A Microfluidic Platform for the Investigation of Transport in Small Blood Vessels

by

Sascha Pinto

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science

Institute of Biomaterials and Biomedical Engineering

University of Toronto

© Copyright by Sascha Pinto, 2012

ii

A Microfluidic Platform for the Investigation of Transport in Small Blood Vessels

Sascha Pinto

Master of Applied Science

Institute of Biomaterials and Biomedical Engineering University of Toronto

2012

Abstract

The microvasculature has the main function of transport of dissolved gases, nutrients and waste

between blood and tissue. Systematically probing transvascular transport rates in these vessels under

well defined conditions is challenging. In vivo and in vitro studies are characterized, respectively, by

limited optical access and control over perfusion concentrations and failure to resemble the structure

and function of an intact organ. In this thesis, I present the development of a microfluidic platform

for investigating molecular transport across mouse mesenteric arteries (150-300μm diameter) in a

controlled physico-chemical microenvironment. Intact vessels are perfused with 4 kDa FITC-

Dextran and the permeation coefficient of this molecule across the vessel wall is quantified using

laser scanning confocal microscopy paired with a 2-D numerical model. Functional viability of the

examined vessel, through phenylephrine and acetylcholine dose responses, is probed, and shear and

phototoxic effects are reported.

iii

Acknowledgments

First and foremost, I would like to thank my parents and my brother for their continued support

throughout this project. Knowing that I could always rely on them (mentally, financially, and

nutrionally) made this project possible.

I would like to acknowledge the help and vast knowledge of Sanjesh Yasotharan, my project brother

throughout these last 2.5 years. From fabrication to soldering, thanks for being a colleague and

teacher.

I would like to thank the members of the Bolz lab, my family away from home. No matter the

frustrations, experiments were simply fun to do with you all.

I would also like to thank the members of the Günther lab. I hope you keep the Starbucks and after

school traditions going.

In addition, I would like to thank my supervisor Axel Günther for his continued support, discussion

and impartment of knowledge, and understanding when I felt homesick. Also, for the timely Ritter

Sports…but only the yellow ones.

I would also like to acknowledge the supervision of our collaborator Steffen-Sebastian Bolz (I will

always know that the hardest part is always ahead), as well as the advice and experimental guidance

from our collaborators Dr. Dan Dumont and Paul Van Slyke, and insight from my committee

members Craig Simmons and Dr. Myron Cybulsky.

Many thanks goes to my close friends back home for their continued understanding and support

these last few months.

Throughout the course of this project, I was funded by NSERC CGS M and FQRNT B1.

Finally, as Meghan has taught me, I would like to thank the mice. There would be no data or results

without them.

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Table of Contents

Acknowledgments .......................................................................................................................................... iii

Table of Contents ........................................................................................................................................... iv

List of Figures ................................................................................................................................................. vi

List of Appendices ........................................................................................................................................ vii

List of Abbreviations ................................................................................................................................... viii

Chapter 1 Introduction ................................................................................................................................. 1

1.1 Hypothesis ............................................................................................................................................. 2

1.2 Specific Aims ......................................................................................................................................... 3

1.3 Background: The Microvasculature and the Endothelium ............................................................ 3

1.4 Previous Work on Transport in the Microvasculature ................................................................... 8

1.5 Thesis Structure .................................................................................................................................. 12

Chapter 2 Experimental Methods ........................................................................................................... 13

2.1 Biological Samples .............................................................................................................................. 13

2.1 Microfluidic Device ............................................................................................................................ 13

2.2 Small Artery Function ........................................................................................................................ 16

2.3 Experimental Procedure .................................................................................................................... 16

2.4 Post-Processing ................................................................................................................................... 19

2.5 Numerical Model ................................................................................................................................ 20

Chapter 3 Results ......................................................................................................................................... 23

3.1 Effect of Shear on Small Artery Function ...................................................................................... 23

3.2 Permeability ......................................................................................................................................... 24

Chapter 4 Discussion .................................................................................................................................. 29

Chapter 5 Conclusion .................................................................................................................................. 33

v

Chapter 6 Future Directions ..................................................................................................................... 34

References ....................................................................................................................................................... 36

Appendices ...................................................................................................................................................... 41

vi

Figure 1.1: Structure of small blood vessels and microvascular transport

Figure 1.2: Paracellular transport

Figure 1.3: Transcellular transport

Figure 2.1: Microfluidic strategy for small artery permeability measurements

Figure 2.2: Confocal measurement using controlled excitation outside of vessel wall

Figure 2.3: Calibration of measured fluorescence intensity as a function of fluorescent marker

concentration

Figure 2.5: Measured concentration profiles as a function of the distance from the vessel

Figure 2.4: 2D numerical model for determining solution of convective diffusive transport problem

Figure 3.1: Smooth muscle and endothelial cell viability in the presence of shear

Figure 3.2: Transient perfusion of fluorescent marker

Figure 3.3: Transient intensity change and steady-state profile

Figure 3.4: Numerical model results and permeability correlation

Figure 3.5: Whole vessel excitation viability results and obtained permeability coefficients

Figure 3.6: Controlled excitation viability results and obtained permeability coefficients

Figure A.1: Microfluidic device design

Figure A.2: Mesh dependence of numerical model

Figure A.3: Effect of Pe number on the transient and steady-state solutions

Figure A.4: VEGF effect on smooth muscle cell and endothelial cell viability

Figure A.5: VEGF effect on permeability coefficient

Figure A.6: Transgenic Tie2-GFP vessel perfusion for local transport study

vii

A.1 Microfluidic Device Design ..................................................................................................................... 41

A.2 Post-Processing Script for Confocal Images ........................................................................................ 42

A.3 Numerical Model ...................................................................................................................................... 47

A.4 VEGF as a Positive Control ................................................................................................................... 49

A.5 Local Transport Study .............................................................................................................................. 51

viii

2-D Two-dimensional

ACh Acetylcholine

AJ Adherens junctions

BBB Blood-brain barrier

Ca Calcium

Cav-1 Calveolin-1

D.R. Dose response

eNOS Endothelial nitric oxide species

EC Endothelial cells

ECI Electric cell-substrate impedance

ECM Extracellular matrix

ESL Endothelial surface layer

FA Focal adhesions

FITC Fluorescein isothiocyanate

FRAP Fluorescence recovery after photobleaching

GFP Green fluorescent protein

IEJ Interendothelial junctional

LIF Laser induced fluorescence

MOPS 3-(N-morpholino)propanesulfonic acid

PAEC Porcine aortic endothelial cells

PE Phenylephrine

PET Polyethylene terephthalate

ix

PDMS Polydimethylsiloxane

ROI Region of interest

SMC Smooth muscle cell

TEER Transendothelial electrical resistance

TJ Tight junctions

VEGF Vascular endothelial growth factor

VEGFR1 Vascular endothelial growth factor receptor-1

VPF Vascular permeability factor

VE Vascular endothelial

ZO Zona occludens

1

The delivery of molecules through the circulatory system is of significant importance for dissolved

gas and nutrient supply, homeostasis and the delivery of drugs for targeted treatment of cancer 1, 2

and neuroinflammatory diseases such as multiple sclerosis 3, where poor pharmacokinetics is

recognized as a leading cause of drug development failure.4 Examples of transported molecules are

water 5, ions, macromolecules, plasma proteins such as albumin, fatty acids and hormones 6, and

leukocytes.6 The rate of molecular movement from blood to the surrounding tissue is determined

by the permeability coefficient, P¸ of the vascular wall. The permeability coefficient is dependent on

many parameters that include genetic background, the vascular bed, vessel size, the

microenvironment of the blood vessel and the solute size and charge.1, 2, 7 The microvasculature,

comprising arterioles, capillaries and venules 8, plays a key role in distributing molecules across and

within different tissues.9 Figure 1.1 illustrates the 100-250 μm arterioles 8, 10, vessels consisting of a

layer of endothelial cells (EC) at the innermost surface surrounded by multiple layers of smooth

muscle cells (SMC). The resistance to transvascular transport in the microvasculature is greatest in

the endothelium2, 7, 11. The understanding of molecular transport in these vessels is closely linked to

the currently available experimental strategies for the measurement of vascular permeability.

Examples include in vivo tissue perfusion and extravisation techniques 12-14, pressure myography

systems 15, 16, in vitro transwell 17 and transendothelial electrical resistance (TEER)18, 19 based

measurements, and microfluidic based strategies.20-22

2

Figure 1.1: Structure of small blood vessels and microvascular transport. Small blood vessels consist

of a layer of endothelial cells that are perfused by blood and are surrounded by multiple layers of

smooth muscle cells. The blood flow carries a variety of molecular payloads that are transported

across the vascular wall. Tranvascular transport in the microvasculature is dominated by the

endothelium, a continuous monolayer of endothelial cells forming the primary barrier to the

movement of substances luminally to abluminally

Previous work in our research group demonstrated a microfluidic platform for studying small artery

structure and function 23,24 that subjected intact small artery segments to a well-defined physical and

chemical environment. With the ability to control fluid movement at the micro-scale, we are able to

probe the effects of vasoactive substances present in a homogeneous and heterogeneous

microenvironment outside of a reversibly fixated vessel. We introduce a unique microfluidic format

that allows molecular transport across an intact small blood vessel to be systematically studied on an

organ level.

1.1 Hypothesis

I hypothesize that a microfluidic based approach for the study of transport in an intact small blood

vessel will result in accurate permeability coefficients through unhindered imaging of the isolated

vessel wall in an environment with well-defined luminal and abluminal flow.

3

1.2 Specific Aims

To carry out and determine the viability of this hypothesis, an experimental strategy comprised of

the following components will be carried out:

i) The design, testing and validation of a microfluidic device for the study of transport

of substances across the vascular wall of a reversibly fixated mouse mesenteric artery

ii) The development of a protocol for probing of artery viability using abluminal flow

(superfusion) of vasoactive substances in the presence of controlled luminal flow

(perfusion) of a fluorescent marker

iii) Steady-state measurements of the permeated abluminal intensity profile of a

fluorescent marker flowing through a fixated artery segment

iv) The development of a numerical model for reversibly obtaining the permeability

coefficient from experimental results

1.3 Background: The Microvasculature and the Endothelium

The microvasculature comprises arterioles, capillaries and venules.8 Resistance arteries, arterioles

capable of diameter changes that alter the hydrodynamic resistance to blood flow 8, 10, 23, 25, are

responsible for 50-60% of the total pressure drop across the vascular system and regulate the flow

of blood to the capillary bed of a particular tissue.8 The capillary bed provides for effective molecular

transport between blood and the surrounding tissue.8 With small vessel dimensions (diameter 5-10

μm)8 and a large total cross-sectional surface area, blood flow is at its lowest in the capillaries,

allowing for a complete exchange of diffusible substances across the vascular wall.8, 26

4

Early microvascular permeability studies were motivated by understanding the structure of the

endothelium, a monolayer of ECs forming the innermost surface of blood vessels, and its

interactions within its immediate environment in its role as a semi-permeable barrier to transport.2

These studies suggested as primary mechanisms for the transport of fluid and solutes across the

vascular wall in the microvasculature paracellular and transcellular transport. Passive paracellular

transport is regulated by the interendothelial junctional complexes (IEJ) 11 formed between adjacent

endothelial cells and the surrounding glycocalyx 27 and extracellular matrix, while active transcellular

transport is regulated by vesicles that selectively transport macromolecules through endocytosis.2

1.3.1 Paracellular Transport

Paracellular transport accounts for most of the water flow and small solute exchange across the

endothelium.5 Adherens junctions (AJs) are considered the primary IEJs affecting vascular

permeability, as they maintain the structural integrity 2 of the endothelium. These junctions are

formed through their major structural protein, vascular endothelial (VE)-cadherin, homophilically

binding and adhering to adjacent cells in a calcium, (Ca)2+, dependent manner.11 A weakening of

these homophilic bonds leads to fluid accumulation in the tissue interstitial space, and is associated

with inflammation and acute lung injury.2 Cell motility and endothelial cytoskeleton reorganization

can affect AJ integrity, as cytoplasmically, the VE-cadherins are bound to β-catenins and γ-catenins,

which further bind to α-catenins and the actin cytoskeleton (Fig. 1.2).

5

Figure 1.2: Paracellular transport. Interendothelial junctions and interactions with the surrounding

extracellular matrix (ECM) regulate paracellular transport. Adherens junctions are composed of the

major structural protein vascular endothelium (VE)-cadherin, which adheres to adjacent endothelial

cells (EC) in a Ca2+-dependent manner. Tight junctions are secondary junctions, primarily found in

the brain microvasculature, composed of the major structural protein occludin. EC interactions with

the ECM is governed by integrins, EC surface receptor proteins that interact with the components

that make up the ECM.

Tight junctions (TJs) are secondary structural junctions in the endothelium, and account for

approximately 20% of IEJs.11 They are heavily concentrated in the brain-microvasculature and aid in

forming the blood-brain barrier 2 and are found to be developed and distributed on the apical

surface of ECs. Their presence is directly linked to decreased vascular permeability, as the arterial

endothelium has an 18-fold greater presence of TJs than the vennular endothelium and is

significantly less permeable.11 TJs are composed of the integral membrane protein occludin, which

through homotypic bonds, associates with the zona occludens protein (ZO-1), linking the occludin

to cingulin, spectrin and the actin cytoskeleton (Fig. 1.2).7

The interaction between the endothelium and surrounding extracellular-matrix (ECM) proteins also

affects paracellular transport. Integrins, receptors for protein in the subendothelial ECM 11, bind to

the ECM at cellular sites known as focal adhesion (FA), interactions that determine the shape the

EC cytoskeleton takes on. A modified EC shape through ECM interactions affects the IEJs,

6

affecting diffusive transport.7 Integrins are composed of transmembrane glycoproteins that interact

with ECM proteins such as collagen, fibrinogen and fibronectin (Fig. 1.2).11

In luminal to abluminal transport, the first barrier interaction water and travelling molecules have is

not with the IEJs formed between adjacent ECs or with the surrounding ECM. The first barrier to

transport is the glycocalyx 7, 27, an aggregation of membrane-bound molecules on the surface of the

endothelial cells. Clefts regulated by IEJs (AJs and TJs) where molecules may pass are affected by

the glycocalyx, acting as a molecular sieve at their opening, and influencing the transport of larger

solutes. Smaller solutes that tend to pass through the gaps formed by TJs and AJs are smaller than

the fiber spacing of the glycocalyx.27

1.3.2 Transcellular Transport

For larger molecules (diameter >4 nm28) unable to pass through the glycocalyx sieve and IEJ gaps,

an active transport mechanism is present that allows fluid and molecules to pass through cells:

transcellular permeability (Fig. 1.3). This receptor-mediated transport relies on transcytosis, an

energy requiring process whereby vesicles containing the macromolecules are trafficked across the

endothelium.2

7

Figure 1.3: Transcellular transport. Transcellular transport is the primary method of transport for

macromolecules (>4 nm), most commonly the plasma protein albumin. Albumin binds to its

specific receptor in calveolae, pits formed on the surface of the endothelium. Transport is then

carried out through endocytosis, where the albumin and a fluid phase is transported through the

cytoplasm, released on the opposing surface of the EC through exocytosis.

The initiator of the process is albumin, the most abundant plasma protein and a regulator of tissue

oncotic pressure.2 All ligands, insulin, lipids and hormones 2 that travel transcellularly are often

bound to albumin. Transport is initiated by extracellular albumin binding to the albumin-binding

protein gp60 on the surfaces of calveolae: pits formed on the luminal and abluminal sides of ECs,

and apparent as free vesicles in the cytoplasm of ECs.2, 28 The binding to gp60 initiates calveolae

fission, regulated by the structural protein calveolin-1 (Cav-1). Further G-protein activation and

signalling 2 results in calveolae internalization, trafficking and scission, whereby the calveolae and its

contents are released through exocytosis.11

8

1.4 Previous Work on Transport in the Microvasculature

Transport and flux of a diffusible substance across the vessel wall is defined by

(1.1)

where J is the mass flow of the substance, P is the permeability coefficient, A is the surface area

available for transport, and ΔC is the concentration difference across the vascular wall.

Several groups investigated small blood vessel permeability through the use of dextrans and

glycogens based tracer particles in the early 1970s.29 The known size of these molecules allowed for

investigation of the effect of molecule size on transport.29 Fluorescently labeled dextrans were

intravenously injected in anaesthetized animals (rats, frogs and mice), and the investigated vascular

bed was exteriorized for study.12 The permeating marker was uptaken in the observed interstitial

space. Images of the surrounding tissue were digitalized and processed to form iso-intensity contour

plots.12 Time-lapsed frames captured the fluorescent intensity profiles at known time points. These

profiles were then paired with a model describing one-dimensional convection and diffusion

transport 12 :

(1.2)

where C is the solute concentration, x is distance from the vessel wall, t is time, V is the solvent

velocity, and D is the diffusion coefficient. Using the known experimental parameters, a numerical

solution of Eq. 1.2 was obtained for different values of D. The experimental results obtained by Fox

and Wayland using this method suggest that the surrounding tissues caused several sources of bias

that lead to overestimated tracer diffusion coefficients.12

Numerical solutions to the transport model for transvascular transport better represented

experimental conditions in further methods developed by Nugent and Jain.30 Their work built on the

9

method previously developed by Fox and Wayland 12, whereby they photometrically measure spatial

intensity distributions over time, and generate intensity distribution profiles.30 Due to the

confinements of their examination chamber, a no-flux condition above and below the tissue was

present. The governing equations and boundary conditions for their model

, , (1.3)

have the following analytical solution

, (1.4)

where C is the solute concentration, D is the apparent diffusion coefficient, x is the distance normal

to the vessel, t is the measured time, λ = x(4Dt)-0.5 and μ is the integration variable.30 Expected

concentrations at the inlet are described by f(t). Previous models used a linear function for f(t), but

after carrying out their experimental work, Nugent and Jain replaced it by a nonlinear function,

resulting in excellent agreement between their developed model and previously obtained data from

other groups.30

In 1986, work by Gerlowski and Jain 31 examined a modified property to characterize mass transport

of blood vessels: permeability coefficient, P. Baxter and Jain 32 further investigated how P is affected

by the presence vasoactive drugs. Based on previous reports identifying the presence of leaky sites in

postcapillary venules due to the administering of vasoactive drugs, Baxter and Jain measured for a

change in vascular permeability of hamster cheek pouch venules following superfusion, delivery

outside of the vessel wall, of histamine and bradykinin.32 A comprehensive review 1 summarizes their

work on the transport of molecules, along with the methods and obtained results from transport

measurements on macroscopic and microscopic levels.

10

Malik’s group developed a protocol for measuring vascular permeability across cultured vascular

endothelial cells.17 Cells were cultured on a microporous filter, and placed in a media filled well. The

tracer was added into the luminal side of chamber, and allowed to permeate across the monolayer.

Concentrations of the abluminal media content determined the permeability through a form of Eq.

1.1. These setups enable microscopic investigation of IEJs, and cellular morphology33 effects on

permeability.

Several efforts have correlated cell morphology and IEJ health to resistance and impedance

measurements of cultured cell monolayers.34, 35 This correlation resulted in the development of

transendothelial electrical resistance (TEER) measurements as the standard in monitoring the

integrity of the endothelium 22, commercially available through a variety of systems (World Precision

Instruments, Millipore).36, 37 TEER based microfluidic platforms have also been developed,21, 22

where electrodes are embedded in a polydimethylsiloxane (PDMS) device and cells are flown into

the device and grown to confluency on the electrodes for measurement.22 Resistance values

obtained across a cultured layer are often compared to known values for a given confluent cell layer

and measurement system,38 while less often, a relationship is made between permeability coefficients

and measured TEER values.18

Cell culture and in vivo methods have several limitations. Results from in vivo methods are potentially

affected by the interstitial space and the surrounding tissue. Uptake of a marker into the tissue

prevents the ability for measuring the transient component of transport as fluorescence builds up in

the immediate vicinity of the vessel wall over time, and newly permeated solute is indistinguishable.

In vitro cell culture methods on the other hand do not fully represent the barrier to transport.33 The

glycocalyx 27 and EC interaction with the surrounding ECM 7 contribute an important transport

11

resistance27, 39, interactions of which are not properly represented through cultured monolayer

transwell, TEER and ECI measurements.33

In vitro experiments by Spaan’s group focused on isolated rat mesentery arteries using a pressure

myograph system paired with confocal microscopy measurements to understand the permeability

barrier.15 Using the lipophilic membrane tracer DiI paired with FITC-Dextran tracer molecules, they

were able to define the endothelium from the DiI peaks, and measured the FITC-Dextran

concentration profile with respect to these peaks. Their results showed that larger (average

molecular size 140 kDa FITC-Dextran) tracer molecules were not able to travel across the lumen as

far as smaller molecules, and that the smallest molecule used (4kDa FITC-Dextran) was able to

penetrate the arterial wall.15 They followed this work by examining the effect of a surface charge

modification on the endothelium. Using 3-(N-morpholino)propanesulfonic acid (MOPS) buffer with

different ionic strengths, they observed that the marker in a lower ionic strength buffer permeated

slower through the ESL than the normal ionic strength MOPS buffer. A limitation of their

experimental protocol, however, was the inability to have differing superfusate and perfusate

solutions, which would have allowed them to study reversible ionic strength modulations.16 A

disadvantage of the pressure myography system is the lack of a well-defined spatial and temporal

control of the superfusate conditions.

Microfluidic devices for cell culture and studies have been widely developed in the last decade 40-45,

and several of these devices have examined transport across cultured EC monolayers, with the

ability to examine flow effects on transport.21, 22, 46 A microfluidic membrane pore system was

recently developed, whereby a superfusate containing a fluorescent marker (FITC-bovine serum

albumin) is flowed through a two-layer microfluidic device. The marker passes over a monolayer of

primary porcine aortic endothelial cells (PAECs) cultured on a polyethylene terephthalate (PET)

12

membrane.20 The marker permeates through the membrane and is detected downstream the bottom

channel using laser induced fluorescence (LIF). The main feature of this method is the application of

fluid flow-induced shear stress on endothelial permeability, a measurement not possible with current

assay kits. A similar flow-based membrane microfluidic strategy paired with TEER measurements

has been developed, allowing for TEER measurement of the confluent cell layer cultured on a

porous membrane, with a similar permeability coefficient measurement technique as Young et al..20

1.5 Thesis Structure

This thesis is organized as follows, Chapter 2 discusses the experimental methods employed,

including device design, experimental setup, numerical model and post-processing methods. Chapter

3 presents the results obtained from the experimental strategy. Chapter 4 provides explanations and

reasoning for the obtained results. Chapter 5 summarizes the findings, and Chapter 6 suggests future

possibilities using the described approach.

13

2.1 Biological Samples

Mesenteric artery segments were isolated from 10-14 week old C57BL/6 mice (Charles-River,

Montreal, Canada) and transgenic Tie2-green fluorescent protein (GFP) mice (Jackson Laboratory,

Bar Harbor, ME, U.S.A).23 Mouse mesenteric arteries were used as a compromise between

physiological relevance and the yield of the employed isolation procedure. Although not a primary

transport vessel, their constrictory and dilatory responses relayed, respectively, smooth muscle and

endothelial cell health.23, 47

2.1 Microfluidic Device

A single layer, 1” x 3” microfluidic device was fabricated using standard photolithographic

processes.48 Masters were fabricated through spinning a layer of SU-8 25 negative photoresist

(Microchem, Newton, MA, U.S.A) at 2000 rpm for 30 secs. on a 2” x 3” dehydrated glass slide

(Thermo Fischer Scientific, Waltham, MA, U.S.A) forming a 25 μm seed layer that was pre-baked

and flood-exposed. Two layers of SU-8 2050 were subsequently spun at 1750 rpm, to obtain a total

feature height of 150 μm. After pre-baking, the slide was exposed to ultraviolet light (wavelength

=365 nm, total energy=240 mJ/cm2, Model 200 Mask Aligner, OAI Inc, San Jose, CA, U.S.A.)

through a transparency mask (CAD/Art Services Inc., Bandon, OR, U.S.A.) containing the device

design (Appendix A.1). The exposed photoresist was post-baked and developed (SU-8 Developer,

Microchem, Newton, MA, U.S.A.) to obtain a finished master. Feature depth and uniformity were

verified through optical profilometer measurements (Wyko NT1100, Veeco Instruments,

14

Woodbury, NY, U.S.A.). PDMS solvent and curing agent (Sylgard 184 Elastomer Kit, Dow Corning,

Midland, MI, U.S.A.) were mixed in a 10:1 ratio and poured onto the master. The PDMS and master

were degassed (280A Vacuum Over, Fisher Scientific, Waltham, MA, U.S.A) in a vacuum and then

cured at 80oC for 1 hr. The PDMS was subsequently peeled from the SU-8 master, and fluidic holes

were punched (24 gauge blunt needles, VWR, West Chester, PA, U.S.A). The PDMS was then

surface treated in oxygen plasma (Harrick, Ithaca, NY, U.S.A.) for 1 min. and bonded to a 1” x 3”, 1

mm thick glass slide (VWR, West Chester, PA, U.S.A) that was surface treated in oxygen plasma for

5 min.. The fabricated device was interfaced to external fluidic connections using a commercially

available manifold (Quorum Technologies, Guelph, Ontario). The manifold contains 10 fluidic

inputs/outputs to interface with the device using luerlock connectors (Idex Health & Science, Oak

Harbor, WA).

Figure 2.1: Microfluidic strategy for small artery permeability measurements. (A) Vasoactive

substances and MOPS buffer are delivered abluminally using computer-controlled syringe pumps.

(B) MOPS buffer and the fluorescent marker (4kDa FITC-Dextran) are delivered luminally using

positive hydrostatic head through an open reservoir. The difference in height between the perfusion

reservoir and superfusion outlet (C) determines the transmural pressure applied on the vessel. (D)

Perfusion flow-rate is controlled using an external syringe pump connected to the loading-well

outlet, downstream of the vessel and in-line with the perfusion channel. (E) The vessel is held in

place using negative hydrostatic head, determined by the difference in height between the

superfusion outlet (C) and the fixation reservoirs (E). (F) On-chip temperature control comprises a

15

thermoelectric element in a closed feedback loop with a thermistor, all thermally bonded to a

sapphire disk.

Isolated vessels were loaded and fixated as previously described by Günther et al..23 The vessel was

placed in the loading well, a fluid filled opening on the device. The vessel was then drawn through a

150 μm high channel, to the inspection area, where it was reversibly fixated at eight points using

negative hydrostatic pressure (Fig. 2.1E). Fixation pressure was defined by the difference in height

between the superfusion outlet and an open, MOPS filled reservoir connected to the fixation

channels. The vessel was exposed to homogeneous flow around the vessel (superfusion, Fig. 2.1A,C)

and through the lumen (perfusion, Fig. 2.1B,D). Superfused substances were delivered using

milliliter syringe pumps (Harvard Apparatus, Holliston, MA) that were computer-controlled using a

custom LabView interface (National Instruments, Austin, TX). Perfusion flow was controlled

through a 3mL syringe placed in a remote syringe pump (Harvard Apparatus, Holliston, MA) that

was interfaced to the lid of the manifold (Fig. 2.1D); used to seal the loading well of the device after

fixation. The syringe pump was placed in withdraw mode to draw fluid through the vessel from an

open reservoir containing the perfusate (Fig. 2.1B). The height of the reservoir with respect to the

superfusion outlet (Fig. 2.1C) determined the transmural pressure applied on the vessel. Heating

was as previously described.23 A thermoelectric element and thermistor (TE Technology, Traverse

City, MI, U.S.A.) were thermally bonded to a 1” sapphire disk, and were in a closed feedback loop

(Fig. 2.1F) with a 37oC set-point. The vessel was heated to 37oC over 30 mins.. Throughout heating,

the transmural pressure was 45 mmHg. After heating, the vessel was pressurized to 60 mmHg, and

subsequently maintained at this level. Throughout heating and pressurization, the artery segment was

superfused with MOPS buffer at a total flow rate of 3 ml/h.

16

2.2 Small Artery Function

Viability of the segment’s structural components was probed through dose response measurements

of the vessel to vasoactive substances. Health of the outer smooth muscle cells was verified through

contractile responses in the presence of increasing concentrations (0.9-3 μM) of phenylephrine (PE)

(Sigma-Aldrich, Oakville, USA).23 Concentrations of PE were modified by varying the relative

administered flow rates of MOPS and PE in the superfusion channel. The health of the

endothelium was subsequently verified through the dilatory response of the vessel.23 The vessel was

pre-constricted (3.0 μm PE), and increasing doses of acetylcholine chloride (ACh) (0.1-10uM)

(Sigma-Aldrich, Oakville, Canada) were applied through varying the flow rates of PE and ACh in the

superfusion channel.23, 49 The total flow rate applied throughout the dose responses was 4ml/h.

Time-lapsed brightfield images and fluorescent images were captured on a laser scanning confocal

microscopy system (transmitted light, Olympus FluoView 1000, Olympus, Melville, NY) and were

post-processed to determine constriction and dilation percentages. Once viability of the artery

segment was verified, a low flow-rate of 0.3ml/h was induced through the fixated artery segment.

2. Experimental Procedure

After flow was induced through the fixated artery segment, the perfusate (Fig. 2.1B) was changed to

a solution of MOPS buffer containing 9 mg/ml 4 kDa fluorescein-isothiacyante labelled dextran

(FITC-Dextran) (Sigma-Aldrich, Oakville, Ontario). FITC-Dextran has been widely used for

microvascular permeability studies 1, 29, 50-52, with the chosen size ensuring transvascular transport

across the studied arterial segment.1 The fluorescent marker was perfused through the vessel, and

started permeating across the vessel wall into the superfusion channel. The marker is excited using a

17

473 nm diode laser, and the reflected intensity was captured at a peak wavelength of 519 nm

(SDM515, Olympus, Melville, NY). The gain of the photomultiplier tube is adjusted to prevent

saturation of the intensity measurement outside of the vessel wall.

Previously, it has been shown that prolonged excitation (>10 mins) of FITC at high excitation light

intensities (>100 Joules/cm2) 53 can cause the release of reactive oxygen intermediates 54 and singlet

oxygen species, resulting in light-induced cellular dysfunction and macromolecular leakage in the

microvasculature.53, 55, 56 In order to prevent damage to the endothelium and SMC layers while

monitoring the permeated molecular concentrations, we have established controlled excitation of the

fluorescent marker outside of the artery segment’s wall, minimizing phototoxicity (Fig. 2.2).

Figure 2.2: Confocal measurement using controlled excitation outside of vessel wall. (a) Standard

512x512 pixel field of view at 20x magnification. (b) Limited field of view for controlled excitation

of regions outside of vessel wall. Small dashed box denotes region of interest (ROI) for transient

measurement of intensity, large dashed box denotes field of view for steady-state measurements. (c)

Fluorescence image of controlled excitation field of view. d) Brightfield image for steady-state

18

measurement of intensity profiles (40x). e) Fluorescent image for measuring steady-state abluminal

intensities. Scale bars = 100 μm.

To monitor the time-dependent concentration change in the superfusion channel, a region of

interest (ROI) was defined outside of the vessel wall, located according to the change in contrast

between the superfusion channel and the SMC and connective tissue layer (Fig. 2.2b-c). The change

in intensity over time within this ROI was monitored at a 20x magnification (LUCPLFLN 20X,

Olympus America Inc. Center Valley, PA). Frames consisted of a 512x512 pixel image (1.254

μm/pixel), obtained at a sampling speed of 20 μs/pixel with Kalman line filtering 57 (K=6) to reduce

noise. Frames were captured at one minute intervals. Once the concentration profile of the

permeated marker achieved steady-state conditions according to a live-plot of the ROI’s integrated

intensity from the in-plane time-lapsed images, the magnification was increased to 40x (optical

zoom), and the bottom half of the superfusion channel was imaged (Fig. 2.2c-d). Five frames were

captured without dwell time, using the same parameters as at 20x, with pixel size decreasing to 0.621

μm/pixel; enabling an improvement in resolution for measurement of the concentration profile of

the permeated marker in the abluminal microenvironment. Similar measurement of the top half of

the superfusion channel followed.

Following transient and steady-state concentration profile measurements, aliquots of known

concentrations (5x10-7- 2.5x10-6 M) of 4 kDA FITC-Dextran were introduced onto the chip and

intensities were measured to obtain intensity-concentration calibration curves at the respective

voltage gains used throughout the measurements (Fig. 2.3).

19

Figure 2.3: Calibration of measured fluorescence intensity as a function of fluorescent marker

concentration. Known increasing concentrations of the fluorescent marker (FITC-Dextran) are

flowed through the device, and intensity measurements are taken at the same z-value and gain

settings as the permeability measurements.

2.4 Post-Processing

The images captured of the abluminal environments at 40x (Fig. 2.2d) were processed using a

custom image processing and curve fitting script (Matlab, Mathworks, Natick, MA, U.S.A.),

Appendix A.2. The script determined the spatial and time-averaged intensity profiles in the

abluminal and converted them to concentration profiles using the obtained calibration curve (Fig.

2.3). A rectangular region was defined (Fig 2.4), where the convective effect of the superfusion

channel’s change in curvature did not affect the concentration boundary profile along the vessel

wall. The canny edge-detection algorithm 58 was then used to determine the location of the vessel

wall within this region, defining a mask for which pixels to analyze. Intensity values were stored as a

function of distance from the vessel wall for each column of pixels within the defined region and a

spatial average was taken from this region (Fig. 2.4). A regression based linear fit was then carried

out on the linear region of the concentration profiles to obtain the value of the linear concentration

20

change as a function of distance away from the vessel wall (Fig. 2.4). This value was paired with

results from a numerical model to obtain the permeability coefficient.

Figure 2.4: Measured concentration profiles as a function of the distance from the vessel. Post-

processed concentration profile taken from defined region, (ii) dashed box, in confocal

measurements as a function of distance from the wall x (i). Linear regression carried out on

experimental results to obtain concentration gradient (dashed line). Error bars are standard

deviations, obtained from spatial averages.

2.5 Numerical Model

A 2D multiphysics numerical model (Comsol v4.2, Burlington, MA) was used to establish the

expected concentration profiles for the experimental conditions. The model used a diffusive-

convective transport study paired with a laminar flow study (Fig. 2.5). Factors affecting the model’s

performance are found in Appendix A.3.

21

Figure 2.5: 2D numerical model for determining solution of convective diffusive transport problem

2. a) Boundary conditions used to generate concentration profiles in abluminal microenvironment

as a function of distance from vessel wall x. Known experimental parameters are inlet flow Qin and

the respective Peclet number Pe, leak velocity uleak , outlet pressure pout, luminal flow Qout and the inlet

concentration Cin . Published values of the free diffusion coefficient, Df , of 4 kDa FITC-Dextran12 as

well as average vessel wall thicknesses10, w, are used. The stiff-spring method is used to define the

flux, N, conditions at the interfaces between the lumen, vessel wall and abluminal

microenvironment. The interface conditions are a function of the partition coefficient, k, stiff-spring

velocity, M, and the concentrations in each region, c1, c2, c3, for the lumen, vessel wall and abluminal

regions respectively. The permeability coefficient, P, is treated as a variable.

The diffusion coefficients as well as the vessel wall thickness and diameter are taken from published

data.10, 12 Convective transport occurs in the luminal and abluminal environments and was controlled

by syringe pumps with known applied flow rates Qin. A leak velocity, uleak, from the superfusion

channel into the fixation channels was previously assessed using simple 2D numerical models.23

Diffusive transport was the main transport mechanism through the vessel wall. The numerical

solution describing the transport through the vessel employed the stiff-spring method.59 This

method ensured continuous flux at the interface 60, with a non-physical velocity M that forced the

concentration differences at the interface to zero. The partition coefficient k, a ratio of the

22

solubilities of the fluorescent marker in MOPS and through the vessel wall, is also a function of the

permeability coefficient of the marker P, the thickness of the vessel wall w and the free diffusion

coefficient of the marker Df :

.60 This relationship was used to evaluate the influence of

different permeability coefficients on the concentration profiles of the permeating marker in the

convective abluminal microenvironment.

23

3.1 Effect of Shear on Small Artery Function

After loading the artery segment onto the microfluidic chip, heating the vessel to 37oC and

pressurizing to 60 mmHg, the viability of the SMC and EC layers was verified through dose

responses to PE and ACh respectively. The vessel was then exposed to a luminal flow at a rate of

5μl/min for 10 mins, applying approximately 1.06 dynes/cm2 on the ECs, and PE and ACh dose

responses were carried out. Figure 3.1 shows a summary of these results (n=4).

Figure 3.1: Smooth muscle and endothelial cell viability in the presence of shear. Dose responses to

a) PE and b) Ach with no perfusion flow and 5 μl/min perfusion of MOPS buffer ~1.05

dynes/cm2. Total superfusion flow rate of 4ml/h. ACh dose response carried out on a 3 μm PE pre-

constricted vessel. Error bars represent standard error (n=4).

24

3.2 Permeability

Once viability was determined, the vessel was perfused with the fluorescent marker 4 kDa FITC-

Dextran. As described in section 2.3, the marker flowed through the fixated artery segment, and

over time, permeated across the endothelial barrier and SMC layers into the convective abluminal

microenvironment.

Figure 3.2: Transient perfusion of fluorescent marker. Whole vessel excitation of fixated vessel

obtained from transgenic Tie2-GFP (20X magnification). a) Brightfield image after heating and

pressurization, t=0s. b) Confocal image of fixated vessel at t=0s. with visible fluorescent

endothelium. c) Perfusion of fluorescent marker, t=660s. d) Steady-state intensity distribution in

abluminal environment, t=2580 s. Scale bars=100 μm.

One instance of the transient permeation of the marker into the abluminal microenvironment over

time is shown in Fig 3.2, and plotted in Fig. 3.3a, where the average pixel intensity within the ROI

(Fig. 2.2b) was plotted over time to the point of steady state along with the filtered change in

intensity from frame to frame. Once steady-state conditions were reached, the intensity profiles on

both sides of the vessel were measured, as shown in Fig. 3.3b.

25

Figure 3.3: Transient intensity change and steady-state profile. Post-processing results of

experimental set in Fig. 3.2. a) Average intensity in ROI defined outside of vessel wall (solid), as

depicted in Fig. 2.2b, and average frame-to-frame intensity difference in ROI (hollow). b) Spatially

averaged intensity profiles in the axial direction, measured at the midline of the vessel for the top

and bottom superfusion channels, as defined in (i). Error bars represent standard deviations.

Post-processing the intensity profiles using the calibration results gave the concentration profile as a

function of distance from the vessel wall. The linear region of this profile was then compared to

results obtained from the numerical model (Fig. 3.4).

26

Figure 3.4: Numerical model results and permeability correlation. a) Concentration and b) velocity

solution for following parameters: P = 1x10-6 cm/s, Df = 6e-7 cm2/s 12, Pe = 240, Qin = 0.25ml/h,

uleak = 10 μm/s, Qout = 0.3ml/h, w = 20μm, pin = 60mmHg, pout = 0 mmHg, Cin = 2.25mM and M =

1m/s c) Concentration profiles in the axial direction as a function of distance from the wall at the

vessel centre line (inset). d) Correlation of concentration gradient and permeability coefficient from

linear regions of concentration profiles in (c).

As different permeability coefficient values are inserted into the numerical model, the concentration

gradient in the axial direction along the vessel center line changes in a linear fashion (Fig. 3.4b). This

linear relationship is used to translate the concentration gradients measured in the experimental

results to permeability coefficients of the vessel wall.

27

Initially, when developing the method, excitation of the fluorescent marker was not carried out in

the controlled manner as described in the experimental section. The initial excitation method

involved analysis of the whole vessel, as seen in Fig. 3.2.. Figure 3.5 shows a summary of the

functional responses (Fig. 3.5a-b) from this whole-vessel excitation, as well as the obtained

permeability coefficients (Fig. 3.5c-d) using the previously described linear-fit method.

Figure 3.5: Whole vessel excitation viability results and obtained permeability coefficients. a) & b)

Dose response measurements of increasing concentrations of PE and Ach respectively. Dose

responses were taken after pressurization and heating with no perfusion flow (D.R. 1), and

subsequently after each perfusion of 4 kDa FITC-Dextran (D.R. 2-4). Error bars represent standard

error. c) Calculated permeability coefficients for each vessel using whole vessel excitation. d)

Summary of results from c). Error bars in c) and d) represent standard deviation.

28

For each loaded and fixated artery segment, the fluorescent marker was sequentially perfused three

times to measure the accuracy of the method. Prior to the first perfusion run and in between each

run, dose responses were taken (Fig. 5a-b) to ensure biological function. Dose responses were

carried out with MOPS buffer perfused through the artery for a 10 min. period prior to the first

dose. In the controlled excitation experimental set, vessel viability and the obtained permeability

coefficients differ from Fig. 3.5, showing an expected consistency in the measured values (Fig. 3.6).

Figure 3.6: Controlled excitation viability results and obtained permeability coefficients. a) & b)

Dose response measurements of increasing concentrations of PE and Ach respectively. Dose

responses are taken after pressurization and heating with no perfusion flow (D.R. 1), and

subsequently after each perfusion of 4 kDa FITC-Dextran (D.R. 2-4). Error bars represent standard

error. c) Calculated permeability coefficients for each vessel using controlled vessel excitation. d)

Summary of results from c). Error bars in c) and d) represent standard deviation.

29

The main advancement achieved with the presented experimental approach is the assessment of

molecular transport of a molecule of a defined size through a mouse mesenteric artery in a well-

controlled (including dynamically changing) physico-chemical microenvironment. Permeability

measurements were complemented with dose-response measurements that assess the functional

viability of the artery endothelial and smooth muscle layers

An important feature of our approach is the capacity for controlled perfusion through the vessel’s

lumen. Shear effects on the endothelium have been broadly investigated.61-65 High shear stresses

applied on the endothelium are known to induce the production of endothelial nitric oxide species

(eNOS), activating the dilation mechanism in arterioles and venules and increasing vascular

permeability.65-67 Vessels obtained from the mouse mesentery bed have been previously shown to

produce eNOS species when encountering wall stresses greater than 10 dynes/cm2.68 With the range

of diameters used throughout these experiments 10, a perfusing flow rate of 5 μl/min was used

throughout the experiments, resulting in approximately 1.06 dynes/cm2 in a perfused 200 μm

diameter vessel.68 Figure 3.1a indicates that the functioning of the SMCs is not affected by luminal

flow, as the constrictory mechanism is preserved in the presence of the applied luminal flow. The

unaffected constrictions suggest that there are no dilatory forces present due to the applied flow to

counteract the constrictory effect of the superfused PE. The presence of a dilatory response to ACh

(Fig. 3.1b) demonstrates functioning of the endothelium in the presence of shear. Dilatory

measurements carried out in the presence of shear show a weakened dilation at high concentrations

30

of ACh. Resistance arteries have been previously shown to have constrictory responses in the

presence of high (>10-6 M) concentration of ACh.69, 70

Figures 3.2-3 provide the experimental results obtained during laser scanning confocal microscopic

measurements taken on the fixated vessels. Figure 3.2b illustrates the autofluorescence of the fixated

vessel at the voltage gain used for measurement. The location of the endothelial barrier is also seen,

utilizing vessels obtained from a transgenic mouse with a GFP tagged to an endothelial specific Tie2

receptor. Figure 3.2c illustrates the transient perfusion of the fluorescent marker through the vessel,

after which, the marker is seen to permeate transvascularly and after some time, the intensity profile

stabilizes (Fig. 3.2d, 3.3a). The transient change in permeation, seen for one vessel in Fig. 3.3a,

shows an early, abrupt rise in the marker’s abluminal concentration, followed by a steadying

interaction with the convective microenvironment. The change in average intensity from frame to

frame is then seen to decrease to zero. The variations in the intensity measurements at steady-state

(Fig. 3.3a) is owed to the fixed pattern noise in the confocal measurement signal due to the high

pixel scan times 71, as well as movement of the vessel due to slight pressure variations in the

perfusion and superfusion flow channels. The steady-state concentration profiles (Fig. 3.3b) show a

linear decrease within close proximity of the vessel wall, the length of which is defined to be the

concentration boundary layer.72

The linear relationship obtained from the numerical model is a result of the transport mechanisms

present, and how they are affected by the vascular wall’s permeability coefficient. After permeation,

a luminal concentration will be present at the interface with the vascular wall, a value of which will

be determined by the inlet concentration and luminal flow rate. Transport through the vascular wall,

a summation of both transcellular and paracellular transport, is assumed to be primarily diffusive for

the molecule used in these experiments (Stokes’ radii of approximately 1.4 nm).50 The concentration

gradient present in the direction of diffusive transport is linear. Therefore, the value of P will

31

determine the concentration present at the interface of the vascular wall with the abluminal

microvironment. Abluminally, the concentration gradient within the concentration boundary layer

will be linear as well.72 Therefore, as P changes in the vascular wall, a linear change results in the

abluminal concentration gradient, as seen in the relationship in Fig. 3.4b..

Figures 3.5-6 summarize the permeability and viability results from the experimental data sets. As

previously mentioned, the first excitation and intensity measurement method focused on the whole

vessel. The viability data (Fig. 3.5a-b) from these experiments saw a weakening of the constrictory

and dilatory response after each perfusion and excitation of 4kDa FITC-Dextran. The weakening of

the vessels endothelium and SMC layer resulted in continually increasing calculated permeability

coefficients (Fig. 3.5c-d). The prolonged laser scanning times (30 sec. intervals, every 1 min. for

approximately 40 mins. for each perfusion of FITC-Dextran) used through the experiment are

thought to have produced free oxygen radicals, damaging the ECs and SMCs.54 The protocol was

therefore modified, so as to have controlled excitation and analysis, as described in section 2.3. With

excitation of the fluorophore outside of the vessel wall, less phototoxicity occurred, as seen in the

PE and ACh dose responses (Fig. 6a-b). There was no decrease in constrictory responses after

subsequent perfusions of FITC-Dextran, and the vessel showed a strong dilatory response to

increasing doses of ACh, ensuring a healthy and functionally intact endothelial layer. The calculated

permeability coefficients reflected the maintained viability, as the obtained values are consistent

throughout sequential perfusions. The average P obtained from controlled excitation was 2.04x10-6

cm/s (Fig. 3.6) with lower and upper limit measured values of 1.437x10-6 cm/s and 3.334x10-6 cm/s

respectively. Errors affecting the measurement of this value include: mesh dependence used in the

numerical model (Appendix A.3), vessel wall size variation affecting representation of numerical

model parameters, vessel wall size affecting abluminal flow profile (effects of abluminal Pe variation

see in Appendix A.3), vessel wall non-uniformity affecting abluminal concentration profile, precision

32

of the linear-fit in post-processing, and spatial and temporal variations within confocal

measurements.

33

A microfluidic platform was developed with the ability to reproducibly quantify the permeability

coefficient of a mouse mesenteric artery to 4 kDa FITC-Dextran. The ability to abluminally deliver

vasoactive substances throughout the perfusion of the fluorescent marker allowed for probing of the

isolated artery segment’s viability and health of the endothelium, the principle barrier to

transvascular transport. Flow was applied through the lumen in a controlled manner and the effect

of the applied shear on the vessel’s viability was quantified. A method for exciting and measuring the

perfused and permeating fluorescent marker was developed so as to reduce phototoxicity and

maintain the integrity of the transport barrier. A post-processing script was developed to analyze

confocal measurements and obtain spatially and temporally averaged steady-state concentration

profiles of the permeated fluorescent marker. Profiles were paired with a numerical model that was

solved for different permeability coefficients. The experimental permeability coefficient was

obtained through backwards fitting the experimental concentration profiles to the solution obtained

from the numerical model. The method obtained permeability coefficients in an accurate manner

(7.06% deviation, n=4) and allowed for viability probing of the whole artery segment in a controlled

microenvironment. This enabled relating of the phototoxic effects produced from whole vessel

illumination to the vessel’s viability and measured permeability coefficient.

34

A proposed strategy to further quantify the developed method is to examine the effect of a

substance known to alter the permeability of the vessel being studied. Vascular endothelial growth

factor (VEGF) has been previously shown to induce hyperpermeability in the microvasculature

through a variety of mechanisms.73-76 Preliminary experiments examining the effect of VEGF on the

constrictory and dilatory responses of a fixated mouse mesenteric artery were carried out (Appendix

A.4), and showed an irreversible weakening effect on the constrictory and dilatory responses,

indicating a biological response. Preliminary permeability experiments examining the effect of

VEGF however showed a negligible increase (Fig. A.4). A revised protocol for examining the effect

of VEGF or other permeability inducing substances (eg. thrombin, histamine and bradykinin 77, 78) is

required.

Permeability is seen to vary in similar sized vessels across different vascular beds. Arterioles from the

brain microvasculature are characterized by an endothelium with a higher prevalence of tight

junctions, resulting in decreased permeability coefficients across the blood brain barrier (BBB).4, 38

Further investigation into the BBB could be performed with the presented method, albeit, with a

modified microfluidic design and fluidic delivery method capable of lower flow rates. Our group has

developed a platform capable of fixating and studying the viability of mice cerebral olfactory

arteries.79 With this platform serving as the groundwork, and with a modified protocol allowing for

perfusion and ACh measurements of the artery segment, permeability coefficients for cerebral

vessels could be obtained.

35

In vivo investigation of local transport mechanisms is currently limited by surrounding tissue and

imaging capabilities. With a microfluidic platform designed on a coverslip glass substrate, the

working distance of the current method (~2 mm) would decrease, increasing magnification and

numerical aperture for cell-level imaging. Paired with a transgenic mouse, which would allow for

localization of the endothelial barrier (Appendix A.5), and imaging techniques that can measure

transport dynamics, such as fluorescence recovery after photobleaching (FRAP)80, 81, local transport

rates and mechanisms can be investigated.

As previously mentioned, a main feature of this platform is the ability to apply controlled perfusion

flow rates. This, with the ability to probe viability in the presence of a luminal flow can make the

platform an ideal drug development assay. The micro-scale fluidic channel dimensions allow for low

reagent use, while controlled superfusion and perfusion flows can administer substances of interest

in a temporal and spatial 23 fashion. Measurement of the permeability of a certain vessel to a specific

drug can improve pharmacokinetics, currently the main cause of failure in drug development.

36

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41

A.1 Microfluidic Device Design

The design of the microfluidic device was based on a previous iteration used in the first publication

of this technology by Günther et al..23 The design was carried out on Autocad (Autocad 2011, San

Rafael, CA, U.S.A), and was designed for:

i) An additional superfusion inlet for the use of ACh

ii) An additional perfusion inlet for introducing the fluorescent marker without

affecting the MOPS buffer flow

iii) Compatibility with the 10-inlet/outlet manifold provided by Quorum

Technologies (Quorum Technologies, Guelph, ON, Canada).

Figure A.1: Microfluidic device design. The device comprises 3 superfusion inlets, 2 perfusion

inlets, 1 superfusion outlet, 2 vacuum lines, 2 fixation lines and a loading well. Locations can be seen

in Fig. 2.1..

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A.2 Post-Processing Script for Confocal Images

The following post-processing scripts were written for use with Matlab R2011a.

Script 1: Process confocal images and output intensities and concentrations

clear variables % enter number of frames imagenum=; % enter total time totaltime=329.593; %calibration um/pixels calibration=0.621; %Linear curve parameters obtained from calibration experiment conc_a=7.04229e3; conc_b=5.74697e-3; %y = 7.04229E+03x + 5.74697E-03 %Directories for outputting files mkdir('Output\Date'); directory='C:\Output\'; directory=strcat(directory,'Date\'); %read brightfield image BF = imread('Figure 3 bf_C002T002.tif'); %Figure and Plot Titles and Fileneames figure_title='Date - T1'; plot_filename='Date - T1'; fileto_cat='Date('; fileto_cat_type=').tif'; %Adjust contrast of brightfield image BF = imadjust(BF); %Perform Canny-Edge detection on Brightfield Image BW = edge(BF,'canny',[.1 0.4],6.6); %Show brightfield image and wait for user to define ROI imshow(BF); %ROI=[xmin ymin width height] ROI_1=getrect(); close all; if (ROI_1(1)<1) ROI_1(1)=1; end if (ROI_1(2)<1) ROI_1(2)=1; end if (ROI_1(1)+ROI_1(3)>512) ROI_1(3)=512-ROI_1(1); end %Define bounds of ROI to analyze xmin=round(ROI_1(1)); xmax=round(ROI_1(1))+floor(ROI_1(3)); ymin=round(ROI_1(2)); ymax=round(ROI_1(2))+floor(ROI_1(4)); %Create a mask to only take certain pixels Canny_image=BW(ymin:ymax,xmin:xmax); %determine the bounds of the region sze=size(Canny_image);

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tempMASK=zeros(sze(1),sze(2)); %Determine location of vessel wall and set it as y_min in each row for column=1:sze(2) row=1; flag=0; while(row<=sze(1)) tempMASK(row,column)=0; if (flag==1) %set mask to be 1 below the wall tempMASK(row,column)=1; end if (Canny_image(row,column)==1) %store locations of wall flag=1; end row=row+1; end end row=row-1; MASK=zeros(512,512); MASK(ymin:ymax,xmin:xmax)=tempMASK;

%Store intensities of confocal images into array called SUM %SUM(x,y,frame number)=intensity %SUM_v2(x,y,frame number)=concentration SUM=zeros(512,512,imagenum); SUM_v2=zeros(512,512,imagenum); for frames=1:imagenum imagename=strcat(fileto_cat,num2str(frames),fileto_cat_type); temp=imread(imagename); temp=im2double(temp); %Convert intensities to concentrations temp_2=(temp-conc_b)/(conc_a); intensity(frames)=0.0; intensity_v2(frames)=0.0; %intensity is a matrix for intensities %intensity_v2 is a matrix for concentrations count=0; for r=1:512 for c=1:512 if MASK(r,c)==1 count=count+1; SUM(r,c,frames)=temp(r,c); SUM_v2(r,c,frames)=temp_2(r,c); intensity(frames)=intensity(frames)+temp(r,c); intensity_v2(frames)=intensity(frames)+temp_2(r,c); end end end %intensity_avg is a matrix for storing intensities/(pixels measured) intensity_avg(frames)=intensity(frames)/count; intensity_v2_avg(frames)=intensity_v2(frames)/count; end

%Take average intensity for frames and standard deviation avg_intensity=mean(intensity); std_intensity=std(intensity);

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%Remove frames if outside standard deviation for i=1:imagenum a=abs(avg_intensity-intensity(i)); if (a<std_intensity) take_frame(i)=1; else take_frame(i)=0; end end %Output frame numbers that are to be included for measurement take_frame framecount=0; for i=1:imagenum if (take_frame(i)==1) framecount=framecount+1; end end %Take total intensities of pixels within ROI and outside of vessel wall %for all frames to be included tmp_1_values=zeros(sze(1),sze(2)); tmp_2_values=zeros(sze(1),sze(2));

for r=ymin:ymax count=0; tmp_1=0; tmp_2=0; rowcount(r-ymin+1,1)=0; for frame=1:imagenum for c=xmin:xmax if (take_frame(frame)==1) tmp_1_values(r-ymin+1,c-xmin+1)=SUM(r,c,frame)+tmp_1; tmp_1=SUM(r,c,frame)+tmp_1; tmp_2_values(r-ymin+1,c-xmin+1)=SUM_v2(r,c,frame)+tmp_2; tmp_2=SUM_v2(r,c,frame)+tmp_2; count=count+1; rowcount(r-ymin+1,1)=rowcount(r-ymin+1,1)+1; end end end end %Calculate average intensities for each column for r=ymin:ymax row_avg_1(r-ymin+1,1)=mean((tmp_1_values(r-ymin+1,:)/(rowcount(r-

ymin+1,1))),2); row_std_1(r-ymin+1,1)=std((tmp_1_values(r-ymin+1,:)/(rowcount(r-

ymin+1,1))),1,2); row_avg_2(r-ymin+1,1)=mean((tmp_2_values(r-ymin+1,:)/(rowcount(r-

ymin+1,1))),2); row_std_2(r-ymin+1,1)=std((tmp_2_values(r-ymin+1,:)/(rowcount(r-

ymin+1,1))),1,2); end max_value1=max(row_avg_1); max_value2=max(row_avg_2);

%Store values into a normalized intensity matrix row_n row_n=row_avg_1/max_value1; a=1;

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for i=1:size(row_avg_1,1) dist1(a,1)=calibration*a; a=a+1; end

%Mark the last point where the intensity is =1 (x_1) x_1=1; for i=1:row if row_n(i,1)==1 x_1=i; end end %Create arrays for outputting intensities and concentrations row_plot_1=row_avg_1(x_1:row,1); row_plot_std_1=row_std_1(x_1:row,1); row_plot_2=row_avg_2(x_1:row,1); row_plot_std_2=row_std_2(x_1:row,1);

a=1; %Create distance vector for i=1:size(row_plot_1,1) dist2(a,1)=calibration*a; a=a+1; end

%Output values into TXT files dlmwrite(strcat(directory,plot_filename,'-raw intensities.txt'), [dist1

row_avg_1 row_std_1], 'delimiter', '\t'); dlmwrite(strcat(directory,plot_filename,'-norm intensities.txt'), [dist1

row_n], 'delimiter', '\t'); dlmwrite(strcat(directory,plot_filename,'-raw concentration.txt'), [dist1

row_avg_2 row_std_2], 'delimiter', '\t'); dlmwrite(strcat(directory,plot_filename,'-intensities.txt'), [dist2

row_plot_1 row_plot_std_1], 'delimiter', '\t'); dlmwrite(strcat(directory,plot_filename,'-concentration.txt'), [dist2

row_plot_2 row_plot_std_2], 'delimiter', '\t'); %Plot concentration profiles and save plot as a PNG h=figure; errorbar(dist2,row_plot_2,row_plot_std_2,'or'); xlabel('Distance from vessel wall (um)'); ylabel('Concentration (M)'); title(strcat('Concentration Profiles - ',figure_title)); %legend(legend1,legend2); print(h,'-depsc','-tiff','-r300',strcat(directory,plot_filename,'.eps')); print(h,'-dpng','-r300',strcat(directory,plot_filename,'.png')) close(h);

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Script 2: Take concentration profiles and carry out a linear fit

%Prior to starting, concentration profiles show be saved as matrix A and

%organized with column headers:(distance,concentration value)

%Store values into matrices newmatrix_X for distance & %newmatrix_Y for concentration newmatrix_X=A(:,1)/1000; newmatrix_Y=A(:,2); loopcondition=size(newmatrix_X,1); %Set settings for minimum linear region %length span=8; %starting point start_position=5; start_frame=start_position+span; %Perform linear fits for all the points starting from starting point and %store the square of the residuals for i=start_frame:loopcondition %if condition for minimal residual

[fitobject,gof]=fit(newmatrix_X(start_position:i),newmatrix_Y(start_position:

i),'poly1'); res(i-start_frame+1,1)=gof.rsquare; end %Find the best fit according to highest r-squared value [y,x]=max(res(:,1)); value(1)=y; location(1)=x+start_frame-1; %Perform a fit for the span with the highest r-squared value, and compute %the values for the fit function

[fobj,gof]=fit(newmatrix_X(start_position:location(1)),newmatrix_Y(start_posi

tion:location(1)),'poly1'); Y=feval(fobj,newmatrix_X(start_position:location(1))); %Output the slope data for use with the results of the numerical model slope_data(1,1)=fobj.p1 slope_data(1,2)=fobj.p2

Results from script 2 are used with results from the numerical model correlation curve (Fig. 3.4b) to

obtain permeability coefficients.

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A.3 Numerical Model

The numerical model developed and outlined in Section 2.5 was used as a solver of the 2-D

convective and diffusive transport equations for our experimental problem. Concentration

distributions as a function of position and time were solved for and used to obtain experimental

permeability coefficients. In developing the model, mesh dependence was determined as a balance

between computational costs and solution accuracy (Fig. A.2).

Figure A.2: Mesh dependence of numerical model. a) Rate of change on concentration obtained at

vessel centerline for different mesh sizes. Mesh sizing was varied between Extra Fine and Extremely

Fine, with numbers of refinements in the lumen varied between low (1) and high (5). b) The resulting

mesh used throughout permeability experiments (extremely fine element size, with refinements in

the vessel wall).

A mesh size setting of Extremely Fine with low refinements in the area representing the vascular wall

resulted in 65500 elements, half the number of elements and an order of magnitude less of degrees

of freedom to solve for than an Extra Fine element size with high refinements. The mesh choice

resulted in a 4.05% difference in average computed values.

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The developed model also served the purpose of determining experimental parameters to be used

during permeability experiments. Inlet flow, Qin, plays a vital role in determining the concentration

profile. Too high of a Qin would result in a thin boundary thickness, while too low of a Qin would

result in a long transient time to reach steady-state conditions. A parametric sweep for different

abluminal Peclet numbers, Pe, was carried out to understand this effect given our experimental

parameters (section 2.3).

Figure A.3: Effect of Pe number on the transient and steady-state solutions. Transient study of time

to steady-state conditions for flow from no flow lumen to superfusion channel for different Pe in

superfusion channel inlet: a) 10, 100, 1000, 10000 , b) 0.01, 0.1, 1. c) Effect of Pe on concentration

profiles along centre-line of vessel, measured from vessel wall in the axial direction.

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A.4 VEGF as a Positive Control

The role of VEGF in affecting vascular permeability has been broadly researched using both in vitro

73, 76, 82, 83 and in vivo 74, 84 methods, so much so that initially VEGF-A was first discovered as vascular

permeability factor (VPF). VEGF is seen to act through a variety of mechanisms to induce

hypermeability, specifically, as described in Olsson et al.’s 2006 review:85 the forming of fenestrations

in the endothelium, the assembly of calveolae into vesiculovacuolar organelles and the induction of

trans-endothelial pores. VEGF regulation of permeability is dependent on the production of

eNOS.85, 86

VEGF was chosen as a positive control to further validate the presented experimental method.

Experiments were carried out to examine the effect of VEGF on EC and SMC functioning (Fig.

A.4a-b), followed by permeability experiments (Fig. A.5).

Figure A.4: VEGF effect on smooth muscle cell and endothelial cell viability. Dose response

measurements to a) PE and b) ACh for four sequential conditions, Static: after heating and

pressurization, no perfusion flow. Flow : 10 mins. of 0.3 μl/h perfusion flow (1.06 dynes/cm2) of

MOPS buffer. VEGF: 10 mins. of perfusion of MOPS + 1 nm VEGF (#V4512. Sigma-Aldrich,

Oakville, ON, Canada). Post-VEGF Flow: 10 mins of 0.3 μl/h perfusion flow of MOPS buffer.

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Figure A.5: VEGF effect on permeability coefficient. Dose response measurements to a) PE and b)

ACh prior to and in between three permeability coefficient measurements c) Run 1: 0.3 μl/h

perfusion flow (1.06 dynes/cm2) of MOPS buffer + 9 mg/ml 4kDa FITC-Dextran, Run 2 (control):

0.3 μl/h perfusion flow of MOPS buffer + 9 mg/ml 4kDa FITC-Dextran + 1 nm VEGF pre-mixed

with 5 nm VEGFR1-Fc (#V6137, Sigma-Aldrich, Oakville, ON, Canada), Run 3: 0.3 μl/h perfusion

flow of MOPS buffer + 9 mg/ml 4kDa FITC-Dextran + 1 nm VEGF.

Figure A.4 shows an irreversible effect of VEGF on the functioning of the SMCs and ECs, through

a decrease in constriction to 2.1 and 3.0 μM PE, and subsequent weakening of the dilatory response

to ACh. This effect was not seen however when carrying out measurement of the permeability

coefficients (Fig. A.5a-b). The measurements control (Fig. A.5c-Run 2) of 1 nm VEGF mixed with 5

nm VEGFR1-Fc for 3 hours at 4oC was used prior to measurements of 1 nm VEGF (Fig. A.5c-Run

3). Modification of the protocol for VEGF use is being examined.

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A.5 Local Transport Study

With a microfluidic platform compatible with a laser scanning confocal microscope, the ability to

carry out high magnification imaging (40-60x) with high numerical apertures can allow for cell-level

imaging. Due to the substrate limitation of our current device design, we are unable to use an

objective greater than 20x with a long working distance (see objective in section 2.3). Efforts have

been made however to perfuse through a vessel isolated from a transgenic Tie2-GFP mouse (Fig.

A.6). Due to emission wavelength overlap between GFP (emission 510nm) and FITC (emission 520

nm), an alternative fluorescent marker was perfused through the fixated artery segment: Texas-Red

Dextran (emission 615 nm) (Invitrogen Molecular Probes, Eugene, OR, U.S.A). Images were

captured on a laser scanning confocal system (Olympus Fluoview 1000, Olympus, Melville, NY).

Figure A.6: Transgenic Tie2-GFP vessel perfusion for local transport study. a) Confocal image of

mesenteric artery obtained from transgenic Tie2-GFP mouse with intensities denoting ECs. (b)

Fixated vessel perfused with 2 mg/ml 10 kDa Texas-red dextran. Scale bars= 100 μm.