1Institut National de la Santé et de la Recherche Médicale, UMR 631, Parc scientifique de Luminy, 2Centre National de la Recherche Scientifique, UMR 6102, Parc scientifique de Luminy, 3Centre d'Immunologie de Marseille-Luminy, Aix-Marseille University, 4École Centrale Marseille, Technopôle de Château-Gombert, 5Institut Fresnel, Aix-Marseille University, 6Centre National de la Recherche Scientifique, UMR 6133, Aix-Marseille University
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Rouger, V., Bertaux, N., Trombik, T., Mailfert, S., Billaudeau, C., Marguet, D., et al. Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT). J. Vis. Exp. (63), e3599, doi:10.3791/3599 (2012).
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.
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
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.
3. Optical Setup
The video-microscopy setup is composed of four major parts:
5. MTT Analysis
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 & 3).
The core MTT analysis is performed over each frame, invoking 3 main tasks (Fig. 3):
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).
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 MTT_example script in appendix.
Further analyses lead to map traces over each cell (Fig. 1C & 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.
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.
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.
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).
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 > 0.05).
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).
However, once the algorithm is fully developed, running it is straightforward, especially since we paid attention at keeping the number of input parameters at minimum (Fig. 3). This renders MTT very robust and easy to implement. When elaborating MTT, we aimed at entirely reconsidering the analytical options used for each task. We wanted to optimize the process along two challenging axes:
Labeling valency is a recurrent issue in single molecule studies. Indeed, direct measure of the dye/target ratio is highly challenging34. However, an alternate way for investigating the putative bias introduced by artefactual multivalent labeling consists in comparing measures with either quantum-dot tags (with putatively several targets per dye, inducing artefactual crosslinking) or organic dye tags (with, at contrary, putatively several dyes per target, biasing only signal intensity and bleaching characteristics). We and others6,36,37 have observed comparable behavior when using either quantum-dots or organic dyes (atto647N in our case, Fig. 4), in terms of diffusion and transition between modes of motion. Variation in diffusion values between the two conditions is lower than the cell-to-cell disparity (Fig. 4C). This shows that labeling valency, even if not strictly monovalent, does not significantly affect SPT results.
Confinement and Molecular Interactions
Transient or stable confinement can be interpreted as the signature of preferential affinity or interaction24-29. Biomembranes are indeed strongly inhomogeneous, with local assemblies dictated by structural features such as hydrophobicity, transmembrane size and interfacial tension. These driving forces lead to spatiotemporal remodeling of functional assemblies with critical roles for i.e. signaling, adhesion or trafficking. Mapping and quantifying such events is expected to provide a key to decipher how a cell integrates continuously presented stimuli. Indeed, for a cell, exhibiting an adequate adaptation through an accurate response requires tuning interactions among signaling partners and their environment. Membrane receptors may interact either with the sub-membrane cytoskeleton fences, membrane structures such as endocytic pits or focal adhesions, or also proteic/lipid partners, involved in signaling domains for instance. In our representation, such events can be investigated through confinement strength and its variations over space and time. This further strengthens the importance of working at the highest achievable space-time resolution, keeping in mind the constraints associated to short timings and high densities, as discussed above.
It is a good practice to compare such dynamic measurements with other approaches. For instance other dynamic microscopy techniques, like FRAP and FCS, provide complementary sensitivity and resolution4,38,39. FRAP provides an ensemble measurement of molecular diffusion, while FCS can reach single-molecule sensitivity, with a very good time resolution. However, both methods are inherently restricted to a local measure (typically within a confocal spot). This motivated us to take advantage and push the limits the spatial possibilities of SPT: investigating a large field of view, to encompass a whole cell at once, together with a locally relevant spatiotemporal accuracy.
Further Developments & Investigations
According to the range of biological questions that may be addressed using single-molecule measurements, MTT can be extended along various directions, at each level: detection, estimation, reconnection and further analyses. For instance, one may consider dealing with more than one molecular population, calling for multicolor detection – as can be performed using dedicated optic or analytic schemes. Estimation can include new relevant descriptors, such as any spectral or polarization information, or the axial z position, for extending tracing into 3D40.
For reconnection, the Brownian and blinking assumptions can be fully reconsidered for a specific problem. Interestingly enough, single-molecule measurements have been extended over the last decade to the so-called nanoscopic regime17,22,23. Although MTT is directly addressing single-molecule localization, it is of crucial importance to consider the case of a fixed sample, which provides a safe solution for an exhaustive localization of a molecular population. However, in such a case, the Brownian assumption is no longer valid. Replacing it by accurately handling a nanoscopic measure, only limited by SNR, is critical for reaching the best nanometric resolution.
Along such developments, based on either multicolor, 3D and/or exhaustive measures, future work is expected to deliver a comprehensive view of molecular static and dynamic data. This will have a direct relevance for cell biology studies, such as deciphering the subtle modalities regulating extra and intracellular signaling.
The source code of MTT is available as open-source software for academic research. It can be downloaded from our web page, at ciml.univ-mrs.fr, by navigating to our team's page, He & Marguet Lab, which provides a link for software description and download. Please note that we ask you for your name and institution only for information, for instance in case of further collaboration or to inform you about a new release or update.
No conflicts of interest declared.
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 Région Provence-Alpes-Côte-d'Azur, Institut National du Cancer, Agence Nationale de la Recherche (ANR-08-PCVI-0034-02, ANR 2010 BLAN 1214 01) & Fondation pour la Recherche Médicale (Equipe labélisée FRM-2009). VR is supported by a fellowship from the Ligue Nationale Contre le Cancer.
|Cos-7 cell line||ATCC||CRL-1651||5,000 cells/well|
|HBSS without Ca2+||GIBCO, by Life Technologies||14175||1 ml|
|0.05% Trypsin EDTA||GIBCO, by Life Technologies||25300||1 ml|
|8-well Lab-tek||Nalge Nunc international||155441||1|
|QDot-605 streptavidin||Invitrogen||Q10101MP||20 mM|
|Biotinylated Fab (for Fab synthesis, see reference 21)|
|Fab from mAb 108||ATCC||HB-9764||200 μg|
|NHS-Biotin||Thermo Fisher Scientific, Inc.||21435||18.5 μg|
|DMEM||GIBCO, by Life Technologies||41965||500 ml|
|Fetal Bovine Serum||Sigma-Aldrich||F7524||50 ml|
|L-Glutamine||GIBCO, by Life Technologies||25030||5 ml|
|HEPES||GIBCO, by Life Technologies||15630||5 ml|
|Sodium Pyruvate||GIBCO, by Life Technologies||11360||5 ml|
|HBSS with Ca2+||GIBCO, by Life Technologies||14025||25 ml|
|HEPES||GIBCO, by Life Technologies||15630||250 μl|
|Inverted microscope||Nikon Instruments||Eclipse TE2000U|
|Fluorescent lamp||Nikon Instruments||Intensilight C-HGFIE|
|1.3 NA 100x objective||Nikon Instruments||Plan Fluor 1.30|
|1.49 NA 100x objective||Nikon Instruments||APO TIRF 1.49|
|Camera||Roper Scientific||Cascade 512 B|
|Thermostated box||Life Imaging Services||The Box|
|Appendix: example Script of MTT supplementary analysis|
%%% Basic examples showing how to recover MTT output results
%%% to plot each trace and to build the histogram
%%% of fluorescence intensities
|if nargin<1 % no file_name provided?
files = dir(’*.stk’);
if isempty(files), disp(’no data in current dir’), return, end
file_name = files(1).name; % default: first stk file
disp([’using’ file_name ’by default’])
|file_param = [file_name ’_tab_param.dat’]; % output file|
|%% Load data
cd(’output23’) % or (‘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 & 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
No_blk_index = tab_blk(itrc, :)>0; % non blinking steps only
plot(tab_i(itrc, No_blk_index), tab_j(itrc, No_blk_index))
xlabel(’i (pixel)’), ylabel(’j (pixel)’)
title([’trace # ’ num2str(itrc)])
disp(’Please strike any key for next trace’), pause
|%% Fluo histogram
N_datapoints = sum(tab_blk(:)>0); % non blinking steps only
hist(tab_alpha(tab_blk>0),2*sqrt(N_datapoints)) % using 2sqrt(N) bins
xlabel(’intensity (a.u.)’), ylabel(’occurrence’)
title(’histogram of particles fluorescence intensity’)