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Related Experiment Video

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

Elizabeth K Unger1, Jacob P Keller2, Michael Altermatt3

  • 1Departments of Biochemistry and Molecular Medicine, Chemistry, Statistics, Molecular and Cellular Biology, and Physiology and Membrane Biology, the Center for Neuroscience, and Graduate Programs in Molecular, Cellular, and Integrative Physiology, Biochemistry, Molecular, Cellular and Developmental Biology and Neuroscience, University of California, Davis, Davis, CA 95616, USA.

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|December 17, 2020
PubMed
Summary
This summary is machine-generated.

Scientists developed a new fluorescent sensor, iSeroSnFR, to precisely measure serotonin release in the brain. This tool aids understanding of brain circuits and psychiatric drug development.

Keywords:
OSTASERTfear-learningfiber photometryfluorescence protein sensoriSeroSnFRmachine learningserotoninsleep-wakesocial behaviors

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Area of Science:

  • Neuroscience
  • Biochemistry
  • Molecular Biology

Background:

  • Serotonin is crucial for cognition and psychiatric drug targets.
  • Current drugs have limited efficacy due to incomplete understanding of serotonergic circuitry.
  • Monitoring serotonin release and transport with high resolution is challenging.

Purpose of the Study:

  • To develop a novel method for high-resolution detection of serotonin.
  • To create a fluorescent sensor for real-time monitoring of serotonin.
  • To investigate serotonin transporter function and drug modulation.

Main Methods:

  • Utilized a machine learning-guided binding-pocket redesign strategy.
  • Developed a high-performance, soluble, fluorescent serotonin sensor (iSeroSnFR).
  • Applied the sensor to detect serotonin release in freely behaving mice.

Main Results:

  • Successfully created and validated the iSeroSnFR sensor for millisecond-scale serotonin detection.
  • Demonstrated iSeroSnFR's utility in monitoring serotonin release during various behaviors in mice.
  • Developed a robust assay for serotonin transporter function and drug modulation.

Conclusions:

  • The iSeroSnFR sensor enables unprecedented optical detection of serotonin dynamics.
  • Machine learning-guided sensor design and iSeroSnFR offer broad applications for neuroscience research.
  • This technology can advance the development of improved psychiatric therapeutics.