Sequence diversity within the HA-1 gene as detected by melting temperature assay without oligonucleotide probes

DMSO Increases Mutation Scanning Detection Sensitivity of High-Resolution Melting in Clinical Samples

BACKGROUND

Mutation scanning provides the simplest, lowest-cost method for identifying DNA variations on single PCR amplicons, and it may be performed before sequencing to avoid screening of noninformative wild-type samples. High-resolution melting (HRM) is the most commonly used method for mutation scanning. With PCR-HRM, however, mutations less abundant than approximately 3%-10% that can still be clinically significant may often be missed. Therefore, enhancing HRM detection sensitivity is important for mutation scanning and its clinical application.

METHODS

We used serial dilution of cell lines containing the TP53 exon 8 mutation to demonstrate the improvement in detection sensitivity for conventional-PCR-HRM in the presence of DMSO. We also conducted coamplification at lower denaturation temperature (COLD)-PCR with an extra step for cross-hybridization, followed by preferential denaturation and amplification at optimized critical temperature (full-COLD-PCR), to further enrich low-level mutations before HRM with or without DMSO, and we used droplet-digital PCR to derive the optimal conditions for mutation enrichment. Both conventional PCR-HRM and full-COLD-PCR-HRM with and without DMSO were used for mutation scanning of TP53 exon 8 in cancer samples containing known mutations and myelodysplastic syndrome samples with unknown mutations. Mutations in other genes were also examined.

RESULTS

The detection sensitivity of PCR-HRM scanning increases 2- to 5-fold in the presence of DMSO, depending on mutation type and sequence context, and can typically detect mutation abundance of approximately 1%. When mutation enrichment is applied during amplification with full-COLD-PCR followed by HRM in the presence of DMSO, mutations with 0.2%-0.3% abundance in TP53 exon 8 can be detected.

CONCLUSIONS

DMSO improves HRM mutation scanning sensitivity with saturating dyes. When full-COLD-PCR is used, followed by DMSO-HRM, the overall improvement is about 20-fold compared with conventional PCR-HRM.

Mixture models for analysis of melting temperature data

Background

In addition to their use in detecting undesired real-time PCR products, melting temperatures are useful for detecting variations in the desired target sequences. Methodological improvements in recent years allow the generation of high-resolution melting-temperature (T_m) data. However, there is currently no convention on how to statistically analyze such high-resolution T_m data.

Results

Mixture model analysis was applied to T_m data. Models were selected based on Akaike's information criterion. Mixture model analysis correctly identified categories in T_m data obtained for known plasmid targets. Using simulated data, we investigated the number of observations required for model construction. The precision of the reported mixing proportions from data fitted to a preconstructed model was also evaluated.

Conclusion

Mixture model analysis of T_m data allows the minimum number of different sequences in a set of amplicons and their relative frequencies to be determined. This approach allows T_m data to be analyzed, classified, and compared in an unbiased manner.

Molecular Beacon–Based Temperature Control and Automated Analyses for Improved Resolution of Melting Temperature Analysis Using SYBR I Green Chemistry

Background: Melting temperature analysis of products amplified with SYBR I Green chemistry is a cheap and effective method for identification of sequence differences. When used in conventional quantitative real-time PCR instruments (qPCR), this method is limited by temperature variations over the heating block and low numbers of fluorescence measurements during the dissociation step, which hamper the ability of most instruments to report accurate and precise melting temperatures.

Methods: We designed a molecular beacon–based temperature indicator probe (Tm-probe) to control for variations in temperatures over the heating block of the instrument. In addition, we wrote an automated curve-fit analysis algorithm of dissociation data to use multiple data points with a gaussian curve fit to extrapolate precise melting temperatures.

Results: Use of the Tm-probe in conjunction with the analysis algorithm and multiple dissociations improved SDs of melting temperatures over a 96-well plate from 0.19 to 0.06 °C

Conclusions: Melting temperature analyses with SYBR I Green chemistry on conventional qPCR instruments can be improved by the use of a Tm-probe in conjunction with curve-fit analysis of data. Resolution improvement up to 3-fold is possible and allows additional melting temperatures to be identified.