Image processing to investigate NEMO recruitment and involvement in mitophagy and inflammatory signaling

Beautiful images are not sufficient to robustly characterize and interrogate a cellular mechanism. In order to show recruitment of NEMO and its relationship to OPTN and other mitophagy factors, we processed hundreds of still and timelapse images and extracted quantifiable data in order to perform statistical analysis comparing different conditions. In many cases, we used software to deconvolve confocal fluorescent images, allowing us to algorithmically surpass the resolution limit. This was especially useful for live cell images that had been collected with low power settings to preserve cell health. We also employed machine learning software to generate binary segmentations and carry out particle analysis on putatively overlapping structures. Finally, in some cases we simply identified fluorescent structures by hand and measured their fluorescent intensities. We approached image analysis in a multitude of creative, effective ways, and importantly we maintained consistency of analysis within experiments in order to present the results with integrity and reproducibility.