Deep learning tools and modeling to estimate the temporal expression of cell cycle proteins from 2D still images

Automatic characterization of fluorescent labeling in intact mammalian tissues remains a challenge due to the lack of quantifying techniques capable of segregating densely packed nuclei and intricate tissue patterns. Here, we describe a powerful deep learning-based approach that couples remarkably precise nuclear segmentation with quantitation of fluorescent labeling intensity within segmented nuclei, and then apply it to the analysis of cell cycle dependent protein concentration in mouse tissues using 2D fluorescent still images. First, several existing deep learning-based methods were evaluated to accurately segment nuclei using different imaging modalities with a small training dataset. Next, we developed a deep learning-based approach to identify and measure fluorescent labels within segmented nuclei, and created an ImageJ plugin to allow for efficient manual correction of nuclear segmentation and label identification. Lastly, using fluorescence intensity as a readout for protein concentration, a three-step global estimation method was applied to the characterization of the cell cycle dependent expression of E2F proteins in the developing mouse intestine. Author summary Estimating the evolution of protein concentration over the cell cycle is an important step towards a better understanding of this key biological process. Unfortunately, experimental designs to monitor proteins in individual living cells are expensive and difficult to set up. We propose instead to consider 2D images from tissue biopsies as snapshots of cell populations to reconstruct the actual protein concentration evolution over the cell cycle. This requires to accurately localize cell nuclei and identify nuclear fluorescent proteins. We take advantage of the powerful deep learning technology, a machine learning approach which has revolutionized computer vision, to achieve these challenging tasks. Additionally, we have created an ImageJ plugin to quickly and efficiently annotate images or correct annotations, required to build training datasets to feed the deep convolutional neural networks.

Automated summary

This study presents a deep learning-based approach for accurate nuclear segmentation and quantification of fluorescent labeling intensity. By applying this approach to 2D fluorescent still images of mouse tissues, the researchers successfully analyzed the cell cycle-dependent protein concentration.