Using a new artificial intelligence identification method, researchers from UC San Diego and his collaborators have made a significant leap forward in understanding human cells, according to a report published Wednesday.
The pilot study — which combines microscopy, biochemical techniques and artificial intelligence in a technique known as Multi-Scale Integrated Cell — revealed about 70 components in a human kidney cell line, half of which had never been seen before.
“If you picture a cell, you probably see the colorful diagram in your cell biology textbook, showing mitochondria, endoplasmic reticulum, and nucleus. But is that the whole story? Absolutely not,” said Trey Ideker, a professor at UCSD’s School of Medicine and Moores Cancer Center. “Scientists have long realized there’s more we don’t know than we know, but now we finally have a way to look deeper.”
The results were detailed in Wednesday’s issue of Nature.
In one example, the researchers saw a group of proteins that formed an unknown structure. In collaboration with UCSD colleague Gene Yeo, they determined that the structure is a novel complex of proteins that bind RNA. The complex is likely involved in splicing, a cellular event that allows for the translation of genes into proteins and helps determine which genes are activated at what time.
The scientists had been interested in mapping the inner workings of cells for years. What is different about MuSIC is its use of deep learning to map the cell directly from cellular microscopy images.
“The combination of these technologies is unique and powerful because it is the first time that measurements at vastly different scales have been brought together,” said first study author Yue Qin, a bioinformatics and systems biology graduate student in Ideker’s lab.
Microscopes allow scientists to see down to the level of a single micron — about the size of some organelles, such as mitochondria. Smaller elements, such as individual proteins and protein complexes, cannot be seen through a microscope. Biochemical techniques, starting with a single protein, allow scientists to get down to the nanometer scale, or a billionth of a meter.
The team trained the MuSIC artificial intelligence platform to look at all the data and construct a model of the cell. The system doesn’t yet map cell contents to specific locations, such as a textbook diagram, in part because their locations aren’t necessarily fixed, the researchers said.
Ideker noted that this was a pilot study to test MuSIC. The team only looked at 661 proteins and one cell type.
“The obvious next step is to blast through the entire human cell and then move to different cell types, people and species,” Ideker said. “Ultimately, we may be able to better understand the molecular basis of many diseases by comparing what’s different between healthy and diseased cells.”
–City news service