Analysis of Protein and Lipid Dynamics Using Confocal Fluorescence Recovery After Photobleaching

Abstract

Fluorescence recovery after photobleaching (FRAP) is a powerful, versatile, and widely accessible tool to monitor molecular dynamics in living cells that can be performed using modern confocal microscopes. Although the basic principles of FRAP are simple, quantitative FRAP analysis requires careful experimental design, data collection, and analysis. In this article, we discuss the theoretical basis for confocal FRAP, followed by step‐by‐step protocols for FRAP data acquisition using a laser‐scanning confocal microscope for (1) measuring the diffusion of a membrane protein, (2) measuring the diffusion of a soluble protein, and (3) analyzing intracellular trafficking. Finally, data analysis procedures are discussed, and an equation for determining the diffusion coefficient of a molecular species undergoing pure diffusion is presented. © 2026 The Author(s). Current Protocols published by Wiley Periodicals LLC.

Basic Protocol 1: How to set up a FRAP experiment

Basic Protocol 2: Confocal FRAP measurements of the lateral diffusion of plasma membrane proteins and lipids

Alternate Protocol 1: Lateral diffusion measurements for a rapidly diffusing soluble protein

Alternate Protocol 2: FRAP analysis of intracellular trafficking kinetics

Basic Protocol 3: Working with FRAP data

Basic Protocol 4: Further analysis of FRAP data to obtain diffusion coefficients

INTRODUCTION

Fluorescence recovery after photobleaching (FRAP) is a fluorescence‐based biophysical tool often utilized to investigate the dynamics of molecules within live cells. Originally developed in the 1970s, FRAP underwent a renaissance with the development of GFP as a tool to visualize proteins in living cells (Kenworthy, 2023; Lippincott‐Schwartz et al., 2001, 2018). Since then, FRAP has proven to be an unusually versatile technique, allowing for the characterization of a wide variety of dynamic processes in cells (Kenworthy, 2023; Lippincott‐Schwartz et al., 2018). While FRAP is often utilized to quantify diffusion and binding kinetics of membrane‐bound or soluble proteins, it can also reveal the kinetics and mechanisms of transport of biomolecules between organelles and intracellular compartments (Lippincott‐Schwartz et al., 2001, 2018; Reits & Neefjes, 2001). In recent years, applications of FRAP have dramatically expanded to investigate processes as diverse as the timescales of protein turnover in muscle (Cadar et al., 2020; Loreau et al., 2023; Ojima et al., 2015; Rudolph et al., 2019; Wang et al., 2022), transcriptional kinetics (Choi et al., 2022; Darzacq et al., 2007; Lenstra et al., 2016; Rosenfeld et al., 2015), kinetics of receptor recycling (Michaluk et al., 2021; Michaluk & Rusakov, 2022), and evolution of morphogen gradients (Kicheva et al., 2012; Sigaut et al., 2014; Soh & Muller, 2018). It has also become an essential tool to probe the biophysical properties of condensates formed by liquid–liquid phase separation both in cells and in vitro (Alberti et al., 2025; Elbaum‐Garfinkle et al., 2015; Feric et al., 2016; Jain et al., 2016; Li et al., 2012; Mitrea et al., 2018; Soranno, 2019; Taylor et al., 2019). Importantly, most commercially available confocal microscopes come equipped to perform FRAP. This combination of high versatility and widespread accessibility have established FRAP as one of the most ubiquitous workhorses in the cell biologist's toolbox.

In a FRAP experiment, a group of fluorescently tagged molecules in a defined region of interest (ROI) is rapidly photobleached using a high‐intensity laser. The exchange of the bleached molecules with unbleached molecules from the surrounding region is then followed over time using low‐intensity excitation light to minimize further photobleaching. The time course of change in fluorescence intensity in the ROI after the photobleaching is called the FRAP curve (Fig. 1). FRAP curves yield information about both the recovery kinetics and the fraction of molecules free to diffuse. Since a wide variety of factors can impact these values, well‐planned FRAP experiments and careful analysis of the data can yield a wealth of information, including but not limited to the underlying mode of diffusion or transport, the organization inside the cell or organelle, binding‐reaction rates, and the size of the diffusing species (Kang & Kenworthy, 2008; McNally, 2008; Mueller et al., 2010; Reits & Neefjes, 2001).

Figure 1.

Confocal FRAP assay. COS‐7 cells were transiently transfected with the GPI‐anchored protein YFP‐GL‐GPI 24 hr prior to FRAP. FRAP was performed at 37°C. (A) Representative time series images of YFP‐GL‐GPI on the plasma membrane of a COS‐7 cell immediately following the photobleach (t = 0). Bar = 10 µm. (B) YFP‐GL‐GPI expressing COS‐7 cell at t = 0 s in the FRAP time series, showing ROIs denoting the bleaching ROI, whole cell, and background regions. (C) FRAP recovery in the bleach ROI, compared to whole‐cell fluorescence and background fluorescence. (D) Recovery curve of YFP‐GL‐GPI corrected for fluorescence decay and background fluorescence. Notation denotes the initial pre‐bleach fluorescence (F _i), the initial post‐bleach fluorescence at t = 0 (F ₀), and the fluorescence after recovery (F _∞). Additionally, the half maximal fluorescence recovery (F _½) denotes the τ _½ position in the recovery curve.

Originally, FRAP measurements were performed using a focused, static laser beam to bleach molecules (Axelrod et al., 2018). A theoretical basis for the analysis of FRAP measurements of lateral diffusion by spot‐photobleaching approaches for a static laser was established shortly after the development of conventional FRAP (Axelrod et al., 1976, 2018). The earliest applications of FRAP were predominantly confined to measurements of cell‐surface proteins or lipids, which could be fluorescently labeled by exogenous probes. The discovery of several fluorescent proteins similar to GFP and development of molecular techniques that allows their tagging or attachment to cellular proteins has expanded the milieu of proteins that can be studied by FRAP. Furthermore, the availability and accessibility of confocal microscopes tuned with ability to perform FRAP has made this technique more user friendly. Advances in mathematical analysis of FRAP data have further advanced the field by making it possible to quantitatively extract diffusion coefficients and kinetic constants from FRAP curves (Axelrod et al., 1976; Braga et al., 2007; Cai et al., 2022; Feder et al., 1996; Hofling & Franosch, 2013; Kang et al., 2009, 2010, 2011; Kicheva et al., 2012; Lubelski & Klafter, 2008; Saxton, 2001; Sprague & McNally, 2005; Sprague et al., 2004, 2006; Taylor et al., 2019; Wachsmuth, 2014; Williamson et al., 2021). Even today, the technique and its applications continue to evolve and to grow. For example, in addition to its widespread applications in cultured cells, FRAP can also be carried out in living organisms (Badrinarayanan et al., 2012; Badrinarayanan & Leake, 2022; Bajanca et al., 2015; Devkota et al., 2017, 2021; Devkota & Pilon, 2018; Ebrahim et al., 2019; Ebrahim & Weigert, 2019; Elowitz et al., 1999; Erami et al., 2016; Ficz et al., 2005; Fuger et al., 2007; Goehring et al., 2010; Greig & Bulgakova, 2021; Kumar et al., 2010; Loreau et al., 2023; Martin & Suzanne, 2022; Salmon et al., 1984; Scheuring et al., 2024; Singh & Lakhotia, 2023; Soh & Muller, 2018; Stricker et al., 2002; Svensk et al., 2016; Takeda et al., 1995; Warrington et al., 2022; Weaver et al., 2008; Weston et al., 2021).

While FRAP is a common tool for measuring dynamics in vivo, other methods for quantifying diffusion exist, including fluorescence photoactivation (Bancaud et al., 2010; Lippincott‐Schwartz et al., 2003; Lippincott‐Schwartz & Patterson, 2008; Patterson, 2008), single‐particle tracking (SPT) (Jaqaman et al., 2008; Kusumi et al., 2014; Manley et al., 2008; Prindle et al., 2023; Shen et al., 2017), fluorescence correlation spectroscopy (FCS) (Bacia & Schwille, 2003; Elson, 2004; Haustein & Schwille, 2007; Jovičić, 2024; Kolin & Wiseman, 2007; Petrasek et al., 2010; Sankaran & Wohland, 2023; Smith, 2024), and image correlation spectroscopy (ICS) (Digman et al., 2005; Gialdini et al., 2025; Kolin & Wiseman, 2007; Petersen et al., 1993; Rossow et al., 2010; Royer et al., 2023). While these are powerful techniques with their own strengths and weaknesses, FRAP has many advantages over these alternative methods. For example, the ratio of mobile to immobile particles within the total population, a variable not easily acquired with other techniques, is accessible using FRAP. Furthermore, FRAP can be performed on most commercially available confocal setups without any special modifications.

In this article, we describe the confocal FRAP methods developed and utilized by our groups to study the dynamics and trafficking of proteins and lipids in living cells (C. A. Day & Kang, 2023; C. A. Day & Kenworthy, 2012; Drake et al., 2010; Goodwin, Drake, Remmert, et al., 2005; Goodwin, Drake, Rogers, et al., 2005; Hinow et al., 2006; Kang & Kenworthy, 2008, 2009; Kang et al., 2009, 2010, 2015, 2019; Kang, 2022; Kenworthy et al., 2004; Kenworthy, 2006, 2007; Kraft & Kenworthy, 2012; Kraft et al., 2014; Kraft, Dowler, et al., 2016; Kraft, Manral, et al., 2016; Wolf et al., 2008). This includes presenting step‐by‐step instructions for setting up a FRAP experiment (Basic Protocol 1), carrying out FRAP experiments to characterize diffusion of membrane associated proteins (Basic Protocol 2), diffusion of soluble proteins (Alternate Protocol 1), and trafficking/transport between organelles (Alternate Protocol 2). In this article, we have also included advice on presenting and interpreting the data (Basic Protocol 3). Additionally, we go into detail regarding the extraction of diffusion coefficients and mobile fractions from FRAP experiments where pure diffusion is being examined (Basic Protocol 4). This protocol should serve as a useful tool for cell biology labs that seek to begin using confocal fluorescence recovery after photobleaching.

Basic Protocol 1. HOW TO SET UP A FRAP EXPERIMENT

This protocol will provide the basic procedures to set up a FRAP experiment and will precede any of the more specific protocols presented for conducting FRAP analysis of slowly diffusing membrane proteins (Basic Protocol 2), diffusion of soluble proteins (Alternate Protocol 1), and intracellular trafficking kinetics (Alternate Protocol 2). Beginning with the growth of cells containing fluorescently tagged proteins, this protocol steps through control experiments to ensure that sufficient production of expressed fluorescently conjugated proteins has been achieved for FRAP analysis and shows users how to establish rough parameters for microscope settings, which will later be refined in the more specifically focused protocols.

The protocol ends with procedures to establish the general microscope configurations that are necessary for the FRAP data acquisition. This setup will need to be repeated for every fluorophore and/or marker you wish to study with FRAP. However, once the proper photobleaching parameters have been worked out for each marker, they can be saved and reused, with little or no adjustment, on subsequent days. These methods are relatively unchanged between FRAP applications, and any special variations between different FRAP applications will be discussed in detail in the Basic and Alternate Protocols that follow. This can be done on live cells, although using fixed cells to define the bleaching parameters at this stage has the advantage that it allows for the optimization of bleaching conditions independent of the temporal resolution of the experiment. This protocol focuses on FRAP measurements of genetically encoded fluorescent fusion proteins as an example. However, FRAP can be carried out on any fluorescently labeled molecule of interest, including proteins labeled with other genetically encoded fluorescent tags, fluorescent lipid analogs, or proteins fluorescently labeled with organic dyes (Braga et al., 2007; Daemen et al., 2016; C. A. Day & Kenworthy, 2012; Dietrich et al., 2001; Kang et al., 2012; Kvist et al., 2018; Loreau et al., 2023; Loren et al., 2015; Morisaki & McNally, 2014; Pincet et al., 2016; Rhodes et al., 2017; Scalettar et al., 1989; Weisgerber & Knowles, 2021).

Materials

• cDNA of protein of interest (e.g., H‐Ras)

• Fluorescent protein vector (e.g., EGFP)

• Mammalian cultured cell lines (e.g., COS‐7, ATCC, cat. no. CRL‐1651)

• Cell culture medium:

  Dulbecco's modified Eagle's medium (DMEM) with phenol red (Gibco, cat. no. 11995‐065)

  10% fetal bovine serum (FBS) (Gibco, cat. no. A5670701)

• Transfection agent (e.g., Lipofectamine 2000, Invitrogen, cat. no. 11668019)

• Imaging medium, e.g.:

  Phenol red‐free DMEM (Gibco, cat. no. 21063‐029)

  10% FBS (Gibco, cat. no. A5670701)

  25 mM HEPES (Current Protocols, 2006)

• 35‐mm glass‐bottom dishes (e.g., MatTek, glass‐bottom microwell dishes)

• Humidified tissue culture incubator, 37°C, 5% CO₂

• Line‐scanning confocal microscope (e.g., Zeiss LSM 880 or comparable system), with the appropriate laser and filter configuration for the fluorophore to be used:

  Argon ion laser (488 nm laser line for bleaching and for imaging EGFP and Alexa‐488)

  HeNe lasers [only necessary if imaging red and far‐red fluorophore (i.e., Cy3, mCherry, Alexa‐555, Cy5, Alexa‐647, etc.)]

  Band‐pass emission filters (i.e., 505 to 550 nm for EGFP and Alexa‐488)

  Acquisition software (e.g., Zeiss LSM)

  Objectives (e.g., 40×/1.3 Oil Plan‐Neofluar lens)

• Stage and objective heater or imaging chamber

• Software for image analysis (e.g., Zeiss LSM software, FIJI)

• Software for data analysis (e.g., Excel, MATLAB)

• 1Prepare a plasmid encoding fluorescent fusion protein DNA from the cDNA of the protein of interest and a fluorescent protein vector (e.g., EGFP) using molecular biology techniques [see Riggs et al. (2001) and other relevant units in Ausubel et al. (2012)].

  Although for the sake of illustration we discuss the use of GFP as a label, other fluorescent tagging strategies can be used. In addition, genome editing strategies such as CRISPR can be used to endogenously tag the protein of interest as an alternative to transfection approaches.

• 2Grow COS‐7 cells in cell culture medium in 35‐mm glass‐bottom dishes.

  Unless restricted for biological reasons, it will make FRAP data collection much easier if an adherent cell line with a morphology well suited for FRAP is selected. For example, when doing FRAP on the plasma membrane, we prefer COS‐7 cells, as they produce large expanses of flat plasma membrane. Similarly, if you wish to monitor a cytoplasmic or nuclear protein, select a cell line with large cytoplasmic or nuclear space. COS‐7 cells are also well suited for these types of experiments. HeLa, HEK, and fibroblasts may also be useful depending on the goal of the experiment.

• 3Transfect cells with the plasmid expression vector produced in step 1 using Lipofectamine 2000 according to the manufacturer's instructions ∼24 hr after plating.

  The choice of tranfection reagent will depend on the type of cell line used for experiment, e.g., in our lab we use Fugene HD for transfecting HeLa cells.

• 4Incubate transfected cells for 24 hr at 37°C in a 5% CO₂ humidified tissue culture incubator to allow transient expression of fluorescent proteins in transfected cells.

  The cells will be ready for FRAP 48 hr after plating.

• 5Rinse the cells with imaging medium and move the cells (in imaging medium) to the line‐scanning confocal microscope.
• 6Perform control experiments to ensure correct production of expressed fluorescent conjugated protein and correct protein localization.

  An excellent place to start is to perform immunofluorescence staining in control cells expressing the untagged endogenous form of the protein. Confirm that the fluorescently tagged fusion protein shows a similar subcellular distribution as the endogenous protein. If possible, functional assays may also be performed to validate that tagging and/or overexpression do not disrupt the normal function of the protein.

  Quantitative FRAP analysis of lateral diffusion is most reliable when there is an optically uniform (or nearly uniform) distribution of the fluorescent particles. If this not the case, other methods may be better suited for characterizing diffusion. See Background Information, “Comparison of FRAP to other techniques sensitive to protein and lipid dynamics,” for more information.

• 7Turn on the confocal microscope according to the manufacturer's operating instructions.
• 8Find a representative cell through the eyepiece and place the cell in the center of the image window.
• 9
  Adjust settings as follows:

  • a.
    Optimize the imaging configuration such that the fluorescence intensity per pixel is high (but not saturating), by adjusting the laser transmission, pinhole, and detector gain.
    To minimize photofading (i.e., the loss of fluorescence due to repetitive imaging), we recommend setting the detector gain high and lowering the laser transmission as much as possible. The goal here is not to take a publication‐quality image but to fill the pixels at the detector so that a good fluorescence signal is obtained.
  • b.
    Collect time‐lapse images of a single cell and further adjust the microscope settings so that a bright image is maintained as much as possible over a sufficient time span required for FRAP (see Basic Protocol 2 and Alternate Protocols 1 and 2 below to select general time courses for your application).
    There is no need to be too meticulous at this point, as these settings will be fine‐tuned later.

• 10Set the image window with the appropriate zoom to the area of interest.

  Generally, when working with COS‐7 cells, a 512 × 512–pixel image at 4× electronic zoom from an image collected using a 40× lens is a good starting point, and the electronic zoom can be adjusted from there depending on the FRAP application.

• 11Using the software ROI selection tool, draw a circular bleach region in the center of the image window. Once a bleach region has been created, save the ROI to the computer for use on subsequent samples.

  Choosing the right‐size bleach ROI can take some time, as it needs to be determined on a case‐by‐case basis. If the bleach region is too small, fluorescence recovery may occur so quickly that it will become difficult to analyze the FRAP curve, or the FRAP curve may be very noisy. On the other hand, the ROI should not be so large that a significant fraction of the total fluorophores is lost due to the bleaching event, as this will artificially lower the mobile fraction. It may take several rounds of performing pilot FRAP experiments to achieve this balance. For plasma‐membrane proteins, cytoplasmic proteins, and nuclear proteins in COS‐7 cells, we suggest 4.1‐, 2.3‐, and 1.7‐µm diameter ROIs, respectively. Note that the geometry and size of the bleach ROI can be varied depending on the goal of particular experiments. For FRAP measurements of diffusion, circular ROIs are recommended to take advantage of analytical FRAP equations developed specifically for this geometry, as discussed in more detail below.

• 12Configure the software to perform the bleach event after multiple pre‐bleach images are scanned.

  These pre‐bleach images provide steady‐state fluorescence intensity before photobleaching, which will be required to normalize the FRAP data later. The number of pre‐bleach images needed varies depending on the type of experiment being carried out and the rate at which the images are acquired. A general rule of thumb is to collect a minimum of three pre‐bleach images when imaging at a rate of ∼1 frame/s to establish a stable fluorescence baseline prior to FRAP.

• 13As a starting point for determining the bleaching conditions, in the software, set the number of bleach iterations between 5 and 20 with no delay between them; this is the number of times that the laser will pass over each pixel in the bleach region during the bleach event.

  The number of bleach iterations required will depend on the power of the bleach laser, the scan speed, the photostability of the fluorophore, and the rate of diffusion of the particle being studied. Between five and twenty passes of the bleaching laser is a reasonable range of bleach scans to start with, but this iteration number will be fine‐tuned in subsequent steps.

• 14The proper bleach laser wavelength for photobleaching will now need to be determined. Select a bleach laser wavelength and set it to the maximum power allowed on your setup.

  Ideally, there should be a large difference between the laser transmission used to repetitively image the sample during the recovery phase and the transmission used for the bleach event itself.

  For GFP or similar fluorophores, run a test FRAP using the 488‐nm argon laser line at 50% output and 100% transmission.

• 15Select the preset “start FRAP/start experiment” button in the software to begin FRAP.

  Note that the exact procedure for setting up the FRAP protocol will vary between confocal systems. For example, if using a Zeiss LSM880, you will need to use several windows in the Zen software, including the Time Series (used to set the total number of images collected and interval between them, as well as when the bleach will be performed), Bleaching (used to set the bleaching parameters), and Regions (used to define the bleach ROI). Once the parameters for FRAP have been established, click on “start experiment” button to start the FRAP.

• 16If needed, repeat steps 14 and 15, using the different laser lines available for photo‐bleaching, until the best laser line for bleaching your specific fluorophore is determined.

  Photobleaching for a specific fluorophore is most efficient when performed with a wavelength of light matched to the maximal excitation energy of that fluorophore. However, argon lasers are typically significantly more powerful than HeNe lasers. For this reason, when bleaching with a fluorophore that does not excite optimally at 488 nm, e.g., Cy3, bleaching with a 488‐nm argon laser line will often produce better bleach depth than could be achieved by bleaching with a less‐powerful HeNe laser tuned to the maximum excitation energy of Cy3.

• 17Once the optimum wavelength of light to bleach with has been determined, the ideal number of iterations for bleaching will need to be established; try changing the iterations from between 1 and 50, saving the data after each attempt. Then, examine the FRAP curves and post‐bleach images side‐by‐side to find a setting that maximizes the bleach depth while minimizing the time required to bleach.

  Once an ideal number of iterations has been selected, this iteration number and wavelength should be reused in subsequent studies.

  The goal here is to find the number of iterations that will produce as short a bleaching event as possible, while also giving good bleach depth. Ideally, this means a bleach depth of 80% or more; however, anything greater than a 50% drop in fluorescence immediately following the bleach is typically usable. Generating sufficient bleach depth must be balanced with maintaining a short bleaching time. If the number of iterations is too high, a large percentage of fluorophores could potentially diffuse out of the bleach region during the bleach. This effect, if uncorrected, can give rise to artificially low values of D and can create an artificially low mobile fraction. This is especially problematic for soluble proteins, as will be discussed in Alternate Protocol 1 below. If a setup cannot be achieved that yields deep bleaching, try adjusting the ROI or changing frame rate (by altering delay, scan speed, image window size, or line averaging), or consider changing fluorophores.

• 18Continue to collect FRAP data after the bleach until recovery has occurred and there is a clear leveling of the fluorescence intensity in the bleached ROI. Additionally, make sure that time points are spaced such that multiple timepoints are collected during the early stages of recovery. Adjust the number of images and the delay between each image to satisfy both requirements.
• 19Once the appropriate number of images has been determined, assess levels of photofading. To do so, carry out an experiment in which the photobleaching step is omitted.

  Ideally, the levels of fluorescence should remain constant for the entire time series. Photofading will result in a systematic loss of fluorescence over time. Keep in mind that a small amount of photofading is acceptable, as it can be corrected for during data processing without impacting the experimental results, as discussed below. However, if significant photofading is seen in the recovery, readjust the pinhole, laser transmission, and detector gain to minimize photofading. If photofading is still a significant problem, consider changing fluorophores.

Basic Protocol 2. CONFOCAL FRAP MEASUREMENTS OF THE LATERAL DIFFUSION OF PLASMA MEMBRANE PROTEINS AND LIPIDS

Basic Protocol 1 discusses the general steps involved in setting up a FRAP experiment. However, the actual data‐collection process will vary depending on the particle or biological process being studied. Here in Basic Protocol 2, we will go through the actual steps of FRAP data acquisition for measurements of diffusion of a membrane protein at the cell surface. Additional applications will be covered in Alternate Protocol 1 (measurements of diffusion of a soluble cytoplasmic or nuclear protein) and Alternate Protocol 2 (analysis of intracellular trafficking kinetics).

Here we present a FRAP protocol that is ideal for slowly diffusing membrane markers. This includes transmembrane proteins and most lipid‐anchored proteins, such as GPI‐anchored proteins or bacterial toxins that bind lipid receptors at the cell surface (as illustrated in Figs. 1 and 2). However, FRAP can also be done on fluorescent lipid analogs, such as the DiIC class of membrane markers, which have much faster rates of diffusion. For these fast‐moving molecules, the method outlined here may not produce the temporal resolution necessary to resolve the recoveries. In that case, the following procedures will need to be modified to allow for faster data acquisition, as outlined for soluble markers in Alternate Protocol 1.

Figure 2.

Confocal FRAP assay of a membrane protein. COS‐7 cells were labeled with 1 µM of Alexa546‐tagged cholera toxin B subunit (A546‐CTxB), which binds ganglioside GM₁ on the plasma membrane. FRAP was then performed using a 4.1‐µm‐diameter bleach spot at 37°C. (A) Representative images of A546‐CTxB during a FRAP experiment. Bar = 10 µm. (B) Raw fluorescence intensity profile of cross‐section through the bleach ROI and fitted curve with mathematically derived effective bleach ROI. (C) Representative recovery curve of Alexa546‐CTxB corrected for fluorescence decay, along with mathematically fitted FRAP curve. (D) Diffusion coefficients of Alexa546‐CTxB derived using the nominal bleach radius r _n and the effective bleach radius r _e for the same FRAP curves. Data show the mean ± SD for 35 cells.

Materials

• See Basic Protocol 1

• 1Follow Basic Protocol 1 to perform the setup procedures for a FRAP experiment.
• 2Locate a representative cell in the eyepiece.
• 3Begin imaging and use the fine z‐adjustment to align the confocal slice to obtain a large, flat expanse of plasma membrane.
• 4At this point, the bleaching parameters and the bleach ROI determined in Basic Protocol 1 (“configuring the confocal LSM for FRAP”) should already be saved on the computer. Load these settings.
• 5Using a 512 × 512–pixel window with the appropriate electronic zoom and the ROI centered in the image window, move the stage in the x and y planes such that the bleach ROI is centered in the flat membrane region.

  The flat membrane region selected should be significantly larger than the bleach ROI (Fig. 1A). If not, find another cell, or create a smaller bleach ROI.

  Note that for ease of data analysis it is recommended that the position of the bleach region within the image window be held constant from cell to cell, i.e., moving the cells as needed rather than moving the bleach region, as this enables batch processing of data sets.

  It will become important during data analysis that a background region outside the cell also be visible in the imaging window (Fig. 1B). If not, collect at least three examples of background under identical conditions.

• 6Perform FRAP by selecting the preset “start FRAP/start experiment” tool on your microscope's software.
• 7Examine the FRAP curve on the computer. As with the FRAP performed in Basic Protocol 1, “Configuring the confocal LSM for FRAP” section, be sure that there are at least three pre‐bleach images, good bleach depth, multiple data points during the recovery period, and minimal photofading due to imaging.

  For a membrane protein, the recovery will generally level off 1 to 2 min after the bleach, although the exact recovery time will vary depending on the size of the bleach ROI and diffusional mobility of a given protein or lipid (Figs. 1 and 2). Note that for slower‐moving particles, line averaging can be used. This will decrease the noise in the data but may increase photofading.

• 8Make any necessary adjustments to the time‐series setup.
• 9Once the experimental setup has been perfected, repeat FRAP on 12 to 14 individual cells per sample group.

  It is acceptable to readjust the detector gain used for collecting pre‐ and post‐bleach images between cells, since the degree of labeling/protein expression will likely change between cells. However, all other settings must be kept the same from cell to cell (including zoom, frame rate and count, scan speed, line averaging, bleach iterations, bleaching laser wavelength and intensity, and geometry and size of the bleach ROI). This allows comparisons to be made between sample groups.

• 10Repeat the experiment on multiple days to ensure reproducibility.

Alternate Protocol 1. LATERAL DIFFUSION MEASUREMENTS FOR A RAPIDLY DIFFUSING SOLUBLE PROTEIN

In addition to measuring slow diffusion, as is the case for most membrane proteins, FRAP can also be employed to study faster‐diffusing molecules, such as soluble proteins in the cytoplasm or nucleus (Figs. 3 and 4). However, the protocol must be changed from the method presented for membrane proteins (Basic Protocol 2) in order to achieve the necessary temporal resolution required when studying soluble markers. To monitor fast diffusion, this protocol illustrates how to crop the image window around the ROI, allowing for faster FRAP data acquisition.

Figure 3.

Confocal FRAP assay of a soluble protein. COS‐7 cells were transiently transfected with mVenus. FRAP of mVenus in the nucleus was performed on the following day, using a 1‐µm‐radius bleach spot. (A) Representative whole‐cell images of mVenus. Bar = 10 µm. (B) Representative images from a FRAP experiment of mVenus in the nucleus. Bar = 10 µm. (C) Normalized fluorescence intensity profile of cross‐section through the bleach ROI and fitted curve with mathematically derived effective bleach ROI. (D) Representative recovery curve of mVenus corrected for fluorescence decay, along with mathematically fitted FRAP curve. (E) Diffusion coefficients of mVenus in the nucleus derived using the nominal radius r _n or the effective radius r _e for the same FRAP curves. Data show the mean ± SD for 12 cells.

Figure 4.

Comparison of FRAP data for different soluble proteins. COS‐7 cells were transfected with the indicated soluble proteins. The following day, confocal FRAP experiments were performed at 37^°C. (A) Representative images collected during a FRAP experiment in COS‐7 cells. Data are shown for measurements in the nucleus. Note that the imaging ROI was cropped to enable more rapid data collection. Scale bar = 2 µm. (B) Recovery curves for EGFP (black circles), EGFP‐LC3 (blue squares), tfLC3 (red triangles), and p53‐GFP (green crosses) in the nucleus of COS‐7 cells. Data show the mean recoveries for 10 cells for each protein from a representative experiment. For clarity, error bars are not shown. (C) Effective diffusion coefficients for EGFP, EGFP‐LC3, tfLC3, and p53‐GFP in the cytosol (black bars) and nucleus (gray bars) of COS‐7 cells. Data show the mean ± SD for 30 cells from a total of 3 independent experiments. Figure adapted from Drake et al. (2010).

Materials

• See Basic Protocol 1

• 1Set up the FRAP experiment following the procedures outlined in Basic Protocol 1.
• 2Locate a representative cell using the eyepieces.
• 3While continuously imaging, use the fine z‐adjustment to align the confocal slice with a large expanse of cytoplasm.
• 4At this point, the bleaching parameters and the bleach ROI determined in Basic Protocol 1, “Configuring the confocal LSM for FRAP” should already be saved on the computer. Load these settings.
• 5Using an imaging window at the appropriate electronic zoom with the ROI centered in the image frame, move the stage in the x and y planes so that the cytoplasm fills the entire image window.
• 6Next, reduce the number of pixels imaged. Using the LSM software, this can be done by specifying a digital zoom or by cropping the image around the ROI to reduce the size of the image window. For best results, make the imaging window a rectangle that is as tall as the bleaching ROI, and at least 3‐fold wider than the bleaching ROI.

  For example, we often use a bleaching ROI 2.3 µm in diameter, centered in a rectangular imaging window of 13.7 × 3.4–µm when photobleaching in the cytoplasm (Fig. 3). To further increase the rate of data acquisition, bidirectional scanning can be used.

  Unlike the FRAP performed on a full 512 × 512–pixel window (as described for membrane proteins), reducing the image window for soluble proteins makes measuring whole‐cell fluorescence decay and background fluorescence at the same time as obtaining the FRAP data difficult. Therefore, do not worry about aligning the image window to show a region outside of the cell (as was done for the membrane protein FRAP). In a subsequent step, we will discuss collecting separate whole‐cell decay and background data that can be used to correct the final FRAP curves.

• 7Collect a single image to verify that the fluorescence intensity is high for most pixels, but with only a few pixels saturated. If not, adjust the pinhole, laser intensity, or detector gain.
• 8Perform FRAP by selecting the preset “start FRAP/start experiment” tool on your microscope's software.

  In the case of very fast‐moving soluble proteins, the bleached molecules will disperse so quickly that a defined ROI may not be recognizable in the post‐bleach image. Instead, the entire image may darken immediately after the bleach. This is well illustrated by comparing the recoveries of EGFP and p53‐EGFP for FRAP experiments performed under the same conditions (Fig. 4). In cases where the bleach region is unrecognizable in the images, a fixed sample can always be used as a control to confirm that bleaching is in fact occurring and that the absence of a discrete bleach spot is due to rapid recoveries.

• 9Examine the FRAP curve on the computer. As with the FRAP performed in Basic Protocol 1, be sure to collect a minimum of three pre‐bleach images, obtain a good bleach depth, collect multiple data points during the recovery phase, and minimize photofading.

  Additionally, the FRAP curves will need to be carried out well past the initial recovery period, which for a soluble protein typically takes 2 to 20 s, depending on the size of the bleach ROI and the protein being studied (see Fig. 3 and Fig. 4).

• 10Make any necessary adjustments to the time series setup. Once the experimental setup has been perfected, repeat FRAP on 12 to 14 individual cells per sample group.

  The degree of labeling/vector expression will likely change between cells. Therefore, it is acceptable to readjust the detector gain between cells. However, all other settings (including zoom, frame rate and count, scan speed, line averaging, bleach iterations, and bleaching laser wavelength and intensity) must be kept the same from cell to cell or else the data cannot be easily grouped for batch analysis later.

• 11Next, collect some control data that will be used later to correct the FRAP curves for photofading caused by imaging. To do this, set up the ROIs just as with the FRAP experiment (steps 1 to 9, above). However, run the experiment without the bleach. Repeat on at least 10 cells.
• 12Finally, collect background fluorescence by running a time series (as in step 10) on the background fluorescence in an area where no cells are present, or for unlabeled cells when autofluorescence is significant.
• 13Repeat the experiment on multiple days to ensure reproducibility.

Alternate Protocol 2. FRAP ANALYSIS OF INTRACELLULAR TRAFFICKING KINETICS

In addition to measuring the diffusion of a particle in a single compartment (i.e., nucleoplasm, cytoplasm, or membrane), FRAP can also be used to measure trafficking between compartments. In this protocol, we will explain how FRAP can be used to measure nucleocytoplasmic transport (Fig. 5). Similarly, by moving the ROI to another organelle, one could measure molecular trafficking between compartments, as has been done to study Ras trafficking to and from the Golgi complex (Goodwin, Drake, Rogers, et al., 2005).

Figure 5.

Confocal FRAP assay of nucleocytoplasmic transport. COS‐7 cells were transiently transfected with EGFP‐LC3 or EGFP. The following day, FRAP experiments were performed to assess the rates of nuclear import and nuclear export. Representative images obtained before the photobleach (pre‐bleach), immediately after the photobleach (t = 0), and during the recovery phase (t = 150 s or 300 s) are shown. (A) To monitor nuclear import, the pool of EGFP‐LC3 or EGFP in the nucleus was photobleached. (B) To monitor nuclear export, the cytoplasmic pool of EGFP‐LC3 or EGFP was photobleached and fluorescence recovery recorded over time. Notice that both the nuclear import and nuclear export of EGFP occur significantly more quickly than for EGFP‐LC3. Bar = 10 µm. Figure adapted from Drake et al. (2010).

Materials

• See Basic Protocol 1

• 1Perform FRAP setup procedures as described in Basic Protocol 1.
• 2Locate a representative cell in the eyepiece.
• 3Use the fine z‐adjustment to align the confocal slice with the center of the nucleus on the computer screen. Then, move the stage in the x and y planes such that the nucleus is centered in the imaging window.
• 4At this point, the bleaching parameters and the bleach ROI determined in Basic Protocol 1, “Configuring the confocal LSM for FRAP” should already be saved on the computer. Load these settings.
• 5Choose an electronic zoom setting that allows imaging of the whole cell. Draw a new ROI around the nucleus. Try to exclude any cytoplasm from the bleaching ROI, if bleaching the nucleus.

  Unlike when measuring diffusion in a single compartment, this ROI does not have to be perfectly circular.

• 6Take a single image to verify that most pixels have a high fluorescence intensity, but with only a few pixels saturated. If this is not the case, adjust the pinhole, laser intensity, or detector gain.
• 7Perform FRAP by selecting the preset “start FRAP/start experiment” tool on your microscope's software.
• 8Examine the FRAP curve on the computer. As with the FRAP performed in Basic Protocol 1, be sure that there are least three pre‐bleach images, good bleach depth (of ≥50% drop in fluorescence), multiple data points during the recovery, and minimal photofading.

  Keep in mind that for this application of FRAP, the recovery times will likely be much longer than when FRAP is performed to measure free diffusion, as transport can be much slower than diffusion. For example, EGFP takes ∼5 min following the bleach to equilibrate between the cytoplasm and nucleus (Fig. 5). However, when examining diffusion of EGFP within the cytoplasm using a small bleach ROI, the post‐bleach fluorescence will reach equilibrium in a matter of seconds (Fig. 4).

• 9Make any necessary adjustments to the time‐series setup. Once the experimental setup has been perfected, repeat FRAP on 12 to 14 individual cells per sample group.

  A new ROI will need to be drawn in each cell, as the size and shape of the nucleus may vary between cells. The degree of labeling/vector expression will also likely change between cells. Therefore, it is acceptable to readjust the detector gain between cells. However, all other settings (including zoom, frame rate and count, scan speed, line averaging, iterations, and bleaching laser wavelength and intensity) must be kept the same from cell to cell, or else the data cannot be easily grouped for batch analysis.

• 10Repeat the experiment on multiple days to ensure reproducibility.

Basic Protocol 3. WORKING WITH FRAP DATA

Before leaving the microscope, it is a good idea to undertake a quick review of the data so that if any major flaw is detected in the experiment, new data can be quickly generated. Also, data tables will need to be generated and exported for subsequent analysis.

Next, it is necessary to normalize the data and plot it for documentation and publication. These procedures and the calculations for acquiring the mobile fraction (M _f) and half time of recovery (t _½) values are presented here. The mobile fraction is the portion of molecules that can undergo diffusion. The t _½ is the point in the recovery curve at which half of the fluorescence recovery has occurred. Since recovery is the result of molecular movement, t _½ is related to the rate of diffusion (or kinetics of transport). It is important to bear in mind that the t _½ values are influenced by several experimental parameters, including the amount of time taken to photobleach the ROI and the ROI spot size. The math involved in determining M_f and t _½ is relatively simple, and this step in the data processing can be easily automated in Excel. Here, we discuss how to perform this analysis manually. However, this process can often be automated using software provided by the confocal microscope manufacturer or with the help of several online tools, e.g., Kohze et al. (2017) and Koulouras et al. (2018). In Basic Protocol 4, “Further analysis of FRAP data to obtain diffusion coefficients,” we present the more complex math involved in extracting diffusion coefficients, which are independent of bleach time or spot size. Approximations can also be used to calculate diffusion coefficients from t _½, as discussed further below.

Materials

• See Basic Protocol 1

• 1On the microscope computer, play back the time‐lapse images collected during the FRAP experiment.

  There are three common artifacts that can render data unusable, and these can be easily identified at this step. (1) The focal plane may have drifted during the FRAP experiment. This will compromise the data, as the focal plane will not stay aligned with the bleach region. This usually produces a FRAP curve that first increases as normal, then decreases as the specimen moves out of focus. (2) Another error that can be observed in the image series is photofading due to imaging. A small degree of decay is acceptable, as it can be corrected later (Fig. 1). (3) A third common artifact is when a large structure (e.g., an endosome) passes through the ROI during the FRAP experiment. This will cause a significant spike or dip in the FRAP curve.

• 2Examine the individual raw FRAP curves for any irregularities. If the curves look acceptable, export the mean fluorescence intensity in the bleach ROI for each time point.

  The times should be contained within the timestamps for each image and will be automatically exported as part of the ROI analysis in the Zeiss software.

• 3In a case where the FRAP was done using the whole image window, as opposed to the cropped window method discussed for cytoplasmic/nuclear proteins, draw an ROI around the whole cell and another around a region in the background (Fig. 1B). Export these data as well.

• 4It is customary to plot the FRAP curve with the first point after the bleach being set as t = 0; to do so, subtract the time of the first post‐bleach image from each timepoint in the series.
• 5
  For further data analysis and to produce publication‐quality graphs, correct the FRAP curves for background fluorescence (F _bkgd), photofading, loss of fluorescent material due to the bleach, and differences in protein expression levels between cells:

  $$F{(t)}_{\mathrm{norm}}=\frac{F{(t)}_{\mathrm{ROI}}-F_{\mathrm{bkgd}}}{F{(t)}_{\mathrm{cell}}-F_{\mathrm{bkgd}}}\times \frac{F{(i)}_{\mathrm{cell}}-F_{\mathrm{bkgd}}}{F{(i)}_{\mathrm{ROI}}-F_{\mathrm{bkgd}}}$$ (1)

  The first part of this normalization accounts for irreversible loss of molecules owing to the bleaching event, as well as any photofading that may have occurred. To correct for this, the bleaching ROI intensity F(t)_ROI is divided by the whole‐cell intensity F(t)cell for each time point F(t). The second part of the normalization rescales the data in terms of percentage of initial fluorescence by multiplying by the initial whole‐cell intensity F(i)_cell divided by the initial intensity in the ROI, F(i)_ROI (Fig. 1D).

• 6Plot F(t) over time for each sample in a cohort on the same graph so that any outliers in the data can be easily identified.
• 7Calculate the mean and standard deviation of F(t) for each sample cohort. Then, plot this mean and SD of F(t) against time.
• 8
  Calculate M_f from the data obtained from the normalized recovery curves (Equation 1) as:

  $$M_{\mathrm{f}}=\frac{(F_{\infty }-F_0)}{(F_{\mathrm{i}}-F_0)}$$ (2)

  where F _∞, F₀ , and F_i are the normalized fluorescence intensities inside the bleach ROI after full recovery (at the asymptote), immediately following the bleach, and before the bleach, respectively (Fig. 1D).

  Mobile fractions can also be reported as the percent mobile molecules. In this case, the product is multiplied by 100.

• 9Average the M_f values for each FRAP curve across all the cells, thus providing statistics for the cell population.
• 10
  As a first approach to data quantification, describe the FRAP recovery curves in terms of the half time of recovery (t_1/2), which can be approximated by fitting to the following equation (Feder et al., 1996):

  $$F(t)=\left[F_0+F_{\infty }(t/t_{1/2})\right]/[1+(t/t_{1/2})]$$ (3)
• 11The t _½ values can then be averaged, thus providing statistics for the cell population.

Basic Protocol 4. FURTHER ANALYSIS OF FRAP DATA TO OBTAIN DIFFUSION COEFFICIENTS

In many cases, the fluorescent molecules of interest are uniformly distributed throughout the cytoplasm or nucleus or within the plasma membrane. As a starting point, under these conditions FRAP data can be analyzed quantitatively using a closed‐form analytical equation describing a purely diffusive process (Kang et al., 2009). Here we describe the protocol for analyzing FRAP data using a single‐component pure‐diffusion model to obtain diffusion coefficients. For quantification of more complex modes of diffusion or transport, including reaction‐diffusion type behavior or anomalous diffusion, alternative mathematical models should be used to extract quantitative results from the FRAP data (Alexander & Lawley, 2022; Axelrod et al., 1976; Berkovich et al., 2011; Braga et al., 2007; Cai et al., 2022; Ciocanel et al., 2024, 2017; Daddysman & Fecko, 2013; Feder et al., 1996; Im et al., 2013; Kang et al., 2009, 2010, 2019; Kang, 2022; Lorenzetti et al., 2025; Lubelski & Klafter, 2008; Mai et al., 2011; Roth et al., 2009; Sprague et al., 2004; Wachsmuth, 2014). In addition, several groups have developed simulation‐based approaches to quantify diffusion from confocal FRAP measurements, offering an alternative to purely analytical approaches (Blassle et al., 2018; Blumenthal et al., 2015; Cowan & Loew, 2023; Kingsley et al., 2018; Wahlstrand Skarstrom et al., 2021).

The initial treatise on extracting diffusion coefficients did not consider scenarios where diffusion is occurring during the bleach (Axelrod et al., 1976). This is not a tremendous concern in some cases where the bleach event is extremely short and/or diffusion is extremely slow. However, this becomes a major concern when using line‐scanning confocal microscopes, as molecules have time to recover during the bleaching event (Weiss, 2004). Additionally, the extent of diffusion during the bleach becomes greater as the rate of diffusion of the molecule becomes greater (Fig. 2 and Fig. 3). We and others have found a method to correct for diffusion during the bleach prior to extracting the diffusion coefficient (Braga et al., 2004; Kang et al., 2009). This is performed by altering the radius of the user‐defined bleach spot (known as the nominal radius or r _n) to an empirically determined radius that considers diffusion that may have occurred during the bleach (called the effective radius or r _e).

To determine the effective radius, the bleaching profile of the ROI must be established. To do this, first obtain the fluorescence intensities as a function of distance along any line bisecting the post‐bleach image (Profile_post). Next, the post‐bleach profile must be normalized. To accomplish this, measure the fluorescence intensities immediately prior to photobleaching (Profile_pre) along the same axis as before. Now the normalized post‐bleach profile is obtained by:

$$\frac{\mathrm{Profil}{\mathrm{e}}_{\mathrm{post}}}{\mathrm{Profil}{\mathrm{e}}_{\mathrm{pre}}}$$ (4)

Assuming the center of the circular bleaching ROI is the origin, the post‐bleach profile can be approximated by an exponential of a Gaussian laser profile:

$$\phi \left(x,y\right)=F_i\exp \left(-K\exp \left(-\frac{2\left(x^2+y^2\right)}{r_e^2}\right)\right)$$ (5)

where F _i = 1 for a normalized post‐bleach profile, K is a bleach depth parameter, and r _e is the effective radius. Both K and r _e contain information about the initial conditions required to solve the diffusion equation and approximate the diffusion that occurred before acquisition of the post‐bleach image (Braga et al., 2004). By the 1^st order approximation, the post‐bleach profile can be further simplified (Kang et al., 2012) to:

$${\phi }^{\ast }\left(x,y\right)=F_i\left(1-K\exp \left(-\frac{2\left(x^2+y^2\right)}{r_e^2}\right)\right)$$ (6)

By solving the diffusion equation in two dimensions with an initial condition given by Equation 5, the FRAP equation can be derived as a closed‐form analytical solution in series form (Kang, Day, et al., 2009):

$$F(t)=F_{\mathrm{i}}\sum _{m=0}^{\infty }\frac{{(-K)}^m{r}_{\mathrm{e}}^2}{m!(r_{\mathrm{e}}^2+m(8\mathrm{Dt}+r_{\mathrm{n}}^2))}{M}_{\mathrm{f}}+(1-M_{\mathrm{f}})F_0$$ (7)

where F_i = 1 for a normalized FRAP curve and r _n is the nominal radius of the circular bleaching ROI. Due to a fast convergence rate of this Taylor series, summation over the first 30 terms approximates Equation 7 excellently. Finally, by fitting normalized FRAP data (Equation 1) with the FRAP equation for D (Equation 7), the diffusion coefficient can be determined.

With an approximated initial condition given by Equation 6, the FRAP equation can also be simplified to:

$$F\left(t\right)=F_i\left(1-\frac{K{r}_e^2}{8\mathrm{Dt}+r_n^2+r_e^2}\right)M_f+\left(1-M_f\right)F_0$$ (8)

Sometimes there is a discrepancy between the initial fluorescence intensities of the FRAP fit and the FRAP data (Equations 1, 7, and 8). This is largely due to the difference between the experimental and theoretical post‐bleach profiles (Equations 4 to 6). We recommend resolving this by numerically solving:

$$F_0=\sum _{m=0}^{\infty }\frac{{(-K)}^m{r}_{\mathrm{e}}^2}{m!\left(r_{\mathrm{e}}^2+m{r}_{\mathrm{n}}^2\right)}$$ (9)

where F₀ is the initial fluorescence intensity of FRAP data.

Approximations can also be used to analyze FRAP data. For example, we derived a simplified equation that can be used to calculate diffusion coefficients for a circular bleach region using r _e and t _½, assuming a pure isotropic diffusion model (Kang et al., 2012). We refer to the resulting diffusion coefficient as D _confocal:

$$D_{confocal}=\left(\frac{r_n^2+r_e^2}{8t_{1/2}}\right)$$ (10)

A workflow to calculate D_confocal using Equation 10 is provided in Figure 6. When r _e = r _n in Equation 10 (i.e., bleaching is instantaneous), this simplifies to:

$$D_{r_n}=0.25\left(\frac{r_n^2}{t_{1/2}}\right)$$ (11)

which is essentially identical to the Soumpasis equation, the FRAP equation for a small circular bleaching spot assuming a uniform laser (Soumpasis, 1983):

$$D_{r_n}=0.224\left(\frac{r_n^2}{t_{1/2}}\right)$$ (12)

Figure 6.

Workflow to determine the apparent diffusion coefficient, D _confocal from τ _1/2, r _n and r _e. Figure is from Kang et al. (2012).

COMMENTARY

Background Information

FRAP was developed using a focused, static laser beam in the early 1970s (Axelrod et al., 2018; Kenworthy, 2023; Lippincott‐Schwartz et al., 2018). A few years later, conventional FRAP theory for quantitative FRAP analysis was established (Axelrod et al., 1976, 2018). At the time, performing FRAP required high levels of expertise and custom‐built rigs. Early FRAP experiments were also primarily restricted to markers that could be exogenously introduced, and the application was specialized for plasma membrane studies. Although GFP was discovered in the 1960s, it was not until the early 1990s that the application of GFP fusion was elucidated. Today, GFP, other genetically encoded fluorescent labels, or organic dyes can be attached to almost any protein of interest, making FRAP possible with a wide variety of proteins. The widespread availability of confocal microscopes has further democratized FRAP.

As the tools for performing FRAP have developed, so have the ways in which scientists apply FRAP. Common variations on FRAP include fluorescence loss in photobleaching (FLIP) (Bancaud et al., 2010; Dundr & Misteli, 2003), inverse FRAP (iFRAP) (Drake et al., 2010; Dundr & Misteli, 2003; R. Huang et al., 2015; Nichols et al., 2001), and photoactivation and photoconversion (Dora et al., 2024; Lippincott‐Schwartz et al., 2003; Matsuda & Nagai, 2014; Mazza et al., 2008; Patterson & Lippincott‐Schwartz, 2002). Additionally, while FRAP is conventionally used to measure diffusion/transport, it is also possible to extract in vivo binding kinetics values from FRAP data (Alexander & Lawley, 2022; Axelrod et al., 1976; Berkovich et al., 2011; Braga et al., 2007; Cai et al., 2022; Ciocanel et al., 2017, 2024; Daddysman & Fecko, 2013; Feder et al., 1996; Im et al., 2013; Kang et al., 2009, 2019; Lorenzetti et al., 2025; Lubelski & Klafter, 2008; Mai et al., 2011; Roth et al., 2009; Sprague et al., 2004; Wachsmuth, 2014). While many exciting variations on and applications of FRAP have been developed, for the purposes of this chapter we have focused on application of FRAP to the study of diffusion and trafficking using a confocal laser‐scanning microscope.

Mathematical description of FRAP

Quantitative FRAP analysis requires a mathematical description of fluorescence recovery for the underlying transport/reaction kinetics, as well as the two different modes of laser excitation: photo‐illumination and photobleaching (Kang et al., 2009). For small bleaching spot size, r _n, it has been reported that the scanning profile of a confocal laser is well approximated by a Gaussian function (Braga et al., 2004):

$$L_{r_{\mathrm{n}}}(x,y)=\frac{2L_0}{\pi r_{\mathrm{n}}^2}\exp \left(-\frac{2(x^2+y^2)}{r_{\mathrm{n}}^2}\right)$$ (13)

where L ₀ is the maximal laser intensity. Similarly, a scanning profile of photo‐illumination mode can be described as εL _rn (x,y) for an attenuation factor ε << 1. If we let F(x,y,t) be the fluorescence intensity at a location (x,y) at time t, then F(x,y,t) is proportional to the illumination mode laser intensity at (x,y) and the number of fluorescent proteins at that location at time t. If the concentration of fluorescent proteins is described by u(x,y,t), then, for an area A defined by:

$$A=\left[x-\frac{dx}{2},x+\frac{dx}{2}\right]\times \left[y-\frac{dy}{2},y+\frac{dy}{2}\right]$$ (14)

the number of fluorescent proteins in A is given by:

$$u\left(x,y,t\right)dxdy$$ (15)

and the local fluorescence intensity in A is computed as:

$$F(x,y,t)=q·\epsilon L_{r_{\mathrm{n}}}(x,y)u(x,y,t)dxdy$$ (16)

where a proportionality constant q is referred to as quantum yield or quantum efficiency.

Finally, the total fluorescence intensity from the ROI can be found by integrating this local fluorescence intensity over the ROI:

$$F(t)=q\epsilon \int {\int }_{{\mathbb{R}}^2}{L}_{r_n}(x,y)u(x,y,t)dxdy$$ (17)

which is called a FRAP equation for u.

Note that different underlying kinetics for u yields different FRAP equations. For free diffusion, the evolution of u(x,y,t) can be described by the diffusion equation, subject to the initial conditions given by a post‐bleach profile right after photobleaching:

$$\left\{\begin{array}{c}{u}_t=D\mathrm{\Delta }u\\ u(x,y,0)=\phi (x,y)\end{array}\right.$$ (18)

where D (µm²/sec) is the diffusion coefficient, and the Laplacian operator is defined by:

$$\mathrm{\Delta }=\frac{{\partial }^2}{\partial x^2}+\frac{{\partial }^2}{\partial y^2}.$$ (19)

φ is given by Equation 5, which describes the post‐bleach fluorescence intensity profile. The solution of the diffusion equation can be found as:

$$\begin{array}{ccc}& & u(x,y,t)={\mathrm{\Phi }}_D\ast \phi \hfill \\ & & =\int \int {\mathrm{\Phi }}_D(x-x^{\prime },y-y^{\prime },t)\phi (x^{\prime },y^{\prime })d{x}^{\prime }d{y}^{\prime }\hfill \end{array}$$ (20)

where the fundamental solution of the diffusion equation Φ_D(x,y,t) is defined as:

$${\mathrm{\Phi }}_D(x,y,t)=\frac{1}{4\pi Dt}\exp \left(-\frac{x^2+y^2}{4Dt}\right)$$ (21)

The post‐bleach profile, φ(x,y) is a function that describes an experimental post‐bleach profile. In theory, φ(x,y) can be obtained by solving the photobleaching equation. Assuming a first‐order photobleaching process with a photobleaching rate α, a governing equation for a photobleaching process of freely diffusing fluorescent proteins can be described as a reaction diffusion equation:

$$\begin{array}{c}{u}_{\mathrm{t}}=D\mathrm{\Delta }u-\alpha L_{r_{\mathrm{n}}}(x,y)u\\ u(x,y,0)=u_0\end{array}$$ (22)

where u ₀ is the pre‐bleach steady‐state fluorescence intensity, which is regarded as a constant.

Unlike the diffusion equation, this photobleaching equation cannot be solved explicitly, so an approximated form of the solution is used for u(x,y,T), where T is the duration of photobleaching. From the empirical observation, φ(x,y) is chosen as an exponential function of Gaussian as in Equation 5.

With this consideration, the FRAP equation for free diffusion becomes (Kang & Kenworthy, 2008; Kang et al., 2010):

$$\begin{array}{ccc}& & F(t)=q\epsilon \int {\int }_{{\mathbb{R}}^2}{I}_{r_{\mathrm{n}}}(x,y)\hfill \\ & & (\int {\int }_{{\mathbb{R}}^2}{\mathrm{\Phi }}_D(x-x^{\prime },y-y^{\prime },t)\phi (x^{\prime },y^{\prime })d{x}^{\prime }d{y}^{\prime })\hfill \\ & & dxdy={\sum }_{n=0}^{\infty }\frac{{(-K)}^n}{n!\left(1+n\left({\gamma }^2+2t/{\tau }_{D_e}\right)\right)}\hfill \end{array}$$ (23)

where γ = r _n/r _e and τ _De = r ² _e/(4D). Using this equation, the τ _De values that provide the best fit to the actual data can be determined, and from there, τ _De = r ² _e/4D can be easily solved for D.

For FRAP equations addressing dynamics other than free diffusion, the governing equation that describes the evolution of fluorescent protein concentration (u) for a given post‐bleach profile as an initial condition must be found first. Then, the integral in Equation 23 should be evaluated. For example, in the case of binding diffusion kinetics, some post‐bleach profiles can be described as φ = βφ₁(x,y;r_e ) + (1 – β)φ₂(x,y;${\overline{r}}_{\mathrm{e}}$ _e) for 0 < β < 1 where βφ₁(x,y;r_e ) and φ₂(x,y ${\overline{r}}_{\mathrm{e}}$) are as defined in Equation 5 (Kang et al., 2010).

Comparison of FRAP to other techniques sensitive to protein and lipid dynamics

Photoactivation and photoconversion are techniques in which fluorescent molecules in a certain area of a cell are selectively converted to an active fluorescent state or different color, and then the fluorescent signal is tracked over time to get information on the mobility of proteins as well as the viscosity of the cellular milieu (Bancaud et al., 2010; Lippincott‐Schwartz et al., 2003; Lippincott‐Schwartz & Patterson, 2008; Patterson, 2008). To do so, photoactivation and photoconversion experiments require specific fluorescent protein variants (Kremers et al., 2009, 2011; Lippincott‐Schwartz & Patterson, 2008; Lukyanov et al., 2005; Nienhaus & Nienhaus, 2022; Rodriguez et al., 2017). Since photoactivation shares the same theoretical framework as FRAP, it can be understood as a complementary counterpart to FRAP where the same analytical equations will apply.

Fluorescence loss in photobleaching (FLIP) is another fluorescence‐based technique that utilizes the photobleaching property of fluorescent proteins (Bancaud et al., 2010; Bonifacino & Lippincott‐Schwartz, 2003; Cole et al., 1996; Hansen et al., 2018; Lippincott‐Schwartz et al., 2001; Wustner et al., 2012; Wustner, 2022). In a FLIP experiment, a photobleaching ROI is selected in a cell and then the ROI is repeatedly photobleached while the whole cell is continuously imaged. From the rate of loss in fluorescence at observation ROIs outside of the photobleaching ROI, FLIP can assess not only the connectivity between the ROIs but also whether a protein moves uniformly or undergoes interactions that impede its transport between the ROIs. Since the total number of fluorophores (photobleached fluorophores plus fluorescent fluorophores) remain constant before and after photobleaching, FRAP and FLIP are complementary, and FLIP data can also be analyzed in a quantitatively similar way to FRAP to obtain kinetic rate constants.

Another group of fluorescence‐based methods, such as fluorescence correlation spectroscopy (FCS) and image correlation spectroscopy (ICS), utilize fluorescence fluctuations in time and/or space rather than photobleaching (Bacia & Schwille, 2003; Elson, 2004; Haustein & Schwille, 2007; Jovičić, 2024; Kolin & Wiseman, 2007; Petrasek et al., 2010; Sankaran & Wohland, 2023; Smith, 2024). Therefore, these methods may serve as independent cross‐validation tools for quantitative FRAP analysis (Im et al., 2013; Kraft et al., 2014; Machan et al., 2016). In FCS, fluorescence fluctuations due to movement of fluorescent molecules in and out of a small volume (confocal volume) is analyzed via a time‐dependent correlation function. One strength of FCS is that it can measure the mean fluorescent protein concentration in the picoliter confocal volume. FCS is best suited for investigating fast kinetics, as slow‐moving molecules may become photobleached as they pass through the confocal volume, creating a serious problem for studying slow diffusion with FCS. FCS generally requires very low concentrations of fluorescent particles to correlate the data, making FCS a good alternative to FRAP when studying low concentrations of markers. One strength of FRAP over FCS is that FCS cannot yield information on the immobile fraction. FCS is also not suited to analyze processes like nucleocytoplasmic trafficking, which require the ability to follow the movements of many molecules over large distances.

Whereas FCS utilizes a time‐correlation function of fluorescence fluctuation from a confocal volume, image correlation spectroscopy (ICS) deals with spatial autocorrelation functions calculated from spatial fluorescence fluctuations in the fluorescence microscopy images (Gialdini et al., 2025; Kolin & Wiseman, 2007; Petersen et al., 1993). Therefore, ICS can be understood as an imaging analog of FCS. ICS can determine velocity and aggregation state of fluorescently labeled proteins, in addition to the fluorescent protein concentration and mobility. A variation of this technique known as Raster Image Correlation Spectroscopy takes advantage of the raster scanning process of confocal microscopes to extract dynamics of fluorescently tagged molecules with characteristic timescales of milliseconds to seconds (Digman et al., 2005; Gialdini et al., 2025; Rossow et al., 2010; Royer et al., 2023). While powerful, this approach requires careful optimization of experimental setting and requires spatial averaging [reviewed in Gialdini et al. (2025)].

The fluorescence microscopy techniques discussed up to this point all report on diffusion of multiple particles at once. On the other hand, single‐particle tracking (SPT) exclusively focuses on the motion of individual particles, and can even visualize the dynamics of multiple molecules simultaneously (Jaqaman et al., 2008; Kusumi et al., 2014; Manley et al., 2008; Prindle et al., 2023; Shen et al., 2017). By analyzing the trajectories of the tracer particles in time, not only the mobility of the tracers but also heterogeneities in their cellular environment can be uncovered. Hence, mobility analysis by FRAP can be validated by SPT analysis. However, this method requires sensitive cameras and tracking software, making it less broadly accessible than FRAP.

Fluorescence‐based techniques have become among the most powerful tools in modern biology. As we have seen, a wide variety of fluorescence‐based techniques for diffusion measurements are currently available, making selection of a proper technical approach challenging. Therefore, it is important to keep in mind the strengths and weaknesses of each technique when choosing how to best answer a given biological question.

Considerations for data analysis

With appropriate mathematical models that describe changes in protein concentration in time, FRAP has been used successfully to analyze lateral diffusion, free diffusion, reaction diffusion, and anomalous diffusion, as well as active transport (Alexander & Lawley, 2022; Axelrod et al., 1976; Berkovich et al., 2011; Braga et al., 2007; Cai et al., 2022; Ciocanel et al., 2017, 2024; Daddysman & Fecko, 2013; Feder et al., 1996; Im et al., 2013; Kang et al., 2009, 2019; Lorenzetti et al., 2025; Lubelski & Klafter, 2008; Mai et al., 2011; Roth et al., 2009; Sprague et al., 2004; Wachsmuth, 2014). Because diffusion coefficients determine the transport and reaction rates of proteins in living cells, they are extremely important for understanding how biological processes are orchestrated in time and space in a living cell.

Since the recovery kinetics are different in different spatial dimensions, 1D, 2D, and 3D FRAP equations must be considered separately. For example, the mean square displacement of proteins undergoing free diffusion with a diffusion coefficient D is approximated as:

$$〈x^2〉=2NDt$$ (24)

where N is the spatial dimensions. Therefore, the number of dimensions in which a particle is free to diffuse will significantly impact both the x and D values.

To yield quantitative measurements of diffusion coefficients and kinetic constants from FRAP measurements, the geometry and size of the bleach region must be considered (Axelrod et al., 1976; Blumenthal et al., 2015; Braeckmans et al., 2007; Deschout et al., 2010; Dey et al., 2021; Kang et al., 2012; Kure, Andersen, Rasmussen, et al., 2020; Smisdom et al., 2011; Soumpasis, 1983; Taylor et al., 2019). In theory, diffusion coefficients obtained by FRAP should be independent of experimental variables, such as bleach spot size or geometry and photobleaching iterations, i.e., the time it takes to bleach. However, in some cases, a dependence on experimental conditions is observed as the result of diffusion and reaction during photobleaching (Weiss, 2004). This is particularly true for large spot sizes, long bleaching events, or rapidly diffusing molecules, as illustrated in Figure 3 and Figure 4. This dependence can be removed by using initial conditions obtained from the experimental post‐bleach profiles, i.e., by measuring the effective radius r_e from the experimental post‐bleach profiles (Braga et al., 2004; Dey et al., 2021; Goehring et al., 2010; Kang, Day, et al., 2009). Other cases where diffusion coefficients may vary as a function of bleach‐spot size is when anomalous diffusion occurs. This effect can be used to obtain information about underlying structures that hinder diffusion, such as the cytoskeletal meshwork (Lenne et al., 2006). Deliberately performing FRAP using different bleach spot sizes is also useful to distinguish lateral diffusion and binding events (Henis et al., 2006; Sprague et al., 2006; Wolfenson et al., 2009).

FRAP analysis also depends on the size and geometry of the cell. It should be noted, for example, that in the analysis of FRAP measurements at the cell surface, the assumption that the plasma membrane is flat is a simplification (Adler et al., 2019). Cells also cannot necessarily be treated as an infinite space because the boundary effects become significant. In addition, due to the finite size of a cell, the recovery curve may not reach the level of pre‐bleach steady state even when there is no immobile pool of proteins in the ROIs due to loss of a significant fraction of fluorescent molecules during the photobleach. Although one can develop a realistic recovery model taking the cell size and boundary effects into consideration (Kingsley et al., 2018), these types of problems often do not have an explicit solution in a closed form, and typically have to be evaluated by numerical computations or simulations, which are not practical for analyzing large amounts of data. Therefore, it is important to find an optimal photobleaching condition where the bleach ROI is small, while at the same time obtaining a reasonable bleaching depth.

Other possible artifacts in FRAP are linked to the properties of the fluorophores themselves, including photofading and reversible photobleaching/photoswitching. Photofading is well characterized by a slow single exponential decay and is easily observed from time‐lapse images obtained under the same imaging conditions used to acquire FRAP curves. Photofading may interfere with analysis of FRAP, especially when long recoveries are being followed. In cases where only a small degree of photofading occurs, the FRAP curves can be corrected using Equation 1 (also see Fig. 1). Additional methods to correct for this effect have also been reported (Kang et al., 2015; Wu et al., 2012).

The photobleaching process in FRAP is often assumed to be an irreversible process. However, this is not always true, as some fluorophores, e.g., GFP, can reversibly recover from photobleaching as the result of photoswitching (sometimes referred to as reversible photobleaching) (Dayel et al., 1999; Dickson et al., 1997; Mueller et al., 2012; Sinnecker et al., 2005). Failure to be aware of this effect can lead to erroneous conclusions (Cadar et al., 2020; Daddysman & Fecko, 2013). The percent of photobleached molecules that undergo this process is dependent on the fluorophore in question as well as bleaching conditions and may represent a measurable percentage of the total bleached fluorophores. It is important to use a photostable fluorophore and to standardize bleaching protocols as well as intensity of the excitation light to minimize photoswitching (Dickson et al., 1997; Mueller et al., 2012; Sinnecker et al., 2005). Procedures for detecting, minimizing, and correcting for the effects of photoswitching of fluorescent proteins have been reported (Daddysman & Fecko, 2013; Mueller et al., 2012). It can also be overcome by using organic dyes coupled to genetically encoded tags (Morisaki & McNally, 2014).

Artifacts in FRAP analysis may also arise from the microscope detection system (Mueller et al., 2008). For example, when the laser intensity is switched from photobleaching mode to photo‐illumination mode, a transient reduction in fluorescence intensity can occur due to detector blinding. Although the effect of detector blinding on the FRAP curve is similar to that of irreversible photobleaching, they are distinguishable in that the detector blinding is also observed for the photo‐illumination mode of laser. In addition, detector blinding happens only when the photobleaching area is relatively large, whereas no detector blinding is observed for small spot bleaching FRAP data. Therefore, a correction for detector blinding may be required for FRAP experiments utilizing large bleaching areas (Mueller et al., 2008).

Other useful equations for benchmarking data analysis

The absolute value of D obtained experimentally contains important information about both the environment and structure of the diffusing molecule. The expected value of D can be approximated by theoretical predictions based on the molecular size, viscosity of the medium, and absolute temperature. The diffusion coefficient of a spherical object in 3D space can be approximated by the Stokes‐Einstein relation:

$$D=\frac{{\kappa }_{B}T}{6\pi \eta r}$$ (25)

where k _B, T, η, and r are the Boltzmann's constant, the absolute temperature, viscosity of the medium, and the radius of the spherical particle, respectively. This equation also provides a way to compare the diffusion coefficients of two different proteins with molecular weights, M ₁ and M ₂ as:

$$\frac{D_1}{D_2}=\sqrt[3]{\frac{M_2}{M_1}}$$ (26)

assuming the proteins can be approximated as spheres (i.e., M _i = (4/3)πr ³). This relation also has a practical importance for predicting whether a protein exists as a freely diffusing monomer, is part of a high‐molecular‐weight complex or reversibly binds to cellular components. For example, we have used this approach to show that the autophagy‐associated protein LC3 exists as part of a higher molecular weight complex based on a comparison of the diffusion coefficients and molecular weights of EGFP and EGFP‐LC3 (Drake et al., 2010; Kraft, Manral, et al., 2016), as well as characterized the size and dynamics of specific LC3‐associated complexes in live cells (Kraft & Kenworthy, 2012; Kraft et al., 2014; Kraft, Dowler, et al., 2016)

For lateral diffusion in membranes, the Saffman–Delbrück equation (Saffman & Delbrück, 1975) can be applied to approximate the diffusion coefficient of membrane proteins with radius r as:

$$D=\frac{{\kappa }_{B}T}{4\pi \mu h}\left(\log \frac{\mu h}{\eta r}-0.5772\right)$$ (27)

where η and µ are viscosities of the aqueous environment and membrane, and h is the thickness of the membrane. In practice however, membrane proteins do not diffuse as rapidly as predicted by this equation in cell membranes due to the complex environment of the cell (C. A. Day & Kang, 2023; Jacobson et al., 1995, 2019; Kure, Andersen, Mortensen, et al., 2020).

Critical Parameters

Use of best practices for live cell imaging and quantitative confocal microscopy

Accurate FRAP measurements require the use of best practices for quantitative confocal microscopy and live cell imaging. Summarizing all these considerations is beyond the scope of this article. For more information, we refer the interested reader to an excellent tutorial by Jonkman et al. (2020).

Choice of fluorophores

The choice of a fluorophore is a critical consideration for FRAP studies, and no single fluorophore excels in all situations. Some important considerations are:

• (1) Can the molecule of interest be labeled with an organic dye, and exogenously added to the cell?

• (2) Is the molecule of interest easily labeled with a genetically encoded tag such as GFP?

If the molecule of interest is a protein and can be easily genetically engineered with either an N‐ or C‐terminal fluorescent protein tag, we recommend using monomeric EGFP (∼27 kDa), Venus, or Emerald. EGFP has a high brightness (defined as the product of quantum yield and molar extinction coefficient) and good photostability during imaging and is easily irreversibly photobleached. Venus is a yellow GFP variant with very high brightness and reasonably good photostability. Emerald is spectrally similar to EGFP and is also an extremely bright and photostable option if photostability is an issue. Another important consideration is that tagging a molecule of interest with a fluorescent protein may alter native function or distribution (Fatti et al., 2025; Han et al., 2015; L. Huang et al., 2014; Snapp, 2009). Therefore, when possible, we recommend confirming that the fluorescent fusion protein retains the native behavior of the molecule of interest. For further discussion of the properties of specific fluorescent proteins and other genetically encoded fluorescent tags, see: Cranfill et al. (2016); R. N. Day and Davidson (2009); Kremers et al. (2011); Nienhaus and Nienhaus (2022); Rodriguez et al. (2017); and Thorn (2017).

For molecules of interest that can be easily isolated and added exogenously to a cell population, using a fluorescent small molecule to tag the protein is advantageous. One example is cholera toxin B subunit, a marker for lipid rafts that is commercially available conjugated with a variety of organic fluorophores. The organic Alexa Fluor dyes are a very good choice of fluorophore if FRAP is to be performed on the tagged molecule. These dyes are extremely bright, photostable, and easily photolyzed under higher‐intensity radiation. This class of fluorescent small molecules is commercially available in a wide range of colors, as well as different variants for the desired means of attaching them to the molecule of interest. For example, Alexa Fluor 488 (which is spectrally similar to GFP) can be ordered in a variety of reactive forms, including amine reactive, carboxylic acid reactive, aldehyde reactive, and thiol reactive, allowing direct labeling of the molecule of interest. As with fluorescent fusion proteins, labeling a protein with a fluorescent dye may alter the native function or distribution of the molecule of interest, and we recommend confirming that the fluorescent fusion protein retains the native behavior of the molecule of interest.

Choice of bleach ROIs and geometries

The quantitative method for obtaining the diffusion coefficient that we have described here requires a circular bleach region. The bleaching ROI should be defined as small as possible to approximate the post‐bleach profile as an exponential of a Gaussian laser profile. In practice, the signal‐to‐noise ratio of the measurements dictates how small the bleaching spot can be defined. In our experience, a bleaching ROI with a 1‐µm radius, using a 1.4 NA 40× objective, is a good starting point. It is important not to change any microscope settings between samples to quantitatively interpret the data. However, depending on the nature of the experiment, any size or shape bleaching geometry may be employed so long as it is used consistently and analyzed using the appropriate FRAP models or simulation approaches. Special considerations for the choice of bleach ROIs in FRAP studies of condensates can be found in Soranno (2019) and Taylor et al. (2019).

Bleaching times and sampling rate

The bleaching time should be minimized, while still resulting in an adequate amount of bleaching. The sampling rate should be maximized to obtain as many early time points in the recovery as possible, while being careful not to increase the amount of unintentional photobleaching (photofading) during imaging.

Troubleshooting

Examples of common problems encountered in FRAP experiments and their solutions are outlined in Table 1.

Table 1.

Troubleshooting Guide for Confocal FRAP Measurements

Problem	Possible cause	Solution
Molecule does not appear to bleach	Bleaching protocol is not sufficiently optimized	Confirm bleaching occurs as expected on a fixed sample; if not, increase the number of bleaching iterations and the bleaching laser intensity
	Molecules are diffusing so rapidly that they recover during the bleaching step	Decrease the size of the region being imaged, decrease the time delay between images, or perform bidirectional scans
	Fluorophore is resistant to photobleaching	Switch to a fluorophore that is more easily bleached
Unintentional bleaching occurs during the recovery phase	Laser intensity used to image during the recovery phase is too high	Reduce the intensity of the excitation light used for imaging while increasing the detector gain or opening the confocal pinhole
		Increase the time between frames and decrease the total number of images collected (ensuring enough data are collected to produce the FRAP curve)
		For slowly moving molecules such as membrane proteins, the intensity from the entire cell ROI can be used for normalization (see Fig. 1)
		Fit a single exponential decay to data collected under identical conditions but without a photobleaching event
	Fluorophore chosen is easily photobleached	Select a different fluorescent marker
Drift in focal plane	Sample has not reached thermal equilibrium with the microscope	Wait for ∼3 min after putting a new sample on the stage to allow it to reach thermal equilibrium prior to imaging
		Engage autofocus

Understanding Results

When performing FRAP for the first time, it may be helpful to evaluate your results with previously determined diffusion coefficients and mobile fractions FRAP. Our lab has used the FRAP methods presented above to characterize the diffusion of a wide range of molecules in cells (Table 2), which may be used as benchmarks. Purified EGFP in aqueous glycerol solutions of known viscosity can also serve as a useful control (Table 2) (Daddysman & Fecko, 2013; Kang et al., 2009; Mazza et al., 2008; Swaminathan et al., 1997). In principle, confocal FRAP experiments can resolve diffusion coefficients ranging over nearly three orders of magnitude (Kang et al., 2012).

Table 2.

Diffusion Coefficients of Various Molecules Determined by FRAP ^{a , b}

Molecule	Intracellular compartment	Cell line/medium	r n (µm)	D (µm2/s)	M f (%)	Citation
Cholera toxin B subunit	Plasma membrane	COS‐7, MEF	2.05	0.2	80	C. A. Day and Kenworthy (2012)
YFP‐GL‐GPI	Plasma membrane	COS‐7, MEF	2.05	1.1	85	C. A. Day and Kenworthy (2012)
YFP‐GT46	Plasma membrane	COS‐7, MEF	2.05	0.5	80	C. A. Day and Kenworthy (2012)
DiIC16	Plasma membrane	COS‐7, MEF	2.05	2.7	85	C. A. Day and Kenworthy (2012)
EGFP‐HRas C181S C184S	ER membrane, Cytoplasm	COS‐7	0.6‐1.1	7.0	84	Kang et al. (2010)
EGFP	Cytoplasm	COS‐7	0.55‐1.375	36‐48	95	Kang et al. (2009); Drake et al. (2010)
Venus	Cytoplasm	COS‐7	0.99	44	99	Kraft and Kenworthy (2012)
EGFP‐LC3	Cytoplasm	COS‐7	1.375	10	93	Drake et al. (2010)
Venus‐LC3	Cytoplasm	COS‐7	0.99	17	98	Kraft and Kenworthy (2012)
EGFP	Nucleus	COS‐7	0.55‐1.375	20‐34	80	Kang et al. (2009); Drake et al. (2010)
Venus	Nucleus	COS‐7	0.99	49	99	Kraft and Kenworthy (2012)
EGFP‐LC3	Nucleus	COS‐7	1.375	10	78	Drake et al. (2010)
Venus‐LC3	Nucleus	COS‐7	0.99	12	99	Kraft and Kenworthy (2012)
p53‐GFP	Nucleus	COS‐7	1.375	2	70	Drake et al. (2010)
EGFP		40% glycerol	1.15	38	90	Kang et al. (2009)
EGFP		50% glycerol	1.15	26	87	Kang et al. (2009)
EGFP		70% glycerol	1.15	11	82	Kang et al. (2009)

^a All FRAP was performed on a Zeiss LSM510 LSCM at 37°C under steady‐state conditions.

^b Abbreviations: DiIC16, 1,1′‐dihexadecyl‐3,3,3′,3′‐tetramethylindocarbocyanine perchlorate; EGFP, enhanced green fluorescent protein; YFP, yellow fluorescent protein; MEF, mouse embryonic fibroblast.

While the diffusion coefficients in Table 2 are representative, it is important to keep in mind that a variety of factors impact these values. In general, membrane proteins diffuse significantly more slowly than soluble proteins due to the higher viscosity of lipid bilayers compared to the cytoplasm or nucleoplasm. Another general rule is that soluble proteins that undergo free diffusion (e.g., GFP) diffuse more rapidly than proteins that exhibit reaction‐diffusion behavior or that are incorporated in large macromolecular complexes (Fig. 4) [also see Hinow et al. (2006); Kraft, Dowler, et al. (2016); Kraft, Manral, et al. (2016); and Kraft et al. (2014)]. Diffusion is also sensitive to temperature and results can vary between cell lines due to differences in underlying biology (C. A. Day & Kenworthy, 2012; Kenworthy et al., 2004). Additionally, the way in which the data is fit can play a major factor in the final diffusion coefficient. It is therefore important to take great care in finding the best model to fit the FRAP curves.

Time Considerations

It may take several sessions on the microscope to optimize a particular FRAP experiment, depending on the degree of complexity. Once all the necessary conditions have been worked out, a FRAP experiment can be completed in 3 to 4 days, with only a small amount of time given to the experiment on most days. On the first day, cells will need to be plated. On the second day, any transient transfections will need to be performed. On the third day, FRAP can be carried out. A minimum of 1 day will be required for data analysis.

The first time FRAP is performed in the lab, Excel spreadsheets and/or MATLAB code may need to be created de novo to analyze the data. This could take a considerable amount of time to accomplish. However, once these tools are assembled, analysis of future FRAP data can be reduced to a few minutes to a few hours.

Author Contributions

Charles Day: Data curation; formal analysis; investigation; methodology; validation; visualization; writing—original draft; writing—review and editing. Minchul Kang: Conceptualization; data curation; formal analysis; investigation; methodology; software; validation; visualization; writing—original draft; writing—review and editing. Lewis Kraft: Data curation; formal analysis; investigation; methodology; software; validation; writing—original draft; writing—review and editing. Anne Kenworthy: Conceptualization; funding acquisition; project administration; supervision; writing—original draft; writing—review and editing.

Conflict of Interest

The authors have no conflict of interest to declare.

Day, C. A. , Kraft, L. J. , Kang, M. , & Kenworthy, A. K. (2026). Analysis of protein and lipid dynamics using confocal fluorescence recovery after photobleaching. Current Protocols, 6, e70298. doi: 10.1002/cpz1.70298

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.