^ CHAPTER FIFTEEN Scanning FCS for the Characterization of Protein Dynamics in Live Cells Zdeněk Petrásek,*'1 Jonas Ries,*'1 and Petra Schwille* Contents 1. Introduction 318 2. Implementation 320 2.1. Scan paths 322 2.2. Calibration of the scan path 326 3. Data Analysis 327 3.1. Calculation of correlation curves 327 3.2. Corrections 330 3.3. Data fitting 332 4. Applications 334 4.1. sFCS in Caenorhabditis elegans embryo 334 4.2. Small-circle sFCS 335 4.3. sFCS with a perpendicular scan path to measure in unstable membranes 337 4.4. Dual-focus sFCS 339 4.5. Dual-color sFCS 340 5. Conclusion 341 References 342 Abstract Scanning fluorescence correlation spectroscopy (sFCS) is the generic term for a group of fluorescence correlation techniques where the measurement volume is moved across the sample in a defined way. The introduction of scanning is motivated by its ability to alleviate or remove several distinct problems often encountered in standard FCS, and thus, to extend the range of applicability of fluorescence correlation methods in biological systems. These problems include poor statistical accuracy in measurements with slowly moving molecules, photobleaching, optical distortions affecting the calibration of the measurement * Biotec, TU Dresden, Dresden, Germany 1 Both authors contributed equally Methods in Enzymology, Volume 472 © 2010 Elsevier Inc. ISSN 0076-6879, DOI: 10.1016/S0076-6879(10)72005-X All rights reserved. 317 318 Zdeněk Petrásek et al. volume, membrane instabilities, etc. Here, we present an overview of sFCS methods, explaining their benefits, implementation details, requirements, and limitations, as well as relations to each other. Further, we give examples of different sFCS implementations as applied to cellular systems, namely large-circle sFCS to measure protein dynamics in embryo cortex and line sFCS to measure protein diffusion and interactions in unstable membranes. Fluorescence correlation spectroscopy (FCS) is a powerful tool to measure local concentrations, molecular weights, translational and rotational diffusion coefficients, chemical rate constants, association and dissociation constants, and photodynamics in vitro as well as in vivo (Bacia and Schwille, 2003; Bacia et ah, 2006; Kim et ah, 2007; Rigler andElson, 2001). It is based on the statistical analysis of intensity fluctuations caused by fluorophores diffusing through a small (~fL) detection volume (for details see Fig. 15.1) and requires only a low concentration («0.1—100 niW) of labeled molecules, minimizing the interference of the labeling with the system. The introduction of commercial confocal FCS systems has promoted the use of FCS so that it can now be considered a well-established technique. However, standard FCS suffers from several limitations of applicability, especially in complex biological systems. Optical artifacts (Enderlein et ah, 2005) such as A B C Figure 15.1 Principle of FCS. (A) The sample is illuminated by focusing the laser beam through an objective. The emitted photons are then spectrally filtered and detected with an avalanche photodiode. A pinhole provides axial confinement to result in a tiny (sub-femtoliter) detection volume. (B) Fluorophores diffusing through the detection volume give rise to a fluctuating intensity trace F(t) from which the autocorrelation curve g(t), which measures the self-similarity of the signal, can be calculated. (C) Parameters of interest, for example, the diffusion time xD or number of particles in the detection volume N, are obtained by fitting a mathematical model to the experimental correlation curve. 1. Introduction Scanning FCS for the Characterization of Protein Dynamics in Live Cells 319 varying cover slide thickness, refractive index mismatch, optical saturation, or aberrations change the size of the detection volume, which precludes the precise calibration necessary for quantitative concentration or diffusion measurements. Slow diffusion in biological samples demands long measurement times—at least 105 times the slowest timescale found in the system (Tcherniak et ah, 2009)—and the long residence times in the detection volume promote photobleaching. Finally, measurements on biological membranes are especially challenging, since even tiny membrane movements or instabilities lead to severe artifacts. To overcome these limitations, modifications of standard FCS have been developed (Dertinger et ah, 2007; Digman et ah, 2005; Ruan et ah, 2004), one of the most successful being sFCS (Petrásek and Schwille, 2008c). sFCS employs a moving detection volume instead of a static detection volume, which has several advantages (Box 1): sampling of a larger volume increases Box 1 When to use sFCS Decide whether to use standard FCS or sFCS. In the following situations sFCS may be superior to standard FCS: • Slow motion. The molecules diffuse (or move) very slowly, with diffusion times in the range of tens (hundreds) of milliseconds or longer. There are not enough fluctuation events during the permissible measurement time and the calculated autocorrelation is too noisy, especially at long lag times. By scanning the sample, the measurement is performed at more locations, thus improving the statistics. • Optical distortions. The measurement is performed within an optically inhomogeneous medium, such as cells or tissues, where the focused laser spot can be deformed, changing its size and shape. This invalidates the calibration of the volume size by an independent measurement, necessary in standard FCS to determine diffusion coefficients and particle numbers. Choose one of the types of sFCS where the volume calibration is replaced by an exact knowledge of the scan path. Small-circle sFCS is useful when the measurement has to be limited to a small part of the sample, while large-circle or line sFCS simultaneously improves the signal-to-noise ratio in case of slowly diffusing molecules by sampling a larger area. • Photobleaching. Due to slow motion, the molecules stay too long in the laser focus and are more likely to become photobleached, resulting in distortions of the correlation curve. Choose a type of sFCS with a larger scan path, whereby more molecules are probed for a shorter time, thus reducing the risk of photobleaching and at the same time improving the statistics. (continued) 320 Zdeněk Petrásek et al. Box 1 (continued) • Membrane motion. When measuring motion on surfaces, for example, in biomembranes or on cell cortex, the surface itself may be moving in an uncontrolled way. Scanning perpendicularly to the surface makes it possible to eliminate these motions in the data analysis. • Complex motion. The molecular motion is more complex, possibly a combination of diffusion and flow with static features, or multicom-ponent or anomalous diffusion. Choose a scan path in combination with spatiotemporal correlation analysis that can reveal complex motion patterns. • Parallel measurement. Need to measure simultaneously at more locations? It may be possible to choose a scan path that passes through all these locations, and the subsequent data analysis produces separate results for each position. the statistical accuracy for slowly moving molecules and leads to shorter measurement times; short residence times in the detection volume reduce the effect of photobleaching; and the scan speed can be determined with high accuracy, eliminating the need for calibrating the detection volume. The application of sFCS is especially beneficial in measurements on biological membranes where diffusion is usually very slow. Here, an alternative implementation of a moving detection volume has proven very useful: choosing a scan path that is perpendicular to the membrane plane eliminates the effect of membrane movements and instabilities (Ries and Schwille, 2006). In the following, we describe experimental steps to perform sFCS measurements (see also Box 2) and discuss applications and limitations. We start with a general description of the common features of sFCS. Then we will discuss specific implementations (circular sFCS and line sFCS with a perpendicular scan path) in detail and present their applications. ^ 2. Implementation sFCS is typically performed on a confocal laser scanning microscope (CLSM) where the sample can be imaged before the scan path is chosen, and which makes it possible to scan the measurement volume during the actual scanning fluorescence correlation spectroscopy (sFCS) measurement across the sample in a user-defined way. The CLSM determines what scan paths can be used. While linear paths of any length and angle are typically possible Scanning FCS for the Characterization of Protein Dynamics in Live Cells 321 Box 2 How to proceed 1. Choose the scan path based on the motivation for using sFCS, and the scan parameters depending on the speed of molecular motion, sample size, etc. 2. Perform the measurement. Ideally, the full raw data (photon arrival times) are saved, and the correlation is calculated by the analysis software. 3. Calculate the correlation. The algorithm in general depends on the scan path, and some preprocessing may be necessary, for example, due to a moving membrane when measuring diffusion within unsupported membranes. 4. Apply corrections for photobleaching and other irregularities, if necessary. 5. Fit the data with a model dependent on the scan path, and obtain the desired parameters. with available commercial instruments, circular paths can usually be realized only with homebuilt setups. Apart from the possibility to scan the measurement volume, the technical requirements for sFCS are identical with those of standard FCS. The main points making a CLSM capable of FCS measurements are briefly summarized in the following paragraphs, while details are extensively described in the literature (Petrov and Schwille, 2008). The critical parameter for a successful FCS measurement is the size and shape of the measurement volume. This is determined by the focusing of the excitation laser beam, and by the detection geometry of the emission signal. The water immersion objective used should have high numerical aperture to produce a small focus and collect a maximum signal, and a correction collar that allows adjustment for different coverslip thicknesses. Exact positioning of the correction collar yields an undistorted focus, a crucial requirement for any FCS measurement. On the detection side, the confocal pinhole should be adjustable, in both its size and its lateral (ideally also axial) position. The pinhole is not necessary if two-photon excitation is employed. Another important element is the detector. The standard is an avalanche photodiode, a photon counting detector with high quantum efficiency. Other important parameters of the detector are low dark count (few hundreds of counts per second or less) and weak afterpulsing (ideally of low amplitude and short decay, microseconds or less). The same detector can be used for photon counting imaging, which is becoming increasingly common nowadays in lowlight, single molecule, and quantitative imaging applications (Becker et at, 2004). 322 Zdeněk Petrásek et al. The signal from the detector consisting of pulses for each detected photon is processed by the computer. It is desirable to store the whole raw data, that is, the arrival times of all photons, with sufficiently high temporal resolution. This gives the user maximal freedom in the subsequent data analysis. Hardware correlators are practical for alignment and monitoring purposes because they provide the autocorrelation in real time, but should be used in sFCS only together with other means for obtaining the raw fluorescence signal. Commercial laser scanning microscope systems that provide FCS capability include Confocor 3 with LSM-510 or LSM-710 microscopes (Zeiss, Jena, Germany), TCS SMD FCS (Leica, Wetzlar, Germany), DCS-120 Confocal FLIM system (Becker & Hickl, Berlin, Germany), and Micro Time 200 (Picoquant, Berlin, Germany). The latter two companies also provide all hardware necessary for an implementation of FCS and sFCS in an existing, either commercial or homebuilt, confocal scanning microscope. LSM-710 (Zeiss) is available with an implemented raster image correlation spectroscopy (RICS, see below) option, a type of sFCS. 2.1. Scan paths The choice of the scan path depends on several factors, mainly on the type of molecular motion motivating the use of sFCS, the sample size and shape, and the options provided by the instrument. All the scan paths considered here lie within the focal plane (do not move to different z positions) and are realized by scanning the laser beam, not the sample stage, which would not be possible to realize at sufficient speed. The scan paths can be divided into two groups: linear and circular. This division is to some extent due to practical reasons. Line scans can be easily performed with commercial CLSMs. Although the commercial scanning microscopes equipped with galvanometer scanners are also capable of circular scans, this option is usually not software-implemented. The user is limited to homebuilt instruments where the scan path can be controlled with full flexibility. The circular path with large radius is more or less equivalent to a linear path, and what is said about one is mostly valid for the other. The only difference is in the calculation of the correlation. With the linear path, the termination of the line and possible changes of direction in case of bidirectional scanning have to be taken into account. With the circular path, all points along the circle are equivalent; therefore, no presorting of the data stream is necessary, simplifying the calculation of the autocorrelation. Additionally, circular scans can be faster than linear scans and feature more constant scanning velocities since they require no rapid acceleration. The most relevant differences between the line and circle scans can be found in the double-line scan, which would be difficult to realize in a circular configuration, and in a small-circle scan, which does not have a line equivalent. Scanning FCS for the Characterization of Protein Dynamics in Live Cells 323 In the following, we briefly describe each scan path and its main area of application (see also Table 15.1). 2.1.1. Single-line and large-circle scans Large-range scans are typically applied in the following situations: when the molecules move slowly and averaging over a larger population is desired, when spatiotemporal correlation of the molecular motion is of interest, or when we wish to perform correlation analysis at many locations along a line simultaneously. By large range we mean scan path lengths many times larger than the linear dimension of the measurement volume. When the molecules move slowly, the FCS measurement at one position suffers from poor statistics, because insufficient numbers of molecules pass through the measurement volume during the measurement time (Tcherniak et al., 2009). This results in low accuracy of the fluorescence correlation especially at long correlation times, affecting the values of recovered parameters (diffusion time and particle number). By scanning the focused excitation beam, the measurement is effectively performed at many locations, their number approximately given by the ratio between the length of the scan path and the size of the volume, thus improving the signal-to-noise ratio. The fluorescence autocorrelation in standard FCS describes the temporal dynamics of molecules leaving the measurement volume. Spatiotemporal correlation curves additionally contain information about the spatial spreading of the molecules from their original location. This additional information is needed to characterize more complex motion than simple diffusion, such as Table 15.1 Scan paths and their properties Scan type Main benefit/application References No scanning Fast diffusion, simple Rigler and Elson (2001) implementation, inhomogeneous sample Single-line, Slow motion, photobleaching, Petrásek et al. (2008b) and large-circle robustness, spatiotemporal Ries et al. (2009a) correlation Small-circle Robustness, precision, Skinner et al. (2005) and small area Petrásek and Schwüle (2008b) Double-line Robustness, precision, Ries and Schwüle (2006) membrane motion Raster Slow motion, Digman et al. (2005) spatiotemporal correlation Perpendicular Membrane motion Ries and Schwüle (2006) to membrane 324 Zdeněk Petrásek et al. multicomponent diffusion, combination with flow, reaction kinetics, or anomalous diffusion. This has been exploited in spatio temp oral image correlation spectroscopy (STICS), a type of correlation analysis applied to a sequence of images (Hebert et al, 2005). sFCS also gives us access to spatio-temporal correlation, as described below. Although sFCS features only one spatial dimension (along the scan path), it has higher temporal resolution than STICS, enabling studies of faster processes. Additionally, the knowledge of spatiotemporal correlation makes it possible to determine from one experiment the diffusion coefficient D and the size of the measurement volume w0. In standard FCS, these two parameters are combined in the diffusion time = w^/ AD, and to calculate D, the volume size has to be determined in an independent measurement with a molecule of a known diffusion coefficient. The effect of possible optical distortions, which can invalidate this calibration in standard FCS, is avoided in sFCS, because the volume size is determined independently from the fit. The spatial calibration of the scan path is determined by the instrument only, can be performed relatively easily, and is generally not affected by the optical properties of the sample. Another useful feature of long scan paths is the reduction of the probability that any observed molecule will be photobleached. Due to the motion of the focused laser beam, the total light dose is distributed over a larger area, and the residence time of individual molecules within the focus is reduced. This lowers the chance of photobleaching compared to situations where the slow-moving molecule diffuses through a stationary focus (Petrásek and Schwille, 2008a; Ries et al, 2009a; Satsoura et al, 2007). When we are interested in correlation analysis at more positions but cannot perform the measurements sequentially, for example, because the sample changes in time, sFCS can be used to perform the measurements in a semi-parallel way. The scan path is chosen so that the beam passes through all the points of interest, the raw data (fluorescence signal) are then sorted depending on the position, and the data from each position are correlated, producing autocorrelation curves at each location. The sorting of the raw data requires that the relation between the fluorescence signal and the position at which it was recorded is known. The size and shape of the scan path is largely governed by the sample. With the exception of the application described in the last paragraph, the sample should be homogeneous along the scan path, since the obtained results represent an average over the measured area. Generally, larger scan paths are preferable due to better averaging. 2.1.2. Small-circle scan Circular paths with a radius comparable to the size of the measurement volume are suitable for robust and accurate measurements of diffusion coefficients in situations when the size of the probed volume is not Scanning FCS for the Characterization of Protein Dynamics in Live Cells 325 known or can be affected by the sample (Petrásek and Schwille, 2008b). The principle is the same as with other correlation methods where spatiotemporal correlation is measured: by introducing another spatial measure (scan radius (Petrásek and Schwille, 2008b), distance covered per unit time (Ries et at, 2009a), distance between two foci (Dertinger et at., 2007)), the diffusion coefficient and the volume size in the model function become decoupled and can be determined independently from the fit, resulting in robustness to optical distortions. Small-circle scan is optimal for this purpose: since the radius is small, the correlation values at all lag times are nonzero, and therefore carry useful information, unlike the large-circle scan, where the correlation values outside the narrow peaks are zero (Fig. 15.2). Another important feature is that the scan with a small radius covers a minimal area, meaning that the measurements can be performed also on samples without large homogeneous areas, as is typical in cells.