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>
	<li>Goni, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+basic+structure+and+dynamics+of+cell+membranes:+an+update+of+the+Singer-Nicolson+model.">The basic structure and dynamics of cell membranes: an update of the Singer-Nicolson model.</a> Biochim Biophys Acta. 1838, 1467-1476 (2014).</li><li>Bagawath-Singh, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytokines+Induce+Faster+Membrane+Diffusion+of+MHC+Class+I+and+the+Ly49A+Receptor+in+a+Subpopulation+of+Natural+Killer+Cells.">Cytokines Induce Faster Membrane Diffusion of MHC Class I and the Ly49A Receptor in a Subpopulation of Natural Killer Cells.</a> Front Immunol. 7, (2016).</li><li>Garcia-Saez, A. J., Schwille, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence+correlation+spectroscopy+for+the+study+of+membrane+dynamics+and+protein/lipid+interactions.">Fluorescence correlation spectroscopy for the study of membrane dynamics and protein/lipid interactions.</a> Methods. 46, 116-122 (2008).</li><li>Machan, R., Wohland, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recent+applications+of+fluorescence+correlation+spectroscopy+in+live+systems.">Recent applications of fluorescence correlation spectroscopy in live systems.</a> FEBS Lett. 588, 3571-3584 (2014).</li><li>Mutze, J., Ohrt, T., Schwille, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence+correlation+spectroscopy+in+vivo.">Fluorescence correlation spectroscopy in vivo.</a> Laser Photon Rev. 5, 52-67 (2011).</li><li>Lidke, D. S., Wilson, B. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Caught+in+the+act:+quantifying+protein+behaviour+in+living+cells.">Caught in the act: quantifying protein behaviour in living cells.</a> Trends Cell Biol. 19, 566-574 (2009).</li><li>Manzo, C., Garcia-Parajo, M. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+review+of+progress+in+single+particle+tracking:+from+methods+to+biophysical+insights.">A review of progress in single particle tracking: from methods to biophysical insights.</a> Rep Prog Phys. 78, 124601 (2015).</li><li>Wiseman, P. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+correlation+spectroscopy:+principles+and+applications.">Image correlation spectroscopy: principles and applications.</a> Cold Spring Harb Protoc. , 336-348 (2015).</li><li>Sankaran, J., Shi, X., Ho, L. Y., Stelzer, E. H., Wohland, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ImFCS:+a+software+for+imaging+FCS+data+analysis+and+visualization.">ImFCS: a software for imaging FCS data analysis and visualization.</a> Opt Express. 18, 25468-25481 (2010).</li><li>Liu, P., Ahmed, S., Wohland, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+F-techniques:+advances+in+receptor+protein+studies.">The F-techniques: advances in receptor protein studies.</a> Trends Endocrinol Metab. 19, 181-190 (2008).</li><li>Manley, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-density+mapping+of+single-molecule+trajectories+with+photoactivated+localization+microscopy.">High-density mapping of single-molecule trajectories with photoactivated localization microscopy.</a> Nat Methods. 5, 155-157 (2008).</li><li>Bacia, K., Haustein, E., Schwille, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence+correlation+spectroscopy:+principles+and+applications.">Fluorescence correlation spectroscopy: principles and applications.</a> Cold Spring Harb Protoc. , 709-725 (2014).</li><li>Haustein, E., Schwille, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence+correlation+spectroscopy:+novel+variations+of+an+established+technique.">Fluorescence correlation spectroscopy: novel variations of an established technique.</a> Annu Rev Biophys Biomol Struct. 36, 151-169 (2007).</li><li>Widengren, J., Thyberg, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FCS+cell+surface+measurements--photophysical+limitations+and+consequences+on+molecular+ensembles+with+heterogenic+mobilities.">FCS cell surface measurements--photophysical limitations and consequences on molecular ensembles with heterogenic mobilities.</a> Cytometry A. 68, 101-112 (2005).</li><li>North, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Seeing+is+believing?+A+beginners'+guide+to+practical+pitfalls+in+image+acquisition.">Seeing is believing? A beginners' guide to practical pitfalls in image acquisition.</a> J Cell Biol. 172, 9-18 (2006).</li><li>Meinhardt, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+NK+cell+magnetic+bead+isolation+methods+on+phenotype+and+function+of+murine+NK+cells.">Influence of NK cell magnetic bead isolation methods on phenotype and function of murine NK cells.</a> J Immunol Methods. 378, 1-10 (2012).</li><li>Bacia, K., Schwille, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Practical+guidelines+for+dual-color+fluorescence+cross-correlation+spectroscopy.">Practical guidelines for dual-color fluorescence cross-correlation spectroscopy.</a> Nat Protoc. 2, 2842-2856 (2007).</li><li>Gendron, P. O., Avaltroni, F., Wilkinson, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diffusion+coefficients+of+several+rhodamine+derivatives+as+determined+by+pulsed+field+gradient-nuclear+magnetic+resonance+and+fluorescence+correlation+spectroscopy.">Diffusion coefficients of several rhodamine derivatives as determined by pulsed field gradient-nuclear magnetic resonance and fluorescence correlation spectroscopy.</a> J Fluoresc. 18, 1093-1101 (2008).</li><li>Petrasek, Z., Schwille, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Precise+measurement+of+diffusion+coefficients+using+scanning+fluorescence+correlation+spectroscopy.">Precise measurement of diffusion coefficients using scanning fluorescence correlation spectroscopy.</a> Biophys J. 94, 1437-1448 (2008).</li><li>Stromqvist, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+modified+FCCS+procedure+applied+to+Ly49A-MHC+class+I+cis-interaction+studies+in+cell+membranes.">A modified FCCS procedure applied to Ly49A-MHC class I cis-interaction studies in cell membranes.</a> Biophys J. 101, 1257-1269 (2011).</li><li>Widengren, J., Mets, U., Rigler, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence+Correlation+Spectroscopy+of+Triplet-States+in+Solution+-+a+Theoretical+and+Experimental-Study.">Fluorescence Correlation Spectroscopy of Triplet-States in Solution - a Theoretical and Experimental-Study.</a> J Phys Chem. 99, 13368-13379 (1995).</li><li>Humpolickova, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Probing+diffusion+laws+within+cellular+membranes+by+Z-scan+fluorescence+correlation+spectroscopy.">Probing diffusion laws within cellular membranes by Z-scan fluorescence correlation spectroscopy.</a> Biophys J. 91, L23-L25 (2006).</li><li>Guo, S. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bayesian+approach+to+the+analysis+of+fluorescence+correlation+spectroscopy+data+II:+application+to+simulated+and+in+vitro+data.">Bayesian approach to the analysis of fluorescence correlation spectroscopy data II: application to simulated and in vitro data.</a> Anal Chem. 84, 3880-3888 (2012).</li><li>Ries, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+suppression+of+sample-related+artifacts+in+Fluorescence+Correlation+Spectroscopy.">Automated suppression of sample-related artifacts in Fluorescence Correlation Spectroscopy.</a> Opt Express. 18, 11073-11082 (2010).</li><li>Chiu, C. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intracellular+kinetics+of+the+androgen+receptor+shown+by+multimodal+Image+Correlation+Spectroscopy+(mICS).">Intracellular kinetics of the androgen receptor shown by multimodal Image Correlation Spectroscopy (mICS).</a> Sci Rep. 6, 22435 (2016).</li><li>Wegel, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+cellular+structures+in+super-resolution+with+SIM,+STED+and+Localisation+Microscopy:+A+practical+comparison.">Imaging cellular structures in super-resolution with SIM, STED and Localisation Microscopy: A practical comparison.</a> Sci Rep. 6, 27290 (2016).</li></ol>

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...

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 ...

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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

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. ...

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using the analysis of cell membrane receptor clusters as a model, we present a detailed protocol for this image analysis task using Fiji (ImageJ) and Matlab routines to: 1) define regions of interest and design masks adapted to these regions; 2) track the particles in fluorescence microscopy videos; 3) analyze the diffusion and intensity characteristics of selected tracks. The quantitative analysis of the diffusion coefficients, types of motion, and cluster size obtained by fluorescence microscopy and image processing provides a valuable tool to objectively determine particle dynamics and the consequences of modifying environmental conditions. In this article we present detailed protocols for the analysis of these features. The method described here not only allows single-molecule tracking detection, but also automates the estimation of lateral diffusion parameters at the cell membrane, classifies the type of trajectory and allows complete analysis thus overcoming the difficulties in quantifying spot size over its entire trajectory at the cell membrane.

Here, we present a protocol for single particle tracking image analysis that allows quantitative evaluation of diffusion coefficients, types of motion and cluster sizes of single particles detected by fluorescence microscopy.

Particle tracking on a video sequence and the posterior analysis of their trajectories is nowadays a common operation in many biological studies. Using ...

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

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

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

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

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

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

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

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