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 ...

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 border="0" cellpadding="0" cellspacing="0" width="1142">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Name of Material/ Equipment</td>
			<td style="width:183px;">Company</td>
			<td style="width:119px;">Catalog Number</td>
			<td style="width:480px;">Comments/Description</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">iXon Ultra 897</td>
			<td style="width:183px;">Andor</td>
			<td>DU-897U-CS0</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Solis</td>
			<td style="width:183px;">Andor</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">CHO-K1</td>
			<td style="width:183px;">ATCC</td>
			<td style="width:119px;">CCL-61</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">ATTO 488 labeled PPE</td>
			<td style="width:183px;">ATTO-TEC GmbH</td>
			<td>AD 488-151</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">DOPE</td>
			<td style="width:183px;">Avanti Polar Lipids, Inc.</td>
			<td style="width:119px;">850725</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">DOTAP</td>
			<td style="width:183px;">Avanti Polar Lipids, Inc.</td>
			<td style="width:119px;">890890</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">100X Penicillin-Streptomycin-Glutamine</td>
			<td>Gibco</td>
			<td>10378-016</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">DMEM/F-12</td>
			<td>Gibco</td>
			<td>21331</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">FBS</td>
			<td>Gibco</td>
			<td>10082147</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">HEPES</td>
			<td style="width:183px;">Gibco</td>
			<td style="width:119px;">15630-106&#160;</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">PBS</td>
			<td style="width:183px;">Gibco</td>
			<td>10010-023</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">SimFCS 3.0</td>
			<td style="width:183px;">Globals Software</td>
			<td style="width:119px;"></td>
			<td>the software can be downloaded here: http://www.lfd.uci.edu/globals/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">DMI6000 with TIRF modulus</td>
			<td style="width:183px;">Leica</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">LAS AF</td>
			<td style="width:183px;">Leica</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Lipofectamine 2000</td>
			<td style="width:183px;">Lipofectamine</td>
			<td>11668019</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Matlab</td>
			<td style="width:183px;">MathWork</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Imagej</td>
			<td style="width:183px;">NIH</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">C-terminal GFP tagged Tranferrin Receptor</td>
			<td style="width:183px;">OriGene</td>
			<td>RG200980</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Agar</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>A5306</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Chloroform</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td style="width:119px;">528730</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Latex beads, fluorescent yellow-green, 30 nm</td>
			<td style="width:183px;">Sigma Aldrich</td>
			<td>L5155</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">SONICA Ultrasonic Cleaners</td>
			<td>SOLTEC</td>
			<td style="width:119px;">ETH S3</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Petri Dishes</td>
			<td style="width:183px;">Willco</td>
			<td>GWSt-3522</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Bio-Format importer for Matlab</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td>http://www.openmicroscopy.org/site/support/bio-formats5/users/matlab/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">ICS-MatLab Tools</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td><a href="https://www.cellmigration.org/resource/imaging/software/ICSMATLAB_28-02-06.zip">https://www.cellmigration.org/resource/imaging/software/ICSMATLAB_28-02-06.zip</a></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Simulation by Matlab Tutorial</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></td>
			<td>https://www.cellmigration.org/resource/imaging/icsmatlab/ICSTutorial.html</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:361px;">Simulation by SimFCS Tutorial</td>
			<td style="width:183px;"></td>
			<td style="width:119px;"></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 measureme...

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

Research

JoVE Journal

Bioengineering

11.3K 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 ...

Live-cell Imaging of Migrating Cells Expressing Fluorescently-tagged Proteins in a Three-dimensional Matrix

Traditionally, cell migration has been studied on two-dimensional, stiff plastic surfaces. However, during important biological processes such as wound healing, tissue regeneration, and cancer metastasis, cells must navigate through complex, three-dimensional extracellular tissue. To better understand the mechanisms behind these biological processes, it is important to examine the roles of the proteins responsible for driving cell migration. Here, we outline a protocol to study the mechanisms of cell migration using the epithelial cell line (MDCK), and a three-dimensional, fibrous, self-polymerizing matrix as a model system. This optically clear extracellular matrix is easily amenable to live-cell imaging studies and better mimics the physiological, soft tissue environment. This report demonstrates a technique for directly visualizing protein localization and dynamics, and deformation of the surrounding three-dimensional matrix. 
Examination of protein localization and dynamics during cellular processes provides key insight into protein functions. Genetically encoded fluorescent tags provide a unique method for observing protein localization and dynamics. Using this technique, we can analyze the subcellular accumulation of key, force-generating cytoskeletal components in real-time as the cell maneuvers through the matrix. In addition, using multiple fluorescent tags with different wavelengths, we can examine the localization of multiple proteins simultaneously, thus allowing us to test, for example, whether different proteins have similar or divergent roles. Furthermore, the dynamics of fluorescently tagged proteins can be quantified using Fluorescent Recovery After Photobleaching (FRAP) analysis. This measurement assays the protein mobility and how stably bound the proteins are to the cytoskeletal network.
By combining live-cell imaging with the treatment of protein function inhibitors, we can examine in real-time the changes in the distribution of proteins and morphology of migrating cells. Furthermore, we also combine live-cell imaging with the use of fluorescent tracer particles embedded within the matrix to visualize the matrix deformation during cell migration. Thus, we can visualize how a migrating cell distributes force-generating proteins, and where the traction forces are exerted to the surrounding matrix. Through these techniques, we can gain valuable insight into the roles of specific proteins and their contributions to the mechanisms of cell migration.

Cellular processes such as cell migration have traditionally been studied on two-dimensional, stiff plastic surfaces. This report describes a technique for directly visualizing protein localization and analyzing protein dynamics in cells migrating in a more physiologically relevant, three-dimensional matrix.

Traditionally, cell migration has been studied on two-dimensional, stiff plastic surfaces. However, during important biological processes such as wound ...

Using Microfluidics Chips for Live Imaging and Study of Injury Responses in Drosophila Larvae

Live imaging is an important technique for studying cell biological processes, however this can be challenging in live animals. The translucent cuticle of the Drosophila larva makes it an attractive model organism for live imaging studies. However, an important challenge for live imaging techniques is to noninvasively immobilize and position an animal on the microscope. This protocol presents a simple and easy to use method for immobilizing and imaging Drosophila larvae on a polydimethylsiloxane (PDMS) microfluidic device, which we call the 'larva chip'. The larva chip is comprised of a snug-fitting PDMS microchamber that is attached to a thin glass coverslip, which, upon application of a vacuum via a syringe, immobilizes the animal and brings ventral structures such as the nerve cord, segmental nerves, and body wall muscles, within close proximity to the coverslip. This allows for high-resolution imaging, and importantly, avoids the use of anesthetics and chemicals, which facilitates the study of a broad range of physiological processes. Since larvae recover easily from the immobilization, they can be readily subjected to multiple imaging sessions. This allows for longitudinal studies over time courses ranging from hours to days. This protocol describes step-by-step how to prepare the chip and how to utilize the chip for live imaging of neuronal events in 3rd instar larvae. These events include the rapid transport of organelles in axons, calcium responses to injury, and time-lapse studies of the trafficking of photo-convertible proteins over long distances and time scales. Another application of the chip is to study regenerative and degenerative responses to axonal injury, so the second part of this protocol describes a new and simple procedure for injuring axons within peripheral nerves by a segmental nerve crush.

Using Microfluidics Chips for Live Imaging and Study of Injury Responses in Drosophila Larvae

Drosophila larvae are an attractive model system for live imaging due to their translucent cuticle and powerful genetics. This protocol describes how to utilize a single-layer PDMS device, called the &#39;larva chip&#39; for live imaging of cellular processes within neurons of 3rd instar Drosophila larvae.

Live imaging is an important technique for studying cell biological processes, however this can be challenging in live animals. The translucent cuticle of ...

A Microfluidic Technique to Probe Cell Deformability

Here we detail the design, fabrication, and use of a microfluidic device to evaluate the deformability of a large number of individual cells in an efficient manner. Typically, data for ~102 cells can be acquired within a 1 hr experiment. An automated image analysis program enables efficient post-experiment analysis of image data, enabling processing to be complete within a few hours. Our device geometry is unique in that cells must deform through a series of micron-scale constrictions, thereby enabling the initial deformation and time-dependent relaxation of individual cells to be assayed. The applicability of this method to human promyelocytic leukemia (HL-60) cells is demonstrated. Driving cells to deform through micron-scale constrictions using pressure-driven flow, we observe that human promyelocytic (HL-60) cells momentarily occlude the first constriction for a median time of 9.3 msec before passaging more quickly through the subsequent constrictions with a median transit time of 4.0 msec per constriction. By contrast, all-trans retinoic acid-treated (neutrophil-type) HL-60 cells occlude the first constriction for only 4.3 msec before passaging through the subsequent constrictions with a median transit time of 3.3 msec. This method can provide insight into the viscoelastic nature of cells, and ultimately reveal the molecular origins of this behavior.

We demonstrate a microfluidics-based assay to measure the timescale for cells to transit through a sequence of micron-scale constrictions.

Here we detail the design, fabrication, and use of a microfluidic device to evaluate the deformability of a large number of individual cells in an ...

3D Orbital Tracking in a Modified Two-photon Microscope: An Application to the Tracking of Intracellular Vesicles

The objective of this video protocol is to discuss how to perform and analyze a three-dimensional fluorescent orbital particle tracking experiment using a modified two-photon microscope1. As opposed to conventional approaches (raster scan or wide field based on a stack of frames), the 3D orbital tracking allows to localize and follow with a high spatial (10 nm accuracy) and temporal resolution (50 Hz frequency response) the 3D displacement of a moving fluorescent particle on length-scales of hundreds of microns2. The method is based on a feedback algorithm that controls the hardware of a two-photon laser scanning microscope in order to perform a circular orbit around the object to be tracked: the feedback mechanism will maintain the fluorescent object in the center by controlling the displacement of the scanning beam3-5. To demonstrate the advantages of this technique, we followed a fast moving organelle, the lysosome, within a living cell6,7. Cells were plated according to standard protocols, and stained using a commercially lysosome dye. We discuss briefly the hardware configuration and in more detail the control software, to perform a 3D orbital tracking experiment inside living cells. We discuss in detail the parameters required in order to control the scanning microscope and enable the motion of the beam in a closed orbit around the particle. We conclude by demonstrating how this method can be effectively used to track the fast motion of a labeled lysosome along microtubules in 3D within a live cell. Lysosomes can move with speeds in the range of 0.4-0.5 &#181;m/sec, typically displaying a directed motion along the microtubule network8.

In this video protocol we track - at high speed and in three dimensions - fluorescently labeled lysosomes within living cells, using the orbital tracking method in a modified two-photon microscope.

The objective of this video protocol is to discuss how to perform and analyze a three-dimensional fluorescent orbital particle tracking experiment using a ...

Live Cell Imaging during Mechanical Stretch

There is currently a significant interest in understanding how cells and tissues respond to mechanical stimuli, but current approaches are limited in their capability for measuring responses in real time in live cells or viable tissue. A protocol was developed with the use of a cell actuator to distend live cells grown on or tissues attached to an elastic substrate while imaging with confocal and atomic force microscopy (AFM). Preliminary studies show that tonic stretching of human bronchial epithelial cells caused a significant increase in the production of mitochondrial superoxide. Moreover, using this protocol, alveolar epithelial cells were stretched and imaged, which showed direct damage to the epithelial cells by overdistention simulating one form of lung injury in vitro. A protocol to conduct AFM nano-indentation on stretched cells is also provided.

A novel imaging protocol was developed using a custom motor-driven mechanical actuator to allow the measurement of real time responses to mechanical strain in live cells. Relevant to mechanobiology, the system can apply strains up to 20% while allowing near real-time imaging with confocal or atomic force microscopy.

There is currently a significant interest in understanding how cells and tissues respond to mechanical stimuli, but current approaches are limited in ...

Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer

We introduce a new method to measure the lateral diffusivity of a surfactant monolayer at the fluid-fluid interface, called fluorescence recovery after merging (FRAM). FRAM adopts the same principles as the fluorescence recovery after photobleaching (FRAP) technique, especially for measuring fluorescence recovery after bleaching a specific area, but FRAM uses a drop coalescence instead of photobleaching dye molecules to induce a chemical potential gradient of dye molecules. Our technique has several advantages over FRAP: it only requires a fluorescence microscope rather than a confocal microscope equipped with high power lasers; it is essentially free from the selection of fluorescence dyes; and it has far more freedom to define the measured diffusion area. Furthermore, FRAM potentially provides a route for studying the mixing or inter-diffusion of two different surfactants, when the monolayers at a surface of droplet and at a flat air/water interface are prepared with different species, independently.

We present a new technique to measure the lateral diffusion of a surface active species at the fluid-fluid interface by merging a droplet monolayer onto a flat monolayer.

We introduce a new method to measure the lateral diffusivity of a surfactant monolayer at the fluid-fluid interface, called fluorescence recovery after ...

Selective Cell Elimination from Mixed 3D Culture Using a Near Infrared Photoimmunotherapy Technique

Recent developments in tissue engineering offer innovative solutions for many diseases. For example, tissue engineering using induced pluripotent stem cell (iPS) emerged as a new method in regenerative medicine. Although this tissue regeneration is promising, contamination with unwanted cells during tissue cultures is a major concern. Moreover, there is a safety concern regarding tumorigenicity after transplantation. Therefore, there is an urgent need for eliminating specific cells without damaging other cells that need to be protected, especially in established tissue. Here, we present a method for specific cell elimination from a mixed 3D cell culture in vitro with near infrared photoimmunotherapy (NIR-PIT) without damaging non-targeted cells. This technique enables the elimination of specific cells from mixed cell cultures or tissues.

Eliminating specific cells without damaging other cells is extremely difficult, especially in established tissue, yet there is an urgent need for a cell elimination method in the tissue engineering field. Here, we present a method for specific cell elimination from a mixed 3D cell culture using near infrared photoimmunotherapy (NIR-PIT).

Recent developments in tissue engineering offer innovative solutions for many diseases. For example, tissue engineering using induced pluripotent stem ...

A Microcontroller Operated Device for the Generation of Liquid Extracts from Conventional Cigarette Smoke and Electronic Cigarette Aerosol

Electronic cigarettes are the most popular tobacco product among middle and high schoolers and are the most popular alternative tobacco product among adults. High quality, reproducible research on the consequences of electronic cigarette use is essential for understanding emerging public health concerns and crafting evidence based regulatory policy. While a growing number of papers discuss electronic cigarettes, there is little consistency in methods across groups and very little consensus on results. Here, we describe a programmable laboratory device that can be used to create extracts of conventional cigarette smoke and electronic cigarette aerosol. This protocol details instructions for the assembly and operation of said device, and demonstrates the use of the generated extract in two sample applications: an in vitro cell viability assay and gas-chromatography mass-spectrometry. This method provides a tool for making direct comparisons between conventional cigarettes and electronic cigarettes, and is an accessible entry point into electronic cigarette research.

Here, we describe a programmable laboratory device that can be used to create extracts of conventional cigarette smoke and electronic cigarette aerosol. This method provides a useful tool for making direct comparisons between conventional cigarettes and electronic cigarettes, and is an accessible entry point into electronic cigarette research.

Electronic cigarettes are the most popular tobacco product among middle and high schoolers and are the most popular alternative tobacco product among ...

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 ...