Pre-amplification critically analysed Jo Vandesompele professor, - PowerPoint PPT Presentation

Pre-amplification critically analysed Jo Vandesompele professor, Ghent University co-founder and CEO, Biogazelle Advanced Methods in RNA quantification (London, UK) May 21, 2009

outline  introduction  450 miRNA pre-amplification  Mestdagh et al., Nucleic Acids Research, 2008  [Mestdagh et al., Genome Biology, in press]  whole mRNAome pre-amplification  prognostic gene signature in cancer patients  Vermeulen et al., The Lancet Oncology, accepted

pre-amplification – the one and only quality criterion  preservation of differential expression (fold changes) before (B) and after (A) sample pre-amplification  [no introduction of bias] (G1S1) B /(G1S2) B = (G1S1) A /(G1S2) A G1 B /G2 B < > G1 A /G2 A gene G, sample S, before B, after A

pre-amplification – the scene  pre: before actual qPCR  amplification: make large amounts of RNA/(c)DNA from limited input  single cell – picograms – nanograms >> micrograms  transcriptome wide  all long RNA molecules  advantages o no prior knowledge of target genes is needed o study can grow  disadvantages o somewhat more expensive  focused pre-amplification  predefined set of sequences  advantages o fast and simple  disadvantages o all targets need to be known in advance

pre-amplification – the players  transcriptome wide  Eberwine method o T7-RNA polymerase based in vitro transcription > antisense RNA o home brew protocols  SMART method (Clontech) o template switch mechanism sense RNA o T7-RNA in vitro transcription  Phi29 based o rolling circle amplification – strand displacement o cDNA  SPIA technology (NuGEN) o hybrid RNA/DNA SPIA primer o cDNA  focused pre-amplification  limited cycle PCR (10-14 cycles)

Ebermine method 3’ 5’ AAAAA 3’ TTTTT 5’ ste 1 streng cDNA-synthese (met T7-oligo(dT) primer) 5’ 3’ AAAAA 3’ 5’ TTTTT ste 1 ronde 3’ AAAAA 5’ de 2 streng cDNA-synthese TTTTT 5’ 3’ antisense RNA-amplificatie 3’ 5’ UUUUU (T7 RNA polymerase) 5’ NNNNNN 3’ 5’ 3’ NNNNNN ste 1 streng cDNA-synthese 5’ (met random hexameren) 3’ UUUUU de 2 ronde 3’ de 5’ AAAAA 2 streng cDNA-synthese (met T7-oligo(dT) primer) 5’ TTTTT antisense RNA-amplificatie 3’ UUUUU 5’ (T7 RNA polymerase)

SMART

SPIA

microRNA pre-amplification  stem-loop megaplex reverse transcription using 20 ng total RNA  limited-cycle pre-amplification (14)  qPCR profiling 450 miRNAs and controls  higher sensitivity  minimal amplification bias (Mestdagh et al., Nucleic Acids Research)

minimal pre-amplification bias ∆∆Cq (|∆Cq NP - ∆Cq P |) NBL-S, IMR-32 ∆∆Cq (|∆Cq NP - ∆Cq P |) NBL-S, IMR-32 8 2.5 8 7 7 2 6 6 5 5 1.5 4 4 1 3 3 2 2 0.5 1 1 0 0 0 10 15 20 25 30 35 10 15 20 25 30 10 15 20 25 30 35 Average Cq NP (NBL-S, IMR-32) Average Cq NP (NBL-S, IMR-32) 0 0 -0.2 -0.1 -0.4 Average ∆∆Cq Average ∆∆Cq -0.2 -0.6 -0.3 -0.8 -0.4 -1 -0.5 -1.2 -1.4 -0.6

single cell profiling 30 30 30 30 A B 25 25 25 25 Cq-value Cq-value 20 20 20 20 15 15 15 15 miR-18a miR-92 10 10 10 10 R 2 = 0.975 R 2 = 0.998 5 5 5 5 0 0 0 0 1 2 4 8 16 32 64 128 0 2 4 6 8 10 12 14 0 2 4 6 8 0 2 4 6 8 10 12 14 total cell number total RNA input (pg) 35 35 35 35 30 30 30 30 25 25 25 25 Cq-value Cq-value 20 20 20 20 15 15 15 15 miR-20b miR-19a 10 10 10 10 R 2 = 0.993 R 2 = 0.996 5 5 5 5 0 0 0 0 1 2 4 8 16 32 64 128 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 0 2 4 6 8 total cell number total RNA input (pg)

Mestdagh et al., Nucleic Acids Research, 2008

outline  background research & goals  neuroblastoma  prognostic marker selection  study design and workflow  RNA quality control  sample pre-amplification  normalization  data-analysis and results

biomarker signature based stratification

biomarker signature based stratification

aim  development and validation of a robust prognostic gene signature for neuroblastoma using real-time qPCR  identifying patients with  increased risk in the current low risk and high risk group  good molecular signature in the current high risk group  better choice of risk-related therapy

neuroblastoma  most frequent extra-cranial solid tumor in children  originates from primitive (immature) sympathetic nervous system cells  1:100,000 children (< 15 years)  20 cases/year Belgium | 700 cases/year USA  15% of childhood cancer deaths  prognosis is dependent on  tumor stage (localized vs. metastatic disease)  age at diagnosis (< or > 1 year)  genetic defects: amplification MYCN, ploidy, loss of 1p, gain of 17q

prognostic classification  misclassifications resulting in overtreatment or undertreatment  need for additional tumor-specific prognostic markers  current microarray gene expression studies  data overfitting  unstable gene lists  lack of overlap  biological & technical noise  much more genes than samples  probe annotation / platform  different risk definition  different data processing and analysis

study workflow • meta-analysis of 7 published microarray gene selection of a top ranking expression studies list of 59 prognostic • literature screening of almost 800 abstracts from markers single-gene studies RNA quality control 423 • two PCR-based assays samples • capillary gel electrophoresis (Experion) sample pre-amplification (WT-Ovation) analysis of 366 primary untreated neuroblastoma tumours using real-time qPCR • Prediction Analysis of Microarrays data-analysis • Kaplan-Meier • Cox proportional hazards

towards real-time PCR signature profiling  100 ng total RNA  30 ng quality control  10 ng unbiased amplification WT-Ovation (NuGEN)  PCR assay design and validation  sensitivity, specificity and efficiency RTPrimerDB (Pattyn et al., 2006, NAR; Lefever et al, 2009, NAR)  absolute standards  real-time PCR using 384-well format  sample maximization strategy (Hellemans et al., Genome Biology, 2007)  366 tumors and 1 gene/plate

WT-Ovation reproducibility 35,00 mean of 5, 15 and 50 ng of total RNA amplified 30,00 25,00 mean Cq (n=3) Stratagene cell line A cell line B cell line C 20,00 15,00 10,00 ACTB RPL13A 18S YWHAZ B2M GAPDH UBC HPRT1 SDHA HMBS genes

WT-Ovation – no amplification bias 100 90 80 cumulative distribution 70 60 50 40 30 20 10 0 0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70 0,80 0,90 1,00 bias (Cq) median bias = 0.36, 90%tile bias = 0.61

WT-Ovation – no amplification bias 100 90  no need for DNase treatment  no need for cleanup of amplified products 80 cumulative distribution 70 60 50 40 30 20 10 0 0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70 0,80 0,90 1,00 bias (Cq) median bias = 0.36, 90%tile bias = 0.61

preservation of differential expression

qPCR reproducibility  within a 384-well plate: 4 x 96 replicates 10,000 # 1,000 # 100 # 10 # 40 35 30 25 20 15 10 5 0 0 50 100 150 200 250 300 350 400

qPCR reproducibility  between two identical 384-well plates  maximum Δ Cq: 0.45 40 35 30 25 20 15 15 20 25 30 35 40

absolute standards FP stuffer RCRP  synthetic control  55 nucleotides  PAGE purification  blocking group  5 points dilution series: 15 molecules > 150.000 molecules

absolute standards  reproducibility across master mixes (5) and instruments (2) 35 30 25 MM1 20 MM2 MM3 MM4 15 MM5 10 5 0 1000000 100000 10000 1000 100 10

absolute standards cross lab comparison 366 samples 5 standards (triplicates) 5 reference genes + 5 other genes

absolute standards cross lab comparison  5 standards (triplicates) Cq qPCR instrument 1, mastermix 1 36 34 32 average Δ Cq standards 30 28 correction Cq samples 26 24 22 20 18 16 16 18 20 22 24 26 28 30 32 34 36 Cq qPCR instrument 2, mastermix 2

absolute standards cross lab comparison  ARHGEF7 gene  366 samples  use of 5 standards (triplicates) for correction Cq qPCR instrument 1, mastermix 1 36 34 32 30 28 26 24 22 20 18 16 16 18 20 22 24 26 28 30 32 34 36 Cq qPCR instrument 2, mastermix 2

rigorous control of RNA quality 423 primary untreated NB (100 ng total RNA) 5’ - 3’ assay (HPRT1): SPUD assay (Nolan et al, 2006): evaluation of mRNA integrity detection of inhibitors 30 ng Computed gel analysis (Experion, Biorad): evaluation of total RNA quality 366 RNA samples

impact of RNA quality on expression stability  differences in reference gene ranking between intact and degraded RNA (Perez-Novo et al., Biotechniques, 2005)

RNA quality parameters 80 50 80 40 60 60 frequency 30 40 40 20 20 20 10 0 0 0 0 5 10 15 20 5 10 15 20 25 2 4 6 8 10 RQI 5’ - 3’ dCq AluSq Cq