3.4.1 Segmentation of Cells
In order to analyse the microscopy images produced in this study on a single cell level, it was necessary to segment each image into mask that could be used to direct analysis at specific regions of the image, i.e. individual cells. To segment individual cells, an image processing protocol was developed using the image analysis software ImageJ (NIH), mak- ing use of the inbuilt macro language to automate the process. A summary of the outputs of each section are shown in Figure 18. Entire folders of images were processed by looping through each image in the folder and performing the analysis/image processing tasks be- fore saving to a new location. Images were first opened using the “Bio-formats importer” plugin, that allowed importing of many different file types, in this case ND2 files as they were produced using the Nikon Elements AR software. Where images were a time-lapse series, each time point was opened separately for individual processing.
Images were split into their individual channels, e.g. green, red, Phase and the fluorescence channels were closed as segmentation was based on the phase contrast images. The smooth function was used to reduce noise in this image. A binary image of the phase
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contrast channel was then produced using the inbuilt automatic thresholding tool with the method set to “Otsu”. This produced a rough outline of the cells requiring further pro- cessing, the watershed function was used to split and adjoining cells to create a binary image of cell outlines and surrounding areas. The fill tool was then used, with the colour set to white, to manually remove the area outside the cells that was included in the thresh- olding limit. This produced a binary image in which the cell areas were represented as black shapes (pixel value 255) and the background/empty regions were white (pixel value 0).
To create a list of cell areas, the “Analyze Particles” tool was used, this selects the outline of each black shape in the image and adds it to a list of “Regions of Interest”. This list was saved for each image and served as the list of cell outlines when performing analysis on the fluorescence channels.
3.4.2 Counting p-bodies
To count the number of p-bodies within the cell, a second macro was written in imageJ to automate the process, again entire directories of images were analysed automatically. This time only the channel with the p-body protein was used for analysis. For each segmented ROI (cell) in the specified images, the “Find Maxima” tool was used to find points of local maximum intensity, which correlated to P-bodies. Several values for noise reduction were
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trialled until a final value of 200 was selected as it gave a consistently similar output to
Figure 18: Demonstration of the cell segmentation and p-body analysis routine.
Cells are first segmented from a bulk image into individual cells. Next an individual ROI, cor- relating to a cell is further segmented in the fluorescence channel using the find maxima algorithm to identify p-bodies (local fluorescence maxima) within the cell. The identified max- ima are analysed for size and intensity to register differences in morphology under various conditions.
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manual selection. The total number of these maxima was recorded in a list for each cell.
3.4.3 Measuring p-body size and intensity
To measure the size and intensity of p-bodies within each cell the p-body counting script was adapted to include further processing steps. After using the “Find Maxima” tool, the sections were output as a binary image with single pixel dots representing the locations of p-bodies. These dots were expanded by 8 pixels and added to the list of ROIs using the “Analyze Particles” tool. This list was then used to cycle through the p-bodies and perform further analysis. For each p-body the ROI was duplicated from the main image, giving just an image of the body. This p-body image was then segmented using the “Otsu” thresh- olding method to give a binary image of the p-body. This binary image was converted to an ROI, once again using the “Analyze Particles” tool and this ROI was overlaid onto the image of the p-body. Finally, the ROI measure function was used to measure the area of the ROI, and hence the p-body, as well as the fluorescence intensity of the p-body based on the total grey value present with in the ROI.
3.4.4 Manual Curation
Each step in this automated imaging analysis protocol was manually curated by pausing the macro at certain points and checking the images for accurate segmentation. P-body selections were also checked for each image to ensure validity of the automated process.
3.4.5 Replicative age quantification
Automated counting of replicative ageing proved difficult using ImageJ due to the limited programming functions available in the standard macro language. Instead of automated counting, a semi-automated process was employed, where images were presented auto- matically and manually input was required for fully processing. For each time lapse series, the series was split into individual time points and saved as separate files. Files were opened in sequence and a user prompt was given instructing the user to select the relevant cell by clicking on it. After clicking on the cell, an image crop containing the cell and a small border was saved in a specified directory. Each cropped image was processed using the above outlined processing methods to produce a series of segmented images with p-body
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location and quantification for each image. The segmented images were used to manually quantify the replicative age of the cells by counting the number of buds per cell.
3.4.6 P-body inheritance
To quantify p-body inheritance, the same processing steps as with replicative ageing were performed, except that the target for the crop was the p-body rather than the mother cell. Once segmentation and processing were complete, the segmentation masks and p-body spot locations were then overlaid to produce a diagrammatic representation of the p-body movement between cells. These overlays were observed when combined as a time-lapse movie and the movement between cells was quantified manually. Inheritance was recorded as positive when the p-body moved between cells without returning to the mother cell before cytokinesis. For each p-body, the number of inheritance events was recorded by following the p-body for the duration of the time lapse acquisition.
3.4.7 Cell cycle phase determination
To determine the position of p-bodies within the cell at various points within the cell cycle, segmentation of cells was performed as previously described. Each segmented image was then manually categorized based on the cellular morphology following the guide (Figure 19). For truly automated analysis of cell cycle stage based on morphology, a nuclear signal is needed to determine the transition from S to G2 to M phase, however not all of the images in this study contained a nuclear tag, and so more simple features were used to
Figure 19: Cell cycle phase was determined based on shape characteristics of phase con- trast cell images. AR represents the aspect ratio of the segmented cell; a higher aspect ratio means a later stage cell. Circ represents the circularity of the cell, a greater value represents a more circular shape, and hence an earlier cell cycle stage. Cells with fluores- cent tags of the Myosin ring and nucleus were used to determine the values of AR and Circ for each transition point in the cell cycle.
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estimate cell cycle stage. The 2 features chosen to indicate cell cycle stage were aspect ratio, where higher values indicated progression further towards cytokinesis, and circularity, where the lower values indicated a progression further into the cell cycle. For each cell/cell- bud these feature values were calculated to give an estimate of cell cycle stage, this was then correlated to p-body position within the cell. Cells with buds were identified manually, after which the aspect ratio and circularity were used to distinguish S and G2/M phase cells with a cut-off of AR = 1.8 and Circ = 0.62
3.4.8 Co-localization of p-bodies and organelles
To determine the p-body’s most common position at various stages of the cell cycle, 2- colour images were subjected to co-localization analysis. Cells were segmented as de- scribed previously, then combined into one large image. The ImageJ Coloc2 plugin was used to analyses these large images, using the segmented image masks as an ROI for this analysis, i.e. the analysis was only performed within the cell boundaries. Coloc2 reports several different measures of co-localization. The co-localization analysis was performed on only subsets of the images corresponding to the different cell-cycle stages as outlined above.
The metric used to analyse p-body colocalisation with the specified organelles was the Mander’s correlation coefficient as outlined below:
𝑀1=
∑ 𝑅𝑖 𝑖,𝑐𝑜𝑙𝑜𝑐𝑎𝑙 ∑ 𝑅𝑖 𝑖
where Ri,colocal = Ri if Gi > 0 and Ri,colocal = 0 if Gi = 0. This represents the fractional overlap
of red and green signals. In simple terms, it measures the intensity of red pixels, where the corresponding green pixel value is not equal to 0. The corresponding measure for green signal is:
𝑀2=
∑ 𝐺𝑖 𝑖,𝑐𝑜𝑙𝑜𝑐𝑎𝑙 ∑ 𝐺𝑖 𝑖
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As this metric measures only values with a non-zero pixel value for the colocalisation marker, the subtraction of background signal is important in order to accurately identify pixels with signal as opposed to background.
Due to the small size and diffuse nature of some of the markers used in this study, the ideal method of thresholding (background subtraction to give 0 values), known as the Costes method proved ineffective as M values were consistently 0 using this method and thresholds gave por segmentation compared to manual observation where clear overlap was visible in micrographs. Instead, an automated threshold level was set by implementing the Otsu threshold in ImageJ (NIH). This algorithm sets the threshold by maximising the inter-class (black vs white) variance and was found to accurately represent the imaged compartments when compared to manual observation. Using this method eliminates bias in threshold setting that could otherwise influence the Mander’s coefficients.
As a control, each analysed image was re-analysed, rotating one channel by 90 degrees, and re-running the analysis using the same threshold value as in the original analysis. This creates a control dataset in which there is only expected to be overlap due to chance. These two data sets were then compared by students t test to assess the significance of the overlap in the experimental set.
3.4.9 P-body tracking and velocity measurement
To follow the movement of p-bodies through the cell-cycle and calculate the changes in their velocity over these periods, the ImageJ plugin MTrackJ as utilized. MTrackJ allows the user to add tracks to a time-lapse image based on mouse clicks and calculate associated values. Fast time-lapse Images were cropped into small sections containing just a few cells, with a central cell in G1 phase of the cell cycle, and MTrackJ was used, clicking on a single p body and overlaying a track only the image after which the results for velocity were calculated using the distance between clicks and frame time interval. Velocities were then correlated to the time that they happened during the cell-cycle based on the recorded time point in the image and manual classification of the cell-cycle stage at that point.
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Development of a robust protocol for analysis of p-bodies
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