In a major leap forward for genetic and biomedical research, two scientists at the University of Missouri have developed a powerful new artificial intelligence tool that can predict the 3D shape of chromosomes inside individual cells — helping researchers gain a new view of how our genes work.
Chromosomes are the tiny storage boxes that hold our DNA. Since each cell has about six feet of DNA packed inside it, it must be folded up tightly to fit. This folding not only saves space — it also controls which genes are active or inactive. But when the DNA doesn't fold the right way, it can disrupt normal cell functions and lead to serious diseases, including cancer.
Historically, scientists have relied on data that averaged results from millions of cells at once. That makes it almost impossible to see the unique differences between individual cells. But the new AI model developed by Yanli Wang and Jianlin "Jack" Cheng at Mizzou's College of Engineering changes that.
"This is important because even cells from the same part of the body can have chromosomes folded in very different ways," Wang, a graduate student and lead author of the study, said. "That folding controls which genes are turned on or off."
Studying single cells is tricky because the data is often messy or incomplete. But the new AI tool is specially designed to work with those challenges. It's smart enough to spot weak patterns in noisy data, and it knows how to estimate a chromosome's 3D shape even when some information is missing.
It also understands how to "see" biological structures correctly, even when they're rotated. Compared to a previous deep learning AI method, Mizzou's tool is more than twice as accurate when analyzing human single-cell data.
The team has made the software free and available to scientists around the world. That means researchers can now use it to better understand how genes function, how diseases start and how to design better treatments.
"Every single cell can have a different chromosome structure," Cheng, a Curators' Distinguished Professor of Electrical Engineering and Computer Science, said. "Our tool helps scientists study those differences in detail — which can lead to new insights into health and disease."
The researchers now plan to improve the AI tool even further by expanding it to build the high-resolution structures of entire genomes. Their goal: to give scientists the clearest picture yet of the genetic blueprint inside our cells.
" Reconstructing 3D chromosome structures from single-cell Hi-C data with SO(3)-equivariant graph neural networks ," was published in NAR Genomics and Bioinformatics.
