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Fluorescence Correlation Spectroscopy to Determine Diffusion Laws: Application to Live Cell Membranes

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Fluorescence Correlation Spectroscopy to Determine Diffusion Laws: Application to Live Cell Membranes Fluorescence Correlation Spectroscopy to determine diffusion laws: application to live cell membranes Laure Wawrezinieck1,2, Pierre-Francois Lenne1,*, Didier Marguet2, Herve Rigneault1 1 Institut Fresnel (CNRS - univ. Aix-Marseille III) Domaine Universitaire de Saint Jerome - 13397 Marseille cedex 20 - France 2 Centre Immunologie Marseille Luminy (CNRS - INSERM - univ. Aix-Marseille II) Parc scientifique de Luminy - 13009 Marseille cedex 9 - France ABSTRACT Fluorescence correlation spectroscopy (FCS) is a mature and powerful technique for measuring diffusion coefficients. In a standard experiment, it measures the spontaneous fluorescence fluctuations arising from a single observation volume defined by confocal optics. However, the study becomes uneasy as soon as the diffusion is impeded by obstacles or specific mechanisms, as it is the case for the cell membrane components in live cells. In this paper, we show that doing FCS measurements at different sizes of observation volumes gives access to the diffusion laws without a priori knowledge of the landscape in which molecules are diffusing. Using this strategy, a measurement of diffusion laws of lipids in monophasic Giant Unilamellar Vesicles and in the plasma membrane of live cells is carried out. Keywords: diffusion, anomalous diffusion, diffusion law, fluorescence correlation spectroscopy, giant unilamellar vesicle, live cell, cell membrane. 1. INTRODUCTION Fluorescence correlation spectroscopy (FCS) was introduced by Elson and Magde1 in 1974 as a performing tool for determining chemical kinetic constants and diffusion coefficients by measuring spontaneous fluorescence fluctuations at thermodynamic equilibrium. FCS is now commonly used to measure low concentrations and diffusion coefficients of fluorescent molecules both in vivo and in vitro. In this case, the fluorescence fluctuations around the equilibrium value are due to random variations of the number of molecules in the observation volume: since this volume is open, molecules can enter or leave it by diffusion. Assuming that the studied molecules move freely in this known volume, a single FCS measurement enables to deduce the diffusion coefficient from the measured diffusion time. When free diffusion can no longer be assumed, as in live cell membranes2-4, the standard FCS treatment becomes uneasy, and a lot less information can be obtained. Without further care on the treatment of FCS data, wrong diffusion coefficients can even be calculated5. Here, we propose to carry out several FCS experiments for different sizes of the observation volume, in order to obtain the diffusion law, which has then to be explained by a diffusion model. With this method, the assumed diffusion model will not intervene in the treatment of experimental data as in a standard FCS treatment, but will only be used for the final interpretation of the results. In this paper, we give details about the method used to change the observation volume sizes. We then apply this method on lipids in Giant Unilamellar Vesicles (GUVs) and on the components of live cell membranes. * Email: [email protected] 2. STANDARD FCS TREATMENT: MEASUREMENT AT A SINGLE WAIST 2.1. FCS experimental realization Fluorescence correlation spectroscopy1 is a versatile technique for in vivo and in vitro investigation of biochemical interactions. It is based on the statistical analysis of spontaneous fluorescence fluctuations in an open volume, which is defined by a focused laser and confocal optics. This method is sensitive to fluorescence fluctuations at the single molecule level: when there are very few molecules in the observation volume, each molecule contributes significantly to the measured signal. As a consequence, FCS experiments will aim at reducing concentrations and observation volumes and at the same time increasing the fluorescence photon yield per molecule, in order to have high signal-to-noise ratios. A usual FCS setup is described in fig. 1. Fluorophores such as Rhodamine 6G (Rh6G), Green Fluorescent Protein (GFP) or Bodipy are excited by the 488 nm line of an Ar+ ion laser. The excitation beam is focused by a high numerical aperture (N.A. = 1.2) microscope objective to a diffraction limited spot. Only the fluorophores within the illuminated region are excited. The fluorescence light is collected through the same microscope objective. Because of the Stokes shift, the emitted fluorescence occurs at a longer wavelength than the excitation: it can be separated from the excitation light by the dichroic mirror before it is directed into the detectors. The chosen detectors are silicon avalanche photodiodes, which have high photon-counting detection efficiencies, and low dark count rates: they have single photon sensitivity. In order to limit the detection volume, a confocal setup is used: a pinhole (20 or 50 µm) is placed in the image plane of the microscope objective, which blocks all light not coming from the focal region. To get a better signal-to-noise ratio, the excitation light has to be blocked. The dichroic mirror deflects the excitation light and transmits the red-shifted emission light. Emission bandpass filters are introduced in front of the detectors to improve the suppression of the scattered laser light. The fluorescence intensity signal is processed by a multiple-tau hardware correlator. This correlator has multiple sampling and delay times6: it measures the autocorrelation function (ACF) with high precision for delays ranging from less than a microsecond to a few minutes. Ar+ ion laser (488 nm) Confocal volume beam correlator expander pinhole emission filter dichroic sample microscope mirror objective APD Fig. 1. FCS setup 2.2. Autocorrelation function calculation and free Brownian motion Fluorescence fluctuations are quantified by calculating the temporal autocorrelation function g(2)(τ) of the recorded intensity signal: ()2 I ()t I (t +τ ) g ()τ = , (1) I ()t 2 where stands for a temporal average. The shape of the autocorrelation function can be predicted from the nature of the underlying molecular process. For a single diffusing species, in the absence of chemical kinetics, concentration fluctuations in the open observation volume are caused by the diffusion of molecules through this volume. In the confocal microscope configuration exposed in 2.1., the observation volume is defined by the convolution of the excitation volume due to the tightly focused laser and the detection volume determined by confocal optics. This observation volume is assumed to be a prolate ellipsoidal Gaussian volume, with an axial waist wz and a lateral waist wxy. In the cases where the diffusion of molecules occurs in 3D or 2D, the autocorrelation functions have respectively the following analytical forms1: ()2 1 1 1 g3D ()τ = 1+ , (2a) N τ 2 1+ w τ τ 1+ z D wxy τ D 1 1 g ()2 ()τ = 1+ , (2b) 2D N τ 1+ τ D w2 where τ = xy , (2c) D 4D and D is the diffusion coefficient. 1.5 experimental ACF 3D free diffusion fit 1.4 1.3 1.2 1.1 autocorrelation function autocorrelation 1.0 0.001 0.01 0.1 1 10 100 1000 time (ms) Fig. 2. Autocorrelation function corresponding to the 3D free diffusion of Rhodamine 6G fluorophores When the results of FCS measurements are compared to the theoretical free Brownian diffusion ACF (fig. 2), three types of information can be obtained. First, the shape of the curve enables to detect the mode of motion of the molecules. In particular, non-Brownian motion, such as directed, confined or anomalous diffusion cannot be fitted by (2a) or (2b). Second, the mean number of molecules in the observation volume can be calculated from the initial correlation amplitude, g(2)(0) = 1+1/<N>. The number of fluorescent molecules is kept small in the observation volume, so that a correlation with significant amplitude is detected: in a volume on the order of the femtoliter (10-15 l), the fluorophore concentration is chosen in the nanomolar range. Finally, the diffusion time τD in the observation volume is the Full Width at Half Maximum (FWHM) of the curve. When this volume is well defined (i.e. wz and wxy are both known), (2c) permits to calculate the diffusion coefficient. 3. STANDARD FCS TREATMENT APPLIED TO LIVE CELL MEMBRANES It has been seen that FCS can be easily used when studying free diffusing molecules. Nevertheless, the ideal case of free Brownian motion does not always apply. For example, the diffusion of molecules in cell membranes or cell organelles is often hindered or even confined by local cellular structures or specific mechanisms (fig. 3): macroscopic diffusion coefficients of proteins and lipids in cell membranes are 5 to 100 times smaller than those in artificial bilayers. free diffusion free diffusion? confined diffusion Fig. 3. Hindered diffusion by domains; the determination of diffusion coefficients depends critically on the confinement. Despite an increasing interest in the architecture of the plasma membrane, a comprehensive view is still lacking. The fluid mosaic model of the membrane was proposed by Singer and Nicolson in 19727, in which the membrane contains no heterogeneities. For the past decades, there has been growing evidence for a lateral organization, in which cholesterol- and sphingolipid-rich domains coexist with more fluid domains enriched in phospholipids with unsaturated hydrocarbon chains8, 9. The data leading to this postulate of membrane microdomains come mainly from biochemical studies: unlike the rest of the membrane, lipid microdomains are resistant to cold detergents such as Triton- X100 and can be extracted10. However, it has been recently shown that Triton-X100 itself may promote the formation of domains in artificial membranes11. A more reliable and dynamic study of the structure of the membrane is necessary: FCS can help drawing a picture of the landscape in which molecules are diffusing. 3.1. Insertion of fluorescent lipids and fluorescent proteins in the plasma membrane of live cells All experiments are carried out on COS-7 cells. We study the dynamics of diffusion of a couple of proteins and lipids, which are thought to partition differently in lipid microdomains.
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