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Protocol for Establishing a Multiplex Image-based Autophagy RNAi Screen in Cell Cultures
在细胞培养中建立基于多重图像的自噬RNAi筛选方法   

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eLIFE
14-Feb 2017

Abstract

Autophagy is a recycling pathway, in which intracellular cargoes including protein aggregates and bacteria are engulfed by autophagosomes and subsequently degraded after fusion with lysosomes. Dysregulation of this process is involved in several human diseases such as cancer or neurodegeneration. Hence, advancing our understanding of how autophagy is regulated provides an opportunity to explore druggable targets and subsequently develop treatment strategies for these diseases. To identify novel autophagy regulators, we developed an image-based phenotypic RNAi screening approach using autophagic marker proteins at endogenous levels (Jung et al., 2017). In contrast to previously performed autophagy screens, this approach does not use overexpressed, tagged autophagy marker proteins but rather detects autophagic structures at endogenous levels. Furthermore, we monitored early and late phases of autophagy in parallel while other screens employed only a single autophagosome marker mostly GFP-LC3B. Here, we describe this multiplex screening protocol in detail and discuss general considerations about how to establish image-based siRNA screens.

Keywords: siRNA screen (siRNA筛选), Immunostaining (免疫染色), Immunofluorescence (免疫荧光), Autophagy (自噬)

Background

Autophagy is an intracellular quality and quantity control pathway by which diverse cytosolic material such as pathogens, organelles or parts thereof, proteins and other macromolecules are engulfed by double membrane structures coined autophagosomes and delivered for bulk lysosomal degradation upon fusion of autophagosomes with lysosomes. Formation of autophagosomes and their maturation to autolysosomes is a highly regulated process. Among the AuTophaGy-related (ATG) genes initially identified in yeast is the ubiquitin-like protein Atg8, which exerts its function in a highly localized manner through its reversible conjugation to the phospholipid phosphatidylethanolamine (PE) located in the recipient autophagosome. Human cells contain six ATG8 family members that can be grouped into two subfamilies: i) microtubule-associated proteins 1A/1B light chain 3A (LC3A), LC3B and LC3C and ii) γ-aminobutyric acid receptor-associated protein (GABARAP), GABARAPL1 and GABARAPL2 (Slobodkin and Elazar, 2013). Given the fact that yeast only harbors one Atg8 isoform, it is unclear whether LC3 and GABARAP proteins are functionally redundant or have unique properties. Members of the GABARAP family have been suggested to function late in autophagy, potentially promoting sealing of IMs or fusion of autophagosomes with lysosomes while LC3-proteins are believed to coordinate the expansion of autophagosomes, thus acting earlier than GABARAP proteins in the pathway (Weidberg et al., 2010). Importantly, overexpression or knockdown of one family member was shown to affect the expression levels of the other LC3 and GABARAP proteins (Weidberg et al., 2010). Therefore, our recent study (Jung et al., 2017) aimed to develop a screening platform for monitoring human ATG8 proteins (i.e., LC3B and GABARAP) at endogenous levels. This distinguishes our approach from other performed genome wide autophagy siRNA screens, in which overexpression of a GFP-tagged version of LC3B was employed (Orvedahl et al., 2011; McKnight et al., 2012). Besides LC3B and GABARAP, we additionally included autophagy marker proteins for the initiation and maturation of autophagosomes such as WIPI2 (WD repeat domain phosphoinositide-interacting protein 2), ATG12 and STX17 (syntaxin 17), respectively. WIPI2 is recruited to phagophores by binding to the phospholipid phosphatidylinositol 3-phosphate (PI3P). Upon PI3P-binding, WIPI2 recruits the mammalian ATG8 lipidation complex comprised of the subunits ATG16L1, ATG5 and ATG12. Subsequently, LC3B is conjugated to PE and in turn can recruit several human ATG8-binding proteins including cargo receptors such as p62 (also known as SQSTM1), which lead to autophagy cargo engulfment. Fusion of autophagosomes with lysosomes requires the SNARE protein STX17. STX17 localizes to closed autophagosomes and associates with SNAP29 and VAMP8 on lysosomes (Ktistakis and Tooze, 2016). Finally, intraluminal components are lysosomally degraded. Application of several autophagic marker proteins such as ATG12, WIPI2 and STX17 in addition to LC3B and GABARAP potentially allows the elucidation of genes specific for early and late phases of the autophagic process. The identification of various known as well as enigmatic autophagy proteins verified our screening approach (Jung et al., 2017).

Materials and Reagents

  1. Pipette tips
  2. 10 cm tissue culture dishes (Corning, Falcon®, catalog number: 353003 )
  3. 384-well imaging plates, CellCarrier-384 Black (PerkinElmer, catalog number: 6007550 )
  4. Cell culture microplate, 96-well, v-bottom (Greiner Bio One International, catalog number: 651180 )
  5. Disposable reagent reservoirs, 25 ml, sterile (VWR, catalog number: 613-1174 )
  6. Disposable reagent reservoirs, 100 ml, sterile (VWR, catalog number: 613-1172 )
  7. Aluminum sealing ape 96 100/CS (Corning, catalog number: 6570 )
  8. U2OS cells (ATCC, RRID:CVCL_0042)
  9. Dulbecco’s modified Eagle’s medium (DMEM) (Thermo Fisher Scientific, GibcoTM, catalog number: 41966029 )
  10. Fetal bovine serum (FBS) qualified, E.U.-approved, South America origin (Thermo Fisher Scientific)
  11. Penicillin-streptomycin (P/S) (10,000 U/ml) (Thermo Fisher Scientific, GibcoTM, catalog number: 15140122 )
  12. L-Glutamine 200 mM (Thermo Fisher Scientific, catalog number: 25030024 )
  13. 0.25% trypsin/EDTA (Thermo Fisher Scientific, GibcoTM, catalog number: 25200056 )
  14. Dimethylsulfoxide (DMSO) (Sigma-Aldrich, catalog number: D8418 )
  15. siRNA library (GE Healthcare, Dharmacon, Cherry-picked library of siRNA pools for the proteins of interest)
  16. Control siRNAs
    Non-targeting control siRNA (GE Healthcare Dharmacon, catalog number: D-001810-10-20 )
    siRNAs for autophagy modulation:
    ATG12 (GE Healthcare Dharmacon, catalog number: J-010212-07 )
    PIK3C3 (GE Healthcare Dharmacon catalog number: J-005250-09 )
    RAB7A (GE Healthcare Dharmacon, catalog number: J-010388-07 )
    Raptor siRNA sequence: GAUGAGGCUGAUCUUACAGUU (MWG)
  17. Water DNase/RNase free, sterile (Thermo Fisher Scientific, GibcoTM, catalog number: 10977035 )
  18. Sodium chloride (NaCl) (Sigma-Aldrich, catalog number: 31434 )
  19. Potassium chloride (KCl) (Sigma-Aldrich, catalog number: P9541-500G )
  20. Sodium phosphate dibasic (Na2HPO4) (Sigma-Aldrich, catalog number: 71640-250G )
  21. Potassium phosphate monobasic (KH2PO4) (Sigma-Aldrich, catalog number: P5379-100G )
  22. Phosphate buffered saline (PBS) (Thermo Fisher Scientific, GibcoTM, catalog number: 14190094 )
  23. Poly-L-lysine solution (Sigma-Aldrich, catalog number: P4707-50ML )
  24. Optimal modified Eagle’s medium (Opti-MEM) (Thermo Fisher Scientific, GibcoTM, catalog number: 31985062 )
  25. Lipofectamine RNAiMax (Thermo Fisher Scientific, InvitrogenTM, catalog number: 13778150 )
  26. Paraformaldehyde (PFA) 4% in PBS (Santa Cruz Biotechnology, catalog number: sc-281692 )
  27. Triton X-100 (VWR, catalog number: 28817.295 )
  28. Bovine serum albumin (BSA) (Sigma-Aldrich, catalog number: A7906-100G )
  29. Anti-ATG12 (Cell Signaling Technology, catalog number: 2010 )
  30. Anti-GABARAP (Abcam, catalog number: ab109364 )
  31. Anti-LC3B (MBL International, catalog number: PM036 )
  32. Anti-STX17 (Sigma-Aldrich, catalog number: HPA001204 )
  33. Anti-WIPI2 (Abcam, catalog number: ab105459 )
  34. Alexa Fluor 488 goat anti-rabbit IgG (Thermo Fisher Scientific, InvitrogenTM, catalog number: A-11008 )
  35. Alexa Fluor 488 goat anti-mouse IgG (Thermo Fisher Scientific, InvitrogenTM, catalog number: A-11001 )
  36. CellMask Deep red stain (Thermo Fisher Scientific, InvitrogenTM, catalog number: H32721 )
  37. DRAQ5 (Cell Signaling Technology, catalog number: 4084S )
  38. Phosphate-buffered saline (PBS, pH 7.4) (see Recipes) 

Note: Plan ahead and order the total amount of reagents needed for the whole screen to avoid running out of any reagent while actually performing the screen.

Equipment

  1. Pipette
  2. Cell culture Incubator (37 °C, 5% CO2)
  3. Centrifuge 5810R (Eppendorf, model: 5810 R , catalog number: 5811000010), rotor A-4-62 with well plate centrifugation inlays (Eppendorf, catalog number: 5810711002)
  4. Cell culture sterile bench
  5. Automated dispenser (MicroFill 96-/384-Well Microplate Dispenser) (BioTek Instruments, model: AF1000A )
  6. 12-channel multichannel pipette (30-300 µl) (NeoLab, catalog number: E-1945)
    Manufacturer: Eppendorf, model: Research® plus .
  7. 12-channel multichannel pipette (10-100 µl) (NeoLab, catalog number: E-1943)
    Manufacturer: Eppendorf, model: R esearch® plus .
  8. Selma pipetting robot 96/25 µl (Analytik Jena, CyBio®, catalog number: OL7001-26-211 ) with 384-well plate adaptor (Analytik Jena, CyBio®, catalog number: OL7001-24-976 )
  9. TipTray 96; 25 µl, PCR certified, sterile (Analytik Jena, CyBio AG, catalog number: OL3800-25-733-P )
  10. Neubauer counting chamber (Marienfeld-Superior, catalog number: 0640110 )
  11. Opera LX High Content Screening System with a 60x water-immersion objective and a robotic plate handler II 230 consisting of:
    1. Opera LX 488/561/640 Microscope (PerkinElmer)
    2. Water Objective 63x (PerkinElmer)
    3. Plate handler II 230 (PerkinElmer)
    Note: The Opera LX is discontinued.

Software

  1. Excel (Microsoft)
  2. Prism 4 (GraphPad)
  3. Barcode generator (http://barcode.tec-it.com/de)
  4. Acapella High Content Imaging Analysis Software (PerkinElmer)

Procedure

Note: All steps before cell fixation require processing under a sterile bench.

  1. Maintaining U2OS cells
    1. Culture U2OS cells in DMEM, supplemented with 10% FBS, 2 mM glutamine as well as 1% P/S and incubated in a humidified cell culture incubator at 37 °C and 5% CO2.
    2. Frequently (~two times a week) passage cells after attaining approximately 80% confluency using 0.25% trypsin/EDTA.
    3. For long-term storage freeze cells at -150 °C in FBS containing 10% DMSO.
      Note: Freeze enough cell aliquots at the same time to perform the entire screen from one batch.
    4. Prepare enough 10 cm tissue culture dishes with U2OS cells to have sufficient cells of 70-80% confluency for the planned siRNA screen at the assay day. For two 384-well plates, one 10 cm dish should be sufficient.

  2. Reverse siRNA transfection of U2OS cells (Figure 1A)


    Figure 1. Scheme of the siRNA screening procedure. A. Lyophilized siRNAs in 96-well plates are solubilized and then diluted with water to receive a Stock2 plate containing siRNAs with a concentration of 1 µM, which is combined with a Lipofectamine RNAiMax-Opti-MEM-Mix (Lipo plate) and transferred to a 384-well imaging plate. U2OS cells are added prior to incubation of the 384-well plates. Finally, cells are fixed, immunostained and imaged followed by image and data analysis. B and C. It is recommended to avoid using the outer rim of the 96 (B) or 384 (C) well plates indicated by black wells to prevent edge effects.

    1. Resuspend lyophilized siRNA pools (Stock1 plate)
      1. Bring ordered 96-well plates containing the lyophilized siRNAs (0.1 nmol) from -80 °C storage to sterile bench after short centrifugation.
      2. Equip pipetting robot (Selma) with new tips.
      3. Dispense 15 µl RNase free water in every well of a 96-well v-bottom plate using a multi-channel pipette (H2O plate).
      4. With the automated pipetting robot resuspend the lyophilized siRNA by transferring 10 µl RNase free water from the H2O plate into the siRNA containing 96-well plates from Dharmacon to obtain a 10 µM stock siRNA solution (Stock1 plate). Mix well by pipetting up and down using the automated pipetting robot in the ‘mixing cycles’ mode.
        Notes:
        1. Be careful to arrange the control siRNAs properly amongst the screening siRNAs (e.g., randomly across the plate but at least one control per lane) while ordering the 96-well plates with lyophilized siRNAs. Thereby, an extra step is avoided where the siRNAs have to be rearranged in a suitable sequence/order for the screen.
        2. Do not use the outer rim of 96-well plates (A1-A12, H1-H12, B1, C1, …, B12, C12, …) to avoid edge effects (Figure 1B, black edges).
        3. Especially whilst using the automated pipetting robot constantly check the orientation of your plate. Always position the well A1 in one specific corner and remember the orientation. Most 96-well plates contain one notched corner for easier orientation.
      5. Discard H2O plate or also use for dilution of Stock2 plate when directly proceeding with protocol.
      6. Cover Stock1 plate with an aluminum seal and store at -80 °C or proceed with protocol.
    2. Count U2OS cells with Neubauer counting chamber
      1. Wash every 10 cm dish containing U2OS cells with 2 ml sterile PBS (see Recipes). Then, add 1.5 ml trypsin per dish and incubate for approximately 5 min at room temperature until the cells are detached.
      2. Block trypsin activity by addition of 8 ml DMEM supplemented with 10% FBS, 2 mM glutamine but without P/S (DMEM(-)P/S).
      3. Pool all cells for the screening assay in one flask (e.g., common sterile 15 ml or 50 ml Falcons).
      4. Transfer 10 µl of the pooled cell solution into a Neubauer counting chamber and count cells under a light microscope.
      5. Dilute U2OS cells with DMEM(-)P/S to obtain a cell density of 7.14 x 104 cells/ml (1,500 cells in 21 µl).
        Note: Perform the cell counting before the 384-well imaging plate preparation to reassure the necessary number of cells is available.
    3. Dilute siRNAs from Stock1 plate to obtain a 1 µM working solution in a Stock2 plate.
      1. The amounts described here are sufficient for one full 384-well imaging plate loaded in quadruplicates. If more 384-well imaging plates are necessary for the screen, adjust the amounts accordingly.
      2. Dispense 15 µl H2O in every well of a 96-well v-bottom plate using a multi-channel pipette (H2O plate).
      3. With the automated pipetting robot using the ‘sample dilution’ mode pipette 9 µl RNase free water from the H2O plate and then add 1 µl from the Stock1 plate into the same tips and release both together in a new 96-well v-bottom plate to obtain a siRNA working solution of 1 µM (Stock2 plate). Mix well by pipetting up and down using the ‘mixing cycles’.
    4. Prepare reverse siRNA transfected U2OS cells in 384-well imaging plates with a final siRNA concentration of 30 nM.
      1. Transfer 30 µl poly-L-lysine into every well of a 384-well imaging plate using a multi-channel pipette and incubate for at least 1 h at room temperature. Remove the poly-L-lysine from the 384-well imaging plate using a multi-channel pipette and discard. Let the 384-well imaging plate dry for a couple of minutes.
      2. Mix 3,382 µl Opti-MEM with 38 µl Lipofectamine RNAiMax and place in reservoir (Lipo-Opti-Mix). For each 384-well this corresponds to 8.9 µl Opti-MEM and 0.1 µl Lipofectamine RNAiMax. The excess amount is prepared to assure enough liquid for the automated pipetting robot.
      3. Transfer 50 µl Lipo-Opti-Mix in every well of a 96-well v-bottom plate using a multi-channel pipette (Lipo plate). Again, do not use the outer rim (Figures 1B and 1C).
      4. Equip automated pipetting robot with new tips.
      5. With the automated pipetting robot using the ‘sample dilution’ mode absorb 9 µl Lipo-Opti-Mix from the Lipo plate and subsequently 0.9 µl siRNA from the Stock2 plate into the same tips and release into the first replicate well of the 384-well imaging plate (Figure 1C, green wells).
        Note: Again, check the orientation of your plate.
      6. Repeat 3 times to receive quadruplicates from one siRNA pool on the 384-well imaging plate (Figure 1C, grey wells).
      7. Incubate siRNA-Lipo-Opti-Mix in 384-well imaging plate for 20 min at room temperature.
      8. Dispense 21 µl DMEM(-)P/S including 1,500 U2OS cells per well (total volume 11.76 ml for one 384 plate) from a reservoir into each well of the 384-well imaging plate using a multi-channel pipette. Ensure that the liquid dropped to the well bottom or gently centrifuge plate if necessary. Gentle mixing by slowly pipetting up and down with a multi-channel pipette is possible but usually not necessary. While mixing, use new tips for every siRNA. 96- and 384-well plates can be centrifuged at 161 x g with the appropriate plate inlays for centrifuges.
        Note: Given that cells quickly sink onto the reservoir bottom (only approximately 1 min equal distribution), mix cells in the reservoir regularly.
      9. Dispense 30 µl DMEM(-)P/S into each well of the 384-well edge (Figure 1C, black wells) using a multi-channel pipette to avoid edge effects.
    5. Incubate 384-well imaging plate in cell culture incubator for 72 h.

  3. Fixation and Immunostaining of cells
    1. Discard liquid from 384-well plates in proper cell culture waste.
    2. U2OS cells in the 384-well plates are fixed with 50 µl 4% PFA per well for 15 min at room temperature pipetted with a multi-channel pipette. Discard liquid in PFA waste in sealed glass bottles and check department instructions for proper waste disposal as suggested by the EH&S Chemical Waste Program.
    3. Wash cells for three times with 100 µl self-made PBS per well using a multi-channel pipette. Remove the PBS by turning the plate upside-down on top of a sink.
    4. Store plate containing 100 µl PBS per well at 4 °C or directly continue immunostaining protocol.
    5. Discard liquid.
    6. Using a multi-channel pipette, permeabilize cells with 50 µl 0.5% Triton X-100 in PBS per well and incubate for 10 min at room temperature. Discard liquid.
    7. Block cells with 100 µl 1% BSA in PBS per well transferred with a multi-channel pipette and incubate for 1 h at room temperature. Discard liquid.
    8. With an automated dispenser, wash cells once with 100 µl self-made PBS per well. Discard liquid.
    9. Prepare primary antibody solution (either anti-ATG12 1:50; anti-GABARAP 1:200; anti-LC3B 1:800; anti-STX17 1:250; or anti-WIPI2 1:500 in 0.1% BSA in PBS) and distribute 20 µl per well with a multi-channel pipette. Incubate cells for 1 h at room temperature and then discard liquid.
      Note: Every 384-well plate is only stained with one primary antibody.
    10. With an automated dispenser, wash cells for three times with PBS as described above.
    11. Prepare secondary antibody solution (anti-rabbit or anti-mouse Alexa Flour 488, 1:1,000 in 0.1% BSA in PBS) and distribute 20 µl per well with a multi-channel pipette. Incubate cells for 1 h at room temperature and then discard liquid.
    12. Prepare cytoplasmic staining solution (HSC CellMask Deep red stain 1:25,000,000 in 0.1% BSA in PBS) and distribute 20 µl per well with a multi-channel pipette. Incubate cells for 1 h at room temperature and then discard liquid.
    13. Prepare nuclear staining solution (DRAQ5, 1:5,000 in 0.1% BSA in PBS) and distribute 20 µl per well with a multi-channel pipette. Incubate cells for 10 min at room temperature and then discard liquid.
    14. With an automated dispenser, wash cells for three times with PBS as described above.
    15. After the third wash, distribute 100 µl sterile PBS per well with a multi-channel pipette and seal the 384-well imaging plate with an aluminum seal to avoid exposure to light. Store plate at 4 °C until image acquisition.
      Note: This last step is performed with a multi-channel pipette and sterile PBS to prolong storability of the imaging plates.

Data analysis

  1. Image acquisition
    1. Choose the 60x water-immersion objective on a PerkinElmer’s Opera High Content Screening System microscope to receive a resolution suitable for intracellular spot detection.
      Note: Phagophores and autophagosomes are detected as intracellular spots with the microscope.
    2. Adjust the necessary laser intensity and plane height according to the used antibody.
      Note: Acquire images sequentially, first in the 488-channel and then in the 633-channel.
    3. Set the plate layout and select the number and distribution of fields per well.
      Note: For U2OS cells 24 fields per well for 4 wells will approximately add up to more than 1,000 cells in all the images.
    4. Save the settings.
    5. Set parameters for the Opera robotic plate handler (e.g., the location of the plates in the plate holder) and save settings to sequentially measure more than one plate.
    6. Label your plates with individual bar codes for each plate using http://barcode.tec-it.com/de. Print the bar codes and glue them onto the plates.
    7. Place all 384-well imaging plates into the plate holders.
      Note: Be aware of the proper orientation of the plate.
    8. Start bulk measurement with the saved settings to automatically image one plate after another with help of the robotic arm.
    9. Remove plates from stacker and keep at 4 °C or discard.
    10. Shut down the Opera microscope.

  2. Image analysis
    1. Open the Acapella High Content Imaging Analysis Software and load your image analysis script for spot detection.
    2. Set the general parameters, e.g., 488 equals channel one, where intracellular spots are detected, 633 equals channel two, where nuclei and cytoplasm are detected, according to the actual measurement.
    3. Set script parameters including contrast, area and the detection algorithm to properly segment nuclei, cytoplasm and spots in every cell. See Figure 2 for example images.


      Figure 2. Image segmentation. Example images for proper image segmentation of the nuclei (left), the cytoplasm (middle) and the spots (right). Spots represent phagophores stained with anti-WIPI2 antibody. Scale bars = 40 µm.

    4. Save all settings and load these parameters into the script next time.
    5. Run the analysis for a couple of images (well mode), which are obtained from assay specific control siRNAs and check the output results. Perform minor adjustments to the parameters if necessary (e.g., contrast can slightly vary for plates immunostained on different days).
      Note: Especially in the script preparation phase always compare the output results with a manual count of your acquired images. Manual counting means that a person actually counts the number of spots by hand. If the image quantification output results don’t match your own manual count, adjust the parameters until you are satisfied with the output result.
    6. Run the script in batch mode for the all the images in the whole 384-well imaging plate and save the output results as Excel file.
    7. Representative example images for all the different antibody stainings are shown in Jung et al., 2017.

  3. Candidate determination
    1. Average raw data of quadruplicates for every siRNA in Excel (in this assay the number of spots) and calculate the standard deviation.
    2. Normalize the output results (number of spots) per siRNA to non-targeting control siRNA for every 384-well plate. See Table 1 for an imaginary data example.

      Table1. Imaginary data example for the staining with LC3B based on Jung et al. (2017), to explain data normalization and candidate selection. Number of spots were quantified using the Acapella Software. For normalization, the number of spots of every siRNA was divided by the number of spots of the non-targeting control siRNA. This yielded a fold change of 3.2 or 0.3 for the positive or negative control siRNAs, respectively. Furthermore, the target siRNAs 2 and 3 would have been selected as candidates for LC3B, according to the standard deviation criterion. As example the average standard deviation for LC3B was approximately 23%, a selected increasing or decreasing candidate siRNA comprises a fold-change of 1.46 or higher (1.0 + 2 x 23%) or 0.54 or lower (1.0 - 2 x 23%), respectively.


    3. Compare the fold-change of all tested siRNAs from all plates with each other.
    4. Classify candidate siRNAs with your selected method e.g., the standard deviation criteria. Here, siRNAs, whose fold-change differ for two or three standard deviations from the normalized sicon (WIPI2 and ATG12 = 3; LC3B, GABARAP and STX17 = 2) are selected as candidates. See Table 1 for an imaginary data example.
    5. Excel can also be used to order and rank the candidates according to the fold-change difference to elucidate the top candidates.
    6. Prism 4 (GraphPad) was applied to generate diagrams and for statistical analysis (ANOVA).
    7. Representative example graphs are shown in Jung et al., 2017.

  4. Perform a deconvolution screen with four individual siRNAs per gene for your top candidates using the same procedure as for the siRNA pool screen described above.
    1. Classify validated candidates, which differ in the standard deviation criterion as above for three out of four siRNAs per gene.
    2. Exclude toxic siRNAs, which showed obvious changes in number of cells as well as in the intensity and area of the nucleus or of the cytoplasm. Remove genes with more than one cytotoxic siRNA for further analysis.

  5. General considerations for screening approaches
    1. Think about the proper cell line for the screening approach. The human osteosarcoma cell line used for this image-based autophagy screen is adherent and provides a big cytoplasm, which makes this cell line appropriate for immunostaining and imaging. Furthermore, autophagy can be induced as well as inhibited in U2OS cells. In addition, siRNA transfection is very efficient in this cell line. According to these properties, U2OS cells are well suited for an image-based autophagy siRNA screen. We assume that e.g., HeLa, A549 or LN229 cells would also be suitable for this screening approach.
    2. Using your method of choice including immunofluorescence, immunoblot or RT-qPCR, check that your assay control siRNAs actually provide an efficient knockdown of the intended target genes and induce the expected phenotype in your chosen cell line.
    3. Elucidate the number of cells per well necessary for a good but not crowded well coverage with your chosen cell line (e.g., ~80% well coverage). As explanation, a lot of empty space in between cells might cause long image acquisition times and image analysis artefacts. Usage of too many cells might cause them to grow on top of each other, which again might introduce image analysis artefacts. To determine the proper cell number, transfect cells with control siRNAs and use several different cell densities (e.g., 500-10,000 cells per well). Perform staining, imaging and analysis to determine the proper number of cells for seeding.
    4. Elucidate the suitable transfection reagent and siRNA (different companies) for your application. Therefore, transfect U2OS cells with different transfection reagents and assay control siRNAs. Perform staining, imaging and analysis to determine the best transfection reagent, which does not interfere with the selected screening pathway (autophagy in this case). Also, different amounts of the selected transfection reagent can be used for optimal knockdown efficiencies.
    5. In this image-based autophagy screen immunofluorescence staining of proteins at endogenous levels with antibodies is applied. To reduce costs the highest possible antibody dilution with a detectable immunofluorescence signal should be determined. Therefore, seed U2OS cells in your imaging plate and stain using a dilution series of your antibody. Perform imaging and analysis to determine the lowest amount of antibody necessary. The dilution series can also be performed with secondary antibodies, the nuclear and the cytoplasm stain.
    6. While determining the proper ‘wet lab’ screening condition, start the script development. Elucidate the correct parameters to detect your phenotype, in this case number of spots. Always compare the number of spots calculated by the script with the actual pictures for a few randomly selected images to make sure that the script is counting properly.
    7. Apply normalization techniques thoroughly (for example, normalization per plate to non-targeting control siRNAs) and carefully select the criteria to define candidates. The 2-3 times fold-change of the aberration of standard deviations is often applied. Other potential methods are Z- and B-score normalization, especially for genome-wide siRNA screens.
    8. Imply cytotoxicity measures into the script, since toxic siRNAs can result in false positive hits. For example, compare number of cells and other general criteria such as area and intensity of the nucleus and the cytoplasm and remove siRNAs with an obvious aberration.
    9. Apply statistics to control your screening conditions and off-target effects such as the correlation of output results between repetitions or between individual and pool siRNAs, respectively.
    10. Think about and establish downstream assays to mechanistically validate your candidates. Typical methods to study autophagy modulation include GFP/RFP-based autophagy flux assays and immunoblot analysis with autophagy markers such as LC3B or p62. Further on, localization of the candidate protein as well as co-localization of the candidate with early and late autophagic markers can be determined by confocal microscopy.

Recipes

  1. Phosphate-buffered saline (PBS, pH 7.4)
    137 mM NaCl
    2.7 mM KCl
    9 mM Na2HPO4
    3 mM KH2PO4

Acknowledgments

This protocol was adapted and modified from Jung et al., 2017, eLife. This work was supported by the Deutsche Forschungsgemeinschaft (DFG) within the framework of the Munich Cluster for Systems Neurology (EXC1010 SyNergy), the Collaborative Research Center (CRC1177) and the European Research Council (ERC, 282333-XABA).

References

  1. Jung, J., Nayak, A., Schaeffer, V., Starzetz, T., Kirsch, A. K., Muller, S., Dikic, I., Mittelbronn, M. and Behrends, C. (2017). Multiplex image-based autophagy RNAi screening identifies SMCR8 as ULK1 kinase activity and gene expression regulator. Elife 6.
  2. Ktistakis, N. T. and Tooze, S. A. (2016). Digesting the expanding mechanisms of autophagy. Trends Cell Biol 26(8): 624-635.
  3. McKnight, N. C., Jefferies, H. B., Alemu, E. A., Saunders, R. E., Howell, M., Johansen, T. and Tooze, S. A. (2012). Genome-wide siRNA screen reveals amino acid starvation-induced autophagy requires SCOC and WAC. EMBO J 31(8): 1931-1946.
  4. Orvedahl, A., Sumpter, R., Jr., Xiao, G., Ng, A., Zou, Z., Tang, Y., Narimatsu, M., Gilpin, C., Sun, Q., Roth, M., Forst, C. V., Wrana, J. L., Zhang, Y. E., Luby-Phelps, K., Xavier, R. J., Xie, Y. and Levine, B. (2011). Image-based genome-wide siRNA screen identifies selective autophagy factors. Nature 480(7375): 113-117.
  5. Weidberg, H., Shvets, E., Shpilka, T., Shimron, F., Shinder, V. and Elazar, Z. (2010). LC3 and GATE-16/GABARAP subfamilies are both essential yet act differently in autophagosome biogenesis. EMBO J 29(11): 1792-1802.

简介

自噬是一种循环途径,其中细胞内货物包括蛋白质聚集体和细菌被自噬吞噬,随后与溶酶体融合后降解。这种过程的失调涉及几种人类疾病,如癌症或神经退行性疾病。因此,提高我们对自噬如何监管的理解提供了探索可药用靶标的机会,并随后制定了这些疾病的治疗策略。为了鉴定新的自噬调节因子,我们开发了一种基于图像的表型RNAi筛选方法,在内源水平上使用自噬标记蛋白(Jung et al。,2017)。与以前进行的自噬屏幕相比,该方法不使用过表达标记的自噬标记蛋白,而是在内源水平检测自噬结构。此外,我们同时监测自噬的早期和晚期阶段,而其他筛选仅使用单个自噬体标志物大多为GFP-LC3B。在这里,我们详细描述了这种多重筛选方案,并讨论了如何建立基于图像的siRNA筛选的一般考虑。
【背景】自噬是一种细胞内质量和数量控制途径,通过这种途径,多种细胞溶质材料如病原体,细胞器或其部分,蛋白质和其他大分子被双重膜结构所吞噬,造成自噬体,并在自噬体与溶酶体融合后进行大量溶酶体降解。自体吞噬体的形成及其对自体溶酶体的成熟是一个高度规范的过程。在酵母中最初鉴定的AuTophaGy相关(ATG)基因是泛蛋白样蛋白Atg8,其通过与位于受体自噬体中的磷脂磷脂酰乙醇胺(PE)的可逆缀合以高度局部化的方式发挥其功能。人类细胞含有六个ATG8家族成员,可以分为两个亚科:i)微管相关蛋白1A / 1B轻链3A(LC3A),LC3B和LC3C和ii)γ-氨基丁酸受体相关蛋白(GABARAP),GABARAPL1和GABARAPL2(Slobodkin和Elazar,2013)。鉴于酵母仅含有一种Atg8同种型,不清楚LC3和GABARAP蛋白是功能上多余的还是具有独特性质。已经建议GABARAP家族的成员在自噬后期起作用,潜在地促进IMs的密封或自体吞噬体与溶酶体的融合,而LC3-蛋白质被认为协调自噬体的扩增,因此比途径中的GABARAP蛋白质起作用(Weidberg et al。等等,2010)。重要的是,一个家族成员的过表达或敲低显示影响其他LC3和GABARAP蛋白的表达水平(Weidberg等,2010)。因此,我们最近的研究(Jung et al。,2017)旨在开发一种用于在内源水平监测人类ATG8蛋白(即LC3B和GABARAP)的筛选平台。这将我们的方法与其他进行的基因组广泛的自噬siRNA筛选区别开来,其中使用GFP标记的LC3B版本的过度表达(Orvedahl等,2011; McKnight等,2012)。除了LC3B和GABARAP之外,我们还分别添加了自噬标记蛋白,用于自噬体的起始和成熟,如WIPI2(WD重复域磷酸肌醇相互作用蛋白2),ATG12和STX17(syntaxin 17)。 WIPI2通过与磷脂磷脂酰肌醇3-磷酸(PI3P)结合而被招募到向标。在PI3P结合后,WIPI2招募了由亚单位ATG16L1,ATG5和ATG12组成的哺乳动物ATG8脂化复合物。随后,LC3B与PE结合,反过来又可引入几种人类ATG8结合蛋白,包括诸如p62(也称为SQSTM1)的货物受体,导致自噬吞噬。自噬体与溶酶体的融合需要SNARE蛋白STX17。 STX17定位于封闭的自噬体,并与溶酶体上的SNAP29和VAMP8结合(Ktistakis和Tooze,2016)。最后,腔内成分溶酶体降解。除了LC3B和GABARAP之外,多种自噬标记蛋白如ATG12,WIPI2和STX17的应用可能有助于阐明自噬过程早期和晚期阶段的特异性基因。各种已知和神秘的自噬蛋白的鉴定验证了我们的筛选方法(Jung et al。,2017)。

关键字:siRNA筛选, 免疫染色, 免疫荧光, 自噬

材料和试剂

  1. 移液器提示
  2. 10厘米组织培养皿(康宁,Falcon ®,目录号:353003)
  3. 384孔成像板,CellCarrier-384 Black(PerkinElmer,目录号:6007550)
  4. 细胞培养微板,96孔,v底(Greiner Bio One International,目录号:651180)
  5. 一次性试剂容器,25毫升无菌(VWR,目录号:613-1174)
  6. 一次性试剂容器,100ml,无菌(VWR,目录号:613-1172)
  7. 铝密封猿96 100 / CS(康宁,目录号:6570)
  8. U2OS细胞(ATCC,RRID:CVCL_0042)
  9. Dulbecco改良的Eagle's培养基(DMEM)(Thermo Fisher Scientific,Gibco TM,目录号:41966029)
  10. 胎牛血清(FBS)合格,E.U.批准,南美洲(Thermo Fisher Scientific)
  11. 青霉素 - 链霉素(P / S)(10,000U / ml)(Thermo Fisher Scientific,Gibco TM,目录号:15140122)
  12. L-谷氨酰胺200mM(Thermo Fisher Scientific,目录号:25030024)
  13. 0.25%胰蛋白酶/ EDTA(Thermo Fisher Scientific,Gibco TM,目录号:25200056)
  14. 二甲基亚砜(DMSO)(Sigma-Aldrich,目录号:D8418)
  15. siRNA文库(GE Healthcare,Dharmacon,樱桃选择的感兴趣的蛋白质的siRNA库文库)
  16. 控制siRNAs
    非靶向对照siRNA(GE Healthcare Dharmacon,目录号:D-001810-10-20)
    用于自噬调节的siRNA:
    ATG12(GE Healthcare Dharmacon,目录号:J-010212-07)
    PIK3C3(GE Healthcare Dharmacon目录号:J-005250-09)
    RAB7A(GE Healthcare Dharmacon,目录号:J-010388-07)
    猛禽siRNA序列:GAUGAGGCUGAUCUUACAGUU(MWG)
  17. 水DNase /无RNA酶,无菌(Thermo Fisher Scientific,Gibco TM,目录号:10977035)
  18. 氯化钠(NaCl)(Sigma-Aldrich,目录号:31434)
  19. 氯化钾(KCl)(Sigma-Aldrich,目录号:P9541-500G)
  20. 磷酸氢二钠(Na 2 HPO 4)(Sigma-Aldrich,目录号:71640-250G)
  21. 磷酸二氢钾(KH 2 PO 4)(Sigma-Aldrich,目录号:P5379-100G)
  22. 磷酸盐缓冲盐水(PBS)(Thermo Fisher Scientific,Gibco TM,目录号:14190094)
  23. 聚-L-赖氨酸溶液(Sigma-Aldrich,目录号:P4707-50ML)
  24. 最佳改良Eagle's培养基(Opti-MEM)(Thermo Fisher Scientific,Gibco TM,目录号:31985062)
  25. Lipofectamine RNAiMax(Thermo Fisher Scientific,Invitrogen TM,目录号:13778150)
  26. PBS中的四聚甲醛(PFA)为4%(Santa Cruz Biotechnology,目录号:sc-281692)
  27. Triton X-100(VWR,目录号:28817.295)
  28. 牛血清白蛋白(BSA)(Sigma-Aldrich,目录号:A7906-100G)
  29. 抗ATG12(Cell Signaling Technology,目录号:2010)
  30. 抗GABARAP(Abcam,目录号:ab109364)
  31. 反LC3B(MBL国际,目录号:PM036)
  32. 抗STX17(Sigma-Aldrich,目录号:HPA001204)
  33. 抗WIPI2(Abcam,目录号:ab105459)
  34. Alexa Fluor 488山羊抗兔IgG(Thermo Fisher Scientific,Invitrogen TM,目录号:A-11008)
  35. Alexa Fluor 488山羊抗小鼠IgG(Thermo Fisher Scientific,Invitrogen TM,目录号:A-11001)
  36. CellMask深红色染色(Thermo Fisher Scientific,Invitrogen TM,目录号:H32721)
  37. DRAQ5(Cell Signaling Technology,目录号:4084S)
  38. 磷酸盐缓冲盐水(PBS,pH 7.4)(见配方)

注意:提前计划,并在整个屏幕上订购所需试剂的总量,以避免在实际执行屏幕时运行任何试剂。

设备

  1. 移液器
  2. 细胞培养培养箱(37℃,5%CO 2
  3. 离心机5810R(Eppendorf,型号:5810R,目录号:5811000010),具有孔板离心嵌体的转子A-4-62(Eppendorf,目录号:5810711002)
  4. 细胞培养无菌台
  5. 自动分配器(MicroFill 96- / 384-Well Microplate Dispenser)(BioTek Instruments,型号:AF1000A)
  6. 12通道多通道移液管(30-300μl)(NeoLab,目录号:E-1945)
    制造商:Eppendorf,型号:研究 ® 加上
  7. 12通道多通道移液管(10-100μl)(NeoLab,目录号:E-1943)
    制造商:Eppendorf,型号:研究 ® 加上
  8. 使用384孔板适配器(Analytik Jena,CyBio )将Selma移液机器人96/25μl(Analytik Jena,CyBio ,目录号:OL7001-26-211)目录号:OL7001-24-976)
  9. TipTray 96; 25μl,PCR认证,无菌(Analytik Jena,CyBio AG,目录号:OL3800-25-733-P)
  10. Neubauer计数室(Marienfeld-Superior,目录号:0640110)
  11. Opera LX高内容筛选系统,具有60x浸水物镜和机器人处理器II 230,包括:
    1. Opera LX 488/561/640显微镜(PerkinElmer)
    2. 水目标63x(PerkinElmer)
    3. 板处理器II 230(PerkinElmer)
    注意:Opera LX已停产。

软件

  1. Excel(Microsoft)
  2. 棱镜4(GraphPad)
  3. 条形码生成器( http://barcode.tec-it.com/de
  4. Acapella高分辨率成像分析软件(PerkinElmer)

程序

注意:细胞固定前的所有步骤都需要在无菌台上进行处理。

  1. 维持U2OS细胞
    1. 在补充有10%FBS,2mM谷氨酰胺以及1%P / S的DMEM中培养U2OS细胞,并在37℃和5%CO 2的潮湿细胞培养箱中孵育。
    2. 通过使用0.25%胰蛋白酶/ EDTA达到约80%融合后,通常(〜每周两次)传代细胞。
    3. 对于在含有10%DMSO的FBS中,在-150℃下长期储存冷冻细胞。
      注意:同时冻结足够的细胞等分试样,以从一个批次中执行整个屏幕。
    4. 准备足够的10厘米组织培养皿与U2OS细胞在测定日期为计划的siRNA筛选具有足够的70-80%融合细胞。对于两个384孔板,一个10厘米的盘应该足够了。

  2. U2OS细胞的反向siRNA转染(图1A)


    图1. siRNA筛选程序的方案。 :一种。将96孔板中的冻干的siRNA溶解,然后用水稀释以接收含有1μM浓度的siRNA的含有2μl的siRNA,其与Lipofectamine RNAiMax-Opti-MEM-Mix(< em> Lipo板)并转移至384孔成像板。在孵育384孔板之前加入U2OS细胞。最后,细胞是固定的,免疫染色的和成像的,随后进行图像和数据分析。 B和C.建议避免使用黑色表示的96(B)或384(C)孔板的外缘以防止边缘效应。

    1. 重新冻结冻干的siRNA池(<1> Stock1板)
      1. 将含有冻干siRNA(0.1nmol)的有序96孔板从-80℃储存到无菌试验台,短时间离心后。
      2. 配备移液机器人(Selma)与新的提示。
      3. 使用多通道移液管( H > O板)。
      4. 使用自动移液机器人,通过从 O 进入含有来自Dharmacon的96孔板的siRNA以获得10μM储备的siRNA溶液(Stock1平板)。在"混合循环"模式下,使用自动移液机器人上下移动进行混合。
        注意:
        1. 在使用冻干的siRNA排序96孔平板的同时,小心地将筛选siRNA之间的对照siRNA适当地排列(例如,随机穿过板,但每个泳道至少有一个对照)。因此,避免了必须以合适的顺序重新排列siRNA以进行筛选的额外步骤。
        2. 请勿使用96孔板(A1-A12,H1-H12,B1,C1,...,B12,C12,...)的外缘以避免边缘效应(图1B,黑色边缘)。
        3. 特别是在使用自动移液机器人时,不断检查您的盘子的方向。始终将井A1置于一个特定的角落,并记住方向。大多数96孔板包含一个切口角,以方便定位。
      5. 直接放弃 H 2 O板或也可用于稀释Stock2板执行协议。
      6. 使用铝合金封盖"Stock1"板,并存放在-80°C或继续执行协议。
    2. 用Neubauer计数室计数U2OS细胞
      1. 用含2ml无菌PBS洗涤含有U2OS细胞的每10cm培养皿(参见食谱)。然后,每个皿加入1.5ml胰蛋白酶,并在室温下孵育约5分钟,直到细胞分离。
      2. 通过加入补充有10%FBS,2mM谷氨酰胺但不含P / S(DMEM( - )P / S)的8ml DMEM阻断胰蛋白酶活性。
      3. 将所有细胞用于一个烧瓶中的筛选测定(例如,常见的无菌15ml或50ml Falcons)。
      4. 将10μl合并的细胞溶液转移到Neubauer计数室中,并在光学显微镜下计数细胞。
      5. 用DMEM( - )P / S稀释U2OS细胞以获得7.14×10 4个细胞/ ml的细胞密度(21μl中的1,500个细胞)。
        注意:在384孔成像板制备前进行细胞计数,以确保可用的细胞数量可用。
    3. 从Stock1板上稀释siRNAs以获得Stock2板中的1μM工作解决方案。
      1. 这里描述的量对于一次装满四份的384孔成像板是足够的。如果屏幕需要更多的384孔成像板,则相应调整量。
      2. 使用多通道移液管( 2 在96孔V底板的每个孔中分配15μlH 2 O 2 > O板)。
      3. 使用"样品稀释"模式移液管的自动移液机器人9μl来自 H ,然后从 Stock1板中加入1μl到相同的提示中,并在一个新的96孔v底板中一起释放,以获得1μM的siRNA工作溶液(Stock2板)。使用"混合循环"上下移动可以很好的混合
    4. 准备反向siRNA转染的U2OS细胞在384孔成像平板,最终siRNA浓度为30 nM。
      1. 使用多通道移液管将30μl聚-L-赖氨酸转移到384孔成像板的每个孔中,并在室温下孵育至少1小时。使用多通道移液管和丢弃,从384孔成像板中取出聚-L-赖氨酸。让384孔成像板干燥几分钟。
      2. 将3,382μlOpti-MEM与38μlLipofectamine RNAiMax混合并置于储存器(Lipo-Opti-Mix)中。对于每个384孔,这对应于8.9μlOpti-MEM和0.1μlLipofectamine RNAiMax。准备多余的量以确保足够的液体用于自动移液机器人。
      3. 使用多通道移液器(Lipo板)在96孔v底板的每个孔中转移50μlLipo-Opti-Mix。再次,不要使用外缘(图1B和1C)。
      4. 配备自动移液机器人新技巧。
      5. 使用"样品稀释"模式的自动移液机器人从Lipos板吸收9μlLipo-Opti-Mix,随后将0.9μl来自"Stock2"板的siRNA进入相同的提示并释放到384孔成像板的第一个重复孔中(图1C,绿色孔)。
        注意:再次检查你的盘子的方向。
      6. 重复3次以从384孔成像板上的一个siRNA池接收一式四份(图1C,灰色孔)。
      7. 在384孔成像板中孵育siRNA-Lipo-Opti-Mix,室温20分钟。
      8. 分配21μlDMEM( - )P / S,包括每孔1500 U2OS细胞(总共体积为11.76 ml,一个384板),使用多通道移液管从384孔成像板的每个孔中分配。确保液体滴到井底,如有必要,请轻轻离心。通过使用多通道移液器上下缓慢吸取的温和混合是可能的,但通常不是必需的。混合时,为每个siRNA使用新的提示。 96孔板和384孔板可以用适当的板式镶嵌离心机进行离心分离。
        注意:鉴于细胞迅速沉入储层底部(只有约1分钟相等分布),定期将储存池混合在储存器中。
      9. 使用多通道移液管将30μlDMEM( - )P / S分配到384孔边缘的每个孔(图1C,黑色孔)中,以避免边缘效应。
    5. 在细胞培养箱中孵育384孔成像板72小时
  3. 细胞的固定和免疫染色
    1. 将384孔板中的液体从适当的细胞培养物中废弃
    2. 将384孔板中的U2OS细胞用50μl4μlPFA每孔固定15分钟,室温用多通道移液管吸移。在密封玻璃瓶中丢弃PFA废物中的液体,并按照EH&amp; S化学废物计划的建议,检查部门的正确处理废物处理说明。
    3. 使用多通道移液管,每孔用100μl自制PBS清洗细胞三次。通过将水平盘上方颠倒的方式取下PBS。
    4. 在4℃下每孔含有100μlPBS的储存板或直接继续免疫染色方案。
    5. 丢弃液体。
    6. 使用多通道移液管,每孔加入50μl0.5μlTriton X-100的细胞,并在室温下孵育10分钟。丢弃液体
    7. 用PBS中的100μl1%BSA封闭细胞,每孔用多通道移液管转移,并在室温下孵育1小时。丢弃液体。
    8. 使用自动分配器,每孔用100μl自制PBS洗涤细胞一次。丢弃液体。
    9. 在PBS中的0.1%BSA中制备初级抗体溶液(抗ATG12 1:50;抗GABARAP 1:200;抗LC3B 1:800;抗STX17 1:250;或抗WIPI2 1:500)并分布每孔20μl,多通道移液器。在室温下孵育细胞1小时,然后弃去液体。
      注意:每个384孔板仅染上一个一抗。
    10. 使用自动分配器,如上所述用PBS洗涤细胞三次。
    11. 准备二抗溶液(抗兔或抗小鼠Alexa Flour 488,1:1,000在PBS中的0.1%BSA),并用多通道移液管每孔分配20μl。在室温下孵育细胞1小时,然后弃去液体
    12. 准备细胞质染色溶液(HSC CellMask深红色1:25,000,000,在PBS中的0.1%BSA),并用多通道移液管每孔分配20μl。在室温下孵育细胞1小时,然后弃去液体
    13. 准备核染色溶液(PBS中0.1%BSA中的DRAQ5,1:5,000),并用多通道移液管分配20μl。在室温下孵育细胞10分钟,然后弃掉液体
    14. 使用自动分配器,如上所述用PBS洗涤细胞三次。
    15. 第三次洗涤后,用多通道移液管每孔分配100μl无菌PBS,并用铝密封件密封384孔成像板,以避免暴露于光线下。将板存放在4°C直到图像采集。
      注意:最后一步是用多通道移液器和无菌PBS进行,以延长成像板的保存能力。

数据分析

  1. 图像采集
    1. 在PerkinElmer's Opera High Content Screening System显微镜上选择60x浸水物镜,以获得适合细胞内斑点检测的分辨率。
      注意:用显微镜将Phagophores和自噬体作为细胞内斑点检测。
    2. 根据使用的抗体调整必要的激光强度和平面高度。
      注意:顺序获取图像,首先在488通道,然后在633通道中。
    3. 设置板布局,并选择每个井的数量和分布。
      注意:对于U2OS细胞,对于4个孔的每个孔的24个场将在所有图像中将近似地加上多于1,000个细胞。
    4. 保存设置。
    5. 设置Opera机器人板处理程序的参数(例如,,板夹在板夹中的位置),并保存设置以顺序测量多个板。
    6. 使用 http://barcode.tec -it.com/de 。打印条形码并将其粘贴到铭牌上。
    7. 将所有384孔成像板放入板支架。
      注意:注意板的正确方向。
    8. 使用保存的设置启动批量测量,以便通过机器人手臂自动对一块板进行自动成像。
    9. 从堆垛机中取出板并保持在4°C或丢弃。
    10. 关闭Opera显微镜。

  2. 图像分析
    1. 打开Acapella高分辨率成像分析软件,并加载您的图像分析脚本进行斑点检测。
    2. 根据实际测量,设置一般参数,例如,488等于通道1,其中检测细胞内斑点,633等于通道2,其中检测细胞核和细胞质。
    3. 设置脚本参数,包括对比度,面积和检测算法,以适当分割每个细胞中的细胞核,细胞质和斑点。参见图2,例如图像。


      图2.图像分割。为核(左),细胞质(中间)和斑点(右)进行适当图像分割的示例图像。斑点代表用抗WIPI2抗体染色的巨噬细胞。比例尺= 40μm
    4. 保存所有设置,并将这些参数下载到脚本中。
    5. 运行分析图像(井模式),从测定特异性对照siRNA获得并检查输出结果。如有必要,对参数进行细微调整(例如,,在不同日期对免疫染色的平板进行对比度略有不同)。
      注意:特别是在脚本准备阶段,总是将输出结果与所获取图像的手动计数进行比较。手工计数意味着一个人实际上手工计数点数。如果图像量化输出结果与您自己的手动计数不匹配,请调整参数,直到对输出结果满意为止。
    6. 在批处理模式下运行脚本,以便将384张全彩色成像板中的所有图像保存为Excel文件。
    7. 所有不同抗体染色的代表性实例图像显示于Jung等人,2017年。

  3. 候选人的决心
    1. Excel中每个siRNA的一式四份的平均原始数据(在该测定中的点数),并计算标准偏差。
    2. 将每个siRNA的输出结果(点数)归一化为每个384孔板的非靶向对照siRNA。有关虚数据示例,请参见表1.

      Table1. Imaginary data example for the staining with LC3B based on Jung et al. (2017), to explain data normalization and candidate selection. Number of spots were quantified using the Acapella Software. For normalization the number of spots of every siRNA was divided by the number of spots of the non-targeting control siRNA. This yielded a fold change of 3.2 or 0.3 for the positive or negative control siRNAs, respectively. Furthermore, the target siRNAs 2 and 3 would have been selected as candidates for LC3B, according to the standard deviation criterion. As example the average standard deviation for LC3B was approximately 23%, a selected increasing or decreasing candidate siRNA comprises a fold-change of 1.46 or higher (1.0 + 2 x 23%) or 0.54 or lower (1.0 - 2 x 23%), respectively.


    3. 将来自所有板的所有测试的siRNA的倍数变化相互比较。
    4. 使用您选择的方法分类候选siRNA,例如,标准偏差标准。这里,选择其与标准化sicon(WIPI2和ATG12 = 3; LC3B,GABARAP和STX17 = 2)的两个或三个标准偏差的折叠变化不同的siRNA作为候选。有关虚数据示例,请参见表1.
    5. Excel也可用于根据折变差异对候选人进行排序和排名,以阐明顶级候选人。
    6. 应用棱镜4(GraphPad)生成图表和统计分析(ANOVA)。
    7. 代表性的示例图显示在Jung等人,2017中。

  4. 使用与上述siRNA泳池筛选相同的步骤,对每个基因进行每个基因四个单独的siRNA的去卷积筛选。
    1. 分类验证的候选人,其标准偏差标准与上述每个基因的四种siRNA中的三种不同。
    2. 排除有毒的siRNAs,其显示细胞数量以及细胞核或细胞质的强度和面积的明显变化。去除具有多于一种细胞毒性siRNA的基因用于进一步分析。

  5. 筛选方法的一般考虑
    1. 考虑筛选方法的适当细胞系。用于这种基于图像的自噬屏幕的人骨肉瘤细胞系是粘附的并且提供大的细胞质,这使得该细胞系适合于免疫染色和成像。此外,U2OS细胞中也可诱导自噬和抑制。此外,siRNA转染在该细胞系中非常有效。根据这些性质,U2OS细胞非常适合于基于图像的自噬siRNA屏幕。我们假定,例如,,HeLa,A549或LN229细胞也适用于该筛选方法。
    2. 使用您的选择方法,包括免疫荧光,免疫印迹或RT-qPCR,检查您的测定对照siRNA实际上是否提供了有效击倒目标基因,并在您选择的细胞系中诱导预期的表型。
    3. 阐明您所选择的细胞系(例如,〜80%井覆盖)所需的良好但不拥挤的井覆盖所需的每孔细胞数。作为解释,细胞之间的空白空间可能会导致长的图像采集时间和图像分析伪像。使用太多的细胞可能会导致它们彼此顶部生长,这可能会引入图像分析伪影。为了确定适当的细胞数目,用对照siRNA转染细胞并使用几种不同的细胞密度(例如每孔500-10,000个细胞)。进行染色,成像和分析以确定播种细胞的适当数量。
    4. 阐明适用于您的应用的转染试剂和siRNA(不同公司)。因此,用不同的转染试剂和测定对照siRNA转染U2OS细胞。进行染色,成像和分析以确定最佳的转染试剂,其不干扰所选择的筛选途径(在这种情况下为自噬)。此外,可以使用不同量的所选择的转染试剂以获得最佳的击倒效率。
    5. 在这种基于图像的自噬屏幕中,应用具有抗体的内源水平的蛋白质的免疫荧光染色。为了降低成本,应确定用可检测的免疫荧光信号进行最高可能的抗体稀释。因此,您的成像板中的种子U2OS细胞和使用稀释系列的抗体进行染色。进行成像和分析以确定所需抗体的最低量。稀释系列也可以用二次抗体,核和细胞质染色进行。
    6. 在确定适当的"实验室"筛选条件的同时,开始脚本开发。阐明正确的参数以检测您的表型,在这种情况下是斑点数。始终将脚本计算的点数与几个随机选择的图像的实际图片进行比较,以确保脚本正确计数。
    7. 彻底应用归一化技术(例如,每个平板标准化为非靶向对照siRNA),并仔细选择标准来定义候选物。经常应用标准偏差的2-3倍倍数变化。其他潜在的方法是Z-和B-得分归一化,特别是对于全基因组siRNA筛选。
    8. 由于有毒的siRNA可能导致假阳性命中,因此对脚本进行细胞毒性测试。例如,比较细胞数量和其他一般标准,例如细胞核和细胞质的面积和强度,并除去具有明显异常的siRNA。
    9. 应用统计数据来控制您的筛选条件和脱靶效应,例如重复之间或个体和池siRNA之间的输出结果的相关性。
    10. 考虑并建立下游测定,以机械验证您的候选人。研究自噬调节的典型方法包括基于GFP / RFP的自噬通量测定和具有自噬标志物如LC3B或p62的免疫印迹分析。此外,候选蛋白的定位以及具有早期和晚期自噬标记的候选者的共定位可以通过共焦显微镜来确定。

食谱

  1. 磷酸盐缓冲盐水(PBS,pH 7.4)
    137 mM NaCl
    2.7 mM KCl
    9mM Na 2 HPO 4
    3mM KH 2 PO 4

致谢

该协议由Jung等人,2017,eLife进行了修改和修改。这项工作得到了慕尼黑系统神经系统集群(EXC1010 SyNergy),协同研究中心(CRC1177)和欧洲研究委员会(ERC,282333-XABA)框架内的德国富士康斯克(DFG)支持。

参考

  1. Jung,J.,Nayak,A.,Schaeffer,V.,Starzetz,T.,Kirsch,AK,Muller,S.,Dikic,I.,Mittelbronn,M.and Behrends,C.(2017)一个class ="ke-insertfile"href ="http://www.ncbi.nlm.nih.gov/pubmed/28195531"target ="_ blank">基于多重图像的自噬RNAi筛选将SMCR8识别为ULK1激酶活性和基因表达调节器。 6.
  2. Ktistakis,NT和Tooze,SA(2016)。消化自噬的扩展机制。 Trends Cell Biol 26(8):624-635。
  3. McKnight,NC,Jefferies,HB,Alemu,EA,Saunders,RE,Howell,M.,Johansen,T.和Tooze,SA(2012)。全基因组siRNA筛选显示氨基酸饥饿诱导的自噬需要SCOC和WAC。 EMBO J 31(8):1931-1946。
  4. Orvedahl,A.,Sumpter,R.,Jr.,Xiao,G.,Ng,A.,Zou,Z.,Tang,Y.,Narimatsu,M.,Gilpin,C.,Sun,Q.,Roth, M.,Forst,CV,Wrana,JL,Zhang,YE,Luby-Phelps,K.,Xavier,RJ,Xie,Y。和Levine,B。(2011)。基于图像的全基因组siRNA筛选鉴定选择性自噬因子。 em> 480(7375):113-117。
  5. Weidberg,H.,Shvets,E.,Shpilka,T.,Shimron,F.,Shinder,V. and Elazar,Z.(2010)。&nbsp; LC3和GATE-16 / GABARAP亚家族在自噬体生物发生中都是必不可少的,但作用不同。 em> 29(11):1792-1802。
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免责声明 × 为了向广大用户提供经翻译的内容,www.bio-protocol.org 采用人工翻译与计算机翻译结合的技术翻译了本文章。基于计算机的翻译质量再高,也不及 100% 的人工翻译的质量。为此,我们始终建议用户参考原始英文版本。 Bio-protocol., LLC对翻译版本的准确性不承担任何责任。
Copyright Jung and Behrends. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Jung, J. and Behrends, C. (2017). Protocol for Establishing a Multiplex Image-based Autophagy RNAi Screen in Cell Cultures. Bio-protocol 7(17): e2540. DOI: 10.21769/BioProtoc.2540.
  2. Jung, J., Nayak, A., Schaeffer, V., Starzetz, T., Kirsch, A. K., Muller, S., Dikic, I., Mittelbronn, M. and Behrends, C. (2017). Multiplex image-based autophagy RNAi screening identifies SMCR8 as ULK1 kinase activity and gene expression regulator. Elife 6.
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