R.U.M was funded by the American Heart Association Pre-doctoral Fellowship (14PRE18560010). Research was funded by the American Heart Association Scientist Development Grant (13SDG14270009) &#38; the NIH (1DP2 CA186752-01 &#38; 1-R01-GM-105646-01-A1) to S.S.

1. DNA Preparation
<ol>
	<li>Design sensor constructs using a modular cloning scheme. Please reference the &#946;2-AR sensor design detailed previously1.</li>
	<li>Prepare DNA according to commercial miniprep kit protocol and elute in 2 mM Tris-HCl solution, pH 8, at concentration &#8805; 750 ng/&#181;l, A260/A280 of 1.7 - 1.9, A260/A230 of 2.0 - 2.29.</li>
</ol>
2. Cell Culture Preparation
<ol>
	<li>Culture HEK-293T-Flp-n cells in DMEM containing 4.5 g/L D-glucose, supplemented with 10% FBS (heat inactivated) (v/v), 1% L-glutamine supplement, 20 mM HEPES, pH 7.5 at 37 &#176;C in humidified atmosphere at 5% CO2. Handle cells in biological safety hood for subsequent steps.</li>
	<li>Allow cells to grow to a confluent monolayer before passaging into six-well dishes. Time to achieve confluency depends on initial plating density. Use plates that come to confluency within 1 - 2 days of plating for six-well plating. A confluent 10 cm tissue culture-treated dish has cell density of approximately 4 x 106 cells/ml. See Figure 1 for image of cell culture growth.</li>
	<li>Wash cells with 10 ml PBS, and trypsinize with 0.25% trypsin (see Discussion, paragraph 2). Plate 8 x 105 cells/well in 2 ml of media in tissue culture-treated six well dishes and allow to adhere for 16 - 20 hr.</li>
</ol>
3. Transfection Conditions
<ol>
	<li>Stagger transfections for constructs that may require different amounts of time to achieve optimal expression (between 20 - 36 hr). Synchronize conditions for a unified experiment time. Also have an untransfected control well at equivalent cell density to be used for background noise and scattering subtraction during analysis.</li>
	<li>Bring transfection reagents to room temperature: reduced serum media, DNA, transfection reagent.</li>
	<li>In a biological safety hood combine reagents in a sterile microcentrifuge tube in the following order: mix 2 &#181;g DNA with 100 &#181;l reduced serum media. Spike 6 &#181;l of transfection reagent into media/DNA mix without touching surface of mixture or the side of the tube. Set up one transfection reaction per well. Transfection conditions can be optimized (1 - 4 &#181;g of DNA, 3 - 6 &#181;l of transfection reagent) to achieve consistent expression levels. See Table 1 for more optimized ratios.</li>
	<li>Incubate mixture at RT in biological safety hood for 15 - 30 min. Do not use reaction if left to incubate for more than 30 min.</li>
	<li>Add reaction to cells in a drop-wise manner across well and gently shake six-well to ensure thorough mixing. Add one reaction per well.</li>
	<li>After 20 hr of expression, monitor fluorescence using tissue culture fluorescence microscope. Assess population expression with 10X objective and protein localization in a cell at 40X. Observe for protein expression at plasma membrane (PM). If substantial internalization is noted, monitor transfection until significant expression is detected at PM.</li>
</ol>
4. Reagent and Equipment Preparation
<ol>
	<li>Prepare 100 mM drug stocks and store at -80 &#176;C: isoproterenol bitartrate (100 mM in dH2O containing 300 mM ascorbic acid). Make on ice/in cold room, and flash-freeze immediately. Aliquots can be made and used up to one year.</li>
	<li>Prepare Cell Buffer (~ 2 ml/condition) and store in a 37 &#176;C water bath. Make fresh each day. Reference Table 2 for Cell Buffer constituents.</li>
	<li>Prepare Drug Buffer (10 ml) and store at room temperature. Reference Table 2 for Drug Buffer constituents.</li>
	<li>Acid wash cuvettes using concentrated HCl. Neutralize with a weak base (1 M KOH), and thoroughly wash cuvettes with dH2O.</li>
	<li>Prepare work station around fluorometer with several boxes of 10, 200, and 1,000 &#181;l pipette tips, a timer set with a 10 min countdown, an accessible vacuum line with tips for cuvette cleaning, delicate task wipes, and squirt bottle with ultrapure H2O.</li>
	<li>Heat external water bath for fluorometer and heat block to 37 &#176;C.</li>
	<li>Turn on fluorometer; set fluorescence collection program for CFP collection to excitation 430 nm, bandpass 8 nm; emission range 450 nm - 600 nm, bandpass 4 nm. For YFP collection only as sensor control (see Discussion) set excitation to 490 nm, bandpass 8 nm, emission range 500 - 600 nm, bandpass 4 nm. CFP collection settings will be used to acquire a FRET spectrum in this experiment.</li>
	<li>Place twelve 1.5 ml microcentrifuge tubes in heat block as shown in Figure 2 below. These tubes are holders for cell aliquot tubes (500 &#181;l microcentrifuge tubes.) Place a small piece of tissue in holders 1 and 7 to cushion the cuvettes placed here. 
	Note: Use separate cuvettes for untreated condition and drug condition to prevent cross-contamination.</li>
</ol>
<img alt="Figure 2" src="/files/ftp_upload/54696/54696fig2.jpg" /> 
Figure 2. Microcentrifuge Tube Set Up and Position Reference in Heat Block. Cuvette for untreated samples is in position 1; cell aliquot tubes are in positions 2 - 6. Cuvette for drug treated samples is in position 7; cell aliquot tubes are in positions 8 - 12. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54696/54696fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="9">
	<li>Fill holders 2 - 6 and 8 - 12 with ~ 750 &#181;l of water to create a mini 37 &#176;C water bath.</li>
	<li>Place ten 500 &#181;l microcentrifuge tubes for cell aliquots into mini-water baths (holders 2 - 6, 8 - 12). Each tube will be an individual repeat of the condition (5 untreated, 5 drug treated).</li>
	<li>Monitor cells for expression (see step 3.6).</li>
</ol>
5. Experiment &#38; Data Collection
<img alt="Figure 3" src="/files/ftp_upload/54696/54696fig3.jpg" /> 
Figure 3. Experimental Schematic.&#160;A detailed step-wise guide for experimental set up and execution. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54696/54696fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol>
	<li>Reference Figure 3 for experimental schematic. When cells are ready to be harvested, based on protein expression detected with fluorescence microscope (see Discussion, paragraph 4): in biological safety hood, gently remove ~1 ml of media, resuspend cells in their culture with a P1,000 and transfer resuspension into a 1.5 ml microcentrifuge tube. 
	Note: Avoid using trypsin as it may digest the N-terminus and/or binding pocket of the GPCR sensor.</li>
	<li>Count cells to ensure proper cell density in resuspension. Optimize resuspension volume for 4 x 106 cells/ml.</li>
	<li>Spin cells in swinging bucket centrifuge at room temperature, 290 x g for 3 min. Remove supernatant after the centrifugation.</li>
	<li>GENTLY resuspend cells in 1 ml Cell Buffer (stored at 37 &#176;C) and repeat step 5.3. During second spin, gather 100 mM drug stock aliquot from -80 &#176;C. Make 1:100 dilution in Drug Buffer for 1 mM working stock and keep at RT.</li>
	<li>After the second centrifugation, remove supernatant and gently resuspend cells in 1 ml of Cell Buffer (4 x 106 cells/ml). Measure OD600 of the sample in the spectrophotometer using 1 ml of cells and 1 ml of Cell Buffer as a blank. Dispense cells in a disposable plastic cuvette and transfer back to a microcentrifuge tube immediately following spectrophotometry.</li>
	<li>For a control untransfected cell condition spectrum, gently resuspend untransfected cells in 1 ml Cell Buffer with P1,000 pipet, add 90 &#181;l of cells to cuvette and acquire FRET spectrum at excitation 430 nm, bandpass 8 nm, emission 450 - 600 nm, bandpass 4 nm. Collect 3 - 5 repeat spectra with fresh 90 &#181;l of cells. Keep stock of cells at 37 &#176;C between specs, resuspend gently with P1,000 between each sample aliquot, and rinse cuvette with ultrapure H2O between samples.</li>
	<li>For experimental conditions, aliquot 90 &#181;l of transfected cells to each of the 500 &#181;l tubes in holders 2 - 6, 8 - 12 in the heat block. Gently resuspend stock of cells with P1,000 pipet between each aliquot.</li>
	<li>After cells are aliquoted, add 10 &#181;l of Drug Buffer to tubes 2 - 6 for untreated condition samples.</li>
	<li>Begin experiment by adding 10 &#181;l of 1 mM drug solution into tube 8, start the timer to countdown from 10 min, and gently mix tube with P200 pipet. Close tube and return to 37 &#176;C heat block.</li>
	<li>Immediately pick up tube 2, mix gently with P200 (use a new tip), add 90 &#181;l of cell suspension to untreated condition cuvette, and place in fluorometer.</li>
	<li>Acquire FRET spectrum at excitation 430 nm, bandpass 8 nm, emission 450 - 600 nm, bandpass 4 nm.</li>
	<li>At 9 min - 10 sec, spike tube 9 with 10 &#181;l of 1 mM drug solution, gently mix with a P200 (use new tip), and return tube to heat block.</li>
	<li>Repeat steps 5.10 - 5.11 with tube 3 and 5.12 with tube 10.</li>
	<li>Repeat steps 5.10 - 5.13 at 1 min intervals (08:10, 07:10, etc.) until spectra are collected for all untreated condition samples, and drug has been added to all drug condition samples. Use a fresh tip for each pipet step to prevent cross-contamination.</li>
	<li>At 5 min - 10 sec, begin mixing tube 8 (drug condition) gently with P200 pipet, add 90 &#181;l of cell suspension to separate cuvette for drug treated sample and place in fluorometer.</li>
	<li>Acquire FRET spectrum (see step 5.11 for settings).</li>
	<li>Repeat steps 5.15 - 5.16 at 1 min intervals (04:10, 03:10, etc.) for remaining drug condition samples (tubes 9 - 12).</li>
	<li>After experiment ends, save project files, thoroughly wash cuvettes with ultrapure H2O, and re-stock tubes for next condition. Take care to prevent cross-contamination in the wash step. Change the tip on the H2O bottle as well as on the vacuum line in between washes.</li>
</ol>
6. Data Analysis
<ol>
	<li>Save and export data files in SPC format to be used for analysis. Analysis programs are available for download from the Sivaramakrishnan Lab publication website.</li>
	<li>Create path files for analysis software which include the analysis programs (v9, v15), untransfected samples files (see step 5.6 for untransfected cell spectrum collection), OUTPUT data file, and comma separated values (CSV) files for data entry.</li>
	<li>Enter following information into CSV file (see sample in Table 3) and designate respective conditions for each sample, including: 
	File name - individual SPC graph files 
	Receptor - designate which GPCR construct was tested (e.g., &#914;2) 
	Binder - designate which peptide variant of the construct was tested (e.g., S) 
	Agonist - designate untreated (N) or drug treated (D) conditions 
	Directory - the path folder in which SPC files are saved, usually organized by date 
	OD - recorded optical density of sample from spectrophotometer</li>
	<li>Enter file names for untransfected samples (step 5.6) to subtract buffer and scattering noise from samples.</li>
	<li>Enter conditions into analysis program.</li>
	<li>Run programs to analyze samples within individual conditions (v9) and across conditions (v15).</li>
	<li>Exclude sample files which are apparent outliers in the data set, or adjust for subtraction by increasing or decreasing OD value of individual files.</li>
	<li>Export data to OUTPUT file for access to calculated FRET ratios (525 nm/475 nm).</li>
	<li>Calculate &#916;FRET by subtracting the average FRET ratio for untreated condition from the individual FRET ratios for treated (drug) conditions.</li>
</ol>

<ol>
	<li>Malik, R. U., 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=Detection+of+G+Protein-selective+G+Protein-coupled+Receptor+(GPCR)+Conformations+in+Live+Cells.">Detection of G Protein-selective G Protein-coupled Receptor (GPCR) Conformations in Live Cells.</a> J. Biol. Chem. 288, 17167-17178 (2013).</li><li>Oldham, W. M., Hamm, H. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heterotrimeric+G+protein+activation+by+G-protein-coupled+receptors.">Heterotrimeric G protein activation by G-protein-coupled receptors.</a> Nat. Rev. Mol. Cell Bio. 9, 60-71 (2008).</li><li>Rasmussen, S. G., 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=Crystal+structure+of+the+&#946;2+adrenergic+receptor-Gs+protein+complex.">Crystal structure of the &#946;2 adrenergic receptor-Gs protein complex.</a> Nature. 477, 549-555 (2011).</li><li>Alexander, N. 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=Energetic+analysis+of+the+rhodopsin-G-protein+complex+links+the+&#945;5+helix+to+GDP+release.">Energetic analysis of the rhodopsin-G-protein complex links the &#945;5 helix to GDP release.</a> Nat. Struct. Mol. Biol. 21 (1), 56-63 (2014).</li><li>Conklin, B. R., Farfel, Z., Lustig, K. D., Julius, D., Bourne, H. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Substitution+of+three+amino+acids+switches+receptor+specificity+of+Gq&#945;+to+that+of+Gi&#945;.">Substitution of three amino acids switches receptor specificity of Gq&#945; to that of Gi&#945;.</a> Nature. 363, 274-276 (1993).</li><li>Conklin, B. R., 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=Carboxyl-Terminal+Mutations+of+Gq&#945;+and+Gs&#945;+That+Alter+the+Fidelity+of+Receptor+Activation.">Carboxyl-Terminal Mutations of Gq&#945; and Gs&#945; That Alter the Fidelity of Receptor Activation.</a> Mol. Pharmacol. 50, 855-890 (1996).</li><li>Onaran, H. O., Costa, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Where+have+all+the+active+receptor+states+gone.">Where have all the active receptor states gone.</a> Nat. Chem. Bio. 8, 674-677 (2012).</li><li>Jares-Erijman, E. A., Jovin, T. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FRET+imaging.">FRET imaging.</a> Nat Biotechnol. 21 (11), 1387-1395 (2003).</li><li>Lohse, M. J., Nuber, S., Hoffman, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluorescence/bioluminescence+resonance+energy+transfer+techniques+to+study+G-protein-coupled+receptor+activation+and+signaling.">Fluorescence/bioluminescence resonance energy transfer techniques to study G-protein-coupled receptor activation and signaling.</a> Pharmacol Rev. 64 (2), 299-336 (2012).</li><li>Vilardaga, J. P., B&#252;nemann, M., Krasel, C., Castro, M., Lohse, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+of+the+millisecond+activation+switch+of+G+protein-coupled+receptors+in+living+cells.">Measurement of the millisecond activation switch of G protein-coupled receptors in living cells.</a> Nat. Biotechnol. 21, 807-812 (2003).</li><li>B&#252;nemann, M., Frank, M., Lohse, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gi+protein+activation+in+intact+cells+involves+subunit+rearrangement+rather+than+dissociation.">Gi protein activation in intact cells involves subunit rearrangement rather than dissociation.</a> Proc. Natl. Acad. Sci. USA. 100 (26), 16077-16082 (2003).</li><li>Stumpf, A. D., Hoffman, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+probes+based+on+G+protein-coupled+receptors+-+added+work+or+added+value.">Optical probes based on G protein-coupled receptors - added work or added value.</a> Brit. J. Pharmacol. 173, 255-266 (2016).</li><li>Sivaramakrishnan, S., Spudich, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systemic+control+of+protein+interaction+using+a+modular+ER/K+&#945;-helix+linker.">Systemic control of protein interaction using a modular ER/K &#945;-helix linker.</a> Proc. Natl. Acad. Sci. USA. 108, 20467-20472 (2011).</li><li>Shaner, N. C., Steinbach, P. A., Tsien, R. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+guide+to+choosing+fluorescent+proteins.">A guide to choosing fluorescent proteins.</a> Nat. Methods. 2 (12), 905-909 (2005).</li></ol>

The authors declare that they have no competing interests.

The tight dynamic range of FRET measurements in this system reinforces the necessity of sensitive quality control in every step of this protocol. The most important steps to ensure a successful FRET experiment are 1) cell culturing, 2) transfection 3) protein expression and 4) timely, precise coordination during the assay execution.
Cell health and maintenance/plating quality can have a significant impact on the signal-to-noise of the experimental system and poor cell health can make it impossible to detect any consistent change in FRET. Conservatively, cells are healthiest for approximately 20 passages, though this may vary based on cell line, handling, and culture conditions. Once cells have difficulty growing as a confluent monolayer, or begin growing consistently in more dendritic patterns (see Figure 1 (b)), experimental background noise will be adversely affected. Careful cell maintenance, including routine media changes and regularly removing non-adherent cells and debris from maintenance plates, will enhance the quality of cells for six-well plating and transfections. Cell clumps, which adversely affect transfection efficiency, can be effectively separated into individual cells by trypsinization of maintenance plates: treat 10 cm confluent dishes with 10 ml of 0.25% trypsin for 30 sec, remove trypsin but leave approximately 200 &#956;l, place dish in 37 &#176;C incubator for 2 - 3 min. Cells will come off dish very easily and are less susceptible to clumping.
It is critical to optimize the transfection step for this experiment. Six-wells must ideally be 60 - 80% confluent for efficient transfection and optimal expression. If too few cells have adhered (&#60; 60%), wait approximately 2 - 6 hr to transfect, or until at least 70% of cells are adhered. Six-wells that are over-confluent (&#62; 80%) will also reduce transfection efficiency. Transfecting at lower cell confluency increases cell death rate. DNA concentration and purity are also critical (see Step 1.2). Using low concentration and/or poor quality DNA preparations negatively affect transfection efficiency Transfection conditions can be adjusted per construct, refer to Table 1 for more information.
Accurate and consistent monitoring of protein expression using a tissue-culture fluorescence microscope is another crucial step in this process. Though this step is subject to individual judgement, it is possible to use other techniques, such as microscopy, to monitor expression over time quantitatively, though they are not detailed here. For constructs we have tested successfully in our system, expression takes approximately 18-36 hr to reach optimal expression. In our experience, constructs that display poor expression during this time window rarely improve after 40 hr. The constructs we have published with have not shown signs of degradation, however this may be an issue for some GPCRs. In our assays, sensor degradation is possible at transfection times over 30 hr. Sensor integrity can be tested using YFP/CFP ratios: 525 nm reading from the YFP-excited (490 nm) spectrum, and 475 nm reading from CFP-excited (430 nm) spectrum. For settings, see step 4.6. Recommended YFP/CFP ratios are in the range of 1.7 - 2.0, with an ideal ratio of 1.8. This value is dependent on the approximate two-fold greater brightness of YFP relative to CFP14. An integral sensor with minimal degradation will therefore contain both fluorophores and have YFP:CFP ratio of approximately 2:1. After sensor quality has been confirmed it is important to confirm protein localization and expression at the plasma membrane. Significant intracellular expression could be a result of either protein degradation, internalization, or ongoing trafficking of the protein. Monitor constructs over time to see if expression is enhanced at the plasma membrane. The next critical challenge in expression is transfection efficiency. Approximately 70% + transfection efficiency is necessary for adequate signal-to-noise detection in this fluorometer system. If fewer cells are transfected, the amount of signal-to-noise may still be detectable but will be much less consistent between samples in one FRET experiment. This will hinder accurate data analysis within the narrow dynamic range of the system. Expression levels also present a significant hurdle to achieving ample signal-to-noise during the experiment. For expression levels detectable by the fluorometer, the ratio of signal between 475 nm and 450 nm (cell scattering) of 1.5 is sufficient for detecting a change in FRET, however optimal expression will have a ratio of approximately 2.0+. For reference, the &#946;2-AR data is collected in a signal-to-noise range of 4 x 105 CPS (450 nm) to 1 x 106 CPS (475 nm), signal-to-noise ratio of 2.5. This ratio will help reduce the amount of variability between samples, which can also affect data analysis. These expression levels are also subject to the sensitivity and optimal alignment of the fluorometer optics; other systems may require different parameters for adequate signal-to-noise optimization.
Cells are extremely sensitive to time and temperature. Once the experimental procedure has begun, gentle handling is essential to avoid cell death. Specific logistical measures can be taken to expedite the process and avoid timely mistakes including preparing the work station, making sure all equipment is functioning properly, and planning out the goals of the experiment beforehand. It is ideal to use cells within 30 min of harvesting, and in our system, this protocol is executed within 20 min. Once the technique is mastered, specifically transfection optimization and the manual dexterity of the exercise itself, this experiment can be used to compare various constructs against each other, generate dose-response curves, and the sensor can be expanded to the full-length G protein.
Though the FRET sensor used here is a unique development in GPCR FRET sensors, this specific experimental setup is detailed as a well-characterized assay for the implementation of the sensor. The fluorometer-based assay allows a large population of cells to be assessed in each experiment and does not rely on purified protein or membrane preps, therefore maintaining an in vivo environment. This experimental design has also been optimized to detect very small changes seen in the system upon agonist stimulation of the GPCR.

G-protein-coupled receptors (GPCRs) are seven-transmembrane receptors. The human genome alone contains approximately 800 genes coding for GPCRs, which are activated by a variety of ligands including light, odorants, hormones, peptides, drugs and other small molecules. Nearly 30% of all pharmaceuticals currently on the market target GPCRs because they play a large role in many disease states2. Despite decades of extensive work done on this receptor family, there remain significant outstanding questions in the field, particularly with regards to the molecular mechanisms that drive GPCR-effector interactions. To date, only one high-resolution crystal structure has been published, providing insight into the interaction between the &#946;2-adrenergic receptor (&#946;2-AR) and the Gs protein3. Together with extensive research in the last three decades, it reiterates one specific structural component that is critical in this interaction: the G&#945; subunit C-terminus. This structure is important for both G protein activation by the GPCR4 and G protein selection5-6. Hence, the G&#945; C-terminus provides a crucial link between ligand stimulation of the GPCR and selective G protein activation.
Research over the last decade suggests that GPCRs populate a broad conformational landscape, with ligand-binding stabilizing subsets of GPCR conformations. While several techniques, including crystallography, NMR and fluorescence spectroscopy, and mass spectrometry are available to examine the GPCR conformational landscape, there is a paucity of approaches to elucidate their functional significance in effector selection7. Here, we outline a F&#1255;rster resonance energy transfer (FRET)-based approach to detect G protein-selective GPCR conformations. FRET relies on the proximity and parallel orientation of two fluorophores with overlapping emission (donor) and excitation (acceptor) spectra8. As the donor and acceptor fluorophores come closer together as a result of either conformational change in the protein or a protein-protein interaction, the FRET between them increases, and can be measured using a range of methods8. FRET-based biosensors have been employed extensively in the GPCR field9. They have been used to probe conformation changes in the GPCR by inserting donor and acceptor in the third intracellular loop and GPCR C-terminus; sensors have been designed to probe GPCR and effector interactions by separately labeling the GPCR and effector (G protein subunits/arrestins) with a FRET pair10; some sensors also detect conformational changes in the G protein11. These biosensors have enabled the field to ask a multitude of outstanding questions including conformational changes in the GPCR and effector, GPCR-effector interaction kinetics, and allosteric ligands12. Our group was particularly interested in creating a biosensor that could detect G protein-specific GPCR conformations under agonist-driven conditions. This biosensor relies on a recently developed technology named SPASM (systematic protein affinity strength modulation)13. SPASM involves tethering interacting protein domains using an ER/K linker, which controls their effective concentrations. Flanking the linker with a FRET pair of fluorophores creates a tool which can report the state of the interaction between proteins12. Previously1 the SPASM module was used to tether the G&#945; C-terminus to a GPCR and monitor their interactions with FRET fluorophores, mCitrine (referred to in this protocol by its commonly known variant, Yellow Fluorescent Protein (YFP), excitation/emission peak at 490/525 nm) and mCerulean (referred to in this protocol by its commonly known variant Cyan Fluorescent Protein (CFP), excitation/emission peak 430/475 nm). From N- to C-terminus, this genetically encoded single polypeptide contains: a full length GPCR, FRET acceptor (mCitrine/YFP), 10 nm ER/K linker, FRET donor (mCerulean/CFP), and the G&#945; C-terminus peptide. In this study, sensors are abbreviated as GPCR-linker length-G&#945; peptide. All components are separated by an unstructured (Gly-Ser-Gly)4 linker which enables free rotation of each domain. The detailed characterization of such sensors was previously performed using two prototypical GPCRs: &#946;2-AR and opsin1.
This sensor is transiently transfected into HEK-293T cells and fluorometer-based live cell experiments measure fluorescence spectra of the FRET pair in arbitrary units of counts per second (CPS) in the presence or absence of ligand. These measurements are used to calculate a FRET ratio between the fluorophores (YFPmax/CFPmax). A change in FRET (&#916;FRET) is then calculated by subtracting the average FRET ratio of untreated samples from the FRET ratio of ligand treated samples. &#916;FRET can be compared across constructs (&#946;2-AR-10 nm-G&#945;s peptide versus &#946;2-AR-10 nm-no peptide). Here, we detail the protocol to express these sensors in live HEK-293T cells, monitor their expression, and the setup, execution, and analysis of the fluorometer-based live cell FRET measurement for untreated versus drug treated conditions. While this protocol is specific for the &#946;2-AR-10 nm-G&#945;s peptide sensor treated with 100 &#181;M isoproterenol bitartrate, it can be optimized for different GPCR-G&#945; pairs and ligands.

<table border="0" cellpadding="0" cellspacing="0" width="1151">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:312px;">B2-AR-10 nm- Gas peptide sensor</td>
			<td style="width:144px;">Addgene</td>
			<td style="width:148px;">47438</td>
			<td style="width:547px;">https://www.addgene.org/Sivaraj_Sivaramakrishnan/</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">GeneJET Plasmid Miniprep Kit</td>
			<td style="width:144px;">Fermentas/Fisher Sci</td>
			<td style="width:148px;">FERK0503</td>
			<td>Elute in 2 mM Tris elution buffer</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:312px;">HEK-293T-Flp-n cells</td>
			<td style="width:144px;">Life Technologies</td>
			<td style="width:148px;">R78007</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">Trypsin (0.25%)</td>
			<td style="width:144px;">Life Technologies</td>
			<td style="width:148px;">25200056</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">DMEM- high glucose</td>
			<td style="width:144px;">Life Technologies</td>
			<td style="width:148px;">11960-044</td>
			<td>Warm in&#160; 37 &#176;C water bath before use</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">FBS, certified, Heat inactivated, US origin</td>
			<td style="width:144px;">Life Technologies</td>
			<td style="width:148px;">10082147</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:312px;">Glutamax I 100x</td>
			<td style="width:144px;">Life Technologies</td>
			<td style="width:148px;">35050061</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">HEPES</td>
			<td style="width:144px;">Corning</td>
			<td style="width:148px;">MT25060CL</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">Opti-MEM</td>
			<td style="width:144px;">Life Technologies</td>
			<td style="width:148px;">31985-070</td>
			<td>Reduced serum media; Bring to room temperature before use</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:312px;">XtremeGene HP transfection reagenet</td>
			<td style="width:144px;">Roche</td>
			<td style="width:148px;">6366236001</td>
			<td>Highly recommended for its consistency. Bring to room temperature before use</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">FluoroMax 4</td>
			<td style="width:144px;">Horiba</td>
			<td style="width:148px;"></td>
			<td>Use with FluorEssence V3.8 software</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:312px;">3-mm path length quartz cuvette</td>
			<td style="width:144px;">Starna</td>
			<td style="width:148px;">NC9729944(16.45F-Q-3/z8.5)</td>
			<td>May require cuvette holder/adaptor for use in Fluorometer, available from Starna</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">Sc100-S3 Heated Circulating water bath pump</td>
			<td style="width:144px;">Fisher Scientific</td>
			<td style="width:148px;">13-874-826</td>
			<td>Warm to 37 &#176;C before use</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:312px;">Thermomixer Heat Block</td>
			<td style="width:144px;">Eppendorf</td>
			<td style="width:148px;">22670000</td>
			<td>Warm to 37 &#176;C before use</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">Ultrapure DNA/RNAse free water</td>
			<td style="width:144px;">Life Technologies</td>
			<td style="width:148px;">10977015</td>
			<td>Use at room temperature</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:312px;">D(+)-glucose, anhydrous</td>
			<td style="width:144px;">Sigma</td>
			<td style="width:148px;">G5767</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">aprotinin from bovine lung</td>
			<td style="width:144px;">Sigma</td>
			<td style="width:148px;">A1153</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">leupeptin hemisulfate</td>
			<td style="width:144px;">EMD</td>
			<td style="width:148px;">10-897</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:312px;">L-ascorbic acid, reagent grade</td>
			<td style="width:144px;">Sigma</td>
			<td style="width:148px;">A0278</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">(-)-isoproterenol (+)-bitartrate</td>
			<td style="width:144px;">Sigma</td>
			<td style="width:148px;">I2760</td>
			<td>Use fresh aliquot each experiment</td>
		</tr>
	</tbody>
</table>

g protein-selective gpcr conformations measured using fret sensors in a live cell suspension fluorometer assay

F&#1255;rster resonance energy transfer (FRET)-based studies have become increasingly common in the investigation of GPCR signaling. Our research group developed an intra-molecular FRET sensor to detect the interaction between G&#945; subunits and GPCRs in live cells following agonist stimulation. Here, we detail the protocol for detecting changes in FRET between the &#946;2-adrenergic receptor and the G&#945;s C-terminus peptide upon treatment with 100 &#181;M isoproterenol hydrochloride as previously characterized1. Our FRET sensor is a single polypeptide consisting serially of a full-length GPCR, a FRET acceptor fluorophore (mCitrine), an ER/K SPASM (systematic protein affinity strength modulation) linker, a FRET donor fluorophore (mCerulean), and a G&#945; C-terminal peptide. This protocol will detail cell preparation, transfection conditions, equipment setup, assay execution, and data analysis. This experimental design detects small changes in FRET indicative of protein-protein interactions, and can also be used to compare the strength of interaction across ligands and GPCR-G protein pairings. To enhance the signal-to-noise in our measurements, this protocol requires heightened precision in all steps, and is presented here to enable reproducible execution.

A generalized schematic of the experiment set up and execution is detailed in Figure 3.
In order to detect a FRET change in the narrow dynamic range of the sensor, it is critical to adhere to the nuances of the system be adhered to. Cell quality is imperative to protein expression as well as consistency in sampling. Figure 1 features images of cultured cells growing in a consistent monolayer (10X) that is optimal for six-well plating and transfection Figure 1 (a) and cells growing in clumped patterns that lead to dendritic shapes Figure 1 (b) which is not recommended for consistent plating. Transfection conditions can also be optimized in order to achieve reproducible expression. Several conditions have been optimized for the recommended transfection reagent used here. Table 1 details these conditions for reduced serum media. DNA, and transfection reagent ratios.
Once all data has been collected and data entered into the CSV file for analysis (see sample in Table 3), the generated results will resemble the Raw FRET spectra data shown in Figure 4 (a) and the normalized, mean FRET spectra shown in Figure 4 (b). In Figure 4 the red spectra are the untreated samples and the blue are the drug treated samples. All of the Raw FRET spectra in Figure 4 (a) have sufficient signal-to-noise range for consistent data analysis (i.e., CPS at 450 nm compared to CPS at 475 nm). With this range, the spectra are smoother and the water peak from the sample (Raman peak is at 500 nm) is also minimal. Low expression levels result in a prominent water peak that interferes with data analysis. Data is normalized at 475 nm, setting the CPS of this value to 1.0 (Figure 4 (b)). In this data set for the &#946;2-AR-10 nm-G&#945;s peptide sensor, there is a significant change at the 525 nm reading between untreated (red) and treated (blue) samples. The &#916;FRET change is calculated from the FRET ratios (525 nm/475 nm) of these data sets and are accessible through the OUTPUT file.
If protein expression is low, there is poor transfection efficiency, or low cell density in the cuvette for fluorescence reading, spectra may appear noisier, as shown in Figure 5. Compared to Figure 4 (a) the signal-to-noise range for this data set is not ideal, at approximately 1.0 - 1.6, with CPSmax of 4 x 105. This low expression level contributes to the jagged spectra seen in the raw data Figure 5 (a), however the data is tight and able to be normalized Figure 5 (b). Though the water peaks (~ 500 nm) does not line up completely between data sets Figure 5 (b) this experiment is still usable for analysis. Figure 6 is representative of an experiment that is inadequate for analysis. While the signal-to-noise of the sample is approximately 2 - 3 for treated (blue) and untreated (red) samples, cell density in the cuvette across samples is too low (450 nm value of 1 x 105) Figure 6 (a). This creates an issue in background subtraction and normalization Figure 6 (b) and the spectra do not align. Subtraction can be adjusted for samples by increasing or decreasing OD in Table 3. However, even with low cell density, the water peak (~ 500 nm) becomes much larger and noisier Figure 6 (c) and inhibits this data set from further analysis.
<img alt="Figure 1" src="/files/ftp_upload/54696/54696fig1.jpg" /> 
Figure 1. Example of Cultured Cell Growth. Cells growing in a monolayer (a) are ideal for consistent plating and transfection. Cells that appear to grow in clumps or with dendritic patterns (b) may not plate consistently in six-wells, may display poor transfection efficiency, and may create inconsistencies in amplitude of the FRET spectrum. Scale bar = 100 &#181;m. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54696/54696fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/54696/54696fig4.jpg" /> 
Figure 4. Representative Data Analysis for &#946;2-AR-10 nm-G&#945;s Peptide &#177; Drug. Representative image of raw data (a) and normalized, averaged data (b) collected with the &#946;2-AR-10 nm-G&#945;s peptide sensor with untreated (red) samples and treated (blue) samples after 5-minute incubation with 100 &#181;M isoproterenol bitartrate. CFPmax emission at 475 nm, YFPmax emission at 525 nm. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54696/54696fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/54696/54696fig5.jpg" /> 
Figure 5. Analysis of Poor Protein Expression with Raw and Normalized Data. Noisy raw data spectra (a) are a result of low expression levels, low cell density per sample, and/or poor transfection efficiency of construct. This data set is still interpretable as normalized (b), although it is not ideal. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54696/54696fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/54696/54696fig6.jpg" /> 
Figure 6. Analysis of Low Cell Density in Fluorometer Cuvette with Raw and Normalized Data Sets. The low cell density per sample, seen in raw data (a) complicates water/background subtraction (b, c) and makes this data set uninterpretable. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54696/54696fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Condition</td>
			<td>0.5x</td>
			<td>0.7x</td>
			<td>1x</td>
			<td>2x</td>
			<td>2x*</td>
		</tr>
		<tr>
			<td>DNA</td>
			<td>1 &#956;g</td>
			<td>1.4&#160; &#956;g</td>
			<td>2&#160; &#956;g</td>
			<td>4&#160; &#956;g</td>
			<td>4&#160; &#956;g</td>
		</tr>
		<tr>
			<td>Reduced serum media</td>
			<td>100&#160; &#956;l</td>
			<td>70&#160; &#956;l</td>
			<td>100&#160; &#956;l</td>
			<td>100&#160; &#956;l</td>
			<td>200&#160; &#956;l</td>
		</tr>
		<tr>
			<td>Transfection reagent</td>
			<td>3&#160; &#956;l</td>
			<td>4.2&#160; &#956;l</td>
			<td>6&#160; &#956;l</td>
			<td>6&#160; &#956;l</td>
			<td>12&#160; &#956;l</td>
		</tr>
		<tr>
			<td colspan="6">*Note: Recommended for exceptionally difficult construct. Caution must be taken, as this condition induces high cell death.</td>
		</tr>
	</tbody>
</table>
Table 1. Transfection Conditions. This table includes the optimized conditions of reagents for transfections into 2 ml wells of HEK-293T cells using the recommended transfection reagent.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2">Cell Buffer</td>
			<td></td>
		</tr>
		<tr>
			<td>Volume</td>
			<td>Reagent</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>9 ml</td>
			<td>ultrapure DNase/RNase free water</td>
			<td></td>
		</tr>
		<tr>
			<td>1 ml</td>
			<td>HBS (10x), pH 7.4, store at 4 &#176;C:</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>200 mM HEPES</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>50 mM KCl</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>450 mM NaCl</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>20 mM CaCl2 - H2O</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>10 mM MgCl2 - H2O</td>
			<td></td>
		</tr>
		<tr>
			<td>100 &#956;l</td>
			<td>20% D-glucose</td>
			<td></td>
		</tr>
		<tr>
			<td>15 &#956;l</td>
			<td>aprotinin (1 mg/ml in dH2O)&#160;</td>
			<td>prevent degradation</td>
		</tr>
		<tr>
			<td>15 &#956;l</td>
			<td>leupeptin (1 mg/ml in dH2O)</td>
			<td>prevent degradation</td>
		</tr>
		<tr>
			<td>100 &#956;l</td>
			<td>ascorbic acid (100 mM in dH2O)*</td>
			<td>stabilize agonist</td>
		</tr>
		<tr>
			<td></td>
			<td>*add immediately before beginning assay</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td colspan="2">Drug Buffer</td>
			<td></td>
		</tr>
		<tr>
			<td>Volume</td>
			<td>Reagent</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>9 ml</td>
			<td>ultrapure DNase/RNase free water</td>
			<td></td>
		</tr>
		<tr>
			<td>1 ml</td>
			<td>HBS (10x), pH 7.4, store at 4 &#176;C:</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>200 mM HEPES</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>50 mM KCl</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>450 mM NaCl</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>20 mM CaCl2 - H2O</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>10 mM MgCl2 - H2O</td>
			<td></td>
		</tr>
		<tr>
			<td>100 &#956;l</td>
			<td>ascorbic acid (100 mM in dH2O)*</td>
			<td>stabilize agonist</td>
		</tr>
		<tr>
			<td></td>
			<td>*add immediately before beginning assay</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 2. Buffer Constituents. This table details the reagents used to make both Cell Buffer and Drug Buffer for use in the experiment. Make both buffers fresh each day of the experiment; store Cell Buffer at 37 &#176;C, and Drug Buffer at room temperature.
<img alt="Table 3" src="/files/ftp_upload/54696/54696tbl3.jpg" />
Table 3. Sample CSV File for Analysis. This sample data file highlights the entry set after one experiment. Multiple experiments can be entered into the same CSV file and can be discerned under the &#39;Additive&#39; column if necessary. Each row must be filled out with the following information: 
File name - individual SPC graph files Receptor - designate which GPCR construct was tested (e.g., &#914;2) 
Binder - designate which peptide variant of the construct was tested (e.g., S) 
Agonist - designate untreated (N) or drug treated (D) conditions 
Directory - the path folder in which SPC files are saved, usually organized by date 
OD - recorded optical density of sample in spectrophotometer 
<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/54696/54696tbl3large.jpg" target="_blank">Please click here to view a larger version of this table.</a>

Watch this Scientific Journal Video about G Protein-selective GPCR Conformations Measured Using FRET Sensors in a Live Cell Suspension Fluorometer Assay at JoVE.com

G Protein-selective GPCR Conformations Measured Using FRET Sensors in a Live Cell Suspension Fluorometer Assay

Simple methods to detect the selective activation of G proteins by G protein-coupled receptors remain an outstanding challenge in cell signaling. Here, F&#1255;rster resonance energy transfer (FRET) biosensors have been developed by pairwise tethering a GPCR to G protein peptides to probe conformational changes at controlled concentrations in live cells.

F&#1255;rster resonance energy transfer (FRET)-based studies have become increasingly common in the investigation of GPCR signaling. Our research group ...

g-protein-selective-gpcr-conformations-measured-using-fret-sensors

Nanyang Technological University

Research

JoVE Journal

Biology

9.5K Views.  University of Minnesota. Simple methods to detect the selective activation of G proteins by G protein-coupled receptors remain an outstanding challenge in cell signaling. Here, Fӧrster resonance energy transfer (FRET) biosensors have been developed by pairwise tethering a GPCR to G protein peptides to probe conformational changes at controlled concentrations in live cells.

Video: G Protein-selective GPCR Conformations Measured Using FRET Sensors in a Live Cell Suspension Fluorometer Assay

Live-cell Imaging and Quantitative Analysis of Embryonic Epithelial Cells in Xenopus laevis

Embryonic epithelial cells serve as an ideal model to study morphogenesis where multi-cellular tissues undergo changes in their geometry, such as changes in cell surface area and cell height, and where cells undergo mitosis and migrate. Furthermore, epithelial cells can also regulate morphogenetic movements in adjacent tissues1. A traditional method to study epithelial cells and tissues involve chemical fixation and histological methods to determine cell morphology or localization of particular proteins of interest. These approaches continue to be useful and provide "snapshots" of cell shapes and tissue architecture, however, much remains to be understood about how cells acquire specific shapes, how various proteins move or localize to specific positions, and what paths cells follow toward their final differentiated fate. High resolution live imaging complements traditional methods and also allows more direct investigation into the dynamic cellular processes involved in the formation, maintenance, and morphogenesis of multicellular epithelial sheets. Here we demonstrate experimental methods from the isolation of animal cap tissues from Xenopus laevis embryos to confocal imaging of epithelial cells and simple measurement approaches that together can augment molecular and cellular studies of epithelial morphogenesis.

Live-cell Imaging and Quantitative Analysis of Embryonic Epithelial Cells in Xenopus laevis

Xenopus embryonic epithelia are an ideal model system to study cell behaviors such as polarity development and shape change during epithelial morphogenesis. Traditional histology of fixed samples is increasingly being complemented by live-cell confocal imaging. Here we demonstrate methods to isolate frog tissues and visualize live epithelial cells and their cytoskeleton using live-cell confocal microscopy.

Embryonic epithelial cells serve as an ideal model to study morphogenesis where multi-cellular tissues undergo changes in their geometry, such as changes ...

Bimolecular Fluorescence Complementation (BiFC) Assay for Protein-Protein Interaction in Onion Cells Using the Helios Gene Gun

Investigation of gene function in diverse organisms relies on knowledge of how the gene products interact with each other in their normal cellular environment. The Bimolecular Fluorescence Complementation (BiFC) Assay1 allows researchers to visualize protein-protein interactions in living cells and has become an essential research tool. This assay is based on the facilitated association of two fragments of a fluorescent protein (GFP) that are each fused to a potential interacting protein partner. The interaction of the two protein partners would facilitate the association of the N-terminal and C-terminal fragment of GFP, leading to fluorescence. For plant researchers, onion epidermal cells are an ideal experimental system for conducting the BiFC assay because of the ease in obtaining and preparing onion tissues and the direct visualization of fluorescence with minimal background fluorescence. The Helios Gene Gun (BioRad) is commonly used for bombarding plasmid DNA into onion cells. We demonstrate the use of Helios Gene Gun to introduce plasmid constructs for two interacting Arabidopsis thaliana transcription factors, SEUSS (SEU) and LEUNIG HOMOLOG (LUH)2 and the visualization of their interactions mediated by BiFC in onion epidermal cells.

Bimolecular Fluorescence Complementation BiFC Assay for Protein-Protein Interaction in Onion Cells Using the Helios Gene Gun

This article illustrates how to properly use the BioRad Helios Gene Gun to introduce plasmid DNA into onion epidermal cells and how to test for protein-protein interactions in onion cells based on the principle of Bimolecular Fluorescence Complementation (BiFC)

Investigation of gene function in diverse organisms relies on knowledge of how the gene products interact with each other in their normal cellular ...

Imaging G-protein Coupled Receptor (GPCR)-mediated Signaling Events that Control Chemotaxis of Dictyostelium Discoideum

Many eukaryotic cells can detect gradients of chemical signals in their environments and migrate accordingly 1. This guided cell migration is referred as chemotaxis, which is essential for various cells to carry out their functions such as trafficking of immune cells and patterning of neuronal cells 2, 3. A large family of G-protein coupled receptors (GPCRs) detects variable small peptides, known as chemokines, to direct cell migration in vivo 4. The final goal of chemotaxis research is to understand how a GPCR machinery senses chemokine gradients and controls signaling events leading to chemotaxis. To this end, we use imaging techniques to monitor, in real time, spatiotemporal concentrations of chemoattractants, cell movement in a gradient of chemoattractant, GPCR mediated activation of heterotrimeric G-protein, and intracellular signaling events involved in chemotaxis of eukaryotic cells 5-8. The simple eukaryotic organism, Dictyostelium discoideum, displays chemotaxic behaviors that are similar to those of leukocytes, and D. discoideum is a key model system for studying eukaryotic chemotaxis. As free-living amoebae, D. discoideum cells divide in rich medium. Upon starvation, cells enter a developmental program in which they aggregate through cAMP-mediated chemotaxis to form multicullular structures. Many components involved in chemotaxis to cAMP have been identified in D. discoideum. The binding of cAMP to a GPCR (cAR1) induces dissociation of heterotrimeric G-proteins into G&gamma; and G&beta;&gamma; subunits 7, 9, 10. G&beta;&gamma; subunits activate Ras, which in turn activates PI3K, converting PIP2 into PIP3 on the cell membrane 11-13. PIP3 serve as binding sites for proteins with pleckstrin Homology (PH) domains, thus recruiting these proteins to the membrane 14, 15. Activation of cAR1 receptors also controls the membrane associations of PTEN, which dephosphorylates PIP3 to PIP2 16, 17. The molecular mechanisms are evolutionarily conserved in chemokine GPCR-mediated chemotaxis of human cells such as neutrophils 18. We present following methods for studying chemotaxis of D. discoideum cells. 1. Preparation of chemotactic component cells. 2. Imaging chemotaxis of cells in a cAMP gradient. 3. Monitoring a GPCR induced activation of heterotrimeric G-protein in single live cells. 4. Imaging chemoattractant-triggered dynamic PIP3 responses in single live cells in real time. Our developed imaging methods can be applied to study chemotaxis of human leukocytes.

Imaging G-protein Coupled Receptor GPCR-mediated Signaling Events that Control Chemotaxis of Dictyostelium Discoideum

Here, we describe detailed live cell imaging methods for investigating chemotaxis. We present fluorescence microscopic methods to monitor spatiotemporal dynamics of signaling events in migrating cells. Measurement of signaling events permits us to further understand how a GPCR-signaling network achieves gradient sensing of chemoattractants and controls directional migration of eukaryotic cells.

Many eukaryotic cells can detect gradients of chemical signals in their environments and migrate accordingly 1.  This guided cell migration is referred as ...

FRET Microscopy for Real-time Monitoring of Signaling Events in Live Cells Using Unimolecular Biosensors

F&ouml;rster resonance energy transfer (FRET) microscopy continues to gain increasing interest as a technique for real-time monitoring of biochemical and signaling events in live cells and tissues. Compared to classical biochemical methods, this novel technology is characterized by high temporal and spatial resolution. FRET experiments use various genetically-encoded biosensors which can be expressed and imaged over time in situ or in vivo1-2. Typical biosensors can either report protein-protein interactions by measuring FRET between a fluorophore-tagged pair of proteins or conformational changes in a single protein which harbors donor and acceptor fluorophores interconnected with a binding moiety for a molecule of interest3-4. Bimolecular biosensors for protein-protein interactions include, for example, constructs designed to monitor G-protein activation in cells5, while the unimolecular sensors measuring conformational changes are widely used to image second messengers such as calcium6, cAMP7-8, inositol phosphates9 and cGMP10-11. Here we describe how to build a customized epifluorescence FRET imaging system from single commercially available components and how to control the whole setup using the Micro-Manager freeware. This simple but powerful instrument is designed for routine or more sophisticated FRET measurements in live cells. Acquired images are processed using self-written plug-ins to visualize changes in FRET ratio in real-time during any experiments before being stored in a graphics format compatible with the build-in ImageJ freeware used for subsequent data analysis. This low-cost system is characterized by high flexibility and can be successfully used to monitor various biochemical events and signaling molecules by a plethora of available FRET biosensors in live cells and tissues. As an example, we demonstrate how to use this imaging system to perform real-time monitoring of cAMP in live 293A cells upon stimulation with a &beta;-adrenergic receptor agonist and blocker.

F&#246;rster resonance energy transfer (FRET) microscopy is a powerful technique for real-time monitoring of signaling events in live cells using various biosensors as reporters. Here we describe how to build a customized epifluorescence FRET imaging system from commercially available components and how to use it for FRET experiments.

F&ouml;rster resonance energy transfer (FRET) microscopy  continues to gain increasing interest as a technique for real-time monitoring  of biochemical ...

Live Imaging of Apoptotic Cell Clearance during Drosophila Embryogenesis

The proper elimination of unwanted or aberrant cells through apoptosis and subsequent phagocytosis (apoptotic cell clearance) is crucial for normal development in all metazoan organisms. Apoptotic cell clearance is a highly dynamic process intimately associated with cell death; unengulfed apoptotic cells are barely seen in vivo under normal conditions. In order to understand the different steps of apoptotic cell clearance and to compare 'professional' phagocytes - macrophages and dendritic cells to 'non-professional' - tissue-resident neighboring cells, in vivo live imaging of the process is extremely valuable. Here we describe a protocol for studying apoptotic cell clearance in live Drosophila embryos. To follow the dynamics of different steps in phagocytosis we use specific markers for apoptotic cells and phagocytes. In addition, we can monitor two phagocyte systems in parallel: 'professional' macrophages and 'semi-professional' glia in the developing central nervous system (CNS). The method described here employs the Drosophila embryo as an excellent model for real time studies of apoptotic cell clearance.

Live Imaging of Apoptotic Cell Clearance during Drosophila Embryogenesis

Here we describe an effective method for studying dynamics of apoptotic cell clearance in vivo. This method employs live Drosophila embryos as a powerful model for monitoring phagocytosis of apoptotic cells using specific labeling of apoptotic cells and phagocytes.

The  proper elimination of unwanted or aberrant cells through apoptosis and  subsequent phagocytosis (apoptotic cell clearance) is crucial for normal  ...

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

Plasma membrane microdomains are features based on the physical properties of the lipid and sterol environment and have particular roles in signaling processes. Extracting sterol-enriched membrane microdomains from plant cells for proteomic analysis is a difficult task mainly due to multiple preparation steps and sources for contaminations from other cellular compartments. The plasma membrane constitutes only about 5-20% of all the membranes in a plant cell, and therefore isolation of highly purified plasma membrane fraction is challenging. A frequently used method involves aqueous two-phase partitioning in polyethylene glycol and dextran, which yields plasma membrane vesicles with a purity of 95% 1. Sterol-rich membrane microdomains within the plasma membrane are insoluble upon treatment with cold nonionic detergents at alkaline pH. This detergent-resistant membrane fraction can be separated from the bulk plasma membrane by ultracentrifugation in a sucrose gradient 2. Subsequently, proteins can be extracted from the low density band of the sucrose gradient by methanol/chloroform precipitation. Extracted protein will then be trypsin digested, desalted and finally analyzed by LC-MS/MS. Our extraction protocol for sterol-rich microdomains is optimized for the preparation of clean detergent-resistant membrane fractions from Arabidopsis thaliana cell cultures.
We use full metabolic labeling of Arabidopsis thaliana suspension cell cultures with K15NO3 as the only nitrogen source for quantitative comparative proteomic studies following biological treatment of interest 3. By mixing equal ratios of labeled and unlabeled cell cultures for joint protein extraction the influence of preparation steps on final quantitative result is kept at a minimum. Also loss of material during extraction will affect both control and treatment samples in the same way, and therefore the ratio of light and heave peptide will remain constant. In the proposed method either labeled or unlabeled cell culture undergoes a biological treatment, while the other serves as control 4.

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

Here we describe a robust method for the fractionation of plant plasma membranes into detergent resistant and detergent soluble membranes based on a mixture of unlabeled and in vivo fully 15N labeled Arabidopsis thaliana cell cultures. The procedure is applied for comparative proteomic studies to understand signaling processes.

Plasma membrane microdomains are features based on the physical properties of the lipid and sterol environment and have particular roles in signaling ...

Single-cell Microinjection for Cell Communication Analysis

Gap junctions are intercellular channels that allow the communication of neighboring cells. This communication depends on the contribution of a hemichannel by each neighboring cell to form the gap junction. In mammalian cells, the hemichannel is formed by six connexins, monomers with four transmembrane domains and a C and N terminal within the cytoplasm. Gap junctions permit the exchange of ions, second messengers, and small metabolites. In addition, they have important roles in many forms of cellular communication within physiological processes such as synaptic transmission, heart contraction, cell growth and differentiation. We detail how to perform a single-cell microinjection of Lucifer Yellow to visualize cellular communication via gap-junctions in living cells. It is expected that in functional gap junctions, the dye will diffuse from the loaded cell to the connected cells. It is a very useful technique to study gap junctions since you can evaluate the diffusion of the fluorescence in real time. We discuss how to prepare the cells and the micropipette, how to use a micromanipulator and inject a low molecular weight fluorescent dye in an epithelial cell line.

We describe here how to perform a single-cell microinjection of Lucifer Yellow to visualize cellular communication via gap-junctions in living cells, and provide some useful tips. We expect that this paper will help everyone to evaluate the degree of cellular coupling due to functional gap junctions. Everything described here could be, in principle, adapted to other fluorescent dyes with molecular weight below 1,000 Daltons.

Gap junctions are intercellular channels that allow the communication of neighboring cells. This communication depends on the contribution of a ...

LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring

The regulation of LDL cholesterol uptake through LDLR-mediated endocytosis is an important area of study in various major pathologies including metabolic disorder, cardiovascular disease, and kidney disease. Currently, there is no available method to assess LDL uptake while simultaneously monitoring for health of the cells. The current study presents a protocol, using a live cell imaging analysis system, to acquire serial measurements of LDL influx with concurrent monitoring for cell health. This novel technique is tested in three human cell lines (hepatic, renal tubular epithelial, and coronary artery endothelial cells) over a four-hour time course. Moreover, the sensitivity of this technique is validated with well-known LDL uptake inhibitors, Dynasore and recombinant PCSK9 protein, as well as by an LDL uptake promoter, Simvastatin. Taken together, this method provides a medium-to-high throughput platform for simultaneously screening pharmacological activity as well as monitoring of cell morphology, hence cytotoxicity of compounds regulating LDL influx. The analysis can be used with different imaging systems and analytical software.

This protocol provides an efficient approach to measuring LDL cholesterol uptake with real time influx rates using a live cell imaging system in various cell types. This technique provides a platform to screen the pharmacological activity of compounds affecting LDL influx while monitoring for cell morphology and hence potential cytotoxicity.

The regulation of LDL cholesterol uptake through LDLR-mediated endocytosis is an important area of study in various major pathologies including metabolic ...

FLIM-FRET Measurements of Protein-Protein Interactions in Live Bacteria.

Protein-protein interactions (PPIs) control various key processes in cells. Fluorescence lifetime imaging microscopy (FLIM) combined with F&#246;rster resonance energy transfer (FRET) provide accurate information about PPIs in live cells. FLIM-FRET relies on measuring the fluorescence lifetime decay of a FRET donor at each pixel of the FLIM image, providing quantitative and accurate information about PPIs and their spatial cellular organizations. We propose here a detailed protocol for FLIM-FRET measurements that we applied to monitor PPIs in live Pseudomonas aeruginosa in the particular case of two interacting proteins expressed with highly different copy numbers to demonstrate the quality and robustness of the technique at revealing critical features of PPIs. This protocol describes in detail all the necessary steps for PPI characterization - starting from bacterial mutant constructions up to the final analysis using recently developed tools providing advanced visualization possibilities for a straightforward interpretation of complex FLIM-FRET data.

We describe here a protocol to characterize protein-protein interactions between two highly-differently expressed proteins in live Pseudomonas aeruginosa using FLIM-FRET measurements. The protocol includes bacteria strain constructions, bacteria immobilization, imaging and post-imaging data analysis routines.

Protein-protein interactions (PPIs) control various key processes in cells. Fluorescence lifetime imaging microscopy (FLIM) combined with F&#246;rster ...

Real-Time Monitoring of Aurora kinase A Activation using Conformational FRET Biosensors in Live Cells

Epithelial cancers are often hallmarked by the overexpression of the Ser/Thr kinase Aurora A/AURKA. AURKA is a multifunctional protein that activates upon its autophosphorylation on Thr288. AURKA abundance peaks in mitosis, where it controls the stability and the fidelity of the mitotic spindle, and the overall efficiency of mitosis. Although well characterized at the structural level, a consistent monitoring of the activation of AURKA throughout the cell cycle is lacking. A possible solution consists in using genetically-encoded F&#246;rster&#8217;s Resonance Energy Transfer (FRET) biosensors to gain insight into the autophosphorylation of AURKA with sufficient spatiotemporal resolution. Here, we describe a protocol to engineer FRET biosensors detecting Thr288 autophosphorylation, and how to follow this modification during mitosis. First, we provide an overview of possible donor/acceptor FRET pairs, and we show possible cloning and insertion methods of AURKA FRET biosensors in mammalian cells. Then, we provide a step-by-step analysis for rapid FRET measurements by fluorescence lifetime imaging microscopy (FLIM) on a custom-built setup. However, this protocol is also applicable to alternative commercial solutions available. We conclude by considering the most appropriate FRET controls for an AURKA-based biosensor, and by highlighting potential future improvements to further increase the sensitivity of this tool.

The activation of the multifunctional Ser/Thr kinase AURKA is hallmarked by its autophosphorylation on Thr288. Fluorescent probes relying on FRET can discriminate between its inactive and activated states. Here, we illustrate some strategies for probe engineering, together with a rapid FRET protocol to follow the kinase activation throughout mitosis.

Epithelial cancers are often hallmarked by the overexpression of the Ser/Thr kinase Aurora A/AURKA. AURKA is a multifunctional protein that activates upon ...

G Protein-selective GPCR Conformations Measured Using FRET Sensors in a Live Cell Suspension Fluorometer Assay

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Chapters in this video

Live-cell Imaging and Quantitative Analysis of Embryonic Epithelial Cells in Xenopus laevis

Bimolecular Fluorescence Complementation (BiFC) Assay for Protein-Protein Interaction in Onion Cells Using the Helios Gene Gun

Imaging G-protein Coupled Receptor (GPCR)-mediated Signaling Events that Control Chemotaxis of Dictyostelium Discoideum

FRET Microscopy for Real-time Monitoring of Signaling Events in Live Cells Using Unimolecular Biosensors

Live Imaging of Apoptotic Cell Clearance during Drosophila Embryogenesis

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

Single-cell Microinjection for Cell Communication Analysis

LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring

FLIM-FRET Measurements of Protein-Protein Interactions in Live Bacteria.

Real-Time Monitoring of Aurora kinase A Activation using Conformational FRET Biosensors in Live Cells