We thank members of our team, particularly MC Blache for technical assistance, as well as M Irla and B Imhof, for their support and fruitful discussions. Figures for deflation and confinement reproduced courtesy of Nature Methods. This project is supported by institutional grants from the CNRS, INSERM and Marseille University, and by specific grants from the R&eacute;gion Provence-Alpes-C&ocirc;te-d'Azur, Institut National du Cancer, Agence Nationale de la Recherche (ANR-08-PCVI-0034-02, ANR 2010 BLAN 1214 01) &amp; Fondation pour la Recherche M&eacute;dicale (Equipe lab&eacute;lis&eacute;e FRM-2009). VR is supported by a fellowship from the Ligue Nationale Contre le Cancer.

In this video, we present a full single particle tracking experiment, using quantum-dots targeted to a specific membrane receptor. The main goal of this experiment consists in discriminating different types of molecular diffusion behaviors measured within the plasma membrane of live cells. Indeed, molecular movements arising at the membrane can typically deviate from Brownian diffusion by being linearly directed or confined within nanodomains26-29, for instance. We aim at simultaneously following as many receptors as technically possible, to provide a snapshot of the variety arising in the dynamics occurring within the membrane of a live cell. This is ultimately expected to allow deciphering of the mechanisms regulating cell surface receptor signaling.
1. Cell Culture
<ol>
 <li> Prepare the cellular sample: use adherent COS-7 cells which endogenously express the epidermal growth factor receptor (EGFR)30. When working with live cells without antibiotics make sure to have no latent sub-detectable contamination by using proper sterile techniques at all times during the preparation. </li>
 <li> Grow cells in complete medium (DMEM with 10% calf serum, 1% glutamine, 1% HEPES and 1% sodium pyruvate, see table of specific reagents below), at 37 &deg;C with 7% CO2, taking care to have them in sub-confluent, exponential growth before spreading them in Lab-Tek. </li>
 <li> On the day before the experiment, spread 5,000 cells/well in 8-well chambers (Lab-Tek) and incubate overnight. Counting cells ensures a constant cell density, hence a reproducible ratio of quantum-dots per cell. </li>
</ol>
2. Cell Labeling
Prepare quantum-dots with a specific coating. Quantum-dots are fluorescent nanoparticles composed of semiconductors. These nanoparticles present a high interest because they are very bright and photostable compared to classical fluorescent probes31,32, which allows achievement of an appropriate signal to noise ratio (SNR) for single molecule imaging.
<ol>
 <li> Before the experiment, produce Fab fragments against EGFR from hybridoma cell line (mAb 108, ATCC HB-9764), by digestion with papain as previously described21. </li>
 <li> Conjugate Fab with biotin, as per manufacturer's instructions (EZ-link sulfo-NHS-LC-biotinylation Kit; Thermo Scientific, Fig. 1A).</li>
 <li> Use quantum-dots functionalized with streptavidin (Invitrogen) and emitting at 605 nm (optimal emission wavelength for brilliance, detection and separation from cellular autofluorescence). </li>
 <li> Prepare the labeling solution in complete medium (see table of specific reagents), in order to saturate the streptavidins present around the quantum-dots, as well as to prevent aggregation and non-specific binding to the cells and to the coverslip. </li>
 <li> Prepare two intermediate solutions: one with quantum-dots-streptavidin and another one with biotinylated Fab, each at 20 nM. </li>
 <li> Mix an equal volume of these two solutions: mix the quantum-dots with Fab in 1:1 ratio to obtain a final working concentration of 10 nM Fab:quantum-dots complex. Using an equimolar ratio favors the formation of mono-functionalized complexes composed of one quantum-dot and one biotinylated Fab fragment. This favors monovalent labeling, limiting artifact observations due to receptor cross-linking33,34. </li>
 <li> Incubate for 15 minutes at 25 &deg;C, using a shaker at 1,200 rotations per minute to prevent aggregation. The mix is then ready to use on living cells. </li>
 <li> Incubate cells in 100 &mu;l of mix for 5 minutes at 37 &deg;C with 7% CO2. </li>
 <li> Wash cells with non-autofluorescent imaging medium (HBSS buffer, 1% HEPES, see table of specific reagents) pre-warmed at 37 &deg;C. </li>
 <li> Carefully remove the excess of labels to prevent unnecessary spoiling the SNR by unbound, out of focus quantum-dots, by extensively washing each well with imaging medium, typically 5 times at room temperature, with at least 5 minutes delay before the last wash. </li>
</ol>
3. Optical Setup
The video-microscopy setup is composed of four major parts: 
<ol>
 <li> An inverted microscope with a specific fluorescence cube (FF01-457/50 excitation, FF495-Di02 dichroic, and FF01-617/73 emission filters, Semrock) and a high numerical aperture (1.3 or 1.49) oil-immersion 100x objective.</li>
 <li>A 100 W mercury lamp (coupled to the microscope through an optic fiber, avoiding interfering with thermoregulation).</li>
 <li> A 512 x 512 pixels high sensitivity EMCCD camera to achieve a sufficient SNR.</li>
 <li>An incubator to keep biological samples at 37 &deg;C during the experiment.</li>
</ol>
4. Acquisition
<ol>
 <li> Select isolated and well-spread cells. This favors the extension of nice, flat lamellipodia, best adapted to look for planar motion of membrane molecules.</li>
 <li> Evaluate the cellular physiological status by its appearance both in transmitted light and in fluorescence: intense vesicular traffic, absence of necrotic or apoptotic signs, low autofluorescence, and average-strong quantum-dot labeling (typically up to 1,000 quantum-dots per cell, see Fig. 1B, C). </li>
 <li> Select a high labeling density, which is critical for simultaneously monitoring as many receptors as possible. This is actually limited both by physiological (surface expression, epitope accessibility, lateral motion) and algorithmic parameters (non-overlapping point-spread functions (PSF), even taking into account blurring due to motion, to allow proper detection and reconnection). </li>
 <li> For each cell, first acquire a brightfield field image (preferentially using DIC, if available) that can further allow checking for the cell aspect and spatial limits of the lamellipodia. </li>
 <li> Save this image using the same name as the video-stack (such as cell1.tif &amp; cell1.stk for instance), in a sub-folder named dic, since, with these conventions, the algorithm can automatically find back the matching image for each stack. </li>
 <li> Acquire 1 to 3 videos per cell, continuously, typically at 36-ms rate, the fastest rate achievable in full frame with this camera. However one can acquire at higher frequencies, up to 1-ms rate, using dedicated CCD with increased sensitivity and/or less pixels. The frame-transfer technology provides negligible delay between frames. </li>
 <li> Electron multiply enhancement should always be set at maximum (just below saturation, if relevant) to allow reaching single molecule sensitivity, with a sufficient SNR, at least above 20 dB (for efficient peak detection1), typically around 25-30 dB. </li>
 <li> Typically acquire 300 frames per video, since trajectories are reconstructed over ~100 frames on average, mostly limited by long blinking events. Video frequency and length can be adapted for a given measure, which could require longer traces for instance. </li>
</ol>
5. MTT Analysis
<ol>
 <li>Select the path to the directory containing the video files to evaluate a given dataset with Matlab or Octave. </li>
 <li> To start the fully automatized analysis1,35, type the command detect_reconnex23 in Octave, or MTT23i in Matlab. This program stands for "version 2.3, with user interface" and is available for download, together with the previous version 2.2. It first displays a graphic interface listing all the parameters used, as shown in Fig. 2. </li>
</ol>
6. Representative Results
MTT automatically analyzes each recorder video, to deliver traces of detected and estimated targets, complemented by further investigations, such as confinement detection. This ultimately allows mapping traces over cell images (Fig. 1C &amp; 3).
MTT Description
The core MTT analysis is performed over each frame, invoking 3 main tasks (Fig. 3):
<ol>
 <li>Detection of the presence or absence of a target (quantum-dot), within a sliding sub-region successively centered around each pixel, where two hypotheses are compared: presence either of a signal, with the PSF modelized as a bi-dimensional Gaussian peak, or only noise, using a threshold insuring low enough false alarm, with less than one spurious detection per frame. This leads to a map of detection probability. Each local maximum is considered as a putative target, insuring that its probability level is higher than a minimum value, set according to the desired Probability of False Alarm (PFA). This limit is set low enough to efficiently discriminate signals from noise, avoiding at best spurious detections (PFA of 10-6 by default, to insure less than one error in 512 x 512 pixel images), while still allowing a sufficiently high probability of detection, reaching the theoretically predicted optimum1. Note that the requirement of using a sub-region implies that the image borders (3 pixels, for a default window of 7 x 7 pixels) cannot be evaluated. </li>
 <li>Estimation, for each detected target, of the relevant parameters, such as sub-pixel position and signal intensity. For every detected target, a least-square Gauss-Newton fit is next performed to estimate the position, width and height of the detected Gaussian. This provides notably the sub-pixel position of the dye (10 to 20 nm accuracy for typical SNR values and immobile or slowly diffusing dyes, increasing up to ~100 nm for dyes diffusing at 0.1 &mu;m2/s). </li>
 <li>Reconnection of the new targets with the traces already built over the previous frames. The set of new targets is matched with the set of previous traces. For this purpose, in order to assign each target to a trace, if possible, all available statistical information obtained from the detection step is used, not only position, but also intensity, width, blinking and associated statistics. Hence, targets are not just assigned to the nearest trace: in case of crossing traces, intensity, speed, width and blinking will be considered. This delivers the statistically optimal reconnection score. This strategy avoids, when possible, biasing the reconnection toward the nearest neighbors. </li>
</ol>
Detected peaks can be a posteriori rejected if their estimation or reconnection fails. A special test handles the detection of new peaks, which would initiate new traces. This test uses a more stringent PFA (10-7), since reconnecting a peak to a trace can be interpreted de facto as a validation of its relevance (this criterion being by definition not applicable for new peaks).
Trajectory Analysis 
Possible transient confinement is next evaluated by a function inversely related to local diffusion24-29. Applying a threshold allows to define confined or not episodes. By iterating these over all traces, we can map membrane dynamics, in terms of transient confinement/slow down events. This can be alternatively represented using either the binary or discrete values of this confinement index. 
By default, MTT automatically performs those tasks, saving 8 peak parameters in a text file: frame number, i and j position, signal intensity, radius, offset and blink, for each video frame (group of 7 rows) and trace (column). These output parameters can be reloaded in Matlab or Octave using the fread_data_spt script for further analyzing i.e. traces or signal intensities, as exemplified in the <a href="https://www.jove.com/files/ftp_upload/3599/MTT_example.txt">MTT_example script</a> in appendix.
Further analyses lead to map traces over each cell (Fig. 1C &amp; 3) and to provide histogram distribution for relevant parameters (such as peak intensities, SNR or local diffusion values). For each file, mean and standard deviation of each parameter are saved in a text file, together with an image of the histograms. Logarithmic distributions, such as for square displacements r2, lead to geometric mean. The diffusion coefficient D is computed from a linear fit over the five first points of the MSD curve. These values provide an overview of an experiment involving for example kinetics of cellular reactions or pharmaceutical/enzymatic treatments affecting membrane organization. Since MTT is an open-source code, this aspect may be readily adapted to any dedicated investigation.
<img src="/files/ftp_upload/3599/3599fig1.jpg" alt="Figure 1" /> 
Figure 1. Monitoring membrane receptors dynamics by MTT. (A) Membrane components, such as the EGFR, are tagged with quantum-dots coupled to biotinylated Fab fragments (schematic drawing with approximately correct scaling of each molecule). (B) Typical fluorescent image acquired from a live COS-7 cell, with 36-ms exposure time, depicting diffraction-limited peaks corresponding to individually labeled receptor. (C) Output of the MTT analysis displaying the reconstituted trajectories of the receptors, overlaid on the brightfield image of the cell.
<img src="/files/ftp_upload/3599/3599fig2.jpg" alt="Figure 2" /> 
Figure 2. MTT input parameters. Running MTT23i opens a graphic user interface listing all input parameters, names and default values, as described in our previous publication1. In the algorithm, space and time parameters (search windows, peak radius, maximum diffusion and blinking) are in dimensionless standard units, pixels and frames. Calibrations can be applied a posteriori to convert output results. Default values, corresponding to a Cascade 512BFT with 100x magnification, are pixel size: 156 nm/pxl and frame delay: 36 ms/frame.
Investigators should optimize a few critical parameters, such as the maximum expected diffusion coefficient ("Diff max") and maximum blinking disappearance ("Toff"). These two space and time limits are almost the only ones that need to be reconsidered for a given experimental condition, the others being set to robust default values. For instance, the number of false alarms is directly set to ensure less than one error per million pixels, hence less than one per frame, which is satisfactory in most cases. All parameters are saved in a text file in the output folder, allowing users to verify afterwards which settings were used for analysis.
<img src="/files/ftp_upload/3599/3599fig3.jpg" alt="Figure 3" /> 
Figure 3. Major steps of the MTT analysis. Starting from an experimental stack of fluorescence images, peaks are sequentially and automatically detected, estimated by a Gaussian fit and reconnected over frames (first range of operations, upper part of the flowchart). Traces can further be analyzed for putative confinement, for instance, ultimately leading to a dynamic map of relevant descriptors (second range of operations, lower part). 
<img src="/files/ftp_upload/3599/3599fig4.jpg" alt="Figure 4" /> 
Figure 4. Labeling valency does not impact MTT. To evaluate the possible bias introduced by artefactual multivalent labeling, MTT analysis was performed to track endogenous EGFR tagged using 2 different schemes, to generate and analyze maps of trajectories. (A) Receptors were tagged with biotinylated Fab and quantum-dots605-streptavidin, as described in the protocol. In this case, the multi-valency of quantum-dots and streptavidins may result in coupling several receptors to a single dye. (B) Receptors were tagged with Fab directly coupled to an organic dye, Atto647N. In this case, one Fab, hence one receptor, can be coupled to more than one dye. (C) Mean square displacement (MSD) curve was computed for all traces for each cell, labeled either with quantum-dot or Atto dyes (left and right graphs, respectively). Diffusion coefficients were computed by linear fit over the five first points of the MSD (red dotted line). Each scheme of labeling led to similar diffusion values (central graph). Qdot: quantum-dots605 (n = 5 cells), Atto: Atto647N (n = 7 cells), ns: not significant (Student's t-test p value &gt; 0.05).

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Methods. 5, 397-399 (2008).</li><li>Bertaux, N., Marguet, D., Rigneault, H., Serg&#233;, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiple-target+tracing+(MTT)+algorithm+probes+molecular+dynamics+at+cell+surface.">Multiple-target tracing (MTT) algorithm probes molecular dynamics at cell surface.</a> Protocol Exchange. , (1038).</li><li>Groc, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Surface+trafficking+of+neurotransmitter+receptor:+comparison+between+single-molecule/quantum+dot+strategies.">Surface trafficking of neurotransmitter receptor: comparison between single-molecule/quantum dot strategies.</a> The Journal of neuroscience : the official journal of the Society for Neuroscience. 27, 12433-12437 (2007).</li><li>Cui, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=One+at+a+time,+live+tracking+of+NGF+axonal+transport+using+quantum+dots.">One at a time, live tracking of NGF axonal transport using quantum dots.</a> Proceedings of the National Academy of Sciences of the United States of America. 104, 13666-13671 (2007).</li><li>He, H. T., Marguet, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+nanodomains+in+living+cell+membrane+by+fluorescence+correlation+spectroscopy.">Detecting nanodomains in living cell membrane by fluorescence correlation spectroscopy.</a> Annu. Rev. Phys. Chem. 62, 417-436 (2011).</li><li>Cebecauer, M., Spitaler, M., Serge, A., Magee, A. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Signalling+complexes+and+clusters:+functional+advantages+and+methodological+hurdles.">Signalling complexes and clusters: functional advantages and methodological hurdles.</a> J. Cell. Sci. 123, 309-320 (2010).</li><li>Kao, H. P., Verkman, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tracking+of+single+fluorescent+particles+in+three+dimensions:+use+of+cylindrical+optics+to+encode+particle+position.">Tracking of single fluorescent particles in three dimensions: use of cylindrical optics to encode particle position.</a> Biophys. J. 67, 1291-1300 (1994).</li></ol>

No conflicts of interest declared.

In single-particle tracking, beside the cell and microscopy aspects, the analysis represents a substantial part of the work. This addresses the algorithm used to perform the three main tasks: detecting, estimating and reconnecting peaks over each frame. But the consequent aspect of this work resides in elaborating the algorithm itself, which may need to be adapted for any new dedicated investigation, essentially for the last, extra steps (such as deciphering modes of motion, interactions or stoichiometry). 
<p class=...

<table border="1">
<tbody>
 <tr>
 <td>Reagent</td>
 <td>Company</td>
 <td>Catalogue number</td>
 <td>Quantity</td>
 </tr>
 <tr>
 <td>Cos-7 cell line</td>
 <td>ATCC</td>
 <td>CRL-1651</td>
 <td>5,000 cells/well</td>
 </tr>
 <tr>
 <td>HBSS without Ca2+</td>
 <td>GIBCO</td>
 <td>14175</td>
 <td>1 ml</td>
 </tr>
 <tr>
 <td>0.05% Trypsin EDTA</td>
 <td>GIBCO</td>
 <td>25300</td>
 <td>1 ml</td>
 </tr>
 <tr>
 <td>8-well Lab-tek</td>
 <td>NUNC</td>
 <td>155441</td>
 <td>1</td>
 </tr>
 <tr>
 <td>QDot-605 streptavidin</td>
 <td>Invitrogen</td>
 <td>Q10101MP</td>
 <td>20 mM</td>
 </tr>
 <tr>
 <td colspan="4">Biotinylated Fab (for Fab synthesis, see reference 21)</td>
 </tr>
 <tr>
 <td>Fab from mAb 108</td>
 <td>ATCC</td>
 <td>HB-9764</td>
 <td>200 &mu;g</td>
 </tr>
 <tr>
 <td>NHS-Biotin</td>
 <td>Thermo Scientific</td>
 <td>21435</td>
 <td>18.5 &mu;g</td>
 </tr>
 <tr>
 <td colspan="4">Complete medium</td>
 </tr>
 <tr>
 <td>DMEM</td>
 <td>GIBCO</td>
 <td>41965</td>
 <td>500 ml</td>
 </tr>
 <tr>
 <td>Fetal Bovine Serum</td>
 <td>SIGMA</td>
 <td>F7524</td>
 <td>50 ml</td>
 </tr>
 <tr>
 <td>L-Glutamine</td>
 <td>GIBCO</td>
 <td>25030</td>
 <td>5 ml</td>
 </tr>
 <tr>
 <td>HEPES</td>
 <td>GIBCO</td>
 <td>15630</td>
 <td>5 ml</td>
 </tr>
 <tr>
 <td>Sodium Pyruvate</td>
 <td>GIBCO</td>
 <td>11360</td>
 <td>5 ml</td>
 </tr>
 <tr>
 <td colspan="4">Imaging medium</td>
 </tr>
 <tr>
 <td>HBSS with Ca2+</td>
 <td>GIBCO</td>
 <td>14025</td>
 <td>25 ml</td>
 </tr>
 <tr>
 <td>HEPES</td>
 <td>GIBCO</td>
 <td>15630</td>
 <td>250 &mu;l</td>
 </tr>
</tbody>
</table>
&nbsp;
<table width="200" border="1">
<tbody>
 <tr>
 <td>Equipment</td>
 <td>Company</td>
 <td>Reference</td>
 </tr>
 <tr>
 <td>Inverted microscope</td>
 <td>Nikon</td>
 <td>Eclipse TE2000U</td>
 </tr>
 <tr>
 <td>Fluorescent lamp</td>
 <td>Nikon</td>
 <td>Intensilight C-HGFIE</td>
 </tr>
 <tr>
 <td>1.3 NA 100x objective</td>
 <td>Nikon</td>
 <td>Plan Fluor 1.30</td>
 </tr>
 <tr>
 <td>1.49 NA 100x objective</td>
 <td>Nikon</td>
 <td>APO TIRF 1.49</td>
 </tr>
 <tr>
 <td>Camera</td>
 <td>Roper Scientific</td>
 <td>Cascade 512 B</td>
 </tr>
 <tr>
 <td>Thermostated box</td>
 <td>Life Imaging Services</td>
 <td>The Box</td>
 </tr>
 </tbody>
</table>
Appendix: example Script of MTT supplementary analysis
function MTT_example(file_name) 
 %%% Basic examples showing how to recover MTT output results 
%%% to plot each trace and to build the histogram 
%%% of fluorescence intensities
if nargin&lt;1 % no file_name provided? 
&nbsp;&nbsp;&nbsp;&nbsp;files = dir('*.stk'); 
&nbsp;&nbsp;&nbsp;&nbsp;if isempty(files), disp('no data in current dir'), return, end 
&nbsp;&nbsp;&nbsp;&nbsp;file_name = files(1).name; % default: first stk file 
&nbsp;&nbsp;&nbsp;&nbsp;disp(['using' file_name 'by default']) 
end
file_param = [file_name '_tab_param.dat']; % output file
%% Load data 
 cd('output23') % or (&#x2018;output22'), according to version used 
% Disclaimer: version 2.2 only generates 7 parameters, 
% an extra parameter, noise, was added in version 2.3
% To read all parameters at once, in a single table 
% tab_param = fread_all_param(file_param); 
% tab_i = tab_param(2:8:end, :); tab_j = ...
% To read all parameters (except frame_number) in separate tables 
% [tab_i,tab_j,tab_alpha,tab_radius,tab_offset,tab_blk,tab_noise] = fread_all_data_spt(file_param); 
tab_i = fread_data_spt(file_param, 3); % index is 3 because trace number &amp; frame number, non informative, are discarded! 
tab_j= fread_data_spt(file_param, 4); 
tab_alpha = fread_data_spt(file_param, 5); 
tab_blk = fread_data_spt(file_param, 8);
%% Loop over traces 
N_traces = size(tab_i,1); 
% Tables are N_traces lines by N_frames colums
for itrc = 1:N_traces 
&nbsp;&nbsp;&nbsp;&nbsp;No_blk_index = tab_blk(itrc, :)&gt;0; % non blinking steps only 
&nbsp;&nbsp;&nbsp;&nbsp; plot(tab_i(itrc, No_blk_index), tab_j(itrc, No_blk_index)) 
&nbsp;&nbsp;&nbsp;&nbsp;xlabel('i (pixel)'), ylabel('j (pixel)') 
&nbsp;&nbsp;&nbsp;&nbsp;title(['trace # ' num2str(itrc)]) 
&nbsp;&nbsp;&nbsp;&nbsp;disp('Please strike any key for next trace'), pause 
end
%% Fluo histogram 
N_datapoints = sum(tab_blk(:)&gt;0); % non blinking steps only 
hist(tab_alpha(tab_blk&gt;0),2*sqrt(N_datapoints)) % using 2sqrt(N) bins 
xlabel('intensity (a.u.)'), ylabel('occurrence') 
title('histogram of particles fluorescence intensity')

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.

Watch this Scientific Journal Video about Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT at JoVE.com

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.

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 ...

mapping-molecular-diffusion-plasma-membrane-multiple-target-tracing

Research

JoVE Journal

Biology

17.1K Views.  Parc scientifique de Luminy. 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.

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

Measuring Near Plasma Membrane and Global Intracellular Calcium Dynamics in Astrocytes

The brain contains glial cells.  Astrocytes, a type of glial cell, have long been known to provide a passive supportive role to neurons. However, increasing evidence suggests that astrocytes may also actively participate in brain function through functional interactions with neurons.  However, many fundamental aspects of astrocyte biology remain controversial, unclear and/or experimentally unexplored. One important issue is the dynamics of intracellular calcium transients in astrocytes. This is relevant because calcium is well established as an important second messenger and because it has been proposed that astrocyte calcium elevations can trigger the release of transmitters from astrocytes. However, there has not been any detailed or satisfying description of near plasma membrane calcium signaling in astrocytes. Total internal reflection fluorescence (TIRF) microscopy is a powerful tool to analyze physiologically relevant signaling events within about 100 nm of the plasma membrane of live cells. Here, we use TIRF microscopy and describe how to monitor near plasma membrane and global intracellular calcium dynamics almost simultaneously. The further refinement and systematic application of this approach has the potential to inform about the precise details of astrocyte calcium signaling. A detailed understanding of astrocyte calcium dynamics may provide a basis to understand if, how, when and why astrocytes and neurons undergo calcium-dependent functional interactions.

We describe how to measure near membrane and global intracellular calcium dynamics in cultured astrocytes using total internal reflection and epifluorescence microscopy.

The brain contains glial cells.  Astrocytes, a type of glial cell, have long been known to provide a passive supportive role to neurons. However, ...

Measuring Plasma Membrane Protein Endocytic Rates by Reversible Biotinylation

Plasma membrane proteins are a large, diverse group of proteins comprised of receptors, ion channels, transporters and pumps.  Activity of these proteins is responsible for a variety of key cellular events, including nutrient delivery, cellular excitability, and chemical signaling.  Many plasma membrane proteins are dynamically regulated by endocytic trafficking, which modulates protein function by altering protein surface expression.  The mechanisms that facilitate protein endocytosis are complex and are not fully understood for many membrane proteins.  In order to fully understand the mechanisms that control the endocytic trafficking of a given protein, it is critical that the protein s endocytic rate be precisely measured.  For many receptors, direct endocytic rate measurements are frequently achieved utilizing labeled receptor ligands.  However, for many classes of membrane proteins, such as transporters, pumps and ion channels, there is no convenient ligand that can be used to measure the endocytic rate.  In the present report, we describe a reversible biotinylation method that we employ to measure the dopamine transporter (DAT) endocytic rate.  This method provides a straightforward approach to measuring internalization rates, and can be easily employed for trafficking studies of most membrane proteins.

Regulated endocytosis governs the cell surface expression levels of the majority of membrane proteins. Here we utilize reducible, membrane impermeant biotinylation reagents to measure the endocytic rate of the dopamine transporter (DAT), a polytopic membrane protein. The method facilitates a straightforward approach to measuring the endocytic rate of most plasma membrane proteins.

Plasma membrane proteins are a large, diverse group of proteins comprised of receptors, ion channels, transporters and pumps.  Activity of these proteins ...

Quantitative Measurement of GLUT4 Translocation to the Plasma Membrane by Flow Cytometry

Glucose is the main source of energy for the body, requiring constant regulation of its blood concentration. Insulin release by the pancreas induces glucose uptake by insulin-sensitive tissues, most notably the brain, skeletal muscle, and adipocytes. Patients suffering from type-2 diabetes and/or obesity often develop insulin resistance and are unable to control their glucose homeostasis. New insights into the mechanisms of insulin resistance may provide new treatment strategies for type-2 diabetes.
The GLUT family of glucose transporters consists of thirteen members distributed on different tissues throughout the body1. Glucose transporter type 4 (GLUT4) is the major transporter that mediates glucose uptake by insulin sensitive tissues, such as the skeletal muscle. Upon binding of insulin to its receptor, vesicles containing GLUT4 translocate from the cytoplasm to the plasma membrane, inducing glucose uptake. Reduced GLUT4 translocation is one of the causes of insulin resistance in type-2 diabetes2,3.
The translocation of GLUT4 from the cytoplasm to the plasma membrane can be visualized by immunocytochemistry, using fluorophore-conjugated GLUT4-specific antibodies.
Here, we describe a technique to quantify total amounts of GLUT4 translocation to the plasma membrane of cells during a chosen duration, using flow cytometry. This protocol is rapid (less than 4 hours, including incubation with insulin) and allows the analysis of as few as 3,000 cells or as many as 1 million cells per condition in a single experiment. It relies on anti-GLUT4 antibodies directed to an external epitope of the transporter that bind to it as soon as it is exposed to the extracellular medium after translocation to the plasma membrane.

This protocol describes a rapid technique to quantify the translocation of GLUT4 from the cytoplasm to the plasma membrane of cells by flow cytometry.

Glucose is the main source of energy for the body, requiring constant regulation of its blood concentration. Insulin release by the pancreas induces ...

Crystallization of Membrane Proteins in Lipidic Mesophases

Membrane proteins perform critical functions in living cells related to signal transduction, transport and energy transformations, and, as such, are implicated in a multitude of malfunctions and diseases. However, a structural and functional understanding of membrane proteins is strongly lagging behind that of their soluble partners, mainly, due to difficulties associated with their solubilization and generation of diffraction quality crystals. Crystallization in lipidic mesophases (also known as in meso or LCP crystallization) is a promising technique which was successfully applied to obtain high resolution structures of microbial rhodopsins, photosynthetic proteins, outer membrane beta barrels and G protein-coupled receptors. In meso crystallization takes advantage of a native-like membrane environment and typically produces crystals with lower solvent content and better ordering as compared to traditional crystallization from detergent solutions. The method is not difficult, but requires an understanding of lipid phase behavior and practice in handling viscous mesophase materials. Here we demonstrate a simple and efficient way of making LCP and reconstituting a membrane protein in the lipid bilayer of LCP using a syringe mixer, followed by dispensing nanoliter portions of LCP into an assay or crystallization plate, conducting pre-crystallization assays and harvesting crystals from the LCP matrix. These protocols provide a basic guide for approaching in meso crystallization trials; however, as with any crystallization experiment, extensive screening and optimization are required, and a successful outcome is not necessarily guaranteed.

The protocols describe the essential steps for obtaining diffraction quality crystals of a membrane protein starting from reconstitution of the protein in a lipidic cubic phase (LCP), finding initial conditions with LCP-FRAP pre-crystallization assays, setting up LCP crystallization trials and harvesting crystals.

Membrane proteins perform critical functions in living cells related to signal transduction, transport and energy transformations, and, as such, are ...

Harvesting and Cryo-cooling Crystals of Membrane Proteins Grown in Lipidic Mesophases for Structure Determination by Macromolecular Crystallography

An important route to understanding how proteins function at a mechanistic level is to have the structure of the target protein available, ideally at atomic resolution. Presently, there is only one way to capture such information as applied to integral membrane proteins (Figure 1), and the complexes they form, and that method is macromolecular X-ray crystallography (MX). To do MX diffraction quality crystals are needed which, in the case of membrane proteins, do not form readily. A method for crystallizing membrane proteins that involves the use of lipidic mesophases, specifically the cubic and sponge phases1-5, has gained considerable attention of late due to the successes it has had in the G protein-coupled receptor field6-21 (<a href="http://www.mpdb.tcd.ie" target="_blank">www.mpdb.tcd.ie</a>). However, the method, henceforth referred to as the in meso or lipidic cubic phase method, comes with its own technical challenges. These arise, in part, due to the generally viscous and sticky nature of the lipidic mesophase in which the crystals, which are often micro-crystals, grow. Manipulating crystals becomes difficult as a result and particularly so during harvesting22,23. Problems arise too at the step that precedes harvesting which requires that the glass sandwich plates in which the crystals grow (Figure 2)24,25 are opened to expose the mesophase bolus, and the crystals therein, for harvesting, cryo-cooling and eventual X-ray diffraction data collection.
The cubic and sponge mesophase variants (Figure 3) from which crystals must be harvested have profoundly different rheologies4,26. The cubic phase is viscous and sticky akin to a thick toothpaste. By contrast, the sponge phase is more fluid with a distinct tendency to flow. Accordingly, different approaches for opening crystallization wells containing crystals growing in the cubic and the sponge phase are called for as indeed different methods are required for harvesting crystals from the two mesophase types. Protocols for doing just that have been refined and implemented in the Membrane Structural and Functional Biology (MS&amp;FB) Group, and are described in detail in this JoVE article (Figure 4). Examples are given of situations where crystals are successfully harvested and cryo-cooled. We also provide examples of cases where problems arise that lead to the irretrievable loss of crystals and describe how these problems can be avoided. In this article the Viewer is provided with step-by-step instructions for opening glass sandwich crystallization wells, for harvesting and for cryo-cooling crystals of membrane proteins growing in cubic and in sponge phases.

Herein is described procedures implemented in the Caffrey Membrane Structural and Functional Biology Group to harvest and cryo-cool membrane protein crystals grown in lipidic cubic and sponge phases for use in structure determination using macromolecular X-ray crystallography.

An important route to understanding how proteins function at a mechanistic level is to have the structure of the target protein available, ideally at ...

Real-time Analyses of Retinol Transport by the Membrane Receptor of Plasma Retinol Binding Protein

Vitamin A is essential for vision and the growth/differentiation of almost all human organs. Plasma retinol binding protein (RBP) is the principle and specific carrier of vitamin A in the blood. Here we describe an optimized technique to produce and purify holo-RBP and two real-time monitoring techniques to study the transport of vitamin A by the high-affinity RBP receptor STRA6. The first technique makes it possible to produce a large quantity of high quality holo-RBP (100%-loaded with retinol) for vitamin A transport assays. High quality RBP is essential for functional assays because misfolded RBP releases vitamin A readily and bacterial contamination in RBP preparation can cause artifacts. Real-time monitoring techniques like electrophysiology have made critical contributions to the studies of membrane transport. The RBP receptor-mediated retinol transport has not been analyzed in real time until recently. The second technique described here is the real-time analysis of STRA6-catalyzed retinol release or loading. The third technique is real-time analysis of STRA6-catalyzed retinol transport from holo-RBP to cellular retinol binding protein I (CRBP-I). These techniques provide high sensitivity and resolution in revealing RBP receptor's vitamin A uptake mechanism.

Here we describe an optimized technique to produce high-quality vitamin A/RBP complex and two real-time monitoring techniques to study vitamin A transport by STRA6, the RBP receptor.

Vitamin A is essential for vision and the growth/differentiation of almost all human organs.  Plasma retinol binding protein (RBP) is the principle and ...

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

Lateral diffusion and compartmentalization of plasma membrane proteins are tightly regulated in cells and thus, studying these processes will reveal new insights to plasma membrane protein function and regulation. Recently, k-Space Image Correlation Spectroscopy (kICS)1&#160;was developed to enable routine measurements of diffusion coefficients directly from images of fluorescently tagged plasma membrane proteins, that avoided systematic biases introduced by probe photophysics. Although the theoretical basis for the analysis is complex, the method can be implemented by nonexperts using a freely available code to measure diffusion coefficients of proteins. kICS calculates a time correlation function from a fluorescence microscopy image stack after Fourier transformation of each image to reciprocal (k-) space. Subsequently, circular averaging, natural logarithm transform and linear fits to the correlation function yields the diffusion coefficient. This paper provides a step-by-step guide to the image analysis and measurement of diffusion coefficients via kICS.
First, a high frame rate image sequence of a fluorescently labeled plasma membrane protein is acquired using a fluorescence microscope. Then, a region of interest (ROI) avoiding intracellular organelles, moving vesicles or protruding membrane regions is selected. The ROI stack is imported into a freely available code and several defined parameters (see Method section) are set for kICS analysis. The program then generates a &#34;slope of slopes&#34; plot from the k-space time correlation functions, and the diffusion coefficient is calculated from the slope of the plot. Below is a step-by-step kICS procedure to measure the diffusion coefficient of a membrane protein using the renal water channel aquaporin-3 tagged with EGFP as a canonical example.

This paper provides a step by step guide to the fluctuation analysis technique k-Space Image Correlation Spectroscopy (kICS) for measuring diffusion coefficients of fluorescently labeled plasma membrane proteins in live mammalian cells.

Lateral diffusion and compartmentalization of plasma membrane proteins are tightly regulated in cells and thus, studying these processes will reveal new ...

Imaging Plasma Membrane Deformations With pTIRFM

To gain novel insights into the dynamics of exocytosis, our group focuses on the changes in lipid bilayer shape that must be precisely regulated during the fusion of vesicle and plasma membranes. These rapid and localized changes are achieved by dynamic interactions between lipids and specialized proteins that control membrane curvature. The absence of such interactions would not only have devastating consequences for vesicle fusion, but a host of other cellular functions that involve control of membrane shape. In recent years, the identity of a number of proteins with membrane-shaping properties has been determined. What remains missing is a roadmap of when, where, and how they act as fusion and content release progress.
Our understanding of the molecular events that enable membrane remodeling has historically been limited by a lack of analytical methods that are sensitive to membrane curvature or have the temporal resolution to track rapid changes. PTIRFM satisfies both of these criteria. We discuss how pTIRFM is implemented to visualize and interpret rapid, submicron changes in the orientation of chromaffin cell membranes during dense core vesicle (DCV) fusion. The chromaffin cells we use are isolated from bovine adrenal glands. The membrane is stained with a lipophilic carbocyanine dye,1,1&#39;-dioctadecyl-3,3,3&#39;,3&#39;-tetramethylindodicarbocyanine, 4-chlorobenzenesulfonate, or&#160;diD. DiD intercalates in the membrane plane with a &#34;fixed&#34; orientation and is therefore sensitive to the polarization of the evanescent field. The diD-stained cell membrane is sequentially excited with orthogonal polarizations of a 561&#160;nm laser (p-pol, s-pol). A 488&#160;nm laser is used to visualize vesicle constituents and time the moment of fusion. Exocytosis is triggered by locally perfusing cells with a depolarizing KCl solution. Analysis is performed offline using custom-written software to understand how diD emission intensity changes relate to fusion pore dilation.

Polarization-based Total Internal Reflection Fluorescence Microscopy (pTIRFM) enables real-time detection of cell membrane dynamics. This article describes the implementation of pTIRFM for the study of membrane remodeling during regulated exocytosis. The technique is generalizable to other processes in cell biology that directly or indirectly involve changes in membrane shape.

To gain novel insights into the dynamics of exocytosis, our group focuses on the changes in lipid bilayer shape that must be precisely regulated during ...

Simultaneous Measurement of Mitochondrial Calcium and Mitochondrial Membrane Potential in Live Cells by Fluorescent Microscopy

Apart from their essential role in generating ATP, mitochondria also act as local calcium (Ca2+) buffers to tightly regulate intracellular Ca2+ concentration. To do this, mitochondria utilize the electrochemical potential across their inner membrane (&#916;&#936;m) to sequester Ca2+. The influx of Ca2+ into the mitochondria stimulates three rate-limiting dehydrogenases of the citric acid cycle, increasing electron transfer through the oxidative phosphorylation (OXPHOS) complexes. This stimulation maintains &#916;&#936;m, which is temporarily dissipated as the positive calcium ions cross the mitochondrial inner membrane into the mitochondrial matrix.
We describe here a method for simultaneously measuring mitochondria Ca2+ uptake and &#916;&#936;m in live cells using confocal microscopy. By permeabilizing the cells, mitochondrial Ca2+ can be measured using the fluorescent Ca2+ indicator Fluo-4, AM, with measurement of &#916;&#936;m using the fluorescent dye tetramethylrhodamine, methyl ester, perchlorate (TMRM). The benefit of this system is that there is very little spectral overlap between the fluorescent dyes, allowing accurate measurement of mitochondrial Ca2+ and &#916;&#936;m simultaneously. Using the sequential addition of Ca2+ aliquots, mitochondrial Ca2+ uptake can be monitored, and the concentration at which Ca2+ induces mitochondrial membrane permeability transition and the loss of &#916;&#936;m determined.

Mitochondria can utilize the electrochemical potential across their inner membrane (&#916;&#936;m) to sequester calcium (Ca2+), allowing them to shape cytosolic Ca2+ signaling within the cell. We describe a method for simultaneously measuring mitochondria Ca2+ uptake and &#916;&#936;m in live cells using fluorescent dyes and confocal microscopy.

Apart from their essential role in generating ATP, mitochondria also act as local calcium (Ca2+) buffers to tightly regulate intracellular Ca2+ ...

Determination of Plasma Membrane Partitioning for Peripherally-associated Proteins

This method provides a fast approach for the determination of plasma membrane partitioning of any fluorescently-tagged peripherally-associated protein using the profiles of fluorescence intensity across the plasma membrane. Measured fluorescence profiles are fitted by a model for membrane and cytoplasm fluorescence distribution along a line applied perpendicularly to the cell periphery. This model is constructed from the fluorescence intensity values in reference cells expressing a fluorescently-tagged marker for cytoplasm and with FM 4-64-labeled plasma membrane. The method can be applied to various cell types and organisms; however, only plasma membranes of non-neighboring cells can be evaluated. This fast microscopy-based method is suitable for experiments, where subtle and dynamic changes of plasma membrane-associated markers are expected and need to be quantified, e.g., in the analysis of mutant versions of proteins, inhibitor treatments, and signal transduction observations. The method is implemented in a multi-platform R package that is coupled with an ImageJ macro that serves as a user-friendly interface.

Here, we present a protocol to perform a quantitative analysis of the level of plasma-membrane association for fluorescently-tagged peripherally-associated protein. The method is based on the computational decomposition of membrane and cytoplasmic component of signal observed in cells labeled with plasma membrane fluorescent marker.

This method provides a fast approach for the determination of plasma membrane partitioning of any fluorescently-tagged peripherally-associated protein ...