High throughput qPCR: tips for analysis across multiple plates
Transcript of High throughput qPCR: tips for analysis across multiple plates
Dr Mikael KubistaFounder and CEO, TATAA Biocenter
Presented by:
High throughput qPCR: tips for analysis across multiple plates
qPCR by sales people is VERY SIMPLE!
Compare to reference sample! Compare to reference gene!
Challenges in high throughput 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
Challenges in high throughput 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
Select threshold
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Fluorescence
Cycles
Select threshold
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Fluorescence
Cycles
Cq: 25.5 29 32.5
ΔCq= 29 – 25.5 = 3.5
ΔCq= 32.5 – 25.5 = 7
ΔCq= 32.5 – 29 = 3.5
Select threshold
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Fluorescence
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Cq: 27 30.5 34
ΔCq= 30.5 – 27 = 3.5
ΔCq= 34 – 27 = 7
ΔCq= 34 – 30.5 = 3.5
Cq’s depend on threshold. ΔCq’s don’t.
GOI RGS1 24 22S2 31 23
Compare relative expression in two samples
Calculate ΔCq
= 24-31 = 22-23
= 24-22
= 31-23
There are two ΔCq’s!
GOI RG ΔCqS1 24 22 2S2 31 23 8ΔCq -‐7 -‐1
Calculate ΔΔCq
= (24-31) – (22-23)
= (24-22) – (31-23)
GOI RG ΔCqS1 24 22 2 ΔΔCqS2 31 23 8 -‐6ΔCq -‐7 -‐1
ΔΔCq -‐6
Calculate Relative Quantity
GOI RG ΔCqS1 24 22 2 ΔΔCqS2 31 23 8 -‐6ΔCq -‐7 -‐1 RQ 64
ΔΔCq -‐6 64 CqΔΔ−= 2
Breaking up a large study into several plates
16 × 24 = 384 reactions
384/96 = 4 plates
”All Samples”
Plate 1 Plate 2 Plate 3 Plate 4
Samples held together (”All Samples” layout)
offsetPlate 1 +1Plate 2 +2
= ((24+1)-(31+1)) – ((22+2)-(23+2))
GOI RG ΔCqS1 25=24+1 24=22+2 1 ΔΔCqS2 32=31+1 25=23+2 7 -‐6ΔCq -‐7 -‐1 RQ 64
ΔΔCq -‐6 64
In real the offsets are not known.Here we assign arbitrary numbers to trace there impact only.
”All Genes”
Plate 1
Plate 2
Plate 3
Plate 4
Genes held together (”All Genes” layout)
offsetPlate 1 +1Plate 2 +2
= ((24+1)-(31+2)) – ((22+1)-(23+2))
GOI RG ΔCqS1 25=24+1 23=22+1 2 ΔΔCqS2 33=31+2 25=23+2 8 -‐6ΔCq -‐8 -‐2 RQ 64
ΔΔCq -‐6 64
”Mixed layout”
Plate 1 Plate 2
Plate 3 Plate 4
“Mixed layout” with two genes and two samples
offsetPlate 1 +1Plate 2 +2
= ((24+2)-(31+1)) – ((22+1)-(23+2))
GOI RG ΔCqS1 26=24+2 23=22+1 3 ΔΔCqS2 32=31+1 25=23+2 7 -‐4ΔCq -‐6 -‐2 RQ 16
ΔΔCq -‐4 16
The Inter-Plate Calibrator (IPC)
GOI RGIPC 20 21
offsetPlate 1 +1Plate 2 +2
= (((24+2)-(20+2))-((31+1)-(20+1))) – (((22+1)-(21+1))-((23+2)-(21+2)))
GOI RGS1 26=24+2 23=22+1S2 32=31+1 25=23+2
IPC_A 21=20+1 22=21+1IPC_B 22=20+2 23=21+2ΔCq -‐6 -‐2ΔΔCq -‐7 -‐1 RQ
ΔΔΔCq -‐6 64
Relative quantification on multiple plates
When expression is normalized to reference genes and samples are compared (ΔΔCq) 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”)
Interplate calibrator
• Interplate calibrators 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/
Challenges in high throughput 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
How many preamplification cycles?
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.
FACSAspirationCapture
FACSAspirationCapture
Cellulyser
No losses!
Freezes profile!
Cell’s expression changes in matter of seconds in response to environmental changes
FACSAspirationCapture
Cellulyser GrandScript
EfficientRT
Anders Ståhlberg, Mikael Kubista, and Michael PfafflComparison of Reverse Transcriptases in Gene Expression AnalysisClinical Chemistry 50, No. 9, 2004
FACSAspirationCapture
Cellulyser GrandScript GrandMasterPreAmp
Efficient Preamp
Highly optimized assays
Ø Dynamic rangeØ SensitivityØ Specificity
gBlocks® Gene Fragments
FACSAspirationCapture
Cellulyser GrandScript GrandMasterPreAmp
High throughputqPCR
GenEx iReport
Challenges in high throughput 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
Compensate for gDNA background: the ValidPrime
+ gDNA specific assay (ValidPrime)+ Reference gDNAOriginal data gene 1 gene 2 gene 3 gene 4 ValidPrime
sample 1 20.1 31.1 22.1 28.2 32.5sample 2 20.5 31.2 22.5 28.9 33.2sample 3 21 31.1 22.9 30.2 32.3sample 4 23.1 31.8 22.5 32.3 34.2sample 5 23.5 30.8 22.8 32 33.1
gDNAstandard 25.8 26.9 26.7 26 27
Laurell et al., Nucleic Acids Research, 2012, 1–10;; Drug Discovery World (2011)
( )ValidPrimegDNA
GOIgDNA
ValidPrimeSample
GOIRT 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
Challenges in high throughput 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
Traditionally RNA integrity is 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!
Molecular approach: ΔAmp and the ERR
Differential amplicons (ΔAmp)
Target
Short (S)Medium (M)
Physical/chemical Degradation
Björkman et al., Differential amplicons (ΔAmp)—a new molecular method to assess RNA integrity. BiomolecularDetectionand Quantification2015.
Enzymatic Degradation
Endogeneous RnaseResistant (ERR) marker
Stability marker
Not detected by
electrophoresis
RNA degradation by formalin detected with ΔAmp
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P P IA - E R R m a rk e r (P ie c e s )
R Q I (P ow d e r )
P P IA - E R R m a rk e r (P ow d e r )
Challenges in high throughput 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
Activation of astrocytes in response to trauma
Astrocytes(principal role in repair)
Single cell expression profiling FACS sorted astrocytes from mouse brain Response to trauma (focal cerebral ischemia)
Comparing genes one by one Gene P-‐ValueAqp9 1.00E-‐08gene 1.00E-‐08gene 1.00E-‐08gene 1.00E-‐08Grin2a 1.00E-‐08Grin2d 1.00E-‐08Grin3 1.00E-‐08Kcna3 1.00E-‐08Snap 1.00E-‐08Gluk1 1.26E-‐07Pdgfr 1.79E-‐06Glun3a 2.78E-‐06Cspg4 4.13E-‐06Vim 8.18E-‐06Kcnk2 3.57E-‐05Gfap 9.98E-‐05Gluk3 0.000416Grin1 0.000867S100b 0.003769Kcnj10 0.004225Gria1 0.012991Kcna5 0.025924Grin2b 0.030311
Approach suffers from multiple testing
ambiguity and low power and does not exploit
correlation
3D PCA classification of single astrocytes – all genes
QC products from TATAA
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
Training modules from TATAA
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 analyiss2 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/
gene expression
PrimeTime® qPCR Assays• Primer and probe sequences provided• Free design tools• Available predesigned for human, mouse, and rat
www.idtdna.com/primetime
Thank you!
Questions?