Name: 用荧光显微镜分析膜受体扩散和簇簇的图像处理协议 (视频) | JoVE Journal
Uploaded: 2019-04-09T14:30:00.000+00:00
Duration: 12 min 15 s
Description: 学习使用ImageJ、MATLAB和uTrack进行单分子追踪的图像处理协议，以量化膜受体的扩散系数和簇簇大小。该方法适用于多种颗粒结构，有助于深入理解细胞水平的生物学过程。

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We are thankful to Carlo Manzo and Maria Garc&#237;a Parajo for their help and source code of the diffusion coefficient analysis. This work was supported in part by grants from the Spanish Ministry of Science, Innovation and Universities (SAF 2017-82940-R) and the RETICS Program of the Instituto de salud Carlos III (RD12/0009/009 and RD16/0012/0006; RIER). LMM and JV are supported by the COMFUTURO program of the Fundaci&#243;n General CSIC.

<ol><li>Yu, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Single-molecule+studies+in+live+cells%5BTitle%5D)+AND+%22Annual+Review+of+Pysical+Chemistry%22%5BJournal%5D)">Single-molecule studies in live cells.</a> Annual Review of Pysical Chemistry. 67 (565-585), (2016).</li>
<li>Mattheyses, A. L., Simon, S. M., Rappoport, J. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Imaging+with+total+internal+reflection+fluorescence+microscopy+for+the+cell+biologist%5BTitle%5D)+AND+%22Journal+of+Cell+Science%22%5BJournal%5D)">Imaging with total internal reflection fluorescence microscopy for the cell biologist.</a> Journal of Cell Science. 123 (Pt 21), 3621-3628 (2010).</li>
<li>Jaqaman, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Robust+single-particle+tracking+in+live-cell+time-lapse+sequences%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">Robust single-particle tracking in live-cell time-lapse sequences.</a> Nature Methods. 5 (8), 695-702 (2008).</li>
<li>Bakker, G. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lateral+mobility+of+individual+integrin+nanoclusters+orchestrates+the+onset+for+leukocyte+adhesion%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+U+S+A%22%5BJournal%5D)">Lateral mobility of individual integrin nanoclusters orchestrates the onset for leukocyte adhesion.</a> Proceedings of the National Academy of Sciences U S A. 109 (13), 4869-4874 (2012).</li>
<li>Kusumi, A., Sako, Y., Yamamoto, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Confined+lateral+diffusion+of+membrane+receptors+as+studied+by+single+particle+tracking+%28nanovid+microscopy%29.+Effects+of+calcium-induced+differentiation+in+cultured+epithelial+cells%5BTitle%5D)+AND+%22Biophysical+Journal%22%5BJournal%5D)">Confined lateral diffusion of membrane receptors as studied by single particle tracking (nanovid microscopy). Effects of calcium-induced differentiation in cultured epithelial cells.</a> Biophysical Journal. 65 (5), 2021-2040 (1993).</li>
<li>Ferrari, R. M., Manfroi, A. J., Young, W. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Strongly+and+weakly+self-similar+diffusion%5BTitle%5D)+AND+%22Physica+D%22%5BJournal%5D)">Strongly and weakly self-similar diffusion.</a> Physica D. 154, 111-137 (2001).</li>
<li>Sbalzarini, I. F., Koumoutsakos, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Feature+point+tracking+and+trajectory+analysis+for+video+imaging+in+cell+biology%5BTitle%5D)+AND+%22Journal+of+Structural+Biology%22%5BJournal%5D)">Feature point tracking and trajectory analysis for video imaging in cell biology.</a> Journal of Structural Biology. 151 (2), 182-195 (2005).</li>
<li>Ewers, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Single-particle+tracking+of+murine+polyoma+virus-like+particles+on+live+cells+and+artificial+membranes%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+U+S+A%22%5BJournal%5D)">Single-particle tracking of murine polyoma virus-like particles on live cells and artificial membranes.</a> Proceedings of the National Academy of Sciences U S A. 102 (42), 15110-15115 (2005).</li>
<li>Manzo, C., Garcia-Parajo, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+review+of+progress+in+single+particle+tracking%3A+from+methods+to+biophysical+insights%5BTitle%5D)+AND+%22Report+on+Progress+in+Physics%22%5BJournal%5D)">A review of progress in single particle tracking: from methods to biophysical insights.</a> Report on Progress in Physics. 78 (12), 124601(2015).</li>
<li>Calebiro, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Single-molecule+analysis+of+fluorescently+labeled+G-protein-coupled+receptors+reveals+complexes+with+distinct+dynamics+and+organization%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+U+S+A%22%5BJournal%5D)">Single-molecule analysis of fluorescently labeled G-protein-coupled receptors reveals complexes with distinct dynamics and organization.</a> Proceedings of the National Academy of Sciences U S A. 110 (2), 743-748 (2013).</li>
<li>Digman, M. A., Dalal, R., Horwitz, A. F., Gratton, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mapping+the+number+of+molecules+and+brightness+in+the+laser+scanning+microscope%5BTitle%5D)+AND+%22Biophysical+Journal%22%5BJournal%5D)">Mapping the number of molecules and brightness in the laser scanning microscope.</a> Biophysical Journal. 94 (6), 2320-2332 (2008).</li>
<li>Schindelin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fiji%3A+an+open-source+platform+for+biological-image+analysis%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">Fiji: an open-source platform for biological-image analysis.</a> Nature Methods. 9 (7), 676-682 (2012).</li>
<li>Martinez-Munoz, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Separating+Actin-Dependent+Chemokine+Receptor+Nanoclustering+from+Dimerization+Indicates+a+Role+for+Clustering+in+CXCR4+Signaling+and+Function%5BTitle%5D)+AND+%22Molecular+Cell%22%5BJournal%5D)">Separating Actin-Dependent Chemokine Receptor Nanoclustering from Dimerization Indicates a Role for Clustering in CXCR4 Signaling and Function.</a> Molecular Cell. 70 (1), 106-119 (2018).</li>
<li>Destainville, N., Salome, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Quantification+and+correction+of+systematic+errors+due+to+detector+time-averaging+in+single-molecule+tracking+experiments%5BTitle%5D)+AND+%22Biophysical+Journal%22%5BJournal%5D)">Quantification and correction of systematic errors due to detector time-averaging in single-molecule tracking experiments.</a> Biophysical Journal. 90 (2), L17-L19 (2006).</li>
</ol>

The authors have nothing to disclose.

The described method is easy to perform even without having any previous experience working with Matlab. However, Matlab routines require extremely accuracy with the nomenclature of the different commands and the localization of the different folders employed by the program. In the tracking analysis routine (step 3), multiple parameters can be modified. The "Setting Gaussian-Mixture Model Fitting" window (step 3.8) controls how U-track will detect single particles on the video. This is done by fitting a Gaussian mixture ...

Membrane proteins embedded in the lipid bilayer are in continuous movement due to thermal diffusion. Their dynamics are essential to regulate cell responses, as intermolecular interactions allow formation of complexes that vary in size from monomers to oligomers and influence the stability of signaling complexes. Elucidating the mechanisms controlling protein dynamics is thus a new challenge in cell biology, necessary to understand signal transduction pathways and to identify unanticipated cell functions.
Some optical methods have been developed to study these interactions in living cells1. Among these, total internal reflection fluorescence (TIRF) microscopy, developed in the early 1980s, allows the study of molecular interactions at or very near the cell membrane2. To study dynamic parameters of membrane protein trajectories obtained from TIRF data in living cells, a single particle tracking method (SPT)is required. Although several algorithms are available for this, we currently use those published by Jaqaman et al.3 that address particle motion heterogeneity in a dense particle field by linking particles between consecutive frames to connect the resulting track segments into complete trajectories (temporary particle disappearance). The software captures the particle merging and splitting that result from aggregation and dissociation events3. One of the output data of this software is detection of the particles along the entire trajectory by defining their X and Y positions in each frame.
Once particles are detected, we apply different algorithms to determine the short timelag diffusion coefficient (D1-4)4,5. By applying the Moment Scaling Spectrum (MSS)6,7,8 analysis or by fitting the &#39;alpha&#39; value by adjustment of the Mean Square Displacement (MSD) to the curve9, we also classify the particles according to the type of trajectory.
Analysis of spot intensity in fluorescence images is a shared objective for scientists in the field10,11. The most common algorithm used is the so-called Number and Brightness. This method nonetheless does not allow correct frame-by-frame intensity detection in particles in the mobile fraction. We have, thus, generated a new algorithm to evaluate these particle intensities frame-by-frame and to determine their aggregation state. Once the coordinates of each particle are detected using U-Track2 software3, we define its intensity in each frame over the complete trajectory, also taking into account the cell background in each frame. This software offers different possibilities to determine the spot intensity and the cell background and, using known monomeric and dimeric proteins as references, calculates the approximate number of proteins in the particle detected (cluster size).
In this article, we describe a careful guide to perform these three steps: 1) detecting and tracking single particles along a video of fluorescence microscopy using U-track; 2) analyzing the instantaneous diffusion coefficient (D1-4) of those particles and the type of movement (confined, free, or directed) of particles with long trajectories by MSS; 3) measuring the spot intensity along the video corrected by the estimated background fluorescence for each spot. This allows cluster size estimation and identification of the photobleaching steps.
The use of this protocol does not require specialized skills and can be performed in any laboratory with cell culture, flow cytometry and microscopy facilities. The protocol uses ImageJ or Fiji (a distribution of ImageJ12), U-track3, and some ad hoc made routines (http://i2pc.es/coss/Programs/protocolScripts.zip). U-track and ad hoc routines run over Matlab that can be installed in any compatible computer.

<table><tbody><tr><td>Human Jurkat cells</td><td>ATCC</td><td>CRL-10915</td><td>Human T cell line. Any other cell type can be analyzed with this software</td></tr><tr><td>pAcGFPm-N1 (PT3719-5)DNA3GFP</td><td>Clontech</td><td>632469</td><td>Different fluorescent proteins can be followed and analyzed with this routine</td></tr><tr><td>Gene Pulse X Cell electroporator&amp;nbsp;</td><td>BioRad&amp;nbsp;</td><td></td><td>We use 280 V, 975 mF, for Jurkat cells.&amp;nbsp; Use the transfection method best working in your hands.&amp;nbsp;</td></tr><tr><td>Cytomics FC 500 flow cytometer&amp;nbsp;</td><td>Beckman Coulter</td><td></td><td></td></tr><tr><td>MoFlo Astrios Cell Sorter&amp;nbsp;</td><td>Beckman Coulter</td><td></td><td>Depending on the level of transfection, cell sorting may not be required.&amp;nbsp; You can also employ cells with stable expression of adequate levels of the receptor of interest.</td></tr><tr><td>Dako Qifikit</td><td>DakoCytomation</td><td>K0078</td><td>Used for quantification the number of receptors in the cell surface.</td></tr><tr><td>Glass bottom microwell dishes</td><td>MatTek corporation</td><td>P35G-1.5-10-C</td><td></td></tr><tr><td>Human Fibronectin from plasma</td><td>Sigma-Aldrich</td><td>F0895</td><td></td></tr><tr><td>Recombinant human CXCL12</td><td>PeproTech</td><td>300928A</td><td></td></tr><tr><td>Inverted Leica AM TIRF</td><td>Leica</td><td></td><td></td></tr><tr><td>EM-CCD camera</td><td>Andor&amp;nbsp;</td><td>DU 885-CSO-#10-VP</td><td></td></tr><tr><td>MATLAB</td><td>The MathWorks, Natick, MA</td><td></td><td></td></tr><tr><td>U-Track2 software</td><td>Danuser Laboratory</td><td></td><td></td></tr><tr><td>ImageJ</td><td>NIH</td><td></td><td>https://imagej.nih.gov/ij/</td></tr><tr><td>FiJi</td><td>FiJI</td><td></td><td>https://imagej.net/Fiji)</td></tr><tr><td>u-Track2 software</td><td></td><td></td><td>Matlab tool.&amp;nbsp; For installing, download .zip file from the web page (http://lccb.hms.harvard.edu/software.html) and uncompress the file in a directory of your choice</td></tr><tr><td>GraphPad Prism</td><td>GraphPad software</td><td></td><td></td></tr></tbody></table>

image processing protocol for the analysis of the diffusion and cluster size of membrane receptors by fluorescence microscopy

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.

The use of this protocol allows the automated tracking of particles detected in fluorescence microscopy movies and the analysis of their dynamic characteristics. Initially, cells are transfected with the fluorescently-coupled protein to be tracked. The appropriate level of receptors presents on the cell surface that allows SPT is obtained by cell sorting (Figure 1). Selected cells are analyzed by TIRF microscopy that generates videos in a format that can be s...

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 ...

New microscopy techniques require the development of adequate tools for the correct comprehension of the biological processes that are studied. This protocol describes the image processing steps required for single-molecule tracking, including estimation of lateral diffusion parameters and quantification of spot size over its entire trajectory at the cell membrane. This protocol combines the use of several software pieces, including ImageJ, MATLAB, and uTrack, as well as some proteins that have been developed specifically for this protocol.This method is illustrated with the tracking of membrane receptors under light microscopy, but it can be applied to any particle-like structure whose trajectories can be tracked over time in a video sequence. This technique relates to basic research and has no direct application in a clinical setting. However, it can be used to aid in the characterization of biological events in both in different diseases and therefore in the identification of new therapeutic targets.This protocol is relevant for the tracking of particles in cellular membranes, as shown in this video, but it can also be applied for the tracking of full cells, bacteria, nanopartic

image-processing-protocol-for-analysis-diffusion-cluster-size

Research

JoVE Journal

Immunology and Infection

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)

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.

Imaging Plasma Membrane Deformations With pTIRFM

This protocol describes how to use total internal reflection fluorescence microscopy to visualize and track single receptors on the surface of living cells and thereby analyze receptor lateral mobility, size of receptor complexes as well as to visualize transient receptor-receptor interactions. This protocol can be extended to other membrane proteins.

High-resolution Spatiotemporal Analysis of Receptor Dynamics by Single-molecule Fluorescence Microscopy

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.

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

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.

Internalization and Observation of Fluorescent Biomolecules in Living Microorganisms via Electroporation

We present a new technique to measure the lateral diffusion of a surface active species at the fluid-fluid interface by merging a droplet monolayer onto a flat monolayer.

Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer

Here, we present a protocol to detect single, SNARE-mediated fusion events between liposomes and supported bilayers in microfluidic channels using polarized TIRFM, with single molecule sensitivity and ~15 msec time resolution. Lipid and soluble cargo release can be detected simultaneously. Liposome size, lipid diffusivity, and fusion pore properties are measured.

SNARE-mediated Fusion of Single Proteoliposomes with Tethered Supported Bilayers in a Microfluidic Flow Cell Monitored by Polarized TIRF Microscopy

PI(4,5)P2 regulates various cellular functions, but its nanoscale organization in the cell plasma membrane is poorly understood. By labeling PI(4,5)P2 with a dual-color fluorescent probe fused to the Pleckstrin Homology domain, we describe a novel approach to study the PI(4,5)P2 spatial distribution in the plasma membrane at the nanometer scale.

Single-molecule Super-resolution Imaging of Phosphatidylinositol 4,5-bisphosphate in the Plasma Membrane with Novel Fluorescent Probes

We describe an imaging approach for the determination of the average oligomeric state of mEGFP-tagged-receptor oligomers induced by ligand binding in the plasma membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&amp;B) analysis.

Oligomerization Dynamics of Cell Surface Receptors in Living Cells by Total Internal Reflection Fluorescence Microscopy Combined with Number and Brightness Analysis

Automated assays using multi-well microplates are advantageous approaches for identifying pathway regulators by allowing the assessment of a multitude of conditions in a single experiment. Here, we have adapted the well-established macropinosome imaging and quantification protocol to a 96-well microplate format and provide a comprehensive outline for automation using a multi-mode plate reader.

Automated Imaging and Analysis for the Quantification of Fluorescently Labeled Macropinosomes

This protocol describes how to use TIRF microscopy to track individual ion channels and determine their activity in supported lipid membranes, thereby defining the interplay between lateral membrane movement and channel function. It describes how to prepare the membranes, record the data, and analyze the results.

Single-Molecule Imaging of Lateral Mobility and Ion Channel Activity in Lipid Bilayers using Total Internal Reflection Fluorescence (TIRF) Microscopy

学习使用ImageJ、MATLAB和uTrack进行单分子追踪的图像处理协议，以量化膜受体的扩散系数和簇簇大小。该方法适用于多种颗粒结构，有助于深入理解细胞水平的生物学过程。

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

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