We acknowledge funding from the Chemical and Biological Metrology programme of the UK's National Measurement Office. We thank Sebastian van de Linde, Miklos Erdelyi and Eric Rees for technical advice and suggestions. We thank Miklos Erdelyi, Eric Rees and Max Ryadnov for helpful comments and discussions on this manuscript.

Note: Throughout this protocol, tips and suggestions are found following certain steps. The notation "*1" indicates that note 1 is relevant to this specific step, and so on.
1. Fluorescent Dextran Coating of Glass
<ol>
	<li>Add 200 &mu;l of 0.01% poly-L-lysine solution to each well of an 8-chamber coverglass and incubate for 10 min. Remove with a pipette to ensure that no is liquid remaining.</li>
	<li>Reconstitute dextran-Alexa 647*1 according to manufacturer's instructions to create a 2 mg/ml stock solution. From this, dilute 0.25 &mu;l into 25 &mu;l of de-ionized water to create a 20 &mu;g/ml solution.</li>
	<li>To create a high density coating of dextran-Alexa 647 solution dilute 20 &mu;l of the 20 &mu;g/ml solution into a total of 200 &mu;l of de-ionized water. For a medium density solution dilute 2 &mu;l in 200 &mu;l of de-ionized water (1:10,000 dilution from the original stock solution). For a low density solution dilute 0.2 &mu;l in 200 &mu;l of de-ionized water (1:100,000 dilution from the original stock solution).</li>
	<li>Add 200 &mu;l of the diluted dextran-Alexa 647 solutions to each well and incubate for 10 min. Longer incubation times can be used which may result in a denser dextran coating. Remove and wash with H2O three times*2</li>
</ol>
Note 1: Other dextran-dye conjugates can be used. Unconjugated dextran can also be conjugated to dyes if desired combinations are not commercially available.
Note 2: The same dextran chambers can be reimaged over a period of weeks although the uniformity of the fluorescence may decrease. Storage at 4 &deg;C in the absence of any buffer is recommended.
2. Fluorescent Actin Filaments on Glass
<ol>
	<li>Add 200 &mu;l of 0.01% poly-L-lysine solution to each well of the chamber and incubate for 10 min*3. Remove with a pipette to ensure that no liquid is remaining.</li>
	<li>Add 90 &mu;l of general actin buffer (reconstituted according to manufacturer's instructions) to each chamber. Add 10 &mu;l of 10 &mu;M preformed actin filament solution and 1 &mu;l phalloidin-Alexa 647 (0.2 units, when dissolved in 1.5 ml of methanol as per manufacturer's instructions) and gently pipette up and down to mix. Incubate for 30 min*4.</li>
</ol>
Note 3: Increasing the volume of the filament solution within the chamber results in fewer filaments binding to the poly-L-lysine coated glass.
Note 4: Incubation times of up to 48 hr at 4 &deg;C have been tested with good results. Actin filament image quality deteriorates once a chamber has been exposed to switching buffer so it is not recommended to use the same sample for more than one day.
3. Microsphere Fluorescent Beads as Fiducial Markers
<ol>
	<li>Dilute 1 &mu;l of TetraSpeck beads into 200 &mu;l PBS and mix.</li>
	<li>Add the diluted beads into an imaging chamber and wait for 15 min*5. Remove the solution and wash 3 times with PBS prior to imaging.</li>
</ol>
Note 5 - the dilution and incubation time can be adjusted to obtain different densities of beads. This protocol typically results in between 3 - 10 beads per 20.5 &mu;m x 20.5 &mu;m field-of-view.
4. Epidermal Growth Factor Cell Staining
<ol>
	<li>Culture HeLa cells in imaging chambers to approximately 80% confluency in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin solution.</li>
	<li>Remove the culture medium and wash with PBS. Then add 500 &mu;l of 4% formaldehyde solution (4 ml 10% formaldehyde solution, 1 ml 10X PBS, 5 ml water) for 10 min. Remove the formaldehyde and wash 3 times with PBS. Do not allow the cells to remain dry and always use PBS buffered solutions to avoid hypotonic stress.</li>
</ol>
CAUTION - formaldehyde is extremely toxic. Ensure that appropriate gloves, eye protection and lab coats are worn. Check local guidelines for disposal of formaldehyde.
<ol start="3">
	<li>Reconstitute EGF-Alexa 647 according to manufacturer's instructions to create a 50 &mu;g/ml stock solution. Add 2 &mu;l of this stock solution to 200 &mu;l of 0.1% BSA solution (in PBS). Remove the PBS from the cells, add the EGF solution and incubate for 30 min.</li>
	<li>Remove solution and wash 3 times with PBS before adding a blocking solution of 0.1% BSA (in PBS) for 15 min. Remove this and wash a further 3 times with PBS prior to imaging.</li>
</ol>
5. Switching Buffer Preparation
<ol>
	<li>Make an enzyme stock solution (A) with 10 &mu;l of catalase (20 &mu;g/ml), 20 &mu;l of 1 M Tris (2-carboxyelthyl) phosphine hydrochloride (4 mM), 2.5 ml glycerin (50%), 125 &mu;l of 1 M KCl (25 mM), 100 &mu;l of 1 M pH 7.5 Tris-HCl (20 mM), 5 mg of glucose oxidase (1 mg/ml) and top up to 5 ml volume with diH20. This is sufficient to make 100 x 50 &mu;l aliquots, which can be stored at -20 &deg;C and used for up to 1 year.</li>
	<li>) Make a glucose stock solution (B) with 4 g glucose (100 mg/ml), 4 ml glycerin (10%) and 36 ml diH20. This is sufficient to make 100 x 400 &mu;l aliquots, which can be stored at - 20 &deg;C and used for up to 1 year.</li>
	<li>Make a reducing agent stock solution (C) with 113.6 mg of MEA-HCl (1M) and 1 ml of diH20. Use fresh or make 10 x 100 &mu;l aliquots, which can be stored at -20 &deg;C and used for up to 1 month (do not refreeze aliquots).</li>
	<li>Just prior to imaging, mix the enzyme (A), glucose (B) and reducing agent (C) stock solutions together in the ratio 50 &mu;l, 400 &mu;l, 100 &mu;l and make up to 1 ml with PBS*6. This buffer will now scavenge oxygen and have a reducing environment (100 mM MEA-HCl)*7. The final pH should be in the range 6.0 - 8.5 (adjustment is not normally necessary)*8.</li>
</ol>
Note 6 - this buffer is suitable for use with carbocyanine-based dyes such as Cy5 and Alexa 647.
Note 7 - the final enzyme concentrations are 50 &mu;g/ml (5 unit/ml) of glucose oxidase and 1 &mu;g/ml (40-60 units/ml) of catalase. The enzymatic activity (units) is given by suppliers and can vary. The calculations for catalase assume that 1 &mu;g/ml final concentration after step 5.4 will contain 50 units of enzyme. Various buffer compositions including similar oxygen scavenging components can be found 21,30,40. For a summary of buffer and dye combinations see references 28,29. Glycerin and TCEP are required for long-term storage at -20 &deg;C.
Note 8 - if imaging actin filaments add 2 mM MgCl2 and 0.2 mM ATP to help stabilize the filaments.
6. Microscope Acquisition of STORM Data (using Alexa 647 dye)
<ol>
	<li>Switch on the STORM/PALM microscope, preferably at least 3 hr before imaging to allow a thermal equilibrium to be reached. Imaging before this has an increased chance of drift.</li>
	<li>Insert the imaging chamber into the microscope stage sample holder. Ensure that the sample is firmly in the holder and is flat.</li>
	<li>Remove the PBS from the imaging chamber and then fill to the top with the switching buffer. Carefully place a coverslip over the top of the chamber, making sure that there are no bubbles at all and that the entire chamber is covered. Top up with additional buffer or PBS if any bubbles are seen otherwise buffer performance can decline.</li>
</ol>
CAUTION - to minimize the chances of lateral and axial drift of the sample in relation to the objective lens it is recommended that the sample and the buffer are left to equilibrate to room temperature for at least 15 min prior to imaging.
<ol start="4">
	<li>Locate a fluorescent structure of interest to be imaged. Select the desired angle of illumination, in this case, a TIRF or highly inclined angle is recommended as this improves the signal-to-noise ratio by minimizing out-of-focus light, which is particularly of benefit for cell samples. Take a reference diffraction-limited image of the structure prior to STORM imaging.</li>
	<li>Increase the excitation laser (with an appropriate excitation wavelength of approximately 640 nm) to a high power, corresponding to approximately 2 kW/cm2 whilst imaging with camera settings of 10 msec exposure and a cycle time of approximately 50 Hz (frames per second). Once the fluorophores have transitioned into a sparse blinking pattern (probably within 10 sec for most samples) collect 10,000 frames*9.</li>
</ol>
Note 9 - 10,000 frames is suitable for the samples described here but it may be necessary to increase the frame number to 50,000 or more for other samples.
7. Reconstructing Super-Resolution Images from Raw Data (using rainSTORM)
<ol>
	<li>Open the rainSTORM.m file from within MATLAB and run (Figure 1A).</li>
	<li>In the rainSTORM window (Figure 1B) select the image file to be analyzed using the browse button. This should be a .tif file (ImageJ can be used to convert other file types into a tif format). Alter the pixel width to match the raw data of the microscope system used to acquire the data*10. Leave the other values to default.</li>
</ol>
Note 10 - A typical EMCCD camera has a pixel size of 16 &mu;m so on a system with a 100X objective lens the pixel size on the image will be 160 nm. On commercial systems the pixel size is usually displayed within the software. Pixel sizes vary between 100 nm and 160 nm for most localization microscopes.
<ol start="3">
	<li>Press the 'Process Images' button and wait until a preliminary image appears. The duration of the processing will depend on the file size, computer specification and the number of candidate blinks within the raw data*11.</li>
</ol>
Note 11 - 10,000 frame sequences of actin and EGF data took 23 sec and 25 sec to process respectively using a PC with an Intel Xeon E5420 CPU. Using the same computer without a parallel processing toolbox in MATLAB, or with a computer without multiple core processing, times were 66 sec and 81 sec respectively.
<ol start="4">
	<li>To generate an updated super-resolution image press the 'Open Reviewer' button and a second window will appear (Figure 1C). Press the 'Adjust Contrast' button in order to change the image display as desired (Figure 1D). In some cases, where the image is very dark the maximum value should be decreased.</li>
	<li>Use the reviewer window (Figure 1C) to generate useful image quality metrics and refine the super-resolution image further. Set the first three quality control parameters to values of 4,000, 0.1 and 0.8-2.0 respectively*12. Update the counts per photon value, which should either be based on a photon calibration measurement or using photon-curve calibration curve values specified by the camera manufacturer for a particular EM gain setting. This is critical for obtaining accurate precision and resolution measurements*13.</li>
</ol>
Note 12 - Signal Counts rejects blinks based on brightness criteria. The higher the value the brighter the blink must be to become accepted as a localization in the final image. This may need to be altered depending on the photon output of the dye and exposure time and sensitivity of the camera. Tolerance rejects blinks if there is significant square error between the signal and the fitted Gaussian i.e. double peaks. PSF Sigma Range rejects blinks if the fitted width of the PSF lies out of this range, i.e. a narrower range can be used to reject out-of-focus signals and large aggregated blobs of constant fluorescence. Using a wider range may result in mislocalization artifacts appearing in the final image. In practice, it is recommended to perform initial quality control using the localization precision cutoff parameter.
Note 13 - for more on resolution metrics see references 23,27. In brief by knowing the number of photons produced by each blink it is possible to calculate the localization precision of each localization and consequently derive overall mean precision estimates for the entire image. Using an Andor iXON DU-897E with a gain setting of 200 the Counts per Photon value is 9.04.
<ol start="6">
	<li>Alter the localization precision cutoff (Figure 1C) to tradeoff between optimizing mean localization precision against localization number in the final image. Values of 30-50 nm are recommended depending on the quality of the data and the sample. Both the histograms and the info.txt files can be used to inform this decision (Figures 1F and 1G).</li>
	<li>The reconstruction scale factor (Figure 1C) default is 5, which will generate super-resolution pixels of 32 nm assuming that the pixel width in the original images is 160 nm. If the raw images have a pixel size of 100 nm this will produce super-resolution images with pixels of 20 nm. Increase the scale factor to produce smaller super-resolution pixels in the final image*14.</li>
</ol>
Note 14- Resolution of localization microscopy depends on the smoothing required for visualization (ie. the pixel size of a simple histogram reconstruction) as well as the localization precision. In practice, the reconstruction pixel size should generally be smaller than the localization precision (see the Mean Precision Estimate metric in the info text file - steps 6.11 &amp; 6.12). In this case the loss of resolution due to reconstruction pixel size will be small 23,27. For example the mean precision estimates in the row and column direction of Figure 4E are 16.1 nm and 16.2 nm. A pixel size of 16 nm has been used to visualize this data (reconstruction scale factor of 10 with an original pixel width of 160 nm).
<ol start="8">
	<li>Modify the limit frame range (Figure 1C) to restrict the reconstruction to subsets of the raw data, for example [2001-inf] would exclude the initial 2,000 frames of raw data.</li>
	<li>Press the 'Run Reviewer' button (Figure 1C) to generate an updated super-resolution image (Figure 1E).</li>
	<li>Press the 'View Histograms' button (Figure 1C), in order to display image quality metrics (Figure 1F)*15.</li>
</ol>
Note 15 - The graph in the top right depicting the number of accepted localizations against camera frame number and the Thompson Localization Precisions histogram in the bottom right (Figure 1F) can be used to inform the localization precision cutoff review option. For example, using a 50 nm cutoff would exclude all the localizations with worse precision from being included in the final super-resolution image.
<ol start="11">
	<li>Press the 'Save Image' button in the reviewer (Figure 1C) in order to save all of this data*16.</li>
</ol>
Note 16 - Between 3 and 5 files can be saved depending on the options selected (sum image, STORMdataImage, STORMprocessed, info &amp; hists), see Figure 1G. The STORM processed image is displayed with modified contrast and color maps. The STORMdataImage is a grayscale image, in which the gray level of each pixel equals the number of localizations which were identified within it.
<ol start="12">
	<li>After examining the data (e.g. Info.txt file and histograms) return to step 7.6 and repeat the subsequent steps with different reviewer parameters if desired. Pressing 'Save Image' in step 7.11 will overwrite the previously saved data.</li>
</ol>
8. Box Tracking and Drift Correction (using rainSTORM)
<ol>
	<li>Follow steps 7.1 - 7.9 to generate an image in the normal way.</li>
	<li>Press the 'Box Tracking' button in the reviewer (Figure 1C) and click and drag a box over the fiducial marker or structure of interest on the reviewed image.</li>
	<li>Wait until a new image appears, which will display the 'Boxed Positions' (see Figures 6B, 6C and 6F for examples). Save this image if desired.</li>
	<li>If the object of interest is a fiducial marker then drift correction can be performed by clicking the 'Set Anchor' button followed by the 'Subtract Drift' button. Finally click 'Run Reviewer' again to generate a new STORM image, which will now have all localizations corrected according to the fiducial marker positions. The bead positions are deleted from the final reviewed image.</li>
	<li>If there are other fiducial markers within the final drift corrected image, these can be removed by pressing 'Box Tracking', 'Delete Boxed' and then 'Run Reviewer' as many times as desired.</li>
</ol>

We have nothing to disclose.

We show with some simple test samples and the freely-available processing software rainSTORM it is possible to optimize STORM super-resolution imaging. These tools and methods will provide useful starting points for new users and ways for experienced users to increase confidence in their microscopes and their use of them to study biological and cellular structures with unprecedented detail. By using a combination of samples with known or well-understood structures and an algorithm that can produce a number of useful image quality metrics, such as mean precision estimates, localization number and histograms showing it is possible to improve image quality and to have greater confidence in the STORM imaging process. There is no one-size fits all approach to acquiring the data as there are a number of different commercial and self-built microscope configurations that can be used. Moreover, there are many sample preparation and image acquisition steps which can influence the final image therefore having software tools and test samples is critical to understanding and optimizing STORM image quality.
In addition these protocols can provide useful and less ambiguous ways of troubleshooting any problems that may arise. For example the dextran sample is a very useful way to identify if there is any laser beam misalignment or uneven illumination of the sample. If there are concerns that the switching buffer may not be working a very simple visual test to see if there is any 'blinking' will help. Actin filaments are a useful way of measuring resolution using FWHM comparisons as well as highlighting drift and mislocalization artifacts. However, it should be noted that as localization-based super-resolution methods can be fluorophore density sensitive, notwithstanding the other contributing factors, such as exposure times, frame rates, laser powers and the way the image is processed, it is impossible to assign a resolution to a particular microscope and assume that all images will have that resolution. What is possible, however, is to measure various aspects of resolution such as mean localization precisions, localization number and drift 27. Even if fiducial markers cannot be incorporated into every experiment they can, at least, be used to characterize a microscope system, its environment and better understand operational considerations such as how much time is required to reach thermal equilibrium after start up. As well as correcting for lateral drift, fiducial markers can be used to characterize and correct for axial drift, although this requires the use of an astigmatic lens and a feedback mechanism to correct the sample position whilst the images are being acquired 43. Commercial super-resolution systems will usually include some sort of stability control or focus-lock mechanism, which may be a more practical solution for correcting focus drift.
There are alternatives to non-specifically coating the glass with dextran, such as using dyes directly or other conjugate molecules, such as secondary antibodies. A limitation of these approaches is that the number of molecules bound to the glass is not known. Alternative filamentous structures that have been used include microtubules 28 and DNA21. However, the combination of very small size and convenient commercially available reagents make actin an attractive alternative, particularly as the separation distance of diverging or branched filaments can also be a useful measurement of system performance 27.
There is room for further development of test samples, despite recent progress in this area 28,29,46,47. In particular, multi-color imaging presents a number of challenges in all 3 main aspects of the imaging, sample labeling, image acquisition (hardware), and software (image processing and alignment). There have a been a number of publications addressing these aspects 4-6 and it is therefore clear that appropriate test samples and methodologies are critical to generating and reliably interpreting these types of images. Indeed, recently it was shown that dSTORM could be used to take two color images of, and resolve, the highly symmetrical nature of the nuclear pore complex and the authors suggested that this would be an ideal way to assess performance of chromatic aberration corrections 45. Another interesting approach is the use of DNA origami to create a nanoruler, which creates the possibility of positioning fluorophores at specific sub-diffraction distances apart 46. This provides a way of assessing resolution and making distance measurements. However, the ultimate aim is to apply these super-resolution techniques to non-idealized samples, such as cells, which are complex three dimensional structures. In this case, a combination of more thorough understanding of the technique combined with software tools that aid the user in acquiring data, processing it in appropriate ways, and providing image metrics is likely to be the ultimate answer. 28,29,47,48

Recently a range of optical microscopy techniques have been developed, typically referred to as super-resolution microscopy or nanoscopy, which can bypass the diffraction limit and generate images with resolutions of 100 nm or better 1,2 . Since the announcement of super-resolution microscopy as being the Nature Methods method of the year in 2008, there has been a great deal of development of localization-based microscopes. For example, there are multi-color applications 3-6 , 3D implementations 7-12, and even live cell applications 5,13-17 . Moreover, as these systems are being commercialized and are now making their way out of optics laboratories into the hands of cell biologists there is likely to be a further increase in their use and application to biomedical questions.
One branch of this family is the localization-based methods which work by building up an image molecule-by-molecule. In order to do this, the majority of fluorescent molecules within the sample must be switched off so that only a few spatially-separated molecules are active at any one time. These molecules are then rapidly switched on and off and many images are taken so that a significant part of the population is imaged to reflect the underlying structure with sub-diffraction precision. A number of approaches have been published including GSDIM 18, PALM 19 , fPALM 20 , STORM 21 and RESOLFT 22. Algorithms that measure the position of a single molecule detection can then be used to locate the actual position of the molecule with precisions better than 20 nm using Gaussian fitting23, for example rapidSTORM 24,25 QuickPALM 26, palm3d 12 and rainSTORM27. When imaging cell or other biological samples, which have a high molecule density, it is therefore necessary to take many thousands of frames of this blinking, spatially separated, signal in order to image a significant proportion of the labeled molecules.
Stochastic optical reconstruction microscopy (STORM) and the highly related dSTORM and GSDIM methods are localization-based super-resolution methods, which have been demonstrated to work with commercially available organic dyes 21. They have since been shown to be applicable to a number of different dyes 28-30, which makes the methodology particularly attractive because of the specificity and relative convenience of well-established immunolabeling approaches to perform fluorescence microscopy. Consequently, there is easy access to a range of dye-antibody and dye-biomolecule conjugates, which have the potential to be used in localization-based super-resolution imaging. Whilst the general principles of STORM and similar approaches are relatively simple, and at first sight the hardware and sample preparation seem relatively straightforward, there are a number of steps in the process which are critical to achieving high quality, artifact-free, super-resolution images.
As with all microscopy techniques the process can be thought of in 3 main steps: first, sample preparation, second, image acquisition and, third, image processing and/or image analysis and interpretation. The additional resolving power and method of generating super-resolution images using a localization-based approach introduces some new problems and considerations 31-33. Starting with sample preparation, when performing immunolabeling, particularly indirect immunofluorescence where a primary and secondary antibody combination is used, it should be noted that the fluorescent dye may be up to 20 nm away from the molecule of interest. In the case of microtubules, this results in diameters of 35-50 nm compared with an expected microtubule diameter of 25 nm 28,30 . Also, the labeling density should meet the Nyquist criteria in order to generate a high quality image 14. For example for an anticipated image resolution of 20 nm along a filamentous structure there should be a dye molecule at least every 10 nm 27. Whilst many dyes have been published to work for localization-based approaches the overall performance of the dye is a mix of at least four important criteria, namely detected photons per switching event (the brightness of each blink - brighter is better), the equilibrium on-off duty cycle (the contrast ratio of dyes in the off versus on state - more off is generally better), the survival fraction (a low bleaching rate is better) and the number of switching cycles (the number of blinks per fluorophore - more is better for high resolution, although 1 blink per fluorophore can be an advantage for molecule counting purposes). The situation is further complicated by the fact that these properties for any given dye can vary in different buffer conditions and with laser power 28,29 . In addition, as well as these unique STORM considerations, image quality can be improved by optimizing sample preparation in the same way as more traditional fluorescence microscopy techniques for example by minimizing autofluorescence by modification of fixation conditions and the use of quenching reagents. Also, minimizing non-specifically bound fluorophores is important, which can be done by adjusting label concentrations, incubation times and blocking and washing steps.
The second step of image acquisition is also critical. The optimum settings on the microscope will depend on the dye, buffer conditions, labeling density, and sample type, e.g. monolayers on glass, cells or tissue samples. The main considerations are camera exposure time, frame number, illumination angle (TIRF, HILO, Epi) and laser power. The aim is to collect as many bright spatially separated blinks as quickly as possible. If the background is very high or the blinks are not very bright, in other words the signal-to-noise ratio is poor, the reconstruction algorithm will not be able to localize the positions as accurately. As well as maximizing the localization precision, i.e. the accuracy with each molecule position can be measured, image quality also depends on a high number of localizations. If an insufficient number of the molecules of interest are imaged, either due to poor labeling efficiency, excessive photobleaching or an inadequate frame number, then the resulting super-resolution image will be punctate and discontinuous as Nyquist criteria will not have been met 14,27.
And finally, the third step, image processing, is particularly important compared with standard fluorescence microscopy techniques. There is a range of freely and commercially available algorithms, which is both an advantage, in terms of choice, and a disadvantage, in that it can be confusing to know which is most appropriate to use. If for example the raw data has well-spaced spatially distinct blinks then a single-molecule fitting algorithm can be highly accurate and efficient, for example by the sparse fitting algorithms available in rainSTORM 27, rapidSTORM 24,25 , palm3d 12 and QuickPALM 26software. If the raw data contains a lot of overlapping signals then algorithms which may be more appropriate include DAOSTORM 34, 3B 35 and deconSTORM 36. Moreover, these algorithms contain thresholds for various criteria such as signal counts, or photons per blink, as well as various different image display options such as visualizing with smoothed Gaussian functions or as histograms with pixel grids, where the super-resolution pixel size can be selected by the user. All of these options change the appearance of the final image and have implications for image interpretation and analysis.
Therefore, we present 3 test samples which will provide the new user with a starting point for dye-based localization-based super-resolution experiments, which for the sake of clarity we will refer to as STORM, and more experienced users the opportunity to further optimize their experiments. First, it is possible to coat the surface of glass with a fluorescent layer of dextran-dye conjugate. This provides a quick and simple way to establish that the microscope, dye and buffer are working to generate blinking fluorescent signal. The method can then be extended by comparing mean localization precision and number metrics generated by rainSTORM to optimize buffer conditions, image acquisition settings and test different dyes. Second, we present a simple protocol for preparing actin filaments on glass which can be readily labeled using phalloidin-dye conjugates. These provide ideal structures to take measurements of resolution 27 and typically the full width half maximum (FWHM) of a filament is given as an empirical estimate of resolution 37. It should be noted that resolution varies between samples and between images within the same sample because resolution in localization-based super-resolution microscopy is a function of fluorophore brightness, background noise and localization density 27 and these are not necessarily constant throughout the specimen. Actin filaments are used to demonstrate potential artifacts such as mislocalizations and drift and how they can be identified with software features within rainSTORM. Moreover, we describe the use of fluorescent fiducial markers to both measure and correct drift. And third, the staining of cells with epidermal growth factor (EGF)-dye conjugates is described. EGF provides a useful indicator of super-resolution image performance, because a proportion of the receptor at the cell surface undergoes endocytosis via vesicles, which are sub-diffraction sized structures of approximately 50-150 nm in diameter 27,38,39 .

<table border="1">
	<tbody>
		<tr>
			<td>Name of Reagent/Material</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Labtek Imaging chamberglass</td>
			<td>Fisher</td>
			<td>CBR-166-390E</td>
			<td>Sample preparation</td>
		</tr>
		<tr>
			<td>Inverted Microscope</td>
			<td>Olympus</td>
			<td>IX71</td>
			<td>* See below for alternatives</td>
		</tr>
		<tr>
			<td>100 X TIRF Objective Lens</td>
			<td>Olympus</td>
			<td>UAPON 100XOTIRF</td>
			<td>* See below for alternatives</td>
		</tr>
		<tr>
			<td>EMCCD Camera</td>
			<td>Andor</td>
			<td>iXon 897</td>
			<td>* See below for alternatives</td>
		</tr>
		<tr>
			<td>640 nm laser</td>
			<td>Toptica</td>
			<td>iBEAM-SMART-640-S</td>
			<td>*150 mW - for imaging Alexa 647 or Cy5 dyes</td>
		</tr>
		<tr>
			<td>LabVIEW</td>
			<td>National Instruments</td>
			<td>&nbsp;</td>
			<td>*Acquisition software (controlling hardware)</td>
		</tr>
		<tr>
			<td>PC</td>
			<td>Dell</td>
			<td>&nbsp;</td>
			<td>Windows XP (64 bit), Intel Xeon E5420 CPU, 16 GB RAM *Instrument control and image processing</td>
		</tr>
		<tr>
			<td>ImageJ</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>Image analysis and processing <a href="http://rsbweb.nih.gov/ij" target="_blank">http://rsbweb.nih.gov/ij</a></td>
		</tr>
		<tr>
			<td>rainSTORM</td>
			<td>Laser Analytics Group (Cambridge) software</td>
			<td>&nbsp;</td>
			<td><a href="http://laser.cheng.cam.ac.uk/wiki/index.php/Resources" target="_blank">http://laser.cheng.cam.ac.uk/wiki/index.php/Resources</a></td>
		</tr>
		<tr>
			<td>MATLAB</td>
			<td>Running rainSTORM</td>
			<td>&nbsp;</td>
			<td><a href="http://www.mathworks.co.uk/products/matlab/" target="_blank">http://www.mathworks.co.uk/products/matlab/</a></td>
		</tr>
		<tr>
			<td>*Example of self-built STORM/PALM microscopes</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>Also see reference 29 <a href="http://code.google.com/p/quickpalm/wiki/Tutorial_CustomMicroscope" target="_blank">http://code.google.com/p/quickpalm/wiki/Tutorial_CustomMicroscope</a></td>
		</tr>
		<tr>
			<td>*Commercial STORM/PALM super-resolution microscopes</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>Leica SR GSD, Nikon N-STORM, Vutara SR-200, Zeiss Elyra P.1, DeltaVision OMX Blaze (Monet Localization) Alternatives to self-built microscopes</td>
		</tr>
		<tr>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>Table 1. Materials &amp; Equipment</td>
		</tr>
		<tr>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>[header]</td>
		</tr>
		<tr>
			<td>PBS (10X)</td>
			<td>Fisher</td>
			<td>VX70013065</td>
			<td>1; 2; 3; 4</td>
		</tr>
		<tr>
			<td>Poly-L-Lysine</td>
			<td>Sigma</td>
			<td>P4707</td>
			<td>1.1; 2.1</td>
		</tr>
		<tr>
			<td>Dextran-Alexa fluor 647</td>
			<td>Invitrogen</td>
			<td>D-22914</td>
			<td>1.2</td>
		</tr>
		<tr>
			<td>General actin buffer</td>
			<td>Cytoskeleton Inc</td>
			<td>BSA01</td>
			<td>2.2</td>
		</tr>
		<tr>
			<td>Preformed-actin filaments</td>
			<td>Cytoskeleton Inc</td>
			<td>AKF99</td>
			<td>2.2</td>
		</tr>
		<tr>
			<td>Phallodin-Alexa fluor 647</td>
			<td>Invitrogen</td>
			<td>A22287</td>
			<td>2.2</td>
		</tr>
		<tr>
			<td>TetraSpeck Microspheres (0.1 micron)</td>
			<td>Invitrogen</td>
			<td>T-7279</td>
			<td>3.1</td>
		</tr>
		<tr>
			<td>HeLa cells</td>
			<td>ATCC</td>
			<td>CCL-2</td>
			<td>4.1</td>
		</tr>
		<tr>
			<td>DMEM</td>
			<td>Fisher</td>
			<td>VX31966021</td>
			<td>4.1</td>
		</tr>
		<tr>
			<td>FBS</td>
			<td>Fisher</td>
			<td>VX10500064</td>
			<td>4.1</td>
		</tr>
		<tr>
			<td>Pen/Step</td>
			<td>Invitrogen</td>
			<td>15070063</td>
			<td>4.1</td>
		</tr>
		<tr>
			<td>Plastic dishes</td>
			<td>Invitrogen</td>
			<td>734-0006</td>
			<td>4.1</td>
		</tr>
		<tr>
			<td>EGF - Alexa fluor 647</td>
			<td>Invitrogen</td>
			<td>E35351</td>
			<td>4.3</td>
		</tr>
		<tr>
			<td>BSA</td>
			<td>Sigma</td>
			<td>A7906</td>
			<td>4.3, 4.4</td>
		</tr>
		<tr>
			<td>Formaldehyde - 10% solution EM grade</td>
			<td>Polysciences</td>
			<td>04018-1</td>
			<td>4.2</td>
		</tr>
		<tr>
			<td>Catalase</td>
			<td>Sigma</td>
			<td>C100</td>
			<td>5.1</td>
		</tr>
		<tr>
			<td>Glucose oxidase</td>
			<td>Sigma</td>
			<td>G2133</td>
			<td>5.1</td>
		</tr>
		<tr>
			<td>KCl</td>
			<td>Sigma</td>
			<td>P9541</td>
			<td>5.1</td>
		</tr>
		<tr>
			<td>TCEP</td>
			<td>Sigma</td>
			<td>C4706</td>
			<td>5.1</td>
		</tr>
		<tr>
			<td>Tris</td>
			<td>Sigma</td>
			<td>93352</td>
			<td>5.1</td>
		</tr>
		<tr>
			<td>Glucose</td>
			<td>Sigma</td>
			<td>C7528</td>
			<td>5.2</td>
		</tr>
		<tr>
			<td>MEA-HCl</td>
			<td>Sigma</td>
			<td>M6500</td>
			<td>5.3</td>
		</tr>
		<tr>
			<td>Glycerin</td>
			<td>Sigma</td>
			<td>G2289</td>
			<td>5.1; 5.2</td>
		</tr>
		<tr>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>&nbsp;</td>
			<td>Table 2. Reagents</td>
		</tr>
	</tbody>
</table>

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.

Watch this Scientific Journal Video about Test Samples for Optimizing STORM Super-Resolution Microscopy at JoVE.com

Test Samples for Optimizing STORM Super-Resolution Microscopy

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 ...

test-samples-for-optimizing-storm-super-resolution-microscopy

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

31.0K Views.  National Physical Laboratory. 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.

Video: Test Samples for Optimizing STORM Super-Resolution Microscopy

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 ...

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 ...

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 ...

Scanning Electron Microscopy (SEM) Protocols for Problematic Plant, Oomycete, and Fungal Samples

Common problems in the processing of biological samples for observations with the scanning electron microscope (SEM) include cell collapse, treatment of samples from wet microenvironments and cell destruction. Using young floral tissues, oomycete cysts, and fungi spores (Agaricales) as examples, specific protocols to process delicate samples are described here that overcome some of the main challenges in sample treatment for image capture under the SEM.
Floral meristems fixed with FAA (Formalin-Acetic-Alcohol) and processed with the Critical Point Dryer (CPD) did not display collapsed cellular walls or distorted organs. These results are crucial for the reconstruction of floral development. A similar CPD-based treatment of samples from wet microenvironments, such as the glutaraldehyde-fixed oomycete cysts, is optimal to test the differential growth of diagnostic characteristics (e.g., the cyst spines) on different types of substrates. Destruction of nurse cells attached to fungi spores was avoided after rehydration, dehydration, and the CPD treatment, an important step for further functional studies of these cells.
The protocols detailed here represent low-cost and rapid alternatives for the acquisition of good-quality images to reconstruct growth processes and to study diagnostic characteristics.

Scanning Electron Microscopy SEM Protocols for Problematic Plant, Oomycete, and Fungal Samples

Problems in the processing of biological samples for scanning electron microscopy observation include cell collapse, treatment of samples from wet microenvironments and cell destruction. Low-cost and relatively rapid protocols suited for preparing challenging samples such as floral meristems, oomycete cysts, and fungi (Agaricales) are compiled and detailed here.

Common problems in the processing of biological samples for observations with the scanning electron microscope (SEM) include cell collapse, treatment of ...

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.

Quantitative multicolor fluorescence microscopy relies on the careful spatial matching of color channels acquired at different wavelengths. Due to ...

Super-Resolution Live Cell Imaging of Subcellular Structures

There has long been a crucial tradeoff between spatial and temporal resolution in imaging. Imaging beyond the diffraction limit of light has traditionally been restricted to be used only on fixed samples or live cells outside of tissue labeled with strong fluorescent signal. Current super-resolution live cell imaging techniques require the use of special fluorescence probes, high illumination, multiple image acquisitions with post-acquisition processing, or often a combination of these processes. These prerequisites significantly limit the biological samples and contexts that this technique can be applied to.
Here we describe a method to perform super-resolution (~140 nm XY-resolution) time-lapse fluorescence live cell imaging in situ. This technique is also compatible with low fluorescent intensity, for example, EGFP or mCherry endogenously tagged at lowly expressed genes. As a proof-of-principle, we have used this method to visualize multiple subcellular structures in the Drosophila testis. During tissue preparation, both the cellular structure and tissue morphology are maintained within the dissected testis. Here, we use this technique to image microtubule dynamics, the interactions between microtubules and the nuclear membrane, as well as the attachment of microtubules to centromeres.
This technique requires special procedures in sample preparation, sample mounting and immobilizing of specimens. Additionally, the specimens must be maintained for several hours after dissection without compromising cellular function and activity. While we have optimized the conditions for live super-resolution imaging specifically in Drosophila male germline stem cells (GSCs) and progenitor germ cells in dissected testis tissue, this technique is broadly applicable to a variety of different cell types. The ability to observe cells under their physiological conditions without sacrificing either spatial or temporal resolution will serve as an invaluable tool to researchers seeking to address crucial questions in cell biology.

Presented here is a protocol for super-resolution live-cell imaging in intact tissue. We have standardized the conditions for imaging a highly sensitive adult stem cell population in its native tissue environment. This technique involves balancing temporal and spatial resolution to allow for the direct observation of biological phenomena in live tissue.

There has long been a crucial tradeoff between spatial and temporal resolution in imaging. Imaging beyond the diffraction limit of light has traditionally ...

Test Samples for Optimizing STORM Super-Resolution Microscopy

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Rozdziały w tym wideo

Super-resolution Imaging of the Bacterial Division Machinery

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

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

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

Scanning Electron Microscopy (SEM) Protocols for Problematic Plant, Oomycete, and Fungal Samples

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

Ground State Depletion Super-resolution Imaging in Mammalian Cells

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

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

Super-Resolution Live Cell Imaging of Subcellular Structures