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Apr 2018
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Three-dimensional Reconstruction and Quantification of Proteins and mRNAs at the Single-cell Level in Cultured Cells
培养细胞中蛋白和mRNA在单细胞水平上的三维重构和量化分析   

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Abstract

Gene expression is often regulated by the abundance, localization, and translation of mRNAs in both space and time. Being able to visualize mRNAs and protein products in single cells is critical to understand this regulatory process. The development of single-molecule RNA fluorescence in situ hybridization (smFISH) allows the detection of individual RNA molecules at the single-molecule and single-cell levels. When combined with immunofluorescence (IF), both mRNAs and proteins in individual cells can be analyzed simultaneously. However, a precise and streamlined quantification method for the smFISH and IF combined dataset is scarce, as existing workflows mostly focus on quantifying the smFISH data alone. Here we detail a method for performing sequential IF and smFISH in cultured cells (as described in Sepulveda et al., 2018) and the subsequent statistical analysis of the smFISH and IF data via three-dimensional (3D) reconstruction in a semi-automatic image processing workflow. Although our method is based on analyzing centrosomally enriched mRNAs and proteins, the workflow can be readily adapted for performing and analyzing smFISH and IF data in other biological contexts.

Keywords: Single-molecule imaging (单分子成像), Centrosome (中心体), Image processing (图像处理), Quantification (量化分析), mRNA distribution (mRNA分布), 3D reconstruction (3D重构), Imaris (Imaris), MATLAB (MATLAB)

Background

smFISH is a technique to visualize individual RNA molecules using multiple short fluorescently-labeled DNA oligonucleotides (“probes”) complementary to the target RNA (Femino et al., 1998; Raj et al., 2008). In this technique, when an ensemble of short fluorescent DNA probes is bound at the target RNA, robust signals are produced as opposed to the weak signals produced by a single probe. This feature enhances the signal-to-noise ratio to reveal the location of the target RNAs, even if a single probe may have off-target binding. smFISH provides information about the RNA abundance and subcellular localization of a given RNA at the single-molecule and single-cell levels. Furthermore, combined with IF to visualize proteins, both RNAs and proteins of interest can be analyzed simultaneously in the same cell. However, tools for analyzing both smFISH and IF data are not broadly available. A widely used tool for analyzing smFISH data is FISH-quant (Mueller et al., 2013; Tsanov et al., 2016). It is a freely available software package that greatly streamlines the image analysis and mRNA spot identification. However, most quantification tools (e.g., Mueller et al., 2013; Lee et al., 2016; Tsanov et al., 2016), including FISH-quant, focus on the quantification of the smFISH data and do not intergrade the analysis of the IF data in the pipeline. Here, we detail a streamlined workflow for performing, acquiring, and analyzing sequential IF and smFISH data via 3D reconstruction in Imaris software, followed by quantifications using MATLAB and R scripts. We use co-translational targeting of pericentrin (PCNT) polysomes to the centrosome during mitosis in adherent cultured cells as an example (Sepulveda et al., 2018) to demonstrate how to use Imaris software to reconstruct the PCNT mRNAs and proteins in 3D confocal z-stacks and to apply MATLAB and R scripts to quantify their intensities, volumes, and relations (e.g., spatial distribution of molecules and overlapping between signals) in a semi-automatic manner. Our protocol can be readily applied to other sequential IF and smFISH experiments to precisely quantify RNAs and proteins in 3D space.

Materials and Reagents

  1. 1-1/2 micro circular glass coverslip–12 mm Diameter (Electron Microscopy Sciences, Hatfield, PA, catalog number: 72230-01)
  2. Cellstar® 24-well cell culture multiwell plate (Greiner Bio-one North America, Inc., Monroe, NC, catalog number: 662160)
  3. 15-cm Petri dish (Corning, New York, catalog number: 351058)
  4. Parafilm (Parafilm M, Bemis, Fisher Scientific, Waltham, MA, catalog number: 13-374-12)
  5. Paper towel (Fisher Scientific, Waltham, MA, catalog number: 19-040-898)
  6. HeLa cells (ATCC, CCL-2) (a gift from Susan Wente, Vanderbilt University, Nashville, TN; RRID: CVCL_0030)
  7. UltraPureTM distilled water (DNase and RNase-Free) (Invitrogen, Carlsbad, CA, catalog number: 10977-015)
  8. Formaldehyde, Para (PFA) (Fisher Chemical, Waltham, MA, catalog number: O4042-500)
  9. Phosphate Buffered Saline (PBS), pH 7.4 (Fisher BioReagents, Pittsburgh, PA, catalog number: BP399-1)
  10. TritonTM X-100 (Fisher BioReagents, Pittsburgh, PA, catalog number: BP151-500)
  11. Rabbit anti-PCNT antibody (Abcam, Cambridge, MA, catalog number: ab4448)
  12. Goat anti-Rabbit IgG (H+L) cross-adsorbed secondary antibody, Alexa Fluor 488 (Invitrogen, Carlsbad, CA, catalog number: A-11008
  13. 37% formaldehyde solution (Fisher BioReagents, Pittsburgh, PA, catalog number: BP531-500)
  14. Formamide (Sigma-Aldrich, Burlington, MA, catalog number: F9037)
  15. Prolong Gold antifade mountant (Life Technologies, Carlsbad, CA, catalog number: P36930)
  16. Stellaris® RNA FISH Hybridization Buffer (Biosearch Technologies, Petaluma, CA, catalog number: SMF-HB1-10)
  17. Stellaris® RNA FISH Wash Buffer A (Biosearch Technologies, Petaluma, CA, catalog number: SMF-WA1-60)
  18. Stellaris® RNA FISH Wash Buffer B (Biosearch Technologies, Petaluma, CA, catalog number: SMF-WB1-20)
  19. Stellaris® RNA FISH probes labeled with Quasar 670 (Biosearch Technologies, Petaluma, CA)
  20. 4′,6-Diamidino-2-phenylindole dihydrochloride (DAPI) (Sigma-Aldrich, Burlington, MA, catalog number: D9542)

Equipment

  1. Leica DMi8 with 63x/1.40 or 100x/1.40 HC PL APO objectives equipped with a spinning disk confocal microscope system (Dragonfly, Andor Technology, Belfast, UK)
  2. iXon Ultra 888 EMCCD camera (Andor Technology, Belfast, UK)

Software

  1. Fusion Software (Andor Technology, Belfast, UK)
  2. Imaris 8.3.1 (Bitplane, Belfast, UK; Imaris, RRID: SCR_007370)
  3. MATLAB (MathWorks, Natick, MA; RRID:SCR_001622)
  4. Microsoft Excel (Microsoft, Redmond, WA; RRID:SCR_016137)
  5. R (R Project for Statistical Computing; RRID: SCR_001905)
  6. ImageJ (Image Processing and Analysis in Java; RRID: SCR_003070)

Procedure

  1. Sequential immunofluorescence (IF) and single-molecule RNA fluorescence in situ hybridization (smFISH)
    Note: We follow the sequential IF and smFISH protocol described by the manufacturer of smFISH probes (Biosearch Technologies, Petaluma, CA) with some modifications detailed below.

    IF
    Note: Use nuclease-free water for preparing all reagents for the sequential IF and smFISH experiment to limit potential RNA degradation.
    1. Place a 12-mm circular coverslip to each well of a 24-well plate. Seed about 5 x 104 cells to each well. Allow cells to attach onto the coverslips.
    2. When the cells reach 75-90% confluency in each well (12-18 h for most human cultured cells), fix cells with 4% paraformaldehyde in phosphate buffered saline (1x PBS) for 10 min at room temperature (RT), followed by two quick washes with 1x PBS.
      Note: To perform quick washes, gently transfer 1 ml of PBS to each well and then aspirate the solution immediately.
    3. Permeabilize cells with 0.1% Triton X-100 in 1x PBS for 5 min at RT, followed by one quick wash with 1x PBS.
    4. Incubate cells (on a 12-mm circular coverslip) with 70 µl of primary antibody solution for 3 h at RT (e.g., 1:1,000 dilution of rabbit anti-PCNT antibody in 1x PBS in our example).
    5. Perform three 5-min washes with 1x PBS and incubate the cells with 70 µl of secondary antibody solution overnight in the dark at 4 °C (e.g., 1:500 diluted anti-rabbit Alexa Fluor 488).
      Note: The dilution factor and incubation time of the primary and secondary antibodies need to be empirically determined; the sample condition described is for detecting PCNT proteins. To perform 5-min washes, gently transfer the coverslip to a well in a 24-well plate containing 1 ml of 1x PBS. Incubate for 5 min at RT without shaking the plate.
    6. Perform three 5-min washes with 1x PBS and post-fix cells with 3.7% formaldehyde in 1x PBS for 10 min at RT.

    smFISH

    1. Wash cells with Wash Buffer A for 5 min and incubate with 67 µl of Hybridization Buffer containing 125 nM smFISH DNA probe mix for 6 h at 37 °C in the dark.
      Note: We use the online Stellaris Probe Designer from Biosearch Technologies to design the FISH probes. We generally use a set of 48 to 96 20-mer DNA probes for each RNA target. For best results, add 10% formamide into 1x Wash Buffer A and Hybridization Buffer immediately before use.
    2. Incubate cells with Wash Buffer A for 30 min at 37 °C, Wash Buffer A containing 0.05 µg/ml DAPI for 30 min at 37 °C, and Wash Buffer B for 3 min at RT.
    3. Mount the coverslip with enough Prolong Gold antifade mountant (e.g., 5 µl for a 12-mm circular coverslip). Let the mountant cure overnight in the dark at RT and seal the edge of the coverslip with nail polish before imaging. 

      Note: For the best results, perform the incubations of antibody and smFISH probes in a humidified container (e.g., a 15-cm Petri dish with a piece of parafilm and wet paper towel inside, wrapped with aluminum foil to block light).


  2. Image acquisition and deconvolution
    Imaging
    Note: When using multiple fluorophores, it is important to carefully select the wavelength ranges, emission filters, and dichroic mirrors to avoid signal bleed-through. Use settings to minimize photobleaching but also maximize the signal-to-noise ratio. A camera with high sensitivity is preferred to obtain good smFISH signals (e.g., an electron multiplying CCD camera). We recommend acquiring images sequentially, channel by channel, starting from the longest wavelength. Below are the general IF and smFISH acquisition settings using the Dragonfly spinning confocal system in the Jao lab.
    1. Objective: 100x NA 1.4 (as well as placing a 1.5x or 2x magnification lens before the camera)
    2. Laser power: between 5-30% of 50-100 mW lasers
    3. Exposure time: 100-250 ms
    4. Gain: 200 for iXon Ultra 888 EMCCD camera
    5. Pinhole: 40 µm
    6. Z-stack interval: 0.3 µm
    7. For assessing colocalization between different fluorescent signals, acquire all channels for each z-stack.

    Deconvolution
    Note: It is important to avoid potential artifacts introduced during the deconvolution process by comparing the pre- and post-deconvolved images. In general, the higher the number of iterations, the more likely image artifacts will be produced. Below are our typical deconvolution settings using the built-in deconvolution algorithm in the Fusion software of the Dragonfly system.
    1. Deconvolution preview mode: ON
    2. Algorithm: Robust (Iterative)
    3. Pre-sharpening: OFF
    4. Edge artifact reduction: none
    5. Denoising filter size: 0.7
    6. Denoising frequency: 4
    7. Number of iterations: 15
    8. Minimum intensity removal: ON
    9. Initial denoising: ON
    10. Normalization: OFF
    11. Iteration acceleration: OFF

Data analysis

  1. 3D reconstruction and image rendering in Imaris
    1. Prior to image processing, in ‘File/Preference/Statistics’, turn on relevant statistical parameters. Below are the parameters we routinely use.
      1. Surfaces: intensity max, intensity mean, intensity sum; position x, position y, position z; total number of surfaces; volume.
      2. Spots: intensity max, intensity mean, intensity sum; position x, position y, position z; total number of spots; volume.
      3. Volume

        Note: The term ‘Statistics’ in Imaris refers to different quantitative descriptions associated with each object (e.g., area, volume, intensity, etc.). It does not refer to a statistical analysis of the data.

    2. Extract and mask a 3D cell outline with the following steps in Imaris:
      1. Open the deconvolved image.
      2. Click ‘Edit/Add Channels’: Select the corresponding original image.
      3. In the menu, select ‘Surpass/Add new surfaces’ (Figure 1A); select ‘Skip automatic creation, edit manually’ (Figure 1B).
      4. Move to the slice position with the clearest outline.
      5. Switch pointer to select mode and select ‘Draw’ option to mark the cell outline (Figures 1C and 1D).
        Note: The cell outline in our analyses was marked based on the visualization of the background noise by adjusting the channel max intensity under ‘display adjustment’. Users may use other membrane or cytoplasmic markers to visualize the cell boundary.
      6. Copy and paste the contour lines to the first and last image planes (Figure 1D).
      7. Select manual ‘Resolution’ and adjust it to the largest size (Figure 1D). Click ‘Create Surface’.
      8. Make cell mask (Figures 1E and 1F): Select ‘Mask All’. The box ‘Duplicate channel before applying mask’ should be selected. Set ‘voxels outside surface’ to ‘0’ and ‘inside surface’ to a high value within the bit depth of your image (e.g., ‘30,000’ in this example). This creates a binary mask of the cell of interest.


        Figure 1. Cell outlining in Imaris. A. Add new surfaces. B. Skip automatic creation and edit manually. C and D. Draw contours lines to mark the cell outline. E and F. Mask channel of the cell outline and set voxels.

    3. Create 3D Surface object(s) for protein(s) of interest in Imaris:
      1. Add a new Surface object (Figure 1A). Click the blue arrow to continue to the next page (Figure 2A). For ‘Source Channel’, select the deconvolved channel of protein of interest. Check the box ‘Smooth’, and the value of ‘Surfaces Area Detail Level’ is automatically calculated and kept as the default. Select ‘Absolute Intensity’ for thresholding (Figure 2B).
        Note: The ‘Smooth’ command applies a Gaussian filter to the image to facilitate the identification of surface objects for noisy images. It reduces the noise and detail included in the rendered surface. If ‘Smooth’ is selected, the ‘Surface Area Detail Level’ parameter is automatically determined from the image. Users can alter this parameter to remove more or less surface details. The higher this parameter, the less detail this surface rendering will include.
      2. ‘Threshold (Absolute Intensity)’ has an automatic value by Imaris (Figure 2C). Increase or lower this threshold to include only the signal of interest. Use the same value across images to be compared.
      3. There is an automatic filter based on voxel size. This can be left at the default of 10 or adjusted depending on the size of interest.
      4. Add Filters: Select ‘Intensity Max of the masked channel number of the cell outline’ generated in step 2. This step restricts the fitting of any protein signals within the outlined cell (Figure 2D).
      5. Click ‘Finish’ to complete the rendering of protein surface (Figure 2D). Examples are in Figure 4.

        Note: If there are multiple proteins of interest per cell, repeat step 3 to generate a surface rendering for each protein.


        Figure 2. 3D reconstruction of protein surfaces. A and B. Select the source channel and set surface area detail level. C. Adjust absolute intensity threshold to capture all signals. D. Add filters to restrict fitting of protein signals within the outlined cell.


    4. Create 3D spot object(s) for RNA of interest in Imaris:
      1. Add a new spot object (Figure 3A).
      2. Select ‘Different Spot Size (Region Growing)’ (Figure 3B).
      3. For ‘Source Channel’, select the deconvolved channel of RNA of interest (Figure 3C).
      4. For ‘Spot Detection’, enter ‘Estimated XY Diameter and Z Diameter’ of RNA of interest (Figure 3C).
        Note: Determine the XY diameter by measuring the average size of several RNA smFISH foci in the original confocal image via ImageJ with reference to a scale bar. The Z diameter is set two times of the XY diameter by Imaris as the default.
      5. Imaris calculates a ‘spot quality’ (Figure 3D) based on intensity differences and shapes for spot rendering. This value should be adjusted to include the signal of interest.
        Note: We found that the spot quality by the default calculation is often inaccurate in marking the true RNA spots. Users need to manually increase or lower the threshold to ensure that the true RNA spots are marked, but the noises (e.g., small dot-like background signals) are excluded.
      6. Add Filter: Select ‘Intensity Max = masked channel number of the cell outline’ generated in step 2. This step restricts the fitting of any RNA signals within the outlined cell (Figure 3D).
      7. Select ‘Spot Region from Absolute Intensity’ (Figure 3E).
      8. Spot Region Threshold is pre-calculated by Imaris (Figure 3F).
        Note: Users may need to manually increase or decrease the region threshold to ensure that all the RNA signals are rendered in the final step.
      9. Click ‘Finish’ to complete the rendering of RNA spots (Figure 3F). Examples are in Figure 4.


        Figure 3. 3D reconstruction of RNA spots. A and B. Create a new spot and select different spot sizes (region growing). C and D. Select source channel and set spot detection. (D) Adjust thresholds and add filters. E and F. Select absolute intensity and adjust region threshold.


        Figure 4. Examples of before (Confocal) and after (3D rendered) 3D reconstruction of the confocal images from a sequential IF and smFISH experiment

    5. This is an additional process to determine if the 3D reconstructed protein surfaces and RNA spots are overlapped.
      1. Click the protein surface object generated in step 3 and mask channel of the protein surface (Figures 5A and 5B): Select ‘Mask All’. Choose the deconvolved channel of the protein surface as Source Channel, and check the box ‘Duplicate channel before applying mask’. Set ‘voxels outside surface’ to ‘0’ and inside surface to the highest voxel values of your imaging system (e.g., ‘30000’ in this example). This will generate a masked channel of protein surface (e.g., Channel number 8 in Figure 5F).
      2. Click the RNA spot object generated in step 4. Add Filter (Figure 5C) and select ‘Intensity Max [channel number] = masked [channel number] of protein surface’ generated in step 5a; this is a binary channel with values either 0 or the high number selected earlier. Apply the upper threshold to select for overlapping RNA spots. Click ‘Duplicate Selection to new Spots’ (Figure 5D).
        Note: If one or more pixels of an RNA spot overlaps with the masked protein surface, it is counted as an overlapping spot.
      3. Repeat the process (step 5b) but apply the low threshold (i.e., spots with a value of 0 in the masked channel) to select for non-overlapping RNA spots. Click ‘Duplicate selection to new Spots’ (Figure 5E).
        Note: If no pixel of an RNA spot overlaps with the masked protein surface, it is counted as a non-overlapping spot.
    6. Repeat steps 1 to 5 for each image. Export the processed images to a folder.
      Note: During image processing, keep the order consistent for generation of cell outlines, protein surfaces, and RNA spots in Imaris (Figure 5F) so that the corresponding numbers of channels, protein surfaces, and RNA spots are identical for a given set of images. This thus allows a consistent definition of protein(s) or RNA(s) of interest when running the MATLAB scripts.


      Figure 5. Detection of the RNA spots that overlap with the protein signals. A and B. Mask protein surface of interest. C-E. Filtering overlapping and non-overlapping RNA spots. F. Illustration of channel numbers, protein surface numbers, and RNA spot numbers.

  2. Process and export data from Imaris using MATLAB scripts
    Note: To set up MATLAB, do the following first:
    1. Open MATLAB software; select ‘HOME/Set Path’: Add MATLAB search path as the location of the image folder
    2. Prior to using ImarisReader for data analysis in MATLAB, download the ImarisReader .zip from the MATLAB Central File Exchange. Save the files within the MATLAB search path.

    Processing note: Below are the examples of using MATLAB and R scripts to quantify the intensities (RNA or protein, total or centrosomal) and RNA distribution relative to the nearest centrosome after rendering of the RNA and centrosomal protein signals in Imaris. See Sepulveda et al., 2018 for examples of the final plots.
    1. Quantification of total RNA and protein intensities
      1. Open ‘total_protein_and_RNA_intensity.m’ (Supplementary files) in MATLAB.
      2. Define ‘fpath’ as the location of the images (e.g., line 14 in Figure 6).
      3. Define protein and RNA of interest for quantification. These are the ‘protein_surface_number’ in line 18 and ‘spot_number’ in line 19 in Figure 6. 
        Note: For example, in Figure 5F, ‘Protein surface number 2’ is the Surface object created for the protein of interest. You will thus define ‘protein_surface_number = 2’ in line 18 in Figure 6. ‘RNA spot number 1’ is the Spot object created for the RNA of interest. You will thus define ‘spot_number = 1’ in line 19 in Figure 6.
      4. Define the original channel numbers for protein and RNA of interest. These are ‘ori_protein_channel_number’ in line 23 and ‘ori_RNA_channel_number’ in line 24 in Figure 6. 
        Note: For example, in Figure 5F, the original channel of protein and RNA of interest are channel 5 and channel 4, respectively. You thus define ‘ori_protein_channel_number = ‘5’’ in line 23 and ‘ori_RNA_channel_number = ‘4’’ in line 24 in Figure 6.
      5. Run the script. The original protein and RNA intensities within the rendered protein surface and RNA spots will be exported into a .csv file in the image folder, which can be opened by Microsoft Excel.


        Figure 6. Quantification of total protein and mRNA intensities from the original (pre-deconvolved) confocal images. A highlight of the MATLAB script lines. See Supplementary files for the complete script.

    2. Quantification of protein intensity at the centrosomes
      Note: This is an example of quantifying protein intensity in a subcellular area of interest. Here we quantify the protein intensity from the two centrosomes during mitosis. The script isolates the two centrosomes from the rest of the cell by defining them as the first and second largest protein surface volumes of the anti-PCNT signals.
      1. Open ‘centrosomal_protein_intensity.m’ (Supplementary files) in MATLAB.
      2. Define ‘fpath’ as the location of the images (e.g., line 14 in Figure 7).
      3. Define the surface number for protein of interest for quantification (e.g., line 17 in Figure 7).
      4. Define the original and deconvolved channel numbers for protein of interest (e.g., in lines 21 and 22, respectively, in Figure 7).
      5. Run the script to obtain a .csv file in the image folder.


        Figure 7. Quantification of the protein intensity at the centrosomes from the original (pre-deconvolved) confocal images. A highlight of the MATLAB script lines. See Supplementary files for the complete script.

    3. Quantification of mRNA distribution relative to the centrosome
      Note: This is an example of quantifying the distribution of mRNA molecules to the centrosome as a function of distance. The intensity of the mRNA signal in each spot is assumed to be proportional to the amount of mRNA in each spot and is used in lieu of mRNA units. The distance from each mRNA spot to each centrosome’s center of mass is calculated, and the mRNA signal is assigned to the closest centrosome. The mRNA spots were binned in 0.5 µm intervals to the centrosome, and the spot intensities in each bin were added as a measure of the amount of mRNA at that distance. This is calculated for each cell and then averaged over all the cells of a given condition and graphed as average mRNA intensity as a function of distance to the closest centrosome within the cell in 3D voxels.
      1. Open ‘mRNA_distribution.m’ (Supplementary files) in MATLAB. 
      2. Define ‘fpath’ as the location of the images (e.g., line 14 in Figure 8).
      3. Define the surface numbers for protein and mRNA for quantification (e.g., line 18 for protein surface and line 19 for RNA spots in Figure 8).
      4. Define the deconvolved channel number of RNA (e.g., line 22 in Figure 8).
        Note: We use RNA intensities from the deconvolved channel to eliminate the noise or background signals.


        Figure 8. Quantification of mRNA distribution relative to the centrosome. A highlight of the MATLAB script lines. See Supplementary files for the complete script.

      5. Run the script to obtain .csv files in the image folder. In each .csv file, each row represents the distance relative to the centrosome, from 0 µm (first row) to 20 µm (last row) with 0.5 µm intervals (Figure 9). Each column represents the amount of mRNA at each distance relative to the nearest centrosome (Figure 9).


        Figure 9. An example of the data in the columns and rows of a .csv file generated by the ‘mRNA_distribution.m’ script

        Note: Below are the steps to use an R script to combine all .csv files from multiple cells into a single spreadsheet for further quantification (e.g., calculation of the normalized intensity as the ratio of each data point over the total intensity in each column).
      6. Group all .csv files into a new folder.
      7. Open ‘column_combination_and_normlization’ (Supplementary files) in R. 
      8. Specify the working directory and the location of the .csv files (e.g., in lines 11 and 12, respectively, in Figure 10). 
      9. Run the R script to obtain two .csv files within the new folder: (1) combined original dataset; (2) combined normalized dataset.


        Figure 10. Using an R script to combine and normalize the data in multiple .csv files. A highlight of the R script lines. See Supplementary files for the complete script.

    4. Quantification of the RNA signals that overlap with the protein signals within a defined distance to the centrosome.
      Note: This is an example of quantifying how much of the PCNT mRNA overlaps with the anti-PCNT N-terminus protein signals within a defined distance from the centrosome; the overlap of these two signals outside of the centrosome suggests that these mRNAs are undergoing active translation. The distance from each overlapping or non-overlapping mRNA spot (separated in Imaris as explained above) to each centrosome’s center of mass is calculated, and the mRNA signal is assigned to the closest centrosome. The mRNA spots were binned in 0.5 µm intervals from the centrosome, and the intensities of the overlapping or non-overlapping spots in each bin were added as a measure of the amount of mRNA at that distance. Percentage of overlapping mRNA spots with protein signals was calculated in each distance per cell. This was then averaged over all the cells of a given condition and graphed as the overlapping mRNA percentage as a function of distance to the closest centrosome within the cell in 3D voxels.
      1. Open ‘overlapping_mRNA_percentage.m’ (Supplementary files) in MATLAB.
      2. Define ‘fpath’ as the location of the images (e.g., line 14 in Figure 11).
      3. Define centrosome protein surface number, overlapping RNA spot number, and non-overlapping RNA spot number (e.g., in lines 18, 19, and 20, respectively, in Figure 11).
        Note: In Figure 5F, RNA spot number 2 and 3 are the Spot objects created for overlapping and non-overlapping RNA spots, respectively. You will thus define ‘overlapping_RNA_spot_number = 2’ in line 19 and ‘nonoverlapping_RNA_spot_number = 3’ in line 20 in Figure 11.
      4. Define the deconvolved channel number for RNA (e.g., line 23 in Figure 11)
      5. Run the script to obtain .csv files in the image folder. The first column represents the distance relative to the centrosome, from 0 µm (first row) to 20 µm (last row) with 0.5 µm intervals. The second column represents the percentage of RNA signals that overlaps with the protein surfaces within a given distance to the centrosome.


        Figure 11. Quantification of the RNA signals that overlap with the protein signals. A highlight of the MATLAB script lines. See Supplementary files for the complete script.

    Notes

    1. Use the same parameters or thresholds for each image during Imaris processing if users aim to compare the absolute intensity or distribution of proteins or RNAs between different experimental conditions.
    2. If MATLAB indicates errors about ImarisReader, run the script of ‘CellsReader.m’ to make sure that ImarisReader is functional before performing data analyses.

    Acknowledgments

    We thank the Jao lab members for discussions and support. This protocol was adapted from the previous work (Sepulveda et al., 2018). Experiments and analyses were performed in part through the use of UC Davis Health Sciences District Advanced Imaging Facility. The work was supported by the New Faculty Startup Funds from University of California, Davis (to L.J).

    Competing interests

    The authors declare no conflicts of interest.

    References

    1. Femino, A. M., Fay, F. S., Fogarty, K. and Singer, R. H. (1998). Visualization of single RNA transcripts in situ. Science 280(5363): 585-590.
    2. Lee, C., Roberts, S. E. and Gladfelter, A. S. (2016). Quantitative spatial analysis of transcripts in multinucleate cells using single-molecule FISH. Methods 98: 124-133.
    3. Mueller, F., Senecal, A., Tantale, K., Marie-Nelly, H., Ly, N., Collin, O., Basyuk, E., Bertrand, E., Darzacq, X. and Zimmer, C. (2013). FISH-quant: automatic counting of transcripts in 3D FISH images. Nat Methods 10(4): 277-278.
    4. Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. and Tyagi, S. (2008). Imaging individual mRNA molecules using multiple singly labeled probes. Nat Methods 5(10): 877-879.
    5. Sepulveda, G., Antkowiak, M., Brust-Mascher, I., Mahe, K., Ou, T., Castro, N. M., Christensen, L. N., Cheung, L., Jiang, X., Yoon, D., Huang, B. and Jao, L. E. (2018). Co-translational protein targeting facilitates centrosomal recruitment of PCNT during centrosome maturation in vertebrates. Elife 7: e34959.
    6. Tsanov, N., Samacoits, A., Chouaib, R., Traboulsi, A. M., Gostan, T., Weber, C., Zimmer, C., Zibara, K., Walter, T., Peter, M., Bertrand, E. and Mueller, F. (2016). smiFISH and FISH-quant - a flexible single RNA detection approach with super-resolution capability. Nucleic Acids Res 44(22): e165.

简介

基因表达通常受空间和时间中mRNA的丰度,定位和翻译的调节。能够在单个细胞中可视化mRNA和蛋白质产物对于理解这种调节过程至关重要。单分子RNA荧光原位杂交(smFISH)的开发允许在单分子和单细胞水平上检测单个RNA分子。当与免疫荧光(IF)组合时,可以同时分析单个细胞中的mRNA和蛋白质。然而,smFISH和IF组合数据集的精确和简化的量化方法很少,因为现有的工作流程主要集中在仅量化smFISH数据。在这里,我们详细介绍了在培养细胞中进行连续IF和smFISH的方法(如Sepulveda et al。,2018中所述)以及随后通过三维(3D)对smFISH和IF数据进行的统计分析在半自动图像处理工作流程中重建。虽然我们的方法基于分析富含中心体的mRNA和蛋白质,但工作流程可以很容易地适用于在其他生物环境中执行和分析smFISH和IF数据。
【背景】smFISH是一种使用与靶RNA互补的多个短荧光标记的DNA寡核苷酸(“探针”)可视化单个RNA分子的技术(Femino et al。,1998; Raj et al。< / em>,2008)。在该技术中,当短荧光DNA探针的整体结合在靶RNA上时,产生稳健的信号,而不是由单个探针产生的弱信号。即使单个探针可能具有脱靶结合,该特征也增强了信噪比以揭示靶RNA的位置。 smFISH提供有关单分子和单细胞水平的给定RNA的RNA丰度和亚细胞定位的信息。此外,结合IF可视化蛋白质,可以在同一细胞中同时分析感兴趣的RNA和蛋白质。但是,用于分析smFISH和IF数据的工具并不广泛。一种广泛使用的分析smFISH数据的工具是FISH-quant(Mueller et al。,2013; Tsanov et al。,2016)。它是一个免费提供的软件包,大大简化了图像分析和mRNA点识别。然而,大多数量化工具(例如,Mueller et al。,2013; Lee et al。,2016; Tsanov et al。 ,2016),包括FISH-quant,专注于smFISH数据的量化,并且不会对管道中的IF数据进行分析。在这里,我们详细介绍了一个简化的工作流程,用于通过Imaris软件中的3D重建执行,获取和分析顺序IF和smFISH数据,然后使用MATLAB和R脚本进行量化。我们在贴壁培养细胞的有丝分裂期间使用pericentrin(PCNT)多核糖体共转录靶向中心体作为例子(Sepulveda et al。,2018),以演示如何使用Imaris软件重建< em> PCNT 3D共聚物z-堆栈中的mRNA和蛋白质,并应用MATLAB和R脚本来量化它们的强度,体积和关系(例如,分子的空间分布和信号之间的重叠)以半自动方式。我们的方案可以很容易地应用于其他顺序IF和smFISH实验,以精确量化3D空间中的RNA和蛋白质。

关键字:单分子成像, 中心体, 图像处理, 量化分析, mRNA分布, 3D重构, Imaris, MATLAB

材料和试剂

  1. 1-1 / 2微圆形玻璃盖玻片-12 mm直径(Electron Microscopy Sciences,Hatfield,PA,目录号:72230-01)
  2. Cellstar ® 24孔细胞培养多孔板(Greiner Bio-one North America,Inc.,Monroe,NC,目录号:662160)
  3. 15厘米培养皿(Corning,纽约,目录号:351058)
  4. Parafilm(Parafilm M,Bemis,Fisher Scientific,Waltham,MA,目录号:13-374-12)
  5. 纸巾(Fisher Scientific,Waltham,MA,目录号:19-040-898)
  6. HeLa细胞(ATCC,CCL-2)(来自田纳西州纳什维尔Vanderbilt大学Susan Wente的礼物; RRID:CVCL_0030)
  7. UltraPure TM 蒸馏水(DNase和RNase-Free)(Invitrogen,Carlsbad,CA,目录号:10977-015)
  8. 甲醛,Para(PFA)(Fisher Chemical,Waltham,MA,目录号:O4042-500)
  9. 磷酸盐缓冲盐水(PBS),pH 7.4(Fisher BioReagents,Pittsburgh,PA,目录号:BP399-1)
  10. Triton TM X-100(Fisher BioReagents,Pittsburgh,PA,目录号:BP151-500)
  11. 兔抗PCNT抗体(Abcam,Cambridge,MA,目录号:ab4448)
  12. 山羊抗兔IgG(H + L)交叉吸附二抗,Alexa Fluor 488(Invitrogen,Carlsbad,CA,目录号:A-11008)
  13. 37%甲醛溶液(Fisher BioReagents,Pittsburgh,PA,目录号:BP531-500)
  14. Formamide(Sigma-Aldrich,Burlington,MA,目录号:F9037)
  15. Prolong Gold防褪色固定剂(Life Technologies,Carlsbad,CA,目录号:P36930)
  16. Stellaris ® RNA FISH杂交缓冲液(Biosearch Technologies,Petaluma,CA,目录号:SMF-HB1-10)
  17. Stellaris ® RNA FISH洗涤缓冲液A(Biosearch Technologies,Petaluma,CA,目录号:SMF-WA1-60)
  18. Stellaris ® RNA FISH洗涤缓冲液B(Biosearch Technologies,Petaluma,CA,目录号:SMF-WB1-20)
  19. 用Quasar 670(Biosearch Technologies,Petaluma,CA)标记的Stellaris ® RNA FISH探针
  20. 4',6-二脒基-2-苯基吲哚二盐酸盐(DAPI)(Sigma-Aldrich,Burlington,MA,目录号:D9542)

设备

  1. Leica DMi8配备63x / 1.40或100x / 1.40 HC PL APO物镜,配备旋转盘共聚焦显微镜系统(Dragonfly,Andor Technology,Belfast,UK)
  2. iXon Ultra 888 EMCCD相机(Andor Technology,英国贝尔法斯特)

软件

  1. Fusion Software(Andor Technology,Belfast,UK)
  2. Imaris 8.3.1(Bitplane,Belfast,UK; Imaris,RRID:SCR_007370)
  3. MATLAB(MathWorks,Natick,MA; RRID:SCR_001622)
  4. Microsoft Excel(Microsoft,Redmond,WA; RRID:SCR_016137)
  5. R(R统计计算项目; RRID:SCR_001905)
  6. ImageJ(Java中的图像处理和分析; RRID:SCR_003070 )

程序

  1. 序贯免疫荧光(IF)和单分子RNA荧光原位杂交(smFISH)
    注意:我们遵循smFISH探针(Biosearch Technologies,Petaluma,CA)制造商描述的顺序IF和smFISH协议,下面将进行一些修改。

    如果
    注意:使用无核酸酶水制备顺序IF和smFISH实验的所有试剂,以限制潜在的RNA降解。
    1. 将12毫米圆形盖玻片放入24孔板的每个孔中。每孔种子约5×104个细胞。允许细胞附着在盖玻片上。
    2. 当细胞在每个孔中达到75-90%汇合时(大多数人培养细胞为12-18小时),在室温(RT)下用磷酸盐缓冲盐水(1x PBS)中的4%多聚甲醛固定细胞10分钟,然后用1x PBS快速洗两次。
      注意:要快速洗涤,轻轻地将1 ml PBS转移到每个孔中,然后立即吸出溶液。
    3. 用0.1%Triton X-100在1x PBS中使细胞在室温下透化5分钟,然后用1x PBS快速洗涤一次。
    4. 将细胞(在12mm圆形盖玻片上)与70μl一抗溶液在室温下孵育3小时(在我们的实施例中,例如,1:1,000稀释的兔抗PCNT抗体在1x PBS中) 。
    5. 用1x PBS进行三次5分钟洗涤,并将细胞与70μl二级抗体溶液在黑暗中于4℃孵育过夜(例如,1:500稀释的抗兔Alexa Fluor 488)。
      注意:一抗和二抗的稀释因子和孵育时间需要根据经验确定;所描述的样品条件用于检测PCNT蛋白质。为了进行5分钟的洗涤,将盖玻片轻轻转移到含有1ml 1x PBS的24孔板中的孔中。在室温下孵育5分钟而不摇动平板。
    6. 用1x PBS进行三次5分钟的洗涤,并在室温下用1x PBS中的3.7%甲醛后固定细胞10分钟。

    smFISH
    1. 用洗涤缓冲液A洗涤细胞5分钟,并与含有125 nM smFISH DNA探针混合物的67μl杂交缓冲液在37°C下在黑暗中孵育6小时。
      注意:我们使用Biosearch Technologies的在线Stellaris Probe Designer设计FISH探针。我们通常对每个RNA靶标使用一组48至96个20聚体DNA探针。为获得最佳效果,请在使用前立即将10%甲酰胺加入1x洗涤缓冲液A和杂交缓冲液中。
    2. 将细胞与洗涤缓冲液A在37℃孵育30分钟,将含有0.05μg/ ml DAPI的洗涤缓冲液A在37℃孵育30分钟,并在室温下洗涤缓冲液B 3分钟。
    3. 使用足够的Prolong Gold防褪色固定剂(例如 。,5μl用于12 mm圆形盖玻片)安装盖玻片。让封固剂在室温下在室温下固化过夜,并在成像前用指甲油密封盖玻片的边缘。&nbsp;
      注意:为了获得最佳效果,在加湿容器中进行抗体和smFISH探针的孵育(例如,一个15厘米的培养皿,里面有一块封口膜和湿纸巾,用铝箔包裹以阻挡光线) 。

  2. 图像采集和解卷积
    成像
    注意:使用多个荧光团时,必须仔细选择波长范围,发射滤光片和二向色镜,以避免信号渗透。使用设置可最大限度地减少光漂白,同时最大限度地提高信噪比。具有高灵敏度的相机优选用于获得良好的smFISH信号(例如,电子倍增CCD相机)。我们建议从最长波长开始逐个通道顺序采集图像。以下是使用Jao实验室中Dragonfly旋转共聚焦系统的一般IF和smFISH采集设置。
    1. 物镜:100x NA 1.4(以及在相机前放置1.5倍或2倍放大镜头)
    2. 激光功率:50-100 mW激光器的5-30%之间
    3. 曝光时间:100-250毫秒
    4. 增益:iXon Ultra 888 EMCCD相机200
    5. 针孔:40μm
    6. Z-堆叠间隔:0.3μm
    7. 为了评估不同荧光信号之间的共定位,获取每个z-堆栈的所有通道。

    解卷积
    注意:通过比较去卷积前后图像,避免在反卷积过程中引入的潜在伪像非常重要。通常,迭代次数越多,产生图像伪像的可能性就越大。以下是使用Dragonfly系统Fusion软件中内置解卷积算法的典型解卷积设置。
    1. 解卷积预览模式:ON
    2. 算法:鲁棒(迭代)
    3. 预锐化:关闭
    4. 边缘伪影减少:无
    5. 去噪滤镜尺寸:0.7
    6. 去噪频率:4
    7. 迭代次数:15
    8. 最小强度消除:ON
    9. 初始去噪:ON
    10. 标准化:关闭
    11. 迭代加速:OFF

数据分析

  1. Imaris的3D重建和图像渲染
    1. 在图像处理之前,在“文件/首选项/统计”中,打开相关的统计参数。以下是我们经常使用的参数。
      1. 表面:强度最大值,强度平均值,强度总和;位置x,位置y,位置z;表面总数;体积。
      2. 斑点:强度最大值,强度平均值,强度总和;位置x,位置y,位置z;总点数;体积。
      3. 体积
        注意:Imaris中的术语“统计”是指与每个对象相关的不同定量描述(例如,面积,体积,强度等)。它没有涉及数据的统计分析。
    2. 使用Imaris中的以下步骤提取并屏蔽3D单元格轮廓:
      1. 打开去卷积图像。
      2. 单击“编辑/添加频道”:选择相应的原始图像。
      3. 在菜单中,选择“Surpass / Add new surfaces”(图1A);选择'跳过自动创建,手动编辑'(图1B)。
      4. 使用最清晰的轮廓移动到切片位置。
      5. 将指针切换到选择模式并选择“绘图”选项以标记单元格轮廓(图1C和1D)。
        注意:我们分析中的细胞轮廓是通过调整“显示调整”下的通道最大强度,基于背景噪声的可视化来标记的。用户可以使用其他膜或细胞质标记来显示细胞边界。
      6. 将轮廓线复制并粘贴到第一个和最后一个图像平面(图1D)。
      7. 选择手动“分辨率”并将其调整到最大尺寸(图1D)。单击“创建曲面”。
      8. 制作单元格掩码(图1E和1F):选择“全部掩码”。应选中“应用遮罩前复制通道”框。将“体表外的体素”设置为“0”和“内部表面”,使其在图像的位深度内设置为较高的值(在此示例中为例如,'30,000')。这将创建感兴趣的细胞的二元掩模。


        图1. Imaris中的细胞轮廓。 A.添加新曲面。 B.手动跳过自动创建和编辑。 C和D.绘制轮廓线以标记单元轮廓。 E和F.屏蔽单元轮廓的通道并设置体素。

    3. 在Imaris中为感兴趣的蛋白质创建3D Surface对象:
      1. 添加一个新的Surface对象(图1A)。单击蓝色箭头继续下一页(图2A)。对于“源通道”,选择感兴趣的蛋白质的去卷积通道。选中“平滑”框,自动计算“曲面区域细节级别”的值并保留为默认值。选择“绝对强度”进行阈值处理(图2B)。
        注意:“平滑”命令将高斯滤镜应用于图像,以便于识别噪声图像的表面对象。它减少了渲染表面中包含的噪点和细节。如果选择“平滑”,则会从图像中自动确定“表面区域细节级别”参数。用户可以更改此参数以删除更多或更少的表面细节。此参数越高,此表面渲染的细节就越少。
      2. '阈值(绝对强度)'具有Imaris的自动值(图2C)。增加或降低此阈值以仅包括感兴趣的信号。在图像中使用相同的值进行比较。
      3. 有一个基于体素大小的自动过滤器。这可以保留默认值10或根据感兴趣的大小进行调整。
      4. 添加过滤器:选择在步骤2中生成的“细胞轮廓的屏蔽通道编号的强度最大值”。此步骤限制在轮廓单元格内的任何蛋白质信号的拟合(图2D)。
      5. 单击“完成”以完成蛋白质表面的渲染(图2D)。示例如图4所示。
        注意:如果每个细胞有多种感兴趣的蛋白质,重复步骤3以产生每种蛋白质的表面呈现。


        图2.蛋白质表面的三维重建。 A和B.&nbsp; 选择源渠道并设置表面区域详细程度。 C.调整绝对强度阈值以捕获所有信号。 D.添加过滤器以限制在概述的细胞内拟合蛋白质信号。

    4. 在Imaris中为感兴趣的RNA创建3D斑点对象:
      1. 添加一个新的点对象(图3A)。
      2. 选择“不同的斑点大小(区域增长)”(图3B)。
      3. 对于“源通道”,选择感兴趣的RNA的去卷积通道(图3C)。
      4. 对于“斑点检测”,输入感兴趣的RNA的“估计的XY直径和Z直径”(图3C)。
        注意:通过ImageJ参照比例尺测量原始共聚焦图像中几个RNA smFISH焦点的平均大小来确定XY直径。 Z轴直径设置为XY直径的两倍,默认为Imaris。
      5. Imaris根据斑点渲染的强度差异和形状计算“斑点质量”(图3D)。应调整此值以包含感兴趣的信号。
        注意:我们发现默认计算的斑点质量在标记真正的RNA斑点时通常是不准确的。用户需要手动增加或降低阈值以确保标记真实的RNA斑点,但不包括噪声(例如,小点状背景信号)。
      6. 添加过滤器:选择在步骤2中生成的“强度最大值=细胞轮廓的屏蔽通道编号”。此步骤限制在轮廓单元格内的任何RNA信号的拟合(图3D)。
      7. 选择“绝对强度点区域”(图3E)。
      8. 现场区域阈值由Imaris预先计算(图3F)。
        注意:用户可能需要手动增加或减少区域阈值,以确保在最后一步中呈现所有RNA信号。
      9. 单击“完成”以完成RNA斑点的渲染(图3F)。示例如图4所示。


        图3. RNA斑点的3D重建 A和B.创建一个新斑点并选择不同的斑点大小(区域增长)。 C和D.选择源通道并设置点检测。 (D)调整阈值并添加过滤器。 E和F.选择绝对强度并调整区域阈值。


        图4.来自顺序IF和smFISH实验的共聚焦图像的之前(共聚焦)和之后(3D渲染)三维重建的示例

    5. 这是确定3D重建蛋白质表面和RNA斑点是否重叠的额外过程。
      1. 单击步骤3中生成的蛋白质表面对象和蛋白质表面的掩蔽通道(图5A和5B):选择“全部掩码”。选择蛋白质表面的解卷积通道作为源通道,并选中“应用遮罩前复制通道”框。将“体外曲面”设置为“0”,将内部曲面设置为成像系统的最高体素值(例如,在此示例中为“30000”)。这将产生蛋白质表面的掩蔽通道(例如,图5F中的通道号8)。
      2. 单击步骤4中生成的RNA斑点对象。添加过滤器(图5C)并选择步骤5a中生成的'强度最大值[通道号] =掩蔽[通道号]蛋白质表面';这是一个二进制通道,其值为0或前面选择的高数字。应用上限阈值以选择重叠的RNA斑点。单击“将选择复制到新点”(图5D)。
        注意:如果RNA斑点的一个或多个像素与蒙面蛋白质表面重叠,则将其视为重叠斑点。
      3. 重复该过程(步骤5b)但应用低阈值(即,在掩蔽通道中值为0的斑点)以选择非重叠RNA斑点。单击“将选择复制到新斑点”(图5E)。
        注意:如果RNA斑点的像素没有与蒙面蛋白质表面重叠,则将其视为非重叠斑点。
    6. 对每个图像重复步骤1到5。将处理过的图像导出到文件夹。
      注意:在图像处理过程中,保持顺序一致,以便在Imaris中生成细胞轮廓,蛋白质表面和RNA斑点(图5F),以便相应的通道数,蛋白质表面和RNA斑点对于给定的相同一组图像。因此,在运行MATLAB脚本时,可以对感兴趣的蛋白质或RNA进行一致的定义。


      图5.检测与蛋白质信号重叠的RNA斑点。 A和B.掩盖目标蛋白质表面。 C-即过滤重叠和非重叠的RNA斑点。 F.通道编号,蛋白质表面编号和RNA斑点编号的图示。

  2. 使用MATLAB脚本处理和导出Imaris的数据
    注意:要设置MATLAB,请首先执行以下操作:
    1. 打开MATLAB软件;选择“HOME / Set Path”:添加MATLAB搜索路径作为图像文件夹的位置
    2. 在MATLAB中使用ImarisReader进行数据分析之前,请从MATLAB Central File Exchange下载ImarisReader .zip。将文件保存在MATLAB搜索路径中。

    处理注意事项:下面是使用MATLAB和R脚本来量化在Imaris中呈现RNA和中心体蛋白信号后相对于最近的中心体的强度(RNA或蛋白质,总体或中心体)和RNA分布的实例。有关最终图的示例,请参见Sepulveda等,2018。
    1. 总RNA和蛋白质强度的定量
      1. 打开' total_protein_and_RNA_intensity.m '(补充文件) )在MATLAB中。
      2. 将'fpath'定义为图像的位置(例如,图6中的第14行)。
      3. 定义感兴趣的蛋白质和RNA用于定量。这些是第18行中的'protein_surface_number'和图6中第19行中的'spot_number'。&nbsp;
        注意:例如,在图5F中,“蛋白质表面编号2”是为感兴趣的蛋白质创建的Surface对象。因此,您将在图6中的第18行定义'protein_surface_number = 2'.'RNA spot number 1'是为感兴趣的RNA创建的Spot对象。因此,您将在图6中的第19行定义“spot_number = 1”。
      4. 定义感兴趣的蛋白质和RNA的原始通道编号。这些是第23行中的“ori_protein_channel_number”和图6中第24行中的“ori_RNA_channel_number”。&nbsp;
        注意:例如,在图5F中,感兴趣的蛋白质和RNA的原始通道分别是通道5和通道4。因此,您在第23行中定义'ori_protein_channel_number ='5'',在图6中第24行定义'ori_RNA_channel_number ='4'。
      5. 运行脚本。渲染的蛋白质表面和RNA斑点中的原始蛋白质和RNA强度将导出到图像文件夹中的.csv文件中,该文件夹可由Microsoft Excel打开。


        图6.从原始(预解卷积)共聚焦图像中定量总蛋白和mRNA强度。 MATLAB脚本系列的一个亮点。请参阅完整脚本的补充文件。 />

    2. 定量中心体的蛋白质强度
      注意:这是量化感兴趣的亚细胞区域中蛋白质强度的一个例子。在这里,我们量化有丝分裂期间两个中心体的蛋白质强度。该脚本通过将两个中心体定义为抗PCNT信号的第一和第二大蛋白质表面体积,将两个中心体与细胞的其余部分隔离开来。
      1. 打开' centrosomal_protein_intensity.m '(补充文件) MATLAB。
      2. 将'fpath'定义为图像的位置(例如,图7中的第14行)。
      3. 定义感兴趣的蛋白质的表面编号以进行定量(例如,图7中的第17行)。
      4. 定义感兴趣的蛋白质的原始和解卷积通道编号(例如,分别在图7中的第21和22行)。
      5. 运行该脚本以获取映像文件夹中的.csv文件。


        图7.来自原始(预解卷积)共聚焦图像的中心体蛋白质强度的定量。 MATLAB脚本系列的一个亮点。请参阅完整脚本的补充文件。 />

    3. 相对于中心体的mRNA分布的定量
      注意:这是量化mRNA分子到中心体的分布随距离变化的一个例子。假定每个斑点中mRNA信号的强度与每个斑点中mRNA的量成比例,并用于代替mRNA单位。计算从每个mRNA斑点到每个中心体质心的距离,并将mRNA信号分配给最近的中心体。将mRNA斑点以0.5μm间隔分箱至中心体,并且添加每个箱中的斑点强度作为该距离处mRNA的量的量度。这是针对每个细胞计算的,然后对给定条件的所有细胞进行平均,并绘制为平均mRNA强度,作为距3D细胞中最近的中心体距离的函数。
      1. 在MATLAB中打开' mRNA_distribution.m '(补充文件) &NBSP;
      2. 将'fpath'定义为图像的位置(例如,图8中的第14行)。
      3. 定义蛋白质和mRNA的表面数量以进行定量(例如,蛋白质表面的第18行和图8中RNA点的第19行)。
      4. 定义RNA的去卷积通道数(例如,图8中的第22行)。
        注意:我们使用去卷积通道的RNA强度来消除噪音或背景信号。


        图8.相对于中心体的mRNA分布的定量。 MATLAB脚本行的一个亮点。请参阅完整脚本的补充文件。 />

      5. 运行脚本以获取映像文件夹中的.csv文件。在每个.csv文件中,每行代表相对于中心体的距离,从0μm(第一行)到20μm(最后一行),间隔为0.5μm(图9)。每列代表相对于最近的中心体的每个距离处mRNA的量(图9)。


        图9.“mRNA_distribution.m”脚本生成的.csv文件的列和行中的数据示例

        注意:以下是使用R脚本将来自多个单元格的所有.csv文件合并到单个电子表格中以进一步量化的步骤(例如,将标准化强度计算为每个数据点与总强度的比率)每一栏)。
      6. 将所有.csv文件分组到新文件夹中。
      7. 在R中打开' column_combination_and_normlization '(补充文件) &NBSP;
      8. 指定工作目录和.csv文件的位置(例如,分别在图10中的第11行和第12行)。&nbsp;
      9. 运行R脚本以获取新文件夹中的两个.csv文件:(1)组合原始数据集; (2)组合归一化数据集。


        图10.使用R脚本组合并规范化多个.csv文件中的数据。 R脚本行的一个亮点。请参阅完整脚本的补充文件。

    4. 定量与蛋白质信号重叠的RNA信号在距离中心体的限定距离内。
      注意:这是量化PCNT mRNA在距中心体一定距离内与抗PCNT N末端蛋白信号重叠的程度的一个例子;这两个信号在中心体外的重叠表明这些mRNA正在进行主动翻译。计算每个重叠或非重叠mRNA斑点(如上所述在Imaris中分离)到每个中心体质心的距离,并将mRNA信号分配给最接近的中心体。将mRNA斑点以0.5μm的间隔从中心体分箱,并且添加每个仓中重叠或非重叠斑点的强度作为该距离处mRNA的量的量度。在每个细胞的每个距离中计算具有蛋白质信号的重叠mRNA斑点的百分比。然后将其在给定条件的所有细胞上取平均值,并绘制为重叠mRNA百分比作为距离到3D体素中细胞内最近的中心体的距离的函数。
      1. 打开' overlap_mRNA_percentage.m '(补充文件)。
      2. 将'fpath'定义为图像的位置(例如,图11中的第14行)。
      3. 定义中心体蛋白质表面编号,重叠RNA斑点编号和非重叠RNA斑点编号( e .g。,分别在第18,19和20行中,图11)。
        注意:在图5F中,RNA斑点编号2和3分别是为重叠和非重叠RNA斑点创建的斑点对象。因此,您将在第19行中定义'overlap_RNA_spot_number = 2',在图11中的第20行中定义'nonoverlapping_RNA_spot_number = 3'。
      4. 定义RNA的去卷积通道编号(例如,图11中的第23行)
      5. 运行脚本以获取映像文件夹中的.csv文件。第一列表示相对于中心体的距离,从0μm(第一行)到20μm(最后一行),间隔为0.5μm。第二列表示在与中心体给定距离内与蛋白质表面重叠的RNA信号的百分比。


        图11.与蛋白质信号重叠的RNA信号的定量。 MATLAB脚本行的一个亮点。请参阅完整脚本的补充文件。 />

    笔记

    1. 如果用户的目的是比较不同实验条件下蛋白质或RNA的绝对强度或分布,则在Imaris处理期间对每个图像使用相同的参数或阈值。
    2. 如果MATLAB指示有关ImarisReader的错误,请运行“CellsReader.m”脚本以确保ImarisReader在执行数据分析之前正常运行。

    致谢

    我们感谢Jao实验室成员的讨论和支持。该协议改编自先前的工作(Sepulveda等,2018)。实验和分析部分通过使用加州大学戴维斯分校健康科学区高级成像设施进行。这项工作得到了加州大学戴维斯分校(致L.J)新教师创业基金的支持。

    利益争夺

    作者宣称没有利益冲突。

    参考

    1. Femino,A。M.,Fay,F。S.,Fogarty,K。和Singer,R。H.(1998)。 原位可视化单个RNA转录本。 Science 280 (5363):585-590。
    2. Lee,C.,Roberts,S.E。和Gladfelter,A。S.(2016)。 使用单分子FISH对多核细胞中转录物进行定量空间分析。 方法 98:124-133。
    3. Mueller,F.,Senecal,A.,Tantale,K.,Marie-Nelly,H.,Ly,N.,Collin,O.,Basyuk,E.,Bertrand,E.,Darzacq,X。和Zimmer,C 。(2013)。 FISH-quant:自动计算3D FISH图像中的成绩单。 Nat方法 10(4):277-278。
    4. Raj,A.,van den Bogaard,P.,Rifkin,S.A.,van Oudenaarden,A。和Tyagi,S。(2008)。 使用多个单独标记的探针对单个mRNA分子进行成像。 Nat Methods 5(10):877-879。
    5. Sepulveda,G.,Antkowiak,M.,Brust-Mascher,I.,Mahe,K.,Ou,T.,Castro,NM,Christensen,LN,Cheung,L.,Jiang,X.,Yoon,D., Huang,B。和Jao,LE(2018)。 共翻译蛋白靶向促进脊椎动物中心体成熟过程中PCNT的中心体募集。 em> Elife 7:e34959。
    6. Tsanov,N.,Samacoits,A.,Chouaib,R.,Traboulsi,AM,Gostan,T.,Weber,C.,Zimmer,C.,Zibara,K.,Walter,T.,Peter,M.,Bertrand ,E。和Mueller,F。(2016年)。 smiFISH和FISH-quant - 一种灵活的单RNA检测方法,具有超分辨能力。 Nucleic Acids Res 44(22):e165。
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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 Jiang et al. 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. Jiang, X., Brust-Mascher, I. and Jao, L. (2019). Three-dimensional Reconstruction and Quantification of Proteins and mRNAs at the Single-cell Level in Cultured Cells. Bio-protocol 9(16): e3330. DOI: 10.21769/BioProtoc.3330.
  2. Sepulveda, G., Antkowiak, M., Brust-Mascher, I., Mahe, K., Ou, T., Castro, N. M., Christensen, L. N., Cheung, L., Jiang, X., Yoon, D., Huang, B. and Jao, L. E. (2018). Co-translational protein targeting facilitates centrosomal recruitment of PCNT during centrosome maturation in vertebrates. Elife 7: e34959.
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