Real-Time PCR Workshop Gene Expression. Applications Absolute and Relative Quantitation
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1 Real-Time PCR Workshop Gene Expression Applications Absolute and Relative Quantitation
2 Absolute Quantitation Easy to understand the data, difficult to develop/qualify the standards Relative Quantitation Easy to make the standards, difficult to understand the data Copy # Fold Chg Applied Biosystems Sample 1 Sample Sample 1 Sample 2
3 Analysis Methods Standard Curve Heart Liver Lung Gene A Gene B Brain Comparative C t 2 - ( Ct) Relative Quantity of Expression Applied Biosystems t = 0 t = 1 h t = 6 h t = 24 h
4 Absolute Quantification To determine the number of nucleic acid molecules with accuracy. Quantification of pathogens Basic research in transcription Quality control Gene therapy DNA damage Applied Biosystems
5 Absolute Standards Want to determine the exact target number or copy number Need to validate the standard copy number. Pure plasmid or in vitro transcribed RNA containing the gene of interest. OD at 260nm Nucleic acid-binding fluorescent dye Radioactive incorporation Applied Biosystems
6 Absolute (External) Standard Target amplicon amplified for a standard curve in tubes separate from samples. The external standards will have the same amplification efficiency as the experimental samples. The standard curve must be run on every plate Applied Biosystems
7 Quantification of Standards Spectrophotometer Fluorometer OD Quantity Careful quantification is critical! Applied Biosystems
8 Absolute Standards for PCR To create an absolute standard you must know the following: a) Genes/genome is constant b) Copy/genome. c) Size of gene. If any not true or not known, a purified version of the gene may be necessary, i.e., gene inserted into a Plasmid Applied Biosystems
9 RNA Absolute Standards for RT/PCR In vitro transcribed RNA (crna) may be necessary. If OD260 is being used to quantify crna, residual NTP s must be removed. crna is very stable when stored frozen in water Applied Biosystems
10 Philosophy of Standard Development Make the standard mimic the sample as much as possible. Genomic standard = genomic samples Plasmid standard = genomic samples crna standard = total RNA samples Applied Biosystems
11 Example: Effect of Amplification Efficiency y = x(1+e) n Case 1: e = 0.8 Case 2: e = 0.9 y = 100 (1+0.9) 30 y = 100 (1+0.8)30 y = 2.3 x y = 4.6 x 10 9 Result: A difference of 0.1 in amplification efficiencies created a 5-fold difference in the final ratio of PCR products Applied Biosystems
12 External Standards used for Absolute Quantitation The standard curve function is to compensate for amplification efficiency and provide quantity units. Detection threshold Standard Unknown Avoid inaccurate quantitation! Cycle no Applied Biosystems
13 Background Non-target nucleic acids found in samples. Plasmid and crna are clean templates which makes them very efficient. Primers may interact with background to produce products that compete with the amplification of the specific product. Extra protein from extraction may inhibit PCR Applied Biosystems
14 Background Solution: Find DNA or RNA that does not contain amplicon target Add this to the Standards Run PCR and test for efficiency differences Applied Biosystems
15 Background Test Background is having no effect Log Rn standard - bkgd standard + bkgd Sample Cycle number background Applied Biosystems
16 Background having Background moderate effect. Spike background into standards to compensate. Log Rn standard - bkgd standard + bkgd Sample Cycle number background Applied Biosystems
17 Background Background having severe effect. Re-isolate or re-design assay. Log Rn standard - bkgd Cycle number standard + bkgd Sample background Applied Biosystems
18 Background Background is amplifying identical target. Redesign assay or choose new background? standard - bkgd standard + bkgd background Log Rn Sample Cycle number Applied Biosystems
19 Absolute Standards Need to validate that standards and samples amplify with the same efficiency Ct y = -3.3x R 2 = log[dilution] Non-linearity of dilution inhibitor effects Applied Biosystems
20 Absolute Standards Goal is to quantify an exact copy number (or number of molecules) of a certain gene in a sample copies 2500 copies 5,000 copies Unk1 C t = ,000 copies 20,000 copies Unk1 Conc=4000 copies Applied Biosystems
21 Absolute vs. Relative Absolute Quantitation Easy to understand the data, difficult to develop/qualify the standards Relative Quantitation Easy to make the standards, difficult to understand the data Copy # Fold Chg Applied Biosystems Sample 1 Sample Sample 1 Sample 2
22 Relative Quantitation To determine fold differences of nucleic acid targets with statistical confidence. Usually RNA targets Research Applications: Gene expression Drug therapy Biomarkers Applied Biosystems
23 Relative Quantification Calibrator The sample used as the basis for comparative results t =0 t=12 t=24 t=48 time Convert purified total RNA to cdna [GOI] [GOI] [GOI] [GOI] [ENDO] [ENDO] [ENDO] [ENDO] Applied Biosystems Control for sample variability
24 Use of a Normalization Gene Endogenous Control, Active Reference Gene Choose a gene that is expressed at a constant level THIS NEEDS TO BE VALIDATED Test representative set of samples that have been normalized via mass (A260) Normalizes for differences in the amount of nucleic acid Any fluctuation in the control gene expression must be due to varying amounts of input RNA or rt-efficiencies Gene of Interest Normalization Gene or IL-2* 18S* Applied Biosystems *for every sample
25 TaqMan Arrays TaqMan Human Endogenous Control Plate Standard 96-well Plates TaqMan Array Cards for Human, Mouse or Rat Endogenous Controls. 16 Commonly used endogenous controls Applied Biosystems
26 TaqMan Endogenous Controls Human Endogenous Controls Product Name Part Number Detects Genomic DNA Accession Number Reporter Quencher Primer Limited Eukaryotic 18S rrna T Yes X03205 FAM MGB No Eukaryotic 18S rrna F Yes X03205 FAM MGB No Eukaryotic 18S rrna E Yes X03205 VIC TAMRA Yes Eukaryotic 18S rrna E Yes X03205 VIC MGB Yes Human ACTB (beta actin) E No NM_ VIC MGB Yes Human ACTB (beta actin) E See Footnote 2 NM_ VIC TAMRA Yes Human ACTB (beta actin) T No NM_ FAM MGB No Human ACTB (beta actin) F No NM_ FAM MGB No Human B2M (beta-2-microglobulin) E No NM_ VIC MGB Yes Human B2M (beta-2-microglobulin) E No NM_ VIC TAMRA Yes Human B2M (beta-2-microglobulin) F No NM_ FAM MGB No Human B2M (beta-2-microglobulin) T No NM_ FAM MGB No Human GAPD (GAPDH) T No NM_ FAM MGB No Human GAPD (GAPDH) F No NM_ FAM MGB No Applied Biosystems
27 Use of a Calibrator Calibrator Sample Express the data compared to a single sample, a 1X sample. Treated vs. Untreated, 0hr vs. 6hr, Normal vs. Diseased. Sample Calibrator or Treated Untreated Expression Fold Change Sample X Sample X Applied Biosystems
28 Relative Standards Make quantitative comparisons of targets using a standard curve (no need for copy number information) Typically a dilution of a sample 1.25x10 3 pg 2.5x10 3 pg 5x10 3 pg Unk1 C t =24.8 1x10 4 pg 2x10 4 pg total RNA Unk1 Conc=4x10 3 pg total RNA Unk2 Conc=2x10 3 pg total RNA Applied Biosystems 2-fold Difference
29 Relative Quantification* Using The Standard Curve Method IL-2 18S Normalized Calibrated pg total RNA pg total RNA IL-2 Expression IL-2 Expression 0 Hr , x Hr , x Hr , x (~4 fold decrease) See Chemistry Guide ( Relative Quantification ) Review of the calculations covered above Standard deviations addressed Applied Biosystems
30 The Comparative C T Method, C T Relative quantification without using standard curves The efficiencies of both PCR reactions are the same. The efficiencies are both close to ~1.0 (E) y = x (1 + e) n Relative Quantification = 2 - average ddct Applied Biosystems
31 Delta Delta Ct Validation Validation -0.1 < Delta Ct Slope > Applied Biosystems
32 From - - Real-Time PCR: Understanding Ct Application Note Applied Biosystems
33 The Comparative C T Method, C T Relative quantification without using standard curves IL-2 18S Normalized Calibrated Fold Ave Ct Ave Ct IL-2 Exp. IL-2 Exp. Difference ( C t ) ( C t ) 2 -( Ct) 0 Hr Hr Hr (~4 fold decrease) NOTE: If both assays are not perfectly efficient then your fold differences will be overestimated. i.e. ddct = -1.2 If E=1, then 2.46 fold difference If E=0.9, then 2.2 fold difference If E=0.5, then 1.6 fold difference Applied Biosystems
34 Relative Quantification Software Comparative C t Calculations Applied Biosystems
35 Integromics Real-Time StatMiner Software: Advanced Data Mining Software Applied Biosystems Real-Time PCR Instrumentation References Bioconductor Algorithms Quality Control Endogenous Control Selection Multiplate (>10) Comparative Ct ( Ct) Analysis Hierarchical Clustering Principal Component Analysis E Applied Biosystems
36 Licensing & Trademarks For Research Use Only. Not for use in diagnostic procedures. Applied Biosystems, LIZ, Celera, and ABI PRISM are registered trademarks and AB (Design), Applera, Celera Discovery System, and PANTHER are trademarks and My Science is a service mark of Applera Corporation or its subsidiaries in the US and/or certain other countries. TaqMan is a registered trademark of Roche Molecular Systems, Inc. All other trademarks are the sole property of their respective owners. The PCR process and 5 nuclease process are covered by patents owned by Roche Molecular Systems, Inc. and F. Hoffmann-La Roche Ltd, and by patents owned or licensed to Applera Corporation. Further information on purchasing licenses may be obtained from the Director of Licensing, Applied Biosystems, 850 Lincoln Center Drive, Foster City, CA USA Applied Biosystems. All rights reserved Applied Biosystems
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