February 9, 2010 Cellular imaging offers a wealth of data about how cells respond to stimuli, but harnessing this technique to study biological systems is a daunting challenge. In a study published online in Genome Research (www.genome.org), researchers have developed a novel method of interpreting data from single-cell images to identify genetic interactions within biological networks, offering a glimpse into the future of high-throughput cell imaging analysis.
For years, scientists have been peering through a microscope at cells as they change appearance in response to different treatments, yet data collection is arduous, largely conducted qualitatively by eye. However, recent technological advances have led to the development of high-throughput image screening methods that can produce extensive datasets of hundreds of different morphological features.
With the ability to collect large imaging datasets, researchers from MIT and Harvard Medical School recognized an opportunity to explore the cellular networks that regulate cell morphology. "These images are an enormous source of data that is only beginning to be tapped," said MIT researcher Bonnie Berger, senior author of the work published today. "We realized we had enough data to go beyond classification and start to understand the mechanism behind the differences in shape."
To meet the challenge of interpreting cell image data, Berger and MIT graduate student Oaz Nir developed a novel computational model to identify genetic interactions using high-dimensional morphological data. Integrating prerequisite knowledge of a pathway, their model maps potential interactions within a network by looking for similar morphological features upon genetic perturbation.
The group demonstrated the method by analyzing the Rho-signaling network in fruit flies, a network that regulates cell adhesion and motility in eukaryotic organisms. I
|Contact: Peggy Calicchia|
Cold Spring Harbor Laboratory