Grant: 5RO1GM086447-02.

1. Sample Preparation
<ol>
	<li>Inoculate LB media with a single colony of strain JB281 [BW25113 / pJB042 (PLac:FtsZ-mEos2)]. Grow overnight in a shaker at 37 &deg;C.</li>
	<li>Dilute the culture 1:1,000 into M9+ minimal media [M9 Salts, 0.4% Glucose, 2 mM MgSO4, 0.1 mM CaCl2, MEM Amino Acids and Vitamins] and grow to mid-log phase (OD600= 0.2-0.3) in the presence of chloramphenicol (150 &mu;g/ml) at room temperature (RT).</li>
	<li>Induce culture with 20 &mu;M IPTG for 2 hr (see Note #1).</li>
	<li>Pellet culture in microcentrifuge (8,000 rpm, 1 min) and resuspend in an equal volume of M9+; repeat. After the second resuspension, continue growth at RT for 2 hr.</li>
	<li>Fix induced culture with 4% paraformaldehyde in PBS (pH=7.4) at RT for 40 min. Pellet culture and resuspend in an equal volume of PBS; repeat. If imaging live cells, skip fixation, pellet culture, and resuspend with equal volume of M9- [M9 Salts, 0.4% Glucose, 2 mM MgSO4, 0.1 mM CaCl2, MEM Amino Acids]; repeat.</li>
	<li>Dilute gold beads 1:10 with resuspended culture. Apply sample to prepared imaging chamber (see step 2.4).</li>
</ol>
[1] Note: The above induction protocol (steps 1.1-1.3) has been optimized for the expression system of strain JB281. Detailed induction conditions may vary for other expression systems or proteins of interest.
2. Assembly of the Imaging Chamber
<ol>
	<li>Make a 3% (w/v) agarose solution in M9-. Melt agarose at 70 &deg;C in a bench-top heat block for 40min. Store melted agarose at 50 &deg;C for up to 5 hr.</li>
	<li>Place a clean microaqueduct slide in the upper half of the imaging chamber with the electrodes facing down. Align the lower gasket on the microaqueduct slide so as to cover the perfusion channels.</li>
	<li>Apply ~50 &mu;l of the melted agarose to the center of the glass slide. Immediately top the agarose gel droplet with a clean, dry coverslip. Allow the gel to solidify at RT for 30 min.
	<ol>
		<li>To clean coverslips: arrange coverslips in a ceramic holder and sonicate for 20 min in rotating baths of acetone, ethanol and 1M KOH in glass jars. Repeat cycle three times, for a total of 9 sonications, followed by a single sonication in dH2O. Store the cleaned coverslips in fresh dH2O. Prior to use, blow dry coverslips with filtered air.</li>
	</ol>
	</li>
	<li>Carefully remove coverslip, leaving gel pad on the microaqueduct slide. Immediately add 1 &mu;l of cell culture sample (see step 1.6) to the top of the gel pad. Wait for ~2 min, allowing solution to be absorbed by the gel pad. Top gel pad with a new clean, dry coverslip. Assemble the whole imaging chamber according to manufacturer's instructions.</li>
</ol>
3. Image Acquisition
<ol>
	<li>Turn on the microscope, camera and lasers. Open MetaMorph software (Molecular Devices).</li>
	<li>Place appropriate excitation/emission filters in the imaging path (Figure 1).</li>
	<li>Set laser powers and acquisition settings accordingly.
	<ol>
		<li>488-nm laser = 15 mW (see Note #2)</li>
		<li>561-nm laser = 75 mW</li>
		<li>405-nm laser = 2 mW &plusmn; neutral density filter (see Note #3)</li>
	</ol>
	</li>
	<li>Lock imaging chamber into the stage via the complementary stage adaptor.</li>
	<li>Designate an appropriate imaging region (100x100 pixel) that is homogenously illuminated by all three lasers.</li>
	<li>Identify a sample area that contains both cells and fiducial markers (gold beads) in close proximity but not overlapping.</li>
	<li>Focus on the surface of cells closest to the coverslip. Acquire one brightfield image with 50 ms integration time and move the focus 0.5 &mu;m into the sample (Figure 3Ai).</li>
	<li>Acquire ensemble green fluorescence image with excitation from the 488-nm laser using a 50 ms integration time (Figure 3Aii).</li>
	<li>Acquire streaming video in red channel with continuous illumination from 405-nm and 561-nm lasers. For fixed cells, a 50 ms frame rate is used for a total of 20,000 frames (~17 min total) with the 405-nm laser power ramped by ~10% every 1,000 frames (see Note #4). For live cells, a 10 ms frame rate is used for a total of 3,000 frames (~30 s total) at a constant 405-nm laser power.</li>
	<li>Move the focus back to the lower surface of the cells and acquire another brightfield image as in step 3.7.</li>
</ol>
[2] Note: The integrated intensity of the green fluorescence of a cell is directly proportional to the total number of FtsZ-mEos2 molecules expressed in the cell. The 488-nm excitation power and ensemble fluorescence acquisition settings should be kept constant for all cells so that the relative FtsZ-mEos2 expression levels in different cells can be compared.
[3] Note: Fixed cells are imaged with the Neutral Density (ND) filter (Figure 1, Optical Component #5) in place in order to achieve a slow activation rate so that each individual molecule can be accurately identified. The ND filter is removed when imaging live cells to increase the activation rate, while maintaining a low probability of two molecules being activated simultaneously in a diffraction-limited area. The faster rate applied to live-cell imaging is needed to decrease the total acquisition time, thereby "freezing" the Z-ring in time and limiting the effect of photodamage to the cell.
[4] Note: The numbers of frames acquired were optimized for our system and are dependent on the particular cellular structure, labeling density, activation rate and whether exhausting the entire pool of fluorophores is important for analysis. In live cells, it is important to obtain a sufficient number of localizations in as short a period of time as possible to avoid the blurring of the image due to movement of the cellular structure.
4. PALM Image Construction
<ol>
	<li>Determine centroid position of each detected spot.
	<ol>
		<li>Load the image stack into Matlab (MathWorks, Inc).</li>
		<li>Crop the image stack to a region around one cell that excludes all fiducial beads.</li>
		<li>Select an intensity threshold for spot detection that is above the background level, but below the average spot intensity. We account for the variation in background level throughout an experiment by calculating the running average of maximum intensity using a 150 frame window. The intensity threshold for each frame is then interpolated from the running average values.</li>
		<li>Subtract the respective threshold from each frame. Prospective spots are identified as three adjacent pixels with intensities above threshold.</li>
		<li>Fit the fluorescence intensity of each prospective spot to a two-dimensional Gaussian function [Equation #1] using a nonlinear least squares algorithm (Figure 2). The localization precision of each spot is calculated using the total number of detected photons [Equation #2]. Any poorly-fit spot with a localization precision greater than 20 nm (fixed cells) or 45 nm (live cells) is discarded (see Note #5). Any spot with an intensity greater than two-fold of the mean intensity, possibly due to overlapping emitters, is also discarded.</li>
		<li>For fixed cells, remove repeated observations of the same molecule by disregarding any spot whose centroid position is within 45 nm of any other spot that preceded it by 6 frames or less. For live cells, all spots are retained.</li>
	</ol>
	</li>
	<li>Calibrate sample drift using fiducial marker movement.
	<ol>
		<li>Crop out a 10x10 pixel region around a fiducial marker.</li>
		<li>Subtract the respective threshold determined in 4.1.3 from each frame.</li>
		<li>Fit the intensity profile in each frame via least-square fitting algorithm assuming a two-dimensional Gaussian shape.</li>
		<li>Calculate displacement between centroid positions relative to frame 1.</li>
	</ol>
	</li>
	<li>Correct the centroid position of each unique molecule identified in 4.1 with the appropriate sample drift calculated in 4.2. Compare the brightfield images acquired in 3.7 and 3.10. If cells are observed to move relative to the fiducial markers, the data is thrown out.</li>
	<li>Superimpose all corrected centroid positions on a single image composed of 15x15 nm pixels. Plot each unique molecule as a unit-area, two-dimensional Gaussian profile centered at the centroid position with a standard deviation equal to the localization precision [Equation #2]. This results in a probability density map, where a pixel's intensity is proportional to the likelihood of finding a molecule in that pixel (Figure 3Aiv).</li>
	<li>Comparing PALM images to ensemble images.
	<ol>
		<li>Replot the centroid positions as in 4.4, but on a plane with 150x150 nm pixels and a standard deviation equal to 250 nm. This generates a PALM image that mimics a diffraction-limited image, which can be used to compare to the diffraction-limited, ensemble image acquired in 3.8 (Figure 3Aiii).</li>
		<li>To confirm that the detected molecules are representative of the whole population, compare the reconstructed ensemble image (Figure 3Aiii, step 4.5.1) with the experimental ensemble fluorescence image (Figure 3Aii, step 3.8) to confirm that both images show structures of similar shape, orientation and relative intensity.</li>
	</ol>
	</li>
</ol>
[5] Note: The minimum required localization precision was determined empirically for each imaging method by plotting the precisions of all molecules for a given sample and selecting an appropriate cutoff.
5. PALM Image Analysis
<ol>
	<li>Measuring Z-Ring Width and Diameter.
	<ol>
		<li>Rotate the PALM image so as to vertically orient the long axis of the cell (Figure 4A).</li>
		<li>Crop out the cellular region containing the Z-ring.</li>
		<li>Calculate the cumulative intensity by projecting an intensity profile along the cell's long axis.</li>
		<li>Fit the intensity profile to a Gaussian distribution. The ring width is defined as the full width half maximum (FWHM) of the fitted Gaussian distribution (Figure 4B).</li>
		<li>Project the cumulative intensity profile of 5.1.2 along the cell's short axis. The ring diameter is defined as the full length of the intensity profile (Figure 4C).</li>
	</ol>
	</li>
	<li>Plotting FtsZ Density (see Note #6).
	<ol>
		<li>Replot the centroid positions as in (4.4), but without the Gaussian profile such that each unique molecule is given a value of 1 and assigned to a single pixel corresponding to its centroid position. In this way, the intensity of each pixel represents the total number of molecules detected in that pixel. The Density PALM image can also be visualized as a contour plot (Figure 5E).</li>
	</ol>
	</li>
	<li>Determining Spatial Resolution.
	<ol>
		<li>Theoretically-achievable spatial resolution: create a representative PSF for the entire sample using the average determined localization precision of all detected molecules and calculate the FWHM (Equation #3).</li>
		<li>Experimentally-achieved spatial resolution: calculate the average displacement between repeat observations of the same molecule.</li>
	</ol>
	</li>
</ol>
[6] Note: We used total internal reflection PALM (TIR-PALM, Figure 1 and 5) to restrict the activation and excitation to a thin layer (~200 nm) above the cell/glass interface. so that only FtsZ molecules associated with the membrane closest to the coverslip will be detected.

<ol>
	<li>Buddelmeijer, N., Beckwith, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assembly+of+cell+division+proteins+at+the+E.+coli+cell+center.">Assembly of cell division proteins at the E. coli cell center.</a> Curr. Opin. Microbiol. 5, 553-557 (2002).</li><li>de Boer, P., Crossley, R., Rothfield, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+essential+bacterial+cell-division+protein+FtsZ+is+a+GTPase.">The essential bacterial cell-division protein FtsZ is a GTPase.</a> Nature. 359, 254-256 (1992).</li><li>Lutkenhaus, J. F., Wolf-Watz, H., Donachie, W. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Organization+of+genes+in+the+ftsA-envA+region+of+the+Escherichia+coli+genetic+map+and+identification+of+a+new+fts+locus+(ftsZ).">Organization of genes in the ftsA-envA region of the Escherichia coli genetic map and identification of a new fts locus (ftsZ).</a> J. Bacteriol. 142, 615-620 (1980).</li><li>Bi, E. F., Lutkenhaus, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FtsZ+ring+structure+associated+with+division+in+Escherichia+coli.">FtsZ ring structure associated with division in Escherichia coli.</a> Nature. 354, 161-164 (1991).</li><li>Erickson, H. P., Taylor, D. W., Taylor, K. A., Bramhill, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bacterial+cell+division+protein+FtsZ+assembles+into+protofilament+sheets+and+minirings,+structural+homologs+of+tubulin+polymers.">Bacterial cell division protein FtsZ assembles into protofilament sheets and minirings, structural homologs of tubulin polymers.</a> Proc. Natl. Acad. Sci. U.S.A. 93, 519-523 (1996).</li><li>Osawa, M., Anderson, D. E., Erickson, H. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reconstitution+of+contractile+FtsZ+rings+in+liposomes.">Reconstitution of contractile FtsZ rings in liposomes.</a> Science. 320, 792-794 (2008).</li><li>Li, Z., Trimble, M. J., Brun, Y. V., Jensen, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+structure+of+FtsZ+filaments+in+vivo+suggests+a+force-generating+role+in+cell+division.">The structure of FtsZ filaments in vivo suggests a force-generating role in cell division.</a> Embo J. 26, 4694-4708 (2007).</li><li>Betzig, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+intracellular+fluorescent+proteins+at+nanometer+resolution.">Imaging intracellular fluorescent proteins at nanometer resolution.</a> Science. 313, 1642-1645 (2006).</li><li>McKinney, S. A., Murphy, C. S., Hazelwood, K. L., Davidson, M. W., Looger, L. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+bright+and+photostable+photoconvertible+fluorescent+protein.">A bright and photostable photoconvertible fluorescent protein.</a> Nat. Methods. 6, 131-133 (2009).</li><li>Fu, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In-vivo+FtsZ-ring+structure+revealed+by+Photoactivated+Localization+Microisocpy+(PALM).">In-vivo FtsZ-ring structure revealed by Photoactivated Localization Microisocpy (PALM).</a> Plos One. , (2010).</li><li>Huecas, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Energetics+and+geometry+of+FtsZ+polymers:+nucleated+self-assembly+of+single+protofilaments.">Energetics and geometry of FtsZ polymers: nucleated self-assembly of single protofilaments.</a> Biophys. J. , (2007).</li><li>Ma, X., Ehrhardt, D. W., Margolin, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Colocalization+of+cell+division+proteins+FtsZ+and+FtsA+to+cytoskeletal+structures+in+living+Escherichia+coli+cells+by+using+green+fluorescent+protein.">Colocalization of cell division proteins FtsZ and FtsA to cytoskeletal structures in living Escherichia coli cells by using green fluorescent protein.</a> Proc. Natl. Acad. Sci. U.S.A. 93, 12998-13003 (1996).</li><li>Dai, K., Lutkenhaus, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+proper+ratio+of+FtsZ+to+FtsA+is+required+for+cell+division+to+occur+in+Escherichia+coli.">The proper ratio of FtsZ to FtsA is required for cell division to occur in Escherichia coli.</a> J. Bacteriol. 174, 6145-6151 (1992).</li><li>Annibale, P., Vanni, S., Scarselli, M., Rothlisberger, U., Radenovic, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+clustering+artifacts+in+photoactivated+localization+microscopy.">Identification of clustering artifacts in photoactivated localization microscopy.</a> Nat. Meth. 8, 527-528 (2011).</li><li>Huang, B., Wang, W., Bates, M., Zhuang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+super-resolution+imaging+by+stochastic+optical+reconstruction+microscopy.">Three-dimensional super-resolution imaging by stochastic optical reconstruction microscopy.</a> Science. 319, 810-813 (2008).</li></ol>

No conflicts of interest declared.

PALM images contain information about molecule counts and positions within a cell, allowing detailed analysis of the distribution and arrangement of target protein molecules that is difficult to achieve by other means. Below we outline precautions that should be taken to extract accurate quantitative information while maintaining the biological relevance of PALM images. We also explore the information that can be best obtained using live vs. fixed cells. Finally, we suggest avenues for obtaining additional super-...

<table border="1">
 <tbody>
 <tr>
 <td>Name of Reagent/Material</td>
 <td>Company</td>
 <td>Catalogue Number</td>
 <td>Comments</td>
 </tr>
 <tr>
 <td>50 x MEM Amino Acids</td>
 <td>Sigma</td>
 <td>M5550</td>
 <td></td>
 </tr>
 <tr>
 <td>100 x MEM Vitamins</td>
 <td>Sigma</td>
 <td>M6895</td>
 <td></td>
 </tr>
 <tr>
 <td>IPTG</td>
 <td>Mediatech</td>
 <td>46-102-RF</td>
 <td></td>
 </tr>
 <tr>
 <td>16% Paraformaldehyde</td>
 <td>Electron Micrsocopy Sciences</td>
 <td>15710-S</td>
 <td></td>
 </tr>
 <tr>
 <td>SeaPlaque GTG Agarose</td>
 <td>Lonzo</td>
 <td>50111</td>
 <td></td>
 </tr>
 <tr>
 <td>50 nm Gold Beads</td>
 <td>Microspheres-Nanospheres</td>
 <td>790113-010</td>
 <td></td>
 </tr>
 <tr>
 <td>FCS2 Imaging Chamber</td>
 <td>Bioptechs</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>Stage Adaptor</td>
 <td>ASI</td>
 <td>I-3017</td>
 <td></td>
 </tr>
 <tr>
 <td>Inverted Microscope</td>
 <td>Olympus</td>
 <td>IX71</td>
 <td></td>
 </tr>
 <tr>
 <td>1.45 NA, 60x Objective</td>
 <td>Olympus</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>IXON EMCCD Camera</td>
 <td>Andor Technology</td>
 <td>DU897E</td>
 <td></td>
 </tr>
 <tr>
 <td>488-nm Sapphire Laser</td>
 <td>Coherent</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>561-nm Sapphire Laser</td>
 <td>Coherent</td>
 <td></td>
 <td></td>
 </tr>
 <tr>
 <td>405-nm CUBE Laser</td>
 <td>Coherent</td>
 <td></td>
 <td></td>
 </tr>
 </tbody>
 </table>

super-resolution imaging of the bacterial division machinery

Bacterial cell division requires the coordinated assembly of more than ten essential proteins at midcell1,2. Central to this process is the formation of a ring-like suprastructure (Z-ring) by the FtsZ protein at the division plan3,4. The Z-ring consists of multiple single-stranded FtsZ protofilaments, and understanding the arrangement of the protofilaments inside the Z-ring will provide insight into the mechanism of Z-ring assembly and its function as a force generator5,6. This information has remained elusive due to current limitations in conventional fluorescence microscopy and electron microscopy. Conventional fluorescence microscopy is unable to provide a high-resolution image of the Z-ring due to the diffraction limit of light (~200 nm). Electron cryotomographic imaging has detected scattered FtsZ protofilaments in small C. crescentus cells7, but is difficult to apply to larger cells such as E. coli or B. subtilis. Here we describe the application of a super-resolution fluorescence microscopy method, Photoactivated Localization Microscopy (PALM), to quantitatively characterize the structural organization of the E. coli Z-ring8.
	PALM imaging offers both high spatial resolution (~35 nm) and specific labeling to enable unambiguous identification of target proteins. We labeled FtsZ with the photoactivatable fluorescent protein mEos2, which switches from green fluorescence (excitation = 488 nm) to red fluorescence (excitation = 561 nm) upon activation at 405 nm9. During a PALM experiment, single FtsZ-mEos2 molecules are stochastically activated and the corresponding centroid positions of the single molecules are determined with &lt;20 nm precision. A super-resolution image of the Z-ring is then reconstructed by superimposing the centroid positions of all detected FtsZ-mEos2 molecules.
	Using this method, we found that the Z-ring has a fixed width of ~100 nm and is composed of a loose bundle of FtsZ protofilaments that overlap with each other in three dimensions. These data provide a springboard for further investigations of the cell cycle dependent changes of the Z-ring10 and can be applied to other proteins of interest.

Illustrated in Figure 3Aiv is a two-dimensional, super-resolution rendering of the Z-ring generated from the PALM imaging method described above. Below, we summarize qualitative and quantitative information that can be obtained from them.
	Qualitatively, we observed that the Z-ring is an irregular structure that adopts multiple configurations (single band or helical arc) that are not distinguishable in conventional fluorescence images (compare Figure 3A-Dii</strong...

Watch this Scientific Journal Video about Super-resolution Imaging of the Bacterial Division Machinery at JoVE.com

Super-resolution Imaging of the Bacterial Division Machinery

We describe a super-resolution imaging method to probe the structural organization of the bacterial FtsZ-ring, an essential apparatus for cell division. This method is based on quantitative analyses of photoactivated localization microscopy (PALM) images and can be applied to other bacterial cytoskeletal proteins.

Bacterial cell division requires the coordinated  assembly of more than ten essential proteins at midcell1,2. Central  to this process is the formation of ...

super-resolution-imaging-of-the-bacterial-division-machinery

Research

JoVE Journal

Biology

11.7K Views.  The Johns Hopkins University School of Medicine. We describe a super-resolution imaging method to probe the structural organization of the bacterial FtsZ-ring, an essential apparatus for cell division. This method is based on quantitative analyses of photoactivated localization microscopy (PALM) images and can be applied to other bacterial cytoskeletal proteins.

Video: Super-resolution Imaging of the Bacterial Division Machinery

Electron Cryotomography of Bacterial Cells

While much is already known about the basic metabolism of bacterial cells, many fundamental questions are still surprisingly unanswered, including for instance how they generate and maintain specific cell shapes, establish polarity, segregate their genomes, and divide. In order to understand these phenomena, imaging technologies are needed that bridge the resolution gap between fluorescence light microscopy and higher-resolution methods such as X-ray crystallography and NMR spectroscopy.
Electron cryotomography (ECT) is an emerging technology that does just this, allowing the ultrastructure of cells to be visualized in a near-native state, in three dimensions (3D), with "macromolecular" resolution (~4nm).1, 2 In ECT, cells are imaged in a vitreous, "frozen-hydrated" state in a cryo transmission electron microscope (cryoTEM) at low temperature (&lt; -180&deg;C). For slender cells (up to ~500 nm in thickness3), intact cells are plunge-frozen within media across EM grids in cryogens such as ethane or ethane/propane mixtures. Thicker cells and biofilms can also be imaged in a vitreous state by first "high-pressure freezing" and then, "cryo-sectioning" them. A series of two-dimensional projection images are then collected through the sample as it is incrementally tilted along one or two axes. A three-dimensional reconstruction, or "tomogram" can then be calculated from the images. While ECT requires expensive instrumentation, in recent years, it has been used in a few labs to reveal the structures of various external appendages, the structures of different cell envelopes, the positions and structures of cytoskeletal filaments, and the locations and architectures of large macromolecular assemblies such as flagellar motors, internal compartments and chemoreceptor arrays.1, 2
In this video article we illustrate how to image cells with ECT, including the processes of sample preparation, data collection, tomogram reconstruction, and interpretation of the results through segmentation and in some cases correlation with light microscopy.

We illustrate here how to use electron cryotomography (ECT) to study the ultrastructure of bacterial cells in near-native states, to "macromolecular" (~4 nm) resolution.

While much is already known about the basic metabolism of bacterial cells, many fundamental questions are still surprisingly unanswered, including for ...

Test Samples for Optimizing STORM Super-Resolution Microscopy

STORM is a recently developed super-resolution microscopy technique with up to 10 times better resolution than standard fluorescence microscopy techniques. However, as the image is acquired in a very different way than normal, by building up an image molecule-by-molecule, there are some significant challenges for users in trying to optimize their image acquisition. In order to aid this process and gain more insight into how STORM works we present the preparation of 3 test samples and the methodology of acquiring and processing STORM super-resolution images with typical resolutions of between 30-50 nm. By combining the test samples with the use of the freely available rainSTORM processing software it is possible to obtain a great deal of information about image quality and resolution. Using these metrics it is then possible to optimize the imaging procedure from the optics, to sample preparation, dye choice, buffer conditions, and image acquisition settings. We also show examples of some common problems that result in poor image quality, such as lateral drift, where the sample moves during image acquisition and density related problems resulting in the 'mislocalization' phenomenon.

We describe the preparation of three test samples and how they can be used to optimize and assess the performance of STORM microscopes. Using these examples we show how to acquire raw data and then process it to acquire super-resolution images in cells of approximately 30-50 nm resolution.

STORM is a recently developed super-resolution microscopy technique with up to 10 times better resolution than standard fluorescence microscopy ...

Sample Preparation for Single Virion Atomic Force Microscopy and Super-resolution Fluorescence Imaging

Immobilization of virions to glass surfaces is a critical step in single virion imaging. Here we present a technique&#160;adopted from single molecule imaging assays which allows adhesion of single virions to glass surfaces with specificity. This preparation is based on grafting the surface of the glass with a mixture of PLL-g-PEG and PLL-g-PEG-Biotin, adding a layer of avidin, and finally creating virion anchors through attachment of biotinylated virus specific antibodies. We have applied this technique across a range of experiments including atomic force microscopy (AFM) and super-resolution fluorescence imaging. This sample preparation method results in a control adhesion of the virions to the surface.

The attachment of virions to a surface is a requirement for single virion imaging by Super-resolution fluorescence imaging or atomic force microscopy (AFM). Here we demonstrate a sample preparation method for controlled adhesion of virions to glass surfaces suitable for use in AFM and super-resolution fluorescence imaging.

Immobilization of virions to glass surfaces is a critical step in single virion imaging. Here we present a technique&#160;adopted from single molecule ...

Super-resolution Imaging of the Cytokinetic Z Ring in Live Bacteria Using Fast 3D-Structured Illumination Microscopy (f3D-SIM)

Imaging of biological samples using fluorescence microscopy has advanced substantially with new technologies to overcome the resolution barrier of the diffraction of light allowing super-resolution of live samples. There are currently three main types of super-resolution techniques &#8211; stimulated emission depletion (STED), single-molecule localization microscopy (including techniques such as PALM, STORM, and GDSIM), and structured illumination microscopy (SIM). While STED and single-molecule localization techniques show the largest increases in resolution, they have been slower to offer increased speeds of image acquisition. Three-dimensional SIM (3D-SIM) is a wide-field fluorescence microscopy technique that offers a number of advantages over both single-molecule localization and STED. Resolution is improved, with typical lateral and axial resolutions of 110 and 280 nm, respectively and depth of sampling of up to 30 &#181;m from the coverslip, allowing for imaging of whole cells. Recent advancements (fast 3D-SIM) in the technology increasing the capture rate of raw images allows for fast capture of biological processes occurring in seconds, while significantly reducing photo-toxicity and photobleaching. Here we describe the use of one such method to image bacterial cells harboring the fluorescently-labelled cytokinetic FtsZ protein to show how cells are analyzed and the type of unique information that this technique can provide.

Super-resolution Imaging of the Cytokinetic Z Ring in Live Bacteria Using Fast 3D-Structured Illumination Microscopy f3D-SIM

Spatiotemporal information about dynamic proteins inside live cells is crucial for understanding biology. A type of super-resolution microscopy called fast 3D-structured illumination microscopy (f3D-SIM) reveals unique information about the cytokinetic Z ring in bacteria: both its bead-like appearance and the rapid dynamics of FtsZ within the ring.

Imaging of biological samples using fluorescence microscopy has advanced substantially with new technologies to overcome the resolution barrier of the ...

Preparation, Imaging, and Quantification of Bacterial Surface Motility Assays

Bacterial surface motility, such as swarming, is commonly examined in the laboratory using plate assays that necessitate specific concentrations of agar and sometimes inclusion of specific nutrients in the growth medium. The preparation of such explicit media and surface growth conditions serves to provide the favorable conditions that allow not just bacterial growth but coordinated motility of bacteria over these surfaces within thin liquid films. Reproducibility of swarm plate and other surface motility plate assays can be a major challenge. Especially for more &#8220;temperate swarmers&#8221; that exhibit motility only within agar ranges of 0.4%-0.8% (wt/vol), minor changes in protocol or laboratory environment can greatly influence swarm assay results. &#8220;Wettability&#8221;, or water content at the liquid-solid-air interface of these plate assays, is often a key variable to be controlled. An additional challenge in assessing swarming is how to quantify observed differences between any two (or more) experiments. Here we detail a versatile two-phase protocol to prepare and image swarm assays. We include guidelines to circumvent the challenges commonly associated with swarm assay media preparation and quantification of data from these assays. We specifically demonstrate our method using bacteria that express fluorescent or bioluminescent genetic reporters like green fluorescent protein (GFP), luciferase (lux operon), or cellular stains to enable time-lapse optical imaging. We further demonstrate the ability of our method to track competing swarming species in the same experiment.

Swarming motility is influenced by physical and environmental factors. We describe a two-phase protocol and guidelines to circumvent the challenges commonly associated with swarm assay preparation and data collection. A macroscopic imaging technique is employed to obtain detailed information on swarm behavior that is not provided by current analysis techniques.

Bacterial surface motility, such as swarming, is commonly examined in the laboratory using plate assays that necessitate specific concentrations of agar ...

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy (iPALM)

Fluorescence microscopy enables direct visualization of specific biomolecules within cells. However, for conventional fluorescence microscopy, the spatial resolution is restricted by diffraction to ~ 200 nm within the image plane and &#62; 500 nm along the optical axis. As a result, fluorescence microscopy has long been severely limited in the observation of ultrastructural features within cells. The recent development of super resolution microscopy methods has overcome this limitation. In particular, the advent of photoswitchable fluorophores enables localization-based super resolution microscopy, which provides resolving power approaching the molecular-length scale. Here, we describe the application of a three-dimensional super resolution microscopy method based on single-molecule localization microscopy and multiphase interferometry, called interferometric PhotoActivated Localization Microscopy (iPALM). This method provides nearly isotropic resolution on the order of 20 nm in all three dimensions. Protocols for visualizing the filamentous actin cytoskeleton, including specimen preparation and operation of the iPALM instrument, are described here. These protocols are also readily adaptable and instructive for the study of other ultrastructural features in cells.

Three-dimensional Super Resolution Microscopy of F-actin Filaments by Interferometric PhotoActivated Localization Microscopy iPALM

We present a protocol for the application of interferometric PhotoActivated Localization Microscopy (iPALM), a 3-dimensional single-molecule localization super resolution microscopy method, to the imaging of the actin cytoskeleton in adherent mammalian cells. This approach allows light-based visualization of nanoscale structural features that would otherwise remain unresolved by conventional diffraction-limited optical microscopy.

Fluorescence microscopy enables direct visualization of specific biomolecules within cells. However, for conventional fluorescence microscopy, the spatial ...

Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm allows for the routine imaging of nanoscale biological complexes and processes. This increase in resolution also means that methods popular in electron microscopy, such as single-particle analysis, can readily be applied to super-resolution fluorescence microscopy. By combining this analytical approach with super-resolution optical imaging, it becomes possible to take advantage of the molecule-specific labeling capacity of fluorescence microscopy to generate structural maps of molecular elements within a metastable structure. To this end, we have developed a novel algorithm &#8212; VirusMapper &#8212; packaged as an easy-to-use, high-performance, and high-throughput ImageJ plugin. This article presents an in-depth guide to this software, showcasing its ability to uncover novel structural features in biological molecular complexes. Here, we present how to assemble compatible data and provide a step-by-step protocol on how to use this algorithm to apply single-particle analysis to super-resolution images.

This manuscript uses the Fiji-based open-source software package VirusMapper to apply single-particle analysis to super-resolution microscopy images in order to generate precise models of nanoscale structure.

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm ...

Ground State Depletion Super-resolution Imaging in Mammalian Cells

Advances in fluorescent microscopy and cell biology are intimately correlated, with the enhanced ability to visualize cellular events often leading to dramatic leaps in our understanding of how cells function. The development and availability of super-resolution microscopy has considerably extended the limits of optical resolution from ~250-20 nm. Biologists are no longer limited to describing molecular interactions in terms of colocalization within a diffraction limited area, rather it is now possible to visualize the dynamic interactions of individual molecules. Here, we outline a protocol for the visualization and quantification of cellular proteins by ground-state depletion microscopy&#160;for fixed cell imaging. We provide examples from two different membrane proteins, an element of the endoplasmic reticulum translocon, sec61&#946;, and a plasma membrane-localized voltage-gated L-type Ca2+ channel (CaV1.2). Discussed are the specific microscope parameters, fixation methods, photo-switching buffer formulation, and pitfalls and challenges of image processing.

This article outlines a protocol for the detection of one or more plasma and/or intracellular membrane proteins using Ground State Depletion (GSD) super-resolution microscopy in mammalian cells. Here, we discuss the benefits and considerations of using such approaches for the visualization and quantification of cellular proteins.

Advances in fluorescent microscopy and cell biology are intimately correlated, with the enhanced ability to visualize cellular events often leading to ...

Application of High-speed Super-resolution SPEED Microscopy in Live Primary Cilium

The primary cilium is a microtubule-based protrusion on the surface of many eukaryotic cells and contains a unique complement of proteins that function critically in cell motility and signaling. Since cilia are incapable of synthesizing their own protein, nearly 200 unique ciliary proteins need to be trafficked between the cytosol and primary cilia. However, it is still a technical challenge to map three-dimensional (3D) locations of transport pathways for these proteins in live primary cilia due to the limitations of currently existing techniques. To conquer the challenge, recently we have developed and employed a high-speed virtual 3D super-resolution microscopy, termed single-point edge-excitation sub-diffraction (SPEED) microscopy, to determine the 3D spatial location of transport pathways for both cytosolic and membrane proteins in primary cilia of live cells. In this article, we will demonstrate the detailed setup of SPEED microscopy, the preparation of cells expressing fluorescence-protein-labeled ciliary proteins, the real-time single-molecule tracking of individual proteins in live cilium and the achievement of 3D spatial probability density maps of transport routes for ciliary proteins.

Recently we mapped the three-dimensional (3D) spatial locations of transport routes for various proteins translocating inside primary cilia in live cells. Here this paper details the experimental setup, the process of biological samples and the data analyses for the 3D super-resolution fluorescence imaging approach newly applied in live primary cilia.

The primary cilium is a microtubule-based protrusion on the surface of many eukaryotic cells and contains a unique complement of proteins that function ...

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

Quantitative multicolor fluorescence microscopy relies on the careful spatial matching of color channels acquired at different wavelengths. Due to chromatic aberration and the imperfect alignment of cameras, images acquired in each channel may be shifted, and magnified, as well as rotated relative to each other in any of the three dimensions. With the classical calibration method, chromatic shifts are measured by multicolor beads attached to the surface of a coverslip, and a number of software are available to measure the chromatic shifts from such calibration samples. However, chromatic aberration can vary with depth, change with observation conditions and be induced by the biological sample itself, thus hindering determination of the true amount of chromatic shift in the sample of interest and across the volume. Correcting chromatic shifts at higher accuracy is particularly relevant for super-resolution microscopy where only slight chromatic shifts may affect quantitative analyses and alter the interpretation of multicolor images. We have developed an open-source software Chromagnon and accompanying methods to measure and correct 3D chromatic shifts in biological samples. Here we provide a detailed application protocol that includes special requirements for sample preparation, data acquisition, and software processing to measure chromatic shifts in biological samples of interest.

High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

Correction of chromatic shifts in three-dimensional (3D) multicolor fluorescence microscopy images is crucial for quantitative data analyses. This protocol is developed to measure and correct chromatic shifts in biological samples through acquisition of suitable reference images and processing with the open-source software, Chromagnon.