Variable Dose Analysis: A Novel High-throughput RNAi Screening Method for Drosophila Cells

Materials and Reagents

1. Pipette tips (Gilson, catalog numbers: F167101, F167103) (storage conditions: RT)
2. Nunc^TM Cell Culture Treated flasks with filter caps (Thermo Scientific, catalog number: 136196) (storage conditions: RT)
3. 96-well tissue culture plate (JET Biofil, catalog number: TCP011096) (storage conditions: RT)
4. 96-well PCR plate (BRAND, catalog number: 781375) (storage conditions: RT)
5. Parafilm M (Bemis, catalog number: HS234526A) (storage conditions: RT)
6. Paper towel (any brand)
7. Drosophila S2R⁺ cells (DGRC, catalog number: 150) (storage conditions: 25 °C)
8. FuGENE^® HD transfection reagent (Promega, catalog number: E2311) (storage conditions: 4 °C)
9. Schneider’s Drosophila Medium (Gibco, catalog number: 21720024) (storage conditions: 4 °C)
10. One Shot^TM Fetal Bovine Serum (Gibco, catalog number: A3382001) (storage conditions: -20 °C)
11. Penicillin-Streptomycin (Gibco, catalog number: 15070063) (storage conditions: -20 °C)
12. ^pActin-GFP (storage conditions: -20 °C)
13. ^pActin-GAl4 (storage conditions: -20 °C)
14. *shRNA library (storage conditions: -20 °C)
15. Phosphate Buffered Saline (PBS) (Gibco, catalog number: 20012019) (storage conditions: RT)
16. Culture media (see Recipes)
    

Notes:

1. ^Available from the Housden lab. 
2. *Available from the DRSC (fgr.hms.harvard.edu) or Housden lab. 
3. RT: Room temperature.

Equipment

1. Flow cytometer (Beckman Coulter, model: CytoFlex S)
2. 25 °C incubator (any supplier)
3. Tissue culture hood (any supplier)
4. Multichannel pipettes (Gilson, model: PIPETMAN^®)
5. Humidifying chamber (N/A)
   

Software

1. Matlab (MathWorks)
2. Excel (Microsoft)
   

Procedure

Note: This protocol is optimized for Drosophila S2R⁺ cells. However, we expect it to be transferrable to any cell type that can be efficiently transfected. All steps involving manipulation of cultured cells should be performed using sterile technique in a suitable tissue culture hood.

1. Preparation of cells for transfection (or ‘Plating Cells’)
   
   1. Culture cells using standard methods in culture media so that they are approximately 80% confluent on the day of transfection.
   2. On the day of transfection, plate 1.5 x 10⁴ cells in 100 µl culture media per well of a 96-well plate.
   3. Place plated cells in a 25 °C incubator for 20 min to 1 h to allow for adhesion prior to addition of transfection mixture (see Step B3).
   Note: The seeding density of cells should be optimized for each cell type and growth condition.
   
   
2. Transfection protocol using FuGENE^® HD transfection reagent
   Notes: 
   
   1. The following transfection protocol will result in ten-fold more transfection mixture than is necessary. This is to prevent the need to pipette very small volumes.
   2. While we typically use FuGENE^® HD reagent, we expect this method to work with any similar reagent.
      
   1. Defrost shRNA library plates with each shRNA at 45 ng/µl in water.
      Note: Library plates should contain positive and negative control reagents, with at least 5 wells of each to allow assessment of noise in the resulting screen data. 
   2. In a 96-well PCR plate, mix 90 ng shRNA plasmid, 20 ng actin-GFP, 90 ng actin-GAL4.
      Note: Although in theory the three plasmids should be delivered together, a higher ratio of shRNA to GFP is needed to minimize the chance that cells would be transfected with GFP but without corresponding delivery of shRNA. In cases where only shRNA is delivered, the cells will be GFP negative and will therefore not be analyzed during cytometry.
   3. Add the appropriate amount of filter-sterilized PBS to bring total volume to 10 µl. 
   4. Add 0.6 µl of FuGENE^® HD transfection reagent to each well and mix gently by pipetting.
      Note: Add FuGENE^® HD transfection reagent directly to the DNA mixture. Do not allow undiluted reagent to contact the sides of the tube. Avoid introducing bubbles when mixing.
   5. Incubate for 20 min at room temperature in a sterile tissue culture hood.
   6. Transfer 1 µl of transfection mixture from each well of the PCR plate to the cell culture plate, careful to not introduce any air bubbles.
      Note: Avoid edge effects by leaving the edge wells untransfected. 
   7. Seal the 96-well plate with Parafilm and incubate cells at 25 °C for 5 days in a humidifying chamber.
      Note: A simple humidifying chamber can be constructed from a plastic bin or Tupperware containing a moist paper towel. Check periodically to make sure the paper towel remains moist and add more water if necessary.
      
      
      
3. Collect data using flow cytometry
   
   1. Initialize and configure the flow cytometer according to the manufacturer’s instructions.
   2. Adjust acquisition settings to record 20,000 events per sample (“All Events”). Adjust gain or laser power settings if necessary to ensure all events are within the detectable range. Make sure that the same settings are applied to every well in the plate. 
   3. Load the plate and acquire data, following the gating strategy shown in Figure 2. Note that the specific methods to load the plate, set up gates and acquire data will vary depending on the cytometer used. Follow manufacturer’s instructions to ensure appropriate set-up. A helpful explanation of the basics of flow cytometry as well as methods used to analyze and interpret data can be found in Jaroszeski and Radcliff (1999).
   4. Export .fcs files for all GFP-expressing cells (Gate P3) for further analysis.
      
      
      Figure 2. Gating strategy for VDA assays. A. Select live cells by plotting forward vs. side scatter and setting a gate as shown. B. Distinguish single cells from doublets by plotting forward scatter–area against forward scatter–height. C. Identify GFP positive cells by plotting GFP fluorescence (FITC filter in this case) against an unused and spectrally distinct filter (APC in this case). D. Statistics view to assess transfection efficiency (P3, % parent).
      Note: VDA does not require a very high transfection efficiency. However, low efficiency (< 20%) can indicate unhealthy cells, which may affect results.
      

Data analysis

Data analysis requires a customized approach depending on the specific cytometer used because .fcs file formats vary between machines. Some cytometers can export data directly in text or excel format whereas others require third-party software to extract data. A Matlab script that works with most cytometers can be obtained here: uk.mathworks.com/matlabcentral/fileexchange/9608-fca_readfcs.

1. Load the .fcs data as described above. 
2. Extract well locations, forward scatter (FSC) data as a proxy for cell size and GFP expression data from the appropriate filter channels. 
3. Divide each GFP value by the corresponding FSC value to normalize for cell size variation. 
4. Generate 500 bins based on the minimum and maximum GFP values and enter data into the bins to obtain a distribution of cells over GFP intensity. 
5. Normalize the area under each distribution curve to 1 by dividing all values by the sum of values for each sample. This removes effects of variable cell number between samples. 
6. Generate an inverted cumulative GFP distribution plot (see Figure 3). Note that inversion of the cumulative distribution is primarily for visualization purposes and is not a necessary step. Record the area under the curve for each well. 

Note: Statistical tests specific to the biological question under investigation can be used to determine RNAi reagents that cause phenotypes significantly different from the positive and negative controls.

Figure 3. Representative results. A. Visualization of the data analysis workflow. B. Inverted cumulative GFP distribution plot [Gene X (green), Gene Y (blue), Gene Z (gold), negative control (black; shRNA targeting the white gene, a well-characterized gene known to have no viability effect in these cells) and positive control (red; shRNA targeting the thread gene, an apoptosis inhibitor which robustly induces cell death when inhibited)]. C. Bar graph of area under curve. Error bars represent SEM for 3 replicate wells of each target gene, and 4 replicate wells of each control in a single 96-well plate. In this case, areas have been normalized to the negative control sample.

Notes

1. We recommend setting up at least 3 biological replicates of each VDA assay plate. 
2. As can be seen in Figure 3, VDA should produce very distinct curves. The curves for control reagents (or reagents used in multiple replicates) should be very tight, almost right on top of each other. Variability might be due to low transfection efficiency or cell conditions (i.e., cell density or health).
   

Recipes

1. Culture media
   500 ml of Schneider's media
   50 ml of fetal bovine serum (FBS)
   5 ml of penicillin/streptomycin
   Filter sterilized
   

Acknowledgments

This work was supported by the Department of Defense Grant W81XWH16-1-0127. We acknowledge help and support from Norbert Perrimon, Stephanie Mohr, members of the Perrimon lab and the Drosophila RNAi Screening Center in developing the VDA protocols presented here. This protocol is optimized from previously published methods (Housden et al., 2017).

Competing interests

The authors have nothing to disclose.

References

1. Boutros, M. and Ahringer, J. (2008). The art and design of genetic screens: RNA interference. Nat Rev Genet 9(7): 554-566.
2. Fischer, B., Sandmann, T., Horn, T., Billmann, M., Chaudhary, V., Huber, W. and Boutros, M. (2015). A map of directional genetic interactions in a metazoan cell. Elife 4: e05464.
3. Housden, B. E., Li, Z., Kelley, C., Wang, Y., Hu, Y., Valvezan, A. J., Manning, B. D. and Perrimon, N. (2017). Improved detection of synthetic lethal interactions in Drosophila cells using variable dose analysis (VDA). Proc Natl Acad Sci U S A 114(50): E10755-E10762.
4. Jaroszeski, M.J. and Radcliff, G. (1999). Fundamentals of flow cytometry. Mol Biotechnol 11(1): 37-53. 
5. Ma, Y., Creanga, A., Lum, L. and Beachy, P. A. (2006). Prevalence of off-target effects in Drosophila RNA interference screens. Nature 443(7109): 359-363.
6. Mohr, S. E., Smith, J. A., Shamu, C. E., Neumuller, R. A. and Perrimon, N. (2014). RNAi screening comes of age: improved techniques and complementary approaches. Nat Rev Mol Cell Biol 15(9): 591-600.
7. Tong, A. H., Lesage, G., Bader, G. D., Ding, H., Xu, H., Xin, X., Young, J., Berriz, G. F., Brost, R. L., Chang, M., Chen, Y., Cheng, X., Chua, G., Friesen, H., Goldberg, D. S., Haynes, J., Humphries, C., He, G., Hussein, S., Ke, L., Krogan, N., Li, Z., Levinson, J. N., Lu, H., Menard, P., Munyana, C., Parsons, A. B., Ryan, O., Tonikian, R., Roberts, T., Sdicu, A. M., Shapiro, J., Sheikh, B., Suter, B., Wong, S. L., Zhang, L. V., Zhu, H., Burd, C. G., Munro, S., Sander, C., Rine, J., Greenblatt, J., Peter, M., Bretscher, A., Bell, G., Roth, F. P., Brown, G. W., Andrews, B., Bussey, H. and Boone, C. (2004). Global mapping of the yeast genetic interaction network. Science 303(5659): 808-813.
8. Zhan, T. and Boutros, M. (2016). Towards a compendium of essential genes - From model organisms to synthetic lethality in cancer cells. Crit Rev Biochem Mol Biol 51(2): 74-85.