New Tool Improves Spatial Analysis of Chromosomes

Computational methods used to fill in missing pixels in low-quality images can also help scientists provide missing information for how DNA is organized in the cell, according to computational biologists at Carnegie Mellon University, who explain that filling in this missing information will make it possible to more readily study the 3D structure of chromosomes and, in particular, subcompartments that may play a crucial role in both disease formation and determining cell functions.

In a paper published today in Nature Communications, the team led by senior author Jian Ma report that they successfully applied their machine learning method to nine cell lines. This enabled them to study differences in spatial organization related to subcompartments across those lines. Previously, subcompartments could be revealed in only a single cell type of lymphoblastoid cells—a cell line known as GM12878—that has been exhaustively sequenced at great expense using Hi-C technology, which measures spatial interactivity among all regions of the genome.

"We now know a lot about the linear composition of DNA in chromosomes, but in the nuclei of human cells, DNA isn't linear," first author Kyle Xiong said. "Chromosomes in the cell nucleus are folded and packaged into 3D shapes. That 3D structure is critical to understanding the cellular functions in development and diseases." Subcompartments are of particular interest because they reflect spatial segregation of chromosome regions with high interactivity.

Using a computational method dubbed SNIPER, the team was able to identify subcompartments in eight cell lines whose interchromosomal interactions based on Hi-C data were only partially known. They also applied SNIPER to the GM12878 data as a control. But Xiong noted that it is not yet known how widely this tool can be used on all other cell types.

"We need to understand how subcompartment patterns are involved in the basic functions of cells, as well as how mutations can affect these 3D structures," Ma said. "Thus far, in the few cell lines we've been able to study, we see that some subcompartments are consistent across cell types, while others vary. Much remains to be learned."