Name: mapping molecular diffusion in the plasma membrane by multiple-target tracing (mtt)
Uploaded: 2012-05-27T14:00:00
Description: Watch this Scientific Journal Video about Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing MTT at JoVE.com

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 recep...

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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 ...

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JoVE Journal

Biology

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, ...

Measuring Plasma Membrane Protein Endocytic Rates by Reversible Biotinylation

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

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Crystallization of Membrane Proteins in Lipidic Mesophases

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

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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 ...

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 ...

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+ ...

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 ...