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GPS for the Nematode
by Jennifer Michalowski
Janelia scientists have made it easier to navigate C. elegans territory.
You'd think the genetics of a creature as small as the eyelash-sized roundworm Caenorhabditis elegans would be simple. But scientists are finding surprising complexity: they now know that more than one genetic pathway can drive the worm's cells to a single developmental fate. The large-scale studies needed to yield this kind of result are possible for two reasons: in C. elegans, the name and fate of each cell on the pathway from egg to adult are well known, and now there's a powerful tool for analyzing this treasure trove of data.
Large collections of drawings and microscope images have been compiled into print and online worm atlases that offer researchers abundant anatomical information. But navigating their pages and relating them to the cells in an image of an actual, individual lab worm can take days for a skilled worm researcher and, in some cases, yield ambiguous results.
Thanks to Janelia Farm scientists Eugene Myers, Hanchuan Peng, and Fuhui Long, the tedious task can now be turned over to a computer. Using a new “digital atlas,” researchers can prepare a worm for microscopy, snap a digital image, and in a few hours retrieve a navigational map of its cells—kind of a WPS, or worm positioning system.
The idea for the digital atlas grew from a conversation between Myers and Stanford University developmental biologist Stuart Kim. Kim's lab group studies changes in gene expression as animals develop and age. They wanted to measure gene activity cell by cell to learn how each cell's genes control its fate.
They needed an efficient way to identify a worm's cells and then match up gene expression profiles to each cell's developmental path. More specifically, they needed a computer program that could discern individual cells in a digital image and then correlate them to the identities documented in the worm atlas.
Image interpretation doesn't come as easily to a computer as it does to a human, Myers says. Variations in an object's shape, inconsistencies in the staining of a sample, and blurry edges contribute to a computer's confusion when it tries to understand what it is “seeing.” Humans draw on prior knowledge for clues as to what an image is likely to represent, he says—“but how do you teach a computer to do that?”
Myers' team started by teaching the computer to recognize the 558 cells in the worm's L1 stage of development—the millimeter-long larval form that emerges from the egg. To make the visual processing logical and accessible for the computer, they divided it into a series of steps (see sidebar, “Break it Down”).
Illustration: Siggi Eggertsson