Many human ailments can be traced to malfunctioning cells. A tumor could develop because a gene was not properly translated into a specific protein or a metabolic disorder is triggered because mitochondria don’t fire correctly. Scientists need to first be able to identify all the relevant factors to identify which parts of a cell might be affected by a disease.
By using biochemistry, microscopy and artificial intelligence, researchers from the University of California San Diego School of Medicine and collaborators have created what they believe may turn out to be a significant leap in understanding human cells.
The technique, also known as Multi-Scale Integrated Cells (MuSIC) was first described in Nature on November 24 2021.
UC San Diego researchers introduce Multi-Scale Integrated Cell (MuSIC), a technique that combines microscopy, biochemistry and artificial intelligence, revealing previously undiscovered cell components that may offer new insights into human development and illness. (Artist’s conceptual rendering.
You may have pictured a cell as a colorful diagram in your cell biology textbook. It includes the nucleus, mitochondria, and endoplasmic. But is that the complete story? It’s not. Scientists have known for a long time that there is more to the universe than we can imagine. However, we now have the ability to look deeper.”
Trey Ideker PhD, Professor, UC San Diego School of Medicine & Moores Cancer Center
Ideker was the lead researcher along with Emma Lundberg, PhD, of KTH Royal Institute of Technology in Stockholm, Sweden and Stanford University.
In the pilot study, MuSIC revealed approximately 70 components in a human kidney cell line, half of which had never been observed before. In one example the researchers noticed a group of proteins forming an unidentified structure. Working with UC San Diego colleague Gene Yeo PhD, they eventually determined the structure to be a brand new complex of proteins that binds to RNA. The complex is likely to be involved in splicing, an important cellular event that enables the translation of genes to proteins, and also helps to determine which genes are activated at which times.
Left: Classic textbook cell diagrams imply all parts are clearly visible and defined. (Credit: OpenStax/Wikimedia). Right: A new cellmap generated by MuSIC technic displays a myriad of novel components. Purple nodes are new components while gold nodes are well-known cell components. The size of the node indicates number of distinct proteins in that component.
One of two methods is used to study the insides of cells as well as the numerous proteins they have: biophysical association or microscope imaging. Imaging allows researchers to add various florescent tags to proteins of interest and monitor their movements and their associations across the microscope’s field of view. Researchers might use an antibody that is specific to a protein to extract it out of the cell and find out what other attachments are associated with it.
The team has been interested in understanding the inner workings of cells for a number of years. MuSIC uses deep learning to map cells directly using photographs of microscopy of cells.
“The combination of these technologies is distinctive and powerful due to the fact that it’s the first time measurements at vastly different scales have been combined,” said study first author Yue Qin Yue Qin, who is a Bioinformatics and Systems Biology graduate student in Ideker’s lab.
Microscopes enable scientists to see down to the level of a single micron, approximately the size of certain organelles, including mitochondria. Lesser components, like proteins or protein complexes in their individual forms aren’t visible under microscopes. Biochemistry techniques, which start with a single protein allow scientists to go down to the nanometer size. (A nanometer is one-billionth of a meter, or 1,000 microns.)
“But how do you bridge the gap between nanometer and micron scale? This has been a problem in biology for many years,” said Ideker. He is also the founder of the UC Cancer Cell Map Initiative and UC San Diego Center for Computational Biology and Bioinformatics. “Turns out you can accomplish this using artificial intelligence, which is using data from multiple sources and asking the system to assemble it into an image of cells.”
The team taught the MuSIC artificial intelligence platform to analyze every data source and create an image of every cell. The system hasn’t yet been able to map the contents of the cell to specific locations, similar to an illustration in a textbook in part due to the fact that their locations aren’t fixed. The locations of the components are fluid and vary based on the cell type and the context.
Ideker said that this was an experiment to test MuSIC. They’ve only looked at 661 proteins and one type of cell.
“The next step is to blow through every human cell,” Ideker stated. “Then we can go on to other cell types individuals, species, and people.” We may eventually be able to identify the molecular basis for many diseases by comparing different characteristics of healthy and diseased cells.
Qin, Y., and. (2021). A multi-scale map showing cell structure by fusing protein images and interactions. Nature. doi.org/10.1038/s41586-021-04115-9.
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