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High throughput qPCR: tips for analysis across multiple plates
Dr. Mikael Kubista's presentation discusses the challenges of high-throughput qPCR, including issues with fitting reactions into a single plate, data analysis reliability, and the costs of necessary controls. It highlights the importance of interplate calibrators to manage variability and emphasizes the necessity for accurate RNA integrity testing. The document also outlines potential methodologies and tools for optimizing gene expression profiling in complex biological samples.
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High throughput qPCR: tips for analysis across multiple plates
- 1. Dr Mikael Kubista Founderand CEO, TATAA Biocenter Presented by: High throughput qPCR: tips for analysis across multiple plates
- 3. qPCR by salespeople is VERY SIMPLE! Compare to reference sample! Compare to reference gene!
- 4. Challenges in highthroughput expression profiling • The number of reactions does not fit into a single plate • Number of target molecules per aliquot varies due to low numbers • Testing for genomic DNA background by performing RT- controls are prohibitively expensive or not feasible • Testing RNA integrity using microfluidics is prohibitively expensive • Data analysis using methods such as t-test is unreliable due to multiple testing ambiguity
- 5. Challenges in highthroughput expression profiling • The number of reactions does not fit into a single plate • Number of target molecules per aliquot varies due to low numbers • Testing for genomic DNA background by performing RT- controls are prohibitively expensive or not feasible • Testing RNA integrity using microfluidics is prohibitively expensive • Data analysis using methods such as t-test is unreliable due to multiple testing ambiguity
- 6. Select threshold 0 5 10 15 20 25 30 35 1 35 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Fluorescence Cycles
- 7. Select threshold 0 5 10 15 20 25 30 35 1 35 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Fluorescence Cycles Cq: 25.5 29 32.5 DCq= 29 – 25.5 = 3.5 DCq= 32.5 – 25.5 = 7 DCq= 32.5 – 29 = 3.5
- 8. Select threshold 0 5 10 15 20 25 30 35 1 35 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 Fluorescence Cycles Cq: 27 30.5 34 DCq= 30.5 – 27 = 3.5 DCq= 34 – 27 = 7 DCq= 34 – 30.5 = 3.5 Cq’s depend on threshold. DCq’s don’t.
- 9. GOI RG S1 2422 S2 31 23 Compare relative expression in two samples
- 10. Calculate DCq = 24-31= 22-23 = 24-22 = 31-23 There are two DCq’s! GOI RG DCq S1 24 22 2 S2 31 23 8 DCq -7 -1
- 11. Calculate DDCq = (24-31)– (22-23) = (24-22) – (31-23) GOI RG DCq S1 24 22 2 DDCq S2 31 23 8 -6 DCq -7 -1 DDCq -6
- 12. Calculate Relative Quantity GOIRG DCq S1 24 22 2 DDCq S2 31 23 8 -6 DCq -7 -1 RQ 64 DDCq -6 64 CqDD 2
- 13. Breaking up alarge study into several plates 16 × 24 = 384 reactions 384/96 = 4 plates
- 14. ”All Samples” Plate 1Plate 2 Plate 3 Plate 4
- 15. Samples held together(”All Samples” layout) = ((24+1)-(31+1)) – ((22+2)-(23+2)) GOI RG DCq S1 25=24+1 24=22+2 1 DDCq S2 32=31+1 25=23+2 7 -6 DCq -7 -1 RQ 64 DDCq -6 64 In real the offsets are not known. Here we assign arbitrary numbers to trace there impact only.
- 16. ”All Genes” Plate 1 Plate2 Plate 3 Plate 4
- 17. Genes held together(”All Genes” layout) = ((24+1)-(31+2)) – ((22+1)-(23+2)) GOI RG DCq S1 25=24+1 23=22+1 2 DDCq S2 33=31+2 25=23+2 8 -6 DCq -8 -2 RQ 64 DDCq -6 64
- 18. ”Mixed layout” Plate 1Plate 2 Plate 3 Plate 4
- 19. “Mixed layout” withtwo genes and two samples = ((24+2)-(31+1)) – ((22+1)-(23+2)) GOI RG DCq S1 26=24+2 23=22+1 3 DDCq S2 32=31+1 25=23+2 7 -4 DCq -6 -2 RQ 16 DDCq -4 16
- 20. The Inter-Plate Calibrator(IPC) GOI RG IPC 20 21 = (((24+2)-(20+2))-((31+1)-(20+1))) – (((22+1)-(21+1))-((23+2)-(21+2))) GOI RG S1 26=24+2 23=22+1 S2 32=31+1 25=23+2 IPC_A 21=20+1 22=21+1 IPC_B 22=20+2 23=21+2 DCq -6 -2 DDCq -7 -1 RQ DDDCq -6 64
- 21. Relative quantification onmultiple plates When expression is normalized to reference genes and samples are compared (DDCq) multiple runs can be merged for common analysis without correction if either: • All genes for all sample are measured together in the same plate (“All genes”) or • All samples for all genes are measured together in the same plate (“All samples”)
- 22. Interplate calibrator • Interplatecalibrators are used to compensate for variations between runs due to instrument settings (base-line correction and threshold settings) • Interplate variation depends on the instrument channel used, but is virtually independent of assay. It is highly discouraged to perform independent inter-plate calibrations per assay! • The Cq of an interplate calibrator must be measured with very high accuracy, else interplate calibration may add more variance to the data than the systematic variation it removes. • Interplate calibrators should be: – Very stable assays – Uncomplicated, purified template at fairly high concentration (20 <Cq < 25) – Run in replicates (minimum triplicates) – The Interplate calibrator shall be stable over time www.tataa.com/products-page/quality-assessment/tataa-interplate-calibrator/
- 23. Challenges in highthroughput expression profiling • The number of reactions does not fit into a single plate • Number of target molecules per aliquot varies due to low numbers • Testing for genomic DNA background by performing RT- controls are prohibitively expensive or not feasible • Testing RNA integrity using microfluidics is prohibitively expensive • Data analysis using methods such as t-test is unreliable due to multiple testing ambiguity
- 24. How many preamplificationcycles? Average number of targets per reaction container should be 35 for accurate analysis. If we assay for 100 targets the original sample should have 3500 of each.
- 25.
- 26. FACS Aspiration Capture Cellulyser No losses! Freezes profile! Cell’s expression changes inmatter of seconds in response to environmental changes
- 27. FACS Aspiration Capture Cellulyser GrandScript Efficient RT Anders Ståhlberg,Mikael Kubista, and Michael Pfaffl Comparison of Reverse Transcriptases in Gene Expression Analysis Clinical Chemistry 50, No. 9, 2004
- 28.
- 30. Highly optimized assays Dynamic range Sensitivity Specificity gBlocks® Gene Fragments
- 31. FACS Aspiration Capture Cellulyser GrandScript GrandMaster PreAmp Highthroughput qPCR GenEx iReport
- 32. Challenges in highthroughput expression profiling • The number of reactions does not fit into a single plate • Number of target molecules per aliquot varies due to low numbers • Testing for genomic DNA background by performing RT- controls are prohibitively expensive or not feasible • Testing RNA integrity using microfluidics is prohibitively expensive • Data analysis using methods such as t-test is unreliable due to multiple testing ambiguity
- 33. Compensate for gDNAbackground: the ValidPrime + gDNA specific assay (ValidPrime) + Reference gDNA Original data gene 1 gene 2 gene 3 gene 4 ValidPrime sample 1 20.1 31.1 22.1 28.2 32.5 sample 2 20.5 31.2 22.5 28.9 33.2 sample 3 21 31.1 22.9 30.2 32.3 sample 4 23.1 31.8 22.5 32.3 34.2 sample 5 23.5 30.8 22.8 32 33.1 gDNA standard 25.8 26.9 26.7 26 27 Laurell et al., Nucleic Acids Research, 2012, 1–10; Drug Discovery World (2011) ValidPrime gDNA GOI gDNA ValidPrime Sample GOI RT CqCqCqCq More accurate and more cost effective than RT(-) controls •15% of human genes have pseudo genes • Pseudo genes usually lack introns • Pseudo genes are often present in multiple copies Calibrated against NIST SRM2372 Human genomic DNA
- 34. Challenges in highthroughput expression profiling • The number of reactions does not fit into a single plate • Number of target molecules per aliquot varies due to low numbers • Testing for genomic DNA background by performing RT- controls are prohibitively expensive or not feasible • Testing RNA integrity using microfluidics is prohibitively expensive • Data analysis using methods such as t-test is unreliable due to multiple testing ambiguity
- 35. Traditionally RNA integrityis tested by electrophoresis RNA extracted from liver tissue. Left at room temperature and analyzed (Bioanalyzer/Experion/Fragment Analyzer) 0min -------------------------------------------------->120min Works quite well, but way too expensive for high throughput applications!
- 36. Molecular approach: DAmpand the ERR Differential amplicons (DAmp) Target Short (S) Medium (M) Physical/chemical Degradation Björkman et al., Differential amplicons (ΔAmp)—a new molecular method to assess RNA integrity. Biomolecular Detection and Quantification 2015. Enzymatic Degradation Endogeneous Rnase Resistant (ERR) marker Stability marker Not detected by electrophoresis
- 37. RNA degradation byformalin detected with DAmp 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 1 4 0 1 6 0 1 8 0 2 0 0 2 2 0 2 4 0 0 2 4 6 8 1 0 0 2 4 6 8 1 0 F o rm a lin e x p o s u re (m in ) DDAmpX-Y RQI L -S E xp e rio n system
- 38. RNA degraded bynucleases detected by ERR 0 2 0 4 0 6 0 8 0 1 0 0 1 2 0 0 2 4 6 8 1 0 0 2 4 6 8 1 0 C T im e in R T (m in ) DDAmpERR RQI R Q I (P ie c e s ) P P IA - E R R m a rk e r (P ie c e s ) R Q I (P o w d e r) P P IA - E R R m a rk e r (P o w d e r)
- 39. Challenges in highthroughput expression profiling • The number of reactions does not fit into a single plate • Number of target molecules per aliquot varies due to low numbers • Testing for genomic DNA background by performing RT- controls are prohibitively expensive or not feasible • Testing RNA integrity using microfluidics is prohibitively expensive • Data analysis using methods such as t-test is unreliable due to multiple testing ambiguity
- 40. Activation of astrocytesin response to trauma Astrocytes (principal role in repair) Single cell expression profiling FACS sorted astrocytes from mouse brain Response to trauma (focal cerebral ischemia)
- 41. Comparing genes oneby one Gene P-Value Aqp9 1.00E-08 gene 1.00E-08 gene 1.00E-08 gene 1.00E-08 Grin2a 1.00E-08 Grin2d 1.00E-08 Grin3 1.00E-08 Kcna3 1.00E-08 Snap 1.00E-08 Gluk1 1.26E-07 Pdgfr 1.79E-06 Glun3a 2.78E-06 Cspg4 4.13E-06 Vim 8.18E-06 Kcnk2 3.57E-05 Gfap 9.98E-05 Gluk3 0.000416 Grin1 0.000867 S100b 0.003769 Kcnj10 0.004225 Gria1 0.012991 Kcna5 0.025924 Grin2b 0.030311 Approach suffers from multiple testing ambiguity and low power and does not exploit correlation
- 42. 3D PCA classificationof single astrocytes – all genes
- 43. QC products fromTATAA Gene panels • Truly Stem Validated primers for 13 markers for stem cell differentiation • CTC GrandPerformance panel for circulating tumor cells CelluLyser Lysis and cDNA Synthesis Kit • CelluLyser For single cell lysis Quality control • ValidPrime to test the quality of analyzed mRNA in complex samples • Exogenous controls DNA and RNA spikes to estimate yields and test for inhibition • InterPlate calibrator kit to remove variation between runs • DAMP and ERR to test RNA integrity Software • GenEx for qPCR data mining
- 44. Training modules fromTATAA 1 day qPCR for miRNA analysis 1 day Sample preparation and quality control 1 day Genotyping with qPCR 1 day Immuno-qPCR 1 day Multiplex PCR 1 day Quality control of qPCR in MDx 1 day CEN/ISO guidelines for the preanalytical process in MDx 2 days Hands-on qPCR 2 days Single cell analysis 2 days Experimental design and statistical data analysis 2 days Digital PCR – Applications and analyiss 2 days NGS – Library construction and quality control 3 days Experimental design and statistical data analysis 3 days Hands-on qPCR Specifications for pre-examination processes • FFPE tissue — RNA • FFPE tissue — DNA • FFPE tissue — Extracted proteins • Snap frozen tissue — RNA • Snap frozen tissue — Extracted proteins • Urine, plasma, serum: Metabolites • Blood — Circulating cell free DNA • Blood — Genomic DNA • Blood — Cellular RNA http://www.tataa.com/courses/
- 45. gene expression PrimeTime® qPCRAssays • Primer and probe sequences provided • Free design tools • Available predesigned for human, mouse, and rat www.idtdna.com/primetime
- 47.