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Nov 2019

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Tandem Tag Assay Optimized for Semi-automated in vivo Autophagic Activity Measurement in Arabidopsis thaliana roots
优化的串联标签检测用于拟南芥根组织中体内半自动化自噬活性测定   

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Abstract

Autophagy is the main catabolic process in eukaryotes and plays a key role in cell homeostasis. In vivo measurement of autophagic activity (flux) is a powerful tool for investigating the role of the pathway in organism development and stress responses. Here we describe a significant optimization of the tandem tag assay for detection of autophagic flux in planta in epidermal root cells of Arabidopsis thaliana seedlings. The tandem tag consists of TagRFP and mWasabi fluorescent proteins fused to ATG8a, and is expressed in wildtype or autophagy-deficient backgrounds to obtain reporter and control lines, respectively. Upon autophagy activation, the TagRFP-mWasabi-ATG8a fusion protein is incorporated into autophagosomes and delivered to the lytic vacuole. Ratiometric quantification of the low pH-tolerant TagRFP and low pH-sensitive mWasabi fluorescence in the vacuoles of control and reporter lines allows for a reliable estimation of autophagic activity. We provide a step by step protocol for plant growth, imaging and semi-automated data analysis. The protocol presents a rapid and robust method that can be applied for any studies requiring in planta quantification of autophagic flux.

Keywords: Plant autophagy (植物自噬), Autophagic activity (自噬活性), Arabidopsis (拟南芥), Tandem tag (串联标签), Autophagic activity in vivo (体内自噬活性), In planta autophagic activity assay (植物中自噬活性检测), High-throughput autophagic activity measurement (高通量自噬活性测定), Autophagic flux (自噬潮)

Background

The Tandem Tag (TT) assay is a widespread approach for quantifying autophagic flux in yeast and mammalian cells (Zhou et al., 2012; Klionsky et al., 2016; Yoshii and Mizushima, 2017). It has been previously described to be applicable for plant cells in a study using tobacco BY-2 cell suspension cultures (Hanamata et al., 2013; Klionsky et al., 2016). Here we provide a detailed protocol for in planta quantification of autophagic flux in epidermal root cells of Arabidopsis thaliana seedlings (Figure 1). The TT assay employs ratiometric quantification of red and green fluorescence and allows to quantify relative induction or inhibition of autophagy. Another advantage of this method is that it provides valuable information on subcellular localization of ATG8, i.e., translocation of ATG8 from the nuclei to cytoplasm (Zhou et al., 2012), incorporation into puncta of different morphologies and motility, and its accumulation in the vacuole.

For this protocol we used stable transgenic Arabidopsis lines expressing the optimized TT (Huang et al., 2015) fused to AtATG8a and driven by a double 35S promoter. The TT-AtATG8a fusion was introduced into wild-type or atg5atg7 double knockout backgrounds, to produce reporter and control lines, respectively (Dauphinee et al., 2019). Under normal growth conditions TT-AtATG8a is localized in the cytoplasm and in the nuclei of the root epidermal cells, but upon induction of autophagy it is gradually translocated to the cytoplasm, then incorporated into autophagosome membranes and delivered together with the cargo to the lytic vacuole. While TagRFP shows relatively high tolerance to the pH of the lytic compartments (Huang et al., 2015), fluorescence of mWasabi is significantly reduced under the same conditions. Thus, ratiometric measurement of the TagRFP and mWasabi fluorescence allows to estimate the delivery rate of the fusion protein to the lytic compartment and eliminates potential bias coming from differences in fusion protein expression. Furthermore, since the assay relies on confocal microscopy image acquisition and analysis, it is possible to obtain time-resolved and dose-dependent data about the changes in autophagic activity. Although we describe only a protocol for detection of the TT-AtATG8a delivery to the vacuole, it is also possible to use the same imaging data to visualize and quantify dynamics of autophagosome formation.

We observed that response to known modulators of autophagic activity, such as AZD8055, which induces autophagy by inhibiting TORC1 activity, and concanamycin A (ConA) (Dauphinee et al., 2019), which inhibits autophagy by inactivating vacuolar vATPase and thus changing the pH of the lytic compartment, in Arabidopsis roots significantly varied depending on the area of the root zone scanned. By comparing data obtained from different root zones, we established that the most reproducible measurements could be obtained by analyzing images of root epidermal cells located in the beginning of the differentiation zone. Nevertheless, we still observed quite significant variation between responses in tricho- and atrichoblasts. Hence, a relatively large number of images was required for the data analysis to obtain a reliable mean value representative of average autophagic activity in root cells.

This assay provides a significant improvement of autophagic activity measurement in planta. It is based on high-throughput image analysis, thus improving reproducibility and robustness of the results. It relies on the use of designated macros written in ImageJ Macro Language (IJM) and R scripts. We ensured that it can be universally used for imaging data obtained using confocal laser scanning microscopes (CLSMs) of various manufacturers. Furthermore, to enable applicability of the protocol for images obtained on different CLSM systems that will naturally vary in efficacy of detection, we added an extra step of threshold values adjustments that will maximize the capacity of the vacuole area selection tool.

Importantly, the TT assay described here allows quantification of autophagy-dependent delivery of ATG8 to the lytic vacuole. Although it can be used as a very good indication for estimating autophagic activity, it is still preferable to combine this assay with other techniques verifying degradation of autophagic cargo e.g., the GFP-ATG8 cleavage assay or long-lived proteins assay (Dauphinee et al., 2019; Klionsky et al., 2016).


Figure 1. A workflow scheme of the Tandem Tag assay. The protocol for the assay comprises six major steps. The time required for each step was estimated based on experienced user progress. It is recommended to use a seed plating guide in the first step to establish equal distance between seedlings and reproducible growth conditions. In the second step, seedlings are grown on the vertically positioned plates to ensure that roots remain on the surface of the medium. We suggest to perform drug treatments in liquid medium for faster, more even and more reproducible delivery of the drugs into the root cells. The required treatment time will be compound-specific. The time required for confocal microscopy might vary depending on microscope configuration and software.

Materials and Reagents

  1. 1.5 ml Eppendorf tubes 
  2. Petri dishes (Thermo Fisher Scientific, catalog number: 150239 )
  3. Pipette tips
  4. 6-well tissue culture plates (Thermo Fisher Scientific, catalog number: 15213338 )
  5. Micro cover glass 25 x 50 mm and 25 x 25 mm (VWR, catalog numbers: 48382-136 and 48366-089 )
  6. Sealing Film, PVC (Phytotechlab, Product ID: A003) or Parafilm (VWR, catalog number: 52859-079 )
  7. Bleach solution (Klorin, Colgate Palmolive)
  8. Tween-20, Polysorbate (VWR, catalog number: 97062 )
  9. 3D printed seed plating guide (https://www.thingiverse.com/thing:4016917)
  10. Murashige and Skoog, MS medium (Duchefa Biochemi, catalog number: M0222 ) liquid medium
  11. MES, 2-(N-morpholino) ethanesulfonic acid (Duchefa Biochemi, catalog number: M1503 )
  12. Sucrose (Duchefa Biochemi, catalog number: S0809 )
  13. KOH (VWR, catalog number: 470302 )
  14. Plant agar (Duchefa Biochemi, catalog number: P1001 )
  15. DMSO (Sigma-Aldrich, catalog number: 276855 ) or other vehicle
  16. AZD8055 (Selleckchem, catalog number: S1555 )
  17. Immersion oil (Zeiss, catalog number: 444960 )
  18. Transgenic Arabidopsis lines used to establish this protocol were published in Dauphinee et al. (2019)
  19. Milli-Q (MQ) water
  20. Liquid 0.5x MS medium (see Recipes)
  21. Solid 0.5x MS medium (see Recipes)
  22. Bleach solution (see Recipes)
  23. ConA 1 mM stock in DMSO (see Recipes)

Equipment

  1. Pipettes for 100-1,000 µl and 1-10 μl
  2. Forceps (Dumont, catalog number: 11251-10 )
  3. 4 °C fridge
  4. Arabidopsis growth cabinet/growth room: 20-22 °C, 50-70% humidity, 150 µM light
  5. Confocal Laser Scanning Microscope (CLSM; Zeiss, LSM 800)

Software

  1. Fiji, the version of ImageJ with included set of plugins (https://fiji.sc/, for this study, we utilized versions 1.51s and 2.0.0-rc-69/1.52i).
  2. AuTToFlux repository containing three ImageJ macro and three R script files (https://github.com/jonasoh/AuTToFlux/archive/master.zip):
    1. CalibrateThreshold.ijm
    2. ImageProcessor.ijm
    3. FluorescenceIntensity.ijm
    4. EvaluateCalibration.R
    5. Control-vs-Reporter.R
    6. Flux-vs-Time.R
  3. R (https://www.r-project.org, we used 3.5.2 and 3.5.1)
  4. RStudio (https://www.rstudio.com/, we used versions 1.1.453 and 1.2.1186).
  5. Git (https://git-scm.com/downloads, we used the version 2.24.1)
  6. R packages:
    1. dplyr (https://CRAN.R-project.org/package=dplyr)
    2. ggplot2 (https://CRAN.R-project.org/package=ggplot2)
    3. readr (https://CRAN.R-project.org/package=readr)

Procedure

  1. Seed sterilization (40 min)
    1. Place ca. 15 μl of seeds of reporter and control lines into 1.5 ml Eppendorf tubes. Generally, the aim is to have at least four-six biological replicates for each treatment and time point planned in the experiment.
    2. Add ca. 1.5 ml of the bleach solution.
    3. Incubate the seeds for ca. 30 min, agitating.
    4. Under sterile conditions, pipette out the bleach solution.
    5. Add sterile water to the seeds. Mix and pipette it out.
    6. Perform the wash with sterile water at least three times to wash out the leftovers of the bleach solution.
    7. Seeds can be vernalized either in the sterile water or as described in the Procesure C.

  2. Seed plating (40 min)
    1. Use 1 ml pipette to transfer single seeds onto a Petri dish with solid 0.5x MS medium. Keep ca. 2-5 mm distance between the seeds in the same row to avoid entanglement of roots. Keep ca. 5 cm distance between the rows (Figures 2A-2B). It is recommended to use a seed plating guide for this procedure (Figure 1, Figure 2A, https://www.thingiverse.com/thing:4016917).
    2. Seal the plates with a strip of Parafilm or sealing film.


      Figure 2. Arabidopsis Tandem Tag autophagic flux assay. A. 3D printed seed plating guide for 9 cm Petri plates. B. Arabidopsis thaliana seeds plated using the seed plating guide (left half) and then grown with the plate positioned vertically for 5 days (right half). C. Arabidopsis seedlings placed in a 6-well plate for treatment. Note that the roots should be submerged gently (white arrow) and should not be floating on the surface (red arrow). D. Seedlings mounted on a glass slide (left) or coverslip (right) for CLSM. E. Approximate beginning of the root differentiation zone F. Images 0, 1 and 2 represent good, bad and ugly quality scans of differentiation zone vacuoles in the reporter line, respectively. G. Vacuole layer masks generated during calibration using the CalibrationThreshold.jim macro. Note that ugly scans F. may lead to complete or partial failure of vacuole detection. H. Sum vacuole areas detected on all analyzed images (2 is the recommended upper threshold value) vs. threshold values generated by the EvaluateCalibration.R script. I. Flux-vs-Time.R script output for vehicle and a compound treatment generated in R using ggplot2. Scale bars = 1.5 cm (B-D); 50 µm (E, F).

  3. Vernalization (24-48 h)
    Incubate the plates in the dark at 4 °C for 24-48 h.

  4. Growth on vertical plates (5 days)
    1. Place the plates into growth conditions (150 µM light, 22 °C for 16 h, 0 µM light, 20 °C for 8 h). Make sure the plates are perfectly vertical to ensure root growth on the top of the medium (Figure 2B).
    2. Grow the seedlings on the plates for ca. 5 days, the root length should reach ca. 2 cm.

  5. Drug treatment (2-24 h)
    1. Pipette 3 ml of liquid 0.5x MS into wells of a 6-well tissue culture plate. Add the chemical compounds of the required concentration.
    2. Using soft forceps, gently pick seedlings from the plates and transfer into liquid 0.5x MS (not more than 20 seedlings/well).
    3. Gently pipette the medium from the well onto the seedlings in the well to submerge the roots (white arrow, Figure 2C).
    4. Seal the plate with a sealing film or as trip of Parafilm.
    5. Incubate the plate under the same growth conditions for the required amount of time.

  6. Mounting the samples (1 min)
    1. Pipette a drop of medium, ca. 50 μl, from a well on the 25 mm x 50 mm cover slip.
    2. Using soft forceps, gently pick seedlings from the well and place it onto the drop of medium making sure that the root is straight. Avoid drying the roots during transfer!
    3. Place up to six seedlings on the cover slip.
    4. Cover the roots with 25 mm x 25 mm cover slip (Figure 2D).
    5. Apply immersion if needed to the objective lens and place the sample on the microscope stage.

  7. Setting up scanning parameters for confocal laser microscopy (30 min, required only once)
    1. To estimate optimal range of settings for scanning, it is advisable to perform a pilot experiment using three control treatments with 500 nM AZD 8055 for 4 h, 500 nM ConA for 6 h and corresponding amount of vehicle (e.g., 0.05% DMSO).
    2. Configure settings for sequential scanning of two channels:
      1. Channel 1 for detection of mWasabi: excitation at 488 nm, emission detection range 490-564 nm.
      2. Channel 2 for detection of TagRFP: excitation at 561 nm, emission detection range 564-700 nm.
      3. It is advisable to use the most sensitive detectors available in the system (i.e., GaAsp or HyD detcors for Zeiss or Leica CLSM, respectively).
      4. Using 40x objective is advisable to obtain images most applicable for automated analysis.
      5. Set switching between channels for each frame to minimize the crosstalk between channels and optimize the pinhole size. If possible, use the pinhole of 1 AU for each of the channels.
      6. If possible, use 16-bit resolution to increase the resolution of intensities.
      7. The sample treated with AZD 8055 will have the weakest fluorescence and should be used to adjust laser intensity and the Detector Gain (Master Gain) to the lowest possible values that do not result in oversaturated pixels.
      8. The sample treated with ConA will have the strongest fluorescence, at least in the green channel, and should be used to re-adjust laser intensity and the Detector Gain (Master Gain) to the lowest possible values that do not result in oversaturated pixels.
      9. The sample treated with the DMSO can be used to verify the applicability of the adjusted settings.
      10. Additionally, noise can be decreased by ramping down the scanning speed or increasing the averaging number. In our experience, scanning at the speed of ca 10 seconds per frame produced images with the quality appropriate for further analysis while also resulting in acceptable time for experiment.

  8. Scanning (ca. 30 min per treatment)
    1. The assay is optimized for measurement of autophagic activity in the epidermal root cells. Downstream image analysis relies on automated detection of the vacuoles in the green fluorescent channel. Thus optimally, images should be made in the focal plane where in each cell vacuole is surrounded by clearly visible cytoplasm.
    2. Most CLSM software will have an option for scanning at selected positions that will significantly decrease the time required for the experiment.
    3. Mark the positions at the beginning of the differentiation zone of the root (Figure 2E).
    4. Start fast scanning mode (live scan) and readjust the focal plane for each position to the middle section through the vacuole of the epidermal cells (Figure 2F with and without cortical cytoplasm)
    5. When all positions are readjusted, acquire and save the images. Please note, that automated statistical analysis will use information provided in the names of images. Please use the following rules to introduce the required parameters into the name of your images:
      1. separate parameters by underscores (_)
      2. Name the files as follows: line_treatment_seedlingX_imageY (e.g., Reporter_50uM.C12_seedling1_image2.czi)
        Where line and treatment are text variables or strings identifying the lines (either “Reporter” or “Control”) and treatments (i.e., “50uM.C12”) used. One treatment must be named “Vehicle”. X and Y are numbers indicating seedling and image replicates thus representing biological and technical replicates, respectively. Note that “seedling” and “image” are fixed strings and only the numbers X and Y are changed from image to image.

Data analysis

Data needs to be processed prior to the analysis. The processing is done in four steps: (i) conversion of confocal images into .TIF files; (ii) adjustment of the threshold values to optimize recognition of the vacuoles. This step must be performed when using the protocol for the first time and, if needed, can be redone for individual experiments; (iii) quantification of fluorescence intensities in the vacuoles; (iv) quantification of the ratios and plotting of the data as control vs reporter lines, or ratios vs time and concentration.
  We provide demo data (https://github.com/jonasoh/AuTToFlux), which can be used to ensure that the analysis works as expected.


  1. Set up the analysis pipeline
    For more detailed instructions see Video 1 (https://youtu.be/6oY3CyvGFPk).
    1. Place all images for processing into a single folder. If several experiments should be processed, place them as subfolders within a single folder.
    2. Download and install Fiji, the version of ImageJ with included set of plugins (https://fiji.sc/) for this study, we utilized versions 1.51s and 2.0.0-rc-69/1.52i).
    3. Download and install R (https://www.r-project.org, we used 3.5.2 and 3.5.1) and RStudio (https://www.rstudio.com/, we used versions 1.1.453 and 1.2.1186).
    4. Either clone the GitHub repository (https://github.com/jonasoh/AuTToFlux) or download it as a zip file. If cloning the repository, RStudio can be used to automate this task: In RStudio, start a new project (File -> New Project -> Version Control -> GIT and input the following URL:
      https://github.com/jonasoh/AuTToFlux. The repository is automatically cloned into the chosen location.
    5. Before running the R scripts for the first time you need to make sure its dependencies are installed. Either use RStudio’s interface for installing packages, and install the packages ggplot2, dplyr, and readr, or install them manually by typing the following command into the console:

      install.packages(c("ggplot2", "dplyr", "readr"))

    Video 1. Repository version control in R studio

  2. Convert images into TIFF files
    For more detailed instructions see Video 2 (https://youtu.be/nKPq0kNvW_U).
    1. Place all images for processing into a single folder. If several experiments are to be processed, place them as subfolders within a single folder.
    2. Run the Image Processor: launch ImageJ and go to Plugins -> Macros -> Run -> locate ImageProcessor.ijm.
    3. The image processor macro opens all compatible images in the target folder and saves them as .TIF files (with the file extension .tif); original acquisition dates of the images are saved in separate files. Multi-image files from experiments scanned using the “Positions“ function are split so that each image is saved as a separate .TIF file.

      Video 2. Image processing

  3. Select proper threshold values
    For more detailed instructions see Video 3 (https://youtu.be/Wkw3VXFj2is). Copy at least three TIFF images generated by the ImageProcessor into a separate folder. Aim to select images representing the best, the worst, and average quality.
    1. Launch ImageJ and go to Plugins -> Macros -> Run -> locate CalibrateThreshold.ijm. This runs the macro. In the file picker immediately presented, locate the folder with representative images. Macro will record the area sizes corresponding to the vacuoles and also save masks as tiff files containing threshold values in their names, e.g., file named *.thr0-3.tiff would correspond to the mask created with the threshold values (0;3). The results overview will be generated as threshold-overview.tif (Figure 2G).
    2. Launch RStudio.
    3. Open the RStudio project file (AuTToFlux.Rproj).
    4. To estimate what threshold values provide the largest selected vacuole area for all analyzed images, run the EvaluateCalibration.R script (select the R script, click Code -> Source and then choose the folder containing the .CSV files generated in the previous step–note: on non-Windows systems you need to type in the pathname of the folder).
    5. The script will plot average sum vacuole areas selected on all analyzed images vs threshold values. Select the threshold value that corresponds to the largest area (Figure 2H).
    6. Using the information gathered from the threshold-overview and the threshold graph, select an appropriate upper threshold value for marking. The masks should not contain any background areas.
    7. Before proceeding with image thresholding and data analysis, ensure that the calibration folder is removed from the directory containing the processed images.

      Video 3. Treshold callibration

  4. Measure fluorescence intensities
    For more detailed instructions see Video 4 (https://youtu.be/6jYqkYXOpiQ).
    1. Run the threshold macro: launch ImageJ -> Plugins -> Macros -> Run -> locate and open the macro file FluorescenceIntensity.ijm.
    2. Select the folder containing images to be analyzed, previously converted using the image processor macro (step B).
    3. The macro will prompt you to select the threshold value determined earlier in the calibration process. For each image the threshold macro automatically selects areas corresponding to the vacuoles using the GFP channel and saves the masks for the selected areas as TIFF files. The masks are then used to quantify intensities of RFP and GFP fluorescence in the corresponding channels of the image. Ratios of vacuolar RFP/GFP fluorescence intensities are saved to .CSV files with matching filenames.

      Video 4. Fluorescence intensity measurement

  5. Analyze data
    To estimate dose and time-dependent responses the RFP/GFP ratios are plotted vs time using Flux-vs-Time.R. For more detailed instructions see Video 5 (https://youtu.be/0_wDY7RN_hk).
    1. Create an info.txt file in the folder in the folder containing the .CSV files for analysis. The initial time of treatment (format YYYY-MM-DD HH:MM, 24-hour time) is user-specified by creating a tab-delimited text file (using any spreadsheet software, e.g., Microsoft Excel) with two columns, which should be saved as info.txt e.g.,
      Treatment     StartTime
      Vehicle          2018-07-06 09:50
      Treatment     2018-07-06 10:00
    2. Launch RStudio.
    3. Locate the R project file in the directory created from the GitHub repository and open it if it is not already opened:
      1. In R select Flux-vs-Time.R, click Code -> Source and then choose the folder containing the .CSV files generated in step D.
      2. A summary of the experiment data is generated with ratios normalized to the vehicle and grouped by treatment and seedling number (Figure 2I). To plot the data, we recommend ggplot2 (https://ggplot2.tidyverse.org/), axes (x=Elapsed time, y=Normalized ratios, color=Treatment)
      3. Further statistical analysis will depend on the treatments present in the experiment and can be performed using R or other software, e.g. Origin, JMP.
      4. The data can be also used to estimate IC50 using R, e.g., with the aid of the drc package (Ritz et al., 2015), or other software such as Prism. 

    Video 5. Flux vs. time

    To demonstrate autophagy-dependent response, RFP/GFP ratios of control lines are plotted vs ratios of reporter lines. For more detailed instructions see Video 6 (https://youtu.be/PQRZ1oOBgws):
    1. Launch RStudio.
    2. Locate the R project file in the directory created from the GitHub repository and open it if it is not already opened:
      1. In R select Control-vs-Reporter.R, click Code -> Source and then choose the folder containing the .CSV files generated in step D.
      2. This script may take as its argument either a folder containing several treatments as subfolders, or a folder without subfolders that contains a single experiment.
      3. The script summarizes RFP/GFP ratios (normalized to vehicle) and groups by line (control or reporter), treatment and seedling number. An unpaired, two-tailed Student’s t-test is used to compare log-transformed normalized means of the control and reporter groups. A summary table named pvals.txt is generated. If P-values derived from permutation (i.e., exact P-values) are desired, uncomment the appropriate sections marked in the R script.

    Note: For both Control-vs-Reporter.R and Flux-vs-Time.R, data is saved both as raw data (as summary-full.txt in the experiment directory) and as per-seedling summary statistics (summary-perseedling.txt), to facilitate analysis using other statistical software.

    Video 6. Control vs. Reporter

Notes

See Table 1 for further troubleshooting steps.

Table 1. Troubleshooting

Recipes

  1. Liquid 0.5x MS medium
    1. 0.5x MS complete with vitamins,10 mM MES, 1% sucrose dissolved in MQ water
    2. Adjust pH to 5.8 using KOH and autoclave for 20’ at 120 °C
    3. Store at 4 °C for max. 2 months
  2. Solid 0.5x MS medium
    1. After adjusting pH of the liquid 0.5x MS medium, add 0.8% Plant agar and autoclave for 20 min at 120 °C
    2. Store at 4 °C for max. 2 months. If needed, the medium can be liquified in the microwave
  3. Bleach solution
    1. 5% Klorin (final concentration: 2.7 g/L sodium hypochlorite), 0.01% Tween-20 in MQ water
    2. Prepare in 50 ml Falcon tube and store at RT
    3. After ca. 1 month Tween-20 might form precipitates in the solution, however, this does not impact the efficacy of the sterilization
  4. AZD 8055 5 mM stock in DMSO
    Store at -20 °C for max. 2 years
  5. ConA 1 mM stock in DMSO
    Store at -20 °C for max. 1 year

Acknowledgments

This project was supported by Carl Tryggers Foundation (to EAM), Natural Sciences and Engineering Research Council (NSERC) of Canada (to AND) and Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (Formas) under Project Number 2016-20031. This protocol was established for the study carried out by Dauphinee et al. (2019)

Competing interests

The authors declare no competing financial interests.

References

  1. Dauphinee, A. N., Cardoso, C., Dalman, K., Ohlsson, J. A., Berglund Fick, S., Robert, S., Hicks, G. R., Bozhkov, P. and Minina, E. A. (2019). Chemical screening pipeline for identification of specific plant autophagy modulators. Plant Physiol 181(3):855-866.
  2. Hanamata, S., Kurusu, T., Okada, M., Suda, A., Kawamura, K., Tsukada, E. and Kuchitsu, K. (2013). In vivo imaging and quantitative monitoring of autophagic flux in tobacco BY-2 cells. Plant Signal Behav 8(1): e22510.
  3. Huang, R., Xu, Y., Wan, W., Shou, X., Qian, J., You, Z., Liu, B., Chang, C., Zhou, T., Lippincott-Schwartz, J. and Liu, W. (2015). Deacetylation of nuclear LC3 drives autophagy initiation under starvation. Mol Cell 57(3): 456-466.
  4. Klionsky, D. J., Abdelmohsen, K., Abe, A., et al. (2016). Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition). Autophagy, 12, 1554-8635 (Online).
  5. Zhou, C., Zhong, W., Zhou, J., Sheng, F., Fang, Z., Wei, Y., Chen, Y., Deng, X., Xia, B. and Lin, J. (2012). Monitoring autophagic flux by an improved tandem fluorescent-tagged LC3 (mTagRFP-mWasabi-LC3) reveals that high-dose rapamycin impairs autophagic flux in cancer cells. Autophagy 8(8): 1215-1226.
    6. Ritz, C., Baty, F. and Strebig, J. C. (2015). Dose-response Analysing Using R. PLoS ONE 10(12): e0146021.

简介

[摘要] 自噬是真核生物的主要分解代谢过程,在细胞稳态中起关键作用。体内自噬活性(通量)的测量是研究该途径在生物体发育和应激反应中的作用的有力工具。在这里,我们描述了串联标记测定的重大优化,用于检测拟南芥幼苗表皮根细胞中植物体内的自噬通量。串联标签由TagRFP 和mWasabi组成 荧光蛋白与ATG8a融合,并在野生型或自噬缺陷型背景中表达,分别获得报告基因和对照。自噬激活后,将TagRFP-mWasabi-ATG8a融合蛋白掺入自噬体中并递送至裂解液泡中。对照和报道细胞液泡中低pH耐受的TagRFP 和低pH敏感的mWasabi 荧光的比例定量可以可靠地估计自噬活性。我们为植物生长,成像和半自动数据分析提供了分步协议。该协议提出了一种快速而可靠的方法,该方法可用于需要植物自噬通量定量的任何研究。

[背景 ] 串联标记(TT)分析是定量酵母和哺乳动物细胞中自噬通量的广泛方法(Zhou 等,2012; Klionsky 等,2016; Yoshii和Mizushima,2017)。先前已经描述了适用于植物细胞中使用tobacc研究?BY-2细胞悬浮培养物小号(Hanamata 等人,2013; Klionsky 。等人,2016 )。在这里,我们提供了拟南芥幼苗表皮根细胞中自噬通量在植物中定量的详细方案(图1)。TT分析采用红色和绿色荧光的比例定量,可以定量自噬的相对诱导或抑制。这种方法的另一个优点是,它提供了有价值的上ATG8,亚细胞定位信息即,从细胞核到CYTOP ATG8易位LASM (周等人,2012 ),纳入不同的形态和运动性的斑点,其在积累液泡。

对于此协议中,我们使用的稳定的转基因拟南芥中表达最优化的TT线(黄等人,2015 )融合至AtATG8a和从动由双35S启动子。将TT-AtATG8a融合体引入野生型或atg5atg7 双敲除背景中,分别产生报告基因和对照品系(Dauphinee et al 。,2019)。在正常的生长条件下,TT-AtATG8a定位在根表皮细胞的细胞质和细胞核中,但是在自噬诱导下,它逐渐转移到细胞质中,然后掺入自噬体膜中,并与cago 一起递送至溶解液泡。尽管TagRFP 对溶胞区的pH 表现出较高的耐受性(Huang 等,2015 ),但在相同条件下mWasabi的荧光显着降低。因此,通过对TagRFP 和mWasabi 荧光的比率测量,可以估算融合蛋白向裂解室的传递速率,并消除了来自融合蛋白表达差异的潜在偏倚。此外,由于该测定依赖于共聚焦显微镜图像的获取和分析,因此可以获得有关自噬活性变化的时间分辨和剂量依赖性数据。尽管我们仅描述了检测TT-AtATG8a传递至液泡的方案,但也可以使用相同的成像数据来可视化和量化自噬体形成的动力学。

我们观察到对自噬活性的已知调节剂的响应,例如通过抑制TORC1活性诱导自噬的AZD8055和伴刀豆球蛋白A (ConA )(Dauphinee 等人,2019),后者通过灭活液泡vATPase 从而改变pH 来抑制自噬。在拟南芥的溶菌区室中,根系根据扫描的根区面积而显着变化。通过比较从不同根区获得的daa ,我们确定可以通过分析位于分化区开始处的根表皮细胞的图像来获得最可重复的测量结果。尽管如此,我们还是观察响应之间相当显著变化毛滴虫-和atrichoblasts 。因此,数据分析需要相对大量的图像以获得代表根细胞中平均自噬活性的可靠平均值。

该测定法显着改善了植物体内自噬活性的测量。它基于高通量图像分析,从而提高了结果的可重复性和鲁棒性。它依赖于使用以ImageJ宏语言(IJM)和R脚本编写的指定宏。我们确保它可以普遍用于使用各种制造商的共聚焦激光扫描显微镜(CLSM)获得的成像数据。此外,为了使该协议适用于在不同CLSM系统上获得的图像,这些图像在检测功效上自然会发生变化,我们增加了阈值调整的额外步骤,该步骤将最大化液泡区域选择工具的功能。

重要的是,此处描述的TT分析可定量自噬依赖性的ATG8传递至裂解液泡。尽管它可以作为估计自噬活性的很好指示,但仍然优选将此方法与其他验证自噬货物降解的技术相结合,例如GFP-ATG8裂解试验或长寿蛋白测定(Dauphinee 等。 ,2019;Klionsky 等,2016)。



D:\ Reformatting \ 2020-1-6 \ 1902885--1285 Alyona Minina 825729 \ Figs jpg \图1.jpg

图1.串联标签测定的工作流程方案。该试验方案包括六个主要步骤。每个步骤所需的时间是根据经验丰富的用户进度估算的。建议在第一步中使用播种导板,以使幼苗之间的距离与可再现的生长条件相等。第二步,在垂直放置的板上生长幼苗,以确保根部保留在培养基表面上。我们建议在液体培养基中进行药物处理,以将药物更快,更均匀和更可重复地递送到根细胞中。所需的治疗时间将取决于化合物。共聚焦显微镜所需的时间可能取决于显微镜的配置和软件。

关键字:植物自噬, 自噬活性, 拟南芥, 串联标签, 体内自噬活性, 植物中自噬活性检测, 高通量自噬活性测定, 自噬潮

材料和试剂


 


1. 1.5毫升Eppendorf管       


2. 培养皿(Thermo Fisher Scientific,目录号:150239 )       


3. 移液器技巧       


4. 6孔组织培养板(Thermo Fisher Scientific,目录号:15213338)       


5. 微型盖玻片25 x 50 mm和25 x 25 mm(        VWR,目录号:48382-136和48366-089)


6. PVC密封膜(Phytotechlab ,产品ID:A003)或Parafilm(VWR,目录号:52859-079)       


7. 漂白液(克罗林,高露洁棕榄)     


8. Tween-20,聚山梨酯(VWR,目录号:97062 )     


9. 3D打印的种子电镀指南(https://www.thingiverse.com/thing:4016917)     


10. 的Murashige 和Skoog,MS培养基(DUCHEFA Biochemi ,目录号:M0222)液体介质  


11. MES,2-(N- 吗啉代)乙磺酸(Duchefa Biochemi ,目录号:M1503)  


12. 蔗糖(Duchefa Biochemi ,目录号:S0809)  


13. KOH(VWR,货号:470302)  


14. 植物琼脂(Duchefa Biochemi ,目录号:P1001)  


15. DMSO(西格玛奥德里奇,目录号:276855)或其他车辆  


16. AZD8055(Selleckchem ,目录号:S1555)  


17. 浸油(Zeiss,目录号:444960)  


18. 用于建立该方案的转基因拟南芥品系发表于Dauphinee 等人。(2019年)  


19. Milli-Q(MQ)水   


20. 液体0.5x MS介质(请参见食谱)   


21. 固体0.5x MS介质(请参阅食谱)   


22. 漂白溶液(请参阅食谱)   


23. DMSO中的ConA 1 mM 库存(请参阅食谱)   


 


设备


 


移液管为100-1,000微升和1-10 μ 升
   镊子(Dumont,货号:11251-10)
   4°C冰箱
   拟南芥生长室等/生长室:20-22°C,湿度50-70 %,1 50 μM的光
   共焦激光扫描显微镜(CLSM; Zeiss,LSM 800)




软件


 


斐济,ImageJ的版本,其中包含一组插件(https://fiji.sc/,对于本研究,我们使用versio ns 1.51s和2.0.0-rc-69 / 1.52i)
AuTToFlux 存储库,其中包含三个ImageJ宏和三个R脚本文件(https://github.com/jonasoh/AuTToFlux/archive/master.zip):
CalibrateThreshold.ijm
ImageProcessor.ijm
荧光强度
评估校准
Control-vs- Reporter.R
通量与时间
R(https://www.r-project.org,我们使用3.5.2和3.5.1)
RStudio(https://www.rstudio.com/,我们使用的版本为1.1.453和1.2.1186)
Git(https://git-scm.com/downloads,我们使用的是2.24.1版本)
R包:
dply r(https://CRAN.R-project.org/package=dplyr)
ggplot2(https://CRAN.R-project.org/package=ggplot2)
读取器(https://CRAN.R-project.org/package=readr)
 


程序


 


                种子灭菌(40分钟)
                             P 花边CA 。15 μ 升的记者和控制线种子放入1.5毫升Eppendorf管的。通常,目标是在实验中计划的每个治疗和时间点至少具有四到六个生物学重复。
                             添加CA 。1.5毫升的漂白剂溶液。
                             培育种子约。30分钟,激动。
                             在无菌条件下,用移液管吸出漂白剂溶液。
                             向种子中加入无菌水。混合并用移液器吸出。
                             用无菌水至少清洗3次以洗去残留的漂白液。
                             种子可以在无菌水中或按照程序C中所述进行春化处理。
 


                接种种子(40分钟)
                             用1 ml移液器将单粒种子转移到含有0.5x MS固体培养基的培养皿中。保持CA 。2 - 的相同行中的种子根避免缠绕到5mm之间的距离。保持CA 。行5之间厘米的距离(图? 重新小号2A- 2 B)。建议对此步骤使用接种平板指南(图1,图2A,https: //www.thingiverse.com/thing:4016917)。
                             用封口膜或密封膜条密封板。
 


D:\ Reformatting \ 2020-1-6 \ 1902885--1285 Alyona Minina 825729 \ Figs jpg \图2.jpg


图2. 拟南芥串联标签自噬通量测定。A. 9厘米培养皿的3D打印种子接种指南。B. 拟南芥使用种子电镀导(左半部分),并铺板种子个烯生长与板垂直放置5天(右半部分)。将拟南芥幼苗置于6孔板中进行处理。请注意,根部应轻轻浸没(白色箭头),并且不要在表面上浮动(红色箭头)。D.将苗安装在CLSM的载玻片(左)或盖玻片(右)上。E. 根分化区的大概起点F. 图像0、1和2分别代表报告基因系中分化区液泡的好,坏和丑陋质量扫描。G. 使用CalibrationThreshold.jim 宏在校准过程中生成的真空层掩模。请注意,丑陋的扫描F.可能会导致液泡检测完全或部分失败。H. 在所有分析图像上检测到的液泡总面积(建议的上限阈值为2)与。EvaluateCalibration.R 脚本生成的阈值。I. 车辆的Flux-vs- Time.R 脚本输出,以及使用ggplot2在R中生成的复合处理。比例尺= 1.5厘米(BD); 50 μm(E,F)。


 


                春化(24-48小时)
在4°C下于黑暗中孵育平板24-48小时。


 


                生长于垂直板秒(5个d AYS )
                             将板置于生长条件下(150 μM光照,22°C持续16 h,0 μM光照,20°C持续8 h)。确保板完全垂直,以确保根在培养基顶部生长(图2B)。
                             在平板上种植幼苗,大约保持10 分钟。5 天后,根长应达到约1 。2厘米
 


                药物治疗(2-24小时)
                             吸取3 ml 0.5x MS液体到6孔组织培养板的孔中。添加所需浓度的化合物。
                             使用软镊子,从平板上轻轻地挑出幼苗,然后转移到0.5x MS液体中(每孔不超过20棵幼苗)。
                             将培养基从孔中轻轻吸移到孔中的幼苗上,以淹没根部(白色箭头,图2C)。
                             用密封膜或封口膜密封板。
                             在相同的生长条件下孵育平板所需的时间。
 


                安装样品(1英里)
用移液器吸取一滴大约ca 的培养基。50 μ 升,从在25井毫米X 50毫米的盖玻片。
使用软镊子,从井中轻轻摘下幼苗,然后将其放在培养基滴中,确保根直。避免在转移过程中干燥根部!
在盖玻片上放置多达6个幼苗。
用25 mm x 25 mm的盖玻片覆盖根部(图2D)。
如果需要,可将其浸入物镜,并将样品放在显微镜载物台上。
 


                设置共聚焦激光显微镜的扫描参数(30分钟,仅需一次)
为了估计最佳的扫描设置范围,建议使用三种对照处理(500 nM AZD 8055持续4 h,500 nM ConA 持续6 h和相应量的媒介物(例如0.05%DMSO))进行中试实验。
配置两个通道的顺序扫描设置:
用于检测mWasabi的通道1 :在488 nm处激发,发射检测范围为490-564 nm 。
用于检测TagRFP的通道2 :在561 nm处激发,发射检测范围为564-700 nm 。
可取的是使用可用的最灵敏的检测器系统中的(即,磷砷化镓或路政署detcors 用于蔡司或徕卡CLSM,分别地)。
建议使用40倍物镜以获得最适合自动分析的图像。
为每个帧设置通道之间的切换,以最小化通道之间的串扰并优化针孔尺寸。如果可能,对每个通道使用1 AU的针孔。
如果可能,请使用16位分辨率来提高强度的分辨率。
用AZD 8055处理的样品将具有最弱的荧光,应用于将激光强度和检测器增益(主增益)调整到尽可能低的值,而不会导致像素过饱和。
用ConA 处理过的样品至少在绿色通道中将具有最强的荧光,应用于将激光强度和检测器增益(主增益)重新调整到尽可能低的值,而不会导致像素过饱和。
经DMSO处理的样品可用于验证调整后设置的适用性。
此外,可以通过降低扫描速度或增加平均次数来降低噪声。根据我们的经验,以每帧约10秒的速度扫描所产生的图像具有适合进一步分析的质量,同时还可以为实验提供可接受的时间。
 


               扫描(CA 。每次处理30分钟)
该测定被优化用于测量表皮根细胞中的自噬活性。下游图像分析依赖于绿色荧光通道中液泡的自动检测。因此,最佳情况下,应在焦平面上成像,在焦平面中每个细胞液泡都被清晰可见的细胞质包围。
大多数CLSM软件都具有在选定位置进行扫描的选项,这将大大减少实验所需的时间。
在根的分化区开始处标记位置(图2E)。
开始快速扫描模式(实时扫描),并通过表皮细胞的液泡将每个位置的焦平面重新调整到中间部分(图2F,有或没有皮质细胞质)
重新调整所有位置后,获取并保存图像。请注意,自动统计分析将使用图像名称中提供的信息。请使用以下规则在图像名称中引入必需的参数:
用下划线(_)分隔参数
命名文件如下:行_ 处理_seedling X _image Y (例如,Reporter_50uM.C12_seedling1_image2.czi )
其中line 和treatment 是文本变量或字符串,用于标识所使用的行(“ Reporter”或“ Control”)和处理(即“ 50uM.C12”)。一种治疗方法必须命名为“车辆”。X 和Y 是表示幼苗和图像重复的数字,因此分别代表生物学和技术重复。请注意,“种子”和“图像”是固定字符串,并且仅数字X 和Y 在图像之间变化。


 


数据分析


 


在分析之前需要处理数据。该处理分四个步骤完成:(i )将共焦图像转换成.TIF文件;(ii)调整阈值以优化对液泡的识别。首次使用该协议时必须执行此步骤,如果需要,可以重新进行单个实验;(iii)定量液泡中的荧光强度;(iv)定量比值和数据作图,以作为对照对报告基因系,或比对时间和浓度。


我们提供了演示数据(https://github.com/jonasoh/AuTToFlux),可用于确保分析按预期进行。


 


建立分析管道
有关更详细的说明,请参见视频1(https://youtu.be/6oY3CyvGFPk)。


                 将所有要处理的图像放入一个文件夹中。如果要处理多个实验,请将它们作为子文件夹放在一个文件夹中。
                 下载并安装Fiji,这是ImageJ的版本,其中包含用于此研究的一组插件(https://fiji.sc/),我们使用的版本为1.51s和2.0.0-rc-69 / 1.52i。
                 下载并安装R(https://www.r-project.org,我们使用3.5.2和3.5.1)和RStudio(https://www.rstudio.com/,我们使用版本1.1.453和1.2。 1186)。
                 克隆GitHub存储库(https://github.com/jonasoh/AuTToFlux)或将其下载为zip文件。如果要克隆存储库,则可以使用RStudio自动执行此任务:在RStudio中,启动一个新项目(文件->新建项目->版本控制-> GIT,然后输入以下URL:
https : //github.com/jonasoh/ AuTToFlux。该库被自动克隆到选定位置。
                 在首次运行R脚本之前,您需要确保已安装其依赖项。使用RStudio的界面来安装软件包,然后安装软件包ggplot2,dplyr 和readr ,或者通过在控制台中键入以下命令来手动安装它们:
 


install.packages (c(“ ggplot2”,“ dplyr ”,“ readr ”))


 


C:\ Users \ Bio-Dandan \ Dropbox \ Refomatting \ 2020-3-05 \ 3535--1902885--1285 Alyona Minina 825729 \ video 1.jpg


视频1. R Studio中的存储库版本控制


 


将图像转换成TIFF文件
?F 或更详细的说明小号EE V 记意2(https://youtu.be/nKPq0kNvW_U)。


将所有要处理的图像放入一个文件夹中。如果要处理多个实验,请将它们作为子文件夹放在单个文件夹中。
运行图像处理器:启动Im ageJ并转到插件->宏->运行->找到ImageProcessor.ijm 。
图像处理器宏将打开目标文件夹中的所有兼容图像,并将它们另存为.TIF文件(文件扩展名为。tif );图像的原始采集日期保存在单独的文件中。使用“位置”功能扫描的实验中的多图像文件被拆分,以便将每个图像保存为单独的.TIF文件。
 


C:\ Users \ Bio-Dandan \ Dropbox \ Refomatting \ 2020-3-05 \ 3535--1902885--1285 Alyona Minina 825729 \ video 2.jpg


视频2.图像处理


 


选择适当的阈值
?F 或更详细的说明见V 记意3(https://youtu.be/Wkw3VXFj2is)。将ImageProcessor 生成的至少三张TIFF图像复制到一个单独的文件夹中。旨在选择代表最佳,最差和平均质量的图像。


启动ImageJ并转到Plugins-> Macros-> Run->定位CalibrateThreshold.ijm 。这将运行宏。在立即显示的文件选择器中,找到具有代表性图像的文件夹。宏将记录与液泡相对应的区域大小,并将掩码另存为名称中包含阈值的tiff文件,例如,名为* .thr0-3.tiff的文件将对应于使用阈值(0; 3)创建的掩码。结果概述将作为阈值概述.tif 生成(图2G)。
启动RStudio。
打开RStudio项目文件(AuTToFlux.Rproj )。
要估算哪些阈值可为所有分析图像提供最大的所选液泡面积,请运行EvaluateCalibration.R 脚本(选择R脚本,单击“代码”->“源”,然后选择包含在上一步中生成d 的.CSV文件的文件夹– 注意:在非Windows系统上,您需要输入文件夹的路径名)。
该脚本将绘制在所有分析图像上选择的平均总液泡面积与阈值的关系图。选择与最大面积相对应的阈值(图2H)。
使用从阈值概述和阈值图收集的信息,选择适当的上阈值进行标记。遮罩不应包含任何背景区域。
在继续进行图像阈值处理和数据分析之前,请确保已从包含已处理图像的目录中删除了校准文件夹。
 


C:\ Users \ Bio-Dandan \ Dropbox \ Refomatting \ 2020-3-05 \ 3535--1902885--1285 Alyona Minina 825729 \ video 3.jpg


视频3. 阈值校准


 


测量荧光强度
有关更详细的说明,请参阅V 记意4(https://youtu.be/6jYqkYXOpiQ)。


运行阈值宏:启动ImageJ->插件->宏->运行->查找并打开宏文件FluorescenceIntensity.ijm 。
选择包含要分析的图像的文件夹,该文件夹先前已使用图像处理器宏进行了转换(步骤B)。
宏将提示您选择在校准过程中较早确定的阈值。对于每个图像,阈值宏会使用GFP通道自动选择与液泡相对应的区域,并将所选区域的遮罩保存为TIFF文件。然后使用掩模对图像相应通道中的RFP和GFP荧光强度进行定量。液泡RFP / GFP荧光强度的比率保存到文件名匹配的.CSV文件中。
 


C:\ Users \ Bio-Dandan \ Dropbox \ Refomatting \ 2020-3-05 \ 3535--1902885--1285 Alyona Minina 825729 \ video 4.jpg


视频4.荧光强度测量


 


分析数据
为了估计剂量和时间依赖性反应,使用Flux-vs - Time.R将RFP / GFP比率与时间作图。有关更详细的说明,请参阅V 记意5 (https://youtu.be/0_wDY7RN_hk)。


在包含要分析的.CSV文件的文件夹中的文件夹中创建一个info.txt文件。治疗的初始时间(格式为YYYY-MM -DD HH:MM,24小时的时间)是用户指定的通过创建一个制表符分隔文本文件(使用任何电子表格软件,例如,Microsoft Excel)中有两列,这应该保存为info.txt,例如 ,
治疗开始时间             


车辆2018-07-06 09:50             


治疗2018-07-06 10:00             


启动RStudio。
在从GitHub存储库创建的目录中找到R项目文件,如果尚未打开,则将其打开:
                                                                  在R中,选择Flux-vs- Time .R ,单击Code- > Source,然后选择包含在步骤D中生成的.CSV文件的文件夹。
                                                                   实验数据的汇总是根据与媒介物归一化的比例生成的,并按处理量和幼苗数进行分组(图2I)。要绘制数据,我们建议使用ggplot2(https://ggplot2.tidyverse.org/),轴(x =经过的时间,y =归一化的比例,颜色=处理)
                                                                  进一步的统计分析将取决于实验中存在的处理方式,并且可以使用R或其他软件(例如Origin,JMP)执行。
                                                                  该数据还可以用于使用估计IC50 R,例如,与所述的辅助DRC 包(里兹等人,2015) ,或其他软件,如棱镜。
 


C:\ Users \ Bio-Dandan \ Dropbox \ Refomatting \ 2020-3-05 \ 3535--1902885--1285 Alyona Minina 825729 \ video 5.jpg


视频5.通量与时间的关系


 


为了证明自噬依赖性反应,绘制了对照品系的RFP / GFP比例与报告基因品系的比例。有关更多详细说明,请参见V ideo 6(https://youtu.be/PQRZ1oOBgws):


启动RStudio。
在从GitHub存储库创建的目录中找到R项目文件,如果尚未打开,则将其打开:
                             在R中,选择Control-vs- Reporter.R ,单击Code- > Source,然后选择包含在步骤D中生成的.CSV文件的文件夹。
                             该脚本可以将包含多种处理方式的文件夹作为子文件夹,也可以将不包含子文件夹的文件夹作为一个实验作为其参数。
                             该脚本总结了RFP / GFP比率(归一化为媒介物)和按品系(对照或报告基因),处理和幼苗数量分组。使用未配对的两尾学生t 检验比较对照组和报告者组的对数转换归一化均值。生成一个名为pvals.txt的摘要表。如果需要从置换派生的P值(即精确的P值),请取消注释R脚本中标记的适当部分。
注意:?F 或两者控制-VS- Reporter.R 和焊剂VS- Time.R ,数据被保存既作为原始数据(如在实验目录摘要-full.txt)和作为每苗汇总统计(总结- perseedling.txt),以利于使用其他统计软件进行分析。


 


C:\ Users \ Bio-Dandan \ Dropbox \ Refomatting \ 2020-3-05 \ 3535--1902885--1285 Alyona Minina 825729 \ video 6.jpg


视频6. 控件与记者


 


笔记


 


有关进一步的故障排除步骤,请参见表1。


 


表1.故障排除


问题


可能的原因





数据点数量少


图像上的高噪点


调整扫描设置以减少噪音


在数据分析过程中调整阈值


 


不集中在液泡的中间部分


确保样品的根在成像过程中没有漂移,并且放置在溶液的最薄层中


 


FluorescenceIntensity.ijm 宏中的阈值错误


请按照过程中的说明估算最佳值并调整宏


Flux-v- Time.R中分配的组不正确


每个时间点的数据点数量不同


该脚本要求每个组的数据点数大致相等。删除或添加数据点以平衡实验。


时间-V- Flux.R 退出与错误“错误的eval (EI ,ENVIR ):文件存在(infofile )是不是真正的”


目录中没有info.txt。


根据说明创建info.txt文件。


 


表1. 继续


Time-v- Flux.R 提供了意外的结果


info.txt中的时间与图像的获取时间不匹配。


确保info.txt中的时间与显微镜记录的时间相对应。


 


info.txt中的处理与文件名不匹配。


确保在文件名和info.txt中给处理名称完全相同。


数据点重复


ImageProcessor 已在同一组图像上运行两次。


删除所有 。tif 目录中的文件,然后直接从显微镜在文件上重新运行ImageProcessor 。


脚本存在错误“ 文件与命名方案不匹配”。


文件命名错误。


仔细检查所有文件名是否与协议中描述的命名方案匹配。


 


菜谱


 


液体0.5x MS介质
0.5x MS含维生素,10 mM MES,1%蔗糖溶解在MQ水中
使用KOH将pH调节至5.8,并在120 °C下高压灭菌20'
最高储存在4°C。2个月
固体0.5x MS介质
调节液体0.5 x MS介质的pH值后,添加0.8 %的植物琼脂并在120 °C 高压灭菌20 分钟
最高储存在4°C。2个月。如果需要,可将介质在微波中液化
漂白液
5%的Klorin (最终浓度:2.7 g / L 次氯酸钠),MQ水中的0.01%Tween-20
准备在50毫升Falcon试管中并保存在室温下
后约。1个月的Tween-20可能在溶液中形成沉淀,但是,这不会影响灭菌效果
DMD中的AZD 8055 5 mM库存
储存在-20°C下 2年


DMSO中的ConA 1 mM 库存
储存在-20°C下 1年


 


致谢


 


该项目得到了卡尔· Tryggers 基金会(授予EAM),加拿大自然科学与工程研究委员会(NSERC)(与AND)以及瑞典环境,农业科学与空间规划研究委员会(Formas )的支持,项目编号为2016-20031。此协议,建立了研究进行由Dauphinee 等。(2019)


 


利益争夺


 


作者宣称没有任何竞争的经济利益。


 


参考文献


 


Dauphinee,AN,Cardoso,C.,Dalman,K.,Ohlsson,JA,Berglund Fick,S.,Robert,S.,Hicks,GR,Bozhkov ,P.和Minina ,EA(2019)。化学筛选管道,用于鉴定特定的植物自噬调节剂。植物生理学181(3):855-866 。
Hanamata,S.,Kurusu,T.,Okada,M.,Suda ,A.,Kawamura,K.,Tsukada ,E.和Kuchitsu (2013)。烟草BY-2细胞中自噬通量的体内成像和定量监测。植物信号Behav 8(1):e22510。
黄河,徐玉,万万,寿X.,钱J.,尤Z.,刘冰,常昌,周T.,Lippincott-Schwartz,J 。and Liu,W.(2015)。饥饿中,核LC3的脱乙酰基驱动自噬的启动。分子池57(3):456-466。
Klionsky,DJ,Abdelmohsen,K.,Abe,A。,等。(2016)。监测自噬的测定方法的使用和解释指南(第3版)。自噬12 :1554-8635(在线)。
周成中,钟文成,周洁。,盛芳芳,方正。,魏燕。,陈燕芳,邓小霞,夏冰,林洁( 2012)。通过改进的串联荧光标记的LC3(mTagRFP-mWasabi-LC3)监测自噬通量,发现高剂量雷帕霉素会损害癌细胞中的自噬通量。自噬8(8):1215-1226。
Ritz,C.,Baty ,F.和Strebig ,J.C .(2015)。使用R进行剂量反应分析。PLoS 一10(12):e0146021。

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Copyright: © 2020 The Authors; exclusive licensee Bio-protocol LLC.
引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Dauphinee, A. N., Ohlsson, J. A. and Minina, E. A. (2020). Tandem Tag Assay Optimized for Semi-automated in vivo Autophagic Activity Measurement in Arabidopsis thaliana roots. Bio-protocol 10(5): e3535. DOI: 10.21769/BioProtoc.3535.
  2. Dauphinee, A. N., Cardoso, C., Dalman, K., Ohlsson, J. A., Berglund Fick, S., Robert, S., Hicks, G. R., Bozhkov, P. and Minina, E. A. (2019). Chemical screening pipeline for identification of specific plant autophagy modulators. Plant Physiol 181(3):855-866.
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