Genome-Wide siRNA Screen for Anti-Cancer Drug Resistance in Adherent Cell Lines

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Cancer Discovery
May 2014



The expression of genes is frequently manipulated in cell lines to study their cellular functions. The use of exogenous small Interfering RNAs (siRNAs) is a very efficient technique to temporarily downregulate the expression of genes of interest [reviewed by Hannon and Rossi (2004)]. A genome-wide siRNA library allows the user to study both the effect of each individual gene on a particular cell phenotype in a high throughput manner and also assess its phenotypic effect relative to all other genes targeted. Several factors that potentially influence the outcome of a screen need to be considered when performing a large siRNA screen (Jiang et al., 2011). Here we present a detailed protocol for a genome-wide screen to identify genes involved in anti-cancer drug resistance using the human siGENOME library from Dharmacon. In this protocol, we focus on resistance to treatment with the Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitor (EGFR-TKI) erlotinib in the lung cancer cell line PC9, which is exquisitely sensitive to EGFR-TKIs (de Bruin et al., 2014). This protocol can be used for other cell lines and other drug treatments, as we expand in the Notes below.

Materials and Reagents

  1. Human lung adenocarcinoma cell line PC9 (RIKEN BioResource Center, catalog number: RCB4455 )
  2. RPMI 1640 Medium containing L-Glutamine (Life Technologies, Gibco®, catalog number: A10491-01 )
    Note: For cell culture, the medium is supplemented with 10% FBS and 1% Penicillin Streptomycin (see details FBS and Pen/Strep below).
  3. Penicillin streptomycin (Life Technologies, Gibco®, catalog number: 15070-063 )
  4. Fetal bovine serum (FBS) (PAA Laboratories GmbH, catalog number: A15-101 )
  5. Opti-MEM® I Reduced Serum Medium (Life Technologies, Gibco®, catalog number: 31985-047 )
  6. 384-well tissue culture plates, black with clear F-bottom (4titude, Anachem, catalog number: 4TI-0201 were used for our screen; currently plates from Greiner bio-one (catalog number: 781091 ) are used
  7. Adhesive foil plate seal (Brandle Plate Sealer, Alpha Biotech)
  8. 5x siRNA buffer (GE Healthcare, catalog number: B-002000-UB-100 )
  9. Human siGENOME SMARTpool siRNA library (GE Healthcare, catalog number: G-005005 )
    Note: We used an older version (from 2005) for our study (de Bruin et al., 2014) that is no longer available. Updated versions will contain other siRNA sequences as well as annotations, which will impact results. We therefore strongly recommend validating identified genes using other approaches.
  10. siRNA controls (see Note 1)
    Negative controls:Human siGENOME non-targeting siRNA 2 (GE Healthcare, catalog number: D-001210-02 ), RISC-Free control siRNA (catalog number: D-001220-01 ), ON-Targetplus non-targeting (catalog number: D-001810-10 )
    Positive controls: Human siGENOME SMARTpool UBB (GE Healthcare, catalog number: M-013382-01 ) and PLK1 (Dharmacon, catalog number: M-003290-01 )
  11. DharmaFECT 2 siRNA transfection reagent (GE Healthcare, catalog number: T-2002-01 ) (see Note 2)
  12. DAPI (Roche Diagnostics, catalog number: 10236276001 )
  13. siGENOME LaminA/C control siRNA (GE Healthcare, catalog number: D-001050-01-05 ) (see Note 3)
  14. Human LaminA/C antibody (catalog number: SC-7292 ) (see Note 3)
  15. Phosphate buffered saline (PBS) (pH 7.0, made in-house)
  16. Erlotinib (Enzo Life Sciences, catalog number: BML-DL279 )


  1. Acumen (TTP LABTECH, mode: eX3 )
  2. ArrayscanVTi HCS microscope (Cellomics)
  3. Biomek FX Liquid handling platform (Beckman Coulter)
  4. Countess Cell counter (Life Technologies)
  5. Liquid dispensor8-channel [for our screen the WellMate (Matrix) was used, currently the FluidX (Xrd-384) is used]
  6. Cytomat 24 °C CO2 incubator 37 °C and 5% CO2
    Note: The Cytomat is a cell culture incubator that has racks built in holding ~500 plates, and the racks rotate to minimize plate edge effects.
  7. Plate washer (BioTek Instruments, model: ELx405 Select CW , Figure 1)
  8. Plate sealer (Brandel Plate Sealer, Alpha Biotech)

    Figure 1. Picture of plate washer

Important considerations

Before performing a large-scale siRNA screen, there are a few crucial points to consider and to optimize in order to obtain the best results (Jiang et al., 2011). These include:

  1. Optimization of the siRNA transfection: Reagent, cell number, siRNA concentration.
  2. Optimization of the drug dose, treatment time length in combination with the siRNA transfection for drug resistant/sensitizing screens.

Details of these optimization steps are provided in the Notes section at the end of this protocol. We recommend performing, if possible, a pilot screen with a range of random siRNAs and selected control RNAs to determine the feasibility and screen read-out prior to the genome-wide screen.


  1. siRNA reverse transfection
    1. Culture PC-9 cells in RPMI medium, supplemented with 10% FBS + 1% Penicillin and Streptomycin.
    2. Resuspend the siRNA SMARTpools library in 1x siRNA buffer (dilute the 5x siRNA buffer using RNase/DNase-free water) to reach a final concentration of 375 nM.
      Note: Upon arrival, the library was resuspended using 1x siRNA buffer into 3.75 µM stock solutions and stored at -20 °C for long-term storage. At this step in the protocol, the stock library was further diluted into 375 nM.
    3. Aliquot the siRNA SMARTpools from library assay plates using the BiomekFX into 384-well assay plates at a final concentration of 11 nM/well (load 2.5 µl/well). Prepare all plates in triplicate for both the untreated and the drug-treated conditions. Use the first 4 columns of each plate for positive and negative siRNA controls (Figure 2 and Note 2), which can be aliquoted at the same time of the library or on the day of the transfection.
      Note: The plates can be prepared beforehand, sealed and stored at -20 °C, making this a suitable stopping point.
    4. On the day of the reverse transfection, thaw the plates at RT for 20 min, and spin briefly for 1 min at 1,000 rpm (180 x g) before removing the seal. Add the siRNA controls in the first 4 columns if not done so at step A3.
    5. Dilute DharmaFECT2 in Opti-MEM media (see Notes 2-4; we used 0.075 µl DharmaFECT2/3 µl) and add 3 µl per well using the 16-channel Xrd-384 dispenser at high speed. Tap the plates to ensure the solutions mix well, and incubate the plates at RT for 15 min.
    6. In the meantime, trypsinize and count the PC9 cells in quadruplicate using the Countess cell counter, and dilute the cell suspension appropriately. For PC9 cells, the final cell number was 500 cells per well in 80 µl. We therefore prepared 15 L RPMI (with FCS and Pen/Strep) containing 93.75E06 cells (see Note 5) for a total of 402 384-well plates, which are 6 replicates of the genome siRNA library (201 plates for the untreated condition and 201 plates for the drug-treated condition).
    7. Add 80 µl cell suspension per well using the 16-channel Xrd-384 dispenser at high speed.
      Note: For an optimal reverse transfection, cells should be added to the siRNA mix at least 15 min and no more than 45 min after step A4.
    8. Incubate cells in incubator at 37 °C for 48 h in a Cytomat incubator to ensure efficient protein knockdown.

      Figure 2. Example of a layout of a 384-well plate. The first four columns were used for control siRNAs, with each row containing the siRNA indicated on the left. The first two columns were left untreated in the drug-treated conditions to confirm the effect of the drug. The remaining twenty columns were used for the genome-wide library, each well containing a different siRNA.

  2. Drug treatment
    1. Remove the medium using the Biotek PlateWasher (Figure 1); ensure that viable cells remain attached to the bottom of the wells.
    2. Add 80 µl RMPI (supplemented with FBS and Pen/Strep) per well with or without drug (see Note 6) using Xrd-384 dispenser at medium speed.
      Note: For the treated condition, we left the first two columns containing control siRNAs without drug to determine the killing efficiency of the drugs in the experiment (Figure 2).
    3. Incubate cells for a further 96 h (see Note 6).

  3. Cell number quantification
    1. Remove media.
    2. Fix the cells by adding 90 µl of 80% ice-cold EtOH using Xrd-384 dispenser at low speed. Store plates for at least 1 h at -20°C.
      Note: Overnight storage at -20 °C is perfectly fine and provides a suitable stopping point in the protocol.
    3. Perform steps C15-18 in batches of 50 plates.
    4. Wash the plates 3x with 100 µl PBS using plate washer.
    5. Add 20 µl of a 1 µg/ml DAPI solution to each well and incubate the cells at RT for 1 h.
    6. Wash the plates with PBS using the plate washer.
    7. Seal the plates with adhesive foil seals.
      Note: Plates can be stored at 4 °C prior to quantification.
    8. Scan the plates using Acumen with the 405 nm laser to quantify the number of cells in each plate (see Note 7 and Figure 3).

      Figure 3. Example of Acumen output of a plate in untreated conditions. A. Once a plate has been scanned by Acumen using laser 405 nm, the number of stained cells is indicated in shades of green for each well. Note that the first four columns contain control siRNAs (see Figure 2). B. Each well can be visualized individually within the Acumen software package. Here, two examples are presented; the left well representing a well comparable to positive control siRNAs, which indicates that the siRNA did not impact, cell viability (>5,000 cells), whereas the right well shows the effect of an siRNA that impacted on cell viability (<500 cells).

  4. Data Analysis
    1. Normalize the data within each plate using a robust Z-score calculation (Birmingham et al., 2009). For each plate, determine the median value of all samples (exclude the first four rows containing controls iRNAs) and subtract this value from each individual well. Divide each well by the median absolute deviation (MAD) of the plate to obtain a robust Z-score for each well. For example, the median sample value within plate A was 1,000 cells with an MAD of 200. If well D10 contains 800 cells, the robust Z-score for well D10 in plate A would be (800-1,000)/200=-1, which indicates that this well contains 1 deviation fewer cells than the median.
      Normalize the data across the plates, separately for the untreated and drug-treated conditions. Determine for each position a smoothed Z-score using the median and MAD at each well position across all plates, in a similar manner as in the previous step. As mentioned above, calculate the smoothed Z-scores separately for the untreated and the drug-treated conditions.
    2. Plot the smoothed Z-scores from the drug-treated condition against those from the untreated condition for each replicate (two conditions in triplicate gives nine comparisons in total).
    3. Determine the ‘line of best fit’ using linear regression.
    4. Calculate the residual difference between each data point (drug treated versus untreated) as the perpendicular distance between each data point and the ‘line of best fit’. A residual difference <0 indicate siRNAs were the viability within the drug condition is lower than would be expected based on the untreated condition, whereas a residual difference >0 indicate siRNAs were the viability within the drug condition is higher than would be expected based on the untreated viability. Thus, siRNAs with a positive residual difference indicate genes that reduce drug sensitivity upon knockdown of the gene.
      Note: The results our genome-wide siRNA screen using this protocol are included in the original manuscript as Supplementary Tables (de Bruin et al., 2014).
    5. For further validation, we selected siRNAs with a smoothed Z-score ≥ -2 in the untreated condition (no cell killing in the absence of the drug) and with a median residual difference ≥ 2 (desensitizing) or ≤ -2 (sensitizing). As described (de Bruin et al, 2014), this selection of siRNAs was further validated by performing a deconvoluted siRNA screen using the Dharmacon Set of 4 Upgrade siGENOME siRNAs.


  1. Positive and negative controls need to be determined for each cell line. For PC9 cells, we used siGENOME siRNAs targeting UBB (ubiquitin B) and siRNAs targeting PLK1 (polo-like kinase 1) as positive killing controls; siGENOME non-targeting siRNA2 (SC2), RISC-Free controlsiRNA (RF) and ON-Targetplusnon-targeting siRNA (ON-NT) were used as negative controls. In addition, we left two columns of control siRNAs untreated in the drug-treated plates and used these columns as treatment control (Figure 2).
  2. Optimal transfection reagent should be determined for each cell line, and depending on forward or reverse transfection. We tested 23 different transfection reagents in a reverse transfection in a 96-well format, using 0.1 and 0.3 µl for each reagent. We used LaminA/C siRNA and quantified both cell number and LaminA/C intensity and the fraction of cells exhibiting LaminA/C staining below a set threshold after staining with an LaminA antibody to assess toxicity and knock-down efficiency for each reagent as described in Note 3.
  3. Lamin A/C staining protocol using 96-well plates: Transfect cells as decribed above in steps A1-7, continue with steps C12-14. Then add 30 µl LaminA/C antibody 1:1,000 dilution in 3% BSA with PBS and incubate plates at RT for 1-2 h. Wash plates 3x with PBS and incubate plates with 30 µl Alexa Fluor488 donkey anti-mouse (1:1,500 dilution in 3% BSA with PBS). Wash plates 3x with PBS and follow with DAPI staining as described in steps C14-17. Scan DAPI (cell number) and LaminA/C intensity (knock-down efficiency) using the Acumen eX3 and Arrayscan VTi microscope.
  4. Having identified the transfection reagent (Note 2), the optimal concentration needs to be determined for use in 384-well plates. For PC9 cells, we tested 0.025, 0.035, 0.045, 0.055, 0.065 and 0.075 µl DharmaFECT 2 per well in 384 well format.
  5. Optimal cell seeding density should be determined for each cell line. For PC9 cells, we tested 500, 750, 1,000 and 1,250 cells per well in a 384-well plate. We aimed at 80-90% confluence at the end of the experiment in untreated condition.
  6. Optimal concentration and incubation time should be determined for each drug and each cell line. For erlotinib, we used 30 nM, a concentration slightly higher than the experimentally determined IC50 value for PC9 cells to bias towards detection of siRNA resulting in resistance.
  7. Preferentially, individual cells should be distinguishable in order to allow scanning by Acumen. The cell line should grow as single-cell layer and not form condensed clumps. Each cell line should be tested prior to performing a screen to determine if cell number can be quantified successfully by the Acumen or Arrayscan. We used the 405 nm laser with 6 mW output, 500 V PMT and sensitivity at 2. These settings depend on the lifetime of the laser and should be optimized for each cell line.


This protocol was developed for a study aiming to identify novel mechanisms of erlotinib resistance (de Bruin et al., 2014). E. B was funded by a fellowship from the Dutch Cancer Society and the High Throughput Screening facility at the Cancer Research UK-London Research Institute is funded by Cancer Research UK.


  1. 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.
  2. de Bruin, E. C., Cowell, C., Warne, P. H., Jiang, M., Saunders, R. E., Melnick, M. A., Gettinger, S., Walther, Z., Wurtz, A., Heynen, G. J., Heideman, D. A., Gomez-Roman, J., Garcia-Castano, A., Gong, Y., Ladanyi, M., Varmus, H., Bernards, R., Smit, E. F., Politi, K. and Downward, J. (2014). Reduced NF1 expression confers resistance to EGFR inhibition in lung cancer. Cancer Discov 4(5): 606-619.
  3. Hannon, G. J. and Rossi, J. J. (2004). Unlocking the potential of the human genome with RNA interference. Nature 431(7006): 371-378.
  4. Jiang, M., Instrell, R., Saunders, B., Berven, H. and Howell, M. (2011). Tales from an academic RNAi screening facility; FAQs. Brief Funct Genomics 10(4): 227-237.


基因的表达经常在细胞系中操作以研究它们的细胞功能。外源小干扰RNA(siRNA)的使用是临时下调感兴趣的基因的表达的非常有效的技术[由Hannon和Rossi(2004)综述]。全基因组siRNA文库允许用户以高通量方式研究每个个体基因对特定细胞表型的作用,并且还评估其相对于所有其他靶向基因的表型效应。当进行大siRNA筛选时,需要考虑潜在地影响筛选结果的几个因素(Jiang等人,2011)。在这里,我们提出了一个详细的协议,使用人类siGENOME库从Dharmacon的基因组范围屏幕识别参与抗癌耐药性的基因。在该方案中,我们关注对表皮生长因子受体酪氨酸激酶抑制剂(EGFR-TKI)厄洛替尼在对EGFR-TKIs敏感的肺癌细胞系PC9中的治疗的抗性(de Bruin等。,2014)。该方案可用于其他细胞系和其他药物治疗,我们在下面的注释中扩展。


  1. 人肺腺癌细胞系PC9(RIKEN BioResource Center,目录号:RCB4455)
  2. 含有L-谷氨酰胺的RPMI 1640培养基(Life Technologies,Gibco ,目录号:A10491-01)
  3. 青霉素链霉素(Life Technologies,Gibco ,目录号:15070-063)
  4. 胎牛血清(FBS)(PAA Laboratories GmbH,目录号:A15-101)
  5. Opti-MEM I减少血清培养基(Life Technologies,Gibco ,目录号:31985-047)
  6. 384孔组织培养板,使用具有透明F底的黑色(4度,Anachem,目录号:4TI-0201)用于我们的筛选;目前使用来自Greiner bio-one(目录号:781091)的平板
  7. 粘合箔板密封(Brandle Plate Sealer,Alpha Biotech)
  8. 5x siRNA缓冲液(GE Healthcare,目录号:B-002000-UB-100)
  9. 人类siGENOME Smartpool siRNA文库(GE Healthcare,目录号:G-005005)
    注意:我们使用的旧版本(从2005年开始)用于我们的研究(de Bruin et al。,2014),不再可用。更新的版本将包含其他siRNA序列以及注释,这将影响结果。因此,我们强烈建议使用其他方法验证鉴定的基因。
  10. siRNA对照(见注1)
    阴性对照:人siGENOME非靶向siRNA 2(GE Healthcare,目录号:D-001210-02),无RISC的对照siRNA(目录号:D-001220-01),ON-靶加上非定位(目录号:D-001810-10)
    阳性对照:人siGENOME SMARTpool UBB(GE Healthcare,目录号:M-013382-01)和PLK1(Dharmacon,目录号:M-003290-01)
  11. DharmaFECT 2 siRNA转染试剂(GE Healthcare,目录号:T-2002-01)(参见注释2)
  12. DAPI(Roche Diagnostics,目录号:10236276001)
  13. siGENOME LaminA/C对照siRNA(GE Healthcare,目录号:D-001050-01-05)(参见注释3)
  14. 人类LaminA/C抗体(目录号:SC-7292)(参见注释3)
  15. 磷酸盐缓冲盐水(PBS)(pH 7.0,内部制造)
  16. 埃洛替尼(Enzo Life Sciences,目录号:BML-DL279)


  1. Acumen(TTP LABTECH,模式:eX3)
  2. ArrayscanVTi HCS显微镜(Cellomics)
  3. Biomek FX液体处理平台(Beckman Coulter)
  4. 伯爵夫人细胞计数器(Life Technologies)
  5. 液体分配器8通道[我们的屏幕使用WellMate(矩阵),目前使用FluidX(Xrd-384)]
  6. Cytomat 24℃CO 2培养箱37℃和5%CO 2
  7. 板洗涤器(BioTek Instruments,型号:ELx405 Select CW,图1)
  8. 板密封器(Brandel Plate Sealer,Alpha Biotech)



在进行大规模siRNA筛选之前,有几个关键点要考虑和优化以获得最佳结果(Jiang等人,2011)。 这些包括:

  1. siRNA转染的优化:试剂,细胞数,siRNA浓度
  2. 优化药物剂量,治疗时间长度与用于耐药/敏化筛选的siRNA转染组合

这些优化步骤的详细信息在本协议末尾的Notes部分中提供。 如果可能,我们建议进行一系列随机siRNA和选择的对照RNA的试验筛选,以确定在全基因组筛选前的可行性和屏幕读数。


  1. siRNA反转录
    1. 在补充有10%FBS + 1%青霉素和链霉素的RPMI培养基中培养PC-9细胞。
    2. 将siRNA SMARTpools文库重悬于1x siRNA缓冲液中(稀释 5x siRNA缓冲液使用RNase/DNase-free水)达到最终 浓度为375nM 注意:抵达后,图书馆 使用1x siRNA缓冲液重悬浮于3.75μM储备溶液和 储存于-20°C长期储存。 在协议的这一步, 将库库进一步稀释到375nM。
    3. 等分 siRNA SMARTpools从文库测定平板使用BiomekFX进入 384孔测定板中,终浓度为11nM /孔(负载2.5 μl/孔)。 准备所有板一式三份为未处理和 药物治疗的条件。 使用每个板的前4列 阳性和阴性siRNA对照(图2和注2),其可以   在图书馆的同一时间或在的当天等分 转染。
    4. 在逆转染的当天,将板在RT下解冻20   min,并在移除之前以1,000rpm(180×g/g)短暂旋转1分钟 封印。 在前4列中添加siRNA对照,如果没有这样做 在步骤A3
    5. 在Opti-MEM培养基中稀释DharmaFECT2(参见注释2-4; 我们使用0.075微升DharmaFECT2/3微升),并添加3微升每孔使用 16通道Xrd-384分配器。 点击板,以确保   溶液混匀,并在RT下温育平板15分钟
    6. 在   同时,胰蛋白酶消化并计数一式四份使用的PC9细胞   Countess细胞计数器,并稀释细胞悬浮液 适当地。 对于PC9细胞,最终细胞数为500个细胞/每个 井在80微升。 因此,我们制备了15L RPMI(含FCS和Pen/Strep) 含有93.75E06细胞(参见注释5),总共402 384孔 平板,其是基因组siRNA文库的6个重复(201个平板 和用于药物处理的201个平板 条件)。
    7. 每孔添加80微升细胞悬浮液使用 16通道Xrd-384分配器。
      注意:为了获得最佳反向   转染,应将细胞加入siRNA混合物至少15分钟 并且在步骤A4之后不超过45分钟。
    8. 孵育细胞在孵育器中37℃在Cytomat孵化器中48小时,以确保有效的蛋白质敲除。

      图2. 384孔板的布局示例。前四个 柱用于对照siRNA,每行含有siRNA   表示在左边。 前两列在处理中未经处理   药物治疗的条件以确认药物的作用。 的 剩余的20列用于全基因组文库 孔含有不同的siRNA。

  2. 药物治疗
    1. 使用Biotek PlateWasher清除介质(图1); 确保活细胞保持附着在孔底部
    2. 每孔加入80μlRMPI(补充有FBS和Pen/Strep) 或无药物(见注6)使用Xrd-384分配器以中速。
      注意:对于处理的条件,我们离开前两列 含有不含药物的对照siRNA以确定杀死 实验中药物的效率(图2)。
    3. 孵育细胞另外96小时(见注6)。

  3. 单元格数量
    1. 删除介质。
    2. 通过加入90μl的80%冰冷的EtOH固定细胞   使用Xrd-384分配器低速。 存储板至少1小时 -20℃。
    3. 以50张为一组执行步骤C15-18。
    4. 用板洗涤器用100μlPBS洗涤板3x。
    5. 向每个孔中加入20μl的1μg/ml DAPI溶液,并在室温下孵育细胞1小时
    6. 用洗板机用PBS洗涤板。
    7. 用粘性箔密封件密封板。
    8. 使用Acumen用405nm激光扫描板以定量每个板中的细胞数(参见注7和图3)。

      图3.在未处理的条件下板的Acumen输出的示例 A.一旦使用激光405nm通过Acumen扫描板,  的染色细胞以绿色阴影表示。注意 前四个柱含有对照siRNA(参见图2)。乙。 每个孔可以在Acumen软件中单独显示 包。这里,呈现两个示例;左井代表a 与阳性对照siRNA相当,这表明 siRNA不影响,细胞活力(> 5,000个细胞) 右孔显示影响细胞活力的siRNA的效应 (<500个细胞)。

  4. 数据分析
    1. 使用鲁棒的Z分数计算来标准化每个板内的数据 (Birmingham et al。,2009)。对于每个板,确定的中值  所有样品(排除包含对照iRNA的前四行)和  从每个井中减去该值。用井分隔每口井 中值绝对偏差(MAD)以获得鲁棒Z评分 。例如,板A中的中值样品值为 1,000个细胞,MAD为200.如果D10含有800个细胞, 板A中孔D10的鲁棒Z评分将为(800-1,000)/200 = -1, 这表明该孔含有比1的偏差更少的细胞  中位数 归一化板上的数据,分别为 未治疗和药物治疗的条件。确定每个位置a 使用在每个孔位置处的中值和MAD来平滑Z分数 所有板,以与前述步骤类似的方式。如上所述 ,分别计算未处理和的平滑Z分数 药物治疗的条件。
    2. 绘制平滑的Z分数   药物处理的条件对来自未处理条件的那些 每个重复(两个条件一式三份给出九个比较 总计)。
    3. 使用线性回归确定"最佳拟合线"。
    4. 计算每个数据点(药物)之间的残差 处理与未处理)作为每个之间的垂直距离 数据点和'最佳拟合线'。 残差<0 表明siRNA在药物条件下的存活率较低 比基于未处理的条件的预期,而a 剩余差异> 0表示siRNA是其中的活力 药物状况高于基于未治疗的预期 可行性。 因此,具有正残留差异的siRNA表明 在基因敲低时降低药物敏感性的基因 注意:   结果我们的全基因组siRNA筛选使用这个协议 包括在原始手稿中作为补充表(de Bruin et   al。,2014)。
    5. 为了进一步验证,我们选择了siRNA 在未处理的条件下平滑的Z评分≥2(在细胞中没有细胞杀伤   不存在药物)并且中值残差≥2 (脱敏)或≤-2(敏化)。 如所描述的(de Bruin等人,2014),通过进行以下步骤来进一步验证该siRNA选择: 解卷积siRNA屏幕使用Dharmacon Set 4升级siGENOME siRNA。


  1. 需要确定每个细胞系的阳性和阴性对照。对于PC9细胞,我们使用靶向UBB(泛素B)和靶向PLK1(polo样激酶1)的siRNA的siGENOME siRNA作为阳性杀伤对照;使用siGENOME非靶向siRNA2(SC2),无RISC的对照siRNA(RF)和ON-Target加非靶向siRNA(ON-NT)作为阴性对照。此外,我们留下两列在药物处理板中未处理的对照siRNA,并使用这些柱作为处理对照(图2)。
  2. 应该为每个细胞系确定最佳的转染试剂,并且取决于正向或反向转染。我们在96孔板的反向转染中测试了23种不同的转染试剂,每种试剂使用0.1和0.3μl。我们使用LaminA/C siRNA并且定量细胞数目和LaminA/C强度,并且在用LaminA抗体染色后显示低于设定阈值的LaminA/C染色的细胞分数,以评估每种试剂的毒性和击倒效率,如注3.
  3. Lamin A/C染色方案:使用96孔板:如上文在步骤A1-7中所述的转染细胞,继续步骤C12-14。然后加入30μlLaminA/C抗体在含3%BSA的PBS中1:1,000稀释,并在室温孵育平板1-2小时。用PBS洗涤板3x并用30μlAlexa Fluor488驴抗小鼠(在含有PBS的3%BSA中1:1,500稀释)孵育平板。用PBS洗涤板3x并按照步骤C14-17中所述的DAPI染色。使用Acumen eX3和Arrayscan VTi显微镜扫描DAPI(细胞数)和LaminA/C强度(击倒效率)。
  4. 鉴定转染试剂(注2)后,需要确定在384孔板中使用的最佳浓度。对于PC9细胞,我们在384孔格式中每孔测试0.025,0.035,0.045,0.055,0.065和0.075μlDharmaFECT 2。
  5. 应为每个细胞系确定最佳细胞接种密度。对于PC9细胞,我们在384孔板中每孔测试500,750,1,000和1,250个细胞。我们的目标是在未处理的条件下在实验结束时80-90%汇合
  6. 应为每种药物和每种细胞系确定最佳浓度和孵育时间。对于厄洛替尼,我们使用30nM,对PC9细胞的略高于实验测定的IC 50值的浓度偏向导致抗性的siRNA的检测。
  7. 优选地,单个细胞应当是可区分的,以允许通过Acumen进行扫描。细胞系应当作为单细胞层生长并且不形成冷凝的团块。每个细胞系应该在进行筛选之前进行测试,以确定细胞数量是否可以通过Acumen或Arrayscan成功量化。我们使用405 nm激光器,6 mW输出,500 V PMT和灵敏度为2.这些设置取决于激光器的寿命,应针对每个细胞系进行优化。


该方案开发用于鉴定厄洛替尼抗性的新机制的研究(de Bruin等人,2014)。 E. B由荷兰癌症协会的奖学金资助,英国癌症研究所的高通量筛选设施由英国癌症研究所资助。


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  2. DE,Melnick,MA,Gettinger,S.,Walther,Z.,Wurtz,A.,Heynen,GJ,Heideman,DA, Gomenz-Roman,J.,Garcia-Castano,A.,Gong,Y.,Ladanyi,M.,Varmus,H.,Bernards,R.,Smit,EF,Politi,K.and Downward, 。 减少NF1表达赋予对肺癌中EGFR抑制的抗性。癌症发现 4(5):606-619。
  3. Hannon,G.J。和Rossi,J.J。(2004)。 使用RNA干扰解锁人类基因组的潜力。 自然< em> 431(7006):371-378。
  4. Jiang,M.,Instrell,R.,Saunders,B.,Berven,H。和Howell,M。(2011)。 来自学术性RNAi筛选设施的故事; Brief Funct Genomics 10(4):227-237。
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引用:de Bruin, E. ., Jiang, M., Howell, M. and Downward, J. (2015). Genome-Wide siRNA Screen for Anti-Cancer Drug Resistance in Adherent Cell Lines. Bio-protocol 5(10): e1474. DOI: 10.21769/BioProtoc.1474.