| Literature DB >> 33333022 |
Elizabeth K Unger1, Jacob P Keller2, Michael Altermatt3, Ruqiang Liang1, Aya Matsui4, Chunyang Dong1, Olivia J Hon5, Zi Yao6, Junqing Sun1, Samba Banala2, Meghan E Flanigan5, David A Jaffe1, Samantha Hartanto1, Jane Carlen1, Grace O Mizuno1, Phillip M Borden2, Amol V Shivange3, Lindsay P Cameron1, Steffen Sinning7, Suzanne M Underhill8, David E Olson1, Susan G Amara8, Duncan Temple Lang1, Gary Rudnick7, Jonathan S Marvin2, Luke D Lavis2, Henry A Lester3, Veronica A Alvarez4, Andrew J Fisher1, Jennifer A Prescher6, Thomas L Kash5, Vladimir Yarov-Yarovoy1, Viviana Gradinaru3, Loren L Looger9, Lin Tian10.
Abstract
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.Entities:
Keywords: OSTA; SERT; fear-learning; fiber photometry; fluorescence protein sensor; iSeroSnFR; machine learning; serotonin; sleep-wake; social behaviors
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Year: 2020 PMID： 33333022 PMCID： PMC8025677 DOI： 10.1016/j.cell.2020.11.040
Source DB: PubMed Journal: Cell ISSN： 0092-8674 Impact factor: 41.582