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U-M scholars develop artificial intelligence that uncovers distinct molecule activities in fluorescence microscopy—and may soon track particles, wildlife, or even astronomical entities

Researchers at the University of Michigan have created an artificial intelligence-driven tool that can swiftly analyze the behavior of a solitary molecule amidst a vast array of data—potentially in mere moments or, at least, overnight.

Grasping the behavior of individual molecules is crucial: it can result in insights into various cellular functions or monitor the onset and progression of diseases. To observe the actions of individual molecules, scientists label them with what is termed a fluorophore. These fluorophores are energized by a laser, and then advanced microscopes are utilized to track the actions of the labeled molecules over time.

However, pinpointing significant behaviors of these labeled molecules necessitates navigating through the extensive data that this type of microscopy typically generates. This task demands an exceptional amount of time, focus, and a bit of luck—and even then, significant details can easily be overlooked.

To counter this challenge, the U-M research team introduced META-SiM. Unlike specialized AI systems that concentrate on a single issue, such as language interpretation, the researchers designed META-SiM as a foundational AI model. Foundation models are expansive AI architectures trained on various experiment types and analyses using enormous datasets. This enables the tool to perform a broad range of analyses and examine entire collections of data to reveal intriguing behaviors deserving further investigation.

The research, funded by the National Institutes of Health, is available in Nature Methods. Jieming Li and Leyou Zhang, former graduate researchers from U-M, spearheaded the project.

While currently concentrating on how signal strength fluctuates over time, representing various states, researchers anticipate that META-SiM’s algorithm can eventually expand beyond molecules to monitor additional phenomena such as single particle diffusion, animal migration trends, or even the trajectory of asteroids within our solar system.

“Our aim is to transition from studying single molecules to examining larger scales. Essentially, data shares commonalities, and this AI algorithm can discern those similarities—and any anomalies—regardless of the scale being analyzed,” remarked senior study author Nils Walter, co-director of the Center for RNA Biomedicine. “We could also monitor, for example, the migration of wildebeests across Kenya and Tanzania, or even potentially celestial bodies traversing the universe.”

The researchers trained META-SiM using millions of simulated traces that mimic a variety of behaviors exhibited by molecules in laboratory settings. However, one practical example of what META-SiM could observe is a common cellular origin of human genetic disorders, as noted by Walter.

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Our body synthesizes a variety of proteins tailored to distinct cell types—such as skin, muscle, bones, or ocular cells—and their respective functions. One mechanism employed in this process involves rearranging segments of genetic material from our DNA in diverse configurations. When correctly combined, this information, known as exons, is transformed into messenger RNA. This mRNA then codes for a protein specifically designed for a particular organ.

However, 60% of human genetic disorders arise due to errors that occur during this splicing of genetic material. META-SiM could hypothetically identify irregular occurrences where the splicing goes awry and subsequently propose treatments to rectify the error.

Co-author and U-M research scientist Alexander Johnson-Buck compares the search for the activity of a single molecule to an elaborate game of Where’s Waldo?, a children’s book series where the objective is to locate a small character dressed in a red hat, glasses, and a red-and-white-striped shirt among throngs of individuals, often wearing similar attire.

“Conducting research on extensive data sets, such as our single molecule fluorescence microscopy data, resembles solving a Where’s Waldo? puzzle where you aim to locate Waldo,” Johnson-Buck remarked. “But perhaps, instead of just one page, he’s concealed across numerous pages, and you might not even know what Waldo truly looks like, with the chance of encountering multiple Waldos.”

While META-SiM still cannot pinpoint Waldo, it has the capability to highlight areas where Waldo might be concealed.

“It speeds up analysis and identifies crucial elements that one would typically have to thoroughly examine the data for over half a year, accomplishing it practically overnight,” Walter commented.

As per Johnson-Buck, “you will still require an expert to interpret that finding and place it within context, but it significantly enhances the speed of the discovery process.”

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