Scientists calculate proteins in a single cell and find 42 million

Scientists have produced what they say is the first reliable count of protein molecules in a simple cell. They hope the finding will lead to new ways to predict diseases such as cancer in more complex human cells.

Predicting diseases could improve with new 'reliable' count of cell proteins, researchers say

Different proteins can be seen as different colours in these yeast cells due to fluorescent tagging. (Brandon Ho)

Proteins are considered the hardest-working components of cells, influencing everything from structure to function — so cell biologists try to determine their numbers and any changes in the count.

The number of protein molecules in a simple cell such as a yeast cell wasn't known with much certainty until now, say researchers from both the University of Toronto and a San Francisco-based biotechnology company that studies aging.

For the first time, they were able to establish a "reliable estimate" of 42 million for the baseline number of those molecules in such a cell, according to a summary of their report published Wednesday in the journal Cell Systems.

Scientists are interested in how things change in a cell when it's diseased, so the researchers hope the finding can be used to help better predict diseases where proteins are overly abundant or lacking.

"Proteins are the fundamental worker of the cell. Everything that happens in a cell is pretty much done by proteins," said the report's senior author, biochemistry professor Grant Brown of the university's Donnelly Centre for Cellular and Biomolecular Research.

Refined baseline can help track changes

"If the function of a cell is going change, for example, if a cell is going to go from being a normal cell to a cancerous cell, one of the many things that has to happen is that the proteins that are doing all the work have to change.

"And if you have a baseline, then you can start to study how they change and use those kinds of changes to figure out where vulnerabilities might be, what kind of cell might respond better to treatment," Brown told CBC News.

Brown's lab is mostly focused on cancer biology and the study of how cells respond to different kinds of drugs that are used in cancer treatment.

Looking at how the "protein landscape" changes is important when treating cells with an anti-cancer drug and for determining who is predisposed to disease, he told CBC News.

For example, if there is a mutation in BRCA1, the major breast and ovarian cancer susceptibility gene, a woman's cells make less BRCA1 protein and that causes that person to be predisposed to cancer. "So the change in the abundance of even a single protein can drive disease," Brown said.

The study was funded by the Canadian Cancer Society.

Others on the team for the simple cell study included Donnelly Centre PhD student Brandon Ho, funded by the Natural Sciences and Engineering Research Council, and Anastasia Baryshnikova, a U of T alum and now a researcher at Calico in California.

They analyzed data from almost two dozen large studies of protein abundance in yeast cells to come up with what Brown said was a "more refined" estimate in the number of protein molecules.

Wider applications 

The work was "entirely computational," using datasets mostly generated by other scientists, Brown said. He added that the method can be applied to all kinds of data to compare different cell types and conditions, including different types of more complex human cells that may contain very different numbers and types of proteins.

"I think it opens up a whole landscape for making comparisons and using data that's already out there, but putting it together in a more understandable form to get an improved result."

Researchers have previously estimated protein levels by sticking a fluorescent tag on protein molecules and inferring their abundance from how much the cells glow.

But differences in instrumentation meant that different labs recorded different levels of brightness emitted by the cells. Other labs measured proteins levels using completely different approaches.

"It was hard to conceptualize how many proteins there are in the cell because the data was reported on drastically different scales," said Ho, who did most of the work in the U of T lab.