Virtual labeling of mitochondria in living cells using correlative imaging and physics-guided deep learning

Mitochondria play a crucial role in cellular metabolism. This paper presents a novel method to visualize mitochondria in living cells without the use of fluorescent markers. We propose a physics-guided deep learning approach for obtaining virtually labeled micrographs of mitochondria from bright-field images. We integrate a microscope's point spread function in the learning of an adversarial neural network for improving virtual labeling. We show results (average Pearson correlation 0.86) significantly better than what was achieved by state-of-the-art (0.71) for virtual labeling of mitochondria. We also provide new insights into the virtual labeling problem and suggest additional metrics for quality assessment. The results show that our virtual labeling approach is a powerful way of segmenting and tracking individual mitochondria in bright-field images, results previously achievable only for fluorescently labeled mitochondria.

Automated summary

This paper presents a novel method to visualize mitochondria in living cells without fluorescent markers. The authors proposed a physics-guided deep learning approach to obtain virtually labeled micrographs of mitochondria from bright-field images. The results showed that the virtual labeling approach significantly outperformed state-of-the-art techniques in segmenting and tracking individual mitochondria.