Method Article

From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope

DOI:

10.3791/51994

⸱

October 9th, 2014

 ,  ,  , 
1NEST Laboratory, Scuola Normale Superiore, 2Center for Nanotechnology Innovation, Instituto Italiano di Tecnologia, 3Laboratory for Fluorescence Dynamics, Department of Biomedical Engineering, University of California, Irvine

In This Article

Summary

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Spatial distribution and temporal dynamics of plasma membrane proteins and lipids is a hot topic in biology. Here this issue is addressed by a spatio-temporal image fluctuation analysis that provides conceptually the same physical quantities of single particle tracking, but it uses small molecular labels and standard microscopy setups.

Abstract

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It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the regulation of many cellular functions. However, due to the fast dynamics and the tiny structures involved, a very high spatio-temporal resolution is required to catch the real behavior of molecules. Here we present the experimental protocol for studying the dynamics of fluorescently-labeled plasma-membrane proteins and lipids in live cells with high spatiotemporal resolution. Notably, this approach doesn’t need to track each molecule, but it calculates population behavior using all molecules in a given region of the membrane. The starting point is a fast imaging of a given region on the membrane. Afterwards, a complete spatio-temporal autocorrelation function is calculated correlating acquired images at increasing time delays, for example each 2, 3, n repetitions. It is possible to demonstrate that the width of the peak of the spatial autocorrelation function increases at increasing time delay as a function of particle movement due to diffusion. Therefore, fitting of the series of autocorrelation functions enables to extract the actual protein mean square displacement from imaging (iMSD), here presented in the form of apparent diffusivity vs average displacement. This yields a quantitative view of the average dynamics of single molecules with nanometer accuracy. By using a GFP-tagged variant of the Transferrin Receptor (TfR) and an ATTO488 labeled 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine (PPE) it is possible to observe the spatiotemporal regulation of protein and lipid diffusion on µm-sized membrane regions in the micro-to-milli-second time range.

Introduction

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Starting from the original “fluid mosaic” model by Singer and Nicolson, the picture of cellular plasma membrane has been continuously updated during the last decades in order to include the emerging role of cytoskeleton and lipid domains1,2.

The first observations were obtained by fluorescent recovery after photobleaching (FRAP) unveiling that a significant fraction of membrane proteins is immobile3-5. These pioneering studies, although very informative, suffered from the relatively poor resolution in space (microns) and time (seconds) of FRAP setups. Also, being an ensemble averaging measurement, FRAP lacks in giving information on single molecule behavior.

In this context, the possibility to specifically label a single molecule with very bright tags (allowing the study of the diffusion process one molecule at a time) has been very successful. Particularly, by pushing the time resolution of the Single Particle Tracking (SPT) approach to the microseconds timescale, Kusumi, et al. gained access to unknown features of lipid and protein dynamics that greatly contributed to the recognition of the role of actin-based membrane skeleton in membrane physiology6,7. These findings generated the so-called the ‘picket and fence’ model, in which lipid and protein diffusion is regulated by actin-based skeleton. However, in order to have access to the huge amount of information provided by SPT many experimental issues have to be addressed. Particularly, the labeling procedure is typically composed by many steps like production, purification and introduction of the labeled species into the system. Furthermore, big labels, like quantum dots or metal nanoparticles, are often required to reach the sub-millisecond timescale and the crosslinking of the target molecules by the label could not be avoided in many cases. Finally, many trajectories have to be recorded to fit statistical criteria and concomitantly a low-density of the label is required to allow tracking.

Compared to SPT, fluorescence correlation spectroscopy (FCS), overcoming many of these drawbacks, represents a very promising approach to study molecular dynamics. In fact, FCS works well also with dim and dense labels, enabling to study the dynamics of fluorescent protein-tagged molecules in transiently transfected cells. Also, it allows reaching high statistics in a limited amount of time. Finally, despite the “high” density of labels FCS provides single molecules information. Thanks to all these properties, FCS represents a very straightforward approach and has been extensively applied to study lipid and protein dynamics both in model membranes and in live-cells8-10. Many different approaches have been proposed to increase the ability of FCS to reveal the details of molecular diffusion. For instance, it was shown that by performing FCS on differently-sized observation areas one can define an “FCS diffusion law” enlightening hidden features of molecular motion11,12. Besides being varied in size, the focal area was also duplicated13, moved in space along lines14-20 or conjugated with fast cameras21,22. Using these ‘spatio-temporal’ correlation approaches, relevant biological parameters of several membrane components were quantitatively described on both model membranes and actual biological ones, thus yielding insight into membrane spatial organization.

However, in all the FRAP and FCS applications described so far the size of the focal area represents a limit in spatial resolution that cannot be overcome. Several super-resolution imaging methods have been recently developed to bypass this limit. Some are based on localization precision, such as stochastic optical reconstruction microscopy (STORM)23,24, photoactivation localization microscopy (PALM)25, fluorescence PALM (FPALM)26, and single-particle tracking PALM (sptPALM)27: the relatively large amount of photons required at each snapshot, however, limits the time resolution of these methods to at least several milliseconds, thus hampering their applicability in vivo.

In contrast, a promising alternative for super resolution imaging have been opened by spatially modulating the fluorescence emission with stimulated emission depletion methods (STED or reversible saturable optical fluorescence transitions (RESOLFT))28,29. These approaches combine the shaping of the observation volume well below the diffraction limit with the possibility to use fast scanning microscopes and detection systems. In combination with fluorescence fluctuation analysis, STED microscopy allowed to directly probe the nanoscale spatiotemporal dynamics of lipids and proteins in live cell membranes30,31.

The same physical quantities of STED-based microscopy can be obtained by a modified spatio-temporal image correlation spectroscopy (STICS32,33) method that is suitable for the study of the dynamics of fluorescently-tagged membrane proteins and/or lipids in live cells and by a commercial microscope. The experimental protocol presented here is composed by few steps. The first one requires a fast imaging of the region of interest on the membrane. Then, the resulting stack of images is used to calculate the average spatial-temporal correlation functions. By fitting the series of correlation functions, the molecular ‘diffusion law’ can be obtained directly from imaging in the form of an apparent diffusivity (Dapp)-vs-average displacement plot. This plot critically depends on the environment explored by the molecules and allows recognizing directly the actual diffusion modes of the lipid/protein of interest.

In more details, as previously shown34, the spatio-temporal auto-correlation function of the acquired image series critically depends on the dynamics of the molecules moving in the collected image series (please note that the same reasoning can be applied in a line acquisition where just one dimension in space is considered). In particular, we define the correlation function as:

figure-introduction-1(1)

where figure-introduction-2 represents the measured fluorescence intensity in the position x, y and at time t, figure-introduction-3 and figure-introduction-4 represents the distance in the x and y directions respectively,figure-introduction-5 represents the time lag, and figure-introduction-6 represents the average. This function can be expressed as:

figure-introduction-7(2)

where ‘N’ represents the average number of molecules in the observation area, figure-introduction-8 represents the convolution operation in space, and figure-introduction-9 represents the autocorrelation of the instrumental waist. This latter can be interpreted as a measure of how the photons of a single emitter are spread out in space due to the optical/recording setup (the so called Point Spread Function, PSF, generally well approximated by a Gaussian function). Finally, figure-introduction-10 represents the probability to find a particle at a distance figure-introduction-11 and figure-introduction-12 after a time delay figure-introduction-13. If we consider a diffusive dynamics, in which particles move randomly in all directions and net fluxes are not present, this function is also well approximated by a Gaussian function where the variance can be identified as the Mean Square Displacement (MSD) of the moving particle. Thus, the waist of the correlation function (also referred as figure-introduction-14), can be defined as the sum of the particle MSDs and the instrumental waist and can be measured by a Gaussian fitting of the correlation function for each time delay. The measured iMSD can be used to calculate an apparent diffusivity of the moving molecules figure-introduction-15 and an average displacementfigure-introduction-16 as:

figure-introduction-17(3)

figure-introduction-18(4)

Few considerations on the experimental setup used can guide the reader throughout the following sections. In order to selectively excite the fluorophores on the basal membrane of living cells we will use a total internal reflection (TIR) illumination, using a commercial TIR fluorescence (TIRF) microscope (details can be found in the material section). Moreover, in order to collect the fluorescence we will use a high magnification objective (100X NA 1.47, high numerical aperture is required for TIRF illumination) and an EMCCD camera (physical size of the pixel on the chip 16 μm). To reach a pixel size of 100 nm we apply an additional magnification lens of 1.6X. As discussed below, a time resolution below 1 msec would be required to properly describe the dynamics of fast membrane lipids below 100 nm. In order to reach this temporal resolution we need to select a region of interest (ROI) smaller than the whole chip of the camera (512 x 512). In this way, the camera will read a reduced number of lines increasing the time resolution. However, in this readout regime the frame time would be limited by the time required to shift the charges from the exposure to the readout chip on the camera and is usually in the order of milliseconds for 512 x 512 pixel EMCCD. To beat this limit, an emerging technology allows shifting the ROI-lines only instead of the whole frame, with a practical effective reduction of the exposed chip size (called Cropped Sensor Mode in our EMCCD). For this configuration to be effective, the chip outside of the ROI must be covered by a couple of slits mounted in the optical path. Thanks to this setup a time resolution down to 10-4 seconds can be achieved. Please note, however, that this approach can be coupled with many different experimental setups, as explained in the ‘discussion’ section.

Demonstration of the method will be provided in live cells, by using both an ATTO488 labeled 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine (ATTO488-PPE) and a GFP-labeled variant of the Transferrin Receptor (GFP-TfR). In the case of ATTO488-PPE this approach can successfully recover an almost constant Dapp as a function of average displacement indicating a mostly free diffusion, as previously reported30,35. By contrast, TfR-GFP shows a decreasing Dapp as a function of average displacement, suggesting partially-confined diffusion6. Moreover, in the latter case it is possible to quantify the local diffusion constant and the average confinement area over many microns on the membrane plane.

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Protocol

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1. System Calibration

  1. Point Spread Function (PSF) calibration
    1. Dilute 10 µl of 30 nm fluorescent bead solution (about 5 μM) in 90 µl of distilled water and then sonicate the solution for 20 min. Cut a square (1 cm x 1 cm) piece of agarose gel (3%) and deposit 10 µl of the solution on the top of the gel. Overturn the piece of gel on the bottom glass of a 2 cm Petri dish and squeeze the drop on the glass.
    2. Turn on the acquisition setup, put the sample in the holder, set the camera exposure and EMgain (100 msec and 1,000 are good parameters but optimize according to the system) and wait for the camera to cool down.
    3. Set camera exposure to 100 msec, camera EMgain to 1,000, acquisition mode to Frame Transfer, 100 repetition and auto save setting.
    4. Using the eyepiece and transmitted light focus on the border of the gel and then move the objective to the center of the gel, adjust the focus and start the laser alignment procedure (in LAS AF, select ‘TIRF setup’ and follow the auto alignment procedure).
    5. Find a field of view with isolated single spots, accurately focus on the brighter spot (that usually represents beads aggregate) as a reference, acquire 100 frames and repeat the step 5-6 times in order to acquire several single spots.
    6. Import the acquired series to a data processing program and average the stack in time (Figure 1A) and select a single isolated bead. Take care to select the smallest ones to avoid particle aggregates.
    7. Fit the selected intensity distribution (an example of single beads profile is presented in Figure 1B) with a Gaussian function using the command “gaussfit” (in the ICS-Matlab tools in the Materials in Matlab). Verify the goodness of the fit by inspecting the obtained residuals (an example of fitted Gaussian profile with the corresponding residuals is presented in Figure 1B).
  2. Camera calibration
    1. Turn on camera and wait for the camera to cool down. Set camera acquisition setting, (i.e., for the used camera we set the exposure to 0.5 msec, camera EMgain to 1,000, acquisition mode to Cropped Mode, the ROI size to 32 x 128, 10,000 repetitions) and start the acquisition of the camera background signal.
    2. Import acquired frame series to a data processing program. Calculate and inspect the average intensity in each pixel in order to verify that the camera background is approximately flat in the selected region of the chip. In Cropped Mode, remove the first and the last few horizontal lines (3 to 10 depending on the size of the ROI) for each frame because the camera background is usually biased in the border lines.
    3. Create a histogram of the values (also defined Digital Level, DL) in acquired images stack (using command ‘hist’ in Matlab) and plot the logarithm of resulting frequency (using semilogy command in Matlab). An example of DL distribution for camera background is presented in Figure 2.
      NOTE: If the camera is working well, the plot will show an approximately Gaussian peak (a parabolic profile in log-scale) representing the distribution of values associated to zero photon followed by an exponential decay (a line with negative slope in log-scale) that represents the distribution of values associated to 1 photon (Figure 2). In particular, the center and the variance of the Gaussian function represent the camera offset and error, respectively, while the decay constant of the exponential part represents an estimation of the DL assigned by the camera to each single photon. In Matlab use the section “CalibrateCamera” of the Script in supporting materials.
    4. Repeat the operation for all the selected Camera EMGain and Gain.

2. Labeled Cell Preparation

  1. To prepare the liposomes required for lipid incorporation 36, dissolve separately 1 mg of DOPE (1,2-dioleoyl-sn-glycero-3-phosphoethanolamine), 1 mg of DOTAP (1,2-dioleoyl-3-trimethylammonium-propane), and 1 mg of PPE-ATTO488 in 1 ml of chloroform. Mix together 0.5 ml of DOPE solution, 0.5 ml of DOTAP solution, and 25 µl of PPE-ATTO488 solution and dry under vacuum for 24 hr. Add 0.5 ml of HEPES buffer 20 mM, vortex for 15 min and sonicate for 15 min at 40 °C.
  2. To prepare the cell, wash 3 times with PBS a p100 dish of confluent CHO-K1 (Chinese Hamster Ovary), add 1 ml of trypsin and store in incubator for 5 min. Suspend detached cells adding 9 ml of DMEM/F12 medium supplemented with 10% of FBS and seed 150 µl of cell solution in a Petri dish containing 800 µl of the same medium.
  3. Store in incubator for 24 hr at 37 °C and 5% CO2. For lipid incorporation, replace cell medium with 500 µl of serum-free medium; after 30 min, add 2 µl of liposomes solution; after 15 min wash with PSB and add new DMEM/F12 medium for imaging.
  4. For Transfection, transfect cells according to Lipofectamine protocol (Manufacturer instructions) using TfR-GFP plasmid and store 24 hr in incubator before imaging.

3. Data Acquisition

  1. Setup preparation
    1. In order to thermostat the microscope, 24 hr before the experiment turn on the incubator.
    2. In order to apply the fastest achievable acquisition time, work in Cropped Sensor Mode (see introduction) and use a first camera for imaging (camera 1) and second camera to select the cell (camera 2). A scheme of the setup configuration is presented in Supplementary Figure S1. Then, to align the two cameras turn on the microscope and wait for the cameras to cool down.
    3. Set on both cameras the parameters for transmitted light imaging (i.e., 20 msec for exposure time, 1 for EM Gain) and put the microscope in Bright Field mode.
    4. Put the samples in the holder and focus using eyepiece, send the light to camera 1 and gently push the slits allowing light only on the ROI used for cell imaging (here a 32 x 32 pixels ROI).
    5. Move a cell in the selected region and send the light to the camera 2, then draw a ROI in the software that control camera 2 in order to have a reference.
  2. Imaging (Figure 3A)
    1. First of all, align the TIRF laser according to the procedure of your setup. In our setup, select the ‘TIRF setup’ and start the auto alignment procedure. When the laser is aligned set 70 nm of penetration depth (about 70°).
    2. Set exposure time to 70 msec and EMGain to 100 on both camera 1 and camera 2; then, select a cell using camera 1, then send the light on camera 2 and accurately focus the cell membrane. Set the minimum exposure on camera 2, 1,000 EMGain, Cropped Sensor Mode, 105 repetitions and set autosave as fits files (Flexible Image Transport System, a format that can be easily managed).
    3. Start the acquisition to record the image series. Release the Gain and the cropped Mode to allow temperature stabilization before acquiring a new cell, then repeat the last two steps in order to acquire 8-10 cells.

4. Calculation of the Mean Square Displacement from Imaging (iMSD)

NOTE: The following protocol can be directly applied to raw data. At the same time, the whole protocol is valid for data acquisitions simulated both in Matlab and in SimFCS. The link to the corresponding tutorials can be found in the ‘Materials’ section.

  1. Calculation by Matlab
    1. Import the acquired series to Matlab using ImportImageSeries script. Calculate the average intensity of each image in time using the command mean on the first 2 dimensions and use plot to see the resulting vector.
    2. If more than 10% of photobleaching is present, discard the series or remove the first part of them. If it is lower, try to correct the effect on the correlation function by subtracting to each image its average intensity, as shown before37.
    3. Calculate the average intensity of each pixel by using mean on the third dimension and see resulting image.
      NOTE: Particular attention is required in order to avoid artifactual correlations. In fact, as previously shown for similar techniques38, cell borders as well as out of focus vesicles could introduce a strong correlation. If the inspection of the average image reveals cell borders or out of focus vesicles, try to exclude the region involved otherwise discard the acquisition. To correct the effect of this immobile structures subtract the average temporal intensity from each pixel39.
    4. Calculate the spatiotemporal correlation (G(ξ,χ,τ)) by using the function CalculateSTICScorrfunc. Remove G(ξ,χ,0) because the correlation due to the shot noise in low-light regime dominates G(0,0,0); the correlation due to the detector dominates the G(±1,0,0), and particle movement during the exposure time could deform G(ξ,χ,τ) for τ=0 by increasing the measured waist (this effect disappears for τ>0)34.
    5. Average G(ξ,χ,τ>0) using a logarithmic time-bin to reduce the noise by using “LogBinStack” function in Supporting Material and then fit the resulting G(ξ,χ,τ) using the function “gaussfit” of the ICS-Matlab tools in the Materials to recover the iMSD (the second column of the resulting array).
    6. Plot the obtained waist σ(τ)2 (iMSD) as a function of time. If the data are too noisy, try to increase the number of acquired frames, increase the laser power, average more G(ξ,χ,τ) together.
  2. Calculation by SimFCS
    1. Open the acquired files with ImageJ using BioFormat importer plugin and save acquired series as Tiff sequence.
    2. Open SimFCS and select RICS tool and select File>Import Multiple Images (Supplementary Figure S2).
    3. Select Fit, insert the correct acquisition parameters and close the fit window (Supplementary Figure S3).
    4. Select Display>Average Intensity>CH1 and verify the presence of photobleaching (Supplementary Figure S4).
    5. If more than 10% of photobleaching is present discard the series or if it is possible load again the image sequence removing the first part of the series.
    6. If bleaching it is lower than 10% select Tools>iMSD>Set Parameters, check ‘Use moving average’, set in the ROI panel on the left a number of frame for the moving average paying attention that the correspondent time is higher than the characteristic diffusion time (for particle moving at 1 µm2sec-1 a time of 10 sec is a good moving average)
    7. Select Tools>iMSD>Calculate iMSD (Supplementary Figure S5) and fit and export the iMSD from the memo pad (Supplementary Figure S6).

5. Calculation of the Diffusion Law from the iMSD

  1. Fit the first few points to extrapolate the intercept (σ02) (5 points are usually enough but more points can be fitted if they show a linear behavior) and compare this value with the previously measured PSF2. If they are comparable, the dynamics of isolated fluorophores are being followed. By contrast, if σ02 >> PSF2 try to acquire faster to ensure that no hidden dynamics are present34.
  2. Calculate the apparent diffusivity (Dapp) and the average displacement (R) using equations 3 and 4 (see Introduction).
  3. Plot Dapp as a function of R to obtain a diffusion law comparable with what is measured with spot variation based FCS12 (Figure 3D).

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Results

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In order to calibrate the instrumental waist, the image of a single fluorescent nano-bead can be measure as described in Protocol step 1.1. A typical fluorescent image of these beads is presented in Figure 1. The fitting of intensity distribution by a 2D Gaussian function gives back good residuals and allows measuring the instrumental waist at 270 nm. This value is in good agreement with the expected diffraction limit estimated by the Rayleigh equation. This calibration is not necessary for the measureme...

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Discussion

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Single particle tracking (SPT) represents one of the most common strategies to study molecular dynamics and it has the great advantage of measuring particle trajectories. This in turn allows probing the behavior of even few labeled particles in a complex system. However, to reach this advantage SPT typically needs a low density of the probe and very bright labels. Particularly, to gain high temporal resolution (μsec range) inorganic probe are usually required (e.g., quantum dots or metal nanoparticles): in ...

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Disclosures

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The authors have nothing to disclose.

Acknowledgements

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This work is supported in part by NIH-P41 P41-RRO3155 and NIH P50-GM076516 (grant to EG), and Fondazione Monte dei Paschi di Siena (grant to FB).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
iXon Ultra 897AndorDU-897U-CS0
SolisAndor
CHO-K1ATCCCCL-61
ATTO 488 labeled PPEATTO-TEC GmbHAD 488-151
DOPEAvanti Polar Lipids, Inc.850725
DOTAPAvanti Polar Lipids, Inc.890890
100x Penicillin-Streptomycin-GlutamineGibco10378-016
DMEM/F-12Gibco21331
FBSGibco10082147
HEPESGibco15630-106 
PBSGibco10010-023
SimFCS 3.0Globals Softwarethe software can be downloaded here: http://www.lfd.uci.edu/globals/
DMI6000 with TIRF modulusLeica
LAS AFLeica
Lipofectamine 2000Lipofectamine11668019
MatlabMathWork
ImageJNIH
[header]
C-terminal GFP tagged Tranferrin ReceptorOriGeneRG200980
AgarSigma AldrichA5306
ChloroformSigma Aldrich528730
Latex beads, fluorescent yellow-green, 30 nmSigma AldrichL5155
SONICA Ultrasonic CleanersSOLTECETH S3
Petri DishesWillcoGWSt-3522
Bio-Format importer for Matlabhttp://www.openmicroscopy.org/site/support/bio-formats5/users/matlab/
ICS-MatLab Toolshttps://www.cellmigration.org/resource/imaging/software/ICSMATLAB_28-02-06.zip
Simulation by Matlab Tutorialhttps://www.cellmigration.org/resource/imaging/icsmatlab/ICSTutorial.html
Simulation by SimFCS Tutorialhttps://www.cellmigration.org/resource/imaging/ppt-pdf/RICS%20Simulations.ppt

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Total Internal Reflection Fluorescence MicroscopyPoint Spread Function CalibrationFluorescent Bead ImagingSpatio temporal AutocorrelationMean Square DisplacementApparent DiffusivityGFP tagged Transferrin Receptor

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