Quantification Killifish LIver image analysis

RNAscope quantification<br>To quantify the rate of hepatocyte efferocytosis, single Z-plane images were analyzed using the image analysis software QuPath v0.4.351. Cell boundaries were predicted using the “Cell Detection” function with parameters as modified from default settings as follows: Nucleus background radius = 5 µm, Nucleus minimum area = 5 µm2, Intensity Threshold = 5. Cells were then classified into three cell types: hepatocytes, erythrocytes, and efferocytosing hepatocytes using the “Train object classifier” function, which used the “Artificial neural network (ANN_MLP)” classifier option. Hepatocytes were cells with nearly exclusive fgg signal, erythrocytes were called as cells containing nearly exclusive cahz signal, and efferocytosing hepatocytes were called as cells containing cahz signal on a background consisting of primarily fgg signal. Any cells below the detection threshold likely represent other cell types and were ignored for this analysis. The ANN_MLP classifier was trained on manually annotated data from three biological replicates and checked manually for accuracy before being applied to all images in the dataset (40 images total). The percentage of each cell type was calculated using Microsoft Excel v16.75. To quantify ncFem1 expression in female and male liver cells, DAPI, ncFem1, and ppIB signals were quantified by using ImageJ2 v2.9.0/1.53t. For each animal, 2 images from 2 liver sections were processed (4 images per animal, 40 images total). Images were split into their different channels (DAPI, ncFem1, and ppIB) and a uniform threshold range (Image &gt; Adjust &gt; Threshold) of 30-255 for each channel was applied to all images. The “Analyze Particles” function on FIJI was used to obtain the areas of all detected particles, which were summed in Microsoft Excel v16.75 to generate the total area of the signal of interest per image. The mean of the total area was then calculated for each animal and the ratio of ncFem1:ppIB area was calculated. Those ratios were then divided by the median male ncFem1:ppIB area to compare ncFem1:ppIB ratio to a male baseline. To test for ncFem1 nuclear localization, images were analyzed in QuPath v0.4.3 and cell boundaries (including nuclear boundaries based on DAPI staining) were predicted as described earlier for efferocytosis classification. To infer nuclear and cytoplasmic signal strength, the mean signal intensity of ncFem1 and ppIB channels over the nucleus and cytoplasm of each cell were exported from QuPath. For each channel, the nuclear:cytoplasmic ratio was reported for each cell in each image. The mean nuclear:cytoplasmic ratio was calculated per image and the median value per animal was taken.