Individual cells throughout the human body can "learn" like brains

A new study suggests that individual cells might be capable of learning — a behavior once thought exclusive to organisms with brains and complex nervous systems. 

“Rather than following pre-programmed genetic instructions, cells are elevated to entities equipped with a very basic form of decision making based on learning from their environments,” explained study co-author Jeremy Gunawardena.

The research, led by scientists at the Center for Genomic Regulation (CRG) in Barcelona and Harvard Medical School in Boston, challenges long-standing assumptions about the fundamental capabilities of cells.

The findings, published in Current Biology, could transform our understanding of life at its most basic level.

Learning without a brain

The researchers focused on habituation, a simple form of learning where an organism gradually stops responding to a repeated stimulus — like how humans tune out the ticking of a clock or flashing lights. 

While habituation has been extensively studied in animals with nervous systems, whether similar behavior exists at the cellular level has been a contentious question.

Early experiments in the 20th century observed behavior resembling learning in the single-celled ciliate Stentor roeselii, but the findings were largely dismissed.

In the 1970s and 1980s, more evidence of habituation emerged in other ciliates, and modern experiments have continued to build on these insights.

“These creatures are so different from animals with brains,” said Rosa Martinez, a co-author of the study and researcher at the CRG.

“To learn would mean they use internal molecular networks that somehow perform functions similar to those carried out by networks of neurons in brains. Nobody knows how they are able to do this, so we thought it is a question that needed to be explored.” 

Decoding cellular “learning”

Instead of working with cells directly in the lab, the researchers turned to computer simulations to track the biochemical reactions that cells rely on to process information. 

By simulating how molecular interactions change when exposed to repeated stimuli, the team uncovered evidence of learning-like behavior.

The study focused on two common molecular circuits: negative feedback loops and incoherent feedforward loops. 

Negative feedback is like a thermostat — it regulates processes by turning them off when a certain threshold is reached. 

Incoherent feedforward loops act like a motion-activated light with a timer, triggering a process and its inhibitor simultaneously.

Memories formed at the cellular level

Simulations revealed that cells use these circuits to fine-tune their responses to repeated stimuli, replicating hallmark features of habituation seen in animals. 

A critical discovery was the role of “timescale separation,” where some biochemical reactions occur much faster than others.

“We think this could be a type of ‘memory’ at the cellular level, enabling cells to both react immediately and influence a future response,” Martinez explained.

Bridging neuroscience and cognitive science

The findings also offer new insights into a long-standing debate between neuroscientists and cognitive researchers about habituation. 

Neuroscientists argue that stronger habituation occurs with more frequent or less intense stimuli, while cognitive scientists emphasize internal changes, finding habituation stronger with less frequent or more intense stimuli.

The study showed that both perspectives are valid. During habituation, cells respond less to frequent or weak stimuli, but after habituation, their response to similar stimuli is stronger. 

“Neuroscientists and cognitive scientists have been studying processes which are basically two sides of the same coin,” Gunawardena said. “We believe that single cells could emerge as a powerful tool to study the fundamentals of learning.”

How cells to learn and remember 

If confirmed through real-world experiments, these findings could have profound implications. 

The ability of cells to “remember” might explain phenomena like cancer cells developing resistance to chemotherapy or bacteria becoming resistant to antibiotics.

Both involve cells appearing to “learn” from their environment to survive.

The researchers used mathematical models to test various scenarios quickly, identifying those worth exploring in laboratory settings.

This approach could save time and resources while accelerating discoveries.

Computational approach to biology

“The moonshot in computational biology is to make life as programmable as a computer, but lab experiments can be costly and time-consuming,” Martinez said. 

Her team works at the Barcelona Collaboratorium, a joint initiative designed to use mathematical modeling to tackle big biological questions.

“Our approach can help us prioritize which experiments are most likely to yield valuable results, saving time and resources and leading to new breakthroughs,” noted Martinez. “We think it can be useful to address many other fundamental questions.”

This research marks a significant step toward understanding learning and memory at the cellular level, potentially reshaping our view of what individual cells are capable of and opening new doors for medical and scientific exploration.

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