Digital PCR for copy number analysis Jo Vandesompele, PhD Biogazelle CSO, UGent professor EMBL Advanced Course Digital PCR, Heidelberg, Germany October 22, 2015
Acknowledgements (A-Z) Lieven Clement, Els Goetghebeur, Bart Jacobs, Peter Pipelers, Olivier Thas, Matthijs Vynck Steve Lefever, Björn Menten, Katrien Vanderheyden, Kimberly Verniers, Nurten Yigit Ariane De Ganck, Nele Nijs Xavier Alba, Jen Berman, Frank Bizouarn, Viresh Pattel, Svilen Tzonev
Agenda • introduction • experiment design • power analysis • sensitivity vs. inhibition vs. availability of input • CNV use cases • advanced data-analysis • droplet classification • combining replicates & multigene normalization • tips & tricks
Full text papers available on Biogazelle website http://www.biogazelle.com > Knowledge center > publications
Biogazelle blog on dPCR vs. qPCR http://www.biogazelle.com/knowledge-center/blog
Digital PCR is emerging as gold standard method for CNV • Biogazelle is reference lab for Bio-Rad’s QX100/200 droplet digital PCR technology • Scalable precision and relative sensitivity (needle in the haystack) (“more is better”) • High accuracy (without calibration) • Excels in quantification of small differences and rare events
Application domains • in principle any nucleic acid quantification study (cost/throughput) • focus on those areas where dPCR excels • small differences • CNV analysis (high copy number range, transgene stability testing, cell-free DNA (NIPT, oncogene amplification) • gene expression (microRNA, splice variants) • rare events • pathogens (e.g. viral load in body fluid such as urine) • mutant cancer cells (tissue, circulating cells or cell-free DNA) • circulating RNA biomarker (cell-free RNA)
dMIQE guidelines for digital PCR • Clinical Chemistry, 2013 • co-authored by Biogazelle founders
dMIQE guidelines have 3 goals 1. Design, perform, and report dPCR experiments that have greater scientific integrity 2. Facilitate replication of published experiments adhering to the guidelines 3. Provide critical information that allows reviewers and editors to assess the technical quality of manuscripts
Power analysis is a crucial aspect of experiment design • Ensure proper setup to find a true difference with statistical significance • Often ignored • Limitations of dPCR power analysis in literature • no or few details on the methods • no incorporation of replicate variability (instead, reactions are (naively) pooled over replicates) • not taking into account of all variables (e.g. replicates, fraction of negative droplets, …) • use of meta-analysis methods (instead of ad hoc statistical method)
Digital PCR power analysis is a function of • true difference you want to see • number of partitions • fraction of negative partitions • number of replicates • alpha value (type I error, false positive rate, 5%) • 97% power to detect a 10% difference in copy number using 3 replicated reactions of each 14,000 partitions with 30% negative partitions • 53% for a 5% difference
Interactive tool to determine power in digital PCR experiments • power for a given condition power ~ number of replicates • ~ fraction of negative partitions ~ number of partitions ~ copy number difference • optimal negative fraction (for max power) ~ copy number difference • Vynck et al., in preparation http://vandesompelelab.ugent.be/power/
Power in function of fraction of negative partitions • difference of 10% • 14,000 partitions • 3 replicates http://vandesompelelab.ugent.be/power/
Power in function of number of replicates • difference of 10% • 14,000 partitions • 95% negatives http://vandesompelelab.ugent.be/power/
Power in function of number of partitions • difference of 15% • 1 replicate • 30% negatives http://vandesompelelab.ugent.be/power/
What is determining the sensitivity of dPCR? • Both qPCR and dPCR can detect 1 molecule (precision is higher for dPCR at low concentrations) • Input amount of nucleic acids • more cDNA to detect a low abundant transcript (e.g. long non-coding RNA) • more circulating cell-free DNA to detect a low frequent mutation intended&sensitivity ng&of&DNA&needed assuming at least 5 positive droplets are needed for confident calling, a 10.000% 0.229 perfectly discriminating assay 1.000% 2.286 between wild type and mutant, 0.100% 22.857 14,000 recovered droplets from 20,000 formed 0.010% 228.571 0.001% 2285.714
Large dynamic range, high precision and accuracy • Correlation between expected and measured concentrations on a gDNA dilution series (ranging from 100 000 copies/reaction to 5 copies/reaction) (320 ng – 16 pg DNA) 6 log10 (measured concentration) 5 copies/ddPCR reaction 4 y = 0.9781x + 0.0695 R ² = 0.99877 3 2 1 0 0 1 2 3 4 5 6 log10 (expected concentration) copies/ddPCR reaction
Unpurified digested genomic DNA inhibits ddPCR if > 30 v/v% 5.5 log10 (measured concentration) 30 25 5.0 20 15 copies/reaction 10 4.5 7.5 y = 1.143x + 3.224 5 R ² = 0.990 4.0 3.5 3.0 0.6 0.8 1.0 1.2 1.4 1.6 1.8 log10 (gDNA concentration) v/v%
cDNA inhibits ddPCR if > 25 v/v% • Influence of cDNA input amounts (ranging from 5 to 45 v/v%) on measured concentration 6.0 y = 0.921x + 3.306 log10 (measured concentration) R ² = 0.999 5.0 25 copies/reaction 20 4.0 15 10 5 3.0 2.0 1.0 0.0 0.6 0.8 1.0 1.2 1.4 1.6 1.8 log10 (cDNA concentration) v/v%
Case 1 – genetic characterization of cell banks • Therapeutic protein production in biopharmaceutical industry • Transgene copy number has influence on expression level • Need for a cell line that is genetically stable throughout the biopharmaceutical manufacturing process • Genetic characterization of Master Cell Bank (MCB) and Working Cell Bank (WCB) • Traditionally by Southern blot analysis - laborious and time consuming • > qPCR method for transgene copy number determination
Case 1 – struggling with qPCR • Transgene copy number analysis • Limited accuracy at higher copy numbers • Compensated by including more PCR replicates and calibrators (D’haene et al., Methods, 2010) • Pilot study: synthetic CN series (1-10 copies) measured with 16 qPCR replicates • Resampling to investigate impact of increased number of replicates & calibrator samples • Conclusion • 8 qPCR replicates and 3 calibrator samples are required for CN analysis at increased copy numbers • Still relatively large deviation from expected copy number in proof of concept study
Case 1 – proof of concept 1 • Copy numbers from duplex assay – gene 1 (performed in triplicate) Copy number S1 S2 S3 S4 S5 S6 S7 S8 expected CN: 0 0 1 2 3 4 5 5 • observed normalized copy numbers tightly agree with expected integer copies
Case 1 – proof of concept 2 • Copy numbers from duplex assay – gene 2 (performed in triplicate) Copy number S1 S2 S3 S4 S5 S6 S7 expected CN: 1 1 4 4 3 0 1 • deviation from expected integer copies for samples 3 and 4
Case 1 – getting integer copy numbers with ddPCR • Copy numbers from duplex assay – gene 2 (XbaI restriction digest) Restriction digest Copy number S1 S2 S3 S4 S5 S6 S7 expected CN: 1 1 4 4 3 0 1 • Restriction digest is required to properly count linked loci (here: tandem repeats)
Case 1 - ddPCR versus qPCR • ddPCR has higher 4.00# accuracy than qPCR • 3.1 x lower standard 3.00# deviation on log2 copy numbers ddPCR • ddPCR% 2.3 x smaller fold changes 2.00# between max and min copy number 1.00# • Less reactions required for ddPCR than for qPCR • 0.00# ddCPR requires no 0.00# 1.00# 2.00# 3.00# 4.00# 5.00# qPCR% external standard or qPCR calibrator sample with known copy number
characterization of cell banks Case 1 – ddPCR based genetic Copy number • 01_WCB 02_WCB • • • Copy number 03_WCB 04_WCB Expected CN: 5 Duplex assay – gene 1 24 samples – WCB 05_WCB 06_WCB 07_WCB 08_WCB 09_WCB 10_WCB 11_WCB 12_WCB 13_WCB 14_WCB 15_WCB 16_WCB 17_WCB 18_WCB 19_WCB 20_WCB 21_WCB 22_WCB 23_WCB 24_WCB Deviation 0.05 0.15 0.25 • 0.1 0.2 0.3 0 01_WCB • • 01_WCB CN Deviation from expected 02_WCB 02_WCB 03_WCB 03_WCB Standard deviation: 0.078 Average: 0.11 04_WCB 04_WCB 05_WCB 05_WCB 06_WCB 06_WCB 07_WCB 07_WCB 08_WCB 08_WCB 09_WCB 09_WCB 10_WCB 10_WCB 1_WCB 11_WCB 12_WCB 12_WCB 13_WCB 13_WCB 14_WCB 14_WCB 15_WCB 15_WCB 16_WCB 16_WCB 17_WCB 17_WCB 18_WCB 18_WCB 19_WCB 19_WCB 20_WCB 20_WCB 21_WCB 21_WCB 22_WCB 22_WCB 23_WCB 23_WCB 24_WCB 24_WCB
Case 1 – ddPCR based genetic characterization of cell banks • ddPCR is very well suited for transgene copy number determination • Genetic characterization of cell banks for therapeutic protein production • Transgene copy number analysis in genetically modified (GM) crop research • Transgenic animal models • Remark: qPCR is the standard approach in biopharmaceutical industry – will take some time to adopt ddPCR
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