RNA interference screening methods to identify proliferation determinants and mechanisms of resistance to immune attack

Abstract

We have used RNA interference (RNAi) screening technology to reveal unknown components of biological signaling pathways including survival mechanisms of estrogen-independent breast cancer cell growth and cancer cell resistance to immune attack. In this chapter, a detailed protocol describing the use of RNAi screening to identify factors important for the proliferation of estrogen-independent MCF7 breast cancer cells will be described. Resistance to therapies that target the estrogen pathway remains a challenge in the treatment of estrogen receptor-positive breast cancer. To address this challenge, small interfering-RNA- (siRNA-)based libraries targeting an estrogen receptor- (ER-) and aromatase-centered network, including 631 genes relevant to estrogen signaling, was designed and constructed for RNAi screening. This protocol will include the following parts: 1) selection of RNAi transfection reagent for specific cells; 2) optimization of RNAi screening conditions using Z’-factor; 3) procedure of ER-network gene siRNA library screening using automated machines under optimized experimental conditions; and 4) method of analysis for RNAi screening data to identify specific determinants important for cell proliferation. 46 genes were found to be selectively required for the survival of estrogen-independent MCF7-derived cells.

Introduction

RNA interference (RNAi) is a biological process that can be exploited to inhibit gene expression by causing the destruction of specific mRNA molecules. Knockdown of specific genes by RNAi technology is often associated with phenotypic changes, which has made RNAi widely used in life science research. Two systems are utilized for high-throughput RNAi screening, one is a lentivirus-based short hairpin RNA (shRNA) library method for screening; the other is chemically-synthesized small interference RNA (siRNA-)-based screening (Boutros & Ahringer, 2008). shRNA-based infection induces stable gene knockdown in cells. siRNA-based transfection induces transient gene knockdown. Lentiviral pooled shRNA libraries consist of lentiviruses containing shRNAs targeting either genomic DNA or a group of genes. Following screening, analysis is required to distinguish target genes, such as chip-based DNA microarray or next generation sequencing (NGS) (Josse et al., 2015; Shuptrine et al., 2017). However, in siRNA-based libraries, siRNAs against each single target gene are individually distributed in the wells of 96-well or 384-well plates. A siRNA library may include many plates depending on the number of targeting genes in this library (Y. W. Zhang, Jones, Martin, Caplen, & Pommier, 2009; Y. W. Zhang et al., 2016). The siRNAs in the library are then reverse-transfected into cells to introduce the siRNA into the cells in the same array they are laid out in the 96- or 384-well plates. Thus for siRNA library screening, no further techniques are required to identify targeting genes.

In our studies, we designed the Estrogen receptor (ER-)-network around 5 seed proteins relevant to estrogen signaling: the ER genes ESR1 (ERα) and ESR2 (ERβ), the estrogen-related receptors ESRRA and ESRRG, and CYP19A1 (aromatase). 631 genes were selected for the ER network. Next, we constructed siRNA-based libraries targeting the ER network genes, which were custom-made from QIAGEN (MD, USA) and arrayed into 96-well plates. siRNAs against the selected genes were distributed into 11 96-well plates. Two siRNAs were selected for each gene and mixed in one well (Y. W. Zhang et al., 2016). The advantage of our method provides high-throughput screening by using automatic machines (Cybio, combi-nl or Wellmate dispenser) to dispense liquid to speed the screening process.

Different types of cancer cell lines had been used for RNAi screening with our methods to identify proliferation determinants and mechanisms of resistance to immune attack (Astsaturov et al., 2010; Murray et al., 2014; Y. W. Zhang et al., 2009; Y. W. Zhang et al., 2016), such as estrogen positive breast cancer MCF7cells, estrogen-independent MCF7 cells (LCC1 and LCC9), triple negative breast cancer MDA-MB-231 cells, epidermoid cancer A431 cells and human fibroblast HFF1 cells. For each cell line, the optimal transfection reagent was determined before RNAi library screening. Z’-factor was taken as a quantitative parameter to control the experimental quality for various cell lines and corresponding transfection reagents. In this example, we utilize ER-network RNAi screening in MCF7 cells to describe the protocol (Y. W. Zhang et al., 2016). It also can be used with other cell lines or other gene network RNAi libraries with minor modifications, such as type of transfection reagent, cell plating density, Cell Titer blue incubation time or RNAi library scale (total number of siRNA library plates), which will be noted. In this example, the protocol will be described in three parts (Figure 1) : 1) Selection of transfection reagents; 2) Z’-factor determination; 3) Screening an RNAi library.

Figure 1.

Outline of the protocol.

Materials and Reagents

1. ER network siRNA library plates (Customized from QIAGEN)

   siRNA-based library targeting Estrogen receptor (ER)- and aromatase-centered network was designed around 5 seed proteins relevant to estrogen signaling: the ER genes ESR1 (ERα) and ESR2 (ERβ), the estrogen-related receptors ESRRA and ESRRG, and CYP19A1 (aromatase). 631 genes were selected as ER network, which was described in supplemental materials. siRNA against each gene was ordered from QIAGEN (Germantown, MD).

2. Pipette tips for CyBio-Well Vario 96 channel simultaneous Pipettor (Thermo Fisher Scientific, Thermo Scientific™, catalog number: 5587)

3. V-bottom 96-well plates (Corning, catalog number: 3357)

4. Flat-bottom 96-well plates (Corning, catalog number: 3595)

5. 50 ml conical tube

6. Corning 0.22 μm vacuum filter system (Corning, catalog number: 431098)

7. T75 flasks (Corning, Costar)

8. Labels with Barcode

9. MCF7 cells (Tissue Culture Shared Resource, Lombardi Cancer Center, Georgetown Univ.)

10. AllStars Negative Control siRNA (QIAGEN, catalog number: 1027281)

11. AllStars Hs Cell Death siRNA (QIAGEN, catalog number: 1027299)

12. AP2A siRNA (QIAGEN, catalog number: SI04371283)

13. GRB14 siRNA (QIAGEN, catalog number: SI00430703)

14. Opti-MEM reduced serum medium (Thermo Fisher Scientific, Gibco™, catalog number: 31985070)

15. IMEM medium (Mediatech, catalog number: 10–024-CV)

16. Trypsin-EDTA (0.5%), no phenol red (Thermo Fisher Scientific, Gibco™, catalog number: 15400054)

17. Charcoal-stripped bovine calf serum (CCS) (Gemini Bio-Products, catalog number: 100–213)

18. Estradiol (Sigma-Aldrich, catalog number: E8875)

19. Cell Titer Blue (Promega, catalog number: G8082)

20. Hank’s balanced salt solution (HBSS) without calcium, magnesium, phenol red (GE Healthcare, Hyclone, catalog number: SH30588.01)

21. siRNA suspension buffer (QIAGEN)

22. Lipofectamine RNAiMAX transfection reagent (Thermo Fisher Scientific, Invitrogen™, catalog number: 13778500)

23. HiPerfect (QIAGEN, catalog number: 301704)

24. Dharmafect 1–4 transfection reagent (GE Dharmacon, catalog numbers: T-2001, T-2002, T-2003, T-2004)

25. RNAiFect (QIAGEN)

26. 70% (v/v) ethanol (filtered via Corning 0.22 μm vacuum filter system)

27. 0.22 μm filtered ddH₂O

Equipment

1. CyBi-Well Vario 96 channel simultaneous Pipettor (CyBio)

   

2. Multidrop Combi nL reagent dispenser (Thermo Fisher Scientific, catalog number: 5840400)

   

3. WellMate microplate dispenser (Thermo Scientific Matrix)

   

4. Envision multi-label plate reader with 560_Ex/590_Em filter set (PerkinElmer, catalog number: 2104–0010)

5. 3R Centrifuge with Ch.003741 rotor (Thermo Fisher Scientific, catalog number: 4393) and swing rectangular buckets with adapters (Thermo Fisher Scientific, catalog number: 75006449)

6. Magnetic stirrer (Thermo Fisher Scientific)

7. 500 ml glass bottle (Corning, Costar)

Part I. Selection of transfection reagents

Because siRNA transfection is the most critical step for RNAi library screening, it is important to select the suitable transfection reagent, which yields high transfection efficiency for a certain cell line. Multiple commercial transfection reagents will be tested by measuring cell viability in cells transfected with negative control siRNAs (siNEG), death control siRNAs (siDEATH), or AP2A1 siRNA (AP2A1 knockdown will yield moderate cell death). The siRNA is reverse-transfected into the tissue culture cells (lipid tranfection reagent and siRNA are mixed first in the plate, and cells are added to the mixture). The procedure for the selection of transfection reagents in breast cancer MCF7 cells is described.

Procedure

Transfection with multiple transfection reagents (lipid) in breast cancer MCF7 cells:

1. Transfect cells in 96-well plate in 7 blocks, for each block (12 wells; see Figure 2 for plate layout):

   • 3 wells: lipid + Opti-MEM medium
   • 3 wells: 20 nM AllStars negative control siRNA (QIAGEN, MD) + lipid + Opti-MEM
   • 3 wells: 20 nM AllStars Death control siRNA (QIAGEN, MD) + lipid + Opti-MEM
   • 3 wells: 20 nM AP2A1 siRNA (QIAGEN, MD) + lipid + Opti-MEM

2. For lipid: diluted lipid (recipe as in Table 1) will be added (15 μl/well) after appropriate dilution in Opti-MEM (Invitrogen, MD). Diluted lipid will be aliquoted into 12 wells.

3. For siRNA: 1 μM siRNA is diluted in Opti-MEM (1:3) and 7 μl diluted siRNA is added to each corresponding well. Each siRNA is dispensed into 21 wells, therefore, 147 μl are needed. To account for pipetting loss, we made 165 μl diluted siRNA: 55 μl 1 μM siRNA + 110 μl Opti-MEM.

4. Split cells, count and calculate dilution for 8,000 cells per well in 100 μl IMEM + 5% charcoal-stripped bovine calf serum (CCS) + 1 nM estradiol (Sigma-Aldrich, MI).

5. Set up a 96-well plate:

   1. Pipette siRNA (Opti-MEM for lipid control wells: A1–12, E1–9), 7 μl/well to following wells: siNEG, wells B1–12 and F1–9; siAP2A1, wells C1–12 and G1–9; siDEATH, wells D1–12 and H1–9.
   2. Pipette lipid mixture (Opti-MEM for medium control wells: E10–12, F10–12, G10–12 and H10–12), 15 μl/well to following wells: A1–12, B1–12, C1–12, D1–12, E1–9, F1–9, G1–9 and H1–9 (see Figure 2 for plate layout).
   3. Incubate for 20 min at room temperature, next add 100 μl MCF7 cells/well with WellMate Microplate dispenser (Thermo Scientific Matrix, USA), then incubate at 37 °C, 5% CO₂ for 5 days.
   4. Five days later, add 20 μl of 1:1 mixture of Cell Titer Blue:HBSS to each well and incubate at 37 °C to allow cells to convert resazurin to resorufin. The fluorescent signal is measured by Envision multi-label plate reader (with excitation wavelength 560 nm/emission wavelength 590 nm) every hour up to 4 h. For this experiment, 2 h is typically the optimal time point to read out with 4 h nearing the maximum signal of the assay where dynamic range is not compromised.

Figure 2.

Layout of transfection reagent selection plate.

Table 1. Recipe of diluted lipid.

To account for pipetting loss, 187.5 μl (12.5 × 15 μl) diluted lipid will be made in Opti-MEM

	Lipid (μl)	Opti-MEM (μl)
Block 1: HiPerfect	9.4	178.1
Block 2: RNAiFect	11.25	176.3
Block 3: DharmaFect 1	6.25	181.3
Block 4: DharmaFect 2	6.25	181.3
Block 5: DharmaFect 3	6.25	181.3
Block 6: DharmaFect 4	6.25	181.3
Block 7: RNAiMax	3.75	183.8

Data analysis

Normalize all cell growth to control cells (in wells E, F, G and H10–11). Next, assess the growth inhibition induced by negative control siRNAs (siNEG, in wells B and F), death control siRNAs (siDEATH, in wells D and H), and AP2A1 siRNA (in wells C and G). Then, compare the effect of various transfection reagents. Viability of AP2A1 siRNA should be within the middle of the dynamic range between siNEG and siDEATH. Select the lipid mixture containing the transfection reagent that provides not only the highest viability with siNEG, but also the lowest viability with siDEATH.

Part II. Z’-factor determination

Before performing the high-throughput RNAi library screening, the experimental conditions should be optimized. Z’-factor, a measure of dynamic range and signal-to-noise, was assessed to determine whether the assay conditions were suitable for screening (Birmingham et al., 2009; J. H. Zhang, Chung, & Oldenburg, 1999). A Z’-factor siRNA plate including negative control siRNAs (siNEG) and death control siRNAs (siDEATH) was used to calculate Z’-factor and test the potential experimental conditions. The procedures of Z’-factor determination was described here.

Procedure

The workflow of high throughput screening using multiple automated machines is shown in Figure 3. Briefly, prepare siRNA plates with concentration of siRNA at 0.24 μM. Next, dispense 10.5 μl of diluted lipid transfection reagent to experimental plates (using Combi-nl), mix with 10 μl of diluted ER library siRNA (using CyBio) and 8,000 cells per well (using WellMate). Cells were incubated for 5 days. Viability was measured by Cell Titre Blue methods. In this procedure of Z’-factor determination, Z’-factor plates are used in the place of the siRNA library plates.

Figure 3.

Workflow of high throughput-RNAi screening

Day 1

Set up a siRNA Z’-factor plate in V-bottom 96-well plate containing 0.24 μM siNEG and siDEATH (using layout shown in Figure 4). To make 0.24 μM siRNA solution: 72 μl 20 μM siRNA is mixed in 5,928 μl siRNA suspension buffer; or 1,440 μl 1 μM siRNA is mixed in 4,560 μl siRNA suspension buffer. One hundred μl of diluted siNEG or siDEATH (0.24 μM) is dispensed into each well (using layout shown in Figure 4). Then siRNA Z’-factor is frozen at −20 °C for later use.

Figure 4.

Layout of siRNA Z’-factor plate

Day 2

Each experimental plate (96-well plate, Costar, Corning, USA) must have 10.5 μl of diluted lipid transfection reagent, 10 μl of siRNA and 8,000 cells per well. Two replicate plates are run at one experiment. The Z’-factor siRNA plate is thawed at room temperature. For two plates (192 wells), 2,016 μl of lipid transfection reagents are required. Due to the loss of machine priming, 3 ml of total transfection reagent is loaded into Combi-nl machine that then distributes 10.5 μl to each well of two experimental plates. The plates are moved to the CyBio machine, and it is used to pipette 10 μl from the siRNA Z’-factor plate into each experimental replicate plate containing the wells with 10.5 μl of diluted transfection. The final concentration of siRNA in cells is 20 nM. Next, cells will be added into experimental plates for culture by Wellmate microplate dispenser.

Procedure and timeline

1. Clean WellMate microplate dispenser with 15 ml of 70% ethanol (pre-filtered by 0.22 μm vacuum filter), then 15 ml of ddH₂O (pre-filtered by 0.22 μm vacuum filter), and lastly 15 ml of IMEM (no serum).

2. Split cells and count. Dilute 8,000 cells in 100 μl for one well (For one 96-well plate, 80,000 cells/ml, need 16 ml, prepare 40 ml in a 50 ml conical tube).

3. Dispense 15 ml Opti-MEM in a 50 ml conical tube.

4. Clean Combi-nL with 7 ml of filtered (0.22 μm) 70% ethanol, 7 ml distilled water (0.22 μm filtered), 7 ml Opti-MEM.

5. Take siRNA Z’-factor plate out from −20 °C and thaw plate at room temperature. Next, spin plate in centrifuge to get liquid in center of wells at 3,500 rpm (1,935 × g), 5 min, room temperature, take plate out immediately to keep condensation from forming.

6. Dilute transfection reagent: 144 μl in 2.86 ml Opti-MEM in a 50 ml conical tube.

7. Dispense 10.5 μl/well of diluted transfection reagent to each Costar 96-well plate (experimental plate) with Combi-nL machine.

8. Start Cybio machine, load plates and distribute siRNAs from siRNA Z’-factor plate to experimental plate (pre-loaded with lipid from step 2g). The loading position for plates: (Figure 5)

   1. siRNA Z’-factor plate–loaded on stack A (left arm) on CyBio.
   2. Experimental plate–loaded on stack A (right arm, with diluted lipid) on CyBio.

9. Run the program (Figure 6) to dispense siRNAs to experimental plates.

10. Wait for 10 min at room temperature for siRNA-lipid complexes to form in experimental plate. While waiting, set up dispensing program on the WellMate dispenser.

11. Use the WellMate microplate dispenser to dispense 100 μl cells/well into each experimental plate, incubate experimental plate at 37 °C, 5% CO₂ for 5 days.

12. Clean WellMate dispenser with 15 ml filtered ddH₂O, then 15 ml filtered 70% ethanol, switch off machine.

13. Clean the Combi-nL dispenser with 7 ml distilled water, and 7 ml of filtered (0.45 μm) 70% ethanol, switch off machine.

Figure 5.

Loading of siRNA plates and experimental plates

Figure 6.

Cybio program to dispense siRNA into experimental plates.

Day 7

Add 20 μl of 1:1 mixture of Cell Titer Blue:HBSS to each well and read out every

Data analysis

1. Based on Cell-Titer Blue read out, calculate the average viability value of siNEG and siDEATH. Calculate Z’-factor(Birmingham et al., 2009; J. H. Zhang, Chung, & Oldenburg, 1999):

   $$\text{Z’-factor}=1-\frac{(3\times \text{S.D.}\text{of}\text{siDEATH}+3\times \text{S.D.}\text{of}\text{siNEG})}{(\text{Average}\text{viability}\text{reading}\text{of}\text{siNEG - Average}\text{viability}\text{reading}\text{of}\text{siDEATH})}$$

   An assay is considered suitable for screening if Z’-factor is in the range of 0.7~1. If Z’-factor is smaller than 0.7, modify assay parameters( such as cell numbers, transfection reagent and siRNA final concentration) and repeat the Z’-factor experiment. Once Z’-factor is within range, continue with large scale RNAi library screening.

2. A representative viability measurement (Figure 7A) and dot plots (Figure 7B) from one Z’-factor test were shown here. In this experiment, average viability reading of siNEG, siDEATH and corresponding standard deviation values (S.D.) were obtained from Figure 7A. Then Z’-factor was calculated as 0.81, therefore, the prescribed conditions used in this experiment were optimal for following siRNA library screening.

Figure 7.

Representative data of Z’-factor test. A. Viability measurement by Cell Titre Blue reading. Purple wells: siDEATH-transfected cells; white wells: siNEG-transfected cells. B. Dot plots of viability measurements. Each dot indicated the value of cell tire blue reading in each well. Upper line of dots indicated those wells containing siNEG-transfected cells; lower line of dots indicated those wells with siDEATH-transfected cells. hour up to 4 h (as described above).

Part III. Screening an RNAi library

Procedure

A. siRNA control preparation for RNAi screening

1. In this article, we utilize an ER-network RNAi library screening as an example to describe the protocol. The ER network siRNA library contains siRNAs against 631 genes, which were custom-made from QIAGEN (MD, USA). siRNAs against those genes were distributed into 11 96-well plates. Two siRNAs were selected for each gene and mixed in one well.

   Notes: For other siRNA library screening, the total number of plates will be determined based on the number of targeted genes and number of targeting siRNAs per gene as well. In summary, the total number and final layout of wells containing siRNA in an RNAi library will modify the screening protocol.

2. Calculate the amount of various control siRNA (0.24 μM) needed to make 11 plates (100 μl each well):

   1. Preparation of negative control siRNA (siNEG)

      $$\frac{14\text{wells}}{\text{plate}}\times \frac{100\mathrm{\mu l}}{\text{well}}\times 11\text{plates}=15,400\mathrm{\mu }\text{l}\text{of}\mathrm{siNEG}(0.24\mathrm{\mu }\text{M})$$

      Accounting for pipetting error, 18 ml of 0.24 μM siNEG will be prepared as followed: 4.32 ml siNEG (1 μM) + 13.68 ml siRNA suspension buffer.
   2. Preparation of DEATH, AP2A1(X) and GRB14(Y) siRNA

      $$\frac{6\text{wells}}{\text{plate}}\times \frac{100\mathrm{\mu }\text{l}}{\text{well}}\times 11\text{plates}=6,600\mathrm{\mu }\text{l}\text{of}\text{Death},\text{X}\text{or}\text{Y}\text{siRNA}\text{at}0.24\mathrm{\mu }\text{M}$$

      Accounting for pipetting error, 8 ml of 0.24 μM DEATH, X and Y siRNA will be prepared as followed: 1.92 ml DEATH, X or Y siRNA (1 μM) + 6.08 ml siRNA suspension buffer.
   3. Preparation of ER network library siRNAs
      For each siRNA mix in one well, prepare 100 μl 0.24 μM siRNA as followed: 24 μl 1 μM stock siRNA + 76 μl siRNA suspension buffer.

B. Estrogen Receptor siRNA Library Screening using RNAiMAX Transfection Reagent in MCF7 cells

Day 1

Autoclave 500 ml glass bottles and magnetic stir bars.

Day 2

1. Set up siRNA Library plates (stock concentration: 0.24 μM, in V-bottom 96-well plates). Layouts are shown in the following 96-well plates (Plates #1–10, Figure 8; Plates #11, Figure 9). The total number of siRNA library plates is 11. Unlabeled wells are for siRNAs against ER network target genes. Labeled wells are for various controls (NEG, DEATH, AP2A1 and GRB14 and MOCK siRNA suspension buffer). 100 μl diluted siRNA was dispensed into each well, stock in −20 °C.

2. Next, prepare 11 flat-bottom 96-well plates as experimental plates. Barcodes for these 11 plates need to be printed and pasted on plates (front and middle). After labeling, these plates are stored in a cell culture hood overnight. Meanwhile, confirm that all materials, including screening media, transfection reagent, plates, transfer pipette tips, and cell cultures are ready.

3. The Cybio program (Figure 10) will pipette 10 μl from 0.24 μM siRNA ER library plate in V-bottom plate, then mixed into Costar 96-well plate that already has 10.5 μl of diluted RNAimax transfection reagent. The final concentration of siRNA in cells was 20 nM.

4. Each experimental plate (Costar 96-well plate) must have 10.5 μl of diluted lipid transfection reagent (0.5 μl RNAimax + 10 μl Opti-MEM medium) added to each

5. well with Combi-nl dispensing machine. 96 wells x 10.5 μl = 1,008 μl, 11 plates 11,088 μl. Accounting for loss from machine priming, make 15 ml for 11 plates. Then load these siRNA and experimental plates on Cybio machine as following (indicated as in Figure 5):

   1. siRNA ER library plates (0.24 μM, 11 plates)–loaded on Left Arm, Stack A, plates ascending from the bottom with lids removed before loading;
   2. Experimental plates (11 plates)–loaded on Right Arm, Stack A; plates with ascending from the bottom with lids removed before loading.

Figure 8.

Layout of ER network siRNA library plate #1–10

Figure 9.

Layout of ER network siRNA library plate #11

Figure 10.

Cybio program to dispense siRNA into RNAi screening experimental plates.

Procedure and timeline

1. Thaw siRNA library plates (stock concentration: 0.24 μM) at room temperature.

2. Clean WellMate microplate dispenser machine with 15 ml of filtered 70% ethanol, then 15 ml of filtered ddH₂O, and 15 ml IMEM medium (without serum).

3. Split MCF7 cells and count. Dilute 8,000 cells in 100 μl (80,000 cells/ml). For one plate: 16 ml of diluted cells are needed. Therefore, for 11 plates: 250 ml diluted cells in 500 ml sterile bottle, with magnet stir bar on the magnetic stirrers, low speed. Plate extra cells in new flasks if necessary.

4. Clean Combi-nl dispensing machine with 7 ml of filtered 70% ethanol, 7 ml filtered ddH₂O, and 7 ml Opti-MEM medium.

5. When the siRNA library plates have thawed, centrifuge them at 3,500 rpm (1,935 x g) for 5 min at room temperature. Take plate out immediately to keep condensation from forming.

6. Dispense 15 ml Opti-MEM in a 50 ml conical tube.

7. Dilute transfection reagent: 720 μl RNAimax in 14.3 ml Opti-MEM in a 50 ml conical tube.

8. Dispense 10.5 μl/well of diluted transfection reagent to 11 experimental plates (Costar 96-well plates).

9. Load experimental plates and siRNA plates on designated stacks of Cybio-machine. siRNA plates: Left Arm, Stack A; experimental plates: Right Arm, Stack A. Lids were removed before loading, and plates ascending from the bottom.

10. Start Cybio machine, run program (Figure 10) to distribute 11 plates of library siRNAs as shown in attached plate layout (Figures 8 and 9). Set up a timer, record the time of each plate being processed. Proceed to the next step while completing this step.

    Note: In this example, the time for running one plate and changing tips was 1 min and 15 sec. The total machine running process will take about 15 min.

11. After the first four experimental plates containing diluted siRNA-lipid mixture are completed, transfer these plates to the cell culture hood. A second person should continue running the remainder of plates on Cybio machine.

12. After waiting 10–15 min from the time of siRNA-lipid mixture, use the WellMate microplate dispenser machine to dispense 100 μl of cells/well to the first four experimental plates, and then transfer to 37°C CO₂ incubator. Wait for another 5 min, then prime the WellMate machine with 5–7 mL cell suspension, dispense cells for the next 4 plates and transfer them to the 37°C CO₂ incubator; then wait for another 5 min, prime machine with 5–7 ml cell suspension, dispense cells for the last 3 plates and transfer them to the 37°C incubator. Document the time each plate received cells.

    Notes

    1. Four plates as a group to be added with cells.
    2. Put WellMate dispenser probe into diluted cells only before adding cells into experimental plates, not earlier, and then prime machine with 5–7 ml cell suspension for each group.

13. Clean WellMate dispenser with 15 ml filtered ddH₂O, then 15 ml filtered 70% ethanol, and switch off the machine.

14. Clean the Combi-nL dispenser with 7 ml distilled water, and 7 ml of filtered (0.45 μm) 70% ethanol, switch off machine.

Day 7

Five days after incubation at 37 °C, 5% CO₂, add 20 μl of 1:1 mixture of Cell Titer Blue:HBSS to each well and read out every hour up to 4 h (as described above).

Data analysis

1. Calculate median viability values (Cell Titer blue readout, arbitrary unit) of each control from the data of corresponding wells, mock (Plate 1–10: A5, A11, C1, F12, H2 and H8; Plate 11, A5, A-G11, C1, F12, H2, H8 and G10); siNEG (all plates: A1, A3, A7, A9, A12, E1, E9, D9, D12, H1, H4, H6, H10 and H12); siDEATH (all plates: B1, B12, A6, E12, G1 and H7); siAP2A1 (all plates: A2, A8, D1, G12, H5 and H9); siGRB14 (all plates: A4, A10, C12, F1, H3 and H11). Determine if AP2A1 or GRB14 siRNA yields median killing of cells, and if death control siRNA kills more than 90% of cells. If so, continue with following data processing and analysis. If not, go back to trouble shoot with technique.

2. Calculate the viability index (VI) of each siRNA-transfected cell normalized to the median value of negative control siRNA-transfected cells:

   $$\text{VI}=\frac{\text{viability}\text{of}\text{siRNA-transfected}\text{cells}}{\text{median}\text{viability}\text{of}\text{negative}\text{control-transfected}\text{cells}}$$

3. Three independent experiments will be needed with average VI of each gene-knocked down-cells is obtained. An arbitrary threshold of VI less than 0.5 can be utilized. These groups of genes whose knockdown induced a loss of 50% viability or more were identified as genes of interest, which were considered as reflecting a robust biological effect and then continued with additional validation.

4. siRNA Validation

   Hits identified by a loss of 50% viability of more following siRNA knockdown (VI ≤ 0.5) underwent validation studies. For each hit identified, four different siRNAs (QIAGEN, MD) targeting the same gene were tested in individual wells. Two out of the four siRNAs were the same target sequences as the siRNAs in the screen, when available. The other two siRNAs were new sequences to test. Cells were screened as described above. If at least two out of four of the siRNAs tested reduced viability by at least 50%, the candidate passed validation as a putative hit.

Notes

1. Each siRNA library screening experiment is repeated three times. In order to reproduce the data efficiently, cell viability of various control siRNAs (NEG, DEATH, AP2A1 and GRB14 and MOCK siRNA suspension buffer) is used to monitor if there is any experimental artifacts or batch-effects. If AP2A1 or GRB14 siRNA yields median killing of cells, and if death control siRNA kills more than 90% of cells, continue with data processing and analysis. If not, the data from a certain batch would not be included for statistical analysis. This will ensure the reproducibility and robustness of this method and analysis.

2. When dealing with multiple plates in one experiment, keep in mind the lipid-siRNA complex formation time should be limited to 10–15 min, record the lipid adding time (CyBio), and calculate the cells dispensing time 10–15 min after (WellMate dispenser). Shorter or longer waiting time can cause variability with transfection efficiency and baseline cell viability, which can be detected using the relevant controls included on each plate.

3. As far as setting up the threshold of VI for identifying hits and validation, we chose 0.5 (VI) in our studies. Those genes resulted in a loss of 50% viability or more following gene knockdown were identified as hits. The criteria of choosing an appropriate threshold is that the threshold must reflect a robust biological effect. The value can be adjusted according to projects.

4. Time recording

   Plate Number	Cybio time	WellMate time
   1
   2
   3
   4
   5
   6
   7
   8
   9
   10
   11

5. Screening check list

   	Complete
   Costar plates
   V-bottom plates
   Media (IMEM)
   CCS
   Opti-MEM
   Trypsin
   Cells
   T75 flasks
   Filtered Mono-Q water
   Filtered 70% Ethanol
   Cell Titer Blue
   Labels w/Barcode
   Control siRNAs
   Autoclaved 500 ml bottle (1) with small stir bar inside
   Calculations