Many cancer survivors live in fear, knowing that the disease may return. Some cancer cells may survive treatment and can go on to form another tumor, delivering a particularly emotional blow to any patient.
“Some therapies seem to be very good at shrinking tumors, but they leave some cells behind,” said Samuel Levy, vice president of Genomic Sciences at Quanticel Pharmaceuticals, a company that is leveraging single-cell genomics to study individual tumor cells. That’s because therapies generally attack specific behaviors of tumor cells, but as Levy points out, “Not every cell in the tumor is doing the exact same thing.”
Pockets of tumor cells may behave differently because of genetic alterations or environmental factors. By identifying these different sub-groups, researchers may one day identify characteristics of these cells called biomarkers that predict which treatments or treatment combinations will ensure that no cancer cell is left behind in a patient. That, however, requires a relatively novel approach: analyzing cells one by one.
Traditionally, cells have been analyzed in bulk—partly because of the assumption that cells in a certain population behaved the same and partly because it was simply easier (in fact, possible at all) to do. When the human genome was first sequenced, it wasn’t the genetic information from any one cell but rather an average genome from thousands—if not millions—of cells. So the most popular sequence ended up in the final product.
I could list a whole host of diseases—autoimmune diseases for instance—where characterizing the behavior and characteristics of single cells may have great value.
But with this bulk approach, it’s easy to miss the rare sub-groups such as cancer stem cells, which researchers believe may be responsible for forming the tumor in the first place. From a clinical perspective, if a therapy eliminates all the different sub-groups within a patient’s tumor, it could be a very effective treatment options for that cancer patient.
Since the human genome was first sequenced, scientists have worked hard to improve the techniques used to amplify DNA into larger quantities and to analyze the genetic material. “The analysis techniques of the past simply required more material than we could pull out of a single cell,” Levy said. “So we had to focus first on optimizing the chemistry until we could actually analyze RNA and DNA from a single cell and trust the results.”
Next, researchers needed the capability to sequence thousands of individual cells quickly and accurately. While a range of single-cell isolation techniques have been refined over the past few years, the Quanticel team has developed their own because they saw limitations with regard to throughput and accuracy in previous approaches.
Today, their platform consists of a robotic arm manned with a camera that identifies single cells on a dish. Then the arm gently sucks up and moves the cell into another plate that keeps the cells separate, known as a microwell plate. The image-and-vacuum approach is gentler and more accurate than other techniques.
“In our approach, we’re not forcing cells into water-in-oil droplets, or handling with multiple micropipettes so there’s less damage to the cells,” Levy said. This could lead to inaccurate or just plain wrong results from any analysis of those cells. “And because we take before-and-after photos, we can guarantee close to 100 percent accuracy in the capture and delivery process.”
Once the single cells are deposited in the microwell plate, researchers can analyze them. Besides single-cell DNA sequencing and gene expression analysis (determining which genes are turned “on” or “off” in a given cell), it won’t be long before researchers can look at the epigenetics (the 3D wrapping of DNA that helps determine whether genes are on or off) and protein profile of single cells.
Recognizing the potential of single-cell analysis to improve cancer treatment and identify new therapeutic targets, Celgene entered into a strategic alliance with Quanticel Pharmaceuticals in 2011 and acquired the company in 2015.
Cancer is just the starting point; single-cell analyses could be valuable in other diseases in which different sub-groups of cells are to blame. “I could list a whole host of diseases—autoimmune diseases for instance—where characterizing the behavior and characteristics of single cells may have great value,” Levy said.