* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download measuring force in the developing zebrafish embryo using
Survey
Document related concepts
Tissue engineering wikipedia , lookup
Cell growth wikipedia , lookup
Cellular differentiation wikipedia , lookup
Signal transduction wikipedia , lookup
Cytoplasmic streaming wikipedia , lookup
Cell encapsulation wikipedia , lookup
Cell culture wikipedia , lookup
Extracellular matrix wikipedia , lookup
Cell membrane wikipedia , lookup
Endomembrane system wikipedia , lookup
Organ-on-a-chip wikipedia , lookup
Transcript
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