We can know more about cells via new technology: MuSIC

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We can know more about cells via new technology: MuSIC

We can know more about cells via new technology: MuSIC.

Scientists have long realized that we don’t know more than we know. Recent research has demonstrated a technique- MuSIC -to use deep learning to draw cells directly from cell microscope images.

In the future, more cells can be known to understand more disease basis.

Most human diseases can be traced to malfunctioning parts of cells, for example, because a gene is not accurately translated into a specific protein, tumors can grow.

Or metabolic diseases occur because mitochondria do not discharge normally. But to understand which parts of a cell can go wrong in a disease, scientists first need to have a complete list of parts.

By combining microscopy, biochemical technology, and artificial intelligence, researchers and collaborators at the University of California (UC) San Diego School of Medicine took an approach that they thought might prove to be a major leap in human cell understanding.

This technology is called Multi-Level Integrated Cellular Atlas ( MuSIC ) and was described in Nature on November 24, 2021 , entitled ” A multi-scale map of cell structure fusing protein images and interactions “.

Dr. Trey Ideker , a professor at the University of California San Diego School of Medicine and Moores Cancer Center , said: “If you imagine a cell, you are likely to draw a color chart of mitochondria, endoplasmic reticulum, and nucleus in your cell biology textbook. But that’s it. The whole story? Absolutely not. Scientists have long realized that we don’t know more than we know, but now we finally have a way to go deeper.”

Ideker led the research with Dr. Emma Lundberg from the Royal Institute of Technology in Sweden and Stanford University .

In preliminary studies, MuSIC revealed about 70 components contained in human kidney cell lines, half of which have never been seen before. In one example, the researchers discovered that a group of proteins formed an unfamiliar structure. In collaboration with UC San Diego colleague Dr. Gene Yeo , they finally determined that the structure is a new protein complex that binds RNA. This complex is likely to be involved in splicing, which is an important cellular event that can translate genes into proteins and help determine which genes are activated when.

The inside of the cell, and the many proteins found there—usually studied using one of two techniques: microscopic imaging or biophysical correlation. Through imaging, researchers add fluorescent tags of various colors to the protein of interest and track their movement and association in the field of view of the microscope. In order to observe the biophysical connection, researchers may use a protein-specific antibody to pull it out of the cell to see what else is attached to it.

For many years, the team has been interested in drawing the inner workings of cells. The difference of MuSIC is that it uses deep learning to draw cells directly from cell microscope images .

Yue Qin , a graduate student in bioinformatics and systems biology in Ideker’s lab and the lead author of the study, said: “The combination of these technologies is unique and powerful because it is the first time that measurements of very different scales have been combined. “

A microscope allows scientists to see as low as a single micrometer, 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 technology, starting from a single protein, allows scientists to reach the nanometer scale.

Founder of the UC Cancer Cell Atlas Project and the UC San Diego Center for Computational Biology and Bioinformatics, Ideker said: “But how to bridge this gap from the nanoscale to the microscale? This has long been a major obstacle for biological sciences. . In fact, you can use artificial intelligence to do it-view data from multiple sources and ask the system to assemble it into a cell model.”

The research team trained the MuSIC artificial intelligence platform to observe all the data and build a cell model. The system has not yet mapped the contents of cells to specific locations like textbook charts, partly because their locations are not necessarily fixed. On the contrary, the component position is variable and changes according to the cell type and situation.

Ideker pointed out that this is a preliminary study to test MuSIC. They only studied 661 proteins and one cell type.

Ideker said: “The next step is obvious, is to explode the entire human cell, and then transfer to different cell types, people and species. Ultimately, by comparing the difference between healthy cells and diseased cells, we may be able to better Understand the molecular basis of many diseases.”

Reference:

https://phys.org/news/2021-11-cells-ai-technique-reveals.html

We can know more about cells via new technology: MuSIC.

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