Visualization and Quantification of Endogenous Intra-Organelle Protein Interactions at ER-Mitochondria Contact Sites by Proximity Ligation Assays

Our PLA protocol allows to identify and to quantify endogenous interaction between proteins at membrane contact sites. We have used it to study the interaction between two endoplasmic reticulum proteins at endoplasmic reticulum mitochondrial contact sites. This technique combines PLA with organelle stainings and analysis of organelles'distances.

It allows to localize and to quantify in interaction between proteins at inter-organelles'membrane contact sites. After performing proximity ligation assay, or PLA, and image acquisition, set up the cell analysis software to start MATLAB and launch an extension by clicking the option Imaris in the Mac operating system or edit menu in Windows. Select the preferences, change to the custom tools panel, and set the path.

To import the images into the software, convert the confocal stack images into a IMS file, either directly through the arena section or using the standalone file converter that allows batch conversion. After the importation, click on edit, go to image properties, and select image geometry to check the image calibration. Check that the voxel size corresponds to the pixel size expected for the actual image in X and Y, and the step applied by the microscope to generate the Z stack in Z.To adjust the contrast of the different channels click edit in the menu, go to the display adjustment, and select show display adjustment.

Adjust each channel independently to optimize the display of each color. This step is essential to set precise thresholds or detect weak objects. Then, click edit and select crop 3D to limit the analysis to a single cell by cropping the image.

To analyze another cell in the same field of view, open the same image again and crop it differently. Detect the PLA signals generated at the location of ORP5 and ORP8 interaction by clicking on the add new spots option, which creates a new set of objects and opens the spot detection wizard. Select the channel on which the spot detection should be performed.

Adjust the estimated XY diameter to help the spot detection algorithm to find the objects of interest. If the chosen value is too high, the nearby objects will fuse, and if the value is too small, aberrant signals may be detected. Now, click on background subtraction to remove the image background before spot detection to enhance the local contrast around the objects of interest.

Adjust the spot detection threshold by keeping the quality as the threshold parameter in the software auto threshold, or slightly modify this value to detect all the objects. Once the spot detection is finished, save the detection parameters and reuse them to process other images. Then, detect the mitochondrial network to generate a surface rendering by clicking on add new surfaces to create a new object and open the surface detection wizard.

Select the channel on which the surface creation should be performed. Apply Gaussian filter to obtain a smoother surface by clicking the smooth checkbox, and setting a threshold indicating the minor details observable on the surface. Afterward, perform a background subtraction to enhance the local contrasts and adjust the threshold to detect the mitochondrial network based on the intensity of the signal.

Apply a filter on the surface to remove small residuals from the threshold by selecting the number of voxels filter on the classify surface window, and play with the upper and lower thresholds to keep only the objects of interest. Once the surface creation is over, save the creation parameters and reuse them to process other images. To generate a distance map outside the created mitochondrial surface, select the mitochondria surface in the scene tree box.

Click image processing and select surfaces function, followed by distance transformation. A MATLAB extension asking the user to choose whether the map should be computed outside or inside the object surface will show up. Select outside surface object to measure the distance between the PLA spots and the surface of the mitochondria.

Once the map is generated, it appears as a new channel in the display adjustment panel. In this channel, every pixel has a value corresponding to the distance to the closest mitochondria. Select the spots previously generated in the scene tree box to measure the distance from each point to the closest mitochondrion, and identify and visualize the closest ones.

Now, select specific values and center intensity of the channel corresponding to the distance map in the statistics and detailed log to measure the value of each spot center, which corresponds to its distance to the closest to mitochondrion in the distance map. Export the data as a CSV file by clicking on the floppy disk icon at the bottom left of the window. To extract a subpopulation of spots based on their distances to mitochondria, select the spots in the scene tree and click on the filters tab.

Add a new filter based on the center intensity of the channel corresponding to the distance map in this window, and extract the spots less than 380 nanometers away from the mitochondria by setting the lower threshold to zero micrometers and the upper threshold to 0.380 micrometers. Perform a duplication step by pressing the duplicate selection to new spots button to focus on the selected spots. Confocal images of endogenous ORP5-ORP8 PLA showed interactions in the HeLa cells in the reticular ER, cortical ER, and ER subdomains in close contact with mitochondria, commonly referred to as mitochondria-associated ER membranes.

3D imaging analysis revealed that about 50%of endogenous ORP5-ORP8 PLA interactions were detected at mitochondria-associated ER membranes. The percentage of ORP5-ORP8 PLA interactions occurring at mitochondria-associated ER membranes in cells co-overexpressing ORP5 and ORP8 was similar to that observed in cells where these proteins were expressed at endogenous levels. ORP5 and ORP8 downregulation induced a massive decrease in the total number of PLA signals, while their co-overexpression increased PLA.

PLA signals were detected in ORP5-PTPIP51 and ORP8-PTPIP51 couples, and their average numbers were similar to the ORP5-ORP8 PLA couple, confirming ORP5 and ORP8 localization at ER-mitochondria membrane contact sites. Further, the interaction of ORP5-ORP8 PLA at ER plasma membrane contacts in a three-way contact site between mitochondria, the ER, and lipid droplets are identified using HeLa cells transfected with PHPLCD-RFP or mCherry-PLN1. As in any image analysis method, the image quality is a key point, so the optimization of the signal is decisive for the automatic detection of the objects.

This technique can be also used to study the associations between two or multiple cellular organelles, as we did in our recent study on ORP5 and ORP8 function at the three-party ER, mitochondria, lipid droplets contact sites. This technique can be helpful in other studies in the intracellular communication field to identify novel multipartite inter-organelle associations.