CD8α-CI-M6PR Particle Motility Assay to Study the Retrograde Motion of CI-M6PR Receptors in Cultured Living Cells

Materials and reagents

Biological materials

1. 70%–80% confluent HeLa cells (ATCC)

   Note: We have used HeLa cells for this analysis because they are easy to transfect and considerably flat for 2D analysis of endosome motility; however, other cell lines can also be used.

   
   

Reagents

1. Plasmid CD8α-CI-M6PR-pIRES Neo2 (a kind gift from Prof. Matthew Seaman)

2. Gibco^TM Dulbecco’s modified Eagle medium (DMEM), high glucose, with GlutaMAX^TM, sodium pyruvate (Thermo Fisher Scientific, catalog number: 10569-010), storage: 2–8 °C

3. Dulbecco’s phosphate buffered saline (DPBS), without calcium and magnesium (Lonza Bioscience, catalog number: 17-512F), storage: 15–30 °C

4. Opti-MEM^® reduced serum media (Thermo Fisher Scientific, Gibco^TM, catalog number: 11058021), storage: 2–8 °C

5. Heat-inactivated fetal bovine serum (FBS) (Thermo Fisher Scientific, Gibco ^TM, catalog number: 10082147), storage: -20 °C

6. Antibiotic-antimycotic (Thermo Fisher Scientific, Gibco^TM, catalog number: 15240-062), storage: -20 °C

7. Gibco^TM 1 M HEPES (Thermo Fisher Scientific, catalog number: 15630-080), storage: 2–8 °C

8. Gibco^TM 100× MEM non-essential amino acid solution (Thermo Fisher Scientific, catalog number: 11140050)

9. Gibco^TM DMEM, high glucose, HEPES, no phenol red (Thermo Fisher Scientific, catalog number: 21063029), storage: 2–8 °C

10. X-tremeGENE^TM HD transfection reagent (Roche^® Life Science Products, catalog number: 6366236001)

11. siRNA transfection reagent Dharmafect-1 (GE Healthcare, catalog number: T-2001-03)

12. Mouse anti-human CD8 monoclonal antibody (BD Pharmingen^TM, catalog number: 555631), storage: 4 °C

13. Alexa Fluor 488-conjugated goat anti-mouse IgG (Thermo Fisher Scientific, catalog number: A11029), storage: 4 °C

14. Citrate anhydrous (HiMedia, catalog number: GRM-1023)

15. Sodium citrate tribasic dihydrate (Tri sodium citrate dihydrate) (Sigma, catalog number: S4641)

    
    

1. Citric acid buffer, pH 4.5 (see Recipes)

   
   

Recipes

1. Citric acid buffer, pH 4.5

   Reagent	Volume
   Citric acid anhydrous	100 mL
   Tri-sodium citrate dihydrate	100 mL
   Total	200 mL

   
   

   0.1 M Citric acid anhydrous

   Reagent	Quantity
   Citric acid anhydrous	2.626 g
   Total	125 mL

   
   

   0.1 M Tri-Sodium Citrate dihydrate

   Reagent	Quantity
   Tri-sodium citrate dihydrate	3.676 g
   Total	125 mL

   Adjust pH to 4.5 with sodium citrate.

   
   

2. Complete medium

   Dulbecco’s modified Eagle medium (DMEM), high glucose, with GlutaMAX^TM, sodium pyruvate supplemented with 10% FBS, 10 mM HEPES, 1× antibiotic-antimycotic, and 1× MEM non-essential amino acid solution

   
   

Laboratory supplies

1. 35 mm μ-dish, high glass-bottom live-imaging dishes (ibidi, catalog number: 81158)

2. BD Falcon 15 mL centrifuge tubes (Corning, Falcon^®, catalog number: 352096)

3. BD Falcon 35 mm cell culture dish (Corning, Falcon^®, catalog number: 353001)

Equipment

1. Cell culture incubator (Eppendorf, New Brunswick^TM, model: Galaxy ^® 170 R)

2. Confocal microscope (ZEISS, model: LSM 710)

3. HulaMixer^TM sample mixer (Thermo Fisher Scientific, catalog number: 15920D)

Software and datasets

1. Fiji software ( https://imagej.net/software/fiji/) 2.15.1

2. ZEN software, ZEN 2012 v. 8.0.1.273 (ZEISS)

Procedure

1. Experimental setup

   Note: This protocol has been modified from CD8α-CI-M6PR trafficking assay [4], aiming to assess the motility of surface-labeled CD8α-CI-M6PR towards the Golgi in HeLa cells treated with siRNAs against a specific gene of interest.

   1. Seed approximately 0.14 ×10⁶ HeLa cells in a 35 mm glass-bottom live-imaging dish and incubate overnight in a cell culture incubator.

      Note: For transfection, seed 0.25 × 10⁶ HeLa cells and incubate overnight in a cell culture incubator.

   2. On the following day, prepare siRNA transfection mix by combining 100 nM siRNA oligos and transfection reagent Dharmafect-1 (Dharmacon) in 200 μL of Opti-MEM and incubate the mixture at room temperature for 30 min. After incubation, add siRNA transfection mix dropwise over cells and incubate for 48 h.

   3. After 48 h of siRNA treatment, transfect the cells with CD8α-CI-M6PR. To prepare the transfection mix, add 2 μg of CD8α-CI-M6PR-pIRES Neo2 plasmid DNA in 200 μL of Opti-MEM and X-tremeGENE^TM HD transfection reagent in a ratio of 1:1 (1 μg DNA: 1 μL transfection reagent). Incubate the transfection mix at room temperature for 30 min, then carefully add it dropwise over cells.

   4. To fluorescently label the primary antibody, incubate the mouse anti-CD8 antibody along with Alexa Fluor 488-conjugated anti-mouse IgG in 1 mL of serum-free DMEM media at 4 °C for 30 min in a tube rotator.

   5. Following 12–14 h of transfection, incubate the complex of anti-CD8α with Alexa Fluor 488-conjugated IgG with cells in a live-cell imaging dish on ice for 30 min to label the surface CD8-CI-M6PR receptors.

      Note: As a control for the non-specific attachment of Alexa Fluor 488-conjugated antibodies to the plasma membrane, execute the antibody labeling and subsequent steps using untransfected cells.

   6. After incubation, carefully aspirate the DMEM media containing anti-CD8α/Alexa Fluor 488-conjugated antibody complex.

   7. To remove any unbound or non-specifically attached antibodies, wash the cells once with DPBS followed by two washes with ice-cold citric acid buffer, pH 4.5. Add approximately 1 mL of citric acid buffer to cells and incubate on ice for 5 min. Discard the buffer and repeat the washing step twice with citric acid buffer. Finally, add 1 mL of DPBS to remove any residual citric acid buffer from the cells.

      Note: Prepare fresh citric acid buffer and use it ice-cold for washes.

   8. After washing, add 2 mL of prewarmed phenol red–free DMEM media supplemented with 10% FBS on cells and incubate it for 10 min.

   9. After incubation, transfer the dish to the humidified live-cell imaging chamber maintained at 37 °C with 5% CO₂ and perform live-cell imaging of the transfected cells showing labeled CD8α-CI-M6PR receptors. Post 10 min chase, labeled CD8α-CI-M6PR receptors present on plasma membrane traffic to TGN due to the retrograde sorting signals present on the cytoplasmic tail of CI-M6PR.

      
      

2. Live-cell imaging and video acquisition

   1. Use 512 × 512 frame size for higher acquisition speed and acquire a timelapse video of cells for approximately 200–300 frames minimum without any time interval.

   2. Record the video of the labeled CD8α-CI-M6PR receptors for a duration not exceeding 15 min, following a 10 min chase, during which they are expected to reach TGN.

      
      

3. Image analysis and quantification using ImageJ/Fiji

   Note: In the manuscript [6], we conducted particle tracking analysis using a semi-automated custom-written program and a MATLAB script [7]. However, in this protocol, we have presented an alternative protocol employing the TrackMate plugin for the analysis of CD8α-CI-M6PR particles.

   1. Open the video file using Fiji. If there are multiple cells in a field, use the free-hand tool from the Fiji tool panel to select a single cell. Duplicate the selection by either using Ctrl+shift+D or go to Image menu and choose the Duplicate option. A pop-up window will appear; select the Duplicate stack option (Figure 2).

      
      

      
      Figure 2. Duplicate video stack and select the cell for analysis

      
      

   2. Remove the background fluorescence using the Edit menu. Select the Clear outside option (Figure 3). Choose yes to the process stack option allowing it to remove the background from all frames.

      
      

      
      Figure 3. Clear outside from all frames of video

      
      

   3. Subtract cytoplasmic background fluorescence from the cell to improve the endosome tracking process. Select Subtract background option in the Process menu in Fiji toolbar. Input the value of Rolling ball radius appropriately according to your background fluorescence in the video file (Figure 4).

      Note: The value of Rolling ball radius should be higher than the largest object in the image.

      
      

      
      Figure 4. Background subtraction in cell

      
      

   4. Perform particle tracking on video file. Select Plugins > Tracking > TrackMate option. A dialog box will open with spatial and temporal information of the data. It provides information on pixel dimensions, number of frames, and also the dimensionality of the video file (2D or 3D lapse video). To proceed with tracking, press the Next button (Figure 5).

      
      

      
      Figure 5. Particle tracking using TrackMate plugin

      
      

   5. Next, a window will open with three options as object detectors. Choose the Difference of Gaussian (DoG) detector, designed to identify round particles like endosomes and being particularly effective for particle sizes below ~5 pixels. Proceed by clicking the Next button (Figure 6).

      
      

      
      Figure 6. Selection of difference of Gaussian (DoG) detector

      
      

   6. In the subsequent DoG detector window, input the values for a few parameters associated with particles to be analyzed, such as Estimated Blob diameter and Quality threshold. For Estimated Blob Diameter, enter the approximate diameter of the endosomes or measure it using the line tool in Fiji. Quality threshold allows you to limit your analysis to a specific number of endosomes, excluding spots/endosomes with quality below the threshold. Click the preview button to see the selected endosomes and assess how well the parameters fit the data. Adjust the Quality threshold to eliminate any false spot intensities from the analysis. Click on Preview to see the selected spots and accordingly adjust the threshold to choose the desired endosomes. You can also see the number of endosomes that were selected for the selected threshold (highlighted here in red outline) (Figure 7). Go to the next window once adjustments have been made.

      
      

      
      Figure 7. Difference of Gaussian (DoG) detector parameters for particles

      
      

   7. Next, choose the HyperStack displayer, which allows you to overlay the end results over the image (Figure 8).

      
      

      
      Figure 8. Choose the HyperStack displayer

      
      

   8. This window allows you to do auto or manual thresholding to select the particles. Choose Auto here. Click Next (Figure 9).

      
      

      
      Figure 9.Thresholding on particles/spots

      
      

   9. At this point, we can see the spots that are selected for analysis. We can run the video and observe the spots moving through all frames. In this window, you can set filters on the spots; however, for now, we will proceed with default settings and click Next (Figure 10).

      Note: You can also filter spots based on parameters like intensity, quality, radius of the particles, frame, or positions (X, Y,) and do a comparative analysis of choices.

      
      

      
      Figure 10. Set filters and choose particles or spots for analysis

      
      

   10. Now, select the simple LAP (linear assignment problem) tracker, ideal for particles undergoing Brownian motion. This tracker creates tracks by linking particles across different frames in the video (Figure 11 ).

       
       

       
       Figure 11. Selection of simple linear assignment problem (LAP) tracker

       
       

   11. Next, a LAP tracker window will open, allowing you to input parameters for particle-to-particle linking. Enter 2 μm in the option for Linking max distance, which defines the maximum distance for two spots to be linked between two frames. Put the value of Gap-closing max distance as 2 μm, meaning that it will not link any two spots that are more than two frames apart. Set Gap-closing max frame gap to 1, which is the maximum frame difference allowed for a spot to be missed for linking (Figure 12).

       
       

       
       Figure 12. Configuration for linking particles by simple LAP tracker

       
       

   12. The next window allows the visualization of the tracks generated by LAP tracker. Click Next (Figure 13).

       
       

       
       Figure 13. Tracks generated by LAP tracker

       
       

   13. This window allows you to set filters on tracks. You can choose any option for viewing tracks, but here we have opted to set the color of the tracks based on maximal velocity. Click Next (Figure 14 ).

       
       

       
       Figure 14. Color filter for visualization of tracks

       
       

   14. The next window allows to further change the display options. Here, you can increase or decrease the size of the spot by changing its radius. Furthermore, you can also change the display of the tracks. Click on Next button. A new panel will open with options to plot different features associated with particle motility as a function of another. To continue, click on Next (Figure 15 ).

       
       

       
       Figure 15. Display options for particles and tracks

       
       

   15. Select Overlay option in the next window and click on Execute. A pop-up window will open, showing the total number of frames. Click on ok. You can save the video with all the captured tracks (Figure 16).

       
       

       
       Figure 16. TrackMate features and analysis

       
       

   16. To obtain measurements associated with particle/spot’s motion such as distance, displacement, maximum speed, and duration of a track, go back to the Display options window (shown in Figure 15) and click on Analysis. Three different windows will open, providing information on quantitative parameters for all the tracks associated with the particle motion (Figure 17).

       
       

       
       Figure 17. Track analysis tables showing different kinetic parameters associated with particle motion

       
       

Data analysis

From the data extracted from the track statistics file or table, we can acquire values such as track duration, displacement, mean velocity, and maximum speed for all the tracks identified by the LAP tracker. In the video, we have detected roughly ~1,000 tracks by linking two spots over time. Each track encompasses values for displacement, duration, and velocity. Displacement signifies the distance between spots in consecutive frames, while duration indicates the time span. Velocity values denote the link velocity defined for each link between two spots. Subsequently, we can compute and plot the median, maximum, minimum, and average velocities from link velocities under different conditions or treatments. Analyze the tracks from approximately 7–10 cells and plot the values for each quantitative parameter against various treatments. Further, calculate the statistical significance of differences in mean values between two or more conditions by utilizing either an unpaired two-tailed Student’s t-test or analysis of variance (ANOVA), respectively. These tests are applicable when the data approximately follows a normal distribution and satisfies assumptions such as homogeneity of variances. For non-normal distributions, which lack symmetry, statistical comparisons are done between the medians of the two groups using Mann-Whitney U test.

Validation of protocol

This protocol has been used and published in the following research article:

1. Rawat, S. et al. (2022). RUFY1 binds Arl8b and mediates endosome-to-TGN CI-M6PR retrieval for cargo sorting to lysosomes. J. Cell Biol. (Figure 10, panel D)

   Note: This article has used a semi-automated custom-written program and a MATLAB script [7], but here we have employed an alternative approach using TrackMate plugin for the particle analysis.

Acknowledgments

The authors adapted and modified this protocol from a previously published study [4] and validated it in a recently described study [6]. The authors would like to thank Prof. Matthew Seaman (University of Cambridge, UK) for sharing the CD8α-CI-M6PR construct. S. Rawat acknowledges fellowship support from University Grants Commission. M. Sharma would like to acknowledge funding support from the Department of Biotechnology (DBT)/Wellcome Trust India Alliance Senior Fellowship (IA/S/19/1/504270), SERB-POWER Grant (SPG/2021/002790), and Janaki Ammal-National Women Bioscientist Award (BT/HRD/NWBA/39/01/2018-19). M. Sharma also acknowledges the infrastructure and financial support from IISER Mohali.

Competing interests

The authors declare no competing financial interests.

References

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