From feulgen to modern methods: marking a century of DNA imaging advances

Microscopy methods for the in vivo study of nanoscale nuclear organization

Eukaryotic genomes are highly compacted within the nucleus and organized into complex 3D structures across various genomic and physical scales. Organization within the nucleus plays a key role in gene regulation, both facilitating regulatory interactions to promote transcription while also enabling the silencing of other genes. Despite the functional importance of genome organization in determining cell identity and function, investigating nuclear organization across this wide range of physical scales has been challenging. Microscopy provides the opportunity for direct visualization of nuclear structures and has pioneered key discoveries in this field. Nonetheless, visualization of nanoscale structures within the nucleus, such as nucleosomes and chromatin loops, requires super-resolution imaging to go beyond the ~220 nm diffraction limit. Here, we review recent advances in imaging technology and their promise to uncover new insights into the organization of the nucleus at the nanoscale. We discuss different imaging modalities and how they have been applied to the nucleus, with a focus on super-resolution light microscopy and its application to in vivo systems. Finally, we conclude with our perspective on how continued technical innovations in super-resolution imaging in the nucleus will advance our understanding of genome structure and function.

Semi-quantitative scoring criteria based on multiple staining methods combined with machine learning to evaluate residual nuclei in decellularized matrix

The detection of residual nuclei in decellularized extracellular matrix (dECM) biomaterials is critical for ensuring their quality and biocompatibility. However, current evaluation methods have limitations in addressing impurity interference and providing intelligent analysis. In this study, we utilized four staining techniques-hematoxylin-eosin staining, acetocarmine staining, the Feulgen reaction and 4',6-diamidino-2-phenylindole staining-to detect residual nuclei in dECM biomaterials. Each staining method was quantitatively evaluated across multiple parameters, including area, perimeter and grayscale values, to establish a semi-quantitative scoring system for residual nuclei. These quantitative data were further employed as learning indicators in machine learning models designed to automatically identify residual nuclei. The experimental results demonstrated that no single staining method alone could accurately differentiate between nuclei and impurities. In this study, a semi-quantitative scoring table was developed. With this table, the accuracy of determining whether a single suspicious point is a cell nucleus has reached over 98%. By combining four staining methods, false positives caused by impurity contamination were eliminated. The automatic recognition model trained based on nuclear parameter features reached the optimal index of the model after several iterations of training in 172 epochs. The trained artificial intelligence model achieved a recognition accuracy of over 90% for detecting residual nuclei. The use of multidimensional parameters, integrated with machine learning, significantly improved the accuracy of identifying nuclear residues in dECM slices. This approach provides a more reliable and objective method for evaluating dECM biomaterials, while also increasing detection efficiency.