This project was supported by the Deutsche Forschungsgemeinschaft (SFB/TR 240, project number 374031971, Project INF) to J. B. and K.G.H.
We thank the Rudolf Virchow Center for financial support and Core Unit Fluorescence Imaging for technical support. Additionally, we thank Ashwin Balakrishnan for thorough proof-reading.

1. Experimental Protocol
<ol>
	<li>Sample preparation 
	NOTE: Perform cell seeding and transfection under sterile conditions.
	<ol>
		<li>Place a cleaned coverslip per well into a 6-well culture plate and wash three times with sterile phosphate-buffered saline (PBS). 
		NOTE: The coverslip cleaning protocol is detailed in Supplementary Note 1.</li>
		<li>To each well, add 2 mL of the complete cell culture medium containing phenol red (supplemented with 10% fetal bovine serum (FBS), 100 &#181;g/mL penicillin and 100 &#181;g/mL streptomycin) and keep the plate aside.</li>
		<li>Culture the CHO cells in the same medium containing phenol red at 37 &#176;C and 5% CO2. Wash the cells with 5 mL of PBS to remove the dead cells.</li>
		<li>Add 2 mL of trypsin and incubate for 2 min at room temperature (RT).</li>
		<li>Dilute the detached cells with 8 mL of medium containing phenol red and mix carefully by pipetting.</li>
		<li>Count the cells in a Neubauer chamber and seed at a density of 1.5 x 105 cells/well in the 6-well cell culture plate containing the coverslips (prepared in step 1.1.1-1.1.2).</li>
		<li>Let the cells grow in an incubator (37 &#176;C, 5% CO2) for 24 h in order to achieve approximately 80% confluency.</li>
		<li>Dilute 2 &#181;g of the desired vector DNA (e.g., CT-SNAP or NT-SNAP) and 6 &#181;L of the transfection reagent in two separate tubes, each containing 500 &#181;L of the reduced-serum medium for each well and incubate for 5 min at RT.</li>
		<li>Mix the two solutions together to obtain the transfection mixture and incubate it for 20 min at RT.</li>
		<li>In the meantime, wash the seeded CHO cells once with sterile PBS.</li>
		<li>Replace the PBS with 1 mL/well of phenol red-free medium supplemented with 10% FBS without any antibiotics.</li>
		<li>Add the entire 1 mL of transfection mixture dropwise to each well and incubate the cells overnight at 37 &#176;C, in 5% CO2.</li>
		<li>For labeling of the SNAP construct, dilute the appropriate SNAP substrate stock solution in 1 mL of the medium supplemented with 10% FBS to obtain a final concentration of 1 &#181;M.</li>
		<li>Wash the transfected cells once with PBS and add 1 mL per well of 1 &#181;M SNAP substrate solution. Incubate the cells for 20 min at 37 &#176;C in 5% CO2.</li>
		<li>Wash the cells thrice with phenol red-free medium and add 2 mL per well phenol red-free medium. Incubate the cells for 30 min at 37 &#176;C in 5% CO2.</li>
		<li>Transfer the coverslips of all samples subsequently into the imaging chamber and wash with 500 &#181;L imaging buffer. Add 500 &#181;L imaging buffer before moving to the FRET-FCS setup.</li>
	</ol>
	</li>
	<li>Calibration Measurements 
	&#8203;NOTE: The FRET-FCS setup is equipped with a confocal microscope water objective, two laser lines, a Time-Correlated Single Photon Counting (TCSPC) system, two hybrid photomultiplier tubes (PMT) and two avalanche photodiodes (APD) for photon collection and the data collection software. It is very important to align the setup every time before live cell measurements. The detailed setup description can be found in Supplementary Note 2. Both lasers and all detectors (two PMTs and two APDs) are always ON during the measurements, as all measurements need to be conducted under identical conditions. For the calibration measurements, use a coverslip from the same lot on which the cells were seeded, this decreases the variation in collar ring correction.
	<ol>
		<li>For adjusting the focus, pinhole and collar ring position, place 2 nM green calibration solution on a glass coverslip and switch on the 485 nm and 560 nm laser. Operate laser in Pulsed Interleaved Excitation (PIE) mode16.</li>
		<li>Focus on the solution and adjust the pinhole and collar ring position such that the highest count rate and smallest confocal volume are obtained to get the maximum molecular brightness.</li>
		<li>Repeat this process for the red channels with 10 nM red calibration solution and a mixture of both, the green and red calibration solution.</li>
		<li>Place the 10 nM DNA solution on a glass coverslip and adjust the focus, pinhole, and collar ring position such that the cross-correlation between the green and red detection channels is highest, i.e., shows the highest amplitude. 
		NOTE: Steps 1.2.1 and 1.2.4 might have to be repeated back and forth to find the optimal alignment. Take 3-5 measurements from each calibration solution for 30 s - 120 s after the focus, pinhole, and collar ring position have been aligned optimally for the green and red detection channels and the confocal overlap volume.</li>
		<li>Measure a drop of ddH2O, the imaging medium, and a non-transfected cell 3-5 times each for 30 s - 120 s to determine the background count rates.</li>
		<li>Collect the instrument response function with 3-5 measurements for 30 s - 120 s. This is optional but highly recommended.</li>
	</ol>
	</li>
	<li>Live cell measurements
	<ol>
		<li>Find a suitable cell by illuminating with the mercury lamp and observing through the ocular. 
		&#8203;NOTE: Suitable cells are alive showing the typical morphology of the respective adherent cell line. The fluorescence of the protein of interest, here a surface receptor, is visible all over the surface. Less bright cells are more suitable than brighter ones due to the better contrast in FCS when a low number of molecules are in focus.</li>
		<li>Switch on both lasers in PIE mode and focus on the membrane by looking at the maximum counts per second. 
		NOTE: The laser power might need to be reduced for the cell samples (less than 5 &#181;W at objective). This depends upon the used fluorophores and the setup.</li>
		<li>Observe the auto- and cross-correlation curves of the eGFP or labelled SNAP-tag attached to the&#160;&#160;&#946;2AR&#160;in the online preview of the data collection software and collect several short measurements (~2 -10) with an acquisition time between 60 -180 s. 
		&#8203;NOTE: Do not excite the cells for a long time continuously as the fluorophores may bleach. However, it will depend on the brightness of each cell, how long the measurements can be, and how many measurements in total can be performed.</li>
	</ol>
	</li>
</ol>
2. Data analysis
<ol>
	<li>Data Export
	<ol>
		<li>Export the correlation curves, G(tc), and count rates, CR, from all measurements.</li>
		<li>Take care to correctly define the &#34;prompt&#34; and &#34;delay&#34; time windows and use the &#34;microtime gating&#34; option in the data correlation software. 
		NOTE: In total, three different correlations are required: (1) autocorrelation of the green channel in the prompt time window (ACFgp), (2) autocorrelation of the red channels in the delay time window (ACFrd), and finally (3) the cross-correlation of the green channel signal in the prompt time window with the red channel signal in the delay time window (CCFPIE). The data export is shown step-by-step for different software in Supplementary Note 3.</li>
	</ol>
	</li>
	<li>Calibration measurements
	<ol>
		<li>Use the autocorrelation functions of the green (ACFgp) and red (ACFrd) fluorophore solutions, and fit them to a 3D diffusion model with an additional triplet term if required (eq. 1) to calibrate the shape and size of the confocal detection volume for the two used color channels: 
		<img alt="Equation 1" src="/files/ftp_upload/62954/62954eq01v2.png" /> eq. 1 
		Here, b is the baseline of the curve, N the number of molecules in focus, tD the diffusion time (in ms), and s = z0/w0 the shape factor of the confocal volume element. The triplet blinking or other photophysics is described by its amplitude aR and relaxation time&#160;tR. 
		NOTE: All variables and symbols used within the protocol are listed in Table 1.</li>
		<li>Use the known diffusion coefficients D for the green26 and red calibration standard27 and the obtained shape factors sgreen and sred to determine the dimensions (width w0 and height z0) and volume Veff of the confocal volume element (eq. 2a-c). 
		<img alt="Equation 2" src="/files/ftp_upload/62954/62954eq02.jpg" />&#160; &#160; &#160;eq. 2a 
		<img alt="Equation 3" src="/files/ftp_upload/62954/62954eq03.jpg" />&#160; &#160; &#160;eq. 2b 
		<img alt="Equation 4" src="/files/ftp_upload/62954/62954eq04.jpg" />&#160;&#160;&#160; &#160;eq. 2c 
		NOTE: Templates for calculation of the calibration parameter are provided as supplementary files (S7).</li>
		<li>Calculate the spectral crosstalk &#945; of the green fluorescence signal (collected in channels 0 and 2) into the red detection channels (channel number 1 and 3) as a ratio of the background-corrected (BG) signals (eq. 3). 
		<img alt="Equation 5" src="/files/ftp_upload/62954/62954eq05.jpg" /> 
		eq. 3</li>
		<li>Determine the direct excitation of the acceptor fluorophore &#948; by the donor excitation wavelength by the ratio of the background-corrected count rate of the red calibration measurements in the &#34;prompt&#34; time window (excitation by green laser) to the background-corrected count rate in the &#34;delay&#34; time window (excitation by red laser) (eq. 4). 
		<img alt="Equation 6" src="/files/ftp_upload/62954/62954eq06v2.png" /> 
		eq. 4</li>
		<li>Calculate the molecular brightness B of both the green and red fluorophores (eq. 5a-b) based on the background-corrected count rates and the obtained number of molecules in focus, N, from the 3D diffusion fit (eq. 1): 
		<img alt="Equation 7" src="/files/ftp_upload/62954/62954eq07.jpg" />&#160; &#160; &#160;eq. 5a 
		<img alt="Equation 8" src="/files/ftp_upload/62954/62954eq08.jpg" />&#160; &#160; &#160;eq. 5b</li>
		<li>Fit both ACFgp and ACFrd as well as CCFPIE&#160;of the double-labeled DNA to the 3D diffusion model (eq. 1). Keep the obtained shape factors, sgreen and sred, constant for ACFgp and ACFrd, respectively. The shape factor for the CCFPIE, sPIE, is usually in between these two values. 
		NOTE: In an ideal setup, both Veff,green and Veff, red would have the same size and overlap perfectly.</li>
		<li>Determine the amplitude at zero correlation time, G0(tc), based on the found values of the apparent number of molecules in focus (Ngreen, Nred and NPIE).</li>
		<li>Calculate the amplitude ratio rGR and rRG for a sample with 100% co-diffusion of green and red fluorophores (eq. 6). Be aware that NPIE does not reflect the number of double-labeled molecules in focus but reflects only the 1/G0(tc). 
		<img alt="Equation 9" src="/files/ftp_upload/62954/62954eq09.jpg" />&#160; and&#160;&#160;<img alt="Equation 10" src="/files/ftp_upload/62954/62954eq10.jpg" />&#160; &#160;&#160;&#160;eq. 6</li>
	</ol>
	</li>
	<li>Live cell experiments
	<ol>
		<li>For single-labeled constructs, fit the cell samples to an appropriate model. For the shown membrane receptor, diffusion occurs in a bimodal fashion with a short and a long diffusion time. Additionally, the photophysics and blinking of the fluorophores have to be considered: 
		<img alt="Equation 11" src="/files/ftp_upload/62954/62954eq011v2.png" /> eq. 7 
		Here, td1 and td2 are the two required diffusion times, and a1 is the fraction of the first diffusion time. 
		NOTE: In contrast to the calibration measurements, in which the free dyes and DNA strands freely diffuse in all directions, the membrane receptor shows only 2D diffusion along the cell membranes. This difference between 3D and 2D diffusion is reflected by the modified diffusion term (compare eq. 1), where tD&#160;in the 2D case does not depend upon the shape factor s of the confocal volume element.</li>
		<li>Calculate the concentration c of green or red labeled proteins from the respective N and Veff using basic math (eq. 8): 
		<img alt="Equation 12" src="/files/ftp_upload/62954/62954eq12.jpg" />&#160; &#160; &#160;eq. 8 
		&#8203;where NA = Avogadro&#39;s number</li>
		<li>For N-terminal SNAP label and intracellular eGFP, fit the two autocorrelations (ACFgp and ACFrd) of the double-labeled sample using the same model as for the single-labeled constructs for the ACFs (eq. 7) and the CCFPIE using a bimodal diffusion model (eq. 9): 
		<img alt="Equation 13" src="/files/ftp_upload/62954/62954eq13.jpg" />&#160; &#160; &#160; eq. 9 
		&#8203;NOTE: For a global description of the system, all three curves have to be fit jointly: The diffusion term is identical for all three curves and the only difference is the relaxation term for the CCFPIE. As photophysics of two fluorophores is usually unrelated, no correlation term is required. This absence of relaxation terms results in a flat CCFPIE at short correlation times. However, crosstalk and direct excitation of the acceptor due to the donor fluorophore might show false-positive amplitudes and should be carefully checked for using the calibration measurements.</li>
		<li>Calculate the concentration c of green or red labeled proteins from the respective N and Veff using equation 8.</li>
		<li>Estimate the fraction or concentration, cGR or cRG, of interacting green and red labeled proteins from the cell samples using the correction factors obtained from the DNA samples, the amplitude ratios rGR and rRG of the cell sample and their respective obtained concentrations (eq. 10). 
		<img alt="Equation 14" src="/files/ftp_upload/62954/62954eq14.jpg" /> and <img alt="Equation 15" src="/files/ftp_upload/62954/62954eq15.jpg" />&#160; &#160; &#160;eq. 10</li>
		<li>For C-terminal SNAP label and intracellular eGFP, fit the two autocorrelations (ACFgp and ACFrd) of the FRET sample as the single-labeled samples (equation 7) and the CCFFRET to a bimodal diffusion model containing an anti-correlation term (equation 11) 
		<img alt="Equation 16" src="/files/ftp_upload/62954/62954eq016v2.png" /> eq. 11 
		where af reflects the amplitude of the total anti-correlation and aR and tR the respective amplitude and relaxation time. 
		NOTE: In case of anti-correlated fluorescence changes due to FRET, one or several anti-correlation terms might be required (eq. 11), resulting in a &#34;dip&#34; of CCFFRET at low correlation times coinciding with a rise in the two autocorrelations (ACFgp and ACFrd). However, be aware that photophysics such as triplet blinking might mask the anti-correlation term by dampening the FRET-induced anti-correlation. A joint analysis supplemented with filtered FCS methods might help to unmask the anti-correlation term. Additionally, technical artifacts stemming from dead times in the counting electronics in the nanoseconds range should be excluded16. A more detailed step-by-step procedure on how to perform the analysis in ChiSurf28 and templates for the calculation of confocal volume or molecular brightness are provided on the Github repository (https://github.com/HeinzeLab/JOVE-FCS) and as supplementary files (Supplementary Note 4 and Supplementary Note 6). Additionally, the python-scripts for batch export of data acquired with the Symphotime software in .ptu format can be found there.</li>
	</ol>
	</li>
</ol>

The authors have no conflicts to declare.

Fluorescence spectroscopy is one of the main methods to quantify protein dynamics and protein-protein interactions with minimal perturbation in a cellular context. Confocal fluorescence correlation spectroscopy (FCS) is one of the powerful methods to analyze molecular dynamics as it is single-molecule sensitive, highly selective, and live-cell compatible1. Compared with other dynamics-oriented&#160;approaches, FCS has a broader measurable time range spanning from ~ ns to ~&#160;s, most importantly covering the fast time scales that are often inaccessible by imaging-based methods. Moreover, it also provides spatial selectivity so that membrane, cytoplasmic, and nucleus molecular dynamics can be easily distinguished 2. Thus, molecular blinking, the average local concentration, and the diffusion coefficient can be quantitatively analyzed with FCS. Intermolecular dynamics such as binding become easily accessible when probing co-diffusion of two molecular species in fluorescence cross-correlation spectroscopy (FCCS) analysis3,4,5 in a dual-color approach.
The main underlying principle in correlation spectroscopy is the statistical analysis of intensity fluctuations emitted by fluorescently labeled biomolecules diffusing in and out of a laser focus (Figure 1A). The resulting auto- or cross-correlation functions then can be further analyzed by curve fitting to eventually derive the rate constants of interest. In other words, the statistical methods FCS and FCCS do not provide single molecule traces like in single particle tracking, but a dynamic pattern or &#34;fingerprint&#34; of a probed specimen with high temporal resolution. When combined with F&#246;rster resonance energy transfer (FRET), intramolecular dynamics such as conformational changes can be monitored at the same time in a common confocal setup5,6. FRET probes the distance of two fluorophores and is often referred to as a molecular &#34;nanoruler&#34;. Energy transfer takes place only when the molecules are in close vicinity (&#60; 10 nm), the emission spectrum of the donor significantly overlaps with the absorption spectrum of the acceptor molecule, and the dipole orientation of the donor and acceptor is (sufficiently) parallel. Thus, the combination of FRET and FCCS provides a technique with very high spatio-temporal resolution. When spatial selectivity, sensitivity as well as live-cell compatibility is required, FRET-FCCS has obvious advantageous over other methods such as Isothermal Titration Calorimetry (ITC)7, Surface Plasmon Resonance (SPR)8, or Nuclear Magnetic Resonance (NMR)9,10 for measuring protein dynamics and interactions.
Despite the capabilities and promise of dual-color fluorescence cross-correlation spectroscopy (dc-FCCS), performing dc-FCCS in live cells is technically challenging due to the spectral bleed-through or crosstalk between the channels3,4, the difference in the confocal volumes due to the spectrally distinct laser lines3,4,11, background signal, and noise or limited photostability of the samples12,13,14,15. The introduction of pulse interleaved excitation (PIE) to FCCS was an important tweak to temporally decouple the different laser excitations for reducing the spectral crosstalk between the channels16. Other correction methods to counter spectral bleed-through17,18,19 and background corrections have also been well-accepted17,18,19. For details and basics on FCS, PIE or FRET the reader is referred to the following references2,4,6,16,20,21,22,23,24.
Here, all necessary calibration experiments and analysis along with the experimental results of a prototypical G-protein coupled receptor, &#946;2-adrenergic receptor (&#946;2AR), for three different scenarios are presented: (1) single-labeled molecules carrying either a &#34;green&#34; (eGFP) or a &#34;red&#34; (SNAP-tag-based labeling)25 fluorophore; (2) a double-labeled construct, which carries an N-terminal SNAP-tag and intracellular eGFP (NT-SNAP) [in this case, both labels are at the same protein. Thus 100% co-diffusion is expected];&#160;and (3) a double-labeled sample, where both fluorophores are on the same side of the cell membrane (CT-SNAP). It carries a C-terminal SNAP-tag and an intracellular eGFP. Here, again both labels are at the same protein with again 100% co-diffusion expected. As both labels are very close to each other, on the same side of the cell membrane, it shows the potential to observe FRET and anticorrelated behavior. All constructs were transfected in Chinese Hamster Ovary (CHO) cells and later labeled with a red fluorescent substrate which is membrane-impermeable for the NT-SNAP construct and&#160;membrane-permeable for the CT-SNAP construct. Finally, simulated data exemplifies the influence of experimental parameters on the FRET-induced anticorrelation, and the effect of protein-protein interactions on the co-diffusion amplitude.
Thus, this protocol provides a complete guide to performing the combined FRET-FCCS in living cells to understand protein dynamics and protein-protein interactions while making aware of technical/physical artifacts, challenges, and possible solutions.

<table><tbody><tr><td>1x Telescope in 4f configuration with five lenses</td><td>Qioptiq, Rhyl, UK</td><td>G063126000</td><td>Optics</td></tr><tr><td>2x Band pass filters Brightline</td><td>AHF, T&amp;uuml;bingen, Germany</td><td>HC 525/50 and HC 600/52</td><td>Filter</td></tr><tr><td>2x Dichroic beam splitter</td><td>AHF, T&amp;uuml;bingen, Germany</td><td>HC BS F38-573</td><td>Filter</td></tr><tr><td>6-well culture plate Nunc</td><td>Thermo Scientific (Waltham, USA)</td><td>140675</td><td>Reagent</td></tr><tr><td>Alexa Fluor 488 NHS Ester (green calibration standard)</td><td>Invitrogen, Life Technologies (Carlsbad, USA)</td><td>A20000</td><td>Reagent</td></tr><tr><td>Alexa Fluor 568 NHS Eater (red calibration standard)</td><td>Invitrogen, Life Technologies (Carlsbad, USA)</td><td>A20003</td><td>Reagent</td></tr><tr><td>ASI stage PZ-2000 XYZ</td><td>Visitron Systems GmbH, Puchheim, Germany</td><td>WK-XYB-PZ-IX71</td><td>Microscope Parts</td></tr><tr><td>Attofluor Cell Chamber, 35 mm diameter for&amp;nbsp; 25 mm round coverslips</td><td>Invitrogen, Life Technologies (Carlsbad, USA)</td><td>A7816</td><td>Glass coverslip holder</td></tr><tr><td>Avalanche photodiode Perkin Elmer (SPCM-AQR-14)</td><td>Laser Components GmbH, Olching, Germany</td><td>SPCM-AQR-14</td><td>Single photon counting detector</td></tr><tr><td>Beamsplitter</td><td>Newport, Darmstadt, Germany</td><td>10FC16PB.3</td><td>Filter</td></tr><tr><td>Biorender (Software)</td><td>Science Suite Inc - o/a BioRender (Toronto, Canada)</td><td>---</td><td>Software used to create GPCR sketch, https://app.biorender.com/</td></tr><tr><td>Chinese hamster ovary (CHO) cell line</td><td>ATCC</td><td>CCL-61</td><td>Cell lines</td></tr><tr><td>ChiSurf (Data analysis Software)</td><td>Thomas-Otavio Peulen, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, USA</td><td>---</td><td>tttrlib-based software to analyze fluorescence correlation data and fluorescence decay histograms, https://github.com/Fluorescence-Tools/chisurf&lt;br/&gt; Tutorial: https://www.youtube.com/watch?v=k9NgYbyLyXk&amp;amp;t=2s&lt;br/&gt; Ref: Peulen et al. J Phys Chem B. 121 (35), 8211-8241, (2017)</td></tr><tr><td>Chloroform</td><td>Sigma-Aldrich (St. Louis, USA)</td><td>472476-2.5L</td><td>Reagent</td></tr><tr><td>DMSO</td><td>AppliChem GmbH (Darmstadt, Germany)</td><td>A3672,0250</td><td>Reagent</td></tr><tr><td>DNA strand (40 bp fluorophore distance)</td><td>IBA Lifesciences GmbH (G&amp;ouml;ttingen, Germany)</td><td>---</td><td>Reagent, 5&amp;rsquo; CGC ACT GAA CAG CAT ATG ACA CGC GAT AGG CTA TCC TGC AGT ACG CT(Alexa568)C AGG 3&amp;rsquo;, 3&amp;rsquo; GCG TGA CT(Alexa488)T GTC GTA TAC TGT GCG CTA TCC GAT AGG ACG TCA TGC GAG TCC 5&amp;rsquo;</td></tr><tr><td>Dulbecco&amp;rsquo;s Modified Eagle Medium: Nutrient Mixture F12 (with and without phenol red)</td><td>GIBCO, Life Technologies (Carlsbad, USA)</td><td>P04-41250, P04-41650</td><td>Reagent</td></tr><tr><td>Ethanol (absolute)</td><td>Sigma-Aldrich (St. Louis, USA)</td><td>34852-1L-M</td><td>Reagent</td></tr><tr><td>Erythrosin B,Dye content &gt;=95 %</td><td>Sigma-Aldrich (St. Louis, USA)</td><td>200964-5G</td><td>Reagent, Instrument Response function, solve in EtOH to 10 mg/mL</td></tr><tr><td>Fetal Bovine Serum (FBS)</td><td>Biochrom&amp;nbsp; (Berlin, Germany)</td><td>S 0615</td><td>Reagent</td></tr><tr><td>Fluorescence Light Source X-Cite 120 Q</td><td>Excelitas Technologies, Ontario, Canada</td><td>XI120-Q-5060</td><td>Microscope Parts</td></tr><tr><td>Fluorescent SNAP-substrate cell : SNAP Cell TMR- STAR</td><td>New England BioLabs (Frankfurt am Main, Germany)</td><td>S9105S</td><td>Reagent</td></tr><tr><td>Fluorescent SNAP-substrate surface : DY-549</td><td>New England BioLabs (Frankfurt am Main, Germany)</td><td>S9112S</td><td>Reagent</td></tr><tr><td>Glass coverslips (Dimensions: diameter 24 mm, thickness 0.13 - 0.16 mm)</td><td>Marienfeld-Superior (Lauda-K&amp;ouml;nigshofen, Germany)</td><td>111640</td><td>Reagent</td></tr><tr><td>Laser Controller</td><td>Picoquant, Berlin, Germany</td><td>910020 (PDL 828-S &quot;SEPIA II&quot;)</td><td>Optics</td></tr><tr><td>Laser lines (480 nm and 560 nm)</td><td>Picoquant, Berlin, Germany</td><td>912485 (LDH-D-C-485), 912561 (LDH-D-TA-560)</td><td>Optics</td></tr><tr><td>Lipofectamine 2000</td><td>Invitrogen, Life Technologies (Carlsbad, USA)</td><td>11668-019</td><td>Reagent</td></tr><tr><td>MFD suite (Software)</td><td>AG Seidel, Heinrich-Heine-University Duesseldorf, Germany</td><td>---</td><td>Software package for analysis of single-molecule fluorescence experiments including e.g. Kristine (correlation of tttr data), Burbulator (simulation of single-molecule experiment), https://www.mpc.hhu.de/software/3-software-package-for-mfd-fcs-and-mfis</td></tr><tr><td>Mounted Achromatic Doublet, ARC: 400-700 nm, f=150 mm, D=25.4 mm</td><td>Thorlabs, Bergkirchen, Germany</td><td>AC254-150-A-ML</td><td>Second part of Beam expander</td></tr><tr><td>Neubauer Chamber (deepness 0.1 mm)</td><td>Marienfeld-Superior (Lauda-K&amp;ouml;nigshofen, Germany)</td><td>640110</td><td>Reagent</td></tr><tr><td>Olympus IX 71 stand</td><td>Olympus, Hamburg, Germany</td><td>IX2-ILL100</td><td>Microscope Parts</td></tr><tr><td>Opti-MEM (Reduced-Serum Medium)</td><td>GIBCO, Life Technologies (Carlsbad, USA)</td><td>31985-047</td><td>Reagent</td></tr><tr><td>Penicillin/Streptomycin</td><td>Sigma-Aldrich (St. Louis, USA)</td><td>049M4857V</td><td>Reagent</td></tr><tr><td>Phosphate-buffered Saline (PBS)</td><td>GIBCO, Life Technologies (Carlsbad, USA)</td><td>14190144</td><td>Reagent</td></tr><tr><td>Pinhole (50 &amp;micro;M)</td><td>Newport, Darmstadt, Germany</td><td>PNH-50</td><td>Pinhole</td></tr><tr><td>PMT Hybrid-40</td><td>Picoquant, Berlin, Germany</td><td>932200 (PMA Hybrid 40)</td><td>Single photon counting detector</td></tr><tr><td>Python scripts (Software)</td><td>Katherina Hemmen, Rudolf-Virchow Center for Integrative and Translational Imaging, University Wuerzburg, Germany</td><td>---</td><td>Collection of self-written Python scripts based on tttrlib (https://github.com/Fluorescence-Tools/tttrlib) used to (1) determine the average count rates, (2) correlate the data and (3) build fluorescence decay histograms, https://github.com/HeinzeLab/JOVE-FCS</td></tr><tr><td>Quad band beamsplitter (zt405/473-488/561/640 rpc phase r uf1)</td><td>AHF, T&amp;uuml;bingen, Germany</td><td>F73-421PH</td><td>Filter</td></tr><tr><td>Single mode fiber polarization keeping, NA = 0.08 with collimator</td><td>Picoquant, Berlin, Germany</td><td>02126</td><td>Optics</td></tr><tr><td>Sodium Hydroxide (NaOH)</td><td>Carl Roth (Karlsruhe, Germany)</td><td>6771.1</td><td>Reagent</td></tr><tr><td>SymPhotime x64 Software (Data collection and data export software)</td><td>Picoquant, Berlin, Germany</td><td>931073 (SPT64-1+2 single user )</td><td>Time-tag time-resolved (tttr) data collection at the self-built FCCS setup, data export</td></tr><tr><td>Time-Correlated Single Photon Counting (TCSPC) system Hydraharp 400</td><td>Picoquant, Berlin, Germany</td><td>930010 (Hydraharp 400)</td><td>Optics</td></tr><tr><td>Trypsin-EDTA</td><td>Sigma-Aldrich (St. Louis, USA)</td><td>T4299-100ml</td><td>Reagent</td></tr><tr><td>Unmounted Achromatic Doublets, ARC: 400 - 700 nm, D=12.7 mm, F=-20 mm</td><td>Thorlabs, Bergkirchen, Germany</td><td>ACN127-020-A</td><td>First part of Beam expander</td></tr><tr><td>Water immersion objective (UPlanSApo 60x/1.20 W)</td><td>Olympus, Hamburg, Germany</td><td>UPLSAPO60XW</td><td>Objective</td></tr></tbody></table>

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.

Watch this Scientific Journal Video about Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells at JoVE.com

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

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 ...

dual-color-fluorescence-cross-correlation-spectroscopy-to-study

Nanyang Technological University

Research

JoVE Journal

Biochemistry

5.1K Views.  Julius-Maximilian University Wuerzburg. Fluorescentie cross-correlation spectroscopie is een statistische methode die tijdsresultaatfluorescentie gebruikt om de dynamische signatuur van G-eiwit-gekoppelde receptoren in levende cellen te lokaliseren. Hier zijn we vooral geïnteresseerd in bèta-2 adrenerge receptoren. Sfingolipide receptoren geven voornamelijk informatie over translationele diffusiedynamiek en met behulp van fluorescentie anisotropie ook rotatiediffusie.Door een extra label te introduceren, kunnen we ook bind...

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

Method for Identifying Small Molecule Inhibitors of the Protein-protein Interaction Between HCN1 and TRIP8b

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the excitability of neurons. The subcellular distribution of these channels in pyramidal neurons of hippocampal area CA1 is regulated by tetratricopeptide repeat-containing Rab8b interacting protein (TRIP8b), an auxiliary subunit. Genetic knockout of HCN pore forming subunits or TRIP8b, both lead to an increase in antidepressant-like behavior, suggesting that limiting the function of HCN channels may be useful as a treatment for Major Depressive Disorder (MDD). Despite significant therapeutic interest, HCN channels are also expressed in the heart, where they regulate rhythmicity. To circumvent off-target issues associated with blocking cardiac HCN channels, our lab has recently proposed targeting the protein-protein interaction between HCN and TRIP8b in order to specifically disrupt HCN channel function in the brain. TRIP8b binds to HCN pore forming subunits at two distinct interaction sites, although here the focus is on the interaction between the tetratricopeptide repeat (TPR) domains of TRIP8b and the C terminal tail of HCN1. In this protocol, an expanded description of a method for purifying TRIP8b and executing a high throughput screen to identify small molecule inhibitors of the interaction between HCN and TRIP8b, is described. The method for high throughput screening utilizes a Fluorescence Polarization (FP) -based assay to monitor the binding of a large TRIP8b fragment to a fluorophore-tagged eleven amino acid peptide corresponding to the HCN1 C terminal tail. This method allows &#39;hit&#39; compounds to be identified based on the change in the polarization of emitted light. Validation assays are then performed to ensure that &#39;hit&#39; compounds are not artifactual.

The interaction between HCN channels and their auxiliary subunit has been identified as a therapeutic target in Major Depressive Disorder. Here, a fluorescence polarization-based method for identifying small molecule inhibitors of this protein-protein interaction, is presented.

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the ...

Fluorescence Anisotropy as a Tool to Study Protein-protein Interactions

Protein-protein interactions play an essential role in the function of a living organism. Once an interaction has been identified and validated it is necessary to characterize it at the structural and mechanistic level. Several biochemical and biophysical methods exist for such purpose. Among them, fluorescence anisotropy is a powerful technique particularly used when the fluorescence intensity of a fluorophore-labeled protein remains constant upon protein-protein interaction. In this technique, a fluorophore-labeled protein is excited with vertically polarized light of an appropriate wavelength that selectively excites a subset of the fluorophores according to their relative orientation with the incoming beam. The resulting emission also has a directionality whose relationship in the vertical and horizontal planes defines anisotropy (r) as follows: r=(IVV-IVH)/(IVV+2IVH), where IVV and IVH are the fluorescence intensities of the vertical and horizontal components, respectively. Fluorescence anisotropy is sensitive to the rotational diffusion of a fluorophore, namely the apparent molecular size of a fluorophore attached to a protein, which is altered upon protein-protein interaction. In the present text, the use of fluorescence anisotropy as a tool to study protein-protein interactions was exemplified to address the binding between the protein mutated in the Shwachman-Diamond Syndrome (SBDS) and the Elongation factor like-1 GTPase (EFL1). Conventionally, labeling of a protein with a fluorophore is carried out on the thiol groups (cysteine) or in the amino groups (the N-terminal amine or lysine) of the protein. However, SBDS possesses several cysteines and lysines that did not allow site directed labeling of it. As an alternative technique, the dye 4&#39;,5&#39;-bis(1,3,2 dithioarsolan-2-yl) fluorescein was used to specifically label a tetracysteine motif, Cys-Cys-Pro-Gly-Cys-Cys, genetically engineered in the C-terminus of the recombinant SBDS protein. Fitting of the experimental data provided quantitative and mechanistic information on the binding mode between these proteins.

Protein interactions are at the heart of a cell&#39;s function. Calorimetric and spectroscopic techniques are commonly used to characterize them. Here we describe fluorescence anisotropy as a tool to study the interaction between the protein mutated in the Shwachman-Diamond Syndrome (SBDS) and the Elongation factor-like 1 GTPase (EFL1).

Protein-protein interactions play an essential role in the function of a living organism. Once an interaction has been identified and validated it is ...

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Single-molecule F&#246;rster resonance energy transfer (smFRET) has become a widely used biophysical technique to study the dynamics of biomolecules. For many molecular machines in a cell proteins have to act together&#160;with interaction partners in a functional cycle to fulfill their task. The extension of two-color to multi-color smFRET makes it possible to simultaneously probe more than one interaction or conformational change. This not only adds a new dimension to smFRET experiments but it also offers the unique possibility to directly study the sequence of events and to detect correlated interactions when using an immobilized sample and a total internal reflection fluorescence microscope (TIRFM). Therefore, multi-color smFRET is a versatile tool for studying biomolecular complexes in a quantitative manner and in a previously unachievable detail.
Here, we demonstrate how to overcome the special challenges of multi-color smFRET experiments on proteins. We present detailed protocols for obtaining the data and for extracting kinetic information. This includes trace selection criteria, state separation, and the recovery of state trajectories from the noisy data using a 3D ensemble Hidden Markov Model (HMM). Compared to other methods, the kinetic information is not recovered from dwell time histograms but directly from the HMM. The maximum likelihood framework allows us to critically evaluate the kinetic model and to provide meaningful uncertainties for the rates.
By applying our method to the heat shock protein 90 (Hsp90), we are able to disentangle the nucleotide binding and the global conformational changes of the protein. This allows us to directly observe the cooperativity between the two nucleotide binding pockets of the Hsp90 dimer.

Here, we present a protocol to obtain three-color smFRET data and its analysis with a 3D ensemble Hidden Markov Model. With this approach, scientists can extract kinetic information from complex protein systems, including cooperativity or correlated interactions.

Single-molecule F&#246;rster resonance energy transfer (smFRET) has become a widely used biophysical technique to study the dynamics of biomolecules. For ...

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 ...

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

Measurement of Force-Sensitive Protein Dynamics in Living Cells Using a Combination of Fluorescent Techniques

Cells sense and respond to physical cues in their environment by converting mechanical stimuli into biochemically-detectable signals in a process called mechanotransduction. A crucial step in mechanotransduction is the transmission of forces between the external and internal environments. To transmit forces, there must be a sustained, unbroken physical linkage created by a series of protein-protein interactions. For a given protein-protein interaction, mechanical load can either have no effect on the interaction, lead to faster disassociation of the interaction, or even stabilize the interaction. Understanding how molecular load dictates protein turnover in living cells can provide valuable information about the mechanical state of a protein, in turn elucidating its role in mechanotransduction. Existing techniques for measuring force-sensitive protein dynamics either lack direct measurements of protein load or rely on the measurements performed outside of the cellular context. Here, we describe a protocol for the F&#246;rster resonance energy transfer-fluorescence recovery after photobleaching (FRET-FRAP) technique, which enables the measurement of force-sensitive protein dynamics within living cells. This technique is potentially applicable to any FRET-based tension sensor, facilitating the study of force-sensitive protein dynamics in variety of subcellular structures and in different cell types.

Here, we present a protocol for the simultaneous use of F&#246;rster resonance energy transfer-based tension sensors to measure protein load and fluorescence recovery after photobleaching to measure protein dynamics enabling the measurement of force-sensitive protein dynamics within living cells.

Cells sense and respond to physical cues in their environment by converting mechanical stimuli into biochemically-detectable signals in a process called ...

Investigating Protein-protein Interactions in Live Cells Using Bioluminescence Resonance Energy Transfer

Assays based on Bioluminescence Resonance Energy Transfer (BRET) provide a sensitive and reliable means to monitor protein-protein interactions in live cells. BRET is the non-radiative transfer of energy from a 'donor' luciferase enzyme to an 'acceptor' fluorescent protein. In the most common configuration of this assay, the donor is Renilla reniformis luciferase and the acceptor is Yellow Fluorescent Protein (YFP). Because the efficiency of energy transfer is strongly distance-dependent, observation of the BRET phenomenon requires that the donor and acceptor be in close proximity. To test for an interaction between two proteins of interest in cultured mammalian cells, one protein is expressed as a fusion with luciferase and the second as a fusion with YFP. An interaction between the two proteins of interest may bring the donor and acceptor sufficiently close for energy transfer to occur. Compared to other techniques for investigating protein-protein interactions, the BRET assay is sensitive, requires little hands-on time and few reagents, and is able to detect interactions which are weak, transient, or dependent on the biochemical environment found within a live cell. It is therefore an ideal approach for confirming putative interactions suggested by yeast two-hybrid or mass spectrometry proteomics studies, and in addition it is well-suited for mapping interacting regions, assessing the effect of post-translational modifications on protein-protein interactions, and evaluating the impact of mutations identified in patient DNA.

Interactions between proteins are fundamental to all cellular processes. Using Bioluminescence Resonance Energy Transfer, the interaction between a pair of proteins can be monitored in live cells and in real time. Furthermore, the effects of potentially pathogenic mutations can be assessed.

Assays based on Bioluminescence Resonance Energy Transfer (BRET) provide a sensitive and reliable means to monitor protein-protein interactions in live ...

Biology

Assessing Protein Interactions in Live-Cells with FRET-Sensitized Emission

F&#246;rster Resonance Energy Transfer (FRET) is the radiationless transfer of energy from an excited donor to an acceptor molecule and depends upon the distance and orientation of the molecules as well as the extent of overlap between the donor emission and acceptor absorption spectra. FRET permits to study the interaction of proteins in the living cell over time and in different subcellular compartments. Different intensity-based algorithms to measure FRET using microscopy have been described in the literature. Here, a protocol and an algorithm are provided to quantify FRET efficiency based on measuring both the sensitized emission of the acceptor and quenching of the donor molecule. The quantification of ratiometric FRET in the living cell not only requires the determination of the crosstalk (spectral spill-over, or bleed-through) of the fluorescent proteins but also the detection efficiency of the microscopic setup. The protocol provided here details how to assess these critical parameters.

F&#246;rster Resonance Energy Transfer (FRET) between two fluorophore molecules can be used for studying protein interactions in the living cell. Here, a protocol is provided as to how to measure FRET in live cells by detecting sensitized emission of the acceptor and quenching of the donor molecule using confocal laser scanning microscopy.

F&#246;rster Resonance Energy Transfer (FRET) is the radiationless transfer of energy from an excited donor to an acceptor molecule and depends upon the ...

Fluorescence Fluctuation Spectroscopy to Study Protein Interaction at Cell Contacts

Source: Dunsing, V. et al. <a href="https://app-jove-com.remotexs.ntu.edu.sg/v/58582">A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts.</a> J. Vis. Exp.&#160;(2018).
This video demonstrates the use of fluorescence fluctuation spectroscopy to detect the interaction among cell surface proteins at cell-cell contacts. By expressing the transmembrane adhesion receptor of interest labeled with a fluorescent protein and mixing two different cell populations harboring two spectrally separated fluorescent labels, the trans-interaction between the receptors of two neighboring cells with different-colored fluorescence is assessed via cross-correlation in the fluctuations of fluorescence intensity.

Source: Dunsing, V. et al. A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts. J. Vis. Exp.&#160;(2018).
...

JoVE Encyclopedia of Experiments

Biological Techniques

Biomolecular Interaction Detection Techniques

Protein-protein interactions

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached proteins diffuse out over time and fluorescently-labeled proteins from other parts of the cell move in, the region becomes fluorescent again. The rate of movement is quantified by plotting the relative fluorescence intensity versus the time taken, which allows for determining the rate with which protein moves within the cell.
F&#246;rster resonance energy transfer (FRET) is a molecular technique used to determine the distance between two proteins. Thus, FRET can be used as a molecular ruler. The technique uses two fluorophore-tagged proteins, one acting as the donor and the other as the acceptor. Upon excitation at a specified wavelength, the donor fluorophore emits fluorescence energy that is absorbed by the acceptor fluorophore. The donor fluorophore returns to the ground state upon energy transfer, while the acceptor fluorophore emits fluorescence that is visualized with a fluorescence microscope. The FRET technique depends on three factors, the distance between interacting proteins, the extent of spectral overlap between the donor and acceptor fluorophores, and the orientation of the donor and acceptor fluorophore during energy transfer. The energy transfer between the correctly oriented donor and acceptor fluorophores can only occur when the distance between the two interacting proteins is 10 nm or less.
Photoinduced Electron Transfer or PET determines the sub-atomic distance between proteins in a cell. In PET, the fluorophore absorbs the light and emits a fluorescence signal through an excited electron. The excited electron is transferred to the receptor. During the energy transfer, a redox reaction occurs, generating charge separation between the donor and acceptor proteins.

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent ...

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

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