人工智能鉴别变异蠕虫的细微差别

Research into the genetic¹ factors behind certain disease mechanisms², illness progression and response to new drugs is frequently carried out using tiny multi-cellular animals such as nematodes（线虫）, fruit flies or zebra fish. Often, progress relies on the microscopic³ visual examination of many individual animals to detect mutants worthy⁴ of further study. Now, scientists have demonstrated an automated⁵ 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 intervention⁶ -- can identify genetic mutations that might not have been detected otherwise.

 

By allowing thousands of worms to be examined autonomously⁷（独立自主地） 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 genes⁸ that affect the formation and development of synapses⁹ 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 fluorescent¹⁰（荧光的） 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.

 

Because of the large number of possible genes involved in these developmental processes, the researchers must examine thousands of worms -- perhaps as many as 100,000 -- to exhaust the search. Lu and her research group had earlier developed a microfluidic "worm sorter" that speeds up the process of examining worms under a microscope, but until now, there were two options for detecting the mutants: a human had to look at each animal, or a simple heuristic algorithm（启发式算法） was used to make the sorting decision. Neither option is objective or adaptable¹¹ to new problems.

 

Lu's system, an optimized¹² version of earlier work by her group, uses a camera to record three-dimensional images of each worm as it passes through the sorter. The system compares each image set against what it has been taught the "wild type" worms should look like. Worms that are even subtly different from normal can be sorted out for further study.

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