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Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning

Unger, Elizabeth K. and Keller, Jacob P. and Altermatt, Michael and Liang, Ruqiang and Matsui, Aya and Dong, Chunyang and Hon, Olivia J. and Yao, Zi and Sun, Junqing and Banala, Samba and Flanigan, Meghan E. and Jaffe, David A. and Hartanto, Samantha and Carlen, Jane and Mizuno, Grace O. and Borden, Phillip M. and Shivange, Amol V. and Cameron, Lindsay P. and Sinning, Steffen and Underhill, Suzanne M. and Olson, David E. and Amara, Susan G. and Temple Lang, Duncan and Rudnick, Gary and Marvin, Jonathan S. and Lavis, Luke D. and Lester, Henry A. and Alvarez, Veronica A. and Fisher, Andrew J. and Prescher, Jennifer A. and Kash, Thomas L. and Yarov-Yarovoy, Vladimir and Gradinaru, Viviana and Looger, Loren L. and Tian, Lin (2020) Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning. Cell, 183 (7). pp. 1986-2002. ISSN 0092-8674. PMCID PMC8025677. https://resolver.caltech.edu/CaltechAUTHORS:20201217-143607354

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.cell.2020.11.040DOIArticle
ORCID:
AuthorORCID
Unger, Elizabeth K.0000-0002-8235-7534
Keller, Jacob P.000-0001-7487-4104
Altermatt, Michael0000-0003-2841-5374
Liang, Ruqiang0000-0002-3075-4554
Matsui, Aya0000-0003-4437-8278
Dong, Chunyang0000-0002-4820-4454
Hon, Olivia J.0000-0003-1086-1421
Flanigan, Meghan E.0000-0002-3185-7459
Jaffe, David A.0000-0003-4773-6982
Hartanto, Samantha0000-0001-8513-5294
Carlen, Jane0000-0002-2538-6670
Mizuno, Grace O.0000-0003-4786-3084
Borden, Phillip M.0000-0003-1653-7067
Shivange, Amol V.0000-0002-4169-2969
Cameron, Lindsay P.0000-0002-8420-7898
Sinning, Steffen0000-0001-6971-6929
Olson, David E.0000-0002-4517-0543
Amara, Susan G.0000-0001-8914-1106
Temple Lang, Duncan0000-0003-0159-1546
Rudnick, Gary0000-0002-7622-4110
Marvin, Jonathan S.0000-0003-2294-4515
Lavis, Luke D.0000-0002-0789-6343
Lester, Henry A.0000-0002-5470-5255
Alvarez, Veronica A.0000-0003-2611-8675
Fisher, Andrew J.0000-0003-3488-6594
Prescher, Jennifer A.0000-0002-9250-4702
Kash, Thomas L.0000-0002-4747-4495
Yarov-Yarovoy, Vladimir0000-0002-2325-4834
Gradinaru, Viviana0000-0001-5868-348X
Looger, Loren L.0000-0002-7531-1757
Tian, Lin0000-0001-7012-6926
Additional Information:© 2020 Elsevier. Received 23 November 2019, Revised 22 June 2020, Accepted 20 November 2020, Available online 16 December 2020. We would like to thank Drs. Liqun Luo (Stanford University) and Jing Ren (MRC) for their critical reading and feedback. This work is based upon research conducted at the Northeastern Collaborative Access Team beamlines, which are funded by the National Institute of General Medical Sciences from the NIH (P30-GM124165). The Pilatus 6M detector on the 24-ID-C beamline is funded by an NIH-ORIP HEI grant (S10-RR029205). This research used resources of the Advanced Photon Source, a US Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under contract DE-AC02-06CH11357. This work was supported by funding to L.T. (BRAIN Initiative U01NS090604, U01NS013522, and DP2MH107056 from NIH), to E.K.U. (Mistletoe Foundation Research Fellowship), and to G.O.M. (ARCS Scholarship), as well as by the Howard Hughes Medical Institute. V.G. is a Heritage Principal Investigator supported by the Heritage Medical Research Institute, the NIH (BRAIN RF1MH117069), the Center for Molecular and Cellular Neuroscience of the Chen Institute, and the Beckman Institute for CLARITY, Optogenetics and Vector Engineering Research. V.A.A. is funded by Intramural Programs of NIAAA and NINDS ZIA-AA000421 and Innovation Award from NIH-DDIR. T.L.K. is funded by the NIH (R01AA019454, P60AA011605, and U24AA025475), and O.J.H. is funded by the NIH (5T32NS007431-20). Author Contributions. E.K.U., J.P.K., M.A., L.L.L., and L.T. conceived of and designed the study. E.K.U. designed the machine-learning method, screened and optimized sensors, and characterized them in purified protein, mammalian cells, cultured neurons, and brain slice, with significant contribution from C.D., D.A.J., and J.S. J.P.K., S.S., and G.R. designed OSTA and stopped-flow experiments, and J.P.K. performed them. M.A. and V.G. designed and performed in vivo fiber photometry and EEG/EMG recording in BLA and mPFC in fear learning and sleep/wake cycles. O.J.H., M.E.F., and T.L.K. designed and performed in vivo fiber photometry experiments in BLA, OFC, and BNST during social interaction. R.L. and V.Y.-Y. designed and performed computational Rosetta modeling. Z.Y. and J.A.P. provided luciferase experimental data for establishing machine-learning methods. J.C. and D.T.L. provided significant insight for the machine-learning methods. J.S. characterized the sensor in acute slice using two-photon imaging. A.M. and V.A.A. designed and performed photometry imaging in acute slice. S.H. and A.J.F. performed crystallography. J.S.M., P.M.B., A.V.S., H.A.L., and L.L.L. provided iAChSnFR0.6 and performed preliminary experiments on serotonin binding. S.B. and L.D.L. synthesized caged serotonin. G.O.M. provided dissociated neuronal cultures. L.P.C. and D.E.O. produced chemical reagents. S.M.U. and S.G.A. provided SSRIs and guidance in cell-assay design. E.K.U., J.P.K., L.L.L., and L.T. wrote the manuscript with significant input from other authors. Declaration of Interests. L.T. and G.O.M. are co-founders of Seven Biosciences. D.E.O. is a founder of Delix.
Group:Heritage Medical Research Institute, Tianqiao and Chrissy Chen Institute for Neuroscience
Funders:
Funding AgencyGrant Number
NIHP30-GM124165
NIHS10-RR029205
Department of Energy (DOE)DE-AC02-06CH11357
NIHU01NS090604
NIHU01NS013522
NIHDP2MH107056
Mistletoe FoundationUNSPECIFIED
ARCS FoundationUNSPECIFIED
Howard Hughes Medical Institute (HHMI)UNSPECIFIED
Heritage Medical Research InstituteUNSPECIFIED
NIHRF1MH117069
Tianqiao and Chrissy Chen Institute for NeuroscienceUNSPECIFIED
Caltech Beckman InstituteUNSPECIFIED
NIHZIA-AA000421
NIHR01AA019454
NIHP60AA011605
NIHU24AA025475
NIH Predoctoral Fellowship5T32NS007431-20
Subject Keywords:serotonin; fluorescence protein sensor; fiber photometry; machine learning; iSeroSnFR; SERT; OSTA; sleep-wake; fear-learning; social behaviors
Issue or Number:7
PubMed Central ID:PMC8025677
Record Number:CaltechAUTHORS:20201217-143607354
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201217-143607354
Official Citation:Elizabeth K. Unger, Jacob P. Keller, Michael Altermatt, Ruqiang Liang, Aya Matsui, Chunyang Dong, Olivia J. Hon, Zi Yao, Junqing Sun, Samba Banala, Meghan E. Flanigan, David A. Jaffe, Samantha Hartanto, Jane Carlen, Grace O. Mizuno, Phillip M. Borden, Amol V. Shivange, Lindsay P. Cameron, Steffen Sinning, Suzanne M. Underhill, David E. Olson, Susan G. Amara, Duncan Temple Lang, Gary Rudnick, Jonathan S. Marvin, Luke D. Lavis, Henry A. Lester, Veronica A. Alvarez, Andrew J. Fisher, Jennifer A. Prescher, Thomas L. Kash, Vladimir Yarov-Yarovoy, Viviana Gradinaru, Loren L. Looger, Lin Tian, Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning, Cell, Volume 183, Issue 7, 2020, Pages 1986-2002.e26, ISSN 0092-8674, https://doi.org/10.1016/j.cell.2020.11.040. (http://www.sciencedirect.com/science/article/pii/S0092867420316123)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107173
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:18 Dec 2020 15:09
Last Modified:26 Aug 2021 18:22

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