Successful gene expression studies using validated qPCR assays Jan - - PowerPoint PPT Presentation

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successful gene expression studies using validated qpcr
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Successful gene expression studies using validated qPCR assays Jan - - PowerPoint PPT Presentation

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Successful gene expression studies using validated qPCR assays Jan Hellemans, CEO Biogazelle webinar October 28 th , 2015 Agenda Requirements for high quality qPCR assays Approaches for qPCR assay validation How good a result

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Successful gene expression studies using validated qPCR assays

Jan Hellemans, CEO Biogazelle webinar October 28th, 2015

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Agenda

  • Requirements for high quality qPCR assays
  • Approaches for qPCR assay validation
  • How good a result can be achieved with highly optimized design

tools

  • How to easily discover additional genes of interest
  • Best practice in qPCR
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Online poll

How would you rank your current knowledge about gene expression assay design? a) basic b) advanced c) expert

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qPCR is reference technology for nucleic acid quantification

  • sensitivity and specificity
  • wide dynamic range
  • speed
  • relatively low cost
  • conceptual and practical simplicity

qPCR is easy to perform ≠ easy to do it right

  • many steps involved
  • all need to be right

Introduction

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R

relative quantification quality control statistical analysis

C

Prepare – cycle – report

P

experiment design samples assays

prepare cycle report

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R

relative quantification quality control statistical analysis

C

Prepare – cycle – report

P

experiment design samples assays

prepare cycle report

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Assay design & validation

design

  • amplicon length
  • primer positions (exonic or intron-spanning)

Considerations & best practices

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SLIDE 8

Assay design & validation

design

  • amplicon length
  • primer positions (exonic or intron-spanning)

Considerations & best practices

gene exonic intron-spanning

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SLIDE 9

Assay design & validation

design

  • amplicon length
  • primer positions (exonic or intron-spanning)
  • transcript coverage

Considerations & best practices

gene transcript 1 transcript 2 transcript 3

2 3 1 2 3 2

coverage

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Assay design & validation

design

  • amplicon length
  • primer positions (exonic or intron-spanning)
  • transcript coverage

in silico verification

  • specificity prediction (retropseudogenes and other

homologues)

  • secondary structure analysis

wet lab validation (experimental)

  • specificity assessment (gel, melt, amplicon sequencing)
  • Cq of NTC (for SYBR assays)
  • amplification efficiency determination (slope, E, SE(E), r2)

Considerations & best practices

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Assay design & validation

design

  • amplicon length
  • primer positions (exonic or intron-spanning)
  • transcript coverage

in silico verification

  • specificity prediction (retropseudogenes and other

homologues)

  • secondary structure analysis

wet lab validation (experimental)

  • specificity assessment (gel, melt, amplicon sequencing)
  • Cq of NTC (for SYBR assays)
  • amplification efficiency determination (slope, E, SE(E), r2)

Considerations & best practices

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Properties of the perfect assay

  • specific for the gene of interest => no off-target amplification
  • detection of all transcript variants
  • detection not affected by polymorphisms => no allelic bias or

drop out

  • amplification efficiency ~100%
  • no gDNA co-amplification
  • no primer dimer formation
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The perfect assay

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Online poll

How do you currently obtain your ‘perfect’ qPCR assay? a) using your own home brewed assays b) buying pre-designed assays (commercial) c) currently not designing any gene expression assays

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The perfect assay

  • For some genes, there is no perfect assay
  • no unique sequence (homology with other genes –

pseudogenes)

... or the best possible

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Gene homology in olfactory receptor genes prevents perfect designs

1532 / 2043 (75%) of genes without perfect design have homologous genes that differ less than 12.5% (2 variations per 16 bases)

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

1 101 201 301 401 501 601 701 801 901 1001 1101 1201 1301 1401 1501 1601 1701 1801 1901 2001

distances (clustalW) between all genes without perfect design

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The perfect assay

  • For some genes, there is no perfect assay
  • no unique sequence (homology with other genes –

pseudogenes)

  • no common sequence among all transcripts
  • regions are excluded because of repeats, secondary

structures, SNPs, homology, ...

  • Make the best possible compromise and report potential issues
  • Design  in silico quality control  wet lab validation

... or the best possible

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Assay design using primerXL

  • database of genomic information (transcripts, SNPs, ...)
  • tools for target region selection (maximize transcript coverage)
  • primer3 design engine
  • analysis of secondary structures and SNPs in primer & probe

annealing regions

  • specificity prediction (BiSearch, bowtie)
  • relaxation cascade (from perfect to best possible)
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Impact of primer mismatches on qPCR assay performance

Lefever, Clin Chem 2013

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BiSearch specificity prediction

  • BiSearch loose
  • 1222222222222222
  • BiSearch strict
  • 1233333333333
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BiSearch specificity prediction

  • BiSearch loose
  • 1222222222222222
  • nly the gene of interest

(FFAR2)

  • BiSearch strict
  • 1233333333333

reads seq gene_list

  • fficial_symbol

location 2843 CATGGCAGTCACCATCTTCTGCTACTGGCGTTTTGTGTGGATCATGCTCTCCCAGCCC

CTTGTGGGGGCCCAGAGGCGGCGCCGAGCCGTGGGGCTGGCTGTGGTGACGC TGCTCAATTTCCTGGTGTGCTTCGGACCTTACAGATCGGAA

ENSG00000126262 FFAR2 19:35940617- 35942667 1897 GTAAGGTCCGAAGCACACCAGGAAATTGAGCAGCGTCACCACAGCCAGCCCC

ACGGCTCGGCGCCGCCTCTGGGCCCCCACAAGGGGCTGGGAGAGCATGATCC ACACAAAACGCCAGTAGCAGAAGATGGTGACTGCCATGAGATCGGAA

ENSG00000126262 FFAR2 19:35940617- 35942667 1535 GTAAGGTCCGAAGCACACCGAGAGCTGGGAGCAGGAGCTACACAGTCTGCTGG

CCTCACTGCACACCCTGCTGGGGGCCCTGTACGAGGGAGCAGAGACTGCTCCT GTGCAGAATGAAGGCCCTGGGGTGGAGATGCTGCTGTCCTCAGAA

ENSG00000141456 AC091153.1 17:4574680- 4607632 1097 CATGGCAGTCACCATCTTCTGAGGACAGCAGCATCTCCACCCCAGGGCCTTCATT

CTGCACAGGAGCAGTCTCTGCTCCCTCGTACAGGGCCCCCAGCAGGGTGTGCA GTGAGGCCAGCAGACTGTGTAGCTCCTGCTCCCAGCTCTCGG

ENSG00000141456 AC091153.1 17:4574680- 4607632 1091 CATGGCAGTCACCATCTTCTGAGGACAGCAGCATCTCCACCCCAGGGCCTTCATT

CTGCACAGGAGCAGTCTCTGCTCCCTCGTACAGGGCCCCCAGCAGGGTGTGCA GTGAGGCCAGCAGACTGTGTAGCTCCTGCTCCCAGCTCTCGGT

ENSG00000141456 AC091153.1 17:4574680- 4607632

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Wet lab validation

PCR composition

  • total volume: 5 μl
  • instrument: Bio-Rad CFX384 (with CFX Automation System)
  • mastermix: Bio-Rad SsoAdvanced SYBR
  • primer conc: 250 nM each

PCR program

  • default cycling protocol for SsoAdvanced SYBR (Ta=60°C)

Samples

  • cDNA: 25 ng (total RNA equivalents – Agilent Universal human reference

RNA = MAQC A)

  • gDNA: 2.5 ng (Roche)
  • NTC: water + carrier (5 ng/μl yeast transfer RNA)
  • synthetic template (pooled 60-mers in concentration range: 20 M – 20

copies)

setup

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Wet lab validation

  • lab validation of 103 053 assays

(human, mouse and rat coding genes)

  • 1 456 142 reactions
  • 3 822 PCR plates (384-well)
  • equivalent to 15 288 PCR plates (96-well)

some numbers

305 m

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Amplification efficiency

  • initial publication: Vermeulen et al., Nucleic Acids Research, 2009
  • Biogazelle approach (easy & cost effective)
  • 60-mer
  • no modifications, standard desalted
  • 7 points dilution series: 20 000 000 > 20 molecules
  • equivalent to full length double stranded template
  • limitation: behavior of first cycles amplifying from cDNA are not

evaluated

synthetic templates

30 nt 3’ 30 nt 5’

ds template ss oligo r2<0.99 1 1 median E 2.00 2.01 average E 2.00 2.01 count E <> [1.90-2.10] 1 3 paired t-test p-value 0.14

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Amplification efficiency

distribution (n = 50 133)

89%

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Amplification efficiency

distribution (n = 50 133)

89% redesign redesign

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Specificity

amplicon sizing ( + melt analysis for SYBR assays)

  • limited sensitivity for detecting low level non-specific

coamplification

  • failure to observe non-specific amplification of sequences

with similar size and/or Tm e.g. expressed pseudogenes or homologous genes next level of specificity assessment

  • in silico specificity predictions by BiSearch
  • massively parallel sequencing of pooled PCR products
  • average coverage > 1000-fold 

lab specificity > 99.9%

  • 50 – 200 times more sensitive than size analysis and Sanger

sequencing

NGS for increased sensitivity

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Specificity

most assays are 100% on-target

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Specificity

0% 25% 50% 75% 100% % on-target

2/3 of non-specific assays may go unnoticed without NGS

0% 20% 40% 60%

0 < x < 0.1 0.1 < x < 0.2 0.2 < x < 0.3 0.3 < x < 0.4 0.4 < x < 0.5 0.5 < x < 0.6 0.6 < x < 0.7 0.7 < x < 0.8 0.8 < x < 0.9 0.9 < x < 1

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Specificity

perfect 60 293 86% acceptable (<10% non-specific) 5 866 8% predicted non-specificity (no specific design found) 1 204 2% failing specificity QC criteria 2 467 4%

the power of in silico verification

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Online poll

How do you validate your assay’s specificity? a) Melt curves b) Size analysis (gel or capillary) c) Restriction digestion with gel analysis d) Sequencing of PCR products

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Online poll

Do you know the MIQE initiative? a) Yes b) No

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MIQE compliant PrimePCR assay

validation data sheet for human, mouse & rat

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  • building on the confidence validated on > 100,000 assays
  • skip wet lab validation
  • 9 organisms
  • assays for SYBR or with probe

27,155 19,762 19,310 25,006 15,307 20,184 19,049 6,572 21,360

PrimePCR assay for 9 extra organisms

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Screening vs targeted

Transcriptome

  • microarray or RNA-seq
  • hypothesis generating
  • few samples (high cost per sample)

Gene panels

  • qPCR (high sensitivity, low cost)
  • selection of genes based on pathway, cell type or disease
  • convenient balance between blind screening and targeted

analysis Individual gene(s)

  • qPCR
  • high flexibility, customization
  • low cost per sample
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% ribo-depletion RNAseq - detection poly-A RNAseq - detection qPCR - detection ribo-depletion RNAseq - quantification poly-A RNAseq - quantification qPCR - quantification

Saturation analysis

MAQC A - % of PrimePCR

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Questions?

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Thank you for your attention!