Name: visualizing protein-dna interactions in live bacterial cells using photoactivated single-molecule tracking
Uploaded: 2014-03-10T20:30:00
Duration: 16 min 21 s
Description: Watch this Scientific Journal Video about Visualizing Protein-DNA Interactions in Live Bacterial Cells Using Photoactivated Single-molecule Tracking at JoVE.com

We acknowledge Justin Pinkney and Johannes Hohlbein for help with the construction of the bespoke microscope and Seamus Holden for the localization software. Rodrigo Reyes-Lamothe is thanked for providing the E. coli strain. The research was funded by the European Commission Seventh Framework Programme Grant FP7/2007-2013 HEALTH-F4-2008-201418, UK Biotechnology and Biological Sciences Research Council Grant BB/H01795X/1, and the European Research Council Grant 261227 to A.N.K.. D.J.S. was funded by a Wellcome Trust Program Grant WT083469. S.U. was supported by a MathWorks doctoral fellowship.

1. Cell Culture
Use sterile culture tubes and pipette tips. The E. coli strain AB1157 polA-PAmCherry carries a C-terminal PAmCherry fusion of Pol1. The fusion was inserted at the native chromosomal location by replacing the wild-type gene using lambda-Red recombination as described in Datsenko et al.8 Functionality of the fusion protein was confirmed as judged by cellular growth rates and sensitivity to the DNA damaging agent methyl methanesulfonate (MMS). More information on construction of the cell strain can be found in Uphoff et al.7, Datsenko et al.8, and Reyes-Lamothe et al.9 Cell cultures are grown in M9 minimal medium to reduce autofluorescence and avoid background particles on the microscope slide. Alternatively, a nutrient-rich defined medium can be used10.
<ol>
	<li>Streak the E. coli strain AB1157 polA-PAmCherry from a frozen glycerol stock on a Luria Broth (LB) agarose plate with selective antibiotics (here, 25 &#181;g/ml kanamycin) and incubate at 37 &#176;C overnight.</li>
	<li>Inoculate a 5 ml LB culture from a single cell colony and grow at 37 &#176;C shaking at 220 rpm for 3 hr.</li>
	<li>Dilute the culture 1:10,000 into 5 ml minimal medium (M9 medium, MEM amino acids + proline, MEM vitamins, 0.2% glycerol) and incubate at 37 &#176;C shaking at 220 rpm overnight.</li>
	<li>The following morning, measure the optical density (OD) using a spectrophotometer and dilute the culture in 5 ml fresh minimal medium to OD 0.025. Grow for 2 hr at 37 &#176;C shaking at 220 rpm to early exponential phase (OD 0.1).</li>
	<li>Concentrate 1 ml of cells in a 1.5 ml microcentrifuge tube by centrifugation at 2,300 x g for 5 min. Remove the supernatant and resuspend the cell pellet in 20 &#181;l residual medium and vortex.</li>
</ol>
2. Microscope Slide Preparation
<ol>
	<li>Prepare a 1.5% low-fluorescence agarose solution in dH2O. Use a microwave to melt the agarose until the solution is clear. Mix 500 &#181;l of the melted agarose solution with 500 &#181;l of 2x minimal medium by gently pipetting up and down a few times.</li>
	<li>Spread the agarose solution evenly on the center of a microscope coverslip (No 1.5 thickness). This has to be done quickly before the agarose cools, avoiding bubbles.</li>
	<li>Flatten the pad with a second coverslip (No 1.5 thickness). To remove background fluorescent particles, coverslips were previously burned in a furnace at 500 &#176;C for 1 hr. Burned coverslips can be stored for weeks at room temperature covered in aluminum foil.</li>
	<li>For DNA damage experiments, prepare an agarose pad containing 100 mM MMS. Follow the procedure in steps 2.1-2.3, but add 8.3 &#181;l MMS to 500 &#181;l of M9 medium before mixing with 500 &#181;l of melted 1.5% agarose, for a final concentration of 100 mM MMS. (Caution! MMS is toxic and mutagenic and must be handled with gloves, mask, goggles, and lab coat).</li>
	<li>Remove the top slide from the pad and add 1 &#181;l of concentrated cell suspension onto the pad. Immobilize the cells by covering the pad with an unused burned coverslip (No 1.5 thickness, matching the microscope objective specification) and by pressing very gently on the slide. Cells should be imaged within 45 min of immobilization before the agarose pad dries. To prevent drying during longer experiments, agarose pads can be sealed using silicon gaskets.</li>
	<li>For DNA-damage experiments, incubate cells immobilized on the agarose pad containing 100 mM MMS for 20 min in a humidified container at room temperature before imaging.</li>
</ol>
3. Preparing Microscopy Data Acquisition
PALM relies on the detection and precise localization of single fluorescent proteins. The sensitivity and optimal alignment of the microscope is critical for the data quality. Single-molecule fluorescence microscopes typically employ Total Internal Reflection (TIR) illumination to enhance the signal-to-noise ratio by exciting only fluorophores within a thin section above the coverslip surface. Here, imaging inside E. coli requires highly inclined illumination11, which can be achieved on a TIRF microscope by slightly decreasing the angle of the excitation light. PAmCherry imaging further requires a 405 nm photoactivation laser and a 561 nm excitation laser. The fluorescence emission is recorded on an electron multiplying CCD (EMCCD) camera at a magnification resulting in a pixel length of 114.5 nm/pixel. For optimal localization precision, the pixel size should approximately match the standard deviation width of the PSF to ensure sufficient sampling without spreading the signal over too many pixels. Figure 3 shows a schematic of a minimal PALM setup. Movie 1 gives an impression of the bespoke microscope building process; see Uphoff et al.7 for a detailed description of the instrument.
<ol>
	<li>Performing routine microscope alignment. Measure the 405 nm and 561 nm continuous wave laser intensities in front of the objective. Adjust the 561 nm intensity to 3.5 mW (~400 W/cm2) and 405 nm intensity to 10 &#181;W (~1 W/cm2). Use a continuously variable neutral density filter wheel that allows gradual adjustment of 405 nm intensity from 0-10 &#181;W. Switch off the laser illumination until the start of the experiment.</li>
	<li>Place the sample on the microscope stage and bring the cells into focus in transmitted light microscopy mode (Figure 4A). The EMCCD camera gain has to be switched off to prevent damage to the camera by overexposure.</li>
	<li>Define a cropped FOV to reduce data size and increase the camera read-out speed.</li>
	<li>Cover the sample from ambient light and switch on the EMCCD camera gain.</li>
	<li>Set the frame rate to 15.26 msec/frame (including 0.26 msec camera readout time). See "Exposure time and excitation intensities" in the Discussion section.</li>
	<li>Display the camera data to check the dark background signal (Figure 4B).</li>
	<li>Switch on the 561 nm laser and check the excitation background signal (Figure 4C).</li>
	<li>Switch on the 405 nm laser for photoactivation of the Pol1-PAmCherry fusion proteins and increase the intensity until fluorescence PSFs appear.</li>
	<li>Adjust the angle of the excitation beam to illuminate only a thin section of the sample close to the coverslip surface.
	<ol>
		<li>To this end, the laser beam is focused into the back focal plane of a 100X NA 1.4 objective (Figure 3). Translating the focusing lens perpendicular to the beam moves the focus away from the center of the objective causing the beam to exit the objective under an angle.</li>
		<li>Aim to maximize the fluorescence intensity and minimize the background signal. Note that strict TIR excitation is optimal to image fluorophores within 100 nm of the coverslip surface, however, imaging DNA-binding proteins associated with the E. coli nucleoid requires deeper illumination up to 0.8 &#181;m.</li>
	</ol>
	</li>
</ol>
4. Data Acquisition
Here, we describe the general protocol for acquisition of a PALM movie. The same procedure applies for imaging Pol1-PAmCherry fusion proteins in undamaged E. coli cells and under continuous DNA damage treatment with MMS. Application of the method to fusion proteins of different molecular weight or copy number per cell will require different acquisition settings (see Discussion section).
<ol>
	<li>Find a new field of view (FOV) of cells in transmitted light microscopy mode and focus the image. Take a camera snapshot to record the cell outlines (Figure 4A).</li>
	<li>Cover the sample from ambient light and switch on the EMCCD camera gain.</li>
	<li>Switch on the 561 nm laser and bleach the cellular autofluorescence and background spots on the coverslip for a few seconds before starting data acquisition. For cells grown and imaged in M9 medium and using burned coverslips there is usually very little fluorescence background; however, prebleaching could be useful for imaging cells in a rich growth medium such as LB. Note that intense illumination is toxic to cells so prebleaching should be kept to a minimum.</li>
	<li>Start the acquisition of a PALM movie under continuous 561 nm excitation at 15.26 msec/frame.</li>
	<li>Switch on the 405 nm laser and gradually increase the intensity over the course of the movie, reaching up to 1 W/cm2. Avoid higher 405 nm intensities that cause cellular autofluorescence. Pay attention to the density of fluorescent molecules - it is important to keep activation rates low such that PSFs are clearly isolated in each frame (Figures 4D-F).</li>
	<li>Record 10,000 frames/movie (depending on the number of molecules to be imaged per cell); one movie typically takes 2-3 min and requires 0.5-1 GB of hard disk space depending on the size of the FOV.</li>
	<li>Repeat the acquisition procedure for multiple FOV. Note that each FOV can only be imaged once because PAmCherry fluorophores get photoactivated and bleached irreversibly.</li>
</ol>
5. Data Analysis
An automated and robust data analysis framework is essential for the performance and efficiency of the method. We use custom software written in MATLAB.
<ol>
	<li>Perform the localization analysis using algorithms described in Crocker et al.12, Holden et al.13, HoldenI et al.14, and Wieser et al.15 PSFs are first identified in a band-pass filtered image using a Gaussian kernel with 7 pixels diameter (Figure 5A). Candidate positions correspond to PSFs with peak pixel intensities above 4.5 times the standard deviation of the background signal (Figure 5B). The locally brightest pixel per candidate PSF serves as initial guess for fitting an elliptical Gaussian function (Figure 5C). The free fit parameters are: x-position, y-position, x-width, y-width, rotation angle, amplitude, and background offset. The elliptical Gaussian mask accounts for molecule during the exposure time, which blurs and deforms the PSF.</li>
	<li>Plot the resulting (x, y) localizations from all frames of the PALM movie onto the transmitted light microscopy image of the same FOV. Localizations of Pol1-PAmCherry should appear within the central area of E. coli cells (Figure 6A). If many localizations appear outside of cells, the localization threshold was set too low or the sample contained background fluorescent particles.</li>
	<li>For automated tracking analysis, the MATLAB implementation of the algorithm described in Crocker et al.12 can be used (see "Diffusion analysis" in the Discussion section). Positions that appear in subsequent frames within a user-defined tracking window are connected to form a trajectory. In the case that multiple localizations occur in the same window, tracks are uniquely assigned by minimizing the sum of step lengths. For a detailed discussion of the various considerations when calculating diffusion coefficients from single-particle tracking data, see Wieser et al.15
	<ol>
		<li>The algorithm uses a memory parameter to account for transient blinking or missed localizations during a track. Here, we set the memory parameter to 1 frame; higher values can be used for tracking fluorophores with long-lived dark states.</li>
		<li>Choose a suitable tracking window based on the following calibration steps. For Pol1, we use 0.57 &#181;m (5 pixels).</li>
		<li>Run the tracking algorithm for a range of tracking window parameters. Calculate the number of measured tracks per cell as a function of the tracking window to identify the smallest possible tracking window that does not split tracks (Figure 6B).</li>
		<li>Plot the resulting tracks on the transmitted light microscopy image of the same FOV to visualize the spatial distribution of molecule movement within cells. Pol1 tracks should display diffusion confined within single cells (Figures 6C-D).</li>
		<li>If a fraction of tracks appears to cross between cells this suggests that separate molecules were erroneously linked because the tracking window was chosen too large and/or the photoactivation rate was too high (Figure 6E).</li>
		<li>Plot the cumulative distribution of the step lengths between consecutive localizations (Figure 6F). The curve rises and saturates smoothly for sufficiently large tracking windows but shows a cutoff edge if the window was chosen too small.</li>
	</ol>
	</li>
	<li>To analyze the diffusion characteristics of Pol1, compute the mean-squared displacement (MSD) between consecutive localizations for each track with a total of N steps): 
	MSD = 1/(N-1) &#8721; i = 1 N-1 (xi+1 &#8211; xi)2 + (yi+1 &#8211; yi)2 . 
	Include only tracks with at least 4 steps (N &#8805; 5 localizations) to reduce the statistical uncertainty in the MSD values.</li>
	<li>Plot a curve of MSD values over a range of lag times by calculating displacements over multiple frames (Figure 6G). The shape of the MSD curve can help to classify the observed molecular motion (Figure 6H).</li>
	<li>Calculate the apparent diffusion coefficient D* per track from the MSD: 
	D* = MSD/(4 &#916;t) &#8211; &#963;loc2/&#916;t . 
	The second term corrects for the estimated localization error (here, &#963;loc = 40 nm and &#916;t = 15.26 msec, see Wieser et al.15).</li>
	<li>Plot a histogram of the measured D* values from all tracks in the FOV (Figure 7A).</li>
	<li>Identify individual Pol1 molecules that appear bound to the chromosome based on the measured D* value per track. Separate the populations of bound (sharp distribution centered at D* ~ 0 &#181;m2/sec) and freely diffusing molecules (broad distribution centered at D* ~ 0.9 &#181;m2/sec) by setting a threshold D* &#60; 0.15 &#181;m2/sec (red bars in Figures 7A and 7D).</li>
	<li>Carry out the localization, tracking, and diffusion analysis for Pol1 in undamaged cells (Figures 7A-C) and in cells under DNA-damage treatment with MMS (Figures 7D-E). The fraction of bound tracks provides a direct quantitative measure of the DNA repair activity of Pol1 in vivo.</li>
</ol>

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The authors declare they have no competing financial interests.

We discuss several key considerations for the success of the experiment.
Choice and expression of the fluorescent fusion protein: There is a large palette of photoactivatable and photoswitchable fluorescent proteins17. The specific choice depends on the microscope characteristics, particularly the lasers and filters available. The combination of 405 nm and 561 nm is ideal for common photoactivatable fluorescent proteins. We chose PAmCherry6 because it is monomeric and showed no aggregation in cells. Furthermore, irreversible photoactivation allows counting the number of activated fluorophores to measure protein copy numbers per cell. Instead of expressing the fusion protein from a plasmid, we prefer chromosomal insertion of the gene encoding for the fusion protein at the wild-type locus. This ensures complete replacement of the protein of interest with the fluorescent version and maintaining the wild-type expression level.
Photoactivation rate: It is important to adjust the photoactivation rate such that on average less than one molecule per cell is in the fluorescent state in any frame of the movie. This depends on the 405 nm intensity and the number of molecules left to be activated. At very low imaging densities, however, not all molecules will be imaged before the end of the movie or very long movies have to be acquired. The number of frames recorded per movie depends on the copy number of fusion proteins per cell and the mean photobleaching lifetime of PAmCherry at the excitation conditions used. The copy number of Pol1 is ~400 molecules/cell1 and the mean value of the exponential photobleaching lifetime distribution was ~4 frames. By increasing the 405 nm intensity gradually, the activation is evenly distributed over the 10,000 frames of the movie. Therefore, each cell is occupied by fluorescent molecules for a total of ~1,600 frames, ensuring little overlap of PSFs and tracking complications in a movie of 10,000 frames.
Exposure time and excitation intensities: Foremost, exposure times need to be sufficiently short to observe sharp PSFs with little motion blurring. However, the frame rate should be chosen to yield observable molecular motion between successive frames beyond the localization uncertainty; otherwise crucial photons are wasted by oversampling the track. The motion of unbound molecules must be sampled at sufficiently long time intervals to be clearly distinguishable from the apparent motion of bound molecules due to the localization uncertainty. When the exposure time is set, the PSF intensity should be adjusted. The localization precision of a PSF increases with the number of photons detected over the duration of a frame. Higher excitation intensities increase the photon emission rate but also the photobleaching rate and background signal. Use the lowest excitation intensity that gives the desired localization precision. For Pol1-PAmCherry we chose 15.26 msec/frame and 3.5 mW 561 nm excitation (400 W/cm2). It is important to confirm cell viability for the particular imaging conditions by monitoring cell growth and morphology before and after data acquisition (see Supplementary Information in Uphoff&#160;et al.7).
Pol1 exhibits a binding time of ~2 sec to a gapped DNA substrate in vivo7; we therefore expect the majority of molecules to be either in the bound or unbound state for the whole duration of a track. Bound molecules appear essentially immobile because chromosome sites have a diffusion coefficient several orders of magnitude lower (~10-5 &#181;m2/sec, Elmore&#160;et al.18) than Pol1 diffusion in the cytoplasm (~1 &#181;m2/sec).
Diffusion analysis: The apparent diffusion coefficient D* is calculated from the MSD of individual tracks, averaged over a minimum of 4 steps (5 frames) to reduce the statistical error. Note that ~75% of molecules bleach within less than 5 frames for the imaging conditions described. Such short tracks do not provide sufficient statistical certainty to distinguish bound and unbound molecules. However, the relative fractions of bound and unbound molecules that report on the protein activity are independent of the total number of tracks analyzed.
It is useful to account for the PSF localization error (&#963;loc) in the calculation of D* because the uncertainty adds an apparent random step to each localization of a molecule15.
To improve the classification of bound and diffusing molecules, we recommend calculating D* both from the single-step displacements and the displacements over the time of two frames. It is then possible to set two separate D* thresholds: D*(15 msec) &#60; 0.15 &#181;m2/sec and D*(30 msec) &#60; 0.075 &#181;m2/sec.
Note that D* is an apparent diffusion coefficient that is affected by cell confinement of the tracks and motion blurring due to diffusion during the exposure time. To extract accurate unbiased diffusion coefficients, it has proven useful to compare the observed motion to simulated data based on a stochastic Brownian motion model5,7. Simulated data can also be used to test data analysis procedures.
Potential applications of this method: We described a general approach for visualizing and quantifying protein-DNA interactions in vivo by the change in the mobility of a protein upon binding to the chromosome. The activities of DNA- or RNA-binding proteins involved in repair, replication, transcription, and chromosome maintenance can thus be followed in real-time at the single-cell level with a spatial resolution below the optical diffraction limit. Photoactivated single-molecule tracking extends conventional tracking methods that are restricted to a few labeled molecules per cell. An alternative method that measures molecular diffusion in vivo is Fluorescence Recovery After Photobleaching (FRAP). While FRAP is very useful for measuring global diffusion characteristics in large cells, it is limited in its ability to resolve several molecular species with different mobilities in a spatially heterogeneous environment, especially for small bacterial cells.
We have applied photoactivated single-molecule tracking to measure DNA-binding activities and subcellular localizations of a range of different proteins in E. coli including Pol1, DNA ligase, Fis protein, DNA polymerase III7, as well as Structural Maintenance of Chromosomes proteins MukB, E, and F19. We anticipate the method can also be adapted to other cell types.

This protocol describes the direct measurement of protein-DNA interactions in living Escherichia coli cells. The technique utilizes the change in the diffusion coefficient of a single fluorescently labeled protein as it binds the chromosome (Figure&#160;1). To demonstrate the method we use DNA polymerase I (Pol1), a prototypical DNA-binding protein that fills DNA gaps in lagging strand replication and excision repair pathways1.
The advent of super-resolution fluorescence microscopy enables visualization of molecular structures in cells with nanometer resolution. Photoactivated Localization Microscopy (PALM) employs fluorescent proteins that can be activated from an initial dark state to a fluorescent state (Figure&#160;2). Only a subset of all labeled molecules is activated at any time to determine their positions in a sequential manner, independently of the total concentration of labeled molecules in the sample2. The localization precision per molecule mainly depends on the size of the fluorescent Point Spread Function (PSF), the number of collected photons, and the background signal3. Many applications of this method focus on the improved visualization of cellular structures. The realization that PALM can be combined with single-molecule tracking4 opened new avenues to directly follow the movement of arbitrary numbers of labeled proteins in living cells. Increased sensitivity and temporal resolution of fluorescence microscopes now allow tracking of single diffusing fluorescent proteins in the bacterial cytoplasm5.
Here, we employ PAmCherry, an engineered fluorescent protein that irreversibly converts from an initial nonfluorescent state to a fluorescent state upon irradiation with 405 nm light6. Activated PAmCherry fluorophores can be imaged by excitation at 561 nm and tracked for several frames until photobleaching. We demonstrate the ability of the method to identify transient DNA-binding events of single proteins using a fusion of Pol1 and PAmCherry. Treatment of cells with methyl methanesulfonate (MMS) causes DNA methylation damage that is turned into gapped DNA substrates by base-excision repair enzymes. Our method shows clear binding of single Pol1 molecules in response to MMS damage7.

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	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:313px;">E.coli strain carrying a chromosomal insertion for a PAmCherry DNA-binding fusion protein</td>
			<td style="width:187px;"></td>
			<td style="width:477px;"></td>
			<td style="width:467px;">created by Lambda-Red recombination</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">MEM amino acids</td>
			<td>Invitrogen</td>
			<td>11130-051</td>
			<td>minimal medium supplement</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">MEM vitamins</td>
			<td>Invitrogen</td>
			<td>11120-052</td>
			<td>minimal medium supplement</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Agarose</td>
			<td>BioRad</td>
			<td>161-3100</td>
			<td>certified molecular biology grade</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Microscope coverslips No 1.5 thickness</td>
			<td>Menzel</td>
			<td>BB024060SC</td>
			<td style="width:467px;">remove background particles by heating slides in furnace at 500 &#176;C for 1h</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Methyl methanesulfonate (MMS)</td>
			<td>Sigma-Aldrich</td>
			<td>129925</td>
			<td>CAUTION: toxic</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">PALM setup</td>
			<td>home-built</td>
			<td></td>
			<td>described in Uphoff et al. 2013</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">MATLAB</td>
			<td>MathWorks</td>
			<td></td>
			<td>for data analysis and visualization</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Localization software</td>
			<td style="width:187px;">custom-written, available online</td>
			<td>http://www.physics.ox.ac.uk/Users/kapanidis/Group/Main.Software.html</td>
			<td style="width:467px;">MATLAB and C++ software package that can be adapted for localization analysis. Cite Holden et al. 2010 if used in publication.</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Tracking software</td>
			<td>available online</td>
			<td><a href="http://physics.georgetown.edu/matlab/">http://physics.georgetown.edu/matlab/</a></td>
			<td style="width:467px;">MATLAB implementation by Blair and Dufresne. Cite Crocker &#38; Grier 1996 if used in publication.</td>
		</tr>
	</tbody>
</table>

visualizing protein-dna interactions in live bacterial cells using photoactivated single-molecule tracking

Protein-DNA interactions are at the heart of many fundamental cellular processes. For example, DNA replication, transcription, repair, and chromosome organization are governed by DNA-binding proteins that recognize specific DNA structures or sequences. In vitro experiments have helped to generate detailed models for the function of many types of DNA-binding proteins, yet, the exact mechanisms of these processes and their organization in the complex environment of the living cell remain far less understood. We recently introduced a method for quantifying DNA-repair activities in live Escherichia coli cells using Photoactivated Localization Microscopy (PALM) combined with single-molecule tracking. Our general approach identifies individual DNA-binding events by the change in the mobility of a single protein upon association with the chromosome. The fraction of bound molecules provides a direct quantitative measure for the protein activity and abundance of substrates or binding sites at the single-cell level. Here, we describe the concept of the method and demonstrate sample preparation, data acquisition, and data analysis procedures.

The concept of photoactivated single-molecule tracking to study protein-DNA interactions in vivo is illustrated in Figure&#160;1. PAmCherry fusion proteins are detected in live E. coli cells in a sequential manner by photoactivating single molecules stochastically with 405 nm light at a frequency of less than one molecule per cell at a time. Activated molecules are imaged under continuous 561 nm excitation. Molecular movement in the cell can be tracked by connecting nearby localizations in a series of frames until irreversible photobleaching. Because the diffusion of DNA-binding proteins is slowed upon binding the chromosome, the apparent diffusion coefficient D* obtained per track directly reports on individual protein-DNA interactions.
Figure&#160;2 demonstrates photoactivation of Pol1-PAmCherry fusion proteins in live E. coli cells. The influence of the 405 nm intensity on the density of fluorescent molecules can be seen in Figure&#160;4. Note that the density is not solely determined by the 405 nm intensity but additionally by the number of molecules that are available for activation; the pool of remaining molecules is depleted over the course of a PALM movie.
Localization analysis is performed for each frame of a PALM movie as illustrated in Figure&#160;5. We measured the localization precision using immobile molecules in fixed cells or bound molecules in live cells. Our acquisition settings gave a localization precision of &#963;loc = 40 nm, in agreement with the theoretical prediction3.
The resulting Pol1 localizations occupy the central area of the cell (Figure&#160;6A), broadly recapitulating the spatial organization of the E. coli nucleoid7. The majority of Pol1 tracks in undamaged cells display diffusion as shown in Figure&#160;6C. A typical cell contains several hundred Pol1 tracks (Figure&#160;6D), consistent with the copy number of approximately 400 Pol1 molecules per E. coli cell1. Figures&#160;6B and 6E-F provide guidance on choosing a suitable tracking window parameter &#8211; if the tracking window is too large, different molecules are more likely to become erroneously linked to a track; if the tracking window is too small, tracks with longer steps will be split. The MSD curve for Pol1 rises linearly for short lag times and saturates at longer lag times due to cell confinement (Figure&#160;6G). Different types of molecular motion can be identified by MSD analysis. Directed motion gives a parabolic curve; Brownian motion is characterized by a straight line; the confined diffusion curve reaches a plateau; an offset of the MSD curve for immobile particles represents the localization uncertainty (Figure&#160;6H). Additional information on single-particle tracking and troubleshooting tips can be found in Arnauld&#160;et al.16
We previously applied the method to measure the DNA-repair activity of Pol1 in response to exogenous DNA alkylation damage7. The D* histogram of Pol1 tracks in undamaged cells shows a dominant population of diffusing molecules (Figures&#160;7A-C). A small fraction of 2.7% bound Pol1 molecules is likely involved in lagging strand replication and repair of endogenous DNA damage. Under continuous 100 mM MMS damage, the population of tracks with D* ~ 0 &#181;m2/sec increases to 13.8% (Figure&#160;7D). These tracks represent individual Pol1 molecules performing DNA repair synthesis to fill single-nucleotide gaps as part of the base-excision repair pathway. The positions of bound tracks show the locations of individual DNA damage and repair sites (Figure&#160;7E-F).
<img alt="Figure 1" fo:content-width="2.5in" src="/files/ftp_upload/51177/51177fig1highres.jpg" width="300" /> 
Figure 1.&#160;Graphical representation of the method. (A) The fluorescent protein PAmCherry can be photoactivated from an initial nonfluorescent state upon irradiation with 405 nm light. The bright state is excited at 561 nm and emits fluorescence around 600 nm until the fluorophore bleaches irreversibly. (B) Controlling the photoactivation rate allows imaging only a single stochastically activated PAmCherry fusion protein per cell at any time while the arbitrarily large pool of molecules that have not yet been activated or have already been bleached remains in a dark state. (C) The position of the fluorescent molecule is determined from the center of the isolated PSF and tracked for several frames until photobleaching. (D) Tracks of many molecules are recorded in a sequential manner. (E-F) The interaction of a DNA-binding protein with a chromosomal target sequence or structure halts the random diffusive motion. Bound and unbound molecules are distinguished by the apparent diffusion coefficient D* extracted from single tracks. The resulting fraction of bound molecules gives a quantitative measure for the activity of a DNA-binding protein in vivo. <a href="https://www.jove.com/files/ftp_upload/51177/51177fig1highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 2" fo:content-width="5in" src="/files/ftp_upload/51177/51177fig2highres.jpg" width="600" /> 
Figure 2.&#160;Photoactivation of Pol1-PAmCherry in live E. coli cells. Scale bars: 1 &#181;m. Schematics are shown underneath each panel. (A) Transmitted light microscopy image of cells immobilized on an agarose pad. (B)&#160;Phototactivating a single PAmCherry fluorophore in one cell. (C) A higher photoactivation rate increases the number of fluorescent molecules. (D) Integrated PAmCherry fluorescence from a PALM movie. <a href="https://www.jove.com/files/ftp_upload/51177/51177fig2highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 3" fo:content-width="4in" src="/files/ftp_upload/51177/51177fig3highres.jpg" width="400" /> 
Figure 3.&#160;Schematic of a minimal PALM setup for photoactivating and imaging PAmCherry fusion proteins. D1: Dichroic mirror (e.g. 550 nm long-pass).&#160;D2: Dichroic mirror (e.g. 570 nm long-pass). L1: Collimating lens. L2: TIR lens. L3: Tube lens. <a href="https://www.jove.com/files/ftp_upload/51177/51177fig3highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 4" fo:content-width="5in" src="/files/ftp_upload/51177/51177fig4highres.jpg" width="500" /> 
Figure 4.&#160;Representative images from a PALM movie with 15.26 msec/frame. Scale bars: 1 &#181;m. (A) Transmitted light image of cells immobilized on an agarose pad. (B) Dark background image measured on the EMCCD camera with the lasers switched off. (C) Excitation background image under continuous 561 nm excitation before photoactivation. (D-F) Increased 405 nm intensity leads to higher photoactivation rates of PAmCherry, imaged under continuous 561 nm excitation. The boxed areas are shown magnified below. (D) Low 405 nm intensity (&#60;1 &#181;W) actives very few fluorescent molecules per FOV. (E) Medium 405 nm intensity (~2 &#181;W) photoactivation results in a good PSF density for localization and tracking analysis. (F) Higher 405 nm intensity (~10 &#181;W) activates more than one fluorescent molecule in some cells, which obscures localization and tracking analysis. <a href="https://www.jove.com/files/ftp_upload/51177/51177fig4highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 5" fo:content-width="5in" src="/files/ftp_upload/51177/51177fig5highres.jpg" width="500" /> 
Figure 5.&#160;Illustration of the localization analysis. Scale bars: 1 &#181;m. (A) Band-pass filtering removes spurious pixel noise and flattens intensity gradients across the FOV. (B) Candidate PSFs are identified in the filtered image based on a user-defined threshold that is chosen to minimize false positive and false negative detections. The threshold corresponds to the minimum intensity of a candidate pixel divided by the background standard deviation (signal-to-noise ratio, SNR). (C) The locally brightest pixel that passes the threshold serves as initial localization guess (orange cross) for a two-dimensional elliptical Gaussian fit. Scale bars: 0.5 &#181;m. The resulting super-resolution localization (blue cross) has an average precision of &#963;loc = 40 nm. <a href="https://www.jove.com/files/ftp_upload/51177/51177fig5highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 6" fo:content-width="5in" src="/files/ftp_upload/51177/51177fig6highres.jpg" width="500" /> 
Figure 6.&#160;Illustration of the tracking analysis. Scale bars: 1 &#181;m. (A) All detected localizations of Pol1-PAmCherry in an example cell. (B) Number of tracks detected in the example cell as a function of the tracking window. Small tracking windows split molecule trajectories, which leads to artifactual high number of tracks. The dashed line indicates our choice for the tracking window parameter (0.57 &#181;m, 5 pixels) -&#160;this gives a good compromise between detecting the full distribution of steps and keeping the trajectories of different molecules intact. (C) Example track of a single Pol1-PAmCherry molecule. (D)&#160;All measured tracks shown in random colors. (E) Tracking artifacts if the tracking window is chosen too large (here 0.8 &#181;m, 7 pixels) or the density of PSFs per frame is too high. (F) Cumulative distributions of the step lengths for tracking windows: 0.34 &#181;m (3 pixels, red line), 0.57 &#181;m (5 pixels, blue line), and 0.80 &#181;m (7 pixels, green line). Note that the 0.34 &#181;m tracking window cuts off steps longer than 0.34 &#181;m which clearly truncates the full distribution of steps. The 0.57 &#181;m tracking window detects almost the same distribution of steps as does the 0.80 &#181;m tracking window. (G) MSD curve shows confined diffusion of Pol1. (H) Schematic MSD curves for directed motion, Brownian motion, confined diffusion, and immobile particles. <a href="https://www.jove.com/files/ftp_upload/51177/51177fig6highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Figure 7" fo:content-width="5in" src="/files/ftp_upload/51177/51177fig7highres.jpg" width="500" /> 
Figure 7.&#160;Direct measurement of the DNA repair activity of Pol1 in live E. coli cells. Scale bars: 1 &#181;m. (A) Histogram of the apparent diffusion coefficient D* for all tracks of 4 or more steps in a FOV of undamaged cells (N = 4,162 tracks). The population of molecules classified as bound is highlighted in red. (B-C) Tracks of Pol1-PAmCherry are shown on a transmitted light microscopy image. Tracks classified as bound according to their diffusion coefficient are shown in red. (D) D* histogram for Pol1 tracks measured in cells immobilized on an agarose pad with 100 mM MMS and incubated for 20 min before imaging (N = 2,128 tracks). The population of bound molecules engaged in DNA repair is shown in red.&#160;(E-F) Pol1-PAmCherry tracks on transmitted light microscopy image showing the tracks of single Pol1 DNA repair events in red. <a href="https://www.jove.com/files/ftp_upload/51177/51177fig7highres.jpg" target="_blank">Click here to view larger image.</a>
<img alt="Video 1" fo:content-width="4in" src="/files/ftp_upload/51177/video.jpg" width="400" /> 
Movie 1.&#160;Building a bespoke PALM setup. <a href="/files/ftp_upload/51177/JoVE_Uphoff_Movie1.avi" target="_blank">Click here to view video.</a>

Watch this Scientific Journal Video about Visualizing Protein-DNA Interactions in Live Bacterial Cells Using Photoactivated Single-molecule Tracking at JoVE.com

Visualizing Protein-DNA Interactions in Live Bacterial Cells Using Photoactivated Single-molecule Tracking

Photoactivated localization microscopy (PALM) combined with single-molecule tracking allows direct observation and quantification of protein-DNA interactions in live Escherichia coli cells.

Protein-DNA interactions are at the heart of many fundamental cellular processes. For example, DNA replication, transcription, repair, and chromosome ...

visualizing-protein-dna-interactions-live-bacterial-cells-using

Research

JoVE Journal

Immunology and Infection

Visualizing Cell-to-cell Transfer of HIV using Fluorescent Clones of HIV and Live Confocal Microscopy

By fusing the green fluorescent protein to their favorite proteins, biologists now have the ability to study living complex cellular processes using fluorescence video microscopy. To track the movements of the human immunodeficiency virus core protein during cell-to-cell transmission of human immunodeficiency virus, we have GFP-tagged the Gag protein in the context of an infectious molecular clone of HIV, called HIV Gag-iGFP. We study this viral clone using video confocal microscopy. In the following visualized experiment, we transfect a human T cell line with HIV Gag-iGFP, and we use fluorescently labeled uninfected CD4+ T cells to serve as target cells for the virus. Using the different fluorescent labels we can readily follow viral production and transport across intercellular structures called virological synapses. Simple gas permeable imaging chambers allow us to observe synapses with live confocal microscopy from minutes to days. These approaches can be used to track viral proteins as they move in from one cell to the next.

This visualized experiment is a guide for utilizing a fluorescent molecular clone of HIV for live confocal imaging experiments.

By fusing the green fluorescent protein to their favorite proteins, biologists now have the ability to study living complex cellular processes using ...

Assessing Stomatal Response to Live Bacterial Cells using Whole Leaf Imaging

Stomata are natural openings in the plant epidermis responsible for gas exchange between plant interior and environment. They are formed by a pair of guard cells, which are able to close the stomatal pore in response to a number of external factors including light intensity, carbon dioxide concentration, and relative humidity (RH). The stomatal pore is also the main route for pathogen entry into leaves, a crucial step for disease development. Recent studies have unveiled that closure of the pore is effective in minimizing bacterial disease development in Arabidopsis plants; an integral part of plant innate immunity. Previously, we have used epidermal peels to assess stomatal response to live bacteria (Melotto et al. 2006); however maintaining favorable environmental conditions for both plant epidermal peels and bacterial cells has been challenging. Leaf epidermis can be kept alive and healthy with MES buffer (10 mM KCl, 25 mM MES-KOH, pH 6.15) for electrophysiological experiments of guard cells. However, this buffer is not appropriate for obtaining bacterial suspension. On the other hand, bacterial cells can be kept alive in water which is not proper to maintain epidermal peels for long period of times. When an epidermal peel floats on water, the cells in the peel that are exposed to air dry within 4 hours limiting the timing to conduct the experiment. An ideal method for assessing the effect of a particular stimulus on guard cells should present minimal interference to stomatal physiology and to the natural environment of the plant as much as possible. We, therefore, developed a new method to assess stomatal response to live bacteria in which leaf wounding and manipulation is greatly minimized aiming to provide an easily reproducible and reliable stomatal assay. The protocol is based on staining of intact leaf with propidium iodide (PI), incubation of staining leaf with bacterial suspension, and observation of leaves under laser scanning confocal microscope. Finally, this method allows for the observation of the same live leaf sample over extended periods of time using conditions that closely mimic the natural conditions under which plants are attacked by pathogens.

We have developed a simple and reproducible protocol to access stomatal response to live bacteria. This method minimizes wounding and manipulation of the leaf as compared to the use of epidermal peels reported previously.

Stomata are natural openings in the plant epidermis responsible for gas exchange between plant interior and environment. They are formed by a pair of ...

Visualizing Dengue Virus through Alexa Fluor Labeling

The early events in the interaction between virus and cell can have profound influence on the outcome of infection. Determining the factors 
that influence this interaction could lead to improved understanding of disease pathogenesis and thus influence vaccine or therapeutic design. Hence, the development 
of methods to probe this interaction would be useful. Recent advancements in fluorophores development1-3 and imaging technology4 can be exploited to 
improve our current knowledge on dengue pathogenesis and thus pave the way to reduce the millions of dengue infections occurring annually.

The enveloped dengue virus has an external scaffold consisting of 90 envelope glycoprotein (E) dimers protecting the nucleocapsid shell, 
which contains a single positive strand RNA genome5. The identical protein subunits on the virus surface can thus be labeled with an amine reactive dye and visualized 
through immunofluorescent microscopy. Here, we present a simple method of labeling of dengue virus with Alexa Fluor succinimidyl ester dye dissolved directly in a sodium 
bicarbonate buffer that yielded highly viable virus after labeling. There is no standardized procedure for the labeling of live virus and existing manufacturer&#x2019;s protocol 
for protein labeling usually requires the reconstitution of dye in dimethyl sulfoxide. The presence of dimethyl sulfoxide, even in minute quantities, can block 
productive infection of virus and also induce cell cytotoxicity6. The exclusion of the use of dimethyl sulfoxide in this protocol thus reduced this possibility. 
Alexa Fluor dyes have superior photostability and are less pH-sensitive than the common dyes, such as fluorescein and rhodamine2, making them ideal for 
studies on cellular uptake and endosomal transport of the virus. The conjugation of Alexa Fluor dye did not affect the recognition of labeled dengue 
virus by virus-specific antibody and its putative receptors in host cells7. This method could have useful applications in virological studies.

Taking advantage of the advancements in fluorophore development and imaging technology, a simple method of Alexa Fluor labeling of dengue virus was devised to visualize the early interactions between virus and cell.

The early events in the interaction between virus and cell can have profound influence on the outcome of infection. Determining the factors 
that ...

Visualization of Bacterial Toxin Induced Responses Using Live Cell Fluorescence Microscopy

Bacterial toxins bind to cholesterol in membranes, forming pores that allow for leakage of cellular contents and influx of materials from the external environment. The cell can either recover from this insult, which requires active membrane repair processes, or else die depending on the amount of toxin exposure and cell type1. In addition, these toxins induce strong inflammatory responses in infected hosts through activation of immune cells, including macrophages, which produce an array of pro-inflammatory cytokines2. Many Gram positive bacteria produce cholesterol binding toxins which have been shown to contribute to their virulence through largely uncharacterized mechanisms.
Morphologic changes in the plasma membrane of cells exposed to these toxins include their sequestration into cholesterol-enriched surface protrusions, which can be shed into the extracellular space, suggesting an intrinsic cellular defense mechanism3,4. This process occurs on all cells in the absence of metabolic activity, and can be visualized using EM after chemical fixation4. In immune cells such as macrophages that mediate inflammation in response to toxin exposure, induced membrane vesicles are suggested to contain cytokines of the IL-1 family and may be responsible both for shedding toxin and disseminating these pro-inflammatory cytokines5,6,7. A link between IL-1&beta; release and a specific type of cell death, termed pyroptosis has been suggested, as both are caspase-1 dependent processes8. To sort out the complexities of this macrophage response, which includes toxin binding, shedding of membrane vesicles, cytokine release, and potentially cell death, we have developed labeling techniques and fluorescence microscopy methods that allow for real time visualization of toxin-cell interactions, including measurements of dysfunction and death (Figure 1). Use of live cell imaging is necessary due to limitations in other techniques. Biochemical approaches cannot resolve effects occurring in individual cells, while flow cytometry does not offer high resolution, real-time visualization of individual cells. The methods described here can be applied to kinetic analysis of responses induced by other stimuli involving complex phenotypic changes in cells.

Methods for purifying the cholesterol binding toxin streptolysin O from recombinant E. coli and visualization of toxin binding to live eukaryotic cells are described. Localized delivery of toxin induces rapid and complex changes in targeted cells revealing novel aspects of toxin biology.

Bacterial toxins bind to cholesterol in membranes, forming pores that allow for leakage of cellular contents and influx of materials from the external ...

Imaging InlC Secretion to Investigate Cellular Infection by the Bacterial Pathogen Listeria monocytogenes

Bacterial intracellular pathogens can be conceived as molecular tools to dissect cellular signaling cascades due to their capacity to exquisitely manipulate and subvert cell functions which are required for the infection of host target tissues. Among these bacterial pathogens, Listeria monocytogenes is a Gram positive microorganism that has been used as a paradigm for intracellular parasitism in the characterization of cellular immune responses, and which has played instrumental roles in the discovery of molecular pathways controlling cytoskeletal and membrane trafficking dynamics. In this article, we describe a robust microscopical assay for the detection of late cellular infection stages of L. monocytogenes based on the fluorescent labeling of InlC, a secreted bacterial protein which accumulates in the cytoplasm of infected cells; this assay can be coupled to automated high-throughput small interfering RNA screens in order to characterize cellular signaling pathways involved in the up- or down-regulation of infection.

Imaging InlC Secretion to Investigate Cellular Infection by the Bacterial Pathogen Listeria monocytogenes

Listeria monocytogenes is a Gram positive bacterial pathogen frequently used as a major model for the study of intracellular parasitism. Imaging late L. monocytogenes infection stages within the context of small-interfering RNA screens allows for the global study of cellular pathways required for bacterial infection of target host cells.

Bacterial intracellular  pathogens can be conceived as molecular tools to dissect cellular signaling  cascades due to their capacity to exquisitely ...

Assessing Bacterial Invasion of Cardiac Cells in Culture and Heart Colonization in Infected Mice Using Listeria monocytogenes

Listeria monocytogenes is a Gram-positive facultative intracellular pathogen that is capable of causing serious invasive infections in immunocompromised patients, the elderly, and pregnant women. The most common manifestations of listeriosis in humans include meningitis, encephalitis, and fetal abortion. A significant but much less documented sequelae of invasive L. monocytogenes infection involves the heart. The death rate from cardiac illness can be up to 35% despite treatment, however very little is known regarding L. monocytogenes colonization of cardiac tissue and its resultant pathologies. In addition, it has recently become apparent that subpopulations of L. monocytogenes have an enhanced capacity to invade and grow within cardiac tissue. This protocol describes in detail in vitro and in vivo methods that can be used for assessing cardiotropism of L. monocytogenes isolates. Methods are presented for the infection of H9c2 rat cardiac myoblasts in tissue culture as well as for the determination of bacterial colonization of the hearts of infected mice. These methods are useful not only for identifying strains with the potential to colonize cardiac tissue in infected animals, but may also facilitate the identification of bacterial gene products that serve to enhance cardiac cell invasion and/or drive changes in heart pathology. These methods also provide for the direct comparison of cardiotropism between multiple L. monocytogenes strains.

Assessing Bacterial Invasion of Cardiac Cells in Culture and Heart Colonization in Infected Mice Using Listeria monocytogenes

Listeria monocytogenes causes fetal infections in pregnant women and meningitis in susceptible populations. Subpopulations of bacteria can colonize cardiac tissue, causing myocarditis in patients and laboratory animals. Here we present a protocol that describes how to assess L. monocytogenes cardiac cell invasion in vitro and cardiac colonization in infected animals.

Listeria monocytogenes is a Gram-positive facultative intracellular pathogen that is capable of causing serious invasive infections in immunocompromised ...

A New Method for Qualitative Multi-scale Analysis of Bacterial Biofilms on Filamentous Fungal Colonies Using Confocal and Electron Microscopy

Bacterial biofilms frequently form on fungal surfaces and can be involved in numerous bacterial-fungal interaction processes, such as metabolic cooperation, competition, or predation. The study of biofilms is important in many biological fields, including environmental science, food production, and medicine. However, few studies have focused on such bacterial biofilms, partially due to the difficulty of investigating them. Most of the methods for qualitative and quantitative biofilm analyses described in the literature are only suitable for biofilms forming on abiotic surfaces or on homogeneous and thin biotic surfaces, such as a monolayer of epithelial cells.
While laser scanning confocal microscopy (LSCM) is often used to analyze in situ and in vivo biofilms, this technology becomes very challenging when applied to bacterial biofilms on fungal hyphae, due to the thickness and the three dimensions of the hyphal networks. To overcome this shortcoming, we developed a protocol combining microscopy with a method to limit the accumulation of hyphal layers in fungal colonies. Using this method, we were able to investigate the development of bacterial biofilms on fungal hyphae at multiple scales using both LSCM and scanning electron microscopy (SEM). This report describes the protocol, including microorganism cultures, bacterial biofilm formation conditions, biofilm staining, and LSCM and SEM visualizations.

This protocol describes a new method to grow and qualitatively analyze bacterial biofilms on fungal hyphae by confocal and electron microscopy.

Bacterial biofilms frequently form on fungal surfaces and can be involved in numerous bacterial-fungal interaction processes, such as metabolic ...

Visualizing the Effects of Sputum on Biofilm Development Using a Chambered Coverglass Model

Biofilms consist of groups of bacteria encased in a self-secreted matrix. They play an important role in industrial contamination as well as in the development and persistence of many health related infections. One of the most well described and studied biofilms in human disease occurs in chronic pulmonary infection of cystic fibrosis patients. When studying biofilms in the context of the host, many factors can impact biofilm formation and development. In order to identify how host factors may affect biofilm formation and development, we used a static chambered coverglass method to grow biofilms in the presence of host-derived factors in the form of sputum supernatants. Bacteria are seeded into chambers and exposed to sputum filtrates. Following 48 hr of growth, biofilms are stained with a commercial biofilm viability kit prior to confocal microscopy and analysis. Following image acquisition, biofilm properties can be assessed using different software platforms. This method allows us to visualize key properties of biofilm growth in presence of different substances including antibiotics.

This protocol describes the visualization of biofilm development following exposure to host-factors using a slide chamber model. This model allows for direct visualization of biofilm development as well as analysis of biofilm parameters using computer software programs.

Biofilms consist of groups of bacteria encased in a self-secreted matrix. They play an important role in industrial contamination as well as in the ...

Highly Multiplexed, Super-resolution Imaging of T Cells Using madSTORM

Imaging heterogeneous cellular structures using single molecule localization microscopy has been hindered by inadequate localization precision and multiplexing ability. Using fluorescent nano-diamond fiducial markers, we describe the drift correction and alignment procedures required to obtain high precision in single molecule localization microscopy. In addition, a new multiplexing strategy, madSTORM, is described in which multiple molecules are targeted in the same cell using sequential binding and elution of fluorescent antibodies. madSTORM is demonstrated on an activated T cell to visualize the locations of different components within a membrane-bound, multi-protein structure called the T cell receptor microcluster. In addition, application of madSTORM as a general tool for visualization of multi-protein structures is discussed.

We demonstrate a method to image multiple molecules within heterogeneous nano-structures at single molecule accuracy using sequential binding and elution of fluorescently labeled antibodies.

Imaging heterogeneous cellular structures using single molecule localization microscopy has been hindered by inadequate localization precision and ...

Visualizing Macrophage Extracellular Traps Using Confocal Microscopy

A primary method used to define the presence of neutrophil extracellular traps (NETs) is confocal microscopy. We have modified established confocal microscopy methods to visualize macrophage extracellular traps (METs). These extracellular traps are defined by the presence of extracellular chromatin with co-expression of other components such as granule proteases, citrullinated histones, and peptidyl arginase deiminase (PAD). The expression of METs is generally measured after exposure to a stimulus and compared to un-stimulated samples. Samples are also included for background and isotype control. Cells are analyzed using well-defined image analysis software. Confocal microscopy may be used to define the presence of METs both in vitro and in vivo in lung tissue.

Macrophage extracellular traps are a newly described entity. This article will concentrate on confocal microscopy methods and how they are visualized in vitro and in vivo from lung samples.

A primary method used to define the presence of neutrophil extracellular traps (NETs) is confocal microscopy. We have modified established confocal ...