Researchers have developed a new computational method that can rapidly track the three-dimensional movements of cells in such data-rich images. Using the method, scientists can essentially automate much of the time-consuming process of reconstructing an animal's developmental building plan cell by cell.
Recent advances in imaging technology are transforming how scientists see the cellular universe, showing the form and movement of once grainy and blurred structures in stunning detail. But extracting the torrent of information contained in those images often surpasses the limits of existing computational and data analysis techniques, leaving scientists less than satisfied.
Now, researchers at the Howard Hughes Medical Institute's Janelia Research Campus have developed a way around that problem. They have developed a new computational method that can rapidly track the three-dimensional movements of cells in such data-rich images. Using the method, the Janelia scientists can essentially automate much of the time-consuming process of reconstructing an animal's developmental building plan cell by cell.
Philipp Keller, a group leader at Janelia, led the team that developed the computational framework. He and his colleagues, including Janelia postdoc Fernando Amat, Janelia group leader Kristin Branson and former Janelia lab head Eugene Myers, who is now at the Max Plank Institute of Molecular Cell Biology and Genetics, have used the methodto reconstruct cell lineage during development of the early nervous system in a fruit fly. Their method can be used to trace cell lineages in multiple organisms and efficiently processes data from multiple kinds of fluorescent microscopes.
The scientists describe their approach in a paper published online on July 20, 2014, in Nature Methods.
"With this fairly fast, simple approach, we can solve easy cases fairly efficiently," Keller says. Those cases make up about 95 percent of the data. "In harder cases, where we might have mistakes, we use heavier machinery."
He explains that in instances where cells are harder to track -- because image quality is poor or cells are crowded, for example -- the computer draws on additional information. "We look at what all the cells in that neighborhood do a little bit into the future and a little bit into the past," Keller explains. Informative patterns usually emerge from that contextual information. The strategy takes more computing power than the initial tactics. "We don't want to do it for all the cells," Keller says. "But we try to crack these hard cases by gathering more information and making better informed decisions."
All of these steps can be carried out as quickly as images are acquired by the microscope, and the result is lineage information for every cell. "You know the path, you know where it is at a certain time point. You know it divided at a certain point, you know the daughter cells, you know what mother cell it came from," Keller says.