This work was supported by a grant from Agence Nationale de la Recherche (ChoAD). We are also grateful to the Fondation ICM (Institut du Cerveau et de la Moelle) for their financial support.

1. Calibration of the FCS Setup
<ol>
<li>Start the confocal microscope, lasers, computers, incubator for temperature and CO2 control.</li>
<li>Make sure the SPAD (Single Photon Avalanche Diode) is on and the fluorescence filter inside the SPAD is well suited to your sample. Check that the SPAD is synchronized in time. Beware to only start your FCS software once your SPAD settings are ready for acquisition.</li>
<li>Prepare a fresh solution of cholera toxin-Alexa488 diluted in PBS to reach a concentration of 1 &mu;g/ml (17.5 nM).</li>
<li>Optimize confocal imaging of the solution using internal detection of the microscope.</li>
<li>Switch to point scanning mode and choose a point in the solution sample. Make sure the duration of the laser illumination is long enough to perform your FCS acquisitions (&gt; 5 minutes).</li>
<li>Switch to external detection by the SPAD and to the FCS software. Monitor your fluorescence signal and make sure it is well suited to the SPAD (enough signal but no saturation, 10 000 to 50 000 counts/s is fine). If necessary, modify z position, gain and/or laser power to achieve correct fluorescence signal.</li>
<li>Acquire 10 sets of 30 seconds measurements. Acquisition time should be optimized for your sample (compromise between the best sampling and photobleaching problems).</li>
<li>Analyze your data (see step 4) to ensure that you have an auto-correlation curve corresponding to one fluorescent species diffusing in solution (equation in Fig. 2) and that the diffusion time obtained after fitting is correct (approximately 0.2 ms) (Fig. 3).</li>
</ol>
2. Staining of Living Cells with Lipid Rafts Marker
<ol>
<li>Plate HEK293 cells on 8-well Labteks coated with poly-L-lysine (1mg/ml) the day before imaging to make sure that their adhesion is correct. Use medium without phenol red to ensure there is no perturbation of the fluorescence signal.</li>
<li>Wash cells twice with HBSS (Hank's Buffered Salt Solution).</li>
<li>Add cholera toxin-Alexa488 (1 &mu;g/ml)/BSA (0,1 %) in 500 &mu;l HBSS to the cells for 30 minutes at 37 &deg;C. Cholera toxin will bind to ganglioside GM1, known to be preferentially partitioned in lipid rafts.</li>
<li>Wash cells twice with HBSS (Hank's Buffered Salt Solution).</li>
</ol>
3. FCS Data Acquisition on Living Cells
<ol>
<li>Place the stained cells on the microscope stage. Make sure temperature and CO2 conditions are optimal otherwise this may lead to artefacts in diffusion times.</li>
<li>Optimize confocal imaging of a cell of interest using internal detection of the microscope (Fig. 4).</li>
<li>Switch to point scanning mode and choose a point in the plasma membrane of the cell of interest. Perform 1-2 live images of the cell to make sure the cell is not moving during acquisition.</li>
<li>Switch to external detection by the SPAD and to the FCS software. Monitor your fluorescence signal and make sure it is well suited to the SPAD (enough signal but no saturation, 10 000 to 50 000 counts/s is fine). If necessary, modify z position, gain and/or laser power to achieve correct fluorescence signal. If this is not enough, you may consider changing staining conditions.</li>
<li>Acquire 10 samples of 30 seconds measurements. As most cells are auto-fluorescent, you may see a decrease in fluorescence during the first acquisitions, due to auto-fluorescence fading.</li>
</ol>
4. FCS Data Analysis
<ol>
<li>Set the correlation interval and generate auto-correlation curves with the FCS software.
The correlation interval will typically range from the shortest resolvable lag time up to the longest time possible (as the statistics of the measurement becomes more and more inadequate when maximum correlation time approaches total acquisition time, the maximum lag time is limited to 80% of the acquisition time on the Picoquant software).</li>
<li>For each sample, export files corresponding to fluorescence fluctuations and auto-correlation as a function of time.</li>
<li>Use a data analysis and fitting software such as Origin Pro to import the files.</li>
<li>For each sample, make sure there is no photobleaching during acquisition ie the fluorescence stays stable during the 30 seconds. Discard any measure displaying photobleaching as this may cause artificial longer diffusion times. </li>
<li>Check that the shape of the remaining FCS curves is correct (see Fig 5). Determine the mean FCS curve. </li>
<li>Fit the curve with the appropriate mathematical model (Fig. 2). This fit will give you the diffusion time(s) and the proportion of molecules diffusing with this (ese) diffusion time(s).</li>
</ol> 
5. Representative Results
An example of an FCS calibration with a cholera toxin-Alexa488 solution is shown in Figure 3. After checking that individual measures of fluorescence as a function of time did not show any photobleaching (Figure 3A), individual and mean FCS curves were calculated. Mean FCS curves were fitted with equations corresponding to various diffusion models (examples in Figure 2). The parameter classically considered to determine the quality of a fit is the coefficient of determination R2. The closer R2 is to 1, the better the fit. In this case, the most accurate model to fit the mean FCS curve is the one describing a population of fluorescent molecules diffusing freely in three dimensions (equation 1 in Figure 2 and Figure 3B). The diffusion time derived from the fit is 0.32 ms. Residuals from curve-fitting (Figure 3C) and R2 factor (0.99906) give an estimate of the quality of the fit.
An example of FCS analysis for cholera toxin-Alexa488 stained HEK293 cells is shown in Figure 5. The multiphasic mean FCS curve shape reveals the existence of populations of fluorescent molecules with different diffusion times. The best fit for this curve corresponds to a model with three populations of fluorescent probes: two with an hindered diffusion (diffusion in two dimensions as in the membrane plane) and one freely diffusing in three dimensions (equation 2 in Figure 2 and Figure 5). This latter population corresponds to fluorescent molecules moving outside of the membrane plane, i.e., either binding or unbinding to their membrane targets, reaching the membrane through the secretion or recycling pathway, or leaving the membrane by endocytosis. The two diffusion times at the membrane, corresponding to cholera toxin bound to GM1, were 2 ms (25% of molecules), corresponding to diffusion outside of lipid rafts, and 75 ms (50% of molecules), corresponding to diffusion in lipid rafts. Please note that any photobleaching during acquisition will lead to artificial longer diffusion times thus possibly creating a bias towards localization of GM1 in lipid raft domains.
<img src="/files/ftp_upload/3513/3513fig1.jpg" alt="Figure 1" /> 
Figure 1. Schematic representation of the FCS set-up (picture modified from Marquer et al.12)
<img src="/files/ftp_upload/3513/3513fig2.jpg" alt="Figure 2" /> 
Figure 2. Example of diffusion models and corresponding equations used to fit autocorrelation curves. The structure parameter S can be written as S = z0/w0 with z0 the effective focal radius along the optical axis at 1/e2 intensity and w0 the effective lateral focal radius at 1/e2 intensity. These values can be extracted from a classical point spread function (PSF) measurement.
<img src="/files/ftp_upload/3513/3513fig3.jpg" alt="Figure 3" /> 
Figure 3. Assessment of diffusion time of cholera toxin-Alexa488 in solution for FCS calibration. A) Fluorescence fluctuation as a function of time for a representative example of a 30 seconds acquisition. B) Mean autocorrelation curve obtained from 10 samples of 30 seconds acquisition fitted with equation 1 (see Figure 2).C) Residuals from curve fitting.
<img src="/files/ftp_upload/3513/3513fig4.jpg" alt="Figure 4" /> 
Figure 4. HEK-293 cell stained with cholera toxin-Alexa488. Cells were imaged on an SP5 confocal microscope (Leica Microsystems, Wetzlar, Germany) with the internal 488nm laser line. Fluorescence was collected with a x60 plan apochromat oil immersion objective between 500 and 650 nm.
<img src="/files/ftp_upload/3513/3513fig5.jpg" alt="Figure 5" /> 
Figure 5. Assessment of diffusion time of cholera toxin-Alexa488 at the plasma membrane of HEK293 cells. A) Mean autocorrelation curve fitted with equation 2 (see Figure 2). B) Residuals from curve fitting. (modified from Marquer et al.12)

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No conflicts of interest declared.

The FCS method presented here enables a sensitive and rapid analysis of the lipid raft partitioning of fluorescent probes of interest in living cells. FCS combines the accuracy of localization of confocal microscopy with the sensitivity of single photon counting. The main difference between FCS and standard biochemical techniques is that FCS enables the absolute determination of the lipid rafts partition of the target and not the relative partition as is the case for DRMs isolation or co-patching.
<p class="jove_cont...

<table border="1">
 <tbody>
 <tr>
 <td>Name of the reagent</td>
 <td>Company</td>
 <td>Catalogue number</td>
 <td>Comments </td>
 </tr>
 <tr>
 <td>Cholera toxin subunit B-Alexa 488</td>
 <td>Invitrogen</td>
 <td>C-34775</td>
 <td>MW (pentamer) = 57 kg/mol</td>
 </tr>
 <tr>
 <td>Confocal microscope</td>
 <td>Leica</td>
 <td>SP5</td>
 <td>&nbsp;</td>
 </tr>
 <tr>
 <td>Incubator for temperature and CO2 control</td>
 <td>Life imaging services</td>
 <td>The Cube and the Box</td>
 <td>&nbsp;</td>
 </tr>
 <tr>
 <td>SPAD (Single Photon Avalanche Diode)</td>
 <td>MPD (Micro Photon Devices)</td>
 <td>PDM serie (100 &mu;m sensitive area) </td>
 <td>&nbsp;</td>
 </tr>
 <tr>
 <td>High pass 488 nm filter </td>
 <td>Semrock</td>
 <td>488 nm blocking edge BrightLine long-pass filter 
 Part # <a href="http://www.semrock.com/FilterDetails.aspx?id=FF01-488/LP-25" target="_blank">FF01-488/LP-25</a> </td>
 <td>&nbsp;</td>
 </tr>
 <tr>
 <td>FCS detection unit</td>
 <td>Picoquant</td>
 <td>Picoharp 300 module </td>
 <td>&nbsp;</td>
 </tr>
 <tr>
 <td>Acquisition and auto-correlation software</td>
 <td>Picoquant</td>
 <td>SymPhoTime</td>
 <td>&nbsp;</td>
 </tr>
 <tr>
 <td>Fitting software</td>
 <td>OriginLab</td>
 <td>OriginPro8 </td>
 <td>&nbsp;</td>
 </tr>
 </tbody>
</table>

determination of lipid raft partitioning of fluorescently-tagged probes in living cells by fluorescence correlation spectroscopy (fcs)

In the past fifteen years the notion that cell membranes are not homogenous and rely on microdomains to exert their functions has become widely accepted. Lipid rafts are membrane microdomains enriched in cholesterol and sphingolipids. They play a role in cellular physiological processes such as signalling, and trafficking1,2 but are also thought to be key players in several diseases including viral or bacterial infections and neurodegenerative diseases3.
Yet their existence is still a matter of controversy4,5. Indeed, lipid raft size has been estimated to be around 20 nm6, far under the resolution limit of conventional microscopy (around 200 nm), thus precluding their direct imaging. Up to now, the main techniques used to assess the partition of proteins of interest inside lipid rafts were Detergent Resistant Membranes (DRMs) isolation and co-patching with antibodies. Though widely used because of their rather easy implementation, these techniques were prone to artefacts and thus criticized7,8. Technical improvements were therefore necessary to overcome these artefacts and to be able to probe lipid rafts partition in living cells.
Here we present a method for the sensitive analysis of lipid rafts partition of fluorescently-tagged proteins or lipids in the plasma membrane of living cells. This method, termed Fluorescence Correlation Spectroscopy (FCS), relies on the disparity in diffusion times of fluorescent probes located inside or outside of lipid rafts. In fact, as evidenced in both artificial membranes and cell cultures, probes would diffuse much faster outside than inside dense lipid rafts9,10. To determine diffusion times, minute fluorescence fluctuations are measured as a function of time in a focal volume (approximately 1 femtoliter), located at the plasma membrane of cells with a confocal microscope (Fig. 1). The auto-correlation curves can then be drawn from these fluctuations and fitted with appropriate mathematical diffusion models11.
FCS can be used to determine the lipid raft partitioning of various probes, as long as they are fluorescently tagged. Fluorescent tagging can be achieved by expression of fluorescent fusion proteins or by binding of fluorescent ligands. Moreover, FCS can be used not only in artificial membranes and cell lines but also in primary cultures, as described recently12. It can also be used to follow the dynamics of lipid raft partitioning after drug addition or membrane lipid composition change12.

Watch this Scientific Journal Video about Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy FCS at JoVE.com

Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy FCS

A technique to probe the lipid raft partitioning of fluorescent proteins at the plasma membrane of living cells is described. It takes advantage of the disparity in diffusion times of proteins located inside or outside of lipid rafts. Acquisition can be performed dynamically in control conditions or after drug addition.

Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy (FCS)

In the past fifteen years the notion that cell membranes are not homogenous and rely on microdomains to exert their functions has become widely accepted. ...

determination-lipid-raft-partitioning-fluorescently-tagged-probes

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비디오: Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy FCS

Measuring the Kinetics of mRNA Transcription in Single Living Cells

The transcriptional activity of RNA polymerase II (Pol II) is a dynamic process and therefore measuring the kinetics of the transcriptional process in vivo is of importance. Pol II kinetics have been measured using biochemical or molecular methods.1-3 In recent years, with the development of new visualization methods, it has become possible to follow transcription as it occurs in real time in single living cells.4 Herein we describe how to perform analysis of Pol II elongation kinetics on a specific gene in living cells.5, 6 Using a cell line in which a specific gene locus (DNA), its mRNA product, and the final protein product can be fluorescently labeled and visualized in vivo, it is possible to detect the actual transcription of mRNAs on the gene of interest.7, 8 The mRNA is fluorescently tagged using the MS2 system for tagging mRNAs in vivo, where the 3'UTR of the mRNA transcripts contain 24 MS2 stem-loop repeats, which provide highly specific binding sites for the YFP-MS2 coat protein that labels the mRNA as it is transcribed.9 To monitor the kinetics of transcription we use the Fluorescence Recovery After Photobleaching (FRAP) method. By photobleaching the YFP-MS2-tagged nascent transcripts at the site of transcription and then following the recovery of this signal over time, we obtain the synthesis rate of the newly made mRNAs.5 In other words, YFP-MS2 fluorescence recovery reflects the generation of new MS2 stem-loops in the nascent transcripts and their binding by fluorescent free YFP-MS2 molecules entering from the surrounding nucleoplasm. The FRAP recovery curves are then analyzed using mathematical mechanistic models formalized by a series of differential equations, in order to retrieve the kinetic time parameters of transcription.

RNA polymerase II transcriptional kinetics are measured on specific genes in living cells. mRNAs transcribed from the gene of interest are fluorescently tagged and using Fluorescence Recovery After Photobleaching (FRAP) the in vivo kinetics of transcriptional elongation are obtained.

The transcriptional activity of RNA polymerase II (Pol II) is a dynamic process and therefore measuring the kinetics of the transcriptional process in ...

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 ...

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) maintain the turnover of all mature blood cell lineages. The coordination of the complex signals leading to specific HSC fates relies upon the interaction between HSCs and the intricate bone marrow microenvironment, which is still poorly understood[1-2].
We describe how by combining a newly developed specimen holder for stable animal positioning with multi-step confocal and two-photon in vivo imaging techniques, it is possible to obtain high-resolution 3D stacks containing HSPCs and their surrounding niches and to monitor them over time through multi-point time-lapse imaging. High definition imaging allows detecting ex vivo labeled hematopoietic stem and progenitor cells (HSPCs) residing within the bone marrow. Moreover, multi-point time-lapse 3D imaging, obtained with faster acquisition settings, provides accurate information about HSPC movement and the reciprocal interactions between HSPCs and stroma cells.
Tracking of HSPCs in relation to GFP positive osteoblastic cells is shown as an exemplary application of this method. This technique can be utilized to track any appropriately labeled hematopoietic or stromal cell of interest within the mouse calvarium bone marrow space.

In Vivo 4-Dimensional Tracking of Hematopoietic Stem and Progenitor Cells in Adult Mouse Calvarial Bone Marrow

The nature of the interactions between hematopoietic stem and progenitor cells (HSPCs) and bone marrow niches is poorly understood. Custom hardware modifications and a multi-step acquisition protocol allow the use of two-photon and confocal microscopy to image ex vivo labeled HSPCs homed within bone marrow areas, tracking interactions and movement.

Through a delicate balance between quiescence and proliferation, self renewal and production of differentiated progeny, hematopoietic stem cells (HSCs) ...

Fluorescence Imaging with One-nanometer Accuracy (FIONA)

Fluorescence imaging with one-nanometer accuracy (FIONA) is a simple but useful technique for localizing single fluorophores with nanometer precision in the x-y plane. Here a summary of the FIONA technique is reported and examples of research that have been performed using FIONA are briefly described. First, how to set up the required equipment for FIONA experiments, i.e., a total internal reflection fluorescence microscopy (TIRFM), with details on aligning the optics, is described. Then how to carry out a simple FIONA experiment on localizing immobilized Cy3-DNA single molecules using appropriate protocols, followed by the use of FIONA to measure the 36 nm step size of a single truncated myosin Va motor labeled with a quantum dot, is illustrated. Lastly, recent effort to extend the application of FIONA to thick samples is reported. It is shown that, using a water immersion objective and quantum dots soaked deep in sol-gels and rabbit eye corneas (&#62;200 &#181;m), localization precision of 2-3 nm can be achieved.

Fluorescence Imaging with One-nanometer Accuracy FIONA

Single fluorophores can be localized with nanometer precision using FIONA. Here a summary of the FIONA technique is reported, and how to carry out FIONA experiments is described.

Fluorescence imaging with one-nanometer accuracy (FIONA) is a simple but useful technique for localizing single fluorophores with nanometer precision in ...

Using Caco-2 Cells to Study Lipid Transport by the Intestine

Studies of dietary fat absorption are generally conducted by using an animal model equipped with a lymph cannula. Although this animal model is widely accepted as the in vivo model of dietary fat absorption, the surgical techniques involved are challenging and expensive. Genetic manipulation of the animal model is also costly and time consuming. The alternative in vitro model is arguably more affordable, timesaving, and less challenging. Importantly, the in vitro model allows investigators to examine the enterocytes as an isolated system, reducing the complexity inherent in the whole organism model. This paper describes how human colon carcinoma cells (Caco-2) can serve as an in vitro model to study the enterocyte transport of lipids, and lipid-soluble drugs and vitamins. It explains the proper maintenance of Caco-2 cells and the preparation of their lipid mixture; and it further discusses the valuable option of using the permeable membrane system. Since differentiated Caco-2 cells are polarized, the main advantage of using the permeable membrane system is that it separates the apical from the basolateral compartment. Consequently, the lipid mixture can be added to the apical compartment while the lipoproteins can be collected from the basolateral compartment. In addition, the effectiveness of the lentivirus expression system in upregulating gene expression in Caco-2 cells is discussed. Lastly, this paper describes how to confirm the successful isolation of intestinal lipoproteins by transmission electron microscopy (TEM).

Caco-2 cells can serve as an in vitro model to study the enterocyte transport of lipids, and lipid-soluble drugs/vitamins. The permeable membrane system separates the apical from the basolateral compartment, while the lentivirus expression system offers an effective gene overexpression method. The isolation of lipoproteins is confirmed by TEM.

Studies of dietary fat absorption are generally conducted by using an animal model equipped with a lymph cannula. Although this animal model is widely ...

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Confocal microscopy provides an accessible methodology to capture sub-cellular interactions critical for the characterization and further development of pre-clinical agents labeled with fluorescent probes. With recent advancements in antibody based cytotoxic drug delivery systems, understanding the alterations induced by these agents within the realm of receptor aggregation and internalization is of critical importance. This protocol leverages the well-established methodology of fluorescent immunocytochemistry and the open source FIJI distribution of ImageJ, with its inbuilt autocorrelation and image mathematical functions, to perform spatial image correlation spectroscopy (ICS). This protocol quantitates the fluorescent intensity of labeled receptors as a function of the beam area of the confocal microscope. This provides a quantitative measure of the state of target molecule aggregation on the cell surface. This methodology is focused on the characterization of static cells with potential to expand into temporal investigations of receptor aggregation. This protocol presents an accessible methodology to provide quantification of clustering events occurring at the cell surface, utilizing well established techniques and non-specialized imaging apparatus.

Antibodies that bind to target receptors on the cell surface can confer conformation and clustering alterations. These dynamic changes have implications for characterizing drug development in target cells. This protocol utilizes confocal microscopy and image correlation spectroscopy through ImageJ/FIJI to quantify the extent of receptor clustering on the cell surface.

Confocal microscopy provides an accessible methodology to capture sub-cellular interactions critical for the characterization and further development of ...

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 ...

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

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of clustering. Here, we describe an imaging approach to determine the average oligomeric state of mEGFP-tagged-receptor homocomplexes in the membrane of living cells. The protocol is based on Total Internal Reflection Fluorescence (TIRF) microscopy combined with Number and Brightness (N&#38;B) analysis. N&#38;B is a method similar to fluorescence-correlation spectroscopy (FCS) and photon counting histogram (PCH), which are based on the statistical analysis of the fluctuations of the fluorescence intensity of fluorophores diffusing in and out of an illumination volume during an observation time. In particular, N&#38;B is a simplification of PCH to obtain information on the average number of proteins in oligomeric mixtures. The intensity fluctuation amplitudes are described by the molecular brightness of the fluorophore and the average number of fluorophores within the illumination volume. Thus, N&#38;B considers only the first and second moments of the amplitude distribution, namely, the mean intensity and the variance. This is, at the same time, the strength and the weakness of the method. Because only two moments are considered, N&#38;B cannot determine the molar fraction of unknown oligomers in a mixture, but it only estimates the average oligomerization state of the mixture. Nevertheless, it can be applied to relatively small time series (compared to other moment methods) of images of live cells on a pixel-by-pixel basis, simply by monitoring the time fluctuations of the fluorescence intensity. It reduces the effective time-per-pixel to a few microseconds, allowing acquisition in the time range of seconds to milliseconds, which is necessary for fast oligomerization kinetics. Finally, large cell areas as well as sub-cellular compartments can be explored.

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&#38;B) analysis.

Despite the importance and ubiquity of receptor oligomerization, few methods are applicable for detecting clustering events and measuring the degree of ...

Probing mRNA Kinetics in Space and Time in Escherichia coli using Two-Color Single-Molecule Fluorescence In Situ Hybridization

Single-molecule fluorescence in situ hybridization (smFISH) allows for counting the absolute number of mRNAs in individual cells. Here, we describe an application of smFISH to measure the rates of transcription and mRNA degradation in Escherichia coli. As smFISH is based on fixed cells, we perform smFISH at multiple time points during a time-course experiment, i.e., when cells are undergoing synchronized changes upon induction or repression of gene expression. At each time point, sub-regions of an mRNA are spectrally distinguished to probe transcription elongation and premature termination. The outcome of this protocol also allows for analyzing intracellular localization of mRNAs and heterogeneity in mRNA copy numbers among cells. Using this protocol many samples (~50) can be processed within 8 h, like the amount of time needed for just a few samples. We discuss how to apply this protocol to study the transcription and degradation kinetics of different mRNAs in bacterial cells.

Probing mRNA Kinetics in Space and Time in Escherichia coli using Two-Color Single-Molecule Fluorescence In Situ Hybridization

This protocol describes an application of single-molecule fluorescence in situ hybridization (smFISH) to measure the in vivo kinetics of mRNA synthesis and degradation.

Single-molecule fluorescence in situ hybridization (smFISH) allows for counting the absolute number of mRNAs in individual cells. Here, we describe an ...

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

The root microbiome plays an important role in plant growth and environmental adaptation. Network analysis is an important tool for studying communities, which can effectively explore the interaction relationship or co-occurrence model of different microbial species in different environments. The purpose of this manuscript is to provide details on how to use the weighted correlation network algorithm to analyze different co-occurrence networks that may occur in microbial communities due to different ecological environments. All analysis of the experiment is performed in the WGCNA package. WGCNA is an R package for weighted correlation network analysis. The experimental data used to demonstrate these methods were microbial community data from the NCBI (National Center for Biotechnology Information) database for three niches of the rice (Oryza sativa) root system. We used the weighted correlation network algorithm to construct co-abundance networks of microbial community in each of the three niches. Then, differential co-abundance networks among endosphere, rhizoplane and rhizosphere soil were identified. In addition, the core genera in network were obtained by the "WGCNA" package, which plays an important regulated role in network functions. These methods enable researchers to analyze the response of microbial network to environmental disturbance and verify different microbial ecological response theories. The results of these methods show that the significant differential microbial networks identified in the endosphere, rhizoplane and rhizosphere soil of rice.

Network analysis was applied to evaluate the association of various ecological microbial communities, such as soil, water and rhizosphere. Presented here is a protocol on how to use the WGCNA algorithm to analyze different co-occurrence networks that may occur in the microbial communities due to different ecological environments.

The root microbiome plays an important role in plant growth and environmental adaptation. Network analysis is an important tool for studying communities, ...