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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵¤ Currently at Incyte Corporation
The previous approaches for nuclei segmentation were compared to Stardist and Cellpose (section 1, supplementary figures 5 and 6). Simulated data was generated to evaluate our approach, leading to a new paragraph in Section 3 as well as the new Figure 4, Supplementary Figures 12, 13 and 14. Section 4 was added to evaluate the estimation of E2Fs concentration evolution over the cell cycle, depending on nuclei segmentation. Figure 5 was added. 9 supplementary tables were added to facilitate the comparison when evaluating the methods over the paper (Supplementary Tables 1 to 9).