We would like to thank Jeffrey Nguyen and Joshua Shaevitz for helping to develop this method. 
Funding: RMM - NIH F32 GM103290-01A1, BPB - Glenn Centers for Aging Research and National Science Foundation PHY-1734030.

1. Sample Preparation
<ol>
	<li>Make E. coli fluorescent for imaging either by engineering them to express a cytoplasmic fluorescent protein6&#160;or using a membrane dye5. Other bacterial species can be used instead of&#160;E. coli.
	<ol>
		<li>&#8203;Transform cells via electroporation with a plasmid that encodes the cytoplasmic mCherry fluorescent protein. 
		NOTE: Different fluorescent proteins of other colors of can be used.</li>
		<li>Grow the transformants on an LB plate with the appropriate antibiotic at 37 &#176;C. Obtain single colonies and identify positive clones by microscopy. Antibiotic resistant colonies that appear red under the microscope contain the plasmid carrying mCherry.</li>
	</ol>
	</li>
	<li>Grow 2 mL of the overnight culture in LB liquid media with the appropriate antibiotics at 37 &#176;C in a shaking incubator. Use either a colony from a plate or a small amount of a freezer stock as the inoculum. 
	NOTE: Use the media necessary for the bacterial cells selected for the experiment.</li>
	<li>Subculture the overnight culture 1:1,000 into 5 mL of fresh LB media and let the cells grow to exponential phase at 37 &#176;C in a shaking incubator for 3&#8722;4 h. Measure the OD600 of the cells to confirm they are in the exponential phase (i.e., OD600 = 0.2&#8722;0.4). 
	NOTE: Imaging can be performed at any growth stage depending on the experiment being performed.</li>
</ol>
2. Slide Preparation
NOTE: It is important to use media that has low autofluorescence. Any medium which has low autofluorescence can be used. In this experiment, 1% agarose M63 minimal media pads and sealing with VaLaP19 is required to image cells in 3D.
<ol>
	<li>Prepare a 1% solution of agarose in 20 mL of minimal media.
	<ol>
		<li>Microwave the solution until the agarose is fully dissolved and the solution appears clear.</li>
		<li>Keep the solution in a 60 &#176;C water bath until ready to use. 
		NOTE: This solution can be solidified and melted as needed or can be aliquoted into smaller amounts that are melted as needed. After multiple uses the media may become discolored and should be replaced.</li>
	</ol>
	</li>
	<li>Melt VaLaP sealant on a hot plate at 80&#8722;100 &#176;C. 
	NOTE: Prepare VaLaP, to be used as a sealant, by melting 50 g each of petroleum jelly, lanolin, and paraffin together in a beaker on a hot plate at 80&#8722;100 &#176;C. This large amount of VaLaP can be stored at room temperature indefinitely and will be enough for &#62;500 slides. Heating at &#62;100 &#176;C will cause it to degrade and its color will darken.</li>
	<li>Place two stacks each of three 20 mm x 20 mm cover slips on the opposite ends of a slide (six total coverslips).</li>
	<li>Pipette 200 &#181;L of agarose pad solution onto the slide. 
	NOTE: If not using an engineered fluorescent stain, a membrane dye may be added to the agarose pad solution before this step. Resuspend the dye in water and add dye to 1 mL of the agarose pad solution to a final concentration of 5 &#181;g/mL.</li>
	<li>Immediately and firmly place a second slide down on the stack of cover slips to flatten the agar and let it solidify for 1 min at room temperature.</li>
	<li>Carefully slide off the top slide.</li>
	<li>Use the large end of a 200 &#181;L pipette tip to cut out individual pads from the gel (~5 mm in diameter) on the slide and discard malformed or unneeded gel. 
	NOTE: If imaging multiple strains or conditions, a separate pad will be needed for each one. If only one strain is to be imaged, make 3&#8722;4 pads to help support the coverslip.</li>
	<li>Pipette cells up and down multiple times to disrupt cell clumps and ensure the culture is well mixed. Pipette 1 &#181;L of subculture from step 1.3 onto a pad. 
	NOTE: Cell shape reconstructions require individual cells that are not touching other cells. If using stationary phase or high cell density cultures, it may be necessary to dilute the sample 1:10 before placing it on the pad.</li>
	<li>Let the sample air dry for 5&#8722;10 min. Ensure that the droplet is completely absorbed into the pad. If any liquid remains, the cells will move around in the liquid and cannot be imaged.</li>
	<li>Place a cover slip on the top of the pads.</li>
	<li>Seal the cover slip with melted VaLaP by gently brushing around the edge of the cover slip&#160;with a cotton swab. Make sure to keep it away from the top of the cover slip where the objective will touch. The VaLaP will harden in a matter of seconds, sealing the sample.</li>
	<li>Immediately image the sample. 
	NOTE: The sample should be imaged as soon as the slide is prepared. The cells can grow on the pads, and if they divide the reconstructions will be more difficult.</li>
</ol>
3. ImagingRequirements
<ol>
	<li>Ensure that the z or focusing axis of the microscope can make precise movements of less than 50 nm. Use z piezo stages (see Table of Materials), available on research grade microscopes, because typical motorized focus devices are unable to provide this precision. 
	NOTE: Assuming the Nyquist-Shannon sampling criterion20, one needs to have the ability to move 0.5x the smallest desired step size, or 2x the desired spatial frequency. For the 100 nm steps needed in this protocol, a stage with a precision of 50 nm or less is required.</li>
	<li>Ensure that the microscope contains a 100x objective with a minimum numerical aperture (NA) of 1.45.
	<ol>
		<li>Collect z-stacks of between 200&#8722;400 different cells to ensure enough cells are obtained for downstream applications. 
		NOTE: The number of cells needed depends on the underlying variability of the sample of interest. Some of the cells collected at this point will not make it through the reconstruction steps.</li>
	</ol>
	</li>
	<li>Ensure that the z-stack matches the settings used for the shape channel if a secondary fluorescent channel (protein, metabolic label, etc.) is measured.</li>
</ol>
4. Imaging
<ol>
	<li>Insert the sealed slide onto the microscope and allow it sit for 5 min to equilibrate the temperature with the surroundings, because the microscope room may be at a different temperature than the sample preparation room.</li>
	<li>Take a fluorescent z-stack of the sample. 
	NOTE: The z-stack should entirely cover the sample with a z-spacing less than the depth of field. For a 1.45 NA 100x objective and ~1 &#956;m thick E. coli cells, 40 steps at 100 nm per step work well. For larger cells or cells that do not lie perfectly flat on the surface, 50 or more steps may be necessary. Include enough steps and ensure that the sample is fully blurred above and below.
	<ol>
		<li>Use the software associated with the microscope (see Table of Materials) to control the microscope.</li>
		<li>Focus on the middle of the cell using the microscope focus wheels. Under ND acquisition check the Z box to take a z-stack. Click the Home button to set the middle of the cell as the starting point. Set the Step size to 0.1 &#181;m and set the Range to 4 &#181;m. Make sure that the Z device is set to the piezo stage.</li>
		<li>Set the fluorescent channels under the Lambda Window to the settings for the fluorescent molecules being imaged. In this experiment GFP and mCherry were used. 
		NOTE: Take an additional z-stack with the same Step Size and Range in the second color channel if the 3D distribution of an additional fluorescent channel is desired. In this experiment, cytoplasmic mCherry was used to determine cell shape and MreB-GFP was used as a second color channel21.</li>
		<li>Ensure that the Order of Experiment is set to lambda (z series) so that it will take a complete z-stack in each color channel before switching.</li>
		<li>Click Run Now to start the image acquisition and save the file onece one or both z-stacks are complete.&#160;</li>
		<li>Move to a new area on the pad and repeat steps 4.2.2-4.2.5.</li>
	</ol>
	</li>
</ol>
5. Cell Reconstruction
<ol>
	<li>Crop individual cells and save the images as a stacked tiff file so that there is only one cell per file. Ensure that this cell is well isolated from any other cells (i.e., roughly 5x the full-width half-maximum of the blurring function in xy. 
	NOTE: This can be done using freely available image analysis software (see Table of Materials).
	<ol>
		<li>Draw a box around an individual cell and Duplicate that cell 2x, once for each channel. Make sure the duplicate hyperstack box is checked and change the channel to either 1 or 2, making sure that the slices include the entire z-stack. 
		NOTE: If imaging only the shape of the cells and not an additional fluorescent protein, only one channel will be present.</li>
		<li>Once both stacks are available go to Images | Stacks | Tools | Concatenate to combine the images with the protein channel first and the shape channel second.</li>
		<li>Save the new image as a tiff file.</li>
	</ol>
	</li>
	<li>Measure the blurring function of the microscope using subdiffraction limited fluorescent beads22. This needs to be done for each microscope and microscope objective but can be performed before or after imaging the samples of interest.
	<ol>
		<li>Average together multiple independent beads with some manual intervention using available software (see Table of Materials). 
		NOTE: The final product should be a 3D image of the blurring function with the same xyz spacing as the samples of interest.</li>
	</ol>
	</li>
	<li>Run the forward convolution cell shape reconstruction scripts using available software. The latest version of these scripts can be freely downloaded from&#160;https://github.com/PrincetonUniversity/shae-cellshape-public.
	<ol>
		<li>Make a folder inside a folder on the desktop that contains the cropped images and the cell_shape_settings_tri.txt file from shae-cellshape-public/exampleData_tri.</li>
		<li>Edit cell_shape_setting_tri.txt to have the correct settings for the experiment of interest. For this experiment, the settings file includes the following lines: 
		nm_per_pixel 70 
		Z_scale 0.65 
		stack_z_size 41 
		stack_t_size 1 
		Fstack_z_size 41 
		Fstack_t_size 1 
		stack_seperation_nm 100 
		Fstack_seperation_nm 100 
		psfScript -999 osuPSF20180726 
		gradient 1 
		 
		NOTE: This text file is organized into sections and each section is parsed into its own variable. While many of the settings do not need to be changed from experiment to experiment, one should make sure that the size of the z-stack (stack_z_size for shape, Fstack_z_size for protein of interest), spacing of the z-stack (stack_seperation_nm, Fstack_seperation_nm), relative focal shift of the microscope (Z_scale)23, the pixel size in xy (nm_per_pixel), and the name of the script that loads the blurring function of the microscope (psfScript) match the experiment. The gradient field should be set to 1 if the shape channel is cytoplasmically filled and 0 if it is a membrane-stained object.</li>
		<li>Run Cell_shape_detector3dConvTriFolder function (shae-cellshape-public/CellShapeDetectorTri/) with the string to the folder location followed by the number of the cell to start on, and the number of&#160;cells you want to run. 
		NOTE: An example for the input will look as follows: Cell_shape_detector3dConvTriFolder (&#39;path to folder with cropped images&#39;, starting index in folder, &#160;# of cells). A typical cell may take between 5&#8722;20 min for the reconstruction to converge and finish.</li>
	</ol>
	</li>
	<li>Screen the cell reconstructions to ensure that they are correct before using the cells for any statistical analysis.
	<ol>
		<li>Run ScreenFits (shae-cellshape-public/shae-fitViewerGui/) to visually screen individual cell reconstructions.</li>
		<li>Click the select folder button when the graphical user interface (GUI) opens, then select the folder with the reconstruction data files (TRI.mat) created in step 5.3.</li>
		<li>Select the box next to the cell reconstruction if a cell appears misshapen or did not fully converge (Figure 3). This could look like a cell with a hole, a flat side, or a branch coming out of it. This will append &#39;FLAG&#39; to the file name so that it can be excluded from any downstream analysis. 
		NOTE: The reconstructed cell can be compared to the original images. This can be especially important if your cells come from a various heterogenous population of shapes.</li>
	</ol>
	</li>
	<li>Run enrichmentSmoothingSpline (shae-cellshape-public/) to create an enrichment profile of the relative concentration of the protein of interest as a function of the Gaussian curvature at the surface.
	<ol>
		<li>Select each folder with TRI.mat files created in step 5.3.3.</li>
		<li>Select the newly created (5.5.1) curve.mat file. 
		NOTE: A curve.mat file will be created for each folder that was selected in 5.5.1.&#160;All of the enrichment profiles will be presented on one graph. If individual graphs are required than run 5.5 for each folder. 
		NOTE: In addition to the precise geometric localization of a fluorescent protein, there are many other ways to analyze the data from the secondary channel, including counting the number, size, and orientation of objects4.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

A critical step in this protocol is the acquisition of high-quality images. To properly reconstruct the cells, there must be enough blurring above and below the cell. Therefore, it is imperative that the z-stack taken covers a large enough distance. The number of steps taken during the image acquisition can be adjusted for each strain. For example, E. coli cells deleted for rodZ are wider and require more steps, and therefore, a greater distance, than wild type cells. If the sample drifts during image acquisition, the reconstruction can have major errors. Therefore, it is important to let the slide come to thermal equilibrium with the microscope before imaging to avoid drift during the z-stack acquisition. Cells should be imaged on pads with low autofluorescence. Media components, such as those found in the common LB medium, have autofluorescence that can cause problems when trying to reconstruct the cells. The density of cells on the imaging pad is important because the reconstruction process is performed independently on each cell. Too few cells will increase the time needed to obtain images of a sufficient number of cells, while too many cells will result in imaging fields that are too dense to easily crop individual cells. Because not all cells reconstruct properly, extra cells should be imaged during the acquisition step and all outputs should be screened before moving forward with statistical analysis (Figure 3).
Many of the limitations for this method are technical. On the microscope to be used, one must have an objective that has a high numerical aperture (typically &#62;1.4), because this enables optical sectioning on the size scale of bacteria. Additionally, the microscope needs to be equipped with a piezo stage that can take small, precise steps in the z-direction. Furthermore, while it is not necessary, access to high-throughput computational resources to run the image analysis software is highly recommended because it will reduce the processing time to reconstruct cells.
One conceptual limitation to the method is that the correct energy scales for weighting the smoothness of the reconstruction relative to the signal-to-noise ratio of the images must be chosen. To validate a choice of parameters, the sizes and shapes of cells should be measured using independent methods such as transmission electron microscopy (TEM) or atomic force microscopy (AFM). As a proof of principle, 3D reconstructions of cells were performed either on an AFM to test for z-accuracy (&#60;50 nm) or on a TEM grid to test for xy-accuracy (&#60;30 nm)3. Such a correlated approach is time consuming and costly. A simpler approach may be to image standard samples such as wild type cells or 1 &#181;m spherical beads. The diameter and sphericity of the reconstructions can be used to ensure that the size and energy scales used in the reconstruction are correct.
This is not the only method that seeks to extract high resolution spatial information from fluorescence microscopy images. Many review articles describe recent advances in the field of super-resolution microscopy24,25. Resolution-enhancing techniques such as deconvolution microscopy26, spinning disk confocal microscopy27, pixel reassignment28, and structured illumination microscopy (SIM)29 seek to improve the resolution of the images acquired by the microscope. These methods are not incompatible with the approach presented. Recently this method was adapted to allow for SIM-based images as inputs9. While the forward convolution method shares some of its underpinnings with deconvolution microscopy, it has a completely different output. Whereas approaches such as deconvolution microscopy seek to improve the resolution of the image, this approach does not generate an image but rather a cell shape reconstruction with roughly 50 nm precision. Single-molecule active-control microscopy techniques based on sparsely labeled samples can provide even higher levels of spatial precision than this method. In many cases, these single molecule approaches require optimization of the fluorescent constructs and can require long acquisition times, making them difficult to use with live or dynamic samples. Each of these methods comes with one or more caveats that this method does not. For instance, the benefits advertised by spinning disk confocal microscopy are not as applicable to monolayers of bacterial cells, where there is not much out of plane light. Furthermore, this method provides a pipeline to acquire accurate 3D cell shapes and protein localization without the need of any specialized fluorophores. This method has minimal hardware requirements (i.e., z piezo, high NA objective) and requires only tens of images per timepoint, allowing one to easily investigate dynamic 3D structures6.
There have been an increasing number of approaches to study the organization of bacterial cells in 3D structures. These include approaches conceptually similar to this that take advantage of high quality, 3D fluorescence images30,31,32. This approach requires well isolated cells and makes no a priori assumptions about cellular geometry. However, to move into dense cellular aggregates or biofilms, the cells are assumed to be rod-like. This lower resolution view still enables investigating packing arrangements of the cells, although the high density of cells in the biofilm prevents analysis of the subcellular localization of specific factors.
In the future, it may be interesting to develop a framework to integrate the single molecule and wide-field approaches with this 3D reconstruction technique. Moreover, it may be possible to include this forward convolution approach with machine vision segmentation tools32 to allow for reconstructions of more dense cell clusters.
Why cells evolved into specific shapes is a complex issue that must reflect the complex environment in which they live. Understanding the evolution and function of cell shapes requires taking precise and accurate measurements of those shapes, which is what this method provides.

Cells of all types regulate their shapes for specific functions. For example, neurons are shaped differently than blood cells and have different functions. Similarly, bacterial cells come in a variety of shapes and sizes, although the purpose of these shapes is not always known1,2. Therefore, it is important that the shape of bacterial cells be accurately determined. The method outlined shows an easily implemented way to collect data suitable for the 3D analysis of most live or fixed bacterial cells.
The method described enables one to take 3D images of bacterial cells in order to accurately represent the 3D cell shape of the sample and to precisely localize proteins within these shapes. Traditional microscopy techniques take 2D images, which is problematic when studying cells that have abnormal or nonsymmetrical shapes, such as mutants of Escherichia coli, or curved bacteria such as Vibrio cholerae and Helicobacter pylori. While high-resolution 3D images are the key input to this method, the method does not return a resolution-enhanced image. Rather, this method reconstructs the 3D surface coordinates and shape of the cell using a forward convolution algorithm using active contours and the apparent blurring function of the microscope3 (Figure 1). It has been used to study the bacterial actin homolog MreB in E. coli4,5,6,7, the novel periskeletal element CrvA in V. cholerae8, and the putative bactofilin CcmA in Helicobacter pylori9 (Figure 2).
The localization of proteins can give insight into their functions. For example, proteins involved in cell division are normally localized to the midcell10,11. High-throughput studies have been undertaken to localize all the proteins of a bacterium in hopes of gaining insight into their functions12. Unfortunately, these studies were performed with 2D imaging and 1D or 2D analysis, making it impossible to measure specific aspects of protein localization, such as localization to cellular geometric features.
For example, MreB, a dynamic protein required for the rod shape of many bacteria, is hypothesized to work by directing the localization of cell wall synthesis, and its localization mirrors the localization of cell wall synthesis7,13. MreB from multiple species shows geometric enrichment4,6,7,14,15. Dynamic surface polymers, such as MreB, may couple the geometry of the surface to the time averaged enrichment profile16 and may be able to orient to specific geometries by minimizing the energy associated with binding to the membrane17. While the importance of twisting, bundling, bending, and dynamics has not been entirely resolved for MreB, it is important to note that accurate measurements of both principal curvatures of a surface require a full 3D representation of the cell. Therefore, to most accurately measure the curvatures to which proteins localize, it is preferential to use 3D, rather than 2D imaging. 3D imaging eliminates the need to computationally estimate those curvatures which cannot be measured in 2D, an estimate that might not be accurate in asymmetrical cells18.
Although 2D imaging of cells is quicker and does not require as much postimaging computational work, 3D imaging provides a more accurate representation of the cell, as well as the ability to measure surface features, such as curvature, which cannot be measured in 2D. Therefore, as 3D imaging becomes more commonplace, new insights into cell shape and protein localization will become possible.

<table><tbody><tr><td>50 nm fluorescent beads</td><td>Invitrogen</td><td>F8795</td><td>these are used to measure the blurring function of the microscope</td></tr><tr><td>Agarose</td><td>sigma-Aldrich</td><td>A9539</td><td></td></tr><tr><td>Cotton Swab</td><td>Puritan Medical Products Company LLC</td><td>S304659</td><td>used to appy VaLaP</td></tr><tr><td>Cover Slips</td><td>VWR</td><td>16004-302</td><td></td></tr><tr><td>Fiji</td><td>ImageJ</td><td>https://fiji.sc</td><td>used to cro cells</td></tr><tr><td>FM4-64</td><td>Invitrogen</td><td>LST3166</td><td>membrane dye used to stain cells</td></tr><tr><td>Huygens Software</td><td>Scientific Volume Imaging</td><td>Huygens essential or professional</td><td>Use to measure blurring function of microscope</td></tr><tr><td>Lanolin</td><td>Sigma-Aldrich</td><td>L7387</td><td>combine with paraffin and petroleum jelly to make VaLaP</td></tr><tr><td>LB growth medium</td><td>BD Difco</td><td>DF0446173</td><td></td></tr><tr><td>M63 medium</td><td>US Biological</td><td>M1015</td><td></td></tr><tr><td>MATLAB</td><td>Mathworks</td><td></td><td>Needed to run forward convolution scripts</td></tr><tr><td>Microscope Slides</td><td>Fisher</td><td>12-550-133</td><td></td></tr><tr><td>NIS Elements</td><td>Nkon</td><td></td><td></td></tr><tr><td>Paraffin</td><td>Sigma-Aldrich</td><td>327212</td><td></td></tr><tr><td>Petroleum Jelly</td><td>Equate</td><td>49035-038-27</td><td></td></tr><tr><td>Piezo z stage</td><td>Nikon</td><td>77011589</td><td></td></tr><tr><td>Pipet tips -p200</td><td>USA Scientific</td><td>1111-0730</td><td></td></tr><tr><td>Pipet tips- p10</td><td>USA Scientific</td><td>1111-3730</td><td></td></tr></tbody></table>

three-dimensional imaging of bacterial cells for accurate cellular representations and precise protein localization

The shape of a bacterium is important for its physiology. Many aspects of cell physiology such as cell motility, predation, and biofilm production can be affected by cell shape. Bacterial cells are three-dimensional (3D) objects, although they are rarely treated as such. Most microscopy techniques result in two-dimensional (2D) images leading to the loss of data pertaining to the actual 3D cell shape and localization of proteins. Certain shape parameters, such as Gaussian curvature (the product of the two principal curvatures), can only be measured in 3D because 2D images do not measure both principal curvatures. Additionally, not all cells lie flat when mounting and 2D imaging of curved cells may not accurately represent the shapes of these cells. Accurately measuring protein localization in 3D can help determine the spatial regulation and function of proteins. A forward convolution technique has been developed that uses the blurring function of the microscope to reconstruct 3D cell shapes and to accurately localize proteins. Here, a protocol for preparing and mounting samples for live cell imaging of bacteria in 3D both to reconstruct an accurate cell shape and to localize proteins is described. The method is based on simple sample preparation, fluorescent image acquisition, and MATLAB-based image processing. Many high-quality fluorescent microscopes can be simply modified to take these measurements. These cell reconstructions are computationally intensive and access to high-throughput computational resources is recommended, although not necessary. This method has been successfully applied to multiple bacterial species and mutants, fluorescent imaging modalities, and microscope manufacturers.

Bacteria come in a wide variety of shapes and sizes that may determine their functions in nature1. The outcome of this procedure is an accurate 3D representation of cells from the forward convolution of a z-stack of images (Figure 1). This method is especially important when dealing with curved cells (Figure 2), or with abnormally shaped cells (Figure 4A), as a 2D representation does not reflect the curvature of the cells accurately. In order to use the forward convolution method (Figure 1A), cells need to be either peripherally stained or have a cytoplasmic stain (Figure 2B left vs. right).
Figure 4 shows MreB localization in the cell. A GFP fusion was made to the bacterial actin protein MreB21 in order to study its precise localization in both wild type and mutant E. coli cells4. Because MreB is associated with the membrane, 3D imaging is required to faithfully reproduce its position in the cell. By making these measurements in 3D, we were able to reconstruct the shapes of both wild type and rodZ mutant cells (Figure 4A). The localization of MreB was shown to be enriched at small Gaussian curvatures, a geometric feature that can only be measured in 3D, in a RodZ dependent manner (Figure 4B).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60350/60350fig1.jpg" /> 
Figure 1: Forward convolution method reconstructs cells with no prior knowledge of cell shape. (A) The final output of the method is a 3D cell reconstruction derived by comparing a test shape with the calibrated blurring function of the microscope. This is compared with the observed z-stack until the observed 3D image matches the hypothetical one. The image shown here is a rendering of the reconstruction of one C. crescentus cell. (B) The reconstruction pipeline iteratively updates the estimated positions of the surface elements based on the observed image stack. For full algorithmic details of the method, see Nguyen3. (C) The reconstruction pipeline starts with no a priori knowledge of the shape of the cell and updates the position and number of surface vertices to match the observed z-stack. Representative images from every 30 steps during the 3D reconstruction are shown from three different angles of a V. cholerae cell. The surface positions of this shape are updated to minimize the difference between the simulated and observed stacks. <a href="https://www.jove.com/files/ftp_upload/60350/60350fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60350/60350fig2.jpg" /> 
Figure 2: Representative 3D reconstructions of three different shaped bacterial species. Three-dimensional reconstructions can be made from cells with their membrane stained (left) or filled with a cytoplasmic fluorophore (right). The shape and curvature of the starting cell is not important, as a bent rod, twisted rod, or straight rod are all able to be accurately reconstructed. The reconstructed surfaces are colored based on the local Gaussian curvature of the surface. Scale bar = 1 &#181;m. <a href="https://www.jove.com/files/ftp_upload/60350/60350fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60350/60350fig3a.jpg" /> 
Figure 3: Reconstructions can fail for multiple reasons. Cells must be screened to ensure that the reconstruction algorithm converged to a reasonable result. Left = representative wild type (top) and rodZ mutant (bottom) E. coli cells that have passed quality control. Right = cells that failed to reconstruct properly and did not pass quality control. Five classes of failed convergence are shown: (i) cells that are too close to the edge of the cropping region leading to a sharp demarcation, (ii) cells that are too close to another cell, (iii) cells that produced a divot, (iv) cells that did not proceed past the initial starting point, and (v) other unknown errors. <a href="https://www.jove.com/files/ftp_upload/60350/60350fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60350/60350fig4.jpg" /> 
Figure 4: Representative data showing protein localization to specific cellular geometries. (A) Three-dimensional reconstructed cells of wild type and rodZ E. coli cells with Gaussian curvature and MreB fluorescence intensity displayed. Scale bar = 1 &#181;m. (B) Enrichment plots of MreB from wild type and rodZ E. coli cells. Values &#62;1 show enrichment and values &#60;1 show depletion of MreB from these cellular regions relative to uniform coverage of the cell. Shaded areas indicate mean &#177;90% bootstrap confidence interval of the mean. The curve for each strain is a cubic smoothing spline and is truncated using a probability threshold for extreme curvatures of p &#62; 5 x 10-3. This figure has been modified from (Bratton et al.)4. <a href="https://www.jove.com/files/ftp_upload/60350/60350fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization at JoVE.com

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

This protocol explains how to prepare and mount bacterial samples for live three-dimensional imaging and how to reconstruct the three-dimensional shape of E. coli from those images.

The shape of a bacterium is important for its physiology. Many aspects of cell physiology such as cell motility, predation, and biofilm production can be ...

three-dimensional-imaging-bacterial-cells-for-accurate-cellular

Research

JoVE Journal

Immunology and Infection

9.9K Views.  Oklahoma State University. This protocol explains how to prepare and mount bacterial samples for live three-dimensional imaging and how to reconstruct the three-dimensional shape of E. coli from those images.

Video: Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

Live-cell Imaging of Migrating Cells Expressing Fluorescently-tagged Proteins in a Three-dimensional Matrix

Traditionally, cell migration has been studied on two-dimensional, stiff plastic surfaces. However, during important biological processes such as wound healing, tissue regeneration, and cancer metastasis, cells must navigate through complex, three-dimensional extracellular tissue. To better understand the mechanisms behind these biological processes, it is important to examine the roles of the proteins responsible for driving cell migration. Here, we outline a protocol to study the mechanisms of cell migration using the epithelial cell line (MDCK), and a three-dimensional, fibrous, self-polymerizing matrix as a model system. This optically clear extracellular matrix is easily amenable to live-cell imaging studies and better mimics the physiological, soft tissue environment. This report demonstrates a technique for directly visualizing protein localization and dynamics, and deformation of the surrounding three-dimensional matrix. 
Examination of protein localization and dynamics during cellular processes provides key insight into protein functions. Genetically encoded fluorescent tags provide a unique method for observing protein localization and dynamics. Using this technique, we can analyze the subcellular accumulation of key, force-generating cytoskeletal components in real-time as the cell maneuvers through the matrix. In addition, using multiple fluorescent tags with different wavelengths, we can examine the localization of multiple proteins simultaneously, thus allowing us to test, for example, whether different proteins have similar or divergent roles. Furthermore, the dynamics of fluorescently tagged proteins can be quantified using Fluorescent Recovery After Photobleaching (FRAP) analysis. This measurement assays the protein mobility and how stably bound the proteins are to the cytoskeletal network.
By combining live-cell imaging with the treatment of protein function inhibitors, we can examine in real-time the changes in the distribution of proteins and morphology of migrating cells. Furthermore, we also combine live-cell imaging with the use of fluorescent tracer particles embedded within the matrix to visualize the matrix deformation during cell migration. Thus, we can visualize how a migrating cell distributes force-generating proteins, and where the traction forces are exerted to the surrounding matrix. Through these techniques, we can gain valuable insight into the roles of specific proteins and their contributions to the mechanisms of cell migration.

Cellular processes such as cell migration have traditionally been studied on two-dimensional, stiff plastic surfaces. This report describes a technique for directly visualizing protein localization and analyzing protein dynamics in cells migrating in a more physiologically relevant, three-dimensional matrix.

Traditionally, cell migration has been studied on two-dimensional, stiff plastic surfaces. However, during important biological processes such as wound ...

Bioengineering

Live-Cell Fluorescence Microscopy to Investigate Subcellular Protein Localization and Cell Morphology Changes in Bacteria

Investigations of factors influencing cell division and cell shape in bacteria are commonly performed in conjunction with high-resolution fluorescence microscopy as observations made at a population level may not truly reflect what occurs at a single cell level. Live-cell timelapse microscopy allows investigators to monitor the changes in cell division or cell morphology which provide valuable insights regarding subcellular localization of proteins and timing of gene expression, as it happens, to potentially aid in answering important biological questions. Here, we describe our protocol to monitor phenotypic changes in Bacillus subtilis and Staphylococcus aureus using a high-resolution deconvolution microscope. The objective of this report is to provide a simple and clear protocol that can be adopted by other investigators interested in conducting fluorescence microscopy experiments to study different biological processes in bacteria as well as other organisms.

This article provides a step-by-step guide to investigate protein subcellular localization dynamics and to monitor morphological changes using high-resolution fluorescence microscopy in Bacillus subtilis and Staphylococcus aureus.

Investigations of factors influencing cell division and cell shape in bacteria are commonly performed in conjunction with high-resolution fluorescence ...

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.

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 ...

Biology

Applying Live Cell Imaging and Cryo-Electron Tomography to Resolve Spatiotemporal Features of the Legionella pneumophila Dot/Icm Secretion System

The Dot/Icm secretion system of Legionella pneumophila is a complex type IV secretion system (T4SS) nanomachine that localizes at the bacterial pole and mediates the delivery of protein and DNA substrates to target cells, a process generally requiring direct cell-to-cell contact. We have recently solved the structure of the Dot/Icm apparatus by cryo-electron tomography (cryo-ET) and showed that it forms a cell envelope-spanning channel that connects to a cytoplasmic complex. Applying two complementary approaches that preserve the native structure of the specimen, fluorescent microscopy in living cells and cryo-ET, allows in situ visualization of proteins and assimilation of the stoichiometry and timing of production of each machine component relative to other Dot/Icm subunits. To investigate the requirements for polar positioning and to characterize dynamic features associated with T4SS machine biogenesis, we have fused a gene encoding superfolder green fluorescent protein to Dot/Icm ATPase genes at their native positions on the chromosome. The following method integrates quantitative fluorescence microscopy of living cells and cryo-ET to quantify polar localization, dynamics, and structure of these proteins in intact bacterial cells. Applying these approaches for studying the Legionella pneumophila T4SS is useful for characterizing the function of the Dot/Icm system and can be adapted to study a wide variety of bacterial pathogens that utilize the T4SS or other types of bacterial secretion complexes.

Applying Live Cell Imaging and Cryo-Electron Tomography to Resolve Spatiotemporal Features of the Legionella pneumophila Dot/Icm Secretion System

Imaging of bacterial cells is an emerging systems biology approach focused on defining static and dynamic processes that dictate the function of large macromolecular machines. Here, integration of quantitative live cell imaging and cryo-electron tomography is used to study Legionella pneumophila type IV secretion system architecture and functions.

The Dot/Icm secretion system of Legionella pneumophila is a complex type IV secretion system (T4SS) nanomachine that localizes at the bacterial pole and ...

4D Imaging of Protein Aggregation in Live Cells

One of the key tasks of any living cell is maintaining the proper folding of newly synthesized proteins in the face of ever-changing environmental conditions and an intracellular environment that is tightly packed, sticky, and hazardous to protein stability1. The ability to dynamically balance protein production, folding and degradation demands highly-specialized quality control machinery, whose absolute necessity is observed best when it malfunctions. Diseases such as ALS, Alzheimer's, Parkinson's, and certain forms of Cystic Fibrosis have a direct link to protein folding quality control components2, and therefore future therapeutic development requires a basic understanding of underlying processes. Our experimental challenge is to understand how cells integrate damage signals and mount responses that are tailored to diverse circumstances.
The primary reason why protein misfolding represents an existential threat to the cell is the propensity of incorrectly folded proteins to aggregate, thus causing a global perturbation of the crowded and delicate intracellular folding environment1. The folding health, or "proteostasis," of the cellular proteome is maintained, even under the duress of aging, stress and oxidative damage, by the coordinated action of different mechanistic units in an elaborate quality control system3,4. A specialized machinery of molecular chaperones can bind non-native polypeptides and promote their folding into the native state1, target them for degradation by the ubiquitin-proteasome system5, or direct them to protective aggregation inclusions6-9.
In eukaryotes, the cytosolic aggregation quality control load is partitioned between two compartments8-10: the juxtanuclear quality control compartment (JUNQ) and the insoluble protein deposit (IPOD) (Figure 1 - model). Proteins that are ubiquitinated by the protein folding quality control machinery are delivered to the JUNQ, where they are processed for degradation by the proteasome. Misfolded proteins that are not ubiquitinated are diverted to the IPOD, where they are actively aggregated in a protective compartment.
Up until this point, the methodological paradigm of live-cell fluorescence microscopy has largely been to label proteins and track their locations in the cell at specific time-points and usually in two dimensions. As new technologies have begun to grant experimenters unprecedented access to the submicron scale in living cells, the dynamic architecture of the cytosol has come into view as a challenging new frontier for experimental characterization. We present a method for rapidly monitoring the 3D spatial distributions of multiple fluorescently labeled proteins in the yeast cytosol over time. 3D timelapse (4D imaging) is not merely a technical challenge; rather, it also facilitates a dramatic shift in the conceptual framework used to analyze cellular structure.
We utilize a cytosolic folding sensor protein in live yeast to visualize distinct fates for misfolded proteins in cellular aggregation quality control, using rapid 4D fluorescent imaging. The temperature sensitive mutant of the Ubc9 protein10-12 (Ubc9ts) is extremely effective both as a sensor of cellular proteostasis, and a physiological model for tracking aggregation quality control. As with most ts proteins, Ubc9ts is fully folded and functional at permissive temperatures due to active cellular chaperones. Above 30 &deg;C, or when the cell faces misfolding stress, Ubc9ts misfolds and follows the fate of a native globular protein that has been misfolded due to mutation, heat denaturation, or oxidative damage. By fusing it to GFP or other fluorophores, it can be tracked in 3D as it forms Stress Foci, or is directed to JUNQ or IPOD.

Cellular viability depends on timely and efficient management of protein misfolding. Here we describe a method for visualizing the different potential fates of a misfolded protein: refolding, degradation, or sequestration in inclusions. We demonstrate the use of a folding sensor, Ubc9ts, for monitoring proteostasis and aggregation quality control in live cells using 4D microscopy.

One of the key tasks of any living cell is maintaining the proper folding of newly synthesized proteins in the face of ever-changing environmental ...

Bioprinting Bacteria in Hydrogel Matrices for the Study of Motility and Growth

3D Printing Bacteria to Study Motility and Growth in Complex 3D Porous Media

Bacteria are ubiquitous in complex three-dimensional (3D) porous environments, such as biological tissues and gels, and subsurface soils and sediments. However, the majority of previous work has focused on studies of cells in bulk liquids or at flat surfaces, which do not fully recapitulate the complexity of many natural bacterial habitats. Here, this gap in knowledge is addressed by describing the development of a method to 3D-print dense colonies of bacteria into jammed granular hydrogel matrices. These matrices have tunable pore sizes and mechanical properties; they physically confine the cells, thus supporting them in 3D. They are optically transparent, allowing for direct visualization of bacterial spreading through their surroundings using imaging. As a proof of this principle, here, the capability of this protocol is demonstrated by 3D printing and imaging non-motile and motile Vibro cholerae, as well as non-motile Escherichia coli, in jammed granular hydrogel matrices with varying interstitial pore sizes.

Author Spotlight: Studying Bacterial Growth in 3D Hydrogel Matrices

This protocol describes a procedure for three-dimensional (3D) printing of bacterial colonies to study their motility and growth in complex 3D porous hydrogel matrices that are more akin to their natural habitats than conventional liquid cultures or Petri dishes.

Bacteria are ubiquitous in complex three-dimensional (3D) porous environments, such as biological tissues and gels, and subsurface soils and sediments. ...

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 ...

Whole&#45;Organ Pathogen Tracking via Fluorescent Reporter Quantification

High&#45;Resolution Three&#45;Dimensional Whole&#45;Organ Tomography of Microbial Infections

Most infections take place within three-dimensional host tissues with intricate anatomy and locally varying host physiology. The positioning of pathogen cells within this diverse environment significantly affects their stress levels, responses, fate, and contribution to the overall progression of the disease and treatment failure. However, due to the technical difficulties in locating &#181;m-sized pathogen cells within cm-sized host organs, this area of research has been relatively unexplored. Here, we present a method for addressing this challenge. We employ serial two-photon tomography and AI-enhanced image analysis to locate individual Salmonella cells throughout the entire spleen, liver lobes, and whole lymph nodes of infected mice. Using fluorescent reporters and in vivo antibody administration, the replication rate of single Salmonella cells, their local interaction with specific immune cells, and bacterial responses to antibiotics can be determined. These methodologies open avenues for a comprehensive examination of infections, their prevention, and treatment within the three-dimensional tissue context.

Author Spotlight: Unraveling Bacterial Responses to Antibiotics and Immune System in Tissues

Here, we describe a procedure that enables organ-wide detection of pathogenic bacteria during infection and quantification of fluorescent reporter activities.

Most infections take place within three-dimensional host tissues with intricate anatomy and locally varying host physiology. The positioning of pathogen ...

Production and Visualization of Bacterial Spheroplasts and Protoplasts to Characterize Antimicrobial Peptide Localization

The use of confocal microscopy as a method to assess peptide localization patterns within bacteria is commonly inhibited by the resolution limits of conventional light microscopes. As the resolution for a given microscope cannot be easily enhanced, we present protocols to transform the small rod-shaped gram-negative Escherichia coli (E. coli) and gram-positive Bacillus megaterium (B. megaterium) into larger, easily imaged spherical forms called spheroplasts or protoplasts. This transformation allows observers to rapidly and clearly determine whether peptides lodge themselves into the bacterial membrane (i.e., membrane localizing) or cross the membrane to enter the cell (i.e., translocating). With this approach, we also present a systematic method to characterize peptides as membrane localizing or translocating. While this method can be used for a variety of membrane-active peptides and bacterial strains, we demonstrate the utility of this protocol by observing the interaction of Buforin II P11A (BF2 P11A), an antimicrobial peptide (AMP), with E. coli spheroplasts and B. megaterium protoplasts.

Here we present a protocol to produce gram-negative Escherichia coli (E. coli) spheroplasts and gram-positive Bacillus megaterium (B. megaterium) protoplasts to clearly visualize and rapidly characterize peptide-bacteria interactions. This provides a systematic method to define membrane localizing and translocating peptides.

The use of confocal microscopy as a method to assess peptide localization patterns within bacteria is commonly inhibited by the resolution limits of ...

Staining and High-Resolution Imaging of Three-Dimensional Organoid and Spheroid Models

In vitro three-dimensional (3D) cell culture models, such as organoids and spheroids, are valuable tools for many applications including development and disease modeling, drug discovery, and regenerative medicine. To fully exploit these models, it is crucial to study them at cellular and subcellular levels. However, characterizing such in vitro 3D cell culture models can be technically challenging and requires specific expertise to perform effective analyses. Here, this paper provides detailed, robust, and complementary protocols to perform staining and subcellular resolution imaging of fixed in vitro 3D cell culture models ranging from 100 &#181;m to several millimeters. These protocols are applicable to a wide variety of organoids and spheroids that differ in their cell-of-origin, morphology, and culture conditions. From 3D structure harvesting to image analysis, these protocols can be completed within 4-5 days. Briefly, 3D structures are collected, fixed, and can then be processed either through paraffin-embedding and histological/immunohistochemical staining, or directly immunolabeled and prepared for optical clearing and 3D reconstruction (200 &#181;m depth) by confocal microscopy.

Here, we provide detailed, robust, and complementary protocols to perform staining and subcellular resolution imaging of fixed three-dimensional cell culture models ranging from 100 &#181;m to several millimeters, thus enabling the visualization of their morphology, cell-type composition, and interactions.

In vitro three-dimensional (3D) cell culture models, such as organoids and spheroids, are valuable tools for many applications including development and ...

Three-dimensional Imaging of Bacterial Cells for Accurate Cellular Representations and Precise Protein Localization

Summary

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Production and Visualization of Bacterial Spheroplasts and Protoplasts to Characterize Antimicrobial Peptide Localization

Staining and High-Resolution Imaging of Three-Dimensional Organoid and Spheroid Models