Abstract
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.
Highlights
Automatic image analysis is at the core of human and animal tissue-based research
Estimating the evolution of protein concentration over the cell cycle is an important step towards a better understanding of this key biological process
We propose a series of deep learningbased approaches to precisely segment nuclei and to identify fluorescently labelled cells in order to analyze the evolution of cell cycle dependent E2F protein concentration in mouse tissues
Summary
Automatic image analysis is at the core of human and animal tissue-based research. quantitation of morphological features or fluorescent labeling in intact mammalian tissues still remains a challenge. Deep convolutional neural networks have demonstrated their superiority for image segmentation [1, 2]. These approaches have outperformed the traditional approaches used in microscopy, such as watershed for nuclei or cell segmentation [5–9]. This machine learning-based approach requires large amounts of annotated data and new strategies have to be developed to process highly complex biological objects acquired with different modalities by considering small training datasets. We propose a series of deep learningbased approaches to precisely segment nuclei and to identify fluorescently labelled cells in order to analyze the evolution of cell cycle dependent E2F protein concentration in mouse tissues
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