Download measuring force in the developing zebrafish embryo using

MEASURING FORCE IN THE DEVELOPING ZEBRAFISH EMBRYO USING AN
EXPRESSIBLE TENSION SENSOR
A DISSERTATION SUBMITTED TO THE DEPARTMENT OF MOLECULAR AND
CELLULAR PHYSIOLOGY AND THE COMMITTEE OF GRADUATE STUDIES OF
STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
ANDREA LEIGH HAMILTON
JUNE 2015
© 2015 by Andrea Leigh Hamilton. All Rights Reserved.
Re-distributed by Stanford University under license with the author.
This work is licensed under a Creative Commons AttributionNoncommercial-Share Alike 3.0 United States License.
http://creativecommons.org/licenses/by-nc-sa/3.0/us/
This dissertation is online at: http://purl.stanford.edu/zp419rt6657
ii
I certify that I have read this dissertation and that, in my opinion, it is fully adequate
in scope and quality as a dissertation for the degree of Doctor of Philosophy.
Ingmar Riedel-Kruse, PhD, Primary Adviser
I certify that I have read this dissertation and that, in my opinion, it is fully adequate
in scope and quality as a dissertation for the degree of Doctor of Philosophy.
Alexander Dunn
I certify that I have read this dissertation and that, in my opinion, it is fully adequate
in scope and quality as a dissertation for the degree of Doctor of Philosophy.
Miriam Goodman
I certify that I have read this dissertation and that, in my opinion, it is fully adequate
in scope and quality as a dissertation for the degree of Doctor of Philosophy.
William Talbot
Approved for the Stanford University Committee on Graduate Studies.
Patricia J. Gumport, Vice Provost for Graduate Education
This signature page was generated electronically upon submission of this dissertation in
electronic format. An original signed hard copy of the signature page is on file in
University Archives.
iii
Abstract
Early vertebrate development is a mechanically dynamic process. Embryos
undergo radical morphologic changes to mold a ball of cells into the recognizable planes
of a frog or fish or mouse. In the last 40 years, the zebrafish has emerged as a powerful
system for studying these early movements in a vertebrate system. Its large, optically
transparent embryos make it a good candidate for advanced microscopy techniques, and
in recent years this model organism has been the setting for a host of cutting edge
microscopy methodologies. One technique that has been comparatively overlooked in
the fish is the use of Fluorescence Lifetime Imaging (FLIM), specifically in conjunction
with Forster Resonance Energy Transfer (FRET). FRET is a powerful technique for
studying real time, in vivo protein dynamics on the nanometer scale. FLIM allows us to
apply this technique in the noisy embryo background without the need for cumbersome
spectral bleed-through corrections. In this work, we adapt previously published controls
for use in the zebrafish system and then take a systematic approach to developing the best
analytical practices for FLIM-FRET in the zebrafish embryo.
After developing FLIM-FRET in the early embryo background, we adapt a
recently published expressible, FRET-based stress sensor capable of measuring
piconewton levels of force in vivo [1]. We insert this Tension Sensor Module (TSMod)
into the zebrafish Epithelial Cell Adhesion Molecule (EpCAM) and use it to make direct
measurements of the tensions experienced during early development. In doing so we
show that the EpCAM molecule holds between 0.5 and 1.5 piconewtons of force during
zebrafish epiboly and that the leading margin is under greater tension than other regions
of the developing embryo. This work represents the first use of FLIM-FRET in the
iv
zebrafish embryo and the first direct measure of molecular tension in a developing
vertebrate.
v
Acknowledgements
This thesis is the accumulation of five and a half years of study and experiment.
Some of it fruitful, some of it not, but all of it my own honest work. For this I thank my
advisor, Professor Ingmar Riedel-Kruse, for letting me find my own path through
research and truly develop as a scientist.
I thank all of my committee members for their help in driving this project over the
years, Professor William Talbot for his expertise in zebrafish development, Professor
Miriam Goodman for her technical and practical advice in approaching scientific
discovery and Professor Alex Dunn for his unwavering enthusiasm and boundless
scientific knowledge. I also wish to thank Schantae Wright for her help in navigating the
administrative quagmire that is graduate school.
I thank all of the Riedel-Kruse lab members who have worked alongside me over
the years. In particular Nate Orloff, Victoria Wu, Xiaofan Jin, David Glass and Zahid
Hossain for making my time in graduate school not just scholarly but fun. I thank Jack
Chai for being the best zebrafish wrangling buddy a person could ask for. And I thank
my cofounding member of the Riedel-Kruse lab, Alice Chung, for being both a stalwart
scientific companion and a dear friend.
And because these last five years have not existed in a vacuum, above all else, I
thank my husband, John. From the moment I told you I was choosing Stanford over your
Berkeley Alma mater, you have been an unwavering source of love and support. I
dedicate this thesis to you.
vi
Table of Contents
Abstract .............................................................................................................................. iv
Acknowledgements ............................................................................................................ vi
List of Tables and Figures.................................................................................................. ix
Chapter 1 Introduction ........................................................................................................ 1
1.1 Background ............................................................................................................... 2
1.2 Force in development ................................................................................................ 3
1.3 Zebrafish Development ............................................................................................. 7
1.4 Fluorescence Microscopy........................................................................................ 12
1.5 Discussion ............................................................................................................... 21
1.6 References ............................................................................................................... 22
Chapter 2 Fluorescence Lifetime Imaging Microscopy (FLIM) for Measuring FRET in
Zebrafish Embryos ............................................................................................................ 25
2.1 Abstract ................................................................................................................... 26
2.2 Introduction ............................................................................................................. 26
2.3 Results ..................................................................................................................... 31
2.4 Discussion ............................................................................................................... 49
2.5 Materials and Methods ............................................................................................ 51
2.6 References ............................................................................................................... 57
Chapter 3 Force Measurements in the Zebrafish Embryo ................................................ 59
3.1 Abstract ................................................................................................................... 60
3.2 Introduction ............................................................................................................. 60
3.3 Results ..................................................................................................................... 66
3.4 Discussion ............................................................................................................... 83
3.5 Materials and Method.............................................................................................. 84
3.6 References ............................................................................................................... 86
Chapter 4 Outlook and Future Work ................................................................................ 88
4.1 Abstract ................................................................................................................... 89
4.2 Re-Evaluating the Fixed Length Controls ............................................................... 89
4.3 Validating the EpCAM TSMod Biosensor ............................................................. 96
4.4 Future Directions ..................................................................................................... 98
4.5 References ............................................................................................................. 101
vii
Appendixes ..................................................................................................................... 102
Appendix A. Construct Sequences ............................................................................. 103
Appendix B. Additional Constructs ........................................................................... 109
Appendix C. Matlab Scripts ........................................................................................ 110
viii
List of Tables and Figures
Figure 1.1 Schematic of early zebrafish development ...................................................... 10
Figure 1.2 FLIM-FRET .................................................................................................... 20
Figure 2.1 FLIM-FRET was used to image EpCAM Constructs ..................................... 34
Figure 2.2 Embryo auto-fluorescence can be removed by thresholding .......................... 45
Figure 2.3 Chi square and photon intensity thresholds are determined based on pixel
spreads............................................................................................................................... 46
Figure 2.4 The FLIM imaging workflow established in Chapter 2 .................................. 51
Figure 3.1 TSMod is built into the zebrafish EpCAM backbone ..................................... 70
Figure 3.2 EpCAM does not rescue Morpholino knockdown .......................................... 71
Figure 3.3 Known controls are used to convert decay time to FRET Efficiency ............. 73
Figure 3.4 EpTS holds force in the developing zebrafish embryo ................................... 79
Figure 3.5 Embryos are imaged at four positions moving from the animal to vegetal pole
........................................................................................................................................... 82
Figure 3.6 The membrane may hold tension in the developing embryo .......................... 82
Table 1. Table showing the status of other TSMod constructs currently in the lab. ..... 109
ix
Chapter 1 Introduction
1
1.1 Background
Force is critical to life. Bump an elbow and you may register a passing
annoyance. Bump it harder and the next day you will have a bruise. Harder yet and you
may find yourself nursing a broken bone, damaged nerves and permanent changes to the
landscape of your arm. We easily intuit the importance of a rigid skull to protect our
underlying brain, a flexible heart to beat rhythmically through the day and expandable
lungs to hold the air we breathe. A pull, a push, a shove, a kick. Fleeting or enduring.
Force moves us, bends us, shapes us.
Nowhere is this more evident than the developing embryo [2]. Great ribbons of
cells must traverse landscapes orders of magnitude larger than their individual size. They
adhere to neighbors and act in sheets. They break connections and travel as independent
entities. They converge, they extend. As a group and as individuals they conform to
adopt the physical requirements of a rigid bone or flexible muscle fiber. These early
movements and the building of physical microenvironments lay the foundation on which
all subsequent development depends. Yet, while the genetic and chemical signals
underlying the developing embryo have long been studied, the mechanical stresses
defining its physical landscape remain poorly understood.
In the descriptive world of early embryology, tracking and describing the gross
movements of developing organisms was foundational to what would ultimately become
the field of developmental biology [3]. But as the biologist’s toolkit expanded to include
more cellular and molecular techniques, the focus shifted to understanding the genetic
and chemical signals responsible for migration and differentiation of embryos. In the
2
past decade, however, pioneering data has emerged suggesting new and important roles
for mechanics in development. Work in cell culture and whole embryos has
demonstrated that force plays a determinative role in developmental processes such as
cell remodeling, tissue layer sorting and differentiation [2, 4-8].
This renewed interest in the mechanical properties of development has been met
with a scramble to provide techniques capable of measuring forces at the molecular,
cellular and whole organism scale. The work presented here attempts to bridge this gap.
We take a newly developed tool for measuring forces at the level of the molecule and use
it to study real time tension dynamics in the developing zebrafish embryo. In doing this,
we report the first use of FLIM-FRET in the zebrafish embryo, show the epithelial
adhesion protein, EpCAM, is under tension in the developing embryo and show the
embryo margin is under higher tension than other regions of the fish.
1.2 Force in development
The importance of mechanical force in development
In recent years force has been shown to play important roles in biological
processes such as cell remodeling and differentiation. Much of this work has focused on
identifying the role of force on individual molecules and cellular complexes. One of the
most well studied models emerging from this work is that of force as a driver of
cytoskeletal and adhesion remodeling, particularly in the cadherin/cytoskeleton complex.
Alpha-catenin helps cadherin bind to the actin cytoskeleton and has been shown to
contain tension-dependent vinculin binding sites [9]. When alpha-catenin is exposed to
3
force, it binds more vinculin, increasing its connections to the actin cytoskeleton and, in
turn, reinforcing adherens junctions in a force-dependent manner. Recent work has
shown that this force dependence is highly reversible, with alpha-catenin acting as a
spring capable of shifting between vinculin adhesion states as force is applied and
removed from its cadherin complex [10]. Vinculin itself has also been shown to hold
tension and recruit adherens junctions in a force dependent manner [1].
Cellular remodeling in response to force has also been observed in the whole
embryo. During zebrafish epiboly, cell divisions in the outer enveloping layer (EVL)
occur in the plane of greatest force and it has been suggested that tension orientation
plays a role in aligning the mitotic spindles [5]. These forces have also been proposed to
drive the spreading of the EVL, allowing for rapid coverage of a large surface area
without an increase in cell size.
In developmental processes where cells migrate as tightly connected sheets,
locally generated forces can also have wide-spread influence on cell migration. A
growing body of work shows that early, large scale cell migrations are often driven by
regionally specific motors at the leading edge of a tissue sheet. During zebrafish epiboly,
the tightly connected cells of the EVL migrate as a sheet from the animal to vegetal pole.
A popular hypothesis dictates that the EVL is towed by a concurrently migrating row of
yolk syncytial nuclei [11], although more recent evidence suggests that these movements
may in fact be driven by contractions of an actin band found circling the embryo at the
margin edge [6, 12]. Similarly, in Drosophila melanogaster, a migrating edge of cells
employ pulsed constrictions to move cell layers through the final steps of dorsal closure
[13, 14].
4
In addition to large-scale movement of tissue in sheets, changes in force are also
capable of driving migration of cells acting as independent entities. This is particularly
evident in a phenomenon called differential adhesion. According to the differential
adhesion hypothesis, cells segregate based on like-like adhesion properties.
Experimentally, this leads to more strongly adhering cells forming the center of cell
aggregates and more loosely adhering cells moving to the outer layers [7, 15]. It is
proposed that these layers form as a direct result of increased surface tension observed in
more tightly adhered cells [15] and that they can directly influence separation of the
differentially adhered ectoderm, mesoderm and endoderm in the developing embryo [7].
Differential adhesion is also observed during zebrafish gastrulation via a cadherin-based
adhesion gradient and it has been proposed that this gradient is in part responsible for
dorsal-ventral cell migration during embryo convergence [16].
While it is perhaps self-evident that force plays a role in highly mechanical
processes like adhesion, remodeling and migration, recent work has shown that exposure
to substrates with different physical properties can also have a direct impact on gene
expression and cell differentiation. In 2006, a pivotal study showed that matrix elasticity,
that is the physical environment experienced by the cell, can influence cell differentiation
in the absence of other contributing signals [4]. Undifferentiated stem cells grown on the
softest surface come to resemble neurons while a slightly stiffer substrate leads to
development of cells that resemble muscle and stiffest surface bone. This work provides
direct evidence for the importance of mechanical microenvironment on cell
differentiation. At the whole organism level, the mesoderm specifying protein, Twist,
has been shown to overexpress in Drosophila in regions exposed to compression [17, 18].
5
Taken together this work indicates the overarching importance of mechanical
force at the gene, molecule, cell, tissue and whole organism levels of development.
Focus must now be placed on developing new tools for measuring the physical properties
of the developing embryo.
Tools for measuring molecular force
While a picture is emerging that suggests mechanical force is important during
development, there remains a dearth of available tools for studying the physical stresses
experienced by molecules, cells and tissues. To the extent these techniques do exist they
often rely on expensive, technical setups or indirect mechanisms for investigating
mechanical force.
Optical traps and atomic force microscopy have emerged as powerful tools for
making direct force measurements at the molecule and cell level, but both require
complex setups and are designed for use with single molecules and cells in culture.
Simpler setups exist, but rely on indirect measurements of force such as traction force
microscopy [19] or growing cells on flexible polymer posts [20]. These types of indirect
measurements may also be used in whole embryos such as monitoring fluid flow with
traceable beads in the developing embryo [21]. While these setups are less technical
from an equipment point of view, they require complex post-analysis which can be a
barrier to entry for more traditional developmental biologists.
A surprisingly powerful tool in the study of developmental force has been whole
embryo compression. These techniques have ranged from precision compression with
tensiometers [8] to blunt changes in geometry through techniques like forcing circular
6
embryos into cylindrical pipettes [6]. These whole embryo manipulations are an
effective way to study the role of force in embryos but are, again, only indirect measures
of force and are not capable of addressing more nuanced perturbations. Strategic laser
ablation offers a more precise ability to investigate forces [5], but the destructive nature
of these experiments limit their function when studying dynamic processes. Theoretical
models have also been used to investigate tissue-level tension in embryos, but such
applications are limited without parallel experimental data [7].
As I will discuss in Chapter 3, in recent years a handful of expressible tension
sensors have become available that rely on fluorescent proteins connected by flexible
linkers that are able to give tension based fluorescence intensity readouts at the
piconewton scale [1, 22]. These sensors have opened the door to investigating real time,
in vivo tension dynamics at the level of the molecule. We adapted these sensors for use
in the zebrafish embryo to make real time measurements of force in the developing
embryo.
1.3 Zebrafish Development
Cell movements in early zebrafish development
In the 1970’s Danio rerio (the zebrafish) emerged as a tool to study vertebrate
development from the work of George Streisinger. Streisinger chose this fish because of
its large, transparent embryos, large clutch sizes and rapid generation times [23]. In the
late 1980s, a large-scale mutant screen established thousands of mutant lines while recent
advances in direct genetic manipulation have made the zebrafish a model widely used in
7
developmental biology today [24]. In recent years the zebrafish embryo has also
emerged as a powerful system for the application of advanced microscopy techniques.
This coupled with its rapid development times make the zebrafish an ideal model system
for our in vivo force measurements.
The zebrafish embryo has been used as a model organism extensively in
developmental biology. It is used to study early morphogenic movements, as well as the
development of a range of higher order complexes such as the heart, kidney, nervous
system and liver. The embryos are used as a model for human disease and regenerative
medicine, and their large clutch sizes make them ideal for use in high throughput
screening. For a review of the zebrafish as a model see reference [25]. More recently the
embryo has been paired with cutting edge microscopy such as super-resolution
techniques [26], whole embryo tracking using light sheet microscopy [27] and dynamic
protein interactions using FRET-based biosensors [28, 29]. We imaged the fish during
some of their earliest morphogenic movements, a developmental time ripe for
considering mechanical force on both the single cell and whole embryo level.
Early zebrafish development is defined by four bulk cell migrations: epiboly,
involution, convergence and extension [30]. Development begins after fertilization with
a single cell perched at the animal pole of a large yolk. The cell undergoes division to
form the blastula. Roughly four hours post fertilization (hpf), epiboly begins and the
blastula is stretched across the yolk toward the vegetal pole. At this stage, three layers
are present. The outermost tissue is the enveloping layer (EVL), a taught, single-cell
epithelia that stretches over the underlying cells and will give rise to the outer periderm
of the fish. The deep cells form a multi-cellular layer beneath the EVL and will give rise
8
to the ecto, endo and mesoderm. Finally, a layer of nucleated yolk forms the yolk
syncytial layer (YSL). These yolk syncytial nuclei (YSN) form tight junctions at the
proximal margin of the blastula and have been purported to help drive epiboly migration
by towing the EVL [11].
When cells reach the mid-point of the yolk, involution begins. Deep cells
undergo an epithelial-mesenchymal transition (EMT), breaking neighboring connections
and migrating under the proximal margin to form an internal layer (the hypoblast) that
will give rise to the mesoderm and endoderm. At the onset of involution the cells also
begin a ventral to dorsal converging migration followed by extension along the animalvegetal axis to form the body plane of the fish.
Throughout these morphogenic steps, cells experience changes in tension at both
the individual and multi-cellular level. During EMT, cells break neighboring connections
and undergo dynamic shape change. Acto-myosin dependent cell contractions are also
observed along the proximal edge of the migrating EVL and these movements have also
been proposed as a motor driving epiboly movements [6, 12]. The EVL itself forms a
tightly connected layer and appears to migrate as a taught, passive sheet. Figure 1.1
provides an overview of early zebrafish development. For a detailed account of zebrafish
development movements see reference [30].
9
Figure 1.1 Schematic of early zebrafish development. Cells migrate from the animal
pole to the vegetal pole during epiboly. The outer EVL monolayer has a stretched
appearance as compared to the underlying deep cells. The YSN form tight junctions with
the EVL margin. As cells migrate vegetally during epiboly they also involute and
undergo convergence and extension to form the tissue and planes of the zebrafish. At the
end of epiboly the zebrafish head forms at the animal pole and the tail at the vegetal pole.
Tension in the zebrafish EVL
To simplify our observations of force in the developing embryo we focused on the
animal to vegetal epiboly migration in the outermost layer of cells, the EVL. The EVL is
a strong candidate for studying tension in the developing embryo. Its external location
and monolayer status simplify imaging while its tightly connected, stretched appearance
indicate it is under tension.
In the zebrafish, the EVL is tightly connected through several adhesion
molecules. E-cadherin is the most prominent of these proteins, forming adherens
junctions with E-Cadherin molecules on neighboring cells. Knockdown of E-Cadherin
through Morpholino injection and elimination through the half baked mutant leads to
10
disruption of cell-cell connections, delay of epiboly in zebrafish and impaired
gastrulation movements, defects which are ultimately lethal to the fish [31, 32]. Tight
junction forming Claudins are also present in the early embryo. Claudin E forms
connections between the EVL margin and migrating YSN nuclei and knockdown of this
molecule via Morpholino injection leads to epiboly delays [33]. Finally, the epithelial
cell adhesion molecule (EpCAM) is also found throughout the EVL in early development
[34]. Maternal/zygotic EpCAM mutants show slowed epiboly in both the EVL and deep
cells but unlike E-Cadherin, maternally zygotic mutants, while sub-viable, are able to be
cultured to adulthood [34]. We will focus more on EpCAM in Chapters 2, 3 and 4.
The earliest suggestion that the EVL holds tension comes from work by
Betchakaku and Trinkhaus in the late 1970s. They showed that when the region
immediately vegetal to the EVL is cut during epiboly, the entire tissue layer retracts
toward the animal pole [11]. This is indicative of a cell sheet under tension and hints that
the tension generator (and potential motor driving the epibolic movements) exists at the
embryo margin. In this early work a series of yolk imbedded syncytial nuclei that both
connect to and migrate with the leading edge of EVL cells were implicated as the driving
force of these early epiboly movements. Additional work has suggested that a
contracting band along the edge of the EVL margin [12] in conjunction with flow friction
[6] also drives epiboly, particularly after the margin crosses embryo equator. In either
case, these work suggest the EVL is a layer under constitutive tension during early
development.
In more recent years, much of the pioneering work studying force during early
zebrafish development, particularly in the EVL and during epiboly, has come from the
11
lab of C.P. Heisenberg. His lab has combined elegant experimental work with strong
analytics to build new models for the role of force in development. By using atomic
force microscopy to measure tension in ex-vivo zebrafish cell culture, they have shown
that cell-cortex tension can directly impact tissue layer sorting, with high tension cells
migrating to the middle of a ball and low tension cells migrating to the outside [7]. They
were able to use this data to simulate separation of the ectoderm from the mesoderm and
endoderm in the whole embryo. As discussed in section 1.2, this again provides evidence
that force is not just a passive byproduct of cell movement and orientation, but that it can
actually effect change in developmental processes.
Most recently the Heisenberg lab has published work using laser ablation to
strategically release tension at the membrane of connected cells in the EVL [5]. By
measuring membrane retraction rates they show that, at the embryo margin, there is an
increase in tension in late epiboly and that cells are under greatest force in the
animal/vegetal direction (direction of migration). This suggests both a spatial and
temporal role for force in the developing embryo.
1.4 Fluorescence Microscopy
Fluorescence microscopy.
Fluorescence has been a tool in the biologist’s toolbox since the 1930s when
fluorescent dyes were first use to stain tissue and bacteria [35]. Fluorescence involves a
light-excitable molecule (a chromophore) that is capable of emitting light at a longer
wavelength than the wavelength with which it has been excited. By passing the shifted,
emitted wavelength through wavelength blocking filters, the fluorophore may be imaged
12
in the absence of light from the excitation source. In biological research, fluorescent dyes
and, more recently, quantum dots, may act on their own to report things like pH. They
may also be cross-linked to macromolecules to showcase spatial, temporal or
concentration information about a protein of interest [35]. Perhaps the most important
advance in the use of fluorescence in the life sciences in recent years has been the
development of genetically-expressed fluorophores that can be directly fused to a protein
of interest, allowing for real time, in vivo tracking of protein dynamics [36-40].
The basic principles of fluorescence microscopy rely on the ability of a
chromophore to absorb and emit light at a slightly shifted wavelength, a process known
as Stokes Shift. A chromophore is capable of absorbing light at a specific range of
wavelengths; if the chromophore is exposed to a wavelength of light within its absorption
spectrum, the energy is absorbed, pushing its electrons it into an excited state by raising
them to higher energy orbitals. As an electron relaxes back to ground state, a photon of
light is released, a process called radiative decay. In addition to losing energy through
photon release, an electron may also lose energy through non-radiative pathways. For
example, energy may be lost through vibration, which drops the election to a lower
orbital without the release of a photon. This loss of energy through non-radiative decay
is the cause of Stokes Shift; the electron ultimately returns to ground state from a lower
orbital, releasing a photon at a longer wavelength (lower energy) than that used in the
initial excitation. By imaging through filters that block all but the light of the emission
spectrum, Stokes Shift allows the chromophore to be visualized without the excitation
light overwhelming the image [35].
13
The discovery and characterization of the naturally occurring Green Fluorescent
Protein (GFP) has made fluorescent imaging a staple in the biologist’s toolbox. GFP
occurs naturally in the fluorescing jellyfish Aequorea Victoria. Its exterior is a
cylindrical beta barrel and buried within is the chromophore core, which may be mutated
to cause shifts in the excitation and emission spectra of the molecule [36]. GFP was first
discovered by Osamu Shimomura in the early1960s [37], however, GFP’s genetic
sequence was not determined for another three decades [38]. In 1994, Martin Chalfie
successfully expressed the GFP fluorophore in bacteria and the nematode worm [39]. In
the late 1990s and early 2000s, Roger Tsien took the GFP molecule and through random
mutagenesis and targeted mutations, he generated a complement of fluorescing proteins
covering a wide range of the visual light spectrum [40]. For this collective work
pioneering the use of genetically encoded fluorophores, Shimomura, Chalfie and Tsien
won the Nobel Prize in Chemistry in 2008. For an overview of the discovery of GFP, see
reference [41]
Förster Resonance Energy Transfer
In traditional widefield, microscopy, spatial resolution is limited by light
diffraction. Ideally, a sample is illuminated at a certain plane and the light reflects back
to the eye or camera in a one to one relationship. In reality, this final image is comprised
of both the in-focus light as well as out-of-focus light generated from out of focus zplanes and light scatter at the sample. In practice, this limits the resolution of an image to
roughly one half the excitation wavelength, typically in the 200nm range. Other factors
may impact resolution including microscope alignment, illumination wavelength, using
the correct cover slip and objective oil, photo-bleaching and signal bleed-through [35].
14
Techniques such as confocal microscopy and two photon excitation help improve
this resolution by, respectively, passing light through a pinhole to remove out of focus
light in the x/y plane and exciting fluorophores in only the in-focus z plane, but these
techniques are only able to bring optical resolution down to the low hundred nanomaters
(microscopyu.com, a collaboration between Nikon and Florida State University provides
good overviews of all these imaging techniques [35]). In contrast, by relying on
measurements of distance dependent energy transfer instead of direct visualization of a
sample, Förster Resonance Energy Transfer (FRET) allows for real time tracking of
protein dynamics at the single nanometer scale, improving spatial resolution by two
orders of magnitude.
FRET takes advantage of the overlapping spectral properties of light excitable
molecules to generate distance-dependent intensity measurements. An acceptor and
donor molecule are selected such that the emission wavelength of the donor overlaps the
excitation wavelength of the acceptor (figure 1.2a). When the donor is excited in close
proximity to the acceptor, the acceptor will absorb some of the donor’s emission energy
and simultaneously excite. This relationship is both distance and orientation dependent,
meaning that the excitation of the acceptor is directly related to its distance from the
donor and that the FRET observed will be skewed if the fluorophore barrels are not in
end-to-end alignment. In practice, this energy transfer is commonly measured in terms of
fluorescence intensity of the acceptor. FRETting molecules typically have a working
distance of single nanometers and as the two fluorophores move closer together, the
energy transfer increases to the power of six. This leads to large changes in energy
transfer (referred to as FRET efficiency) with relatively small changes in distance,
15
making the method exquisitely sensitive to small changes in proximity (for more review
on FRET see references [28, 42]).
Interestingly, much of the foundational work that has led to the modern day use of
FRET is inspired by photosynthesis [42]. The capture and movement of light-derived
energy across the large surface area of the chlorophyll to the photosynthetic reaction
centers is based on the same non-radiative energy transfer utilized in FRET sensors. The
mathematical descriptions of such resonance energy transfer were first described by
Theodor Förster in the late 1940s [42, 43]. Early FRET measurements employed
fluorophore pairs made of metals and dyes capable of being ionically or covalently bound
to a molecule of interest. These early FRET experiments were used to study the structure
of tRNAs and the interactions between actin and myosin [44]. FRET has since expanded
to take advantage of the explosion of expressible fluorescent proteins omnipresent in the
world of molecular biology today to build biosensors capable of reporting novel, real
time, in vivo molecule dynamics [45].
While a more thorough discussion of FRET-based sensors may be found in
Chapter 2, the proximity based output may be used to study both intramolecular events
(the donor and acceptor are located in a single molecule and cleave or adopt a geometry
that alters the FRET efficiency) or intermolecular dynamics (two molecules are
independently tagged and FRET when they come into close proximity). In addition to
using FRET to directly study protein dynamics, the technique has also been used to create
biosensors capable of reporting the presence of proteins and small molecules, such as the
well-known cameleon sensor whose FRET profile reports the presence and absence of
calcium [46]. FRET has been used in cell culture as well as live organisms, including
16
plants, yeast, nematodes, fruit flies, zebrafish and mice [28, 29, 47-52]. While the use of
FRET in zebrafish has not yet been extensive, we believe the large, optically transparent
embryo is an excellent system for employing a range of FRET-based biosensors. In this
work we present the expression of a FRET-based tension sensor in the zebrafish embryo
to measure forces in the single piconewton range.
Fluorescence Lifetime Imaging Microscopy (FLIM)
There are a handful of microscopy strategies for making FRET measurements.
The most typical method is ratiometric, acceptor based intensity imaging. In this method,
the donor is excited and an emission image is taken of the FRET excited acceptor
channel. Because each pixel of the acceptor intensity image is comprised of many
individual FRET pairs, the overall intensity of this acceptor image will be determined
both by the amount of FRET energy exchange and the overall number of molecules
present in a pixel. To correct for this, a second image is taken to capture the total
acceptor intensity by exciting and imaging the acceptor independent of the donor. This
image is then used to normalize the FRET image, ultimately giving a FRET ratio that
explains the percentage of acceptor signal observed in a pixel that is generated by donor
energy transfer.
Several challenges emerge with this intensity-based method. First, in theory the
FRET ratio only represents the acceptor excited by donor. In practice, overlap of the
fluorophore spectra cause some amount of acceptor to be directly excited by donor
excitation and some proportion of donor emission to bleed into the acceptor emission
channel (fig 1.1a). Independent experiments for donor only and acceptor only
fluorophores are required to generate bleed-through constants for each fluorescent protein
17
to remove non-specific fluorophore excitation and emission. These constants are used to
quantify the bleed-through by normalizing them to their total signal for each experimental
image. This means that in addition to the FRET and mEYFP excitation images, one must
acquire an mTFP excitation/emission image. This can lead to either long experimental
times, which can pose a challenge in the dynamic developing embryo, or the use of a
channel splitter which can be difficult to later overlap and cause mismatch in bleedthrough correction and the FRET to acceptor intensity ratio. Without bleedthough
correction the FRET efficiency calculation is a pure ratio (FRET/acceptor). The
experimentally generated bleed-throughs are multiplied by the total donor and acceptor
values and then subtracted from the FRET value. This makes the correction non-linear
and the corrected results are not proportional to the original ratio. Thus, small error in
both the correction coefficient and alignment between the FRET, donor and acceptor
images add compounding layers of error to any ratiometric FRET calculation.
An additional challenge arises in the inability of ratiometric FRET to differentiate
the intensity contribution of auto-fluorescence. This is a particular concern in the early
zebrafish embryo given the presence of a large, auto-fluorescing yolk. Most FRET-based
sensors employ a binary approach to their respective measurements, the pair adopts a
geometry such that the fluorophores are either FRETTing or not FRETting. In these
cases we are looking for a single, large change in FRET efficiency and error propagation
from bleed-through correction, image alignment or auto-fluorescence artifacts can be
more easily absorbed. In Chapter 3, we will discuss the use of an analog, FRET-based
tension sensor capable of making a theoretically infinite number of discreet FRET
measurements based on the stresses experienced by its host protein. In this system, small
18
discrepancies in any of these non-linear correction factors can cause disproportionately
large artifacts in the final FRET efficiency calculation.
To avoid these pitfalls, we imaged embryos using Fluorescence Lifetime Imaging
Microscopy (FLIM). As discussed earlier, fluorescence occurs when an excited electron
relaxes back down to its ground state. The time required for this relaxation is unique to a
given fluorophore and is also influenced by the non-radiative pathways available for
energy loss (figure 1.2b). FLIM measurements are made directly on the donor molecule,
eliminating the need for bleed-through correction or aligning multiple images for
ratiometric analysis. Because fluorophores and auto-fluorescence have different
lifetimes, the donor can be separated out at the exclusion of the acceptor and background
noise. For a review of FLIM see reference [53]. To date, FLIM has only been used once
in the zebrafish embryo and never in conjunction with FRET [29]. Here, for the first time
we establish FLIM-FRET for use in the developing zebrafish embryo. A more
comprehensive overview of FLIM as well as the specifics of our imaging approach will
be discussed in Chapter 3.
19
Figure 1.2 FLIM-FRET a) The mTFP emission spectra overlaps the mEYFP excitation
spectra (horizontal black lines). For typical ratiometric imaging, mTFP is excited at 458
(blue band) and imaged between 550 and 600nm (green band). The overlapping
spectrum can lead to bleed-through artifacts. b) When a donor molecule loses energy
through non-radiative quenching by an acceptor molecule the time it takes to return to
ground state decreases. FLIM allows us to measure these changes. We will take
advantage of this technique to make FLIM-FRET measurements in the TSMod force
sensor (shown un-stretched and stretched at bottom).
20
1.5 Discussion
We have now discussed the emerging role of force as a determining factor in
development. The study of the mechanical properties experienced by the developing
embryo have been hindered, however, by a dearth of tools to make direct, in vivo
measurements at the molecular level. We have also shown the zebrafish embryo is a
strong candidate for coupling with advanced optical techniques and that its rapid
development makes it an ideal environment for studying large-scale morphogenesis, a
process likely to experience significant force on the molecular, cellular and whole
embryo scale. In this work, we unite these concepts by building an EpCAM based
tension reporter for use in the EVL during zebrafish epiboly and employing the advanced
microscopy techniques of FLIM and FRET. In this way, we build a powerful system for
studying real-time force dynamics in the developing embryo.
21
1.6 References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
Grashoff, C., et al., Measuring mechanical tension across vinculin reveals regulation of
focal adhesion dynamics. Nature, 2010. 466(7303): p. 263-6.
Heisenberg, C.P. and Y. Bellaiche, Forces in tissue morphogenesis and patterning. Cell,
2013. 153(5): p. 948-62.
Keller, R., Developmental biology. Physical biology returns to morphogenesis. Science,
2012. 338(6104): p. 201-3.
Engler, A.J., et al., Matrix elasticity directs stem cell lineage specification. Cell, 2006.
126(4): p. 677-89.
Campinho, P., et al., Tension-oriented cell divisions limit anisotropic tissue tension in
epithelial spreading during zebrafish epiboly. Nat Cell Biol, 2013. 15(12): p. 1405-14.
Behrndt, M., et al., Forces driving epithelial spreading in zebrafish gastrulation. Science,
2012. 338(6104): p. 257-60.
Krieg, M., et al., Tensile forces govern germ-layer organization in zebrafish. Nat Cell Biol,
2008. 10(4): p. 429-36.
Schotz, E.M., et al., Quantitative differences in tissue surface tension influence zebrafish
germ layer positioning. HFSP J, 2008. 2(1): p. 42-56.
Yonemura, S., et al., alpha-Catenin as a tension transducer that induces adherens
junction development. Nat Cell Biol, 2010. 12(6): p. 533-42.
Kim, T.J., et al., Dynamic Visualization of alpha-Catenin Reveals Rapid, Reversible
Conformation Switching between Tension States. Curr Biol, 2015. 25(2): p. 218-24.
Betchaku, T. and J.P. Trinkaus, Contact relations, surface activity, and cortical
microfilaments of marginal cells of the enveloping layer and of the yolk syncytial and
yolk cytoplasmic layers of fundulus before and during epiboly. J Exp Zool, 1978. 206(3):
p. 381-426.
Koppen, M., et al., Coordinated cell-shape changes control epithelial movement in
zebrafish and Drosophila. Development, 2006. 133(14): p. 2671-81.
Solon, J., et al., Pulsed forces timed by a ratchet-like mechanism drive directed tissue
movement during dorsal closure. Cell, 2009. 137(7): p. 1331-42.
Martin, A.C., M. Kaschube, and E.F. Wieschaus, Pulsed contractions of an actin-myosin
network drive apical constriction. Nature, 2009. 457(7228): p. 495-9.
Foty, R.A. and M.S. Steinberg, The differential adhesion hypothesis: a direct evaluation.
Dev Biol, 2005. 278(1): p. 255-63.
von der Hardt, S., et al., The Bmp gradient of the zebrafish gastrula guides migrating
lateral cells by regulating cell-cell adhesion. Curr Biol, 2007. 17(6): p. 475-87.
Desprat, N., et al., Tissue deformation modulates twist expression to determine anterior
midgut differentiation in Drosophila embryos. Dev Cell, 2008. 15(3): p. 470-7.
Farge, E., Mechanical induction of Twist in the Drosophila foregut/stomodeal
primordium. Curr Biol, 2003. 13(16): p. 1365-77.
Wang, J.H. and J.S. Lin, Cell traction force and measurement methods. Biomech Model
Mechanobiol, 2007. 6(6): p. 361-71.
Sniadecki, N.J. and C.S. Chen, Microfabricated silicone elastomeric post arrays for
measuring traction forces of adherent cells. Methods Cell Biol, 2007. 83: p. 313-28.
Wang, G., H.J. Yost, and J.D. Amack, Analysis of gene function and visualization of ciliagenerated fluid flow in Kupffer's vesicle. J Vis Exp, 2013(73).
22
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
Meng, F. and F. Sachs, Visualizing dynamic cytoplasmic forces with a compliancematched FRET sensor. J Cell Sci, 2011. 124(Pt 2): p. 261-9.
Streisinger, G., et al., Production of clones of homozygous diploid zebra fish (Brachydanio
rerio). Nature, 1981. 291(5813): p. 293-6.
Streisinger, G., et al., Segregation analyses and gene-centromere distances in zebrafish.
Genetics, 1986. 112(2): p. 311-9.
Grunwald, D.J. and J.S. Eisen, Headwaters of the zebrafish -- emergence of a new model
vertebrate. Nat Rev Genet, 2002. 3(9): p. 717-24.
Gabor, K.A., et al., Super resolution microscopy reveals that caveolin-1 is required for
spatial organization of CRFB1 and subsequent antiviral signaling in zebrafish. PLoS One,
2013. 8(7): p. e68759.
Keller, P.J., et al., Reconstruction of zebrafish early embryonic development by scanned
light sheet microscopy. Science, 2008. 322(5904): p. 1065-9.
Kardash, E., J. Bandemer, and E. Raz, Imaging protein activity in live embryos using
fluorescence resonance energy transfer biosensors. Nat Protoc, 2011. 6(12): p. 1835-46.
Tsuruwaka, Y., et al., Real-time monitoring of dynamic intracellular Ca(2+) movement
during early embryogenesis through expression of yellow cameleon. Zebrafish, 2007.
4(4): p. 253-60.
Warga, R.M. and C.B. Kimmel, Cell movements during epiboly and gastrulation in
zebrafish. Development, 1990. 108(4): p. 569-80.
Kane, D.A., K.N. McFarland, and R.M. Warga, Mutations in half baked/E-cadherin block
cell behaviors that are necessary for teleost epiboly. Development, 2005. 132(5): p.
1105-16.
Babb, S.G. and J.A. Marrs, E-cadherin regulates cell movements and tissue formation in
early zebrafish embryos. Dev Dyn, 2004. 230(2): p. 263-77.
Siddiqui, M., et al., The tight junction component Claudin E is required for zebrafish
epiboly. Dev Dyn, 2010. 239(2): p. 715-22.
Slanchev, K., et al., The epithelial cell adhesion molecule EpCAM is required for epithelial
morphogenesis and integrity during zebrafish epiboly and skin development. PLoS Genet,
2009. 5(7): p. e1000563.
http://www.microscopyu.com. [website] 2000-2013 2013.
Ormo, M., et al., Crystal structure of the Aequorea victoria green fluorescent protein.
Science, 1996. 273(5280): p. 1392-5.
Shimomura, O., F.H. Johnson, and Y. Saiga, Extraction, purification and properties of
aequorin, a bioluminescent protein from the luminous hydromedusan, Aequorea. J Cell
Comp Physiol, 1962. 59: p. 223-39.
Prasher, D.C., et al., Primary structure of the Aequorea victoria green-fluorescent
protein. Gene, 1992. 111(2): p. 229-33.
Chalfie, M., et al., Green fluorescent protein as a marker for gene expression. Science,
1994. 263(5148): p. 802-5.
Tsien, R.Y., The green fluorescent protein. Annu Rev Biochem, 1998. 67: p. 509-44.
Miyawaki, A., Green fluorescent protein glows gold. Cell, 2008. 135(6): p. 987-90.
Clegg, R.M., The History of FRET. Reviews in Fluorescence, 2006. 2006: p. 1-45.
Forster, T., Energy migration and fluorescence. 1946. J Biomed Opt, 2012. 17(1): p.
011002.
dos Remedios, C.G., M. Miki, and J.A. Barden, Fluorescence resonance energy transfer
measurements of distances in actin and myosin. A critical evaluation. J Muscle Res Cell
Motil, 1987. 8(2): p. 97-117.
23
45.
46.
47.
48.
49.
50.
51.
52.
53.
Periasamy, A. and R.N. Day, Visualizing protein interactions in living cells using digitized
GFP imaging and FRET microscopy. Methods Cell Biol, 1999. 58: p. 293-314.
Emmanouilidou, E., et al., Imaging Ca2+ concentration changes at the secretory vesicle
surface with a recombinant targeted cameleon. Curr Biol, 1999. 9(16): p. 915-8.
Aoki, K., Y. Kamioka, and M. Matsuda, Fluorescence resonance energy transfer imaging
of cell signaling from in vitro to in vivo: basis of biosensor construction, live imaging, and
image processing. Dev Growth Differ, 2013. 55(4): p. 515-22.
Hamers, D., et al., Development of FRET biosensors for mammalian and plant systems.
Protoplasma, 2014. 251(2): p. 333-47.
Kamioka, Y., et al., Live imaging of transgenic mice expressing FRET biosensors. Conf
Proc IEEE Eng Med Biol Soc, 2013. 2013: p. 125-8.
Cai, D., et al., Mechanical feedback through E-cadherin promotes direction sensing
during collective cell migration. Cell, 2014. 157(5): p. 1146-59.
Krieg, M., A.R. Dunn, and M.B. Goodman, Mechanical control of the sense of touch by
beta-spectrin. Nat Cell Biol, 2014. 16(3): p. 224-33.
Keow, J.Y., K.M. Herrmann, and B.D. Crawford, Differential in vivo zymography: a
method for observing matrix metalloproteinase activity in the zebrafish embryo. Matrix
Biol, 2011. 30(3): p. 169-77.
Becker, W., Fluorescence lifetime imaging--techniques and applications. J Microsc, 2012.
247(2): p. 119-36.
24
Chapter 2 Fluorescence Lifetime Imaging Microscopy (FLIM) for Measuring FRET
in Zebrafish Embryos
25
2.1 Abstract
FLIM-FRET is a powerful tool for tracking protein dynamics in vivo. By
measuring a distance dependent energy transfer, FRET permits imaging of protein
dynamics at the single nanometer scale, an order of magnitude smaller than traditional
widefield fluorescence microscopy. One of the main drawbacks to FRET, however, is
that the ratiometiric imaging technique commonly used to make FRET measurements
requires a potentially confounding spectral bleedthrough correction. Pairing FRET with
FLIM lifetime imaging eliminates the need for this correction, further improving the
accuracy of an already powerful tool.
While FLIM-FRET has been used in cell culture as well as a number of model
organisms, to date, this technique has not been validated in the zebrafish embryo model
system. Here, we adapt previously published controls for use in the zebrafish system and
then take a systematic approach to developing the best analytical practices for
investigating a FRET-FLIM data set. By doing this we were able to identify and treat
embryo auto-fluorescence, replicate FRET data that had been previously reported in cell
culture and establish FLIM-FRET as a useful tool in the zebrafish embryo.
2.2 Introduction
Förster Resonance Energy Transfer
Förster Resonance Energy Transfer, also known as Fluorescence Resonance
Energy Transfer (FRET) is a powerful tool for studying real time spatial and temporal
protein dynamics at the single nanometer range. As discussed in section 1.4, the use of
26
expressible fluorescent proteins has been an enormous boon to biologists over the past
two decades. Through the non-radiative loss of energy through vibration, these proteins
are able to be excited at one wavelength and then specifically imaged at a second, a
process known as Stokes Shift. FRET also takes advantage of non-radiative energy loss
through a distance dependent pathway known as resonance energy transfer. In brief, a
donor molecule is paired with an acceptor such that its emission energy overlaps with the
excitation spectra of the acceptor (fig 2.1a). This process is distance dependent to the
power of six, meaning that there is a steep increase in energy transfer as the two
molecules move closer together. The acceptor fluorescence intensity increases as the two
molecules come closer together, and the exponential relationship between distance and
energy transfer means the intensity output provides information about the distance
between the acceptor and donor at the single nanometer scale. For more details on FRET
see section 1.4.
FRET is highly sensitive to small changes at the nanometer scale. Most
expressible biosensors built on this technology work as a binary tool at the level of
individual the molecule, reporting either a FRET or no FRET state. In these cases, the
two fluorophores are able to adopt an on or off state through cleavage or change in
geometry of a FRET pair linker (intramolecular FRET) or through direct interaction
between two FRETTing flourophores on different molecules (intermolecular FRET). An
example of a common FRET biosensor that uses the intramolecular linker approach is a
calcium reporter called cameleon which uses a donor and acceptor connected by a
calmodulin linker [1]. Calmodulin takes on a more compact geometry upon binding of
calcium, bringing the two fluorophores closer together and leading to an increase in
27
FRET efficiency. At the macroscopic level observed by the microscope, the FRET signal
will be dependent on calcium concentration, allowing the binary sensor to report a
spectrum of results. A common sensor that takes the intermolecular approach reports the
activation state of the GTPase protein, Rac [2]. In this system the Rac protein is tagged
with a donor molecule but not the acceptor. The acceptor is bound to a reporter molecule
based on the Rac binding p21-activated kinase protein (PAK). Rac binds PAK only
when Rac is in its activate state, thus the PAK reporter only experiences FRET exchange
when Rac is activated, giving a direct report of Rac’s conformational state. These two
FRET reporters are just a small sampling of the many biosensors that have been built in
the technology and a more comprehensive review may be found in [3].
While much of the current FRET biosensor work has been carried out in cell
culture, FRET has also been used in live organisms, including plants, yeast, nematodes,
fruit flies and mice [3-7]. In zebrafish, FRET has been employed in a small but highly
regarded body of work. A FRET-based intermolecular biosensor has been used in to
study the location and activation state of Rac during germ cell migration [8]. The FRETbased cameleon calcium sensor been used to study real time calcium dynamics in the
developing embryo through bulk imaging of the zebrafish embryo [9] and a transgenic
zebrafish line hosting this sensor has also recently been developed [10]. A
metalloproteinase sensor has also been used to study matrix dynamics in the fish [11].
While the use of FRET in zebrafish has thus far been limited, we believe the large,
optically transparent embryo is an excellent system for employing a range of FRET-based
biosensors. In this work we will present the first use of a tension sensing FRET
biosensor known as TSMod [12] in the zebrafish embryo.
28
FLIM imaging
As described earlier in this section, the acceptor molecule in a FRET pair displays
an exponential increase in fluorescence intensity as the donor molecule is brought into
closer and closer proximity. The most common method for imaging FRET takes
advantage of this phenomenon by translating the change in acceptor intensity into a
change in FRET efficiency. This is done by imaging the acceptor through FRET (excite
the donor, image the acceptor) and normalizing it to the directly excited acceptor (excite
the acceptor, image the acceptor).
As described in section 1.4, a number of challenges emerge when using this
method known as ‘stimulated emission’. The primary concern is removing the non-linear
spectral bleed-through that comes from direct excitation of the acceptor by the donor
laser and leaching of excited donor through the acceptor filter (fig. 2.1a). Correcting this
bleed-through requires both independent experiments to create a correction factor for
donor and acceptor bleed-through as well as an additional donor/donor image for every
experimental condition. We eliminate the need for bleed-through correction by
employing the FLIM technique for FRET imaging. In brief, fluorophores have a
characteristic time in which it takes for them to go from an excited to ground state. The
more energy that is lost through non-radiative pathways the faster the return to ground.
By measuring the time required for this return, the FRET efficiency may be determined
without the complications of spectral bleed-through (fig 2.1b).
In the case of the TSMod biosensor discussed in this work, a FRET pair is
connected by an elastic-like linker, allowing the sensor to give a distance dependent read
out of tension as the sensor is stretched and relaxed. In this scenario, as the acceptor is
29
pulled further from the donor there is a loss of non-radiative energy transfer and a
subsequent increase in time required for the excited fluorophore to relax back to ground
state (fig 2.1b). By employing FLIM we may make direct measurements of this time
change, eliminating the need for any intensity based calculations.
We use the time domain approach to make these FLIM measurements. With this
technique, a pulsed laser is used to periodically excite the donor fluorophore. A detector
captures and records individual emission photons until the exited donor returns to its
ground state. This is repeated until enough photons are gathered to build a decay curve
for each pixel of the image. The ultimate data output is a plot of photon number as a
function of time displaying number of photons captured as a response to time from the
light pulse. An exponential curve may be fit to the histogram to calculate a discreet
decay time for donor molecules in a given pixel. When a FRET pair is separated by a
large distance its donor decay time will be long and when the fluorophores are close
together the donor decay will be short. For a review of FLIM see [13].
While FLIM-FRET has been used widely in cell culture, to date the only
published work using the FLIM technique in the zebrafish employs the technique to
separate auto-fluorescence from a GFP fluorophore [14] and it has never, to our
knowledge, been used in conjunction with FRET. Here we establish FLIM-FRET
microscopy as a tool in the zebrafish embryo. In Chapter 3 we will use FLIM-FRET to
study force in the developing embryo using the TSMod linker discussed at the top of this
section [12], to report distance dependent FRET efficiency based on the stresses
experienced by its host protein. For this reason we also consider the dynamic range of
FLIM-FRET in zebrafish relative to the decay times produced using the TSMod sensor.
30
For the purposes of this study we work with controls and TSMod embedded in the
zebrafish Epithelial Cell Adhesion Molecule (EpCAM). Further rationale behind the
EpCAM molecule will be described in Chapter 3.
2.3 Results
Expression and Survival of FRET Constructs
We built several constructs to test the efficacy of FLIM-FRET in the early
zebrafish embryo. For our FRET pair we chose the monomeric teal fluorescent protein
(mTFP) and the enhanced monomeric yellow fluorescent protein (mEYFP) as the
respective donor and acceptor [15]. The original TSMod sensor uses an mTFP/Venus
pair [12] however Venus did not express in our fish system. mEYFP has a similar
excitation and emission spectra to Venus and the mTFP/mEFYP donor/acceptor pair has
also been published using the TSMod linker [16]. Due to the similarities in the mEYFP
and Venus fluorophores we do not expect this substitution to produce a meaningful
difference in FRET efficiency and we will discuss the two interchangeably when
comparing our data to previously published work. The excitation and emission spectra
for mTFP and mEYFP are outlined in figure 2.1a.
In addition to the TSMod insert, two fixed length control constructs were built
using the mTFP and mEYFP fluorophores. These controls used rigid linkers of known
distance to connect the FRETting fluorophore pair. The first control is a short 5 amino
acid (5aa) linker and the second a longer 229 amino acid linker from the tumor necrosis
factor receptor associated domain (TRAF) [17]. The 5aa linker keeps the FRET pair in
31
close proximity and therefore should report a high FRET efficiency while the TRAF
linker keeps the pair far apart and should report a low FRET efficiency. Both control
linkers have been previously published using the mTFP/Venus FRET pair and are
reported to have a 55% (5aa) and an 11% (TRAF) FRET efficiency [15].
Because our ultimate reason for developing FLIM-FRET microscopy in the
zebrafish embryo is to make force measurements using the EpCAM/TSMod biosensor,
the TSMod, 5aa and TRAF constructs were built into the zebrafish EpCAM backbone.
EpCAM is a transmembrane glycoprotein. It forms adhesion complexes through trans
interactions with EpCAM molecules on neighboring cells and by binding cytoplasmically
to the actin cytoskeleton through alpha-actinin [18-20]. For our sensors, the TSMod,
TRAF and 5aa FRET reporters were placed immediately after base pair 858 (amino acid
286), flanking the membrane in the cytoplasmic domain (Fig 2.1c, see Chapter 3 for
location rationale). In addition to these three constructs, we built a truncated version of
EpCAM-TSMod in which a stop codon was placed in the actin binding domain,
prematurely terminating the molecule. By removing this cytoplasmic anchor point we
prevent the construct from holding tension. This construct will ultimately be used to test
EpCAM-TSMod’s ability to hold tension in Chapter 3.
Throughout this work the constructs will be referred to as follows:
1) Full length EpCAM + TSMod = EpTS
2) Truncated EpCAM + TSMod = EpTR
3) Full length EpCAM + 5aa = Ep5aa
32
4) Full length EpCAM + TRAF = EpTRAF. A modified free floating mTFP
construct was also used as a control and will be referred to simply as mTFP. A summary
of the constructs can be found in figure 2.1d. All constructs were over-expressed as
mRNA in wildtype zebrafish embryos (TL strain) via injection at the 1-4 cell stage. At 46 hours post fertilization (the onset of RNA expression) embryo survival was near or
equal to one hundred percent in all conditions, the same as in un-injected controls (fig
2.1e). We also measured EpTS survival at one day post fertilization (1DPF) and
observed nearly one hundred percent survival in both the injected and un-injected fish
(data shown in Chapter 3, Fig 3.1d). Chapter 3 summarizes TSMod injected embryo
survival and phenotypes. Embryos expressing the EpCAM based sensors showed the
expected membrane localization while the un-embedded mTFP showed the expected
universal expression (fig 2.1f). The EpCAM sensors were overexpressed as transient
mRNA leading to two days of universal expression throughout the embryo tissue.
During early development, nascent EpCAM expression is limited to the outermost
tissue called the Enveloping Layer (EVL) [21]. The EVL is a single-layer epithelium that
appears in late blastula and engulfs the outermost part of the embryo throughout epiboly.
Compared to the underlying deep cells, the EVL has a thin, stretched geometry and it has
been proposed that the vegetal movements of the whole embryo observed during epiboly
are driven, in part, by this external tissue layer [22, 23]. We reasoned that the EVL was
ideal for our study of tissue tension with the EpCAM-based sensor since the EVL is the
natural EpCAM environment, has stretched geometry, has a proposed role in large scale
morphogenesis and provides ease of imaging. All data presented will be taken from this
tissue layer. Additionally, unless otherwise specified, all data were collected at the
33
animal pole. Collecting the required 50 million photons per image can take over a minute
and this region is the most static during early development. It is also not confounded by
yolk auto-fluorescence as can happen in regions closer to the margin edge.
Figure 2.1 FLIM-FRET was used to image EpCAM Constructs. a) The mTFP emission
spectra overlaps the mEYFP excitation spectra (horizontal black lines). For typical
ratiometric imaging, mTFP is excited at 458 (blue band) and imaged between 550 and
600nm (green band). The overlapping spectrum can lead to bleed-through artifacts. b)
When a donor molecule loses energy through non-radiative quenching by an acceptor
molecule the time it takes to return to ground state decreases. FLIM allows us to measure
these changes. c) EpCAM has extracellular (Extra) transmembrane (TM) and
Cytoplasmic (Cyto) domains. It binds to EpCAM molecules on neighboring cells
through homopholic dimerization and to the actin cytoskeleton through alpha-actinin.
TSMod is inserted in the cytoplasmic domain. d) Four FRET constructs were built and
imaged using FLIM. e) All constructs and un-injected controls show nearly 100%
survival at the onset of expression (total embryo n from left to right: 646, 371, 374, 49,
67, 36). f) All constructs show membrane localization and as well as a cytoplasmic pool
(embryos imaged at the animal pole between 6 and 10hpf, 40x magnification, scale =
50um).
34
Fitting FLIM decay data
Once an embryo has been imaged, the photon histogram generated during FLIM
imaging must be fit to an exponential curve. Depending on the curve exponential,
multiple decay times for multiple FRET states can be calculated from a single curve.
Achieving the best fit, however, requires a trade off in acquisition time, spatial resolution
and number of measurable components.
The plot for any given pixel includes photons generated from donor molecules
under multiple FRET configurations, as well as photons from auto-fluorescence. The
number of exponentials used to fit the curve represents the number of discrete decay
times the calculation is capable of reporting. The more decay times desired, the more
photons required, roughly an order of magnitude for each additional component. This
corresponds to a ten-fold increase in acquisition time. In the case of the early embryo
data discussed here, a collection of fifty million photons per image takes roughly one
minute and represented the maximum collection time before cell boundaries blurred from
movement. Thus there is a trade-off between FLIM precision and acquisition time. In
essence, we can calculate decay values using fewer components, increasing the likelihood
of contamination by auto-fluorescence artifacts, or increase photon counts by binning,
losing spatial resolution and potentially confounding calculations with non-specific
regions of the embryo. The balance of all these factors turns out to be a non-negligible
problem and creates a near infinite parameter space for both fitting the raw FLIM data
and determining which pixels represent meaningful decay times. Here we present a
35
rational approach to imaging, fitting and analyzing a FLIM dataset in the zebrafish
embryo.
Before imaging, embryos were pronase dechorioanted and mounted in 0.8%
agarose. Expression of the sensor mRNA began at approximately 4HPF and all embryos
presented here were imaged between 6 and 10HPF, roughly 50% to 100% epiboly. FLIM
data were collected using a scanning confocal fit with a pulsed two-photon laser and a
high-speed hybrid detector capable of collecting individual photons of light. The FLIM
data was then stored as a histogram of photons collected over time for each pixel of the
image. Roughly fifty million photons were gathered for each image to populate a
512x512 image space. Due to the uneven distribution of the membrane localized
EpCAM, this roughly translated to a histogram bin maximum of 10s of photons for each
collected pixel. We also used pixel binning to increase the bin maximums for each pixel.
This will be discussed in more detail later.
After imaging, a line was fit to the photon histograms to generate a decay curve
for each bin of pixels, ultimately to calculate a donor decay time. Constructs with high
FRET efficiencies (FRET pair close together) decayed back to baseline quickly and
reported a short decay time while constructs with low FRET efficiencies (FRET pair far
apart) reported a long decay time. All fitting was performed using the Becker and Hickl
(B&H) SPCImage software. The software defines three parameters for fitting FLIM data
1) bin size 2) intensity threshold and 3) number of components. Binning allowed us to
increase the photon counts used to fit the histogram decay curve, thus improving the
overall fit and number of available components. More parameters are available in the
36
SPCImage software but these are the three we focused on, a complete overview of
settings may be found in the methods section.
To perform a one component fit on the histogram data, the histogram maximum in
regions of interest (in our case the cell membrane) should be greater than one hundred
photons and for a two component fit it should be greater than one thousand photons [24].
Typically several thousand photons are required to generate a bin maximum in the 10s
and without binning, the raw FRET data falls one to two orders of magnitude below these
requirements. The software allows binning such that all photon counts from the central
pixel and its surrounding bin are lumped to generate a single decay histogram. The bin is
rolling which maintains the original 512x512 image size, but decreases spatial resolution.
A bin of zero is the raw, single pixel count data, a bin of one includes the central pixel
and its eight immediate neighbors; a bin of two radiates two layers out from the central
pixel to incorporate a total of twenty five pixels. The intensity threshold looks at the
histogram maximum in a pixel bin and removes anything below the set value. The
component parameter dictates the number of discrete signals expected in any pixel and
defines the fit exponential. Because the TSMod sensor can adopt any number of states,
the number of independent signals from any pixel is theoretically infinite, but we assume
a good enough fit to discern differences at the population level with a one or two
component fit.
We first analyzed un-injected control embryo data to determine the background
and auto-fluorescence in the embryos. These embryos were collected, treated and imaged
under the same conditions as their expressing counterparts. As with the injected
embryos, fifty million photons were collected for each image, in this instance
37
representing a worst case scenario where all signal was generated from auto-fluorescence.
In the un-injected controls, auto-fluorescence was observed throughout the embryo, with
the intensity growing more pronounced at the membrane (fig 2.2a). At the animal pole
the auto-fluorescence had two distinct peaks, one at 400ps and a second at 1900ps (fig.
2.2b). TSMod reports mTFP decay times in the low 2000ps [12]. The 400ps peak is
easily discernable from TSMod but it was not be possible to resolve the autofluorescence at the 1900ps peak from the TSMod data from a multicomponent fit. While
it was not be possible to fully separate the overlapping auto-fluorescence signal from the
pixels expressing TSMod, we could eliminate all pixels that were primarily autofluorescence using an intensity based threshold.
To eliminate the auto-fluorescence signal, we tested intensity thresholds within
the SPCImage software. Because these embryos were imaged to the full fifty-million
photons, they represented the longest possible acquisition times and therefore the
threshold required to remove auto-fluorescence artifact from a worst case image (fig. 2.2
a, d). We began by fitting the un-injected embryos with a bin of one or two and then
incrementally increased the intensity threshold until no signal was observed across the
entire image. For a bin of one, a threshold of twenty was sufficient to remove all image
auto-fluorescence. At a bin of two, auto-fluorescence was removed at a threshold of
forty. The thresholds remained the same independent of a one or two component fit (fig
2.2d). In addition to the animal pole, we also investigated auto-fluorescence at three
additional cross sections increasingly vegetal to the animal pole (fig. 2.2c and d) and
found no change in threshold requirements to remove auto-fluorescence from the
expressing cells. It should be noted that images at position 4 contained a large amount of
38
yolk auto-fluorescence and that this noise required a substantially higher threshold for
removal (ninety for a bin of one and 205 for a bin of two). However, we did not consider
yolk in any of the FRET calculations and we therefore deemed the lower thresholds
appropriate at all positions imaged.
Next we imaged embryos expressing the free floating mTFP construct to
determine the expected decay time in the absence of FRET. This value represented the
maximum observable decay time. Again, all imaging conditions were kept consistent
with those used for the EpCAM embedded sensors. mTFP images were taken at 910HPF at the animal pole. There should only be one component (mTFP) in each pixel,
and a bin of one was sufficient to reach the one thousand photon count histogram
minimum. For this reason we fit these data using a one component fit, a bin of one and
the corresponding intensity threshold of twenty. The data were fit in SPCImage and then
exported to Matlab where we applied a hand-drawn mask to include only the EVL cells
and remove any non-expressing regions of the image. A chi squared threshold of 0.9 was
also applied to narrow the data set to well fit pixels. Because the data were fit with a bin
of one, pixel decay times were calculated from a sum of their own and their neighbor
data. To avoid overweighting neighbors, a rolling filter was applied such that when a
pixel containing data was observed (a pixel not removed by the intensity threshold, mask,
or chi- square threshold) all of its immediately surrounding neighbors were removed.
Because each pixel was independently fit and intensity thresholded, we consider the pixel
to be a discreet data point and thus our n is determined by the remaining number of pixels
after image processing. The processed mTFP decay times showed the expected single
peak histogram centered around a decay time of 2600ps. The average decay at the animal
39
pole was 2.57ns (SD +/-0.15ns, n = 2034 pixels, 3 embryos). This is well in line with the
published mTFP decay time of 2.60 (SD +/- 0.11ns), reported in cell culture [15].
It should be noted that later in this chapter we will discuss using a chi square
threshold of 1.07 to process EpCAM embedded sensors. We were unable to build a
functioning EpCAM embedded mTFP sensor, which is why we chose to use the free
floating version. We also tested the 1.07 threshold on the mTFP data set. The average
decay time in this case was 2.56ns, (SD 0.16ns, n = 17256). Although this difference
appears vanishingly small, it is statistically significantly different than the 2.57ns (SD +/0.15ns, n = 2034 pixels) that we observe at the 0.9 threshold (p<0.0004), however the
significance is primarily due to the extremely large sampling size at a chi square of 1.07.
Due to the high overall fluorescence intensity of the free floating mTFP images, the “n”
from these images was one to three orders of magnitude larger than that observed in the
membrane localized data. A chi square threshold of 0.9 keeps us within the same
magnitude as the brightest EpCAM embedded pixels and for this reason we opted to use
the 0.9 data for all fitting and comparisons. Unfortunately, because we were unable to
build the EpCAM-mTFP construct this is not a perfect comparison (the mTFP decay time
calculations also include more regions of the cell than the membrane only EpCAM data),
but given how close our data overlaps that published in the literature we assume it
sufficient for the work presented here.
The controls discussed thus far allow us to establish the no-signal limit (uninjected control) and the no-FRET limit (mTFP alone) expected in our experiments. We
then turned to the more complicated analysis of finding the fit parameters for an actively
FRETting pair of fluorophores. From the un-injected control data we determined a
40
threshold of twenty was appropriate for a bin of one and a threshold of forty was
appropriate for a bin of two. Looking at histogram data for individual pixel fits in the
SPCImage software showed a bin of one is appropriate to generate a histogram with the
one hundred photon count peak maximum for a one component fit while a bin of two
gave the one thousand required for a two component fit. With these boundaries in mind,
we fit all data in two ways: 1) a one component fit with a bin of one and a threshold of
twenty and 2) a two component fit with a bin of two and a threshold of forty. After
fitting images using the SPCImage software, decay values, pixel intensities and chi
square values were exported to Matlab for further analysis.
To test which of these two methods for determining mTFP decay times provided a
better fit for the histogram data in a FRETting system, we calculated the average chi
square value per pixel across the entire EpCAM based data set (Ep5aa, EpTRAF, EpTS
and EpTR). A smaller chi square value represents a better fit. All data was collected for
EpTS and EpTR between 6 and 10hpf while EpTRAF and Ep5aa were collected between
9-10HPF. All images were taken at the animal pole. When we plotted histograms of the
chi square data, we observed that each set of embryo conditions had a small number of
high value chi square outliers (chi-square value > 1000). Visual inspection of such
outliers typically reveals the fit hasn’t converged at all. Aside from these outliers, both
the one and two component fits returned to base line around a chi square value of two (fig
2.3a and b). Therefore, to avoid artificially inflating the chi square means with
individual, high value pixels, a preliminary chi threshold was set to two before continuing
with this analysis.
41
After loosely thresholding the chi square data as outlined above, the range of
mean chi square values observed for each of the EpCAM based sensors was small, all
falling between 1.23 and 1.28. Again, we considered each pixel to be a separate
measurement for the purpose of these calculations. EpTS and EpTRAF showed a small,
but statistically significant decrease in chi square with the one component fit while EpTR
and Ep5aa showed a small, but statistically significant decrease in chi square with the two
component fit (fig 2.4d). Taken together, the one component fit showed a vanishingly
small yet statistically significant decrease in chi square value over the two component fit
(1.2499 SD +/-0.16 n = 69599 pixels vs. 1.2505 SD +/-0.22, n = 74886 pixels p<0.001).
Given the similarity in chi square values, we determined that both a one component and
two component exponential were sufficient for generating an appropriate fit to the decay
data, but that the one component fit had a small but non-negligible improvement in fit.
One possible explanation is that typical expression of the EpCAM based biosensors at the
membrane was one to three pixels in width. The larger bin required for the two
component fit may therefore pick up unexpected artifacts from the cytosol, driving poor
fit in a sub-population of pixels and leading to the non-normal distribution of chi square
values. Regardless, the lower overall chi average, normal chi distribution and less
membrane/cytosol overlap in expressing pixels led us to choose the one component fit
with a bin of one (bin width of three pixels) as the most appropriate for analyzing
membrane localized decay signals. As we will see later, these parameters are, in fact,
sufficient for measuring differences between Ep5aa and EpTRAF in the embryo
background.
42
After fitting the raw histogram data in SPCImage using the parameters established
above, each pixel then contained three pieces of information: 1) a single mTFP decay
time, 2) a chi square value and 3) an intensity value representing the number of photons
in each binned pixel. Using the intensity based image, masks of the expressing
membrane regions were created for each image using ImageJ. Because there was some
subjectivity in identifying membranes, the cell boundaries were initially found using the
Trainable Weka Segmentation plugin. This learning algorithm take a subset of manually
selected membrane regions and uses it to find membrane in all images. This provides a
much faster approach to identifying membranes than manually selecting all the cell
boundaries. After running the plugin, the images were manually checked and any
obvious non-membrane regions were removed. The decay times, chi square values and
intensity values were then exported to Matlab where the mask was applied to eliminate
all non-membrane pixels.
Next, we applied a chi square threshold to the remaining decay times to eliminate
any poorly fit pixels. This threshold was determined using the decay time for mTFP
alone that we previously established. Because TSMod is based on a FRETting mTFP
molecule, its maximum decay time (no FRET) must not be more than that of mTFP
alone. When all data from the per pixel chi square values of EpTS and EpTR (7-10HPF,
positions 1-4) were plotted against their corresponding decay times, we saw that below a
chi square value of 1.07, no decay time is above the expected mTFP decay time of 2.6ns
(fig 2.3b, left). For this reason, we threshold out all TSMod biosensor data with a
corresponding chi square value of 1.07 or above. We use this threshold for both fixed
length controls as well. It should also be noted that after processing the data using the
43
method outlined here, all decay times for Ep5aa, the highest possible FRET efficiency,
control fell above 1000ps (fig. 2.3d), well above the observed auto-fluorescence peak of
400ps indicating that our fit parameters do a good job of removing pixels where
fluorescence is dominated by auto-fluorescence.
As the final processing step, a pixel intensity threshold was applied to normalize
the disparate intensities observed with different sensors. In particular, this threshold
addressed the concern of inter-molecular FRETTing. Ideally, all FRET energy exchange
should occur between the donor and its linked acceptor pair. However, because FRET
transfer is driven by proximity, it is possible that two separate EpTS molecules in a
crowded environment could transfer energy directly from the donor to a neighbor’s
acceptor. Here we used pixel intensity as a proxy for molecule density. As can be seen
in figure 2.3c, visual inspection of the relatively high intensity EpTR appears to show a
decrease in decay time correlating with an increase in molecular density beyond a pixel
intensity of 300 photons. We also observed a large difference in pixel intensity range
both between sensors and between the between different embryo regions imaged with the
same sensor. A pixel intensity range of 100-200 photons gave good coverage for all
EpCAM sensors across all embryo positions. For this reason we eliminated all pixels
outside of this range.
From the analysis described here we determined that a bin of 1, threshold of 20,
single component fit with a chi square threshold of 1.07 and a pixel intensity range of
100-200 photons generated a robust data set for processing images of the EpCAM
embedded FRET constructs expressed in the embryo. By applying this rational approach
44
to fitting FLIM-FRET data, we were able to quickly and efficiently move from a nearly
infinite parameter space to a well-motivated set of fit conditions.
Figure 2.2 Embryo auto-fluorescence can be removed by thresholding. a) Autofluorescence is observed in un-injected fish embryos (left) but can be removed by
applying a threshold of 20 (right) (animal pole, ~10hpf, bin of one). b) Autofluorescence donor decay peaks are observed in un-injected controls around 400 and
1900 ps (images 9-10hpf, all pixels plotted from 3 embryos imaged at the animal pole).
c) Four positions were imaged in the embryo from animal pole (top) to margin (bottom).
d) A threshold of 20 was sufficient to remove all auto-fluorescence at a bin of one and 40
for a bin of two. This did not change with number of fit component. Each cluster
represents one embryo, three embryos were imaged at position one, two at position two
and one at positions three and four (embryos imaged 9-10hpf).
45
Figure 2.3 Chi square and photon intensity thresholds are determined based on pixel
spreads. a, b) The chi square values for all pixels from TSMod based constructs are
plotted as histograms after one (a) or two (b) component fitting (EpTS and EpTR, animal
pole, imaged 6-11hpf. c) Chi square values were plotted against decay time for all
TSMod based sensors from each of the four positions outlined in figure 2.3c. No decay
46
values above the reported and our measured 2.6ns mTFP decay time are observed below
a chi square value of 1.07 leading us to use this as the chi threshold for EpTS and EpTR.
The green box shows last pixel above 2.6ns. The yellow box shows the next longest
decay value falls below 2.6ns. d) EpTRAF and Ep5aa were also plotted (right, blue =
EpTRAF, pink = Ep5aa) and the same 1.07 chi square threshold was applied. e, f) To
establish a photon intensity threshold, all photon intensity data for all pixels at all
locations outlined in figure 2.3c were plotted for EpTS (e) and EpTR (f) against decay
time. Red = pos 1, orange = pos 2, green = pos 3 and blue = pos 4. EpTR shows a visual
decrease in decay time beyond an intensity of 300 photons, likely indicating intermolecular FRETting. Visual inspection shows all regions for both EpTS and EpTR are
well represented between an intensity of 100 and 200 photons. This was used as a
threshold to match the data sets. g) The average chi square values at the animal pole of
each indicated sensor with one component and two component fitting. Lumped data
(‘all’) report the same average chi square value at 1.25 (For 1 component EpTS “n” =
22391 pixels, 19 embryos, EpTR = 37295 pixels, 25 embryos, Ep5aa = 6188 pixels, 5
embryos, EpTRAF 3725 pixels, 4 embryos; 2 component EpTS “n” = 25255, EpTR =
38591, Ep5aa = 6938, EpTRAF = 4103) . e) The measured decay values for controls
(blue) compared to previously reported values (orange). EpTS falls between the dynamic
range established by EpTRAF and Ep5aa. All measured constructs are different at
p<0.0001 (“n” mTFP = 2034 pixels, 3 embryos; EpTRAF = 303 pixels, 3 embryos,
Ep5aa = 428 pixels, 4 embryos, EpTS = 1384 pixels, 17 embryos)
FLIM-FRET produces meaningful signals in zebrafish embryos
With the fit parameters determined, we can now ask if FLIM-FRET is an
appropriate tool to use in the developing zebrafish embryo. As discussed above, we were
able to replicate previously published mTFP decay times in the fish background. Next
we evaluated the high and low FRET controls (Ep5aa and EpTRAF) at the animal pole.
After imaging, the FLIM data was fit as described above, a membrane mask was applied
and any pixels with a chi value greater than 1.07 or intensity outside the 100-200
intensity range were removed. In our embryo system, the EpCAM based fixed length
controls reported an average decay time of 2.37ns (SD +/- 0.16ns, n = 303 pixels, 3
embryos) for low efficiency EpTRAF and 1.80ns (SD +/-0.13ns, n = 428 pixels, 4
embryos) for high efficiency Ep5aa (fig 2.3e). Reported values for mTFP-TRAF-Venus
47
and mTFP-5aa-Venus are 2.22ns (SD +/- 0.07ns) and 1.11ns (SD +/- 0.06ns),
respectively [15]. Unlike the free floating mTFP construct which showed a nearly
identical decay time in our embryo compared to the reported values in cell culture, both
fixed length controls showed a relatively longer decay time in our system. This indicated
an overall lower transfer of energy between the donor and acceptor. Our mTFP/mEYFP
FRET pair also showed only slightly more than half the expected dynamic range (a
1.11ns difference between the two controls in cell culture and only a 0.58ns difference in
the embryo, fig 2.3e).
There are a number of possible reasons for this discrepancy in decay time. The
most likely candidates include: the use of mEYFP instead of Venus as the FRET pair
acceptor, artifacts from working in the relatively noisier embryo background and changes
in linker geometry due to embedding in EpCAM. In the cell culture reports, the mTFP
and Venus were imaged as a free floating FRET pair so it is also possible that although
both 5aa and TRAF are reported to be rigid linkers, they still might experience some
tension when embedded in a stressed host protein [15, 17]. While FRET will be changed
if the fixed length sensors are pulled apart end to end, it can also be changed if the barrels
are pulled so that they no longer sit parallel to one another. FRET transfer is optimal
when the two barrels are aligned end to end. If this geometry is altered when the TSMod
sensor is embedded into a protein, the FRET transfer will also become less efficient.
Regardless of genesis of this shift, the Ep5aa and EpTRAF showed highly
statistically significant differences in decay times in the zebrafish embryo background
(p<0.0001), indicating that we can make meaningful FLIM-FRET measurements in the
zebrafish embryo. Furthermore, according to both previously published work and our
48
own data, TSMod reports meaningful tension signals in the low 2000ps range [12]. This
signal both falls between and is statistically different from both EpTRAF and Ep5aa
sensors (p<0.0001), indicating that we can measure FRET data in a dynamic range
appropriate for TSMod. From this data, along with the previously discussed mTFP decay
results we concluded that FLIM imaging is a valid tool for making FRET measurements
in the zebrafish embryo.
2.4 Discussion
FLIM-FRET is a powerful tool for separating different fluorescent signals in vivo
at the single nanometer scale. To our knowledge, it has not been previously used in the
zebrafish embryo. Here, we report the first successful use of FLM-FRET during early
zebrafish development. We present a strategy for treatment of auto-fluorescence in the
relatively high background noise of the embryo and take a systematic approach to postimaging data processing to provide a logical guide for fitting data from the near infinite
FLIM analysis parameter space. By applying strict thresholds using chi square and image
intensity values, we were able to narrow our final data set to only the best fit pixels. By
narrowing the range of pixels to only include regions where decay time does not visually
skew with intensity we also provide a non-experimental way to limit artifacts from intermolecular FRETting (as will be discussed in Chapter 4 co-injecting EpCAM embedded
mTFP and mEYFP and looking for FRET transfer would be a conclusive way to rule out
inter-molecular FRET). An overview of the workflow developed here may be found in
figure 2.4.
49
By approaching FRET analysis in this systematic way, we successfully measured
control FRET within the noisy, dynamic zebrafish background. We replicated mTFP
decay data previously reported in cell culture and demonstrated the ability to resolve a
clear difference in the fixed length TRAF and 5aa controls [15]. We further showed the
dynamic range measured using our FLIM/FRET system is appropriate for measuring
differences in tension using the TSMod biosensor.
Due to their large size and optical transparency, zebrafish have emerged as a
powerful model system for coupling with microscopy They have been widely studied
using confocal and epi-fluorescence techniques and more recently they have been paired
with more advanced microscopy techniques such as super-resolution [25], light sheet
microscopy [26] and a growing set of FRET-based biosensors [8]. Adding FLIM to the
toolbox of zebrafish microscopy techniques provides a powerful method for resolving
differences in cell microenvironments and expanding the potential use of FRET
biosensors, particularly for analog reporters such as the TSMod sensor. With these
methods in place we are now able to move on to test EpTS in the fish and determine if
the biosensor is capable of reporting in vivo tension measurements in a vertebrate system.
50
Figure 2.4 The FLIM imaging workflow established in Chapter 2. Embryos were
imaged using Leica SP5 2 photon/confocal upright microscope, the data was then fit in
the B&H SPCImage software using a bin of 1, single component fit and a maximum
histogram threshold of 20. Masks of the embryo membrane regions were generated in
ImageJ. Mask application, chi and photon thresholds and neighbor removal were
performed in Matlab to generate the final image. Pixel averaging was also performed in
Matlab.
2.5 Materials and Methods
DNA and RNA preparation
Zebrafish EpCAM cDNA was ordered from Thermo Scientific (catalog number
MDR1734-9140155). TSMod, TRAF and 5aa inserts were gifts from the Dunn lab. To
build the EpCAM backbone for inserting TSMod, TRAF and 5aa zebrafish EpCAM
cDNA was inserted into pCS2+ using the EcoRI restriction site. A 5’NcoI 3’AgeI insert
site was built into EpCAM-PCS2+ at amino acid 858 (amino acid 286) using site directed
mutagenesis using Pfu turbo polymerase. The resulting construct was then digested with
51
NcoI and AgeI and ligated with either TSMod, 5aa or TRAF also digested with AgeI
NcoI using T4 ligase. To build the truncated version of EpCAM-TSMod a TAA stop site
was inserted three amino acids after the TSMod insert. See Appendix A for full
sequences of all constructs.
To linearize DNA for RNA preparation, DNA constructs were digested overnight
with NotI. mRNA was prepared off the linearized DNA template using an Ambion
mMessage mMachine SP6 kit (AM1340) and purified using the Qiagen RNeasy
MinElute Cleanup Kit (Cat# 74204). Purified mRNA was diluted in RNAse free water to
200-400 ng/ul and stored at -20C. Before injection, it was further diluted in 0.05%
phenol red to a final concentration of 25ng/uL.
Zebrafish Embryo Preparation
Embryos were obtained by mating TL wild-type zebrafish. Embryos were
collected and injected within 30 minutes of laying (between the 1 and 4 cell stage) and
incubated at 28°C when not in use.
Displacement driven microinjection system was used to deliver 2.1nL (52.5pg) of
mRNA to the embryo. Between 4 and 6 hours post fertilization embryos were screened
for survival and fluorescence. Between 5 and 7HPF, embryos suitable for mounting were
dechorionated in 1mg/mL pronase in Hanks Balanced Salt Solution (HBSS). The
reaction was quenched with 16mM EDTA in HBSS and embryos were then rinsed 1x in
HBSS and 2x in zebrafish E2 buffer. 0.8% low melt agarose was prepared with E2 buffer
and stored at 37°C. Agarose was allowed to cool for several minutes before mounting
52
embryos in a World Precision Instruments FluoroDish. The agarose was allowed to
solidify for at least half an hour before imaging.
Imaging
FRET FLIM images were generated using a Leica SP5 2 photon/confocal upright
microscope equipped with a HCX APO L 20x NA 1.0 water immersion lens. An
additional 2x zoom was also applied for a total of 40x magnification. Fluorophores were
excited using a Spectra Physics MaiTai, DeepSee ultrafast pulsed laser system tuned to
870nm and emitted through a 475/28 BP mTFP filter (donor emission: 461-489nm) with
a BS506 dichroic. Photon counts were generated using a Becker and Hickl (B&H)
Simple Tau 150 with a HPM-100-40 high speed hybrid detector and the B&H Spcm64
software package.
During mounting, the embryo always fell into one of two orientations: animal pole up
(AP) or dorsal/ventral axis up (DV). If the embryo was in the AP orientation a z plane
was found that included EVL cells at the most animal position of the embryo (defined as
position 1). Fifty million photons were collected using the B&H software and the image
was saved as an sdt file. After collecting the AP image, the embryo was scrolled through
vegetally in the z plane to collect a second image that included both deep cells and a ring
of EVL cells approximately 50-100um outward from the central animal pole position
(position 2). If the embryo was in the DV orientation one image was taken of the EVL
cells immediately at the margin (where the cells give way to yolk, position 4) followed by
a second that included both deep cells and EVL cells in a concentric ring 50 to 150um
animal to the margin (position 3).
53
Image Processing
After imaging, the raw data from the microscope was imported into the B&H
SPCImage software as sdt files. No processing is performed on the data prior to this step.
The imported decay curves were then fit to the photon histogram bins using the B&H
SPCImage software package. A single sdt file from the imaging experiment was
imported using the B&H software to act as a template for future batch image processing.
All calculations were performed in the ‘Channel 1’ tab which contains the mTFP decay
data (channel 2 stores the acceptor channel will not be used). To fit data we used a bin of
1 (3x3 pixels), threshold of 20 (max bin height) and component of 1. For fitting
experiments, a bin of 2 (5x5 pixels), threshold of 40 and component of 2 was also used.
T1 and T2 were automatically fit (default setting). Shift, scatter and offset were all set to
automatic (left unchecked), allowing the software to determine the value that gave the
best fit for each pixel. The model constraints were left in their default settings which
were as follows: fit model multiexponential decay, parameter constraints 20ps to 3e
+010ps, minimum ratio 1, algorithmic settings iterations 5, chi^2 max 10, offset calculate
from channel 0 to channel 31, fix shift before calculating image, use multiple threads
when calculating image, collection time (experiment) 1000s, dead time (detector) 150ns.
After processing the single template image, all other images were processed in batch
mode. After fitting, decay time, chi square vales and pixel intensity (photon number) for
each pixel were exported as asc files for further processing in Matlab. At this point each
pixel is defined as the center of a bin.
A mask of each image’s cell boundaries was generated in Fiji. To do this, a copy of
the photon intensity asc file was resaved as a tif and used for all membrane selection.
54
The Trainable Weka Segmentation plugin was used to create a template to automatically
find the cell boundaries by providing trainable examples of membrane and cytosol. Once
the program was trained, it was applied to all tif-converted photon intensity images. Each
newly generated mask was then thresholded to 0.7 using the ImageJ thresholding tool and
any remaining non-membrane pixels were manually removed. Because the free floating
mTFP images do not have membrane localization, the Weka program was not used and
instead a threshold of 0.7 was directly applied to the tif images and a mask was handdrawn to capture the fluorescing regions.
After fitting and mask creation, all remaining image processing was completed
using custom Matlab scripts. The Fiji generated mask was applied to each of the
exported asc files. Next the chi square thresholds (0.9 for mTFP and 1.07 for all EpCAM
embedded sensors) and photon intensity thresholds (100-200) were applied. The rolling
search was then applied to avoid overweighting pixels represented in neighbor bins. The
search began in the upper left corner of an image and moves through the pixels from left
to right and up to down. When a pixel containing data was identified, all its immediate
neighbors were also searched. If any neighbor contains data (data that were not removed
by any prior thresholding) the pixel was removed while the data in the origin pixel
remained (in future work this would be better served as a 2 pixel space to fully ensure no
redundancy in data). Finally, after applying these filters any image with fewer than 2
pixels remaining was also removed (this was done to permit the use of statistics on single
embryos, work that will not be further discussed here). Mean decay times for each
embryo in the data set were determined by averaging the pixels remaining after
thresholding. Each pixel was considered an independent measurement.
55
For un-injected controls, auto-fluorescence was analyzed directly in the
SPCImage software. Un-injected controls from each of the four positions (3 from
position 1, 2 from position 2, 1 from position 3 and 1 from position 4) were imaged to a
final count of fifty million photons to represent a worst case scenario image comprised of
only background and auto-fluorescence. Decay times were fit in SPCImage using a
single or double component fit, and a bin of 1 or 2. The thresholds were then adjusted to
determine the point where all auto-fluorescence was removed.
56
2.6 References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
Emmanouilidou, E., et al., Imaging Ca2+ concentration changes at the secretory vesicle
surface with a recombinant targeted cameleon. Curr Biol, 1999. 9(16): p. 915-8.
Kraynov, V.S., et al., Localized Rac activation dynamics visualized in living cells. Science,
2000. 290(5490): p. 333-7.
Aoki, K., Y. Kamioka, and M. Matsuda, Fluorescence resonance energy transfer imaging
of cell signaling from in vitro to in vivo: basis of biosensor construction, live imaging, and
image processing. Dev Growth Differ, 2013. 55(4): p. 515-22.
Hamers, D., et al., Development of FRET biosensors for mammalian and plant systems.
Protoplasma, 2014. 251(2): p. 333-47.
Kamioka, Y., et al., Live imaging of transgenic mice expressing FRET biosensors. Conf
Proc IEEE Eng Med Biol Soc, 2013. 2013: p. 125-8.
Cai, D., et al., Mechanical feedback through E-cadherin promotes direction sensing
during collective cell migration. Cell, 2014. 157(5): p. 1146-59.
Krieg, M., A.R. Dunn, and M.B. Goodman, Mechanical control of the sense of touch by
beta-spectrin. Nat Cell Biol, 2014. 16(3): p. 224-33.
Kardash, E., J. Bandemer, and E. Raz, Imaging protein activity in live embryos using
fluorescence resonance energy transfer biosensors. Nat Protoc, 2011. 6(12): p. 1835-46.
Tsuruwaka, Y., et al., Real-time monitoring of dynamic intracellular Ca(2+) movement
during early embryogenesis through expression of yellow cameleon. Zebrafish, 2007.
4(4): p. 253-60.
Mizuno, H., et al., Transgenic zebrafish for ratiometric imaging of cytosolic and
mitochondrial Ca2+ response in teleost embryo. Cell Calcium, 2013. 54(3): p. 236-45.
Keow, J.Y., K.M. Herrmann, and B.D. Crawford, Differential in vivo zymography: a
method for observing matrix metalloproteinase activity in the zebrafish embryo. Matrix
Biol, 2011. 30(3): p. 169-77.
Grashoff, C., et al., Measuring mechanical tension across vinculin reveals regulation of
focal adhesion dynamics. Nature, 2010. 466(7303): p. 263-6.
Becker, W., Fluorescence lifetime imaging--techniques and applications. J Microsc, 2012.
247(2): p. 119-36.
McGinty, J., et al., In vivo fluorescence lifetime optical projection tomography. Biomed
Opt Express, 2011. 2(5): p. 1340-50.
Day, R.N., C.F. Booker, and A. Periasamy, Characterization of an improved donor
fluorescent protein for Forster resonance energy transfer microscopy. J Biomed Opt,
2008. 13(3): p. 031203.
Borghi, N., et al., E-cadherin is under constitutive actomyosin-generated tension that is
increased at cell-cell contacts upon externally applied stretch. Proc Natl Acad Sci U S A,
2012. 109(31): p. 12568-73.
Koushik, S.V., et al., Cerulean, Venus, and VenusY67C FRET reference standards. Biophys
J, 2006. 91(12): p. L99-L101.
Balzar, M., et al., Cytoplasmic tail regulates the intercellular adhesion function of the
epithelial cell adhesion molecule. Mol Cell Biol, 1998. 18(8): p. 4833-43.
Litvinov, S.V., et al., Ep-CAM: a human epithelial antigen is a homophilic cell-cell
adhesion molecule. J Cell Biol, 1994. 125(2): p. 437-46.
Balzar, M., et al., The structural analysis of adhesions mediated by Ep-CAM. Exp Cell Res,
1999. 246(1): p. 108-21.
57
21.
22.
23.
24.
25.
26.
Slanchev, K., et al., The epithelial cell adhesion molecule EpCAM is required for epithelial
morphogenesis and integrity during zebrafish epiboly and skin development. PLoS Genet,
2009. 5(7): p. e1000563.
Koppen, M., et al., Coordinated cell-shape changes control epithelial movement in
zebrafish and Drosophila. Development, 2006. 133(14): p. 2671-81.
Warga, R.M. and C.B. Kimmel, Cell movements during epiboly and gastrulation in
zebrafish. Development, 1990. 108(4): p. 569-80.
Hickl, B., SPCImage 5.0 Data Analysis Software for Fluorescence Lifetime Imaging
Microscopy. 2015. p. 1-32.
Gabor, K.A., et al., Super resolution microscopy reveals that caveolin-1 is required for
spatial organization of CRFB1 and subsequent antiviral signaling in zebrafish. PLoS One,
2013. 8(7): p. e68759.
Keller, P.J., et al., Reconstruction of zebrafish early embryonic development by scanned
light sheet microscopy. Science, 2008. 322(5904): p. 1065-9.
58
Chapter 3 Force Measurements in the Zebrafish Embryo
59
3.1 Abstract
Along with more basic morphogenesis, evidence exists that mechanical force can
play a determinative role in processes such as differentiation and tissue sorting [1-3].
Yet, while the chemical and genetic signals underlying early development have long been
studied, the mechanical stresses defining the physical landscape of the embryo remain
poorly understood, particularly at the cell and molecule scale. An expressible Förster
Resonance Energy Transfer (FRET) based probe has been developed to measure
piconewton force levels in vivo [4]. We adapt this Tension Sensor Module (TSMod) for
use in the zebrafish embryo using the epithelial cell adhesion molecule, EpCAM, as a
host protein. In doing so we show that EpCAM holds between 0.5 and 1.5 piconewtons
of tension in the zebrafish embryo and that TSMod can be used to make meaningful cell
and tissue scale force measurements in a developing vertebrate system.
3.2 Introduction
FRET-based TSMod makes tension measurements in vivo
As discussed in Chapter 2, a number of FRET-based biosensors have been
developed to study in vivo protein and small molecule dynamics. Most, however, rely on
either a single conformational change or protein/protein interaction. In these cases any
change in FRET represents only one of two potential states. In the last decade, however,
several biosensors have emerged with an analog approach to FRET, making them capable
of reporting a range of geometries. These new sensors have focused on flexible linkers to
create real time tension reporters [4-7].
60
Much of the pioneering work in expressible FRET tension sensors has come from
the lab of Frederick Sachs. In 2008, the Sachs lab published a FRET pair linked with a
flexible alpha helix and successfully made strain measurements in filamin, spectrin,
alpha-actinin and collagen backbones [5]. This work was done primarily in cell culture
but also included a small pilot study showing that the collagen molecule COL-19 could
register stress in the C. elegans nematode, providing a hint of the potential for these
sensors in living organisms. This work was followed up in 2011 by a second tension
sensor using spectrin repeats as the linker [6]. This sensor was inserted into alpha-actinin
and used to make physiologically relevant measurements about strain during migration
and hypotonic swelling in cell culture.
In 2010, the lab of Martin Schwartz published a separate sensor [4]. In this
version, the expressible FRET pair are connected with a flexible linker based on the forty
amino acid flagelliform silk protein from the orb-weaving spider [4, 8]. This biosensor,
called the Tension Sensor Module (TSMod) was embedded the structural protein vinculin
and used to show the molecule holds tension and that adhesion assembly in the cell is
dependent upon vinculin being under strain. TSMod has been calibrated using an optical
tweezers and shown to accurately measure force in the single piconewton range, a scale
relevant to stresses experienced by individual molecules. Since its publication, the
TSMod biosensor has been used to study tension dynamics of host molecules in cell
culture using E-cadherin [9] and VE-Cadherin [10]. Additionally it has been studied in
spectrin in the nematode [11] and E-cadherin in the fruit fly [12].
While TSMod has now been well validated in a number of proteins and
expression systems, to our knowledge it has never been used in a vertebrate. Here, we
61
express the TSMod biosensor in a zebrafish-appropriate host molecule and use it to study
real time tension dynamics in the developing zebrafish embryo.
Zebrafish EpCAM as a Biosensor Backbone
We note several considerations in the selection of a host protein to serve as the
backbone for TSMod insertion in the zebrafish embryo. First and foremost, the host
protein must hold force. For the purposes of this study a candidate molecule must have at
least two anchor points and have a function and location likely to experience tension in
the developing embryo. The molecule should have the potential to investigate interesting
phenomenon on the molecular and cellular scale as well as the ability to reveal global
tissue properties. Finally, insertion of TSMod into the host protein should not disrupt its
function and, if function is disrupted, it must still be able to report tension and have
limited consequences on the fish as a whole. These criteria led us to a deep literature
search of structural and adhesion molecules. While we cloned TSMod into a variety of
these proteins (see table in Appendix B), we ultimately chose of the zebrafish Epithelial
Cell Adhesion Molecule (EpCAM) as our pilot host for studying TSMod in the zebrafish
embryo.
EpCAM is a transmembrane glycoprotein found at the cell membrane of epithelia.
It functions as an adhesion molecule, forming self-self complexes with EpCAM
molecules on neighboring cells [13] and connecting cytoplasmically to the actin
cytoskeleton through alpha-actinin [14]. EpCAM is well conserved through the
vertebrate world [15-17] and unlike the more classical family of cadherin adhesion
proteins, EpCAM interactions are not calcium dependent [13]. Functionally, EpCAM
plays both a structural and signaling role in the cell. In aggregation studies, EpCAM is
62
responsible for cell clustering [13. ] Although in a contradictory role, it has been shown
to inhibit E-cadherin adhesion by disrupting its ability to bind to the actin cytoskeleton
through alpha-catenin [18, 19]. In a similarly contradictory role, EpCAM is associated
both with up-regulation and down-regulation of epithelial derived cancers [20]. More
recent work suggests the EpCAM cytoplasmic domain, in addition to serving as an
anchor into the cytoskeleton, is capable of up-regulating cell proliferation as a direct
nuclear signaling molecule [21] and plays a role in Wnt signaling [22].
Structurally, EpCAM is comprised of an extracellular domain, a transmembrane
domain and a cytoplasmic domain [14, 16, 17, 23]. The extracellular domain is
comprised of a thyroglobulin-like domain as well as EGF-like repeats. Both of these
regions have been implicated in EpCAM forming homophilic bonds [17, 23]. Recent
publication of the crystal structure of the extracellular EpCAM domain shows the
thyroglobulin domain forms cis interactions with EpCAM neighbors on the same cell and
it is proposed that these cis dimer then forms trans tetramers with EpCAM molecules on
neighboring proteins [17]. Flanking the membrane on the extracellular side is a cysteine
poor region followed by the transmembrane domain. While early models depict EpCAM
as protruding linearly from the cell membrane, the new crystal structure suggests the
extracellular domain is actually quite compact and extrudes only 5 nm from the cell
membrane [17].
EpCAM’s cytoplasmic domain is small compared to the overall molecule, yet this
domain is critical to both EpCAM’s structural and signaling functions. The internal
domain connects EpCAM to the actin cytoskeleton through multiple alpha-actinin sites
[14]. The cytoplasmic domain is also capable of binding to proteins involved in Wnt
63
signaling, thus acting as a critical developmental regulator [22]. It also has multiple
cleavage sites and is capable of migrating directly to the nucleus [21].
In the zebrafish embryo, EpCAM also plays a role in adhesion and signaling.
Throughout epiboly EpCAM is expressed only in the zebrafish enveloping layer (EVL),
the outer epithelia of the fish that eventually becomes the fish periderm. By 24 hpf, its
expression is restricted to the EVL and the basal keratinocyte layer immediately below
[24]. Maternal/zygotic EpCAM mutants show slowed epiboly in both the EVL and deep
cells and smoother boundaries between EVL cells. While in cell culture EpCAM is
proposed to prevent E-Cadherin from acting as an adhesion molecule, in zebrafish
epiboly the two molecules appear to have a compounding effect on adhesion [24].
Unlike E-Cadherin whose mutants show a strong lethal phenotype in zebrafish [25, 26],
loss of EpCAM is not lethal and homozygous maternal/zygotic mutant embryos can be
raised to adulthood [24]. In addition to its epiboly defects, EpCAM mutants show a
delay in development of the inner ear and slowed deposition of lateral line neuromasts
[15]. Recent work in the zebrafish has also shown that EpCAM is capable of binding
Lrp6 and that this interaction is critical for downstream regulation of Wnt2bb and
ultimately the development of hepatic liver cells [22].
EpCAM as a Tension Reporter in the Embryo
Ultimately we chose EpCAM as the host protein to build a zebrafish tension
sensor because it meets all of our outlined criteria: 1) has the potential to hold tension as
connected via connection to neighboring cells or the cytoskeleton, 2) has the potential to
show both molecular and tissue level dynamics and 3) relatively passive in the embryo.
The final point is perhaps easiest to consider. While genetic alteration of any protein runs
64
the risk of compromising an organism’s “natural” phenotype, EpCAM’s non-essential
role in the zebrafish makes it a good candidate for use as a global tension sensor as
changes in expression should have limited effect on the developing embryo.
To the first point we can consider EpCAM both at a molecular and tissue level.
EpCAM’s internal anchor is alpha-actinin [23]. As mentioned, alpha-actinin has been
studied with expressible FRET tension sensors and is shown to holds force during
migration in cell culture [6]. And while EpCAM itself has not been directly shown to
hold tension, E-Cadherin, another adhesion protein, has [9], indicating that molecules in
the adhesion family are good candidates for reporting strain. Given the likelihood that
EpCAM experiences tension, we must also consider whether it exists in an environment
that is likely to experience meaningful force. As discussed in section 1.3 the EVL
experiences tension at both the level of the individual cell and the whole tissue during
epiboly. This tension is enough to be measured in basic retraction assays and differs both
spatially and temporally [27, 28]. Given EpCAM’s up-regulation in the EVL during
epiboly, we believe this combination of molecule, developmental stage and tissue
location is a strong candidate for making tension measurements in the embryo.
While making a direct, molecular level tension measurement in a vertebrate
embryo is a novel accomplishment on its own, our final consideration is whether an
EpCAM-TSMod sensor can reveal meaningful insights into embryo biology. At the
molecular level, EpCAM’s location at the membrane has the potential to reveal
interesting dynamics about local forces experienced during cell adhesion as well those
generated by the cytoskeleton. EpCAM’s role in epiboly also makes it a strong candidate
for studying forces between sheets of cells undergoing large scale migrations. While we
65
will focus on EpCAM as a proxy for tension in adhesion and migration, its additional
roles in signaling and cancer open the doors for future studies in these systems.
3.3 Results
In Chapter 2 we established that meaningful FRET-FLIM measurements can be
made in the developing zebrafish embryo. We also established a protocol for fitting
decay data using the SPCImage software and for thresholding based on chi square and
photon intensity. Next we applied these principles to the full length EpCAM TSMod
sensor (EpTS) to determine if we could make tension measurements in the developing
embryo.
Placement of TSMod in the Zebrafish EpCAM Molecule
In Chapter 2 we discussed EpTS as an EpCAM based tension sensor with TSMod
built into EpCAM’s cytoplasmic domain. Initially, however four versions of the
EpCAM-TSMod sensor were developed with TSMod placed in varying locations
throughout the EpCAM molecule. In EpCAM-TSMod version 531 TSMod was inserted
extracellularly in the EGF-like domain of zebrafish with EpCAM placed immediately
after base pair 531 (amino acid 177). In version 693 it was located extracellularly in the
cysteine-poor region immediately after base pair 693 (amino acid 231). In version 765
(amino acid 255) it was placed to flank the outside of the extracellular membrane. In
version 858 (amino acid 286) it was inserted cytoplasmically immediately inside the
membrane. Human EpCAM has two alpha-actinin sites, one flanking the membrane
66
further toward the c-terminus [14]. EpCAM-TSMod 858 likely disrupts the first of these
sites, but not the second. Figure 3.1a summarizes the four constructs.
We screened all four versions for survival at the onset of expression (4-6HPF) and
one day post fertilization. EpCAM-TSMod versions 531 and 765 showed poor survival
at both 4-6HPF and 1DPF while versions 693 and 858 showed nearly 100% survival at
both time points, similar to the un-injected controls (fig 3.1b and c). Early models of
EpCAM portray its extracellular domain as protruding in a long, straight line from the
membrane. Quite recently, however, key components of the human EpCAM structure
were solved showing that the extracellular domain is, in fact, quite compact [17]. For this
reason it is unsurprising that external 531 and 765 inserts cause lethal disruptions to the
functional domains of the fish. With this new crystal structure in mind, it is perhaps more
surprising that the 693 insert allowed the fish to survive in equal measure to control.
When we followed up with confocal imaging of individual cells in the embryo, however,
we found that the cytoplasmic 858 version showed substantially better membrane
localization compared to extracellular 693. This indicated that although embryos with
this extracellular insert survive, their ability to localize the protein was compromised (fig
3.1b). This is consistent with previous work done in cell culture indicating the
importance of the extracellular domain to EpCAM localization at cell-cell boundaries
[23].
Having identified EpCAM-TSMod version 858 as superior to the other constructs
in terms of survival and localization, we next imaged EpCAM-TSMod 858 injected
embryos using brightfield microscopy to look for large scale defects in the injected fish.
Injected embryos appeared phenotypically normal at both 5.5 and 24HPF (fig 3.1e). At
67
5.5HPF EpCAM TSMod expression was observed and at 24HPF the EpCAM-TSMod
injected embryos showed universal expression of the construct. Given that EpCAMTSMod 858 did not affect survival or phenotype, expressed during our desired time
course and localized to the cell membrane, we opted to use the cytoplasmic 858 version
of the sensor to study tension in the fish embryo. From here on out it will simply be
referred to as EpTS.
While embryos injected with EpTS appeared phenotypically normal, we also
checked the construct’s functionality by using a Morpholino knockdown of the zebrafish
EpCAM. We were able to recreate the shrunken otolith (inner ear) phenotype previously
reported at two days post fertilization in the zebrafish embryo [24], but were unable to
rescue it using the EpTS construct (fig 3.2a and b). We also observed a previously
unreported delay in zebrafish epiboly with the Morpholino knockdown. Although
epiboly retardation can be a side effect of Morpholino toxicity, it is also observed in
transgenic EpCAM mutants [24]. Embryos injected with EpCAM Morpholino took
approximately 30 additional minutes to reach the shield stage as compared to un-injected
controls. We were not, however, able to rescue this phenotype with the addition of EpTS
(fig. 3.2c).
While the Morpholino results were not able to confirm that EpTS behaves as a
fully functional EpCAM molecule, one of the reasons we chose EpCAM was that change
in expression was likely to have little impact on the developing embryo [24]. A rescue
would solidify our confidence that EpTS localizes and binds at its two anchor points as
expected. However, it is not necessary that EpCAM retains its full function in in EpTS.
Our most important requirement is that it can register a force and thus be used as a
68
tension sensor in the embryo. Further, while the otolith phenotype discussed above has
been reported in both Morpholino knockdowns and in the full EpCAM mutant, it is not
observed until two days post fertilization, beyond the time scope of these studies and thus
not necessarily an appropriate indicator of whether EpTS behaves as EpCAM during
early epiboly. While the inability to rescue the epiboly delay was more relevant to our
time course, epiboly retardation can also be the result of general Morpholino toxicity. A
rescue in the maternal zygotic mutant is needed to fully understand EpTS’ ability to
replace EpCAM during epiboly in vivo [24] although even in the maternal zygotic mutant
the phenotype is very hard to see and eventually rescue (Hammerschmidt personal
communication).
69
Figure 3.1 TSMod is built into the zebrafish EpCAM backbone. a) Four versions of
EpCAM TSMod were built by inserting TSmod into various regions of zebrafish EpCAM
extracellular (531, 693 and 765) and cytoplasmic (858) domains. b) While both EpCAM
TSMod 693 (left) and EpCAM TSMod 858 (right) show comparable survival, EpCAM
TSMod 858 has superior membrane localization (scale bar = 50um, embryos imaged at
the margin). c, d) EpAM TSMod 531 and 765 show mortality at onset of expression at 46HPF (c) and 1DPF (d, n for 4-6HPF from left to right 646, 17, 35, 10, 371, n for d from
left to right 61, 8, 21, 8 ,22 ). e, f) EpCAM TSMod 858 (EpTS) looks phenotypically
normal at the onset of expression (e, 5.5HPF) and at 1DPF (f). Expression continues
throughout 24 hours (e and f, far right). Scale = 500um.
70
Figure 3.2 EpCAM does not rescue Morpholino knockdown. a) Embryos injected with
EpCAM targeted morpholino (+Ep MO) show the previously reported decrease in otolith
vesicle size at one day post fertilization. b) Otolith vesicle size could not be restored by
addition of EpTS (“n” =5, error = 1SD). c) A previously unreported decrease in time in
time to shield (~30min) was also observed in Ep MO knockdowns. This increase was not
rescued by addition of EpTS (“n” = 5, error = 1SD).
71
EpTS holds tension in zebrafish embryos
Next we demonstrate EpTS reports tension at the membrane using two controls:
1) A truncated version of EpTS that does not hold tension and 2) a chemical inhibition of
the cytoskeleton using the microtubule depolymerizing agent, nocodazole (fig 3.4a).
In addition to EpTS, a cytoplasmically-truncated version of EpCAM-TSMod was
built with a stop codon inserted one amino acid after the end of the TSMod insert
(EpTR). EpCAM binds the actin cytoskeleton through alpha-actinin and human EpCAM
has been shown to have two separate alpha-actinin binding sites [14]. The first sits
immediately inside the membrane and is likely disrupted by the insertion of TSMod. The
second sits near the c-terminal of the protein. Cytoplasmic mutants of EpCAM have
been shown to both bind alpha-actinin with only the second binding domain intact [14]
and we thus expected EpTS to maintain the ability to connect to the actin cytoskeleton
while EpTR, with the removal of the second site, should not. Without this second anchor
point, EpTR should not hold tension. Like EpTS, EpTR showed almost 100% survival at
the onset of expression at 4-6hpf and localized to the membrane (fig 2.1 f and 3.1 c). A
second control with the extracellular domain removed was also built, but this sensor did
not localize to the membrane (data not shown). This phenomena has been previously
observed in cell culture studies using human EpCAM [23] and we did not pursue this
control sensor further.
In Chapter 2, FLIM was developed as a tool for making FRET measurements in
the zebrafish embryo. mTFP decay times were determined for mTFP alone, low FRET
EpTRAF and high FRET Ep5aa. FRET efficiency values have been reported for free
floating versions of these constructs in cell culture: 0% for mTFP alone, 11% for TRAF
72
and 55% for Ep5aa [29]. We used these known values to generate an equation for
converting our measured FLIM decay times into FRET efficiency percentages. The
known efficiency for each of the three controls was plotted against its corresponding
measured decay time in the zebrafish embryo. We then fit a line to the three points and
returned the following equation for determining a FRET efficiency value from an
observed mTFP decay time (fig 3.3a and b):
Equation 3.1 y = -0.0007x + 1.8786.
Although our measured values for the fixed length controls were larger than the reported
decay times, the line had a R2 value of 0.995. While this is not a string test of our model,
we consider it sufficient to fit our data going forwards.
Figure 3.3 Known controls are used to convert decay time to FRET Efficiency. a) Free
floating mTFP and fixed legth control constructs with known FRET efficiencies are
imaged. b) By plotting the average measured decay time for all embryo pixels against the
expected FRET efficiency we can fit a line to generate equation 3.1 (error = 1SEM, “n”
mTFP = 2034 pixels, 3 embryos; EpTRAF = 303 pixels, 3 embryos; Ep5aa = 428 pixels,
4 embryos).
73
FRET-FLIM data for both EpTS and EpTR was collected between 7 and 10hpf
and analyzed as described in Chapter 2. Briefly, constructs were overexpressed as
mRNA and embryos were mounted and imaged. Individual photons were gathered using
FLIM microscopy to generate histograms of photon decay for each pixel. This histogram
was binned, thresholded by intensity and fit to a single component exponential curve
using the B&H SPCImage software. An mTFP decay time, chi square value and photon
intensity value was generated for each pixel and these images were exported to Matlab
where a membrane defining mask was applied. Finally, the images were thresholded by
chi square and photon intensity. With these final thresholds applied, the remaining decay
values were fit to equation 3.1 to convert mTFP decay time to a FRET efficiency value
for each pixel. It should be noted that in the TSMod system, FRET efficiency is
inversely related to tension: a low FRET efficiency shows the FRET pair are far apart and
thus under high tension while a high FRET efficiency indicates the pair are close together
and experiencing low tension.
Before comparing the membrane-bound EpTS and EpTR, we first considered the
two constructs in their unbound, unstressed states. Both EpTR and EpTS showed
significant cytoplasmic pools at the animal pole. EpCAM forms its neighbor and
cytoskeletal attachments at the membrane, so this cytoplasmic pool should represent
untethered EpCAM that is not under tension. To determine if there is an inherent
difference in FRET efficiency between the EpTS and EpTR constructs we first compared
the efficiencies of their unbound pools. Masks were created to capture only the cell
cytoplasm and were applied in Matlab as described in the results section of Chapter 2.
All other processing was identical to that of membrane bound EpTS and EpTR. An
74
average of all post-processed pixels for all images gave a FRET efficiency of 48.0%
(SEM +/- 0.24%, n = 1094 pixels, 23 embryos) for cytoplasmic EpTS and 48.2% (SEM
+/- 0.13%, n = 4460 pixels, 23 embryos) for cytoplasmic EpTR (fig. 3.4c). These values
are nearly identical and not considered statistically different (p>0.5). This indicates that
TSMod has a similar starting geometry in both EpTS and EpTR and that FRET efficiency
is not affected by the small differences in their EpCAM backbones.
We next compared the membrane bound versions of EpTS and EpTR. If EpTS
holds tension at the membrane we expect to see an overall decrease in FRET efficiency
compared to the truncated control. Indeed when the average animal pole signal was
observed for both of these constructs, EpTS had a highly statistically significant decrease
in FRET efficiency of 1.3 percentage points compared to the truncated control (EpTS:
44.61%, SEM +/-0.22%, n = 1384 pixels, 17 embryos; EpTR: 45.91%, SEM +/- 0.19%,
n = 2046 pixels, 26 embryos; p<0.0001, fig 3.4c). TSMod FRET efficiency has been
calibrated [4], and a change of 5 percentage points represents an increase or decrease of
approximately 2pN of force per molecule. Thus, while the difference in FRET efficiency
between EpTS and EpTR appears small, it represents a difference or roughly 0.5pN of
force, a biologically relevant shift [4, 9].
This change in FRET efficiency is further evidenced when we compare the
cytoplasmic pools to their respective membrane localized sensors. Both EpTS and EpTR
show lower FRET efficiency (higher tension) at the membrane than in the cytoplasm, a
decrease of 3.4 and 2.2 percentage points respectively (fig 3.4c). EpTR is unable to bind
actin and should not experience tension at the membrane, making the decrease in FRET
efficiency from its cytoplasmic pool to membrane a curious result. The truncated sensor
75
maintains its extracellular anchor point and is still able to form self-self adhesions with
EpCAM on neighboring cells. It is therefore possible that this decrease in FRET
efficiency is due to a more restricted geometry at the membrane compared to the
cytoplasm. FRET transfer between two molecules is optimal when the barrels of the
fluorophore are aligned end to end. Any configuration that pushes the barrels out of this
optimal geometry will lead to an underrepresentation of FRET transfer, one possible
explanation for the decrease in FRET efficiency we observe from EpTR in the
cytoplasmic pool to EpTR bound at the membrane. Alternatively there may simply be a
baseline difference in the decay time for the FRET pair in different locations. Free
floating mTFP reports a slightly longer decay time in the nucleus of cells compared to the
cytosol [29]. It is therefore possible that the FRETing TSMod fluorophores are
influenced by their microenvironments in a way that drives a higher FRET efficiency in
the cytosol than at the membrane. Regardless, this change is substantially less than the
change observed between cytosolic and membrane bound EpTS.
EpTS showed a 3.4 percentage point decrease in FRET efficiency between the
cytosol and margin, a 1.5 times change compared to EpTR (fig 3.4c). Depending on the
genesis of the FRET change observed between the membrane and cytosol of EpTR (a
change in conformation in EpTR would indicate the EpTS cytosol to membrane
comparison is most apt while a change in environment would indicate comparing EpTS
to EpTR at the membrane is best), we suggest that EpTS shows a decrease in FRET
efficiency from its bound to unbound state of between 1.3 and 3.4 percentage points.
This translates to full length EpCAM experiencing between 0.5-1.5pN of force at the
membrane. The adhesion molecule E-Cadherin is estimated to experience 1-2pN of force
76
generated by the actin cytoskeleton [9] putting our results well in line with the growing
understanding of the mechanical properties of actin bound adhesion molecules.
Finally, we treated the embryos with the microtubule depolymerizing agent
nocodazole to investigate the effect of cytoskeletal destabilization on the EpCAM sensor.
We chose nocodazole over other cytoskeletal inhibitors because its use has been
previously reported in zebrafish [30, 31]. Although EpCAM is anchored cytoplasmically
through actin binding, we expect that any large scale disruption of the cytoskeleton
should be registered by a structurally bound protein. In addition to nocodazole, we
attempted to disrupt the actin cytoskeleton via the myosin inhibitor blebbistatin which has
also been well reported in zebrafish [32], however embryos treated with this small
molecule inhibitor no longer displayed EpCAM localization at the membrane (data not
shown). EpCAM’s loss of membrane localization with disruption of the actin
cytoskeleton has been previously reported in cell culture using cytochalasin D [14], and
further attempts to study EpTS through actin knockdown were not pursued.
Nocodazole was added to the mounting agarose and allowed to incubate with
embryos expressing either EpTS or EpTR for a minimum of one hour before imaging.
Again, all imaging occurred at the animal pole between 7 and 10hpf. While the EVL
appeared largely unaffected by the addition of nocodazole, the underlying deep cells
expressing EpTS and EpTR took on a wider, rounder phenotype compared to untreated
embryos (fig 3.4e). This may be because the deep cells make and break connections
throughout epiboly while the EVL remains more tightly bound. Regardless, the relaxed
appearance of the deep cells furthers the idea that microtubule inhibition leads to a
decrease in overall cortical tension.
77
To ensure nocodazole did not cause artifacts in FRET imaging, we first compared
EpTR expressing embryos with and without nocodazole treatment. As can be observed
in figure 3.4d, EpTR embryos reported an average FRET efficiency of 45.9% (SEM +/0.19%, n = 2046 pixels, 26 embryos) while nocodazole treated embryos reported a
similar efficiency of a 45.5% (SEM +/- 0.38%, n = 356 pixels, 5 embryos). These values
were not statistically different (p=0.08), indicating addition of nocodazole did not lead to
a change in FRET efficiency in unstressed EpCAM. Next we compared EpTS with and
without nocodazole addition. Here we observed a 1.7 percentage point increase in FRET
efficiency with the addition of nocodazole (EpTS: 44.6%, SEM +/- 0.22%; n = 1384
pixels, 17 embryos; EpTS + nocodazole: 46.3%, SEM +/-0.32%, n = 584 pixels, 5
embryos). This difference was highly statistically significant (p<0.0001) further
validating that EpTS is a biosensor capable of measuring changes in tension at the
membrane. Additionally, it confirmed that inhibition of microtubules can lead to an
overall relaxation of the cortical cytoskeleton. It should also be noted that the difference
between EpTS and EpTR with nocodozole was not significantly different. This implies
the presence of nocodazole may act to increase our measured donor decay times, possibly
through local microenvironment changes in the cell. However, if this is the case the
FRET efficiency value for EpTS with nocodozole reported here would be artificially
skewed lower compared to reality. Given that we are looking for an increase in FRET
efficiency in EpTS with nocodozole relative to untreated EpTS, we believe our data
stands and that, if anything, we are underrepresenting the loss of tension in EpTS as a
result of microtubule inhibition.
78
From these data we determined that EpCAM is under tension in the developing
embryo and that the EpTS construct is a functional force sensor capable of making
measurements in vivo. Our nocodazole data, in addition to further validating EpTS is
under tension, hints at the potential to test hypotheses regarding mechanical properties of
cells in a live vertebrate system.
Figure 3.4 EpTS holds force in the developing zebrafish embryo. a) Full length EpTS is
compared to a cytoplasmically truncated control and EpTS in embryos treated with the
microtubule depolymerizing agent nocodazole. b) Masks were made to differentiate
between EpTS and EpTR FRET efficiency in the membrane (left) and cytoplasmic pool
(right), scale = 50um. c) EpTS and EpTR do not show differences in FRET Efficiency
79
in the cytoplasmic pool. EpTS shows a statistically significant decrease in FRET
efficiency (increase in tension) compared to EpTR and its own cytoplasmic pool. A
smaller decrease in FRET efficiency was also observed between EpTR and its
cytoplasmic pool (p< 0.0001animal pole, 7-10HPF, “n” EpTS memb = 1384 pixels, 17
embryos; EpTR memb = 2046 pixels, 26 embryos; EpTS cyto = 1094 pixels, 15 embryos;
EpTR cyto = 4460 pixels, 23 embryos). d) EpTS shows a significant increase in FRET
efficiency (decrease in tension) with the addition of the microtubule inhibitor nocodazole.
This change is not observed between when nocodazole is added to EpTR (p< 0.0001
animal pole, 7-10hpf, “n” EpTS and EpTR same as in c, EpTS + Noc = 584 pixels, 5
embryos; EpTR + Noc = 356 pixels, 5 embryos). e) While embryos exposed to
nocodazole appear phenotypically normal in the outer EVL, the internal deep cells show
a markedly rounded and larger phenotype indicating a global relaxation of the
cytoskeleton (error = 1 SEM, animal pole, 9.5-10hpf, scale bar = 50um).
EpCAM holds more tension at the margin than other regions of the embryo
Finally, we used the EpTS sensor to determine whether or not epithelial tension
varied in a systematic manner within the EVL of the developing zebrafish embryo. To do
this we imaged embryos expressing either EpTS or EpTR in four locations moving
vegetally from the animal pole to the margin (the row of cells at the leading edge of the
embryo migration). Until this point, all measurements had been made at the animal pole,
what we will now call position 1. Moving from the animal to vegetal pole we will now
refer to these regions as position 1 (animal pole), position 2 (50-100um vegetal to the
animal pole), position 3 (a concentric ring 50-100um animal to the margin at its furthest
point) and the position 4 (margin) (fig 3.6a). All measurements were made from 7-10
HPF and processed as previously described. For cells imaged at the margin, masks were
made to include only the one cell deep row most closely abutting the yolk. For positions
2 and 3 all visible EVL cells are included (fig. 3.5 a and b).
We first looked at the truncated sensor in each of the four positions to determine
if there was any baseline difference in FRET in different embryo regions. Indeed we saw
80
statistically significant differences between many of the positions (fig 3.6b). While this
is not ideal, as previously discussed when comparing the cytoplasm to margin data, cell
microenvironments are capable of influencing FRET efficiency [9]. The differences
observed in these regions may reflect biological realities in differences in pH, membrane
protein environment or changes in auto-fluorescence between the cytosol and the
membrane. Alternatively, until this point all data had been collected at the animal pole, a
region which is primarily comprised of EVL. In these animal pole images it was
relatively straight-forward to identify EVL membrane regions. Images taken at positions
2-3 often included deep cells and had a more confounding geography, making it more
difficult to select the appropriate tissue layer and cell regions.
Because of the large changes we observed in baseline donor decay at each embryo
region, we compared EpTS data in each position relative to its corresponding truncated
control. As seen in figure 3.6c, there was a small (p<0.05) increase in tension in
positions 1 and 3 as compared to position 2. There was a larger (p<0.01) increase in
tension between position 2 and position 4. Regardless of the exact statistics, as figure
3.5c shows, tension at the margin was two to five times that of any of the other regions
measured in this experiment, representing a difference of nearly a full piconewton of
force. This is consistent with hypothesis suggesting the leading edge of the EVL is an
integral player in driving the epiboly movements observed through early development.
While this experiment in and of itself does not prove conclusively that the margin is a
high tension region of the embryo, it does merit follow up and points to EpTS as a tool to
make meaningful investigations into biological phenomena in a living vertebrate. The
work until now has focused primarily on the animal pole and as a result our pixel “n” at
81
positions 3 and 4 are roughly an order of magnitude smaller than those at position 1 and
2. We suggest increasing the “n” for positions 3 to see if a more robust experiment helps
to flesh out these trends.
Figure 3.5 Embryos are imaged at four positions moving from the animal to vegetal pole.
a) From left to right example images of position 1, position 2, position 3 and position 4
and b) example images of the masks used to identify EVL cells. Scale bar = 50um.
Figure 3.6 The membrane may hold tension in the developing embryo. a) Four
positions were imaged moving from the animal at position 1 vegetally to the margin at
position 4. b) EpTR shows significantly differences in FRET efficiency in different
positions of the embryo (error = SEM, 7-10HPF, p <0.001, “n” pos 1 = 2046 pixels, 23
embryos, pos 2 = 3702, 26 embryos, pos 3 = 374 pixels, 11 embryos, pos 4 = 133 pixels,
7 embryos). c) When average EpTS FRET efficiency is compared to EpTR in the same
82
positions, the expected decrease in FRET Efficiency (increase in tension) is observed at
each region, however only small differences in efficiency are observed between the
normalized region (error = SEM, 7-10HPF, p<0.05, p<0.01).
3.4 Discussion
TSMod is a relatively new tool that has opened the door to studying tension in
vivo. Its use in live vertebrate embryos further expands the toolkit for studying the
physical landscape of early development. Here we compare membrane bound EpCAM
embedded with the tension sensor module, TSMod, to its unbound cytoplasmic pool and
a truncated control. We show that the zebrafish EpCAM molecule holds tension in the
developing zebrafish and that these forces are on order with tension reported in other
adhesion molecules [4, 9]. Additionally, we demonstrate that we are able to cause a
decrease in EpCAM tension by relaxing cytoskeletal tension with the membrane
depolymerizing agent nocodazole. To our knowledge, this is the first time such direct
measurements of molecular tension have been made in the fish embryo.
By expanding our work to consider whole regions of the fish, we show that in
addition to using this tool to study molecular and cellular scale tensions, we can also use
it to investigate forces acting on tissue and whole embryo scales. While we were unable
to show strong statistical differences in tension within different regions of the fish, the
qualitative increase in tension at the margin compares favorably to hypotheses about the
margin as a motor driving epiboly movements. Both the yolk syncytial nuclei (YSN) and
actin band surrounding the leading edge of the migrating EVL margin have been
proposed to act as force generators, actively pulling the EVL and its underlying deep
cells through the epiboly movements that ultimately envelope the yolk [32, 33]. It also
83
matches recent work using laser ablation which found cells at the margin bear significant
tension compared to those at the animal pole [28].
The zebrafish embryo has been a valuable model system for the study of early
embryo development. In particular, its large, optically transparent embryo has been an
ideal backdrop for pairing with advanced microscopy techniques. Here, we prove FLIMFRET is a viable tool in the fish background, thus adding an additional technique to an
already powerful toolkit. Given the role of the zebrafish as an emerging model for
regeneration, the ability to measure tissue level forces in the vertebrate embryo will be a
valuable tool in our ability to study not only development, but also tissue engineering,
regenerative medicine and wound healing.
3.5 Materials and Method
DNA and RNA preparation
EpTS, EpTR, Ep5aa and EpTRAF were prepared as described in Chapter 2.
Additionally, EpCAM-TSMod 531, 693 and 765 were built by site directed mutagenesis
of 5’NcoI 3’AgeI insert sites into their respective locations within zebrafish EpCAMPCS2+ followed by insertion of an Nco1/Age1 digested TSMod. See Appendix A for
full sequences of all constructs.
Zebrafish Embryo Preparation
Embryo mating, injection and storage were completed as described in Chapter 2.
For morpholino experiments 2.1nL of 150uM EpCAM Morpholino-1 (Morpholino
84
sequence: ACTAAAACCTTCATTGTGAGCGAGA, synthesized by Gene-tools LLC)
was injected into embryos at the 1-4 cell stage. For rescue experiments they were coinjected and 52.5pg of EpTS. For nocodazole treated embryos 0.8% low melt agarose
was prepared in zebrafish E2 buffer stored at 37C. For nocodazole treated embryos,
nocodazole was added to the 0.8% low melt agarose mounting solution to a final
concentration of 4 μg/mL immediately before mounting. Nocodazole treated embryos
were allowed to incubate for at least one hour before imaging.
Survivals of EpCAM-TSMod 858, 531, 693 and 765 were taken at both 4-6hpf
and 1dpf. 63X imaging of membrane localization of EpCAM-TSMod 858 and 693 was
completed using the Leica SP5 2 photon/confocal upright microscope and exciting the
mEYFP acceptor fluorophore. Bright field and epi-florescence imaging of EpTS at 5.5
and 24hpf was completed using a Leica dissection scope and imaging with total
luminescence or mEYFP epi- florescence.
Embryo Imaging and Image Processing
All embryo imaging and image processing was completed as described in Chapter
2. All EpTS and EpTR data was generated between 7 and 10HPF. EpTRAF and Ep5aa
data were generated between 9 and 10HPF. For cytoplasmic quantification of truncated
and full length sensors, a mask was generated by manually selecting the cytosol regions
based on the previously generated membrane masks. Quantification of FRET Efficiency
was completed using custom Matlab scripts.
85
3.6 References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
Engler, A.J., et al., Matrix elasticity directs stem cell lineage specification. Cell, 2006.
126(4): p. 677-89.
Krieg, M., et al., Tensile forces govern germ-layer organization in zebrafish. Nat Cell Biol,
2008. 10(4): p. 429-36.
Farge, E., Mechanical induction of Twist in the Drosophila foregut/stomodeal
primordium. Curr Biol, 2003. 13(16): p. 1365-77.
Grashoff, C., et al., Measuring mechanical tension across vinculin reveals regulation of
focal adhesion dynamics. Nature, 2010. 466(7303): p. 263-6.
Meng, F., T.M. Suchyna, and F. Sachs, A fluorescence energy transfer-based mechanical
stress sensor for specific proteins in situ. FEBS J, 2008. 275(12): p. 3072-87.
Meng, F. and F. Sachs, Visualizing dynamic cytoplasmic forces with a compliancematched FRET sensor. J Cell Sci, 2011. 124(Pt 2): p. 261-9.
Kim, T.J., et al., Dynamic Visualization of alpha-Catenin Reveals Rapid, Reversible
Conformation Switching between Tension States. Curr Biol, 2015. 25(2): p. 218-24.
Becker, N., et al., Molecular nanosprings in spider capture-silk threads. Nat Mater, 2003.
2(4): p. 278-83.
Borghi, N., et al., E-cadherin is under constitutive actomyosin-generated tension that is
increased at cell-cell contacts upon externally applied stretch. Proc Natl Acad Sci U S A,
2012. 109(31): p. 12568-73.
Conway, D.E., et al., Fluid shear stress on endothelial cells modulates mechanical tension
across VE-cadherin and PECAM-1. Curr Biol, 2013. 23(11): p. 1024-30.
Krieg, M., A.R. Dunn, and M.B. Goodman, Mechanical control of the sense of touch by
beta-spectrin. Nat Cell Biol, 2014. 16(3): p. 224-33.
Cai, D., et al., Mechanical feedback through E-cadherin promotes direction sensing
during collective cell migration. Cell, 2014. 157(5): p. 1146-59.
Litvinov, S.V., et al., Ep-CAM: a human epithelial antigen is a homophilic cell-cell
adhesion molecule. J Cell Biol, 1994. 125(2): p. 437-46.
Balzar, M., et al., Cytoplasmic tail regulates the intercellular adhesion function of the
epithelial cell adhesion molecule. Mol Cell Biol, 1998. 18(8): p. 4833-43.
Villablanca, E.J., et al., Control of cell migration in the zebrafish lateral line: implication of
the gene "tumour-associated calcium signal transducer," tacstd. Dev Dyn, 2006. 235(6):
p. 1578-88.
Balzar, M., et al., The structural analysis of adhesions mediated by Ep-CAM. Exp Cell Res,
1999. 246(1): p. 108-21.
Pavsic, M., et al., Crystal structure and its bearing towards an understanding of key
biological functions of EpCAM. Nat Commun, 2014. 5: p. 4764.
Litvinov, S.V., et al., Epithelial cell adhesion molecule (Ep-CAM) modulates cell-cell
interactions mediated by classic cadherins. J Cell Biol, 1997. 139(5): p. 1337-48.
Winter, M.J., et al., Expression of Ep-CAM shifts the state of cadherin-mediated
adhesions from strong to weak. Exp Cell Res, 2003. 285(1): p. 50-8.
van der Gun, B.T., et al., EpCAM in carcinogenesis: the good, the bad or the ugly.
Carcinogenesis, 2010. 31(11): p. 1913-21.
Maetzel, D., et al., Nuclear signalling by tumour-associated antigen EpCAM. Nat Cell
Biol, 2009. 11(2): p. 162-71.
86
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
Lu, H., et al., EpCAM is an endoderm-specific Wnt derepressor that licenses hepatic
development. Dev Cell, 2013. 24(5): p. 543-53.
Balzar, M., et al., Epidermal growth factor-like repeats mediate lateral and reciprocal
interactions of Ep-CAM molecules in homophilic adhesions. Mol Cell Biol, 2001. 21(7): p.
2570-80.
Slanchev, K., et al., The epithelial cell adhesion molecule EpCAM is required for epithelial
morphogenesis and integrity during zebrafish epiboly and skin development. PLoS Genet,
2009. 5(7): p. e1000563.
Kane, D.A., K.N. McFarland, and R.M. Warga, Mutations in half baked/E-cadherin block
cell behaviors that are necessary for teleost epiboly. Development, 2005. 132(5): p.
1105-16.
Babb, S.G. and J.A. Marrs, E-cadherin regulates cell movements and tissue formation in
early zebrafish embryos. Dev Dyn, 2004. 230(2): p. 263-77.
Betchaku, T. and J.P. Trinkaus, Contact relations, surface activity, and cortical
microfilaments of marginal cells of the enveloping layer and of the yolk syncytial and
yolk cytoplasmic layers of fundulus before and during epiboly. J Exp Zool, 1978. 206(3):
p. 381-426.
Campinho, P., et al., Tension-oriented cell divisions limit anisotropic tissue tension in
epithelial spreading during zebrafish epiboly. Nat Cell Biol, 2013. 15(12): p. 1405-14.
Day, R.N., C.F. Booker, and A. Periasamy, Characterization of an improved donor
fluorescent protein for Forster resonance energy transfer microscopy. J Biomed Opt,
2008. 13(3): p. 031203.
Lu, F.I., C. Thisse, and B. Thisse, Identification and mechanism of regulation of the
zebrafish dorsal determinant. Proc Natl Acad Sci U S A, 2011. 108(38): p. 15876-80.
Jesuthasan, S. and U. Stahle, Dynamic microtubules and specification of the zebrafish
embryonic axis. Curr Biol, 1997. 7(1): p. 31-42.
Koppen, M., et al., Coordinated cell-shape changes control epithelial movement in
zebrafish and Drosophila. Development, 2006. 133(14): p. 2671-81.
Behrndt, M., et al., Forces driving epithelial spreading in zebrafish gastrulation. Science,
2012. 338(6104): p. 257-60.
87
Chapter 4 Outlook and Future Work
88
4.1 Abstract
The work presented in this thesis represents the first successful use of FLIMFRET in the zebrafish embryo. We also present the first use of the TSMod tension
sensor module in a vertebrate. While we believe this work is convincing as it stands,
there are several gaps remaining, notably the discrepancy between our observed decay
times in the Ep5aa and EpTRAF sensors and those published in the literature and
finalizing our validation of the EpTS sensor through a physical perturbation. Here we
present our recommendations for the next experiments with the EpCAM based sensors
developed in this paper, as well as future directions for FRET-based tension sensors in
the zebrafish embryo.
4.2 Re-Evaluating the Fixed Length Controls
We believe that the work shown here fully demonstrates the effectiveness of
FLIM-FRET in the zebrafish embryo. We successfully show that we can measure the
expected decay time in a free floating mTFP molecule and that we can measure a
difference in decay time for FRET pairs joined with linkers of different length.
The one noticeable question remaining is the origin of the difference between our
observed fixed length decay times and those reported in the literature [1]. In summary,
the expected decay times for 5aa and TRAF are 1.11ns and 2.22ns, respectively, while we
measured our Ep5aa and EpTRAF at 1.80ns and 2.38ns. The free floating mTFP we
imaged in the embryo background was nearly identical to that observed in cell culture
reporting decay times of, respectively, 2.57ns and 2.56ns. The three likeliest causes of
the differences observed between the fixed length controls are 1) The addition of a
89
TSMod host protein, the literature work was done with free floating fixed length FRET
pairs while our work was completed with EpCAM embedded controls; 2) the imaging
background, the literature reports 5aa and TRAF in cell culture, our results are shown in
the zebrafish embryo background; and 3) The post-imaging data fitting. We will now
discuss these three potential sources of error and future work that will help identify which
are contributing to our final decay measurements.
Free floating fixed length controls
In the literature results, the 5aa and TRAF fixed length sensors are free floating in
the cell [1]. The decay times described here are in the zebrafish. Additionally, in the
literature the fixed length FRET pairs are free floating, while in our experiments they
have been embedded in the EpCAM host protein. It should be noted that the sensors
have been expressed in the nematode worm, and the FLIM results observed in this work
are in good agreement with those published in the literature [2]. To investigate the
differences that embedding the sensor and imaging in the fish background may cause,
free floating 5aa and TRAF can also be built into the PCS2+ vector and expressed as
mRNA in the fish embryo. If these results better overlap with the decay times observed
in the literature, we will have strong evidence that embedding the FRET pair in EpCAM
leads to decay time artifacts. From here we may ask further questions about the cause of
the change.
As described in section 2.2, embedding the sensor may cause a change in decay
time in one of two ways. First, a change in microenvironment may cause changes in
FRET transfer efficiency through things like a change in pH or the addition of molecules
capable of non-specifically accepting energy from the mTFP donor. In this case simply
90
moving the sensor to the membrane may be sufficient to alter the observed decay time.
The second potential source of change from embedding in EpCAM comes from altering
the sensor geometry. EpCAM may force the fixed length sensors into an un-expected
geometry causing the fluorophore barrels to misalign. In this case the efficiency of the
resonance energy transfer will decrease even if there is no net increase in fluorophore
distance. Similarly, although both 5aa and TRAF are expected to act as rigid linkers, it is
possible that when they are held between two anchors at the membrane instead of floating
freely in the cytoplasm, the molecules experience some sort of tension leading to the
observed decrease in decay time.
By expressing free floating TSMod in the embryo we can look at decay times at
different regions in the cell and compare the overall decay time at both the cytoplasm and
membrane. If the free floating sensor shows a uniform decay time that matches the decay
times described in the literature, then we can be confident in saying the differences in the
times reported here are due to the EpCAM backbone forcing the sensor into some sort of
alternative geometry. If we see that free floating 5aa and TRAF have a decreased decay
time when imaged close to the membrane then it is likely that there is either some sort of
microenvironment effect. If, however, the free floating measurements look similar to
decay times of EpCAM embedded 5aa and TRAF, then we must consider that the
difference is either a result of our embryo background or our post-imaging calculations.
It should be noted that while the simple experiment would be to compare the
EpTRAF and Ep5aa signals in the membrane to that in the cytoplasmic pool,
unfortunately neither of the fixed length controls measured in the embryo gave a
cytoplasmic pool signal sufficient to make meaningful calculations.
91
Evaluating artifacts from the embryo background
The decay time measurements we compare our results to were made in cell
culture. It is possible that measuring in the complicated embryo background causes decay
time artifacts. Preliminary experiments have been completed successfully expressing
EpTS and EpTR in MDCK cells (data not show). Currently, our EpTRAF and Ep5aa
sensors only exist in the pCS2+ vector which is not suitable for expression in cell culture.
These constructs may be sub-cloned into a suitable vector, expressed in cell culture and
then imaged using FLIM-FRET. If EpTRAF and Ep5aa show an increase in decay time
in cell culture as compared to the embryo, then it is likely that we are experiencing
embryo-specific artifacts. It is possible that these artifacts are a result of differences in
the embryo microenvironment or from including auto-fluorescence when we fit our decay
times.
As discussed in section 1.4, FLIM is a powerful tool for segregating disparate
signals through differences in decay times. However, we were unable to take full
advantage of this capability because our photon counts were too low to utilize multicomponent fitting. If cell culture results indicate the source of the fixed length
discrepancy is due to the embryo background it will be worth revisiting the multicomponent fitting. We can do this experimentally by increasing the expression of EpTS
in the embryo (by injecting more mRNA for example). We can also do this in silico by
using larger bins to increase the number of photons in a curve. We could also move to
entirely new fitting regimes, an option which will be discussed in the next section.
Identifying superior methods for fitting FLIM data
92
While we were able to show significant differences between the fixed length
controls, these values were significantly higher than those reported in cell culture [1].
These differences could be biological in origin. As discussed here and in Chapter 2
FRET signals may be affected by local microenvironments and FRET pair orientation.
However it is also possible that the observed difference is an artifact of the initial decay
curve fitting.
We found the SPCImage software challenging to use in conjunction with the
embryo data. Since our ultimate goal is to compare whole areas of the embryo, ideally
we would bin photons from all membrane regions in an image before fitting the data.
This would potentially give enough photons to meaningfully fit the data using two and
even three component fitting. The SPCImage software offers no option for importing
externally generated masks, and while it is possible to select small regions of interest
directly in the software, this capability is non-intuitive and selecting all membrane
boundaries using the available tools in the software would have been prohibitively time
intensive.
Due to these software limitations, we had to fit data at a smaller binning scale.
One of the strengths of FLIM is its ability to separate auto-fluorescence using multiexponential fitting, but the limited number of photons in our pixel bins led us to use the
one component fit, ultimately leaving one of FLIM’s most potent capabilities unused. It
is possible to export the raw, photon bin data from the spc files and in the future it would
be worth developing custom software specifically designed for working with the embryo
data.
93
Alternatively, a strategy called the phasor approach could also be employed to
look at the embryo data [3]. In phasor, each decay curve is turned into a vector and
plotted in a two dimensional space. Instead of a decay time, each component in a sample
has a unique x,y position and different species end up clustered in different regions of the
plot. The phasor output is a simple way to visualize the different FRETting components
of a sample and eliminates the need for the confounding fitting encountered using the
exponential fit parameters in SPCImage.
Final considerations on FLIM-FRET in the embryo
Again, we believe the work outlined in Chapter 2 successfully demonstrates
FLIM-FRET as tool for use in the zebrafish embryo background. But determining the
ultimate cause of the discrepancy between the previously reported fixed length control
decay times and those measured here will give us a better understanding of EpCAM as a
host molecule and the challenges of using FLIM-FRET in the embryo background. A
final source of error we touch briefly on in Chapter 2 is intermolecular FRET, or FRET
transfer between donor and acceptor fluorophores that are not part of the same FRET
pair. Intermolecular FRET should cause an overall increase in FRET efficiency as
denoted by a faster donor decay time. In our EpCAM embedded sensors we see the
opposite trend, that Ep5aa and EpTRAF have an increase donor decay time as compared
to previously published results. For this reason we do not believe our results are the
result of intermolecular FRET. In our experiments we attempt to correct for any potential
intermolecular FRET by using intensity as a proxy for molecular density and thresholding
our results in an intensity regime that appears roughly linear. We could also test this
experimentally by building EpCAM constructs expressing only mTFP or mEYFP and co94
injecting them as mRNA. In this circumstance we would expect to see donor decay
matching that of mTFP alone and any decrease in decay time would presumably stem
from intermolecular FRET.
As a final consideration, the YFP category of fluorophores are characterized by
slow maturation times. Venus is a noticeable exception because its maturation time has
been increased through targeted mutation [4]. The cell culture data discussed here was
generated using an mTFP/Venus FRET pair [1]. If our mEYFP/mTFP-based sensor has a
relatively larger portion of FRET pairs with immature acceptor molecules, our signal will
be skewed towards the longer decay times represented by the donor-only state. We were
unsuccessful at expressing Venus in the zebrafish embryo (data not shown), however if
successful expression were achieved it would be interesting to note if fixed length
mTFP/Venus sensors built into the EpCAM backbone would show the same
discrepancies discussed here.
Finally, it should also be mentioned that there is a real possibility that we will
never fully match the reported values. Anecdotally, we have heard of others reporting
slightly lower than expected FRET efficiencies using the 5aa and TRAF linkers in a
FRET system. Unfortunately, the challenge of reproducibility is simply a reality when
working with complex biological systems. Further refining the ultimate measurement
output could make it more conducive to measuring small changes in decay time like those
found in TSMod, but regardless of whether we ultimately show reproducibility to the
previously published data, the work shown here demonstrates that we can measure
statistically significant differences between different FRET linkers using FLIM-FRET in
the zebrafish embryo.
95
4.3 Validating the EpCAM TSMod Biosensor
Testing EpTS Functionality
While we were able to make convincing tension measurements using the EpCAM
TSMod biosensor, we believe several steps are still needed to fully validate the use of
TSMod as a tension sensor in the embryo. First, as discussed 3.2, we were unable to
rescue the Morpholino knockdown of EpCAM. While we successfully recreated the
decreased otolith size reported in the literature, otolith size was unchanged by coinjection of EpTS with the Morpholino. While proving EpTS acts as a functioning
EpCAM molecule in zebrafish development is not a requirement for it to be a useful
tension biosensor, it would be a helpful confirmation that the embryo is correctly
localizing and binding. A maternal/zygotic mutant exists [5] and shows epiboly defects.
It would be useful to attempt a rescue using EpTS in this mutant background. In the
absence of this, a titration experiment comparing epiboly rates and cell phenotypes at
varying concentrations of EpTS would help better elucidate any phenotypic changes
induced by expression of the EpTS construct.
A second option for investigating biological functionality of the EpTS sensor is to
move out of the fish and into a cell culture system. EpCAM adhesion behavior is well
characterized in cell culture and is a simple assay for testing functionality [6, 7]. By
moving EpTS into a cell culture expression system, we could then express it in the
adhesion molecule lacking fibroblastic L cell line. A functional EpCAM based construct
should cause the cells to noticeably aggregate. In our case we would expect expression
96
of EpTS to lead to cell aggregation while EpCAM negative and cells expressing EpTR
would not.
Validating EpTS reports tension
The gold standard for showing TSMod holds tension includes three separate
experiments 1) Difference in tension compared to a truncated control. 2) Difference in
tension compared to a chemical perturbation of the cytoskeleton and 3) Direct physical
perturbation of the system [2, 8, 9]. We have successfully demonstrated 1 and 2 and
believe this work is sufficient to demonstrate EpCAM holds tension at the membrane.
However a physical manipulation of EpTS through laser ablation or other mechanical
perturbation would serve as a final confirmation that the sensor does, in fact, register
meaningful tension.
Finally, while demonstration that EpCAM holds tension in the developing embryo
is a valuable discovery of itself, we would ultimately like to use EpTS to understand
tension dynamics at the cell and tissue level. The nocodazole and margin data discussed
in Chapter 3 provide a glimpse into the potential of this sensor as way to visualize less
specific stresses in the developing fish, but now that we have demonstrated proof of
concept we hope the sensor will be used to study other novel processes. For example, a
preliminary attempt was made to watch temporal changes at both the animal pole and
margin. While a trend was observed showing a decrease in tension over time at the
animal pole and an increase at the margin, the number of embryos imaged was not
sufficient to draw meaningful conclusions.
97
4.4 Future Directions
Finally, not presented here but also relevant to this work, we have built the
TSMod into a variety of additional zebrafish host molecules including N-cadherin,
vinculin and prickle (see Appendix B). Exploration of any of these molecules using
TSMod would be a valuable future work. As a long term goal, we hope to incorporate
EpTS or one of these other sensors into a transgenic fish line. Because our experiments
are performed using transiently expressed mRNA, we are temporally limited to the onset
of expression (~4HPF) to RNA degradation (~2DPF). While there are many dynamic
processes to explore in this window, ultimately, the building of a transgenic EpTS line
would permit the study of tension at all stages of embryo development, allowing easy
integration of tension mapping to the developmental biologist’s toolkit.
With a functional tension sensor in the fish we can test hypothesis regarding the
role of force in the developing embryo. With our current sensor, we can consider
questions about EpCAM itself. As discussed in Chapter 1, Vinculin, a molecule involved
in E-Cadherin adhesion, is recruited to focal adhesions in a tension dependent manner [9,
10]. Likewise, we can ask if EpCAM also recruits structural proteins when it experiences
strain. EpCAM also plays a role in nuclear signaling and EpTS would allow us probe
potential relationships between force and EpCAM signaling [11]. This would be a
particularly prudent path of investigation given that EpCAM signaling is purportedly
involved in cancer pathways [12].
We can also use EpTS as a generic sensor to help us understand tension dynamics
in the embryo as a whole. One of the simplest places to start would be to determine if the
yolk syncytial nuclei (YSN) or contractions in the EVL leading edge act as a tension
98
generating motor during epiboly [13-15] . We can measure the tension experienced at the
leading edge of the margin and watch the tension profile after laser ablation of the YSN.
If the YSN apply significant tension, we expect to see a drastic drop in tension profile at
the leading edge after ablation. As a follow-up, we can build a second sensor into the
Claudin E molecules that connect the YSN to the leading edge of the margin [16] and
directly compare YSN and leading edge tension.
While the work presented here has focused on imaging in the EVL, the EpTS
sensor also expresses in the deep cells (see Figure 3.4 in Chapter 3). Although native
EpCAM does not express universally in the deep cell layer during epiboly [5], it can still
be used to provide a read out of force. For example, a tension sensor expressing in all
cell layers would allow us to directly test the differential adhesion hypothesis during
different stages of development. On a whole organism level this would mean comparing
the tension experienced by the ectoderm, mesoderm and endoderm and seeing if altering
tension in different layers (by, for example, over or under-expressing adhesion proteins)
leads to direct changes in both tissue tension and layer segregation. Differential adhesion
is also observed during zebrafish gastrulation via a cadherin-based adhesion gradient
[17]. It has been proposed that this gradient is in part responsible for dorsal-ventral cell
migration during embryo convergence. Here too we can use the tension sensor to
quantitatively probe the role of force during a major development step.
An in vivo embryo tension sensor would also allow us to ask questions about how
mechanical microenvironments influence differentiation. As discussed in Chapter 1,
growing stem cells on substrates of different tensile strengths is sufficient to irreversibly
determine differentiation [18]. Having a method to directly measure the tensions
99
experienced by cells in the developing embryo would allow us to correlate the forces
experienced in vivo to cell fate.
Perhaps the most exciting future for tension sensors in the fish embryo is in whole
embryo force mapping. When I began this project, my hope had been to make a whole
embryo map of tension in the EVL during epiboly. At the time, a 3D time lapse of
zebrafish embryo development had just been published using scanned light sheet
microscopy [19]. I imagined having a similar map outlining the forces experienced by
cells in the fish. While this clearly fell outside of the work I was able to achieve for this
dissertation, having a map of real-time, whole embryo data showing the mechanical
microenvironments experienced by cells during early development would be an
invaluable resource. Just as GFP enables the study of in vivo protein dynamics, having
expressible, fluorescence-based tension sensors will permit simple, microscopy-based
tracking of the mechanical properties of the developing embryo.
100
4.5 References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
Day, R.N., C.F. Booker, and A. Periasamy, Characterization of an improved donor
fluorescent protein for Forster resonance energy transfer microscopy. J Biomed Opt,
2008. 13(3): p. 031203.
Krieg, M., A.R. Dunn, and M.B. Goodman, Mechanical control of the sense of touch by
beta-spectrin. Nat Cell Biol, 2014. 16(3): p. 224-33.
Digman, M.A., et al., The phasor approach to fluorescence lifetime imaging analysis.
Biophys J, 2008. 94(2): p. L14-6.
Nagai, T., et al., A variant of yellow fluorescent protein with fast and efficient maturation
for cell-biological applications. Nat Biotechnol, 2002. 20(1): p. 87-90.
Slanchev, K., et al., The epithelial cell adhesion molecule EpCAM is required for epithelial
morphogenesis and integrity during zebrafish epiboly and skin development. PLoS Genet,
2009. 5(7): p. e1000563.
Litvinov, S.V., et al., Epithelial cell adhesion molecule (Ep-CAM) modulates cell-cell
interactions mediated by classic cadherins. J Cell Biol, 1997. 139(5): p. 1337-48.
Litvinov, S.V., et al., Ep-CAM: a human epithelial antigen is a homophilic cell-cell
adhesion molecule. J Cell Biol, 1994. 125(2): p. 437-46.
Meng, F. and F. Sachs, Visualizing dynamic cytoplasmic forces with a compliancematched FRET sensor. J Cell Sci, 2011. 124(Pt 2): p. 261-9.
Grashoff, C., et al., Measuring mechanical tension across vinculin reveals regulation of
focal adhesion dynamics. Nature, 2010. 466(7303): p. 263-6.
Kim, T.J., et al., Dynamic Visualization of alpha-Catenin Reveals Rapid, Reversible
Conformation Switching between Tension States. Curr Biol, 2015. 25(2): p. 218-24.
Maetzel, D., et al., Nuclear signalling by tumour-associated antigen EpCAM. Nat Cell
Biol, 2009. 11(2): p. 162-71.
van der Gun, B.T., et al., EpCAM in carcinogenesis: the good, the bad or the ugly.
Carcinogenesis, 2010. 31(11): p. 1913-21.
Betchaku, T. and J.P. Trinkaus, Contact relations, surface activity, and cortical
microfilaments of marginal cells of the enveloping layer and of the yolk syncytial and
yolk cytoplasmic layers of fundulus before and during epiboly. J Exp Zool, 1978. 206(3):
p. 381-426.
Behrndt, M., et al., Forces driving epithelial spreading in zebrafish gastrulation. Science,
2012. 338(6104): p. 257-60.
Koppen, M., et al., Coordinated cell-shape changes control epithelial movement in
zebrafish and Drosophila. Development, 2006. 133(14): p. 2671-81.
Siddiqui, M., et al., The tight junction component Claudin E is required for zebrafish
epiboly. Dev Dyn, 2010. 239(2): p. 715-22.
von der Hardt, S., et al., The Bmp gradient of the zebrafish gastrula guides migrating
lateral cells by regulating cell-cell adhesion. Curr Biol, 2007. 17(6): p. 475-87.
Engler, A.J., et al., Matrix elasticity directs stem cell lineage specification. Cell, 2006.
126(4): p. 677-89.
Keller, P.J., et al., Reconstruction of zebrafish early embryonic development by scanned
light sheet microscopy. Science, 2008. 322(5904): p. 1065-9.
101
Appendixes
102
Appendix A. Construct Sequences
All constructs were cloned into the PCS2+ backbone using the EcoRI restriction site.
mTFP (note we eliminated the first 8 amino acids of the mTFP clone- as compared to
canonical mTFP- by neglecting to insert an upstream start site for mRNA expression.
Given the similarity of our mTFP results to those observed in the literature we do not
believe this mistake impacts the work described here).
ATGGGGGTCATCAAGCCCGACATGAAGATCAAGCTGAAAATGGAAGGGAAT
GTAAACGGGCACGCGTTTGTAATCGAGGGTGAGGGAGAGGGGAAACCATAC
GATGGAACGAATACAATCAATCTTGAGGTCAAGGAGGGCGCACCTCTCCCGT
TTTCGTACGATATTCTGACGACTGCATTCGCCTACGGTAACCGGGCCTTTACC
AAGTATCCAGACGATATCCCCAACTATTTCAAGCAGAGCTTCCCGGAGGGGT
ATTCGTGGGAACGAACGATGACCTTTGAGGACAAGGGCATTGTGAAAGTAAA
GTCCGACATTTCGATGGAGGAGGATTCCTTCATCTACGAAATCCACCTCAAA
GGAGAGAACTTTCCCCCGAACGGTCCGGTGATGCAGAAAAAGACGACAGGA
TGGGACGCGTCAACCGAGAGGATGTATGTGCGGGATGGAGTATTGAAGGGG
GACGTGAAGCATAAACTTCTGTTGGAGGGTGGAGGGCACCATCGCGTGGATT
TCAAAACGATCTACCGCGCCAAGAAAGCAGTCAAGCTCCCTGACTATCATTT
CGTGGACCACAGAATTGAAATCTTGAATCACGATAAAGATTACAATAAGGTG
ACTGTATATGAAAGCGCGGTCGCTAGGAACTCAACAGATGGAATGGATGAAC
TTTACAAGttaa
EpTS
ATGAAGGTTTTAGTTGCCTTGTTTGTTGTGGCATTGGTTGATGTAACTTCACA
ATgtacttgtaaaacaatgaagtgggcaaactgtgatgactcgtgctcatgcagtcttacattaactgaatcttccaagcaaaccc
ttgactgttctaagtTGGTTCCCAAGTGCTTCCTCATGAAAGCAGAGATGTATCGTGCC
CGTCACAACTTGGGCACAAGAAAAACTGGGAAGCCAGATGAGAATGCCTTTG
TGGACAATGATGGCATCTATGACCCAGAATGTCAGAGTGATGGGAAATTCAA
GGCAGTCCAGTGTAACAACACTGAAGTATGCTGGTGCGTCAACAGTGCTGGT
GTACGAAGAAGTGACAAAAAAGACAAGAACATAAAGTGCGAGCCTGCGGAG
ACCTAttgggttcgtgcagaaatgacgcacaaaagcgtggatgtgcccattgatgtcgctaatctgaggatGGGGATT
GAGAACGCTCTGCAGCAACGTTACTTTTTGGATAAGAACTTTGTCTCTGAAGT
TCAGtatgacaaggatgccaggctcattgtggtggatgtcaaaaaagataagaacgaccgtactacagatctgtccctgatga
cttattacctcgagaaagatATCAAAGTTAAGCCCCTGTTTTCCGATGAAAAACCATTTGT
GCTTAGTGTTCAGGGAAAAAATGTTACAATGGAGAATGTCCTGATCTACTAT
103
GTAGATGACAAAGCACCCACCTTCACCATGcagaagCTAACTGGTGGTATCATT
GCTGTCATTGTTGTAGTCAGCTTGATTGTGATTGGAGGATTTCTGGTTCTGttcttt
cttgcacggcgacagccatggTCCGGAGTCTCGAAAGGCGAAGAAACAACTATGGGGGT
CATCAAGCCCGACATGAAGATCAAGCTGAAAATGGAAGGGAATGTAAACGG
GCACGCGTTTGTAATCGAGGGTGAGGGAGAGGGGAAACCATACGATGGAAC
GAATACAATCAATCTTGAGGTCAAGGAGGGCGCACCTCTCCCGTTTTCGTAC
GATATTCTGACGACTGCATTCNCCTACGGTAACCGGGCCTTTACCAAGTATCC
AGACGATATCCCCAACTATTTCAAGCAGAGCTTCCCGGAGGGGTATTCGTGG
GAACGAACGATGACCTTTGAGGACAAGGGCATTGTGAAAGTAAAGTCCGAC
ATTTCGATGGAGGAGGATTCCTTCATCTACGAAATCCACCTCAAAGGAGAGA
ACTTTCCCCCGAACGGTCCGGTGATGCAGAAAAAGACGACAGGATGGGACG
CGTCAACCGAGAGGATGTATGTGCGGGATGGAGTATTGAAGGGGGACGTGA
AGCATAAACTTCTGTTGGAGGGTGGAGGGCACCATCGCGTGGATTTCAAAAC
GATCTACCGCGCCAAGAAAGCAGTCAAGCTCCCTGACTATCATTTCGTGGAC
CACAGAATTGAAATCTTGAATCACGATAAAGATTACAATAAGGTGACTGTAT
ATGAAAGCGCGGTCGCTAGGAACTCAACAGATGGAATGGATGAACTTTACAA
GTTAATTAAGGGTCCAGGCGGTGCCGGTCCCGGAGGTGCTGGCCCAGGAGGC
GCAGGGCCCGGAGGCGCTGGCCCCGGTGGTGCTGGTCCTGGGGGAGCAGGC
CCTGGTGGCGCCGGTCCCGGCGGTGCTGGCGCGCCAGTGAGCAAGGGCGAG
GAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAA
ACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACG
GCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTG
GCCCACCCTCGTGACCACCTTCGGCTACGGCCTGCAGTGCTTCGCCCGCTACC
CCGACCACATGAAGCAGCACGACTTCTTCAAGTCCGCCATGCCCGAAGGCTA
CGTCCAGGAGCGCACCATCTTCTTCAAGGACGACGGCAACTACAAGACCCGC
GCCGAGGTGAAGTTCGAGGGCGACACCCTGGTGAACCGCATCGAGCTGAAG
GGCATCGACTTCAAGGAGGACGGCAACATCCTGGGGCACAAGCTGGAGTAC
AACTACAACAGCCACAACGTCTATATCATGGCCGACAAGCAGAAGAACGGC
ATCAAGGTGAACTTCAAGATCCGCCACAACATCGAGGACGGCAGCGTGCAGC
TCGCCGACCACTACCAGCAGAACACCCCCATCGGCGACGGCCCCGTGCTGCT
GCCCGACAACCACTACCTGAGCTACCAGTCCAAACTGAGCAAAGACCCCAAC
GAGAAGCGCGATCACATGGTCCTGCTGGAGTTCGTGACCGCCGCCGGGATCA
CTCTCGGCATGGACGAGCTGTACAAGACTAGTaccggtaaggcccactacagtaaagcacagG
CCAGAGAGATGGAGACAATTTCTTAA
EpTR
ATGAAGGTTTTAGTTGCCTTGTTTGTTGTGGCATTGGTTGATGTAACTTCACA
ATgtacttgtaaaacaatgaagtgggcaaactgtgatgactcgtgctcatgcagtcttacattaactgaatcttccaagcaaaccc
ttgactgttctaagtTGGTTCCCAAGTGCTTCCTCATGAAAGCAGAGATGTATCGTGCC
CGTCACAACTTGGGCACAAGAAAAACTGGGAAGCCAGATGAGAATGCCTTTG
TGGACAATGATGGCATCTATGACCCAGAATGTCAGAGTGATGGGAAATTCAA
GGCAGTCCAGTGTAACAACACTGAAGTATGCTGGTGCGTCAACAGTGCTGGT
GTACGAAGAAGTGACAAAAAAGACAAGAACATAAAGTGCGAGCCTGCGGAG
104
ACCTAttgggttcgtgcagaaatgacgcacaaaagcgtggatgtgcccattgatgtcgctaatctgaggatGGGGATT
GAGAACGCTCTGCAGCAACGTTACTTTTTGGATAAGAACTTTGTCTCTGAAGT
TCAGtatgacaaggatgccaggctcattgtggtggatgtcaaaaaagataagaacgaccgtactacagatctgtccctgatga
cttattacctcgagaaagatATCAAAGTTAAGCCCCTGTTTTCCGATGAAAAACCATTTGT
GCTTAGTGTTCAGGGAAAAAATGTTACAATGGAGAATGTCCTGATCTACTAT
GTAGATGACAAAGCACCCACCTTCACCATGcagaagCTAACTGGTGGTATCATT
GCTGTCATTGTTGTAGTCAGCTTGATTGTGATTGGAGGATTTCTGGTTCTGttcttt
cttgcacggcgacagccatggTCCGGAGTCTCGAAAGGCGAAGAAACAACTATGGGGGT
CATCAAGCCCGACATGAAGATCAAGCTGAAAATGGAAGGGAATGTAAACGG
GCACGCGTTTGTAATCGAGGGTGAGGGAGAGGGGAAACCATACGATGGAAC
GAATACAATCAATCTTGAGGTCAAGGAGGGCGCACCTCTCCCGTTTTCGTAC
GATATTCTGACGACTGCATTCNCCTACGGTAACCGGGCCTTTACCAAGTATCC
AGACGATATCCCCAACTATTTCAAGCAGAGCTTCCCGGAGGGGTATTCGTGG
GAACGAACGATGACCTTTGAGGACAAGGGCATTGTGAAAGTAAAGTCCGAC
ATTTCGATGGAGGAGGATTCCTTCATCTACGAAATCCACCTCAAAGGAGAGA
ACTTTCCCCCGAACGGTCCGGTGATGCAGAAAAAGACGACAGGATGGGACG
CGTCAACCGAGAGGATGTATGTGCGGGATGGAGTATTGAAGGGGGACGTGA
AGCATAAACTTCTGTTGGAGGGTGGAGGGCACCATCGCGTGGATTTCAAAAC
GATCTACCGCGCCAAGAAAGCAGTCAAGCTCCCTGACTATCATTTCGTGGAC
CACAGAATTGAAATCTTGAATCACGATAAAGATTACAATAAGGTGACTGTAT
ATGAAAGCGCGGTCGCTAGGAACTCAACAGATGGAATGGATGAACTTTACAA
GTTAATTAAGGGTCCAGGCGGTGCCGGTCCCGGAGGTGCTGGCCCAGGAGGC
GCAGGGCCCGGAGGCGCTGGCCCCGGTGGTGCTGGTCCTGGGGGAGCAGGC
CCTGGTGGCGCCGGTCCCGGCGGTGCTGGCGCGCCAGTGAGCAAGGGCGAG
GAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAA
ACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACG
GCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTG
GCCCACCCTCGTGACCACCTTCGGCTACGGCCTGCAGTGCTTCGCCCGCTACC
CCGACCACATGAAGCAGCACGACTTCTTCAAGTCCGCCATGCCCGAAGGCTA
CGTCCAGGAGCGCACCATCTTCTTCAAGGACGACGGCAACTACAAGACCCGC
GCCGAGGTGAAGTTCGAGGGCGACACCCTGGTGAACCGCATCGAGCTGAAG
GGCATCGACTTCAAGGAGGACGGCAACATCCTGGGGCACAAGCTGGAGTAC
AACTACAACAGCCACAACGTCTATATCATGGCCGACAAGCAGAAGAACGGC
ATCAAGGTGAACTTCAAGATCCGCCACAACATCGAGGACGGCAGCGTGCAGC
TCGCCGACCACTACCAGCAGAACACCCCCATCGGCGACGGCCCCGTGCTGCT
GCCCGACAACCACTACCTGAGCTACCAGTCCAAACTGAGCAAAGACCCCAAC
GAGAAGCGCGATCACATGGTCCTGCTGGAGTTCGTGACCGCCGCCGGGATCA
CTCTCGGCATGGACGAGCTGTACAAGACTAGTaccggtaagTAA
Ep5aa
ATGAAGGTTTTAGTTGCCTTGTTTGTTGTGGCATTGGTTGATGTAACTTCACA
ATgtacttgtaaaacaatgaagtgggcaaactgtgatgactcgtgctcatgcagtcttacattaactgaatcttccaagcaaaccc
ttgactgttctaagtTGGTTCCCAAGTGCTTCCTCATGAAAGCAGAGATGTATCGTGCC
105
CGTCACAACTTGGGCACAAGAAAAACTGGGAAGCCAGATGAGAATGCCTTTG
TGGACAATGATGGCATCTATGACCCAGAATGTCAGAGTGATGGGAAATTCAA
GGCAGTCCAGTGTAACAACACTGAAGTATGCTGGTGCGTCAACAGTGCTGGT
GTACGAAGAAGTGACAAAAAAGACAAGAACATAAAGTGCGAGCCTGCGGAG
ACCTAttgggttcgtgcagaaatgacgcacaaaagcgtggatgtgcccattgatgtcgctaatctgaggatGGGGATT
GAGAACGCTCTGCAGCAACGTTACTTTTTGGATAAGAACTTTGTCTCTGAAGT
TCAGtatgacaaggatgccaggctcattgtggtggatgtcaaaaaagataagaacgaccgtactacagatctgtccctgatga
cttattacctcgagaaagatATCAAAGTTAAGCCCCTGTTTTCCGATGAAAAACCATTTGT
GCTTAGTGTTCAGGGAAAAAATGTTACAATGGAGAATGTCCTGATCTACTAT
GTAGATGACAAAGCACCCACCTTCACCATGcagaagCTAACTGGTGGTATCATT
GCTGTCATTGTTGTAGTCAGCTTGATTGTGATTGGAGGATTTCTGGTTCTGttcttt
cttgcacggcgacagccatggTCCGGAGTCTCGAAAGGCGAAGAAACAACTATGGGGGT
CATCAAGCCCGACATGAAGATCAAGCTGAAAATGGAAGGGAATGTAAACGG
GCACGCGTTTGTAATCGAGGGTGAGGGAGAGGGGAAACCATACGATGGAAC
GAATACAATCAATCTTGAGGTCAAGGAGGGCGCACCTCTCCCGTTTTCGTAC
GATATTCTGACGACTGCATTCNCCTACGGTAACCGGGCCTTTACCAAGTATCC
AGACGATATCCCCAACTATTTCAAGCAGAGCTTCCCGGAGGGGTATTCGTGG
GAACGAACGATGACCTTTGAGGACAAGGGCATTGTGAAAGTAAAGTCCGAC
ATTTCGATGGAGGAGGATTCCTTCATCTACGAAATCCACCTCAAAGGAGAGA
ACTTTCCCCCGAACGGTCCGGTGATGCAGAAAAAGACGACAGGATGGGACG
CGTCAACCGAGAGGATGTATGTGCGGGATGGAGTATTGAAGGGGGACGTGA
AGCATAAACTTCTGTTGGAGGGTGGAGGGCACCATCGCGTGGATTTCAAAAC
GATCTACCGCGCCAAGAAAGCAGTCAAGCTCCCTGACTATCATTTCGTGGAC
CACAGAATTGAAATCTTGAATCACGATAAAGATTACAATAAGGTGACTGTAT
ATGAAAGCGCGGTCGCTAGGAACTCAACAGATGGAATGGATGAACTTTACAA
GTTAATTAAATCCGGACTCAGATCTGGCGCGCCAGTGAGCAAGGGCGAGGAG
CTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACG
GCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCA
AGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCC
CACCCTCGTGACCACCTTCGGCTACGGCCTGCAGTGCTTCGCCCGCTACCCCG
ACCACATGAAGCAGCACGACTTCTTCAAGTCCGCCATGCCCGAAGGCTACGT
CCAGGAGCGCACCATCTTCTTCAAGGACGACGGCAACTACAAGACCCGCGCC
GAGGTGAAGTTCGAGGGCGACACCCTGGTGAACCGCATCGAGCTGAAGGGC
ATCGACTTCAAGGAGGACGGCAACATCCTGGGGCACAAGCTGGAGTACAACT
ACAACAGCCACAACGTCTATATCATGGCCGACAAGCAGAAGAACGGCATCA
AGGTGAACTTCAAGATCCGCCACAACATCGAGGACGGCAGCGTGCAGCTCGC
CGACCACTACCAGCAGAACACCCCCATCGGCGACGGCCCCGTGCTGCTGCCC
GACAACCACTACCTGAGCTACCAGTCCAAACTGAGCAAAGACCCCAACGAG
AAGCGCGATCACATGGTCCTGCTGGAGTTCGTGACCGCCGCCGGGATCACTC
TCGGCATGGACGAGCTGTACAAGACTAGTaccggtaaggcccactacagtaaagcacagGCCA
GAGAGATGGAGACAATTTCTTAA
EpTRAF
106
ATGAAGGTTTTAGTTGCCTTGTTTGTTGTGGCATTGGTTGATGTAACTTCACA
ATgtacttgtaaaacaatgaagtgggcaaactgtgatgactcgtgctcatgcagtcttacattaactgaatcttccaagcaaaccc
ttgactgttctaagtTGGTTCCCAAGTGCTTCCTCATGAAAGCAGAGATGTATCGTGCC
CGTCACAACTTGGGCACAAGAAAAACTGGGAAGCCAGATGAGAATGCCTTTG
TGGACAATGATGGCATCTATGACCCAGAATGTCAGAGTGATGGGAAATTCAA
GGCAGTCCAGTGTAACAACACTGAAGTATGCTGGTGCGTCAACAGTGCTGGT
GTACGAAGAAGTGACAAAAAAGACAAGAACATAAAGTGCGAGCCTGCGGAG
ACCTAttgggttcgtgcagaaatgacgcacaaaagcgtggatgtgcccattgatgtcgctaatctgaggatGGGGATT
GAGAACGCTCTGCAGCAACGTTACTTTTTGGATAAGAACTTTGTCTCTGAAGT
TCAGtatgacaaggatgccaggctcattgtggtggatgtcaaaaaagataagaacgaccgtactacagatctgtccctgatga
cttattacctcgagaaagatATCAAAGTTAAGCCCCTGTTTTCCGATGAAAAACCATTTGT
GCTTAGTGTTCAGGGAAAAAATGTTACAATGGAGAATGTCCTGATCTACTAT
GTAGATGACAAAGCACCCACCTTCACCATGcagaagCTAACTGGTGGTATCATT
GCTGTCATTGTTGTAGTCAGCTTGATTGTGATTGGAGGATTTCTGGTTCTGttcttt
cttgcacggcgacagccatggTCCGGAGTCTCGAAAGGCGAAGAAACAACTATGGGGGT
CATCAAGCCCGACATGAAGATCAAGCTGAAAATGGAAGGGAATGTAAACGG
GCACGCGTTTGTAATCGAGGGTGAGGGAGAGGGGAAACCATACGATGGAAC
GAATACAATCAATCTTGAGGTCAAGGAGGGCGCACCTCTCCCGTTTTCGTAC
GATATTCTGACGACTGCATTCNCCTACGGTAACCGGGCCTTTACCAAGTATCC
AGACGATATCCCCAACTATTTCAAGCAGAGCTTCCCGGAGGGGTATTCGTGG
GAACGAACGATGACCTTTGAGGACAAGGGCATTGTGAAAGTAAAGTCCGAC
ATTTCGATGGAGGAGGATTCCTTCATCTACGAAATCCACCTCAAAGGAGAGA
ACTTTCCCCCGAACGGTCCGGTGATGCAGAAAAAGACGACAGGATGGGACG
CGTCAACCGAGAGGATGTATGTGCGGGATGGAGTATTGAAGGGGGACGTGA
AGCATAAACTTCTGTTGGAGGGTGGAGGGCACCATCGCGTGGATTTCAAAAC
GATCTACCGCGCCAAGAAAGCAGTCAAGCTCCCTGACTATCATTTCGTGGAC
CACAGAATTGAAATCTTGAATCACGATAAAGATTACAATAAGGTGACTGTAT
ATGAAAGCGCGGTCGCTAGGAACTCAACAGATGGAATGGATGAACTTTACAA
GTTAATTAAGGGAGAGAGCCTGGAGAAGAAGACGGCCACTTTTGAGAACATT
GTCTGCGTCCTGAACAGGGAGGTGGAGAGGGTGGCCATGACTGCCGAGGCCT
GCAGCCGGCAGCACCGGCTGGACCAAGACAAGATTGAAGCCCTGAGTAGCA
AGGTGCAGCAGCTGGAGAGGAGCATTGGCCTCAAGGAGTCTGCGTCCTGAAC
CCTGGCGATGGCTGACTTGGAGCAGAAGGTCTTGGAGATGGAGGCATCCACC
TACGATGGGGTCTTCATCTGGAAGATCTCAGACTTCGCCAGGAAGCGCCAGG
AAGCTGTGGCTGGCCGCATACCCGCCATCTTCTCCCCAGCCTTCTACACCAGC
AGGTACGGCTACAAGATGTGTCTGCGTATCTACCTGAACGGCGACGGCACCG
GGCGAGGAACACACCTGTCCCTCTTCTTTGTGGTGATGAAGGGCCCGAATGA
CGCCCTGCTGCGGTGGCCCTTCAACCAGAAGGTGACCTTAATGCTGCTCGAC
CAGAATAACCGGGAGCACGTGATTGACGCCTTCAGGCCCGACGTGACTTCAT
CCTCTTTTCAGAGGCCAGTCAACGACATGAACATCGCAAGCGGCTGCCCCCT
CTTCTGCCCCGTCTCCAAGATGGAGGCAAAGAATTCCTACGTGCGGGACGAT
GCCATCTTCATCAAGGCCATTGTGGACCTGACAGGGCTCTCCGGACTCAGAT
CTTGGCGCGCCAGTGAGCAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCA
107
TCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGG
CGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGC
ACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCTTCGGCT
ACGGCCTGCAGTGCTTCGCCCGCTACCCCGACCACATGAAGCAGCACGACTT
CTTCAAGTCCGCCATGCCCGAAGGCTACGTCCAGGAGCGCACCATCTTCTTCA
AGGACGACGGCAACTACAAGACCCGCGCCGAGGTGAAGTTCGAGGGCGACA
CCCTGGTGAACCGCATCGAGCTGAAGGGCATCGACTTCAAGGAGGACGGCA
ACATCCTGGGGCACAAGCTGGAGTACAACTACAACAGCCACAACGTCTATAT
CATGGCCGACAAGCAGAAGAACGGCATCAAGGTGAACTTCAAGATCCGCCA
CAACATCGAGGACGGCAGCGTGCAGCTCGCCGACCACTACCAGCAGAACAC
CCCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAGCTAC
CAGTCCAAACTGAGCAAAGACCCCAACGAGAAGCGCGATCACATGGTCCTGC
TGGAGTTCGTGACCGCCGCCGGGATCACTCTCGGCATGGACGAGCTGTACAA
GACTAGTaccggtaaggcccactacagtaaagcacagGCCAGAGAGATGGAGACAATTTCTTA
A
108
Appendix B. Additional Constructs
Construct
Insertion Site
EpCAM
See thesis
data
Vinculin
Between BP
884 and 885
Ezrin
Between BP
472 and 473
PrickleLike
Between BP
643 and 644
N-Cadhein
Between BP
791 and 792
Itga9
Between BP
14 and 15
Immuno-Staining
membrane localizes
(MDCK and HELA
cells)
membrane localizes
(MDCK, not
HELA)
membrane localizes
(MDCK, not
HELA)
expressionlocalization not
known (unknown)
no expression
observed (cells
unknown)
no expression
observed (cells
unknown)
Zebrafish
Notes
expressed
see thesis data
expressed
need high power mag
to observe
localization in
embryo
not tried
observed in embryo
but did not localize
not tried
possible best next
construct to pursue
not tried
n/a
not tried
n/a
Table 1. Table showing the status of other TSMod constructs currently in the lab.
All proteins are from the zebrafish. Note, all of these experiments (aside from EpCAM)
are in early stages. Results should be repeated.
109
Appendix C. Matlab Scripts
1) Bring exported SPCImage asc files and tif mask into Matlab, apply mask, apply
thresholds, apply rolling filter, save as .mat structure
%function [ output_args ] = Untitled2( input_args )
% bring images into Matlab, apply mask, thresholds and rolling filter
FileName = 'all_b1_t20_c1_2_chi0pt9_noneighbor_photon100to200'; %name
of file where exported SPCImage files live
%set chi square threshold
threshold = 1.07;
DataPath=['C:\Users\MCP-Andrea\Desktop\matlab\' FileName '\t1\'];
%location of files, change as appropriate
files=dir([DataPath, '\*asc']); %pulls asc files in data path
FileSize = size(files); %Determines number of asc files
FileNameRegExp =
'([^_]+)_([^_]+)_([^_]+)_([^_]+)_([^_]+)_([^_]+)_([^_]+)_([^_]+).asc';
for FileNumber = 1:FileSize;
CurrentFile = files(FileNumber).name
Result_Old = dlmread(['C:\Users\MCP-Andrea\Desktop\matlab\'
FileName '\t1\' CurrentFile]);
Names = regexp(CurrentFile, FileNameRegExp, 'tokens');
a = Names{1}{1};
b = Names{1}{2};
c = Names{1}{3};
d = Names{1}{4};
e = Names{1}{5};
f = Names{1}{6};
g = Names{1}{7};
NameMatch = [a '_' b '_' c '_' d '_' e '_' f '_' g '_'];
%Read in files
%mask = imread(['C:\Users\MCP-Andrea\Desktop\matlab\' FileName
'\evl_mask\' (NameMatch) 'photons.tif']);
mask = imread(['C:\Users\MCP-Andrea\Desktop\matlab\' FileName
'\evl_mask\' (NameMatch) 'photons.tif']);
mask = imcomplement(double(mask));
mask = im2bw(mask);
Photons_Old = dlmread(['C:\Users\MCP-Andrea\Desktop\matlab\'
FileName '\photons\' (NameMatch) 'photons.asc']);
Chi_Old = dlmread(['C:\Users\MCP-Andrea\Desktop\matlab\' FileName
'\chi\' (NameMatch) 'chi.asc']);
%Apply mask
Result_New = Result_Old .* mask;
Photons_New = Photons_Old .* mask;
Chi_New = Chi_Old .* mask;
[x, y] = size(Chi_New);
110
%Aply thresholds as desired
for i = 1:x
for j = 1:y
if Chi_New(i,j) > threshold || Chi_New(i,j) <
0.1||Photons_New(i,j) > 200 || Photons_New(i,j) < 100
Chi_New(i,j) = 0;
Photons_New(i,j) = 0;
Result_New(i,j) = 0;
end
%Apply rolling filter
if i == 1
Result_New(i, j) =
end
if j == 1
Result_New(i, j) =
end
if i == 512
Result_New(i, j) = 0;
end
if j == 512
Result_New(i, j) =
end
if Result_New(i, j) >
n = 1;
end
if Result_New(i, j) >
Result_New(i+1, j)
end
if Result_New(i, j) >
Result_New(i+1, j)
end
if Result_New(i, j) >
Result_New(i+1, j)
end
if Result_New(i, j) >
Result_New(i+1, j)
end
if Result_New(i, j) >
Result_New(i+1, j)
end
if Result_New(i, j) >
Result_New(i+1, j)
end
if Result_New(i, j) >
Result_New(i, j+1) > 0
Result_New(i+1, j)
end
if Result_New(i, j) >
Result_New(i+1, j)
end
0;
0;
0;
0
0 && Result_New(i+1, j) > 0;
= 0;
0 && Result_New(i-1, j) > 0
= 0;
0 && Result_New(i+1, j+1) > 0
= 0;
0 && Result_New(i+1, j-1) > 0
= 0;
0 && Result_New(i-1, j+1) > 0
= 0;
0 && Result_New(i-1, j-1) > 0
= 0;
0 && Result_New(i, j) > 0 &&
= 0;
0 && Result_New(i, j-1) > 0
= 0;
end
end
111
%Build structre PostCalc with results (images)
PostCalc.result = Result_New;
PostCalc.original = Result_Old;
PostCalc.photons = Photons_New;
PostCalc.photonsorig = Photons_Old;
PostCalc.chiorig = Chi_Old;
PostCalc.chi = Chi_New;
PostCalc.mask = mask;
%Build structre PostCalc with results (calculations)
PostCalc.value_mean = mean(nonzeros(PostCalc.result));
PostCalc.result_std = std(nonzeros(PostCalc.result));
PostCalc.result_nnz = nnz(PostCalc.result);
PostCalc.result_median = median(nonzeros(PostCalc.result));
PostCalc.result_weight = 1/(PostCalc.result_std.^2);
PostCalc.result_wmean = PostCalc.result_weight .*
PostCalc.value_mean;
PostCalc.chi_mean = mean(nonzeros(PostCalc.chi));
PostCalc.chi_std = std(nonzeros(PostCalc.chi));
PostCalc.chi_median = median(nonzeros(PostCalc.chi));
if PostCalc.result_nnz < 2;
continue
else
%save result
save(['C:\Users\MCP-Andrea\Documents\MATLAB\FRET_Calculations\'
FileName '\' NameMatch], 'PostCalc')
end
end
2) Create Regular Expression for Selecting Image Files (allows one to choose
parameters to compare from command line)
function [ FileNameRegExp ] = FLIM_condition_v001(construct, treatment,
location, fish, experiment)
%FLIM_CONDITION_V001 Creates a regular expression for selecting image
files.
%
% FLIM_condition_v001(construct, treatment, location, fish, experiment)
%
% Used to select a common set of files by experimental parameters.
% The filename pattern used by all experiments is
% construct_treatment_location_fish_experiment. No escaping of input
% parameters is done.
% Parameters:
% construct - mRNA injected
% treatment - chemical perturbation
% location - Position 1, 2, 3, or 4
% fish - Fish number
% experiment - date of experiment
112
% Parameters may be set to 0 to indicate that a parameter should act as
a wildcard.
% Return value - a string containing the desired regular expression.
FileNameRegExp = '';
if (construct)
FileNameRegExp = [FileNameRegExp '(' construct ')_'];
else
FileNameRegExp = [FileNameRegExp '([^_]+)_'];
end
if (treatment)
FileNameRegExp = [FileNameRegExp '(' treatment ')_'];
else
FileNameRegExp = [FileNameRegExp '([^_]+)_'];
end
if (location)
FileNameRegExp = [FileNameRegExp '(' location ')_'];
else
FileNameRegExp = [FileNameRegExp '([^_]+)_'];
end
if (fish)
FileNameRegExp = [FileNameRegExp '(' fish ')_'];
else
FileNameRegExp = [FileNameRegExp '([^_]+)_'];
end
if (experiment)
FileNameRegExp = [FileNameRegExp '(' experiment ')_'];
else
FileNameRegExp = [FileNameRegExp '([^_]+)_'];
end
FileNameRegExp = [FileNameRegExp '([^_]+)_([^_]+)']
3) Create regular expression to make groups of image files
function [ Set ] = FLIM_Set_v001(Num, varargin)
%FLIM_SET_V001 Creates regular expressions to find one or more groups
of image files.
%
% FLIM_Set_v001(Num, varargin)
%
% Creates one or more regular expressions for selecting image files by
% experimental parameters.
% Parameters:
% Num - The number of groups to select
% varargin - Experimental parameters in groups of 5. Each group
consists of
% construct, treatment, location, fish, and experiment.
%
113
% Return value - a cell array containing strings containing the desired
regular expressions.
L = length(varargin);
if ( L ~= (Num * 5))
errstr = num2str(L);
myerr = MException(strcat('Wrong number of arguments: ', errstr, '
should be a multiple of 5, with values for construct, treatment,
location, fish, and experiment'));
throw(myerr);
end
Set = cell(Num,1);
I = 1;
for CompNum = 1 : Num;
construct = varargin{I};
treatment = varargin{I + 1};
location = varargin{I + 2}
fish = varargin{I + 3}
experiment = varargin{I + 4};
Cond = FLIM_condition_v001(construct, treatment, location, fish,
experiment);
Set(CompNum,1) = cellstr(Cond);
I = I + 5;
end
end
4) Find One or More Groups of Image Files (Finds files from groups created in 3 looking
at structures saved in 1)
function [ FileNames, NameDetails ] = FLIM_FindFiles_v001(Num, Folder,
varargin)
%FLIM_FINDFILES_V001 Finds one or more groups of image files.
%
% FLIM_FindFiles_v001(Num, Folder, varargin)
%
% Used to select one or more groups of files by experimental
parameters.
% Num - The number of groups to select
% Folder - The subfolder under
% C:\Users\MCP-Andrea\Documents\MATLAB\FRET_Calculations to search.
% varargin - Experimental parameters in groups of 5. Each group
consists of
% construct, treatment, location, fish, and experiment.
%
% Return value - Two cell arrays, the first containing the filenames in
% each set. The second containing the experimental parameters for each
set.
114
DataPath = ['C:\Users\MCP-Andrea\Documents\MATLAB\FRET_Calculations\'
Folder '\']
Files = dir([DataPath, '*mat'])
FileLength = length(Files)
Set = FLIM_Set_v001(Num, varargin{:});
FileNames = cell(Num, 1);
NameDetails = cell(Num, 1);
for SetNum = 1:Num
RegExp = Set{SetNum};
F = cell(FileLength, 1);
P = cell(FileLength, 7);
for FileNum = 1:FileLength
ImageName = Files(FileNum).name;
if ~isempty(regexp(ImageName, RegExp))
F{FileNum} = cellstr(ImageName);
Names = regexp(ImageName, RegExp, 'tokens');
a = Names{1}{1};
b = Names{1}{2};
c = Names{1}{3};
d = Names{1}{4};
e = Names{1}{5};
f = Names{1}{6};
g = Names{1}{7};
P{FileNum} = [cellstr(a), cellstr(b), cellstr(c),
cellstr(d), cellstr(e), cellstr(f), cellstr(g)];
end
end
FileNames{SetNum} = F(~cellfun('isempty',F));
NameDetails{SetNum} = P(~cellfun('isempty',P));
end
end
5) Perform calculations for “n” = pixels including FRET efficiency conversion
function [ output_args ] = FLIM_calc_im_avg_allpix_v001( Num, Folder,
varargin )
%FLIM_CALC_IM_AVG_ALLPIX_V001 FRET, list all pixels for data set and
perform calculations (mean, median, t test, etc for "n" = pixel) and
convert to FRET efficiency from decay time if desired
%
% FLIM_calc_im_avg_allpix_v001(Num, Folder, varargin)
%
% Num - The number of groups to select
% Folder - The subfolder under
% C:\Users\MCP-Andrea\Documents\MATLAB\FRET_Calculations to search.
% varargin - Experimental parameters in groups of 5. Each group
consists of
% construct, treatment, location, fish, and experiment.
%
% Return value - Two cell arrays, the first containing the filenames in
% each set. The second containing the experimental parameters for each
set.
115
DataPath = ['C:\Users\MCP-Andrea\Documents\MATLAB\FRET_Calculations\'
Folder '\'];
Files = FLIM_FindFiles_v001(Num, Folder, varargin{:});
NumCond = length(Files);
Mean_all = zeros(NumCond,1);
Median_all = zeros(NumCond,1);
StDev_all = zeros(NumCond,1);
BigMean = zeros(3);
BigCount = 0;
BigCount2 = 0;
for Cond = 1:NumCond
FoundFiles = Files{Cond};
NumFiles = length(FoundFiles);
Calc.Sets{Cond}.Means = zeros(NumFiles, 2);
Calc.Sets{Cond}.StDevs = zeros(NumFiles, 2);
Calc.Sets{Cond}.nnz = zeros(NumFiles, 1);
Sum = zeros(NumFiles, 1);
PixList = zeros(NumFiles, 262144);
for FileNum = 1: NumFiles;
BigCount = BigCount + 1;
FileName = strcat(DataPath, FoundFiles{FileNum});
load(FileName{1});
pix = (PostCalc.result(:));
PixList(FileNum, :) = pix;
BigMean(BigCount, :) = [PostCalc.value_mean, Cond,
PostCalc.result_std];
end
PixListAll = PixList(:);
NNZPix = nonzeros(PixListAll);
%Apply FRET efficiency conversion (decay time to FRET efficiency)
if desired
for ZCheck = 1:length(NNZPix);
NNZPix(ZCheck) = -0.0007 .* NNZPix(ZCheck) + 1.8786;
end
if Cond == 1
x = NNZPix;
end
if Cond == 2
y = NNZPix;
end
%Find mean/median/etc. for all pixels from all images, remove comma
to print
n = nnz(NNZPix)
Mean_all(Cond) = mean(NNZPix);
Median_all(Cond) = median(NNZPix);
StDev_all(Cond) = std(NNZPix);
SEM_all(Cond) = (std(NNZPix))/(sqrt(n));
%hist(NNZPix, 100);
end
116
%
% run t test on groups if desired
[h,p] = ttest2(x,y)
%Make plot of pixel means if desired
errorbar(fig_mean, Mean_all,StDev_all, 'ro', 'MarkerSize', 10)
%format figure
%set(fig_mean, 'XTickLabel', {'','Full Pos1','Trunc Pos 1','Full
Pos 2','Trunc Pos 2','Full Pos 3','Trunc Pos 3', 'Full Pos 4', 'Trunc
Pos 4', ''})
%
set(fig_mean, 'XTickLabel', {'','Full AP','Trunc AP','Full
Marg','Trunc Marg',''})
%
%rotateXLabels(fig_mean, 45);
%
ylim(fig_mean, [1800 2500]);
%
xlabel('Location','FontSize',12)
%
ylabel('Decay Time (ns)','FontSize',12)
%
%set(fig_mean, 'XTick', [1 2 3 4 5 6], 'FontSize',10)
%
% hold on
%
%plot(fig_mean, BigMean(:,2), BigMean(:,1), '+', 'MarkerSize',
5);
%axis(fig_mean, [0 10 1900 2400]);
%set(fig_mean, 'XTickLabel', {'1','AP 2','AP 3', 'Marg 1','Marg
2','Marg 3'})
end
117