{"147291":{"#nid":"147291","#data":{"type":"news","title":"Automated Worm Sorter Detects Subtle Differences in Tiny Animals Used in Genetic Research","body":[{"value":"\u003Cp\u003EResearch 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.\u003C\/p\u003E\u003Cp\u003ENow, scientists have demonstrated an automated system that uses artificial intelligence and cutting-edge image processing to rapidly examine large numbers of individual \u003Cem\u003ECaenorhabditis elegans\u003C\/em\u003E, a species of nematode widely used in biological research. Beyond replacing existing manual examination steps using microfluidics and automated hardware, the system\u2019s ability to detect subtle differences from worm-to-worm \u2013 without human intervention \u2013 can identify genetic mutations that might not have been detected otherwise.\u003C\/p\u003E\u003Cp\u003EBy 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 \u003Cem\u003EC. elegans\u003C\/em\u003E.\u003C\/p\u003E\u003Cp\u003EDetails of the research were reported August 19th in the advance online publication of the journal \u003Cem\u003ENature Methods\u003C\/em\u003E. The research has been supported by the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Alfred P. Sloan Foundation.\u003C\/p\u003E\u003Cp\u003E\u201cWhile 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,\u201d said \u003Ca href=\u0022http:\/\/www.chbe.gatech.edu\/faculty\/lu\u0022\u003EHang Lu\u003C\/a\u003E, the project\u2019s lead researcher and an associate professor in the \u003Ca href=\u0022http:\/\/www.chbe.gatech.edu\/\u0022\u003ESchool of Chemical \u0026amp; Biomolecular Engineering\u003C\/a\u003E at the Georgia Institute of Technology. \u201cThis technique found differences that would have been almost impossible to pick out by hand.\u201d\u003C\/p\u003E\u003Cp\u003ELu\u2019s 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.\u003C\/p\u003E\u003Cp\u003EOne 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 \u201cwild type\u201d worms indicate inappropriate developmental patterns caused by the genetic mutations.\u003C\/p\u003E\u003Cp\u003EBecause of the large number of possible genes involved in these developmental processes, the researchers must examine thousands of worms \u2013 perhaps as many as 100,000 \u2013 to exhaust the search. Lu and her research group had earlier developed a microfluidic \u201cworm sorter\u201d 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 was used to make the sorting decision. Neither option is objective or adaptable to new problems.\u003C\/p\u003E\u003Cp\u003ELu\u2019s system, an optimized 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 \u201cwild type\u201d worms should look like. Worms that are even subtly different from normal can be sorted out for further study.\u003C\/p\u003E\u003Cp\u003E\u201cWe feed the program wild-type images, and it teaches itself to recognize what differentiates the wild type. It uses this information to determine what a mutant type may look like \u2013 which is information we didn\u2019t provide to the system \u2013 and sorts the worms based on that,\u201d explained Matthew Crane, a graduate student who performed the work. \u201cWe don\u2019t have to show the computer every possible mutant, and that is very powerful. And the computer never gets bored.\u201d\u003C\/p\u003E\u003Cp\u003EWhile the system was designed to sort \u003Cem\u003EC. elegans\u003C\/em\u003E for a specific research project, Lu believes the machine learning technology \u2013 which is borrowed from computer science \u2013 could be applied to other areas of biology that use model genetic organisms. The system\u2019s hardware and software are currently being used in several other laboratories beyond Georgia Tech.\u0026nbsp; \u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u201cOur automated technique can be generalized to anything that relies on detecting a morphometric \u2013 or shape, size or brightness difference,\u201d Lu said. \u201cWe can apply this to anything that can be detected visually, and we think this could be expanded to studying many other problems related to learning, memory, neuro-degeneration and neural developmental diseases that this worm can be used to model.\u201d\u003C\/p\u003E\u003Cp\u003EIndividual \u003Cem\u003EC. elegans\u003C\/em\u003E are less than a millimeter long and thinner than a strand of hair, but have 302 neurons with well-defined synapses. While research using single cells can be simpler to do, studies using the worms are good in vivo models for many important processes relevant to human health.\u003C\/p\u003E\u003Cp\u003EOther researchers who contributed to this paper include student Jeffrey Stirman from Georgia Tech\u2019s interdisciplinary program in bioengineering, Professor James Rehg from Georgia Tech\u2019s School of Interactive Computing, and three researchers from the Department of Biology at Stanford University\u2019s Howard Hughes Medical Institute: Chan-Yen Ou, Peri Kurshan, and Professor Kang Shen.\u003C\/p\u003E\u003Cp\u003EThe autonomous processing facilitated by the new system could allow researchers to examine more animals more rapidly, potentially opening up areas of study that are not feasible today.\u003C\/p\u003E\u003Cp\u003E\u201cWe are hoping that the technology will really change the approach people can take to this kind of research,\u201d said Lu.\u0026nbsp; \u201cWe expect that this approach will enable people to do much larger scale experiments that can push the science forward beyond looking what individual mutations are doing in a specific situation.\u201d\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EThe project described was supported by Award Numbers R01GM088333, R21EB012803 and R01AG035317 from the National Institutes of Health. This material is also based on work supported by the National Science Foundation under Grant No. CAREER CBET-0954578. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National lnstitutes of Health or the National Science Foundation.\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECitation\u003C\/strong\u003E: Matthew Crane, Jeffrey Stirman, Chan-Yen Ou, Peri Kurshan, James Rehg, Kang Shen \u0026amp; Hang Lu, \u003Cem\u003EAutonomous screening of C. elegans identifies genes implicated in synaptogenesis\u003C\/em\u003E, DOI: 10.1038\/NMETH.2141\u003Cbr \/\u003E\u003Cbr \/\u003E\u003Cstrong\u003EResearch News \u0026amp; Publications Office\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cstrong\u003EGeorgia Institute of Technology\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cstrong\u003E75 Fifth Street, N.W., Suite 309\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cstrong\u003EAtlanta, Georgia\u0026nbsp; 30308\u0026nbsp; USA\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cbr \/\u003E\u003Cstrong\u003EMedia Relations Contact\u003C\/strong\u003E: John Toon (404-894-6986)(\u003Ca href=\u0022mailto:jtoon@gatech.edu\u0022\u003Ejtoon@gatech.edu\u003C\/a\u003E)\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EWriter\u003C\/strong\u003E: John Toon\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EScientists have demonstrated an automated system that uses artificial intelligence and cutting-edge image processing to rapidly examine large numbers of individual nematodes, a tiny animal widely used in biological research.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"An automated system allows rapid study of tiny animals used in genetic research."}],"uid":"27303","created_gmt":"2012-08-19 11:31:40","changed_gmt":"2016-10-08 03:12:40","author":"John Toon","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2012-08-19T00:00:00-04:00","iso_date":"2012-08-19T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"147271":{"id":"147271","type":"image","title":"Automated Worm Sorter2","body":null,"created":"1449178763","gmt_created":"2015-12-03 21:39:23","changed":"1475894782","gmt_changed":"2016-10-08 02:46:22","alt":"Automated Worm Sorter2","file":{"fid":"195117","name":"automated-worm-sorter129.jpg","image_path":"\/sites\/default\/files\/images\/automated-worm-sorter129_0.jpg","image_full_path":"http:\/\/tlwarc.hg.gatech.edu\/\/sites\/default\/files\/images\/automated-worm-sorter129_0.jpg","mime":"image\/jpeg","size":787310,"path_740":"http:\/\/tlwarc.hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/automated-worm-sorter129_0.jpg?itok=cA8E67h3"}},"147261":{"id":"147261","type":"image","title":"Automated Worm 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Sorter3","file":{"fid":"195118","name":"automated-worm-sorter174.jpg","image_path":"\/sites\/default\/files\/images\/automated-worm-sorter174_0.jpg","image_full_path":"http:\/\/tlwarc.hg.gatech.edu\/\/sites\/default\/files\/images\/automated-worm-sorter174_0.jpg","mime":"image\/jpeg","size":898313,"path_740":"http:\/\/tlwarc.hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/automated-worm-sorter174_0.jpg?itok=5TdOgRnI"}}},"media_ids":["147271","147261","147281"],"groups":[{"id":"1188","name":"Research Horizons"}],"categories":[{"id":"141","name":"Chemistry and Chemical Engineering"},{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"146","name":"Life Sciences and Biology"}],"keywords":[{"id":"2556","name":"artificial intelligence"},{"id":"898","name":"Hang Lu"},{"id":"204","name":"image processing"},{"id":"40871","name":"image recognition"},{"id":"7346","name":"nematode"},{"id":"167750","name":"School of Chemical \u0026 Biomolecular Engineering"},{"id":"169516","name":"synapse"}],"core_research_areas":[{"id":"39441","name":"Bioengineering and Bioscience"},{"id":"39431","name":"Data Engineering and Science"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EJohn Toon\u003C\/p\u003E\u003Cp\u003EResearch News \u0026amp; Publications Office\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022mailto:jtoon@gatech.edu\u0022\u003Ejtoon@gatech.edu\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E(404) 894-6986\u003C\/p\u003E","format":"limited_html"}],"email":["jtoon@gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}