The study was led by researchers in the lab of Lin Tian, Ph.D., principal investigator at the University of California Davis School of Medicine. Current methods can only detect broad changes in serotonin signaling. In this study, the researchers transformed a nutrient-grabbing, Venus flytrap-shaped bacterial protein into a highly sensitive sensor that fluorescently lights up when it captures serotonin. Previously, scientists in the lab of Loren L. Looger, Ph.D., Howard Hughes Medical Institute Janelia Research Campus, Ashburn, Virginia, used traditional genetic engineering techniques to convert the bacterial protein into a sensor of the neurotransmitter acetylcholine. The protein, called OpuBC, normally snags the nutrient choline, which has a similar shape to acetylcholine. For this study, the Tian lab worked with Dr. Looger's team and the lab of Viviana Gradinaru, Ph.D., Caltech, Pasadena, California, to show that they needed the added help of artificial intelligence to completely redesign OpuBC as a serotonin catcher.

The researchers used machine learning algorithms to help a computer 'think up' 250,000 new designs. After three rounds of testing, the scientists settled on one. Initial experiments suggested that the new sensor reliably detected serotonin at different levels in the brain while having little or no reaction to other neurotransmitters or similarly shaped drugs. Experiments in mouse brain slices showed that the sensor responded to serotonin signals sent between neurons at synaptic communications points. Meanwhile, experiments on cells in petri dishes suggested that the sensor could effectively monitor changes in these signals caused by drugs, including cocaine, MDMA (also known as ecstasy) and several commonly used antidepressants.

Finally, experiments in mice showed that the sensor could help scientists study serotonin neurotransmission under more natural conditions. For instance, the researchers witnessed an expected rise in serotonin levels when mice were awake and a fall as mice fell asleep. They also spotted a greater drop when the mice eventually entered the deeper, R.E.M. sleep states. Traditional serotonin monitoring methods would have missed these changes. In addition, the scientists saw serotonin levels rise differently in two separate brain fear circuits when mice were warned of a foot shock by a ringing bell. In one circuit -- the medial prefrontal cortex -- the bell triggered serotonin levels to rise fast and high whereas in the other -- the basolateral amygdala -- the transmitter crept up to slightly lower levels. In the spirit of the BRAIN Initiative, the researchers plan to make the sensor readily available to other scientists. They hope that it will help researchers gain a better understanding of the critical role serotonin plays in our daily lives and in many psychiatric conditions.

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Materials provided by NIH/National Institute of Neurological Disorders and Stroke. Note: Content may be edited for style and length.

Journal Reference:

1. 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, 2020; 183 (7): 1986 DOI: 10.1016/j.cell.2020.11.040