September 1, 2008 (Vol. 28, No. 15)

Catherine Shaffer

Usage in Copy Number Variation Gains in Popularity

There have been many advances in qPCR since it was first introduced, and applications for it have proliferated widely. The idea of quantitating the PCR product after each cycle is a simple one, and yet creative applications of the concept can be most ingenious. It is for this reason that Select Biosciences is hosting its first qPCR conference, “Advances in qPCR,” in Stockholm next month.

Among the hottest of hot topics is copy number variation. The idea that the number of copies of a gene could vary drastically between normal individuals, and also that copy number outside the normal range could be responsible for genetic disease—apart from any mutation—created a sensation in the field of genomics when several papers on the subject came out nearly simultaneously in 2006.

It’s not surprising that qPCR is a powerful tool for studying copy number variation. Applied Biosystems has been developing assays for specific and accurate detection of copy number for researchers studying copy number variation. Kelly Li, Ph.D., senior staff scientist, will present two studies at the meeting that will demonstrate how the TaqMan qPCR assay can be a powerful tool to meet challenges in differential detection of copy number changes.

The first study to be discussed by Dr. Li is with C4 (complement factor 4), which has two different protein isoforms, C4A and C4B. The copy number variation of C4A and C4B leads to different susceptibilities to autoimmune and inflammatory diseases.

There are only five base differences within a 17-bp-long sequence between C4A and C4B, however, their functions are radically different. For example, lower copy number of C4A is a risk factor for SLE, and higher copy number of C4A is a protective factor for SLE. Additionally, there are different sizes of C4: C4L (long form) and C4S (short form). It is quite a challenge to design assays that can differentiate between C4A and C4B, as well as C4L and C4S.

Dr. Li’s second investigational gene involves OTC (ornithine carbamoyltransferase), an enzyme that catalyzes the second step of the urea cycle. Deficiency of OTC causes severe hyperammonemia. In addition to point mutations, deletions involving one or more exons have been found in 10–15% of patients with OTC deficiency. The challenge is to quickly and specifically detect the intragenic copy number changes that may cause the disease.

Results of these studies show the utility of the TaqMan assay for qPCR-based studies in copy number variation. “TaqMan probe provides specificity that regular qPCR assays don’t have,” says Dr. Li. “Most researchers who run regular qPCR for copy number put the test assay and reference assay in separate wells, and well-to-well variation could affect data quality. It also doubles the cost of reagents. With our Taqman assay, research runs in duplex format, which cancels out many variations, minimizes noise, and yields cleaner data. It also saves on the cost of master mix.”


James Flanagan, Ph.D., senior research fellow at University College London Cancer Institute, studies epigenetics, looking primarily at DNA methylation. Using the Methylight asssay (from Qiagen) and primers targeted at both methylated and unmethylated DNA, it is possible to determine the methylation status of the DNA. Dr. Flanagan has a number of lines of research focusing on DNA methylation.

“We are investigating genetic changes in a stem cell model using Methylight in a primary screen to find out whether DNA hypermethylation is occurring, looking for a particular repetitive sequence,” he says. Dr. Flanagan will present the results of this research in Stockholm.

The primary strength of using a qPCR assay to detect methylation is the exquisite sensitivity of it. It can detect one copy in ten thousand. On the other hand, sometimes this strength can be a weakness. The high sensitivity can create laboratory artificacts. “It could be good or bad depending on how you use the experiment,” Dr. Flanagan adds.

DNA methylation can shed light on important changes in the organism. As part of his overall research goals, Dr. Flanagan hopes to reveal how viruses such as KSHV ultimately transform normal cells into cancer cells. He believes that changes in methylation could be important in this process.

Single-Cell Expression Profiling

Expression profiling is a popular application for qPCR. However, the traditional expression profile takes an average of a collection of cells. At the individual cell level, gene expression, even in homogeneous samples, has profiles that may vary significantly from the average.

Mikael Kubista, Ph.D., professor at the TATAA Biocenter, is presenting his work on single-cell expression profiles and also showing some examples of subcellular expression profiling. By monitoring how cells respond differently to stimuli, Dr. Kubista hopes to learn more about basic biological mechanisms, finding better ways to search for drug targets or monitor therapy for diseases such as cancer.

“We already know that a tumor is very heterogeneous,” says Dr. Kubista. “Small populations of cells in a tumor are responsible for metastasis. By doing single-cell studies, it is possible to find and analyze critical cells.”

Most exciting is multiway expression profiling. This work is enabled by robust high-throughput technologies: cell sorters that can collect large numbers of individual cells, reagents for one-step extraction RT-PCR, instruments capable of handling 384- at present, and soon 1,536-well plates, and dedicated software for analysis.

Future applications of this method are expected to be exceedingly powerful. For example, when applied to human clinical samples, it can be used to monitor the expression of many genes, in different tissues, as a function of time to optimize individualized treatments. “This was not really feasible with microarrays,” adds Dr. Kubista.


One thing that all of these applications of qPCR have in common is that they generate a great deal of data, and that computerized statistical methods are necessary to sort it out. When data analysis is carried out by a software package, it is often opaque to the scientist, and so it is not always apparent when things need to be improved.

When Terry Hyslop, Ph.D., director of biostatistics for Thomas Jefferson University (, embarked on the data analysis for a clinical trial of guanylyl cyclase c as a prognostic biomarker for colon cancer, she noticed something interesting about the data coming out of their qPCR assays.

“We got the machine here and started seeing data coming out of it. The way the q process happens, it does not take into account the experimental realities. As a statistician, I know there’s a lot of data and assumptions in the process not being utilized. We found that if we incorporated these characteristics, the model provided a much more precise answer in terms of reducing the variability of what’s measured,” she explains.

Dr. Hyslop and her colleagues developed an algorithm, creating a mathematical curve to fit all of the data coming out of the machines. Using this algorithm, they reduced the noise in the experiment and lowered the error rate by 30–60%. Additionally, by using a newer quantification process, they eliminated the need to run a set of standard curve samples in each plate because the standards are internal to the experiment. The algorithm can add up to significant savings in the design and execution of new studies.

“When you go to plan a new study, the variability won’t be as high, so you won’t need as many patients in the study to answer the same question,” says Dr. Hyslop.

Unlike his colleagues at the conference, George Weinstock, Ph.D., associate director, Genome Center, Washington University, is not presenting an improvement or new application of qPCR. Rather, he is presenting a method for targeted genome sequencing that avoids the use of all forms of PCR. The method is an alternative to developing PCR primers, amplifying a particular region, and then sequencing the amplified fragments. It is a chip-based oligonucleotide array, utilizing 454 Life Sciences; a Roche Diagnostics

This is an important reminder that one should not be married to any single method or technology, and that a good scientist uses the best tools available even if that means moving out of one’s comfort zone. Researchers will continue to find ingenious new applications for qPCR, especially in combination with exciting next-generation tools in related disciplines.

Previous articleGSK Pays $125M Upfront for Part of Valeant’s Neurology Program
Next articleThe Japanese Acquisition Binge