This study focuses on assessing mitochondrial health in astrocytes using a Mito-timer protein. The Mito-timer serves as a biosensor, allowing for the differentiation between healthy and stressed mitochondria through fluorescence imaging.
Begin with an astrocyte culture transduced to express a Mito-timer protein localized to their mitochondria.
Mito-timer acts as a mitochondrial biosensor, emitting green fluorescence in healthy mitochondria and transitioning to red fluorescence in stressed or damaged ones.
Microscopically select large and isolated astrocytes for imaging.
Using different excitation wavelengths, capture images for green and red fluorescence.
To analyze the images, select frames for both red and green channels, then choose merge channels.
Apply auto shading correction and run the image enhancement algorithm to enhance image clarity.
Select the desired morphological parameters of mitochondria for analysis.
Using the mean intensity tab, measure the mean intensities of red and green fluorescence.
Calculate the ratio between red and green intensities, which indicates the overall mitochondrial health of the astrocyte.
Assess the astrocytic mitochondrial system, at least three to five days after the lentiviral infections with LV-G1-MitoTimer. Select five astrocytes per well with a mitochondrial network expressing sufficient levels of LV-G1-MitoTimer using a magnification of 40 times, taking care to select astrocytes as flat and large as possible and not located in clusters of cells. Then, capture fluorescence images using sequential excitation at 490 nanometers for the green channel and 550 nanometers for the red channel with green and red fluorescent signals, and using a magnification of 150 times for each coordinate.
Select the first frame for red and green channels for each image sequence by clicking on ND Processing and Select Frame. Then, merge the red and the green channels by selecting conversions and Merge Channel. To correct image shading, select Pre-Processing and then Auto-Shading Correction. Apply the rolling ball algorithm by selecting Pre-Processing and Rolling Ball.
To generate binary masks for each mitochondrion, select Segmentation and Threshold. Remove any objects truncated by the border by selecting Binary Processing and Touching Border. Select Measurement, and then Object Area, EQ Diameter, Length, Width, Roughness, Circularity, or Elongation to measure surface area, diameter, length, width, roughness, circularity, or elongation respectively.
Compose a group with these measurements and rename it as morpho data. Then select Measurement and then select Mean Intensity to measure the mean green and red intensities, and Ratio to measure the red by green ratio.
Now, compose a group with these measurements and rename it as ratio data. Finally, export the table to a CSV file by selecting Reference and Table to CSV, and then save the GA 3 script of analysis by selecting Save As.
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