Ferritinophagy: Assessing the Selective Degradation of Iron by Autophagy in Human Fibroblasts

1. Culturing and seeding primary cells

1. Fetch the vial with frozen cells from the liquid nitrogen tank, and keep it on ice until reaching the cell culture room. In a previously sterilized hood, hold the vial in the hand so the cell suspension thaws. Resuspend the cells, and transfer them to a 10 cm cell culture plate with 9 mL of prewarmed DMEM with 10% fetal calf serum (FCS) and 1% penicillin-streptomycin (P/S). Shake gently so that the cells distribute evenly on the plate, and incubate overnight at 37 °C and 5% CO₂
2. The following morning, look at the cells to confirm that they are attached. Change the medium to fresh DMEM + 10% FCS + 1%P/S, and let the cells grow until they reach 80% confluency.
3. Remove the culture medium, wash 2x with DPBS, add 1 mL of trypsin to the plate, and incubate it at 37 °C and 5% CO₂ for 10 min. Add 9 mL of DMEM + 10% FCS to the plate, and resuspend the cells.
4. Transfer the cell suspension to a 15 mL centrifuge tube. Determine the cell count manually using a Neubauer chamber, and dilute the cell suspension in DMEM + 10% FCS until a cell concentration of 40,000 cells/mL is achieved.
5. In a glass-bottom 96-well plate, seed 100 µL/well of the cell suspension. Incubate at 37 °C and 5% CO₂ for 16 h.

2. Treatments

1. Prepare the following treatments according to the Table of Materials: control medium, control medium plus deferasiox (DFX) for iron chelation, or control medium plus ferric ammonium citrate (FAC) for iron loading. Prepare all the treatments in both the presence and absence of a lysosomal inhibitor (bafilomycin A1) for the assessment of the ferritinophagy flux.
2. Remove the culture medium from the plate, and replace it with the corresponding treatment (100 µL/well).
3. Incubate at 37 °C and 5% CO₂ for 6 h.

3. Fixation and staining

1. Aspirate the treatment medium, and wash 2x with DPBS (Table of Materials).
2. Add 100 µL of 3.7% paraformaldehyde (PFA, Table of Materials) per well. Fix the cells for 20 min at room temperature in darkness.
3. Remove the PFA, and wash the cells 2x with PBS/T (Table of Materials).
4. Block in PBS/T containing 1% bovine serum albumin (BSA) for 1 h at 4 °C.
5. Prepare the primary antibody (Table of Materials) mix in PBS/T (ferritin antibody 1:50, LAMP2 antibody 1:50).
6. Wash the cells 2x with PBS/T, and apply 50 µL of the antibody mix per well. Wrap the plates with parafilm, and incubate overnight at 4 °C.
7. The following morning, prepare the secondary antibody (Table of Materials) mix (1:200) in PBS/T.
8. Wash the cells 2x with PBS/T, and apply 50 µL of the secondary antibody mix per well. Incubate at 4 °C for 1 h in the dark.
9. Prepare 5 µg/µL 4',6-diamidino-2-phenylindole (DAPI, Table of Materials) solution in room-temperature PBS.
10. Wash the cells 2x with PBS/T, and add 100 µL/well of DAPI stain. Incubate for 20 min at room temperature in the dark.
11. Wash the cells 2x with PBS, and add 50 µL of PBS to each well. Wrap the plates with parafilm, and store at 4 °C in the dark.

4. Automated imaging using confocal laser-scanning microscopy

1. Exchange the PBS in the plate for 50 µL of fresh PBS at room temperature.
2. Cover the plate with aluminum foil and take it to the microscopy room.
3. Turn the confocal laser-scanning microscope on, set a sample holder for 96-well plates on the microscope table, and select an appropriate objective (here: 40x). Refer to Table 1 for details.
4. Adjust the imaging settings (Table 1).
   1. In Smart Setup, select the lasers and filters, and assign them to tracks (Tracks 1-3) for optimal image quality and speed (Table 1).
   2. When selecting the type of experiment, click on Tiles to enable the option of selecting and saving the determined X,Y positions on the plate.
   3. In the Imaging Setup module, visualize the acquisition channels, the tracks assigned to them, and their excitation and emission wavelengths (Table 1).
   4. In the Acquisition Mode module, select the following: Scan Speed: 6; Direction: bidirectional; Averaging: 2x; Bits per pixel: 8.
   5. In the Channels module, adjust the laser power, pinhole, and master gain for each channel individually (Table 2). Fine-tune these parameters to achieve properly exposed images without oversaturating them.
   6. Click on the Focus strategy module.
      1. Select Combine Software Autofocus and Definite Focus.
      2. Under Reference Channel and Offsets, select the most stable/brightest channel as a reference (normally DAPI or 488 nm).
      3. Under Stabilization Event Repetitions and Frequency, select Standard.
   7. In the Software Autofocus module, select the following: Mode | Intensity; Search | Smart; Sampling | Fine; Relative Range.
   8. Click on the Tiles module.
      1. Select Positions | single positions.
      2. Under Advanced Setup, navigate through the plate, identify a position for imaging, and click on the + button under the Position Setup tab.
      3. Label each position with additional information that will be recorded on the image metadata. To do so, open the Properties tab, click on the wheel icon next to the Category dropdown menu, and click on Nuevo to open a new dialog box where the new category name is to be entered. To assign a category to a certain position, select the position, click on the Properties tab, go to the Categories dropdown menu, and select the corresponding label.
         ​NOTE: It is recommended to create a category for each treatment to label each position with the treatment that the cells were subjected to.
      4. Under Sample Carrier, select Multiwell 96-Cellvis glass bottom #0. Click on Calibrate to calibrate the microscope to the plate surface. Choose between 1 point or 7 point calibration depending on the system's specifications.
      5. Under Options, select a tile overlap of 10%. Under Travel in Tile Regions, select Comb. Tick the following: use stage speed from stage control, use stage acceleration from stage control, and image pyramid during acquisition.
5. Run image acquisition, and save the image results (czi files) and the image metadata (csv files) on a portable hard drive.
6. Export the images as TIFF files.

5. High-throughput image analysis

1. Create a separate metadata spreadsheet file with the following columns: Position, Well, Condition, Series, and Set. The appropriate data to fill these columns are retrieved from the original metadata file that comes from confocal laser-scanning microscopy.
2. Download the open-source software CellProfiler 4.2.4 (https://cellprofiler.org/previous-releases).
3. Launch CellProfiler 4.2.4 by double-clicking, and import the CellProfiler pipeline (Supplemental File 1) by drag and drop.
   NOTE: Find below the steps to compile the CellProfiler pipeline (Supplemental File 1) for the automated recognition of fibroblasts using DAPI staining (Module 1, nuclei recognition, see Table 2) and background fluorescence in single cells derived from AF488 (Module 2, cell recognition, see Table 2). Puncta (termed objects) recognition is determined by the puncta size and the puncta fluorescence intensity over background (Module 3, puncta recognition, see Table 2). Finally, according to the cell boundaries, the puncta recognized by the software are assigned to the corresponding cells (Assign relationships). A detailed description of how to implement a CellProfiler pipeline for autophagy studies has been published previously⁹. In addition, Table 2 contains the CellProfiler settings used in this study. Moreover, Supplemental File 2 displays the CellProfiler pipeline (Supplemental File 1) with screenshots taken in consecutive order and referring to the modules detailed in Table 2.
4. Before adapting the CellProfiler pipeline (Supplemental File 1), drag and drop individual .czi image files or a folder containing multiple .czi image files to the Image module.
5. Customize the image recognition pipeline. In the Names and Types module, replace the c1-c3 code with a code that allows the software to identify and sort the single-channel images to be analyzed.
6. Tune the pipeline settings for the IdentifyPrimaryObjects and IdentifySecondaryObjects modules by optimizing the numeric entries in the modules. The values used in this study are presented in Table 2.
7. To track the adjustments, select the Test Mode option by clicking on the Start Test Mode button. Then click the Step button between the modules. Wait for pop-up windows (the original input image, the recognition outlines drawn over the original image, and the analysis result) showing how an image would be analyzed with the settings that were just entered.
8. Once the pipeline is optimized, end the test mode. Execute the analysis by activating the Exit Test Mode, followed by clicking on the Analyze Images button.
9. After the analysis is complete, verify the accuracy of the cell and puncta detection by comparing the input images with the overlay outputs (cell boundaries, puncta) generated by CellProfiler. If not satisfactory, adjust the numerical values in the different modules (Table 2) until the analysis result is sufficiently accurate.

6. Data analysis

1. After running the analysis, wait for the software to create a set of tabular text files with the results. If using the provided CellProfiler pipeline (Supplemental File 1), look for the following output files:
   1. The CellProfilerOutputCells file, which lists the image number assigned by the software, the cell number assigned to single cells, the original file name, and the path indicating where the files are stored, the number of puncta (ferritin, LAMP2) per cell, and the mean puncta area (ferritin, LAMP2) per cell.
   2. The CellProfilerOutputExperiment file, which provides information on the technical details related to the experiment (e.g., the software version, the image channels, the metadata tags).
2. Open these files as spreadsheets.
3. Proceed to conduct the statistical analysis of the puncta counts and puncta co-localization numbers using the preferred statistics software (Table of Materials).