एकाधिक – लक्ष्य अनुरेखण एक घर में जीवित कोशिकाओं के प्लाज्मा झिल्ली के भीतर अलग – अलग लेबल अणुओं पर नज़र रखने के लिए विकसित एल्गोरिथ्म है. कुशलता का पता लगाने, आकलन और उच्च घनत्व अनुरेखण पर समय के साथ अणुओं nanoscale झिल्ली गतिशीलता की जांच के लिए एक उपयोगकर्ता के अनुकूल, व्यापक उपकरण प्रदान करने के लिए.
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.
एकल कण ट्रैकिंग में, सेल और माइक्रोस्कोपी पहलुओं के बगल में, विश्लेषण काम का एक बड़ा हिस्सा प्रतिनिधित्व करता है. यह तीन मुख्य कार्य करने के लिए उपयोग किया एल्गोरिथ्म पते का पता लगाने, आकलन और प्रत्येक ?…
The authors have nothing to disclose.
हम हमारी टीम के सदस्यों, तकनीकी सहायता के लिए विशेष रूप से एम सी Blache, के रूप में अच्छी तरह से एम Irla और बी Imhof के उनके समर्थन और उपयोगी विचार विमर्श के लिए, धन्यवाद. अपस्फीति और प्रसूति के लिए आंकड़े प्रकृति के तरीके के सौजन्य से reproduced. इस परियोजना से CNRS, INSERM और मार्सिले विश्वविद्यालय के, और Provence-Alpes-Côte-d'Azur क्षेत्र, राष्ट्रीय Institut du कैंसर, Agence Nationale डे ला Recherche (से विशिष्ट अनुदान द्वारा संस्थागत अनुदान द्वारा समर्थित है ANR-08-PCVI 0034-02, ANR २,०१० 1214 BLAN 01) और Fondation डालना ला Recherche MEDICALE (Equipe labélisée एफ आर एम 2009). VR लीग Nationale Contre ले कैंसर से एक फैलोशिप द्वारा समर्थित है.
Reagent | Company | Catalogue number | Quantity |
Cos-7 cell line | ATCC | CRL-1651 | 5,000 cells/well |
HBSS without Ca2+ | GIBCO | 14175 | 1 ml |
0.05% Trypsin EDTA | GIBCO | 25300 | 1 ml |
8-well Lab-tek | NUNC | 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 Scientific | 21435 | 18.5 μg |
Complete medium | |||
DMEM | GIBCO | 41965 | 500 ml |
Fetal Bovine Serum | SIGMA | F7524 | 50 ml |
L-Glutamine | GIBCO | 25030 | 5 ml |
HEPES | GIBCO | 15630 | 5 ml |
Sodium Pyruvate | GIBCO | 11360 | 5 ml |
Imaging medium | |||
HBSS with Ca2+ | GIBCO | 14025 | 25 ml |
HEPES | GIBCO | 15630 | 250 μl |
Equipment | Company | Reference |
Inverted microscope | Nikon | Eclipse TE2000U |
Fluorescent lamp | Nikon | Intensilight C-HGFIE |
1.3 NA 100x objective | Nikon | Plan Fluor 1.30 |
1.49 NA 100x objective | Nikon | APO TIRF 1.49 |
Camera | Roper Scientific | Cascade 512 B |
Thermostated box | Life Imaging Services | The Box |
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<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’])
end
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
end
%% 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’)