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RNA Interference Screening to Identify Proliferation Determinants in Breast Cancer Cells
RNA干扰筛查鉴定乳腺癌细胞增殖的决定因素   

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Abstract

RNAi screening technology has revealed unknown determinants of various biological signaling pathways in biomedical studies. This protocol provided detailed information about how to use RNAi screening to identify proliferation determinants in breast tumor cells. siRNA-based libraries targeting against Estrogen receptor (ER)-network, including 631 genes
relevant to estrogen signaling, was constructed for screening in breast cancer cells. Briefly, reverse transfection of siRNA induced transient gene knockdown in MCF7 cells. First, the transfection reagent for MCF7 cells was selected. Next, the Z’-score assay was used to monitor if screening conditions yielded efficiently. Then, the ER-network siRNA library screening was preceded by automatic machines under optimized experimental conditions.

Keywords: RNA interference (RNAi)(RNA干扰(RNAi)), Screening(筛查), Estrogen receptor (ER)(雌激素受体(ER)), Breast cancer(乳腺癌), Z’-score(Z评分), Multidrop Combi-nL reagent dispenser(多滴Combi nL试剂分配器), WellMate microplate dispenser(WellMate微孔板分配器), CyBio automatic dispenser(CyBio自动分配器)

Background

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 lentivus-based short hairpin RNA (shRNA) library screening; the other is chemical synthesized small interference RNA (siRNA)-based screening (Boutros et al., 2008). shRNA-based transfection induces stable gene knockdown in cells. siRNA-based transfection induces transient gene knockdown. Lentiviral pooled shRNA libraries contain lentiviruses with shRNAs targeting against either genomic DNA or a group of genes. Following analysis is required to distinguish target genes after screening, such as chip-based DNA microarray or next generation sequencing (NGS). However, in siRNA-based libraries, siRNAs against each single target gene are distributed in each well of 96-well or 384-well plates. A siRNA library may include many plates depending on the number of targeting genes in this library. 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 as ER network. Next, we constructed siRNA-based libraries targeting against ER network genes into 96-well plates, which were custom-made from QIAGEN (MD, USA). siRNAs against those genes were distributed into 11 x 96-well plates. Two siRNAs were selected for each gene and mixed in one well (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 in RNAi screening with our methods (Astsaturov et al., 2010; Murray et al., 2014; Zhang et al., 2016), such as estrogen positive breast cancer MCF7, estrogen-independent MCF7 (LCC1 and LCC9), triple negative breast cancer MDA-MB-231, epidermoid cancer A431 and human fibroblast HFF1 cells etc. For each cell line, the optimal transfection reagent has to be determined before RNAi library screening. Z’-score is taken as a quantitative parameter to control the experiment quality for various cell lines and corresponding transfection reagents. In this assay, we utilize ER-network RNAi screening in MCF7 cells as an example to describe the protocol (Zhang et al., 2016). It also fits other cell lines or other gene network RNAi library with minor modification, 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 article, these protocols will be described in three parts: 1) Selection of transfection reagents; 2) Z’-score determination; 3) Screening an RNAi library.

Materials and Reagents

  1. Pipette tips for CyBi-Well Vario 96 channel simultaneous Pipettor (Thermo Fisher Scientific, Thermo ScientificTM, catalog number: 5587 )
  2. V-bottom 96-well plates (Corning, catalog number: 3357 )
  3. Flat-bottom 96-well plates (Corning, catalog number: 3595 )
  4. 50 ml conical tube
  5. Corning 0.22 µm vacuum filter system (Corning, catalog number: 431098 )
  6. T75 flasks (Corning, Costar)
  7. Labels with Barcode
  8. MCF7 cells (Tissue Culture Shared Resource, Lombardi Cancer Center, Georgetown Univ.)
  9. AllStars Negative Control siRNA (QIAGEN, catalog number: 1027281 )
  10. AllStars Hs Cell Death siRNA (QIAGEN, catalog number: 1027299 )
  11. AP2A siRNA (QIAGEN, catalog number: SI04371283 )
  12. GRB14 siRNA (QIAGEN, catalog number: SI00430703 )
  13. Opti-MEM reduced serum medium (Thermo Fisher Scientific, GibcoTM, catalog number: 31985070 )
  14. IMEM medium (Mediatech, catalog number: 10-024-CV )
  15. Trypsin-EDTA (0.5%), no phenol red (Thermo Fisher Scientific, GibcoTM, catalog number: 15400054 )
  16. Charcoal-stripped bovine calf serum (CCS) (Gemini Bio-Products, catalog number: 100-213 )
  17. Estradiol (Sigma-Aldrich, catalog number: E8875 )
  18. Cell Titer Blue (Promega, catalog number: G8082 )
  19. Hank’s balanced salt solution (HBSS) without calcium, magnesium, phenol red (GE Healthcare, HycloneTM, catalog number: SH30588.01 )
  20. ER network siRNA library plates (Customized from QIAGEN)
  21. siRNA suspension buffer (QIAGEN)
  22. Lipofectamine RNAiMAX transfection reagent (Thermo Fisher Scientific, InvitrogenTM, 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. AccuSpin 3R Centrifuge with Ch.003741 rotor (Thermo Fisher Scientific, catalog number: 4393 ) and swing rectangular buckets with adapters (Thermo Fisher Scientific, catalog number: 75006449 )
  5. Magnetic stirrer (Thermo Fisher Scientific)
  6. Envision multi-label plate reader with 560Ex/590Em filter set (PerkinElmer, catalog number: 2104-0010 )
  7. 500 ml glass bottle (Corning, Costar)

Part I. Selection of transfection reagents

Procedure

Transfection with multiple lipids (transfection reagents) in MCF7 cells

  1. Transfect cells in 96-well plate in 7 blocks, for each block (12 wells):
    3 wells: lipid + Opti-MEM
    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 aliquot into 12 wells.

    Table 1. Recipe of diluted lipid. To account for pipetting loss, 187.5 µl (12.5 x 15 µl) diluted lipid will be made in Opti-MEM.


  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 1 for plate layout).


      Figure 1. Layout of transfection reagent selection plate. Efficiency of a variety of transfection reagents was tested in MCF7 cells.

    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.
    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.

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’-score determination

Procedure

Day 1
Set up a siRNA Z’-score plate in V-bottom 96-well plate containing 0.24 µM siNEG and siDEATH (using layout shown in Figure 2). 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 2). Then siRNA Z’-score plate is frozen at -20 °C for later use.


Figure 2. Layout of siRNA Z’-score 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. For two plates (192 wells), 2,016 µl transfection reagents are required. Due to the loss of machine priming, 3 ml of total transfection reagent is loaded on Combi-nL machine that aliquot 10.5 µl to each well in two experimental plates. After thawing at room temperature, pipette 10 µl from the siRNA Z’-score plate into each experimental replicate plate containing 10.5 µl of diluted transfection reagent in each well on CyBio machine. 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). Plate cells in new flasks if necessary.
  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’-score 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 x 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 the each Costar 96-well plate (experimental plate) by Combi-nL machine.
  8. Start Cybio machine, load plates and run program (Figure 3) to distribute siRNAs from siRNA Z’-score plate to experimental plate (pre-loaded with lipid from step 2g). The loading position for plates:
    1. siRNA Z’-score plate–loaded on stack A (left arm) on CyBio.
    2. Experimental plate–loaded on stack A (right arm, with diluted lipid) on CyBio.
  9. 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.
  10. Use the WellMate microplate dispenser to dispense 100 µl cells/well into each experimental plate, incubate experimental plate at 37 °C, 5% CO2.
  11. Clean WellMate dispenser with 15 ml filtered ddH2O, then 15 ml filtered 70% ethanol, switch off machine.
  12. Clean the Combi-nL dispenser with 7 ml distilled water, and 7 ml of filtered (0.45 µm) 70% ethanol, switch off machine.


    Figure 3. Cybio program to dispense siRNA into experimental plates. A. The first part of program; B. The second part of program.

    Day 7
    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. Based on Cell-Titer Blue read out, calculate the average viability value of siNEG and siDEATH. Calculate Z’-score: Z’-score = 1 - (3 x S.D. of siDEATH + 3 x S.D. of siNEG)/(Average viability reading of siNEG - Average viability reading of siDEATH) (Zhang et al., 1999; Birmingham et al., 2009). The reasonable range of Z’-score is between 0.7-1. If Z’-score is smaller than 0.7, re-assess assay parameters and repeat the Z’-score experiment. Once Z’-score is within range, continue with large scale RNAi library screening.
  2. A representative viability measurement (Figure 4A) and dot plots (Figure 4B) from one Z’-score test were shown here. In this experiment, average viability reading of siNEG, siDEATH and corresponding standard deviation values (S.D.) were obtained from Figure 4A. Then Z’-score was calculated as 0.81, which was in the range of 0.7-1, therefore, assay parameters used in this experiment were optimal for siRNA library screening.


    Figure 4. Representative data of Z’-score 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.

Part III. Screening an RNAi library

Procedure

  1. SiRNA 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 x 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)



        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



        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.

  2. 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 5; Plates #11, Figure 6). 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.


      Figure 5. Layout of ER network siRNA library plate #1-10


      Figure 6. Layout of ER network siRNA library plate #11

    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 7) 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.


      Figure 7. Cybio program to dispense siRNA into RNAi screening experimental plates. A. The first part of program; B. The second part of program.

    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 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:
      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.

      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 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 7) to distribute 11 plates of library siRNAs as shown in attached plate layout (Figures 5 and 6). 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. Wait for another 5 min, then prime the WellMate machine with 5-7 ml cell suspension, dispense cells for the next 4 plates; then wait for another 5 min, prime machine with 5-7 ml cell suspension, dispense cells for the last 3 plates. 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
        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 troubleshoot with technique.
  2. Calculate the viability index (VI) of each siRNA-transfected cell normalized to the median value of negative control siRNA-transfected cells:

    VI = (viability of siRNA-transfected cell)/median viability of negative control-transfected 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. We had once screened a total of 44 experimental plates for one RNAi screening experiment. 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.
  2. 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.
  3. Time recording


  4. Screening check list

Acknowledgments

This protocol was adapted from previous work Zhang et al. (2016). We thank Wei Xu, Alan Zwart, David Goldstein and Annie Zuo for their technical assistance. The authors were supported by R01CA050633, CA51880, U54 CA149147 (to LMW), R01CA63366 and R21CA181287 (to EAG).

References

  1. Astsaturov, I., Ratushny, V., Sukhanova, A., Einarson, M. B., Bagnyukova, T., Zhou, Y., Devarajan, K., Silverman, J. S., Tikhmyanova, N., Skobeleva, N., Pecherskaya, A., Nasto, R. E., Sharma, C., Jablonski, S. A., Serebriiskii, I. G., Weiner, L. M. and Golemis, E. A. (2010). Synthetic lethal screen of an EGFR-centered network to improve targeted therapies. Sci Signal 3(140): ra67.
  2. Birmingham, A., Selfors, L. M., Forster, T., Wrobel, D., Kennedy, C. J., Shanks, E., Santoyo-Lopez, J., Dunican, D. J., Long, A., Kelleher, D., Smith, Q., Beijersbergen, R. L., Ghazal, P. and Shamu, C. E. (2009). Statistical methods for analysis of high-throughput RNA interference screens. Nat Methods 6(8): 569-575.
  3. Boutros, M., Ahringer, J. (2008). The art and design of genetic screens: RNA interference. Nat Rev Genet 9(7): 554-566.
  4. Murray, J.C., Aldeghaither, D., Wang, S., Nasto, R. E., Jablonski, S. A., Tang, Y., Weiner, L. M. (2014). c-Abl modulates tumor cell sensitivity to antibody-dependent cellular cytotoxicity. Cancer Immunol Res 2(12):1186-1198.
  5. Zhang, J. H., Chung, T. D. and Oldenburg, K. R. (1999). A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screen 4(2): 67-73.
  6. Zhang, Y. W., Nasto, R. E., Varghese, R., Jablonski, S. A., Serebriiskii, I. G., Surana, R., Calvert, V. S., Bebu, I., Murray, J., Jin, L., Johnson, M., Riggins, R., Ressom, H., Petricoin, E., Clarke, R., Golemis, E. A. and Weiner, L. M. (2016). Acquisition of estrogen independence induces TOB1-related mechanisms supporting breast cancer cell proliferation. Oncogene 35(13): 1643-1656.

简介

RNAi筛选技术已经揭示了生物医学研究中各种生物信号通路的未知决定因素。 该协议提供了有关如何使用RNAi筛选来鉴定乳腺肿瘤细胞中增殖决定簇的详细信息。 基于siRNA的文库针对雌激素受体(ER) - 网络,包括631个基因
与雌激素信号相关,构建用于筛选乳腺癌细胞。 简单地说,siRNA在MCF7细胞中的逆转染诱导瞬时基因敲低。 首先选择MCF7细胞转染试剂。 接下来,使用Z'评分测定来监测筛选条件是否有效产生。 然后,在优化的实验条件下,ER-网络siRNA文库筛选之前是自动机器。
【背景】RNA干扰(RNAi)是一种生物过程,可通过引发特异性mRNA分子的破坏而被利用来抑制基因表达。通过RNAi技术敲除特定基因通常与表型变化相关联,这使得RNAi广泛用于生命科学研究。两种系统用于高通量RNAi筛选,一种是基于慢病毒的短发夹RNA(shRNA)文库筛选;另一种是基于化学合成的小干扰RNA(siRNA)筛选(Boutros等人,2008)。基于ShRNA的转染诱导细胞中稳定的基因敲低。基于siRNA的转染诱导瞬时基因敲低。慢病毒合并的shRNA文库含有针对基因组DNA或一组基因的shRNA的慢病毒。需要进行以下分析以区分筛选后的靶基因,例如基于芯片的DNA微阵列或下一代测序(NGS)。然而,在基于siRNA的文库中,针对每个单个靶基因的siRNA分布在96孔或384孔板的每个孔中。 siRNA文库可以包括许多平板,这取决于该文库中靶向基因的数目。对于siRNA文库筛选,不需要进一步的技术来鉴定靶基因。
在我们的研究中,我们设计了与雌激素信号相关的5种种子蛋白的雌激素受体(ER) - 网络:ER基因ESR1(ERα)和ESR2(ERβ),雌激素相关受体ESRRA和ESRRG和CYP19A1(芳香化酶)。选择631个基因作为ER网络。接下来,我们将针对ER网络基因的基于siRNA的文库构建到96孔板中,其由QIAGEN(MD,USA)定制。将这些基因的SiRNA分布到11×96孔板中。为每个基因选择两个siRNA并在一个孔中混合(Zhang等人,2016)。我们的方法的优点是通过使用自动机器(Cybio,Combi-nL或Wellmate分配器)分配液体来加快筛选过程,提供高通量筛选。
不同类型的癌细胞系已经用我们的方法用于RNAi筛选(Astasaturov等人,2010; Murray等人,2014; Zhang 雌激素阳性乳腺癌MCF7,与雌激素无关的MCF7(LCC1和LCC9),三阴性乳腺癌MDA-MB-231,表皮样癌A431和人成纤维细胞HFF1细胞等。等。对于每种细胞系,必须在RNAi文库筛选之前确定最佳转染试剂。将Z'评分作为定量参数,以控制各种细胞系和相应转染试剂的实验质量。在本实验中,我们利用MCF7细胞中的ER-网络RNAi筛选作为实例来描述方案(Zhang等人,2016)。它还适合其他细胞系或其他基因网络RNAi文库,其中包括转染试剂的类型,细胞电镀密度,细胞滴度蓝孵化时间或RNAi文库量表(siRNA文库平板的总数)等。在本文中,这些方案将分为三部分:1)选择转染试剂; 2)Z'评分确定; 3)筛选RNAi文库。

关键字:RNA干扰(RNAi), 筛查, 雌激素受体(ER), 乳腺癌, Z评分, 多滴Combi nL试剂分配器, WellMate微孔板分配器, CyBio自动分配器

材料和试剂

  1. CyBi-Well Vario 96通道同时移液器(Thermo Fisher Scientific,Thermo Scientific TM ,目录号:5587)的移液器吸头
  2. V底96孔板(Corning,目录号:3357)
  3. 平底96孔板(Corning,目录号:3595)
  4. 50ml圆锥管
  5. Corning0.22μm真空过滤系统(Corning,目录号:431098)
  6. T75烧瓶(康宁,Costar)
  7. 带有条形码的标签
  8. MCF7细胞(组织培养共享资源,伦巴第癌症中心,乔治城大学)
  9. AllStars Negative Control siRNA(QIAGEN,目录号:1027281)
  10. AllStars Hs细胞死亡siRNA(QIAGEN,目录号:1027299)
  11. AP2A siRNA(QIAGEN,目录号:SI04371283)
  12. GRB14 siRNA(QIAGEN,目录号:SI00430703)
  13. Opti-MEM降低血清培养基(Thermo Fisher Scientific,Gibco TM,目录号:31985070)
  14. IMEM介质(Mediatech,目录号:10-024-CV)
  15. 胰蛋白酶-EDTA(0.5%),无酚红(Thermo Fisher Scientific,Gibco TM,目录号:15400054)
  16. 炭蒸汽牛血清(CCS)(Gemini Bio-Products,目录号:100-213)
  17. 雌二醇(Sigma-Aldrich,目录号:E8875)
  18. 细胞滴度蓝(Promega,目录号:G8082)
  19. 不含钙,镁,酚红的汉克平衡盐溶液(HBSS)(GE Healthcare,Hyclone TM,目录号:SH30588.01)
  20. ER网络siRNA文库(QIAGEN定制)
  21. siRNA缓冲液(QIAGEN)
  22. Lipofectamine RNAiMAX转染试剂(Thermo Fisher Scientific,Invitrogen TM,目录号:13778500)
  23. HiPerfect(QIAGEN,目录号:301704)
  24. Dharmafect 1-4转染试剂(GE Dharmacon,目录号:T-2001,T-2002,T-2003,T-2004)
  25. RNAiFect(QIAGEN)
  26. 70%(v / v)乙醇(经Corning0.22μm真空过滤系统过滤)
  27. 0.22μm过滤的ddH 2 O - / -

设备

  1. CyBi-Well Vario 96通道同时移液器(CyBio)
  2. Multidrop Combi-nL试剂分配器(Thermo Fisher Scientific,目录号:5840400)
  3. WellMate微量滴定仪(Thermo Scientific Matrix)
  4. AccuSpin 3R离心机,带Ch.003741转子(Thermo Fisher Scientific,目录号:4393)和带适配器的旋转矩形桶(Thermo Fisher Scientific,目录号:75006449)
  5. 磁力搅拌器(Thermo Fisher Scientific)
  6. 具有560个Ex / 590 过滤器组(PerkinElmer,目录号:2104-0010)的Envision多标签读板器
  7. 500毫升玻璃瓶(康宁,科斯塔)

第一部分转染试剂的选择

程序

在MCF7细胞中转染多种脂质(转染试剂)

  1. 对于每个块(12孔),在7个块中的96孔板中转染细胞:
    3孔:脂质+ Opti-MEM
    3孔:20nM AllStars阴性对照siRNA(QIAGEN,MD)+脂质+ Opti-MEM
    3孔:20nM AllStars死亡对照siRNA(QIAGEN,MD)+脂质+ Opti-MEM
    3孔:20nM AP2A1 siRNA(QIAGEN,MD)+脂质+ Opti-MEM
  2. 对于脂质:在Opti-MEM(Invitrogen,MD)中适当稀释后,将加入稀释的脂质(表1中的配方)(15μl/孔)。稀释的脂质将等分成12孔
    表1.稀释脂质的配方为了考虑移液损失,将在Opti-MEM中制备187.5μl(12.5×15μl)稀释的脂质。


  3. 对于siRNA:在Opti-MEM(1:3)中稀释1μMsiRNA,并将7μl稀释的siRNA加入每个对应的孔中。每个siRNA分配到21个孔中,因此需要147μl。为了解释移液损失,我们制备了165μl稀释的siRNA:55μl1μMsiRNA + 110μlOpti-MEM。
  4. 在100μlIMEM + 5%炭剥离牛牛血清(CCS)+ 1nM雌二醇(Sigma-Aldrich,MI)中分解细胞,计数并计算每孔8000个细胞的稀释度。
  5. 设置一个96孔板:
    1. 吸管siRNA(用于脂质对照孔的Opti-MEM:A1-12,E1-9),7μl/孔至以下孔:siNEG,孔B1-12和F1-9; siAP2A1,孔C1-12和G1-9; siDEATH,井D1-12和H1-9。
    2. 移液脂质混合物(中等对照孔的Opti-MEM:E10-12,F10-12,G10-12和H10-12),15μl/孔至以下孔:A1-12,B1-12,C1-12,D1 -12,E1-9,F1-9,G1-9和H1-9(参见图1的板布局)。


      图1.转染试剂选择板的布局。在MCF7细胞中测试各种转染试剂的效率。

    3. 在室温下孵育20分钟,然后用WellMate微孔板分配器(Thermo Scientific Matrix,USA)加入100μlMCF7细胞/孔,然后在37℃,5%CO 2温度下孵育。
    4. 五天后,向每个孔中加入20μl1:1 Cell Titer Blue:HBSS混合物,37℃孵育,使细胞将resazurin转化为resorufin。荧光信号由Envision多标签阅读器(激发波长560nm /发射波长590nm)每小时至4小时测量。对于该实验,2小时通常是4小时读出的最佳时间点,接近测定动态范围不受影响的最大信号。

数据分析

使所有细胞生长归一化以控制细胞(在孔E,F,G和H10-11中)。接下来,评估由阴性对照siRNA(siNEG,孔B和F),死亡对照siRNA(siDEATH,在孔D和H中)和AP2A1 siRNA(在孔C和G中)诱导的生长抑制。然后,比较各种转染试剂的效果。 AP2A1 siRNA的活力应在siNEG和siDEATH之间的动态范围的中间。选择含有转染试剂的脂质混合物,其不仅具有最高的生存力与siNEG,而且也是siDEATH的最低存活力。


第二部分Z'分数确定

程序

第1天
在含有0.24μMsiNEG和siDEATH(使用图2所示的布局)的V底96孔板中设置siRNA Z'刻板。制备0.24μMsiRNA溶液:72μl20μMsiRNA在5,928μlsiRNA悬浮液中混合;或1,440μl1μMsiRNA在4,560μlsiRNA悬浮液中混合。将100μl稀释的siNEG或siDEATH(0.24μM)分配到每个孔中(使用图2所示的布局)。然后将siRNA Z'评分板在-20℃冷冻以备后用。


图2. siRNA Z'评分板的布局

第2天
每个实验板(96孔板,Costar,Corning,USA)必须具有10.5μl稀释的脂质转染试剂,10μlsiRNA和每孔8,000个细胞。两个重复板在一个实验中运行。对于两个板(192个孔),需要2,016μl转染试剂。由于缺少机器启动,将3ml总转染试剂加载到Combi-nL机上,每台样品在两个实验板中分别加入到每个孔中。在室温下解冻后,从CySIO机上的每个孔中,将每个含有10.5μl稀释的转染试剂的每个实验重复平板移取10μl的siRNA Z'评分板。细胞中siRNA的最终浓度为20nM。接下来,细胞将被添加到实验板中以供Wellmate微孔板分配器培养。

过程和时间线

  1. 用15ml 70%乙醇(0.22μm真空过滤器预过滤),然后加入15ml ddH 2 O(0.22μm真空过滤器预过滤),最后15次ml IMEM(无血清)
  2. 分裂细胞和计数。在一个孔中稀释8,000个细胞(100μl)(对于一个96孔板,80,000个细胞/ ml,需要16ml,在50ml锥形管中制备40ml)。如果需要,新瓶中的细胞细胞。
  3. 在50ml锥形管中分配15 ml Opti-MEM。
  4. 用7ml过滤的(0.22μm)70%乙醇,7ml蒸馏水(0.22μm过滤),7ml Opti-MEM清洁组合物。
  5. 将siRNA Z'刻板从-20℃取出并在室温下解冻。接下来,将离心机中的旋转板在3,500rpm(1,935×g / g)的孔中心获得液体,室温5分钟,立即取出以保持冷凝形成。
  6. 稀释转染试剂:将144μl在2.86ml Opti-MEM中的50ml锥形管中
  7. 通过Combi-nL机将10.5μl/孔稀释的转染试剂分配到每个Costar 96孔板(实验板)上。
  8. 启动Cybio机器,装载板和运行程序(图3)将siRNA从siRNA Z'评分板分发到实验板(预加载步骤2g的脂质)。板材的装载位置:
    1. 在CyBio上的堆叠A(左臂)上的siRNA Z'评分板。
    2. 实验板装载在堆叠A(右臂,稀释脂质)CyBio上。
  9. 在室温下等待10分钟,以在实验板中形成siRNA-脂质复合物。等待时,在WellMate分配器上设置分配程序。
  10. 使用WellMate微孔板分配器将100μl细胞/孔分配到每个实验板中,在37℃,5%CO 2/2下孵育实验板。
  11. 用15 ml过滤的ddH 2 O,然后15 ml过滤70%乙醇清洗WellMate分配器,关闭机器。
  12. 用7毫升蒸馏水和7毫升过滤的(0.45微米)70%乙醇清洗Combi-nL分配器,关闭机器。


    图3. Cybio程序将siRNA分配到实验板中。 A.程序的第一部分; B.程序的第二部分。

    第7天
    向每个孔中加入20μlCell Titer Blue:HBSS的1:1混合物,每小时读出4小时(如上所述)。

数据分析

  1. 基于Cell-Titer Blue读出,计算siNEG和siDEATH的平均生存值。计算Z'评分:Z'-score = 1 - (siDEATH的3×SD + 3×SD的siNEG)/(siNEG的平均生存力读数 - siDEATH的平均生存力读数)(Zhang等, em>,1999; Birmingham等人,2009)。 Z'-score的合理范围在0.7-1之间。如果Z'评分小于0.7,则重新评估测定参数并重复Z'评分实验。一旦Z'评分在范围内,继续进行大规模的RNAi文库筛选。
  2. 这里显示了一个Z'评分测试的代表性生存力测量(图4A)和点图(图4B)。在该实验中,从图4A获得siNEG,siDEATH和相应的标准偏差值(S.D.)的平均生存力读数。然后,Z'评分计算为0.81,在0.7-1的范围内,因此本实验中使用的测定参数对于siRNA文库筛选是最佳的。


    图4. Z'评分测试的代表性数据。 :一种。 Cell Titre蓝色阅读的生存力测量。紫色井:siDEATH转染细胞;白井:siNEG转染的细胞。 B.生存力测量的点图。每个点表示每个孔中细胞轮胎蓝色读数的值。点上方表示含有siNEG转染细胞的孔;较低的点线表示这些具有siDEATH转染细胞的孔。

第三部分。筛选RNAi库

程序

  1. 用于RNAi筛选的SiRNA制备
    1. 在本文中,我们以ER网络RNAi文库筛选为例来描述方案。 ER网络siRNA文库含有针对631个基因的siRNA,其由QIAGEN(MD,USA)定制。将针对这些基因的siRNA分布到11×96孔板中。为每个基因选择两个siRNA并在一个孔中混合。
      注意:对于其他siRNA文库筛选,将基于目标基因的数目和每个基因的靶向siRNA数量来确定平板的总数。总之,RNAi文库中含有siRNA的孔的总数和最终布局将修改筛选方案。
    2. 计算制备11个平板(每孔100μl)所需的各种对照siRNA(0.24μM)的量:
      1. 阴性对照siRNA(siNEG)的制备



        考虑到移液误差,将按以下方法制备18 ml的0.24μMsiNEG:4.32ml siNEG(1μM)+ 13.68ml siRNA悬浮液。
      2. DEATH,AP2A1(X)和GRB14(Y)siRNA的制备



        记录移液误差,8ml0.24μMDEATH,X和Y siRNA将按如下制备:1.92ml DEATH,X或Y siRNA(1μM)+ 6.08ml siRNA悬浮液。
      3. ER网络文库siRNA的制备
        对于一个孔中的每个siRNA混合物,制备如下所示的100μl0.24μMsiRNA:24μl1μM储备siRNA +76μlsiRNA悬浮液缓冲液。

  2. 使用RNAiMAX转染试剂在MCF7细胞中的雌激素受体siRNA文库筛选
    第1天
    高压灭菌500毫升玻璃瓶和磁力搅拌棒。

    第2天
    1. 设置siRNA库平板(库存浓度:0.24μM,在V底96孔板中)。布局图显示在以下96孔板(图5中的板#1-10,图6的板#11)中。 siRNA文库平板的总数为11.未标记的孔用于针对ER网络靶基因的siRNA。标记的孔用于各种对照(NEG,DEATH,AP2A1和GRB14和MOCK [siRNA悬浮液缓冲液])。将100μl稀释的siRNA分配到每个孔中,储备在-20℃。


      图5. ER网络siRNA文库板#1-10的布局


      图6. ER网络siRNA文库板#11的布局

    2. 接下来,准备11个平底96孔板作为实验板。这些11个板的条形码需要打印并粘贴在板(前面和中部)上。标记后,将这些板储存在细胞培养罩中过夜。同时,确认所有材料,包括筛选培养基,转染试剂,平板,移液器吸头和细胞培养物都已准备就绪。
    3. Cybio程序(图7)将从V底板的0.24μMsiRNA ER文库中吸取10μl,然后混合到已经具有10.5μl稀释的RNAimax转染试剂的Costar 96孔板中。细胞中siRNA的终浓度为20nM

      图7. Cybio程序将siRNA分配到RNAi筛选实验板。 A.程序的第一部分; B.程序的第二部分。

    4. 每个实验板(Costar 96孔板)必须使用Combi-nL分配机将每孔加入10.5μl稀释的脂质转染试剂(0.5μlRNAimax +10μlOpti-MEM培养基)。 96孔×10.5μl=1,008μl,11个板11,088μl。计算机启动的损失,为11块板15毫升。然后将这些siRNA和实验板载入Cybio机器如下:
      1. 装载在左臂,堆叠A上的siRNA ER库平板(0.24μM,11个板),在装载前从底部升起盖子,盖子被移除;
      2. 实验板(11板)装载在右臂,堆叠A;板材从底部上升,盖子在装载前取下。

      过程和时间轴
      1. 在室温下解冻siRNA文库平板(库存浓度:0.24μM)
      2. 清洁WellMate微孔板分配机,装有15ml过滤的70%乙醇,然后加入15ml过滤的ddH 2 O和15ml IMEM培养基(不含血清)。
      3. 分解MCF7细胞并计数。以100μl(80,000个细胞/ ml)稀释8,000个细胞。对于一个板:需要16ml稀释的细胞。因此,对于11板:将250ml稀释的细胞在500ml无菌瓶中,用磁力搅拌棒上的磁力搅拌器,速度低。如果需要,新瓶中的细胞细胞。
      4. 使用7 ml过滤的70%乙醇,7 ml过滤的ddH 2 O和7 ml Opti-MEM培养基清洗Combi-nL分配机。
      5. 当siRNA文库板解冻时,在室温下以3500rpm(1,935×g)离心5分钟。立即取出,以防止形成冷凝。
      6. 在50ml锥形管中分配15 ml Opti-MEM。
      7. 稀释转染试剂:在50ml圆锥管中的14.3ml Opti-MEM中的720μlRNAimax。
      8. 将10.5μl/孔稀释的转染试剂分配至11个实验板(Costar 96孔板)
      9. 在Cybio机的指定堆上装载实验板和siRNA板。 siRNA板:左臂,堆叠A;实验板:右臂,堆叠A.装载前取下盖子,板从底部上升。
      10. 启动Cybio机器,运行程序(图7),如附件板布局(图5和图6)所示,分发11个文库siRNA片段。设置一个定时器,记录每个正在处理的板的时间。完成此步骤时,继续下一步。
        注意:在这个例子中,运行一个盘子和更换提示的时间是1分15秒。总机运行过程大约需要15分钟。
      11. 在完成含有稀释的siRNA-脂质混合物的前四个实验板之后,将这些板转移到细胞培养罩。第二个人应该继续在Cybio机器上运行余下的盘子。
      12. 在等待从siRNA-脂质混合物的时间开始10-15分钟后,使用WellMate微孔板分配器将100μl细胞/孔分配到前四个实验板。再等5分钟,然后用5〜7ml细胞悬浮液灌注WellMate机器,分配4个平板的细胞;然后等待5分钟,主机用5-7毫升细胞悬浮液,分配最后3块板的细胞。记录每个板块接收的细胞。
        注意:

        1. 将WellMate分配器探针放入稀释的细胞中,只有在将细胞添加到实验板之前不再更早,然后将每个组的5-7 ml细胞悬液加入机器。
      13. 用15ml过滤的ddH 2 O,然后15ml过滤的70%乙醇清洗WellMate分配器,并关闭机器。
      14. 用7毫升蒸馏水和7毫升过滤的(0.45微米)70%乙醇清洗Combi-nL分配器,关闭机器。

        第7天
        向每个孔中加入20μlCell Titer Blue:HBSS的1:1混合物,每小时读出4小时(如上所述)。

数据分析

  1. 从相应孔的数据(模板1-10:A5,A11,C1,F12,H2和H8;板11,A5,A-G11)计算每个对照的中位数生存力值(细胞滴度蓝色读数,任意单位) ,C1,F12,H2,H8和G10); siNEG(所有板:A1,A3,A7,A9,A12,E1,E9,D9,D12,H1,H4,H6,H10和H12); siDEATH(所有板:B1,B12,A6,E12,G1和H7); siAP2A1(所有板:A2,A8,D1,G12,H5和H9); siGRB14(所有板:A4,A10,C12,F1,H3和H11)。确定AP2A1或GRB14 siRNA是否产生细胞中值杀死,如果死亡对照siRNA杀死超过90%的细胞。如果是这样,请继续进行以下数据处理和分析。如果没有,请回头用技巧拍摄麻烦。
  2. 计算每个siRNA转染细胞的活力指数(VI),归一化为阴性对照siRNA转染细胞的中值:

    VI =(siRNA转染细胞的存活力)/阴性对照转染细胞的中位生存力

  3. 需要进行三次独立的实验,获得每个基因敲除细胞的平均VI。可以利用VI的任意阈值小于0.5。这些基因组的敲除诱导了50%的生存力或更多的损失被鉴定为感兴趣的基因,这被认为是反映强大的生物学效应,然后继续进行额外的验证。
  4. siRNA验证
    通过损失更多的以下siRNA敲低(VI≤0.5)的50%存活率确定的命中进行了验证研究。对于每个确定的命中,在单个孔中测试靶向相同基因的四种不同的siRNA(QIAGEN,MD)。四种siRNA中的两种与可用的筛选中的siRNA相同。其他两种siRNA是新的测序序列。如上所述筛选细胞。如果测试的四个siRNA中至少有两个测试将可行性降低了至少50%,则候选人通过验证作为推定的命中。

笔记

  1. 我们一次筛选了一个44个实验板用于一个RNAi筛选实验。在一个实验中处理多个板时,请记住脂质siRNA复合物形成时间应限制在10-15分钟,记录脂质添加时间(CyBio),并在(WellMate)后10-15分钟计算细胞分配时间饮水机)。更短或更长的等待时间可能导致转染效率和基线细胞存活率的变异性,可以使用每个平板上包含的相关对照进行检测。
  2. 就VI设定阈值和验证的阈值而言,我们在研究中选择了0.5(VI)。这些基因导致基因敲除后50%存活力或更多的损失被鉴定为命中。选择适当阈值的标准是阈值必须反映出强大的生物效应。该值可根据项目进行调整。
  3. 时间录制


  4. 筛选检查单

致谢

这个协议是从以前的Zhang等人的作品(2016)中改编而成。感谢Wei Xu,Alan Zwart,David Goldstein和Annie Zoo的技术援助。作者受到R01CA050633,CA51880,U54 CA149147(LMW),R01CA63366和R21CA181287(至EAG)的支持。

参考

  1. Astasaturov,I.,Ratushny,V.,Sukhanova,A.,Einarson,MB,Bagnyukova,T.,Zhou,Y.,Devarajan,K.,Silverman,JS,Tikhmyanova,N.,Skobeleva,N.,Pecherskaya, A.,Nasto,RE,Sharma,C.,Jablonski,SA,Serebriiskii,IG,Weiner,LM和Golemis,EA(2010)。< a class =“ke-insertfile”href =“http: .ncbi.nlm.nih.gov / pubmed / 20858866”target =“_ blank”>以EGFR为中心的网络的合成致死屏幕,以改善靶向治疗。 Sci Signal 3(140) :ra67。
  2. Birmingham,A.,Selfors,LM,Forster,T.,Wrobel,D.,Kennedy,CJ,Shanks,E.,Santoyo-Lopez,J.,Dunican,DJ,Long,A.,Kelleher,D.,Smith ,Q.,Beijersbergen,RL,Ghazal,P.and Shamu,CE(2009)。< a class =“ke-insertfile”href =“http://www.ncbi.nlm.nih.gov/pubmed/ 19644458”target =“_ blank”>用于分析高通量RNA干扰屏幕的统计方法。 Nat方法6(8):569-575。
  3. Boutros,M.,Ahringer,J.(2008)。< a class =“ke-insertfile”href =“https://www.ncbi.nlm.nih.gov/pubmed/18521077”target =“_ blank”遗传筛选的艺术和设计:RNA干扰。 Nat Rev Genet 9(7):554-566。
  4. Murray,JC,Aldeghaither,D.,Wang,S.,Nasto,RE,Jablonski,SA,Tang,Y.,Weiner,LM(2014)。  c-Abl调节肿瘤细胞对抗体依赖性细胞毒性的敏感性。 2(12):1186-1198。
  5. Zhang,JH,Chung,TD and Oldenburg,KR(1999)。  用于评估和验证高通量筛选测定的简单统计参数。生物学屏幕 4(2):67-73。
  6. Zhang,YW,Nasto,RE,Varghese,R.,Jablonski,SA,Serebriiskii,IG,Surana,R.,Calvert,VS,Bebu,I.,Murray,J.,Jin,L.,Johnson, Riggins,R.,Ressom,H.,Petricoin,E.,Clarke,R.,Golemis,EA and Weiner,LM(2016)。  获得雌激素独立性诱导支持乳腺癌细胞增殖的TOB1相关机制。致癌基因 35(13) :1643-1656。
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Copyright: © 2017 The Authors; exclusive licensee Bio-protocol LLC.
引用:Zhang, Y., Nasto, R. E., Jablonski, S. A., Serebriiskii, I. G., Surana, R., Murray, J., Johnson, M., Riggins, R. B., Clarke, R., Golemis, E. A. and Weiner, L. M. (2017). RNA Interference Screening to Identify Proliferation Determinants in Breast Cancer Cells. Bio-protocol 7(15): e2435. DOI: 10.21769/BioProtoc.2435.
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