Name: from fast fluorescence imaging to molecular diffusion law on live cell membranes in a commercial microscope
Uploaded: 2014-10-09T15:00:00.000+00:00
Duration: 15 min 10 s
Description: Watch this Scientific Journal Video about From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope at JoVE.com

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).

1. System Calibration
<ol>
	<li>Point Spread Function (PSF) calibration
	<ol>
		<li>Dilute 10 &#181;l of 30 nm fluorescent bead solution (about 5 &#956;M) in 90 &#181;l 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 &#181;l 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.</li>
		<li>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.</li>
		<li>Set camera exposure to 100 msec, camera EMgain to 1,000, acquisition mode to Frame Transfer, 100 repetition and auto save setting.</li>
		<li>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 &#8216;TIRF setup&#8217; and follow the auto alignment procedure).</li>
		<li>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.</li>
		<li>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.</li>
		<li>Fit the selected intensity distribution (an example of single beads profile is presented in Figure 1B) with a Gaussian function using the command &#8220;gaussfit&#8221; (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).</li>
	</ol>
	</li>
	<li>Camera calibration
	<ol>
		<li>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.</li>
		<li>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.</li>
		<li>Create a histogram of the values (also defined Digital Level, DL) in acquired images stack (using command &#8216;hist&#8217; 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 &#8220;CalibrateCamera&#8221; of the Script in supporting materials.</li>
		<li>Repeat the operation for all the selected Camera EMGain and Gain.</li>
	</ol>
	</li>
</ol>
2. Labeled Cell Preparation
<ol>
	<li>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 &#181;l 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 &#176;C.</li>
	<li>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 &#181;l of cell solution in a Petri dish containing 800 &#181;l of the same medium.</li>
	<li>Store in incubator for 24 hr at 37 &#176;C and 5% CO2. For lipid incorporation, replace cell medium with 500 &#181;l of serum-free medium; after 30 min, add 2 &#181;l of liposomes solution; after 15 min wash with PSB and add new DMEM/F12 medium for imaging.</li>
	<li>For Transfection, transfect cells according to Lipofectamine protocol (Manufacturer instructions) using TfR-GFP plasmid and store 24 hr in incubator before imaging.</li>
</ol>
3. Data Acquisition
<ol>
	<li>Setup preparation
	<ol>
		<li>In order to thermostat the microscope, 24 hr before the experiment turn on the incubator.</li>
		<li>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.</li>
		<li>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.</li>
		<li>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).</li>
		<li>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.</li>
	</ol>
	</li>
	<li>Imaging (Figure 3A)
	<ol>
		<li>First of all, align the TIRF laser according to the procedure of your setup. In our setup, select the &#8216;TIRF setup&#8217; and start the auto alignment procedure. When the laser is aligned set 70 nm of penetration depth (about 70&#176;).</li>
		<li>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).</li>
		<li>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.</li>
	</ol>
	</li>
</ol>
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 &#8216;Materials&#8217; section.
<ol>
	<li>Calculation by Matlab
	<ol>
		<li>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.</li>
		<li>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.</li>
		<li>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.</li>
		<li>Calculate the spatiotemporal correlation (G(&#958;,&#967;,&#964;)) by using the function CalculateSTICScorrfunc. Remove G(&#958;,&#967;,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(&#177;1,0,0), and particle movement during the exposure time could deform G(&#958;,&#967;,&#964;) for &#964;=0 by increasing the measured waist (this effect disappears for &#964;&#62;0)34.</li>
		<li>Average G(&#958;,&#967;,&#964;&#62;0) using a logarithmic time-bin to reduce the noise by using &#8220;LogBinStack&#8221; function in Supporting Material and then fit the resulting G(&#958;,&#967;,&#964;) using the function &#8220;gaussfit&#8221; of the ICS-Matlab tools in the Materials to recover the iMSD (the second column of the resulting array).</li>
		<li>Plot the obtained waist &#963;(&#964;)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(&#958;,&#967;,&#964;) together.</li>
	</ol>
	</li>
	<li>Calculation by SimFCS
	<ol>
		<li>Open the acquired files with ImageJ using BioFormat importer plugin and save acquired series as Tiff sequence.</li>
		<li>Open SimFCS and select RICS tool and select File&#62;Import Multiple Images (Supplementary Figure S2).</li>
		<li>Select Fit, insert the correct acquisition parameters and close the fit window (Supplementary Figure S3).</li>
		<li>Select Display&#62;Average Intensity&#62;CH1 and verify the presence of photobleaching (Supplementary Figure S4).</li>
		<li>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.</li>
		<li>If bleaching it is lower than 10% select Tools&#62;iMSD&#62;Set Parameters, check &#8216;Use moving average&#8217;, 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 &#181;m2sec-1 a time of 10 sec is a good moving average)</li>
		<li>Select Tools&#62;iMSD&#62;Calculate iMSD (Supplementary Figure S5) and fit and export the iMSD from the memo pad (Supplementary Figure S6).</li>
	</ol>
	</li>
</ol>
5. Calculation of the Diffusion Law from the iMSD
<ol>
	<li>Fit the first few points to extrapolate the intercept (&#963;02) (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 &#963;02 &#62;&#62; PSF2 try to acquire faster to ensure that no hidden dynamics are present34.</li>
	<li>Calculate the apparent diffusivity (Dapp) and the average displacement (R) using equations 3 and 4 (see Introduction).</li>
	<li>Plot Dapp as a function of R to obtain a diffusion law comparable with what is measured with spot variation based FCS12 (Figure 3D).</li>
</ol>

The authors have nothing to disclose.

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 (&#956;sec range) inorganic probe are usually required (e.g., quantum dots or metal nanoparticles): in this case a complex procedure of production, labeling and insertion into the system is necessary. Compared to SPT the present method shows some key advantages. First of all, this approach can be used in conjugation with fluorescent proteins. Thus, compared to SPT, a higher temporal resolution is achieved (on the same label) thanks to the lower amount of photons required34. More in detail, this property allows pushing the temporal resolution below 10&#8722;3 sec also when using encodable fluorescent proteins, and this timescale gives exclusive access to the nanoscale dynamics of membrane constituents. Finally, it is worth noting that molecular diffusion laws are described by analyzing the full space-time correlation function, with no need to track each molecule.
The comparison with STED-based FCS is also interesting. In a STED-FCS measurement the average transit time of molecules for decreasing observation volumes is measured by the temporal correlation of the fluorescence signal. This allows obtaining a local measurement of the molecular dynamics also below the diffraction limit. In the presented approach the diffusion law is measured as the average of all the particles moving in the selected ROI, observed by means of standard, diffraction-limited, observation volume. However, reported results demonstrate that this method is not limited by diffraction, but only by the temporal resolution available. In fact, although a diffraction-limited acquisition is used to detect fluctuations (analogously to what is done in other super-resolution techniques, such as PALM and STORM), molecular displacements well below the diffraction limit can be (directly) calculated, as already demonstrated by using STICS to measure molecular flows32. Moreover, unlike STED-FCS, this approach can be easily applied to a wide range of commercial and existing microscopy setups, such as raster scanning microscopes or wide field camera-based microscopes. It is worthy of mention that STED-FCS measurements of molecular diffusion laws strictly require a fluorophore-dependent calibration of the size of the instrumental waist. Oppositely, the measurement presented here does not require a system calibration (needed only for the estimation of particle size).
The actual resolution in the measurement of particle displacements by the presented method depends on how accurately we can measure the correlation function. Consequently, it is not inherently limited by diffraction, analogously to the SPT case where the resolution depends on how accurately the particle &#8220;image&#8221; is measured. To measure a significant correlation in less than 1 min for the proposed experiments, few photons (usually below 10 photons) for each particle in each frame are enough. In fact, the contribution of all the observed particles is averaged together when the correlation function is calculated, even if particles are not isolated. This property is intrinsic of fluctuation correlation methods and allows using dim and dense labels, such as fluorescent proteins transfected in live cells.
With this in mind it appears clear that the minimum measurable displacement depends on the diffusivity of the particle and on the time resolution of the imaging setup. As an example, please consider diffusion of molecules on the cell membrane, where the maximum measured diffusivity for proteins or lipids is around 5 &#956;m2sec-1. Under these conditions, we need a time resolution of approximately 10-4 sec to catch an average displacement of 50 nm. This time resolution can be achieved by fast scanning microscopes along single lines or by fast EMCCD camera, where time resolution coincides to the exposure time, as showed here.
An additional essential requirement for this method to accurately describe molecular dynamics is a correct spatial sampling. In fact, in order to fit the correlation function we need a spatial sampling (pixel size) lower than the waist of the instrumental PSF. In most commercial microscopes (confocal or wide field), the PSF waist spans from 200 nm to 500 nm (depending mainly on the numerical aperture of the selected objective and on the wavelength used) and can be easily measured by a calibration experiment using nano-sized fluorescent beads. Thus, a pixel size of 70-150 nm (3 times lower than the instrumental waist) can be enough. However, the pixel size can be adapted to the system under study taking into account a simple rule: lower the pixel size, higher the accuracy in the description of the correlation function. Furthermore, the minimum size of the image to be acquired has to be at least 3-times larger than the maximum displacement of interest (plus the instrumental waist). This is required in order to reach a good convergence of the fitting algorithm and a statistically significant sampling of molecular displacements. As an example, to study average molecular displacements smaller than few hundreds of nanometers (e.g., 200 nm) an image size of few microns is enough. Moreover, the total number of pixels (taking constant the pixel size) impacts on the quality of the correlation function. In fact, a larger image allows averaging more information in the correlation function, even if at expense of the time resolution. Concerning the camera-based system used here, please note that the physical size of the pixel on the chip is fixed. Consequently, decreasing the pixel size lowers the signal in each pixel (that depends on the square of the pixel size), decreases the field of view, and requires higher magnification power. On the other hand, in a scanning system, where the observation area is fixed, decreasing the pixel size usually results in an increased pixel number at the expense of the time resolution.
Few details about the detector used have to be discussed. Unlike single-photon detectors, EMCCD systems measure an average intensity (digital level, DL) that is not directly proportional to the collected light due to the presence of an offset. Even if this offset is low compared to the dynamic range of the camera (few hundred compared to 216 in 16 bit readout) and negligible in experiments where many photons are collected, it has to be taken into account to obtain a correct normalization of the correlation function. Also, the offset can be used as a reference in low-light conditions in order to identify the amount of signal collected. Moreover, in order to estimate the average amount of photons that are collected during the acquisition, the average digital level associated to each collected photon has to be measured. This quantity can be retrieved by exposing the camera to a very low light intensity (e.g., the background light in the room); in fact, in this case, we can reasonably assume that just single photons reach the camera, i.e. the measured intensity can be related to zero or one photon only.
Finally, let us comment on how some alternative acquisition systems (i.e., different microscopy setups) can be used to perform the presented measurements. First of all, the &#8216;W&#8217;factor in Equation 2 (that represents the autocorrelation of the instrumental PSF) can be adapted to the particular acquisition system used in order to fit the experimental correlation function. As previously shown34, an easy case is the acquisition whit a laser scanning microscope when the scanning speed is significantly higher than particle dynamics. In such a case, in fact, the movement of particles during the acquisition time (i.e., line time) can be considered negligible and the correlation function is well approximated by a Gaussian function. In the context of the emerging imaging technologies, an interesting approach is based on the possibility to produce very thin light sheets (1-2 &#956;m) through the sample41. The light sheet allows selective illumination of a single plane (Single plane illumination microscopy, SPIM) in the sample and, combined with a camera-based acquisition system, fast optical sectioning in 3D42. Because of these characteristics, SPIM has been successfully conjugated with FCS43 and could represent a valid tool to extend the presented analysis to 3D environments, such as the cytoplasm or the nucleus of living cells.
Summarizing, from an experimental point of view this approach requires only the access to a microscope equipped with a fast acquisition module. The protein of interest can be tagged with any fluorescent protein or organic fluorophore, thus allowing also for multicolor imaging. In this context, we envision the possibility to use cross-iMSD analysis to select sub-populations of molecules and reveal interactions and co-diffusion on live cell membranes. Finally, we believe that this approach may represent a powerful tool to study proteins and/or lipids undergoing dynamic partitioning within nanodomains on the plasma membrane. In this case, the highly-variable size and lifetime of the nanodomains introduce an additional level of complexity in real data which would require further methodological implementations including 2-color imaging, local analysis (e.g., 2D pair correlation) and/or fluorescence anisotropy.

Starting from the original &#8220;fluid mosaic&#8221; 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 &#8216;picket and fence&#8217; 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 &#8220;high&#8221; 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 &#8220;FCS diffusion law&#8221; 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 &#8216;spatio-temporal&#8217; 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 &#8216;diffusion law&#8217; 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:
<img fo:content-width="2.3in" src="/files/ftp_upload/51994/51994eq1.jpg" width="230" />(1)
where <img fo:content-width=".5in" src="/files/ftp_upload/51994/xyt.jpg" width="50" /> represents the measured fluorescence intensity in the position x, y and at time t, <img fo:content-width=".08in" src="/files/ftp_upload/51994/e.jpg" width="8" /> and <img fo:content-width=".1in" src="/files/ftp_upload/51994/x.jpg" width="10" /> represents the distance in the x and y directions respectively,<img fo:content-width=".1in" src="/files/ftp_upload/51994/t.jpg" width="10" /> represents the time lag, and <img fo:content-width=".2in" src="/files/ftp_upload/51994/brackets.jpg" width="20" /> represents the average. This function can be expressed as:
<img fo:content-width="2.3in" src="/files/ftp_upload/51994/51994eq2.jpg" width="230" />(2)
where &#8216;N&#8217; represents the average number of molecules in the observation area, <img fo:content-width=".10in" src="/files/ftp_upload/51994/circlex.jpg" width="10" /> represents the convolution operation in space, and <img fo:content-width=".5in" src="/files/ftp_upload/51994/ex.jpg" width="50" /> 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, <img fo:content-width=".5in" src="/files/ftp_upload/51994/ext.jpg" width="50" /> represents the probability to find a particle at a distance <img fo:content-width=".10in" src="/files/ftp_upload/51994/e.jpg" width="10" /> and <img fo:content-width=".10in" src="/files/ftp_upload/51994/x.jpg" width="10" /> after a time delay <img fo:content-width=".10in" src="/files/ftp_upload/51994/t.jpg" width="10" />. 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 <img fo:content-width=".3in" src="/files/ftp_upload/51994/2t.jpg" width="30" />), 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 <img fo:content-width=".4in" src="/files/ftp_upload/51994/dappt.jpg" width="40" /> and an average displacement<img fo:content-width=".4in" src="/files/ftp_upload/51994/rt.jpg" width="40" /> as:
<img fo:content-width="1.7in" src="/files/ftp_upload/51994/51994eq3.jpg" width="170" />(3)
<img fo:content-width="1.7in" src="/files/ftp_upload/51994/51994eq4.jpg" width="170" />(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 &#956;m). 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 &#8216;discussion&#8217; 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.

<table><tbody><tr><td>iXon Ultra 897</td><td>Andor</td><td>DU-897U-CS0</td><td></td></tr><tr><td>Solis</td><td>Andor</td><td></td><td></td></tr><tr><td>CHO-K1</td><td>ATCC</td><td>CCL-61</td><td></td></tr><tr><td>ATTO 488 labeled PPE</td><td>ATTO-TEC GmbH</td><td>AD 488-151</td><td></td></tr><tr><td>DOPE</td><td>Avanti Polar Lipids, Inc.</td><td>850725</td><td></td></tr><tr><td>DOTAP</td><td>Avanti Polar Lipids, Inc.</td><td>890890</td><td></td></tr><tr><td>100x Penicillin-Streptomycin-Glutamine</td><td>Gibco</td><td>10378-016</td><td></td></tr><tr><td>DMEM/F-12</td><td>Gibco</td><td>21331</td><td></td></tr><tr><td>FBS</td><td>Gibco</td><td>10082147</td><td></td></tr><tr><td>HEPES</td><td>Gibco</td><td>15630-106&amp;nbsp;</td><td></td></tr><tr><td>PBS</td><td>Gibco</td><td>10010-023</td><td></td></tr><tr><td>SimFCS 3.0</td><td>Globals Software</td><td></td><td>the software can be downloaded here: http://www.lfd.uci.edu/globals/</td></tr><tr><td>DMI6000 with TIRF modulus</td><td>Leica</td><td></td><td></td></tr><tr><td>LAS AF</td><td>Leica</td><td></td><td></td></tr><tr><td>Lipofectamine 2000</td><td>Lipofectamine</td><td>11668019</td><td></td></tr><tr><td>Matlab</td><td>MathWork</td><td></td><td></td></tr><tr><td>ImageJ</td><td>NIH</td><td></td><td></td></tr><tr><td colspan="4">[header]</td></tr><tr><td>C-terminal GFP tagged Tranferrin Receptor</td><td>OriGene</td><td>RG200980</td><td></td></tr><tr><td>Agar</td><td>Sigma Aldrich</td><td>A5306</td><td></td></tr><tr><td>Chloroform</td><td>Sigma Aldrich</td><td>528730</td><td></td></tr><tr><td>Latex beads, fluorescent yellow-green, 30 nm</td><td>Sigma Aldrich</td><td>L5155</td><td></td></tr><tr><td>SONICA Ultrasonic Cleaners</td><td>SOLTEC</td><td>ETH S3</td><td></td></tr><tr><td>Petri Dishes</td><td>Willco</td><td>GWSt-3522</td><td></td></tr><tr><td>Bio-Format importer for Matlab</td><td></td><td></td><td>http://www.openmicroscopy.org/site/support/bio-formats5/users/matlab/</td></tr><tr><td>ICS-MatLab Tools</td><td></td><td></td><td>&lt;a href=&quot;https://www.cellmigration.org/resource/imaging/software/ICSMATLAB_28-02-06.zip&quot;&gt;https://www.cellmigration.org/resource/imaging/software/ICSMATLAB_28-02-06.zip&lt;/a&gt;</td></tr><tr><td>Simulation by Matlab Tutorial</td><td></td><td></td><td>https://www.cellmigration.org/resource/imaging/icsmatlab/ICSTutorial.html</td></tr><tr><td>Simulation by SimFCS Tutorial</td><td></td><td></td><td>https://www.cellmigration.org/resource/imaging/ppt-pdf/RICS%20Simulations.ppt</td></tr></tbody></table>

from fast fluorescence imaging to molecular diffusion law on live cell membranes in a commercial microscope

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&#8217;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 &#181;m-sized membrane regions in the micro-to-milli-second time range.

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 measurement of particle dynamics but it is required to measure the apparent particle size.
A typical frequency distribution of camera background is presented in Figure 2. The peak at about 180 DL is due to the camera response to no photon, and it represents the contribution of Analog Digital (AD) converter. This contribution can be approximated as a Gaussian distribution to estimate the offset and the variance introduced by the signal recording. Above 200 DL the digital level distribution becomes exponential (linear in logarithmic scale) and represents the average camera response to a single photon. Fitting this part with an exponential distribution allows the measurements of the average DL assigned to each single photon. The higher is the ratio between the average DL assigned to each photon and the AD converter error, the lower will be the noise in the calculated correlation function. Moreover, the average single photon response allows the estimation of the camera dynamic range.
A diagram of the complete experimental procedure is summarized in Figure 3 and a picture of Atto488-PPE insertion into the membrane is represented in Figure 4A. A representative TIRF image of the basal membrane of a CHO cells labeled with Atto488-PPE is presented in Figure 4B. Several bright spots may be present outside the cell due to liposomes stacked on the glass. They can be discarded by selecting a ROI on a membrane portion mostly uniform in fluorescence (i.e., the cellular plasma membrane). As expected the measured diffusion law (Figure 4C) for this lipid is flat, indicating a mostly free diffusion as previously shown by STED-FCS measurements30,35. It is worth mentioning that all the shown displacement values are below the diffraction limit, clearly indicating the ability of this approach to super-resolve average molecular displacements well below the diffraction limit and down to few tens of nanometers.
A schematization of TfR-GFP dimer insertion into the membrane is represented in Figure 5A. Many studies showed that the cytoplasmic tail of this receptor interacts with the membrane skeleton, which in turn acts as a fence for the receptor mobility12,40. A representative TIRF image of a CHO cell expressing TfR-GFP is presented in Figure 5B. Low fluorescence intensity cells should be preferred, as the membrane is closer to the native condition and the probability of artifacts related to the over-expression is minimized. In addition, the central part of the cell should be avoided, as the effects of out-of-focus fluorescence (from cytoplasm, for instance) may be present. As expected the measured diffusion law (Figure 5C) for TfR-GFP shows a first flat behavior below 100 nm, with an average Dapp of about 0.7 &#181;m2sec-1, followed by consequent rapid decrease in apparent diffusivity down to 0.2 &#181;m2sec-1 (the value typically measured by diffraction-limited FCS12). This result shows that our approach can easily measure the average displacement of GFP labeled proteins with a resolution of few tens of nanometers. Moreover the spatial scale at which Dapp starts to decrease sets the characteristic spatial scale of protein partial confinement by the membrane skeleton at around 120 nm, in keeping with previous estimates6.
<img alt="Figure 1" fo:content-width="7in" src="/files/ftp_upload/51994/51994fig1highres.jpg" width="700" /> 
Figure 1.&#160;Calibration of Point Spread Function. (A)&#160;Pseudocolor image of an isolated bead and beads aggregates. (B) 3D plot of the intensity profile of an isolated bead shows a well-defined Gaussian profile. (C)&#160;Fit of the intensity distribution by a Gaussian function (upper panel) with the corresponding residuals (lower panel). The good agreement between the fitted distribution and the measured intensity profile is also a proof that the instrumental PSF can be approximated by a Gaussian function. <a href="https://www.jove.com/files/ftp_upload/51994/51994fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" fo:content-width="6in" src="/files/ftp_upload/51994/51994fig2highres.jpg" width="600" /> 
Figure 2.&#160;Calibration of Camera response to single photons. The figure shows the Digital Level (DL) distribution for camera background in a 32 x 128 ROI, exposure 0.5 msec, in Cropped Sensor Mode. The peak at about 180 DL represents the camera response to no photons. Particularly, it represents the contribution of the Analog Digital (AD) converter and can be approximated by a Gaussian function to estimate the offset and the variance introduced by the signal recording. Above 200 DL the distribution of digital levels becomes exponential and represents the average camera response to a single photon. The measurement of these parameters allows estimating the density of photons that are recorded during the acquisition. <a href="https://www.jove.com/files/ftp_upload/51994/51994fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" fo:content-width="6in" src="/files/ftp_upload/51994/51994fig3highres.jpg" width="600" /> 
Figure 3.&#160;Schematization of the method. (A) Wide field imaging by EMCCD camera is applied to reach sub-millisecond resolution, while TIRF microcopy is exploited to provide accurate optical sectioning of the plasma membrane. (B) The resulting stack of images is autocorrelated in order to calculate the average spatial-temporal correlation function. This correlation function is well approximated by a Gaussian function (see Introduction) and it spreads out in time according to particle displacements. (C) Thus, in order to quantify the spreading of the correlation function due to molecular displacement, fitting with a Gaussian function is performed. This allows measuring the molecular &#8216;diffusion law&#8217; directly from imaging, in the form of apparent diffusivity vs average displacement plot. (D) Thanks to this plot, molecular diffusion modes can be directly identified with no need for an interpretative model or assumptions about the spatial organization of the membrane. In fact, freely diffusing molecules will display a constant apparent diffusivity as their mobility does not depend on the spatial scale of the measurement. By contrast, partially confined molecules will display a quite constant apparent diffusivity for displacements smaller than confinement size, then a decreasing diffusivity for spatial scales bigger than confinement size. Thus, the appearance of a reduction in the apparent diffusivity can be interpreted as a fingerprint of transient confinement, while the related spatial scale can be used to estimate the spatial extension of the confinement. <a href="https://www.jove.com/files/ftp_upload/51994/51994fig3highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" fo:content-width="5in" src="/files/ftp_upload/51994/51994fig4highres.jpg" width="500" /> 
Figure 4.&#160;ATTO488-PPE diffusion law in live cell membranes.&#160;(A) Schematic representation of ATTO488-PPE insertion in cell membrane. (B) TIRF image of CHO basal membrane labeled with ATTO488-PPE: a ROI (red box) is selected in a mostly uniform part of the cell, avoiding cell border and highly fluorescent spots. (C) The diffusion law measured in the selected ROI shows a flat behavior confirming a free diffusion model for this component. <a href="https://www.jove.com/files/ftp_upload/51994/51994fig4highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" fo:content-width="5in" src="/files/ftp_upload/51994/51994fig5highres.jpg" width="500" /> 
Figure 5.&#160;TfR-GFP diffusion law in live cell membranes.&#160;(A) Schematic representation of TfR-GFP insertion in cell membrane: the cytoplasmic tail of the receptor interacts with the membrane skeleton, that acts as a fence for receptor mobility. (B) TIRF image of CHO expressing TfR-GFP: a ROI is selected preferring low expressing cells to avoid artefacts due to overexpression. (C) The diffusion law of TfR (black dots), unlike PPE (gray line, taken from Figure 4), shows the typical behavior of partially confined diffusion where a first flat part is followed by a decrease in Dapp. <a href="https://www.jove.com/files/ftp_upload/51994/51994fig5highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope at JoVE.com

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

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.

It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the ...

from-fast-fluorescence-imaging-to-molecular-diffusion-law-on-live

Nanyang Technological University

Research

JoVE Journal

Bioengineering

11.4K Views.  Scuola Normale Superiore. 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.

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

Fluorescence Lifetime Imaging of Molecular Rotors in Living Cells

Diffusion is often an important rate-determining step in chemical reactions or biological processes and plays a role in a wide range of intracellular events. Viscosity is one of the key parameters affecting the diffusion of molecules and proteins, and changes in viscosity have been linked to disease and malfunction at the cellular level.1-3 While methods to measure the bulk viscosity are well developed, imaging microviscosity remains a challenge. Viscosity maps of microscopic objects, such as single cells, have until recently been hard to obtain. Mapping viscosity with fluorescence techniques is advantageous because, similar to other optical techniques, it is minimally invasive, non-destructive and can be applied to living cells and tissues.
Fluorescent molecular rotors exhibit fluorescence lifetimes and quantum yields which are a function of the viscosity of their microenvironment.4,5 Intramolecular twisting or rotation leads to non-radiative decay from the excited state back to the ground state. A viscous environment slows this rotation or twisting, restricting access to this non-radiative decay pathway. This leads to an increase in the fluorescence quantum yield and the fluorescence lifetime. Fluorescence Lifetime Imaging (FLIM) of modified hydrophobic BODIPY dyes that act as fluorescent molecular rotors show that the fluorescence lifetime of these probes is a function of the microviscosity of their environment.6-8 A logarithmic plot of the fluorescence lifetime versus the solvent viscosity yields a straight line that obeys the F&ouml;rster Hoffman equation.9 This plot also serves as a calibration graph to convert fluorescence lifetime into viscosity.
Following incubation of living cells with the modified BODIPY fluorescent molecular rotor, a punctate dye distribution is observed in the fluorescence images. The viscosity value obtained in the puncta in live cells is around 100 times higher than that of water and of cellular cytoplasm.6,7 Time-resolved fluorescence anisotropy measurements yield rotational correlation times in agreement with these large microviscosity values. Mapping the fluorescence lifetime is independent of the fluorescence intensity, and thus allows the separation of probe concentration and viscosity effects. 
In summary, we have developed a practical and versatile approach to map the microviscosity in cells based on FLIM of fluorescent molecular rotors.

Fluorescence Lifetime Imaging (FLIM) has emerged as a key technique to image the environment and interaction of specific proteins and dyes in living cells. FLIM of fluorescent molecular rotors allows mapping of viscosity in living cells.

Diffusion is often an important rate-determining step in chemical reactions or biological processes and plays a role in a wide range of intracellular ...

Fluorescence detection methods for microfluidic droplet platforms

The development of microfluidic platforms for performing chemistry and biology has in large part been driven by a range of potential benefits that accompany system miniaturisation. Advantages include the ability to efficiently process nano- to femoto- liter volumes of sample, facile integration of functional components, an intrinsic predisposition towards large-scale multiplexing, enhanced analytical throughput, improved control and reduced instrumental footprints.1
In recent years much interest has focussed on the development of droplet-based (or segmented flow) microfluidic systems and their potential as platforms in high-throughput experimentation.2-4 Here water-in-oil emulsions are made to spontaneously form in microfluidic channels as a result of capillary instabilities between the two immiscible phases. Importantly, microdroplets of precisely defined volumes and compositions can be generated at frequencies of several kHz. Furthermore, by encapsulating reagents of interest within isolated compartments separated by a continuous immiscible phase, both sample cross-talk and dispersion (diffusion- and Taylor-based) can be eliminated, which leads to minimal cross-contamination and the ability to time analytical processes with great accuracy. Additionally, since there is no contact between the contents of the droplets and the channel walls (which are wetted by the continuous phase) absorption and loss of reagents on the channel walls is prevented.
Once droplets of this kind have been generated and processed, it is necessary to extract the required analytical information. In this respect the detection method of choice should be rapid, provide high-sensitivity and low limits of detection, be applicable to a range of molecular species, be non-destructive and be able to be integrated with microfluidic devices in a facile manner. To address this need we have developed a suite of experimental tools and protocols that enable the extraction of large amounts of photophysical information from small-volume environments, and are applicable to the analysis of a wide range of physical, chemical and biological parameters. Herein two examples of these methods are presented and applied to the detection of single cells and the mapping of mixing processes inside picoliter-volume droplets. We report the entire experimental process including microfluidic chip fabrication, the optical setup and the process of droplet generation and detection.

Droplet-based microfluidic platforms are promising candidates for high throughput experimentation since they are able to generate picoliter, self-compartmentalized vessels inexpensively at kHz rates. Through integration with fast, sensitive and high resolution fluorescence spectroscopic methods, the large amounts of information generated within these systems can be efficiently extracted, harnessed and utilized.

The development of microfluidic platforms for performing chemistry and biology has in large part been driven by a range of potential benefits that ...

Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy

Full insight into the mechanisms of living cells can be achieved only by investigating the key processes that elicit and direct events at a cellular level. To date the shear complexity of biological systems has caused precise single-molecule experimentation to be far too demanding, instead focusing on studies of single systems using relatively crude bulk ensemble-average measurements. However, many important processes occur in the living cell at the level of just one or a few molecules; ensemble measurements generally mask the stochastic and heterogeneous nature of these events. Here, using advanced optical microscopy and analytical image analysis tools we demonstrate how to monitor proteins within a single living bacterial cell to a precision of single molecules and how we can observe dynamics within molecular complexes in functioning biological machines. The techniques are directly relevant physiologically. They are minimally-perturbative and non-invasive to the biological sample under study and are fully attuned for investigations in living material, features not readily available to other single-molecule approaches of biophysics. In addition, the biological specimens studied all produce fluorescently-tagged protein at levels which are almost identical to the unmodified cell strains ("genomic encoding"), as opposed to the more common but less ideal approach for generating significantly more protein than would occur naturally ('plasmid expression'). Thus, the actual biological samples which will be investigated are significantly closer to the natural organisms, and therefore the observations more relevant to real physiological processes.

Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy

Here we demonstrate the protocols for performing single-molecule fluorescence microscopy on living bacterial cells to enable functional molecular complexes to be detected, tracked and quantified.

Full insight into the mechanisms of living cells can be achieved only by investigating the key processes that elicit and direct events at a cellular ...

Biology

Single Molecule Fluorescence Microscopy on Planar Supported Bilayers

In the course of a single decade single molecule microscopy has changed from being a secluded domain shared merely by physicists with a strong background in optics and laser physics to a discipline that is now enjoying vivid attention by life-scientists of all venues 1. This is because single molecule imaging has the unique potential to reveal protein behavior in situ in living cells and uncover cellular organization with unprecedented resolution below the diffraction limit of visible light 2. Glass-supported planar lipid bilayers (SLBs) are a powerful tool to bring cells otherwise growing in suspension in close enough proximity to the glass slide so that they can be readily imaged in noise-reduced Total Internal Reflection illumination mode 3,4. They are very useful to study the protein dynamics in plasma membrane-associated events as diverse as cell-cell contact formation, endocytosis, exocytosis and immune recognition. Simple procedures are presented how to generate highly mobile protein-functionalized SLBs in a reproducible manner, how to determine protein mobility within and how to measure protein densities with the use of single molecule detection. It is shown how to construct a cost-efficient single molecule microscopy system with TIRF illumination capabilities and how to operate it in the experiment.

Preparation of protein-functionalized planar glass-supported lipid bilayers, determination of protein mobility within and measurement of protein densities is shown here. A roadmap to building a noise-reduced Total Internal Reflection microscope is outlined, which allows visualizing single bilayer-resident fluorochromes with high spatiotemporal resolution.

In the course of a single decade single molecule microscopy has changed from being a secluded domain shared merely by physicists with a strong background ...

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells

Dynamic biological processes in living cells, including those associated with plasma membrane organization, occur on various spatial and temporal scales, ranging from nanometers to micrometers and microseconds to minutes, respectively. Such a broad range of biological processes challenges conventional microscopy approaches. Here, we detail the procedure for implementing spot variation Fluorescence Correlation Spectroscopy (svFCS) measurements using a classical fluorescence microscope that has been customized. The protocol includes a specific performance check of the svFCS setup and the guidelines for molecular diffusion measurements by svFCS on the plasma membrane of living cells under physiological conditions. Additionally, we provide a procedure for disrupting plasma membrane raft nanodomains by cholesterol oxidase treatment and demonstrate how these changes in the lateral organization of the plasma membrane might be revealed by svFCS analysis. In conclusion, this fluorescence-based method can provide unprecedented details on the lateral organization of the plasma membrane with the appropriate spatial and temporal resolution.

This article aims to present a protocol on how to build a spot variation Fluorescence Correlation Spectroscopy (svFCS) microscope to measure molecular diffusion at the plasma membrane of living cells.

Dynamic biological processes in living cells, including those associated with plasma membrane organization, occur on various spatial and temporal scales, ...

Biochemistry

Conducting Multiple Imaging Modes with One Fluorescence Microscope

Fluorescence microscopy is a powerful tool to detect biological molecules in situ and monitor their dynamics and interactions in real-time. In addition to conventional epi-fluorescence microscopy, various imaging techniques have been developed to achieve specific experimental goals. Some of the widely used techniques include single-molecule fluorescence resonance energy transfer (smFRET), which can report conformational changes and molecular interactions with angstrom resolution, and single-molecule detection-based super-resolution (SR) imaging, which can enhance the spatial resolution approximately ten&#160;to twentyfold compared to diffraction-limited microscopy. Here we present a customer-designed integrated system, which merges multiple imaging methods in one microscope, including conventional epi-fluorescent imaging, single-molecule detection-based SR imaging, and multi-color single-molecule detection, including smFRET imaging. Different imaging methods can be achieved easily and reproducibly by switching optical elements. This set-up is easy to adopt by any research laboratory in biological sciences with a need for routine and diverse imaging experiments at a reduced cost and space relative to building separate microscopes for individual purposes.

Here we present a practical guide of building an integrated microscopy system, which merges conventional epi-fluorescent imaging, single-molecule detection-based super-resolution imaging, and multi-color single-molecule detection, including single-molecule fluorescence resonance energy transfer imaging, into one set-up in a cost-efficient way.

Fluorescence microscopy is a powerful tool to detect biological molecules in situ and monitor their dynamics and interactions in real-time. In addition to ...

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT)

Our goal is to obtain a comprehensive description of molecular processes occurring at cellular membranes in different biological functions. We aim at characterizing the complex organization and dynamics of the plasma membrane at single-molecule level, by developing analytic tools dedicated to Single-Particle Tracking (SPT) at high density: Multiple-Target Tracing (MTT)1. Single-molecule videomicroscopy, offering millisecond and nanometric resolution1-11, allows a detailed representation of membrane organization12-14 by accurately mapping descriptors such as cell receptors localization, mobility, confinement or interactions.
We revisited SPT, both experimentally and algorithmically. Experimental aspects included optimizing setup and cell labeling, with a particular emphasis on reaching the highest possible labeling density, in order to provide a dynamic snapshot of molecular dynamics as it occurs within the membrane. Algorithmic issues concerned each step used for rebuilding trajectories: peaks detection, estimation and reconnection, addressed by specific tools from image analysis15,16. Implementing deflation after detection allows rescuing peaks initially hidden by neighboring, stronger peaks. Of note, improving detection directly impacts reconnection, by reducing gaps within trajectories. Performances have been evaluated using Monte-Carlo simulations for various labeling density and noise values, which typically represent the two major limitations for parallel measurements at high spatiotemporal resolution.
The nanometric accuracy17 obtained for single molecules, using either successive on/off photoswitching or non-linear optics, can deliver exhaustive observations. This is the basis of nanoscopy methods17 such as STORM18, PALM19,20, RESOLFT21 or STED22,23, which may often require imaging fixed samples. The central task is the detection and estimation of diffraction-limited peaks emanating from single-molecules. Hence, providing adequate assumptions such as handling a constant positional accuracy instead of Brownian motion, MTT is straightforwardly suited for nanoscopic analyses. Furthermore, MTT can fundamentally be used at any scale: not only for molecules, but also for cells or animals, for instance. Hence, MTT is a powerful tracking algorithm that finds applications at molecular and cellular scales.

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT

Multiple-Target Tracing is a homemade algorithm developed for tracking individually labeled molecules within the plasma membrane of living cells. Efficiently detecting, estimating and tracing molecules over time at high-density provide a user-friendly, comprehensive tool to investigate nanoscale membrane dynamics.

Our goal is to obtain a comprehensive description of molecular processes occurring at cellular membranes in different biological functions. We aim at ...

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using the analysis of cell membrane receptor clusters as a model, we present a detailed protocol for this image analysis task using Fiji (ImageJ) and Matlab routines to: 1) define regions of interest and design masks adapted to these regions; 2) track the particles in fluorescence microscopy videos; 3) analyze the diffusion and intensity characteristics of selected tracks. The quantitative analysis of the diffusion coefficients, types of motion, and cluster size obtained by fluorescence microscopy and image processing provides a valuable tool to objectively determine particle dynamics and the consequences of modifying environmental conditions. In this article we present detailed protocols for the analysis of these features. The method described here not only allows single-molecule tracking detection, but also automates the estimation of lateral diffusion parameters at the cell membrane, classifies the type of trajectory and allows complete analysis thus overcoming the difficulties in quantifying spot size over its entire trajectory at the cell membrane.

Here, we present a protocol for single particle tracking image analysis that allows quantitative evaluation of diffusion coefficients, types of motion and cluster sizes of single particles detected by fluorescence microscopy.

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using ...

Immunology and Infection

Internalization and Observation of Fluorescent Biomolecules in Living Microorganisms via Electroporation

The ability to study biomolecules in vivo is crucial for understanding their function in a biological context. One powerful approach involves fusing molecules of interest to fluorescent proteins such as GFP to study their expression, localization and function. However, GFP and its derivatives are significantly larger and less photostable than organic fluorophores generally used for in vitro experiments, and this can limit the scope of investigation. 
We recently introduced a straightforward, versatile and high-throughput method based on electroporation, allowing the internalization of biomolecules labeled with organic fluorophores into living microorganisms. Here we describe how to use electroporation to internalize labeled DNA fragments or proteins into Escherichia coli and Saccharomyces cerevisi&#230;, how to quantify the number of internalized molecules using fluorescence microscopy, and how to quantify the viability of electroporated cells. Data can be acquired at the single-cell or single-molecule level using fluorescence or FRET. The possibility of internalizing non-labeled molecules that trigger a physiological observable response in vivo is also presented. Finally, strategies of optimization of the protocol for specific biological systems are discussed.

Studies of biomolecules in vivo are crucial for understanding molecular function in a biological context. Here we describe a novel method allowing the internalization of fluorescent biomolecules, such as DNA or proteins, into living microorganisms. Analysis of in vivo data recorded by fluorescence microscopy is also presented and discussed.

The ability to study biomolecules in vivo is crucial for understanding their function in a biological context. One powerful approach involves fusing ...

Live Cell Fluorescence Microscopy to Observe Essential Processes During Microbial Cell Growth

Core cellular processes such as DNA replication and segregation, protein synthesis, cell wall biosynthesis, and cell division rely on the function of proteins which are essential for bacterial survival. A series of target-specific dyes can be used as probes to better understand these processes. Staining with lipophilic dyes enables the observation of membrane structure, visualization of lipid microdomains, and detection of membrane blebs. Use of fluorescent-d-amino acids (FDAAs) to probe the sites of peptidoglycan biosynthesis can indicate potential defects in cell wall biogenesis or cell growth patterning. Finally, nucleic acid stains can indicate possible defects in DNA replication or chromosome segregation. Cyanine DNA stains label living cells and are suitable for time-lapse microscopy enabling real-time observations of nucleoid morphology during cell growth. Protocols for cell labeling can be applied to protein depletion mutants to identify defects in membrane structure, cell wall biogenesis, or chromosome segregation. Furthermore, time-lapse microscopy can be used to monitor morphological changes as an essential protein is removed and can provide additional insights into protein function. For example, the depletion of essential cell division proteins results in filamentation or branching, whereas the depletion of cell growth proteins may cause cells to become shorter or rounder. Here, protocols for cell growth, target-specific labeling, and time-lapse microscopy are provided for the bacterial plant pathogen Agrobacterium tumefaciens. Together, target-specific dyes and time-lapse microscopy enable characterization of essential processes in A. tumefaciens. Finally, the protocols provided can be readily modified to probe essential processes in other bacteria.

Understanding the function of essential processes in bacteria is challenging. Fluorescence microscopy with target-specific dyes can provide key insights into microbial cell growth and cell cycle progression. Here, Agrobacterium tumefaciens is used as a model bacterium to highlight methods for live cell imaging for characterization of essential processes.

Core cellular processes such as DNA replication and segregation, protein synthesis, cell wall biosynthesis, and cell division rely on the function of ...

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

Summary

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Chapters in this video

Fluorescence Lifetime Imaging of Molecular Rotors in Living Cells

Fluorescence detection methods for microfluidic droplet platforms

Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy

Single Molecule Fluorescence Microscopy on Planar Supported Bilayers

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells

Conducting Multiple Imaging Modes with One Fluorescence Microscope

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT)

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Internalization and Observation of Fluorescent Biomolecules in Living Microorganisms via Electroporation

Live Cell Fluorescence Microscopy to Observe Essential Processes During Microbial Cell Growth