The hierarchical packing of euchromatin domains can be described as multiplicative cascades.
Amra NoaHui-Shun KuanVera AschmannVasily ZaburdaevLennart HilbertPublished in: PLoS computational biology (2021)
The genome is packed into the cell nucleus in the form of chromatin. Biochemical approaches have revealed that chromatin is packed within domains, which group into larger domains, and so forth. Such hierarchical packing is equally visible in super-resolution microscopy images of large-scale chromatin organization. While previous work has suggested that chromatin is partitioned into distinct domains via microphase separation, it is unclear how these domains organize into this hierarchical packing. A particular challenge is to find an image analysis approach that fully incorporates such hierarchical packing, so that hypothetical governing mechanisms of euchromatin packing can be compared against the results of such an analysis. Here, we obtain 3D STED super-resolution images from pluripotent zebrafish embryos labeled with improved DNA fluorescence stains, and demonstrate how the hierarchical packing of euchromatin in these images can be described as multiplicative cascades. Multiplicative cascades are an established theoretical concept to describe the placement of ever-smaller structures within bigger structures. Importantly, these cascades can generate artificial image data by applying a single rule again and again, and can be fully specified using only four parameters. Here, we show how the typical patterns of euchromatin organization are reflected in the values of these four parameters. Specifically, we can pinpoint the values required to mimic a microphase-separated state of euchromatin. We suggest that the concept of multiplicative cascades can also be applied to images of other types of chromatin. Here, cascade parameters could serve as test quantities to assess whether microphase separation or other theoretical models accurately reproduce the hierarchical packing of chromatin.
Keyphrases
- dna damage
- deep learning
- genome wide
- gene expression
- transcription factor
- convolutional neural network
- optical coherence tomography
- single molecule
- high resolution
- single cell
- dna methylation
- computed tomography
- oxidative stress
- stem cells
- artificial intelligence
- machine learning
- ultrasound guided
- high throughput
- mesenchymal stem cells
- electronic health record
- mass spectrometry
- big data
- quantum dots
- data analysis
- cell free
- circulating tumor cells