Turn on the laser scanning confocal microscope and place the slide on the slide holder. For image visualization and acquisition, select the 20X objective lens. From the confocal software, select sequential scan mode to avoid crosstalk between the BODIPY 493/503 and DAPI.
Then excite the BODIPY 493/503 die using the 488 nanometer argon laser line and the DAPI stain using the 405 nanometer laser line. Set the emission ranges at 493 to 589 nanometers for BODIPY and 410 to 464 nanometers for DAPI. Next, configure the microscope settings by adjusting frame sizes, scan speed, and averaging which includes number two times bit depth as 12 bits, direction as bidirectional, scan area digital zoom minus one, and pinhole minus one area unit.
Adjust the gain and digital gain settings. Ensure there are no saturated pixels as indicated by the range indicator. After accurately identifying the lipid droplets, acquire the image with the BODIPY and DAPI channels.
To create wide area images, switch to a 10X subjective lens. Then activate tile scan mode and configure it to generate five by five mosaic images. To generate 3D and orthogonal views, apply a drop of immersion oil on the top of the cover glass and change the objective lens to a 40X lens.
Select the Z-stack mode. Adjust the Z-plane to define the first and last positions for image capture, ensuring an optical slice thickness of approximately 0.5 microns to capture all droplets in focus. Select the ortho module and create orthogonal views.
Acquire 3D images by selecting the 3D module following transparency rendering mode. After image acquisition, begin processing and analyzing single plane images using CellProfiler. Upload the images in TIF format by clicking on the images mode in the top left corner of the CellProfiler interface.
Use the names and types module to sort between BODIPY droplet and DAPI nuclei stained images based on the filenames. For lipid droplet analysis, use only BODIPY stained images. Initiate pipeline construction by clicking on adjust modules and select the color to gray module to convert the images into gray scale.
Then using identify primary objects module, define lipid droplets between 6 to 300 pixels and a threshold correction factor of one to detect lipid droplets within the gray scale images. To measure the pixel intensity of the identified lipid droplets, add a measure object intensity module. Incorporate an additional filter objects module to ensure that only the strongest signals are quantified while less intense signals are excluded from the final lipid droplet analysis.
Next, to measure the lipid droplets related to the output data, add the measure object size shape module. Then add an overlay outlines module to overlay the identified droplet on the original image and thus ensure that the segmentation looks accurate on the unprocessed image as well. Once done, add an export to spreadsheet module and click on the analyze images button from the bottom left corner.
Compared to the control, HFD-fed animals presented a pronounced dyslipidemic profile and subtle increase in circulating triglycerides levels. Wide area images revealed increased lipid droplets across the livers of HFD-fed animals. Analysis with CellProfiler revealed an increased number of hepatic lipid droplets, augmented BODIPY fluorescence intensity, 360%area ratio, and larger diameters.
Size distribution in HFD-fed animals revealed an increase in macro vesicular hepatic lipid droplets of more than nine microns and a reduction in microvesicles of less than three microns.
