Optical label-free determination of mitochondrial dynamics using deep learning*

Association of mitochondria and cancer growth has been a topic of interest for researchers over many decades [1-2]. Understanding the changes in the structure and function of mitochondria in cancer cells typically involves electron and fluorescence microscopies. These methods, although widely adopted in the biomedical field due to the high resolution of electron microscopy and high specificity of fluorescence microscopy, are still not well suited for long term observation of live cells as electron microscopy involves destructive sample preparation and fluorescence microscopy carries inherent risk of phototoxicity and photobleaching. Here, we present an optical, label-free, deep learning enabled mitochondria detection technique for live mammalian cells and demonstrate the applicability of the proposed method in characterizing the dynamics of mitochondria in HeLa, Neuroglioblastoma, and CHO cells. We observed that as compared to the whole cell, mitochondria are more deterministic in their dynamics as indicated by a statistically significant reduction of the diffusion coefficient between them.

1. Kroemer, G., 2006. Mitochondria in cancer. Oncogene, 25(34).

2. Vyas, S., Zaganjor, E. and Haigis, M.C., 2016. Mitochondria and cancer. Cell, 166(3), pp.555-566.