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Assay Tutorials : Oct 15, 2009 (Vol. 29, No. 18)

High-Resolution Melt Analysis Applications

Alternative to Probe-Based Genotyping Seeks to Overcome Drawbacks
  • Viresh Patel, Ph.D.

Genomics is rapidly expanding beyond merely identifying the genetic makeup of organisms to encompass studies of how variations in genotype impact physiological function. Applications involving analysis of the genome include mutation discovery, SNP genotyping, DNA mapping, genetic screening, and population studies. It is hoped that better insight into how changes in genotype translate into effect upon phenotypic expression will lead to earlier disease diagnosis and targeted therapies.

One of the primary tools engaged in the pursuit of genomic discovery is PCR. Specifically, post-PCR analysis techniques are used for genotyping and mutation detection. Traditional methods—including denaturing high-performance liquid chromatography, single-stranded conformation polymorphism (SSCP), temperature gradient capillary electrophoresis, and restriction fragment length polymorphism (RFLP)—may require the purchase of costly laboratory equipment.

Probe-based genotyping, a new, more sensitive technique, can be performed on any standard real-time PCR instrument, but requires specially labeled probes, which are also expensive. This approach can require considerable time and effort for optimization.

Until recently, what has been lacking is a low-cost-per-sample method that also enables high-throughput sample processing. High-resolution melt (HRM) analysis is an alternative to probe-based genotyping assays that overcomes the cost, time, and labor-intensity challenges. HRM does not require special instrumentation as it can be performed on existing real-time PCR systems. This simple, yet highly sensitive and accurate tool is quickly becoming a mainstream part of the genetic analysis workflow.

DNA melt-curve analysis—applying temperature to melt and characterize the resulting curve profiles of double-stranded DNA samples—has proven useful for scanning for sequence variations, primarily to confirm the specificity of primers by ensuring no primer-dimers are present in quantitative PCR assays. The goal of this well-established method has been to prevent nonspecific amplification and improve data accuracy.

Recent advances in dye chemistry, instrument sensitivity, and data-acquisition rates have led to the next generation of this technique—HRM analysis. A closed-tube, post-PCR analysis method that requires no post-PCR handling, HRM analysis generates DNA melt-curve profiles sufficiently specific and sensitive for mutation scanning, methylation analysis, and genotyping.

HRM analysis is performed to discriminate nucleotide sequence differences between samples. HRM analysis also enables mixed DNA fragments to be distinguished from each other—important for SNP genotyping of wild-type, heterozygous, and homozygous mutant individuals.

Three basic tools are used in HRM analysis:

  • Specialized PCR reagents and dsDNA binding dye—third-generation saturating, low-toxicity dyes can be used in high concentrations to yield strong melt curves and do not interfere with amplification during PCR.
  • Real-time PCR instrumentation should offer sensitive detection for accurate quantitation, target discrimination and precise thermal control.
  • HRM analysis-compatible software generates and analyzes melt-curve profiles and clusters samples with similar properties.

Because of its simplicity and abbreviated workflow, HRM analysis offers a cost-effective, yet accurate alternative to probe-based genotyping assays such as SSCP, RFLP, and DNA sequencing.

Workflow

After DNA is amplified in a real-time PCR instrument using a saturating dye-based master mix, the PCR product is melted using high data-acquisition rates to generate melt curves. Software developed specifically for HRM analysis, such as Precision Melt Analysis™ software from Bio-Rad Laboratories enables analysis of the melt curves for genotyping and mutation scanning.

Melt curves are analyzed in the software using three basic steps:

  • All samples are normalized along the fluorescence axis such that their average relative fluorescence value at the premelt signal is set to 100% and postmelt signal is set to 0%. This serves as a visual aid to interpret the data.
  • To magnify the differences in the melting curves between samples, each melt curve is subtracted from a user-defined reference melt curve and the fluorescence differences between samples are plotted. Similarly curved shapes will be clustered automatically into groups representing different genotypes/sequences.
  • Temperature shifting (optional) makes it easier to distinguish heterozygous from wild-type homozygous samples. Curves can be shifted along the high-end of the temperature axis to meet at the same specific temperature so that curve shapes are more accurately compared.

This simple post-PCR workflow has far fewer steps than traditional screening applications, reducing the resources required to compare DNA samples by sequence, length, content, or complementarity.

Case Studies

Perhaps the most challenging issue facing researchers seeking to identify the genetic factors involved in the proliferation of breast cancer is the polymorphic nature of genes commonly involved in expression of the disease. Kim de Leeneer, a Ph.D. student at the Center for Medical Genetics Ghent (CMGG) is researching the genetics of breast cancer, specifically the BRCA1 and BRCA2 genes.

de Leeneer conducted her first HRM experiments in January 2007, screening 212 positive control samples for breast cancer. Until these experiments, the primary tools used at CMGG to screen for the genetic inheritance of breast cancer were denaturing gradient gel electrophoresis (DGGE) and direct sequencing of both large exons 11 of BRCA1 and BRCA2.

Traditional sequencing experiments were conducted in parallel to compare results and verify accuracy of the new technique. All controls were recognized, so de Leeneer and colleagues began converting traditional assays to HRM analysis.

Initially, all HRM analysis results were confirmed by sequencing: results demonstrated 100% sensitivity and 98.7% specificity of HRM analysis, with few false positives. As the researchers at CMGG developed confidence in HRM analysis they began processing sample assays in single replicates. Only aberrant melting curves get sequenced to confirm the presence of a genetic variant.

As a prescreening tool, HRM analysis makes it easy to identify samples with genetic variants—and with a significant reduction in cost and time required over traditional methods. In de Leeneer’s lab, HRM has reduced, by approximately one-third, the costs and workloads compared to DGGE and direct sequencing.

The main focus of Alessandro Martino’s studies in the department of biology at Pisa University is SNP-based pharmacogenetics for multiple myeloma. Currently, he is studying the rat gene expression that triggers repair responses in heart perfusion following heart failure. Martino’s experiments mainly center on membrane transporters, cytokines, and other pathways that potentially modulate drug response and survival rates after chemotherapy. Studies involve analyzing SNP mutations—primarily class I and II, but also some class IV, in MM patient blood samples.

Until recently, the primary techniques used for SNP analysis were dual-labeled hydrolysis probe assays, but Martino has tested HRM analysis with the aim to replace probe-based screening methods. In initial HRM experiments, researchers used the same primer pairs and reagents as with dual-labeled hydrolysis probe assays.

These early HRM experiments were run in parallel with probe-based assays, and Martino observed good correlation between the two. These studies demonstrated that melting temperature of the amplicon and primer pair specificity are influenced by the primer pairs used, so Martino began to develop primer pairs specifically for HRM studies.

Replacing a self-made reagent mix with a commercial supermix further optimized experiments. The new reagent enabled amplification of targets that were previously problematic. HRM analysis also improved their results with allelic discrimination experiments over probe-based assays. Because allelic groups cluster using HRM-based methods, they can be identified via melting curves generated by the software.

Currently, the laboratory conducts ~200 HRM experiments per week, although up to 200 experiments can be run in a single day. As more of the laboratory’s key instrumentation is replaced with HRM-compatible models, Martino believes that HRM has the potential to replace many probe-based assays.