Studying mitochondrial network dynamics using phase imaging with computational specificity (PICS)

In this study, we use phase imaging with computational specificity (PICS) to detect unlabeled mitochondria in live cells and monitor their dynamics over time.This is a two-step study with first phase involving detection of mitochondria in phase images using deep learning. HCT116 cells with GFP tagged mitochondria were imaged with a correlative SLIM and fluorescence imaging instrument, resulting in pairs of registered phase and fluorescence images per field of view. A deep neural network, EfficientNetB2+U-Net, was trained on the phase - fluorescence image pairs. Our network can predict mitochondria from the SLIM images with a SSIM of 0.9. The second step involves monitoring the effects of anticancer drugs on the mitochondria network dynamic, dry mass of mitochondria content, and their correlation with the overall cell health and drug efficacy. This method can potentially be translated into a tool for label-free efficacy evaluation of mitochondria inhibiting drugs for cancer therapy.