This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: As an embryo develops from a small cluster of stem cells, those once "blank slate" cells begin to take on more specialized roles like brain, liver or muscle cells and organize themselves into three-dimensional structures such as tissues and organs. The fate of each cell—what type of specialized cell it will become—depends on which genes are turned on or off in the cell.
These patterns of gene activity shape the cell's structure and function, enabling it to take on a specific role in the body. But this decision isn't up to individual cells. They constantly send and receive chemical signals to and from neighboring cells, which help them understand where they are, what stage of development they are in and what they should become.
These messages spread through multistep sequences called signaling pathways, which translate external signals into specific changes in gene activity in the cell. For researchers, being able to retrace the sequence of instructions a cell has received would offer a powerful way to understand how tissues develop and how these processes go awry in disease. But this has been difficult to achieve because scientists have long assumed that the effects of signaling pathways vary widely across cell types, meaning they would need to map each pathway separately in each cell type—an arduous and painstaking process.
Now, in a new study led by Whitehead Institute member Pulin Li and graduate student Nicholas Hutchins, researchers have discovered that each signaling pathway leaves behind a unique "fingerprint"—a distinctive pattern of gene activity that reflects the particular signals the cell has encountered. Importantly, these fingerprints are consistent across different cell types for the same signaling pathway, which means that instead of mapping each cell type separately, scientists can reconstruct signaling histories across many cell types using these pathway-specific fingerprints. This discovery was made possible through a machine learning model called IRIS.
This model can detect fingerprints of different signaling pathways and pinpoint which signals a cell received at different stages of development inside an embryo, even for cell types it hasn't encountered before. This AI-driven approach marks a major advance over traditional methods, which require researchers to experimentally test every pathway in every possible cell type, and opens the possibility of comprehensively mapping the signaling histories of every cell inside a mouse or human embryo at an unprecedented scale. "Think of voice recognition systems like Siri, which are trained mainly in English, but then use that training to help them recognize other languages," says Li, who is also an assistant professor of biology at the Massachusetts Institute of Technology (MIT).
"This is called transfer learning, and this is why IRIS can work across many different cell types." The researchers' detailed findings, published in the journal Nature Methods on Sept. 8, could accelerate stem cell engineering for regenerative medicine and improve the creation of organoids—miniature, 3D models that mimic real organs—for studying disease mechanisms and testing new drugs. This is because once researchers learn the pattern of signals that drives a stem cell to become a specific cell type, they can recreate those signals to control the fate of stem cells in a lab or medical setting. Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights. d research that matter—daily or weekly.
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