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Deep Learning Algorithms Identify Structures in Living Cells

Researchers are using artificial intelligence to pick out the features of brightfield microscopy images.

Written byDiana Kwon
| 4 min read
images taken with brightfield microscopy

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ABOVE: MODIFIED FROM THE ALLEN INSTITUTE

For cell biologists, fluorescence micro­scopy is an invaluable tool. Fusing dyes to antibodies or inserting genes coding for fluorescent proteins into the DNA of living cells can help scientists pick out the location of organelles, cytoskeletal elements, and other subcellular structures from otherwise impenetrable microscopy images. But this technique has its drawbacks. There are limits to the number of fluorescent tags that can be introduced into a cell, and side effects such as photo­toxicity—damage caused by repeated exposure to light—can hinder researchers’ ability to conduct live cell imaging.

These issues were on biomedical engineer Greg Johnson’s mind when he joined the Allen Institute for Cell Science in Seattle in 2016. Johnson, whose doctoral work at Carnegie Mellon University had focused on creating computational tools to model cellular structures (see “Robert Murphy Bets Self Driving Instruments Will Crack Biology's Mysteries” here), was hired as part ...

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Meet the Author

  • Diana is a freelance science journalist who covers the life sciences, health, and academic life. She’s a regular contributor to The Scientist and her work has appeared in several other publications, including Scientific American, Knowable, and Quanta. Diana was a former intern at The Scientist and she holds a master’s degree in neuroscience from McGill University. She’s currently based in Berlin, Germany.

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