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New Method Uses Input Sample Quantity to Eliminate Need for Control Genes in qPCR

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NEW YORK (GenomeWeb) — Scientists at the State University of New York's Downstate campus have published a method that could obviate the need to measure control, or housekeeping, genes in quantitative PCR. Using a simple mathematical formula, the technique normalizes efficiency-corrected cycle threshold data to input sample quantity, and is applicable to both standard and high-throughput reverse transcription qPCR.

In a study published earlier this month in PLoS One, the researchers compared the input quantity method to two other commonly used algorithms — a comparative Ct method and an efficiency corrected method — and found comparable results using a set of clinical samples.

According to the study, the input quantity method can be used to determine fold-change differences between samples and controls. By using a commercially available cDNA standard, it can also be adapted to derive absolute number of transcripts per cell or unit volume. In this way, results are made comparable across experiments or platforms.

In an interview with PCR Insider, Alison Baird, a professor of neurology and medical director of the stroke program at SUNY Downstate, said her team developed the input quantity method out of necessity while searching for blood biomarkers of stroke.

"With clinical samples, it's very difficult to know the right control genes to use, and our experience has been that many are not stable under stroke conditions," Baird said.

In a previous expression profiling study of circulating leukocytes for stroke detection, Baird said her group tested 10 potential control genes and found "a dramatic lack of stability across multiple leukocyte subsets for the majority of them," she said.

For in vitro or ex vivo studies, performing additional experiments to identify and validate control genes is less of an issue, she said. But the lack of baseline, pre-stroke, expression levels and the limited volume of typical patient samples makes this approach very difficult for standard qPCR.

With her colleague Mateusz Adamski, Baird developed the new method and found a way to express it as a mathematical derivation.

In a nutshell, the method adds a value of one to the experimentally determined efficiency, raises that sum to the cycle threshold value for a given gene, and divides this by the input sample quantity.

"For example, say we have a transcript that has 95 percent [amplification] efficiency. That would be one plus 0.95, which is 1.95, to the power of let's say 10, if the Cq value is 10, and then divided by the cell count, whatever that was," Baird said.

Baird noted that for clinical samples using whole blood, total white cell counts can be done by the clinical laboratory and are highly accurate. In the lab, cell numbers of peripheral blood mononuclear cells can be determined using a hemocytometer. The input variable can be based on the volume that's used, or on the tissue weight, and the result is expression level per unit, she said.

Baird and Adamski also attempted to adhere as closely as possible to MIQE guidelines in their study. "To perform reliable PCR you have to stick to [the guidelines], and this was a helpful resource to make sure we are doing everything that is required to create repeatable data," Adamski said.

"For high-throughput qPCR, the guidelines are still in the process of catching up with regular qPCR," Baird added.

The team hopes to use their method in the future to examine miRNA profiles in stroke.

With this in mind, they have honed in on an All-in-One extraction kit from Norgen Biotek for sample preparation. The kit is designed for precious or small samples, and yields DNA, large RNA, small RNA, and protein. Adamski explained that this was the only kit they were able to find which could separate all four of those elements, and the protocol for one molecular subset takes about 20 minutes for eight samples, he said.

Baird and Adamski are now using the input quantity method in combination with HT-RT-qPCR to analyze array data and determine leukocyte subsets that may harbor biomarkers of stroke. "There's a definite need for more rapid diagnosis of stroke given that the evaluation time currently, using imaging, is long and the time window for treatment is short," Baird said.

The group has no plans to patent the input quantity method. "We do have other things that are under protection, but we did not proceed with protection of [this method], so it is freely available," she said.

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