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Introduction to High Resolution Melt Analysis
1. HRM Analysis
Francisco Bizouarn
International Field Application Specialist
Gene Expression Division
2. Overview
• Introduction to High Resolution Melt (HRM)
• Applications
• Important Considerations
• Assay Design and Optimization
• Precision Melt Analysis software
3. High Resolution Melt
• Post-PCR melt analysis method
• Discriminates dsDNA based on sequence length, GC
content or strand complementarity
• Detects a single base difference
• Rapid, inexpensive sequence screening method
– Mutation sequence can be unknown
– Samples are further processed to identify mutation sequence
• Increased specificity and sensitivity
Sample PCR HRM
4. HRM Applications
• Mutation discovery/gene scanning
• SNP genotyping 95% of all applications
• DNA methylation analysis
• Species identification
• DNA fingerprinting
• Screening for loss of heterozygosity
• Allelic prevalence in a population
• Characterization of haplotype blocks
• HLA compatibility testing
• Identification of candidate predisposition genes
5. Melt Curve Analysis
• After real-time PCR amplification, a melt curve is performed in
presence of a DNA binding “saturation dye”
• Melting temperature (Tm)
– DNA is half double and half single-stranded
– Depends on nucleotide content and length
Double Single
Stranded DNA Stranded
Tm
6. Melt Curve Analysis
• Distinguish products based on their Tms
– Plot negative rate of change of fluorescence vs. temp (-dI/dT)
for easy discrimination of products based on their Tms
8. SNP Genotyping
• A single base substitution, prevalent to 1% in a population
• Use HRM analysis to identify samples containing known single
nucleotide polymorphisms
• Not all SNPs are equally easy to differentiate
SNP Class Base Change Rarity (in human genome)
1 C/T and G/A 64%
2 C/A and G/T 20%
3 C/G 9%
4 A/T 7%
SNP classes defined by Venter et al (2001)
9. HRM Genotyping Curves
• The observed fluorescence curve shown in black is the
composite of all four possible duplexes
C
G
T
A
C
^
v
A
T
^
v
G
10. Mendelian Genetics Review
Homozygote WT Heterozygote Homozygote mutant
C C T
C T T
Allele 1 C C T
G G A
Allele 2 C T T
G A A
C
Melting C G T
G T A
A
C
Duplexes C T T
G A
A
G
11. Normalization
• Pre-melt (initial) and post-melt (final) fluorescence
signals of all samples are normalized to relative values of
100% and 0%
• Eliminates differences in background fluorescence
between curves
12. Difference Plot
• Magnify curve differences by subtracting each curve from
the most abundant type or from a user-defined reference
• Sets a baseline, so small differences become visible
13. Cluster Analysis
• The software clusters similar curve shapes automatically
into groups representing different genotypes (sequences)
• The software then auto-calls samples to a genotype
depending on where their curve shape clusters
C/T
C/C
T/T
14. Class 1 SNP Mutation G>A
• Hemochromatosis (HFE), C282Y mutation
• 75bp amplicon
• Genomic DNA from human blood, using SsoFAST Eva Green Mix
• Melt Study results from 3 melt files (12 samples per genotype)
G/G
A/A
G/A
15. Class 3 SNP Mutation C>G
• Hemochromatosis (HFE) gene, H63D mutation
• 100bp amplicon
• Genomic DNA from human blood, using SsoFAST Eva Green Mix
• 10 samples of each genotype
C/C
G/G
C/G
16. Class 4 SNP Mutation A>T
• Hemochromatosis (HFE) gene, S65C mutation
• 100bp amplicon
• Genomic DNA from human blood, using SsoFAST Eva Green Mix
• 3 samples of each genotypes
18. Methylation
• 2-5% of the cytosines in the genome are methylated
• 5th base
• Epigenetic information - may change over time
• No effect on base pairing
Mtase=methyltransferase
Mtase S-Adenosyl Methionine
SAH
SAM
19. Methylation Sites
• Only cytosine preceding guanines are methylation sites- CpG
• CpG- dinucleotides are unevenly distributed in the genome
• CpG-islands 1000-2000 bases long with 10-20 times higher CpG
content
• Promoter regions and first exon of many protein coding genes
20. Methylation and PCR
• Taq polymerase does not distinguish between cytosine and 5-
methylcytosine
• After the first PCR cycles, all 5-,methyl-cytosine will be
substituted for non-methylated cytosines
• PCR on untreated methylated DNA will erase the epigenetic
information
21. Traditional Approach:
Bisulfite Treatment
• Converts non-methylated Cytosines to Uracil
• Methylated Cytosines remain intact
• During PCR the Uracils are replaced by Thymine
• GC pairs are shifted to AT at non-methylated CpGs and non
CpG Cytosines resulting in a Tm change
22. Methylation Data Analysis
• Bisulfite treated DNA with all CpG-sites methylated will have
higher Tm than if non-methylated
• C:G vs A:T
• Bisulfite treated samples where:
Higher Tm in samples with promoter methylated in all cells
compared to samples with promoter methylated in only
30% of the cells
23. Methylation Assay
• CDH1 (Cadherin E),
• 113bp amplicon
• iQ SYBR Green Supermix
• Methylated gDNA diluted with unmethylated gDNA
24. HRM – Species identification
Mycoplasma species identification with SsoFast EvaGreen Supermix
and Precision Melt Analysis Software
• Samples amplified directly from tissue culture supernatants
• rpoB gene amplified (400 to 600 bp products)
• 4 known and 10 unknown species
M. ureolyticum M. argninig
M. pirum M. fermentans
M. hyorhinis M. hominis
M. orale M. gallisepticum
M. salivarium M. laidlawii
25. Mutation Discovery
• aka Mutation screening, SNP discovery, DNA re-sequencing, gene
scanning
• Rapidly screen many samples to identify the few that have mutations
• HRM gene scanning can rapidly identify samples with a mutation, however,
further analysis is still required to identify the mutation
– DNA sequencing
– Compare against known genotype profiles using HRM
– Perform HRM DNA Matching experiments
26. DNA Matching
• aka species identification, compatible donor screening
• Quickly identify similar sequences
– forensic sibling identification, screening for pathogenic or
antibiotic-resistant bacterial species etc
• Mix samples together and then perform HRM
• Downstream Analysis Still Required
• Not all homozygotes can be distinguished
• May require heteroduplex analysis where
specific rations of known genotypes are
added to unknown samples and melted
27. Genotyping for Sequence
Insertions/Deletions/Repeats
• Looking for trinucleotide repeats
• 4 possible genotypes
• Fig. C. shows all possible duplexes formed from the 4
genotypes
29. Amplicon Melting
• HRM assays are comparisons of
dissociation patterns.
– If amplicons dissociated a a specific
temperature we would see the following.
ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGT
TGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCA
– Amplicons dissociate at a rate that will vary
according to sequence homology, salt,
length, etc…
AATTAAT TAATTAATTA
ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGT
TGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCA
TTAATTA ATTAATTAAT
30. Mendelian Genetics Review
Homozygote WT Heterozygote Homozygote mutant
C C T
C T T
Allele 1 C C T
G G A
Allele 2 C T T
G A A
C
Melting C G T
G T A
A
C
Duplexes C T T
G A
A
G
32. Small differences
Homozygotes produce a single
re-associated products
A
T
A
T
Heterozygotes produce a mixed
population of re-associated products
T
A T T
T A
A
A
34. Amazingly
• Very small differences can
be detected.
– The juxtaposition of bases
A
T
A
T
T
A
T
A
35. What’s really going on?
Max or Total Signal
Baseline Noise Level
(dye, DNA, instrument)
Change of
florescence due to
temperature effect
(buffering and pH)
Change of
florescence resulting
from the melting of
double stranded DNA
36. DNA melting phase
• Comprised of;
– Continued change in
florescent signal due to
buffering effect.
– Dissociation of amplicons
that are the desired
amplification product
– Dissociation of amplicons
that are not the desired
amplification product.
– Dissociation of template
DNA (to a lesser extent)
37. Mechanics of High Resolution
Melt
What are HRM profiles?
• The quick version
– The dissociation curves of dye
bound PCR amplification
products are double baselined
and subsequently compared to
one another for differences in
their melting profiles.
• The in depth version
– Signal vs noise ratio difference
analysis between samples at
varying temperatures.
38. How is differentiation done
• These fluorescent results are
plotted with signal intensity on the
“Y” axis.
• Data for the various temperature
increments is plotted sequentially
on the “X” axis.
• Data is then double baselined
using values before and after the
melt phase.
• Data is re-scaled (normalized)
such that each profile ranges
from 0 to 1 in in fluorescent
intensity.
39. How is differentiation done
• The differentiation process is
repeated for each
temperature point at which
the data was collected.
• The larger the difference in
fluorescent readings at a
specific temperature, the
larger the difference in in the
“Difference RFU” graph
40. How is differentiation done
• Subsequently, the smaller
the difference in fluorescent
readings at a specific
temperature, the smaller the
difference in in the
“Difference RFU” graph.
• Large fluorescent differences
make analysis simpler.
41. Amplicon Size
• HRM assays are comparisons of dissociation patterns
– It stands to reason that the larger the signal difference between samples, the easier it
is to differentiate and identify them.
– As such, smaller amplicons generally work better than larger ones.
ACGTACGTACGTACGTACGTAC A GTACGTACGTACGTACGTACGT
TGCATGCATGCATGCATGCATG T CATGCATGCATGCATGCATGCA
ACGTACGTACGTACGTACGTAC G GTACGTACGTACGTACGTACGT
TGCATGCATGCATGCATGCATG C CATGCATGCATGCATGCATGCA
– The larger the amplicon the lower the over signal difference as the signal differences is
partially obscured by the overall noise.
ACGTACGTACGTACGTACGTACACGTACGTACGTACGTACGTAC A GTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTAC
TGCATGCATGCATGCATGCATGTGCATGCATGCATGCATGCATG T CATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATG
ACGTACGTACGTACGTACGTACACGTACGTACGTACGTACGTAC G GTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTAC
TGCATGCATGCATGCATGCATGTGCATGCATGCATGCATGCATG C CATGCATGCATGCATGCATGCATGCATGCATGCATGCATGCATG
42. Amplicon Size
50 bp amplicon
• Comparison of A/A and T/T
heterozygotes
• 300nM primer
• Optimal annealing temp
• Clustering and percent
confidence are increased
500 bp amplicon
using smaller amplicons
43. 96 well A/A A/T T/T assay
• The most difficult SNP class
– 50pb amplicon
– Auto analysis
– One failed wells out of 96
– Rest all accurately clustered
44. Melting Resolution
• This widely distributed table leads to some confusion.
SNP Class Base Change Typical Tm Shift Rarity (in human genome)
1 C/T and G/A Large (>0.5oC) 64%
2 C/A and G/T 20%
3 C/G 9%
4 A/T Very Small (<0.2oC) 7%
SNP classes defined by Venter et al (2001)
What does this column really mean?
45. Resolution increments necessary for
HRM
• It is often assumed that to perform “High Resolution Melt” it is
necessary to take readings across a temperature range using
very small increments.
• More important than the ability to perform thin “slices” is the
capability to detect small differences.
• Although smaller increments than those used in classic qPCR
assays should be used, depending on the assay increments of
0.5 degrees can be used.
• Critical parameters to successful HRM assays include proper
assay design, good analysis software, clean template and
uniformity in assay setup.
46. Class 4 SNP 50pb amplicon
Melt inc 0.1oC
Melt inc 0.2oC
47. Class 4 SNP 50pb amplicon
Melt inc 0.3oC
Melt inc 0.4oC
48. Class 4 SNP 50pb amplicon
Melt inc 0.5oC
• SNP detection can be performed with increments of 0.5 degrees
(max tested in this set of assays) when using small amplicons
even with class 4 SNP’s.
• Heterozygote samples are the easiest to spot and generally give
a very high confidence level when auto clustering.
49. Class 3 SNP 100 pb amplicon
Melt inc 0.1oC
Melt inc 0.2oC
50. Class 3 SNP 100 pb amplicon
Melt inc 0.3oC
Melt inc 0.4oC
51. Class 3 SNP 100 pb amplicon
Melt inc 0.5oC
• Simplicity of the SNP is also a factor in melt analysis.
• Here a class 3 SNP is easily clustered at 0.5 degree increments.
54. Target Sequence
• SNP analysis
– Identify the right sequence
– Search for SNPs based on gene, location or function
– Find variation sites (avoid variations that impact melt curves)
• Methylation Assays
– CpG sites in primers and within the sequence
– Fragment length
• NCBI SNP Databases
– http://www.ncbi.nlm.nih.gov/SNP
55. Primer Design
• Primer guidelines
– BLAST primer sequences
– 18-24 bases
– 40-60% G/C
– Balanced distribution of G/C and A/T bases
– Annealing between at 55-65 C
– No internal secondary structures (hair-pins)
• Primer pairs
– Similar Tms, within 2-3 C
– No significant complementarity (> 2-3bp), especially in 3’ ends
• Primer binding sites
– Avoid targets with secondary structure
– Avoid pseudogenes
56. Experiment Considerations
• Amplify a single product at high efficiency
– No primer-dimers or non-specific products
• Generate sufficient PCR product (C(t)s 30)
• Samples need equal PCR efficiencies and plateau
fluorescence for comparison
• Analyze short PCR products, the smaller the better
• Uniform reaction mix/sample concentrations
• Capture data over at least a 10 C melt curve range
57. Amplicon Design
• Use similar criteria as for SYBR Green assay
• Short amplicons maximize differences in melting behavior
between similar sequences
– 70-150bp is desired (50-250bp acceptable)
– Longer amplicons yield more complex profiles, with multiple melting
domains, consider melt domain complexity (M-fold)
– Avoid areas of secondary structure and high GC content
– Local sequence context can influence mutation detection
– Overall GC content and position of the mutation in the fragment does
not have a significant effect on mutation detection
• Determine folding characteristics at annealing temperature
(DINAMelt)
58. PCR Evaluation
• Poor PCR optimization = Poor HRM resolution
• No primer-dimers or non-specific products
– Thermal gradient optimization of annealing temperature
– Increase annealing temperature/decrease MgCl2 concentration to
increase specificity
– Run No Template Controls (NTC)
– Optimize primer concentrations (100nM steps)
• Efficient PCR amplification, want similar plateau fluorescence
• Generate sufficient product, C(t) values below 30
– Degraded material or too little material, increase concentration
• Do not use UNG enzymes for methylation assays
Always check amplification curves prior to HRM analysis
59. PCR Optimization
• Starting material is key to good results
– Handle samples properly prior to analysis
– Use a consistent amount of starting material
• Amplification
– Use a Hot-Start, high-fidelity enzyme
– Shorten protocol steps
– Optional: hold at 72 C after amplification
– 95 C to denature PCR products
• Hold at 40-50 C for heteroduplex formation
60. HRM Troubleshooting
• Problems with interpretation of HRM results
– Include controls for each known variant in the test population
– Make fresh reaction mixes for each new run
– Run plates within two hours of preparation
– Perform melt curve immediately following amplification
• Inconsistent melt behavior can also occur due to variations in:
– MgCl2 and buffer salts
– Taq storage buffer additives
– Intercalating dye type and concentration
– Reaction volumes
– Melt ramp rate
61. HRM Assays Summary
• Consistency in reaction setup and reagent use is necessary for
comparisons of samples
• Protocols vary depending on the application
• Results vary depending on DNA template quality and the
sequence
• Avoid primer-dimers and additional products that effect melting
behavior
• Use negative controls and standards
• Any ambiguous samples should be sequenced
63. Workflow
1. Set up reactions using HRM compatible reagents
SsoFAST Eva Green Supermix
2. Run amp + high resolution melt protocol on CFX96 or CFX384
98°C for 2 min
98°C for 2-5s
55-60°C for 2-10sec (plate read)
Go to step 2, 39 more times
98°C for 1 min
70°C for 1 min
Melt curve 75°C to 95°C increment
0.2°C 10 sec hold (plate read)
3. In Precision Melt Analysis software, import the data file (.pcrd)
to create a melt file (.melt)
64. Data Analysis
• Multiple views of data, with easy interpretation of results
• Analyze multiple experiments from a single plate using the Well
Group feature
66. Percent Confidence
• Provides a percentage chance that a given well is correctly
categorized within the assigned cluster
• It is based on the number of standard deviations the sample is
from the mean of the cluster. This assumes that the found
"cluster means and standard deviations" are accurate
descriptions of the real probability distributions of the data
