Nature杂志在2009年5月7日的主页文章中大篇幅地介绍了美国Fluidigm公司的微液流芯片在单细胞表达中的应用。

A closer look at the single cell

Megan Scudellari
Nature Reports Stem Cells
Published online: 7 May 2009 | doi:10.1038/stemcells.2009.71

Analyses of individual stem cells are gaining momentum, but technological barriers persist

Stem cells are defined by their remarkable ability to self-renew and differentiate into specialized cells. But even after careful sorting, a single population of stem cells is dynamic: some divide rapidly and others more slowly; some differentiate, others self-renew; some can give rise to more lineages than others. Because of this variation, population studies of stem cells are unable to accurately address essential questions, such as defining discrete steps from a single stem cell to a complex population of cells.

Image of a blood stem cell dividing in real time. (Reya lab, Duke University)

"There are very few people who pay attention to the advantages and importance of studying single cells," says Ron McKay, chief of the Laboratory of Molecular Biology at the National Institute of Neurological Disorders and Stroke in Bethesda, Maryland. "They talk as if they do. They use a FACS machine and act as if they have single-cell data. But they don't. They have data on a population, and that's a completely different thing."

Although single-cell analysis is still too new to have generated a wealth of literature about the lineages a single stem cell can follow, the genes it can express and the way it behaves, voices from many different fields are emphasizing its importance.

Taking a look

Imaging individual cells is one of the most sought-after achievements in cell biology, but it is perhaps the most challenging. To image single stem cells, researchers use time-lapse photography to take pictures at a high resolution every 2–3 minutes, often for days at a time. Not only must the cells' movements be constantly tracked to keep them in frame, the thousands of resulting images must be processed, manually scrutinized and statistically analyzed.

At the University of Waterloo in Ontario, Canada, chemical engineer Eric Jervis generates lineage trees of many generations of stem cells and makes movies of individual cells differentiating or self-renewing. But the research requires robotic microscopes, canyons of hard-drive space and custom software. Even with the help of talented students, the equipment design took a year to complete1. "It's technically challenging. It requires a renaissance-type researcher to be able to span robotic imaging, database and data mining and stem cell biology. If you don't have all three, you can't do anything," says Jervis.

Despite his willingness to share the details of his system, Jervis is more likely to receive cell samples in the mail for imaging than he is to have other labs wanting to adopt the technique, he says. At the long-term live-cell imaging core facility for the Canadian Stem Cell Network, Jervis now receives samples each week from labs across Canada in need of his tools.

In the United States, even with the support of the National Institutes of Health infrastructure, it took McKay more than six years to construct his stem cell imaging system: building a live-cell chamber, constructing a microscope and camera to track and image the cells, and developing software to handle the data. "We spent an enormous amount of time figuring out how to do these experiments. But in the end, I think it was worth it," says McKay. Using the system, he recently published an analysis of how central nervous system stem cells transition from one state to another, including the cells' response to ciliary neurotrophic growth factor, a cytokine thought to direct neural stem cells toward an astrocytic fate2.