We thank Dr. Vladana Vukojevi&#231;, Center for Molecular Medicine, Karolinska Institutet for the maintenance of the Zeiss Confocor 3 instrument and for helpful tips regarding cell measurements.&#160;This study was funded by grants from Vetenskapsr&#229;det (grant number 2012- 1629), Magnus Bergvalls stiftelse, and from Stiftelsen Claes Groschinskys minnesfond.

<ol>
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The authors have nothing to disclose.

This protocol for FCS can be used for the assessment of the molecular dynamics of surface molecules on all types of immune cells (murine, human, or other species). FCS measures spatio-temporal molecular dynamics down to single-molecule resolution in live cells. The molecular density, as well as the diffusion rate and clustering dynamics of the proteins of interest, can be extracted from the autocorrelation curves.
The fluorescent labeling is of pivotal importance for successful FCS experiments. Proteins of interest on the cell membrane have traditionally been labeled using antibodies, and antibodies are often most convenient for use on surface proteins in na&#239;ve immune cells. Unlabeled antibodies must be directly conjugated to photo-stable fluorescent dyes with a high molecular brightness. The selection of the fluorophore for antibody conjugation is important for the quality of the measurements. Standard labels for flow cytometry, such as fluorescein and phycoerythrin-based dyes, are not recommended due to rapid bleaching. Photo-stable dyes for direct conjugation to antibodies are currently provided by several companies. The manufacturers usually provide satisfactory protocols for conjugation and purification of the resulting conjugated antibody, and this can be done in a few hours. It is also possible to perform FCS using fluorescent proteins, but special care should be taken to minimize the laser power, since fluorescent proteins are in general more prone to bleaching than the most photo-stable chemical fluorophores. According to previous experience, over-expression of proteins often leads to a considerable decrease in the diffusion rate due to crowding, which makes the use of fluorescent proteins unsuitable.
Pinhole calibration prior to the measurements is also a critical step. The fluorophores used for determining the size of the focal volume must have known diffusion coefficients (see Equation 2). Use free fluorophores corresponding closely to the emission spectra of the antibody labels to determine the size of the focal volume. This must be done to assure the optimal signal efficiency and proper detection of potential cross-correlations between color channels. Perform an FCS laser power series of the freely diffusing fluorophores to ensure that the system gives the expected output signal over a range of powers. Compare the CPM at the same laser power between experiments to ensure consistency between experiments.
In the analysis step, it is essential to put reasonable ranges and starting values for the variables when fitting the models. Use previously published knowledge from similar cell systems to find good starting points. Theoretically, FCS is a quantitative technique, but since optimal conditions seldom occur, a certain degree of caution has to be taken when interpreting the results. All types of fluctuations will be recorded, regardless of the origin of such fluctuations. Therefore, it is useful to have as much information as possible about the experimental system in order to exclude possible error sources. For instance, movement of the whole cell membrane will give rise to fluctuations with longer TauD (Figure 3A), whereas putative contaminants of the free fluorophores in the solution will diffuse with at least ten times shorter TauD.
This basic protocol can be modified in several ways. If two proteins are labeled with different fluorophores, co-diffusion (an indirect measure of interaction) can be assessed by expanding the methodology to detect the cross-correlation. The extent to which these proteins bind to each other in the cell membrane is represented by the height of the cross-correlation curve relative to the height of the autocorrelation curves of the individual color channels. Cross-correlation is denoted as Ch1-Ch2 in the measurement software. The precise analysis of cross-correlation requires controls for cross-talk and is thus somewhat more complicated than the protocol presented here. A detailed description of how to proceed, as an extension to this basic protocol, can be found in Str&#246;mqvist et al.13. To optimize the positioning in the z-direction, z-scanning FCS can be used22. According to previous experience, it has been satisfactory to do this manually. It is also possible to fit multiple diffusion coefficients to an FCS autocorrelation curve23. This requires previous knowledge of the number of subpopulations with different diffusion rates and of the approximate diffusion rates of at least one of the subpopulations. Otherwise, there will be too many free variables, which will render the fitting unreliable. A typical example would be a surface protein whose diffusion rate is considerably slowed down by the binding of a ligand. Finally, cleaning the trace of outliers without entirely removing repeats is possible24.
The lack of a fluorescent signal is a common problem to troubleshoot. For freely diffusing fluorophores, use the halogen lamp to check if the drop is fluorescent. If the drop is not fluorescent, mix a new batch of fluorophores with a slightly increased concentration (within the nM range) and try again. Check that the focus is within the fluorescent drop, the lasers are turned on in the software, the laser power is sufficient, the correct emission filters and detectors are chosen, and the right channels in the &#34;FCS Measurement&#34; window are activated (see step 3.6). Start the laser and look from the side to visually confirm that the laser light reaches the sample. Avoid looking straight into the laser source. If the selected emission filter encompasses the laser wavelength, a shutter will automatically block the light path to avoid damage to the detector. If all the settings are fine but no light arrives, re-start the lasers, the FCS system, and the computer. Ask an expert if the lack of a fluorescent signal persists. A simplified procedure applies if the labeled cells do not display fluorescence. Confirm the presence of fluorescence with the halogen lamp; turn on the lasers, select the laser channels, and adjust the laser power. Select a position on the cell membrane, either using the &#34;Crosshair&#34; or &#34;Add position&#34; option (see step 6.4). Switch to the next sample if the signal is still too low.
One important aspect of the fluctuation basis of the technique is that molecules have to be reasonably mobile to be detected. Very slowly moving fractions of molecules or molecules trapped within areas smaller than the focus during the whole measurement time can therefore not be measured. This leads to a possible underestimation of surface densities and an overestimation of the diffusion rate. Bleaching can also contribute to a putative underestimation of both the density and TauD, since molecules will have a higher probability of being bleached the longer time they spend in the laser-illuminated focal volume. Influence from the background (fluorescence from outside of the focal plane) would, on the other hand, artificially increase the number of detected molecules and decrease the measured CPM. Previously, the total maximal error in the absolute density determination was estimated to be around 40%20. However, it is usually possible to compare different biological samples to each other, since the error sources are, in most cases, equal throughout the experiments. Another potential error source is that antibodies are bivalent, meaning that each antibody can potentially bind to two target molecules. The authors did not observe this phenomenon, but it cannot be guaranteed that this is a global feature for other antibodies. The potential influence of bivalence must therefore be tested individually for each antibody. The combination of specific primary and labeled secondary antibodies must never be used due to the high risk of inducing artificial clusters from two successive bivalent labels. If cells have instead been transfected with fluorescent protein-labeled versions of the protein of interest, every protein will have only one label. However, this requires transfection, which is not always desirable and may not even be possible (e.g., if using immune cells directly isolated from human blood). Finally, single-point FCS does not record images. If images are required for publication or other purposes, these must be captured separately using the imaging part of the software. Always capture images after FCS measurements to avoid unnecessary bleaching of the cell surface.
Unlike FCS, traditional confocal-based microscopy, such as FRAP, and image correlation methodologies cannot quantify fluorescence fluctuations at the single-molecule level6. Image correlation methodologies measure the number of molecules indirectly and with lower resolution, but they can assess the variability of molecular diffusion and clustering over the entire imaged area25. Standard SPT measured by confocal microscopy has an ideal level of labeling, orders of magnitude lower than FCS7. Therefore, the density of labeled proteins cannot be measured by standard SPT. The combination of SPT with other microscopy techniques (e.g., stochastic optical reconstruction microscopy or photoactivation localization microscopy) allows the density and movement to be assessed, but it requires very specialized dyes or proteins11. Super-resolution techniques, such as structured illumination microscopy and stimulated emission depleted microscopy, often require fixed samples and a combination of primary and secondary antibodies for labeling26. Localization is therefore very precise, while the dynamics of the system cannot often be assessed. In comparison to SPT and super-resolution techniques, FCS also requires significantly less computer power and time for the analysis and extraction of results. Flow cytometry is the workhorse of immunology and can be favorably combined with FCS. Cell sorting can, for example, be followed by subsequent measurements on selected cell populations if photo-stable dyes had been used prior to the sorting. FCS is thus a methodology that can be used alone or in combination with other established methods.
This protocol can be used to identify currently unknown features of immune cell dynamics and interactions within the cell membrane at the single-molecule level. Furthermore, features of whole populations versus selected subsets can be compared. It also has a potential role as a selection step for immune cell therapy. Since the measurements do not consume the cell, unlike most other methods for investigating the functionality of immune cells, individual cells can potentially be recovered and cultured. Thus, after the analysis of the FCS curves, promising cells can be extracted and expanded for additional applications, including putative clinical applications.

Many immune cells functions rely on molecular diffusion and interactions within membranes. Biological membranes are complex, and many factors that may be important for the function of immune cells can influence the speed of translational diffusion of proteins within cellular membranes1. We recently showed that natural killer (NK) cells, lymphocytes belonging to the innate immune system, exhibit differential diffusion of two studied proteins at the cell membrane depending on the state of NK cell activation2.
Fluorescence correlation spectroscopy (FCS) is a technique that is capable of quantifying molecular diffusion rates within biological membranes. It reports the average diffusion rate of fluorescently labeled molecules in a fixed volume, typically the focus of a confocal microscope. It is based on the measurement of the fluctuations in fluorescence that occur upon molecular movement in a system in steady state. FCS has been widely used for studying the diffusion of fluorescent dyes and proteins, both in solution and within lipid membranes. Other output parameters affecting the diffusion rate can also be indirectly studied in this fashion (e.g., conformational changes of proteins or interactions of molecules on cell membranes)3,4. FCS stands out compared to other techniques due to its high sensitivity, allowing the possibility for single-molecule detection. It works well for molecular concentrations in the nanomolar to millimolar range, which is typical for endogenous expression levels of most proteins5. Furthermore, FCS can give an approximation of the absolute number of proteins within the studied volume, while most other techniques only give relative information about protein expression levels. Other methods to measure molecular diffusion rates within membranes include fluorescence recovery after photobleaching (FRAP), single particle tracking (SPT), multiple pinhole FCS, and image correlation methods. FRAP and image correlation methods are ensemble techniques, which generally do not give information about the absolute number of molecules10. Compared to SPT, the throughput of FCS is higher in regard to characterizing the population average. The analysis is also less demanding since the average diffusion rate of the molecules present within the laser focus is measured, rather than the rate of single molecules. Also, unless specialized microscopes are available11, SPT cannot give any information about concentrations, since standard SPT labeling must be very low to allow for the identification of single molecules. On the other hand, FCS requires the molecules under study to be mobile. It will simply not detect any putative immobile fractions or molecules moving very slowly. The diffusion rate of molecules that reside within the focus longer than approximately one tenth of the acquisition time will not be correctly represented in FCS measurements3,12. Therefore, diffusion coefficients recorded by FCS tend to be faster than diffusion rates reported from techniques like FRAP and SPT, where the close-to-immobile and very slow fractions are taken into account as well. SPT will also give a more detailed description of the variability of diffusion rates within the molecular population than FCS will.
FCS quantifies the fluctuation of fluorescence intensity over time within the excited volume. In the case of membrane measurements, this translates to the illuminated area of the membrane. In this paper, we utilize the fact that such fluctuations are induced by molecules exhibiting Brownian diffusion and are thus moving in and out of the excitation volume. There are also several other possible sources for the fluctuations in the fluorescence signal, such as blinking or the presence of a triplet state in the fluorophores, environmental effects, binding-unbinding of the ligand, or movement of the entire cell membrane. These putative error sources need to be taken into consideration when designing an FCS experiment in order to accurately interpret the results12,13. Typically, lateral diffusion rates in biological membranes are low due to crowding and interactions, both between membrane proteins and between proteins and the cytoskeleton. Historically, the use of FCS in membranes has thus been hampered by the lack of photo-stable fluorophores, which are required to avoid bleaching during the extended transit times through the excitation focus14. However, today, there are plenty of options for suitable photo-stable dyes. Significant improvements in detectors and other hardware also allow the detection of fluorescent proteins and dyes of lower brightness. Here, a basic protocol for the application of FCS using murine primary lymphocytes, where the protein of interest is labeled with a fluorescently tagged antibody, is described. An approach to fit the autocorrelation curves in order to extract the diffusion coefficient and the molecular density is also shown. The protocol aims at being easily followed by immunologists with no previous experience of techniques to study the diffusion of molecules. However, a basic understanding of confocal microscopy is expected (to gain this basic understanding, see reference15). This protocol can relatively easily be adapted to other suspension cells, both cell lines and primary cells. For more experienced FCS users, more refined analysis methods exist, some of which are described in the discussion.

<table><tbody><tr><td>MACS NK Cell Isolation Kit mouse II</td><td>Miltenyi Biotec Norden&lt;sup&gt;AB&lt;/sup&gt;</td><td>130-096-892</td><td>Negative selection of NK cells</td></tr><tr><td>Fetal Bovine Serum</td><td>Sigma-Adlrich</td><td>F7524</td><td>Heat inactivated</td></tr><tr><td>Phosphate Buffered Saline</td><td>-</td><td>-</td><td>Made in house&amp;nbsp;</td></tr><tr><td>Roswell Park Memorial Institute medium 1640</td><td>PAA The Cell Culture Company&amp;nbsp;</td><td>E15-848</td><td>Transparent medium</td></tr><tr><td>Antibody clone 2.4G2</td><td>Thermo Fischer Scientific</td><td>553140</td><td>For blocking Fc-receptors.</td></tr><tr><td>Anti-Ly49A antibody&amp;nbsp;</td><td></td><td></td><td>Monoclonal antibody made in house and conjugated in house to Alexa fluor 647</td></tr><tr><td>Clone JR9.318</td><td></td><td></td><td></td></tr><tr><td>Anti-H-2Dd antibody&amp;nbsp;</td><td>BD Pharmingen</td><td>558915</td><td>Conjugated in house to MFP488</td></tr><tr><td>Clone 34.5.8S</td><td></td><td></td><td></td></tr><tr><td>MFP488</td><td>Mobiotech</td><td>MFP-A2181</td><td>Fluorescent dye for antibody conjugation.</td></tr><tr><td>Poly-L-Lysine</td><td>Sigma-Aldrich</td><td>P8920</td><td>Diluted in distilled water (1.10)</td></tr><tr><td>Poly-L-Lysine (20 kDa) grafted with polyethylene glycol (2 kDa)</td><td>SuSoS AG</td><td>PLL(20)-g[3.5]-PEG(2)</td><td>Diluted in PBS (pH 7.4) to 0.5 mg/ml.</td></tr><tr><td>Rhodamine 110 chloride</td><td>Sigma-Aldrich</td><td>432202</td><td>Known diffusion coefficient: 3.3 &amp;times; 10&lt;sup&gt;&amp;minus;10&lt;/sup&gt; m&lt;sup&gt;2&lt;/sup&gt;/sec &lt;sup&gt;19&lt;/sup&gt;</td></tr><tr><td>Alexa fluor 647</td><td>Thermo Fisher Scientific&amp;nbsp;</td><td>A20006</td><td>Known diffusion coefficient: 4.4 &amp;times; 10&lt;sup&gt;&amp;minus;10&lt;/sup&gt; m&lt;sup&gt;2&lt;/sup&gt;/sec &lt;sup&gt;20&lt;/sup&gt;</td></tr><tr><td>Confocal microscope&amp;nbsp;</td><td>Zeiss</td><td>LSM510</td><td></td></tr><tr><td>Software: Confocor 3</td><td>Zeiss</td><td></td><td></td></tr><tr><td>Software: Matlab with curve fitting toolbox</td><td>Matlab</td><td>Version R2013b</td><td></td></tr><tr><td>Nunc Lab-Tek Chambered Coverglass</td><td>Thermo-scientific&amp;nbsp;</td><td>155411</td><td>8 wells, 1.0 borosilicate bottom</td></tr></tbody></table>

molecular diffusion in plasma membranes of primary lymphocytes measured by fluorescence correlation spectroscopy

Fluorescence correlation spectroscopy (FCS) is a powerful technique for studying the diffusion of molecules within biological membranes with high spatial and temporal resolution. FCS can quantify the molecular concentration and diffusion coefficient of fluorescently labeled molecules in the cell membrane. This technique has the ability to explore the molecular diffusion characteristics of molecules in the plasma membrane of immune cells in steady state (i.e., without processes affecting the result during the actual measurement time). FCS is suitable for studying the diffusion of proteins that are expressed at levels typical for most endogenous proteins. Here, a straightforward and robust method to determine the diffusion rate of cell membrane proteins on primary lymphocytes is demonstrated. An effective way to perform measurements on antibody-stained live cells and commonly occurring observations after acquisition are described. The recent advancements in the development of photo-stable fluorescent dyes can be utilized by conjugating the antibodies of interest to appropriate dyes that do not bleach extensively during the measurements. Additionally, this allows for the detection of slowly diffusing entities, which is a common feature of proteins expressed in cell membranes. The analysis procedure to extract molecular concentration and diffusion parameters from the generated autocorrelation curves is highlighted. In summary, a basic protocol for FCS measurements is provided; it can be followed by immunologists with an understanding of confocal microscopy but with no other previous experience of techniques for measuring dynamic parameters, such as molecular diffusion rates.

A typical result will generate an autocorrelation curve with a transit time in the range of 10 msec to 400 msec for membrane proteins. The number of molecules can vary between 0.5 to around 200 per &#956;m2 for endogenously expressed proteins. Check carefully that the CPM is not lower than expected. This may mean that there is an influence of the background signal. As a rule of thumb, the CPM signal on cells that is accepted for analysis should not be lower than 33% of the CPM for freely diffusing antibodies at the same laser power. The CPM should scale linearly with the laser power. The autocorrelation curve for primary immune cell surface receptors is expected to be smooth, with the steepest part between 0.001 and 1 sec. See Figure 2 for a representative autocorrelation curve and its time trace.
As mentioned briefly in the introduction, there are several cases where FCS is not suitable for measuring molecular diffusion due to the influence of other sources of fluorescence fluctuations, which contribute to confounding the signal. Figure 3 shows examples of cases in which a particular cell or measurement repeat should be discarded. It has already been mentioned that an extremely low signal (very low CPM) is cause for discarding the cell. Another reason for discarding the individual cell is that the cell is moving (Figure 3A). In the case of bleaching (Figure 3B) or if large clusters are present (Figure 3C), the measurements should be discarded if the effect is considerable or present throughout the whole measurement. If this feature is only present in one or two repeats, these individual repeats may be discarded, but the remaining repeats may still be used.
It is important to both use the correct model to fit the data and also to check carefully that the fit closely overlaps with the autocorrelation curve. In Figure 4, examples of good and bad curve fits are shown. Pay close attention to the part of the curve representing diffusion (the most steeply sloping part). It is also important to ascertain that both the start and the end of the sloping part of the curve are well-fitted by the model. If the fit is bad, without apparent problems such as moving, bleaching, or clusters, modify the starting values and/or the upper and lower limits (within a reasonable range) before making a final decision to discard the cell.
<img alt="Figure 1" src="/files/ftp_upload/54756/54756fig1.jpg" /> 
Figure 1: FCS software interface. Shown here are the main windows necessary to operate the software tool for FCS acquisition and imaging, as described in the protocol. Window A, the &#34;Configuration Control&#34; window is the control window for the beam path, laser channels, and filters for imaging. Window B is the &#34;Scan Control&#34; window for imaging and shows the scanning settings. Window C is the &#34;Measurement&#34; window, the window for the setup of the FCS measurement settings. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54756/54756fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54756/54756fig2.jpg" /> 
Figure 2: Representative trace and autocorrelation curves of diffusing H-2Dd major histocompatibility class I surface molecules in freshly isolated murine NK cells. The upper panel in the figure shows the fluorescence fluctuation as a function of time throughout the whole time trace of 7x 10 sec measurements. The lower panel represents the averaged autocorrelation curve from these seven repeats. The height of the autocorrelation curve is inversely proportional to the concentration of mobile fluorescently labeled H-2Dd entities within the focal volume. The x-axis value at the steepest part of the slope of the curve, half of the amplitude, represents the average residence time of fluorescently labeled H-2Dd molecules within the focal volume. The measurement presented is a part of the data set published in reference2. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54756/54756fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/54756/54756fig3.jpg" /> 
Figure 3: Examples of problematic cell traces. (A) A moving cell, as exemplified by the trace not having a stable basal level. The top panel shows an example where the whole cell has moved out of focus after approximately 35 sec, as represented by the very low signal after this time point. In the lower panel, the effect is more subtle, with undulations apparent at an interval of several seconds. (B) Cell displaying bleaching, the height of the trace decreasing with time. Compare the height of the trace at the start of measurement to that at the end of the measurement. (C) The presence of large clusters, represented by spikes in the trace. In the upper panel, one large cluster at 20-25 sec is present. In the lower panel, several clusters are present throughout the measurement. The measurements presented are a part of the data set published in reference2. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54756/54756fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/54756/54756fig4.jpg" /> 
Figure 4: Representative autocorrelation curves with curve-fitting and residuals. The red and blue vertical lines represent the limits of the time window used for fitting. (A) Example of a representative fit of a 2-dimensional diffusion model to a sample autocorrelation curve. In the upper panel, the green curve (the model) overlays the blue curve (the acquired autocorrelation), indicating a good fit. The lower panel shows the residual from the curve fitting. The fluctuations around 1 sec, which are not fitted well, are typical for cell measurements and are tolerable. (B) Example of a bad fit of 2-dimensional diffusion to the same autocorrelation curve. The fitted model does not overlay the autocorrelation curve, and it does not converge at 1. The lower panels in (A and B) show the residual from the curve fitting. The residuals are significantly larger for the bad fit, especially for the diffusion part of the curve. The measurement presented is a part of the data set published in reference2. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54756/54756fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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Molecular Diffusion in Plasma Membranes of Primary Lymphocytes Measured by Fluorescence Correlation Spectroscopy | Immunology and Infection | Video

Molecular Diffusion in Plasma Membranes of Primary Lymphocytes Measured by Fluorescence Correlation Spectroscopy

A method to measure protein diffusion in membranes of primary immune cells using fluorescence correlation spectroscopy (FCS) is described. In this paper, the use of antibodies for fluorescent labeling is illustrated.

Fluorescence correlation spectroscopy (FCS) is a powerful technique for studying the diffusion of molecules within biological membranes with high spatial ...

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10.8K Views.  Karolinska Institutet. A method to measure protein diffusion in membranes of primary immune cells using fluorescence correlation spectroscopy (FCS) is described. In this paper, the use of antibodies for fluorescent labeling is illustrated.

Video: Molecular Diffusion in Plasma Membranes of Primary Lymphocytes Measured by Fluorescence Correlation Spectroscopy

Methods to Assess Beta Cell Death Mediated by Cytotoxic T Lymphocytes

Type 1 diabetes (T1D) is a T cell mediated autoimmune disease. During the pathogenesis, patients become progressively more insulinopenic as 
insulin production is lost, presumably this results from the destruction of pancreatic beta cells by T cells. Understanding the mechanisms of beta cell death during 
the development of T1D will provide insights to generate an effective cure for this disease. Cell-mediated lymphocytotoxicity (CML) assays have historically used the 
radionuclide Chromium 51 (51Cr) to label target cells. These targets are then exposed to effector cells and the release of 51Cr from target 
cells is read as an indication of lymphocyte-mediated cell death. Inhibitors of cell death result in decreased release of 51Cr.
 As effector cells, we used an activated autoreactive clonal population of CD8+ Cytotoxic T lymphocytes (CTL) isolated from a mouse stock transgenic for both the alpha and beta chains of the AI4 T cell receptor (TCR). Activated AI4 T cells were co-cultured with 51Cr labeled target NIT cells for 16 hours, release of 51Cr was recorded to calculate specific lysis 
Mitochondria participate in many important physiological events, such as energy production, regulation of signaling transduction, and apoptosis. The study of beta cell mitochondrial functional changes during the development of T1D is a novel area of research. Using the mitochondrial membrane potential dye Tetramethyl Rhodamine Methyl Ester (TMRM) and confocal microscopic live cell imaging, we monitored mitochondrial membrane potential over time in the beta cell line NIT-1. For imaging studies, effector AI4 T cells were labeled with the fluorescent nuclear staining dye Picogreen. NIT-1 cells and T cells were co-cultured in chambered coverglass and mounted on the microscope stage equipped with a live cell chamber, controlled at 37&deg;C, with 5% CO2, and humidified. During these experiments images were taken of each cluster every 3 minutes for 400 minutes.
Over a course of 400 minutes, we observed the dissipation of mitochondrial membrane potential in NIT-1 cell clusters where AI4 T cells were attached. In the simultaneous control experiment where NIT-1 cells were co-cultured with MHC mis-matched human lymphocyte Jurkat cells, mitochondrial membrane potential remained intact. This technique can be used to observe real-time changes in mitochondrial membrane potential in cells under attack of cytotoxic lymphocytes, cytokines, or other cytotoxic reagents.

Cell-mediated lymphocytotoxicity (CML) assays can be used to test autoreactive responses and study mechanisms of cell death in vitro. However, using live-cell confocal microscopic imaging techniques with fluorescent dyes, the type and kinetics of cell death as well as the pathways utilized can be studied in greater detail.

Type 1 diabetes (T1D) is a T cell mediated autoimmune disease.  During the pathogenesis, patients become progressively more insulinopenic as 
insulin ...

piggyBac Transposon System Modification of Primary Human T Cells

The piggyBac transposon system is naturally active, originally derived from the cabbage looper moth1,2. This non-viral system is plasmid based, most commonly utilizing two plasmids with one expressing the piggyBac transposase enzyme and a transposon plasmid harboring the gene(s) of interest between inverted repeat elements which are required for gene transfer activity. PiggyBac mediates gene transfer through a "cut and paste" mechanism whereby the transposase integrates the transposon segment into the genome of the target cell(s) of interest. PiggyBac has demonstrated efficient gene delivery activity in a wide variety of insect1,2, mammalian3-5, and human cells6 including primary human T cells7,8. Recently, a hyperactive piggyBac transposase was generated improving gene transfer efficiency9,10.
Human T lymphocytes are of clinical interest for adoptive immunotherapy of cancer11. Of note, the first clinical trial involving transposon modification of human T cells using the Sleeping beauty transposon system has been approved12. We have previously evaluated the utility of piggyBac as a non-viral methodology for genetic modification of human T cells. We found piggyBac to be efficient in genetic modification of human T cells with a reporter gene and a non-immunogenic inducible suicide gene7. Analysis of genomic integration sites revealed a lack of preference for integration into or near known proto-oncogenes13. We used piggyBac to gene-modify cytotoxic T lymphocytes to carry a chimeric antigen receptor directed against the tumor antigen HER2, and found that gene-modified T cells mediated targeted killing of HER2-positive tumor cells in vitro and in vivo in an orthotopic mouse model14. We have also used piggyBac to generate human T cells resistant to rapamycin, which should be useful in cancer therapies where rapamycin is utilized15.
Herein, we describe a method for using piggyBac to genetically modify primary human T cells. This includes isolation of peripheral blood mononuclear cells (PBMCs) from human blood followed by culture, gene modification, and activation of T cells. For the purpose of this report, T cells were modified with a reporter gene (eGFP) for analysis and quantification of gene expression by flow cytometry.
PiggyBac can be used to modify human T cells with a variety of genes of interest. Although we have used piggyBac to direct T cells to tumor antigens14, we have also used piggyBac to add an inducible safety switch in order to eliminate gene modified cells if needed7. The large cargo capacity of piggyBac has also enabled gene transfer of a large rapamycin resistant mTOR molecule (15 kb)15. Therefore, we present a non-viral methodology for stable gene-modification of primary human T cells for a wide variety of purposes.

piggyBac Transposon System Modification of Primary Human T Cells

We describe a method to genetically modify primary human T cells with a transgene using the non-viral piggyBac transposon system. T cells modified to using the piggyBac transposon system exhibit stable transgene expression.

The piggyBac transposon system is naturally active, originally derived from the cabbage looper moth1,2. This non-viral system is plasmid based, most ...

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring cells. Of interest, only few assays are capable of specifically probing such interactions directly in living cells. Here, we present an assay to measure the binding of proteins expressed at the surfaces of neighboring cells, at cell-cell contacts. This assay consists of two steps: mixing of cells expressing the proteins of interest fused to different fluorescent proteins, followed by fluorescence fluctuation spectroscopy measurements at cell-cell contacts using a confocal laser scanning microscope. We demonstrate the feasibility of this assay in a biologically relevant context by measuring the interactions of the amyloid precursor-like protein 1 (APLP1) across cell-cell junctions. We provide detailed protocols on the data acquisition using fluorescence-based techniques (scanning fluorescence cross-correlation spectroscopy, cross-correlation number and brightness analysis) and the required instrument calibrations. Further, we discuss critical steps in the data analysis and how to identify and correct external, spurious signal variations, such as those due to photobleaching or cell movement.
In general, the presented assay is applicable to any homo- or heterotypic protein-protein interaction at cell-cell contacts, between cells of the same or different types and can be implemented on a commercial confocal laser scanning microscope. An important requirement is the stability of the system, which needs to be sufficient to probe diffusive dynamics of the proteins of interest over several minutes.

This protocol describes a fluorescence fluctuation spectroscopy-based approach to investigate interactions among proteins mediating cell-cell interactions, i.e. proteins localized in cell junctions, directly in living cells. We provide detailed guidelines on instrument calibration, data acquisition and analysis, including corrections to possible artefact sources.

A variety of biological processes involves cell-cell interactions, typically mediated by proteins that interact at the interface between neighboring ...

Biochemistry

From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope

It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the regulation of many cellular functions. However, due to the fast dynamics and the tiny structures involved, a very high spatio-temporal resolution is required to catch the real behavior of molecules. Here we present the experimental protocol for studying the dynamics of fluorescently-labeled plasma-membrane proteins and lipids in live cells with high spatiotemporal resolution. Notably, this approach doesn&#8217;t need to track each molecule, but it calculates population behavior using all molecules in a given region of the membrane. The starting point is a fast imaging of a given region on the membrane. Afterwards, a complete spatio-temporal autocorrelation function is calculated correlating acquired images at increasing time delays, for example each 2, 3, n repetitions. It is possible to demonstrate that the width of the peak of the spatial autocorrelation function increases at increasing time delay as a function of particle movement due to diffusion. Therefore, fitting of the series of autocorrelation functions enables to extract the actual protein mean square displacement from imaging (iMSD), here presented in the form of apparent diffusivity vs average displacement. This yields a quantitative view of the average dynamics of single molecules with nanometer accuracy. By using a GFP-tagged variant of the Transferrin Receptor (TfR) and an ATTO488 labeled 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine (PPE) it is possible to observe the spatiotemporal regulation of protein and lipid diffusion on &#181;m-sized membrane regions in the micro-to-milli-second time range.

Spatial distribution and temporal dynamics of plasma membrane proteins and lipids is a hot topic in biology. Here this issue is addressed by a spatio-temporal image fluctuation analysis that provides conceptually the same physical quantities of single particle tracking, but it uses small molecular labels and standard microscopy setups.

It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the ...

Bioengineering

Rapid Analysis of Chromosome Aberrations in Mouse B Lymphocytes by PNA-FISH

Defective DNA repair leads to increased genomic instability, which is the root cause of mutations that lead to tumorigenesis. Analysis of the frequency and type of chromosome aberrations in different cell types allows defects in DNA repair pathways to be elucidated. Understanding mammalian DNA repair biology has been greatly helped by the production of mice with knockouts in specific genes. The goal of this protocol is to quantify genomic instability in mouse B lymphocytes. Labeling of the telomeres using PNA-FISH probes (peptide nucleic acid - fluorescent in situ hybridization) facilitates the rapid analysis of genomic instability in metaphase chromosome spreads. B cells have specific advantages relative to fibroblasts, because they have normal ploidy and a higher mitotic index. Short-term culture of B cells therefore enables precise measurement of genomic instability in a primary cell population which is likely to have fewer secondary genetic mutations than what is typically found in transformed fibroblasts or patient cell lines.

DNA repair pathways are essential for maintenance of genomic integrity and preventing mutation and cancer. The goal of this protocol is to quantify genomic instability by direct observation of chromosome aberrations in metaphase spreads from mouse B cells using fluorescent in situ hybridization (FISH) for telomeric DNA repeats.

Defective DNA repair leads to increased genomic instability, which is the root cause of mutations that lead to tumorigenesis. Analysis of the frequency ...

Spot Variation Fluorescence Correlation Spectroscopy for Analysis of Molecular Diffusion at the Plasma Membrane of Living Cells

Dynamic biological processes in living cells, including those associated with plasma membrane organization, occur on various spatial and temporal scales, ranging from nanometers to micrometers and microseconds to minutes, respectively. Such a broad range of biological processes challenges conventional microscopy approaches. Here, we detail the procedure for implementing spot variation Fluorescence Correlation Spectroscopy (svFCS) measurements using a classical fluorescence microscope that has been customized. The protocol includes a specific performance check of the svFCS setup and the guidelines for molecular diffusion measurements by svFCS on the plasma membrane of living cells under physiological conditions. Additionally, we provide a procedure for disrupting plasma membrane raft nanodomains by cholesterol oxidase treatment and demonstrate how these changes in the lateral organization of the plasma membrane might be revealed by svFCS analysis. In conclusion, this fluorescence-based method can provide unprecedented details on the lateral organization of the plasma membrane with the appropriate spatial and temporal resolution.

This article aims to present a protocol on how to build a spot variation Fluorescence Correlation Spectroscopy (svFCS) microscope to measure molecular diffusion at the plasma membrane of living cells.

Dynamic biological processes in living cells, including those associated with plasma membrane organization, occur on various spatial and temporal scales, ...

Fluorescence Recovery after Merging a Droplet to Measure the Two-dimensional Diffusion of a Phospholipid Monolayer

We introduce a new method to measure the lateral diffusivity of a surfactant monolayer at the fluid-fluid interface, called fluorescence recovery after merging (FRAM). FRAM adopts the same principles as the fluorescence recovery after photobleaching (FRAP) technique, especially for measuring fluorescence recovery after bleaching a specific area, but FRAM uses a drop coalescence instead of photobleaching dye molecules to induce a chemical potential gradient of dye molecules. Our technique has several advantages over FRAP: it only requires a fluorescence microscope rather than a confocal microscope equipped with high power lasers; it is essentially free from the selection of fluorescence dyes; and it has far more freedom to define the measured diffusion area. Furthermore, FRAM potentially provides a route for studying the mixing or inter-diffusion of two different surfactants, when the monolayers at a surface of droplet and at a flat air/water interface are prepared with different species, independently.

We present a new technique to measure the lateral diffusion of a surface active species at the fluid-fluid interface by merging a droplet monolayer onto a flat monolayer.

We introduce a new method to measure the lateral diffusivity of a surfactant monolayer at the fluid-fluid interface, called fluorescence recovery after ...

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

We present a protocol and workflow to perform live cell dual-color fluorescence cross-correlation spectroscopy (FCCS) combined with F&#246;rster Resonance Energy transfer (FRET) to study membrane receptor dynamics in live cells using modern fluorescence labeling techniques. In dual-color FCCS, where the fluctuations in fluorescence intensity represent the dynamic &#34;fingerprint&#34; of the respective fluorescent biomolecule, we can probe co-diffusion or binding of the receptors. FRET, with its high sensitivity to molecular distances, serves as a well-known &#34;nanoruler&#34; to monitor intramolecular changes. Taken together, conformational changes and key parameters such as local receptor concentrations and mobility constants become accessible in cellular settings.
Quantitative fluorescence approaches are challenging in cells due to high noise levels and the vulnerability of the sample. Here we show how to perform this experiment, including the calibration steps using dual-color labeled &#946;2-adrenergic receptor (&#946;2AR) labeled with eGFP and SNAP-tag-TAMRA. A step-by-step data analysis procedure is provided using open-source software and templates that are easy to customize.
Our guideline enables researchers to unravel molecular interactions of biomolecules in live cells in situ with high reliability despite the limited signal-to-noise levels in live cell experiments. The operational window of FRET and particularly FCCS at low concentrations allows quantitative analysis at near-physiological conditions.

We present an experimental protocol and data analysis workflow to perform live cell dual-color fluorescence cross correlation spectroscopy (FCCS) combined with F&#246;rster Resonance Energy transfer (FRET) to study membrane receptor dynamics in live cells using modern fluorescence labeling techniques.

We present a protocol and workflow to perform live cell dual-color fluorescence cross-correlation spectroscopy (FCCS) combined with F&#246;rster Resonance ...

Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy (FCS)

In the past fifteen years the notion that cell membranes are not homogenous and rely on microdomains to exert their functions has become widely accepted. Lipid rafts are membrane microdomains enriched in cholesterol and sphingolipids. They play a role in cellular physiological processes such as signalling, and trafficking1,2 but are also thought to be key players in several diseases including viral or bacterial infections and neurodegenerative diseases3.
Yet their existence is still a matter of controversy4,5. Indeed, lipid raft size has been estimated to be around 20 nm6, far under the resolution limit of conventional microscopy (around 200 nm), thus precluding their direct imaging. Up to now, the main techniques used to assess the partition of proteins of interest inside lipid rafts were Detergent Resistant Membranes (DRMs) isolation and co-patching with antibodies. Though widely used because of their rather easy implementation, these techniques were prone to artefacts and thus criticized7,8. Technical improvements were therefore necessary to overcome these artefacts and to be able to probe lipid rafts partition in living cells.
Here we present a method for the sensitive analysis of lipid rafts partition of fluorescently-tagged proteins or lipids in the plasma membrane of living cells. This method, termed Fluorescence Correlation Spectroscopy (FCS), relies on the disparity in diffusion times of fluorescent probes located inside or outside of lipid rafts. In fact, as evidenced in both artificial membranes and cell cultures, probes would diffuse much faster outside than inside dense lipid rafts9,10. To determine diffusion times, minute fluorescence fluctuations are measured as a function of time in a focal volume (approximately 1 femtoliter), located at the plasma membrane of cells with a confocal microscope (Fig. 1). The auto-correlation curves can then be drawn from these fluctuations and fitted with appropriate mathematical diffusion models11.
FCS can be used to determine the lipid raft partitioning of various probes, as long as they are fluorescently tagged. Fluorescent tagging can be achieved by expression of fluorescent fusion proteins or by binding of fluorescent ligands. Moreover, FCS can be used not only in artificial membranes and cell lines but also in primary cultures, as described recently12. It can also be used to follow the dynamics of lipid raft partitioning after drug addition or membrane lipid composition change12.

Determination of Lipid Raft Partitioning of Fluorescently-tagged Probes in Living Cells by Fluorescence Correlation Spectroscopy FCS

A technique to probe the lipid raft partitioning of fluorescent proteins at the plasma membrane of living cells is described. It takes advantage of the disparity in diffusion times of proteins located inside or outside of lipid rafts. Acquisition can be performed dynamically in control conditions or after drug addition.

In the past fifteen years the notion that cell membranes are not homogenous and rely on microdomains to exert their functions has become widely accepted. ...

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

Lateral diffusion and compartmentalization of plasma membrane proteins are tightly regulated in cells and thus, studying these processes will reveal new insights to plasma membrane protein function and regulation. Recently, k-Space Image Correlation Spectroscopy (kICS)1&#160;was developed to enable routine measurements of diffusion coefficients directly from images of fluorescently tagged plasma membrane proteins, that avoided systematic biases introduced by probe photophysics. Although the theoretical basis for the analysis is complex, the method can be implemented by nonexperts using a freely available code to measure diffusion coefficients of proteins. kICS calculates a time correlation function from a fluorescence microscopy image stack after Fourier transformation of each image to reciprocal (k-) space. Subsequently, circular averaging, natural logarithm transform and linear fits to the correlation function yields the diffusion coefficient. This paper provides a step-by-step guide to the image analysis and measurement of diffusion coefficients via kICS.
First, a high frame rate image sequence of a fluorescently labeled plasma membrane protein is acquired using a fluorescence microscope. Then, a region of interest (ROI) avoiding intracellular organelles, moving vesicles or protruding membrane regions is selected. The ROI stack is imported into a freely available code and several defined parameters (see Method section) are set for kICS analysis. The program then generates a &#34;slope of slopes&#34; plot from the k-space time correlation functions, and the diffusion coefficient is calculated from the slope of the plot. Below is a step-by-step kICS procedure to measure the diffusion coefficient of a membrane protein using the renal water channel aquaporin-3 tagged with EGFP as a canonical example.

This paper provides a step by step guide to the fluctuation analysis technique k-Space Image Correlation Spectroscopy (kICS) for measuring diffusion coefficients of fluorescently labeled plasma membrane proteins in live mammalian cells.

Lateral diffusion and compartmentalization of plasma membrane proteins are tightly regulated in cells and thus, studying these processes will reveal new ...

Molecular Diffusion in Plasma Membranes of Primary Lymphocytes Measured by Fluorescence Correlation Spectroscopy

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