Measuring Small-molecule Inhibition of Protein Interactions in Live Cells Using FLIM-FRET

[Abstract] This protocol was designed to quantitatively measure small-molecule displacement of proteins in live mammalian cells using fluorescence lifetime imaging microscopy–Förster resonance energy transfer (FLIM-FRET). Tumour cell survival is often dependent on anti-apoptotic proteins, which bind to and inhibit pro-apoptotic proteins, thus preventing apoptosis. Small-molecule inhibitors that selectively target these proteins (termed BH3-mimetics) are therefore a promising avenue for the treatment of several cancers. Previous techniques used to study the efficacy of these drugs often use truncated versions of both pro- and anti-apoptotic proteins, as they are membrane bound and hydrophobic in nature. As a result, the true efficacy of these drugs to displace full-length pro-apoptotic proteins in their native environment within a cell is poorly understood. This protocol describes FLIM-FRET methods to directly measure the displacement (or lack of displacement) of full-length Bcl-2 family proteins in live mammalian cells.

Note: We often find day to day variability in the photon count, despite not making any changes to laser power or filters. As a result, we use this fluorescein sample as a reference point to ensure we consistently achieve the same photon counts as previous experiments. Additionally, we measure the mono-exponential lifetime of fluorescein as a control for the instrument.
3. Lower the objective, and apply a small bead of water. Ensure the water resupply tube is in close proximity to the droplet. Place the 384-well plate into the holder on the microscope, and begin raising the objective until the water droplet makes contact with the bottom of the plate. 4. Move the stage to a well containing cells, and use the brightfield to focus on the cells. 5. Once focused on the cells, select "z-reset" on the microscope, and move 100 μm above the cells to ensure the focal plane in within the solution. Move the stage to the well containing fluorescein, turn off brightfield, cover the stage with a black dark fabric, and select L100 on the microscope to connect the light path to the detectors.  three different wavelengths to pass, enabling the examination of the three most common fluorescent proteins (cerulean, green and red fluorescent protein). Filter #4 will pass the emission of Venus and reflect the light of mCer3, resulting in the signal of Venus for Channel-1 (Ch1) and mCer3 for Channel-2 (Ch2).
8. Under the "Multi-well Plate" tab, select the correct plate-map configuration as your experiment.
For ours, we are using a 384 well plate from Corning as it works for the PerkinElmer plate.
9. Under "Device Monitor", select the small microscope icon, and select Alba to direct light from the microscope to the instrument. 10. Select the green circle directly below to open the shutters. You should now be able to see the photon counts from Ch1 and Ch2 directly below. We often strive to achieve photon counts between 1 x 10 5 -1.5 x 10 5 for both channels by adjusting the laser power or using the "Motor Alignment" to adjust the pin hole location. Using either one of these tools to achieve consistent photon counts between experiments means the data collected from multiple experiments can be directly comparable. Record the photon counts for Ch1 and Ch2 then uncheck the green circle to close the shutters. 11. The next step is to ensure we are measuring the correct lifetime for fluorescein. Under the "Scan Settings" tab, change the pixel dwell time to 0.5 (ms), and averaging mode set to "none". Also, uncheck both "Position Series" and "Macro Position series" directly below.
12. Click "filename" and enter the name of "reference". 14. Next, on the microscope turn on the brightfield, reset the stage from 100 back to 0 (focal height of cells), and move to your most top-left well containing your cells (Since the stage moves left to right, up to down to collect data). Confirm you are focused on your cells in the most top-left view within the most top-left well using the eyepiece and brightfield. Press "Zero reset" on the microscope, select "Scanner Auto-Focus" to lock the focal plane. Finally, press "L100" to switch from the brightfield to the detector. name of the cells and experiment) and click "start". By the end of the experiments, a ". iss" file will be automatically stored in your selected path.
18. Be sure to check the water supply has enough reservoir for the time of image acquisition.

Data Analysis
A. VistaVision analysis 1. The three files we need for downstream analysis are the intensities from Ch1 (Venus), Ch2  3. Your ISS data should look very similar to Figure 3. Click the "Set intensity threshold for lifetime analysis" button on the left hand side, and input a minimum of 80 for Ch-2. Click okay. Pixels with a Ch2 intensity less than 80 will be discarded from the lifetime data (this variable may change based on your own fluorescence data).

it refers to.
4. Next, click the "lifetime fit" button found on the left column. 5. On the right-hand side under the lifetime fitting select the following from their respective dropdown menus; select Ch2 since we are concerned with mCer3 lifetime, select series to apply settings to all images in this stack, select Bin of 3 (this means photons from neighbor pixels will be binned together to fit lifetime). Finally, input T1 and T2 gates as 12 and 96 respectively.
Note: T1 and T2 gates are correlated with the PIE time setting for this microscope. We limit the gate scope after mCer3 excitation and before the disappearance of the emission.
6. Directly below, set Comps to 1, and click the green "play" button. 7. Once finished, click "Export Data", select "Export Frame Analysis Data", and select "Comp-1 Tau" and "Chi-square". Click the save button, and select the same folder where Ch1 and Ch2 tiff files are currently saved. Click okay.
Note: The "Chi-square" file is our quality record, which could be useful to troubleshoot if we notice abnormal data in later steps.
B. ImageJ Macro 1. For file organization, each transfection/treatment group should have its own designated folder.
In this example, and as seen in Figure 4, all data is stored under the file named " V Bad". Within this folder, you need to make four additional ones named "analysis", "Ch1", "Ch2", "tau". Within each folder place the respective data renamed as the following; For Ch1 and Ch2 tiff image stacks, name them as "1_ch1" and "1_ch2". If you have additional files from additional wells, they can be named as "2_ch1" and "2_ch2". The lifetime tiff should be renamed to "1t", again additional files from additional wells can be renamed to "2t". 10 www.bio-protocol.org/e3401  5. The macro will prompt you to "Choose Source Directory". Select your " V Bad" folder (or whichever folder contains your data). 6. Input the number of images contained within your tiff stacks. Once you input this value, the script will automatically start to run. When the analysis is done, a new window will appear stating "Analysis complete". Click okay and close ImageJ. automatically saved your data into three separate excel files named; "Ch1", "Ch2" and "tau".
Copy the mean (column C) from each excel file, and paste into a new Excel workbook called "Bin_analysis". Paste Ch1 data into Column A, Ch2 data into Column B, and tau data into Column C. Use row 1 for each column as a space to label the data. For each of these columns of data, each row represents the average values for a ROI. specifically, the value obtained from taking the mean intensity for Venus divided by the mean intensity of mCer3 for each ROI. The acceptor/donor ratio acts as a surrogate for the relative amount of protein concentrations used to plot binding curves. In column D ("A/D") take Ch1 data and divide it by Ch2 (i.e., In cell D2, type: = A2/B2), and extend this to the bottom of the Excel workbook.
3. Now, in cell E1, assign the name "Bin". This column "Bin" assigns each ROI to a different acceptor/donor ratio bin for averaging and plotting data. To do this, copy and paste the following 4. Finally, in cell F1, name this column "FRET" as it will contain the calculated FLIM-FRET, the average value of energy transfer that occurred for each ROI. This value is calculated by the following formula: where,  paste mean FLIM-FRET efficiency (column B of your pivot table) into "Mean" column next to "X" in GraphPad. Column E in Excel is your calculated standard error of the mean "SEM" for FRET values. Copy Column E and paste it under the "SEM" labeled column in GraphPad. Finally, copy Column C, the number of ROIs per bin, and paste this data under the column labeled "N" in GraphPad.
3. Within GraphPad on the left hand side, select Data1 under the "Graphs" folder. Your data should appear on a simple XY-plot with unlabeled axes. Label the X-axis by selecting it and typing "Ratio of Venus to mCer3". Label the Y-axis by selecting it and typing "FLIM-FRET Efficiency %". 4. The next step is to fit the data. Under the "Analysis" tab at the top, select "Fit a curve with nonlinear regression". The new window that appears is called "Parameters: Nonlinear Regression". Within this new window select the tab called "Fit" and select "One site-Specific binding with Hill slope". 5. Next, select the tab called "Compare" and select the question "Do the best-fit values of selected unshared parameters differ between data sets?". Below a new box should appear labeled "Choose one or more parameters". Select the "Bmax" box.
Note. This step should only be used when all data sets are from the same protein [or slightly changed mutants like VBad-2A which has 2 amino acids changed via site-directed mutagenesis , since we expect the Bmax value to be the same for all.
6. Under the "Confidence" tab, select "Calculate CI of parameters" and select the confidence interval of 95%. Below under "Confidence or prediction bands", check the box to "Plot confidence/prediction bands", and again select the 95% value. Also ensure the box for "Show SE of parameters" is checked. Finally, click okay to fit your data. 7. Your data should now be fitted with the Hill slope equation and display the 95% confidence interval for this fit. A new sheet is created on the left hand side under "Results" and should be labeled as "Nonlin fit of Data1". If you go back to the "Data table" and input the value of 0.5 under the "X" column, an additional folder will appear under "Results" and be labeled as "Interpolated Y values". 8. Repeat all the above steps under "Data Analysis" for your other FLIM-FRET groups, plotting their data within the same GraphPad file. In our example, our groups were Venus-Bad ( V Bad),  9. To quantify resistance to displacement (Resistance (%)), we compare the FLIM-FRET efficiency (%) between V Bad, and V Bad + 10 μM ABT-263 at the ratio of Venus to mCer3 of 0.5. To find the FLIM-FRET efficiency (%) value at this acceptor/donor ratio, input the value of 0.5 in the "data table" (in the ratio column below all raw data), then select the tab under "Results" called "Interpolate Y values". The FLIM-FRET efficiency (%) value will be calculated automatically according to the fit model.
Note: The standard error of the mean describes the accuracy of our measurement while the 95% confidence interval describes how well our data fits the model. 10. To calculate Resistance (%), we use the following formula: where, R denotes Resistance (%), A is the interpolated mean FLIM-FRET efficiency value for 14 www.bio-protocol.org/e3401