Morphological Characterization of Vascular Toroid Building

Morphological Characterization of Vascular

Toroid Building Blocks

By

Marianne Kanellias

B.S., Worcester Polytechnic

Institute, 2017

A thesis submitted in partial fulfillment of the requirements for the Degree of

Master of Science in the Department of Molecular Pharmacology, Physiology and

Biotechnology, and the Center of Biomedical Engineering at Brown University

Providence, Rhode Island

May 2019

ii

Signature Page

This thesis by Marianne Kanellias is accepted in its present form by the Depart-

ment of Molecular Pharmacology, Physiology, and Biotechnology as satisfying the thesis

requirements for the degree of Master of Science

Signed: Date:

Dr. Jeffrey Morgan, Advisor

Signed: Date:

Dr. Jacquelyn Schell, Reader

Signed: Date:

Dr. Anubhav Tripathi, Reader

Approved By Graduate Council

Signed: Date:

Andrew G. Campbell, Dean of the Graduate school

iii

AcknowledgementsI would first like to thank my family for their incredible support and for listening

when I ramble excitedly about science. You have always cheered me on and I can’t thank

you enough for that. And to Stephen, for always being there.

I want to give a huge thank you to everyone in the Morgan Lab for making this

experience so enjoyable and fostering a great environment for growing. Thank you to Dr.

Jeffrey Morgan for the opportunity to contribute in the Morgan Lab and for your invaluable

advising and perspective. Thank you to Kali Manning for letting me play a piece in your

awesome work, for all of the training and advice, and co-miserating about primary cells.

Thank you to Beth, Blanche, Ben, Gianna, Andrew, and Caitlin for all of your help and

expertise.

Thank you to Dr. George Pins for my first opportunity in research, and to Dr.

Megan Chrobak for being a great mentor. Finally, I would like to thank all of the friends

and teachers along the way that have played a part in making this thesis possible.

iv

Contents

Acknowledgements iii

Abstract x

1 Background 1

1.1 3D Cell Culture: Microtissues . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Self-Assembly in Microtissues . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Importance of Biological Lumens . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 The Human Vascular System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.1 Macro-structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.2 Micro-structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Tunica Adventitia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Tunica Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Tunica Intima . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4 Clinical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.4.1 Current Vascular Grafts . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.4.2 Design Criteria for a Vascular Graft . . . . . . . . . . . . . . . . . . . . 12

1.4.3 Current Tissue-Engineered Approaches . . . . . . . . . . . . . . . . . . 12

1.4.4 Scaffold-based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.4.5 Scaffold-free Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Cell-sheet Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Bioprinting Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Microtissue Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

1.5 The Funnel-Guide Microtissue Building Platform . . . . . . . . . . . . . . . . 19

1.5.1 Need for Vascular Toroid Characterization . . . . . . . . . . . . . . . . 22

v

2 Introduction 23

3 Materials and Methods 25

3.1 Hydrogel Micromold Casting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2 Cell Culture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.3 Microtissue Seeding and Preparation . . . . . . . . . . . . . . . . . . . . . . . . 26

3.4 Fluorescent Staining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.5 Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.6 Data Analysis and Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4 Analysis of Tori Inside Mold 30

4.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5 Analysis of Tori Outside Mold 43

5.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

6 Summary and Future Directions 58

7 Conclusion 60

8 Appendix 61

Bibliography 69

vi

List of Figures

1.1 Microtissue formation process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Diagram of vascular system flow organization . . . . . . . . . . . . . . . . . . 5

1.3 Diagram of blood vessel tunica . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4 Funnel-Guide microtissue manipulation system . . . . . . . . . . . . . . . . . 20

4.1 Formation of toroid-shaped microtissues . . . . . . . . . . . . . . . . . . . . . 31

4.2 Modulation of seeding density for HUVEC tori . . . . . . . . . . . . . . . . . . 31

4.3 Seeding density affects pop-off rate of HUVEC tori . . . . . . . . . . . . . . . . 32

4.4 Passage number of HUVEC cells in microtissues . . . . . . . . . . . . . . . . . 34

4.5 HUVEC tori contraction dependent on location in micro-mold. . . . . . . . . . 35

4.6 96-well toroid mold formation process. . . . . . . . . . . . . . . . . . . . . . . 36

4.7 Proof-of concept of co-culture vascular tori sorting. . . . . . . . . . . . . . . . 37

4.8 Proof-of concept of co-culture vascular tori sorting with bright field removed. 39

5.1 HUVEC tori undergo morphological changes out of mold over time. . . . . . 44

5.2 Live/dead viability of microtissues at 24 hours. . . . . . . . . . . . . . . . . . . 44

5.3 Image analysis process for measuring cross-sectional area. . . . . . . . . . . . 45

5.4 Cross-sectional area decreases for HUVEC tori over 20 hours. . . . . . . . . . 45

5.5 Dimensional consistency of building blocks at T=0 and 20 hours. . . . . . . . 47

5.6 Quantification of HUVEC tori dimensions over 20 hours. . . . . . . . . . . . . 48

5.7 Normalized HUVEC tori dimensional changes over 20 hours. . . . . . . . . . 49

5.8 HUVEC tori rates of change over time intervals. . . . . . . . . . . . . . . . . . 50

5.9 Side-view images to validate toroid height. . . . . . . . . . . . . . . . . . . . . 51

5.10 Experimental tori volume decreases over time over 20 hours. . . . . . . . . . . 52

vii

5.11 Experimental volume versus theoretical volume is comparable, and can be

used to calculate Volume per cell. . . . . . . . . . . . . . . . . . . . . . . . . . . 53

8.1 Setup template of tori survival data for statistical analysis . . . . . . . . . . . . 63

viii

List of Tables

1.1 Characteristics of venous and arterial vessels . . . . . . . . . . . . . . . . . . . 6

1.2 Design criteria of vascular graft . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.3 Comparison of autologous vessel alternatives for blood vessel replacement . 14

1.4 Comparison of macro-tissue fabrication platforms. . . . . . . . . . . . . . . . . 21

ix

List of Symbols

AC cross-sectional area µm2

DO outer diameter µm

DL lumen diameter µm

T wall thickness µm

VE experimental volume µm3

VT theoretical volume µm3

x

Abstract

Due to the global burden of cardiovascular disease, there exists a need for readily

available blood vessel replacements that can integrate into the patient’s native vasculature.

Microtissue methods seek to address this need by providing cellular aggregates that behave

closest to in vivo tissues. Furthermore, microtissues can be utilized as building blocks for

larger graft structures. The Funnel-Guide macro-tissue building platform allows for stack-

ing of toroid microtissues to create a fused, lumen structure in a high-throughput manner.

Previous work showed that more contractile cell types used in these microtissues result in a

less consistent and uneven macro-tissue tube. The work of this thesis aims to characterize

the morphological properties of these vascular microtissues in order to provide consistent

“building blocks” to use in the Funnel-Guide. Tori composed of primary endothelial cells

were observed both inside and outside mold structures for rates of contractility and overall

dimensional changes. The effect of seeding density and passage number were evaluated on

toroid contractility in-mold to develop a Pop-off assay. Outside of the mold, vascular tori at

two densities were observed to contract and present a window of time after self-assembly

and before lumen closure where there was highest consistency in building parts. The re-

sults of this thesis seek to provide knowledge of the contractile properties of endothelial

cell microtissues and develop metrics to evaluate future building microtissues of other cell

types.

1

Chapter 1

Background

1.1 3D Cell Culture: Microtissues

Recreating tissues of the human body has the potential to progress the world of

healthcare through disease modelling, tissue regeneration, and drug screening. For any

of these applications, the development of a new model system entails in vitro cellular and

in vivo animal testing before reaching clinical human use. Animal models are undeniably

useful - but the high cost, difficulty in isolating particular systems, and biologic/genetic dif-

ferences from the human body has led researchers to focus on the development of in vitro

models (Hartung (2008)). Conventional in vitro two-dimensional (2D) culture involves cells

growing in a monolayer on a flat substrate. This stiff, flat environment provides only lateral

growth- unlike most cellular microenvironments in vivo (Duval et al. (2017)). A recent devel-

opment in in-vitro testing is the ability to grow cells in three dimensions (3D). Compared to

2D culture, culturing in 3D allows for more spatial cell-cell and cell-ECM contact, and there-

fore a more robust and accurate model of biological systems (Duval et al. (2017), Page, Flood,

and Reynaud (2013), and Ravi et al. (2015). New techniques to culture cells in 3D involve

microtissue culture, organoid culture, seeding into scaffolds or hydrogels, organ-on-a-chip,

and 3D bioprinting (Fang and Eglen 2017).

The Morgan Lab group has developed a system for 3D tissue culture, creating

tunable microtissues as model building blocks. These microtissues are reliant only on cell-

produced ECM, therefore allowing for more in vivo-like architecture, cellular densities, and

matrix composition than other 3D methods (Dean et al. (2007) and Fang and Eglen (2017).

The process for creating microtissues begins with polymeric mold negatives which are filled

Chapter 1. Background 2

with melted agarose. Once the agarose is solidified and removed it becomes a positive mold

containing micro-recesses in a desired shape. Cellular suspensions are pipetted into the

molds and cells settle in the recesses via gravity. Additionally, the non-adherent surface

properties of agarose allows cells to preferentially adhere to one another rather than the

mold. Once in contact with one another in the micro-recesses, the cells self-assemble to form

the microtissues. A diagram depicting this process is shown in Figure 1.1. The ability of

mono-dispersed cells to self-assemble into tissues is key to this micro-mold technology.

Figure 1.1: Microtissue formation process for spheroid shapes. The castagarose gel with seeding chamber containing micro-wells (A) is filledwith mono-dispersed cells in media (B). Within 30 minutes the cells set-tle towards the bottom of the seeding chamber (C) until they aggregatein the micro-wells and self-assemble into microtissues (D) (Rago, Chai,

and Morgan, 2009).

1.1.1 Self-Assembly in Microtissues

The self-assembly process, describing mono-dispersed cells aggregating into multi-

cellular tissues, is the driving force behind morphogenesis and organogenesis in vivo (Duguay,

Foty, and Steinberg (2003) and Svoronos et al. (2014)). This process occurs passively when

individual cells migrate toward one another following chemical and mechanical cues in the

environment. When in contact, cell adhesion and self-assembly occurs spontaneously. This

phenomena can be described by the differential adhesion hypothesis introduced by Stein-

berg in 1964. (Steinberg 1964, 1970) The hypothesis states that self-assembly is driven by

free energy to minimize the surface area: volume ratio between cells (Foty and Steinberg

(2004)). The free energy available by cells is largely dependent on surface tension, which

is linked to complex cadherin- and cytoskeletal-mediated interactions (Dean and Morgan

(2008) and Foty and Steinberg (2004)). Furthermore, surface tension can also play a role in


cell-sorting between multiple cell types in the same aggregate, as displayed particularly in

morphogenesis (Foty et al. (1996)). These self-assembly phenomena can be applied in tissue

engineering to produce microtissues of custom shapes and cellular composition (Dean et al.

(2007), Livoti and Morgan (2010), and Svoronos et al. (2014)). Reproducing these qualities

of a tissue of interest is important for maintaining physiological relevancy of a tissue engi-

neered model. There is arguably no more relevant shape in the body than a tube, described

as a “fundamental unit of organ design” (Lubarsky and Krasnow (2003)).

1.2 Importance of Biological Lumens

Early metazoans, composing of small groups of cells, relied on simple cellular

transport for homeostasis. However, as metazoans grew in size and complexity, larger sys-

tems of transport were required to carry liquids, gases, and metabolites through the body

(Iruela-Arispe and Beitel (2013) and Lubarsky and Krasnow (2003)). These systems are com-

posed of tubes with open channels, or lumens. The human body is composed of many

lumen systems including:

• Vascular system

• Digestive system

• Respiratory system

• Renal system

• Exocrine systems

These lumens share the common feature of a lining of endothelial or epithelial cells which act

as a selective barrier and keep the lumens open. When this lining is disrupted, fatal disease

states can occur, making treatment or replacement of these lumens critical. Perhaps the most

crucial lumen system in the metazoan body is the vascular system, providing every tissue

with required nutrients and transport of waste to maintain vitality. Due to the prevalence

of lumens in the human body, understanding relevant disease states and providing tissue

replacements is of great interest to the medical field. Using tissue engineering, particularly

a modular approach, to develop accurate lumen containing tissues in vitro would be an

important first step to accomplishing these goals.


1.3 The Human Vascular System

1.3.1 Macro-structure

The vascular system, present in all multicellular organisms, acts as the crucial high-

way to circulate blood continuously through the body. As such, the vascular system is the

first functioning organ system to form in developing embryos (Ribatti (2015)). The main

functions of vascular circulation are to: 1) remove cellular waste and 2) provide oxygen and

nutrients to tissues and their cells. The former function is fulfilled by the venous branches,

and the latter by the arterial branches. The vessels of these systems are extensive since the

diffusion limit of oxygen is 100-200 µm (Rouwkema et al. (2009)). Normal cellular processes,

like cellular respiration, create waste such as diffused CO2 gas which require transport out

of the body. The venous system begins on a micron scale with the smallest-diameter ves-

sels called capillaries. Cellular waste diffuses into the capillaries via paracellular transport,

determined by osmotic pressure. The deoxygenated, waste-filled blood is then flowed to-

wards the heart and lungs through the next larger vessel called venules. The blood is further

transported through small veins, large veins, and finally the vena cava which connects to the

heart.

The arterial system provides cells of the body with oxygenated blood and nutri-

ents. The arterial system begins at the pulmonary vein, which takes the now-oxygenated

blood from the lungs to the heart. The blood moves through the heart chambers and out of

the aorta. The aorta is the largest vessel in the arterial system and experiences the highest

flow rate and blood pressure of the vascular vessel types (Hall (2016)). After the aorta, the

blood moves through large arteries, small arteries, arterioles, and finally the capillaries. The

venous and arterial capillaries combine in structures called capillary beds, with vessels aver-

aging 8 µm in diameter to allow passage of red blood cells (Woods et al. (2016)). Transfer of

gases, nutrients, small biomolecules, and waste occur under low pressure and smaller flow

rates in the capillary beds (Miller (2012)). A summary diagram of the vascular system flow

circuit is shown in Figure 1.2.

Larger mechanical properties of blood vessels are also important to maintain the

blood flow system. Blood vessels largely behave as a viscoelastic material under stress,


Figure 1.2: Diagram of the flow from venous (blue) to arterial systems(red), from Pearson Education c© (Pearson Education Inc. n.d.). (Pathway

of Blood through the Pulmonary and Systemic Circuits)

however there are innate differences between the mechanics of veins and arteries (Wang

et al. (2016)). Some of these differences are outlined in Table 1.1. In summary, veins are

less elastic and therefore stiffer and have less muscle content. Veins also contain a series

of one-way valves similar to cardiac vales in that they prevent backflow. In comparison,

arteries are more elastic through higher composition of elastin and have thicker muscular

walls to accommodate for more blood flow. These blood vessel properties are a result of

their micro-structure and can be overthrown by disease states as described in the Clinical

Background.


Table 1.1: Comparison of venous and arterial vessel characteristics (Mon-cada and Higgs, 2006; Hall, 2016; Bronzino, 2000; Woods et al., 2016; Gau-vin et al., 2011; Iwasaki et al., 2008; Gray, 1918; Konig et al., 2008; Cherng,

Jackson-Weaver, and Kanagy, 2010; Caro et al., 2011)

1.3.2 Micro-structure

Cellular composition and organization in blood vessels is generally consistent through

vessel sizes. Blood vessels in the body require a combination of mechanic stiffness, fatigue

strength, and flexibility; while responding to vasoactive stimuli. Therefore, blood vessels

are a composite of three layers: the tunica externa or adventitia, tunica media, and tunica

intima – each with distinct properties. A diagram of the blood vessel layers is shown in

Figure 1.3.

Tunica Adventitia

The outermost layer of blood vessels is called the tunica adventitia, or externa. It

is composed of mostly connective tissues, autonomic nerves, and the vasa vasorum. These

connective tissues, mainly collagen and elastin matrix, integrate the vessel to surrounding


Figure 1.3: Diagram comparing the tunica layers of arteries and veinsfrom Pearson Education c© (Pearson Education Inc. n.d.). (Structure of

arteries and veins)

tissues. Fibroblasts in the adventitia are responsible for maintaining this matrix, which also

supports the nerves and vasa vasorum, or vascular blood supply (Cherng, Jackson-Weaver,

and Kanagy (2010)). The adventitia also contains resident tissue lymphocytes and endothe-

lial/smooth muscle progenitor cells (Majesky et al. (2011)). Known functions of the adven-

titia have been increasing, currently including cell trafficking, vessel growth and repair, and

mediating communication between endothelial cells of the tunica intima and smooth mus-

cle cells of the tunica media (Majesky et al. (2011)). Overall, the tunica adventitia acts as an

active moderator of the functions of the intimal and medial layers.

Tunica Media

The tunica media is the middle and thickest layer of vessels, consisting of circum-

ferentially aligned smooth muscle cells (SMC). Smooth muscle, specifically vascular smooth


muscle, differs from skeletal muscle in a few aspects. Smooth muscle cells are more spindle-

like and use G-coupled protein ligand receptors instead of ion channel receptors during

contraction. Smooth muscle also contains less myosin filaments and therefore do not form

muscle striations (Hill and Olson (2012)). Instead the SMCs form fibers arranged in lamellae,

along with elastic fibers also in layered rings. These differences allow for vascular smooth

muscle to contract in a slower, controlled manner and hold contractions for longer periods

of time (Woods et al. (2016)). The thickness and amount of elastic fibers in the tunica me-

dia is determined by the size of the vessel. Larger vessels have thicker walls and are more

elastic, whereas in the smallest vessels contractile pericytes, or mural cells, take the place of

smooth muscle cells and often integrate with the endothelium (Bergers and Song (2005)).

The muscular structure of the tunica media allows for vasodilation and vasocon-

striction of the vessels. The factors controlling these mechanisms can be summarized into in-

trinsic and extrinsic factors (Carroll (2007)). Intrinsic, or local, regulations include endothelial-

derived vasoactive factors (like Nitric oxide (NO)), metabolic control of blood flow, autoreg-

ulation, and temperature changes. Extrinsic, or neurogenic, factors include the sympathetic

nervous system acting on adrenergic receptors and circulating humoral molecules like An-

giotensin II (Woods et al. (2016)). These factors control the body’s blood pressure and control

the amount of blood flow to specific organs.

Tunica Intima

The innermost concentric layer lining blood vessels is the tunica intima. The tunica

intima is composed of a thin sheet of endothelial cells (the endothelium) and a basal lam-

ina. The endothelium is less than 0.2 µm thick and is composed of continuous endothelial

cells (Yau, Teoh, and Verma (2015)). These endothelial cells are connected through cell-cell

adhesions which allow for selective transport and can differ in type based on location. Most

of the junctions are tight junctions and adherens junctions, supported by molecules such

as platelet endothelial cell adhesion molecule (PECAM) and vascular endothelial cadherin

(VE-cadherin) (Cerutti and Ridley (2017)). The basal lamina, or inner elastic membrane, is

extracellular matrix produced by the endothelium- mainly collagen IV and elastin proteins.


These extracellular matrix proteins provide flexibility for the inner layer and a cell attach-

ment bed (Cherng, Jackson-Weaver, and Kanagy (2010))).

Being in direct contact with the bloodstream, the endothelium plays an important

homeostatic role in anti-thrombosis, vessel tone and growth, and smooth muscle cell func-

tion (Moncada and Higgs (2006) and Yau, Teoh, and Verma (2015)). Healthy endothelium

creates an antithrombotic surface by inhibiting enzymes of the clotting cascade (Yau, Teoh,

and Verma (2015)). Vessel tone is regulated through released vasodilation factors (e.g. nitric

oxide (NO)) or vasoconstriction factors (e.g. endothelin-1 (ET-1)) acting on neighboring vas-

cular smooth muscle cells (Sandoo et al. (2010)). The endothelium also drives blood vessel

growth, or angiogenesis, by releasing a multitude of growth factors including transform-

ing growth factor beta (TGFβ) and vascular endothelial growth factor (VEGF), and directed

by inhibitors (Moncada and Higgs (2006)). A unique property of the endothelium (and the

epithelium in other lumens) is the polarization of the constituent cells. During cellular po-

larization, cell-cell contact is focused on the lateral side, and cell-ECM contact is focused on

the basal side (Iruela-Arispe and Beitel (2013)). This segregation of function directs cell pro-

liferation to grow the vessel by stratifying or enlarging it. In addition to initiating growth,

the endothelium also maintains patency of the vascular lumens through inhibiting excessive

growth of smooth muscle cells in the tunica media (Scott-Burden and Vanhoutte (1994)).

During damage to blood vessels or presence of foreign bodies, activated endothe-

lial cells contribute to both the blood coagulation and inflammatory cascades. Damage to the

endothelium amplifies production of thrombin through the production of Tissue Factor (TF),

as well as platelet aggregation via Von-Willebrand Factor (vWF). The presence of thrombin

and platelet factors results in thrombosis, or blood clotting. Additionally, the tunica intima

is important for immune cell trafficking during inflammation, allowing the white blood cells

(or leukocytes) to pass from the bloodstream to surrounding tissues. Cell-surface molecules

like P-selectin allows adhesion of the leukocytes and induces trans-endothelial migration

and diapedesis (Moncada and Higgs (2006) and Nourshargh and Alon (2014)). Although

the process of thrombosis and inflammation are important for preventing blood loss and

fighting infections respectively, they can lead to diseased states if unchecked, as discussed

in Clinical Background.


1.4 Clinical Background

Cardiovascular diseases (CVD) was the cause of 31% of global deaths in 2016, and

has been the leading cause of death for the past 80 years (Greenlund et al. (2006) and World

Health Organization (WHO) (2017)). This mortality is also predicted to rise to an estimated

23.3 million deaths by 2030 (Mathers and Loncar (2006)). Cardiovascular disease can man-

ifest in various forms, some of the most prominent CVD’s are supravalvular aortic stenosis

(SVAS), aneurism, and atherosclerosis. SVAS is known to be caused by a genetic mutation in

the ELN gene which encodes tropoelastin proteins, resulting in a defective elastic lamellae

in the vessel and overgrowth of the muscular layer (Mazurek et al. (2017) and Micale et al.

(2010)). This overgrowth causes the vessel to occlude, restricting blood flow. In contrast,

aneurisms involve thinning of the muscular layer and vessel wall which creates turbulent

flow, risk of rupture and thrombosis. There is no direct cause of aneurisms but they are

thought to be influenced by “multiple environmental and genetic factors” (Mazurek et al.

(2017)). Finally, atherosclerosis is the most common CVD and is linked to many genetic, di-

etary, and environmental factors. The result is a raised level of blood low-density lipoprotein

cholesterol (LDL-c) which causes circulating macrophages to adhere to the endothelium and

cause a growing plaque inside the vessel wall. This plaque can fissure, leading to infarction

(heart attack) or stroke (Barquera et al. (2015)). Heart attacks and stroke resulting from CVD

cost more than $320 billion annually in “healthcare costs and lost productivity” (Center for

Disease Control Foundation (CDC Foundation) (2015)). These dysfunctions of the vascular

system require understanding through accurate disease models and long-lasting treatment

options.

These diseases are first treated pharmacologically or with minimally-invasive tech-

niques like stents. However, the vessel occlusion or aneurism often can require surgical in-

tervention in the form of vascular bypass surgery. Vascular bypass surgery uses vascular

grafts to replace a vessel or re-direct blood flow and accounts for over 370,000 procedures

in the US in 2018 (Benjamin et al. (2018)). The first vascular grafts began with using the

patient’s own autologous vessels, first recorded in 1948 (Kunlin (1951)). With limited acces-

sibility of autologous vessels, researchers in the 1960-70’s began to develop synthetic grafts


that could replace vessels. Early synthetic grafts were met with high thrombosis rates and

low patency. Current vascular grafts, including allograft and xenograft options, still present

these issues and have not lessened morbidity and mortality (Carrabba and Madeddu (2018)

and Prabhakaran et al. (2018)). Overall, there exists a clinical need for vascular grafts that

can remain non-inflammatory and adapt to the patient’s existing vasculature.

1.4.1 Current Vascular Grafts

Current solutions on the market for blood vessel replacements include autologous

vessels or grafts composed of synthetic and/or biological materials. Autologous tissue from

a patient’s own internal thoracic artery (ITA) or saphenous vein (SV) remains the gold stan-

dard, especially in smaller vessels. While immunogenically these autologous grafts present

an ideal solution, harvesting requires invasive surgery and can result in significant complica-

tions (Pashneh-Tala, MacNeil, and Claeyssens (2015)). Especially for the elderly or patients

with pre-existing conditions, the quality of harvested vessels is not always reliable as well

(Carrabba and Madeddu (2018)). Furthermore, autologous grafts are unsatisfactory for ar-

teriovenous vascular (AV) dialysis access procedures, with up to 50% failure rate at 2 years

(Tillman et al. (2012)). When autologous grafting is not an option, exogenous-sourced grafts

are the remaining commercial option for patients.

The majority of commercially-available vascular grafts are synthetic polymer grafts,

remaining virtually unchanged since their introduction in the 1970’s. The most common

materials include expanded Polytetrafluoroethylene (ePTFE, or Gore-Tex R©), Polyester (PE),

and Polyethylene Terepthalate (PET, or Dacron R©). These grafts are classified as Class II

medical devices and require sufficient mechanical and biological similarity to native vessels

(Food and Drug Administration (FDA) (2000)). Mechanical requirements include achiev-

ing similar burst pressure and tensile strength values to native vessels (4000 mmHg and

1000 kPa, respectively, as cited in Table 1.1), as well as cyclic fatigue resistance (Interna-

tional and (ASTM) (2013)). Biological requirements include thrombosis and immunogenic

testing (Food and Drug Administration (FDA) (2000)). Synthetic grafts have proved satis-

factory long-term patency (around 90%) in large diameter vessels >6 mm (Brewster (1997)).

Although mechanically strong, excessive stiffness has led to compliance mismatch and lack


of patient adaptability (Greenwald and Berry (2000) and Kumar et al. (2012)). The foreign

polymer surface also creates thrombotic responses in the bloodstream, leading to patency

complications particularly in small-diameter vessels (Carrabba and Madeddu (2018) and

Kumar et al. (2012)).

Surface modifications of synthetic grafts have been attempted to reduce thrombo-

sis via increased hydrophobicity, and increase endothelialization using cell-specific bind-

ing molecules (Ren et al. (2015)). However, a double-edged sword arises where increasing

surface hydrophobicity also decreases endothelial cell attachment, leading to no change in

outcomes (Ravi, Qu, and Chaikof (2009) and Ren et al. (2015)). Infection rates are also an

issue with synthetic grafts, where studies of hemodialysis patients found infections rates of

3-35% (Bachleda et al. (2012) and Benrashid et al. (2017)). Overall, synthetic vascular grafts

are a cost-effective replacement for larger vessels, but lack in compliance and inertness after

implantation. Thus, there exists a need for small diameter vessel grafts (<6 mm) that can

integrate with patient vasculature mechanically and biologically.

1.4.2 Design Criteria for a Vascular Graft

The vasculature system acts as an important bloodstream barrier and regulator and

is mechanically tuned to its environment. Therefore, successful replacement constructs must

strive to match native properties biologically and mechanically (Benrashid et al. (2016) and

Fernandez (2014)). A construct must also fulfill commercial constraints to become available

to patients. A list of important design criteria, in no particular order, are listed in Table 1.2.

1.4.3 Current Tissue-Engineered Approaches

Researchers are currently developing Tissue-Engineered Vascular Grafts (TEVG)

to overcome the limitations of available commercial options. These novel approaches also

have the potential to provide additional beneficial design features as outlined in Table 1.3. In

tissue engineering there are two main approaches: the ‘top-down’ and ‘bottom-up’ approach

(Lu, Li, and Chen (2013)). The ‘top-down’, or scaffold-based, approach uses pre-existing

scaffolds as a 3D template and populate with cells. Conversely, the ‘bottom-up’ approach


Table 1.2: Biological, mechanical, and commercial design criteria for avascular graft (Catto et al., 2014; Greenwald and Berry, 2000; Kumar et

al., 2012)

uses natural self-assembly forces to create building blocks for use to construct larger 3D

tissues. Both approaches been used in vascular tissue engineering, using a wide array of

synthetic and biological materials, as summarized in Table 1.3. Synthetic polymer grafts are

also included for reference.


Table 1.3: Comparison of autologous vessel alternatives for blood ves-sel replacement Pashneh-Tala, MacNeil, and Claeyssens, 2015; Roeder,Lantz, and Geddes, 2001; Amiel et al., 2006; Olausson et al., 2012; Cattoet al., 2014; El Assar et al., 2012; L’Heureux et al., 2007; Dahl et al., 2011;Shin’oka et al., 2005; Syedain et al., 2014; Enomoto et al., 2010; Koch etal., 2010; Tillman et al., 2008; Itoh et al., 2015; Jakab, Norotte, and Marga,

2012; Munaz et al., 2016; Kelm et al., 2010; McAllister et al., 2009


1.4.4 Scaffold-based Methods

Scaffold-based methods aim to use existing or pre-fabricated materials as a frame-

work to support cellular tissue growth. Thus, scaffolds can provide directionality for cell

growth as well as mechanical strength. Though with increased scaffolding material present,

cell-cell communication and attachment is limited (Svoronos et al. (2014)). As described in

previous sections, cell-cell (endothelial, smooth muscle cell, and fibroblast) contact is crucial

for full functionality in in vivo blood vessels. Addition of scaffold material can also impede

nutrient diffusion, as well as hinder heterotypic cell culture and physiologically-relevant cell

densities (Mironov et al. (2009)). With this in mind, mechanical strength is crucial to graft

survival, and achieving it without a scaffold can require more processing (Pashneh-Tala,

MacNeil, and Claeyssens (2015)).

Scaffold-based methods utilize materials including decellularized tissue, biological

matrix polymers, synthetic polymers, or polymer composites. Decellularized grafts utilize

human and non-human vessels and balance preserving ECM architecture while removing

immunogenic cellular materials from the tissue using a combination of methods (Crapo,

Gilbert, and Badylak (2012)). Decellularized vascular grafts have been unable to surpass

their synthetic competitors since the 1960’s, due to studies showing xenografts showing

“no clear advantage” in terms of patency and higher cost (Pashneh-Tala, MacNeil, and

Claeyssens (2015)). Common decellularized graft failures include thrombosis, infection, and

aneurism – hypothesized to originate from the lack of cellularity (Carrabba and Madeddu

(2018)). Cell-seeding after decellularization may reduce complications, but manufacturing

requires optimization for widespread adoption (Olausson et al. (2012)). Groups have at-

tempted to bypass decellularization by recreating natural ECM scaffolding using biological

polymers such as collagen, elastin, fibrin, and silk fibroin. These materials retain most of

their cell-adhesion binding sites and are designed to be bioactive during cellular integra-

tion. Weinberg and Bell aimed to recreate vascular tunica with a gel-based and Dacron R©-

supported externa, media, and intima; modified to produce a physiological stress-strain

response but ultimately lacked in tensile strength and burst pressures (Weinberg and Bell

(1986) and Pashneh-Tala, MacNeil, and Claeyssens (2015)). Biological polymer scaffolds


largely lack in mechanical strength and degradation resistance, which can be increased us-

ing a bioreactor but adds more production cost and time (Carrabba and Madeddu (2018)).

Polymer scaffolds can be composed of synthetic, biological or composite poly-

mers. Biodegradable synthetic polymer scaffolds such as Polylactic acid (PLA) and Poly-

ε-caprolactone (PCL) are designed to serve as a temporary support scaffold for tissue in-

growth of a damaged vessel. Work of Niklason and colleagues used a PGA scaffold seeded

with autologous endothelial and smooth muscle cells (Humacyte R©), which achieved 2000

mmHg burst pressures, but required 12 weeks to produce (Niklason et al. (1999)). Clinical

trials with decellularized versions showed no immune rejection with satisfactory patency

at 6 months, but 28% patency at 12 months (Lawson et al. (2016)). Current challenges with

biodegradable scaffolds are ensuring complete degradation of polymer and constructing

proper alignment of collagen in the vessel to achieve suitable mechanical properties (Dahl,

Vaughn, and Niklason (2007) and Dahl et al. (2007)). In order to balance the biocompatibil-

ity of biological scaffolds and strength of polymer scaffolds, composite polymers have been

designed. These scaffolds have been demonstrated in vivo, with patency up to 6 months

for a P(L/D)LA and fibrin gel graft with autologous cells (Koch et al. (2010)). These hybrid

polymer grafts show promising preclinical results, although their complexity in production

remains a limitation. In summary, scaffold-based approaches can provide the optimal me-

chanical strength, but only when biocompatibility and cell binding are surrendered.

1.4.5 Scaffold-free Models

Cell-sheet Methods

Scaffold-free tissue engineering utilizes cellular self-assembly to engineer tissues

with more physiologically-relevant compositions. Three tissue engineered self-assembled

(TESA) approaches have been taken to replace artificial vascular grafts: cell-sheet manipu-

lation, bioprinting, and microtissue aggregates. The earliest vascular TESA approach, cell

sheet engineering, used thermo-responsive 2D surfaces to allow removal of cells along with


deposited ECM (Chen et al. (2015)). Vascular cell sheets were then wrapped around a man-

drel and matured in a pulsatile bioreactor to obtain physiological burst pressures, as demon-

strated by L’Heureux and colleagues in 1998 (L’Heureux et al. (1998)). Tubes produced ad-

equate amounts of ECM and each individual layer rendered an average production time

of 7.5 months (McAllister et al. (2009)). Clinical trials of these cell-sheet grafts, patented

Cytograft R©, used autologous fibroblasts and endothelial cells of 10 end-stage renal disease

patients, which were implanted for AV access. Three grafts experienced failure due to di-

lation and thrombosis, though remaining grafts maintained 60% patency at 6 months. The

long production time for these grafts led to the development of an off-the-shelf version with-

out endothelial seeding, trademarked LifelineTM. Results of a three patient clinical trial in

2014 for AV access indicated the grafts could be stored and resist infection (Wystrychowski

et al. (2014)). However, the longest patency observed was 11 months and thrombosis re-

mained an issue, requiring re-intervention for all patients. Cell-sheet methods hold promise

for creating vascular tubes with physiological strength, however they require long produc-

tion times and significant physical manipulation to cells when removing as a sheet (Carrabba

and Madeddu (2018)).

Bioprinting Methods

A difficulty with scaffold-free methods is the ability to create and support complex

cellular structures, such as the branching vascular tree, in a high-throughput manner. The

field of bioprinting aims to solve this issue by utilizing 3D rapid prototyping technology.

Microtissues and sacrificial polymers are deposited sequentially on non-adherent surfaces

and function as “ink”. Work done by the Forgacs group demonstrated the feasibility of their

bioprinter to print strips of SMC and fibroblast spheroids and non-adherent agarose in lay-

ers to form various tube shapes. The bioprinter system was able to manufacture branching,

tubular shapes which fused in 7 days. The constructs reached 773 mmHg burst pressure

(compared to 1600 mmHg for native vein) with 21 additional days in a bioreactor (Konig

et al. (2008) and Norotte et al. (2009)). There are some biological limitations when manipu-

lating cellular materials, including shear stress at the nozzle that can cause cell lysis (Munaz

et al. (2016)). Another concern involves some constructs to be kept in air to polymerize the


sacrificial polymers, which can damage cells, and even if perfusion microchannels are incor-

porated they cannot be perfused during printing (Ip et al. (2018) and Manning, Thomson,

and Morgan (2018)). Another consideration is maintaining sterility within the production

space (Carrabba and Madeddu (2018)). Barring these biological constraints, bioprinting has

shown the benefits of incorporating a rapid prototyping platform to produce vasoactive

macro-tissues in complex shapes. Though recent technological developments of rapid pro-

totyping have the potential to benefit tissue engineering in complexity and reducing pro-

duction time, optimization for large-scale production remains to be seen.

Microtissue Methods

Microtissue approaches utilize cellular self-assembly to form microtissues without

the use of external scaffold materials. Using methods such as the hanging drop or cellular

aggregation techniques, mono-dispersed cells in suspension are allowed to aggregate spon-

taneously. Microtissue shapes are also modular and can allow for more complex shapes,

such as tori and honeycombs. These micro-tissues can fuse together to form macro-tissues

and can be maintained in a bioreactor. Microtissue methods avoid detrimental shear forces

from removing cell sheets or extruding through a bioprinter nozzle, while maintaining the

benefits of cell-only composition (Carrabba and Madeddu (2018)). Work by Kelm and col-

leagues obtained a confluent, tubular structure using hanging-drop cultures of endothe-

lial and fibroblast cells cultured in a bioreactor for 14 days. These microtissues demon-

strated “prevascularization capacity”, no observed thrombosis, and enhanced ECM produc-

tion compared to cell-sheets.(Kelm et al. (2010)). Another approach involves the “Kenzan

method” using multicellular spheroids impaled on a microneedle array to arrange spatially

for fusion (Moldovan, Hibino, and Nakayama (2017)). An in vivo rodent study showed graft

endothelialization and patency after 5 days, took only 8 days to produce, but achieved only

half the strength of native vessels (Itoh et al. (2015)).

Finally, work by Rolle et al. developed smooth muscle cell rings using an agarose

mold system similar to the Morgan lab method, and fused them into a tube via stacking on

a mandrel (Gwyther et al. (2011)). A robotic system was also developed for automation of

removing smooth muscle cell rings from the mold via a punch system, and stacking onto


a mandrel (Nycz et al. (2019)). The individual microtissues were able to achieve 100-150

kPa ultimate tensile strength after 8 days in culture, however mechanical analysis of a fused

tube has yet to be published. Overall, limitations for the scale-up of microtissue methods are

the diffusion of nutrients and oxygen with larger structures, mechanical strength, and total

length of production time (Kelm et al. (2006) and Pashneh-Tala, MacNeil, and Claeyssens

(2015)). An automated assembly and bioreactor system could expedite the production pro-

cess using rapid manufacturing technology and could lessen these problems. Microtissues

have the potential for modular, biologically-functional tissue units, and a platform system

to expedite the production process would be greatly desired.

1.5 The Funnel-Guide Microtissue Building Platform

Scaffold-free tissue engineering holds the potential to provide tissue models that

replicate in vivo tissues closer than current scaffold-based and synthetic approaches. Cur-

rently, major constraints of microtissue approaches are: diffusion of nutrients, mechanical

strength, and longer production times. The first limitation of nutrient diffusion could be

circumvented by the microtissue shape itself – one that contains lumens to flow nutrient-

rich media through. In the Morgan group, we have developed various microtissue mold

shapes, one of which is a toroid shape containing a lumen. This toroid shape can easily be

adapted for creating vasculature and can allow for various cell types and densities. Limiting

the amount of manufacturing support structures such as mandrels and sacrificial polymers

would also allow for further diffusion. The second limitation of mechanical strength (e.g.

burst pressure and tensile strength) has been previously overcome with other cell-based

methods using pulsatile-flow bioreactors (Dahl, Vaughn, and Niklason (2007), Huang and

Niklason (2014), and Syedain et al. (2014)). To achieve these mechanics however, multiple

days or weeks of bioreactor conditioning were required. Long production times of microtis-

sue grafts can be shortened by using rapid prototyping technology and automated systems,

however, the time allotted for cellular proliferation cannot currently be avoided.

Recently the Morgan lab has developed a scaffold-free microtissue manipulation

platform, the Funnel-Guide, to produce tubular macrotissues (Manning, Thomson, and


Morgan (2018)). The Funnel-Guide uses a funnel to direct toroid and honeycomb-shaped

microtissues into a stacking chamber where they are aligned using gravity in a submerged

environment. When the microtissues are in contact they fuse together into a contiguous tis-

sue, independent of cell type and size, as previously observed (Livoti and Morgan (2010) and

Manning, Thomson, and Morgan (2018)). Perfusion can then occur through the aligned lu-

mens. The system offers a simple, submerged additive manufacturing method for stacking

microtissue building blocks. The system can also greatly benefit from automation, which

would increase efficiency without sacrificing accuracy, as previously demonstrated by the

Bio-Pick, Place, and Perfuse (Bio-P3) system (Blakely et al. (2015)). A diagram of the Funnel-

Guide technology is shown in Figure 1.4.

Figure 1.4: Funnel-Guide microtissue manipulation system consisting ofa free-fall space (10 mm height) for the toroid to right itself, a guidingfunnel with specified angle, and a square stacking chamber (A). StackedHepG2 cell tori (45 total) are shown in the stacking chamber (B). Scalebar is 2 mm. Adapted from Manning et al., 2018 Manning, Thomson,

and Morgan, 2018.

Compared to the Kenzan method for microtissue stacking, the Funnel-Guide is

non-contacting, which can avoid cellular damage and forces from microneedle impaling.

The Funnel-Guide also allows for the use of larger, toroid shapes that can easily be fused

into tube shapes requiring fewer building parts (Manning, Thomson, and Morgan (2018)).

The Rolle robotic system uses stacked tori similar to the Funnel-Guide, however it manually


manipulates the microtissues, which can create harmful stresses on the cells. Further viabil-

ity staining on the microtissues can determine any cellular damage from manipulation or by

contacting the steel mandrel. Furthermore, the Rolle system relies on tori to contract around

the vertically suspended mandrels, which resulted in some failures by sliding off (Nycz et

al. (2019)). A non-contacting system would be beneficial to allow for less-contractile micro-

tissues (e.g. endothelial cell-based) to be used. A summary of microtissue manipulation

platforms, including the Funnel-Guide is described in Table 1.4.

Table 1.4: Comparison of macro-tissue fabrication platforms.(Moldovan, Hibino, and Nakayama, 2017; Nycz et al., 2019; Jakab,Norotte, and Marga, 2012; Ke and Murphy, 2018; Manning, Thomson,

and Morgan, 2018)

Using the Funnel-Guide technology, toroid microtissues can easily be manipulated

into fused, macro-tissue tubes. These tubes are physiologically relevant to the biological lu-

mens that are ubiquitous throughout in the human body, particularly the vascular system.

The vascular system is not only crucial to survival, but has been a major focus of researchers

today due to the high role of cardiovascular disease on global morbidity and mortality. Due

to this disease there has remained a need for not only clinical blood vessel replacements, but

accurate models to understand the mechanisms of disease and possible treatments. There-

fore, a model biological blood vessel with the highest relevancy to native tissues could fulfil

this need. Using Morgan Lab microtissue technology, tori can be produced using vascu-

lar cell types (e.g. endothelial, smooth muscle, and fibroblast) at high cell density which


could model a cross-section of vascular tissue. Furthermore, using the Funnel-Guide plat-

form, these vascular tori could be stacked as building blocks and fused in order to produce

a blood vessel-like tube. The Funnel-Guide is particularly useful for creating blood vessels

in that it is optimized for tori and can accommodate different sizes by scaling the stacking

chamber (Manning, Thomson, and Morgan (2018)). Properties of a successfully built blood

vessel include being completely fused and having no defects or holes. These properties are

largely dependent on the uniformity of the vascular tori building blocks, hence characteri-

zation of the vascular tori is the first step towards forming a blood vessel model.

1.5.1 Need for Vascular Toroid Characterization

Cell-based microtissues can be utilized as building blocks for larger tissue struc-

tures. To improve the quality of the tubular macrotissues formed using the Funnel-Guide,

the self-assembly and functionality of each toroid building block can be evaluated before fu-

sion. Unlike synthetic building blocks, variation between multicellular parts is more preva-

lent. To create a tube structure that allows perfusion through a central lumen, stacked tori

must be consistent in: outer diameter, lumen diameter, and cell density at the time of build-

ing. It has been observed that microtissues undergo an initial period of self-assembly from

monodispersed cells (Dean et al. (2007)). After initial self-assembly, tissue contraction and

compaction occurs in order to minimize surface area (Livoti and Morgan (2010) and Man-

ning, Thomson, and Morgan (2018)). This contraction has been observed to be dependent

on time after self-assembly, cell-type, agarose mold shape and cellular density. Therefore, in

order to create the most consistent building blocks, tori must be observed in variations of

these conditions to ascertain the optimal tissue for building while maintaining functionality.

23

Chapter 2

Introduction

Currently there exists a need for conduit grafts for vascular bypass surgery that

are readily manufactured and provide high vascular functionality. Cardiovascular disease

(CVD) was responsible for 31% of global deaths in 2016, and has been the leading cause of

death for the past 80 years (Greenlund et al. (2006) and World Health Organization (WHO)

(2017)). When blood vessels become occluded due to CVD, vascular bypass surgery is used

to re-direct and restore blood flow; accounting for over 370,000 procedures in the US in 2018

(Benjamin et al. (2018)). Using the patient’s own autologous vessel is ideal for compatibility,

however quality varies and harvesting requires invasive surgery (Carrabba and Madeddu

(2018) and Pashneh-Tala, MacNeil, and Claeyssens (2015)). Commonly, synthetic polymer

grafts (ex. ePTFE or PET) are used due to their ease of manufacturing and satisfactory pa-

tency in large (> 6mm in diameter) vessels (Brewster (1997)). However, the synthetic poly-

mer surface creates issues with thrombosis, infection, and low patency in small-diameter

vessels (Catto et al. (2014) and Pashneh-Tala, MacNeil, and Claeyssens (2015)). Tissue en-

gineered blood vessels (TEBV) have been developed using scaffold-based and scaffold-free

methods in order to overcome these limitations.

While scaffold-based grafts can provide structural support with less culturing, scaffold-

free approaches deliver higher cell density and cell-cell contact using only endogenous ma-

terials (Mironov et al. (2009) and Rupaimoole et al. (2017)). Cell-sheet approaches, such as

that of L’Heureux and colleagues, wrap 2D cultured cells around a mandrel and can achieve

physiologic strength but require months in bioreactor culture (L’Heureux et al. (1998) and

McAllister et al. (2009)). Microtissue-based approaches utilize cellular self-assembly to form

Chapter 2. Introduction 24

building block units that can be stacked to form larger structures in days (Livoti and Mor-

gan (2010) and Mironov et al. (2009)). The Kenzan group uses a microneedle array to impale

spheroid microtissues, forming a tube construct (Moldovan, Hibino, and Nakayama (2017)).

These spheroids are large (400–600 µm), which can interfere with diffusion, and require

physical manipulation to construct into macro-tissues. Generally, microtissue approaches

are limited in production time, nutrient diffusion throughout the tissue, and mechanical

strength. Microtissue-based approaches have the potential to create modular, biologically-

functional vascular models, and a platform system could potentially help overcome these

limitations.

Our group has produced non-adherent microtissue molds in various shapes and

can be used with multiple cell types and densities (Dean et al. (2007). The annular, toroid

microtissue shape allows for perfusion through the center lumen and can be stacked and

fused to form a tube (Livoti and Morgan (2010)). In order to efficiently stack these build-

ing blocks, we have developed a Funnel-Guide platform that provides a submerged, non-

contact method of stacking tori with automation potential (Manning, Thomson, and Morgan

2018). In contrast to other microtissue platforms such as bioprinting and robotic manipula-

tion, the Funnel-Guide avoids unnecessary shear stresses and allows for constant submer-

sion of the construct in media (Mironov et al. (2009), Munaz et al. (2016), and Nycz et al.

(2019)). To ensure alignment during stacking, consistency of tori building parts at time of

building is crucial. Tori microtissues have been observed to undergo time-dependent con-

traction after self-assembly in order to minimize surface area (Livoti and Morgan (2010) and

Manning, Thomson, and Morgan (2018)). Therefore, characterization of the morphologi-

cal changes of vascular building parts over time can determine the optimal conditions for

stability during stacking. In this thesis, endothelial, smooth muscle, and co-culture tori mi-

crotissues were observed in constrained (in the mold) and unconstrained (outside the mold)

settings to determine the optimal conditions for fabricating the tori building blocks. These

determined conditions will further facilitate the fabrication of larger macrotissue structures

using the Funnel-Guide microtissue manipulation system.

25

Chapter 3

Materials and Methods

3.1 Hydrogel Micromold Casting

Polymeric 3D Petri Dish R© tori micro-molds (Microtissues, Inc., Providence, RI)

and Powder UltraPureTM Agarose (Invitrogen, Carlsbad, CA) were sterilized by autoclave.

Agarose solution was produced by adding sterile water to obtain 2% agarose (weight/vol-

ume). The solution was heated until clear and uniform, then pipetted to fill the 3D Petri

Dish R© micro-molds. Air bubbles were released from the recesses using a sterile spatula and

the agarose gels were allowed to solidify. Each agarose micro-mold contained a seeding

chamber with recessed micro-wells for forming tori, each with a peg in the center to create

a lumen. Peg angles were designed to be at 90 degrees from the horizontal. Gels were re-

moved via spatula and transferred into 6-well plates. The gels were then equilibrated using

serum-free media culture medium overnight at 37C and 5% CO2. Final dimensions of the

equilibrated, agarose tori micro-molds contained 36 micro-wells with 600 µm diameter pegs

in the center, surrounded by 400 µm wide trough bottom on all sides.

To evaluate tori outside of the molds, 2% agarose solution was created as described

above. Molten agarose was then added to each well of a 96-well plate in order to cover

the well bottom and create a level, non-adherent surface. After hardening, the plate was

equilibrated with serum-free growth medium and incubated at 37C and 5% CO2.

96-well toroid molds were formed using a stainless steel negative mold that con-

tains a 4x8 array of negative toroid micro-molds extruded from a base. Molten agarose

is pipetted into the peg recess of the negative mold so that an air pocket does not form.

The polymer negative is then inverted and pressed into the 96-well plate containing 90µL

Chapter 3. Materials and Methods 26

agarose per well until the agarose solidifies. The negative is then removed and the process

is repeated until all the wells contain an agarose toroid micro-mold. Pegs were at The plate

is then equilibrated with serum-free media at 37C and 5% CO2 until seeding.

3.2 Cell Culture

Human umbilical vein endothelial cells (HUVEC) were expanded in endothelial

growth medium (EGM) with added supplements (PromoCell, Heidelberg, Germany) sup-

plemented with 1% penicillin/streptomycin (MP Biomedicals, LLC). Human aortic smooth

muscle cells (HAoSMC) were expanded in smooth muscle growth medium (SMGM) with

added supplements (PromoCell, Heidelberg, Germany) and 1% penicillin/streptomycin (MP

Biomedicals, LLC). Cultures were incubated at 37C with 5% CO2, and growth media was

exchanged every 48 hours. For co-culture experiments, a 50:50 HUVEC: HAoSMC growth

medium mixture was used to submerge the co-culture tori.

3.3 Microtissue Seeding and Preparation

Microtissues were formed using a protocol described previously (Livoti and Mor-

gan 2010). To summarize, confluent cell cultures were trypsinized and counted to obtain the

desired density for seeding into micro-wells. Cell suspension was pipetted into each mold

and the plates were incubated for 30 minutes in growth media to allow for self-assembly of

the micro-tissues. For analysis of tori inside of molds, plates were then directly transferred

to an inverted light microscope for imaging. For analysis of tori outside of molds, tori were

first allowed to self-assemble and incubate for another 6 hours. Then, tori were released

from the pegs by gently titrating with a large-bore pipette until the tori were loose in the

mold chamber media. Tori were individually transferred to equilibrated, agarose-coated

96-well plates and submerged in growth media for imaging.


3.4 Fluorescent Staining

For viability of cells in toroid microtissues, LIVE/DEAD R© Viability/Cytotoxicity

Assay Kit (Invitrogen) was used. A staining solution with 1µM calcein-AM and 4µM EthD-

1 in serum-free, HUVEC media was prepared. Toroid microtissues were washed three times

with serum-free media and incubated in the dye solution for 1 hour. Tori were then imaged

using Zeiss confocal microscope with an AxioCam MRm camera (Carl Zeiss Micro-Imaging,

Thornwood, NY) with X-Cite 120 fluorescence illumination system (EXFO Photonic Solu-

tions, Mississauga, Ontario, Canada). Exposure for the red channel was increased to 500 ms

for better visibility of the EthD-1 stain. For co-culture microtissues, HUVECs were stained

with CellTracker Green (CMFDA, Thermo Fisher Scientific, Waltham, MA) and HAoSMCs

were stained with CellTrackerTM Red (CMTPX, Thermo Fisher Scientific, Waltham, MA).

CellTrackerTM solutions were prepared to a final concentration of 5µM in serum-free media.

Flasks were rinsed with PBS and incubated with respective dye solutions for 30 min at 37C

with 5% CO2. Flasks were then rinsed again and equilibrated with growth media for 30 min.

Cells were then passaged and formed into microtissues following standard protocol.

3.5 Imaging

Tori were imaged inside and outside of molds via Zeiss microscope on bright field

setting. Full-gel images were taken by stitching multiple images at 10X magnification using

Zen software functionality (Carl Zeiss Micro-Imaging, Thornwood, NY). To obtain time-

lapse images, single or stitched snapshots were obtained every 30 minutes for 20 hours.

Imaging of co-culture tori stained with CellTracker dyes were imaged using a Zeiss Axio

Observer Z1 equipped with an AxioCam MRm camera (Carl Zeiss Micro-Imaging, Thorn-

wood, NY) and an X-Cite 120 fluorescence illumination system (EXFO Photonic Solutions,

Mississauga, Ontario, Canada). Side-view imaging of tori was performed by transferring

tori using a widened-bore pipette into a cuvette (Dynalon Corporation, Rochester, NY) filled

with growth media. Images were captured using a horizontally-positioned DinoLite digital

microscope (BigC Dino-Lite, Torrance, CA) with a calibration ruler for size reference.


3.6 Data Analysis and Statistics

For analysis of tori inside micro-molds, Kaplan-Meier curves were created with

toroid contraction off the peg as event criteria. Significance between survival curves were

tested using Python and the Lifelines survival analysis package (including NumPy, Pandas,

Matplotlib) (Davidson-Pilon et al. 2018; Hunter 2007; McKinney 2010; Oliphant 2006). Tests

were performed using a pairwise Log-rank analysis (P < 0.05) with post hoc Bonferroni

correction. For measurement of cross-sectional area (AC) of tori outside of micro-molds,

images were processed using ImageJ (National Institutes of Health, Bethesda, MD) using a

thresholding macro for all images (See Appendix 8). Briefly, contrast threshold of images

were adjusted to convert the toroid into a masked, solid shape. The outer shape and the

lumen space were then selected and measured using the ROI manager and measured. In

post-processing, lumen area was subtracted from total outer area for each toroid to obtain

AC , and measurements were reported as mean ± standard deviation. Lumen closure was

determined if the AC of the lumen was less than 1964 µm2 (50 µm diameter).

Measurements of toroid outer diameter (DO) and lumen diameter (DL) were ob-

tained using Zen Blue software by taking an average of a vertical and horizontal mea-

surement of the circular shape, reported as mean ± standard deviation. Lumen closure

was determined with an average lumen diameter less than 50 µm. Thickness (T) of tori

walls were calculated by: (DO – DL)/2. Tests for significance between curves were per-

formed using two-tailed, unpaired t-test with Welch’s correction (p<0.05). Measurements of

toroid AC , lumen diameter, outer diameter, and wall thickness were normalized to the T=0

measure to observe overall trends in rates. Normalized rates of lumen and outer diameter

over time were graphed by seeding density and analyzed for significance using a One-way

ANOVA (p<0.05) with post hoc Dunn’s multiple comparisons test. Measurements of toroid

height (H) using side-view imaging were taken using ImageJ (National Institutes of Health,

Bethesda, MD).

Average HUVEC cell volume was determined by measuring the diameter of single

cells on an agarose surface and assuming a spherical morphology with volume equal to

43(π(rcell)3), where rcell is the radius of the cell body. Theoretical volume (VT ) of tori was


calculated by multiplying the calculated single-cell volume by the seeding density for each

sample. Experimental volume (VE) of tori was calculated by modelling experimental tori as

a 3D torus with an elliptical vertical wall cross-section. Volume of the toroid was obtained

using the equation: VE=(πhr)(2πR), where h is the semi-minor axis of the ellipsoid cross-

section (H ÷ 2, where H is the total measured height of the toroid), r is the semi-major axis

(or radius, calculated as T ÷ 2), and R is the major radius of the top-down shape (calculated

as (r + (DL ÷ 2)). Tori volume between groups was compared using an unpaired, two-tailed

t-test with Welch’s correction.

30

Chapter 4

Analysis of Tori Inside Mold

4.1 Results

A modular, scaffold-free approach to creating a vascular tube could allow for the

most physiologically-relevant replacement for native blood vessels. To allow for a uniform

tube with known morphological properties, the modular components of this vessel first

require characterization. The modular, building block design consists of a toroid-shaped

microtissue consisting of only cells and endogenous ECM. Briefly, formed agarose micro-

molds are seeded with mono-dispersed cells, which settle into micro-recesses via gravity

and self-assemble to form a uniform microtissue (Fig. 4.1). When evaluating these microtis-

sues morphologically, features of interest include: lumen patency, cellular compaction, and

overall dimensional changes and consistency over time with respect to microtissue compo-

sition.

For these building parts, primary human umbilical vein endothelial (HUVEC) cells

were used to form tori using the micro-mold method. This primary cell type is contractile

by nature and will actively compact microtissue shape over time. Optimal seeding densities

were chosen by evaluating 25,000-150,000 (25K- 150K) cells/toroid in mold after 20 hours

(Fig. 4.2). Tori seeded at 25K did not remain as intact tori after 20 hours, and conversely 150K

tori overflowed from wells and did not remain individual. Therefore the optimal seeding

density for HUVEC tori was determined to be between 50,000 and 100,000 cells/toroid when

using the 6-well configured mold with 36 tori recesses.

Chapter 4. Analysis of Tori Inside Mold 31

Figure 4.1: Formation of toroid-shaped microtissues. First, a set volumeof cell suspension is pipetted over a media-equilibrated agarose mold.The mold contains a number of cylindrical recesses with central pegs foreach toroid (A). Next, mono-dispersed cells in the suspension (B) settleto the bottom of the cylindrical recesses via gravity and self-assemble

around the pegs to form tori (C).

Figure 4.2: HUVEC tori were seeded into agarose micro-molds at variousseeding densities: 25,000 (25K), 50,000 (50K), 100,000 (100K) and 150,000(150K) cells/toroid. Tori were imaged immediately after self-assembly,then after 20 hours in mold to determine the optimal seeding density forintact, uniform tori. Tori at 25K were unable to remain intact, compared

to 150K tori which overflowed the micro-recesses. Scale bar = 500µm.

To determine the morphological changes of HUVEC tori inside agarose molds over

time, tori were seeded and imaged in gels over 20 hours. Over time tori contracted around


the central peg and were observed to “pop-off” of the peg at a certain rate. The change in

rate of “pop-off” was tested under multiple factors: seeding density of tori, passage num-

ber of cells used, and location within the gel. To evaluate the effect of seeding density on

HUVEC tori contraction, tori were seeded at 50,000 (50K) [n=534], 75,000 (75K) [n=78] and

100,000 (100K) [n=108] cells/toroid in micro-molded agarose gels. The tori were allowed

to self-assemble for 30 minutes into micro-tissues before imaging using time-lapse for 20

hours with stitched images at 10X in order to establish when the tori contracted off the pegs.

Percent survival of toroid contraction off the pegs was plotted on a Kaplan-Meier survival

curve for each seeding density (Fig. 4.3). The 50K group was determined to be significant

from the higher densities via pairwise Log-rank analysis (P < 0.05) with post hoc Bonfer-

roni, indicating that lower density tori are less contractile. The 50K group also showed less

variation in rate (standard deviation from mean) than the higher seeding densities.

Figure 4.3: Seeding density affects pop-off rate of HUVEC tori. HUVECtori were seeded at 50K, 75K, and 100K cells/toroid in agarose micro-molds. Tori were imaged after self-assembly for 20 hours using time-lapse. Tori were plotted for percent survival (tori remaining on pegs)for each seeding density (A). The 50K density was found to have a higherrate of survival than 75K and 100K densities (pairwise Log-rank test with

post hoc Bonferroni, P < 0.0001).


Primary-sourced cells, such as HUVEC cells, undergo phenotypic changes as they

age when cultured in a 2D environment. The effects of these phenotypic changes in a 3D

environment is largely unknown. To evaluate the effect of passage number of cells used on

tori contraction, HUVEC tori were seeded at 50K density in a range of passages from P.5-

P.14 and imaged via time-lapse, stitched composite images for 20 hours. Survival curves for

each passage tori were plotted over time, with no obvious trend (Fig. 4.4A). Tori were then

grouped into “Early Passages” (P.5- P.8) and “Late Passages” (P.9- P.14) passages based on

HUVEC culture convention and plotted over time (Fig. 4.4B). A trend was observed where

tori containing late passage (i.e. older) cells overcame the mold peg via contraction at a

significantly faster rate, as determined by a pairwise Log-rank analysis (P < 0.05). Mean

survival time for early passages was 14.6 ± 0.29 hours and late passages was 12.6 ± 0.3

hours.

During standard toroid seeding procedure, vertical pipetting of cell suspension

above the center of the gel was hypothesized to push cells towards outer edges. In order

to determine the effect of this possible variance in toroid mechanics, tori were seeded at

50K [n= 17 gels] and imaged via time-lapse, stitched composite images for 20 hours (Fig.

4.5A). Tori were organized by radial proximity to the gel center in an “outer”, “middle”,

and “inside” ring (Fig. 4.5B). Percent survival of tori was plotted over time for each group

(Fig. 4.5C). Analysis showed a higher contraction rate for outer ring tori compared to inner

ring tori (pairwise Log-rank analysis with post hoc Bonferroni, **p<0.01). Contraction off of

pegs began at 2.5, 4.0 and 5.0 hours and mean survival times were 13.09 ± 0.3, 14.5 ± 0.35,

and 15.5 ± 0.62 hours for the outer, middle, and inner rings, respectively.

To investigate the effect of multiple cell types in a microtissue toroid, HUVEC and

human aortic smooth muscle (HAoSMC) cells were incorporated into tori as a proof-of-

concept. These co-culture tori were seeded into agarose gel plates designed for one tori

micro-mold per 96-well (Fig.4.6). The diagram describes a mold negative containing one

toroid micro-mold per well (Fig. 4.6A) that is pressed into a 96-well plate filled with a small

volume of molten agarose (Fig. 4.6B). The agarose molds are then allowed to solidify, before

being equilibrated and seeded following standard protocol (Fig. 6C). HUVEC and HAoSMC

cells were stained with green CMFDA and red CMTPX CellTracker dye, respectively and


Figure 4.4: Passage number of HUVEC cells in microtissues. Tori at 50Kwere then seeded at a range of seeding densities from P.5- P.14 and ob-served over time with no obvious trends (A). Seeding densities were clas-sified as “Early Passages” (P.5- P.8) and “Late Passages” (P.9- P.14), andtheir respective survival curves were plotted (B). Earlier passages werefound to have higher rates of toroid survival (pairwise Log-rank test, P <

0.0001).

passaged. Microtissues at 50K and 100K seeding density were formed by mixing cell sus-

pensions to obtain various ratios of HUVEC: HAoSMC (1:1, 1:2, 2:1, 1:3 and 3:1). Tori were

allowed to self-assemble, then were transferred to a microscope for time-lapse imaging in

bright field, green and red channel for 24 hours at 10X magnification (Fig. 4.7). Low seeding

volume resulted in loss of cell suspension for both seeding densities, therefore only the 100K

density samples formed complete tori - but can be assumed to be lower in actual density.


Figure 4.5: Passage number of HUVEC cells in microtissues. Tori at 50Kwere then seeded at a range of seeding densities from P.5- P.14 and ob-served over time with no obvious trends (A). Seeding densities were clas-sified as “Early Passages” (P.5- P.8) and “Late Passages” (P.9- P.14), andtheir respective survival curves were plotted (B). Earlier passages werefound to have higher rates of toroid survival (pairwise Log-rank test, P <

0.0001).

Control tori composed of only HUVEC or HAoSMC cells (Fig. 4.7A) contracted around the

pegs after self-assembly (Fig. 4.7B). In 24 hours, HUVEC tori formed a smooth ring around

the peg, however the HAoSMC tori were less contracted and more ragged in appearance.


Figure 4.6: 96-well toroid mold formation process. In order to form onetoroid micro-mold per 96-well, a set volume of agarose was pipetted intoeach well and a negative piece (with the negatives of each peg filled withagarose) (A) was pressed into multiple wells and allowed to solidify (B).The negative was then removed and the gels equilibrated with mediumand seeded to form microtissues, forming around the central peg, as de-picted in the diagram of a single plate well (C, courtesy Benjamin Wilks).


Figure 4.7: Proof-of concept of co-culture vascular tori sorting. HUVECand HAoSMC cells were stained with CellTracker green and red, respec-tively, and cellular suspensions were combined at various ratios to formco-culture tori at 100K density in individual 96-well toroid gels. Toriwere imaged at 10X magnification. Control tori with only HUVEC andHAoSMC cells followed a standard contraction from T=0 (A) to T=24(B) hours. Co-culture tori seeded at various HUVEC: HAoSMC ratios(columns labeled) (C) also contracted over time, with some breakages

observed due to irregular seeding at T=24 hours (D).


Co-culture tori with a mixture of HUVEC and HAoSMC cells (Fig. 4.7C) all con-

tracted around the peg over 24 hours, resulting in some breakages (Fig. 4.7D). For better

distinction of the cell types, the bright field channel was omitted and green and red channels

isolated for T=0 (Fig. 4.8A-C) and T=24 (Fig. 4.8D-F). For the equal mix (1:1) tori, HUVEC

and HAoSMC cells can be seen evenly mixed at T=0 (Fig. 4.8A). At 24 hours, one portion

of the toroid that was further contracted also showed to be more visibly saturated with red-

stained HAoSMC cells, whereas the rest of the microtissue remains mixed (Fig. 4.8D). For

ratios 1:2 and 1:3 where the HAoSMCs were dominant, distinguishing the location of HU-

VEC cells was difficult at 24 hours (Fig. 4.8D, column 2 and 4). However, for ratios 2:1 and

3:1 where the HUVECs were dominant, a sorting phenomenon was observed where the HU-

VEC cells preferentially lined the outside edges and the HAoSMC cells preferred the center

(Fig. 4.8D, column 3 and 5).


Figure 4.8: Proof-of concept of co-culture vascular tori sorting with brightfield removed. HUVEC and HAoSMC cells were stained with Cell-Tracker green and red, respectively, and cellular suspensions were com-bined at various ratios to form co-culture tori at 100K density in indi-vidual 96-well toroid gels. Tori were imaged at 10X via time-lapse over24 hours. Control tori with only HUVEC and HAoSMC cells followeda standard contraction from T=0 (A) to T=24 (B) hours. Co-culture toriseeded at various HUVEC: HAoSMC ratios (columns labeled) (C) alsocontracted over time, with some breakages observed due to irregularseeding at T=24 hours (D). Images of co-culture tori are also picturedin only green and red channels at T=0 and 24 hours (E, F) to show the

organization of each cell type. Scale bars = 500 µm.


4.2 Discussion

After self-assembling in toroid molds, HUVEC toroid microtissues were observed

to contract around the central peg, climbing upwards until the microtissue popped off the

peg. This climbing phenomena was also recorded in previous work using tori composed

of normal human fibroblast (NHF) and rat hepatoma (H35) cells Youssef et al. (2011)). A

toroid climbing assay was developed in this study to measure the rate of vertical climbing

between cell types in tori and was used to calculate the overall work output of the micro-

tissue. This climbing assay provided detailed information of the tissue before pop-off, how-

ever for the Funnel-Guide application, a higher throughput method was needed to evaluate

and compare contraction rates between microtissues under various conditions. Therefore,

when looking at a mold containing 36 individual tori, the event of “pop-off” was used as

a “death event” in a Kaplan Meier curve, so when plotted over time one can calculate the

rate of pop-off as well as the survival (stay on peg) chance for each experimental group. In

this Pop-off assay, rates of pop-off for microtissues can be used as a measure of contractility,

where higher contractile tissues will pop-off sooner than less contractile tissues. The ability

of the Pop-off assay to evaluate multiple gels with 36 tori each also increases the throughput

from previous assays. Limitations of this assay are the reduction in information conveyed

from the previous toroid climbing assay and that video data analysis is still manual, which

could be improved by video recognition software. Overall the Pop-off assay was developed

as a rapid comparison of the contractility of multiple tori which, like the toroid climbing

assay, can give a larger picture of the complex cellular mechanisms involved in cellular ad-

hesion and contraction.

Using the Pop-off assay, HUVEC tori were compared by varied seeding density,

varied passage of cells, and between tori inside the same gel. First, tori at 50K, 75K, and

100K seeding densities were compared by rate of pop-off. The lowest seeding density, 50K

tori, were found to be significantly lower rate of pop-off than the higher seeding densities.

These tori could be less contractile due to fewer cells available to provide contractile force

to move the microtissue up the peg. Alternatively, the 100K tori could still be in the process

of completing self-assembly and provide more contractile force as ECM is restructuring and


cell-cell adhesions are strengthening. When considering building parts for a larger 3D struc-

ture, building parts with the most stable phenotype are desired. Therefore, using vascular

tori at the lower 50K seeding density would be recommended over a higher seeding density.

From a manufacturing standpoint, using a lower seeding density per building part would

also conserve cell materials and cost.

Being a primary-sourced cell line, HUVEC cells undergo cellular senescence as they

age, resulting in phenotypic changes. Over multiple cell divisions telomeres are shortened

and become dysfunctional, causing the arrest of cellular proliferation as well as decreased

nitric oxide (NO) production and increased permeability of endothelial monolayers in vitro

D Krouwer et al. (2012) and Erusalimsky (2009)). These changes have been associated with

the apparent detrimental role of age in cardiovascular disease El Assar et al. (2012)). Further-

more, senescent endothelial cells have been identified in vivo specifically in atherosclerotic

lesions D Krouwer et al. (2012)). Little work has been done to evaluate the effect of primary

cell senescence in 3D microtissues, therefore evaluation of HUVEC microtissue behavior is of

interest to the study of cardiovascular disease. HUVEC cells were cultured from P.5- P.14 and

seeded into tori microtissues for evaluation with the Pop-off assay, where no clear trend of

rate emerged by increasing passage number. When considering the recommended passage

number of P.8 for HUVEC culture, grouping by Early (P.5- P.8) and Late (P.9- P.14) passages

showed a significant increase in pop-off rate for later passages. This result is interesting as it

seems to indicate higher contractility in older HUVEC cells. Genomic and proteomic work

have indicated changes in cytoskeletal proteins in senescent HUVEC cells, which could in-

fluence the contraction mechanisms and output more force Chang et al. (2005) and Kamino

et al. (2003)). Future work could consider using high-passage HUVEC tori when modelling

cardiovascular disease. Based on these experiments, it would be recommended to maintain

HUVEC cells for the Funnal-Guide below P.8 as recommended due to this apparent increase

in contractility at higher passages.

When seeding microtissue tori, cellular suspension is pipetted vertically over the

center of the gel. The force of this added suspension has been suspected to push more cells

towards the outer edges of the seeding chamber, leading to an uneven distribution of cells

between tori. Tori in a 6x6 (36 total) tori gel were grouped by proximity to the center in


an inner, middle, and outer ring. Contraction rate of the outer ring tori were found to be

significantly higher than the inner ring tori, validating the presence of inconsistencies. This

increase in outer tori contraction was most likely due to a larger amount of cells, which

increase the tissue contraction force as seen from the difference between high and low seed-

ing density tori. A solution to produce more consistent tori would be a mold that contains

an individual gel per tori for seeding. Although reducing the throughput of microtissue

production, the resulting consistency would be beneficial at this stage.

Endothelial, smooth muscle, and fibroblast cells composing a blood vessel are to-

gether crucial for vasoactivity, and many tissue engineered blood vessels incorporate a com-

bination of these cells in order to achieve vasoactivity. As a proof-of concept for future

incorporation of additional cell types into toroid microtissues, HUVEC and HAoSMC cells

were co-cultured at various ratios in microtissues to observe their behavior. Both cell types

were stained with CellTracker dye to observe their location in the microtissue over 24 hours.

Control tissues contracted as expected, and did not pop off the peg due to mold design,

however the HAoSMC tissues were more irregular and rough at the edges. The 1:1 mixed

tori and other tori with majority HAoSMC did not exhibit any sorting trends over 24 hours.

Conversely, ratios with majority HUVEC were seen to exhibit a sorting behavior where the

HAoSMC migrated to the center and HUVEC moved to the periphery. This sorting behavior

between two cell types was also observed in NHF and HUVEC co-cultured spheroids, where

the more contractile (i.e. NHF) cells preferentially adhered to form a stable core Napolitano

et al. (2007)). Drawbacks of this proof-of concept experiment were that the microtissues were

low in seeding density due to a lack of mold optimization, and that a longer observation of

the samples would have been beneficial to observe later sorting changes. Overall, these re-

sults are promising for future work with co-cultured vascular tori with distinct functional

layers.

43

Chapter 5

Analysis of Tori Outside Mold

5.1 Results

When inside the mold, the central agarose peg was seen to affect tori contraction ki-

netics, therefore evaluation of tori was performed outside of the mold after self-assembly. To

determine the morphological changes of HUVEC tori over multiple days, tori were seeded at

50,000 (50K) and 100,000 (100K) cells/toroid in micro-molded agarose gels. The tori were al-

lowed to self-assemble for 30 min and were then incubated in-mold for an additional 6 hours

to ensure the tori could maintain their structural integrity when handling. Tori were re-

moved from the molds and placed in individual 96-wells coated with non-adhesive agarose

(Fig 5.1). Observing the tori through images taken every 24 hours determined that the high-

est contraction activity was within 0-24 hours out of mold for the 50K [n=27 tori] (Fig 5.1A)

and 100K group [n=15 tori] (Fig. 5.1B).

A limitation of microtissues is the diffusion of nutrients into the center of the tis-

sue as size increases. Although difficult to ascertain the viability of cells at the core due

to diffusion limit of staining molecules, a live/dead stain was performed in order to deter-

mine cellular viability at the microtissue surface. Tori were seeded at 50K and 100K and al-

lowed to incubate in mold for 24 hours. Tori were then transferred by widened-bore pipette

into individual, agarose-coated wells and stained with a calcein AM (1 µM) and ethidium

homodimer-1 (EthD-1, 4 µM) solution for 1 hour. High calcein AM signal was shown on the

surface of the microtissue comparative to EthD-1 signal, indicating high cell viability after

physical transferring via pipette (Fig. 5.2).

Chapter 5. Analysis of Tori Outside Mold 44

Figure 5.1: HUVEC tori undergo quantifiable morphological changesover time. HUVEC tori were formed by seeding into micro-moldedagarose gel at 50,000 cells/toroid (50K) and 100,000 cells/toroid (100K).Self-assembled tori were removed from agarose molds after 6 hours incu-bation and placed on a non-adherent agarose surface. Images were taken

every 24 hours for 50K (A) and 100K tori (B). Scale bars = 250 µm.

Figure 5.2: Live/dead viability of microtissues at 24 hours. Live/deadstain indicates cell viability of outer cells for HUVEC tori at 50K and100K at 24 hours after transferring to individual wells with pipette be-fore staining. Contrast and brightness of images were increased by +40%.

Scale bars = 200 µm


Tori activity was then observed more closely during T=0-20 hours via snapshot im-

ages every 30 minutes. Contraction of tori over 20 hours was first evaluated with measure-

ments of cross-sectional area (AC) over time. In order to quantify AC (µm2) of the samples

an image analysis method was developed using ImageJ software to isolate the darker toroid

shape from the background and inner lumen area (Fig. 5.3). Average AC was then plot-

ted for T=0 and T=20 in mm2 (Fig. 5.4). Over 20 hours 100K tori remained larger than the

50K tori (two-tailed, unpaired t-test with Welch’s correction, ∗∗∗∗p<0.0001), but both groups

decreased in area over 20 hours. (Fig. 5.4B).

Figure 5.3: Image analysis process for measuring cross-sectional area(AC). ImageJ software was used to measure AC via thresholding soft-ware features. First, the original “.tiff” image was converted to greyscale(I), and the auto-thresholding feature (II) was used to create a silhouetteimage (III). By subtracting the lumen space from the full area (IV, colored

blue), the toroid AC can be obtained (V, colored green).

Figure 5.4: Cross-sectional area decreases for HUVEC tori over 20 hours.100K tori remained larger in cross-sectional area (AC) than 50K tori at T=0(A) and T=20 hours (B). Significance determined via two-tailed, unpaired

t-test with Welch’s correction (∗∗∗∗p<0.0001).


To quantify average outer diameter (DO), lumen diameter (DL) and thickness (T),

measurements were taken using Zen software. Toroid thickness, T, was obtained by: ((DO –

DL)÷2). Significance between the measurements of both seeding densities was determined

via two-tailed, unpaired t-test with Welch’s correction. Measurements of outer diameter

(DO) and thickness (T) were plotted at T=0 and T=20 hours (Fig. 5.5). At T=0, DO and T of

the 100K tori were significantly larger than the 50K samples (∗∗∗∗p<0.0001) (Fig. 5.5A). There

was no significant difference between 50K and 100K lumen diameters (DL), most likely due

to the shaping influence of the central peg. A higher variation (standard deviation from

mean) in 50K tori DO and T was also observed at T=0 compared to the 100K samples. At

T=20 hours, DO and DL for both groups decreased, however the 100K group DL decreased

to be significantly lower than the 50K group (∗∗∗∗p<0.0001) (Fig. 5.5B). Lumen closure was

determined to be when theDL measured less than 50 µm average. At 20 hours, 13 of 15 100K

samples closed, whereas the 50K group experienced no lumen closures. The lumen closures

of the 100K group occurred on average at T=16 ± 1.3 hours. Thickness of the 100K group as

a result also increased to be significantly wider than the 50K group (∗∗∗∗p<0.0001).

In order to compare overall temporal changes between seeding densities over the

20 hour window, measurements of AC , DO, DL, and T were plotted over time (Fig. 5.6).

Over time, a decrease is shown for A, DO andDL, as expected. The increase in thickness due

to reduction of DL was significantly higher for 100K tori, increasing around 87 µm whereas

the 50K tori thickness was only observed to increase by around 32 µm (Fig. 5.6D).

In order to evaluate the rates of contraction, average AC , DO, DL, and T were nor-

malized to their T=0 values and graphed over the 20-hour period (Fig. 5.7). No signifi-

cant difference was found between rates of AC , DO, and DL for the 50K and 100K groups

(two-tailed, unpaired t-test with Welch’s correction (p=0.3911, p=0.5165, and p=0.0593, re-

spectively)) (Fig. 5.7A-C). However, a significant difference was observed wherein the 100K

tori experienced a higher increase of thickness over time than the 50K group (two-tailed,

unpaired t-test with Welch’s correction, ***p<0.001) (Fig. 5.7D). To compare overall rates of

outer and lumen diameters, rates were calculated as % change for 5-hour intervals, and plot-

ted over time (Fig. 5.8). It was observed that the highest rates of contraction for all groups

were from T=0-5 hours. Rates of lumen diameter were found to be significantly higher


Figure 5.5: Dimensional consistency of building blocks at T=0 and 20hours. HUVEC tori at 50,000 cells/toroid (50K, grey) and 100,000 cells/-toroid (100K, black) were measured by outer diameter (DO), lumen diam-eter (DL) and wall thickness (T) and compared at T=0 (A) and T=20 hours(B). The 100K group was larger in all measurements except for lumen di-ameter, as can be seen in the diagram of an average toroid from eachgroup at T=0 (A, right). Comparing the same measurements at T=20, thetwo groups decreased in dimensions proportionally except in the lumen,

where the 100K group decreased more drastically (B, right).

in contraction than the outer diameters, independent of cell number, through a Two-way

ANOVA with Tukey’s multiple comparisons test (see Fig. 5.8 caption). When considering

building block consistency, the time points with the lowest deviation between sample di-

mensions would be the optimal time to use for building. It was observed that the highest

standard deviation occurred for both seeding densities from 0-5 and 15-20 hours, therefore


Figure 5.6: Quantification of HUVEC tori dimensions over 20 hours. HU-VEC tori at 50K and 100K were seeded into a micro-molded agarose gel.Self-assembled tori were placed on a non-adherent agarose surface andimaged every 30 minutes. The cross-sectional area (“A” in inset) wasplotted over 20 hours for 50K [n=27 tori] () and 100K [n=15 tori] HU-VEC tori (•) (A). Toroid outer diameter (DO) and lumen diameter (DL)for both groups were measured and plotted over 20 hours (B, C). Cross-sectional wall thickness (“T” in inset) for both groups were also plotted(D). Asterisks indicate level of significance via two-tailed, unpaired t-test

with Welch’s correction, ∗∗∗p<0.001 and ∗∗∗∗p<0.0001.

the optimal recommended time to use tori for building would be between 5-15 hours.

Dimensional measurements taken from vertical microscopy were validated using

side-view microscopy (Fig. 5.9). HUVEC tori were seeded at 50K [n=30] and 100K [n=33]

densities and allowed to incubate for an additional 6 hours after self-assembly. Half of the

tori were then transferred individually to a media-filled cuvette, where they were imaged

using a calibration ruler as reference (Fig. 5.9A). After 20 hours the remainder of the sam-

ples were removed and imaged similarly. Both seeding densities experienced an increase

in height over 20 hours, however the 100K group increased in height more drastically than

the 50K group (two-tailed, unpaired t-test with Welch’s correction, ∗∗p<0.01 for 100K and


Figure 5.7: Normalized HUVEC tori dimensional changes over 20 hours.Average AC , DO, DL, and T for 50K () and 100K (•) HUVEC tori werenormalized to T=0 measurements and plotted over 20 hours. No sig-nificant difference in rates was determined between seeding densitiesfor AC , DO, and DL (two-tailed, unpaired t-test with Welch’s correction,p>0.05). A significant difference was found between rates of toroid thick-ness change, where the 100K group showed a larger increase over time

(two-tailed, unpaired t-test with Welch’s correction, ∗∗∗p<0.001)

p=0.0571 for 50K) (Fig. 5.9B). Overall the 100K group was significantly higher in height

than the 50K group at both time points (two-tailed, unpaired t-test with Welch’s correction,

∗∗∗∗p<0.0001).

To quantify total toroid compaction in three dimensions, measurements from ver-

tical and side-view imaging were used to calculate the volume of tori over time. This calcu-

lation was adapted from the standard equation for volume of a torus: V=(πr2)(2πR), where

r and R are the minor and major radii, respectively. This equation assumes the vertical cross

section of the wall is perfectly circular, however HUVEC microtissues were found in reality

to be slightly flattened in height. Therefore, an adjusted equation for volume can be used

substituting the area of an ellipse instead, with semi-minor axis, h, and semi-major axis (or


Figure 5.8: HUVEC tori rates of change over time intervals. HUVEC toriat 50K [n=27 tori] and 100K [n=15 tori] cells/toroid were observed overtime on a non-adherent surface. Normalized rates of contraction of outerand lumen diameter were plotted over 5-hour intervals, where the high-est rates were seen from hours T=0-5 for both groups. Superscript sym-bols indicate significant difference from Outer 50K (∗), Outer 100K (†),Lumen 50K (‡), or Lumen 100K (Y) groups (2-way ANOVA with Tukey’smultiple comparisons test, bolded symbol for p<0.0001, un-bolded for

p<0.001).

radius), r (Fig. 5.10A). The final equation used for experimental volume (VE) of an ellip-

tical torus was: VE=(πhr)(2πR). The minor radius, r, was obtained by dividing toroid wall

thickness by half, and the major radius, R, was obtained by adding half the lumen diame-

ter to r for each sample (Fig. 5.10B). For height, h, an average was used for each respective

experimental group. Toroid volume (µm3) plotted for each group, where 100K tori [n=15]

were higher in volume than 50K tori [n=27] (two-tailed, unpaired t-test with Welch’s cor-

rection, ∗∗∗∗p<0.0001). Both groups showed a significant decrease in volume over 20 hours

(two-tailed, unpaired t-test with Welch’s correction, ∗∗∗∗p<0.0001), however the magnitude

of percent change was different (Fig. 5.10C). Although the 100K tori were overall higher

in volume, the 50K tori experienced a much greater percent decrease comparatively (two-

tailed, unpaired t-test with Welch’s correction, ∗∗∗∗p<0.0001).


Figure 5.9: Side-view images to validate toroid height. Tori at 50Kand 100K were then transferred using a pipette into a media-filled cu-vette and imaged side-on at T=0 and T=20 hours (A). Scale bar = 1 mm.Toroid height was plotted for the two seeding densities at both timepoints (B). Significant differences were found between 50K and 100K atboth time points, however only the 100K group increased significantly inthickness over time (two-tailed, unpaired t-test with Welch’s correction,

∗∗∗∗p<0.0001 and ∗∗p=0.001).

To ascertain if the decrease in overall volume seen in both seeding densities was

proportional to the amount of cells in the microtissue, volume was divided by total cells. The

original seeding density number was used for both time points, assuming minimal cell death

or proliferation. Significance was determined by two-tailed, unpaired t-test with Welch’s

correction, where ns= “not significant” and ∗∗∗∗p<0.0001. When compared, the 50K and

100K group begin with comparable volumes/cell, however after 20 hours the 50K group

decreased significantly more in volume/cell (Fig. 5.11). Average volume per cell for 50K tori

was 2196.92± 189.1 µm3 at T=0 and 1537.41± 161.2 µm3 at T=20 hours. Average volume per

cell for 100K tori was 2287.88± 98.24 µm3 at T=0 and 1839.79± 67.07 µm3 at T=20. To verify

these findings, the average volume of a HUVEC cell was calculated by taking measurements

of diameter of single HUVEC cells on agarose (Fig 5.11B). The average diameter of a HUVEC

cell was determined to be 16.2 ± 2.3 µm, and therefore the volume of a theoretical HUVEC

cell (assuming spherical in shape) was then calculated to be 2185.12 µm3. Multiplying this

number by the seeding density for each group allowed a calculation of the theoretical toroid

volume (VT ). Comparing to the experimental VE value for each group at T=0, the values are


Figure 5.10: Experimental tori volume decreases over time over 20 hours.Toroid volume was calculated using a modified equation that assumesan elliptical wall cross-section with minor height, h, and minor radius, r(A). Top-view diameter measurements were utilized to obtain the majorradius, R, and minor radius, r (B). Tori volume at 50K [n=27] and 100K[n=15] seeding densities was compared, showing a significant differencebetween both groups as well as a significant decrease within groups (two-tailed, unpaired t-test with Welch’s correction, ∗∗∗∗p<0.0001) (C). Plottingpercent decrease, the change in 50K tori volume was significantly more

than the 100K group (D).

comparable, indicating accurate volume estimates of the tori (Fig 5.11C).


Figure 5.11: Experimental volume versus theoretical volume is compa-rable, and can be used to calculate Volume per cell. Theoretical volume(VT ) was calculated by first obtaining diameters of HUVEC cells on anon-adherent surface (A) and calculating the estimated volume of a HU-VEC cell. The VT for 50K and 100K samples at T=0 was compared tothe experimental (VE) calculated previously and was found to be com-parable (B). Using the validated (VE), volume per cell was calculated,showing a decrease in volume/cell for both seeding densities over time.Significance determined using two-tailed, unpaired t-test with Welch’s

correction, ∗∗∗∗p<0.0001).


5.2 Discussion

The Pop-off assay developed for toroid evaluation in-mold could not describe the

temporal changes in tori outside of the physical mold influence. Hence, it was necessary to

remove the tori from the mold after self-assembly to evaluate their morphological changes

over time. This evaluation of tori on a non-adherent surface was previously performed for

rat hepatoma (H35) tori to evaluate the influence of micromold design and seeding density

on toroid dimensions Livoti and Morgan (2010)). Being less contractile than HUVEC cells,

the lumens of H35 tori did not close until after 10 days, whereas HUVEC tori lumen were

found to close before 2 days on a non-adherent surface. Therefore, the time window for

evaluating HUVEC tori contraction kinetics was determined to be from 0-48 hours, and

more specifically 0-24 hours when the most contractile changes occur. Over this time, tori of

low and high seeding densities were seen to contract in size, wherein some lumens closed.

First, a proof-of-concept for cellular viability was performed on HUVEC tori at 24

hours. Verification of cellular viability in the core of microtissues has remained a challenge

in the field of tissue engineering. Even with confocal microscopy, a diffusion limit to dye

penetration remains in microtissues, with up to 75% fluorescent loss at 75µm into the mi-

crotissue Leary et al. (2018)). Nonetheless, imaging of tori dyed with Live/Dead (Calcein

AM/ Ethiduim homodimer-1) stain can provide information of viability of cells along the

top surface of the microtissue. Since damage to microtissues during transfer via pipette was

a concern, 24-hour tori were stained in individual wells after transfer. Results showed high

viability fluorescence after transfer for both seeding densities. This high viability was also

determined of HUVEC and liver hepatocellular carcinoma (HepG2) tori after transfer in pre-

vious work (Manning, Thomson, and Morgan 2018). Further studies could evaluate tori at

longer time points using confocal imaging to look at cross-sections through the microtissue.

Using bright field images of tori over 20 hours, tori dimensions were quantified

in cross-sectional area (AC), outer diameter (DO), lumen diameter (DL), and wall thickness

(T). For cross-sectional area, an image analysis protocol was developed using ImageJ soft-

ware to isolate the toroid shape as a silhouette and measure area. The process relied on the


software to distinguish the outline of the toroid from the background, which was more dif-

ficult on later time points where more cell debris made the edge less distinct. Nonetheless,

cross-sectional area of tori decreased for both seeding densities over time. Measuring DO

and DL was done manually, which could have created some human error, however taking

an average of horizontal and vertical measurements was expected to lessen this error. At

T=0, variation of the 50K tori DO and T was higher, possibly due to cells not completely

filling the seeding micro-well and leaving incomplete edges and varied measurements. In

order to overcome these inconsistencies it would be recommended to test slightly higher

seeding densities to fine-tune the minimum required density. However, the arguably more

important dimension DL was almost identical between the two seeding densities due to the

cells maintaining the shape of the mold peg. Over 20 hours, both tori groups were shown to

decrease in DO and DL over time, however the 100K tori decreased in DL significantly more.

This change can also be shown by the drastic increase in thickness for the 100K tori. Over-

all, DL was the most changed over time for both groups, which is perhaps the most crucial

measurement to allow media flow through building parts. Therefore, 100K tori would not

be recommended to be used due to their more unstable lumens and shorter time for closure.

For building part consistency, tori should be used when their rate of contraction

and dimensions are the most consistent. When plotting the percent change of DO and DL

over 5-hour intervals, the highest percent change was observed for both measurements be-

tween 0-5 hours, independent of seeding density. Based on this result and time-lapse video

data, the average rate of self-assembly seems to be highest at the start and taper over time.

Logically, once cells in a microtissue have reached the configuration to minimize the surface

area: volume ratio there would be fewer subsequent morphological changes. Furthermore,

the standard deviation in percent change was shown to be lowest from 5-15 hours, suggest-

ing that tori at those time would be at the same stage of self-assembly and therefore more

consistent.

To evaluate the tori in three dimensions, side-view images were taken of tori to

obtain measurements of height. This process involved transferring tori individually into

a media-filled cuvette via pipette, which could have physically affected the toroid shape.

An increase in toroid height over 20 hours was observed, and can be compared to similar


increases in height (or, thickness) during H35 self-assembly Livoti and Morgan (2010)). In-

creasing toroid height indicates that the force of self-assembly and minimizing surface area

by the cells is able to overcome the force of gravity on them. More interesting is the higher

increase in height for 100K tori of 19.4% compared to a 13% increase for 50K, which was con-

trary to the original hypothesis that more cells would limit height expansion due to weight.

Experimental volume (VE) was calculated using top-down and side-view measurements,

showing an overall decrease in volume for both groups. An average height was used for

each sample group therefore the accuracy of experimental volume is not ideal, though it can

be used for general comparison.

Toroid volume decrease could be the result of two morphological changes: either 1)

cell bodies moving closer together or 2) the volume of the cell bodies themselves decreasing.

Further studies could investigate this distinction by fluorescently dyeing the cytoplasmic

area of a small portion of the cells within a microtissue and measure if there is any increase or

decrease in cytoplasmic area over time. Notably, the 50K tori had a higher percent decrease

in volume than the larger tori, but it unclear which change this decrease resulted from.

A hypothesis for this seeding density difference could be that, in having fewer cells, the

50K tori are more further self-assembled than the 100K tori due to fewer cell-cell adhesions

required for full self-assembly. Therefore, the 50K tori would have a head-start in fully

compacting the microtissue to its most stable form. Evaluating both tori over a longer period

of time could show if the 100K tori eventually reach the same percent decrease in volume

relative to the 50K tori.

To verify the VE results, theoretical volume (VT ) of the tori was estimated by using

a value for the theoretical volume of a HUVEC cell. The theoretical volume of a HUVEC cell

was obtained by taking diameter measurements of HUVEC cells on a non-adherent surface

to obtain an average diameter of 16.1 µm. This value is reasonable, being within range

of two published values for HUVEC cell diameter (14-15 µm and 17 µm) Milo, Jorgensen,

and Springer (Diameter of Human Umbilical Vein Endothelial - Human Homo sapiens - BNID

108899) and Milo, Jorgensen, and Springer (Size of HUVEC (human umbilical vein endotheli

- Human Homo sapiens - BNID 108923)). Assuming a spherical cell shape, the theoretical

volume was calculated to be around 2185.12 µm3. Multiplying this number by the seeding


density of each sample can obtain the VT for each seeding density (assuming minimal space

between cell bodies). Comparing to VE , the VT values were found to be 0.53% higher than

the 50K and 4.49% than the 100K samples. A higher VT estimation of the 100K volume could

suggest that the 100K samples are more compact than the 50K tissues or that their cell bodies

have reduced in size. However from this data that distinction is still indefinite and could

be revealed through further studies measuring cell body size inside of a microtissue over

time. Overall, results showed that a measurement of toroid volume can be obtained that is

comparable to theoretical estimates, and can be used in the future to further study the effect

of self-assembly on the cellular components of microtissues.

58

Chapter 6

Summary and Future Directions

In summary, vascular toroid microtissues can be produced and their dimensions

are time and seeding-density dependent. In order to build a tube structure that is consis-

tent using the Funnel-Guide, the building parts must be consistent in dimensions as well.

Therefore, contractility was evaluated inside and outside the microtissue mold to determine

the time of optimal building part consistency. The Pop-off assay was developed to ascer-

tain relative contractile strengths of tori under variable seeding density, passage number,

and location within gel. This Pop-off assay could be improved in combination with image

recognition software to produce a high-throughput assay of toroid contractility. Further es-

timation of toroid contractile power can also be determined using the toroid climbing assay

from previous work. Results from the Pop-off assay showed that higher seeding densities

are more contractile, therefore lower seeding density tori (50K) were recommended to be

used in the Funnel-Guide. Next, lower contractility was observed in HUVEC tori using

cells from recommended passages (below P.8), so culturing below those passages was rec-

ommended. These results are also a first step towards assessing the effects of primary cell

senescence in 3D culture. Next, varying contractility between tori within the same gel was

observed, which was a result of mold design. A solution would be to use the 96-well mold as

used in the co-culture proof-of-concept so that the tori are seeded separately. Finally, mixing

HUVEC and HAoSMC cells in co-culture in the 96-mold showed indication of self-sorting

which could indicate the possibility of distinct cell layers in vascular co-culture tori.

To evaluate HUEVC tori outside of mold influence, HUVEC tori were observed

on non-adherent agarose over time and measured dimensionally. Over 20 hours, 50K and

Chapter 6. Summary and Future Directions 59

100K tori decreased in outer and inner diameters, and increased in height. A higher vari-

ability was observed for measurements of the 50K tori, suggesting that a balance exists be-

tween having enough cells to form a consistent shape and limiting contraction. A suggestion

would be to raise the minimum toroid seeding density slightly to fine-tune this balance. The

100K tori were shown to decrease significantly more in lumen diameter and increase sig-

nificantly more in height than the 50K tori. Contraction rate of change was found to be the

highest and most variable from 0-5 hours. The time interval with the least variability in rate

of change is ideal for using building parts, therefore using microtissue tori from 5-15 hours

should result in the most consistent tube. Volume was also calculated and was shown to

decrease over time, and was verified by comparing to a theoretical volume calculated from

estimated HUVEC cell volume.

Future directions for this work would be to take tori that fulfill the recommenda-

tions put forth by this thesis and use them in the Funnel-Guide to validate that the result

would be the most dimensionally-consistent tube. Next step would be replication of the

In-mold and Out-of-mold experiments with other vascular cell types, namely HAoSMC and

vascular fibroblasts. A limitation with using primary HAoSMC is the slow growth rate and

low yield, which made attempts at the same experiments difficult. Rat aortic smooth muscle

cells could be utilized for their higher growth rate, however the results would be less appli-

cable to human data. After individually characterizing cell type tissues for contractility, a

next step could be co-culturing the tori to first look at sorting behavior and then analyzing

how co-culturing affects the contractile behavior of the toroid as a whole. Finally, in order to

create a functioning vascular graft with these multiple cell types, histological and mechan-

ical testing must be done in order to compare with in vivo native vessels. As the Funnel-

Guide technology can be applied to other lumen systems in the body, this work presents an

advancement in optimizing vascular tori building blocks for creating larger vascular tubes.

60

Chapter 7

Conclusion

Cardiovascular disease remains a large burden on global health, inciting a need

to understand its mechanisms and provide long-term solutions. Microtissue methods hold

promise for providing an in vivo-like replacement for blood vessels damaged from this dis-

ease. Moreover, microtissues have the ability to be used as building blocks for larger 3D

structures such as a lumen structure through manually stacking. The Funnel-Guide sys-

tem was developed previously as a non-contacting platform to stack microtissues in a high-

throughput manner. The system is designed to be used with toroid microtissues, which al-

low for media flow through the center for nutrient diffusion. However, a limitation to using

the Funnel-Guide with vascular cell microtissues is the contractility of the building blocks,

creating inconsistent tubes. The work of this thesis aimed to characterize these vascular

building blocks to determine the optimal time for stability as well as assess the morphology

of endothelial tori self-assembly as a whole. Results showed that vascular tori contract in

a time and seeding density-dependent manner. Furthermore, vascular tori contraction is

most consistent from 5-15 hours after self-assembly and at lower seeding densities. Metrics

for assessing contractility of tori building blocks, such as the Pop-off assay, were developed

in order to lay the groundwork for assessment of tori of different or multiple cell types.

Ultimately, the metrics produced in this thesis to evaluate vascular tori can be used in the

development of a consistent, macro-tissue vascular tube for treatment of damaged vascula-

ture.

61

Chapter 8

Appendix

Appendix A: Image Analysis Macro Code

This macro expedites some of the image processing of .tiff images of tori outside

of mold on flat, agarose substrate. The code uses the AutoThreshold function to set to (0,

81) for all images to increase contrast of shadowed toroid on white background. The macro

then converts the toroid shape to a Mask and selects the wand tool for manual selection

of shapes. Manually-selected lumen areas can be subtracted from the larger toroid area to

obtain the cross-sectional area of the toroid.

Instructions for use:

1. Install code as ImageJ (Fuji) macro:

run("8-bit");

setAutoThreshold("Default");

//run("Threshold...");

//setThreshold(0, 81);

setOption("BlackBackground", false);

run("Convert to Mask");

//setTool("wand");

run("ROI Manager...");

2. Open .tif or .jpg image for analysis.

3. Ensure scale is correct.

4. Run macro code.

Chapter 8. Appendix 62

5. Manually select lumen space, hit “Add” in ROI manager, then outer toroid shape and hit

“Add” again.

6. Select “Measure” in ROI manager to save the Area measurements (µm2).

7. Close image and repeat process for images in batch.

8. When finished, copy measurement data into Excel and subtract lumen spaces from total

(outer) area for each toroid to obtain the cross-sectional area.


Appendix B: Survival Analysis Code

Python code adapted from Benjamin Wilks using LifeLines package (Davidson-

Pilon et al. (2018)) to analyze tori groups based on passage number, seeding density, or

location within gel via Log Rank analysis with/without Bonferroni correction for multiple

comparisons. Example of Exel Sheet data template can be seen in Fig. 8.1.

Figure 8.1: Setup template of tori survival data for analysis. Data wasarranged with time in column ’A’ in hours (converted to minutes in col-umn ’B’). Experimental groups (e.g. "50K") were added as columns, withthe total amount of events (e.g. toroid pop-off) at each time for each row.To the right of each experimental group column contains the total cen-sored samples at each time for said group (e.g. "Censor.1"). Note thatsubsequent censor columns are titled distinctly. Finally, the foot of each

column contains the sum of all events or censors.


Passage Number Analysis

# coding : utf−8

import pandas as pd

import osos . l i s t d i r ( )

# L i s t s a l l f i l e s t h a t can be imported in the mapped f o l d e r

f i lename = ’HUVEC_Pop−o f f _ L i f e l i n e _ A n a l y s i s _ p a s s a g e groups . xlsx ’ #Choose f i l e to be read

df_PGroup = pd . read_exce l ( f i lename ) #Names the parsed data f i l e as " df_PGroup "

df_PGroup = df_PGroup . drop ( ’ Hour ’ , a x i s =1)

# Units of time need to be i n t e g e r values

df_PGroup . head ( )

# Displayes a preview of the data f i l e t h a t i s read

EarlyP = df_PGroup [ [ ’ Minutes ’ , ’ Early_P ’ , ’ Censor_E ’ ] ]

LateP = df_PGroup [ [ ’ Minutes ’ , ’ Late_P ’ , ’ Censor_L ’ ] ]

# Creat ing v a r i a b l e s f o r each experimental group ( Time to Event , Event column , Censor column )

EarlyP . head ( )

from l i f e l i n e s . u t i l s import surv iva l_events_ f rom_tab le

T1 , E1 = surv iva l_events_ f rom_tab le ( EarlyP , observed_deaths_col = ’ Early_P ’ , censored_col = ’ Censor_E ’ )

T2 , E2 = surv iva l_events_ f rom_tab le ( LateP , observed_deaths_col= ’ Late_P ’ , censored_col = ’ Censor_L ’ )

# Assigns T ( time ) and E ( event ) v a r i a b l e s f o r the " surv iva l_events_ f rom_tab le " funct ion , one s e t per experimental group

T = [ ]

E = [ ]

g1 = [ ’ Early_P ’ ]∗ len ( T1 )

g2 = [ ’ Late_P ’ ]∗ len ( T2 )

group = [ ]

group . extend ( g1 )


T . extend ( T1 )

T . extend ( T2 )

E . extend ( E1 )

E . extend ( E2 )

df_PGroup2 = pd . DataFrame ( )

df_PGroup2 [ ’ T ’ ] = T

df_PGroup2 [ ’ E ’ ] = E

df_PGroup2 [ ’ group ’ ] = group

df_PGroup2 . head ( )

T = df_PGroup2 [ ’ T ’ ]

E = df_PGroup2 [ ’ E ’ ]

group = df_PGroup2 [ ’ group ’ ]

EarlyP = ( df_PGroup2 [ ’ group ’ ] == ’ Early_P ’ )

LateP = ( df_PGroup2 [ ’ group ’ ] == ’ Late_P ’ )

# This s e c t i o n turns the T and E v a r i a b l e s i n t o index−able arrays and c r e a t e s another vers ion of the data

# f i l e " df_PGroup2 " to update i t

from l i f e l i n e s import KaplanMeierFi t ter

kmf = KaplanMeierFi t ter ( )

kmf . f i t ( T , event_observed=E , ) import m a t p l o t l i b as mpl

from m a t p l o t l i b import pyplot as p l t

%m a t p l o t l i b i n l i n e

# p l t . s t y l e . use ( r ’C:\ Users\bwilks\Desktop\ a r t i c l e . mplstyle ’ )

p l t . f i g u r e ( f i g s i z e = ( 3 . 5 , 3 . 5 ) , dpi =600)

p l t . y l a b e l ( ’ Survival ’ )



ax = p l t . subplot ( 1 1 1 )

kmf_Outer = KaplanMeierFi t ter ( )

ax = kmf . f i t ( T [ EarlyP ]/2 , E [ EarlyP ] , l a b e l = ’ Early Passages ’ ) . p l o t ( ax=ax , ci_show=False , l i n e s t y l e = ’ dotted ’ , c o l o r = ’ black ’ )

l i n e = ax . l i n e s [ 0 ]

p r i n t ( l i n e . get_xydata ( ) )

kmf_Middle = KaplanMeierFi t ter ( )

ax = kmf . f i t ( T [ LateP ]/2 , E [ LateP ] , l a b e l = ’ Late Passages ’ ) . p l o t ( ax=ax , ci_show=False , l i n e s t y l e = ’− . ’ , c o l o r = ’ grey ’ )

p l t . x l a b e l ( ’ Hours ’ )

# ax . se t_yl im ( [ 0 , 1 . 0 5 ] )

# ax . se t_x l im ( [ 0 , 7 . 0 5 ] )

# p l t . x t i c k s ( np . arange ( 0 , 8 , s tep =1 ) )

# p l t . s a v e f i g ( ’ s u r v i v a l . png ’ )

s t a t s = p a i r w i s e _ l o g r a n k _ t e s t ( T , group , E , alpha = 0 . 9 9 )

s t a t s . summary

#Did not use Bonferroni c o r r e c t i o n − only one pairwise comparison

Seeding Density Analysis

# coding : utf−8

import pandas as pd

import os

# L i s t s a l l f i l e s t h a t can be imported in the mapped f o l d e r

os . l i s t d i r ( )

#Choose f i l e to be read

fi lename = ’HUVEC_Pop−o f f _ L i f e l i n e _ A n a l y s i s _ d e n s i t y . x lsx ’

df = pd . read_exce l ( f i lename ) #Names the parsed data f i l e as " df "

df = df . drop ( ’ Hour ’ , a x i s =1) # Units of time need to be i n t e g e r values

df . head ( ) # Displayes a preview of the data f i l e t h a t i s read


sd_50K = df [ [ ’ Minutes ’ , ’ 5 0K’ , ’ Censor . 1 ’ ] ]

sd_75K = df [ [ ’ Minutes ’ , ’ 7 5K’ , ’ Censor . 2 ’ ] ]

sd_100K = df [ [ ’ Minutes ’ , ’ 1 0 0K’ , ’ Censor . 3 ’ ] ]

sd_50K . head ( )



T1 , E1 = surv iva l_events_ f rom_tab le ( sd_50K , observed_deaths_col = ’50K’ , censored_col = ’ Censor . 1 ’ )



sd_50K . head ( )

T = [ ]

E = [ ]

g1 = [ ’ 5 0K’ ]∗ len ( T1 )

g2 = [ ’ 7 5K’ ]∗ len ( T2 )

g3 = [ ’ 1 0 0K’ ]∗ len ( T3 )

group = [ ]





T . extend ( T1 )

T . extend ( T2 )

T . extend ( T3 )

E . extend ( E1 )

E . extend ( E2 )

E . extend ( E3 )

df_2 = pd . DataFrame ( )

df_2 [ ’ T ’ ] = T

df_2 [ ’ E ’ ] = E

df_2 [ ’ group ’ ] = group

df_2 . head ( )

T = df_2 [ ’ T ’ ]

E = df_2 [ ’ E ’ ]

group = df_2 [ ’ group ’ ]

sd_50K = ( df_2 [ ’ group ’ ] == ’50K’ )

sd_75K = ( df_2 [ ’ group ’ ] == ’75K’ )

sd_100K = ( df_2 [ ’ group ’ ] == ’100K’ )

# This s e c t i o n turns the T and E v a r i a b l e s i n t o index−able arrays and c r e a t e s another vers ion of the data f i l e

#" df_2 " to update i t



kmf . f i t ( T , event_observed=E , )

import m a t p l o t l i b as mpl


get_ipython ( ) . run_line_magic ( ’ matplot l ib ’ , ’ i n l i n e ’ )






kmf_50k = KaplanMeierFi t ter ( )

ax = kmf . f i t ( T [ sd_50K ]/2 , E [ sd_50K ] , l a b e l = ’50K ’ ) . p l o t ( ax=ax , ci_show=False , l i n e s t y l e = ’ dotted ’ , c o l o r = ’ black ’ )

l i n e = ax . l i n e s [ 0 ]



ax = kmf . f i t ( T [ sd_75K ]/2 , E [ sd_75K ] , l a b e l = ’75K ’ ) . p l o t ( ax=ax , ci_show=False , l i n e s t y l e = ’− . ’ , c o l o r = ’ grey ’ )


ax = kmf . f i t ( T [ sd_100K ]/2 , E [ sd_100K ] , l a b e l = ’100K ’ ) . p l o t ( ax=ax , ci_show=False , l i n e s t y l e = ’ dashed ’ , c o l o r = ’ s i l v e r ’ )


# ax . se t_yl im ( [ 0 , 1 . 0 5 ] )

# ax . se t_x l im ( [ 0 , 7 . 0 5 ] )



from l i f e l i n e s . s t a t i s t i c s import l o g r a n k _ t e s t p a i r w i s e _ l o g r a n k _ t e s t

s t a t s = p a i r w i s e _ l o g r a n k _ t e s t ( T , group , E , alpha =0 .99 , Bonferroni = True )

s t a t s . summary

#Used Bonferroni c o r r e c t i o n to c o r r e c t f o r mult ip le pairwise comparisons


Toroid Location Analysis

# coding : utf−8

import pandas as pd

import os

os . l i s t d i r ( ) # L i s t s a l l f i l e s t h a t can be imported in the mapped f o l d e r

f i lename = ’HUVEC_Pop−o f f _ L i f e l i n e _ A n a l y s i s _ l o c a t i o n . xlsx ’

#Choose f i l e to be read

df_Locat ion = pd . read_exce l ( f i lename ) #Names the parsed data f i l e as " df_Locat ion "

df_Locat ion = df_Locat ion . drop ( ’ Hour ’ , a x i s =1) # Units of time

# need to be i n t e g e r values

df_Locat ion . head ( )

# Displayes a preview of the data f i l e t h a t i s read

Outer = df_Locat ion [ [ ’ Minutes ’ , ’ Outer Ring ’ , ’ Censor_O ’ ] ]

Middle = df_Locat ion [ [ ’ Minutes ’ , ’ Middle Ring ’ , ’ Censor_M ’ ] ]

Inner = df_Locat ion [ [ ’ Minutes ’ , ’ Inner Ring ’ , ’ Censor_I ’ ] ]


Outer . head ( )


T1 , E1 = surv iva l_events_ f rom_tab le ( Outer , observed_deaths_col= ’ Outer Ring ’ , censored_col = ’Censor_O ’ )

T2 , E2 = surv iva l_events_ f rom_tab le ( Middle , observed_deaths_col= ’ Middle Ring ’ , censored_col = ’Censor_M ’ )

T3 , E3 = surv iva l_events_ f rom_tab le ( Inner , observed_deaths_col= ’ Inner Ring ’ , censored_col = ’ Censor_I ’ )


T = [ ]

E = [ ]

g1 = [ ’ Outer Ring ’ ]∗ len ( T1 )

g2 = [ ’ Middle Ring ’ ]∗ len ( T2 )

g3 = [ ’ Inner Ring ’ ]∗ len ( T3 )

group = [ ]




T . extend ( T1 )

T . extend ( T2 )

T . extend ( T3 )

E . extend ( E1 )

E . extend ( E2 )

E . extend ( E3 )

df_Locat ion2 = pd . DataFrame ( )

df_Locat ion2 [ ’ T ’ ] = T

df_Locat ion2 [ ’ E ’ ] = E

df_Locat ion2 [ ’ group ’ ] = group

df_Locat ion2 . head ( )

T = df_Locat ion2 [ ’ T ’ ]

E = df_Locat ion2 [ ’ E ’ ]

group = df_Locat ion2 [ ’ group ’ ]

Outer= ( df_Locat ion2 [ ’ group ’ ] == ’ Outer Ring ’ )

Middle= ( df_Locat ion2 [ ’ group ’ ] == ’ Middle Ring ’ )

Inner= ( df_Locat ion2 [ ’ group ’ ] == ’ Inner Ring ’ )

# This s e c t i o n turns the T and E v a r i a b l e s i n t o index−able arrays and c r e a t e s another vers ion of the data f i l e " df_Locat ion2 "


# to update i t



kmf . f i t ( T , event_observed=E , )

import m a t p l o t l i b as mpl


get_ipython ( ) . run_line_magic ( ’ matplot l ib ’ , ’ i n l i n e ’ )






kmf_Outer = KaplanMeierFi t ter ( )

ax = kmf . f i t ( T [ Outer ]/2 , E [ Outer ] , l a b e l = ’ Outer ’ ) . p l o t ( ax=ax , ci_show=False , l i n e s t y l e = ’ dotted ’ , c o l o r = ’ black ’ )

l i n e = ax . l i n e s [ 0 ]


kmf_Middle = KaplanMeierFi t ter ( )

ax = kmf . f i t ( T [ Middle ]/2 , E [ Middle ] , l a b e l = ’Middle ’ ) . p l o t ( ax=ax , ci_show=False , l i n e s t y l e = ’− . ’ , c o l o r = ’ grey ’ )

kmf_Inner = KaplanMeierFi t ter ( )

ax = kmf . f i t ( T [ Inner ]/2 , E [ Inner ] , l a b e l = ’ Inner ’ ) . p l o t ( ax=ax , ci_show=False , l i n e s t y l e = ’ dashed ’ , c o l o r = ’ s i l v e r ’ )


# ax . se t_yl im ( [ 0 , 1 . 0 5 ] )

# ax . se t_x l im ( [ 0 , 7 . 0 5 ] )



from l i f e l i n e s . s t a t i s t i c s import logrank_tes t , p a i r w i s e _ l o g r a n k _ t e s t

s t a t s = p a i r w i s e _ l o g r a n k _ t e s t ( T , group , E , alpha =0 .99 , Bonferroni = True )

s t a t s . summary

#Used Bonferroni c o r r e c t i o n to c o r r e c t f o r mult ip le pairwise comparisons

69

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