The human body is composed of trillions of cells with their actions coordinated throughout numerous tissues by the complex processes of cell signaling. But despite an ostensible sameness, numerous differences appear when these cells are examined individually. In the history of cell signaling research—which has already elucidated how signaling errors may lead to diseases like cancer and diabetes—only recently have scientists pursued what effect signaling has on the single-cell level.
A recent paper published in Nature has shown that all cells do not respond equally during a signaling cascade. Researchers tested the effect of stimuli on 5000 individual cells, and found that within a cell type, each can react differently to the same stimulus. “Not all cells are created equal and they all seem to have their own minds about how to function,” says Savas Tay, a postdoctoral research associate at the Stanford Department of Bioengineering and first author on the paper. “Only measurements with single-cell resolution will allow one to understand how individual cells function.”
Studies on cell signaling have previously used qPCR in bulk assays to test the response of groups of cells, but researchers have often wondered if the results accurately reflected what happened on an individual basis. The introduction of a new microfluidic chip, invented by Stephen Quake and his research team three years ago (1), has made high-throughput single-cell assays possible. The chip consists of silicon layers and microscopic gates that control fluid flow, which enables automation of multiple simultaneous experiments and long-term assessment of the cells. In Tay’s experimental design, a camera took pictures at fixed intervals to record the response of each cell as it was stimulated on the chip.
The team focused their studies on a pathway involving nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), a DNA-binding transcription factor. Irregularities in NF-κB regulation have been linked to a bevy of problems, including autoimmune disease and memory issues.
“[The] NF-κB signaling pathway is definitely one of those complex, almost chaotic-looking biological systems that happens to be involved in everything from inflammation to development to diseases like cancer,” says Tay. “However, the degree of accuracy and control in such seemingly complex biological pathways surprised me in a positive way. Everything that happens in biology is for a reason, and we systems biologists are looking to understand such ‘design principles’ using non-traditional methods.”
The researchers exposed mouse fibroblast cells to varying amounts of tumor necrosis factor-alpha (TNF-α), a signaling molecule that stimulates cells to express genes such as ripk2 and ccl2, both of which can lead to apoptosis. Using both quantitative and digital PCR, the expression levels of those genes were measured for each cell. By measuring the amount of gene expression, the researchers were able to see exactly how sensitive each individual cell response was.
“We thought we would be done with it quickly,” said Tay of the research, “but the level of detail and the surprising nature of the results kept us busy.” While almost all cells responded to the stimuli at high concentrations, very few did so at low concentrations. And while it was not possible to accurately predict if a certain cell was going to respond, some appeared to be more sensitive than others.
The major issue that the Stanford team faces now is the enormous amount of data that was generated. Initial experiments were completed in less than a year, but the process of sifting through the millions of images and data points has only just begun.
Up against the task of finding the biologically important results from within the data deluge, the team turned to mathematician Tomasz Lipniacki, from the Institute of Fundamental Technological Research in Poland, to help create a model that would reproduce the dynamics of the NF-κB pathway. “He already had a model that worked nicely at a limited region of our data space,” said Tay, “so we changed it to fit our single-cell data; now it works under all conditions and is applicable to many cell types.”
Tay acknowledged that his work with single-cell studies is far from over. “Our study introduced more questions than it answered, and I plan to further investigate the mechanisms behind digital, stochastic signaling in cells. We are also looking into other signaling molecules and their roles in NF-κB signaling.”
In addition to furthering the understanding of cell signaling, the conclusions from this study could shed light on why some drugs work for some people and not others. Tay used the example of cancer, noting the fact that some tumors respond to chemotherapy while others don’t. “I plan to improve the microfluidic technology and adapt it to other types of problems,” he said.
The paper “Single-cell NF-κB dynamics reveal digital activation and analogue information processing” was published June 27 in Nature.
1. Gómez-Sjöberg, R., A.A. Leyrat, D.M. Pirone, C.S. Chen, and S.R. Quake. 2007. Versatile fully automated, microfluidic cell culture system. Anal. Chem. 79:8557-8563.