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Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning
Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review
DOI
- https://doi.org/10.1016/j.cell.2020.11.040
Final published version
- Elizabeth K. Unger, University of California at Davis ,
- Jacob P. Keller, Howard Hughes Medical Institute ,
- Michael Altermatt, California Institute of Technology ,
- Ruqiang Liang, University of California at Davis ,
- Aya Matsui, National Institute on Alcohol Abuse and Alcoholism ,
- Chunyang Dong, University of California at Davis ,
- Olivia J. Hon, University of North Carolina ,
- Zi Yao, University of California at Irvine ,
- Junqing Sun, University of California at Davis ,
- Samba Banala, Howard Hughes Medical Institute ,
- Meghan E. Flanigan, University of North Carolina ,
- David A. Jaffe, University of California at Davis ,
- Samantha Hartanto, University of California at Davis ,
- Jane Carlen, University of California at Davis ,
- Grace O. Mizuno, University of California at Davis ,
- Phillip M. Borden, Howard Hughes Medical Institute ,
- Amol V. Shivange, California Institute of Technology ,
- Lindsay P. Cameron, University of California at Davis ,
- Steffen Sinning
- Suzanne M. Underhill, National Institutes of Health ,
- David E. Olson, University of California at Davis ,
- Susan G. Amara, National Institutes of Health ,
- Duncan Temple Lang, University of California at Davis ,
- Gary Rudnick
- Jonathan S. Marvin, Howard Hughes Medical Institute ,
- Luke D. Lavis, Howard Hughes Medical Institute ,
- Henry A. Lester, California Institute of Technology ,
- Veronica A. Alvarez, National Institute on Alcohol Abuse and Alcoholism ,
- Andrew J. Fisher, University of California at Davis ,
- Jennifer A. Prescher, University of California at Irvine ,
- Thomas L. Kash, University of North Carolina ,
- Vladimir Yarov-Yarovoy, University of California at Davis ,
- Viviana Gradinaru, California Institute of Technology ,
- Loren L. Looger, Howard Hughes Medical Institute ,
- Lin Tian, University of California at Davis
Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which has been hampered by our inability to monitor serotonin release and transport with high spatial and temporal resolution. We developed and applied a binding-pocket redesign strategy, guided by machine learning, to create a high-performance, soluble, fluorescent serotonin sensor (iSeroSnFR), enabling optical detection of millisecond-scale serotonin transients. We demonstrate that iSeroSnFR can be used to detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep/wake transitions. We also developed a robust assay of serotonin transporter function and modulation by drugs. We expect that both machine-learning-guided binding-pocket redesign and iSeroSnFR will have broad utility for the development of other sensors and in vitro and in vivo serotonin detection, respectively.
| Original language | English |
|---|---|
| Journal | Cell |
| Volume | 183 |
| Issue | 7 |
| Pages (from-to) | 1986-2002.e26 |
| ISSN | 0092-8674 |
| DOIs | |
| Publication status | Published - Dec 2020 |
| Externally published | Yes |
- fear-learning, fiber photometry, fluorescence protein sensor, iSeroSnFR, machine learning, OSTA, serotonin, SERT, sleep-wake, social behaviors
Research areas
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ID: 207674258