Name: highly multiplexed, super-resolution imaging of t cells using madstorm
Uploaded: 2017-06-24T12:30:00.000+00:00
Duration: 8 min 43 s
Description: Watch this Scientific Journal Video about Highly Multiplexed, Super-resolution Imaging of T Cells Using madSTORM at JoVE.com

We thank Xufeng Wu for access to the STORM microscope. This research was supported by the Intramural Research Program of the National Cancer Institute (NCI) Center for Cancer Research and the National Heart Lung and Blood Institute (NHLBI).

Caution: Please consult all relevant material safety data sheets (MSDS) before use. Several of the chemicals used in this protocol are toxic and carcinogenic. Please use all appropriate safety practices when performing the protocol including the use of engineering controls (fume hood, glovebox) and personal protective equipment (safety glasses, gloves, lab coat, full length pants, closed-toe shoes).
1. Multiplexed Imaging of Activated T Cells
<ol>
	<li>Directly conjugate antibodies with Alexa-647 (A647) dye using the Alexa 647 antibody labeling kit. Follow the labeling protocol provided by the manufacturer. 
	NOTE: Dialysis of antibody solution in 1x PBS is recommended before this step to increase antibody labeling efficiency.</li>
	<li>Spin labeled antibodies for 5 min at 20,800 rcf and collect supernatant to remove aggregated antibodies.</li>
	<li>Preparation of fluorescent nano-diamond-coated coverslips
	<ol>
		<li>Coat 8-well coverslip chambers (see reagent/material list) with 250 &#956;L 0.01% Poly-L-Lysine (PLL) for 15 min, aspirate, and dry at 65 &#176;C for 30 min. (No rinse before drying.)</li>
		<li>Prepare a dilution of 100 nm FNDs in 1x PBS. Test various dilutions to ensure enough FNDs are visible each field of view in step 1.2.7 (we are using a 1:200 dilution of FNDs provided by the manufacturer).</li>
		<li>Vortex the diluted FNDs for 1 min.</li>
		<li>Sonicate the FND supernatant for 30 s at a high-power setting.</li>
		<li>Incubate the sonicated FND supernatant in a PLL-coated 8-well coverslip chamber for 30 min at room temp.</li>
		<li>Wash 5x with 1x PBS and visualize the FND-coated chamber using 647 nm laser excitation on the TIRF microscope. Ideally, 4-10 individual FNDs should be visible in a field of view given the appropriate dilution of FNDs in step 1.2.2 (we are using a 100X objective with 256 x 256 camera pixel setting to yield 61 x 61 &#956;m field of view) with at least one FND present in each quadrant of the imaging field. If the FNDs appear to be clustered (i.e. FNDs with significantly higher signal than surrounding FNDs and a point spread function larger than 200 nm), centrifuge FNDs at 6,800 rcf for 1 min, and repeat the coating procedure using the FND supernatant.</li>
		<li>Add 250 &#956;l of anti-CD3 antibody (10 &#956;g/mL) in each well and incubate for 1 hr at 37 &#176;C, or overnight at 4 &#176;C.</li>
		<li>Remove solution and add 1x PBS for 30 sec. Repeat this wash step 5 times (wash 5x).</li>
	</ol>
	</li>
	<li>Activation of Jurkat T cells
	<ol>
		<li>Spin 1 ml of Jurkat T cells at 800 rcf for 6 min and resuspend in 300 &#956;l 1x HBS solution. Jurkat T cells should ideally be at a concentration of 0.5-1.0 x 106 cells/ml before spinning.&#160;1x HBS solution consists of HEPES buffer saline with 1% Bovine serum albumin as described previously22.</li>
		<li>Add 150 &#956;L of 1x HBS solution in each well and incubate at 37 &#176;C for 15 min.</li>
		<li>Add 50 &#956;L resuspended Jurkat T cells to each well and incubate for 3 min at 37 &#176;C.</li>
		<li>Add 300 &#956;L of 4% Paraformaldehyde to each well and incubate for 30 min at 37 &#176;C.</li>
		<li>Wash 3x with 1x PBS.</li>
		<li>Permeabilize cells by adding 250 &#956;L of 0.1% Triton-X solution for 5 min at room temp.</li>
		<li>Wash 3x with 1x PBS.</li>
		<li>Add 250 &#956;L 1% fish gelatin solution in 1x PBS to each well for 30 min at room temp.</li>
		<li>Wash 3x with 1x PBS.</li>
	</ol>
	</li>
	<li>Imaging activated Jurkat T cells using madSTORM
	<ol>
		<li>Add 200 &#956;L of labeled antibody at 0.1-0.5 &#956;g/mL to fixed cells for 1 hr at room temp.</li>
		<li>Wash 5x with 1x PBS.</li>
		<li>Add 1 ml of STORM buffer and cover the chamber with a glass coverslip to limit exposure to air. The STORM buffer has been described previously10. We recommend making new STORM buffer for each round of imaging and spinning the STORM buffer at 20,800 rcf for 2 min to remove precipitates.</li>
		<li>Using low 647 nm laser power in TIRF mode, locate a stained cell with at least 3 FNDs in the field of view. To aid in locating FNDs, concomitant excitation with 568 nm laser can be used to increase the brightness of the FND signal.&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160;&#160; 
		NOTE: The performance of averaged fiducial correction described in section 2.1 improves with inclusion of more FNDs.</li>
		<li>Increase 647 nm laser power and acquire images. We typically acquire 10,000 frames at 200 ms exposure, 125 mW 647 nm laser, 100X TIRF objective lens (1.49 NA), 100X EM gain (5 MHz at 16 bit, conversion gain 1). Details of our imaging setup have been described previously10.</li>
		<li>Wash 5x with 1x TBS.</li>
		<li>Add 1 ml of elution buffer (3.5 M MgCl2, 20 mM PIPES, 0.1% Tween-20, pH 6.5) and incubate at room temp for 1 min.</li>
		<li>Repeat elution 3 times. (exact elution conditions and the number of elution rinses needed to remove the signal must be checked for each antibody used).</li>
		<li>Wash 3x with 1x TBS and add 1 ml of 1x PBS.</li>
		<li>Photobleach using 647 nm laser at high power (125 mW) with 405 nm laser at 2-5 mW to photo-activate A647 dyes in dark state. Wait until all remaining signal from non-eluted antibody is photobleached. Typically this takes 2-5 s of laser exposure. Acquiring sample images (~100 frames) using the STORM settings in 1.3.5 after elution and photobleaching is recommended to confirm removal of signal. 
		We typically remove 99.8% of signal using this method. We recommend testing the elution efficiency of each antibody before use in madSTORM as shown previously10.</li>
		<li>Add 250 &#956;L 4% Paraformaldehyde for 15 min. This prevents reverse crosslinking of fixed molecules in the cell.</li>
		<li>Wash 3x with 1x PBS.</li>
		<li>Repeat steps 1.3.1-1.3.12 for sequential labeling of multiple targets. Figure 1 shows a composite image of multiple molecules in an activated Jurkat T cell acquired using madSTORM.</li>
	</ol>
	</li>
</ol>
2. Drift Correction and Alignment of Multiplexed Image Stacks
<ol>
	<li>Drift correction of madSTORM image stacks
	<ol>
		<li>Open images using ImageJ. We recommend converting the image files to a TIFF format before opening in ImageJ. Use rectangle ROI to select a single FND. Play the time lapse to make sure that the FND is visible in every frame of the image stack. Use Run Analysis in the ThunderSTORM plugin to localize the FND. For the averaged fiducial correction method described in 2.1.5, it is important that a single FND is localized in each image frame (i.e. number of identified peaks should be equal to the number of frames).</li>
		<li>If the FND is not identified in every frame, select another FND. If multiple peaks are located in a single frame, remove the peak that deviates the most from previous peaks.</li>
		<li>Repeat 2.1.2 for each FND in the image stack. Keep in mind that some FNDs should not be included in the drift correction process so they can serve as an independent gauge for drift correction precision as described in step 2.1.7.</li>
		<li>For faster, less precise drift correction, use either the cross correlation or fiducial correction algorithms included in ThunderSTORM to correct the FND images. Typically, we use 100 bins, 5 magnification, and 0.1 trajectory smoothing factor for cross correlation and 10 max distance, 0.1 min marker visibility ratio, and 0.01 trajectory smoothing factor for fiducial correction. Cross correlation and fiducial correction will yield a correction file which can be applied to other FNDs to test the drift correction precision.</li>
		<li>For more stringent, more precise drift correction, use the averaged fiducial correction (AFC) algorithm as described previously10. This process does not require optimization of parameters and yields higher localization precision than predicted by SMLM localization algorithms. However, averaged drift correction requires at least 4 FNDs and for the FNDs to be localized in every frame of the image stack. AFC drift correction performs better with inclusion of more FNDs because a large number of localized FNDs will lessen the distorting effect of an outlier peak in a given frame.</li>
		<li>Localize all point spread functions within the image stack using ThunderSTORM as described previously10. We typically use pixel size of 100 nm, photoelectrons per A/D count of 4.28, base level A/D count of 100, EM gain of 100, PSF: Integrated Gaussian localization, 5 pixel fitting radius, maximum likelihood fitting method, and 1.3 pixel initial sigma.</li>
		<li>Apply the drift correction factor acquired from FNDs to localizations from the entire image stack. Visually inspect the final image to ensure proper drift correction (Figure 2). The resulting drift correction factor should be independently tested on FNDs not included in the AFC procedure to measure the level of drift correction precision. The precision of drift-corrected, independent FNDs should be close to the average precision/uncertainty value calculated from the localization algorithm utilized in step 2.1.6.</li>
		<li>Repeat steps 2.1.1-2.1.7 for the madSTORM image stacks of sequentially labeled antibodies. Keep in mind, that localizing the same set of FNDs in each madSTORM image stack is critical for the following alignment procedure.</li>
	</ol>
	</li>
	<li>Alignment of madSTORM images
	<ol>
		<li>Calculate the centroid position of the drift corrected FNDs in each madSTORM image. To do this, find the mean x and y axis positions for each localized FND, and average the mean x and y positions of all FNDs in each madSTORM image. The detailed algorithm for this step has been described previously10. Align sequential madSTORM images using the centroid positions of FND fiducial markers. To perform the alignment, calculate the offset between two localized madSTORM images by subtracting the centroid position of FNDs in the second madSTORM image from the first. Then subtract the offset amount from all localizations in the second madSTORM image.</li>
		<li>We recommend using the first madSTORM image as a reference image and to repeat this alignment step between the first and third, first and fourth, etc. Test the alignment precision by measuring the offset between FNDs not included in the alignment process. Large offsets may indicate that different sets of FNDs were used when calculating the centroid positions of sequential madSTORM images.</li>
		<li>If so, steps 2.2.1-2.2.2 should be repeated using the same set of FNDs to perform alignment. 
		NOTE: Custom codes for both the AFC drift correction and alignment procedures are provided.</li>
	</ol>
	</li>
</ol>

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We have nothing to disclose.

The sequential multiplexing, drift correction, and alignment procedures in madSTORM allow precise, highly multiplexed visualization of heterogenous structures in cells.10 In addition, madSTORM avoids the limitations of multi-color STORM such as chromatic aberration and sub-optimal photoswitching/emission properties of non-far red dyes9,12. As the elution step significantly reduces steric interference from sequentially bound antibodies, madSTORM can be used to perform repeated imaging of cell samples beyond the 6-10 rounds of multiplexed imaging achieved by previous techniques9,15,21. Moreover, the sequential elution and staining steps are not restricted to SMLM and can be used for other imaging techniques such as confocal microscopy, SIM and two-photon microscopy. However, given that madSTORM is a sequential immunostaining technique, this technique is limited by the availability of antibodies for each targeted molecule. Each antibody should be carefully tested to ensure specificity, labeling efficiency, and elution ability.
The acquisition settings for madSTORM were chosen to maximize detection of individual photoswitching events, allowing us to achieve 2.5 nm average localization precision. However, the high precision comes at the expense of time, requiring ~3 h for each round of madSTORM imaging as discussed previously10. For faster image acquisition at lower localization precision (5-10 nm), a shorter exposure time and higher EM gain are recommended (e.g. 20 ms exposure, 300X EM gain, 17 MHz at 16 bit, conversion gain 3). Furthermore, multi-color imaging (e.g. Atto-488, Alexa-568, and Alexa-647 nm) can be performed for each round of madSTORM imaging to increase the scale of multiplexing, albeit with a decrease in alignment precision due to chromatic aberration.
We have successfully performed up to 25 rounds of madSTORM imaging using this protocol. As the entire procedure is performed with the coverslip chamber mounted on the microscope stage, it is important to secure the coverslip chamber in the same position. To do this utilize auto focus mode and mount the chamber on the microscope stage using metal clamps. If significant stage drift has occurred, use known positions of FND fiducial markers to re-center the cell sample.
Simultaneous visualization of aligned madSTORM images can be challenging. To simultaneously view 7 or fewer madSTORM images, use Color/merge channels in ImageJ to create a pseudo-colored composite image. For 8 or more images, it is advisable to catagorize the madSTORM images into different groups, merge each group into a composite image, and apply a single LUT color to each composite image.
We used 100 nm nitrogen vacancy-center nano-diamonds due to their brightness and adherence to PLL-coated coverslips. FNDs smaller than 80 nm did not adhere well to PLL-coated coverslips, and FNDs larger than 100 nm were significantly brighter than Alexa-647 dye when excited using 647 nm laser, limiting optimization of camera acquisition settings for Alexa-647 emission. We recommend experimenting with different FND sizes or colors if the 100 nm nitrogen vacancy-center FNDs are not optimal for your imaging setup. Alternatively, nano-gold particles or fluorescent beads can be used as fiducial markers, but are not optimal for the averaged point correction procedure used below in section 2.110. Finally, for NV- vacancy FNDs, the emission intensity can be decreased by up to ~50% in situ by the application of a static magnetic field23. The decrease in intensity is roughly linear up to ~500 Gauss at which point the effect saturates. In practice, the intensity of diamonds in the field of view can be decreased to the desired degree by bringing a permanent magnet closer to the microscope slide from the top.
Lastly, we optimized the elution buffer for removal of antibodies targeting the molecular components of the T cell microcluster. madSTORM is not limited to TCR microclusters, however, as microtubules, mitochondria, F-actin, Vimentin and other molecular structures have been imaged using this technique10. For antibodies targeting other cellular compartments, additional steps may be required to disrupt epitope binding such as higher temperature, lower pH and longer incubation of elution buffer. Conversely, the elution buffer can be used without Tween-20 to target sensitive membrane structures, preventing disruption of non-ionic interactions. We foresee in future experiments that the elution buffer composition will be tailored for individual antibodies to optimize elution efficiency and sample preservation. Furthermore, beyond the application in activated T cells, madSTORM may be feasible with other systems including in vitro molecular complexes, different cell types, and tissue sections. Various parameters of the madSTORM protocol, such as fixation, permeabilization, antibody staining, and elution buffer composition will need to be adjusted for each targeted system.

A variety of super-resolution microscopy techniques have been developed to overcome the diffraction limit of light microscopy (~200 nm). Among these is a category of techniques called single molecule localization microscopy (SMLM) which includes photo-activation localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM). SMLM techniques share in the use of fluorophores that can be switched between on (fluorescent) and off (dark/photo-switched) states, allowing sequential localization of fluorescence from single molecules1,2,3.
Due to its compatibility with commercially available dyes and microscopes, direct STORM (dSTORM) has become a widely adopted SMLM technique4. dSTORM can routinely achieve ~10 nm localization precision, defined as the uncertainty in calculating the center of a diffraction-limited point spread function (PSF). However, despite the high precision estimated using localization algorithms5,6,7, accurate determination of the actual location of single molecules has been hampered by a number of issues. First, mechanical movement of the microscope stage during image acquisition adds significant uncertainty to localization precision. As SMLM images are obtained over thousands of time-lapse frames, nano-scale movements of the microscope stage can significantly compromise the precision of the final super-resolution image8. To compensate for stage movement during image acquisition, stage drift is commonly estimated from regression-based fitting of binned localizations from the image itself (cross correlation) or sequential localizations from fiducial markers (fiducial correction)1,9. However, these methods require optimization of multiple parameters for each image stack, and cannot account for stage movements at short time scales such as mechanical vibration. Gold nano-particles and multi-color fluorescent beads have been used as fiducial markers in SMLM, but they are not photo-stable, and result in significantly lower precision after drift correction than the nitrogen vacancy-center fluorescent nano-diamonds (FNDs) used in madSTORM10.
In addition to the diffraction limit, light microscopy is further restricted by spectral limits. Simultaneous visualization of multiple targets requires fluorescent probes with non-overlapping spectral profiles, generally restricting fluorescence-based light microscopy to 6 colors and SMLM to 2-3 colors4,11,12. Moreover, non-linear chromatic aberration causes misalignment of multicolor images, which require extensive alignment procedures using multi-colored fiducial markers8,13. To overcome these limits, previous studies have imaged multiple targets using repetitive photobleaching or chemical quenching of sequentially bound fluorophores14,15,16,17,18,19. While these methods can overcome the spectral limits of microscopy, fluorescence bleaching is known to be a toxic process20, and prolonged bleaching or quenching may cause unwanted side effects such as loss of crosslinking. Furthermore, the accumulation of fluorescent probes could lead to steric blocking of binding sites in the sample, preventing large-scale multiplexing and robust targeting of epitopes. To avoid such steric interference, a recent study achieved multiplexing using stochastic exchange of freely diffusing protein fragments21. Whereas this method allows dense labeling of cellular structures, it requires extensive biochemical preparation to isolate peptide fragments, cannot locate single molecule positions, and does not readily facilitate large-scale multiplexing using commercially available probes. We present a detailed video protocol describing the sequential binding and elution of fluorescently antibodies for multiplexed, antibody size-limited dSTORM (madSTORM) imaging, and use of fluorescent nano-diamonds to achieve precise drift correction and alignment.

<table><tbody><tr><td>8 well coverslip chamber</td><td>Lab-tek</td><td>155409</td><td></td></tr><tr><td>0.1% Poly-L-Lysine solution</td><td>Sigma-Aldrich</td><td>P8920</td><td></td></tr><tr><td>NV-100 nm Fluorescent Nano-diamond</td><td>Adamas</td><td>Red FND</td><td></td></tr><tr><td>anti-CD3&amp;epsilon; antibody</td><td>BD Biosciences</td><td>555329</td><td></td></tr><tr><td>Bovine serum albumin, Fraction V</td><td>KSE Scientific</td><td>98-100P</td><td></td></tr><tr><td>Triton-X</td><td>Sigma-Aldrich</td><td>T9284</td><td></td></tr><tr><td>10% Paraformaldehyde</td><td>EMS</td><td>15712</td><td>Carcinogen</td></tr><tr><td>Gelatin from fresh water fish skin</td><td>Sigma-Aldrich</td><td>G7041</td><td></td></tr><tr><td>Alexa 647 antibody labeling kit</td><td>Thermo Fisher</td><td>A20186</td><td></td></tr><tr><td>Magnesium Chloride hexahydrate</td><td>Sigma-Aldrich</td><td>138924</td><td></td></tr><tr><td>PIPES</td><td>Sigma-Aldrich</td><td>P6757</td><td></td></tr><tr><td>Tween-20</td><td>Fisher Scientific</td><td>BP337-500</td><td></td></tr><tr><td>2-Mercaptoethanol</td><td>Sigma-Aldrich</td><td>M7154</td><td>Highly toxic, air sensitive</td></tr><tr><td>Cysteamine</td><td>Sigma-Aldrich</td><td>30070</td><td>Highly toxic</td></tr><tr><td>Cyclooctatetraene 98%</td><td>Sigma-Aldrich</td><td>138924</td><td>Highly toxic, air sensitive</td></tr><tr><td>10x PBS</td><td>KD Medical</td><td>RGF-3210</td><td></td></tr><tr><td>10x TBS</td><td>KD Medical</td><td>RGF-3385</td><td></td></tr></tbody></table>

highly multiplexed, super-resolution imaging of t cells using madstorm

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

The sequential elution and staining method was used to produce the multiplexed madSTORM image of microclusters and other structures in an activated Jurkat T cell (Figure 1, check figure alignments). Each pseudo-colored image represents one round of madSTORM imaging acquired in steps 1.1.1 to 1.3.13. As described previously10, the field of view should be monitored for residual signal from previous, non-eluted antibody to avoid crosstalk between multiplexed targets. Moreover, as the elution efficiency can vary among antibodies, the order of sequential multiplexing should be arranged from the best to worst eluting antibody to minimize steric blocking of epitopes. The final madSTORM image in Figure 1 has been corrected for drift and alignment using AFC and FND fiducial markers as described in section 2.
FNDs were used to perform AFC on localizations from a single, independent FND (Figure 2A, 2B) and an activated Jurkat T cell (Figure 2C, 2D). The AFC drift correction was performed as described in steps 2.1.1-2.1.8 using the algorithms provided. First, visual inspection of the image after drift correction (Fig. 2C) is recommended to ensure proper application of the drift correction algorithm. Second, measuring the standard deviation of localizations in both the X and Y axis is recommended to confirm the precision achieved by AFC. As reported previously10, AFC typically yields better precision than expected by various localization algorithms.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55997/55997fig1.jpg" /> 
Figure 1: (A) An activated Jurkat T cell, sequentially imaged using multiple rounds of madSTORM imaging. LAT (red), pLAT-Y171 (magenta), pLAT-Y191 (pink), pLAT-Y226(purple), PLC&#947;1 (light blue), pSLP76-Y128 (green), pSRC-Y416 (aqua), pTCR&#950;-Y142 (blue), TOM20 (yellow), &#945;Tubulin (orange), ZAP70 (brown). Scale bar = 2.5 &#956;m. (B) Expanded image of boxed region in A. Scale bar = 500 nm. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55997/55997fig1large.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/55997/55997fig2.jpg" /> 
Figure 2: SMLM image of a single FND localized in 30,000 image frames (A) before and (B) after drift correction with averaged fiducial correction. Scale bars = 20 nm (A and B). SMLM image of an activated Jurkat T cell stained with anti-phosphorylated SLP76 (Y128) antibody and FND fiducial markers (C) before and (D) after drift correction with averaged fiducial correction. Scale bar = 2 &#956;m. <a href="https://cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/55997/55997fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Highly Multiplexed, Super-resolution Imaging of T Cells Using madSTORM at JoVE.com

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

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

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

highly-multiplexed-super-resolution-imaging-of-t-cells-using-madstorm

Universidad San Sebastian

Research

JoVE Journal

Immunology and Infection

7.3K Views.  National Institutes of Health. We demonstrate a method to image multiple molecules within heterogeneous nano-structures at single molecule accuracy using sequential binding and elution of fluorescently labeled antibodies.

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

A TIRF Microscopy Technique for Real-time, Simultaneous Imaging of the TCR and its Associated Signaling Proteins

Signaling is initiated through the T Cell Receptor (TCR) when it is engaged by antigenic peptide fragments bound by Major Histocompatibility Complex (pMHC) proteins expressed on the surface of antigen presenting cells (APCs). The TCR complex is composed of the ligand binding TCR&alpha;&beta; heterodimer that associates non-covalently with CD3 dimers (the &#949;&delta; and &#949;&gamma; heterodimers and the &zeta;&zeta; homodimer)1. Upon engagement of the receptor, the CD3 &zeta; chains are phosphorylated by the Src family kinase, Lck. This leads to the recruitment of the Syk family kinase, Zap70, which is then phosphorylated and activated by Lck. After that, Zap70 phosphorylates the adapter proteins LAT and SLP76, initiating the formation of the proximal signaling complex containing a large number of different signaling molecules2. 
The formation of this complex eventually results in calcium and Ras-dependent transcription factor activation and the consequent initiation of a complex series of gene expression programs that give rise to T cell differentiation2. TCR signals (and the resulting state of differentiation) are modulated by many other factors, including antigen potency and crosstalk with co-stimulatory/co-inhibitory, chemokine, and cytokine receptors 3-4. Studying the spatial and temporal organization of the proximal signaling complex under various stimulation conditions is, therefore, key to understanding the TCR signaling pathway as well as its regulation by other signaling pathways.
One very useful model system to study signaling initiated by the TCR at the plasma membrane in T cells is glass-supported lipid bilayers, as described previously5-6. They can be utilized to present antigenic pMHC complexes, adhesion, and co-stimulatory molecules to T cells-serving as artificial APCs. By imaging the T cells interacting with the lipid bilayer using total internal reflection fluorescence microscopy (TIRFM), we can restrict the excitation to within 100 nm of the space between the glass and the cell surface 7-8. This allows us to image primarily the signaling events occurring at the plasma membrane. As we are interested in imaging the recruitment of signaling proteins to the TCR complex, we describe a two-camera TIRF imaging system wherein the TCR, labeled with fluorescent Fab (fragment antigen binding) fragments of the H57 antibody (purified from hybridoma H57-597, ATCC, ATCC Number:HB-218) which is specific for TCR&beta;, and signaling proteins, tagged with GFP, may be imaged simultaneously and in real time. This strategy is necessary due to the highly dynamic nature of both the T cells and of the signaling events that are occurring at the TCR. This imaging modality has allowed researchers to image single ligands 9-11 as well as recruitment of signaling molecules to activated receptors and is an excellent system to study biochemistry in-situ12-16.

The compartmentalization of proteins either within the plasma membrane or into intracellular locations is one regulatory mechanism that can greatly influence signaling outcomes; hence, to understand signaling it is important to study the spatial and temporal behavior of the proteins involved. We describe here a TIRF microscopy based system to study signal transduction in T cells, but is broadly applicable.

Signaling is initiated through the T Cell Receptor (TCR) when it is engaged by antigenic peptide fragments bound by Major Histocompatibility Complex ...

Visualizing Surface T-Cell Receptor Dynamics Four-Dimensionally Using Lattice Light-Sheet Microscopy

The signaling and function of a cell are dictated by the dynamic structures and interactions of its surface receptors. To truly understand the structure-function relationship of these receptors in situ, we need to visualize and track them on the live cell surface with enough spatiotemporal resolution. Here we show how to use recently developed Lattice Light-Sheet Microscopy (LLSM) to image T-cell receptors (TCRs) four-dimensionally (4D, space and time) at the live cell membrane. T cells are one of the main effector cells of the adaptive immune system, and here we used T cells as an example to show that the signaling and function of these cells are driven by the dynamics and interactions of the TCRs. LLSM allows for 4D imaging with unprecedented spatiotemporal resolution. This microscopy technique therefore can be generally applied to a wide array of surface or intracellular molecules of different cells in biology.

The goal of this protocol is to show how to use Lattice Light-Sheet Microscopy to four-dimensionally visualize surface receptor dynamics in live cells. Here T cell receptors on CD4+ primary T cells are shown.

The signaling and function of a cell are dictated by the dynamic structures and interactions of its surface receptors. To truly understand the ...

Biochemistry

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is an effective methodology for tumor evaluation that improves the understanding of the tumor microenvironment. The visualization of protein expression in formalin-fixed, paraffin-embedded&#160;tissues is achieved when a specific fluorophore is annealed to an antibody-bound barcode via complementary oligonucleotides and then sample imaging is performed; indeed, this method allows for the use of customizable panels of more than 40 antibodies in a single tissue staining reaction. This method is compatible with fresh frozen tissue, formalin-fixed, paraffin-embedded tissue, cultured cells, and peripheral blood mononuclear cells, meaning that researchers can use this technology to view a variety of sample types at single-cell resolution. This method starts with a manual staining and fixing protocol, and all the antibody barcodes are applied using an antibody cocktail. The staining fluidics instrument is fully automated and performs iterative cycles of labeling, imaging, and removing spectrally distinct fluorophores until all the biomarkers have been imaged using a standard fluorescence microscope. The images are then collected and compiled across all the imaging cycles to achieve single-cell resolution for all the markers. The single-step staining and gentle fluorophore removal not only allow for highly multiplexed biomarker analysis but also preserve the sample for additional downstream analysis if desired (e.g., hematoxylin and eosin staining). Furthermore, the image analysis software enables image processing-drift compensation, background subtraction, cell segmentation, and clustering-as well as the visualization and analysis of the images and cell phenotypes for the generation of spatial network maps. In summary, this technology employs a computerized microfluidics system and fluorescence microscope to iteratively hybridize, image, and strip fluorescently labeled DNA probes that are complementary to tissue-bound, oligonucleotide-conjugated antibodies.

Multiplexed barcoding image analysis has recently improved the characterization of the tumor microenvironment, permitting comprehensive studies of cell composition, functional state, and cell-cell interactions. Herein, we describe a staining and imaging protocol using the barcoding of oligonucleotide-conjugated antibodies and cycle imaging, which allows for the use of a high-dimensional image&#160;analysis technique.

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is ...

Cancer Research

Multiplex Immunofluorescence and Spatial Image Analysis of the Tumor Microenvironment

Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

The tumor microenvironment (TME) is composed of a plethora of different cell types, such as cytotoxic immune cells and immunomodulatory cells. Depending on its composition and the interactions between cancer cells and peri-tumoral cells, the TME may affect cancer progression. The characterization of tumors and their complex microenvironment could improve the understanding of cancer diseases and may help scientists and clinicians to discover new biomarkers.
We recently developed several multiplex immunofluorescence (mIF) panels based on tyramide signal amplification (TSA) for the characterization of the TME in colorectal cancer, head and neck squamous cell carcinoma, melanoma, and lung cancer. Once the staining and scanning of the corresponding panels are completed, the samples are analyzed on an image analysis software. The spatial position and the staining of each cell are then exported from this quantification software into R. We developed R scripts that allow us not only to analyze the density of each cell type in several tumor compartments (e.g. the center of the tumor, the margin of the tumor, and the stroma) but also to perform distance-based analyses between different cell types.
This particular workflow adds a spatial dimension to the classical density analysis already routinely performed for several markers. mIF analysis could allow scientists to have a better understanding of the complex interaction between cancer cells and the TME and to discover new predictive biomarkers of response to treatments, such as immune checkpoint inhibitors, and targeted therapies.

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment  

In this article, a protocol for manual tyramide signal amplification (TSA) multiplex immunofluorescence (mIF) combined with image analysis and spatial analysis is described. This protocol can be used with formalin-fixed paraffin-embedded (FFPE) sections for the staining of two to six antigens per slide depending on the slide scanner available in the laboratory.

The tumor microenvironment (TME) is composed of a plethora of different cell types, such as cytotoxic immune cells and immunomodulatory cells. Depending ...

Mapping the Tumor Immune Landscape: An In-Depth Multiplex IHC Analysis Protocol

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. Multiplex immunohistochemistry is a valuable tool to visualize and identify different types of immune cells in tumor tissues while retaining its spatial information. Here we provide detailed protocols to analyze lymphocyte, myeloid, and dendritic cell populations in tissue sections. Starting from cutting formalin-fixed paraffin-embedded sections, automatic multiplex staining procedures on an automated platform, scanning of the slides on a multispectral imaging microscope, to the analysis of images using an in-house-developed machine learning algorithm ImmuNet. These protocols can be applied to a variety of tumor specimens by simply switching tumor markers to analyze immune cells in different compartments of the sample (tumor versus invasive margin) and apply nearest-neighbor analysis. This analysis is not limited to tumor samples but can also be applied to other (non-)pathogenic tissues. Improvements to the equipment and workflow over the past few years have significantly shortened throughput times, which facilitates the future application of this procedure in the diagnostic setting.

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

This protocol describes in detail how immune cell characterization of the tumor microenvironment using multiplex immunohistochemistry is carried out.

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. ...

Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data

Filamentous-actin plays a crucial role in a majority of cell processes including motility and, in immune cells, the formation of a key cell-cell interaction known as the immunological synapse. F-actin is also speculated to play a role in regulating molecular distributions at the membrane of cells including sub-membranous vesicle dynamics and protein clustering. While standard light microscope techniques allow generalized and diffraction-limited observations to be made, many cellular and molecular events including clustering and molecular flow occur in populations at length-scales far below the resolving power of standard light microscopy. By combining total internal reflection fluorescence with the super resolution imaging method structured illumination microscopy, the two-dimensional molecular flow of F-actin at the immune synapse of T cells was recorded. Spatio-temporal image correlation spectroscopy (STICS) was then applied, which generates quantifiable results in the form of velocity histograms and vector maps representing flow directionality and magnitude. This protocol describes the combination of super-resolution imaging and STICS techniques to generate flow vectors at sub-diffraction levels of detail. This technique was used to confirm an actin flow that is symmetrically retrograde and centripetal throughout the periphery of T cells upon synapse formation.

To investigate flow velocities and directionality of filamentous-actin at the T cell immunological synapse, live-cell super-resolution imaging is combined with total internal reflection fluorescence and quantified with spatio-temporal image correlation spectroscopy.

Filamentous-actin plays a crucial role in a majority of cell processes including motility and, in immune cells, the formation of a key cell-cell ...

Imaging the Human Immunological Synapse

The purpose of the method is to generate an immunological synapse (IS), an example of cell-to-cell conjugation formed by an antigen-presenting cell (APC) and an effector helper T lymphocyte (Th) cell, and to record the images corresponding to the first stages of the IS formation and the subsequent trafficking events (occurring both in the APC and in the Th cell). These events will eventually lead to polarized secretion at the IS. In this protocol, Jurkat cells challenged with Staphylococcus enterotoxin E (SEE)-pulsed Raji cells as a cell synapse model was used, because of the closeness of this experimental system to the biological reality (Th cell-APC synaptic conjugates). The approach presented here involves cell-to-cell conjugation, time-lapse acquisition, wide-field fluorescence microscopy (WFFM) followed by image processing (post-acquisition deconvolution). This improves the signal-to-noise ratio (SNR) of the images, enhances the temporal resolution, allows the synchronized acquisition of several fluorochromes in emerging synaptic conjugates and decreases fluorescence bleaching. In addition, the protocol is well matched with the end point cell fixation protocols (paraformaldehyde, acetone or methanol), which would allow further immunofluorescence staining and analyses. This protocol is also compatible with laser scanning confocal microscopy (LSCM) and other state-of-the-art microscopy techniques. As a main caveat, only those T cell-APC boundaries (called IS interfaces) that were at the right 90&#176; angle to the focus plane along the Z-axis could be properly imaged and analyzed. Other experimental models exist that simplify imaging in the Z dimension and the following image analyses, but these approaches do not emulate the complex, irregular surface of an APC, and may promote non-physiological interactions in the IS. Thus, the experimental approach used here is suitable to reproduce and to confront some biological complexities occurring at the IS.

This protocol images both the immunological synapse formation and the subsequent polarized secretory traffic towards the immunological synapse. Cellular conjugates were formed between a superantigen-pulsed Raji cell (acting as an antigen-presenting cell) and a Jurkat clone (acting as an effector helper T lymphocyte).

The purpose of the method is to generate an immunological synapse (IS), an example of cell-to-cell conjugation formed by an antigen-presenting cell (APC) ...

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density and the location of immune cells in the TME have important diagnostic and prognostic values. Multiomic profiling of the TME has exponentially increased our understanding of the numerous cellular and molecular networks regulating tumor initiation and progression. However, these techniques do not provide information about the spatial organization of cells or cell-cell interactions. Affordable, accessible, and easy to execute multiplexing techniques that allow spatial resolution of immune cells in tissue sections are needed to complement single cell-based high-throughput technologies. Here, we describe a strategy that integrates serial imaging, sequential labeling, and image alignment to generate virtual multiparameter slides of whole tissue sections. Virtual slides are subsequently analyzed in an automated fashion using user-defined protocols that enable identification, quantification, and mapping of cell populations of interest. The image analysis is done, in this case using the analysis modules Tissuealign, Author, and HISTOmap. We present an example where we applied this strategy successfully to one clinical specimen, maximizing the information that can be obtained from limited tissue samples and providing an unbiased view of the TME in the entire tissue section.

Here, we describe a simple and accessible strategy for visualizing, quantifying, and mapping immune cells in formalin-fixed paraffin-embedded tumor tissue sections. This methodology combines existing imaging and digital analysis techniques with the purpose of expanding the multiplexing capability and multiparameter analysis of imaging assays.

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density ...

Test Samples for Optimizing STORM Super-Resolution Microscopy

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

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

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

Biology

Imaging Intermediate Filaments and Microtubules with 2-dimensional Direct Stochastic Optical Reconstruction Microscopy

The cytoskeleton, composed of actin microfilaments, microtubules, and intermediate filaments (IF), plays a key role in the control of cell shape, polarity, and motility. The organization of the actin and microtubule networks has been extensively studied but that of IFs is not yet fully characterized. IFs have an average diameter of 10 nm and form a network extending throughout the cell cytoplasm. They are physically associated with actin and microtubules through molecular motors and cytoskeletal linkers. This tight association is at the heart of the regulatory mechanisms that ensure the coordinated regulation of the three cytoskeletal networks required for most cell functions. It is therefore crucial to visualize IFs alone and also together with each of the other cytoskeletal networks. However, IF networks are extremely dense in most cell types, especially in glial cells, which makes its resolution very difficult to achieve with standard fluorescence microscopy (lateral resolution of ~250 nm). Direct STochastic Optical Reconstruction Microscopy (dSTORM) is a technique allowing a gain in lateral resolution of one order of magnitude. Here, we show that lateral dSTORM resolution is sufficient to resolve the dense organization of the IF networks and, in particular, of IF bundles surrounding microtubules. Such tight association is likely to participate in the coordinated regulation of these two networks and may, explain how vimentin IFs template and stabilize microtubule organization as well as could influence microtubule dependent vesicular trafficking. More generally, we show how the observation of two cytoskeletal components with dual-color dSTORM technique brings new insight into their mutual interaction.

The overall goal of this methodology is to give the optimal experimental conditions from sample preparation to image acquisition and reconstruction in order to perform 2D dual color dSTORM images of microtubules and intermediate filaments in fixed cells