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>

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<li>Mueller, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((STED+nanoscopy+reveals+molecular+details+of+cholesterol-+and+cytoskeleton-modulated+lipid+interactions+in+living+cells%5BTitle%5D)+AND+%22Biophys.+J%22%5BJournal%5D)">STED nanoscopy reveals molecular details of cholesterol- and cytoskeleton-modulated lipid interactions in living cells.</a> Biophys. J. 101 (7), 1651-1660 (2011).</li>
<li>Kleusch, C., Hersch, N., Hoffmann, B., Merkel, R., Csiszar, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fluorescent+lipids%3A+functional+parts+of+fusogenic+liposomes+and+tools+for+cell+membrane+labeling+and+visualization%5BTitle%5D)+AND+%22Molecules%22%5BJournal%5D)">Fluorescent lipids: functional parts of fusogenic liposomes and tools for cell membrane labeling and visualization.</a> Molecules. 17 (1), 1055-1073 (2012).</li>
<li>Ries, J., Chiantia, S., Schwille, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Accurate+determination+of+membrane+dynamics+with+line-scan+FCS%5BTitle%5D)+AND+%22Biophys.+J%22%5BJournal%5D)">Accurate determination of membrane dynamics with line-scan FCS.</a> Biophys. J. 96 (5), 1999-2008 (2009).</li>
<li>Kolin, D. L., Wiseman, P. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Advances+in+image+correlation+spectroscopy%3A+measuring+number+densities%2C+aggregation+states%2C+and+dynamics+of+fluorescently+labeled+macromolecules+in+cells%5BTitle%5D)+AND+%22Cell+Biochem.+Biophys%22%5BJournal%5D)">Advances in image correlation spectroscopy: measuring number densities, aggregation states, and dynamics of fluorescently labeled macromolecules in cells.</a> Cell Biochem. Biophys. 49 (3), 141-164 (2007).</li>
<li>Digman, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Measuring+fast+dynamics+in+solutions+and+cells+with+a+laser+scanning+microscope%5BTitle%5D)+AND+%22Biophys.+J%22%5BJournal%5D)">Measuring fast dynamics in solutions and cells with a laser scanning microscope.</a> Biophys. J. 89 (2), 1317-1327 (2005).</li>
<li>Ritchie, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Detection+of+non-Brownian+diffusion+in+the+cell+membrane+in+single+molecule+tracking%5BTitle%5D)+AND+%22Biophys.+J%22%5BJournal%5D)">Detection of non-Brownian diffusion in the cell membrane in single molecule tracking.</a> Biophys. J. 88 (3), 2266-2277 (2005).</li>
<li>Voie, A. H., Burns, D. H., Spelman, F. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Orthogonal-plane+fluorescence+optical+sectioning%3A+three-dimensional+imaging+of+macroscopic+biological+specimens%5BTitle%5D)+AND+%22J.+Microsc%22%5BJournal%5D)">Orthogonal-plane fluorescence optical sectioning: three-dimensional imaging of macroscopic biological specimens.</a> J. Microsc. 170, 229-236 (1993).</li>
<li>Huisken, J., Swoger, J., Del Bene,, Wittbrodt, F., J,, Stelzer, E. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Optical+sectioning+deep+inside+live+embryos+by+selective+plane+illumination+microscopy%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">Optical sectioning deep inside live embryos by selective plane illumination microscopy.</a> Science. 305 (5686), 1007-1009 (2004).</li>
<li>Wohland, T., Shi, X., Sankaran, J., Stelzer, E. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Single+plane+illumination+fluorescence+correlation+spectroscopy+%28SPIM-FCS%29+probes+inhomogeneous+three-dimensional+environments%5BTitle%5D)+AND+%22Opt.+Express%22%5BJournal%5D)">Single plane illumination fluorescence correlation spectroscopy (SPIM-FCS) probes inhomogeneous three-dimensional environments.</a> Opt. Express. 18 (10), 10627-10641 (2010).</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><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 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

11.4K 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 ...

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 ...

Visualizing Intracellular SNARE Trafficking by Fluorescence Lifetime Imaging Microscopy

Soluble N-ethylmaleimide sensitive fusion protein (NSF) attachment protein receptor (SNARE) proteins are key for membrane trafficking, as they catalyze membrane fusion within eukaryotic cells. The SNARE protein family consists of about 36 different members. Specific intracellular transport routes are catalyzed by specific sets of 3 or 4 SNARE proteins that thereby contribute to the specificity and fidelity of membrane trafficking. However, studying the precise function of SNARE proteins is technically challenging, because SNAREs are highly abundant and functionally redundant, with most SNAREs having multiple and overlapping functions. In this protocol, a new method for the visualization of SNARE complex formation in live cells is described. This method is based on expressing SNARE proteins C-terminally fused to fluorescent proteins and measuring their interaction by F&#246;rster resonance energy transfer (FRET) employing fluorescence lifetime imaging microscopy (FLIM). By fitting the fluorescence lifetime histograms with a multicomponent decay model, FRET-FLIM allows (semi-)quantitative estimation of the fraction of the SNARE complex formation at different vesicles. This protocol has been successfully applied to visualize SNARE complex formation at the plasma membrane and at endosomal compartments in mammalian cell lines and primary immune cells, and can be readily extended to study SNARE functions at other organelles in animal, plant, and fungal cells.

This protocol describes a new method allowing for the quantitative visualization of complex formation of SNARE proteins, based on F&#246;rster resonance energy transfer, and fluorescence lifetime imaging microscopy.

Soluble N-ethylmaleimide sensitive fusion protein (NSF) attachment protein receptor (SNARE) proteins are key for membrane trafficking, as they catalyze ...

Immunology and Infection

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 ...

Multicellular Spheroid Formation for FLIM-Based Metabolic and Hypoxia Analysis

Production and Multi-Parameter Live Cell Fluorescence Lifetime Imaging Microscopy (FLIM) of Multicellular Spheroids

Multicellular tumor spheroids are a popular 3D tissue microaggregate&#160;model for reproducing tumor microenvironment, testing and optimizing drug therapies&#160;and using bio- and nanosensors in a 3D context. Their ease of production, predictable size, growth, and observed nutrient and metabolite gradients are important to recapitulate the 3D niche-like&#160;cell microenvironment. However, spheroid heterogeneity and variability of their production methods can influence overall cell metabolism, viability, and drug response. This makes it difficult to choose the most appropriate&#160;methodology, considering the requirements in size, variability, needs of biofabrication, and use as in vitro 3D tissue models in stem and cancer cell biology. In particular, spheroid production can influence their compatibility with quantitative live microscopies, such as optical metabolic imaging, fluorescence lifetime imaging microscopy (FLIM), monitoring of spheroid&#160;hypoxia with nanosensors, or viability. Here, a number of conventional spheroid formation protocols are presented, highlighting their compatibility with the live widefield, confocal, and two-photon microscopies. The follow-up imaging to analysis&#160;pipeline with multiplexed autofluorescence FLIM and, using various types of cancer and stem cell spheroids, is also presented.

Author Spotlight: Standardizing Spheroid Formation Methods for Metabolic and Oxygenation Analysis Using Fluorescence Lifetime Imaging Microscopy

Here, we describe different multicellular spheroid formation methods to perform follow-up multi-parameter live cell microscopy. Using fluorescence lifetime imaging microscopy (FLIM), cellular autofluorescence, staining dyes, and nanoparticles, the approach for analysis of cell metabolism, hypoxia, and cell death in live three-dimensional (3D) cancer and stem cell-derived spheroids is demonstrated.

Multicellular tumor spheroids are a popular 3D tissue microaggregate&#160;model for reproducing tumor microenvironment, testing and optimizing drug ...

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 ...

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

Summary

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

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Visualizing Intracellular SNARE Trafficking by Fluorescence Lifetime Imaging Microscopy

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

Production and Multi-Parameter Live Cell Fluorescence Lifetime Imaging Microscopy (FLIM) of Multicellular Spheroids

Internalization and Observation of Fluorescent Biomolecules in Living Microorganisms via Electroporation