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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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&#252;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&#252;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&#160; 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 
			Tutorial: https://www.youtube.com/watch?v=k9NgYbyLyXk&#38;t=2s 
			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&#246;ttingen, Germany)</td>
			<td>---</td>
			<td>Reagent, 5&#8217; CGC ACT GAA CAG CAT ATG ACA CGC GAT AGG CTA TCC TGC AGT ACG CT(Alexa568)C AGG 3&#8217;, 3&#8217; GCG TGA CT(Alexa488)T GTC GTA TAC TGT GCG CTA TCC GAT AGG ACG TCA TGC GAG TCC 5&#8217;</td>
		</tr>
		<tr>
			<td>Dulbecco&#8217;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 &#62;=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&#160; (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&#246;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 &#34;SEPIA II&#34;)</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&#246;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 &#181;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&#252;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

Research

JoVE Journal

Biochemistry

4.9K Views.  Julius-Maximilian University Wuerzburg. We present an experimental protocol and data analysis workflow to perform live cell dual-color fluorescence cross correlation spectroscopy (FCCS) combined with Förster Resonance Energy transfer (FRET) to study membrane receptor dynamics in live cells using modern fluorescence labeling techniques.

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 Scaffold Liposomes to Reconstitute Lipid-proximal Protein-protein Interactions In Vitro

Studies of integral membrane proteins in vitro are frequently complicated by the presence of a hydrophobic transmembrane domain. Further complicating these studies, reincorporation of detergent-solubilized membrane proteins into liposomes is a stochastic process where protein topology is impossible to enforce. This paper offers an alternative method to these challenging techniques that utilizes a liposome-based scaffold. Protein solubility is enhanced by deletion of the transmembrane domain, and these amino acids are replaced with a tethering moiety, such as a His-tag. This tether interacts with an anchoring group (Ni2+ coordinated by nitrilotriacetic acid (NTA(Ni2+)) for His-tagged proteins), which enforces a uniform protein topology at the surface of the liposome. An example is presented wherein the interaction between Dynamin-related protein 1 (Drp1) with an integral membrane protein, Mitochondrial Fission Factor (Mff), was investigated using this scaffold liposome method. In this work, we have demonstrated the ability of Mff to efficiently recruit soluble Drp1 to the surface of liposomes, which stimulated its GTPase activity. Moreover, Drp1 was able to tubulate the Mff-decorated lipid template in the presence of specific lipids. This example demonstrates the effectiveness of scaffold liposomes using structural and functional assays and highlights the role of Mff in regulating Drp1 activity.

Using Scaffold Liposomes to Reconstitute Lipid-proximal Protein-protein Interactions In Vitro

This paper describes a method for assessing the interactions and assemblies of integral membrane proteins in vitro with various partner factors in a lipid-proximal environment.

Studies of integral membrane proteins in vitro are frequently complicated by the presence of a hydrophobic transmembrane domain. Further complicating ...

Visualization of Protein-protein Interaction in Nuclear and Cytoplasmic Fractions by Co-immunoprecipitation and In Situ Proximity Ligation Assay

Protein-protein interactions are involved in thousands of cellular processes and occur in distinct spatial context. Traditionally, co-immunoprecipitation is a popular technique to detect protein-protein interactions. Subsequent Western blot analysis is the most common method to visualize co-immunoprecipitated proteins. Recently, the proximity ligation assay has become a powerful tool to visualize protein-protein interactions in situ and provides the possibility to quantify protein-protein interactions by this method. Similar to conventional immunocytochemistry, the proximity ligation assay technique is also based on the accessibility of primary antibodies to the antigens, but in contrast, proximity ligation assay detects protein-protein interactions with a unique technique involving rolling-circle PCR, while conventional immunocytochemistry only shows co-localization of proteins.
Nuclear factor 90 (NF90) and RNA-binding motif protein 3 (RBM3) have been previously demonstrated as interacting partners. They are predominantly localized in the nucleus, but also migrate into the cytoplasm and regulate signaling pathways in the cytoplasmic compartment. Here, we compared NF90-RBM3 interaction in both the nucleus and the cytoplasm by co-immunoprecipitation and proximity ligation assay. In addition, we discussed the advantages and limitations of these two techniques in visualizing protein-protein interactions in respect to spatial distribution and the properties of protein-protein interactions.

Visualization of Protein-protein Interaction in Nuclear and Cytoplasmic Fractions by Co-immunoprecipitation and In Situ Proximity Ligation Assay

Protein-protein interactions can occur in both the nucleus and the cytoplasm of a cell. To investigate these interactions, traditional co-immunoprecipitation and modern proximity ligation assay are applied. In this study, we compare these two methods to visualize the distribution of NF90-RBM3 interactions in the nucleus and the cytoplasm.

Protein-protein interactions are involved in thousands of cellular processes and occur in distinct spatial context. Traditionally, co-immunoprecipitation ...

Biotinylated Cell-penetrating Peptides to Study Intracellular Protein-protein Interactions

Here we present a protocol to study intracellular protein-protein interactions that is based on the widely used biotin-avidin pull-down system. The modification presented includes the combination of this technique with cell-penetrating sequences. We propose to design cell-penetrating baits that can be incubated with living cells instead of cell lysates and therefore the interactions found will reflect those that occur within the intracellular context. Connexin43 (Cx43), a protein that forms gap junction channels and hemichannels is down-regulated in high-grade gliomas. The Cx43 region comprising amino acids 266-283 is responsible for the inhibition of the oncogenic activity of c-Src in glioma cells. Here we use TAT as the cell-penetrating sequence, biotin as the pull-down tag and the region of Cx43 comprised between amino acids 266-283 as the target to find intracellular interactions in the hard-to-transfect human glioma stem cells. One of the limitations of the proposed method is that the molecule used as bait could fail to fold properly and, consequently, the interactions found could not be associated with the effect. However, this method can be especially interesting for the interactions involved in signal transduction pathways because they are usually carried out by intrinsically disordered regions and, therefore, they do not require an ordered folding. In addition, one of the advantages of the proposed method is that the relevance of each residue on the interaction can be easily studied. This is a modular system; therefore, other cell-penetrating sequences, other tags, and other intracellular targets can be employed. Finally, the scope of this protocol is far beyond protein-protein interaction because this system can be applied to other bioactive cargoes such as RNA sequences, nanoparticles, viruses or any molecule that can be transduced with cell-penetrating sequences and fused to pull-down tags to study their intracellular mechanism of action.

This is a protocol to study intracellular protein-protein interactions based on the biotin-avidin pull-down system with the novelty of combining cell-penetrating sequences. The main advantage is that the target sequence is incubated with living cells instead of cell lysates and therefore the interactions will occur within the cellular context.

Here we present a protocol to study intracellular protein-protein interactions that is based on the widely used biotin-avidin pull-down system. The ...

Dissipative Microgravimetry to Study the Binding Dynamics of the Phospholipid Binding Protein Annexin A2 to Solid-supported Lipid Bilayers Using a Quartz Resonator

The dissipative quartz crystal microbalance technique is a simple and label-free approach to measure simultaneously the mass uptake and viscoelastic properties of the absorbed/immobilized mass on sensor surfaces, allowing the measurements of the interaction of proteins with solid-supported surfaces, such as lipid bilayers, in real-time and with a high sensitivity. Annexins are a highly conserved group of phospholipid-binding proteins that interact reversibly with the negatively charged headgroups via the coordination of calcium ions. Here, we describe a protocol that was employed to quantitatively analyze the binding of annexin A2 (AnxA2) to planar lipid bilayers prepared on the surface of a quartz sensor. This protocol is optimized to obtain robust and reproducible data and includes a detailed step-by-step description. The method can be applied to other membrane-binding proteins and bilayer compositions.

Here, we present an experimental protocol that can be employed to determine the binding affinities and mode of interaction of label-free phospholipid-binding protein annexin A2 with immobilized solid-supported bilayers (SLB) by simultaneously measuring the mass uptake and the viscoelastic properties of the protein annexin A2.

The dissipative quartz crystal microbalance technique is a simple and label-free approach to measure simultaneously the mass uptake and viscoelastic ...

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 ...

Using In Vitro Fluorescence Resonance Energy Transfer to Study the Dynamics Of Protein Complexes at a Millisecond Time Scale

Proteins are the primary operators of biological systems, and they usually interact with other macro- or small molecules to carry out their biological functions. Such interactions can be highly dynamic, meaning the interacting subunits are constantly associated and dissociated at certain rates. While measuring the binding affinity using techniques such as quantitative pull-down reveals the strength of the interaction, studying the binding kinetics provides insights on how fast the interaction occurs and how long each complex can exist. Furthermore, measuring the kinetics of an interaction in the presence of an additional factor, such as a protein exchange factor or a drug, helps reveal the mechanism by which the interaction is regulated by the other factor, providing important knowledge for the advancement of biological and medical research. Here, we describe a protocol for measuring the binding kinetics of a protein complex that has a high intrinsic association rate and can be dissociated quickly by another protein. The method uses fluorescence resonance energy transfer to report the formation of the protein complex in vitro, and it enables monitoring the fast association and dissociation of the complex in real time on a stopped-flow fluorimeter. Using this assay, the association and dissociation rate constants of the protein complex are quantified.

Protein-protein interactions are critical for biological systems, and studies of the binding kinetics provide insights into the dynamics and function of protein complexes. We describe a method that quantifies the kinetic parameters of a protein complex using fluorescence resonance energy transfer and the stopped-flow technique. &#160;

Proteins are the primary operators of biological systems, and they usually interact with other macro- or small molecules to carry out their biological ...

Application of Biolayer Interferometry (BLI) for Studying Protein-Protein Interactions in Transcription

A transcription factor&#160;(TF) is a protein that regulates gene expression by interacting with the RNA polymerase, another TF, and/or template DNA. GrgA is a novel transcription activator found specifically in the obligate intracellular bacterial pathogen Chlamydia. Protein pulldown assays using affinity beads have revealed that GrgA binds two &#963; factors, namely &#963;66 and &#963;28, which recognize different sets of promoters for genes whose products are differentially required at developmental stages. We have used BLI to confirm and further characterize the interactions. BLI demonstrates several advantages over pulldown: 1) It reveals real-time association and dissociation between binding partners, 2) It generates quantitative kinetic parameters, and 3) It can detect bindings that pulldown assays often fail to detect. These characteristics have enabled us to deduce the physiological roles of GrgA in gene expression regulation in Chlamydia, and possible detailed interaction mechanisms. We envision that this relatively affordable technology can be extremely useful for studying transcription and other biological processes.

Application of Biolayer Interferometry BLI for Studying Protein-Protein Interactions in Transcription

Interactions of transcription factors (TFs) with the RNA polymerase are usually studied using pulldown assays. We apply a Biolayer Interferometry (BLI) technology to characterize the interaction of GrgA with the chlamydial RNA polymerase. Compared to pulldown assays, BLI detects real-time association and dissociation, offers higher sensitivity, and is highly quantitative.

A transcription factor&#160;(TF) is a protein that regulates gene expression by interacting with the RNA polymerase, another TF, and/or template DNA. GrgA ...

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.

Quantitative Multiplex Immunoprecipitation (QMI) uses flow cytometry for sensitive detection of differences in the abundance of targeted protein-protein interactions between two samples. QMI can be performed using a small amount of biomaterial, does not require genetically engineered tags, and can be adapted for any previously defined protein interaction network.

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic ...