Johann To Berens Philippe, Schivre Geoffrey, Theune Marius, Peter Jackson, Sall Salimata Ousmane, Mutterer Jérôme, Barneche Fredy, Bourbousse Clara, Molinier Jean
Institut de biologie moléculaire des plantes du CNRS, 67000 Strasbourg, France.
Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, Centre National de la Recherche Scientifique, Inserm, Université PSL, 75230 Paris, France.
Epigenomes. 2022 Oct 5;6(4):34. doi: 10.3390/epigenomes6040034.
The combination of ever-increasing microscopy resolution with cytogenetical tools allows for detailed analyses of nuclear functional partitioning. However, the need for reliable qualitative and quantitative methodologies to detect and interpret chromatin sub-nuclear organization dynamics is crucial to decipher the underlying molecular processes. Having access to properly automated tools for accurate and fast recognition of complex nuclear structures remains an important issue. Cognitive biases associated with human-based curation or decisions for object segmentation tend to introduce variability and noise into image analysis. Here, we report the development of two complementary segmentation methods, one semi-automated () and one based on deep learning (), and their evaluation using a collection of nuclei with contrasted or poorly defined chromatin compartmentalization. Both methods allow for fast, robust and sensitive detection as well as for quantification of subtle nucleus features. Based on these developments, we highlight advantages of semi-automated and deep learning-based analyses applied to plant cytogenetics.
不断提高的显微镜分辨率与细胞遗传学工具相结合，使得对细胞核功能分区进行详细分析成为可能。然而，需要可靠的定性和定量方法来检测和解释染色质亚核组织动态，这对于破译潜在的分子过程至关重要。能够使用适当的自动化工具准确、快速地识别复杂的核结构仍然是一个重要问题。与基于人工的策展或对象分割决策相关的认知偏差往往会在图像分析中引入变异性和噪声。在这里，我们报告了两种互补分割方法的开发，一种是半自动的（），另一种是基于深度学习的（），并使用一组具有对比鲜明或定义不明确的染色质分区的细胞核对它们进行评估。这两种方法都能够快速、稳健且灵敏地检测以及量化细微的细胞核特征。基于这些进展，我们强调了应用于植物细胞遗传学的半自动和基于深度学习分析的优势。
