TR10: Single-Cell Analysis
Norman Dovichi believes that detecting minute differences between individual cells could improve medical tests and treatments.
Analyzing individual cells allows researchers to distinguish between a uniform population of cells (above left) and a group of cells with members having, say, different protein content (above right). The ability to recognize such differences could be essential to understanding diseases such as cancer or diabetes.
This article is one in a series of 10 stories we’re running this week covering today’s most significant emerging technologies. It’s part of our annual “10 Emerging Technologies” report, which appears in the March/April print issue of Technology Review.
We all know that focusing on the characteristics of a group can obscure the differences between the individuals in it. Yet when it comes to biological cells, scientists typically derive information about their behavior, status, and health from the collective activity of thousands or millions of them. A more precise understanding of differences between individual cells could lead to better treatments for cancer and diabetes, just for starters.
The past few decades have seen the advent of methods that allow astonishingly detailed views of single cells–each of which can produce thousands of different proteins, lipids, hormones, and metabolites. But most of those methods have a stark limitation: they rely on “affinity reagents,” such as antibodies that attach to specific proteins. As a result, researchers can use them to study only what’s known to exist. “The unexpected is invisible,” says Norman Dovichi, an analytical chemist at the University of Washington, Seattle. And most every cell is stuffed with mysterious components. So Dovichi has helped pioneer ultrasensitive techniques to isolate cells and reveal molecules inside them that no one even knew were there.
Dovichi’s lab–one of a rapidly growing number of groups that focus on single cells–has had particular success at identifying differences in the amounts of dozens of distinct proteins produced by individual cancer cells. “Ten years ago, I would have thought it would have been almost impossible to do that,” says Robert Kennedy, an analytical chemist at the University of Michigan-Ann Arbor, who analyzes insulin secretion from single cells to uncover the causes of the most common type of diabetes.
And Dovichi has a provocative hypothesis: he thinks that as a cancer progresses, cells of the same type diverge more and more widely in their protein content. If this proves true, then vast dissimilarities between cells would indicate a disease that is more likely to spread. Dovichi is working with clinicians to develop better prognostics for esophageal and breast cancer based on this idea. Ultimately, such tests could let doctors quickly decide on proper treatment, a key to defeating many cancers.
A yellow, diamond-shaped sign in Dovichi’s office warns that a “laser jock” is present. Dovichi helped develop the laser-based DNA sequencers that became the foundation of the Human Genome Project, and his new analyzers rely on much of the same technology to probe single cells for components that are much harder to detect than DNA: proteins, lipids, and carbohydrates.
For proteins, the machines mix reagents with a single cell inside an ultrathin capillary tube. A chemical reaction causes lysine, an amino acid recurring frequently in proteins, to fluoresce. The proteins, prodded by an electric charge, migrate out of the tube at different rates, depending on their size. Finally, a laser detector records the intensity of the fluorescence. This leads to a graphic that displays the various amounts of the different-sized proteins inside the cell.
Although the technique reveals differences between cells, it does not identify the specific proteins. Still, the analyzer has an unprecedented sensitivity and makes visible potentially critical differences. “For our cancer prognosis projects, we don’t need to know the identity of the components,” Dovichi says.
Dovichi is both excited about the possibilities of single-cell biology and sober about its limitations. Right now, he says, analyses take too much time and effort. “This is way early-stage,” says Dovichi. “But hopefully, in 10, 20, or 30 years, people will look back and say those were interesting baby steps.”