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Measuring Protein Diffusion in Living Cells by Raster Image Correlation Spectroscopy (RICS)

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Measuring Protein Diffusion in Living Cells by Raster Image Correlation Spectroscopy (RICS) Measuring Protein Diffusion in Living Cells by Raster Image Correlation Spectroscopy (RICS) A Thesis Submitted for the Award of Master of Science Raz Shimoni CENTRE OF MICRO-PHOTONICS The Faculty of Engineering and Industrial Sciences (FEIS) Swinburne University of Technology, Melbourne, Australia PETER MACCALLUM CANCER CENTRE St Andrews Place, East Melbourne, Australia Supervised By: Prof. Sarah Russell, Dr. Ze’ev Bomzon, Prof. Min Gu January 2010 Dedicated to my partner Olga iii ”Anyone who has never made a mistake has never tried anything new.” -Albert Einstein (1879-1955) iv Abstract T ime-lapse fluorescence imaging has revolutionized studies of biology in the last 15 years. In addition to the now routine tracking of bulk fluorescence, for instance of a protein moving into the nucleus in response to an extracellular signal, technologies are now emerging that enable much more sophisticated analysis of the motion and interactions of proteins within living cells. The potential of these approaches to elucidate biological processes is clear, but they have not yet been developed and validated for broad use by biologists. This thesis describes the adaptation of a recently introduced method, Raster Image Correlation Spectroscopy (RICS). RICS is a novel approach to assess the dynamic properties of fluorescent macromolecules in solutions and within living cells by confocal laser scanning microscopy. Based on RICS theory, we developed novel software with which to analyse confocal images and to measure diffusion coefficients of the fluorophores. This new software has several advantages compared with published RICS software, and its ability to give accurate diffusion coefficient values was characterized under a range of settings. Once a RICS routine was established, it was applied to measure the diffusion coefficient of PAK-interacting exchange factor (βPIX) within living fibroblast cells as a paradigm for RICS analysis. The interaction between βPIX and the adaptor protein, Scribble, plays a critical role in cell polarity and actin polymerization. These preliminary measurements indicate the potential of RICS in elucidating the dynamics of proteins within living cells, and demonstrate how the use of RICS will open new opportunities in the cell biology research. Acknowledgments The last two years have been an amazing experience for me. I have been introduced to novel technologies in the BioPhotonics field, interacted with leading biologists and physicists, met new friends from different nationalities and travelled extensively around beautiful Australia. It was a great honour for me to be a part of the Centre of Micro-Photonics (CMP) at Swinburne University of Technology and I am grateful for this opportunity. I would like to thank my research supervisors- Professor Sarah Russell, the group leader of Immune Signalling at the Peter MacCallum Cancer Centre and the head of the Cell Biology group in the CMP and Dr. Zeev Bomzon for this opportunity and for their kind support along the way. Of course, without financial support all this could not be possible. For the generous financial support that allowed me to conduct my research I would like to thank Professor Min Gu- the Director of the CMP and the Faculty of Engineering and Industrial Sciences (FEIS) at Swinburne University of Technology. v vi I would like to acknowledge the contribution of my supervisor Dr. Zeev Bomzon to the RICSIM. Dr. Bomzon built the initial stage of the RICSIM GUI, including the image-processing filters procedures. I would like to thank Mandy Ludford-Menting the senior research assistant from Sarah Russell‘s lab at Peter MacCallum Cancer Centre for teaching me to generate and to validate the cell lines that were used for this thesis, and I thank Kim Pham, a PhD student from the CMP for providing the EYFP- βPIX∆CT construct and the EYFP-βPIX cells. I would also like to thank Dr. Andrew Clayton and Dr. Noga Kozer from Ludwig Institute for Cancer Research for supplying BaF3 cell lines including supportive materials, GFP samples, and for our fruitful discussions. I thank the Nanostructured Interfaces and Materials Group at the department of Chemical and Biomolecular Engineering, the University of Melbourne, for contributing the PVPON. For the microscopy training and for taking care that the microscope equipment is in the best condition - I would like to thank Sarah Ellis, the core manager of the microscopy unit at the Peter MacCallum Cancer Centre. For his professional help with the flow cytometry and our interesting conversa- tions, I would like to thank Ralph Rossi from the Peter MacCallum Cancer Centre. I would like to extend my thanks to all the CMP members and my colleagues from Russell’s group for providing a supportive intellectually environment with a friendly atmosphere. Finally, I would like to thank my family and close friends in Israel and Australia who supported and encouraged me throughout this research. vii Declaration I declare that: n This thesis contains no material of any other degree or diploma, except where due reference is made in the text of the thesis. n To the best of my knowledge, this thesis contains no material previously published or written by another person except where due reference is made in the text of the thesis. n Contributions of respective workers are mentioned in this thesis. Raz Shimoni Abbreviations 1-D One-dimensional 2-D Two-dimensional 3-D Three-dimensional A/D Analog-to-Digital ACF Autocorrelation Function AF488 Alexa R Fluor dye 488 nm AOBS Acoustic Optical Beam Splitter AOTF Acoustic Optical Tuneable Filters APD Avalanche Photodiode Detector ATP Adenosine Triphosphate βPIX Beta PAK- Interacting Exchange Factor βPIX∆CT βPIX mutant that lack (-TNL) BSA Bovine Serum Albumin c Speed of light (≈3×108 m·s−1) C Concentration CHO Chinese Hamster Ovary CLSM Confocal Laser Scanning Microscopy viii ix D Diffusion coefficient (µm2/s) DLS Dynamic Light Scattering DMEM Dulbecco’s Modified Essential Medium DMSO Dimethyl Sulphoxide Dstop βPIX∆CT ECL Enhanced Chemiluminescence EDTA Ethylenediamietetraacetate EGF Epidermal Growth Factor EGFP Enhance-Green-Fluorescence Protein EGFR EGF-Receptor EYFP Enhance-Yellow-Fluorescence Protein f Fourier Transform f −1 Inverse Fourier Transform FACS Fluorescence Activated Cell Sorting FCS Fluorescence Correlation Spectroscopy FFS Fluorescence Fluctuation Spectroscopy FFT Fast Fourier Transform fl femtoliter (10−15 liter) FRAP Fluorescence Recovery After Photobleaching FRET Fluorescence Resonance Energy Transfer G(0,0) amplitude of 2-D ACF before normalization g(0,0) amplitude of 2-D normalized ACF g(ξ,0) horizontal vector of the normalized ACF g(0, ) vertical vector of the normalized ACF GDP Guanosine diphosphate GEF Guanine nucleotide Exchange Factors GFP Green Fluorescence Protein GTP Guanosine triphosphate GUI Graphical User Interface x h Planck constant (≈6.62×10−34 J·s) HRP Horse Radish Peroxidase I(X,Y) Intensity of pixel at coordinates (X,Y) in 2-D matrix I(t) Intensity value at time in a vector ICM Image Correlation Microscopy ICS Image Correlation Spectroscopy ICCS Image Cross Correlation Spectroscopy IF ImmunoFluorescence ii index image from series jj index counter −23 −1 KB Boltzmann constant (≈1.38×10 J·K ) kDa Kilo Dalton µg micro-gram (10−6 gram) µl micro-liter (10−6 liter) µM microMolar µs micro-second (10−6 second) M Molarity MEF Mouse Embryonic Fibroblasts mg milli-gram (10−3 gram) ml milli-liter (10−3 liter) mM milli-Molar (10−3 Molar) mQ H2O milliQ water ms milli-second (10−3 second) MSD Mean Square Displacement N Number of particles n length of discrete intervals refractive index NA Numerical Aperture 23 −1 Na Avogadro constant (≈6.02×10 mol ) nM nano-Molar (10−9 Molar) xi ns nano-second (10−9 second) PAK p21-activated serine threonine kinase PAO Phenylarsine oxide PBS Phosphate Buffer Saline PCH Photon Counting Histogram pH Power of Hydrogen PMT Photomultiplier Tube PSD Power Spectrum Density PSF Point Spread Function PVPON Poly(N-vinyl pyrrolidone) Q Quantum yield r radius rcf relative centrifugal force RICS Raster Image Correlation Spectroscopy ROI Region of Interest RPMI Roswell Park Memorial Institute medium RT Room Temperature s seconds S/N, SNR Signal to Noise ratio SPT Single-Particle Tracking STICS Spatial-Temporal Image Correlation Spectroscopy T absolute Temperature (K) t time, index image from series Tiff Tagged Image File V Volt, Volume Veff effective volume WT Wild Type X height of an image in pixels X(t) trajectories of individual particle Y width of an image in pixels xii Symbols δ fluctuation " excitation efficiency γ correction shape factor η signal-to-noise ratio constant ι instrumental counting efficiency λ wavelength µ micro (10−9) ν viscosity π mathematical constant (π≈3.14159) θ angular aperture ρ density of material τ lag time (characteristic delay time) τD diffusion time τ l line time τ p pixel time ξ spatial displacement along X-axis spatial displacement along Y-axis !xy XY-waist of the PSF (µm) !z Z-waist of the PSF (µm) Contents Abstract iv Acknowledgmentsv List of Abbreviations viii Contents xvii List of Figures xx 1 Introduction- The Biological Context1 1.1 βPIX and Scribble in Cell Polarity . .2 1.2 βPIX-Scribble Interaction in RAC1/Cdc42 Mediated Actin Polymerization . .4 1.3 The Research Questions and an Outline of the Chosen Methodology7 2 Theoretical Background 10 2.1 Introduction . 10 2.1.1 The diffusion coefficient in cell biology . 13 Fick’s 1st law for diffusion . 13 Brownian motion and Einstein-Smoluchowski equation . 15 The Stokes-Einstein relation . 18 2.1.2 The principles of fluorescence . 20 xiii CONTENTS xiv 2.1.3 Fluorescence proteins technology and traditional respective fluorescence based techniques . 21 Time-lapse fluorescence microscopy . 23 Computational image analysis of fluorescence microscopy images . 23 Single-particle tracking . 24 Fluorescence Recovery After Photobleaching (FRAP) . 24 Forster Resonance Energy Transfer (FRET) . 26 Summary . 27 2.2 Principles of Fluorescence Correlation Spectroscopy (FCS) .
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