Study: Single-cell imaging can identify gene interactions
In a study published online Feb. 9 in Genome Research, researchers showed that high-throughput single-cell image-based data from genetic screens can be used to identify genetic interactions and to infer signaling relations.
Bonnie Berger, PhD, professor of applied mathematics at the Massachusetts Institute of Technology in Cambridge, Mass., and colleagues evaluated the efficacy of a systematic computational model for identifying genetic interactions using single-cell images from genetic screens and applying it to RhoGAP/GTPase regulation in Drosophila.
"These images are an enormous source of data that is only beginning to be tapped," said Berger. "We realized we had enough data to go beyond classification and start to understand the mechanism behind the differences in shape."
The researchers "knocked-down" components of the RhoGAP network using RNA interference and then imaged thousands of fly cells, gathering measurements of cell perimeter, nuclear area and more than 150 other morphological features for each cell. These data were then passed through the computational framework to produce a set of high-confidence interactions, according to Berger.
The research group found that by making combinatorial knockdowns of Rho network components, their computational method was able to accurately infer Rho-signaling network interactions more precisely than when using only data from single knockdowns. Rho pathway has been implicated in cancer and other diseases in humans and the authors believe that these predicted interactions will be excellent candidates for future study.
The study demonstrated that high-throughput morphological image-based data can be used to identify genetic interactions and to infer signaling relations. "This work provides a glimpse into the future," Berger concluded, "where looking under the microscope manually at cells one-by-one is replaced with automated high-throughput processing of many cellular images."
Bonnie Berger, PhD, professor of applied mathematics at the Massachusetts Institute of Technology in Cambridge, Mass., and colleagues evaluated the efficacy of a systematic computational model for identifying genetic interactions using single-cell images from genetic screens and applying it to RhoGAP/GTPase regulation in Drosophila.
"These images are an enormous source of data that is only beginning to be tapped," said Berger. "We realized we had enough data to go beyond classification and start to understand the mechanism behind the differences in shape."
The researchers "knocked-down" components of the RhoGAP network using RNA interference and then imaged thousands of fly cells, gathering measurements of cell perimeter, nuclear area and more than 150 other morphological features for each cell. These data were then passed through the computational framework to produce a set of high-confidence interactions, according to Berger.
The research group found that by making combinatorial knockdowns of Rho network components, their computational method was able to accurately infer Rho-signaling network interactions more precisely than when using only data from single knockdowns. Rho pathway has been implicated in cancer and other diseases in humans and the authors believe that these predicted interactions will be excellent candidates for future study.
The study demonstrated that high-throughput morphological image-based data can be used to identify genetic interactions and to infer signaling relations. "This work provides a glimpse into the future," Berger concluded, "where looking under the microscope manually at cells one-by-one is replaced with automated high-throughput processing of many cellular images."