Extracted from SYNOVIAL FLUID .................................. 76
3.4. DISCUSSION ................................................................................................................ 83
3.5. LMITATIONS ............................................................................................................... 91
3.6. CONCLUSIONS ........................................................................................................... 94
FUTURE DIRECTIONS .................................................................................................96
APPENDIX .....................................................................................................................100
REFERENCES ...............................................................................................................109
CURRICULUM VITAE ................................................................................................113
vi
LIST OF FIGURES AND TABLES
LIST OF FIGURES……………………………………………………………………………Pg. #
Figure 1-1: Molecular Structure of Hyaluronic Acid ....................................................................... 2
Figure 2-1: Transmission Electron Micrograph Images Produced by HIM .................................. 27
Figure 2-2: Coulter Counter Schematic Illustration ....................................................................... 27
Figure 2-3: Ohmic Relationship Representation in a Coulter Counter .......................................... 28
Figure 2-4: Schematic of HA Translocation Across a SS-Nanopore ............................................. 30
Figure 2-5: Nanopore Platform Assembly & Components. ........................................................... 34
Figure 2-6: SS-nanopore Measurement of Polydisperse HA ......................................................... 37
Figure 2-7: Gel Electrophoresis of PolyHA Samples .................................................................... 38
Figure 2-8: Rate of Translocation Dependency on Voltage and Concentration for HA ................ 39
Figure 2-9: Mechanical Shearing of PolyHA Using Sonication Energy. ...................................... 41
Figure 2-11: MonoHA Dwell Time and Change in Conductance Analysis. ................................. 44
Figure 2-12: Calibration Curve of Mean ECD vs. Molecular Weight for MonoHA ..................... 45
Figure 2-13: Calibration Curve of Mean ECD vs. Molecular Weight for MonoHA (54kDa-
2.4MDa) Using Different LPFF values and Threshold StDev. ...................................................... 49
Figure 2-14: Calibration Curve of Mean ECD vs. Molecular Weight for MonoHA Across the
MW Range of 54kDa-2.4MDa Using Different Applied Voltage Conditions. ............................. 50
Figure 3-1: Schematic Illustration of the Extraction of HA from Biological Fluids ..................... 60
Figure 3-2: Phase Lock Separation Schematic for HA Isolation from Biological Fluids .............. 62
Figure 3-3: Surgically Induced Carpal Chip, on Horses’ Radial Carpal Bone joint ...................... 63
Figure 3-4: Affinity Extraction Method for HA from Biological Fluids ....................................... 65
Figure 3-5: Determination of the Dynabead-bVG1 Complex’s ability to capture HA .................. 69
Figure 3-6: Preliminary Isolation of Spiked in HA in Human Plasma ......................................... 72
Figure 3-7: Isolation of Spiked MonoHA (250kDa) in Human Plasma, Using Phenol-Chloroform
Extraction.. ..................................................................................................................................... 73
Figure 3-8: HA Extraction from Left Limb of Equine Model Grade 3 Induced OA ..................... 77
Figure 3-9: Timepoint Comparison Left Limb for Induced Carpal Chip (Horse Boss) ............... 79
Figure 3-10: Timepoint Comparison of Extracted HA from Equine Sample (BL Day 0 and Day
12) Using Gel Electrophoresis and SS-nanopore System .............................................................. 81
Figure 3-11Time Comparison of Extracted HA from Equine Sample (PL Day 0 and PL Day 5)
using Gel Electrophoresis and Using SS-nanopore System ........................................................... 82
Figure 3-12: Nomogram of Percent Overlap of Normal Distributions, Lk-Norm Method ............ 88
Figure A-0-7: Procedural Iterations of Isolation Schematics of Spiked in MonoHA in Human
Plasma .......................................................................................................................................... 108
APPENDIX SECTION…………………………………………………………………..…….Pg.#
Figure A- 0-1: Mean ECD Measurements for PolyHA Samples at Different Concentrations and
Applied Voltages. ........................................................................................................................ 100
Figure A- 0-2: Mean ECD Calculation for Mechanically Sheared PolyHA Using Increasing
Sonication Energy ........................................................................................................................ 101
Figure A-0-3: Descriptive Statistics for ECD Histogram Analysis for Different MW-HA ......... 102
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Figure A-0-4: Histogram Analysis for Event Dwell Time and Change in Conductance for
MonoHA Samples Ranging From 54kDa-2.4MDa ..................................................................... 103
Figure A-0-5: Calibration Curve Calculation between ECD vs. MW of MonoHA ..................... 104
Figure A-0-6: Comparative Histogram Analysis for MonoHA Analysis Using different LPFF and
Standard Deviation Threshold Values. ........................................................................................ 105
LIST OF TABLES………………………………………………………………………..…….Pg.# Table 1: Normal HA Concentration in Different Biological Fluids and Tissues in Humans ......... 4
Table 3: Estimated MW of HA using Preliminary Calibration Curve Equation of ECD vs.MW of
HA .................................................................................................................................................. 46
Table 4: Optimization of Dynabeads M-280 to bVG1 Concentration Ratio ................................. 66
Table 5: Relative Yield of HA Capture Using the Dynabeads-Proteoglycan Complex ................ 70
Table 6: HA Extraction Yield, from Spiked MonoHA in Human Plasma Sample ........................ 75
Table 7: Optimization of Dynabeads-to-bVG1 Concentration ...................................................... 75
APPENDIX SECTION…………………………………………………………………..…….Pg.#
Table A-0- 1 Calibration Curve to Associate ECD to MW Using Different Filter Frequency and
Event Threshold Detection Set-Up Conditions ............................................................................ 106
Table A-0- 2 Tukeys Pairwise Difference of Least Square Means for the use of Different SS-
Nanopore Platform Voltages ........................................................................................................ 107
Table A-0- 3 Calibration Curve to Associate ECD to MW Using Different Voltage Set-Up
Conditions .................................................................................................................................... 108
Table A-0- 4: Gaussian Distribution Statistical Analysis for extracted MonoHA from Plasma
(81kDa) compared to a control sample of equal MW .................................................................. 108
viii
LIST OF ABBREVIATIONS
CD44: Cluster of differentiation 44, cell surface glycoprotein
ECD: Event Charge Deficit; or integrated area under graph Conductance vs. Time
ECM: Extracellular matrix
ΔG: Change in Conductance measured in Siemens
GAG: Glycosaminoglycan
HA: Hyaluronic Acid
HAS1, HAS2, and HAS3: Types of Hyaluronan Synthase enzymes
HMW-HA: High Molecular Weight Hyaluronic Acid
LMW-HA: Low Molecular Weight Hyaluronic Acid
LYVE1: Lymphatic vessel endothelial hyaluronan receptor 1
MALLS-SEC: Multi-Angle Laser Scattering-Size Exclusion Chromatography
MonoHA: Monodisperse Hyaluronic Acid
MW: Molecular Weight
PolyHA: Polydisperse Hyaluronic Acid
RHAMM: Receptor for hyaluronic acid mediated motility
SF: Synovial Fluid
sHA: Serum Hyaluronic Acid
SS-Nanopore: Solid State Nanopore
TGS-6: Tumor necrosis factor-inducible gene 6, hyaluronan binding protein
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ABSTRACT
Hyaluronic acid (HA; hyaluronan) has emerged as a powerful indicator for a range of
diseases and physiological conditions, including cancer, osteoarthritis, and wound healing after
injury. Evidence has shown that low molecular weight HA is pro-inflammatory while high
molecular weight HA is anti-inflammatory, suggesting the potential importance of HA size as an
independent biomarker. However, current methods to measure HA are time consuming, rarely
distinguish HA by molecular weight and are not easily integrated in the clinical setting. Thus, there
is a need for improved HA detection and quantification methods that can address this need.
It was therefore, the aim of this thesis to propose a novel method involving the use of a
Solid-State nanopore platform developed by Hall et al. for the quantitative analysis of HA. This
platform consists on a nanometer-scale pore through which biomolecules can be driven
electrophoretically, yielding characteristic electrical signals at the single-molecule level, which can
be then translated into useful information about the molecule’s size. The advantage of this platform
is that it has the potential to be easily integrated in the clinical setting, is less time consuming (than
conventional detection methods of HA) and can potentially offer quantitative information of
molecular size differences of HA with high resolution while using low sample volumes.
This work also explored the feasibility of potentially using this platform in a clinical
setting, showing proof of concept of detection and quantification of HA extracted from biological
fluids such as plasma and synovial fluid, where changes in HA concentration and molecular weight
distribution have significant implications in disease states. Given this research goal, a newly
proposed protocol for the extraction of HA from plasma and synovial fluid was also presented in
this work.
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1. CHAPTER1- INTRODUCTION
1.1. HYALURONIC ACID
Hyaluronic Acid (HA) was first discovered by Karl Mayer and his colleague John Palmer
in 1934, when isolated from the vitreous body of a cow’s eye. This led to the unprecedented finding
of a macromolecule that is fundamental in many diverse biological processes, serving as a
lubricating, hydrating, binding and immunomodulatory agent. This linear polysaccharide is
ubiquitous in the body found predominantly in connective tissue such as skin, umbilical cord,
synovial fluid, vitreous humor and in systemic circulation. In addition, HA is also found in the
lungs, brain, and muscle tissue. This polysaccharide is naturally occurring, over a wide range of
molecular weight (MW) from 1000kDa-8000kDa (under normal physiological conditions). The
MW of HA depends on the number of repeating disaccharide units per HA polymer strand.
Interestingly, differences in MW, change its chemical and physical properties which gives rise to
the many roles (sometimes antagonist in function) that this molecule plays in both normal and
pathological conditions in the body (were smaller fragments <200kDa are predominantly seen).
(Cowman, Lee, Schwertfeger, McCarthy, & Turley, 2015; Dicker et al., 2014; Kogan, Soltes, &
Mendichi, 2007; Necas, Bartosikova, Brauner, & Kolar, 2008).
It is not surprising that HA is a widely studied macromolecule because of its diverse
biological roles, its potential applications in biomedical engineering (due to its material properties),
in addition to emerging as powerful biomarker in the clinical setting for a range of inflammatory
and physiological conditions such as cancer, wound healing, and osteoarthritis.
It is the aim of this chapter to elucidate the important biological role that this
macromolecule plays in the body, and how its unique physical and chemical properties allow it to
become versatile substance for different biomedical applications.
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1.1.1. Chemical Structure
HA is a simple, linear, and stable polysaccharide that is part of the Glycosaminoglycan
(GAGs) group of polysaccharides as it is composed of up repeating disaccharide units. It is made
up of an alternating amino sugar (N-acetylglucosamine) and a uronic sugar (D-glucuronic acid),
linked through β1-3 and β1-4 glyosidic bonds forming a stable chair like configuration (Figure
1-1) (Kogan et al., 2007; Torvard C Laurent, Laurent, & Fraser, 1996). Unlike other GAGs, HA is
not sulfated and its macromolecules are naturally found in its salt form in solution, adopting single
helical/coil configuration when in the presence of counter ions Na+, K+ Ca 2+, Mg 2+ (Cowman &
Matsuoka, 2005). Nevertheless, a double helix configurations can be seen in dilute conditions when
placed in a microenvironment containing H+/K+,Rb+,NH4+ (Cowman & Matsuoka, 2005). Its
helical configuration is attributed to the relative non-polar axial hydrogens that face outwardly, in
relation to equatorial side chains that are more polar and tend to face inward which create the coil
configuration. Hydrogen bonding between the hydroxyl groups along the chain give HA a highly
stable backbone (Necas et al., 2008). HA It is highly negatively charged in physiological pH and
extremely hydrophilic due to its internal, highly polar inner coil functional groups giving the
molecule the extraordinary ability to trap 1000 times its weight in water (Necas et al., 2008), hence
its hydrating function.
Figure 1-1: Molecular structure of Hyaluronic Acid
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1.1.2. Physical Structure
HA disaccharide units have and average MW weight of ~0.4kDa with and average length
of 1nm (T C Laurent, Laurent, & Fraser, 1995). The coil configuration of HA in solution has a
radius that increases exponentially with increasing MW giving it a large hydrodynamic (Cowman,
Schmidt, Raghavan, & Stecco, 2015).
The viscoelastic properties of HA are dependent on its overall concentration and the mean
MW distribution. Typically, single oligomers of this molecule have a linear rod-like structure while
longer chains of HA behave as semi-flexible coils. However, as concentration approximates
1mg/ml (known as the entanglement concentration point) HA chains become stiffer and interact
with one another forming a 3D network/mesh of double helixes and the solution begins to exhibit
viscoelastic properties. At these concentrations, its viscosity increases exponentially (to ~3.3
power). When solutions of this concentration are exposed to low shear stress its viscosity can be
~106 higher than its solvent, while when exposed to higher shear its viscosity can drop 1000-fold
(Torvard C Laurent et al., 1996).
The shear dependent behavior of HA in solution is attributed to the rate of recovery of the
polymer chains in their ability to regain their undisturbed shape when shear forces are applied.
When exposed to higher shear rates the molecules become stretched in the direction of flow and
hence their contribution to the viscosity reduces and overall the solution behaves elastically. On
the other hand, at lower shear rates the polymer chains can reorient themselves and regain their
original relaxed shape remaining highly viscous. (Cowman, Schmidt, et al., 2015). These physical
properties make this substance ideal for tissues are subject to cyclical loading (i.e. muscles and
joints) as it can adjust to changing shear forces giving shock absorbing capabilities and help prevent
frictional damage between tissue structures.
These rheological properties are well conserved in a pH range of 4.0-9.0 however,
increases in temperature on the other hand has the effect of reducing the apparent viscosity
4
significantly. HA is highly stable in a wide range of temperatures ranging between 5-105°C,
however stability is time dependent in temperatures above 50°C (Cowman, Schmidt, et al., 2015).
1.1.3. Biological Role
1.1.3.1. HA Synthesis and Degradation and Biodegradation
Unlike other GAGs, HA is not synthesized through the Golgi apparatus; instead it is
synthesized by membrane-bound enzymes called hyaluronan synthases (HAS). These enzymes are
located in the inner face of the plasma membrane (Kogan et al., 2007) which add disaccharide units
as it is simultaneously translocases HA into the extracellular space through HAS pores. There are
mainly three HAS isozymes responsible for HA synthesis known as HAS1, HAS2 and HAS3.
Despite the fact that HA is considered a simple polysaccharide, these three isozymes differ in their
reaction rates (hence differing in their ability to synthesize HA) and MW polymer strands that they
are able to produce (Dicker et al., 2014). In general, HAS1 and HAS2 have been shown to be
responsible for large HA size chains 2000kDa while HA3 can produce smaller HA strands between
100-1000kDa (Dicker et al., 2014). The spatial distribution and relative concentration of these
isozymes in tissues are crucial for the maintenance of overall mean MW distribution of HA and
concentrations throughout the body. Table 1 below provides a summary of relative concentration
of HA distribution in the human body and biological fluids.
Table 1: Normal HA concentration in different biological fluids and tissues in humans
Tissue Type or Bodily fluid Concentration (μg/ml) * Overall Mean MW
Umbilical Cord 4100 Low-High
Dermis 200-500 Mid-Range
Vitreous Body 140-340 High
Epidermis 100 Mid-Range
Synovial Fluid 1400-3600 High
Urine 0.1-0.3 Low
Serum 0.01-0.1 Low-Mid-Range
*Concentration numbers were obtained from Kogan et al., 2007.
5
Since the molecular function of HA is highly dependent on its size, these three HAS
enzymes give the ability to control and mediate different cellular behavior based on the coordinated
activation and thus synthesis of varying MW-HA. Not surprisingly, during different disease states
and morphogenic processes the activity of HAS vary. (Dicker et al., 2014).
On the other hand, hydrolysis of HA in mammalian tissue is the result of three types
enzymes known as hyaluronidases (with isoforms Hyal 1 and Hyal2 being the two most common),
β-D-Glucoronidase and β-N-acetyl-hexosaminidase. In general HA is removed from the
extracellular matrix (ECM) and drained into the lymphatic system and further degraded in local
lymph nodes (~40%) (Kogan et al., 2007). Hyaluronidases which are ubiquitous to somatic cells,
fragment larger polymer chains into smaller intermediate fragment sizes of HA by the coordinated
action of Hyal2 binding to HA extracellularly, which is then internalized and degraded by Hyal1.
Similarly, HA can also be catabolized by the other enzymes above which can be found in peripheral
tissue as well as by reactive oxygen species (ROS). Degradation activity of HA is a highly
controlled process, as changes in MW not only affects localized viscosity of HA in tissue structures,
but also their binding affinity to other macromolecules and cell surface receptor, which can trigger
several molecular pathways. Therefore, degradation rate of HA, as well as monitoring for
enzymatic activity and their spatial distribution has become important in relation to understanding
normal molecular processes and progression of disease conditions.
HA in synovial fluid (SF) is primarily degraded by ROS under both normal and
pathological conditions, which is then carried out into joint tissue where enzymatic activity further
degrades it, followed by degradation in the liver. HA has a turnover rate of 2-3 weeks in SF, with
a half-life of 20 hours. Comparatively, HA in circulation has a half-life of 2-6min and has a total
turnover rate of 10-100mg/day (Fraser, Laurent, & Laurent, 1997; Torvard C Laurent et al., 1996;
Necas et al., 2008).
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Experiments carried out by Lebel et al. determined that HA found in systemic circulation
is cleared primarily by the liver (accounting for 33% of clearance rate) and via pulmonary excretion
(accounting for 63% of excretion). The remaining HA clearance is carried out by the kidneys and
in lower percent the gastrointestinal track (GT) tract (Necas et al., 2008).
1.1.3.2. Binding Affinities and HA receptors
A critical characteristic of HA is its ability to bind to cells and components of the ECM
through specific and non-specific binding. This is important as HA plays an important role in
structural support and scaffold organization of the ECM for many connective tissue structures. It
also, plays a central role in cellular migration.
The interaction of HA with a class of proteins called proteoglycans (such as aggrecan,
versican and neurocan) form what are known hyaladherins. These complexes are important in:
stabilizing the ECM integrity, controlling passage of substances through the network of proteins
and HA linked chains, and acting as diffusion barriers for certain substances. In addition,
Hyaladherins are important in controlling cellular migration, and as they aggregate to tissue
structures, the HA bound to these proteins help in hydration of the tissue. This is particularly
important in hydration of the cartilage surfaces as high molecular weight HA (HMW-HA) binds to
aggrecan present in cartilage surface, increasing water retention, and maintaining the
biomechanical compressive ability of these surfaces.
HA also binds to cell surface receptor known as cluster of differentiation 44 (CD44),
expressed in a variety of cells. Depending on the CD44 variant expressed the binding affinity to
HA changes which in turn, this complex can trigger different cellular pathways. For example,
HWM-HA/CD44 complex has been identified to influence cellular aggregation and attachment of
cells to the ECM, as well to be involved in the fast turnover rate of the ECM. On the other hand,
Low MW-HA (LMW-HA)/CD44 complex is involved in mediating lymphocyte migration in
inflammatory stages and play a role in stimulating angiogenesis. In pathological conditions,
7
overexpression HMW-HA/CD44 has been associated to tumor growth while LMW-HA/CD44 has
been associated to play a role in tumor progression increasing its vascularity. Therefore, MW of
HA is highly related to the cellular process it is involved with.
Another important cell receptor HA binds to, is Receptor for Hyaluronan-Mediated
Motility (RHAMM) which is found on the cell surface, cytosol, and nucleus. HA bound to
RHAMM regulates cellular response to growth factors and participate in cellular motility (Necas
et al., 2008). When cells extracellularly express this receptor, it binds to CD44 and HA.
Interestingly, differences in expression of CD44 variants, as well as RHAMM expression determine
HA fragments size affinity and subsequent cellular pathway it activates. For example, this complex
is responsible to induce cellular migration and help in the reorganization in the ECM, in addition
to also be involved in cellular growth and differentiation. In pathological conditions, the association
of HA to RHAMM has been studied associated to metastasis of cancerous tissue.
Another HA receptor is the lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1)
which is an extracellular link domain that binds to HA. It is an important glycoprotein in the
lymphatic system regulating HA transport and degradation in lymph nodes. Since HA can trap
debris elements in its entangled structure it is believed that HA bound to LYVE1 play a vital
immunogenic role.
Lastly tumor necrosis factor-inducible gene 6 which (TGS-6), expressed by macrophages
is an important binding protein. TGS-6 is an important hyaladherin that can modulate macrophage
activity to become pro or anti-inflammatory. This role is highly dependent on the MW-HA and the
presence of inter-alpha-inhibitor (IαI). The TGS-6 and LMW-HA along with the IαI form a
complex which stops the release of signals for up-regulation of enzymes that degrade the ECM
during the proliferative stage of wound healing. However, TGS-6 with HMW-HA without the
8
presence of IαI can have the effect of activating macrophage activity and its role in debris and
bacterial removal and thus behave pro-inflammatory.
1.1.3.3. Biological Function
The biological importance of HA can be appreciated in its role under normal healthy
conditions (associated with HMW-HA chains) and in its pivotal role in inflammatory, and
pathological conditions in tissue (mostly associated with the presence of LMW-HA).
Under healthy conditions HA maintains water homeostasis in tissues. This role is
attributed to two main HA characteristics which are: its ability to retain large amounts of water
(aiding in tissue hydration) and the fact that the osmotic pressure of the 3D architecture of HA
increases with increased concentration of HA (preventing among other things rapid water loss from
tissues). The latter biological characteristic, can further help as a diffusion barrier from the bulk
movement of fluids, regulate transport of molecules, and even work to immobilize certain large
molecules of interest. This is particularly important in the ECM, as it can compartmentalize protein,
cells and other molecules and thus contribute to its organization. HA’s role in tissue hydration is
most evident in its role to maintain skin firmness and elasticity.
The interaction with cell surface receptors and hyaladherins attributes HMW-HA its ability
to induce cell signaling and initiate intracellular processes as well as cellular motility in the ECM.
This is key in tissue regeneration, repair and in morphogenic processes. In addition, HMW-HA
(>5MDa) has been associated to be anti-angiogenic and immunosuppressive (Dicker et al., 2014).
The viscoelastic properties and mesh-like structure of HA (seen in HMW range) is
important as it acts as a space filler between muscle bundles acting to lubricate them to prevent
damage from frictional gliding. In joints, HA found in SF aids in hydration lubrication and plays a
biomechanical role in compressive shock absorbing abilities of joints and prevents their frictional
damage. Also, importantly in joints, HA plays a ROS scavenger protective role. Proteoglycans like
aggrecan deposit on the collagen matrix of cartilage giving its structural integrity while
9
glycoproteins like lubricin which contain surface active phospholipids (SAPL) attach to its surface
and act as a lubricating agent (Schwarz & Hills, 1998). However, stress induced from shear forces
in joints can contribute to the release of ROS which can deteriorate the cartilage surfaces by
degradation of the above-mentioned macromolecules. HA in SF has been identified to react rapidly
with ROS serving the role as a protective barrier from cartilage wear and tear (Schwarz & Hills,
1998).
Lastly HA of LMW-HA (~<300kDa) has been attributed for its role in immunomodulation,
given its ability to activate macrophages which in turn release pro-inflammatory cytokines.
Similarly fragments of this size have been associated for its role in promoting angiogenesis and are
crucial in tissue injury to signal for disturbance of homeostatic conditions (Torvard C Laurent et
al., 1996; Price, Myers, Leigh, & Navsaria, 2005).
In pathological and chronic inflammatory conditions, a shift towards increased HA
concentration particularly in LMW-HA (resulting from enzymatic or ROS fragmentation) have
been of interest for its clinical application as a useful biomarker in disease onset and progression.
Mainly HA levels and MW distribution are monitored, in cases of degenerative conditions like
osteoarthritis (OA), and during wound healing following burn injuries, and more recently in certain
types of cancer. Therefore, HA has been shown to be a powerful biomarker for several disease
onset and progression.
1.1.4. HA’s role in Disease States and Potential Use as a Biomarker
HA has become an increasingly popular biomolecule of study due to its vital biological
roles and inter-play between normal and diseased states. Different sizes of HA can affect cellular
behaviors differently. For instance, LMW-HA fragments tend to be more bioactive than HMW-HA
and are seen to compete for their binding sites modifying the cascade of biological signals in the
tissue microenvironment. It is not surprising that in a disease states where there is disruption of the
tissue microenvironment, there is both a reduction in MW and concentration, of HA, which when
10
unregulated have been seen to exacerbate inflammatory conditions. Thus, HA size and amount can,
serve as a biomarker for the disease diagnosis and progression. This overexpression of HA can be
measured in both biological fluids (i.e. blood and synovial fluid) as well as in the tissue
microenvironment. Nevertheless, the specific role of HA in relation to its MW in disease states is
still being studied, as well as evaluation of its prognostic reliability as a clinical biomarker,
particularly because in many disease state HA cannot be studied in isolation from other biological
macromolecules that contribute to the disease. (Cowman, Lee, et al., 2015)
1.1.4.1. Osteoarthritis
Osteoarthritis (OA) is degenerative disease that affects the articular joints occurring most
often in the knees, hips, lower back, neck, and smaller joints (i.e. fingers). Articular cartilage under
normal conditions covers the end surface portion of bones, to ensure a smooth gliding movement
in joints providing a lubricating tissue layer that prevents their frictional damage. The breakdown
of cartilage over time causes pain, swelling and inflammation. Chronic, wear, and tear of cartilage
can expose bone to frictional damage as they glide past each other leading to spur like growth in
the exposed bone (which contributes to surface roughness that further aggravates frictional
damage). Chipping of the bone is often observed in advanced cases of OA, and these bone
fragments remain in the articular cavity, which further exacerbate the inflammatory response,
which in turn contributes to further increase cartilage damage. Although it can occur in people of
all ages it is most prevalent in individuals over 65 and common risk factors include age, obesity,
joint overuse and prior history of joint injury and genetics (Arthritis Foundation (Organization),
n.d.).
It is understood that one of the mechanisms of action which induces cartilage damage is
stress related ROS, which are released in the joint cavity. ROS have the effect of disrupting and
fragmenting HMW-HA, into smaller fragments which in turn disrupts the binding affinity of
proteoglycans and glycoproteins which make up the biomatrix of cartilage structure (i.e. aggrecan)
11
as well as provide its lubricating layer (i.e. SAPLs) (Antonacci et al., 2013; Nitzan, Nitzan, Dan, &
Yedgar, 2001). The decrease in HMW-HA induces an inflammatory and immunostimulatory
response, which further triggers cytokine and enzymatic activities further disrupting the cartilage
layer. The shift towards more LMW-HA also reduces the viscosity of SF, which translates to
reduced compressive ability of the joint leading to bone-on-bone frictional damage. Consequently,
changes in SF-HA’s overall MW and concentrations have been studied for its association to onset
and disease progression of OA when compared to normal joints of healthy individuals (accounting
for age and gender). However, the use of HA as a potential biomarker for OA diagnosis or OA
progression remains controversial, as there may be other confounding effects that contribute to OA
progression. The remains questions regarding the exact role of HA and its MW distribution in SF
during OA.
Normal SF-HA has been observed to have a mean molecular weight of 6-7Mda, however
during OA, HA can be partially degraded which results in shift of MW distribution of HA to be
<1000kDa. HA concentration is also lower in arthritic knees compared to normal healthy knees,
for the same age group. It should be noted, however, that HA size distribution and concentration in
SF can also change with age without OA (Temple-Wong et al., 2016). Despite, this HA has been
as a useful predictor for disease prognosis in moderate to severe cases of OA when extracted from
the joint’s SF. Likewise, changes to LMW-HA have been positively associate to pain in patients
with OA (Band et al., 2015; Temple-Wong et al., 2016).
On the other hand, serum levels of HA (sHA) have also been studied for its biomarker
potential for OA, since extraction of HA from serum represents a less invasive technique than
extraction from an already affected joint. However, sHA does not provide a clear diagnostic
potential for primary to moderate OA (grade 1-3), and only provide useful disease information for
sever cases of OA (grade 4), since studies have shown insignificant changes in concentration levels
when comparing normal to disease state OA that have been previously classified using radiographic
12
techniques (Band et al., 2015; Kaneko et al., 2013). However, there remain questions as to how
sHA correlates to joint HA or SF-HA distribution. It should be noted that sHA measurements can
be affected by the natural turnover changes throughout the day, as well as variability in circulating
HA due to other biological processes or comorbid complication that contribute to different
circulating HA fragments, which are unrelated to OA.
Therefore, the role of MW-HA distribution and concentration in OA patients is still being
evaluated, yet clinically it is being used as a reference for identifying rate of the inflammatory
progression of OA in an effort to help guide disease intervention strategies (Band et al., 2015).
1.1.4.2. Injury/trauma (wound healing)
Following tissue injury after trauma a characteristic series of events takes place for the
resolution of the wound. These events include inflammation, tissue granulation, reepithelization,
and remodeling (W. Y. J. Chen & Abatangelo, 1999). HA plays a significant role throughout these
processes, given its role in modulating the microenvironment of the ECM by binding to cellular
receptors that promote cellular migration and induce protein expression responsible for wound
closure and remodeling. These cellular cascade of events are highly dependent on HA’s MW
(D’Agostino et al., 2015).
During the initial stages of wound healing following the injury, there is significant rise in
the concentration of HMW-HA, primarily due to its ability to bind with fibrinogen to form the
initial clot. The role of HMW-HA is also important during this stage as it facilitates macrophage
migration to the site of the inury for removal of tissue debri and bacteria through phagocytosis (W.
Y. J. Chen & Abatangelo, 1999). Following the initial clot, HWM-HA is degraded during the acute
inflammatory phase of wound healing and there is an accumulation of LMW-HA (30-450kDa),
which is the result of both enzymatic activity and the presence of ROS. This degration is important
as these smaller fragments contribute to the activation of macrophages (by binding to their CD44
receptors) which breakdown injured tissue and further release proinflammatory cytokines (like
13
TNF-α, IL- 1β and IL-8) as well promote fibroblast, smooth muscle cells and endothelial cells to
migrated to the injured tissue. LMW-HA is also important in the acute phase of inflammation as it
activates immune cells through its binding to toll like receptors (TLR) which stimulate B-
Lymphocytes to migrate the injured tissue (Litwiniuk, Krejner, & Grzela, 2016).
Following inflammation, the formation of granular tissue is characterized by immature
ECM composed of fibroblasts, inflammatory cells, fibronectin, temporary collagen network (type
III) and GAGs (like HA), whose purpose is to close the wound site and provide the environment
for subsequent reepithelization. This matrix has a rapid turnover rate, by enzymatic degradation.
HA is present in high concentrations primarily to facilitate cell migration due to its ability to bind
to locomotive promoting receptors like RHAMM and opening spaces in the ECM that facilitate
cellular movement aiding in its organization. Interestingly, LMW-HA, also plays a role in
moderating inflammation by protecting the newly formed matrix from enzymatic and ROS damage.
In a similar, anti-inflammatory manner HA also forms complexes which help reduce inflammation.
An example of these complexes occurs when HA binds to TGS-6 (expressed by fibroblasts) along
with IαI. This complex has the effect of inhibiting the role of matrix metaloproteinases (MMPs)
that degrade the newly-formed tissue matrix. Therefore HA helps in stabilizing and preserving the
granulation tissue. During reepithelization HA synthesis shifts towards HMW with the purpose of
promoting once again the hydrating strutucture and stability to the extracellular space.
LMW-HA is also associated with angiogenesis during wound healing. HA-CD44 binding
stimulates enzymatic activity of MMPs which create space in the matrix that promotes the
migration of endothelial cells from nearby vessels. As these fragments migrate and proliferate they
form new vessels in the tissue. (Litwiniuk et al., 2016; Pardue, Ibrahim, & Ramamurthi, 2008).
Given the diverse role HA plays during wound healing, HA has emerged as an important
biomolecule of interest. For example, in-vivo experiments have shown than during ECM
14
remodeling, the application of exogenous HMW-HA in a concentration dependent manner promote
a more ordely and increased collagen deposition. On the other hand, instances of tranasplanted
tissue rejection have been primarily associated to higher concentrations of LMW-HA. It has also
been shown that LMW-HA <50kDa enhances endothelial cell proliferation promoting greater
vascularization of the tissue. Meanwhile the effect of HA chains >1000kDa showed to diminish
vascular proliferation and EC cultures showed to produce a decrease expression of proangiogenic
cytokines (Pardue et al., 2008). The role of HA in the epidermis was shown in CD44 knockout
mice to show below normal levels of HA accumulation which resulted in decreased skin elasticity
and to strongly affect tissue repair following injury (W. Y. J. Chen & Abatangelo, 1999).
These studies and several others, have led to the better understanding of the complex
mechanisms and roles of HA during wound healing. These studies have also aided in the
development of wound dressings and tissue grafts to improve wound healing. As further research
is conducted in wound healing, it is posible that HA may become a viable biomarker for wound
healing and potentially helo in guiding decision-making for therapeutic strategies.
1.1.4.3. Cancer
Cancer is characterized by the abnormal growth of cells and their unregulated proliferation
which can lead to the formation of a tumor mass when localized, and in some cases has the ability
to invade other parts of the body when becoming metastatic. Cancerous cells, where the are
believed to be generated as a consequence of genetic mutation of one or more cells, where the
underlying cause of mutation can be associated to both external and hereditary conditions. Tumor
growth is highly dependent on increased vascularization in order to support the energetic demand
of the growth of the cancerous tissue. Due to the uncontrolled cellular growth and division, tumor
cells often overexpress biomolecules (i.e. proteins and polysacharides) cytokines and growth
factors as compared to healthy tissue which generate changes in local chemical and physical
microenvironment which in some cases can be identified and measured.
15
Overexpression of HA in cancerous tissue has been identified as a potential biomarker for
ovarian (increased by 100-fold) , prostatic (increased by 7-fold), breast and bladder carcinomas
when compared to normal stromal levels of HA in these tissues (Cowman, Lee, et al., 2015; Yuan,
Amin, Ye, De La Motte, & Cowman, 2015). The cancerous microenvironment is rich in HA and
its sharp increase in concentration in many cases is implicated in their progression. Particularly,
LMW-HA is believe to be crucial in cancer progression because of its ability to modulate
angiogenesis and tumor-genesis. In addition, the HA rich microenvironment near the tumor is ideal
to promote cellular proliferation, aid in cellular migration as well improve nutrient difussion to the
site of the tumor. Similarly other studies have identified smaller fragments of HA particularly
between 4-25 oligosacharides (20-100kDa) to promote cancer metastasis. It is hypothesized that
HA fragments originate because of overexpressing HA degrading enzymes such as a Hyal 1 and
Hyal2, during development of certain cancers. Smaller HA fragments have been known to loosen
cell anchorage as well as promote the degradation of the basement membrane which facilitate
cellular detachment from the main tumor allowing cancerous cells to spread through the lymphatic
or circulating system. More importantly it is the biding of HA to CD44 receptors which contribute
to the locomotive function of the cancerous cells and affect their cellular adhesion to the ECM.
Therefore, both local tumor HA levels and HAS enzymatic activity could be monitored for cancer
progression.
It has been observed that higher Hyal1 levels found in both urine and blood samples
correlate to a negative prognostic diagnosis of bladder cancer (Cowman, Lee, et al., 2015).
Similarly, Hyal1 is also monitored in prostate cancer, whereby Hyal1 levels have correlated with
tumor progression in 84% of the cases(Dicker et al., 2014) . On the other hand, higher concentration
levels of HA and versican in prostatic stromal cells correlate with its metastatic potential and early
detection(Melrose, Numata, & Ghosh, 1996; Ricciardelli et al., 1998; Ricciardelli, Sakko, Ween,
Russell, & Horsfall, 2009; Sakko et al., 2000). Lastly, HA levels have also been shown to be a
16
important predictor to study cancerous cell lines. An example of this is the fact that highly invasive
endometrial cancer cell lines show to overexpress Hyal2 levels when compared to less invasive
cell lines.
Early detection of cancer, is key in delivering effective treatments. Thes is also a need for
improved methods for faster and accurate detection of cancer. Cellular expression of HA and HA
producing enzymes have been shown to correlate with cancer onset, progression and in some cases
have shown to be correlated to patient outcomes. The appeal of using HA as a biomarker is based
on the fact that in some cases it can be readily available and identified in systemic circulation and
in other bodily fluids like urine, as well as provide in some cases more reliable quantitative
information than traditional histological techniques to monitor its progression.
1.2. HYALURONIC ACID SYNTHESIS AND QUANTIFICATION
1.2.1. Laboratory Synthesis
1.2.1.1. Production of Hyaluronic Acid
There are primarily two high yield sources for the extraction of HA for laboratory and
commercial purposes that are animal tissue or bacterial fermentation. Primarily these two sources
yield polydisperse HMW-HA (Boeriu, Springer, Kooy, van den Broek, & Eggink, 2013; Necas et
al., 2008).
HA extracted from animal tissue has been derived from several sources including the
vitreous body, umbilical cord as well as synovial fluid of rabbits, porcine and bovine tissue in
addition to being extracted from the cartilage of sharks (Boeriu et al., 2013). However, the most
abundant source of HA in animal tissue is seen in a rooster’s comb, which is used for large scale
industrial applications and FDA approved for use in medical applications in humans (Boeriu et al.,
2013). Animal derived HA goes through an extensive purification process, particularly when used
for medical grade products, in an effort to reduce possible contamination with unwanted residual
proteins and potential viruses that can be carried by the animal (Boeriu et al., 2013). Consequently,
17
these harsh purification steps lower its yield, by causing HA breakdown. Hence, HA derived from
bacterial fermentation is preferred.
Bacterial fermentation for extraction of HA is done on A and C Streptococci bacterial
strains, as well as from animal bacterial strains like P. multocida. The advantage of these methods
is that manipulation of growth medium and bioreactor conditions can accelerate their metabolism
(Boeriu et al., 2013) and be tailored to produce HA of specific MW; thereby reducing the
polydispersity of the HA which is commonly seen in animal tissue. Finally, bacterial production
gives the distinct advantage of being selective of mutant strains that have been identified for
producing smaller and specific MW (Necas et al., 2008). One of the drawbacks of using pathogenic
bacteria are the toxins released from their catabolic processes (Boeriu et al., 2013). Therefore,
genetically engineered bacterial organisms are used. This consists on introducing the HA synthase
enzymes from pathogenic bacteria into hyaluronan producing bacterial organisms like E, Coli
which are nonpathogenic for humans.
Lastly, a recent approach to HA synthesis consists of invitro synthesis of HA from isolated
HA synthase, primarily when needing a specific MW HA. This enzymatic polymerization allows
to control MW and yield by controlling intrinsic enzymatic properties such as the presence of sugar
monomers, and controlling the nucleotide sugar precursors (Boeriu et al., 2013). Nevertheless, this
technology is still not practical for larger scale application given its low yield.
1.2.2. HA Quantification Methods
1.2.2.1. Laboratory Techniques for HA Detection and Quantification
To obtain HA from biological specimens such as tissues, biological fluids or cell cultures,
HA goes through a series of three basic steps. The first step consists of generating a soluble form
of HA regardless of its biological origin. Second, the soluble form of HA is removed from other
biomolecules, which is achieved through digesting or denaturing unwanted proteins and removing
lipids and other large and small constituents from the solution. For example during this step the use
18
of digestive enzymes like Proteinase K or Proteinase E are commonly used (Cowman, Schmidt, et
al., 2015; Lee & Cowman, 1994; Yuan et al., 2015). Similarly heat or denaturing substances (i.e.
chloroform) are also used to disrupt HA binding to other biomolecules. The addition of acetone
and organic solvents are used to remove lipids. Vital to the process of isolating the aqueous phase
containing HA from the rest of the constituents, the use of techniques like phase separation,
centrifugation, dialysis, and ethanol precipitation are readily used. Finally, once most other
elements have been removed from the mixture, HA is a specifically isolated using binding affinity
method. One such method consists of using protein specific binding to HA, which in some cases
are seen coating magnetic beads for selective removal of HA. This isolation strategy has been
previously used to isolate HA from breast milk and synovial fluid (Hill et al., 2013; Yuan et al.,
2015). Variations of affinity methods also include running the aqueous HA sample through gel
beds that bind specifically to HA (Cowman, Schmidt, et al., 2015).
To quantify concentration levels of HA, and characterize the different MW there are
several laboratory techniques that are currently used. Summarized below are some of the most
common techniques used.
Enzyme-linked immunosorbent assay (ELISAs) is one of the most common techniques that
allow determining relative concentration levels of HA. This technique consists of using an
immobilized primary antibody (anti-HA monoclonal antibody), which is then incubated with a
secondary antibody conjugate (typically HA-Horseradish Peroxidase), and the HA aqueous sample.
Both HRP and HA compete for binding sites of the monoclonal antibody in the wells, thus often
called a competing assay. A substrate is then incubated with the assay that reacts with the HRP
complex giving off a blue color. The addition of what is known as a stop solution is added which
stops the reaction between the substrate and HRP complex and in doing so discoloration of the
complex to a yellow color occurs. Spectrophotometry is used to determine relative intensity of the
yellow color in the plate as such is inversely proportional to HA concentration. While this technique
19
has been used to determine molecular mass as low as 6.4kDa, the degree of variability is increased
significantly when testing molecular weights below 27kDa (Haserodt, Metin, & Dweik, 2011).
Larger molecular weights up to 2MDa have been detected with this technique. Different ELISA
kits differ in their ability to detect MW ranges with high precision and accuracy and hence previous
knowledge of approximate sample MW helps to select the HA-ELISA kit to use that is most
appropriate.
Gel electrophoresis is another commonly used technique to obtain semi-quantitative
measurements for MW determination of HA. This technique works by separating different MW of
HA in solution based on their movement through a porous gel as they are pulled according to their
electrical bias under the influence of an electric field. The speed at which the molecules move
across the gel is inversely related to the length of the polymer chain of HA, and consequently
smaller HA molecules travel further down the gel than larger molecules. To determine
corresponding MW a standard solution or ladder (containing a mixture of known molecular weight)
needs to be incorporated into one of the lanes of the well. Consequently, additional image software
tools need to be used to obtain an approximation of the molecular weight relative to the standard.
Size resolution of the different MW weights of HA using electrophoretic measurements has been
improved changing concentration composition of the gels. PAGE primarily is more suited for
higher resolution of molecular weight between 5-100k while predominantly agarose gels showed
to have best resolution in MW in the range 80-1500kDa (Bhilocha et al., 2011; Cowman et al.,
2011; Lee & Cowman, 1994).
Multi-Angle Laser Scattering-Size Exclusion Chromatography (MALLS-SEC) has been
use for the isolation of HA given its high sensitive for characterization of HA. This method consists
on controlling the isolation of HA based on the relative size that is present in the mixture, as the
sample is ran through a column with calibrated pore size diameters. The different pore sizes
effectively trap different MW size of HA across the length of the column (Barth, Boyes, & Jackson,
20
1996). In combination with a Light scattering detector the conformation and molecular size
uniformity of the sample can be determined. While this method provides with a high detection
sensitivity of MW characterization of HA, prior knowledge of MW size range is needed for the
adequate pore size selection.
Other studies have shown the feasibility of size determination and isolation using Ion
Exchange Chromatography (IEX) (Yuan et al., 2015), High Performance Liquid Chromatography,
Mass Spectroscopy an Spectrophotometry (L. Chen, Liu, Luo, & Hu, 2014; Homer, Denbow, &
Beighton, 1993).
1.3. NANOPORES:
1.3.1. What are Nanopores?
In many biological transport processes, there are specialized protein structures that serve
as transport channels for substances across cells membranes. Inspired by these naturally occurring
channels, which can be highly selective for the passage of substances, the field of molecular
detection on a single molecule level emerged (Fologea, Ledden, McNabb, & Li, 2008; Haque, Li,
Wu, Liang, & Guo, 2014; Kowalczyk, Grosberg, Rabin, & Dekker, 2012). nanopores, as its name
implies, are nanometric openings that rest on either a synthetic material, or embedded in a lipid
bilayer, which are used to interrogate on a single molecule level the passage of charge molecules.
These Nanopore apertures are strategically placed in between reservoirs containing an
electrolyte solution, and thus serve to restrict the passage of ions when an applied voltage is
generated across these reservoirs. As such, these nanopores play the equivalent role of a resistor in
a basic electrical circuit, with the current measured across these pores behaving in an Ohmic
fashion. Notably modifying the size aperture, changes the electrical dynamic of the system whereby
increasing the size aperture increases the passage of ions thus reducing the overall resistance that
is felt across. The electric field that is generated due the applied voltage also has the effect of being
able to pull electrically charged molecules of interest according to their electrical bias. Therefore,
21
when charged molecules of interest temporarily cross these pores, the otherwise consistent
electrical signal is interrupted. The instances of changes in the current signals mark the passage of
molecules crossing these pores, thus allowing for their detection and quantification.
There are mainly three types of classifications of nanopores that are used for molecular
detection which are: nanopores of biological origin (consisting of embedded pores in a lipid
bilayer), Synthetic nanopores (that are openings fabricated onto solid material substrates typically
made out of Si3N4, Aluminum Oxide or graphene), and nanopores which are a combination of both
biological and synthetic components usually referred to as hybrid nanopores (Fologea et al., 2008).
1.3.2. Types of Nanopores
1.3.2.1. Biological
The classic and first ever studied natural occurring Nanopore for molecular detection was
the α-hemolysin channel. α-hemolysin is a toxin that is secreted by Staphylococcus aureus
bacterium which essentially creates a transmembrane pore in the lipid membrane of cells. The pores
created are of typically consistent size diameters of ~1.4nm (Fologea et al., 2008). These were the
first to be studied for sensing the passage of small organic molecules, proteins and single strand
DNA/RNA molecules. These pores can readily be inserted in lipid bilayers; however to due to the
small size pore they are restricted to detecting molecular sizes of 1nm in diameter (Fologea et al.,
2008).
Since then other biological nanopores have been investigated for the use of molecular
detection including the pores generated by MspA (Mycobacterium smegmatis porin A) among
others. The advantages of using these biological pores over its synthetic counterparts, is that they
are highly consistent in their pore dimension, are thermally functional over a wide temperature
range and can be genetically engineered such that they can be more selective in their detection
capabilities (Fologea et al., 2008).
22
1.3.2.2. Synthetic
Synthetic nanopores most commonly known as Solid State nanopores (SS-nanopores) are
an alternative to biological pores. These, have been widely used given the fact that they can be
fabricated to defined geometries and can be finely controlled to specific dimensions. These pores
are also mechanically robust, and are both chemically and more thermally stable than the lipid
membrane on to which natural nanopores are created. Typically, these nanopores are fabricated on
silicon base membranes, Aluminum oxide, membranes or graphene (Fologea et al., 2008). The
choice of these materials depends on the level of sensitivity that is needed for molecular detection,
cost consideration and set up conditions to which the pore is going to be subjected, for which
material consideration is crucial.
There are many fabrication strategies used to create these synthetic nanopores including
ion etching, ion beam sculpting and laser etching techniques that generate different pore sizes
primarily based on exposure time of the substrate to the ablating/etching technique used (Yang et
al., 2011). These techniques can precisely generate nanometric aperture over a wide range of
diameters. For this research SiN based Solid-State Nanopore are used. However unlike other SiN
pore fabrication technique, these pores have been fabricated using a Helium Ion Microscope (HIM)
technique (Yang et al., 2011) which has shown to have more fine tune control over the size of the
pore that is created, over conventional techniques previously used.
Hybrid nanopores consist of the combination of both biological nanopores embedded on a
synthetic membrane. The advantage of using these pores is the fact that the size range of natural
pores and their molecular detection selectivity is highly conserved, while leveraging the mechanical
advantage of resting a more robust structure provided by the synthetic material, unlike the lipid
bilayer which is fragile. An example of this is using an embedded α-hemolysis channel onto a solid
SiN substrate (Fologea et al., 2008).
23
An additional approach for hybrid pores involves the use of DNA origami structures
embedded in SiN solid substrates. The advantage of this novel approach is the fact that they are
chemically selective for the passage of substances and their geometry can be selectively fined tuned
(Fologea et al., 2008).
Nevertheless, as with all hybrid pores one of the key drawbacks, is that in some occasions
a proper seal between the solid substrate and the biological pore is not always created, allowing for
current leakage and thus limiting the usefulness of this technology for molecular detection(Fologea
et al., 2008).
The advantage of using biological pore over synthetic pores is that the diameter of the pores
is extremely reproducible, and is much more precise than what synthetic fabrication techniques can
deliver. On the other hand, they can be genetically engineered to have chemical selectivity and are
less labor intensive to produce. Nevertheless, one of the biggest drawbacks is the fact that their
supporting lipid layers are very fragile and as such they are not easily integrated into nanodevices
with the same ease that SS-nanopores can (Fologea et al., 2008). In addition, unlike synthetic pores,
they are not reusable. Therefore, the choice between Synthetic and Biological nanopores depends
on the application for which it is needed.
1.3.3. Application of Nanopores in Single Molecule Detection
Research involving the use of nanopores for molecular detection has been heavily focused
on the one side, in detecting nucleic acid and polymer structures based on their electrophoretic
passage through Nanopore structures. In several studies nanopores have been used to study lengths
of DNA (on both a single and double stranded configurations), as well as their folding
conformations, with an effort to understand their translocation mechanism to potentially use this
technology for DNA sequencing, given the fact that this tool can be used for length differentiation.
Nevertheless, it has proven to be very challenging, since the passage of these molecules happens
24
extremely fast, that the instrument is not able to detect the electrical changes on a single base pair
level which is needed for DNA sequencing.
On the other hand, this technology has also been used for the practical application of
sensing epigenetic modification, microRNAs and Protein-nucleic acid interactions as it has been
previously studied in the Hall lab (Zahid, Zhao, He, & Hall, 2016). The relevancy of studying this
is that this technology can be used as sensing approach for potential biomarkers in many disease
states.
1.4. RESEARCH MOTIVATION
The overarching motivation for this research was based on the fact that Hyaluronic Acid is
highly important biopolymer, that has been studied for the multitude of physiological roles it plays
in both healthy and pathological conditions (as it is the case for osteoarthritis; OA). Nevertheless,
its role remains controversial, particularly since it has been shown to have antagonist roles believed
to be dependent on the molecular size of HA as it has been previously highlighted in this chapter.
Advancing the understanding of the role of HA heavily relies on the laboratory techniques that are
used to detect and quantify HA to be highly sensitivity, when it comes to differentiating molecular
size differences. Nevertheless, current technologies are time consuming, not easily integrated in a
clinical setting, and often are limited to provide semi-quantitative results. Consequently, given the
above-mentioned reasons, highlights the importance of accurately detecting HA, which leads to the
two main research goals that this document addresses in the subsequent chapters.
The first aim of this research (presented in Chapter 2 of this document), was to show the
feasibility of detecting molecular size differences of HA, using the novel technology of a Solid-
State nanopore platform developed by Hall et. al, since this technology has been used in the past
for the quantitative detection of size differences of charge biopolymers (i.e. DNA), using low
sample volumes, with high degree of sensitivity. Given HA’s charged properties and its semi-
flexible linear structure this technology was leveraged for its detection. Highlighted in the results
25
section presented in Chapter 2, this technology showed promising potential, to be used for its ability
to discriminate between HA of different molecular sizes.
The second research aim was explored in Chapter 3 of this document, which addressed the
feasibility of potentially using this platform in a clinical setting, by showing proof of concept of
detection and quantification of HA extracted from biological fluids such as plasma and synovial
fluid. To accomplish this goal, a newly proposed protocol for the extraction of HA from plasma
and synovial fluid was presented in this work and assessed for its feasibility for extracting HA with
high degree of purity. The material extracted with this method was then analyzed through SS-
nanopore system. Finally, equine derived SF samples from an OA model, were analyzed with this
system and the results were compared with those obtained from conventional laboratory techniques
to serve as proof of concept of the feasibility of the extraction protocol, and the SS-nanopore system
in identifying MW size distribution differences.
It was the goal of this research to highlight the potential of translating this technology into
the clinical setting as it can have a significant impact in the advancement of understanding diseases
conditions such as OA.
26
2. CHAPTER 2-MOLECULAR ANALYSIS OF HA USING SS-NANOPORE
2.1. INTRODUCTION
2.1.1. Solid State-Nanopore Platform
2.1.1.1. Fabrication of Silicon Nanopores (HIM)
Solid-State nanopores have been widely used as an emerging technology for the
quantitative detection of charged biomolecules on a single molecule level (including DNA, RNA,
proteins, and protein complexes) based on their electrophoretic signal trace. The use of this
technique has shown to offer high degree of sensitivity, resolution, and reproducibility of results
(Li et al., 2001; Yang et al., 2011). For this reason, this technology was intended for the molecular
detection and quantification of HA of different MW, given the need of an improved laboratory
technique that was both accurate, less time consuming and capable of providing quantitative
characterization. However, to maintain reproducibility of the results, it was crucial that the
fabrication method used to generate the nanopores, operates reliably within tight geometrical
tolerances, to consistently create same size nanometric holes. SS-nanopore detection heavily relies
on the dimensions of the nanopore to be consistent, since changes in this dimension, alter the
electrical signal readings that are picked up, as the biomolecules are being probed. To control this,
the use of a high-resolution SS-nanopore fabrication developed by Hall et al. al (Yang et al., 2011)
was used in this research.
This fabrication technique consisted on using a highly focused beam of a Helium Ion
Microscope (HIM) which ablates the material of the thin SiN membrane (that rests on a Si chip) in
a controlled fashion generating a nanometric aperture as seen in Figure 2-1. The aperture size was
controlled by the dose (ion/nm2) and the time exposure of the localized beam that either rastered or
performed a single shot exposure of the He+ beam over the SiN window. This approach was capable
of high resolution and reproducibility and able to achieve sizes as low as 2.5nm (Yang et al., 2011).
27
Figure 2-1: Transmission Electron Micrograph Images Produced by HIM Showing the Effect of He+
beam Exposure Time (At a Constant pA Dose) on the Size Diameter of the SS-nanopore
2.1.1.2. Nanopore Platform Basic Principles and Set-up
Detection of biomolecules using Solid-State nanopores, works on the same operating
principles of a Coulter counter (used for quantification of red blood cells and their size distribution)
(Purdue University, n.d.). This technology works by monitoring the temporary impedance that is
caused as charged particles of interest are pulled electrically through an orifice between two
reservoirs containing an electrolyte solution, whilst an electrical current is recorded concurrently
(because of the movement of ions across this same orifice) (Figure 2-2).
Figure 2-2: Coulter Counter Schematic Illustration, Marking the Passage of a Red Blood Cell Across
an Orifice Separating Two Chambers with Ionic Solution
28
The orifice size, establishes the resistance of the circuit between the two reservoirs, by
physically delimiting the available space for the passage of ions. This electrical resistance behaves
in an Ohmic fashion, and remains constant despite changes in applied voltage, by proportionally
adjusting the current baseline value (Figure 2-3).
Figure 2-3: Ohmic Relationship Representation in a Coulter Counter. The slope of the graph of
Current vs. Voltage is the overall electrical resistance of the system.
However, as a larger molecule pass through the orifice (i.e. Red Blood Cell; or RBC), it
temporarily occupies the space that would otherwise allow for the passage of ions, causing a
temporary increased electrical resistance in the circuit, resulting in a proportional drop in the current
signal. Since the drop in the current signal is proportional to the volume of the molecule that
transverses the orifice, the electrical readings can be used to probe the size distribution (based on
the drop in current) and the number of molecules in the solution (based on the number of instances
the current drops from its otherwise baseline value).
The SS-nanopores however, works on a much smaller scale, capable of probing smaller
molecules, as it has been shown for biomarker detection of charged biomolecules such as,
epigenetic modification in DNA, microRNAs as well as protein-DNA interactions (Fologea et al.,
2005, 2008). Given the fact the Hyaluronic Acid is a negatively charged biopolymer that is naturally
occurring over a broad range of molecular sizes (lengths), (Cowman, Lee, et al., 2015) the use of
SS-nanopores is a promising technology for the molecular characterization of HA.
The SS-nanopore platform used in this research from the Hall Lab (Virginia Tech- Wake
Forest School of Biomedical Engineering, Winston-Salem, NC), used a 4.4mm silicon chip with a
29
thin free-standing SiN membrane on to which a nanometric opening was fabricated (refer to
Section 2.1.1.1 for their fabrication process). This SS-nanopore was then place between two flow
cell chambers, containing an electrolyte solution, with one of the chambers also containing the
molecule of interest (in this case HA) (Refer to Figure 2-5 for an illustration of the SS-nanopore
Platform). A voltage was then applied creating a localized electric field which would drive the
movement of the charged particle across the nanometer opening according to its electrical bias.
Following the schematic in Figure 2-4 below, as ions passed uninterrupted across the pore
a current signal, denoted as the baseline current, was measured. However, when a charged
molecule approximates the pore it temporarily blocked the passage of ions across the nanometer
opening, which translated into a current signal drop. This drop in current signal persisted for as
long as it took for the molecule to cross the pore, at which point the current returned to the original
baseline value established earlier. Therefore, each time a molecule crossed the nanopore it was
denote as an “Event”. There are two parameters that are used to describe an Event: (a) change in
current drop or the depth of the event, and (b) the event duration or “dwell time”, which is a measure
of how long it takes for the molecule to move across. Integrating the area under the event, gives the
metric called Event Charge Deficit (ECD). Since these event parameter are affected by the
translocation properties of the molecule including: its diamter, length and folding conformation,
ECD is a more comprehensive parameter that encompasses these contributions taking into account
both event dwell time and change in conductance.
It was hypothesized that given the ability of the SS-nanopore platform to detect the passage
of charged biopolymers, that it should be able to detect the passage of HA and more importantly,
that changes in MW of HA would yield distinct electrical event signals, allowing for
characterization of HA with great sensitivity.
30
Figure 2-4: Schematic of HA Translocation Across a SS-nanopore
2.1.2. Research Goals
The primary research aim of this chapter was to show the application of the SS-nanopore
platform technology for the quantitative analysis of HA, and show its ability to discriminate with
high degree of sensitivity differences in MW. Initial results illustrated in this chapter served to
demonstrate the feasibility of this technique for its characterization and lay the foundation for future
optimization studies.
2.2. METHODS
2.2.1. HA Loading Experimental Conditions
2.2.1.1. HA Sample Preparation
Monodisperse and Polydisperse samples of HA were used in this study for the
characterization of HA, using SS-nanopore platform. A description of the sample preparation and
storage is summarized below.
31
2.2.1.1.1 Monodisperse HA (MonoHA) Solution Preparation
Purified MonoHA samples were provided by Hyalose Inc. (Oklahoma City, OK), derived
invitro from recombinant Pasteurella multocida hyaluronan synthase. A total of seven samples of
MonoHA sampled with mean MW of 54kDa, 81kDa, 130kDa, 237kDa, 545kDa, 1076kDa and
2384kDa were used, whose MW was confirmed by the supplier using MALLS-SEC, and reported
to be within ± 5% of the reported mean MW. Purified samples were speed vacuumed and delivered
in the form of a dry powder in aliquots of 50μg each. Aliquots of each of the samples were dissolved
in DI, to a concentration of 1μg/μl, and stored at 4°C for immediate use. The remainders of samples
were kept at -20°C for stable long-term storage.
2.2.1.1.2 Polydisperse HA (PolyHA)
PolyHA, referring to a natural blend of different MW of HA with no size exclusion, was
obtained in powder form. PolyHA sample were dissolved in DI water to a concentration of 1μg/μl.
The concentration was determined by weighing 1mg of dry powder of HA, followed by dissolving
the content of the vial.
2.2.1.1.3 Mechanical Shearing of PolyHA using Ultra sonication
Since the goal for this research was to differentiate HA according to its MW using the SS-
nanopore platform, initial evaluation of changes in MW distribution and size was tested by
mechanically fragmenting PolyHA, using ultrasonic shearing. PolyHA samples in a concentration
of 1μg/μl were placed in microTUBE AFA fiber snap-caps (Covaris, Woburn, MA) to a final
volume of 50 μl, which were mechanically sheared using Covaris S220 focused-ultrasonicator.
Sonication was carried out on five individual polydisperse samples, each exposed to increasing
sonication time ranging from 5-25sec in 5sec increments, in a 7°C water bath operating under the
following conditions: peak incident power of 175W, 200 cycles per burst, and a duty factor of
10%. After sonication, the samples were stored at 4°C for immediate analysis using SS-nanopore
32
platform. HA fragmentation was confirmed by gel electrophoresis on a 0.5% agarose gel, using the
methods described in the proceeding section.
2.2.1.2. HA Molecular Mass Determination (Gel Electrophoresis)
Gel electrophoresis, was used as a validated laboratory technique to verify MW of HA
samples. An agarose solution was prepared to a 0.5% concentration using 1xTAE buffer, which
was briefly heated using a microwave, and then poured over the casting tray and allowed to cool at
room temperature for 30 minutes to form a thin gel. After removal of the gel’s comb and casting
tray side walls, the gel along with its supporting tray was transferred to the electrophoresis unit and
covered with 400ml of 1xTAE buffer fully submerging the gel. All measured MonoHA and
PolyHA samples were aliquot to a 12μl volume in 0.15 NaCl solution, such that all samples
contained a minimum of 1μg -3μg of total HA mass. This HA total mass amount was based on
previous work done by Lee & Cowman, 1994 and Yuan et al., 2015, that suggested similar range,
for proper visualization of HA in the gel when imaged. In addition, 3μl of Orange 6X gel loading
dye (Cat. No. B7002S, New England Biolabs Inc.) was added to the aliquot samples for a total
volume of 15μl loaded into each well. Electrophoresis was performed for 3 ½ hrs. at 34V at room
temperature.
The gel was then transferred onto a container where it was submerged in a solution of
0.005% Stains All (Cat. No 7423-31-6, Sigma-Aldrich) in 50% absolute ethanol (200 proof, Cat.
No. BP2818500, Fisher BioReagents), being careful to protect the Stains All solution from light
exposure as previously described by Hong Gee Lee et. al (Lee & Cowman, 1994). The gel was
incubated overnight at room temperature in a light-protective container. To destain the gel the
Stains All solution was exchanged for a 10% absolute ethanol (200 proof, Cat. No. BP2818500,
Fisher BioReagents) solution while still protecting the gel from light exposure and allowed to sit at
room temperature for 8 hours, refreshing the ethanol solution at least twice during this phase. The
use of Kim wipes was used to remove excess ethanol solution.
33
Finally, the gel was then exposed to ambient room light for approximately 30 minutes at
room temperature, before being photographed under UV light, and using a white light conversion
screen (Cat No. 1708289, Bio-Rad) with the ChemiDoc XRS+ system (Cat No. 1708265). Manual
adjustment of image capture setting of brightness and contrast was performed to improved image
quality of the gel.
Post-processing of the imaged gel was done using ImageJ (Version 1.50i, NIH, USA),
which is image analysis software, with built in tools specifically for gel analysis. This software was
used to determine relative sample concentrations, and obtain qualitative MW distribution profiles.
Refer to user guide instructions at https://imagej.nih.gov/ij/docs/guide/146-30.html for further
details.
2.2.1.3. SS-nanopore Platform Set-Up and Nanopore Diameter Determination
Commercially available 4.4mm silicon chips with a thin free-standing SiN membrane
(Norcada, Inc. Alberta, Canada) were obtained to fabricate the SS-nanopore. Individual pores were
created on the thin SiN using an Orion Plus helium ion microscope (Carl Zeiss, Peabody, MA) as
previously described (Yang et al., 2011). All fabricated nanopores were stored in 50% ethanol
solution prior to their use. Before any measurements, the nanopore was rinsed with DI water and
absolute ethanol (200 proof, Cat. No. BP2818500, Fisher BioReagents) then dried with filtered air
and subsequently plasma sterilized on each side for two minutes (30 W). After air plasma exposure,
the nanopore chip was placed in between two custom-made Ultem 1000 flow cells, denoted as cis-
and trans- chambers (Figure 2-5). An ionic solution of 1MNaCl and 1xTris-EDTA buffer was
loaded onto both chambers for initial determination of pore size diameter. Ag/AgCl electrodes were
position on each of the inlet ports of the respective flow cell and a voltage was applied. Current
measurement through the electrodes was recorded using an Axopatch 200B patch clamp amplifier
(Molecular Devices, Sunnyvale, CA) at a rate of 200kHz with a 100 kHz four-pole Bessel filter
34
and analyzed with custom in-house software were an additional 5kHz low-pass filter was applied
to all collected data.
Figure 2-5: Nanopore platform assembly & components. SS-Nanopore used for the experiments with
HA was ~6-8nm in diameter.
To verify the nanopore diameter size, a linear relationship between applied voltage and
current (IV curve) was measured. In addition, the stability of the baseline signal was also verified
across a range of voltages. The resistance measured across the pore from the IV curve is
proportional to the nanopore diameter, and should remain constant, under measured conditions of
salt type, concentration, and pore membrane thickness.
2.2.1.4. HA Loading in SS-nanopore Platform and Data Acquisition Parameters
All experiments were carried out using nanopores of ~6-8nm in diameter. Having
determined the pore size, the ionic solution in the Trans-chamber was exchange for a 6MLiCl-
1xEDTA (pH 8), while the HA sample was loaded onto the Cis-chamber to a final concentration
of 50 μg/μl in 6MLiCl. Unless otherwise noted all experiments were ran under this same loading
conditions. A voltage was then applied across the chambers, to generate a localized electric field to
drive the movement of HA across the pore according to its electrical bias. Each sample was tested
at different voltages ranging from 100-400mV. Temporary conductance blockades in the current
trace, because of the single HA molecule translocation, were recorded as events. An event was
35
defined as changes in conductance ≥ 5σ from baseline, with dwell times ranging from 25 μs to 2.5
ms. Each of the events was quantified based on the Event Charge Deficit (ECD) or the area enclosed
by each of the “events” recorded from the current signal (Figure 2-4: Schematic of HA
Translocation Across a SS-nanopore).
To determine event rate, measurements of uninterrupted current traces of identical time
durations for each data set were recorded. Data was saved every 3.2 seconds, and the standard
deviation of the event rates was used as an indication of measurement error.
2.3. RESULTS
Detection of molecules using Solid-State nanopores is highly dependent on the molecule’s
size and charge, which affect the translocation speed, and the ability of the system to resolve the
passage of the molecules. Since the movement of molecules across the pore, are primarily driven
by electrophoretic and electro-osmotic forces, factors like solvent conditions (both the type and the
concentration they are found in solution) play a significant role in generating high signal to noise
ratio. Initial experiments were aimed at trying to find both the type of ionic solution and molar
concentration that would be optimal to yield resolvable events through the SS-nanopore,
considering that it was the first time that this system was being used to probe the detection of
Hyaluronic Acid. While initial attempts to measure HA through the pore were unsuccessful using
NaCl and KCl solutions between 1M-4M concentrations, resolvable events were observed when
changing buffer conditions to LiCl and using a concentration of 6M across the two chambers (as
mentioned in the METHODS Section 2.2), while keeping the pore size constant of approximate
diameter 6-8nm. For this reason, all of the experiments for the characterization of HA were
conducted using this solvent and concentration. In addition, unless otherwise stated, analysis of
results was carried out at a voltage of 200mV as they showed to provide with optimal
characterization of HA of different MW.
36
2.3.1. Polydisperse HA
To verify the use of SS-nanopore for the analysis of HA, commercially available HA with
a polydisperse length distribution (PolyHA), was first investigated. Figure 2-6-A below, showed a
representative current trace of PolyHA translocations, yielding a series of easily-resolved blockades
that each mark the passage of a single molecule. The graph also showed a magnified view of the
typical translocation event, showing differences in ECD values for translocation events. Since
polymer length is proportional to the MW of the polymer, these two terms can be used
interchangeably. In addition, this graph also showed that the events were observed only towards a
positive bias, in agreement with the expected electrophoretic direction of HA as a negatively
charged molecule. Consequently, this behavior was indicating that electrophoretic forces dominate
the translocations of HA across the SS-nanopore.
For event analysis, the use of ECD was considered as the metric to characterize event
translocations (measured in electron charge; commonly reported in kilo electron charge), since it
was a more comprehensive value that accounts for variability in the event duration/dwell time and
amplitude (changes in conductance), that are caused by the random conformation of one molecule
to the next as they pass through the nanopore. A histogram plot of the ECD measurements for this
PolyHA yielded a broad Gaussian distribution profile with a magnitude range of 102-106 ke (Figure
2-6-B). This distribution profile was significant, since the sample tested was expected to have a
broad range of molecular sizes, and hence it would be expected that the electrical trace of the event
translocations should also reflect qualitatively these MW differences considering the established
dependence of SS-nanopore ECD on biopolymer length. Therefore, this wide distribution agreed
with the qualitative expectation for a polydisperse mixture of HA.
37
Figure 2-6: SS-nanopore Measurement of PolyHA. (a) Representative trace of translocation events at
200mV. Event translocations are only seen under a positive bias. The lower trace represents the signal
trace containing events, along with a magnified view of typical events, demonstrating different ECDs.
(b) Histogram of PolyHA-ECD measurements, yielding a broad population distribution.
The PolyHA sample was also analyzed using gel electrophoresis. Figure 2-7-A below
provides evidence of the polydispersity of the mixture of HA in the sample, seen as a broad band
in the loaded wells, with a wide range of MW. ImageJ was used, to plot the relative greyscale
intensity distribution across the length of the lane to generate a semi-quantitative analysis of the
distribution profile of the sample. This plot worked by relating greyscale magnitude values to
relative concentration of the sample. Since, migration distance of a sample in a gel (measured in
pixel units with ImageJ), correlated to MW, the use of greyscale intensity and migration distance
were used to generate a relative semi-quantitative representation of the MW distribution profile of
the sample. Figure 2-7-B below showed a wide Gaussian distribution profile for MW, which
qualitatively agreed with the profile distribution of ECD for the measured translocation events,
further corroborating the potential of correlating molecular size differences with ECD
measurements obtained from SS-nanopore measurements.
38
Figure 2-7: Gel Electrophoresis of PolyHA Sample loaded with different concentrations (B.) Relative
intensity profile of the imaged gel, and the associated migration distance of the sample using ImageJ
analysis software.
To confirm that the events recorded represent actual molecular translocations rather than
non-translocative interactions, events were first recorded with a positive voltage bias, causing the
HA to move from the cis-to-trans direction (given the electrode placement). After approximately
30sec of data recording, the polarity of the voltage was reversed and events were recorded.
Observing the signal readings, events were seen to occur in the opposite direction with the change
in polarity (preserving the direction of HA translocation according to the electrical bias) and their
rate was seen to decay rapidly. Since there was significantly lower concentration of HA material
present in the trans- chamber relative to the cis- side (where the sample was originally loaded) the
decay in event recapturing (when reversing the polarity) was expected, as the probability of
translocation event decreased with concentration.
PolyHA analysis was repeated with three different concentrations (25, 50 and 75 ng/μl)
and their event properties were measured across a range of 150-400mV in increments of 50mV.
There were no observable changes in the event properties between the different concentrations.
This assessment was based on calculating the mean ECD value from the histogram analysis of
Event Count vs. ECD histogram for each sample tested at the specified concentration and applied
39
voltage. A Gaussian Fit was done on each of these histogram plots from which the mean ECD value
(calculated at the peak max value of the distribution) was obtained (Refer to appendix section for
Figure A- 0-1-A). The mean ECD for the experimental iterations were plotted against the voltage
yielding a plot with unvarying ECD values across the different voltages with overlapping reported
standard deviations for the populations (Refer to appendix section for Figure A- 0-1-B).
Nevertheless, the capture rate was found to both be directly dependent on sample concentration
(Figure 2-8-A) and to consistently have a linear dependency on applied voltage (Figure 2-8-B),
indicating a diffusion-limited translocation behavior.
Figure 2-8: Rate of translocation dependency on: (a) voltage (testing over a 150-400mV range) and (b)
concentration for HA using PolyHA sample at (25, 50 and 75μg/μl, red, blue and green respectively)
2.3.2. Sheared Polydisperse HA
Considering that the goal of using this platform was to differentiate between MW of HA,
an initial evaluation was done using mechanically sheared PolyHA using sonication energy (Refer
to Methods Section 2.2.1.1.3). Sonication energy had the effect of fragmenting HMW-HA, into
smaller fragments of LMW. Gel electrophoresis analysis was done on the samples (Figure 2-9- A)
which confirmed that increasing sonication energy had the effect of generating HA fragments of
40
smaller MW, seen reflected in a greater migration distance of the samples in the gel with increased
sonication time in comparison to the non-sonicated sample. Likewise, the gel showed a wider
distribution of MW of HA for the non-sonicated sample compared to the bands seen with increasing
sonication energy.
The samples were then evaluated using SS-nanopore system. Similar histogram analysis as
done previously to report mean ECD, was performed on the recorded data (Refer to Appendix
Section Figure A- 0-1). Using the sonication times 0 and 10s for comparison, there was a clear
relative change in distribution profile, from the unsonicated sample (showing a broad range of ECD
measurements in Green) with a FWHM value of 50.12ke compared to the 10s sonication
distribution (in blue) showing a qualitatively narrower distribution with a FWHM value of 6.76ke
(Figure 2-9-B). In addition, the graph shows that there was shift towards a lower ECD mean value
with increasing sonication energy, which corresponded to a sample with lower MW as seen from
the gel analysis. Plotting the migration distance of the samples relative to the loading well (using
ImageJ analysis) in Figure 2-9-C, along with the sample’s mean ECD, the SS-nanopore
demonstrated that there was a decrease in the mean ECD with progressively smaller fragments of
HA. This behavior was indicative that the SS-nanopore system could discriminate between HA of
different MW, using ECD measurements as a metric to characterize the translocations of different
size HA.
41
Figure 2-9: Mechanical shearing of PolyHA using sonication energy, for MW fragmentation of HA
(A.) Gel electrophoresis analysis of mechanically shear PolyHA for different sonication time periods
using a constant sonication energy of 175 Watts (B.). Representative normalized distribution of the un-
sonicated PolyHA sample (green) and the 10sec sonicated sample (blue) (C.) Migration distance and
mean ECD measurements for each of the sample’s normalized distributions, obtained from the median
value of their respective Gaussian distribution profiles.
42
2.3.3. Monodisperse HA Characterization
To further assess the ability of the SS-nanopore system of screening for MW of HA, a set
of commercially available Monodisperse HA samples of known MW were investigated with a
narrow distribution size of ± 5%. These samples were provided from the collaboration with Dr.
Paul DeAngelis chief Scientist for Hyalose LLC (Oklahoma City, OK). The individual MW sizes
tested were ~54kDa, 81kDa, 130kDa, 237kDa, 545kDa, 1.1MDa and 2.4MDa. Initially gel
electrophoresis analysis was done on the samples which as seen in Figure 2-10-A, visually verified
the difference in size of the samples with great degree of uniformity seen as a narrow band for lanes
1-5 (seen from left to right). On the other hand in lanes 6 and 7 there is visible smearing of the
sample towards a range of smaller MW which can be indicative of fragmentation.
The same analysis condition was used for the MonoHA population as the PolyHA samples
when running them through the SS-nanopore System. The results from Figure 2-10-B, showed
individual narrow population distributions for normalized distribution for ECD measurements for
the range of MW between 54kDa-545kDa, with a mean ECD calculated from the Gaussian fit to
the histogram plots generated for each sample (Refer to Appendix Section for descriptive statistics
Figure A-0-3)
43
Figure 2-10: Characterization of MonoHA samples using the SS-nanopore system based on evaluating
their Mean ECD. (A.) Gel electrophoresis of MonoHA samples across the range of 54kDa-2.4MDa. (B.)
Normalized distribution profiles for the different MonoHA samples tested.
For the analysis of the mean ECD for MW samples 1.1MDa and 2.4MDa the use of multi
peak analysis was performed (Figure A-0-3). This was since it was observed from the distribution,
a main peak followed by ECD measurement corresponding to smaller ECD values. Therefore, the
mean ECD was calculated from the central predominant peak of the distribution. The lower range
of ECD measurements were interpreted to be the result of smaller fragments of MW-HA that were
present in the solution, which agreed with what was observed in the gel analysis above, for these
two MonoHA samples.
44
Nevertheless, the key observation of these results, was that throughout the range of the
MonoHA (except for the 54-81kDa) there was a shift towards higher Mean ECD with increasing
MW of the sample. These changes in ECD were observed to be driven by increases in the dwell
time of the translocations with increasing MW of the samples and to some extent contributions to
changes in the amplitude of the events (or changes in conductance). This behavior was best
illustrated when breaking down ECD measurements according to its constituent components of
translocation dwell time and conductance change for the recorded events for the different samples.
Figure 2-11: MonoHA dwell time and change in conductance analysis. Histogram plot analysis for
MonoHA samples (54kDa-2.4MDa) was done and fitted to a Gaussian curve to obtain mean dwell time
values and change in conductance for the different samples respectively (Appendix Section Figure A-
0-4 (A.) Graphical representation of mean dwell time event duration with respect to molecular weight
of MonoHA. B.) Provides a graphical representation of the mean event conductance with respect to
molecular weight.
Figure 2-11-A and B, show the individual contribution of the Mean Dwell Time and
Conductance for the set of MonoHA samples. It was observed that Dwell Time follows a power
law fit, of increasing Dwell time with MW. On the other hand, the variability in change in
conductance could be an indication to changes in the conformation of the different molecular sizes
of HA as they passed through the pore (i.e. folding of polymer chains). Hence to account for the
individual contributions of these two parameters ECD measurements were used instead.
45
Nevertheless, it is outside of the scope of this research, to evaluate the difference in conformational
structure of translocation events with respect to MW.
The discrepancy in the 54kDa-81kDa range in terms of following an increasing ECD with
increase of MW could be attributed to the resolution capabilities of the SS-nanopore platform given
the current set-up conditions used and as such the sensitivity of the system to resolve LMW-HA
decreases, seen reflected in these results not following the trend seen above. Regardless, the system
showed the capability of discriminating HA based on MW.
Further analysis showed that when plotting Mean ECD with respect to MW of MonoHA
Figure 2-12 was observed to obey a power-law dependence on MW of HA. Which was important
as this allowed translating the electrical signals that are picked up from SS-nanopore platform into
useful information of the Molecular weight of HA for its characterization.
Figure 2-12: Calibration Curve of Mean ECD vs. Molecular Weight for MonoHA (54kDa-2.4MDa).
The graph is observed to follow a power law dependency with of Mean ECD with MW of HA.
46
Initial calculations yielded the following empirical Equation1 below for the dependence
of ECD with MW of HA (Refer to Appendix Section Figure A-0-5 for a detailed description for
the determination for Equation 1 below).
𝑬𝑫𝑪 = 𝟎. 𝟎𝟒𝟖𝟒𝟐 ∗ (𝑴𝑾𝟐.𝟏𝟒𝟗𝟗) (Equation 1)
Refer to Table 2 below to for statistical analysis description of results obtained of the
estimated MW that was calculated based on experimentally obtained Mean ECD of the MonoHA
MW, using Equation 1 above.
Table 2: Estimated MW of HA using Preliminary Calibration Curve Equation of ECD vs.MW of HA
Original
Molecular
Weight (kDa)
(+/- 5%)
Measured
ECD Using
SS-
nanopore
(ke)
Upper
(Std.
Dev)
Lower
(Std.
Dev)
Confidence
Interval
(Upper
Bound)
Confidence
Interval
(Lower
Bound)
Estimated
Molecular
Weight Using
Equation 1
(kDa)
81 878 358 605 702 1186 96
130 1478 516 793 1012 1555 122
237 4692 1467 2135 2875 4185 209
545 32063 8212 11040 16096 21638 510
1076 218801 61921 86364 121364 169274 1246
2384 883365 252267 353476 494443 692814 2385
2.4. DISCUSSION
From the results obtained above, it was seen that the SS-nanopore system was able to
effectively characterize differences in MW-HA based on ECD measurements. There was a power
law relationship established between MW and ECD which allowed translating electrical signals
into MW information of HA.
The current set-up conditions used to analyze HA, involve the use of a 6M LiCl
concentration across the two chambers. The choice of using LiCl over other solvent solutions (of
different counterions like K+ and Na+ ) was based on previous studies (Kowalczyk, Wells,
Aksimentiev, & Dekker, 2012), that showed to improve the resolution of the passage of
47
biomolecules given its ability to slow down the translocation speed compared to the other
electrolyte solutions. Therefore, considering that initial experiments were not able to resolve
translocation events of HA using NaCl or KCl the choice of using LiCl was warranted. The reason
for the ability of LiCl to aid in slowing down translocation speed was investigate by Kowalczyk,
Wells, Aksimentiev, & Dekker, 2012 which attributed the reduction in speed to the bond strength
and the duration of the bound counterion to the charge biomolecule being probed. The counter ion
cloud has the effect of reducing the translocation speed of the molecule of which Li shows to have
the highest strength followed by Na+ and then K+. As ions bind to the molecule the ions transfer
the force they experience from the electric field to the molecule rather than dissipate it into the
solution which has the effect of reducing its translocation velocity.
On the other hand, the increased electrolyte concentration to 6M was also employed to
improve resolution of the translocation of HA after having tried with 1M, 3M and 4M electrolyte
solutions. Previous literature (Wanu et al. 2009 and Hatlo, Panja, & Van Roij, 2011) supported
the observation that ionic concentration, played a role in slowing down the translocation as the
increased presence of counter ions contributed to charge screening of the biomolecule, which not
only reduced the capture probability of the molecule (given its lower apparent charge) and thus
being able to resolve more clearly one event from the next, but also slowed down the translocation
speed of the molecules through the pore. Therefore, after several iterations, 6MLiCl solution
showed to be able to best resolve HA. Needless to say, that improved resolution, particularly for
LMW-HA as seen in the histogram plots can be improved further, as discussed in FUTURE
DIRECTIONS Section of this document. The ionic solution, was kept at a pH of 8. While pH has
an effect on changing translocation characteristics, further studies are warranted in order to
understand its effect.
The current experiments used a filter frequency of 5 kHz and characterize events with a
conductance depth that was 5σ from the established baseline measurements. The choice for these
48
software event recording conditions, was based on a study where an improved resolution particular
in the LMW-HA, was observed using these settings compared to the results using a Low Pass Filter
Frequency (LPFF) of 10 kHz and changing from a 5σ to a 4σ measurement threshold. Histogram
analysis (from which Mean ECD measurements were obtained) showed qualitatively an improved
resolution between MW populations (using same binning values) particularly for LMW-HA using
the 5 kHz and 5σ settings (Refer to Figure A-0-6 in the Appendix section for histogram
representation of the measured data). In order to quantify this observable effect, the same procedure
used earlier to establish the calibration curve for the Mean ECD to Molecular Weight was used
which yielded the following results seen in Figure 2-13 below. The calibration curve showed to
be most accurate when 5kHz was being used as a LPFF, and showed to have improved predictive
capability of correlating ECD measurements with actual MW (Refer to Table A-0- 1 in the
Appendix Section of this document). The results showed that this calibration curve with a 5kHz
LPF had a percent error of less >20% irrespective if using a 5 or 4σ event detection threshold,
compared to when using a 10kHz LPFF that the estimation error increased to <30% for both St-
Dev. thresholds utilized.
49
Figure 2-13: Calibration Curve of Mean ECD vs. Molecular Weight for MonoHA (54kDa-2.4MDa)
Using Different LPFF values and Threshold StDev. Software setup conditions.
A similar analysis was performed for the choice of voltage used for characterization of HA.
Were the 200mV and 300mV showed to have the best resolution of LMW-HA given the fact that
when performing a Tukeys pairwise Difference of Least Squares Means (using SAS statistical
software) the system was able to detect mean differences as far apart of 81kDa and 237kDa
compared to when using 400mV that detectable mean differences were not observed (Table A-0-
2) in this range. Nevertheless, when fitting a calibration curve to the data as done previously, the
percent error of the estimated MW was the least when using a 200mV, with an estimated error
ranging between 1-18% across the range of MW tested, compared to 300mV voltage that was less
precise for estimating MW above 1.1MDa with an error ranging between 1-48%. On the other hand,
50
a 400mV measurements had a percent error ranging between 2-42% (Refer to Table A-0- 3 in the
Appendix Section of this document)
Figure 2-14: Calibration Curve of Mean ECD vs. Molecular Weight for MonoHA Across the MW
Range of 54kDa-2.4MDa Using Different Applied Voltage Conditions.
For this reason, preliminary set-up conditions involve the use of a 6MLiCl, 5kHz LPF and
a voltage of 200mV for the characterization experiments of HA that followed. Nevertheless, this
conditions were highly dependent upon the size of the Nanopore, as studies have shown that the
Signal to Noise Ratio (SNR) are negatively affected with increased pore size (Smeets, Keyser,
Dekker, & Dekker, 2008) and therefore to adjust for these noise conditions, set-up conditions are
modified to better resolve translocations. Currently an approximate pore diameter of 6-8nm was
51
used and the observations suggest that the chosen set-up conditions are adequate for the pore size
chosen. Nevertheless, they are still not optimal for LMW-HA differentiation below 81kDa, for
which further studies involving different pore size will need to be conducted across the range of
voltages explored in this current study as well as the software set-up conditions for event
characterization. Furthermore, it is necessary that repeated experiments be carried out for the
discrete MW available to verify the reproducibility of the results and improve the calibration curve
fitting that can be generated for translating ECD measurements into MW.
Currently, the determination of the Mean ECD for MWs above 1.1MDa was performed
using a multi-peak analysis as mentioned in the results section given the smearing effect attributed
to smaller fragments observed in the gel. For this reason, the Gaussian fit used to determine Mean
ECD corresponded to the one which included the most predominant peak in the original
distribution. Because of the nature of the calculated Gaussian fit using a multi-peak analysis, the
estimated standard error is significantly lower, compared to samples of LMW were the true
Gaussian fit for the entire population was used. This has the effect of skewing the results and as
such, hinders the accuracy of the correlation between ECD measurements and MW. Nevertheless,
the results show promising evidence of the ability of the system to detect molecular size differences
of HA already offering far more quantitative information with higher degree of sensitivity than
traditional gel electrophoretic measurements for HA detection.
An important finding of the results presented, was the fact that event rate and voltage scaled
linearly with concentration of HA. This finding was important as it not only agreed with previous
experiments involving the use of SS-nanopore to test other biopolymers (demonstrated in work
performed by Zahid, Zhao, He, & Hall, 2016), but showed the feasibility of this technology for
potential assessment of unknown concentrations of HA, based on the applied voltage, and rate
calibration curves that can be generated from this analysis. Another important aspect from these
results was that the system showed to enable events detection in an efficient rate of ~0.5 s-1 (at a
52
voltage of 200mV) with concentrations as low as 0.025μg/μl. This implied that ~1,800 events could
be potentially recorded in the time spam of 1hr, providing with a significant number of events from
which statistical analysis of the event distribution can be drawn. This finding was of crucial
importance since it showed that sample analysis can be conducted in a reasonable timeframe with
sample concentration levels that are physiologically relevant to those that are found in blood
plasma. This observation also showed the unparalleled advantage of using this system over other
conventional semi quantitative laboratory techniques (like gel electrophoresis) which required of
concentrations that are typically 10xfold of what has been currently used in the SS-Nanopore
platform under non-optimized parameter. Likewise, it was also significantly less time consuming
considering data processing times with the SS-nanopore system were done in ≥1hr time window
(depending on concentration used) compared to electrophoretic gel methods that can take between
1-3days to complete. While lower concentrations can potentially be used, if needed, the processing
time would be most affected. The majority of experiments carried out with the SS-Nanopore system
in this research, were conducted using a concentration of 0.05μg/μl which typically in the span of
an hour can easily yield >5000 events. While increasing voltage could potentially decrease the
detection time frame, there is a potential trade-off of loss in resolution for LMW (as seen in the
results) if current conditions of buffer concentration, transmembrane isotonicity, pore size and
thickness are kept the same (Shekar et al., 2016). In addition, an increase in voltage increased the
potential instances of clogging, which was detrimental for our experiments. For this reason, further
studies to improve SNR are needed with the potential of not only improving the resolution of lower
MW but also aimed to have a sensible molecular capture rate.
One vital aspect that still needs to be considered is the fact that the rate of translocation is
very likely to be dependent on the MW of HA molecule based on studies that have demonstrated
this phenomenon in other biopolymers (Grosberg & Rabin, 2010). This is important particularly
when analyzing polydisperse mixtures of varying MW of HA, since the distribution of the
53
translocation of the events must be adjusted to the rate at which they are captured, in order to
objectively show the actual distribution profile. Another aspect to consider, is that the current
determination for MW heavily relies on the distribution profile’s width of the Gaussian fit to
determine the mean ECD measurement. Currently, it is not clear if ECD variability for the
individual MW translocations, is consistent across the range of MWHA tested. For this reason, our
current metric’s sensitivity to correlate ECD with molecular weight might not be most optimal
without having prior understanding of the translocation behavior dependency on MW and the range
of ECD variability that can be observed. Potentially the results obtained from the analysis will need
to be adjusted to account for ECD variability. Initial results however, show that there was variability
in the Gaussian fit’s width across the range of 54kDa-2.4kDa. It is worth noting that the number of
recorded events for each of the MW tested was consistent, ranging from 344-8,000 events captured
respectively for the different MW tested. This had the potential to inadvertently skew the
distribution of the results.
Having subsequently measured purified MonoHA, it was observed that ECD mean
measurements consistently and predictable increased with increasing MW across the range tested,
following a power law dependence. Nevertheless, since size resolution becomes increasingly more
difficult with LMW explains the observed deviation for the 54kDa MW measurement in obeying
the power law trend. On the other hand, given the present non-optimized conditions the
translocation speed at which this MW was passing, could have been close to the instruments
inherent sensitivity. For this reason, this data point was removed from the calibration curve
calculation. This however, had the effect of causing the calibration curve to show the largest
observed percent error when associating ECD measurements to LMW of HA. On the other hand,
since the number of experiments performed on the same sample size was small, the statistical power
54
and thus inferences that can be made from the data were limited. Increased sample sizes are needed
to assess the reliability and reproducibility of the results of the SS-nanopore platform.
The system was able to effectively translate electrical signals into useful physical
information of the biopolymer’s MW. In addition, the behavior of HA translocations agreed with
translocation phenomenon observed in other linear biopolymers. The exponent calculated from the
calibration curve of 2.1499 is indicative that the Log vs. Log plot of MW and ECD is not linear
(otherwise the exponent would be closer to 1). This could be attributed to the fact conformational
changes (due to folding configurations) particularly for HMW-HA would affect the translocation
characteristics.
2.5. LIMITATIONS
One current limitation of this research was the fact that comparative analysis of HA between
the SS-nanopore and the laboratory technique used to verify for its potential feasibility (gel
electrophoresis) does not provide the same level of sensitivity, substantially limiting the verification
and validation of the SS-nanopore system. Therefore, other techniques (like ELISAs HPLC or
Spectrophotometric analysis) should be used in conjunction to gel electrophoresis to assess: MW
distribution of the extracted HA solution.
On the other hand, current detection capabilities were limited by the size range, and
detection precision that the system in its present conditions was tested under. The current
calibration curve used to relate ECD to MW had a limited statistical power given that is based on
a small sample size for each of the MW tested. Therefore, to improve the assessment of HA content
the calibration curve needed to be refined based on increasing experimental measurements of the
individual MW tested. In addition, future set-up conditions should be more thoroughly evaluated
for their contributions in increasing SNR and improving resolution of the system. Factors such as
pore size and thickness and buffer solution gradient should be investigated serially, in order to
determine which combination provides with the best resolution for MW, across the range of
55
voltages and different system set-up data collection filters. Another important limitation of this
system, was the fact that our current metric to assess mean ECD measurement does not take into
account the possible range variability that might exist depending on the MW of HA being
measured. For this reason, further studies on identifying which best metric most accurately reflects
the translocation behavior differences, might be needed. This is since our metric to determine Mean
ECD relies on the distribution range.
Considering that translocation rate dependency on MW has not been established, the
analysis for a polydisperse mixture tested using our system might not be accurate, given that the
results of the distribution might reflect the actual population distribution of the mixture rather,
reflect the physical phenomenon of the translocation behavior and capture rate of having the
individual masses present in the mixture.
On the other hand, it has been observed throughout these measurements that clogging
incidents occured most frequently with HMW-HA, which in some instances rendered the nanopore
useless. Therefore, considering that this technology is intended to be evaluated for its potential use
for HA detection in biological sample like synovial fluid (where HA is found in larger MW than
what it has been presently tested), it is likely that larger pore sizes might be needed to prevent
clogging incidents. The increase in pore size while reducing the clogging incidence, might limit the
resolution of detection of LMW-HA.
Finally, another limitation of the system is that it requires of a highly pure sample of HA.
This is since the system does not intrinsically probe for HA rather it indiscriminately detects
translocation events of charged particles as they interact with the pore. For this reason, impurities
and other molecules can adversely affect the results. Given the objective of using this technology
in the clinical setting, it is necessary that the extraction techniques of HA produce a high purity
solution.
56
2.6. CONCLUSIONS
In conclusion, the SS-nanopore platform showed the ability to determine differences in MW
of HA, based on the ECD measurements. The results showed that across the range of MW tested,
ECD consistently increases with increasing MW following a power law dependency. Importantly,
is has shown to operate and be able to differentiate MW of HA in a range that has been identified
of having physiological relevance in its role as a pro- or anti-inflammatory biopolymer (~ 250kDa-
1000kDa).
The SS-Nanopore system showed to operate using small amounts of HA, almost 10xfold
less than conventional laboratory techniques used to detect HA, while providing quantitative results
in a sensible time manner. This was of crucial importance, as it highlighted the ability of the system
to address the technological need of having an assay that can precisely detect and quantify MW-
HA differences in a quantitative manner that could be easily integrated in a laboratory or clinical
setting.
An important finding was the fact that HA translocations behaved in a manner that agreed
with previously studied linear biopolymers, showing a linear dependence on applied rate and
voltage with concentration. This was important, as it allowed to better control setting parameters
that can be adjusted to improve the resolution of the instrument.
Finally, the preliminary results obtained from this system showed the potential of using this
platform for evaluation of HA in biological fluids. Nevertheless, it is important that the extraction
protocol is capable of obtaining samples of HA that are highly pure to avoid the introduction of
impurities that can otherwise skew the results and contribute to clogging incidents.
57
3. CHAPTER 3- ISOLATION OF HA FROM BIOLOGICAL SAMPLES
3.1. INTRODUCTION
The use of the Solid-State nanopore platform was a promising candidate for the analysis of
HA, given its ability to detect charged biomolecules on a single-molecule level. Initial results,
illustrated in the previous chapter, showed that HA of varying MW could be probed using the SS-
nanopore platform, and that the molecular translocations yielded predictable and reproducible
electrical signals associated to its MW. In addition, this platform operated over a wide range of
MW-HA (54kDa-2.4MDa), which is comparable to what it is typically found in-vivo in biological
fluids, such as blood plasma and what is mid- range MW in SF, making the platform relevant for
clinical applications that involve HA characterization. Likewise, this platform showed an
unparalleled advantage over other MW-HA detection techniques, given the fact that it was less time
consuming, and in principle there was no minimum concentration (other than non-zero) of HA that
is needed for its measurement, in contrast to other techniques that require large sample volumes.
However, the implication of using a very low concentration was that it increased the amount of
time that it takes to complete the analysis given that the rate of measurable events varied linearly
with concentration. Another promising advantage of using this platform was that even without
optimized platform settings (as discussed in the previous chapter), the current preliminary data
showed finer resolution in MW than commonly used laboratory techniques (e.g. Gel
electrophoresis) for molecular weight detection and quantification. Finally, the use of this
technology has the potential to be used in a clinical setting given its ability to become a modular
and portable technology in contrast to, traditionally used complex techniques, that are not easily
integrated in the hospital setting, are more time consuming, and in most cases, provide only semi-
quantitative analysis.
While the advantages of using this system have been described, it is also vital to note that
since the system itself does not intrinsically probe for HA translocations, rather it indiscriminately
58
detects the passage of charged molecules, and it heavily relies on the test sample being highly pure.
Impurities, can potentially affect the signal readings (i.e. adding unwanted noise, artifacts to the
recorded signal or event record false positive events). In addition, larger impurities can permanently
block off the pore rendering is useless for further use. For this reason, highly specific binding
affinity strategies for HA isolation are the ideal candidate to extract HA from biological fluids to
obtain pure samples of HA.
While variations of the isolation strategies presented in this chapter have been previously
employed for DNA purification and in the extraction of HA from biological fluids like human
breast milk (Yuan et al., 2015), the newly developed protocol presented in this chapter had not been
explored for the extraction of HA from biological fluids such as blood plasma and SF. The choice
of isolating HA from these two biological fluids was important, given that changes in the overall
MW of HA over time, (particularly in SF) have been associated to onset and disease progression
of OA (refer to Section 1.1.4.1). Therefore, being able to effectively extract HA, for its analysis
with the SS-nanopore system was critical to be able to translate this technology into a clinical
setting.
Therefore, it was the aim of this chapter to assess the reliability of the methodology used
to isolate HA from biological fluids. The first portion of this chapter was dedicated to show proof
of concept that the implemented protocol is effective in isolating HA, and more importantly that it
preserved the integrity of the HA without fragmenting it. The second portion of this chapter was
focused on the application of the isolation method, and the use of the SS-nanopore platform for
MW analysis of HA extract from SF from horses, that are part of an Equine model for OA. This
was done in an effort to show proof of concept that this technology can provide quantitative
information with finer resolution than traditional laboratory techniques used to evaluate changes of
HA over time.
59
3.2. METHODS
To accomplish high purity of the extracted HA, the protocol used for this purpose could be
summarized in four steps (Refer to Figure 3-1 for a schematic illustration of the process).
First Step: consisted of obtaining a soluble form of HA, that was removed from proteins,
lipids and larger molecules that can be found bound to HA or are free floating. The binding affinity
of bound molecules to HA were disrupted through enzymatic digestion or heat treatment.
Second Step: The sample was then processed through a phase separation technique, using
phenol-chloroform which dissolves and compartmentalizes the organic component of the biological
fluid from the aqueous phase containing HA, which was collected through pipetting.
Third Step: Involved generating complex that was highly selective with a strong binding
affinity to HA to selective capture it from the aqueous phase generated in Step 2, to obtain a highly
pure sample. This complex was formed in a stepwise process which consisted on using
commercially available magnetic beads coated with streptavidin. Streptavidin is known to form
stable covalent bonds with biotin, while concomitantly biotin binds to proteoglycans such as
Versican (forming biotinylated versican). The result was a Magnetic Bead-Proteoglycan complex
formation when these elements are incubated together. Importantly, Versican has a high binding
affinity and specificity to HA, leading to the fourth step of the isolation strategy.
Fourth Step: Using the binding affinity of HA to versican through its G1 domain, HA found
in the aqueous phase (generated in second step of this process) can be selectively captured when
incubated with the Magnetic beads-Proteoglycan complex. A washing step followed the incubation
to guarantee that only bound HA was attached to the complex, while remnant impurities were
washed away. Having selectively isolated HA from the aqueous phase, localized heat could then
be used to disrupt the Versican and HA interaction (by denaturing the Versican) leaving HA free
floating in solution while the complex component attached to the magnetic beads could be locally
60
suspended in solution using a magnet. This allowed the extraction of highly pure aqueous HA
through pipetting. The detail description of these steps are explained below.
Figure 3-1: Schematic illustration of the Extraction of HA from Biological Fluids
3.2.1. HA Isolation/Extraction Methods
3.2.1.1. Streptavidin Magnetic Beads Preparation:
Streptavidin coated magnetic beads of a concentration 10mg/ml (Dynabeads M-280, Cat
No. 11206D, Invitrogen), were resuspended in the original vial by vortexing for 10s. 250μl of
Dynabeads were transferred to a 500μl vial and placed under a magnet for 2min to collect them on
one end of the vial, while removing the solution in which the beads originally were dissolved in
through aspiration. A washing buffer was added to the beads consisting of a solution of 1x PBS,
0.05% Tween solution (300μl), and mixed gently. The Dynabeads were then placed under a magnet
and the washing buffer supernatant was removed. This washing step was repeated three times, at
which point and additional three washing steps were performed, using however 1xPBS solution
only. After the washing steps, the Dynabeads were suspended in 50μl of 1xPBS.
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3.2.1.2. Dynabeads M-280 Incubation with bVG1 (Complex Formation)
Previously washed magnetic beads were then incubated with commercially available
Biotinylated-Versican G1 Domain (bVG1, Cat No. G-HA02, Echelon Biosciences) by adding a
total of 21μl of bVG1 of a concentration of 1.23μg/μl, with the 1x PBS solution containing the
Dynabeads. Incubation was carried out for one hour at room temperature while on a rocker to form
the Dynabeads-bVG1 complex. Once the complex was formed the sample was then placed on a
magnet to remove the supernatant and washed three times with 150 μl of 1x PBS, after which point
the complexed Dynabeads were set aside to be used with an aqueous phase solution containing HA.
The ratio of Dynabeads to bVG1 was calculated based on an experiment described in Section
3.2.1.5
3.2.1.3. Isolation of HA from Biological Fluids in aqueous solution
To effectively use the affinity extraction method to obtain a highly pure solution containing
HA, the first step consisted on isolating HA in a soluble form from the biological sample. This was
followed by purification strategies intended to remove both large and small biomolecules (e.g.
lipids and proteins), by means of enzymatic digestion, or through applied heat, such that any bound
macromolecule could be selectively removed from aqueous phase containing HA. While the overall
protocol presented in this chapter for isolating HA from SF and plasma was the same, there were
slight differences, which are explained below.
3.2.1.1.1. HA Purification from Human Plasma:
To assess viability of isolating HA from plasma, preliminary HA isolation protocol was
performed with a spiked in sample of MonoHA, of known MW in plasma.
For this purpose, a volume of 500μl of human derived plasma was thawed in a 37°C water
bath for 15min, after which it was centrifuged at room temperature for 15min at 15,00x g units
using the Brushless Microcentrifuge Denville 260D (Harvard Bioscience Inc., Holliston MA) to
removed larger blood plasma constituents. The sample was then removed from the micro centrifuge
62
and the plasma supernatant was aspirated with a pipette, and set aside. A volume of 10 μl of known
MW MonoHA sample (at a concentration of 1μg/μl) was spiked into the approximate 500μl of
plasma volume recovered after centrifugation. At a concentration of 0.78U/ml Proteinase K was
added and the spiked plasma was incubated overnight in a 37°C water bath.
The sample was then placed in a Phase Lock Gel Tube (PLG, Cat No. 10847-802,
QuantaBio®) and an equal volume of Phenol-Chloroform solution as the sample (Cat No.
AC327111000, Fisher Scientific) was added to the PLG Tube. The vial was vigorously shaken and
then placed in the Brushless Microcentrifuge Denville 260D (Harvard Bioscience Inc., Holliston
MA) at 14,000 rpm for 15min. Centrifugation of the PLG had the effect of separating the aqueous
HA containing phase from the organic phase which was trapped in or below the interphase barrier
(Figure 3-2) along with the Phenol-Chloroform. After this, an additional equal volume of
Chloroform as the starting sample volume was added to the PLG vial which was micro centrifuge
to the previous settings. The aqueous phase inside the PLG tube was extracted and set aside.
Figure 3-2: Phase Lock Separation Schematic for HA isolation from Biological Fluids
3.2.1.1.2. Equine Model of OA Sample Description & HA-SF Isolation
Equine synovial fluid samples were provided from our collaborator, Dr. Heidi Reesink,
from the College of Veterinary Medicine of Cornell University (Ithaca, NY, USA). The samples of
SF were obtained from 2-5-year-old adult horses, that showed normal carpal joints, evaluated
radiographically, before they are conditioned and are surgically induced a carpal chip defect in one
63
of their frontal legs while performing a sham operation on their contralateral leg’s joint to serve as
an internal control for the horse as part of the Equine Model for OA.
Initially these horses are conditioned to treadmill exercises, after which and initial SF fluid
sample was extracted from the horses from both the sham and surgically induced carpal chip joint
serving as the baseline levels of HA or Day 0. To surgically induce OA onset in one of the horses’
leg, an 8mm chip was created and removed arthroscopically from the dorsal carpal rim of the radial
carpal bone of one of the two carpal joints. The exposed subchondral bone was then debrided using
an arthroburr, to generate a larger defect of 15mm (Figure 3-3). The debris generate from the
procedure was not removed from the synovial cavity. Following two weeks after the induced
subchondral fragmentation without operative intervention, the horses were subject to a 30min
treadmill, 5 days a week training regimen, which would have the effect on the chipped limb to
initiate OA. Synovial fluid samples from the horses was extracted from both the sham and induced
OA leg on Days 4, 7, 14 and thereafter weekly post initial surgery. A portion of the SF extracted
from the horses was shipped to the Hall Lab and Rahbar Lab (Virginia Tech- Wake Forest School
of Biomedical Engineering, Winston-Salem, NC), to carry out analysis of the HA content of the
samples using the SS-Nanopore System. The equine SF samples were kept at -80°C. A detailed
description the purification and isolation of the HA from these samples is described below.
Figure 3-3: Surgically induced Carpal Chip, on Horses Radial Carpal bone joint
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3.2.1.1.3. Synovial Fluid Isolation:
Synovial Fluid from Equine source was set aside in an Eppendorf, and placed in a 37°C
water bath for 15min to thaw. Adjusted per volume of synovial fluid used, Proteinase K was added
to a concentration of 0.78U/ml as previously used in literature by Yuan et al, and unlike plasma it
was allowed to incubate for 15min in a 37°C water bath. Initial isolation experiments with synovial
fluid samples used a larger than need volume, (of 600μl) to assess viability of experimental
protocol. Given, the higher HA content per milliliter of synovial fluid than blood plasma, in larger
mammals (Kogan et al., 2007), and having successfully isolated HA from SF, subsequent
experiments were carried out using 50 μl volumes instead, yielding sufficient extracted HA material
for both gel electrophoresis and SS-Nanopore Analysis.
From this point forward, the SF samples followed the same experimental steps for HA
isolation used for the human plasma samples described above (Refer to Section 3.2.1.1.1)
3.2.1.4. Affinity Extraction of Aqueous HA with Dynabeads-bVG1 complex:
The isolated aqueous phase containing the HA from both plasma and synovial fluid was
incubated with the Dynabeads-bVG1 complex prepared in Section 3.2.1.2, by placing the aqueous
phase content into the Eppendorf containing the Dynabeads-bVG1 complex on a rocker for 24hrs
at room temperature.
Once the incubation period had finalized, the sample was placed under a magnet to suspend
the Dynabeads-complex conjugated with HA, while the supernatant was pipetted. Three washes
with 1xPBS were conducted, using the magnet to suspend the beads while pipetting out the
supernatant. The supernatant was either discarded or set aside and properly labelled for future
analysis. After the third wash, DI water was added to the sample to a final volume of 50μl (even
though a smaller volume could be used depending on the desired concentration level of HA in the
sample).
65
The sample was then placed on a heating block at 95°C for 15min. Applied heat had the
effect of denaturing versican, and thus disrupting the affinity of the bound HA to the Dynabeads-
complex releasing the polymer strands into the DI water (Figure 3-4). Lastly, the sample was then
placed under a magnet and the aqueous solution containing purified HA was pipetted out and set
aside, which was then stored at -4°C for later analysis using the SS-nanopore system or other
laboratory techniques.
Figure 3-4: Affinity Extraction Method for HA from Biological Fluids
3.2.1.5. Optimization of Beads to Versican
To increase the capture yield of HA using streptavidin coated magnetic beads (Dynabeads
M-280, Cat No. 11206D, Invitrogen), the optimal bead to biotinylated versican (bVG1, Cat No. G-
HA02, Echelon Biosciences) was established. A serial dilution of versican while maintaining the
Dynabeads M-280 concentration constant, allowed determining optimal ratio using
spectrophotometric analysis. A detailed procedure is summarized below.
50μl of resuspended Dynabeads M-280 was collected in 4 different 1.5ml Eppendorf vials
and subsequently labelled. The Dynabeads were washed using the protocol specified above (See
Section 1). Using
Table 3 below the corresponding amount volume of bVG1 (at a concentration of 1.23μg/μl) was
added to the Dynabeads suspension. Additional 1xPBS was introduced to the samples to bring the
solution to a final volume of 100μl.
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Table 3: Optimization of Dynabeads M-280 to bVG1 Concentration Ratio
Ratio
Vial Beads bVG1 Magnetic
Beads (μl)
bVG1
Volume (μl)
Total 1x PBS
Volume (μl)
1 1 : 0 50 0.00 50.00
2 1 : 0.2 50 0.81 49.19
3 1 : 0.6 50 2.44 47.56
4 1 : 1** 50 4.07 45.93
5 1 : 1.50 50 6.10 43.90
**IMPORTANT NOTE: Dynabeads Manufacturer’s instruction specify a binding affinity of
10ug of Biotinylated Ligand (in this case the ligand was assumed to be versican) for every 1000μg
of Dynabeads. Consequently, the “1:1 ratio” designation is given to the mixed quantity of
Dynabeads to bVg1 preparation that was mix per manufacturer’s specification (~100:1). The other
vials contain relative proportional amounts of bVG1 to the “1:1 ratio” sample. The volumes
reported on the table arise from the sample’s original packaging concentrations of 1.23μg/μl and
1mg/ml of bVG1 and Dynabeads respectively, which then satisfy the ratios specified in table
relative to the 1:1 ratio.
The samples were incubated for 1 hour, while on a rocker at room temperature, to form the
Dynabead-bVG1 complex. Once the incubation had finalized, starting with one of the vials, it was
placed under a magnet for 2min to allow for the complexed Dynabeads to collect at the bottom of
the vial, and the supernatant was aspirated with a pipette. The supernatant was set aside and labelled
accordingly to the Dynabeads: bVG1 ratio used. This procedure was repeated to collect the
supernatant material for all 5 samples.
Using the NanoDrop Spectrophotometer 2000 (Thermo Fisher Scientific, Waltham Ma),
and the predefined factory settings for protein detection, the spectral baseline was obtained from a
67
pure 1xPBs solution, for which the absorbance value was correlated to a 0 μg/μl concentration of
bVG1. Spectral measurements were obtained by micro-pipetting 1μl of the sample onto the
Spectrophotometer’s sample platform. A total of three spectral readings were obtained for each of
the samples and the averaged measured relative concentration value of bVG1 was reported.
3.2.2. Gel Electrophoresis Verification of Extracted HA
Gel electrophoresis was used to verify the content of HA in SF and in the HA spiked human
blood plasma samples. The protocol used for preparation and loading of the gel was the same as
that used in Section 2.2.1.2, with the exception that extracted HA material diluted in DI, (after the
Dynabead-bVG1 affinity extraction step was performed), was loaded instead. Since the amount of
HA spiked in the blood was known (~5μg) and so was the final volume to which it was diluted after
the extraction (Section 3.2.1.4), the samples used for gel electrophoresis were calculated such that
the total mass of HA was between 1μg-3μg in a total volume of 12μl.
On the other hand, since the amount of HA was unknown for the SF samples, different
loading volumes were used. However, the volumetric mixture of: 8μl of extracted HA sample in
DI, with 4μl of 0.15 NaCl solution, in addition to 3μl of Orange 6X gel loading dye (Cat. No.
B7002S, New England Biolabs Inc.), leading to a final volume of 15μl per lane provided with
overall visible results in the gel.
3.3. RESULTS
3.2.3. Evaluation of HA Extracted from PLASMA
Binding Ability of Dynabeads-bVG1 Complex to Capture HA
To test for the ability of the Dynabeads-bVG1 complex to capture HA, the first experiment
consisted on performing a trial run incubation of HA with the Dynabeads-bVG1 complex. Using a
mass ratio of 100:1, the Dynabeads were incubated with bVG1 for an hour at room temperature, to
generate the complex. The complex was washed (as per instructions in Section 3.2.1.1) and
incubated in with 85μl of DI water containing 5μg of MonoHA (of 237kDa MW). The final volume
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was brought to 150μl with the addition of 1xPBS solution. Incubation time with the HA sample
was of 1hr. After incubation, the sample was washed with 150μl of 1xPBS per instructions in
Section 3.2.1.4, and the volume used for each of the washes was collected. After the final wash,
70μl of DI water was added to the Dynabeads-bVG1 complex containing the HA, and the heat
treatment as per specifications in Section 3.2.1.4 was performed, to collect a pure aqueous form of
HA. The collected material from the washes along with the purified HA aqueous solution was
analyzed using gel electrophoresis (using loading volumes specified earlier in Section 2.2.1.2). A
controlled sample containing a known concentration of 200ng/μl of 237kDa MonoHA was loaded
onto the first lane of the gel. Results of the gel could be seen below in Figure 3-5.
Qualitatively the imaged gel showed that the Dynabeads-bVG1 complexes were capable
of capturing HA seen reflected in the last lane of the gel, as it showed a distinct band, at the same
migration distance from the well as the control sample containing diluted MonoHA of the same
MW (237kDa). In addition, it showed visual evidence that the extraction process described,
preserved the integrity of HA molecules, since the gel yielded a compact material band without
visual smearing.
69
Figure 3-5: Determination of the Dynabead-bVG1 Complex’s ability to capture HA, analyzed using
gel electrophoresis. The photographed gel was taken after exposure to UV light. 0.5% agarose was
used a long with 0.005% Stains all in 50% ethanol as an HA detection agent
To calculate the relative percent recovery of HA by the Dynabeads-bVG1 complex, the
built-in image based gel analysis tool from Image J. software (Version 1.50i, NIH, USA), was used.
The relative grey scale intensity area was measured for each of the bands, using the same
rectangular enclosed selection for all the lanes of the gel. The first lane was used as the
control/baseline band to numerically correlate the measured intensity area with sample
concentration (refer to Table 4 for numerical values). Knowing that the “Control lane/ Lane 1”
had an approximate concentration of 200ng/μl, the relative concentration of the second lane was
calculated to be 17.64ng/μl given its relative measured intensity area using the following relation:
[𝑪𝒐𝒏𝒕𝒓𝒐𝒍 𝒍𝒂𝒏𝒆 (
𝒖𝒈
𝒖𝒍)
𝑨𝒓𝒆𝒂 𝒐𝒇 𝑪𝒐𝒏𝒕𝒓𝒐𝒍 𝑳𝒂𝒏𝒆] ×𝑨𝒓𝒆𝒂 𝒐𝒇 𝑼𝒏𝒌𝒏𝒐𝒘𝒏 𝑺𝒂𝒎𝒑𝒍𝒆 ≈ [𝑼𝒏𝒌𝒏𝒐𝒘𝒏 𝑺𝒂𝒎𝒑𝒍𝒆 (
𝒖𝒈
𝒖𝒍)] (Equation 2)
Adjusting for the volume of DI used in the 1st wash (of 150μl), implies that there was a
relative mass amount of 2.64μg in the solution. Knowing that an original mass of 5μg was incubated
70
with the Dynabeads-complex in the first place, it implies that 55% of the total mass did not get
captured by the beads. This material capture yield could be attributed to several factors among
which can be the effect of the Dynabeads being oversaturated with HA and as such all the available
binding sites were occupied leaving unbound HA behind in excess. In addition, inherit binding
inefficiencies of the beads or suboptimal isolation protocol conditions (i.e. incubation times) could
all play a role in the capture potential of the beads. This was further explored in the discussion
section of this chapter
To calculate the approximate amount of HA recovered in the isolation step (lane 5); a
similar image analysis was done using the first lane as a reference. A relative concentration of
7.91ng/ μl was initially calculate, however when adjusting for the volume of DI used (of 70μl) to
dilute the recovered amount of HA, the approximate calculated mass extracted is approximately
0.55μg. This value implies that 11% of the original material was recovered by the beads under the
conditions specified.
Table 4: Relative Yield of HA Capture Using the Dynabeads-Proteoglycan Complex
Lane Label Area (a.u)
Apparent
Concentration
(ng/μl)
Apparent Mass
of HA (μg)
1 Control 9928.3 200 N/A
2 1st Wash 3654.8 17.64 2.64
5 Isolated HA 2250.5 7.91 0.55
Extraction Spiked in MonoHA from Human Plasma
The first set of attempts in extracting HA from plasma did not involve the use of a Phenol-
Chloroform extraction step, instead relied on the binding affinity alone of the Dynabeads-
proteoglycan complex to HA, using enzymatic activity alone to disrupted potential bound elements
to HA to purify the sample. Considering the significantly low amount of naturally occurring HA
in blood serum (0.01-0.1ug/ml), the first strategies of separating HA consisted on using larger
volumes (10ml) to increase the total mass present in the solution and hence increase the potential
71
yield of the extraction process. In addition, considering that incubation volume might influence the
capture rate of HA in solution, the sample was speed vacuumed, which served to both concentrate
the solution and reduce the incubation volume by ~70%. To assess the effectiveness of the
extraction process, a spiked amount of MonoHA (81kDa) was added to the plasma sample before
initiating the extraction process, with the expectation that most of the extracted material should
correspond to the same molecular weight as the spiked in material in the first place. (Refer to
Appendix Section Figure A-0-7-i, showing a schematic summary of the experimental procedure
used). The Speed vacuumed solution was incubated with the beads serially 500μl at a time for 1hr
each, with the same beads. Nevertheless, the extraction results were unsuccessful to be resolved in
the gel, and Nanopore platform.
The next set of experiments involved using a smaller volume as the one used in the first
experiment when testing for the Dynabeads-bVG1 complex’s ability to capture HA when spiked in
1xPBS. A 500μl volume of plasma, spiked with 5μl volume containing a MonoHA sample of
81kDa (at a concentration of 1μg/μl) was used. Given that instead of 1xPBS solution, plasma was
being used, it allowed testing if the incubation volume, or intrinsic fluid properties (i.e. viscosity,
plasma protein binding affinities to HA, or other biomolecules causing steric hindrance), were
hampering the ability of the beads to capture HA, which would explain why the previous
experiments were unsuccessful to extract HA. For this experiment the sample was not speed
vacuumed (given its already small volume), and the use of either proteinase K or heat treatment
alone was evaluated for its ability in digesting or denaturing plasma proteins respectively (Refer to
Appendix Section Figure A-0-7-ii for a schematic summary of the procedure used). While
extracted HA was not visually resolved with gel electrophoresis either (Figure 3-6-A), measurable
ECD values of the translocation events of the extracted 81kDa MonoHA (from the sample that used
Proteinase K) were able to be recorded (Figure 3-6-B), when analyzed with SS-Nanopore platform.
The same set-up conditions as specified in Section 2.2.1.4 were used to analyze this sample. Results
72
shown below were based on using a 200mV voltage and a ~6.5nm pore. The mean ECD (data point
in black) calculated from the Gaussian distribution profile of the measured ECD values for the
event translocations of the 81kDa MonoHA sample (refer to inset in Figure 3-6-B) was found to
not be statistically significant (significance level set at 0.05, p=0.76, ANOVA single factor), from
the control sample of dissolved 81kDa MonoHA in a 6MLiCl solution alone (data point in red
corresponding to 81kDa MW) when measured with the same dimension size pore in the SS-
nanopore system. These results were evidence, of the ability of the extraction process to isolate
HA from plasma while preserving its integrity, and the ability of the platform to operate using low
sample concentrations, that could not be otherwise resolved by gel electrophoresis.
Figure 3-6: Preliminary isolation of spiked in HA in human blood plasma (A.) Shows the imaged gel,
were Lane 1 corresponds to the control sample and lane 2 and 3 correspond to plasma samples spiked
in with 81kDa MW of MonoHA after extraction process (B.) Shows the calibration curve of ECD vs.
Molecular weight, where data point in black corresponds to the measured 81kDa MonoHA sample
73
isolated from Plasma. Inset graph shows population distribution for the MonoHA control (81kDa
MonoHA sample) (red) vs. the isolated 81kDa MonoHA (black). Mean ECD, was found to not be
statistically significant (p>0.05, One-way ANOVA)
In attempting to improve the yield of the extracted HA from plasma, the use of a phenol-
chloroform step was incorporated in the extraction process, given its ability to separate the organic
from the aqueous HA containing phase, and remove impurities that might otherwise compete with
HA’s binding affinity with Versican (Methods Section 3.2.1.1.1). For this experiment, the use of
MonoHA of known MW of 250kDa was used. Figure 3-7-A showed that the extracted MonoHA
material was resolved in the gel (lane 7), and the sample migrated the same distance from the well
as the control sample (lane 1) of equal MW. Lanes 3 and 4 of the gel correspond to the aqueous
phase material, after the first Phenol-Chloroform extraction, and the second “Chloroform only”
extraction phase respectively, intended to identify if there was material loss in extraction process.
Figure 3-7: Isolation of Spiked MonoHA (250kDa) in Human Plasma, Using Phenol-Chloroform
Extraction. (A.) Shows a photographed 0.5% agarose gel using 0.005% Stains all in 50% ethanol as
74
detection agent after being exposed to UV light. The table below the gel describes the content of each
well loaded to 20μl volumes (B.) Histogram plot analysis for MonoHA samples for the control sample
(red, MW 250kDa) and extracted HA from plasma sample (blue, MW 250kDa). Fitted to their
distribution a Gaussian curve was used to obtain mean ECD for the different samples respectively (C.)
Graphical representation of mean ECD and their respective standard deviation as a measure of the
Gaussian distribution FWHM value.
A normalized histogram plot was generated for the measured ECD for the translocations
events recorded using the SS-Nanopore platform, for both the control sample (in red, Figure 3-7-
B), compared to the extracted material (in blue). The results showed similar symmetric distribution
and ECD range between the samples. Even though, there was a slight shift in the Gaussian profile
of the extracted HA towards a higher mean ECD (calculated from the corresponding ECD peak
value at the top of the Gaussian fit), compared to the control sample, the two samples were shown
to not be statistically significant running a single factor ANOVA (Significance level at 0.05,
p=0.066) (Figure 3-7-C). Refer to Appendix Section Table A-0- 4: Gaussian Distribution Statistical
Analysis for extracted MonoHA from Plasma (81kDa) compared to a control sample of equal MW for a
summary table of the statistical analysis results.
From the gel, the extraction yield of HA was calculated to be <3.5% using built-in gel
analysis tool from Image J. software (Version 1.50i, NIH, USA). This was calculated using the
method described earlier, by relating the intensity area to the known concentration of the sample
loaded, and then taking into consideration its dilution volume. The concentration of Lanes 3 was
calculated to be ~ 19ng/μl while the relative concentration in Lane 5 was of ~15 ng/μl, adjusting to
the dilution volume there was a calculated material loss in terms of relative grey scale intensity of
~ <17% during the PLG extraction phase steps. The dilution volumes for Lane’s 5 and 7 were both
known.
75
Table 5: HA Extraction Yield, from Spiked MonoHA in Human Plasma Sample
Lane # Lane Content
Calculated
Intensity
Area (a.u)
Calculated
Concentration
(ng/μl)
Mass
(μg)
3
1st Phenol-
Chloroform
_Extraction
0.18703 19.00 9.50
5 2nd Chloroform
Extraction 0.15538 15.78 7.89
7 Isolated HA 0.06896 7.01 3.50*
The 3.50μg measurement in Table 5 is based on relative intensity for a 500μl volume.
Scaling this amount to the dilution volume of 50 μl (the volume to which the isolated HA was
diluted), corresponds to an actual mass amount of 0.35μg of apparent HA extracted after using the
Dynabeads-bVG1 Complex affinity extraction step.
To optimize the extraction yield of HA from biological fluids, the optimal concentration
ratio between Dynabeads and biotinylated Versican was studied, to maximize the number of
Dynabeads complexes created and thus improve capture yield as described in Section 3.2.1.5. The
relative concentration of bVG1 was measured (using the correlated baseline wavelength absorbance
reading of pure 1xPBS as a calibration standard that does not contain bVG1) for each of the
supernatant samples. The higher the relative concentration measured was indicative of excess of
bVG1 relative to the concentration of Dynabeads used.
Table 6: Optimization of Dynabeads-to-bVG1 Concentration Ratio
Vial Beads bVG1 Relative Concentration
of bVG1 Volume (μg/μl) Std. Dev
1 1 : 0 0 0
2 1 : 0.2 0.005 0.002
3 1 : 0.6 0.001 0.001
4 1 : 1** 0.003 0.001
5 1 : 1.50 0.038 0.014
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From Table 6 above the results showed that the optimal incubation ratio of Dynabeads to
bVG1 is between 1:1 and 1:1.5 (which in term of actual mass ratio is of 100μg:1μg or higher
respectively). This was since the calculated relative concentration showed a significant increase in
the supernatant bVG1 concentration relative to the other incubation volumes when the
concentration was above 1:1 ratio, indicative that bVG1 has saturated the amount of the Dynabeads’
available binding sites. While there was a minimal relative loss below this concentration ratio, it
was attributed to the instruments measurement sensitivity.
3.2.4. Evaluation of HA Extracted from SYNOVIAL FLUID
Initial results of successful extraction of HA from SF using the methods described above,
used SF samples extracted from the left carpal joint cavity of a horse which was clinically diagnosed
as having grade 3 OA. Initial measurements used 600μl of SF sample. Evidence of extracted HA
from SF was verified using gel electrophoresis (Figure 3-8-A) using the protocol described in
Section 3.2.2. Lanes 1, 3 and 5 correspond to intermediary process extraction steps, while Lane 7
on the other hand corresponds to the HA material extracted after using the Dynabeads-bVG1
complex affinity extraction step. The results showed that there was visual shift towards a lower
MW in lane 7 compared to the other lanes. This shift however, was attributed to several reasons
such as experimental loading conditions or the effect (if any) of the affinity extraction step having
a bias towards a particular range of MWHA which would consequently skew the result accordingly.
These reasons were further explored in the discussion section of this chapter.
Using ImageJ analysis software, a semi-quantitative analysis was performed on the
extracted HA material in Lane 7 of the gel using the relative grey scale color intensity (across the
length of the individual lanes) to correlate it to the loaded sample’s relative concentration.
Considering that a higher grey scale intensity value corresponded to higher sample concentration,
and since the migration distance of the sample in the gel was inversely proportional to the MW of
the sample loaded, these two metrics (migration distance and greyscale intensity) were used to
approximately quantify changes in sample’s MW distributions and relative concentration. A grey
77
scale intensity graph was plotted (Figure 3-8-B) (having accounted for the effects of the
contributions of the background’s grey scale intensity values), which showed a HMW peak region
of high intensity grey scale values, followed by relatively uniform grey scale intensity values across
the remainder length of the lane. This behavior was corroborated with what was visually
appreciated in the gel. These results showed preliminary evidence of the ability of the extraction
process to capture a HA over wide range of MW, which was important given the goal of using this
protocol to extract HA from SF samples to be analyzed with the SS-nanopore system.
Figure 3-8: HA Extraction from Left Limb of Equine Model Grade 3 Induced OA
The extracted sample from lane 7 was then analyzed using the SS-Nanopore system
(Figure 3-8-C), showing evidence for the first time of measurable translocation events of HA
derived from a biological sample using this technology. A 6-8nm pore diameter was used for this
78
measurement, and the same system set-up conditions specified in Section 2.2.1.4 at a 200mV were
used. The calibration curve that related ECD to MW (Refer to Figure 2-12 under the Results section
of Chapter 2 of this document), was used to plot the distribution profile and MWHA range of the
sample. The sample showed to have an approximate MW range of 500kDa-10MDa. Additionally,
the results also showed that the MW distribution was qualitatively similar to the MW profile
distribution that was appreciated in the gel. These results not only corroborate the feasibility of
using the extraction process to obtain an aqueous HA sample that could be used for the subsequent
analysis with the SS-nanopore system, but also given the fact that qualitatively the gel and SS-
nanopore results were in agreement, provided preliminary evidence of the feasibility of using the
SS-nanopore system as a tool for the analysis of HA derived from SF. Finally, since (according to
the 200mV calibration curve used), the measured HA translocation events corresponded to a MW
range that extended beyond what was previously measured, (using commercially purchase
synthetic samples ranging in MW of 54kDa-2.4MDa); was of critical importance as it demonstrated
preliminary evidence that further supported the potential of this platform to be used for HA analysis
from SF samples. This was since, HA in joints is present over a wide range of naturally occurring
HMW that can be larger than 2.4MDa in MW.
To show proof of concept of the potential feasibility of using the SS-Nanopore system for
future clinical assessment of HA content in SF, preliminary results were analyzed using SF derived
from two horses (Refer to Section 3.2.1.1.2). The HA content in the SF, extracted at two different
time points from their respective frontal carpal joint cavity (that was previously surgically induced
to develop OA) was analyzed using conventional gel electrophoresis and the SS-nanopore system.
The HA content in the extracted SF analyzed for the horse named Boss was extracted at
time points Day 0 and at Day 12, while for the other horse named Phoebe, the SF was extracted at
time points Day 0 and Day 5. Figure 3-9-A below corresponds the gel analysis of the HA content
in the SF extracted from Boss’s left frontal carpal joint cavity. The designation BL= “Boss Left” is
79
in referenced to the horse’s name and left limb from where the SF was extracted. The gel
electrophoresis results/image for both horses were courtesy of Dr. Heidi Reesink (our project
collaborator from the College of Veterinary Medicine of Cornell University Ithaca, NY, USA).
Both time point samples from BL, show a polydisperse distribution of HA that falls in MW
range of between 500kDa and >6MDa. Using ImageJ, the imaged gel was analyzed for relative
grey scale intensity for the different extraction day time points. In addition, the migration distance
from the well (in pixel units) was correlated to a MW value using the HA ladder’s migration
distance (in the first lane) as a standard (Figure 3-9-B). The graph gives a detailed semi-quantitative
appreciation of the MW profile of the samples, and showed a clear shift from Day 0 to Day 12
towards overall HMW-HA, in addition, showed MW values >6 MDa, on Day 12.
Figure 3-9: Day Comparison Left Limb for Induced Carpal Chip (Horse Boss)
To analyze the HA found in the extracted SF using the SS-nanopore system, the protocol
described earlier in the method’s section of this chapter was used to process the samples. Extracted
HA was analyzed using the SS-Nanopore system with the same setting and sample loading
80
conditions used for the Grade 3 OA sample analyzed earlier, with the exception that a 7.5nm
diameter pore was used. ECD measurements were scaled to MW, based on the calibration curve as
done before, yielding the profile distribution for MW for both time points as seen in Figure 3-10
below. The graph was then transformed into a normalized distribution, for comparison purposes
between the two-time points.
The graph in Figure 3-10-A showed a similar mass distribution profile compared to the
gel analysis (Figure 3-9-A) showing a shift on Day 12 towards HMW-HA (outlined in black) (for
MW primarily >~500kDa) relative to Day 0 (outlined in red). More importantly the SS-nanopore
results yielded qualitatively similar behavior in terms of MW range (300kDa->~6MDa) and
distribution profile to the gel analysis of the same sample, demonstrating consistent results. Further
analysis was performed on the ECD data to show the relative cumulative MW distribution, which
showed that there was higher concentration of HMW-HA in Day 12 than there was in Day 0
(Figure 3-10-B) (also consistent with what was seen in the gel analysis).
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Figure 3-10: Time Comparison of Extracted HA from Equine Sample (BL Day 0 and Day 12) Using
Gel Electrophoresis and SS-Nanopore System
The same gel electrophoretic analysis was performed to compare the two-day time points
(Day 0 and Day 5) from the other horse’s SF-HA of its right limb. The designation PR= “Phoebe
Right” is in referenced to the horse’s name and to indicate the limb from which it was extracted.
From the imaged gel (Figure 3-11-A), a relative intensity plot was generated against MW, using
the same approach as before. The intensity profile (Figure 3-11-B) provided semi-quantitative
results indicating that there was a relative shift from Day 0 to Day 5 towards an overall LMW-HA,
with an overall MW range estimated to be between 1000kDa- to- >7MDa for Day 0 in comparison
to Day 5 where the range was approximated to be between ~1000kDa-to <7MDa.
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The SF samples were processed using the extraction protocol used before, for both time
points, and were then analyzed using the SS-Nanopore system. Results were captured using the
same system settings and loading sample conditions as before (e.g. 200mV voltage, 6MLiCl buffer
etc.) and the measured ECD values from the translocations of HA were correlated to MW using the
same calibration curve as before, yielding the graph seen in Figure 3-11-C. The results from both
the SS-nanopore and its corresponding gel results agreed in showing a similar MW distribution for
the two day time points, and the shift in MW described previously.
Figure 3-11Time Comparison of Extracted HA from Equine Sample (PL Day 0 and PL Day 5) using
Gel Electrophoresis and Using SS-Nanopore System
The fact that the results obtained using gel electrophoresis, matched in both overall MW
range and distribution and relative MW shift for both equine samples with the results of the SS-
83
nanopore system provided evidence regarding the reliability and consistency of the SS-Nanopore
system to analyze HA from SF over a wide MW range, and importantly it showed that the extraction
process was effective in isolating HA over the clinically relevant range of MW naturally occurring
in synovial fluid joints.
3.4. DISCUSSION
The results above showed the extraction process was successful in extracting HA from
biological samples preserving its integrity. Importantly the observed results from the gel-
electrophoresis analysis agreed with the results obtained from the SS-nanopore system. In addition,
the results from the SS-nanopore system showed increased resolution for MW differentiation than
the conventional gel technique.
While the results showed that the extraction yield of HA in plasma was significantly low
(of ~3.5%) it is worth noting that since the current protocol conditions have not been optimized to
identify the binding capacity of Dynabead-bVG1 complex to HA, the spiked amount of HA that
was used may have already been present in excess in the solution relative to the capture capacity
of the Dynabeads-bVG1 complex amount used. Therefore, the apparent 3.5% recovered yield, may
have already occupied the totality of the binding sites for HA, hence inadvertently showing a lower
yield than there was. A dilution series of HA amount in solution relative to a constant Dynabead-
bVG1 concentration can be performed to address the binding capacity of this extraction technique,
using an HA ELISA Kit assay to assess relative concentration extraction yield, which can be further
corroborated with rate vs. concentration measurement through the SS-Nanopore platform.
Nevertheless, if the 3.5% was in fact the approximate yield recovery, other factors not yet
evaluated, may have played a role in the extraction process having a low capture yield such as: the
incubation time of the Dynabeads-complex with HA, and the volume of solution that was incubated
at a time.
84
Assuming the interaction of the HA to the Dynabeads complex, happened stochastically,
driven by HA diffusing in solution, then the solution volume had a significant effect on the diffusion
rate of HA whereby larger volumes increased the distance that the molecules in solution must travel
(thus decreased the rate of diffusion) and reduced the probability of the interaction of HA with the
Dynabeads-complex. For this reason, incubation time may have played a significant role, since
depending on the sample volume it may have required a longer incubation period. While the latest
experiments of HA extraction from plasma included an overnight incubation instead of the 1-3hr
incubation window for the affinity extraction step, (which seemed to increase the apparent
extraction yield as it was resolved using gel electrophoresis) the effect of incubation time could not
be evaluated adequately given that it was not tested in isolation. This was since other protocol
variables were changed including: the introduction of the Phenol-Chloroform step, in addition to
an increased spiked in HA concentration. Therefore, comparative yield recovery could not be
assessed objectively with preceding experiments in which larger solution volumes were incubated
with the Dynabeads while keeping the incubation time constant. For this reason, both a time and
volume dependency studies need to be conducted for the optimization of HA extraction process.
Given that the Spiked in HA in plasma was successfully measured through the SS-
nanopore prior to the introduction of the Phenol Chloroform step, raised the question whether the
Phenol-Chloroform step was necessary. When testing the spiked 5μg of 81kDaMW of MonoHA in
500μl of plasma, and using Proteinase K treatment and centrifugation (to remove unwanted protein
and other impurities), SS-nanopore measurements were obtained. More importantly the
translocation event count distribution and mean ECD measurement was shown to not be statistical
significantly different to the measured results obtained when running the MonoHA solution of the
same MW that served as the control. While the isolated material was not visible in gel, compared
to when the Phenol-Chloroform extraction step was introduced, it is worth noting that a higher
concentration of spiked in HA was used (10μg of HA for 500μl of plasma) for the latter experiment.
85
Therefore, rather than attesting to the protocol’s apparent inefficiency, it highlighted the detection
sensitivity limitation of traditional gel electrophoretic techniques (compared to the SS-Nanopore
detection sensitivity), given that they require larger volumes of sample. Therefore, revisiting the
contribution of Phenol-Chloroform step in improving HA capture yield should be assessed in
parallel to the original plasma experiments that involved the use of Proteinase K and centrifugation
alone, however performed under the same experimental conditions (i.e. volume of solution,
incubation time and spiked in HA of the same MW). However, for future verification experiments,
the use of PAGE based electrophoresis as opposed to the agarose based gel should be used. This is
since studies conducted by Bhilocha et al., 2011, have shown that this assay has better resolution
and detection sensitivity for HA than traditional agarose gel. Therefore, it is plausible that the
material extracted without involving the Phenol-Chloroform step could have been resolved in
PAGE.
Despite of this, the Phenol Chloroform step has the added benefit of better purifying the
aqueous content containing HA, given its ability to remove the organic phase content in solution.
Therefore, this added purification step may be the major contributing factor for the improved
relative HA capture yield that was already seen in the latter experiments, rather than the amount of
spiked in HA present accounting solely for the improved yield. This is since, impurities in solution
may hinder the binding affinity of the Dynabeads-complex to HA, whereby Proteinase K and
centrifugation alone does remove them. This was evident since, prior measurements in the SS-
nanopore system that used material that was not extract using phenol chloroform, showed to have
a higher incidence of clogging instances of the nanopore, which were not as prevalent compared to
when the Phenol-Chloroform extraction step was introduced. Nevertheless, the crucial finding of
this observation was that since the HA was not resolved using traditional gel electrophoresis, it
highlighted the importance of using the SS-nanopore platform given its greater detection sensitivity
and operating using low sample volumes.
86
Another important aspect to consider with the current extraction protocol is the fact that it
has not been established whether the affinity extraction method had a MW size dependent bias.
Since the Versican-HA interaction is not fully understood in current literature, it was unknown
when incubating a polydisperse mixture of different size HA with the complexed Dynabeads, that
different size HA would have different binding affinity with the Dynabeads-complex. Not to
mention that since different MW particles diffuse in solution at different rates it is plausible that
the HA-Dynabeads complex interactions occur more frequently with LMW-HA than with
HMWHA given their comparatively faster diffusion rate. Consequently, the extraction protocol
might inadvertently be excluding HA of a particular MW range that has comparatively lower
binding affinity and thus biasing the results. Current experiments are being carried out to rule out
if any; a MW bias of this extraction protocol, which is further explained in the FUTURE
DIRECTIONS Section of this document. This was an important aspect to explore given the goal of
this research to show the feasibility of using this extraction protocol in conjunction with the SS-
nanopore platform for clinical evaluation of changes in HA concentration and relative size over
time, given the associated implication in disease states. Therefore, the accuracy and reliability of
this assessment highly depends on the extraction protocol not having a MW bias.
The results from the extraction of HA from plasma show promising preliminary evidence
of the feasibility of using the affinity extraction method for HA isolation, this was since in both
instances where the 81kDa and 250kDa the SS-Nanopore measurements were taken there was no
statistical significant difference between the means when performing a single factor ANOVA,
showing evidence to suggest that the calculated mean derived from the same sampled population.
Important, to this analysis was the fact that the gel results (at least for the 250kDa sample tested
that could be verified through gel electrophoresis) showed qualitatively the same migration
distance, between the spiked sample that was isolated through the extraction protocol and the
control sample. Similarly, the results of the extracted material showed to conserve similar size
87
uniformity (i.e. narrow band) to what was observed for the control sample, showing reasonable
evidence of the preserved structural integrity of the sample post extraction treatment as there was
no significant visible smearing of the extracted material in the gel. Furthermore, this observation
was also a testament of the high purity of the extracted solution of HA. Nevertheless, further
experiments could be conducted to assess the purity of the extracted sample using ELISAs or
spectrophotometric analysis for glycosaminoglycan determination similar to those performed by
L. Chen, Liu, Luo, & Hu, 2014; Dong et al., 2010. This is since other impurities, particularly other
Glycosaminoglycan, might be present in the solution, which were not effectively isolated through
this protocol.
Further extending the comparative analysis for the extracted vs control sample of HA,
another metric to provide substantive evidence of sample similarity was to quantify the percent
normal distribution overlap for both distributions, as a statistical metric to assess population
changes. Using the descriptive summary statistics obtained from the plotted histogram Figure 3-7-
B the Lk-nomogram method, (Linacre, 1996) in Figure 3-12 below was used. This method showed
that there was an overlap of >80% of the distribution data. This computation is based on each of
the populations sample’s mean and standard deviation as specified below, which is then correlated
to the approximated overlap percent region. This served as an important metric that could be used
to better characterize distribution profiles and their relative shifts. A similar percent overlap was
also observed when calculated for the 81kDa MW sample extracted compared to the control. While
more precise calculations for the calculated intersection area between two Gaussian curves can be
used, the nomogram methodology offers a rough approximation. This metric has the potential to
be used to specify the degree by which a particular distribution of a specific MW over time changes.
88
Figure 3-12: Nomogram of Percent Overlap of Normal Distributions, Lk-Norm Method
The results showed that this protocol was also successful in the isolation of HA from
synovial fluid, shown in the gel electrophoresis results belonging to the horse sample with a grade
3 osteoarthritic classification. The gel however showed an apparent shift in the isolated material
towards a lower MW seen in the last lane of the gel compared to the other lanes. As explained
previously, the material loaded in the preceding lanes corresponded to SF material at different
protocol experimental steps. However, this apparent shift could have been attributed to sample
overloading. Inherently in SF, there is a high concentration of HA and of an overall higher MW
than plasma, ranging in the mega-dalton MW range. Since gel electrophoresis set-up conditions
had not changed from when using control known concentrations of HA and presumably of lower
MW than what is naturally found in SF (i.e. time, voltage and loading volumes); might not be
adequate for the evaluation of HA extracted from SF. This is highly plausible given the fact that
there was no prior knowledge of the concentration HA in SF and thus it is likely that what is observe
is the gels was overloaded material that did not adequately migrated. Since there is an expected
loss in capture yield through the affinity extraction step the overloading effect may not have been
89
a factor in the last lane of the gel, and thus reflecting the actual MW distribution profile of HA in
this SF sample.
Nevertheless, another possible explanation for this shift was the fact that the extraction
method as explained earlier, might have MW affinity bias, that was not being considered, and thus
skewing the results, (in this case) towards a LMW-HA. To address this issue a dilution series of
the material obtained from each of the preceding lanes to Lane7 of the gel could be loaded onto a
new a gel. This experiment would serve for a dual purpose to address both the notion of the lanes
being overloaded (whereby with increased dilution of the material, the distribution would be
expected to be similar to lane 7 if there was if fact excess of HA loaded in the lanes) and secondly
address qualitatively if there was an affinity bias from extraction technique towards LMWHA. The
latter would be observed if after the dilution series, the migration of the sample HA was not similar
to that which was seen in Lane7. Nevertheless, the fact that HA was extracted from SF using the
same protocol as that used for plasma was a promising result in demonstrating the versatility of this
protocol for extraction of HA from different biological fluids.
The preliminary results obtained from the HA material from the Equine samples are of
critical importance in demonstrating the feasibility of the protocol and the SS-nanopore system to
have the potential to be used in a clinical setting. This was since both the gel and Nanopore result
measurements agreed qualitatively with the MW range calculated from ECD measurements (with
the current non-optimize calibration settings of the system), that could be approximately estimated
in the gel using the HA ladder.
Interestingly, for Day 12 results for the horse named Boss (Figure 3-9) the shift in MW
occured toward a HMW, contrary to what would be expected after the onset of osteoarthritic
condition, where it was previously observed in other experimental studies for larger mammals that
the overall MW concentration and size tend to both decrease in magnitude when compared to a
healthy joint (Band et al., 2015; Filková et al., 2009; Temple-Wong et al., 2016). This observation
90
however could be attributed biological variability between subjects and time point at which the HA
was extracted from the SF cavity, as the MW size distribution was seen to be affected through
natural turnover throughout the day. Additionally, since the trauma was generated on an otherwise
healthy horse, and OA is a degenerative chronic process, the changes manifested in the induced
OA model may not necessarily be comparable or had time to fully manifest themselves in this
particular specimen, at the time point in which it was examined. It is also possible that the induced
trauma may have increased the HA production by hyaluronidases and thus HA of larger MW may
have been replenished within the 12 day span. For this reason having the results of the contralateral
leg would have shown if in fact there was a significant shift compared to the otherwise healthy joint
of the horse from which HA was extracted.
This observation was key since, it is still unclear in orthopedic research the direct
contributions of MWHA and its association to OA, and also the particularly role that
Hyaluronidases plays in OA and HA synthesis replenishment, in chronic vs. traumatic conditions.
This highlights the need for future work to avoid analyzing HA in isolation, but to also take into
account other biological processes that are believed to play a role in HA degradation and OA
progression. On the other hand, the results observed for the other horse show a shift towards a
LMW-HA following the expected trend in molecular size change. However, this change was
observed at day 5 as opposed to day 12.
Another aspect to consider from the results was the fact that the HA extraction technique
from SF used by our collaborators was different from the one that was employed in this current
research which therefore hindered the ability to perform an objective comparative assessment of
the SS-nanopore measurements and those seen in the gel. The technique used by our collaborators
does not involve a phenol-chloroform purification step or affinity extraction capture of HA. Instead
the protocol consisted on collecting the sample from the horse which was then, spun down at 4000g
x 60 minutes to pellet the cellular material. The collected supernatant (containing the HA) was then
91
diluted in 1:20 DPBS and digested with Proteinase K (1mg/ml) overnight to be then ran on the gel.
Since it has not been verified through other laboratory techniques that impurities are completely
removed from the solution, (i.e. undigested protein or other constituents) they may still be present
in the gel analysis, thus potentially altering the MW distribution profile. And since the staining
solution (Stains All 95%) indiscriminately stains for different molecules, it was not possible to tell
from the gel image itself if the extracted material was in fact HA. Therefore, for future comparative
qualitative evaluation of the material observed in the Gel and that which is measured through the
nanopore, the HA in SF should be subject to the same extraction protocol conditions.
For these particular experiments, it is not expected that pH of the synovial fluid had any
effect between the control and injured limb considering that previous reasearch studies have shown
that the pH of synovial fluid does not vary significantly (between 7.5-7.8) (Henrietta Jebens &
Eileen Monk-jones, n.d.). HA is known to be stable between 4-9 therefore, there is no expectation
that intrinsic pH changes between the samples should affect the results. In addition, the samples,
were placed in a 6MLiCL containing 1X EDTA solution before being analyzed in the SS-nanopore
sytem, helping to control for pH variability.
3.5. LMITATIONS
While the preliminary results obtained from the extraction protocol of HA and the
feasibility of its application in analyzing HA from biological fluids were promising, there are some
current limitations that need to be considered for improved characterization of HA using this
protocol.
A current limitation of the research was that the laboratory technique used to verify for the
extraction yield of the extracted HA (gel electrophoresis) does not provide with both the level of
sensitivity and quantification that was required to validate the feasibility of the extraction,
considering that it only provided with qualitative evidence of the presence or absence of extracted
material and an approximate MW range. While the use of image analysis techniques of the gels
92
aided in providing quantitative results, these were only rough approximations. The current
technique heavily relied on the quality of the gel results (which have been seen to be variable), and
image post processing techniques which are not standardized (i.e. brightness and contrast
adjustments), thus having a significant effect on the results. Molecular mass estimates were based
on using a sample that contained a mixture of known MW (or ladder), to which migration distance
in pixel units is approximated to a mass value. Nevertheless, the usefulness and reliability of using
this technique heavily relies on the ladder extending past the range of the tested samples. This was
not the case for the gel results obtained from the biological samples, especially for HMW-HA, for
which accurate determination of MW became an issue. Therefore, other techniques (like ELISAs
HPLC or Spectrophotometric analysis) should be used in conjunction to gel electrophoresis to
assess: MW distribution of the extracted HA solution, in order to determine the feasibility of this
extraction protocol.
The feasibility of using this extraction protocol in isolating HA from plasma still needs to
be evaluated. Considering that there is a significantly reduced naturally occurring HA concentration
in plasma (0.1-0.01μg/ml), it might hamper the ability of the protocol to extract HA from plasma,
after accounting for material loss in the extraction process. While optimization strategies can
improve the sample yield, it is likely that other experimental conditions might need to be modified.
For example, there might be a need to use large sample volumes of plasma, for which the approach
of using either higher concentration of Dynabead-bVG1 complex needs to be increased and or the
incubation time. This creates a practical limitation of the potential application of this protocol for
HA plasma extraction in a clinical setting considering the sample volumes needed, processing times
and potential increased costs. While, it is known that in disease conditions there is a natural rise in
the HA content in the blood, the protocol should still be able to effectively extract HA from plasma
from a healthy control sample for comparison purpose. As mentioned earlier, laboratory techniques
that are more sensitive in detecting HA need to be used to validate the extraction protocol and its
93
content. In addition, SS-nanopore evaluations can be performed in spiked plasma samples
containing physiologically relevant amount of in HA of a known MonoHA MW, using the latest
extraction protocol to observe if HA can be effectively extracted and detected with the SS-nanopore
system. Similarly, a control sample of un-spiked plasma should also be evaluated for its HA
content.
Another limitation of the current extraction protocol was the fact that it used commercially
available Biotinylated-Versican, which is not cost effective given the amount that it was
needed/extraction. This might hinder the ability of translating the use of this protocol in the clinical
setting. Therefore, it is worth exploring the use of a more cost-effective proteoglycans that can be
used for HA selective extraction (i.e. Aggrecan or CD44). Additionally, current processing times
for the isolation of HA from Plasma or SF requires of approximately 24hours. While, optimization
strategies can potentially reduce this timeframe, it can limit the potential application for certain
clinical diagnostic evaluations in which HA levels need to be evaluated in a time sensitive manner.
Therefore, it is necessary to keep in mind as a long-term goal, the clinical space in which this
technology is best suited and thus allocate resources for extraction protocol optimization
experiments accordingly.
Given the concern regarding potential experimental bias in the extraction technique of HA,
limits the extent to which the results presented in this chapter for the Equine Model analysis can be
assesses objectively. This is since; the extracted material might not adequately reflect the true
biological MW distribution. Likewise, since preliminary biological specimen results are based on
a single measurement for each of the horses SF sample tested, it reduces the statistical power and
reliability of the assessment. While multiple experimental measurements can be taken on the same
biological sample to corroborate that the MW distribution changes between different time points
are in fact real, we are limited to the physical to amount of the biological sample that is available.
For this reason, a statistical power analysis to understand the minimum sample size requirement is
94
necessary. The use of single factor ANOVA and Tukeys pairwise comparison can be used for
assessment of sample size differences. Furthermore, is also necessary that the reproducibility and
reliability of the SS-nanopore system itself be determined, since the accuracy of the biological
assessment is highly dependent on the sensitivity and reliability of the system to detect differences
in molecular size of HA.
While the results obtained using the SS-nanopore platform for the Equine model samples
qualitatively show similar MW distribution trends as those observed in the gel, they are limited to
show only the differences in the Horse’s OA limb and for only two-time points. Future work will
include performing measurements on the different samples of SF for both the injured and
contralateral joint for the different horses.
Another limitation for the comparative analysis presented in this chapter, was that there
was not yet an established metric to best characterize comparative differences for polydisperse
distributions. Current analysis focused on showing cumulative percent differences in molecular
mass, as well as provided the molecular range of HA. Nevertheless, it is also important to address
relative concentration changes of the samples to provide a more thorough analysis.
Finally, since the HA content from the horses’ SF fluid that was loaded in the gels was
obtained using different extraction techniques, compared to the HA material that was used to obtain
measurements with the SS-nanopore system, limited the reliability of the analysis when performing
a qualitative comparison. Future experiments must, ensure that the extraction protocol for HA is
consistent for all experiments.
3.6. CONCLUSIONS
In conclusion, the extraction protocol despite its current non-optimized conditions showed
potential feasibility for the extraction of HA from biological fluids such as plasma and synovial
95
fluid, given the fact that it preserved the structural integrity of the material and it was able to obtain
a highly pure HA solution from the biological fluid.
Likewise, the results highlighted in this chapter showed evidence for the first time of
analysis of HA using the SS-nanopore system from biological specimens, which are qualitatively
comparable to the results observed using conventional laboratory techniques for HA analysis. This
system showed to provide with improved detection sensitivity over conventional electrophoretic
techniques given its ability to detect HA at lower sample concentrations, as well as provide
unparalleled sensitivity for the quantitative measurements of HA. Nevertheless, future experiments
need to be conducted to address pending concerns regarding the reliability of the extraction protocol
in accurately extracting HA without any intrinsic bias. Likewise, in order to validate the adequacy
of this extraction protocol increased sample size of the experimental observations needs to be
performed to demonstrated reproducibility and reliability of both the extraction process and SS-
nanopore analysis.
Finally, efforts to improve the extraction yield of HA, as well as aiming to reduce the overall
cost of the extraction technique, are much needed to show the feasibility of its used in clinical
setting particularly if it is intended for analysis of HA content in plasma given its naturally
occurring low concentration compared to SF. Nevertheless, the results presented show preliminary
evidence of potentially translating this extraction protocol in conjunction with the SS-nanopore
technique into in the clinical setting as a reliable clinical tool for HA evaluation.
96
FUTURE DIRECTIONS
To better characterize MW of HA, there are a couple of avenues that still need to be explored,
particularly targeted to better resolve for LMW-HA. An evaluation of pore sizes and thickness is
necessary across the range of the measured voltages done in this study, as this can aid in increasing
the SNR. A decrease in the thickness of the pore has the effect of increasing the amplitude of the
recorded events, while at the same time a reduced thickness, decreases the event duration. Therefore
performing experiments whereby the systematic combinations of these two factors are changed can
aid in determining best resolution set-up characteristics across the range of MW of HA.
On the other hand, experimenting with a salt gradient between the chambers might have the
effect to reduce the translocation time significantly. In experiments conducted by Fologea et al.
2005, it has been shown that introducing a salt gradient across the chambers can have the effect of
reducing translocation speeds of up to 40 times. Therefore, systematically changing the gradient
can improve the resolution of LMW-HA since the events would occur with translocations times
well above the temporal resolution of the instruments. This will also allow increasing the filter
frequency and thus effectively better characterize the translocation event differences which are
currently limited with a lower LPFF.
While current results use a pore size of 6-8nm, testing other pore diameters might be
beneficial for better resolving HA, and potentially reducing the incidence of clogging particularly
for HMWHA. While a smaller pore would have the benefit of causing the translocation amplitude
to be more pronounced and thus improve the sensitivity of MW detection, it can also increase the
incidence of clogging as it reduces the physical space for the molecule to transverse through.
Therefore, larger MW of HA might get tangled in the pore more often. On the other hand, while
increasing the pore size has the advantage of reducing potential clogging, the resolution of the
system is affected as the conductance depth might get lost in the noise of the baseline signal, which
is very likely given the current LPFF used in the current set-up conditions and St Dev. threshold.
97
Therefore, investigating the best combination of these conditions would help in identifying the
optimal set-up conditions. These changes in conditions will be evaluated using the approach present
in the discussion session of Chapter 3 whereby the goodness of fit of ECD vs. MW and percent
error that is estimated from the calibration curve, will allow determining which conditions are best
suited for MW differentiation. On the other hand, a pragmatic approach to detection of MW of HA
over a wide range might also involve the use of different size pores and thickness as well as
respective set-up conditions depending on the range of anticipates MW of HA that is to be tested.
Currently the capture rate dependency on MW will be evaluated whereby an HA solution
containing equal molar amount of three distinct MW of HA (100kDa, 500kDa and 2.4MDa); will
be tested through the Nanopore system. This experiment will have the ability (depending on the
relative capture concentration) to determine the capture rate bias, which can be used to adjust
accordingly the ECD event count distribution results when analyzing polydisperse mixtures of HA.
Finally, given the systematic experimental changes that are intended to be tested, it is
important to revisit if ECD is the most adequate metric to evaluate MW size differences. Likewise,
it is important to understand if the approach of utilizing Mean ECD best highlights MW difference.
This will be dependent, on increasing the sample size of the measured experiments, with the ability
to improve the statistical power of the results obtained, while at the same time provide evidence of
reproducibility and accuracy of the system’s ability to detect HA.
With regards to the experimental protocol for the extraction of HA from biological fluids,
it is important that the potential MW affinity bias be understood. For this purpose the same ladder
of HA of 100kDa, 250kDa and 2.4MDa, (with equal molar concentration) will be subjected to the
extraction protocol. Comparing the results of HA through gel electrophoresis by loading a sample
of the ladder (that has not been subject to the extraction protocol) next to the extracted material will
allow showing using image analysis software of changes in relative capture yield of the different
MW of HA across the individual lanes. The results from this experiment will affect how the
98
Dynabeads-bVG1 complex is incubated with HA, and potentially it might involve having to
perform multiple incubation iterations of the same sample volume utilizing fresh Dynabead-bVG1
every time. It is also important to understand the range of MW that the Dynabeads-bVG1 can
extract.
Other future experiments with regards to the extraction protocol should be aimed to
optimize capture yield. For this purpose, there are three important experiments that need to be
evaluated:
The first experiment is aimed at understanding the binding capacity of the Dynabead-bVG1
with HA, which will allow determining the anticipated capture yield of HA and downstream
protocol steps needed to concentrate the recovered samples. This will be addressed testing a dilution
series of a known MonoHA MW, against a fixed amount of Dynabead-bVG1 complex
concentration, using the same volume, solution conditions and incubation time. Recovering the
supernatant material and running it through PAGE, or an HA ELISA Kit will allow determining
relative concentration changes. Similarly, the material can be tested using the SS-Nanopore
platform by measuring uninterrupted current traces of equal time durations for the samples tested
to determine the rate of translocations, which should correlate to the relative concentration of HA
in solution.
The second set experiments are aimed to understand the volume and incubation time
dependency on the capture yield. For this, a minimum of three different volumes of 1xPBS with
the same MonoHA spiked in concentration incubated with a fixed amount Dynabead-bVG1
concentration for different time periods (i.e. 3hr, 6hr, 12hrs and 24hrs.) ran in a triplicate fashion
for a total of 12 samples can be used for this purpose. The supernatant material after the incubation
will be collected and set aside, and the captured material will be eluted in the same volume across
all samples with localized heat. The aqueous solution containing HA will be measured using PAGE,
or an ELISA HA Kit along with the SS-Nanopore to quantify relative concentrations of HA. This
99
way it will allow determining the volume and time dependency on capture yield by means of using
a multiple regression model analysis. This experiment however is highly dependent on the capture
binding capacity of the Dynabeads, for which it might be beneficial to address these three elements
in a single large experiment. The reason for testing with a single MonoHA population is because
the binding affinity bias might also play a role in capture yield.
The third experiment is to understand if external factors such as an increase in incubation
temperature can have an effect in improving capture yield. This is since a rise in temperature
increases the rate of diffusion of molecules in solution, which might have the effect of improving
the probability of the interaction of HA with the Dynabeads-complex in the allotted incubation time
and thus potentially improving the capture yield. Since rise in temperature increases the diffusion
rate, this might offset the effects of incubation volume, potentially negatively affecting capture
yield as it is predicted. This would be particularly beneficial when extracting HA from Plasma,
since larger sample volumes are needed, given the relative low naturally occurring concentrations
of HA present to begin with.
Finally, the extracted material using the proposed protocol should be evaluated for its
purity. This will allow determining if unwanted impurities still remain in the aqueous extracted
phase which can affect the results of the SS-Nanopore system. An experiment to test for this can
be design by preparing a solution containing known biomolecules (i.e. proteins and lipids)
comparable to what would be found in a biological sample in a solution of PBS, with a spiked in
known MonoHA concentration. Performing the extraction protocol, an either using
spectrophotometric analysis (with a previously established baseline standard measurement for a
control sample of MonoHA in PBS solution alone) or using an ELISA (previously prepared to be
selective for the capture of the unwanted material), can aid in assessing the efficacy of the protocol
in removing unwanted material. The purpose of using MonoHA is to understand if there are bound
elements to HA post extraction.
100
APPENDIX
CHAPTER 2:
Figure A- 0-1: Mean ECD Measurements for PolyHA Samples at Different Concentrations and
Applied Voltage.
101
Figure A- 0-2: Mean ECD Calculation for Mechanically Sheared PolyHA using Increasing
Sonication Energy
102
Figure A-0-3: Descriptive Statistics for ECD Histogram Analysis for Different MW-HA
103
Figure A-0-4: Histogram Analysis for Event Dwell Time and Change in Conductance for MonoHA
Samples Ranging From 54kDa-2.4MDa
104
Figure A-0-5: Calibration Curve Calculation between ECD vs. MW of HA
From the B Plot, the slope gives the exponent to which the power function ECD is related
to Molecular Weight (MW) which is 2.1499. Therefore, the expression below:
𝐸𝐶𝐷 = k ∗ (𝑀𝑊2.1499)
To determine the scalar quantity k. The gradient from the plot ECD vs. MW2.1499 yields
the scalar quantity 0.04842. The final equation is then:
𝐸𝐶𝐷 = 0.04842 ∗ (𝑀𝑊2.1499)
105
Figure A-0-6: Comparative Histogram Analysis for MonoHA Analysis Using different LPFF and
Standard Deviation Threshold Values.
106
Table A-0- 1 Calibration Curve to Associate ECD to MW Using Different Filter Frequency and
Event Threshold Detection Set-Up Conditions
107
Ta
ble
A-0
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Sq
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ren
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S-N
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Pla
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olt
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es
108
Table A-0- 3 Calibration Curve to Associate ECD to MW Using Different Voltage Set-Up Conditions
CHAPTER 3:
Figure A-0-7: Iterations of Procedural Isolation Schematic of Spiked in MonoHA in Human Plasma
Table A-0- 4: Gaussian Distribution Statistical Analysis for extracted MonoHA from Plasma (81kDa)
compared to a control sample of equal MW
109
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CURRICULUM VITAE
Education: M.S in Biomedical Engineering
Virginia Tech-Wake Forest University, Winston-Salem NC
GPA 3.7/4.0
Present
B.S in Biomedical Engineering
Illinois Institute of Technology, Chicago IL 3.4/4.0
2012
Work
Experience:
Cook Medical Inc-Product Management Intern.
Bloomington IN, USA, Division: Urology
May 2016-Aug 2016
ConMed Corporation- Mechanical Engineer I.
Westborough MA, USA, Division: Endoscopic Advanced
Visualization
Feb 2013-Nov 2014
Boston Scientific Corporation-R&D Engineer I
Marlborough, MA, USA, Division: R&D Urology Women’s
Health
June 2012-Feb 2013
DAAD Research Internship Science and Engineering
(RISE)
Kaiserslautern University of Technology Germany,
Division: Bioengineering
May 2010- Aug 2010
Depuy Synthes Inc.- Product Development Engineer Intern
West Chester, PA, USA, Division: Neurology &
Craniomaxillofacial
May 2009- Aug 2009
Recognitions Student Travel Award ISHAS Conference
MS Student Scholarship Wake Forest University
1st Place White Board Challenge Design Competition MIT
1st Place Poster Global Health Design Competition Rice
University
1st IPRO Design Competition Illinois Institute of
Technology
DAAD Research Scholarship (Kaiserslautern, Germany)
Merit Based International Student Scholarship Illinois
Institute of Technology
June2017
Aug 2015
Aug 2011
May 2011
May2011
May2010
Aug 2007
Skills Software: Solidworks, MATLAB, Microsoft Office
(Excel, Power Point, Word & Outlook), Oracle database, R
Statistical software.
Languages: Fluent in English and Spanish.
