A New Sensor Can Monitor Serotonin Levels in Real Time

New tools could help us better understand the workings of this neurotransmitter.

Posted Dec 28, 2020

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Serotonin has "rock star" status as one of the most famous neurotransmitters known to man. Scientists and laypersons alike recognize serotonin for its vital role in human cognition and mental health. Nevertheless, despite being the focus of countless studies over the past century, serotonin remains surprisingly mysterious.

A Brief History and Timeline of Serotonin and SSRIs

Scientists first discovered serotonin in 1937 and identified its structure as 5-HT in 1948. Researchers linked serotonin to depressive symptoms for the first time in the early 1970s. In 1987, the first selective serotonin reuptake inhibitor (SSRI) was approved by the FDA for the treatment of depression and sold under the brand name Prozac in the United States. (Eli Lilly's Sarafem brand of fluoxetine is the same drug.)

Soon after its launch, the world's first SSRI antidepressant (fluoxetine hydrochloride) was hyped as "happiness in a blister pack." Prozac's patent owner (Eli Lilly) spent decades developing this so-called "wonder drug" and perpetuated high expectations among consumers fueled by strategic marketing campaigns and media buzz.

At its sales peak in 1998, Prozac generated $2.8 billion in revenue. Since 2001, when Eli Lilly's patent expired, fluoxetine has been widely sold as a generic drug by other pharmaceutical companies.

Unfortunately, despite being the most widely prescribed antidepressant on the planet, we now have evidence that approximately 30 percent of patients with major depressive disorders don't respond to fluoxetine-based SSRIs (Rush et al., 2006).

The Million-Dollar Question: Do SSRIs Only Inhibit Serotonin Reuptake?

Hypothetically, SSRIs increase serotonin levels in the brain by blocking serotonin transporter (SERT) functions, which inhibits the reuptake of 5-HT. However, this rudimentary theory of how SSRIs work in the brain is debatable.

In the early 2000s, studies in rats (Malberg et al., 2000) showed that chronic antidepressant treatment was associated with increased adult neurogenesis in the hippocampus; other studies in mice (Zhou et al., 2005) found that SSRIs affect both serotonin and dopamine transporters. Another study in mice (Baudry et al., 2016) found that SSRIs target serotonin transporters via microRNA-16 (miR-16) in ways that also affect noradrenergic neurons.

In recent years, countless studies have tried to pin down precisely how SSRIs work in the brain and the role that low levels of serotonin actually play in the pathophysiology of depression. (See "Why SSRIs Take About 14 Days to Kick In.")

Now, thanks to advances in artificial intelligence (AI), scientists have developed a new highly sensitive serotonin sensor that gives us fresh insights into how and when this enigmatic neurotransmitter is released in the brain.

The iSeroSnFR Sensor Monitors the Brain's Serotonin Levels in Real Time

This month, a team of researchers announced that they've created a sensor with the ability to "detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep/wake transitions." These findings (Unger et al., 2020) were published on December 16 in the journal Cell

This new serotonin sensor (iSeroSnFR) was designed with the help of machine learning algorithms. Unlike current methods used to measure the brain's serotonin levels, initial findings suggest that this new sensor can "reliably detect serotonin at different levels in the brain while having little or no reaction to other neurotransmitters or similarly shaped drugs."

The iSeroSnFR sensor lights up fluorescently when it captures some 5-HT and can monitor the brain's serotonin levels in real time. Experiments conducted in Petri dishes suggest that the sensor can effectively monitor changes in serotonin levels triggered by commonly used antidepressants as well as other illicit drugs (e.g., MDMA, cocaine).

Notably, other experiments in mice by Unger et al. show that the iSeroSnFR sensor can monitor the naturally occurring neurotransmission of serotonin in living organisms (in vivo) as they go about their day-to-day life.

For example, the researchers observed an uptick in serotonin levels when sleeping mice awoke and a decrease in serotonin as they fell asleep. A more significant drop in serotonin was observed when mice entered rapid eye movement (REM) sleep states. According to a recent news release, "traditional serotonin monitoring methods would have missed these changes."

Additionally, the researchers pinpointed how serotonin levels rose at various rates when fear was triggered via two different brain circuits: In the medial prefrontal cortex circuit, fear caused serotonin levels to spike, whereas fear-based activation of the basolateral amygdala circuitry caused serotonin levels to creep up more slowly.

This research was funded in part by the NIH's Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative. In keeping with this initiative's collaborative spirit, the researchers plan to make their new serotonin sensor available for other scientists' experiments. According to the news release, "They hope that [this sensor] will help researchers gain a better understanding of the critical role serotonin plays in our daily lives and in many psychiatric conditions."


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 (First published: December 16, 2020) DOI: 10.1016/j.cell.2020.11.040