Public release date: 19-Aug-2012 [ | E-mail | Share ]

Contact: John Toon jtoon@gatech.edu 404-894-6986 Georgia Institute of Technology Research News

Research into the genetic factors behind certain disease mechanisms, illness progression and response to new drugs is frequently carried out using tiny multi-cellular animals such as nematodes, fruit flies or zebra fish.

Often, progress relies on the microscopic visual examination of many individual animals to detect mutants worthy of further study.

Now, scientists have demonstrated an automated system that uses artificial intelligence and cutting-edge image processing to rapidly examine large numbers of individual Caenorhabditis elegans, a species of nematode widely used in biological research. Beyond replacing existing manual examination steps using microfluidics and automated hardware, the system's ability to detect subtle differences from worm-to-worm without human intervention can identify genetic mutations that might not have been detected otherwise.

By allowing thousands of worms to be examined autonomously in a fraction of the time required for conventional manual screening, the technique could change the way that high throughput genetic screening is carried out using C. elegans.

Details of the research were scheduled to be reported August 19th in the advance online publication of the journal Nature Methods. The research has been supported by the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Alfred P. Sloan Foundation.

"While humans are very good at pattern recognition, computers are much better than humans at detecting subtle differences, such as small changes in the location of dots or slight variations in the brightness of an image," said Hang Lu, the project's lead researcher and an associate professor in the School of Chemical & Biomolecular Engineering at the Georgia Institute of Technology. "This technique found differences that would have been almost impossible to pick out by hand."

Lu's research team is studying genes that affect the formation and development of synapses in the worms, work that could have implications for understanding human brain development. The researchers use a model in which synapses of specific neurons are labeled by a fluorescent protein. Their research involves creating mutations in the genomes of thousands of worms and examining the resulting changes in the synapses. Mutant worms identified in this way are studied further to help understand what genes may have caused the changes in the synapses.

One aspect the researchers are studying is why synapses form in the wrong locations, or are of the wrong sizes or types. The differences between the mutants and the normal or "wild type" worms indicate inappropriate developmental patterns caused by the genetic mutations.

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Artificial intelligence helps detect subtle differences in mutant worms