The SmartTrap that has been developed by researchers at the University of Gothenburg.
Image Credit: Martin Selin/ University of Gothenburg
Scientific Frontline: Extended "At a Glance" Summary: SmartTrap AI Optical Tweezers
The Core Concept: SmartTrap is an open-source artificial intelligence platform that fully automates optical tweezers, enabling the autonomous manipulation and measurement of microscopic biological components, such as individual DNA molecules and living cells.
Key Distinction/Mechanism: Unlike traditional optical tweezers that rely on constant human oversight and manual adjustment, SmartTrap integrates image analysis, real-time deep learning, precise fluid control, and closed-system feedback to independently capture, position, and analyze particles in three dimensions.
Major Frameworks/Components:
- Optical Tweezers: Laser-based instruments that exert radiation pressure to trap and physically maneuver nanoscale targets.
- Real-Time Deep Learning: Advanced neural networks that analyze live visual data to guide the instrument's decisions instantaneously.
- Automated Fluid Control: Custom hardware subsystems designed to handle continuous sample loading and environmental manipulation without manual input.
- Autonomous Closed-Loop Feedback: A self-regulating operational loop that permits the system to design, execute, and repeat experimental sequences continuously.
Branch of Science: Biophysics, Artificial Intelligence, Molecular Biology, and Nanotechnology.
Future Application: The platform is anticipated to act as a catalyst for pharmaceutical research and development, particularly for accelerating the discovery of RNA-based therapies and expanding high-throughput structural studies of DNA dynamics.
Why It Matters: By eliminating the operational bottleneck of manual human labor, the AI platform performs highly demanding biophysical tasks—such as single-molecule DNA stretching—ten to one hundred times faster than skilled human operators, bringing industrial-scale automation to microscopic laboratory research.
By teaching an AI to use optical tweezers, researchers from the University of Gothenburg, Chalmers University of Technology, and several international universities have sped up the analysis of life’s smallest components. The AI platform captures particles, takes measurements, and loads new samples, all without human intervention.
Just as self-driving cars navigate traffic without a human behind the wheel, laboratory instruments are now being developed that can design, carry out, and repeat experiments independently, 24 hours a day. In an article recently published in Nature Methods, researchers describe how they developed an AI system, named SmartTrap, capable of speeding up the operation of optical tweezers.
Nobel Prize-Awarded Technology
Optical tweezers use finely focused laser beams to grasp and manipulate objects that are a thousand times thinner than a human hair—individual DNA molecules, living cells, and microscopic particles. In 2018, Arthur Ashkin was awarded the Nobel Prize in Physics for the development of optical tweezers.
“The optical tweezers have revealed how molecular motors power our cells, how DNA is copied and repaired, and how diseases such as malaria and sickle cell anemia affect the function of red blood cells,” says Giovanni Volpe, research leader at the University of Gothenburg.
AI Speeds Up the Process
Until now, these powerful instruments have required a trained researcher to oversee every step and make all the decisions. This limitation results in low throughput, long working days, and experiments that may vary slightly from one researcher to another.
The researchers’ AI eliminates this problem. By combining image analysis with real-time deep learning, customized electronics, precise fluid control, and feedback within a closed system, the AI platform operates completely autonomously. SmartTrap captures the particles, positions them with nanometer precision in three dimensions, performs measurements, and loads new samples for the next test.
The AI platform underwent several rigorous tests. The AI sorted and characterized hundreds of particles per hour. It performed single-molecule DNA stretching, one of the most technically demanding analyses in biophysics. The AI managed to carry out 10–15 experiments per hour. It also investigated the mechanical stiffness of red blood cells and mapped nanoscale electrostatic forces between particle pairs at different salt concentrations.
Performed Better
“For a human operator of optical tweezers, these experiments take much longer, like ten or one hundred times longer, assuming the operator doesn’t get bored or need to take some breaks. We also found that the AI performed as well as or better than a skilled human operator in every case,” says Giovanni Volpe.
The SmartTrap AI is built on open-source software and designed to serve as a shared platform for the industry. As smart microscopy matures, AI platforms such as this will transform laboratories in the same way that automation transformed the manufacturing industry, according to Giovanni Volpe.
“Will Greatly Expand Our Studies”
“We are delighted to have been part of this study. This fall, we will have an identical instrument in our laboratory, which will enable us to greatly expand our studies of the structure and dynamics of RNA and DNA,” says Fredrik Westerlund, professor at the Department of Life Sciences at Chalmers.
Together with Marcus Wilhelmsson, professor in the Department of Chemistry and Chemical Engineering at Chalmers, he contributed biophysical expertise to the project, particularly in the parts of the study involving DNA and RNA.
“I am proud of and pleased with this collaborative effort, and I look forward with great interest to seeing how, for example, the pharmaceutical industry will be able to use this technology in its research and development of RNA-based therapies,” says Marcus Wilhelmsson.
Published in journal: Nature Methods
Title: SmartTrap: automated precision experiments with optical tweezers
Authors: Martin Selin, Antonio Ciarlo, Giuseppe Pesce, Lars Bengtsson, Joan Camunas-Soler, Vinoth Sundar Rajan, Fredrik Westerlund, L. Marcus Wilhelmsson, Isabel Pastor, Felix Ritort, Steven B. Smith, Carlos Bustamante, and Giovanni Volpe
Source/Credit: Chalmers University of Technology | Olof Lönnehed, University of Gothenburg and Susanne Nilsson Lindh, Chalmers
Edited by: Scientific Frontline
Reference Number: biph062226_01