In recent years, researchers have come to the realization that there may be more value to be gained from studying gene expression in single cells than in cell populations.
This is particularly true when studying gene expression in excised cancerous tissue samples, which often comprise a sea of wild-type cells riddled with a relatively small number of cells harboring mutations associated with cancer or resistance to anti-cancer drugs.
To address this, a number of commercial molecular biology tool vendors have introduced kits and instrument platforms to enable such analyses on single cells. In addition, digital PCR platforms, while not specifically geared toward single-cell studies, can be used to detect small numbers of mutated genes — even down to a single copy — among thousands or millions of wild-type genes in a cell population.
Researchers from the University of Southern California are attempting to combine the best attributes of all of these approaches into a single, high-throughput, PCR-based method for analyzing genetic heterogeneity in tissue samples.
In September, the group, led by Emil Kartalov, was awarded $246,000 for the first year of a two-year project to develop its technique, which involves using photolithography to design a chip containing a matrix of millions of microwells that can be placed directly onto morphologically intact tissue slices.
Then, the researchers perform individual allele-specific endpoint PCR reactions in each of the microwells, which are small enough to contain a handful of cells. Then they use a microarray scanner to detect fluorescence from amplification products to investigate genetic heterogeneity across entire tissue samples.
"One of the potential benefits of our method is [that] it analyzes the entire sample, and it doesn't have any bias to it," Kartalov says. "Since all the cells are going to be analyzed at the same time in a highly parallel fashion, you don't have to … commit to a particular section of the sample and nothing else, so we won't miss anything."
Kartalov notes that their method has parallels to digital PCR. "But the issue is that [digital PCR] completely loses the morphological information" because a sample must first be homogenized and then dispersed among thousands of individual droplet-based PCR reaction volumes, Kartalov says. "Our system retains the morphological information, so we know which signal came from which part of the tissue. We have a picture of the tissue sample, and a picture of what mutations are present in which part of the tissue, and you can superimpose them and produce the equivalent of a biochemical microscope."
Currently, the group's prototype platform features matrices in which each individual well has a diameter of about 400 microns, which, when placed upon a typical tissue slice, will encapsulate several hundred cells. This is not truly a single-cell approach, but Kartalov notes that reducing the number of cells in each allele-specific PCR reaction from millions down to several hundred "greatly improves the probability that the allele-specific phase to work appropriately."
However, Kartalov says that since submitting its NIH grant proposal, the team has demonstrated that it can shrink individual reaction volumes down to about 50 microns — small enough to capture just a few cells — and may be able to go even smaller.
Kartalov says that the method would be initially ideal for research use only, to interrogate the cellular basis of diseases like cancer. But eventually it may work in the clinic to detect resistance mutations in cancer cells and to pinpoint exactly where in a tumor those mutations are occurring.
"If you don't analyze the entire sample, then maybe you're missing something," he says. "All it takes for the drug to fail is a very … small percentage of the mutant cells to survive, repopulate, and eventually kill the patient."