To begin the quantification of stained IMAT lipid droplet metrics, open Confocal Stacks in ImageJ. Open the Threshold User Interface by selecting Image, then Adjust, then Threshold. In the user interface, select Intermodes as the thresholding type and ensure Dark Background is selected.
Then click on Apply to run a thresholding algorithm. Run the watershed algorithm to separate touching lipid droplets by selecting Process, then Binary, and Watershed. Select Yes in a dialog box to process all the images in the stack to add a thin black line dividing larger areas of solid white.
To set the maximum and minimum particle sizes, open the original image and outline the smallest and largest lipid droplet in view using the Oval tool. Then add these shapes to the ROI Manager by typing T.Select Analyze, then Set Measurements. In the dialog box, select the settings, check Area only, and click OK.Then select Measure from the ROI Manager.
Use the two area measures from this results window as the size range and analyze particles. To identify the region of interest, or ROIs, with the analyze particles algorithm. Select Analyze, then Analyze Particles.
A dialog box opens to set the selection settings. To output the ROI Measurements, select Analyze, then Set Measurements, and select Area, Centroid, Fit Ellipse, and Stack Position. Click on OK.Then select Measure from the ROI Manager.
The data in the results table can be selected and copied into Excel for further analysis. For a detailed assessment of individual lipid droplet metrics and distribution, the BODIPY fluorescently labeled lipid droplets were imaged via confocal microscopy. Thresholding and shape segmentation provided a good first pass for generating ROIs for each lipid droplet.
However, manual edits were needed to correct the errors. Deep lipid droplets, which appeared dim on the image, can be added by hand using the Oval tool in ImageJ. A group of lipid droplets mistakenly identified as a single ROI can also be corrected by deleting and replacing the original ROI with multiple new ROIs.
Where a single lipid droplet was identified as a unique ROI in multiple slices, the duplicate ROIs were consolidated into a single ROI.
