development of cell culture processes on digital ... · this chapter introduces the fundamental...

Development of Cell Culture Processes on Digital Microfluidic

Platforms

by

Sam H. Au

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy – Biomedical Engineering

Institute of Biomaterials and Biomedical Engineering

University of Toronto

© Copyright by Sam H. Au (2013)

ii

Development of Cell Culture Processes on Digital Microfluidic

Platforms

Sam H. Au

Doctor of Philosophy – Biomedical Engineering

Institute of Biomaterials and Biomedical Engineering

University of Toronto

2013

Abstract

In vitro microenvironments used for culturing and studying living cells have remained virtually

unchanged for the last five decades. Mammalian cells are routinely seeded as monocultures onto

rigid, homogeneous, two dimensional substrates – systems with limited physiological relevance.

Microfluidics has the potential to significantly improve cell models to better mimic native tissue

or disease states in addition to a host of other benefits such as improved throughput, reduced

consumable requirements and seamless integration with a number of analysis techniques. Digital

microfluidics, a fluid handling technique which manipulates discrete droplets over micro-

electrode patterned surfaces, may be a valuable tool for a number of cell applications

incorporating all of the above potential advantages. Cell culture and analysis is a new application

for digital microfluidics – the first report of such was in 2008. As a result, a number of technical

impediments must be addressed before cells can be effectively and routinely studied on these

microfluidic devices. These impediments include: a) rapid device failure due to protein

biofouling on hydrophobic device surfaces, b) the unexplored possibility of detrimental effects

on cell fitness arising from the electrokinetic manipulation of droplets and c) the lack of robust

iii

systems capable of long term automated culture and integrated analysis of cells. The aim of the

work described in this thesis is to advance digital microfluidics for biologically relevant cellular

culture and analysis by addressing each of these challenges. The technical advancements gained

from these studies can then be used to develop a proof-of-concept digital microfluidic platform

for modeling liver tissue. In summary, this work describes advances towards physiologically-

relevant culture and analysis of living cells on digital microfluidics, a technology which has the

potential to become a valuable tool for the biomedical research community.

iv

To him who has had the experience no explanation is necessary, to him who has not, none is

possible.

- Ram Dass

v

Acknowledgments

First and foremost I wish to express deep gratitude to my many research mentors. I count myself

extremely lucky to have studied under each of these skilled scientists: Aaron Wheeler, whose

enthusiasm, generosity and, most importantly, patience are the best qualities anyone could ask

for in a graduate supervisor. Veronica Carvalhal for teaching me the principles of intelligent

experimental design – like most things in life, what you get out depends on what you put in;

Arindom Sen for teaching me that when it comes to cell culture, nurture beats nature; and Poki

Yuen and Vasiliy Goral for showing me that even though there are an infinite number of

problems to be solved and an infinite ways to solve them, we advance by finding one solution to

one problem at a time.

To my committee members, Professors Jonathan Rocheleau, Alison McGuigan, Christopher Yip

and Eugenia Kumacheva for their guidance and wise counsel. A special thanks to my external

committee member, Professor David Juncker, for making the trip to add his expertise.

For sparking my interest in the sciences, I am eternally grateful to some very special teachers:

Mr. Wereley, Mr. Wrightson, Mr. Cantrill and Mr. Edmiston. Thanks for making math and

science so much fun, I still can’t believe that they actually pay people to do this.

I am especially grateful to some very supportive Wheeler Lab members: Ryan Fobel for

countless discussions on parenthood, caloric-restriction, lock-picking, tax law, get-rich-quick

schemes and fungal mind-control; Alphonsus Ng, who, ever generous with his resources and

time, can be counted to be in the lab 24/7 so that no lab member would ever have to work alone;

Vivienne Luk for her glass always-full optimism; and Andrea Kirby for giving me a life-

threatening addiction to carrot cake. And to the many other Wheelerites who I have had the

vi

awesome privilege of counting not only as colleagues, but also as friends: Mohamed

Abdelgawad, Kihwan Choi, Irwin Eydelnant, Lindsey Fiddes, Lorenzo Gutierrez, Mais Jebrail,

Sydney Kuipers, Paresh Kumar, Nelson Lafreniere, Jared Mudrik, Nauman Mufti, Brendon

Seale, Mahesh Sarvothaman, Motashim Shamsi, Steve Shih, Suthan Srigunapalan and Hao Yang.

There are a number of people outside the Wheeler Lab people who have contributed much

knowledge and inspiration to this academic journey: Dean Chamberlain, Evan Mills and Gary

Mo. Much gratitude to Henry Lee and Yimin Zhou for patiently solving countless equipment

failures in the cleanroom. And to the innumerable other people who have lent me helping hands,

ideas, cells, reagents, solvents, slides, DNA sequences or pipette tips but whose contributions I

may have forgotten, my gratitude is greater than my memory.

For reminding me that there is a world outside the 4th

floor of CCBR, profound thanks to Stefan

Cusi and Dorcas Lam. Finally, I am especially grateful to my brother and parents for lovingly

supporting me in countless ways through over 2 decades of education.

vii

Overview of Chapters

The use of digital microfluidics (DMF) for cell culture and analysis has a number of potential

benefits over traditional macro scale techniques. However, as cell applications are a relatively

new use for this technology, many fundamental challenges must be addressed before DMF can

be reliably used for routine analysis. This thesis describes my work towards addressing several of

these challenges:

Chapter 1 – Microfluidics and Cell Studies

This chapter introduces the fundamental physics of microfluidic devices with a focus on

dimensionless numbers which help describe fluid dynamics on the microscale. Some common

uses of microfluidic devices for cell-based applications are summarized followed by a review of

digital microfluidics and its use for cell applications. Finally, challenges associated with the use

of DMF platforms for cell-based applications are introduced.

Chapter 2 – Pluronic Additives to Inhibit Device Failure

Device failure when using protein-rich solutions required for cell culture is a major impediment

to developing DMF as a useful tool for cellular analysis. The Wheeler Lab previously developed

techniques in which pluronics (block co-polymers with tailorable hydrophobic/hydrophilic chain

lengths) were doped into working solutions to slow device failure. However, the use of pluronics

as an anti-fouling agent had not been optimized, especially in the context of cell culture

applications. The goal of the work described in this chapter was to determine a) which

viii

parameters of pluronics are important in delaying/preventing device failure, b) the mechanisms

behind the anti-fouling properties of the co-polymer and c) how these co-polymers interact with

cells.

Chapter 3 – Effects of Digital Microfluidic Actuation on Cell Fitness

Since DMF uses electric fields to drive droplets, it is important to characterize if and how these

electric fields may influence cell health and fitness. Although previous studies have shown little

affect on cell viability and growth rates, there may be more subtle effects on cells as a result of

DMF manipulation. The goal of the work described in this chapter was to examine the effects of

DMF-operation on the genome-level responses of mammalian cells. A number of responses were

analyzed to this end including heat shock activation, DNA integrity, and genomic expression

profiles.

Chapter 4 – Integrated Microorganism Culture and Analysis

An advantage of microfluidics, and DMF in particular, is the ability to combine a number of

traditionally tedious and labour-intensive experimental steps onto a single integrated platform. A

DMF platform was developed for multi-day culture and analysis of bacteria, algae and yeast in a

highly automated fashion.

ix

Chapter 5 – Microfluidic Liver Organoid Platform

This chapter describes ongoing work-in-progress. The liver is a vital organ for metabolism,

detoxification and hormone production. There is therefore interest in developing in vitro liver

models to study small molecule interactions, pharmacokinetics and disease states. Building upon

the work in Chapters 2-4, the goal of this project is to evaluate the suitability of digital

microfluidics for creating liver models which better mimic the in vivo microenvironment than

models created in traditional cell culture platforms. The use of co-cultured, encapsulated cells in

3D hydrogel matrices were explored to this end.

x

Author Contributions

The work contained within this thesis was made possible by the contributions of many skilled

hands and bright minds.

Chapter 2 – Paresh Kumar (former visiting graduate student from the India Institute of

Technology) and I conducted device longevity experiments and contact angle measurements. He

also aided with device fabrication. Dr. Lindsey Fiddes (former graduate student at the University

of Toronto) provided training on goniometer operation. I conducted critical micelle concentration

determination assays, cell compatibility studies and statistical analyses. Dr. Gary Mo (former

graduate student at the University of Toronto) contributed helpful discussions. This work is

published in Langmuir. Au, S.H.; Kumar, P.; Wheeler, A.R. "A New Angle on Pluronic

Additives: Advancing Droplets and Understanding in Digital Microfluidics" Langmuir, 2011, 27,

8586-8594.

Chapter 3 – Ryan Fobel (graduate student at the University of Toronto) contributed device

modeling expertise, design of droplet temperature measurements systems and insightful

discussions. Dr. Dean Chamberlain (postdoctoral fellow at the University of Toronto) and Dr.

Lindsey Fitzgerald (former graduate student at the University of Toronto) both helped with RT-

qPCR training and operation. Julie Tsao and Carl Virtanen (both employees of the University

Health Network Microarray Centre (Toronto, Canada) ran and analyzed microarray experiments.

Professor Joel Voldman (Massachusetts Institute of Technology) and Dr. Salil Desai (former

graduate student at the Massachusetts Institute of Technology) contributed GFP-HSE cells. I

fabricated devices, cultured cells and exposed cells to DMF manipulation, hypothermia controls

http://microfluidics.utoronto.ca/papers/Au_Kumar_2011.pdf

http://microfluidics.utoronto.ca/papers/Au_Kumar_2011.pdf

xi

and arsenite controls. I also conducted cell-stress evaluation studies, DNA integrity assays,

RNA isolation and qPCR. This work is published in Integrative Biology, In Press 2013

Chapter 4 – Dr. Steve Shih (former graduate student at the University of Toronto) ran algae

experiments and designed the device automation system. I conducted bacteria and yeast

experiments, cell death assays and bacterial transformation. Dr. Evan Mills (former graduate

student at the University of Toronto) gifted us bacteria, Dawn Edmonds (lab manager at the

University of Toronto) provided us with yeast and training on yeast culture and Dr. Kamlesh

Patel (Sandia National Laboratories) provided training for algae experiments. This work is

published in Biomedical Microdevices: Au, S.H.; Shih, S.C.C.; Wheeler, A.R. "Integrated

Microbioreactor for Culture and Analysis of Bacteria, Algae and Yeast" Biomedical

Microdevices, 2011, 13, 41-50.

Chapter 5 – Dr. Dean Chamberlain (postdoctoral fellow at the University of Toronto) provided

biological guidance. Shruthi Mahesh (former volunteer lab assistant at the University of Toronto)

helped with protocol development. I designed photomasks, fabricated devices and conducted dye

mixing, viability, contractility, albumin and enzymatic activity experiments. A manuscript is in

preparation.

Professor Aaron Wheeler contributed guidance, expertise and direction to all of the work

described in this document.

http://microfluidics.utoronto.ca/papers/BAY_paper.pdf

http://microfluidics.utoronto.ca/papers/BAY_paper.pdf

xii

Table of Contents

Abstract ........................................................................................................................................... ii

Acknowledgments ........................................................................................................................... v

Overview of Chapters ................................................................................................................... vii

Author Contributions ...................................................................................................................... x

List of Figures .............................................................................................................................. xvi

List of Tables ............................................................................................................................. xviii

List of Equations .......................................................................................................................... xix

Abbreviations ................................................................................................................................ xx

List of Foundations and Funding Sources ................................................................................. xxiii

Chapter 1: Microfluidics and Cell Studies ...................................................................................... 1

1.1 Fundamentals of Microfluidics ........................................................................................... 1

1.2 Microfluidics for Cell Applications .................................................................................... 5

1.3 Digital Microfluidics for Cell Applications ........................................................................ 8

1.4 Thesis Objectives .............................................................................................................. 17

Chapter 2: Pluronic Additives to Inhibit Device Failure .............................................................. 18

2.1 Introduction ....................................................................................................................... 19

2.2 Experimental ..................................................................................................................... 21

2.2.1 Reagents and Materials ......................................................................................... 21

2.2.2 Device Fabrication ................................................................................................ 22

2.2.3 DMF Longevity Assay .......................................................................................... 22

2.2.4 Contact Angle Measurements ............................................................................... 25

2.2.5 Cell Growth and Viability Assay .......................................................................... 26

2.2.6 Critical Micelle Concentration Determination ...................................................... 26

2.2.7 Statistical Analysis ................................................................................................ 27

xiii

2.3 Results and Discussion ..................................................................................................... 27

2.3.1 Device Lifetime .................................................................................................... 29

2.3.2 Droplet Wetting .................................................................................................... 35

2.3.3 Compatibility with Cells ....................................................................................... 39

2.4 Conclusions ....................................................................................................................... 41

Chapter 3: Effects of Digital Microfluidic Actuation on Cell Fitness .......................................... 43

3.1 Introduction ....................................................................................................................... 43

3.2 Experimental ..................................................................................................................... 45

3.2.1 Device Fabrication and Operation ........................................................................ 45

3.2.2 Cell Culture and Stress Conditioning ................................................................... 46

3.2.3 Cell Stress Evaluation and Flow Cytometry ......................................................... 47

3.2.4 Single Cell Gel Electrophoresis COMET Assays ................................................. 48

3.2.5 Microarrays and qPCR .......................................................................................... 48

3.2.6 Temperature Measurements .................................................................................. 51

3.3 Results and Discussion ..................................................................................................... 51

3.3.1 Preliminary Experiments and Cell-based Stress Sensors ...................................... 51

3.3.2 DNA Integrity ....................................................................................................... 54

3.3.3 Gene Expression – Microarrays ............................................................................ 56

3.3.4 Gene Expression – qPCR ...................................................................................... 63

3.3.5 Droplet Heating ..................................................................................................... 65

3.4 Conclusions ....................................................................................................................... 68

Chapter 4: Integrated Microorganism Culture and Analysis ........................................................ 69

4.1 Introduction ....................................................................................................................... 69

4.2 Experimental ..................................................................................................................... 71

4.2.1 Macroscale Cultures .............................................................................................. 71

xiv

4.2.2 Device Fabrication ................................................................................................ 72

4.2.3 Device Operation .................................................................................................. 73

4.2.4 Microscale Cultures .............................................................................................. 75

4.2.5 Growth Curve Generation ..................................................................................... 77

4.2.6 Cell Death Assays ................................................................................................. 78

4.2.7 Transformation ...................................................................................................... 78

4.3 Results and Discussions .................................................................................................... 79

4.3.1 Microbioreactor Design ........................................................................................ 79

4.3.2 Microorganism Culture ......................................................................................... 82

4.3.3 Downstream Processing and Analysis .................................................................. 86

4.4 Conclusions ....................................................................................................................... 88

Chapter 5: Microfluidic Liver Organoid Platform ........................................................................ 90

5.1 Introduction ....................................................................................................................... 90

5.2 Experimental ..................................................................................................................... 91

5.2.1 Device and SU-8 Barrier Fabrication ................................................................... 91

5.2.2 Device top and bottom plates were Cell Handling and Preparation ..................... 93

5.2.3 Device Operation Protocols .................................................................................. 94

5.2.4 Mixing Analysis .................................................................................................... 95

5.2.5 Viability and Contractility Assays ........................................................................ 96

5.2.6 Albumin Analysis ................................................................................................. 97

5.2.7 Cytochrome P450 3A4 Activity Assay ................................................................. 97

5.3 Preliminary Results and Discussion .................................................................................. 99

5.3.1 Organoid Confinement, Feeding, and Mixing ...................................................... 99

5.3.2 Organoid Contractility and Viability .................................................................. 102

5.3.3 Albumin Activity ................................................................................................ 105

xv

5.3.4 Cytochrome P450 Enzymatic Activity ............................................................... 106

5.4 Future Work .................................................................................................................... 109

Conclusions and Future Directions ............................................................................................. 110

References ................................................................................................................................... 119

xvi

List of Figures

Figure 1.1 General digital microfluidic device schematic. ............................................................ 9

Figure 1.2 Schematic of hydrophilic adhesion pads .................................................................... 14

Figure 2.1 Schematic device lifetime assay operation. ................................................................ 24

Figure 2.2 Device longevity assay ‒ initial screen. ...................................................................... 31

Figure 2.3 Device longevity assay ‒ concentration dependance. ................................................ 33

Figure 2.4 Non-potentiated contact angles .................................................................................. 37

Figure 2.5 Electrodynamic contact angles ................................................................................... 39

Figure 2.6 Pluronic cytotoxicity ................................................................................................... 41

Figure 3.1 Cell-based stress sensor results. .................................................................................. 53

Figure 3.2 Quantification of DNA integrity. ................................................................................ 55

Figure 3.3 Microarray heat map ................................................................................................... 58

Figure 3.4 Microarray expression comparisons ........................................................................... 59

Figure 3.5 Droplet temperature in digital microfluidics .............................................................. 66

Figure 4.1 Schematic of BAY microbioreactor ........................................................................... 74

Figure 4.2 Operation of BAY microbioreactor ............................................................................ 80

xvii

Figure 4.3 Microorganisms on device .......................................................................................... 83

Figure 4.4 Microorganism growth curves .................................................................................... 84

Figure 4.5 Microorganism viability and transformation .............................................................. 87

Figure 5.1 Digital microfluidic organoid platform ...................................................................... 92

Figure 5.2 General automated droplet exchange procedure ........................................................ 95

Figure 5.3 Dye-mixing study ..................................................................................................... 101

Figure 5.4 Organoid contractility. .............................................................................................. 104

Figure 5.5 Organoid viability. .................................................................................................... 104

Figure 5.6 Organoid albumin secretion assay. ........................................................................... 106

Figure 5.7 Cytochrome P450 3A4 activity. ............................................................................... 108

xviii

List of Tables

Table 2.1 Physical properties of Pluronics ................................................................................... 29

Table 3.1 Stress and apoptosis gene summary ............................................................................. 62

Table 3.2 qPCR validation of Dusp1 ........................................................................................... 64

Table 4.1 BAY microreactor parameters ..................................................................................... 76

Table 4.2 Microorganism doubling time comparison .................................................................. 85

Table 5.1 Collagen-cell suspension components ......................................................................... 94

Table C.1 Current state of digital microfluidics for cell applications. ....................................... 115

xix

List of Equations

Equation 1.1 Reynolds Number ..................................................................................................... 2

Equation 1.2 Péclet Number .......................................................................................................... 3

Equation 1.3 Capillary Number ..................................................................................................... 4

Equation 1.4 Lippman-Young Law ............................................................................................. 10

Equation 1.5 Lippman-Young derived driving force .................................................................. 11

Equation 1.6 Electromechanical Framework ............................................................................... 11

Equation 1.7 Electromechanical Framework derived driving force ............................................ 12

Equation 2.1 Actuation time log-normal curve fit ....................................................................... 25

Equation 4.1 Doubling time ......................................................................................................... 77

Equation 4.2 Growth rate ............................................................................................................. 77

Equation 5.1 Unbiased estimator of standard deviation .............................................................. 96

xx

Abbreviations

AC Alternating current

BAY Bacteria, algae, yeast

Ca Capillary Number

CCD Charge-coupled device

CMC Critical micelle concentration

COMET Single cell gel electrophoresis assay

CS Calf serum

CYP Cytochrome P450

DC Direct current

DEP Dielectrophoresis

DI Deionized

DMF Digital microfluidic(s)

ECM Extracellular matrix

EDTA Ethylenediaminetetraacetic acid

EMF Electromagnetic field(s)

xxi

EthD-1 Ethidium homodimer-1

EWOD Electrowetting on dielectric

FBS Fetal bovine serum

GFP Green fluorescent protein

HLB Hydrophilic-lipophilic balance

HSE Heat shock element

ITO Indium tin oxide

OD Optical density

PBS Phosphate buffered saline

PCB Printed circuit board

PCR Polymerase chain reaction

Pe Péclet Number

PEO Polyethylene oxide

PP Peak-to-peak

PPO Polypropylene oxide

QC Quality control

xxii

qPCR Real time polymerase chain reaction

Re Reynolds Number

RMS Root-mean square

S.D. Standard deviation

UV Ultraviolet

V Voltage

Vis Visible

YFP Yellow fluorescent protein

xxiii

List of Foundations and Funding Sources

National Sciences and Engineering Research Council of Canada

Canadian Institutes of Health Research

1

Sam H. Au Microfluidics and Cell Studies

Chapter 1

Microfluidics and Cell Studies

1.1 Fundamentals of Microfluidics

The premise of microfluidics and micro-total analysis systems are to miniaturize and integrate

bench-top (macro-scale) laboratories processes. Microfluidic devices are commonly defined as

tools with micron-scale features that are capable of manipulating microliter or smaller volumes1,

2.

The most obvious benefits of miniaturization3 are significantly reduced consumables and reagent

use which can reduce costs and may allow for reductions in the volumes of rare and/or patient

samples. Moreover, smaller sample volumes can increase the number of replicates or

experimental conditions which can be conducted at once (parallelization)4. Miniaturization also

significantly decreases the physical size of total analysis systems3, which enables the

development of portable tools such as handheld blood glucose monitors. The reduction of

volume and length scales can also drastically alter the fundamental physics of fluid behaviour1.

This leads to other benefits of microfluidics over traditional macro-scale fluid handling such as

the ability to establish well defined chemical gradients3, shorten analysis times and improve

detection sensitivities5, 6

. These principles are briefly described below; a more in detailed review

of the physics of microscale flows can be found in an excellent review paper by Squires and

Quake1.

2


Surface Area to Volume Ratios

The surface area of an object or liquid is proportional to the square of the characteristic length

scale while the volume is proportional to the cube of that length scale. Therefore the surface area

to volume ratio increases linearly with decreasing length scale. The small length scales in

microfluidic systems result in high surface area to volume ratios which in turn can substantially

increase heat and mass transfer rates, which can be used to speed up the rates of

exothermic/endothermic reactions but may also be detrimental – for example, there are often

increased biofouling rates in microsystems. Addressing this latter consideration is the primary

motivation for Chapter 2.

Inertial and Viscous Forces

Inertia is the resistance of objects/liquids to changing their current state of motion. Viscosity is

the resistance of a fluid to stress-induced deformation. To describe the balance between these

forces, the dimensionless Reynolds number (Re) is used:

L is the characteristic dimension [m]

U is the mean fluid velocity [m/s]

ρ is the fluid density [kg/m3]

µ is the fluid dynamic viscosity [Pa.s]

For fluids in motion, these conflicting forces dictate whether the flow is turbulent (chaotic) or

laminar (deterministic). Since the characteristic dimensions in microfluidics are typically on the

order of microns (10-6

m), fluid flows in most microfluidic systems are usually viscosity

𝑅𝑒 ≝𝐼𝑛𝑒𝑟𝑡𝑖𝑎𝑙 𝑓𝑜𝑟𝑐𝑒𝑠

𝑉𝑖𝑠𝑐𝑜𝑢𝑠 𝑓𝑜𝑟𝑐𝑒𝑠=

𝐿𝑈𝜌

𝜇 (1.1)

3


dominated resulting in laminar flows profiles with Re < 2000. Laminar flow regimes are

characterized by linear deterministic flows with no mixing of streamlines (i.e., fluid flows

parallel to the walls of a straight channel or pipe). In microsystems, the orderly flow results in no

cross-currents, eddies or mixing which is necessary for the formation of linear fluid flow

gradients but can render mixing a challenge.

Convection and Diffusion

The orderly flow profiles in microsystems have substantial impact upon microfluidic mass

transfer. Convective mass transfer in fluid flows occurs when streamlines mix together (bulk

fluid mixing) resulting in increased homogeneity. In most systems, convection is much faster

than diffusive mixing, which relies on the random stochastic motion of particles to reach increase

homogeneity. The relative dominance of convection and diffusion can be described by the

dimensionless Péclet number (Pe):

L is the characteristic dimension for mass transfer [m]


D is the diffusion coefficient [m2/s]

Because of the small length scales in microfluidic systems, mass transfer is typically dominated

by diffusion which allows for the formation of well defined chemical gradients. Also in contrast

to macro-scale systems, the small length scales in microfluidic systems often reduce the time

required for diffusion-dominated systems to become well-mixed, meaning that diffusive mass

transfer alone may be sufficient in some microsystems. In the work described in Chapter 4,

diffusion alone is insufficient to properly mix dividing yeast cells due to their relative large size

𝑃𝑒 ≝𝐶𝑜𝑛𝑣𝑒𝑐𝑡𝑖𝑜𝑛

𝐷𝑖𝑓𝑓𝑢𝑠𝑖𝑜𝑛=

𝑈𝐿

D (1.2)

4


and hence low diffusion coefficient – therefore a continuous mixing system was implemented to

improve homogeneity.

Viscous and Interfacial Forces

The increased surface area to volume ratios in microfluidic devices also influence microscale

fluid behaviour. A fluid in contact with a solid (e.g. the surface of a microfluidic device)

experiences both interfacial forces (liquid-solid interaction energy) and viscous forces. The

dimensionless capillary number (Ca) is used to describe the balance between these forces:

µ is the fluid dynamic viscosity [Pa.s]


γ is the surface or interfacial tension [N/m]

The capillary number can be used to predict the motion of or control fluids in microfluidic

devices. For example capillary forces can be used to draw fluid through microchannels and the

contact angle/wetting of fluids onto surfaces is an important factor in the biofouling of DMF

devices (described in Chapter 2).

Applications and Driving Forces

The first applications of microfluidics were in analytical chemistry4. Chromatography

7 and

capillary electrophoresis8 both benefit from improved sensitivities and resolutions brought on by

flow through smaller dimensions. Since then, the applications for microfluidics have expanded

many-fold4 to include genomics

9, proteomics

9, polymerase chain reaction

10, drug discovery

11,

biochemical assays12

, crystallization13

, cell culture/analysis,14-16

and many others. In this work I

focus on cell applications of DMF, which is described in more detail in section 1.2.

𝐶𝑎 ≝𝑉𝑖𝑠𝑐𝑜𝑢𝑠 𝑓𝑜𝑟𝑐𝑒𝑠

𝐼𝑛𝑡𝑒𝑟𝑓𝑎𝑐𝑖𝑎𝑙 𝑓𝑜𝑟𝑐𝑒𝑠=

µU

γ (1.3)

5


The driving forces and configurations for fluid handling in microfluidic systems have also

increased many-fold. Early work relied upon pressure-driven7 or electroosmotic

8 flows.

Microfluidic technologies now incorporate many more fluid handling modalities which can be

divided into five main categories2: capillary forces, pressure-driven, centrifugal, electrokinetic

and acoustic. There are a number of trade-offs associated with each modality such as plug flow

liquid-profiles in electroosmotic flow vs. laminar streamline liquid-profiles in pressure-driven

flow2. This thesis focuses on the electrokinetic modality of digital microfluidics (DMF)

(described in more detail in section 1.3).

1.2 Microfluidics for Cell Applications

Mammalian cells, bacteria, algae and yeast are used widely in biotechnology, biopharmaceutical

production, drug discovery, genomics, proteomics and studies in fundamental biology. The

generic advantages of microfluidics, which are applicable to nearly all applications, such as

reduced reagent use and increased throughput are also useful for cell applications. For example,

cell-based microfluidic platforms with 100 chamber perfusion-flow devices17

, automated 96

chamber devices driven by peristaltic pumps18

and 160 chamber devices containing thousands of

encapsulated drops19

have been developed. Moreover, the miniaturization of traditional macro-

scale processes has enabled the integration of microfluidic cell manipulation with sorting and

downstream analyses such as qPCR14, 15

. These micro total analysis systems significantly reduce

the amount of manual sample handling required which reduces labour requirements, user-to-user

variation and experimental error. However, microfluidics also offers a number of additional

benefits which are particularly suited to cell applications.

6


Microenvironment Control

The cellular microenvironment influences all cellular activities including growth, division,

differentiation, motility, survival and phenotypic behaviour14

. Microfluidics offers increased

control of cellular microenvironments14-16

in comparison to traditional cell culture tools such as

flasks and well-plates. This enables the generation of physiologically relevant cell environments

and permits the study of the influence of microenvironmental factors on cell behaviour.

In contrast to traditional cultures where cells are often maintained in static liquid media,

microfluidic systems can maintain cells under highly tunable flow and shear rates. This is

particularly useful for systems which experience complex pulsatile flow profiles in vivo such as

the cardiovascular20

and respiratory21

systems. The development, differentiation and behaviour

of these tissue systems often depend on the presence of shear stress which cannot be accurately

and uniformly applied with tissue culture flask systems.

The ability to create laminar (Re < 2000), diffusion dominated (low Pe) flows in microfluidics

can be used to create well defined gradients14

. One application of gradients is the study of cell

migration in response to differential concentrations of soluble factors or cytokines. In this

manner, microfluidics has been used to study cell migration across well defined chemical22

and

oxygen23

gradients. More complex gradients can be generated on microfluidics as well. For

example, gradients and flows in two or more dimensions can be created in microfluidic systems24

which can be used to study more complex biological systems such as the emergence of antibiotic

resistance in bacterial populations25

. In addition, microfluidic systems can be used to create

multiple liquid streams which are in contact but remain unmixed due to slow diffusion-

dominated mass transfer rates. Signal propagation and dynamics can be studied by exposing

single cell or tissue units to these unmixed streams26

.

7


The micron or nanometer-scale features created by microfluidic fabrication technologies can also

be used to model mammalian physiology. For example, organs are often organized around

basement membranes and basal laminae which are comprised of extracellular matrix and serve to

separate different cell types and fluids. Microfluidic tools have been created with artificial

membrane mimics to better model physiological processes and cellular behaviour15, 27

.

Micrometer-scale control in microfluidics can also be used to create 3D cell culture systems

which are better models of in vivo microenvironments since cells in all multi-cellular organisms

are organized into three dimensions. Synthetic or natural hydrogels are often used in

microsystems as cell scaffolds or extracellular matrix (ECM)3, 14, 28, 29

. The precise spatial control

available in microfluidic systems can be used to create well defined spatial geometries29

which

are important for preventing the development of necrotic cores. Also, for thermosetting

hydrogels such as collagen and agarose, the high surface area to volume ratio in microfluidic

systems can be capitalized on to rapidly set gels with high uniformity29

.

Single Cell and Population Analyses

The micro-scale features of microfluidic devices allows for the capture30, 31

, analysis30, 31

and

manipulation32

of single cells, often in picoliter-volume droplets33

. This enables the study of not

only single isolated cells, but also individual cells within a population to gain a better

understanding of the population as a whole. For example, the majority of traditional cell-based

assays provide a single (mean) read-out for the entire cell population (e.g. mean

fluorescent/luminescent intensity in ELISA assays). This is an appropriate metric for

homogeneous, normally distributed populations. However, a single mean is an inappropriate

measure for many cell applications since even clonal cell populations exposed to ―identical‖

treatments result in significant phenotypic heterogeneity within the popluation34

. In these cases,

8


examining the state of individual cells provides a far better measure of the population. Flow

cytometry is an example of a similar technique which is capable of studying the state of

individual cells within a population, but usually requires the availability of appropriate

fluorescent tags. The wide range of detection modalities that can be coupled with microfluidics

permits the detection of ―secreted antibodies… intracellular, cell-surface or secreted proteins and

for quantifying catalytic or regulatory activities‖ (as aptly phrased by Mazutis et al.35

) of single

cells as well.

1.3 Digital Microfluidics for Cell Applications

Digital microfluidics, also known as electrowetting on dielectric (EWOD), is an electrokinetic

method of microscale fluid manipulation36, 37

. Droplets (of nanoliter to milliliter volumes) can be

mixed, split, merged and dispensed, the four basic operations required for most liquid

experiments. A generic device performing these basic operations is depicted in Figure 1.1A.

9


Figure 1.1 General digital microfluidic device schematic. (A) Photograph of a digital

microfluidic device demonstrating droplet mixing, splitting, merging and dispensing. (B) Side-

view schematic of a two-plate digital microfluidic device38

. Reproduced by permission of The

Royal Society of Chemistry.

Digital microfluidic devices are fabricated with electrodes patterned underneath an insulating

dielectric layer. Surfaces in contact with working liquids are coated in a hydrophobic layer to

reduce droplet-surface interfacial force. A schematic of a generic DMF device is presented in

Figure 1.1B. To achieve control over the small volumes in microfluidics requires the ability to

fabricate features of micron or nanometer-scale dimensions. As a result, DMF (and microfluidics

in general) has benefited from fabrication techniques previously developed for microelectronics2,

4. Some examples of these techniques used to fabricate DMF devices include metal deposition,

10


photolithography, etching and spin-coating39

. The DMF fabrication techniques used in the work

described in this thesis are described in sections 2.2.2, 3.2.1 and 4.2.2.

Physics of Droplet Motion

Multiple models have been developed to describe the forces behind DMF-driven droplet motion.

Two different theoretical approaches will be described here, the electrowetting model and the

electromechanical model, both of which reach consensus on DMF driving forces40

.

In the electrowetting model, charges accumulate at the interface of a charged conductive material

and a non-conductive material (e.g. Fig 1.1B – between the charged electrode and dielectric layer

of a DMF device). The accumulated charges apply an interfacial force which is especially strong

at triple contact lines – that is at solid-liquid-gas interfaces (e.g. Fig 1.1B – at the Teflon-liquid-

air interface of a DMF device). This interfacial force then distorts deformable liquids resulting in

a change in contact angle. The equation governing this contact angle change is the Lippman-

Young law40

:

θ is the deformed contact angle due to applied voltage

θ0 is the contact angle without applied voltage

C is the capacitance of the dielectric layer between the liquid and the electrode [F]

γLG is the liquid-gas interfacial tension [N/m]

V is the applied RMS voltage [V]

ε0 is the permittivity of free space [F/m]

εr is the relative permittivity of dielectric [F/m]

d is the dielectric thickness [m]

Note that the contact angles of Equation 1.4 are static contact angles and do not account for

droplet motion after deformation. In this model, droplet motion occurs due to liquid contact

cosθ = cosθ0 +C

2γLGV2 = cosθ0 +

ε0εr

2γLG dV2 (1.4)

11


angle asymmetry resulting in capillary forces which serve to bring droplets back into symmetry.

The driving force in this model can be expressed as40

:

F is the driving force [N]

L is the length of the triple contact line overlapping the actuated electrode (reduces to the

length of charged electrodes perpendicular to the direction of droplet motion) [m]

Another approach to determining driving forces is the electromechanical framework which

models DMF devices as electric circuits with each component represented as a capacitor and

resistor in parallel (Figure 1.1B). Highly resistive elements (such as the dielectric and

hydrophobic layers) reduce to capacitors. In this model, energy is stored in the capacitors as a

function of frequency and droplet position40

:

E is the energy in the system [J]

f is the applied frequency [Hz]

x is the droplet position along the axis perpendicular to motion [m]

The i subscript in Equation 1.6 refers to each layer of the DMF device (Fig. 1.1B) directly above

or below the liquid or filler portions of the electrode. Note that the voltage drop across each layer

is a function of the magnitude of the applied frequency, layer permittivity and layer thickness.

Differentiating the energy calculated in Equation 1.6 with respect to x yields force:

F = LγLG cosθ − cosθ0 =ε0εrL

2dV2

E(f,𝑥) = L

2 𝑥

ε0εri Vi2(j2πf)

𝑑𝑖𝑖

𝑙𝑖𝑞𝑢𝑖𝑑

+ (𝐿 − 𝑥) ε0εri Vi

2(j2πf)

𝑑𝑖𝑖

𝑓𝑖𝑙𝑙𝑒𝑟

(1.5)

(1.6)

12


Note that the electromechanical model does not describe the fundamental nature of the forces

driving droplet motion, only that stored energy results in an applied force. The advantage of the

electromechanical model is that it takes into account the frequency dependency of the applied

voltage droplets across each layer and portion of the device. This is important if one wishes to

accurately model systems where the voltage drops across layers other than the dielectric are non-

negligible. However, for most DMF systems, the forces calculated by the electrowetting and

electromechanical models reach consensus. For manipulation of conductive liquids (such as cell

media) and air (used for all experiments in this thesis), the energy stored in the filler portion of

the electromechanical model is negligible in comparison to the energy stored in the liquid portion

(εr,liquid >> εr,filler ). Moreover, the energy stored in the liquid layers are negligible in comparison

to the energy stored in the parylene (dielectric) and Teflon (hydrophobic) layers because of the

differences in relative thicknesses (ddielectric ≈ 2-6 µm, dhydrophobic ≈ 235 nm vs. dliquid/filler ≈ 140-280

µm). Therefore, by grouping the dielectric and hydrophobic layers into a general ―dielectric

layer‖ for modeling purposes, Equation 1.7 simplifies to Equation 1.5.

Cell Applications on Digital Microfluidics

A wide range of cell-based applications have been conducted on microfluidic platforms (section

1.2). The majority of these studies were conducted in microflow systems in which an aqueous

fluid is delivered through enclosed micron-scale channels (microchannels). A variation on this

modality, often referred to as droplets in channels28, 41

, is also commonly used for cell

applications. In this mode, one phase (often aqueous) is typically delivered through

F f =∂E(f, 𝑥)

∂𝑥=

L

2

ε0εri Vi2(j2πf)

𝑑𝑖𝑖

𝑙𝑖𝑞𝑢𝑖𝑑

− ε0εri Vi

2(j2πf)

𝑑𝑖𝑖

𝑓𝑖𝑙𝑙𝑒𝑟

(1.7)

13


microchannels as droplets using an immiscible carrier phase (often oil). In comparison to either

of these systems, digital microfluidics manipulates droplets on open planar surfaces typically

filled with air. Each of these modalities (channels, droplets-in-channels, or DMF) is useful for

different cell applications. For example, the establishment of well defined chemical gradients is

difficult in the discrete droplets of DMF or droplet in channel systems but is easily established in

microchannels22

. The encapsulation of thousands or millions of single cells in multiple isolated

serial reactors is difficult in microchannel or DMF systems but can be performed with extremely

high throughput in droplet in channel systems39

. The ability to rapidly reconfigure fluidic

networks for cell and reagent manipulation is difficult to perform in microchannels or droplet in

channel systems but is a trivial operation in DMF systems37

.

Cell studies are an attractive application for DMF because of its ability to precisely manipulate

droplets of different volumes and constituents, rapidly reconfigure fluidic paths, handle cells

with low-shear stress and integrate with numerous analysis modalities. The first report of DMF

used for cell applications was in 2008. Jurkat T-cells, a suspension cell line, were manipulated,

grown and assayed for viability on a DMF platform42

. Since then a number of DMF platforms

have been developed for cells. Cells in suspension were manipulated on hybrid dielectrophoresis

(DEP)-DMF devices43

or combined DMF and optoelectronic tweezer manipulation44

. Adherent

mammalian cell have been grown, subcultured and transfected on dried extracellular matrix

protein spots45

(Figure 1.2). Mammalian cell lines46

and primary cells47

have been cultured and

analyzed on hydrophilic spots of the top-plate by Teflon lift-off and cell lines have been seeded

onto hydrophilic spots of the bottom-plate formed by parylene lift-off48

. DMF platforms have

also been developed to encapsulate NIH-3t3 cells in agarose hydrogel discs49

, to manipulate

yeast and zebrafish embyros51

and for long-term culture and analysis of bacteria, algae and

14


yeast52

(Chapter 3). A multiplexed DMF apoptosis assay was performed on a DMF platform50

which capitalized on the laminar flow profiles of DMF droplet manipulation to permit the

analysis of apoptotic cells delaminating from device surfaces. The explanation for this

phenomenon is that the Reynolds number (Eqn. 1.1) for a typical DMF setup (2 mm diameter

droplet, droplet velocity of 3.33 mm/s) manipulating an aqueous solution at room temperature, is

approximately seven. This is well inside the laminar regime.

Figure 1.2 Schematic of hydrophilic adhesion pads used to culture adherent mammaliancells45

–

Reproduced by permission of The Royal Society of Chemistry

Challenges for Digital Microfluidic Cell Applications

There are a number of challenges associated with the application of DMF for cell-based studies.

Addressing these challenges will be useful to researchers conducting cell-based research on

DMF platforms in the future such that more complex applications can be completed with more

reliability.

One critical challenge is biofouling, which can lead to catastrophic device failure as droplets are

unable to move away from fouled regions. This is a particularly severe problem for cell

applications for a number of reasons. First, serum-containing cell media used to maintain

mammalian cells are protein-rich solutions. The inherent amphiphilic nature of proteins and their

relatively large molecular weights contribute to their rapid adsorption to many material

15


surfaces51

. Second, hydrophobic surfaces such as the Teflon-AF© surfaces used on DMF

devices, are typically fouled more rapidly by proteins than hydrophilic surfaces52

. Third, similar

to most microfluidic systems, DMF platforms have greatly increased surface area to volume

ratios in comparison to macro-scale counterparts. For example, the (liquid-solid) surface area to

volume ratio of a typical DMF system (two-plate, 2 mm diameter droplets, 140 µm spacer) is

approximately 14286 m-1

while for a common macro-scale cell culture format (100 µL in one

well of a flat bottom 96 well plate) it is approximately 952 m-1

, an order of magnitude difference.

Fourth, the accumulation of amphiphilic protein species on DMF devices increases the interfacial

interaction forces between cell media and contact surfaces. The Capillary Number (Eqn. 1.3) for

droplets on DMF is typically well under 1 (for an aqueous droplet moving at 3.33 mm/s, Ca ≈

0.00005) meaning interfacial forces dominate over viscous forces. Therefore increased interfacial

force due to biofouling increases droplet wetting which increases the effective surface area for

biofouling. An increased surface area then leads to an increased rate of biofouling etc., a feed-

forward loop. These four properties contribute to the immovability of droplets of cell media

containing 10% fetal bovine serum on DMF devices without the implementation of anti-

biofouling strategies. Although additives53

and disposable dielectrics54

have been previously

developed to combat biofouling on DMF devices, long-term robust manipulation of cell media

and other protein-rich solutions cannot become a reality without improved anti-fouling

technologies.

A second critical challenge is the potential for detrimental effects of electromagnetic fields

(EMF), which are known to harm cells and living tissue under some circumstances. Although

there is much debate on the mechanisms and the extent of these phenomena, EMF have been

reported to induce cell death 55-57

, cell stress responses 58-61

, DNA damage 62, 63

, tumorigenesis 64-

16


67, and a number of other cellular processes

68. These phenomena have been studied primarily for

―extremely low frequencies‖ (50/60 Hz) commonly used in power transmission and electrical

appliances and ―ultra high frequencies‖ (MHz-GHz) used for mobile phone and wireless

communications. Between these frequency ranges, however, little work has been done on the

potential effects of EMF on cell viability and behavior. Although DMF manipulates droplets by

applying potentials of hundreds of volts at frequencies of approximately 1-18 kHz, little work

has been done to characterize the potential for effects of DMF actuation and associated EMF on

cell fitness. Without a comprehensive study of putative effects of DMF manipulation on cell

fitness, especially subtle genome-level effects, researchers are at risk of mistaking DMF-driven

artifacts as biologically significant results.

A third challenge is long-term device operation. Many cell-based applications such as cell-based

qPCR, immunocytochemistry, metabolomics and cytotoxicity assays require extensive sample

handling and preparation which can increase cost, labour and experimental error with each

additional manual handling step. One of the major advantages of microfluidics is integration of

multiple steps onto a single platform which significantly reduces the required manual handling

steps. A challenge however for many cell applications is phenotypic variation in cell types

(described in section 1.2), which is often complicated by non-uniform in vitro cellular

microenvironments. This is especially true for experiments which require long-term cell culture

processes since cells may be exposed to differential environmental factors for days at a time.

Actively mixing droplets improves the distribution of soluble factors and cells which

homogenizes the cellular microenvironments. Although cell culture and analysis has been

previously integrated onto microfluidic devices42

, many biologically-relevant complex cell

17


applications cannot be conducted on digital microfluidics until robust platforms capable of

continuous mixing over multiple days to maintain homogeneity are developed.

1.4 Thesis Objectives

The primary goal of the work described in this thesis is to address the challenges described above

in section 1.3. Specific aims toward achieving this goal are outlined below:

1) Improve device longevity by delaying or inhibiting the biofouling of protein-rich cell

solutions on DMF surfaces without negatively affecting cell fitness (Chapter 2).

2) Investigate potential genome-level effects of DMF manipulation on mammalian cells

(Chapter 3).

3) Develop a DMF platform capable of robust, multi-day culture and analysis of a number

of microorganisms (Chapter 4).

4) Develop a physiologically relevant in vitro liver model on DMF for pharmacology

applications (Chapter 5).

18

Sam H. Au Pluronic Additives to Inhibit Device Failure

Chapter 2

Pluronic Additives to Inhibit Device Failure

Biofouling in microfluidic devices limits the type of samples which can be handled and the

duration for which samples can be manipulated. Despite the cost of disposing fouled devices,

relatively few strategies have been developed to tackle this problem. Here, we have analyzed a

series of eight amphiphilic droplet additives, Pluronic co-block polymers of poly(propylene

oxide) (PPO) and poly(ethylene oxide) (PEO), as a solution to biofouling in digital microfluidics

using serum-containing cell culture media as a model fluid. Our analysis shows that species with

greater PPO content are superior for enabling droplet motion and reducing biofouling. Two of

the tested species, L92 and P105, were found to lengthen device lifetimes by 2-3 times relative to

additives used previously when used at optimal concentrations. Pluronics with low PEO content

like L92 were found to be cytotoxic to an immortalized mammalian cell line, and therefore, we

recommend that Pluronic additives with high PEO content and greater or equal to 50% PEO

composition, like P105, be used for digital microfluidic applications involving cells. Finally,

contact angle measurements were used to probe the interaction between Pluronic-containing

droplets and device surfaces. Strong correlations were found between various types of contact

angle measurements and the capacity of additives to reduce biofouling, which suggests that

contact angle measurements may be useful as a tool for rapidly screening new candidates for the

potential to reduce biofouling. We propose that the work in this chapter will be useful for

scientists and engineers who are developing digital microfluidic platforms for a wide range of

applications involving protein-containing solutions, and in particular, for applications involving

cells.

19


2.1 Introduction

Biofouling, or unwanted adsorption of biomolecules to surfaces, is a serious problem for a wide

range of biomedical applications including implanted medical devices, bioreactors, and filtration

membranes52, 69-72

. Biofouling is exacerbated in microfluidic devices because of the high surface

area to volume ratios in these systems. A number of strategies exist to combat fouling in

channel-based microfluidics73-77

, yet few strategies have been developed to prevent fouling in

digital microfluidic (DMF) systems. In this chapter, I describe work developing a strategy for

minimizing fouling caused by the use of protein solutions (with a special emphasis on cell

culture media) to maximize the lifetime of DMF devices.

Digital microfluidics is a fluid-handling technique in which droplets are manipulated on an open

surface by applying electrical potentials to an array of electrodes embedded underneath an

insulator37

. Because of its ability to precisely dispense, mix, merge and split discrete droplets,

DMF is becoming an increasingly popular tool for biological and biochemical applications78

,

including cell-based assays42, 45, 79, 80

, enzyme assays81-84

, immunoassays85-87

, processing of

samples for proteomic analysis88-93

, applications involving DNA94-96

, and clinical sample

processing and analysis.97

DMF device surfaces are typically coated with a fluorinated polymer

such as Teflon-AF®91

; unfortunately, these types of surfaces are susceptible to unwanted protein

adsorption98, 99

. This is particularly problematic for digital microfluidics ‒ when proteins adsorb

and accumulate, the hydrophobic device surface becomes hydrophilic, which slows and

eventually stops aqueous droplet motion, resulting in reduced device lifetimes. Cell culture

media is particularly challenging for DMF ‒ the biofouling caused by high concentrations of

serum (a complex mixture of proteins and other factors) in such solutions makes droplets

immobile on many kinds of DMF devices.

20


Previous strategies for reducing the amount of protein adsorption to surfaces of digital

microfluidic devices include immersion in water-immiscible oils100

, careful modulation of

applied voltage polarities101

, the use of replaceable plastic films54

, and operation on

superhydrophobic surfaces102, 103

. The first strategy100

is useful for some applications, but is not a

universal solution, as these oils are incompatible with miscible solvents such as ethanol or

methanol and nonpolar solutes may partition from aqueous droplets into the oil matrix. The

second strategy101

is also useful in certain circumstances, but is less effective for complex

solutions where different protein species may present positive or negative charges at

physiological pH. The third strategy54

is useful for preventing cross-contamination (a new film

can be used for each experiment), but does not solve the problem of biofouling within a given

experiment. The fourth strategy102, 103

is effective at reducing biofouling, but these surfaces are

often difficult to fabricate and cannot tolerate even a small amount of detergent (such as 0.01%

Tween 20)103

.

The Wheeler Lab has recently developed a general strategy for reducing fouling in DMF relying

on the inclusion of Pluronic additives to droplets used in DMF systems53

. Pluronics are tri-block

copolymers of poly(ethylene oxide) (PEO) and poly(propylene oxide) (PPO) and are known to

reduce protein104-106

and cell107

adsorption to surfaces. In earlier work53

, two types of Pluronics

(Pluronic F68 and Pluronic F127) were evaluated for their capacities to limit protein adsorption

and increase device lifetimes. Since that initial report, the pluronic additive strategy has been

applied to a diverse range of applications14-18, 24-26

on digital microfluidics with no indication of

adverse effects; for example, the activity of alkaline phosphatase is unaltered even in high

concentrations of additive18

(0.1% Pluronic F127). Of course, there may be applications in which

21


pluronics is problematic; in such cases, the additive may be removed using digital microfliudic

solid-phase extraction108

.

Although Pluronic additives have been demonstrated to be useful for digital microfluidics, the

recent applications of DMF devices for increasingly complex processes such as cell culture and

assays42, 45, 79, 80

(which necessitate the long-term actuation of solutions containing high

concentrations of proteins such as cell culture media and cell lysate) led us to conduct a more

exhaustive study to find a better solution for biofouling. Here, we have evaluated eight different

Pluronic formulations over a range of concentrations based on their (a) ability to enable the long-

term actuation of protein-containing solutions, (b) effects on surface wettabilities, and (c)

compatibility with mammalian cell adhesion and proliferation. Our objectives were to discover a

superior additive to increase DMF device lifetimes (i.e., to reduce analyte losses during fluid

handling and prevent droplet sticking when working with protein solutions), and to characterize

the mechanism(s) by which Pluronic additives enable the actuation of protein containing

solutions by DMF. We speculate that this study will be useful for scientists and engineers who

are developing digital microfluidic analysis platforms for applications involving protein-

containing solutions, and in particular, for applications involving cells.

2.2 Experimental

2.2.1 Reagents and Materials

Unless specified otherwise, reagents were purchased from Sigma-Aldrich (Oakville, ON). Most

Pluronics (BASF Corp., Germany) were generously donated by Brenntag Canada (Toronto, ON);

Pluronic F-68 was from Sigma-Aldrich. Parylene-C dimer was obtained from Specialty Coating

Systems (Indianapolis, IN). Teflon-AF was from DuPont (Wilmington, DE), and A-174 silane

was from GE Silicones (Albany, NY).

22


2.2.2 Device Fabrication

Digital microfluidic devices were fabricated in the University of Toronto Emerging

Communications Technology Institute (ECTI) fabrication facility. Glass substrates bearing

patterned chromium electrodes (used as bottom plates of DMF devices) were formed by

photolithography and etching as described previously42

using photomasks printed with 20,000

dpi resolution by Pacific Arts and Design (Toronto, ON). After patterning, the substrates were

primed for parylene coating by immersing them in silane solution (isopropanol, DI water, and A-

174, 50:50:1 v/v/v) for 15 min, allowing them to air-dry and then washing with isopropanol.

After priming, substrates were coated with Parylene-C (6.9 µm) and Teflon-AF (235 nm).

Parylene was applied by evaporating 15 g of dimer in a vapor deposition instrument (Specialty

Coating Systems), and Teflon-AF was spin-coated (1% in Fluorinert FC-40, 2000 rpm, 60 s) and

then post-baked on a hot-plate (160 °C, 10 min). To enable the application of driving potentials,

the polymer coatings were locally removed from the contact pads by gentle scraping with a

scalpel. Unpatterned top plates were formed by spin-coating indium tin oxide (ITO) coated glass

substrates (Delta Technologies, Stillwater, MN) with Teflon-AF (235 nm, as above).

2.2.3 DMF Longevity Assay

A longevity assay was developed to evaluate the potential for Pluronic additives to increase

device lifetime. The bottom plate of the device used for this assay featured a linear array of 3

square (4×4 mm) actuation electrodes with inter-electrode gaps of 30 µm. Devices were

assembled with a patterned bottom plate and an unpatterned ITO–glass top plate separated by a

spacer formed from 2 pieces of double-sided tape (total spacer thickness 140 µm). To actuate

droplets, driving potentials (200 VPP) were generated by amplifying the output of a function

generator (Agilent Technologies, Santa Clara, CA) operating at 5 kHz. Droplets were

23


sandwiched between the two plates and actuated by applying driving potentials between the top

electrode (ground) and sequential electrodes on the bottom plate via exposed contact pads on the

bottom plate. Droplet actuation was monitored and recorded by a CCD camera mounted on a

lens.

The longevity assays were used to evaluate RPMI 1640 cell culture medium with 10% fetal

bovine serum (FBS) (Life Technologies/Invitrogen Canada, Burlington, ON) containing one of

eight Pluronic additives at a concentration ranging from 0.0 to 0.15% (w/v). Each concentration

was evaluated 3 times on 3 different devices. During each assay, a 4-µL droplet was actuated in a

five-step process as depicted in Figure 2.1. Briefly, (step 1) with a droplet over electrode 1,

electrode 2 was charged to initiate droplet motion (Figure 2.1A/B). (Step 2) Once the droplet had

travelled to the middle of electrode 2, electrode 3 was charged (while electrode 2 remained

charged) (Figure 2.1C). This ensured smooth droplet transitions between electrodes without

pause. (Step 3) Once the droplet had travelled onto electrode 3, the potential was removed from

electrode 2 (Figure 2.1D). (Step 4) The potential was removed from electrode 3 once the droplet

reached the end of the electrode, and the droplet was moved back to its original position (Figure

2.1E-F). (Step 5) Steps 1-4 were repeated in the same manner reversing directions at the end of

each cycle until the device failed. As droplets containing cell media were actuated across

electrodes in this manner, the speed of the droplets decreased over time. Device failure was

defined as any case in which a droplet required more than 15 seconds to complete a movement

step from one electrode to the next. The number of steps and the time until device failure were

recorded for each condition.

24


Figure 2.1 Schematic device lifetime assay operation. Grey squares represent uncharged (non-

potentiated) electrodes and yellow squares represent charged (potentiated) electrodes. In (A), an

electrical potential is applied to electrode 2 to initiate droplet motion. In (B), the droplet begins

to move onto electrode 2. In (C), when droplet is halfway over electrode 2, a potential is applied

to electrode 3 to ensure continuous droplet motion. In (D), the potential is removed from

electrode 2 once droplet reaches electrode 3. In (E), once droplet has moved to the end of

electrode 3, a potential is applied to electrode 2 to change the direction of movement. In (F), the

droplet begins to move across electrode 2.

25


For concentration dependent studies, the actuation times as a function of Pluronic concentration

in cell media were fit to a lognormal curve:

t is the actuation time [s]

c is the concentration of Pluronic in cell media [mol/L]

A, width are derived constants

2.2.4 Contact Angle Measurements

Contact angle measurements were conducted on single-plate DMF devices (i.e., no top-plate)

with a single 1×1 cm square electrode. In each experiment, a 4-µL droplet of RPMI 1640 cell

culture medium with 10% FBS was positioned on top of the electrode, and the contact angle was

measured using the sessile drop fitting method on a Drop Shape Analysis System (Krüss

DSA100, Hamburg, Germany). Each droplet contained one of eight Pluronic additives at a

concentration ranging from 0 to 0.15% (w/v), and each concentration was evaluated 2 times on 2

different devices. In some experiments, the non-potentiated contact angles (i.e., contact angles of

droplets with no potentials applied) were measured every 2.5 minutes for 20 minutes. In other

experiments, a grounded platinum wire (0.25 mm diameter) was inserted into the top of each

droplet, and the electrodynamic contact angles were measured before, during, and 10 seconds

after the application of a 30 second 200 Vpp 5 kHz potential. Electrodynamic contact angle

hysteresis was defined as the difference between the contact angle observed directly before the

application of potential and that observed after the potential is withdrawn (allowing for a few

seconds for the droplet to stabilize).

𝑡 = 𝑡0 + 𝐴𝑒 (−𝑙𝑛 𝑐 𝑐0

2

𝑤𝑖𝑑𝑡 𝑕

(2.1)

26


2.2.5 Cell Growth and Viability Assay

Pluronic cytotoxicity experiments were conducted using the Chinese Hamster Ovary (CHO) cell

line. Cells were grown in T-25 flasks in an incubator at 37°C with 5% CO2. At the beginning of

each experiment, cells were detached using a solution of trypsin (0.25% w/v) and EDTA (1 mM)

for 5 minutes and then centrifuged at 173 x g for 5 minutes. The supernatant was removed and

the cells were resuspended at 19,000 cell/cm2 in complete cell culture media (50% DMEM, 40%

Ham’s F12, 10% FBS) containing 0.02% (w/v) Pluronic L62, L64, F68, L92 or P105 and seeded

into 24-well plates. The well plates were stored in an incubator at 37°C with 5% CO2, and each

day for 3 days, cells were collected (using trypsin/EDTA and washing, as above) from wells and

counted using a hemocytometer (Hausser Scientific, Horsham, PA) using the trypan blue

exclusion method. Cells were imaged using a Leica DM2000 microscope (Leica Microsystems

Canada, Richmond Hill, ON). All cell experiments were conducted in triplicate.

2.2.6 Critical Micelle Concentration Determination

Critical micelle concentrations (CMCs) of Pluronic F68, L92 and P105 in RPMI 1640 cell

culture media were determined using the Pyrene solubilization method109

. Briefly, 10 µL aliquots

of 60 µM pyrene in acetone were pipetted into 1.5 mL microcentrifuge tubes and the acetone was

allowed to evaporate. 1 mL aliquots of media containing 0.005%-5.0% (w/v) Pluronics were

added to each tube such that the final pyrene concentration was 6 × 10-7

M and the tubes were

incubated at 65°C for 3 hours and then at 25°C overnight. Samples were transferred to quartz

cuvettes and analyzed using a Fluoromax-3 fluorescence spectrometer (Horiba Jobin Yvon,

Edison, NJ) with excitation at 333 nm and 339 nm. The ratios of the emission intensities at 380

nm resulting from both excitation wavelengths were used for CMC determination. For dilute

solutions of Pluronics this ratio is independent of concentration, but as the concentrations are

27


raised, the ratio is observed to increase. The Pluronic concentrations at which the ratios begin to

increase were taken as the CMCs.109

Media without serum was used for these measurements

because the serum was found to interfere with the analysis.

2.2.7 Statistical Analysis

Statistical analysis was conducted using JMP Statistical Discovery Software (SAS Institute,

Cary, NC). Linear least squares regression was applied to the maximum time and number of

steps as a function of Pluronic molecular weight, PPO chain length, PEO chain length, percent

PEO content, hydrophilic-lipophilic balance, initial non-potentiated contact angle, change in

non-potentiated contact angle over 20 minutes, contact angle during application of potential,

difference in contact angles during and after application of potential, and difference in contact

angles before and after application of potential (i.e., the electrodynamic contact angle hysteresis).

2.3 Results and Discussion

Digital microfluidics (DMF) is a fluid-handling technique in which discrete microdroplets can be

dispensed, merged, mixed and split. As DMF becomes an increasingly popular tool for biological

and biochemical applications, methods for reducing biofouling are imminently needed. This is

particularly the case for applications involving cells42, 45, 79, 80

, which require complex, long-term,

and multi-step experiments. A previous study53

reported the capacity of two solution additives,

Pluronics F68 and Pluronics F127, to reduce the extent of biofouling in digital microfluidics.

Pluronics (also known as poloxamers) are PEO and PPO tri-block copolymers (PEOm – PPOn –

PEOm) with variable PEO and PPO content which controls the degree of hydrophobicity.

The eight Pluronics species evaluated in this work (L35, F38, L44, L62, L64, F68, L92, and

P105), listed in Table 2.1, were chosen to cover a wide range of physical and chemical

28


properties. For example, the PPO chain lengths vary from 16 units (L35 and F38) to 54 units

(P105) and the PEO content varies from 20% (L62 and L92) to 80% (F38 and F68). For each

PEO content percentage, two different PPO lengths were chosen ‒ for example, L62 and L92

each have 20% PEO content, but have average PPO lengths of 30 and 47 units, respectively. To

identify a strategy for preventing biofouling and to gain a better understanding of the

mechanisms behind device fouling and inhibition thereof, we evaluated the eight different

Pluronics over a broad range of concentrations based on their (a) ability to enable the long-term

actuation of complex protein containing solutions by digital microfluidics, (b) effect on droplet

wetting, and (c) compatibility with mammalian cellular adhesion and proliferation.

29


Table 2.1 Physical properties of Pluronics (PEOm – PPOn – PEOm) used in this study

Pluronic Average

Molecular

Weighta

Ave.

PPO

Chain

Length

(n)

Ave.

PEO

Chain

Length

(m)

% PEO

Content

Hydrophilic-

Lipophilic

Balance

(HLB)a

CMC

in

Media

25°C

(%

wt/v)b

Batch/Lot

Numberc

L35 1900 16 11 50 18-23 WPOE579B

F38 4700 16 46 80 >24 WPMD569B

L44 2200 21 11 40 12-18 WPHD524B

L62 2500 30 8 20 1-7 USXW112110

L64 2900 30 13 40 12-18 USXW110132

F68 8400 30 75 80 >24 1.05 097K2410

L92 3650 47 10 20 1-7 0.02 WPIE577B

P105 6500 54 38 50 12-18 0.27 WPIC572B

a Provided by Manufacturer

b Critical Micelle Concentrations in RPMI 1640 cell culture medium were measured using the pyrene solubilization

method as described in the Methods and Materials section.

c Pluronics were obtained from BASF Corp (a generous donation from Brenntag Canada), except for F68 which was

obtained from Sigma Aldrich

2.3.1 Device Lifetime

Eight species of Pluronics were screened at a concentration of 0.02% (wt/v) for the ability to

prolong the motion of droplets of cell culture medium containing 10% fetal bovine serum. This

initial concentration of Pluronics (0.02%) was chosen to balance two factors ‒ on one hand,

concentrations of 0.05% (F6845

) and 0.08% (F12753

) are known to be useful for droplet

manipulation in DMF; on the other hand, some Pluronics have been shown to be toxic to cells at

moderate-to-high concentrations110

that vary depending on which species is used. Thus, 0.02%

30


was used as an initial concentration to balance these two effects ‒ high enough to facilitate

droplet movement but low enough to potentially reduce cell toxicity.

The data from the initial screen is shown in Figure 2.2 and it leads us to three conclusions. First,

Pluronic PPO chains must be above a threshold of ~30 molecular units to enable motion of

droplets of cell culture media containing 10% serum. As shown, droplets failed to move when

PPO chain lengths were 21 or less (L44, F38 and F35) (identical to the case in which no

additives were present), whereas chain lengths above 30 enabled droplet motion (F64, F68, L62,

L92 and P105). Second, the maximum actuation time and the number of successful droplet

movement steps before device failure generally increased with increasing PPO chain lengths.

Specifically, for a given ratio of PEO to PPO, the longer PPO chain Pluronic had superior droplet

movability ‒ for example, L44 (21 PPO units) did not enable droplet motion while L64 (30 PPO

units) did. Third, PPO chain-length had a greater influence on droplet movability than percent

PEO. For example, for Pluronics with a PPO chain length of 30 molecular units, PEO contents of

20% (L62), 40% (L64) and 80% (F68) all enabled droplet motion. These findings are consistent

with literature reports of the importance of longer PPO lengths104-106

for reducing protein

adsorption to surfaces.

31


Figure 2.2 Device longevity assay ‒ initial screen. Droplets containing cell culture media with or

without one of eight different Pluronic additives at 0.02% (w/v) were actuated repeatedly across

a device until movement failure was observed. The maximum actuation time (left axis) and

maximum number of droplet steps (right axis) for the different Pluronics species are arranged

(left-to-right) by increasing PPO unit length. Error bars are ± 1 S.D.

The effects of the two best-performing additives from the initial screen, Pluronics L92 and P105,

were then investigated as a function of concentration, along with the previous standard for DMF

applications involving cells, F68. As shown in Figure 2.3, device lifetime was concentration-

dependent for all of the Pluronics tested, with optimal concentrations of 0.05%, 0.02% and

0.02% (wt/v) for F68, L92 and P105, respectively. The condition used previously45

for

manipulation of droplets of cell media, 0.05% Pluronic F68, facilitated a maximum actuation

time of 762 ±162 s. Pluronic L92 at 0.02% enabled actuation for 2227 ± 178 s, and Pluronic

P105 at 0.02% enabled actuation for 1470 ±176 s, both of which were statistically significant

2000

1500

1000

500

0

Ma

x A

ctu

atio

n T

ime (

s)

None F38 F35 L44 F68 L64 L62 L92 P105

Pluronic

400

300

200

100

0

Ma

x A

ctu

atio

n S

teps

Time

Steps

32


improvements over F68 at 0.05% (p < 0.05). Interestingly, Pluronics L92 and P105 were most

effective at a narrow distribution of concentrations, while Pluronic F68 was effective over a

broader range of concentrations.

33


Figure 2.3 Device longevity assay ‒ concentration dependance. Droplets containing cell culture

media with Pluronics F68 (A), L92 (B) and P105 (C) at a range of different concentrations were

actuated repeatedly across a device until movement failure was observed, recording the

maximum actuation time (left axis) and maximum number of droplet steps (right axis). The

maximum actuation time data were fit to lognormal curves. Error bars are ± 1 S.D.

L92

B3000

2500

2000

1500

1000

500

0

Max A

ctu

ation T

ime (

s)

0.140.120.100.080.060.040.02

Pluronic Concentration (%)

400

300

200

100

0

Max A

ctu

atio

n S

teps

L92 Time

L92 Steps

AF68

1000

800

600

400

200

0

Max A

ctu

ation T

ime (

s)

0.140.120.100.080.060.040.02Pluronic Concentration (%)

250

200

150

100

50

0

Max A

ctu

atio

n S

teps

F68 Time

F68 Steps

P105C 2000

1500

1000

500

0

Max A

ctu

ation T

ime (

s)

0.140.120.100.080.060.040.02

Pluronic Concentration (%)

300

250

200

150

100

50

0

Max A

ctu

atio

n S

teps

P105 Time

P105 Steps

34


Pluronic micelles have been shown to be effective at encapsulating a number of biomolecules111,

112. Thus, one possibility for the beneficial effects of Pluronic additives on DMF device longevity

is the encapsulation of biomolecules in Pluronic micelles, which may prevent biomolecules from

interacting with the device surface. However, the critical micelle concentrations (CMCs) of

Pluronics F68, L92 and P105 in media were measured to be 1.05%, 0.02% and 0.27% (wt/v),

respectively. As shown in Figure 2.3, the concentrations for F68 and L92 which result in

maximum device longevity are far lower than the CMCs of these species, which suggests that

micelles are not required for improved device performance. Moreover, we propose that the data

in Figure 2.3 suggests that Pluronic-protein interactions (even at sub-CMC concentrations) are

unlikely to be the source of the beneficial effects of Pluronic additives on device longevity. We

estimate the molar concentration of protein in the experimental system to be 45-68 mM

[assuming 3.0-4.5% (wt/v) concentration of protein in serum (as provided by supplier) and 66

kDa average protein molecular weight (of the most abundant protein species, albumin)], while

the molar concentrations of Pluronic F68, L92, and P105 in Figure 2.3 that correlate with the

best device performance are estimated to be 60 µM, 55 µM, and 33 µM, respectively (given the

average molecular weights listed in Table 1). Thus, there are approximately 3 orders of

magnitude more protein molecules than Pluronic molecules in these systems, which suggest that

interactions between proteins and pluronic molecules do not explain the observed effects. Rather,

we propose that the most likely explanation is that Pluronic molecules form a temporary coating

on droplet interfaces, preventing proteins from interacting with the device surfaces. This

assertion is supported by the results of Chen et al.113

, which demonstrated that Pluronic

molecules dissolved in aqueous solvents preferentially form dense ordered layers at solution/air

and solution/solid interfaces.

35


2.3.2 Droplet Wetting

After evaluating the effect of Pluronics on device longevity (described above), we evaluated the

effects of the same panel of Pluronic additives on contact angles measured for droplets

positioned on device surfaces. We note that this is superficially similar to a wide body of

literature114-118

from the early 2000s that sought to model digital microfluidics in terms of

"electrowetting" ‒ i.e., the reduction in contact angle of a droplet upon application of an external

electrical field. We are skeptical of electrowetting as a fluid manipulation model, given that

liquids with no electrowettting behaviour are movable on digital microfluidic devices119

. Thus,

the contact angle measurements presented here were not used to explore the mechanism of

droplet movement, but rather to probe the nature of the effects of Pluronic additives on protein

adsorption to surfaces.

As a first step, the contact angles were measured for droplets of cell culture media with 10% fetal

bovine serum containing 0.02% (wt/v) of each of the eight different Pluronic additives in a non-

potentiated state (i.e., with no voltage applied). A representative picture of such a droplet is

shown in Figure 2.4A. Upon observation of the results, the additives were categorized into two

classes. (1) In some cases (Figure 2.4B), droplet contact angles decrease as a function of time.

This behaviour is typified by media not containing any additives (blue squares in Fig. 2.4B), and

is likely an effect of protein adsorption to the surface as time progresses -- as more protein

adsorbs, the surface becomes more hydrophilic, resulting in lower contact angles. Interestingly,

the additives that exhibit this behaviour (with decreasing contact angles as a function of time)

were incapable of supporting droplet movement (see Figure 2.1) with one exception: Pluronic

F68. (2) In other cases (Figure 2.4C), droplet contact angles are fairly constant as a function of

time. This behaviour is typified by liquids not containing any proteins, like DI water (red upside-

36


down triangles in Figure 2.4C). We propose that the additives that facilitate this behaviour (L64,

L62, L92, and P105) for protein-containing solutions are effective at preventing protein

adsorption on this time scale. Note that the two behaviours (reducing contact angle vs. constant

contact angle as a function of time) correlate approximately with PPO chain length. With the

exception of Pluronic F68, the additives in Figure 2.4B have PPO chain lengths <30, and the

additives in Figure 2.4C have chain lengths ≥30.

The non-potentiated contact angle data in Figure 2.4B and 2.4C show another trend. Immediately

after depositing the droplet on the surface, the non-potentiated contact angles were lower for

additives in Figure 2.4C than for the additives in Figure 2.4B. This is a useful observation, which

we propose may be useful for screening additives for utility for digital microfluidics. In fact,

when examined quantitatively, there is an inverse correlation (R2 = 0.78) between maximum

actuation time on DMF devices (from Figure 2.2) and the initial non-potentiated contact angle,

which is plotted in Figure 2.4D. For example, the two Pluronic additives which enabled the

longest lifetimes were L92 and P105 (from Figure 2.2), and these additives were observed to

have the lowest initial non-potentiated contact angles of 82° and 85°. It is likely that the primary

reason for droplet movement failure in the device longevity assay is the adsorption of proteins to

the device surface. Under this assumption, the data in Figure 2.4D suggests that the more

attracted the Pluronic molecules are to the surface (resulting in lower initial contact angles), the

greater the extent of protection of the surface from protein adsorption. This finding is consistent

with literature on the influence of wettability on protein adsorption – in general, lower contact

angles for aqueous droplets on uncharged surfaces are known to be associated with reduced

protein adhesion.120-122

Pluronics with longer PPO chains assemble more rapidly on hydrophobic

37


device surfaces and also are be more difficult to displace once assembled.51, 123

Both of these

phenomena are likely to be useful for preventing protein adsorption on DMF device surfaces.

Figure 2.4 Non-potentiated contact angles. Picture (A) of a non-potentiated droplet of cell

culture media on a Teflon-AF coated surface. Contact angles of media containing 0.02% (wt/v)

Pluronics were measured for 20 minutes, and then categorized as having large changes over time

(B) or remaining constant (C). Linear least squares regression (D) of maximum droplet actuation

time (from Fig. 2.2) vs. non-potentiated contact angle (R2 = 0.78). Error bars are ± 1 S.D.

After evaluating the relationship between non-potentiated contact angle and device longevity, we

turned our attention to electrodynamic contact angles. In DMF devices, when an aqueous droplet

positioned on an insulator is positioned over a charged electrode (i.e., with potential applied), the

droplet is observed to assume a wetted geometry (Fig. 2.5A). When the electrical potential is

then removed, the droplet is observed to recover to a non-wetted geometry on the now de-

charged electrode (Fig. 2.5B). The electrodynamic contact angles were measured for the series of

C

Initial, Non-potentiated

0

500

1000

1500

2000

Max A

ctu

atio

n T

ime (

s)

F38L35L44

F68

L64

L62

L92

P105

80 85 90 95 100

Initial Non-potentiated Contact Angle

Linear Fit

Max time (s) = 9293.9891 - 92.962056*Passive Starting Angle

RSquare

RSquare Adj

Root Mean Square Error

Mean of Response

Observations (or Sum Wgts)

0.782406

0.746141

403.986

702.5521

8

Summary of Fit

Model

Error

C. Total

Source

1

6

7

DF

3521027.7

979227.9

4500255.6

Sum of

Squares

3521028

163205

Mean Square

21.5743

F Ratio

0.0035*

Prob > F

Analysis of Variance

Intercept

Passive Starting Angle

Term

9293.9891

-92.96206

Estimate

1855.19

20.01416

Std Error

5.01

-4.64

t Ratio

0.0024*

0.0035*

Prob>|t|

Parameter Estimates

Linear Fit

Bivariate Fit of Max time (s) By Passive Starting Angle

R2 = 0.78

D

A BContact Angle Reduces as a Function of Time

110

100

90

80

70

60

Sta

tic C

onta

ct

Angle

20151050

Time (min)

Media

F38

L35

L44

F68

110

100

90

80

70

60

Sta

tic C

onta

ct

Angle

20151050

Time (min)

Water

L64

L62

L92

P105

Contact Angle Stable as a Function of Time

38


cell culture media formulations containing Pluronics; as far as we are aware, this is the first time

electrodynamic contact angles have been measured for Pluronic-containing liquids. As was the

case for non-potentiated contact angles, there was a strong inverse correlation (R2 = 0.85)

between maximum actuation time on DMF devices (from Figure 2.2) and charged contact angle,

which is plotted in Figure 2.5C. For example, the Pluronic additives with the most beneficial

effects on device longevity, L92 and P105, had the lowest charged contact angles of 44.6° and

44.5°. Moreover, there was an even stronger correlation (R2 = 0.88) between the maximum

actuation time on DMF devices and the change in contact angles between the charged and de-

charged states, which is shown in Figure 2.5D. Again, L92 and P105 had the highest observed

contact angle changes of 45.5° and 45.4°. Interestingly, no correlations were found between

electrodynamic contact angle hysteresis (i.e., the difference between non-potentiated contact

angles measured before and after charging the electrode) and maximum actuation time or

PPO/PEO chain lengths. Regardless, the contact angle results presented in Figures 2.4-5 are

useful predictors of the effects of additives on device lifetime, and we suggest that future

investigation of new additives may want to use similar measurements as a screen for beneficial

effects.

39


Figure 2.5 Electrodynamic contact angles. Pictures of droplets on a device during (A) and after

removal (B) application of a 200 VPP potential. Linear least squares regression of maximum

droplet actuation time (from Fig. 2.2) vs. electrodynamic contact angle during charging (R2 =

0.85) (C) and vs. the difference in contact angle between the charged to de-charged states (R2 =

0.88) (D).

2.3.3 Compatibility with Cells

A primary goal for the work in this chapter was to identify Pluronic additives that do not

interfere with cell culture. To this end, Chinese Hamster Ovary (CHO) cells were cultured for 3

days in the presence of the five best performing Pluronics species from the initial screens at

concentrations of 0.02% (wt/v) to characterize their affects on viability (as a marker for toxicity)

and cell density (as a marker for effects on proliferation). Upon observation of the results, the

40


five additives were categorized into two classes, non-toxic and toxic, and the data are

summarized in Figure 2.6. As shown, Pluronics F68 and P105 were found to be non-toxic to

CHO cells and had little or no affect on proliferation (Figure 2.6A). Moreover, as shown in 2.6C,

these two additives had little or no impact on cell morphology or adhesion to the substrates. In

contrast, Pluronics L62, L64 and L92 had significant cytotoxicity (with viabilities decreasing to

less than 20% within 3 days) and in addition prevented cell proliferation (Figure 2.6B). This

observation is supported by previous work110

in which Pluronic L64 was found to be cytotoxic to

epithelial cell lines and primary microphages in vitro. Interestingly, the Pluronics species

categorized as non-toxic had higher PEO content (50% or higher) and higher hydrophilic-

lipophilic balance (HLB) values than the species categorized as toxic (Table 2.1). We speculate

that the more lipophilic Pluronics (with lower HLB) are more likely to interact with and disrupt

phospholipid bilayers, compromising cell membrane integrity. It is clear that Pluronics P105 and

F68 at 0.02% are not detrimental to CHO cells. It is reasonable to assume that many

immortalized cell lines will fare similarly, but we caution that cell phenotypes vary considerably,

so effects should be tested before use.

41


Figure 2.6 Pluronic cytotoxicity. Viability (right axes) and density (left axes) of CHO cells

cultured over 3 days without Pluronics, in Pluronics F68 and P105 at 0.02% (wt/v) (A) and in

Pluronics L62, L64 and L92 at 0.02% (wt/v) (B). Photomicrographs for CHO cells after 3 days

cultured Pluronics-free, in 0.02% F68 and 0.02% P105 (C). Cells show comparable growth rates

and morphologies in the presence of F68 and P105 to standard Pluronics-free conditions.

2.4 Conclusions

We evaluated a series of Pluronic block copolymers as additives for use with digital

microfluidics to reduce protein adsorption and prolong device lifetime. Of the formulations

tested (which included Pluronics F68, which has been used extensively for this purpose), the

additives that yielded the best device performance were Pluronics L92 and P105 at 0.02% (wt/v).

For applications involving mammalian cells, however, P105 is a better choice as L92 is cytotoxic

C

1.5x105

1.0

0.5

0.0

Via

ble

Cell

Concentr

ation (

cell/

mL)

7248240

Time (h)

100

80

60

40

20

0

Via

bility

(%)

L62 Cell Density

L62 Viability

L64 Cell Density

L64 Viability

L92 Cell Density

L92 Viability

1.5x105

1.0

0.5

0.0

Via

ble

Cell

Concentr

ation (

cell/

mL)

7248240

Time (h)

100

80

60

40

20

0

Via

bility

(%)

Pluronics-free Cell Density

Pluronics-free Viability

F68 Cell Density

F68 Viability

P105 Cell Density

P105 Viability

Toxic

Non-Toxic

A

B

Pluronics-free

F68

P105

42


to Chinese hamster ovary cells at 0.02%. More generally, when selecting Pluronic additives to

improve device longevity in DMF devices, we recommend that Pluronic species should have

long PPO chain lengths (30 units or greater). For use with cells, greater PEO content (50% or

more) is likely to be important. To this end, Pluronic species such as F88 and F108 (which were

not evaluated in this chapter) are likely good candidates because they have high PEO content and

PPO chain lengths within the acceptable range determined in this study. In additional

experiments (data not shown), F88 at 0.06% was evaluated to be more effective anti-fouling

additive that is amenable to cells and was used for cell experiments in Chapter 3. It is important

to note that concentration dependence should be evaluated for any future Pluronic additive

candidates since droplet movability is highly dose dependent. Finally, if full device longevity

assays cannot be performed (as may be the case if a large matrix of conditions is being

evaluated), we recommend that contact angles may be a useful screen ‒ lower initial non-

potentiated and electrodynamic/charged contact angles, and higher contact angle differences

between charged and de-charged states correlate with improved performance on device. As we

advance our understanding of the mechanisms behind biofouling and biofouling prevention in

digital microfluidic devices, we will greatly increase its suitability for an ever-greater range of

applications.

43

Sam H. Au Effects of Digital Microfluidic Actuation on Cell Fitness

Chapter 3

Effects of Digital Microfluidic Actuation on Cell Fitness

The potential benefits of using new technologies such as microfluidics for life science

applications are exciting, but it is critical to understand and document potential biases imposed

by these technologies on the observed results. In this chapter, the first study of genome-level

effects on cells manipulated by digital microfluidics is described. These effects were evaluated

using a broad suite of tools: cell-based stress sensors for heat shock activation, single-cell

COMET assays to probe changes in DNA integrity, and DNA microarrays and qPCR to evaluate

changes in genetic expression. The results lead to two key observations. First, most DMF

operating conditions tested, including those that are commonly used in the literature, result in

negligible cell-stress or genome-level effects. Second, for DMF devices operated at high driving

frequency (18 kHz) and with large driving electrodes (10 mm x 10 mm), there are significant

changes in DNA integrity and differential genomic regulation. We hypothesize that these effects

are caused by droplet heating. We recommend that for DMF applications involving mammalian

cells that driving frequencies be kept low (≤10 kHz) and electrode sizes be kept small (≤5 mm)

to avoid detrimental effects.

3.1 Introduction

Microfluidic approaches are growing in popularity; for example, the ―organ on a chip‖ concept

has attracted attention as a potentially disruptive new tool for drug discovery and screening11, 124-

127. But as interest in these techniques grows, so too does the need to better understand the effects

that microfluidic culture conditions have on cell phenotype, fitness, and health. For example,

microchannel-based cell culture is known to cause cells to experience significant changes in

glucose consumption, proliferation, and stress levels128

. Likewise, exposure to

44


polydimethylsiloxane (PDMS), a common material used to form microchannels, has been found

to alter gene expression129

. Thus, we posit that while the potential benefits of using new

technologies (such as microfluidics) for life science applications are exciting, it is critical that we

take the time to understand and document the potential biases imposed by these technologies on

the observed results.

The topic of how the microenvironment in microfluidic systems alters cell fitness is of particular

interest for digital microfluidics (DMF), a fluid-handling technique in which droplets are

manipulated on an open surface by applying electrical potentials to an array of electrodes

embedded under an insulator40

. A typical DMF device is shown in Figure 1.1, which highlights

the capacity to dispense, mix, merge, and split discrete droplets. These operations are attractive

for cell-based applications, and DMF has recently become popular for handling43, 44, 46, 48, 130-132

and culturing42, 45, 50, 133

mammalian cell lines, micro-organisms80, 134

, primary mammalian

cells47

, and 3D cell constructs49

.

Unfortunately, the scientific literature contains little information about the effects of digital

microfluidic droplet actuation on cell health. Barbulovic-Nad et al.42, 45

reported that mammalian

cells exposed to one set of DMF operating conditions had similar viabilities and proliferation

rates when compared to control (non-actuated cells), and Au et al.80

reported similar results for

bacteria, algae, and yeast. However viability and proliferation are crude measures of cell fitness

which may ignore the vast range of subtle effects that may be caused by DMF actuation – stress

responses, DNA damage, differential DNA expression, etc. It is important to catalogue these

potential effects, particularly if DMF becomes useful for screening for cell phenotype differences

in the pharmaceutical industry50

. Moreover, it would be useful to evaluate the potential effects of

45


DMF droplet manipulation over a wide range of operating conditions to determine which should

be used and which should be avoided.

This chapter describes work evaluating the genome-level effects of DMF actuation on cells. A

number of cell fitness indicators were evaluated: heat shock stress response135, 136

, DNA

integrity62, 63

and changes in genetic transcription levels56, 58, 68

. We propose that the results

described here will serve as a useful guide for the rapidly growing number of research groups

that are adopting digital microfluidics as a tool for applications involving cells. Moreover, we

propose that the suite of tests described here (cell stress sensors, COMET assays, and

microarrays/qPCR) represents a useful measuring stick for probing the effects of any new

technology that is applied to applications involving cells.

3.2 Experimental

Unless specified otherwise, reagents used in this chapter were purchased from Sigma-Aldrich

(Oakville, ON). Parylene-C dimer was obtained from Specialty Coating Systems (Indianapolis,

IN). Teflon-AF was from DuPont (Wilmington, DE), and A-174 silane was from GE Silicones

(Albany, NY).

3.2.1 Device Fabrication and Operation

Digital microfluidic devices were fabricated in the University of Toronto Emerging

Communications Technology Institute (ECTI). Bottom plates bearing square electrodes arranged

in 2 x 2 arrays of 10, 5, 2.5, or 2 mm wide square chromium electrodes (used as bottom plates of

DMF devices) were formed by photolithography and coated with a Parylene-C insulating layer

(6.9 µm) and Teflon-AF hydrophobic layer (235 nm) as described previously42, 80

. Unpatterned

top plates were formed by spin-coating indium tin oxide (ITO) coated glass substrates (Delta

46


Technologies, Stillwater, MN) with Teflon-AF (235 nm, as above) and were assembled with

bottom plates by adherent spacers formed from four pieces of double-sided tape (total spacer

thicknesses 280 µm). To drive droplets or expose cells to potentials, square AC potentials were

applied to actuation electrodes on the bottom plate relative to the counter-electrode on the top-

plate in a circular pattern using a custom high voltage switching system (Astinco, Inc., Markham,

ON, Canada).

3.2.2 Cell Culture and Stress Conditioning

WeHi-3B cells (ATCC, Manassas, VA) were grown for 3-4 days at 37°C and 5% CO2 in IMDM

media supplemented with 10% fetal bovine serum (Life Technologies, Inc., Burlington, ON,

Canada), 2 mM L-glutamine (Life Technologies, Inc.), 100 IU/mL penicillin, and 100 µg/mL

streptomycin. After reaching confluency, spent media was exchanged for fresh media, and

thereafter, the media was exchanged every day for three days. Each aliquot of spent media was

centrifuged (300 g, 5 min), the supernatant filtered through a 0.2 µm syringe filter (PALL

Canada Ltd., Saint-Laurent, QC), and the filtrate frozen at -20°C until use. Wild-type Ba/F3 pro-

B murine suspension cells (ATCC) were cultured in complete growth media consisting of RPMI-

1640 supplemented with 10% fetal bovine serum (Life Technologies, Inc.), 10% WeHi-

conditioned media (as above), 100 IU/mL penicillin, 100 µg/mL streptomycin, and 0.06% (wt/v)

pluronic F88 at 37°C and 5% CO2. Stably transformed GFP-HSE Ba/F3 cells137, 138

were treated

similarly with the exception of the addition of 500 µg/mL G418 geneticin (Life Technologies) to

the complete media to maintain selective pressure. Wild-type or GFP-HSE Ba/F3 cells in early

log-phase growth were centrifuged (300 g, 5 min), the supernatant was removed, and the pellet

was resuspended in complete growth media at 2 x 106 cells/mL. In DMF experiments, 70 µL, 18

µL or 5 µL aliquots of the wild-type or GFP-HSE Ba/F3 cell suspension were loaded onto

47


devices and actuated continuously in a circular pattern over 4 electrodes (at approximately 3.33

mm/s) under conditions of varying driving potential (200, 400, 625, or 650 Vpp), driving

frequency (1, 10, or 18 kHz), actuation time (5 or 15 min), and electrode size (2x2, 2.5x2.5, 5x5,

or 10x10 mm).Untreated control cells were handled identically by loading aliquots onto devices,

but without applying driving voltages (i.e., the droplets remained stationary). Heat shock controls

were carried out on 20 or 70 µL aliquots of cell suspension which were loaded onto non-active

devices (as above) and heated on a PMC digital hot plate (Thermo Fisher Scientific, Waltham,

MA) such that droplets were maintained at 42°C, 47°C or 52°C for 5 or 15 minutes. Droplet

temperature was measured with a thermocouple as described in section 3.2.6 information, to

control the actual temperature to within 1°C of set-point. Sodium arsenite controls were carried

out on 100 µL aliquots of cell suspension supplemented with sodium arsenite and incubated for 5

minutes. Several replicates of cells exposed to each condition (DMF actuated, non-actuated

control, heat-shock control, arsenite control) were generated for protein expression/flow

cytometry, COMET assays, oligonucleotide microarrays, and qRT-PCR assays, as described

below.

3.2.3 Cell Stress Evaluation and Flow Cytometry

Aliquots of untreated, DMF-treated, heat-treated, and arsenite-treated GFP-labeled cells were

diluted with 9.9 mL phosphate buffered saline (PBS), centrifuged (300 g, 5 min) and the

supernatant removed. Each aliquot was then resuspended in 1 mL of fresh media (with G418

geneticin), transferred into a well of a 24 well plate, and incubated for 24 hours at 37ºC/5% CO2.

Cells were then collected, centrifuged (300 g, 5 min), the supernatant removed and resuspended

into 2.5 mL PBS. GFP expression was determined using an Epics XL flow cytometer (Beckman

Coulter Canada, Mississauga, ON, Canada) and analyzed using Expo32 Software (Beckman

48


Coulter). The cells were sampled at a rate of ~100 events/s, excited using a 488 nm laser, with

the fluorescent signature detected through a 530/30 nm filter until 5000-20,000 cells were

detected per condition. Histograms of fluorescent intensity were plotted on a logarithmic scale.

3.2.4 Single Cell Gel Electrophoresis COMET Assays

Assays were conducted using a Comet Assay Kit (Trevigen, Inc., Gaithersburg, MD) according

to the manufacturer’s protocols. Briefly, in each assay, a suspension of Ba/F3 cells was diluted to

3 x 105 cells/mL in ice-cold PBS (Mg

2+/Ca

2+ free) immediately after stress or control treatment

(as above). This suspension was combined with agarose solution, spread on a microscope slide

and allowed to gel. Slides were then immersed and incubated in prechilled lysis solution (45

min) and subsequently alkaline unwinding solution (45 min). The slides’ contents were then

electrophoresed in a horizontal gel electrophoresis system (VWR International, LLC, Radnor,

PA) at 0.7 V/cm in a 4°C cold room (60 min) before washing twice in DI water and once in 70%

ethanol. Slides were dried before adding SYBR green and imaging with a Leica DM2000

microscope (Leica Microsystems, Inc., Concord, ON, Canada). Percent fragmented DNA in tail

and Olive moments were quantified using Cometscore™ software (AutoComet, TriTek Corp,

Sumerduck, VA). Each condition was conducted in triplicate with at least 100 cells counted per

sample.

3.2.5 Microarrays and qPCR

For each oligonucleotide microarray or qPCR experiment, a suspension of Ba/F3 cells was

diluted into 1 mL pre-warmed complete growth media in a tissue culture treated well plate (BD,

Franklin Lakes, NJ) immediately after stress or control treatment (as above). The cells were

allowed to recover for 1 hour at 37°C, and then RNA was extracted using Arcturus Picopure

49


RNA Isolation kits (Applied Biosystems, Inc., Foster City, CA) according to the manufacturer’s

guidelines. Purified RNA was stored at -80°C and thawed on ice directly before analysis.

Microarray studies were conducted at the University Health Network Microarray Centre

(Toronto, ON, Canada). Prior to hybridization, each sample was evaluated on an Agilent

Bioanalyzer (Agilent Technologies Canada, Inc., Mississauga, ON) to ensure that it met QC

thresholds (RNA integrity number>9)139

. 60-200 ng of total RNA per sample was labeled using

an Illumina TotalPrep-96 RNA Amplification Kit (Life Technologies) according to the

manufacturer’s protocol. 1.5 ng of the generated cRNA was randomized and hybridized onto

mouse WG-6 v2.0 BeadChip platforms (Illumina, Inc., San Diego, CA) by incubation at 58ºC for

18 hours. Beadchips were washed and stained according to the manufacturer’s protocol and

scanned using an iScan array scanner (Illumina). The dataset is publically available at the Gene

Expression Omnibus (GEO Accession number: GSE43507). Data quality was validated prior to

normalization using Illumina® internal quality control metrics as well the R(v2.10.0)

Bioconductor framework with the Lumi package140

.

Microarray data analysis was conducted using Genespring v.11.5.1 (Agilent Technologies). Two

batches of microarray data were normalized using the Empirical Bayes ComBat accommodation

of batch effects141

using an R script. Each array batch contained 3 untreated control biological

replicates for cross-batch normalization. Data was normalized using a standard quantile method

followed by a ―per probe‖ median centered normalization. A total of 45281 probes were

represented on the mouse array. The data was filtered such that only probes in the upper 80th

percentile of the distribution of intensities were retained so that probes without signal would not

confound subsequent analysis. The filtered set contained 37133 probes. An unsupervised

clustering algorithm using a Pearson centered correlation as a distance metric with average

50


linkage rules was used to build hierarchical trees. Next, an ANOVA was performed using the

Benjamini-Hochberg false discovery rate correction with a multiple testing correction threshold

of p < 0.05. Results were presented in log2 fold change versus untreated controls. Venn diagrams

of significantly expressed probes were created using Venny

(http://bioinfogp.cnb.csic.es/tools/venny/index.html). Putative gene functions were designated

from the AmiGO gene ontology database (http://amigo.geneontology.org) and the Information

Hyperlinked Over Proteins database (http://www.ihop-net.org)142

. Each condition was conducted

in triplicate (n=3) except for non-actuated controls which had a total of 6 replicates (n=6).

For qPCR experiments, total RNA was extracted and purified as above. Reverse transcription

was completed using a Quantitect Reverse Transcription Kit (Qiagen, Inc., Toronto, ON)

according to the manufacturer’s guidelines. The expression stability of beta-2-microgobulin

(B2m) and glyceraldehyde 3-phosphate dehydrogenase (Gapdh) were evaluated in pilot qPCR

runs. Primers (Life Technologies, Inc.) were as follows: B2m forward and reverse primers -

TTCTGGTGCTTGTCTCACTGA and CAGTATGTTCGGCTTCCCATTC; Gapdh forward and

reverse primers - AGGTCGGTGTGAACGGATTTG and

TGTAGACCATGTAGTTGAGGTCA; Dusp1 forward and reverse primers -

GTTGTTGGATTGTCGCTCCTT and TTGGGCACGATATGCTCCAG respectively. qPCR

was conducted on a 7900HT qRT-PCR system (Applied Biosystems, Inc.) using Quantifast

SYBR green PCR kits (Qiagen, Inc.). Three biological replicates and at least two technical

replicates were conducted for cells exposed to each condition. Relative quantification was

conducted according to Pfaffl143

and Rieu and Powers144

with the expression of Dusp1

normalized to both B2m and Gapdh housekeeping genes. Baselines, windows of linearity (by

amplicon group) and Cq thresholds (by sample group) were detected by LinRegPCR145

. The

http://bioinfogp.cnb.csic.es/tools/venny/index.html

http://amigo.geneontology.org/

http://www.ihop-net.org/

51


RNA used in qPCR experiments was from the same pool for RNA used for microarray

experiments, whenever possible. Two-tailed student t-tests were conducted to determine p-

values.

3.2.6 Temperature Measurements

70 µL, 18 µL or 5 µL droplets of RPMI-1640 cell culture media supplemented with 10% fetal

bovine serum and 0.06% (wt/v) Pluronic F88 (Brenntag Canada, Toronto, ON) were loaded onto

DMF devices bearing 10 mm x 10 mm, 5 x 5 mm or 2.5 x 2.5 mm electrodes respectively. The

electrodes were then charged for 15 minutes continuously using the methods and system

described in section 3.2.1, with 400 VPP driving potentials at 1, 10, or 18 kHz. A K-type

KMQSS-010U-6 thermocouple (Omega Engineering, Inc., Laval, Canada) was inserted between

the two plates to measure the temperature in each droplet as a function of time. Three replicate

measurements were collected for each of the nine electrode size/frequency combinations.

3.3 Results and Discussion

3.3.1 Preliminary Experiments and Cell-based Stress Sensors

In preliminary experiments, Ba/F3 pro-B murine cells that had been stably transfected to express

green fluorescent protein (GFP) under the transcriptional control of heat shock element (HSE)

promoter137, 138

were used as cell-based sensors to probe for stressful stimuli. GFP-HSE cell

sensors were exposed to a range of DMF operating conditions, including driving potentials of

200-650 Vpp and frequencies of 1-18 kHz applied to droplets actuated continuously for five

minutes on devices with 2 x 2 mm driving electrodes. Stress responses (reported by the

expression of GFP) were evaluated by flow cytometry (Fig. 3.1). As shown, the stress response

of cells actuated by digital microfluidics across all of the conditions (Fig. 3.1B-F) was similar to

untreated control cells (Fig. 3.1A) (mean fluorescent intensities of 1.0 or less). In contrast, cells

52


exposed to chemical or heat shock controls were observed to have increased expression of GFP

relative to untreated controls (Fig. 3.1G-H). This is a useful finding, as heat shock proteins

(many of which are under transcriptional control of HSE) are known to be upregulated under

many different types of stress135, 136

, and it appears that DMF operation in the conditions

described above does not trigger this pathway. There may be other cell responses that might not

be captured by these experiments, which led us to select a sub-set of operating conditions for

further experiments. Specifically, a protocol was developed in which one driving potential (400

Vpp) and three different driving frequencies (1, 10, and 18 kHz), were applied to droplets

containing cells that were continuously actuated for 15 minutes. To enable the use of microarray

analysis (which requires relatively large numbers of cells), we tested the effects of devices with

larger actuation electrodes (up to 10 mm x 10 mm, useful for actuating 70 L droplets) than

those used in the preliminary experiments. These conditions were tested on non-transfected wild-

type Ba/F3 cells and were used for all experiments described below.

53


Figure 3.1 Cell-based stress sensor results. Flow cytometry histograms of GFP-HSE Ba/F3 cells

(GFP transcription under transcriptional control of heat shock element promoter). Cells were (A)

untreated for 5 min, manipulated continuously by DMF on devices with 2 x 2 mm electrodes for

5 min at: (B) 200 Vpp and 10 kHz, (C) 400 Vpp and 1 kHz, (D) 400 Vpp and 18 kHz, (E) 625 Vpp

and 1 kHz, or (F) 650 Vpp and 15 kHz, or (G) heat-shock treated at 42ºC for 5 min, or (H)

exposed to sodium arsenite at 200 µg/mL for 5 min.

Untreated

A

Arsenite control

Co

unts

DMF 200 Vpp 10 kHz

42ºC control

Co

unts

GFP Intensity GFP Intensity

B

DMF 400 Vpp 1 kHz

C

DMF 400 Vpp 18 kHz

D

G H

Co

unts

Co

unts

Co

unts

Co

unts

DMF 625 Vpp 1 kHz

E

Co

unts

DMF 650 Vpp 15 kHz

F

Co

unts

54


Interestingly, as described in the section 3.3.5, some of the DMF operating conditions used in

these experiments resulted in increased droplet temperatures. This observation was unexpected

(and is the first report of this phenomenon), but it added to our motivation to evaluate the effects

of DMF actuation on the cells in a series of assays for DNA damage and expression.

3.3.2 DNA Integrity

To evaluate the effects of DMF actuation on DNA damage, we conducted COMET assays, an

established method of evaluating the extent of DNA damage146

by subjecting individual cells

fixed within a hydrogel to electrophoresis. Intact (undamaged) DNA remains immobilized in the

fixed cells, while fragmented (damaged) DNA electrophoreses out of the cells, forming a

characteristic ―comet-like‖ pattern. As shown in Figure 3.2, DNA damage was observed for cells

actuated by DMF at 18 kHz (Fig. 3.2C), but not at 1 kHz (Fig. 3.2B). Heat-shock controls at

42°C, 47°C and 52°C also showed significant fragmentation (Fig. 3.2D-F). Quantification by

percent DNA found in the tails and the Olive moment (the product of the length and the fraction

of DNA in each tail) indicate that the DNA damage observed for cells actuated at 18 kHz or in

any of the three heat-shock controls was significantly higher than that observed for the untreated

controls (p < 0.01) while cells actuated on DMF at 1 kHz and untreated controls were not

significantly different (Figure 3.2G). Since cells were assayed immediately after DMF actuation,

it is unlikely that the observed DNA damage was a result of apoptotic fragmentation. Heat shock,

on the other hand, has been reported to be a cause of double stranded DNA breaks62

, potentially

through protein damage147

or the bystander effect, in which dying cells release factors lethal to

neighbouring cells148

. Since droplets actuated on DMF devices with 10 mm x 10 mm electrodes

at 18 kHz were (unintentionally) heated (section 3.3.5), the observed DNA fragmentation for the

18 kHz case may be a result of droplet heating.

55


Figure 3.2 Quantification of DNA integrity. Representative photomicrographs of cells assayed

for DNA damage using the single cell gel electrophoresis COMET assay for (A) untreated

controls, DMF-treated for 15 min at 400 Vpp on devices with 10 x 10 mm electrodes at

frequencies of (B) 1 kHz or (C) 18 kHz, or heat-shock treated at (D) 42°C, (E) 47°C or (F) 52°C.

Scale bars represent 50 µm. (G) Percent fragmented DNA (solid red bars) and Olive moment

(striped blue bars) were quantified by CometScore™. Error bars represent one standard deviation

(n=3).

G

Untreated control

A

42 C control

B

DMF - 1 kHz

C

DMF - 18 kHz

D

E

47 C control

F

52 C control

56


In summary, the data in Figure 3.2 suggests that the DNA integrity of cells exposed to DMF

manipulation at 400 Vpp on devices with 10 x 10 mm electrodes was strongly frequency

dependent, with DNA fragmentation ranging from negligible at 1 kHz, to significant at 18 kHz.

3.3.3 Gene Expression – Microarrays

To evaluate if DMF actuation causes changes in DNA expression, oligonucleotide microarrays

were conducted; the complete data set is publically available at the Gene Expression Omnibus

(GEO Accession number: GSE43507). Differentially expressed genes were identified using an

ANOVA with false discovery rate (FDR) q-value cut-off of 0.05. Using a 2-fold change

threshold, 196 unique genes were differentially expressed among the various conditions, and a

heat map of these genes is shown in Figure 3.3. The numbers of differentially expressed genes

observed for cells exposed to the different conditions relative to control cells were 3, 85, 65, 48,

and 82, for DMF/1 kHz, DMF/18 kHz, 42°C control, 47°C control, and 52°C control,

respectively.

Figure 3.3 suggests that there were few effects on gene expression caused by DMF actuation at

1 kHz. For example, the hierarchical clustering of conditions (the top axis in Figure 3.3)

indicates that cells manipulated by DMF at 1 kHz group with untreated controls, in contrast to

cells manipulated by DMF at 18 kHz, which group closely with the externally heated controls.

The three genes that were differentially expressed relative to untreated controls under actuation

by DMF at 1 kHz are ubiquitin-conjugating enzyme E2C (Ube2c), destrin (Dstn) and a predicted

pseudogene (ECG635570). The functions of these genes (Ube2c: cell division; Dstn: cytoskeletal

organization; ECG635570: unknown) are not related to known forms of cell stress (heat,

oxidation, osmotic pressure, etc.) or apoptosis. And as shown in the Venn diagrams in Figure

57


3.4A, these three genes were not differentially expressed in cells exposed to heat-shock. Finally

the magnitudes of the differential expression for these genes were modest (+2.3, +2.2 and +2.1

fold changes for Ube2c, Dstn, and ECG635570); in some analyses, much higher thresholds are

used to identify differentially expressed genes149, 150

.

58


Figure 3.3 Microarray heat map. Each gene shown has a ≥2-fold difference determined by an

ANOVA using the FDR Benjamini and Hochberg multiple testing correction (p<0.05). Cells

were either untreated (n=6), manipulated by DMF for 15 min at 400 Vpp on devices with 10 x 10

mm electrodes at 18 kHz or 1 kHz (n=3 ea.), or heat-shock treated at 42°C, 47°C or 52°C (n=3

ea.). Hierarchies were generated with a Pearson centered correlation tree building algorithm as a

distance metric with average linkage rules. Green shaded cells and red shaded cells represent log

fold-change down-regulated or up-regulated expression versus the mean, respectively.

-4 log fold +4 log fold

DMF 18 kHz

47 C control

52 C control

42 C control

DMF1 kHz

Untreated Control

Hierarchial Clustering (conditions)

Hie

rarc

hia

lC

lust

eri

ng (

genes

)

59


Figure 3.4 Microarray expression comparisons. Venn diagrams comparing the number of

significant probes with ≥2-fold (absolute) change relative to untreated controls common

between: (A) cells manipulated on DMF for 15 min at 400 Vpp on devices with 10 x 10 or 5 x 5

mm electrodes at 1 kHz or 18 kHz frequencies and heat-shock controls (42°C, 47°C or 52°C);

and (B) heat-shock control cells.

52°C control 47°C control 42°C control

DMF –1 kHz

DMF –18 kHz

A

B

60


In contrast to actuation at 1 kHz frequencies, the data in Figure 3.3 clearly shows that cells

exposed to 18 kHz DMF actuation at 400 Vpp on devices with 10 x 10 mm electrodes experience

dramatic changes in gene expression relative to untreated controls. In fact, the hierarchical

clustering of conditions (the top axis in Figure 3.3) suggests that the DMF/18 kHz expression

profile is most closely related to those of the 52°C and 47°C controls. This is consistent with the

observations described in section 3.3.5, which suggest that the conditions imposed on these cells

(i.e., 70 L droplets manipulated on 10 x 10 mm electrodes with 400 Vpp at 18 kHz for 15 min)

are associated with temperature increases from ambient to the 47°C-52°C range. The similarity

between DMF/18 kHz and the 52°C and 47°C controls is further highlighted in the Venn

diagrams in Figure 3.4A—there are 23 and 29 gene expression overlaps with the 52°C and 47°C

controls, respectively. These numbers are large relative to the gene expression overlaps between

the three temperature controls themselves (Fig. 3.4B). Thus, we hypothesize that electrically

driven heating is one of the sources of the differential expression observed for the cells actuated

at 400 Vpp and 18 kHz for 15 min.

Many of the 85 genes that are differentially expressed for cells actuated at 18 kHz are known to

be related to stress and/or apoptosis – these genes are listed in Table 3.1. The magnitudes of the

differential expression of many of these genes are large – for example Fos, Egr2, and Dusp1 are

expressed at -30.5, -7.6, and -7.5-fold relative to untreated controls, respectively (note that the

corresponding fold changes for cells exposed to DMF actuation at 1 kHz are near unity). Eight of

the genes in Table 3.1 are involved in MAPK signaling pathways, which are known to be

activated by heat, oxidation, osmotic pressure, DNA damage and ischemic stresses151

.

Specifically (from Table 3.1), Fos152, 153

, Egr2154

, Dusp1155-158

, Osm159

, Mcm7160

, Dusp2161

,

Ier3162

, and Zfp36163

are all components of or interact with the MAPK pathway. Interestingly,

61


seven of these eight genes are down-regulated for DMF/18 kHz actuation rather than up-

regulated, which is the expected response. Note that heat shocked control cells also showed

down-regulation of these stress genes as well (Table 3.1), suggesting that this phenomenon is a

result of the cells’ inherent heat shock response and not simply an artifact of DMF manipulation.

However, this phenomenon merits future study.

62


Table 3.1 Stress and apoptosis gene summary. Sub-set of differentially expressed genes for

DMF/18 kHz operation on devices with 10 x 10 mm electrodes (with a ≥2 absolute fold change

relative to untreated controls) related to stress and/or apoptosis. Putative functions designated by

AmiGO gene ontology database and Information Hyperlinked Over Proteins (iHOP)142

.

-30

-20

-10 0

10

Fos E

gr2

Dusp1

Axud1

Osm

M

cm

7

Dusp2

Ier3 Z

fp36

Prf1 P

lk3

Nisch

Myd116

Hspd1

Hspa8

Fosl2 Atf4

D

MF

- 18 kH

z

D

MF

- 1 K

Hz

Symbol Entrez

Gene ID

Annotation DMF -

18 kHz

FC

DMF -

1 kHz

FC

52 C

control

FC

47 C

control

FC

42 C

control

FC

Putative Function

Fos 14281 FBJ osteosarcoma

oncogene

-30.5 -1.3 -9.8 -5.4 -2.1 Response to oxidative stress and

extracellular stimulus, positive regulation

of transcription

Egr2 13654 early growth response 2 -7.6 -1.5 -1.0 -1.1 -1.7 Response to stress and DNA damage,

positive regulation of transcription,

myelination, cellular response to organic

substance

Dusp1 19252 dual specificity

phosphatase 1

-7.5 -1.1 -6.9 -6.3 1.0 Response to oxidative stress, cell cycle,

MAPK activity, regulation of apoptosis

Axud1 215418 cysteine-serine-rich

nuclear protein 1

-.5.3 -1.3 -4.3 -3.6 -2.1 Apoptotic process, regulation of

transcription

Osm 18413 oncostatin M -4.9 -1.3 -4.1 -2.6 -3.6 Response to heat, apoptosis, positive

regulation of cell proliferation and

MAPK cascade

Mcm7 17220 minichromosome

maintenance deficient 7

4.0 1.0 -1.1 -1.0 -1.1 Response to DNA damage stimulus, cell

cycle, cell proliferation

Dusp2 13537 dual specificity

phosphatase 2

-3.9 -1.3 -2.0 -1.6 -2.0 Response to oxidative stress, inactivation

of MAPK activity, regulation of

apoptotic process

Ier3 15937 immediate early

response 3

-3.8 -1.1 -3.1 -2.1 -3.0 Response to stress, regulation of

apoptosis and ROS metabolic process

Zfp36 22695 zinc finger protein 36 -3.6 -1.2 -3.8 -3.5 -2.0 Response to stress, mRNA catabolism

Prf1 18646 perforin 1 3.1 1.6 1.5 4.1 8.5 Apoptotic process, cytolysis, immune

response to tumor cell, defense response

to virus

Plk3 12795 polo-like kinase 3 -3.0 -1.3 -2.9 -2..7 -2.1 Response to DNA damage stimulus,

response to ROS, response to osmotic

stress, G1/S transition

Nisch 64652 nischarin 2.6 1.2 1.4 2.0 1.0 Apoptotic process, cell communication,

negative regulation of cell migration

Myd116 17872 protein phosphatase 1,

regulatory (inhibitor)

subunit 15A

-2.4 -1.1 -2.2 -1.7 1.1 Response to stress, apoptotic process,

regulation of translation

Hspd1 15510 heat shock protein 1

(chaperonin)

-2.3 -1.2 -1.7 -2.3 -1.1 Response to stress, B-cell activation, B

cell proliferation, protein folding,

regulation of apoptosis

Hspa8 15481 heat shock protein 8 -2.3 -1.2 1.0 -2.1 1.3 Response to stress, protein folding,

regulation of cell cycle, regulation of

transcription

Fosl2 14284 fos-like antigen 2 -2.1 -1.5 -2.4 -2.5 -1.5 Apoptosis, regulation of fibroblast

proliferation, regulation of transcription,

response to hypoxia

Atf4 11911 activating transcription

factor 4

-2.1 1.2 -1.3 -1.5 -1.2 Response to ER stress, GABA signalling

pathway, regulation of transcription

-30

-20

-10

0

10

Fos Egr2 Dusp1 Axud1 Osm Mcm7 Dusp2 Ier3 Zfp36 Prf1 Plk3 Nisch Myd116 Hspd1 Hspa8 Fosl2 Atf4

DMF - 18 kHz

DMF - 1 KHz

Fold Change

-30 -20 -10 0 +10

63


In summary, it is evident that for actuation for 15 minutes at 400 Vpp on devices with 10x10 mm

electrodes, the frequency of the applied electric field plays a pivotal role in the magnitude and

nature of cell responses to actuation. Low frequency (1 kHz) operation led to the up-regulation of

only 3 probes (with modest magnitudes), none of which are involved in cell stress or death;

while high frequency (18 kHz) operation led to the significant modulation of 85 probes, many of

which are implicated in stress or death responses.

3.3.4 Gene Expression – qPCR

qPCR of dual specificity phosphatase 1 (Dusp1) was chosen as an orthogonal test because it

exhibited strong differential expression from microarray results (described above), and because it

is known to be a gene that responds rapidly to heat stress155, 164, 165

. In addition, Dusp1 is a key

element regulating the MAPK pathway155-158

, which (as described above) appears to be a vital

component of the cellular responses observed in the microarray data. B2m and Gapdh were

chosen as reference genes for these experiments because they demonstrated high stability across

the different conditions in preliminary qPCR validation experiments (stability values less than

0.03 as determined by NormFinder software)166

.

In qPCR experiments, cells were treated on DMF devices with 5 x 5 mm electrodes (in addition

to 10 mm x 10 mm), and with 10 kHz driving frequency (in addition to 18 kHz and 1 kHz). As

shown in Table 3.2, of all the DMF conditions, only cells actuated at the highest frequency (18

kHz) and on the largest (10 mm x 10 mm) electrodes led to a statistically significant (p<0.05)

modulation of Dusp1 expression while cells actuated on smaller electrodes (5 mm x 5 mm) at the

same high frequency and cells actuated at lower frequencies (10 kHz and 1 kHz) on large

electrodes did not have a statistically significant difference relative to untreated controls. This

finding is consistent with the observations of droplet temperature described in section 3.3.5. In

64


addition, the qPCR data is consistent with the microarray data in that Dusp1 expression was

down-regulated by treatment at 47°C and 52°C, but not at 42°C or with any of the other DMF

operating conditions. This finding reinforces the discovery that significant stress responses are

only observed for large electrodes and high actuation frequencies (i.e., those conditions that are

associated with large temperature changes).

Table 3.2 qPCR validation of Dusp1. Microarray and qPCR fold changes (relative to untreated

controls) of dual specificity phosphatase 1 (Dusp1) for cells treated by DMF manipulation at

different frequencies and electrode sizes or without manipulation at various temperatures (n=3).

Conditions with Dusp1 PCR fold-changes significantly different (p<0.05) than untreated controls

are shaded.

ConditionDusp1 Microarray

Fold Change

Dusp 1 PCR Fold

ChangePCR p-value

10 mm 18 kHz -7.5 -3.5 <0.01

5 mm 18 kHz - 1.4 >0.05

10 mm 10 kHz - -1.0 >0.05

5 mm 10 kHz - 1.4 >0.05

10 mm 1 kHz -1.1 -1.3 >0.05

5 mm 1 kHz - 1.0 >0.05

52°C control -6.9 -5.4 <0.025

47°C control -6.3 -2.3 <0.01

42°C control 1.0 1.0 >0.05

65


In summary, the qPCR data agrees with the microarray results – the transcription levels of a

model stress gene for cells exposed to DMF operation at 400 Vpp for 15 min have strong

frequency dependence. This is consistent with previous observations of frequency-dependent cell

stress and death for cells manipulated on dielectrophoresis (DEP) systems138, 167, 168

. This is

notable and interesting, given that DEP systems, which typically include modest or no electrical

insulation and much higher (~MHz) frequencies, are quite different than the DMF system

described here. Furthermore, the qPCR results (supported by the cell-based stress sensor results)

indicate a strong dependence on electrode size – smaller DMF driving electrodes result in

negligible changes in DNA expression over a range of different operating conditions. This is

consistent with the hypothesis that the changes observed in cell fitness (both in transcription

profiles and in DNA integrity) are caused by droplet heating. Similar hypotheses have been

proposed for DEP-driven effects169

.

3.3.5 Droplet Heating

The results of the temperature measurement experiments are shown in Figure 3.5. In some cases,

droplet temperatures increased significantly– up to 25°C above ambient – during application of

DMF driving potentials. While such effects are purposefully generated in specialized DMF

devices modified to include resistive heaters (typically driven by DC potentials),170, 171

the data

shown in Figure 3.5 were generated from droplets positioned on standard devices (with no

heaters) driven by standard AC driving potentials. As far as we are aware, this phenomenon has

never before been reported.

66


Figure 3.5 Droplet temperature in digital microfluidics. Images of 70 µL (top left), 18 µL (top

right) and 5 µL (bottom) droplets on digital microfluidic devices (A). Graphs of the temperatures

of droplets subjected to driving potentials on devices bearing 10.0 x 10.0 mm (red triangles), 5.0

x 5.0 mm (blue circles) and 2.5 x 2.5 mm (green squares) square electrodes at 400 Vpp at (B) 18

kHz, (C) 10 kHz and (D) 1 kHz frequencies. Error bars represent one standard deviation (n=3),

and curves were added to guide the eye.

B

C

D

A

55

50

45

40

35

30

25

Tem

pera

ture

(°C

)

8006004002000

Time (s)

18 kHz 10 mm electrode

18 khz 5 mm electrode

18 kHz 2.5 mm electrode

38

36

34

32

30

28

26

24

Tem

pera

ture

(°C

)

8006004002000

Time (s)




27.5

27.0

26.5

26.0

25.5

25.0

24.5

24.0

Tem

pera

ture

(°C

)

8006004002000

Time (s)




67


The data in Figure 3.5 suggest several trends. First, the conditions tested that are closest to those

used regularly for digital microfluidics (i.e., 2.5 mm x 2.5 mm electrodes at 1 kHz or 10 kHz

frequencies) have near-negligible effects on droplet temperature, with average temperature

increases of 0°C and 2.3°C respectively. Second, elevated frequency or electrode size alone

results in minor effects – e.g., 2.5 mm electrode/18 kHz and 10 mm electrode/1 kHz result in

average temperature increases of 3.9°C and 2.8°C, respectively. Third, the large heating effects

(i.e., temperature increases greater than 5°C) were only observed for conditions with both

elevated electrode size and frequency.

It should be noted that the droplet temperatures in Figure 3.5 were measured in stationary

droplets (not moving droplets, as in actual DMF experiments). We expect that the temperatures

recorded in Figure 3.5 are an over-estimation of the temperatures of droplets in motion, as

mobile droplets such as those used for manipulating cells, were repeatedly moved to device

regions which have had time to cool, allowing heat to be dissipated more rapidly.

A potential mechanism for the temperature increases represented in Figure 3.5 is resistive (Joule)

heating, which is commonly observed in MEMS devices169

. As frequency increases, the

impedance of the digital microfluidic circuit decreases172

, resulting in an increase in current

which will increase the Joule heating. Another candidate is dielectric heating, which is caused by

the frictional loss of energy of rotating dipoles in the presence of an applied electric field173

. The

power generated by dielectric heating scales with the square of electric field strength and linearly

with applied frequency. Although DMF operation uses a much lower frequency range than those

which are typically used for dielectric heating (MHz-GHz), the physical scale of DMF devices

may render dielectric heating significant because large field strengths can be achieved over very

short distances. Both mechanisms of heating are consistent with the frequency-dependent

68


increases in temperature shown in Figure 3.5, and in other experiments, much higher frequency

waveforms led to even greater temperature changes (data not shown). In on-going work, we are

evaluating these effects in more detail, but these observations are not the main focus of the work

presented here.

3.4 Conclusions

The goal of the work described in this chapter was to determine if the manipulation of

mammalian cells by digital microfluidics causes measurable genomic effects. In an attempt to

discover the boundaries of such (putative) effects, a range of different DMF operating conditions

were evaluated, including driving voltage and frequency, electrode size, and actuation time. The

results indicate that cells manipulated by DMF under such conditions exhibit a broad range of

responses, ranging from negligible to significant. In particular, for cells actuated on small

electrodes (5 mm x 5 mm or less), negligible detrimental effects were observed for all operating

conditions tested. In contrast, for cells actuated on large electrodes (10 x 10 mm), DNA damage

and protein expression changes in stress-related genes were observed for high-frequency (18

kHz) DMF operation, but not for low-frequency operation (10 or 1 kHz). The genomic

expression pattern for cells exposed to high-frequency operation/large electrodes showed

similarity to heat shocked cells, which was consistent with observations that DMF operation

under these conditions causes droplet heating. Future study is merited for alternate conditions

and different cell types, but the results described here suggest that DMF experiments involving

cells are best suited for operation at low frequencies on devices with small electrodes to avoid

excessive droplet heating, DNA damage and/or changes in gene expression. Also, the droplet

heating phenomenon may have potential uses which require rapid heating such as qPCR.

69

Sam H. Au Integrated Microorganism Culture and Analysis

Chapter 4

Integrated Microorganism Culture and Analysis

This chapter describes the development of a micro-scale bioreactor for automated culture and

density analysis of microorganisms. The microbioreactor is powered by digital microfluidics

(DMF) and because it is used with bacteria, algae and yeast, we call it the BAY microbioreactor.

Previous miniaturized bioreactors have relied on microchannels which often require valves,

mixers and complex optical systems. In contrast, the BAY microbioreactor is capable of

culturing microorganisms in distinct droplets on a format compatible with conventional bench-

top analyzers without the use of valves, mixers or pumps. Bacteria, algae and yeast were grown

for up to 5 days with automated semi-continuous mixing and temperature control. Cell densities

were determined by measuring absorbances through transparent regions of the devices, and

growth profiles were shown to be comparable to those generated in conventional, macro-scale

systems. Cell growth and density measurements were integrated in the microbioreactor with a

fluorescent viability assay and transformation of bacteria with a fluorescent reporter gene. These

results suggest that DMF may be a useful tool in automated culture and analysis of

microorganisms for a wide range of applications.

4.1 Introduction

Microorganisms such as bacteria, algae, and yeast are important for a wide range of applications.

For example, bacteria and yeast are used extensively for protein production174-176

and genomic

studies177, 178

, and algae is a potential source of biofuel production179, 180

. These types of cells are

cultured in specialized growth media, often accompanied by active mixing and temperature

control, and algae cultures have an added requirement of light as an energy source. A common

70


method for monitoring growth profiles is to measure the absorbance of the culture at a specific

wavelength. As biomass accumulates, the absorbance increases in a manner that is predictable

and correlated with the density of cells in suspension.

In commercial applications, microorganisms are often grown in bioreactors with volumes up to

thousands of liters, but prior to large-scale culture, smaller systems (for example, microwell

plates bearing hundreds of microliters) are used to screen for optimum conditions for growth and

analyte production181

. There is great interest in developing miniaturized culture systems to

further reduce the costs of consumables, increase throughput and reduce manual labour

requirements. Most such efforts have relied on enclosed networks of microchannels; for

example, microfluidic devices have been developed to grow bacteria and yeast with integrated

sensors182, 183

and/or the ability to precisely control media delivery rates184, 185

. Moreover, there is

great potential for integrating channel-based microorganism culture systems with other

operations such as dielectrophoretic sorting186, 187

or even single-cell analysis188, 189

. However, a

disadvantage of microchannel-based culture systems is that they are not well-suited to

absorbance-based cell density measurements because of short path-lengths. Although devices

with integrated optics190-193

or systems that allow for direct counting of cells 194, 195

offer some

relief from these problems, fabrication of such devices is complicated and time-consuming, and

can lead to high fabrication costs. In addition, parallelization in microchannel-based systems is

challenging, especially for perfusion systems183-185

.

This chapter describes work developing a proof-of-principle microbioreactor relying on digital

microfluidics (DMF)37

. In DMF, fluid droplets are controlled in parallel on an open surface by

applying electrical potentials to an array of electrodes coated with a hydrophobic insulator (for a

comprehensive review of device geometries and fabrication techniques, see196

). DMF has

71


become a popular tool for biochemical applications, including mammalian cell-based assays42, 79,

197, enzyme assays

100, 198, 199, immunoassays

85, 86, protein processing

88, 89, 91, 200-202, the polymerase

chain reaction203

, and clinical sample processing and analysis204

. However, at the time that this

work was completed, there was only one report of the use of DMF with microorganisms – Son

and Garrell demonstrated that droplets containing yeast could be moved on a DMF system134

.

The work in this chapter demonstrates that DMF is capable of automated growth and density

analysis of several different types of microorganisms. To validate the new technique, the growth

characteristics of bacteria, algae, and yeast were measured and compared to those of

microorganisms grown and analyzed using conventional macroscale techniques. Furthermore, a

viability assay and a genetic transformation were implemented on-chip to illustrate how the

platform can be integrated with down-stream analyses after up-stream culture and density

measurement.

4.2 Experimental

Unless specified otherwise, reagents were purchased from Sigma-Aldrich (Oakville, ON).

Escherichia coli DH5α were generously donated by Prof. Kevin Truong (Institute of

Biomaterials and Biomedical Engineering, University of Toronto). Saccharomyces cerevisiae

BY4741 (S288C Background) were generously donated by Prof. Igor Stagjlar (Department of

Medical Genetics and Microbiology, University of Toronto). Cyclotella cryptica (CCMP 332)

algae and associated culture reagents were purchased from the Center for Culture of Marine

Phytoplankton (Maine, NE).

4.2.1 Macroscale Cultures

Bacteria and yeast were grown in 3 mL aliquots of media (LB broth and YPD broth,

respectively) in vented tubes in a shaking incubator (37°C/225 rpm and 30°C/200 rpm/45°

72


inclination, respectively). To generate growth curves, 0.3 mL aliquots of saturated culture (OD600

= 2.76 ± 0.02 for bacteria and OD600 = 6.95 ± 0.14 for yeast) were inoculated into 2.7 mL fresh

broth, and absorbance at 600 nm of diluted aliquots were measured periodically using a UV/Vis

spectrophotometer (Eppendorf, Westbury, NY). Algae was grown in 30-60 mL aliquots of f/2

medium (CCMP, Maine, NE) supplemented with biotin and cyanocobalamin (2 nM final

concentration ea., CCMP) in vented bottles at 14°C with agitation by magnetic stir bar (60 rpm),

with continuous illumination by a 60 W lamp positioned 20 cm from the culture. Algae were

maintained by weekly subculture at inoculation densities of ~9.0 x 105 cells/mL. To initiate

growth curves, exponentially proliferating algae were harvested by centrifugation (2000 g, 12

min) and inoculated in medium at a density of 7.0 x 104

cells/mL, and absorbance at 660 nm was

measured periodically using a UV/Vis spectrophotometer (Shimadzu, Burlington, ON). All

cultures were evaluated by microscopy (Leica DM2000, Leica Microsystems Canada, Richmond

Hill, ON) and were grown and evaluated in triplicate.

4.2.2 Device Fabrication

Devices were fabricated in the University of Toronto Emerging Communications Technology

Institute (ECTI) fabrication facility. Fabrication supplies included parylene-C dimer from

Specialty Coating Systems (Indianapolis, IN), Teflon-AF from DuPont (Wilmington, DE), and

A-174 silane from GE Silicones (Albany, NY). Silane solution comprised isopropanol, DI water,

and A-174 solution (50:50:1 v/v/v).

Glass substrates bearing patterned chromium electrodes (used as bottom plates of DMF devices)

were formed by photolithography and etching as described previously42

using photomasks

printed with 20,000 dpi resolution by Pacific Arts and Design (Toronto, ON). After patterning,

devices were primed for parylene coating by immersing them in silane solution for 15 min,

73


allowing them to air-dry and then washing with isopropanol. After priming, devices were coated

with Parylene-C (6.9 µm) and Teflon-AF (235 nm). Parylene was applied by evaporating 15 g of

dimer in a vapor deposition instrument (Model PDS 2010 LABCOATER® 2, Specialty Coating

Systems, Indianapolis, IN), and Teflon-AF was spin-coated (1% w/w in Fluorinert FC-40, 2000

rpm, 60 s) and then post-baked on a hot-plate (160 °C, 10 min). To facilitate the application of

driving potentials , the polymer coatings were locally removed from the contact pads by gentle

scraping with a scalpel. Unpatterned top plates were formed by spin-coating indium tin oxide

(ITO) coated glass substrates (Delta Technologies, Stillwater, MN) with Teflon-AF (235 nm, as

above).

4.2.3 Device Operation

As depicted in Figure 4.1, the BAY microbioreactor comprises a reactor region (four 10.5 x 9.5

mm electrodes arranged in a 2 x 2 array) mated to a sample region (three rows of eleven 3 x 3

mm electrodes) and a reservoir region (three 6 x 6 mm electrodes, one for each sample row).

Each of the droplet actuation electrodes (shown) are connected by microfabricated wires to

contact pads (not shown for clarity) to facilitate application of driving potentials. Each of the

three rows includes an L-shaped electrode which defines a 1.5 x 1.5 mm transparent window for

absorbance measurements. Prior to an experiment, reagents were pipetted onto the appropriate

electrodes on a bottom plate, and then an unpatterned, transparent top ITO-coated plate was

positioned onto the device, sandwiching the droplets between the two plates. The spacing

between the two plates was defined by 350-µm thick spacers formed from five-high stacks of

double-sided tape. Driving potentials of 400-500 Vrms were generated by amplifying the output

of a function generator (Agilent Technologies, Santa Clara, CA) operating at 1-5 kHz, and

74


droplets were actuated by applying driving potentials between the top electrode (ground) and

sequential electrodes on the bottom plate.

Figure 4.1 Schematic of BAY microbioreactor. A reactor region contains the mother drop, from

which daughter droplets are dispensed for analysis in the sample region or mixed with reagents

dispensed from the reservoir region. L-shaped electrodes in the sample region define 1.5 x 1.5

mm transparent windows which are used for absorbance measurements

Droplet motion was managed using an automated control system. Briefly, a computer running a

custom LabVIEW (National Instruments, Austin, TX) program interfaced to a DAQPAD 6507

(National Instruments, Austin, TX) controls the states of a network of high-voltage relays

(RT424012F, Tyco Electronics, Berwyn, PA). The inputs of the relays are connected to the

function generator/amplifier (see above), and the outputs of the relays mated to the contact pads

on the bottom plate of a device via a 40-pin connector. In practice, the user manually loads

75


reagents into the microbioreactor and then inputs a series of desired droplet movement steps,

after which all droplet actuation is controlled automatically by the system.

4.2.4 Microscale Cultures

The media used for microscale culture were identical to those used for macroscale culture

(section 4.2.1), but supplemented with Pluronic F68 (bacteria and yeast 0.1 % w/v, algae 0.02%).

Pluronic additives reduce the adhesion of cells42, 197

and proteins53

to DMF device substrates and

have additives species with high hydrophilic-lipophilic balance (such as F68) have no

detrimental effects on cell vitality or proliferation as described in Chapter 2. Prior to use, devices

were sterilized by rinsing in 70% ethanol, and microorganisms were grown in ~70 µL aliquots

termed ―mother drops‖ in the reactor region. During culture, the devices were stored in a

humidified chamber (a sealed Petri dish saturated with water vapor), and the mother drops were

actuated in a circular pattern at programmed intervals. Temperatures were controlled by means

of a digital hot-plate (bacteria and yeast) or by storage in a chilled room (algae). Algae cultures

were positioned under a 60 W lamp (at a distance of 20 cm) for continuous illumination. The

parameters for each type of culture are listed in Table 4.1.

76


Table 4.1 BAY microreactor parameters. Media, temperature, mixing frequency and absorbance

measurement frequecny used for microfluidic culture and analysis of bacteria, algae, and yeast

E. coli S. cerevisiae C. cryptica

Growth Media

LB broth YPD broth f/2

supplemented

with biotin and

cyanocobalamin

Temperature (°C)

37 30 14

Mixing Frequency

(min)

2.5

2.5 120

Absorbance

Measurement

Frequency (h)

1 2 24

To generate growth curves, microbial cultures were initialized by inoculating culture fluid into

fresh media, using identical procedures and densities to those used in the macroscale (see section

3.2.1). Mother drops containing bacteria, algae, or yeast were then grown with automated semi-

continuous mixing. For absorbance measurements, three ~7 µL daughter droplets were dispensed

from the mother drop onto the sample region and were driven to the L-shaped electrodes at

designated intervals (see Table 4.1). The microbioreactors were then positioned onto the tops of

transparent 96 well-plates and inserted into a PHERAstar microplate reader (BMG Labtech,

Durham, NC) for absorbance measurements at 600 nm for bacteria/yeast and 660 nm for algae.

The absorbances were collected using a well-scanning program, in which 8 separate

measurements were collected from pre-determined spots in a ~2.25 mm2 area. The absorbances

of the three daughter droplets were averaged together and were background-corrected by

subtracting the average absorbance (collected once at the beginning of each experiment)

measured from droplets containing only media on the same devices. After measuring the optical

77


densities, the daughter droplets were translated by DMF actuation back to the reactor region

where they were re-combined with the mother drop for continued culture.

4.2.5 Growth Curve Generation

To generate growth curves for each microorganism, OD measurements from the well plate reader

and benchtop spectrometers were plotted in natural log scale. The data were then baseline

corrected (subtracting the lowest value) and re-scaled (dividing by the highest, corrected

macroscale value) to generate growth curves in the range of 0-1 for comparison between

macroscale and microscale profiles. For each data point, three replicate measurements were

obtained and the average and standard deviations were plotted as a function of time. Doubling

times were calculated as follows:

𝑇

Td is the doubling time [s]

K is the growth rate [s-1

]

Cn is the cell density (as determined by absorbance) at timepoint n [cell/L]

tn is the elapsed time at timepoint n [s]

Doubling time values were determined during early log phase growth and compared using a two-

tailed t-test.

=

𝑙𝑜𝑔 𝐶2

𝐶1

(𝑡2 − 𝑡1)

(4.1)

(4.2)

78


4.2.6 Cell Death Assays

The viability of S. cerevisiae yeast grown in BAY microbioreactors was assayed using the

nucleic acid dye Ethidium homodimer-1 (EthD-1) (Invitrogen Molecular Probes, Eugene, OR).

Prior to operation, a 20 µL mixture of 2 µM EthD-1 in PBS supplemented with 0.05% F68 was

added to one reservoir and another mixture of 2 µM EthD-1 in PBS supplemented with 0.05%

F68 and 0.05% (v/v) Triton X-100 was added to another. Yeast were then inoculated as

described above and incubated at 30°C with automated mixing for 4 hours before the assay was

started. The assay was completed by dispensing daughter droplets of yeast from the mother drop

and merging each of them with droplets containing Triton X-100 and dye dispensed from the

reservoirs. The combined droplets were mixed in the sample region by actuation along the linear

path 10 times. The microbioreactor was incubated at 30°C for 1 hour and then visualized for

fluorescence over a square sample electrode.

4.2.7 Transformation

E. coli bacteria were transformed in a BAY microbioreactor with a pTriEx vector encoding

yellow fluorescent protein (YFP) and ampicillin resistance (generously donated by Kevin

Truong, University of Toronto). Prior to operation, a 20 µL mixture of 200 ng plasmid DNA and

0.20 M CaCl2 in LB broth without antibiotic supplemented with 0.05% F68 was added to a

reservoir. Bacteria (without ampicillin resistance) were then inoculated as described above and

incubated at 37°C with automated mixing for 1 hour before transformation. After confirming that

the cultures were at early log phase densities (as above), the microbioreactor was placed on ice

for 5 minutes, after which a daughter droplet was combined with an equal volume droplet

dispensed from the reservoir and mixed in the sample region by actuating the droplet in the

sample region approximately 10 times. The microbioreactor was chilled on ice for 1 hour,

79


rapidly heated on a hot plate at 42°C for 50 seconds and then cooled on ice for an additional 1

minute. The microbioreactor was incubated at 37°C for 1 hour with automated mixing after

which the droplet containing transformed bacteria was spread on an LB agar plate containing

ampicillin at 100 mg/L and incubated overnight at 37°C to allow colony formation.

4.3 Results and Discussions

4.3.1 Microbioreactor Design

A wide range of applications for microorganisms, particularly those involving screening of

different conditions, would benefit from automated, micro-scale culture techniques. The work in

this chapter describes the development of an automated microbioreactor using digital

microfluidics that is capable of culture, analysis and transformation of microorganisms in

droplets. This device is called the ―BAY‖ microbioreactor, after the three microorganism species

used here: bacteria (E. coli), algae (C. cryptica) and yeast (S. cerevisiae). The primary function

of the BAY microbioreactor is long-term cell culture. As shown in Figure 4.1, the device was

designed such that culture takes place in a ~70 L aliquot of media called a ―mother drop.‖ In

conventional bioreactors, cultured cells are gently and continuously mixed to ensure uniform

distribution of dissolved gases, nutrients, and the cells themselves185, 186

. In the BAY

microbioreactor, this function was accomplished by manipulating the mother drop in a circular

path at regular intervals. As has been reported elsewhere205-207

, when droplets are actuated in

similar paths in array-based DMF systems, droplet contents are mixed at rates up to 10-50x faster

than by diffusion alone. As shown in Figure 4.2A, in the current work, each circular mix step

comprised a sequence of four movements between adjacent electrodes. While future designs may

be developed for more efficient mixing (using more complex DMF movement paths such as

figure-eights206

), in this work, a simple circular path was observed to be adequate.

80


Figure 4.2 Operation of BAY microbioreactor. In (A), the mother drop was mixed by an

automated control mechanism. The drop was moved in a circular pattern on the four large

electrodes (which facilitates active mixing) at specific time intervals (i.e., every 2.5 minutes for

bacteria and yeast and every 2 hours for algae). In (B), daughter droplets were dispensed from

the mother drop to facilitate absorption measurements and were returned to the mother drop after

measurement

81


To facilitate automated microorganism culture, the BAY microbioreactor was designed to be

compatible with absorbance measurements, which serve as an indicator of cell density. As shown

in Figure 4.2B, in each such measurement, three daughter droplets were dispensed successively

from the mother drop and were driven onto L-shaped electrodes for analysis using a well plate

reader. The frequency of these measurements (i.e., every 1 h for bacteria, 2 h for yeast and 24 h

for algae) was determined by the growth rates of the organism. After the measurements, the

daughter droplets were returned to mother drops to continue incubation (thus maintaining the

volume of the culture). The L-shaped electrodes were designed with transparent regions that are

one quarter (1/4) of the area of the square electrode. Other ratios of window to electrode area

(e.g. 1/2 and 1/8) were evaluated, but were found to be sub-optimal, as droplets either were not

reliably moved over the window (1/2) or the window was too small for reproducible

measurements in the plate reader (1/8). While the strategy of using an L-shaped electrode

worked well in the current design, future devices might be formed using transparent driving

electrodes for simultaneous incubation and analysis.

A significant advantage of the new technique is the simplicity of the analysis, especially when

evaluation of many different conditions is required. The L-shaped electrodes in BAY devices

were designed to be 9 mm from each other, matching the pitch of a 96-well plate, and

absorbances were measured by inserting devices into a multiwell plate reader. As described

elsewhere for other applications42, 89, 198

, we propose that compatibility with off-the-shelf optical

detectors is an attractive feature of DMF-based systems. The optical path-length for absorbance

measurements in the BAY devices is determined by the spacer thickness between top and bottom

plates in the device. A 350 µm spacer was used for the work reported here, but DMF is

compatible with inter-plate spacers of up to several millimeters (data not shown).

82


Other advantages of this method are the ease with which active mixing can be incorporated and

the inherent batch mode of operation. For the former, many microscale reactors rely on diffusion

for mass transport, which is inefficient for cell-sized particles208

. Specially designed

micromechanical mixers can provide some relief for this limitation in microchannels183, 209

; in

contrast, simple repetitive manipulation of droplets in the DMF microbioreactor is sufficient for

active mixing without added complexity. Although the current generation of BAY devices was

designed for a single culture, the format, which matches the pitch and dimensions of multiwell

plates and detectors, is likely scalable in future generations for analysis of different culture and

analysis conditions in parallel. We propose that future generations of BAY systems may be

useful for growth and screening of many populations of organisms (e.g. S. cerevisiae gene

deletion libraries210

).

4.3.2 Microorganism Culture

To compare the growth rates of microorganisms grown in BAY microbioreactors to those

cultured by conventional means, bacteria, yeast and algae grown in both systems (micro- and

macro-scale) were interrogated with absorbance measurements over 8, 12 or 120 hours of

culture. As described in the section 4.2, the culture conditions in both systems (macro-scale or

BAY) were similar. Cell proliferation with minimal clumping was observed in both systems for

all three microorganisms over the course of the experiments (Figure 4.3). As shown in Figure 4.4

and Table 4.2, the growth rates of bacteria, algae, and yeast were similar for the macro- and

micro-scale; this is striking, given the significant differences between the systems (volumes,

electrostatic actuation, detectors, etc.).

83


Figure 4.3 Microorganisms on device. Photomicrographs of daughter droplets positioned on L-

shaped electrodes containing (A) E. coli at 0 and 8 hours, (B) C. cryptica at 0 and 5 days, and (C)

S. cerevisiae at 0 and 12 hours in BAY microbioreactors. Scale bars are 500 µm

84


Figure 4.4 Microorganism growth curves. Representative growth curves of (A) E. coli, (B) C.

cryptica, and (C) S. cerevisiae grown in macroscale (circles) or in BAY microbioreactors

(triangles). Absorbance measurements were taken at 600 nm for bacteria/yeast and 660 nm for

algae and were normalized to the highest value. Macroscale measurements were conducted using

benchtop UV/Vis spectrophotometers while microscale measurements were conducted on BAY

microbioreactors using a well-plate reader. Samples were evaluated in triplicate and error bars

represent one standard deviation.

A - Bacteria

B - Algae

C - Yeast

85


Table 4.2 Microorganism doubling time comparison. Exponential doubling times of bacteria,

yeast and algae in macro and micro-scale formats. P-values compare the difference between

macro- and micro-scale.

E. coli S. cerevisiae C. cryptica

Macro-scale

Doubling Time (h)

0.79±0.06 1.80±0.24 37.0±1.2

Micro-scale

Doubling Time (h)

1.23 ±0.43 1.88±0.15 42.6±2.4

P-value 0.08 0.31 0.02

We speculate that the variations in the growth rates between the micro- and macro-scale systems

may be caused by a number of factors. The most likely is temperature differences since small

fluctuations in incubation temperature can result in vastly different growth rates in bacteria, algae

and yeast211-213

. This difference between the macro- and micro-scale systems is most relevant

during absorbance measurements – in the macro-scale system, small aliquots were collected

from the main culture, measured for density, and then disposed (while the main culture remained

at temperature). In the micro-scale system, the entire device (including the main culture)

remained at room temperature (in the plate reader) during density measurements. In the future, a

plate reader with temperature control might be used to correct for this. Other potential sources of

variation may include differences in mixing efficiency and imprecise temperature control on hot

plates in comparison to incubators. The greater variances in the optical density measurements in

the microscale are most likely a result of the combination of shorter path lengths in the

microscale (350 µm versus 1 cm) and differences in the analysis tools (well-plate reader versus

benchtop spectrophotometer).

86


4.3.3 Downstream Processing and Analysis

A key advantage of microfluidic systems is the potential for integration of multiple processes

onto a single platform. To illustrate this point with the BAY microbioreactor, two different

downstream processes were integrated: a fluorescent viability assay of yeast and genetic

transformation of bacteria. In the former, the viability of S. cerevisiae grown in the BAY

microbioreactor was assayed on-chip with a fluorescent nucleic acid stain (Ethidium homodimer-

1). Dye with or without the surfactant, Triton X-100, was loaded into device reservoirs, the yeast

were grown for 4 hours and then daughter droplets dispensed and mixed with reagents dispensed

from reservoirs. Figures 4.5A-C are representative images collected in this assay, which

demonstrates the toxicity of Triton-X 100 at this concentration. Here, fluorescence was used a

read-out for cell death; in the future, we propose that many other probes or assays relying on

fluorescence or luminescence are likely compatible with the BAY microbioreactor.

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Figure 4.5 Microorganism viability and transformation. Representative images of yeast viability

assay and bacterial transformation. Yeast grown for 4 hours in BAY microbioreactor were

incubated with 2 µM Ethidium homodimer-1. (A) and (B) are brightfield and fluorescence

images of yeast not exposed to Triton X-100, and (C) is a fluorescence image of yeast exposed

to0.05% (v/v) Triton X-100. Brightfield images of yeast exposed to Triton X-100 were similar to

(A) and are not shown. Comparison of the images reveals that over 99% of the yeast were non-

viable after treatment with Triton X-100. (D) is a fluorescent image of an LB agar plate spread

with YFP-transformed bacteria. Scale bars represent 60 µm.

Yeast (-Triton) Fluorescence

Yeast (+Triton) Fluorescence

Yeast (-Triton) Bright Field

A

Transformed Bacteria

B

C D

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In the latter application, genetic manipulation was implemented on-chip; this is particularly

relevant for the BAY reactor, as this process is typically performed in early to mid-log phase

growth214

. To demonstrate this integrated process, E. coli were grown and their optical densities

were measured to confirm that they were in early to mid-log phase. The droplets containing the

bacteria were then transformed with a YFP reporter gene in a step-wise process involving several

thermal cycling steps and exposure to calcium chloride (which facilitates gene uptake). After

transformation, the bacteria were spread on an ampicillin agar plate overnight to confirm

successful transformation (Figure 4.5D).

The work in this chapter demonstrates a device architecture that can be used for microliter-scale

culture of bacteria, algae, and yeast, and integration with down-stream processing. But the

principle of using digital microfluidics for microorganism culture, analysis and manipulation

could extend to an even wider range of organisms and high-throughput technologies (e.g. two-

hybrid screens). Devices can be configured to accommodate droplets ranging from nanoliters to

milliliters215

, and electrodes can be arranged into virtually unlimited numbers of spatial

configurations. For example, future designs might be developed for self-contained culture of

limited supply or dangerous species, yielding information on gene expression, protein

interactions, and biological interactions within living cells, microarray or parallel-scale culture

(e.g., on 384-well or 1536-well formats)216-218

, or integration with microchannels219, 220

for other

types of analyses188, 189, 204

.

4.4 Conclusions

A platform for the integrated growth and cell density analysis of microorganisms in distinct

microdroplets has been developed using digital microfluidics (DMF). These microbioreactors

operate with long term automated semi-continuous DMF-driven mixing and are compatible with

89


a diverse range of organisms and processes. The new technique may be beneficial for microbial

applications that require miniaturization or parallelization in highly customizable formats,

especially those which require complex, multi-step, multi-day processes.

90

Sam H. Au Microfluidic Liver Organoid Platform

Chapter 5

Microfluidic Liver Organoid Platform

5.1 Introduction

The liver is a vital organ responsible for metabolism, protein synthesis, bile production,

detoxification and drug clearance. Understanding, modeling and predicting the rate and scope of

liver processing of pharmaceutical candidates are critical components of drug discovery and

development. An ideal liver model for the pharmaceutical industry would be simple,

inexpensive, scalable and most importantly, strongly mimic liver function. Unfortunately, the

two-dimensional in vitro models that have been developed to study liver function and activity

often poorly mimic three-dimensional in vivo tissue. This is widely recognized as a bottleneck in

the pharmaceutical industry, one which has led to the requirement for exhaustive and expensive

multi-phase clinical testing of drug candidates221, 222

. We hypothesized that the unique attributes

of digital microfluidics for cell applications (described in section 1.3) might permit the

development of new systems capable of generating and evaluating liver models which are more

physiologically relevant than current best practices. Specifically, in contrast to other microfluidic

liver models223, 224

, we proposed that DMF would be useful to develop methods for the

formation, maintenance and analysis of cell-dense 3D constructs with precise control over

environmental parameters such as extracellular stiffness, cell density and biochemical stimuli of

individual tissue constructs. We further speculated that the new system would be useful for

systematically evaluating parameters such as construct size and matrix stiffness, to maximize

hepatic function.

In this chapter, I describe efforts to develop a digital microfluidic system capable of growing

three-dimensional liver organoid cultures, which were evaluated in terms of viability,

91


contractility, albumin production and enzymatic activity, towards the final goal of improving

upon current in vitro liver models. This work builds on the findings developed in the main text of

this thesis in the areas of anti-biofouling strategies (chapter 2), bias-free cell manipulation on

DMF (chapter 3) and long-term integrated analysis (chapter 4). While digital microfluidics has

been used previously to grow cells in three dimensions in a hydrogel matrices49, 225

, to our

knowledge, this represents both the first three-dimensional co-culture system and first ―organ on

a chip‖ work on digital microfluidics. Although work is on-going in some sections of this

project, preliminary results suggest that this method could become a useful tool for in vitro

screening for liver activity.

5.2 Experimental

Unless specified otherwise, reagents were purchased from Sigma-Aldrich. Parylene-C dimer was

obtained from Specialty Coating Systems. Teflon-AF was from DuPont, and A-174 silane was

from GE Silicones. All working solutions in sections 5.2.2-5.2.5 were supplemented with 0.06%

(wt/v) Pluronic F88 (BASF Corp.) to inhibit droplet fouling. All experiments were conducted

with three or more replicates.

5.2.1 Device and SU-8 Barrier Fabrication

Device top and bottom plates were fabricated at the University of Toronto Emerging

Communications Technology Institute in the same manner as the work described in sections

2.2.2, 3.2.1 and 4.2.2 except that additional steps were required to create SU-8 organoid retention

barriers. Briefly, before spin-coating Teflon® onto parylene-coated bottom plates, substrates

were pre-heated on a hot-plate at 95ºC for 5 minutes before spin coating ~5 mL SU-8 3035

(Microchem Corp.) for 10 s at 500 rpm followed immediately by a second 30 s spin at 1000 rpm.

SU-8 coated substrates were ramp heated (~3ºC/min) on hotplates from 65ºC to 95 ºC for 20 min

92


before ramp cooling (~3ºC/min) to 65ºC. Substrates were exposed through a photomask (formed

with ―negative‖ features) with 20,000 dpi resolution (Pacific Arts and Design) for 10 seconds

and then ramp heated on hotplates from 65ºC to 95 ºC for 5 min before ramp cooling to 65ºC.

Substrates were developed for 10 min in SU-8 developer, washed with isopropanol, dried with

nitrogen gas and baked at 170ºC for 10 min before Teflon® coating as described in section 2.2.2.

Top and bottom plates were separated by two pieces of double sided tape for a gap spacing of

140 µm.

Figure 5.1 depicts the device design and geometry. Briefly, the bottom-plate device design

comprises 65 electrodes, including a 2 x17 array of 2.2 x 2.2 mm electrodes, five ―large‖

reservoirs (10.0 x 6.5 mm) and four ―small‖ reservoirs (8.4 x 4.0 mm). Each large reservoir was

connected to the array by two 2.2 x 2.2 mm electrodes, while each small reservoir was connected

to the array by four 1.5 x 1.5 mm electrodes. The 1.5 x 1.5 mm electrodes served as ―organoid

culture regions‖, each with an SU-8 retention barrier. Each retention barrier featured fourteen

200 x 50 x 70 µm oval or rectangular pillars separated by 50 µm gaps.

Figure 5.1 Digital microfluidic organoid platform for construct creation, maintenance and

evaluation. (A) Photograph of device showing reservoirs and 4-plex design, (B)

Photomicrograph of SU-8 retention barrier and retained organoid, (C) Top- view (top) and side-

view (bottom) schematics of DMF device.

Large Reagent

Reservoirs

Organoid DMF Device Organoid Culture RegionSmall Reagent

Reservoir

SU-8 Retention

Barrier

Liver

OrganoidDigital Microfluidic

Electrodes

A

BC

Extraction Port

93


5.2.2 Device top and bottom plates were Cell Handling and Preparation

HepG2 cells and NIH-3t3 cells were maintained separately in feed media (50/50 DMEM/F12

with 8% fetal bovine serum (FBS), 2% calf serum (CS), 100 IU/mL penicillin and 100 µg/mL

streptomycin) by passaging every 3-4 days in T-25 flasks (Corning, Inc.). For use in forming

organoids, flasks containing the two cell types were trypsinized with 0.25% trypsin-EDTA for 5

minutes at 37ºC followed by resuspension in separate centrifuge tubes in feed media at 4.0 x 107

cell/mL concentrations. Collagen-cell suspensions were prepared on ice in 1.5 mL

microcentrifuge tubes by combining the solutions listed in Table 5.1 using 3D collagen cell

culture kits (Millipore, Inc.) where required.

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Table 5.1 Collagen-cell suspension components. Volumes (µL) of components used to create

collagen-cell suspensions which gel to become organoids.

Component Co-culture

0.9 mg/mL

Collagen

Mono-culture

0.9 mg/mL

Collagen

Co-culture

1.5 mg/mL

Collagen

Mono-culture

1.5 mg/mL

Collagen

Collagen I 80 80 80 80

5X DMEM 20 20 20 20

Feed Media 166 181 61 70

10% (wt/v) F88 1.8 1.8 1.1 1.1

Neutralization buffer 2.5 2.5 2.5 2.5

4.0 x 107 HepG2/mL 15 15 9.1 9.1

4.0 x 107 NIH-3t3/mL 15 0 9.1 0

5.2.3 Device Operation Protocols

Droplets were manipulated by applying 220 Vpp, 5 kHz sinusoidal potentials to electrodes using

an automated high voltage switching system226

. To create liver organoids, device top and bottom

plates were washed with 70% ethanol and allowed to air dry in a laminar flow hood before

assembly with double sided tape. Then 6.0 µL aliquots of collagen-cell suspensions were

electrokinetically loaded onto 8.4 x 4.0 mm reservoirs and 315 nL droplets were dispensed onto

1.5 x 1.5 mm square electrodes adjacent to SU-8 retention barriers. The droplets were allowed to

gel (forming organoids) for 1 hour at 37ºC/5% CO2. The organoids were then ―fed‖ with feed

media using the general automated droplet exchange procedure, depicted in Figure 5.2. The

general automated droplet exchange procedure is a standard protocol designed to bring fresh

media or reagents to organoids and to remove spent media from devices for subsequent analysis.

Briefly, 12 µL aliquots of feed media (or other reagents, as described below) were loaded into

10.0 x 6.5 mm reservoirs and then 1.36 µL droplets were dispensed onto the 2.2 x 2.2 mm

electrode array. Up to four of these droplets were independently delivered to organoid-containing

95


droplets, and the merged contents were mixed by actuation across five linear electrodes in the

organoid culture region. Media in excess of 630 nL (equivalent to the volume associated with

two 1.5 x 1.5 mm electrodes) were then dispensed from merged droplet for extraction either to

waste or for subsequent analysis from the edge of the device using a blunt tip 24 gauge needle

connected to a 1 mL syringe.

Figure 5.2 General automated droplet exchange procedure for reagent/dye exchange and sample

extraction: (1) Feed droplets dispensed from large reservoir, (2) Feed drops aligned with

organoids droplets, (3) Feed and organoids droplets merged, (4) Merged droplets mixed, (5)

Excess volume split from merged droplets, (6) Excess volume/waste removed at removal port.

5.2.4 Mixing Analysis

An experiment using dyes was devised to characterize mixing in the organoid culture region

(Figure 5.3A). 630 nL PBS droplets containing blue food dye (representing media containing

organoids) were positioned adjacent to SU-8 barriers. 1.36 µL droplets of PBS were then

dispensed and merged with the dye-containing droplets following the general automated droplet

exchange procedure. Linear actuation of the merged droplet from the nearest 2.2 x 2.2 mm

electrodes to the small (8.4 x 4.0 mm) reservoirs and back again constituted one mix cycle. A

total of 6 mix cycles were conducted with photos taken immediately after mixing and at the end

96


of each mix cycle (as depicted in Figure 5.3.A) for analysis with ImageJ software. Four regions

of interest were defined encompassing the majority of each of four merged droplets at the end of

each mix cycle to maintain consistency. Then, images were split into red, green and blue

channels, and the histogram function was used within the regions of interest in each red channel

image to determine the standard deviation of the dye intensity as an estimate of unmixed

heterogeneity. The unbiased estimate of the standard deviation was used to estimate the standard

deviation of the sample deviation according to equation 5.1227

.

SD is the unbiased estimate of the standard deviation of the population standard deviation

s is the sample standard deviation

Γ(·) is the gamma function

n is the number of sample elements

5.2.5 Viability and Contractility Assays

Liver organoids were formed on device, incubated at 37ºC/5% CO2 and maintained by feeding

with feed media using the general automated droplet exchange procedure (as described above)

every day for four days. On the fourth day, PBS droplets containing 5.86 µM calcein AM and

11.72 µM ethidium homodimer-1 were merged with organoids droplets (merged droplet final

concentrations of 4 µM and 8 µM respectively). Merged droplets were mixed and excess media

split from organoid cultures using the general automated droplet exchange procedure (described

above). Organoids were incubated at room temperature for 30 minutes before washing with PBS

droplets using the general automated droplet exchange procedure and analysis using microscopy

(Leica DM2000, Leica Microsystems Canada).

𝑆𝐷 𝑠 = 𝑠𝛤

𝑛 − 12

𝛤 𝑛 2 𝑛 − 1

2−

𝛤 𝑛 2

𝛤 𝑛 − 1

2

2

(5.1)

97


5.2.6 Albumin Analysis

Liver organoids were formed and maintained as above (section 5.2.3) except that droplets

containing spent media were removed from devices during the general automated droplet

exchange procedure on days 1-4 and frozen in 0.6 mL microcentrifuge tubes at -80ºC until

analysis. Human albumin levels were quantified using a Human Albumin ELISA Kit (Abnova

Corporation, Taipei, Taiwan) following the manufacturer’s recommended guidelines. The

measured albumin levels were dilution-adjusted by multiplying the values by 3.16 (the ratio of

the merged droplet volume [1.99 µL] to that of the culture organoids volume [0.63 µL]) to obtain

the concentration of albumin in organoid culture droplets.

5.2.7 Cytochrome P450 3A4 Activity Assay

Liver organoids were formed in the same manner as above (section 5.2.3) to form HepG2 &

NIH-3t3 co-culture constructs. The general automated droplet exchange procedure was used to

introduce feed media containing reagents and remove an equal volume of excess liquid to

organoid cultures daily. Three populations of organoids were treated for three consecutive days:

control, induced, and induced-inhibited. Control organoids were fed on days one and two with

1.36 µL feed droplets containing 1.46% (v/v) ethanol (to a final concentration in the organoid

droplet of 1.0 %). Induced and induced-inhibited organoids were fed on day one with 1.36 µL

feed droplets containing 14.6 mM dexamethasone and 1.46% (v/v) ethanol (to concentrations in

the organoid droplet of 10.0 mM and 1.0 %, respectively) and on day two with feed droplets

containing 10.0 mM dexamethasone and 1.0% ethanol. Control and induced organoids were fed

on day three with 1.36 µL feed droplets containing 0.146% (v/v) ethanol (to 0.10% final

concentrations in the organoid droplets). Induced-inhibited organoids were fed on day three

with1.36 µL feed droplets containing 14.6 mM ketoconazole and 0.146% (v/v) ethanol (to

98


concentrations in the organoid droplet of 10.0 mM and 0.10%, respectively). For all conditions,

one hour after the feeding on day three, 1.36 µL droplets of feed media containing 14.6 mM

Vivid® BOMR dye (Life Technologies, Inc.) were added to each organoid culture to obtain a

final dye concentration of 10.0 mM.

For comparison to two-dimensional formats, on day zero, 50 µL of PBS containing 0.1 mg/mL

neutralized collagen I were dispensed into each well of tissue culture treated polystyrene flat-

bottom 96 well plates (Corning, Inc.), incubated at 37ºC/5% CO2, aspirated dry and allowed to

air dry for 30 minutes in a laminar biosafety cabinet. 1.0 x 105 HepG2 cells and 3.0 x 10

4 NIH-

3t3 cells were seeded into 100µL feed media per well and incubated at 37ºC/5% CO2. Analogous

three-day control, induced, and induced-inhibited conditions were defined and implemented as

for DMF (as above). In place of the general automated reagent exchange procedure, each feed

was implemented by aspirating out well contents and replacing them with 100 L aliquots of the

new contents (to the same final concentrations as described above).

For both microscale and macroscale cultures, BOMR dye was metabolized into a fluorescent

substrate and the intensity was determined immediately upon adding the dye and every 15

minutes afterwards for 1 hour, with incubation at 37ºC/5% CO2 between time-points, using a

Pherastar multiwell plate reader (BMG Labtech) at 530/620 nm wavelength excitation/emission.

Fluorescent intensity was normalized to the starting intensity for each organoid culture droplet or

culture well and enzymatic activity was estimated by the rate at which the fluorescent intensity

increased over time.

99


5.3 Preliminary Results and Discussion

5.3.1 Organoid Confinement, Feeding, and Mixing

As a first step towards generating a physiologically relevant liver model, we explored the use of

digital microfluidics in creating liver ―organoids‖ using hepatocytes in a hydrogel matrix. A new

digital microfluidic platform was developed to this end (Figure 5.1). Since organoids were

suspended rather than adherent to surfaces, a means to confine the three dimension constructs

was required. As shown (Figure 5.1B/C), arrays of SU-8 features formed on DMF bottom plates

served as retention barriers to spatially localize organoids while allowing for media exchange.

The only other use of SU-8 barriers in a DMF system that we are aware of was reported by

Mousa et al.97

, who used them for a different purpose (to aid in partitioning non-mixing solvents

for liquid-liquid extraction). We propose that this strategy is a useful new method for integrating

digital microfluidics with the culture of cells in 3D hydrogels.

As shown in Figure 5.2, a general automated droplet exchange procedure was developed for

organoids cultured adjacent to the retention barrier. As described in the experimental section, in

this procedure, droplets of feed media or other reagents were driven to the organoid culture

region and merged (and mixed) with the organoid-containing droplet. Excess media was then

driven away from the organoid, either to waste or for subsequent analysis. Reagent

concentrations in feed droplets were thus diluted to 68.5% of original concentrations once

merged with organoid-containing droplets. In typical experiments, organoids were maintained

for multiple days with a general automated droplet exchange of feed media every 24 hours.

A critical aspect of the general automated droplet exchange procedure is mixing. Nutrients must

be effectively delivered to organoids, waste products sufficiently removed and soluble analytes

such as albumin predictably diluted into extracted droplets. To examine this process, a dye

100


mixing experiment was devised. Figure 5.3A shows sample frames of the dye mixing experiment

and Figure 5.3B plots the dye distribution as quantified by the variance of dye intensity within

the merged droplets at the end of each mix cycle. The red channel was used to determine the

mixing efficiency since the blue dye absorbs most strongly in the red spectrum allowing for

strong contrast versus the bright device background. The merged droplets were well mixed

within only 2 cycles (4 total paths across 5 linear electrodes) since subsequent mix cycles failed

to reduce the variance in dye intensity. This suggests that the mixing procedure incorporated

sufficient convective/advective mixing. The presence of SU-8 features may have contributed to

the mixing efficiency by introducing hydrodynamic instabilities228

. Nonetheless, 5 cycles (10

paths) was chosen for all experiments described here to ensure adequate mixing.

101


Figure 5.3 Dye-mixing study to characterize the mixing efficiency of the automated droplet

exchange procedure. (A) Sample frames of the mixing experiment. 1. Feed droplets dispensed

and aligned with dye drops, 2. Feed and dye droplets merged, 3. First mix cycle begun by

actuating merged droplets towards small reservoirs, 4. First mix cycle ended by actuating merged

droplets onto 2.2 x 2.2 mm electrode, 5. Second mix cycle begun, 6. Second mix cycle ended.

(B) Standard deviation of red channel intensity within merged droplets (red channel) at the end

of each mix cycle. Error bars represent the unbiased estimate of the standard deviation of the

sample standard deviation. Conducted in quadruplicate.

A

B60

50

40

30

20

10

Sta

ndard

Devia

tion -

Red C

hannel

6543210

Mix Cycles

102


5.3.2 Organoid Contractility and Viability

Liver organoids were formed, cultured, and analyzed using the system described above. In initial

work, organoids were formed from HepG2 cells suspended in collagen, which may direct cells

into phenotypes that more closely resemble those found in vivo229

. But a simple suspension of

immortalized hepatocytes in collagen may lack the microenvironmental cues required for liver-

like phenotypic activity. To increase physiological relevance, organoids should (a) possess the

means to remodel the extracellular matrix (ECM) by generating additional matrix components

such as collagen, elastin, fibronectin and proteoglycans to supplement the (initially homogenous)

collagen matrix and (b) include stromal cells which may provide biochemical signals required

for hepatocyte activity224

. Here we chose to evaluate the use of NIH-3t3 fibroblasts as a

component of the DMF liver organoids, as they are known to secrete ECM proteins and to

actively remodel and contract hydrogels29

in three-dimensional cell culture.

Matrix remodeling and contraction are likely important components of organoids for a number of

reasons. First, even a modest hydrogel contraction can significantly increase cell densities. For

example, an isotropic contraction to half the original length scale results in an 8-fold reduction in

total volume, or an 8-fold increase in cell density before taking in account cell division or other

processes. This allows for the study of cell densities that are close to those of native tissue (~109

cell/cm3 in liver

230). Second, hydrogel contraction coupled with matrix protein remodeling can

bring cells into physical contact with each other, which is important because cell-cell contact of

hepatocytes is known to inhibit division related processes while increasing liver-specific

functions231

. Third, hydrogel contraction also increases matrix stiffness, which affects a wide

range of cellular processes including growth, morphology and migration232

.

103


The effects of two parameters during DMF organoid formation were evaluated: the presence of

NIH-3t3 fibroblasts and the concentration of collagen I in pre-gelled organoid suspensions.

Figure 5.4 depicts liver organoids seeded with or without 2 x 106 cell/mL NIH-3t3 and at low

(0.9 mg/mL) or high (1.5 mg/mL) collagen I concentrations on day zero and after four days in

culture. As expected, the presence of NIH-3t3 fibroblasts substantially increased the contraction

of organoids over 4 days relative to organoids without fibroblasts. Collagen density also played a

role in this process, with high collagen density inhibiting the magnitude of contraction. The

presence or absence of fibroblasts at the concentrations used in this study had a greater impact on

the contraction than did the change in collagen density. Importantly, the diameter of organoids,

even when seeded with NIH-3t3 cells in low density collagen, did not decrease to smaller than

the gaps in the retention barrier (~50 µm). In the near future, the degree of contraction for each

of the conditions will be quantified using photoanalysis software. Similar levels of contraction in

hydrogels seeded with 3t3 fibroblasts have been previously reported29

in pooled hydrogel

systems (i.e. many hydrogels in a chamber). However, as far as we are aware this is the first

report of work in which the contraction of individually addressable hydrogel constructs can be

monitored over time. DMF is uniquely suited for creating individually suspended hydrogels on

an open platform which are free to contract in three dimensions.

The viabilities of liver organoids over four days were also assayed to determine the effects of

culture conditions, including the degree of contraction, on cell health. As shown in Figure 5.5,

the vast majority of cells remained viable after a week as determined by calcein-AM staining

with very few dead cells (determined by ethidium homodimer-1staining) in any of the tested

conditions. This suggests that there is adequate diffusion of nutrients into and adequate diffusion

of waste products out of hydrogel organoids.

104


Figure 5.4 Organoid contractility. Photomicrographs of representative organoids cultured on

DMF platform on Day 0 (top) and Day 4 (bottom) after gel formation. Organoids were seeded

with HepG2 cells (2 x 106

cell/mL) with or without NIH-3T3 fibroblasts (2 x 106 cell/mL each)

in low (0.9 mg/mL) or high (1.5 mg/mL) density collagen. Scale bar represents 200 µm.

Figure 5.5 Organoid viability. Organoids cultured on a DMF platform 4 days after gel formation

in brightfield (top), stained for viability with calcein-AM (middle) or stained for cell death with

ethidium homodimer-1 (bottom). Organoids were seeded with HepG2 cells (2 x 106 cell/mL)

with or without NIH-3T3 fibroblasts (2 x 106 cell/mL each) in low (0.9 mg/mL) or high (1.5

mg/mL) density collagen. Scale bar represents 100 µm.

SU-8 Barriers

105


5.3.3 Albumin Activity

The production of albumin, a class of protein which constitutes approximately half of all proteins

in human plasma233

, is a primary function of hepatocytes and the secretion of albumin is

commonly used as a measure of liver-specific phenotypic activity. For example, HepG2 cells

cultured in three dimensional hydrogel matrices (similar to the liver organoids developed in this

work) are known to have increased levels of albumin secretion relative to two dimensional

formats223

. To assay the amount of albumin secreted by liver organoids formed and cultured on

DMF devices, media was collected during daily feeds and assayed for human albumin using an

ELISA kit (Figure 5.6). For the first 3 days, no significant difference in albumin levels was

observed between HepG2 organoids and HepG2/NIH-3t3 organoids, but by day 4, the co-

cultured organoids significantly outperformed mono-culture organoids. This is consistent with

previous work234-236

which suggests that co-cultured with fibroblasts improve the functional

activity of hepatocytes. Many of the factors discussed in section 5.3.1, including fibroblast-

induced hydrogel contraction, may have contributed to these results. Interestingly, there were no

statistical differences in albumin levels between organoids cultured in low or high collagen

densities. Because of the superior function in co-cultured organoids, Cytochrome P450 activity

studies (section 5.3.4) were conducted with co-cultured systems only.

106


Figure 5.6 Organoid albumin secretion assay. Concentration of secreted albumin in liver

organoid media collected during daily feeds determined using a human albumin ELISA kit.

Organoids were created with HepG2, with (closed symbols) or without (open symbols) NIH-3t3

fibroblasts and in either low (0.9 mg/mL) (purple/blue circles) or high (2.9 mg/mL) (red/green

squares) collagen. Error bars represent 1 standard deviation (n=3).

5.3.4 Cytochrome P450 Enzymatic Activity

Cytochrome P450 (CYP) is a superfamily of proteins found primarily in the liver which are

responsible for the catalysis of organic substances237

. These enzymes are of particular interest to

the pharmaceutical industry because they metabolize many drugs and antibiotics. In addition,

some small molecules are known to interfere with CYP enzymatic activity, delaying the

clearance of other drugs or toxins in vivo238

. In this work, the activity of human Cytochrome

P450 3A4 (CYP3A4), a CYP isoform, was evaluated after incubation of liver organoids with

compounds known to induce or inhibit CYP3A4 enzymatic activity. Dexamethasone, an anti-

inflammatory and immunosuppressant drug was used as a CYP3A4 inducer, and ketoconazole,

an anti-fungal drug, was used as a CYP3A4 inhibitor. CYP3A4 activity was monitored using a

12x103

10x103

8x103

6x103

4x103

2x103

Alb

um

in C

oncentr

ation (

ng/m

L)

4321

Day

HepG2 - Low Collagen

HepG2 - High Collagen

HepG2 + NIH3t3 - Low Collagen

HepG2 + NIH3t3 - High Collagen

107


fluorogenic substrate (BOMR) with specificity to that isoform239, 240

in control, induced, or

induced-inhibited HepG2/NIH-3t3 co-cultures grown either as 2D monolayers in well plates or

as 3D organoids in DMF devices. As shown in Figure 5.7A, the rates of substrate metabolism by

HepG2/NIH-3t3 co-cultured cells in well-plates were indistinguishable, regardless of the

condition. In contrast, the rates of substrate metabolism were clearly distinguishable in DMF-

cultured organoids (Figure 5.7B) with dexamethasone-treated organoids having higher activity

than control cells and dexamethasone and ketoconazole-treated organoids showing the lowest

level of activity. Hepatocyte CYP activity has been shown to be higher in three dimensional

systems than in traditional two dimensional formats241

, which may explain the higher HepG2

liver-specific function in the DMF platform relative to conventional 2D cultures. Another

attribute of the DMF system which may contribute to this difference may be that the detection

limits for fluorescent read-outs on DMF devices are often superior to those of comparable assays

implemented on macroscale well plates42

; a phenomenon that is likely a result of increased signal

from the reflective metal layer on devices.

The data in Figure 5.7 suggests that the new DMF/organoid system may represent an

inexpensive option for screening pharmaceutical candidates for CYP metabolism. Interestingly,

HepG2 cells are typically eschewed by the pharmaceutical industry (in favour of much more

expensive primary hepatocytes) for metabolism tests because of the inability (in conventional

formats) of HepG2 cells to model CYP activity21, 242

. For example, there are no significant

differences in transcriptional regulation for human CYP3A4 in HepG2 cells in response to doses

of Beta-naphtoflavone, Phenobarbitol or Rifampicin242

, which is consistent with the data in

Figure 5.7A. But as shown in Figure 5.7B, the CYP activity of DMF-cultured organoids can be

both induced and repressed by small molecules. If similar responses can be observed for other

108


compounds, this will be a particularly attractive feature of the DMF organoid model system,

without requiring the use of expensive primary cells.

Figure 5.7 Cytochrome P450 3A4 activity. Cells were untreated (blue circles), incubated with 10

mM dexamethasone for 48 hours prior to assay (red squares), or incubated with 10 mM

dexamethasone for 48 hours plus 10 mM ketoconazole for 1 hour (green diamonds) prior to

assay. Assays and cultures were conducted on HepG2/NIH-3t3 co-cultures in (A) two-

dimensional format in 96 well plates or (B) three-dimensional organoids on DMF device.

400

300

200

100

0

-100Norm

aliz

ed

Flu

ore

sce

nce

In

ten

sity (

Arb

itra

ry)

6050403020100

Time (hr)

Untreated Control

Dexamethasone

Dexamethasone & Ketoconazole

A

B400

300

200

100

0

Norm

aliz

ed

Flu

ore

sce

nt

Inte

nsity (

Arb

itra

ry)

6050403020100

Time (hr)

Untreated Control

Dexamethasone

Dexamethasone & Ketoconazole

109

Sam H. Au

5.4 Future Work

Liver organoids consisting of co-cultured HepG2 and NIH-3t3 cells in 3D collagen matrices

have been successfully created, maintained and assayed for albumin secretion and enzymatic

activity on DMF. In the immediate future, organoid contractility will be quantified. A second

potential future experiment is to probe the response of more physiologically representative cells

such as primary human hepatocytes or engineered cell lines242

grown in organoids. Liver

organoids created with more active cells more may further improve physiologically relevance

versus current best practices, and the relatively fewer numbers of cells used to form organoids

relative to well-plate cultures may make this a cost-effective option for screening. A third

potential direction of study would be to examine the cytotoxicity of small molecules to liver

organoids as a measure of hepatotoxicity. Acetominophen would be a prime candidate for these

studies since it has been suggested that acetaminophen overdose causes about 39% of acute liver

failure cases in the United States243

.

110

Sam H. Au Conclusions and Future Directions

Conclusions and Future Directions

Digital microfluidics is emerging as a useful tool for a wide range of applications including cell-

based studies. There are however a number of unresolved impediments to implementing

experiments with living cells on DMF. This thesis describes work addressing some of these

impediments including device failure caused by protein biofouling, potential effects of DMF

manipulation on cell fitness, and a lack of robust microfluidic platforms for long-term integrated

cell culture and analysis. This section outlines advances made in these key areas which should

benefit digital microfluidic researchers and the biomedical community as a whole. Future

directions for not only these areas but also for other challenges facing DMF for cell applications

are also discussed.

Pluronic Additives to Inhibit Device Failure (Chapter 2)

DMF device failure is a significant problem when using protein-rich solutions such as complete

cell media. This work advances the field in a number of directions. First, a panel of anti-fouling

additives was screened at different concentrations. Additives were found which prolonged device

lifetimes when manipulating serum-containing cell media by 2-3 times relative to previous best

practices. Second, the hydrophilic-lipophilic balance of the additive was found to be an

important factor affecting cell viability and growth rates. Third, a novel technique for rapidly

screening the biofouling rates of a number of candidate additives was developed such that the

work of researchers addressing this impediment in the future can be expedited. This work

enables researchers to reliably operate DMF devices for cell applications for longer periods

without undesired cell death. This is a key development since many potentially groundbreaking

111


cell culture applications on DMF may require long-term manipulation of protein-solutions (e.g.

proteomics and metabolomics). Future work in this area should approach this problem from

multiple angles. For example, other researchers have examined the anti-fouling potential of

graphene oxides on DMF using the same screening methods that we outlined in chapter 2244

.

Other anti-fouling strategies such as alternate device surfaces comprised of anti-fouling block-

copolymers but which still exhibit a low interfacial interaction with aqueous solutions245

should

be investigated. Superhydrophobic surfaces, which often present significantly reduced contact

surface areas, may also significantly reduce biofouling rates246

. Another potential direction

would be thermally-switchable polymers which have been developed to release adsorbed

proteins from the surfaces of microfluidic devices247

.

Effects of Digital Microfluidics on Cell Fitness (Chapter 3)

The goal of the work described in chapter 3 was to identify DMF operating parameters which

may have detrimental effects on cell fitness such that these parameters can be avoided in the

future. A cell-based stress sensor sensitive to heat shock induction was employed to screen a

wide range of operating voltages and frequencies. None of the tested conditions, even at voltages

far greater than those typically used for droplet manipulation (625-650 Vpp), activated the sensor.

To search for other effects, genome-wide microarray studies and DNA integrity assays were

conducted for low and high frequency operation at high voltages. The majority of operating

conditions showed no or negligible effects on gene regulation or DNA fragmentation vs.

untreated controls. Only operation at high voltages (400 Vpp), high frequencies (18 kHz) and on

large electrodes (10 mm x 10 mm) resulted in substantial changes in gene expression and DNA

fragmentation (the presence of only two of these three parameters was insufficient to induce a

112


response). It was determined that droplet heating (above 37ºC) due to DMF actuation was the

most likely cause of these effects. This work demonstrates that DMF operation at moderate

voltages, frequencies and electrode sizes and induced EMF result in no detrimental effects on

cells. However, at higher voltages, frequencies and electrode sizes, researchers should evaluate

the potential of droplet heating and cell effects. The validation that the vast majority of DMF

operating parameters are compatible with cells is an important finding since without this work,

DMF researchers performing cell applications would be unable to determine if experimental

results were a result of manipulated variables or an simply artifact of DMF manipulation. In the

future, researchers should examine the mechanism(s) behind droplet heating in DMF devices.

Adequate control over the heating mechanism(s) may be potentially useful for increasing

reaction kinetics, studying hyperthermia or cell incubation applications on DMF.

Integrated Microorganism Culture and Analysis (Chapter 4)

Many cell applications require the integration of well mixed cell cultures with long-term analysis

tools. The work described in chapter 4 represents the first digital microfluidic platform for the

multi-day culture and analysis of microorganisms including bacteria, algae and yeast. Semi-

continuous mixing was incorporated into the system with an automated control system. Cell

densities were determined using on-chip absorbance measurements and growth rates for all

organisms were found to be similar to those of counterparts cultured in the macro-scale. The

viability of yeast cells was analyzed and a proof-of-concept toxicity assay was conducted. This

work, the first demonstration of microorganism growth and analysis on DMF, paves the way for

more complex long-term cell applications on DMF systems, especially those which require

multi-day active mixing; examples include protein-protein interactions using yeast two-hybrid

113


systems248

or multi-day metabolite-tracking experiments for metabolomics249

. In addition, the

system could be improved by miniaturizing absorbance or fluorescent detection tools such that

they can be incorporated into a complete micro total analysis system. The high intensity and low

size of light emitting diodes may be useful to this end250

.

Microfluidic Liver Organoid Platform (Chapter 5)

Building upon the work of chapters 2-4, a DMF platform was developed for creation of ―liver-

on-a-chip‖ organ models. In the first demonstration of 3D co-culture on DMF, hepatocyte and

fibroblast cells were encapsulated into neutralized collagen hydrogels, which were formed,

maintained and assayed on device as ―organoid‖ liver models. Extracellular albumin levels (a

measure of metabolic activity) increased daily for three days for both mono-culture (hepatocyte

only) and co-culture organoids. However, by day 4, mono-culture organoids had reduced

albumin secretion while co-culture organoids continued to increase albumin levels suggesting

that stromal cells improved the phenotypic activity of hepatocytes. For use as in physiologically-

relevant in vitro pharmacology studies, the activity of liver-specific enzyme Cytochrome P450

3a1 in DMF-cultured organoids was found to be induced and inhibited by tested small molecule

compounds. When similar macro-scale well-plate cultures were assayed, the induction and

inhibition of the same enzyme were undetectable. Thus, the DMF platform is uniquely

compatible with the formation and maintenance of physiologically relevant liver-specific tissue

constructs with relatively low-activity liver cell lines. In the future, I propose that similar systems

could be developed to accommodate constructs modeling other organ systems such as the

cardiovascular, neural and respiratory systems27

. I propose that the ease splitting, transporting

114


and mixing discrete droplets on DMF makes it a promising tool for developing a ―human-on-

chip‖ platform for modeling systemic multi-organ interactions251

.

State of the Art and Future Directions

As a new technology, there are a great number of challenges facing DMF for cell applications.

Table C.1 summarizes the current state of DMF technology for cell applications and potential

directions for advancement.

115


Table C.1 Current state of digital microfluidics for cell applications. Challenges, current

progress and future research directions for cell applications in digital microfluidics.

Challenge Description State of the Technology Potential Directions

2D Culture

Formats

Robust methods of

culturing suspension or

adherent cell types on

DMF platforms for routine

analysis must be

developed.

A wide range of cell types

cultured on native

hydrophobic surfaces 42-44,

80 and on hydrophilic

patches45-48

.

Co-culture of different

cell types in well-

defined geometries252

e.g. with

micropatterned

ECM253

.

3D Culture

Formats

Device features and

protocols must be

developed to generate

better DMF mimics of in

vivo microenvironments.

Cell encapsulated in

hydrogels49

and on-chip

evaluation of phenotypic

activity of organoids

(Chapter 5).

Hydrogel formation on

hydrophilic patches for

cell culture225

.

DMF-based models of

other organ systems

may be of interest to

biologists27

.

The incorporation of

systemic interactions

e.g. ―human-on-chip‖

DMF platform251

.

Biofouling

Device longevity is

severely inhibited by

biomolecule adsorption

onto DMF device surfaces.

Additives such as

Pluronics block co-

polymers53, 254

(Chapter 2)

and Graphene oxides244

.

Replaceable films254

and

filling devices with water-

immiscible oils100

.

Modifications to

device surfaces e.g.

fouling resistant block

co-polymer245

and

superhydrophobic

surfaces246

Thermally-switchable

polymers for release

of adsorbed

proteins247

Detrimental

Cell Effects

The electrokinetic

manipulation of droplets

may influence cell fitness

and phenotypic behaviour.

Only one study examining

the detrimental effects of

DMF actuation beyond

crude measures of growth

and viability38

(Chapter 3).

Investigate droplet

heating mechanisms

e.g. Joule172

or

dielectric173

heating.

Study genome-level

effects on other cell

types e.g. primary,

stem or neuronal.

116



Integrated

Analysis

The incorporation of more

analysis modalities enables

more complex cellular

analysis on DMF micro

total analysis systems.

Integrated absorbance80

(chapter 4) and

fluorescent42, 50

detection

systems for cell

applications.

Impedance-based systems

for on-chip cell

quantification255

.

Other analysis tools

developed for DMF

but as of yet not

applied to cell

applications e.g.

surface plasmon

resonance256

and

electrochemistry257

.

Consolidation of

current tools for more

complex studies of

cell states (e.g.

integrated

manipulation, lysis

and qPCR96

).

Humidity

Humid environments,

especially long durations

and at elevated

temperatures (e.g. inside

cell incubators) can be

catastrophic to DMF

devices. Water creeps

underneath the dielectric

layer (e.g. entering devices

at parylene-glass

boundaries) leading to

electrical shorts and

electrolysis when

operated.

Parylene C is a superior

conformal coating for

preventing water

penetration258

but adhesion

to glass can be poor.

A silane solution is used to

improve the bonding of

parylene C to the glass

substrates259

. A mitigation

strategy is the use water-

resistant tape on parylene-

glass boundaries.

Improve parylene

adhesion to glass e.g.

modifying

vaporization

temperature259

.

Switch to dielectrics

with stronger glass

adhesion e.g., spin on

glass260

.

Seal parylene-glass

boundaries e.g.

waterproof epoxies.

Temperature

Control

Temperature control on

DMF devices may be

beneficial for a number of

cell applications e.g.

incubation,

hyperthermia/hypothermia

studies, cell lysis,

cryopreservation,

thermotaxis.

Microfabricated resistive

heaters170, 171

.

Device operation on

simple heating elements

(Chapter 4).

Peltier systems similar

to those previously

integrated into other

microfluidic

formats261

.

Droplet heating by

DMF actuation

(Chapter 3).

117



Throughput

The inherent variability

and of cell systems and the

interest in screening

thousands of small

molecule targets in

addition to

genomic/proteomic studies

requires significant

parallelization .

Connecting (busing)

electrodes increases the

number of activatable

electrodes given voltage

switch limitations262

.

Cross-referencing (NxM

grid arrays)263, 264

e.g. a

15 x 15 array provides 225

effective grid

electrodes264

.

Thin film transistors can

be used to create large

arrays of individually

addressable electrodes

(e.g. 64 x 64 = 4096

electrodes)265

.

Complex multi-layer

PCB design permits

direct wiring to

potentially thousands

of individually

addressable

electrodes266-268

.

Incorporation of thin

film transistor

fabrication and

control systems for

routine DMF use.

Cost

If DMF devices are to be

adopted for routine use,

consumable costs must be

comparable to current low-

cost fabrication techniques

used for competitive

technologies e.g. injection

molding.

Disposable dielectric

coatings so that bottom

plates can be re-used54

.

Rapid copper prototyping

by photolithography and

laser printing269

.

PCB fabrication266-268

.

Paper substrates used

for other microfluidic

formats270

.

Increase demand to

capitalize on economy

of scale.

Automation

The adoption of DMF

technology by the

biomedical community

requires that end users

need only minimal training

to operate devices.

Automated control

systems have been

developed271

including an

open source feedback

system capable of

handling 320 simultaneous

outputs226

.

Automated droplet

generator uses feedback to

reduce droplet splitting

error272

.

Further reduce

required user input

e.g. implement code

to automatically route

droplets in the

minimum number of

steps262

such that

cross-contamination

can be prevented273

.

118


Digital microfluidics has the potential to improve the speed, relevance and cost of biomedical

cell-based research. However, a number of major impediments exist for this new technological

application. This thesis describes work to advance DMF technology in a number of key areas

including device biofouling, potential cell effects of DMF manipulation and integration of long-

term cell culture and analysis. The discoveries described in this thesis permits biomedical

researchers to conduct complex, long-term, multi-step, on-chip cell-based analyses using digital

microfluidics without severe biofouling or undesired changes to genomic expression. However, a

significant amount of work still remains to be done not only in these areas but in others as well.

Only through the concerted efforts of many researchers can robust, routine cell application on

DMF platforms become a reality.

119

Sam H. Au References

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