QPCR for quantification of synthetic ecosystems: hurdles and solutions

QPCR for quantification of synthetic ecosystems: hurdles and solutions
UMONS _ Proteomic and Microbiology lab Microbial resource managment (IAP P7/25) MRM in engineered and natural ecosystems

Q-PCR for quantification of synthetic ecosystems: hurdles and solutions
offered by UMons, tutorial workshop: This workshop will be more of an overview of qPCR issues that were experienced by the people at UMons for accurate quantification of synthetic ecosystems. They will introduce a new way of making appropriate standard curves for SYBR-based assays which correct for non-target PCR bias.

QPCR PCR => Polymerase Chain Reaction = DNA Amplification
Q => Quantitative (Real Time) A PCR is a method used to specifically amplify a peace of DNA by over amplification. You want more DNA either because you need this peace of DNA for cloning, either because you want to see if ti is present, either whatever… The QPCR is particular in the fact that you can visualise the amplification in a REal Time. From the resulting curves, you can deduce the initial fragment quantity.

House style Definitions/Basics Experimental Design Analysis
Targets & References Probing VS Priming DNA extraction Relative or Absolute Quantification Analysis 1 species analysis Community analysis

Definitions

Basics in QPCR/Vocabulary
Amplification Curve # of copies/[DNA] Time Cycle Step And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles

Exponential Amplification of DNA # of copies/[DNA] Time Cycle Step Exponential Amplification Base 2 And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles

Exponential Amplification of DNA # of copies/[DNA] Time Cycle Step 16 8 4 2 Baseline 1 And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles 1 2 3 4

Efficiency # of copies/[DNA] Time Cycle Step Exponential Amplification Base 2 16 8 4 2 1 And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles 1 2 3 4

Efficiency = the Rate of DNA amplification # of copies/[DNA] Time Cycle Step Exponential Amplification Base Efficiency ~ base 2 16 8 4 0 < E < 1 0 % < E% < 100% Eff = (1+E) ~2 2 1 And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles 1 2 3 4

End Point VS Real Time 1 2 3 4 5 # of copies/[DNA] Cycle 16 8 4 2 1 And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles 1 2 3 4 End point PCR

End Point VS Real Time 1 2 3 4 5 # of copies/[DNA] Cycle 16 8 4 2 1 And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles [DNA]i 1 2 3 4 [DNA]i End point PCR

Threshold Fluorescence Cycle 16 8 Threshold 4 2 1 And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles Fluoi 1 2 3 4 Fluoi

Ct or CP or Cq = threshold Fluorescence Cycle 16 8 Threshold 4 2 1 And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles Fluoi 1 2 3 4 Fluoi Ct Ct

Ct or CP or Cq = threshold Fluorescence Cycle 16 8 Threshold 4 2 1 And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles Fluoi 1 2 3 4 D Fluoi Ct Ct D

Ct or CP or Cq = threshold Fluorescence Cycle 1 Target = 1 Threshold 16 8 Threshold 4 2 1 And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles Fluoi 1 2 3 4 D Fluoi Ct Ct D

Ct or CP or Cq = threshold Fluorescence Cycle 1 Target = 1 Threshold 16 8 Threshold ~ Baseline 4 Threshold 2 Baseline 1 And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles Fluoi 1 2 3 4 D Fluoi Ct Ct D

Ct or CP or Cq = threshold And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles FluoCycle 1 = Fluoi x 21 100 % E Eff ~ 2 FluoCycle 2 = Fluo1 x 21 => FluoCycle 2 = Fluoi x 22

Ct or CP or Cq = threshold FluoCycle 2 = Fluoi x 22 FluoTh = Fluoi x 2Ct Fluoi = FluoTh 2Ct And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles Fluoi = FluoTh x 2-Ct

Targets and References
Genes to analyse May be sensitive to condition No variation NORMALIZATION

Genes to analyse May be sensitive to condition No variation QTarget Fluoi Target => QReference Fluoi Reference

Fluoi = FluoTh x 2-Ct Fluoi Target = FluoTh Target x 2-Ct Target Fluoi ref = FluoTh ref x 2-Ct ref Fluoi Target FluoTh Target x 2-Ct Target FluoTh ref x 2-Ct ref = Fluoi ref FluoTh ref = FluoTh Target

Fluoi = FluoTh x 2-Ct Fluoi Target = FluoTh Target x 2-Ct Target Fluoi ref = FluoTh ref x 2-Ct ref Fluoi Target FluoTh Target x 2-Ct Target FluoTh ref x 2-Ct ref = Fluoi ref Fluoi Target 2-Ct Target 2-Ct ref = Fluoi ref Fluoi Target 2-Ct Target - - Ct ref = Fluoi ref 2Ct ref - Ct Target =

Fluoi = FluoTh x 2-Ct Fluoi Target = FluoTh Target x 2-Ct Target Fluoi ref = FluoTh ref x 2-Ct ref Fluoi Target FluoTh Target x 2-Ct Target FluoTh ref x 2-Ct ref = Fluoi ref Fluoi Target 2-Ct Target 2-Ct ref = Fluoi ref Fluoi Target 2-Ct Target - - Ct ref = Fluoi ref 2Ct ref - Ct Target = Fluoi Target EffTarget-Ct Target EffReference-Ct ref = Fluoi ref

1. Amplification Curve => Fluo/QDNA/# copies = f(Cycle/Time) 2. Efficiency => Rate of Exponential Amplification ~ 2 3. Baseline => Limit of sensitivity 4. Threshold => Selected Fluo/QDNA/# copies used for calculation => The same for 1 target => The closest to Baseline 5. Ct/CP/Cq => # of cycle required to reach the threshold 6. [DNA]i = f(Fluoi) => Fluoi = FluoTh x Eff-Ct => (Eff ~ 2) 7. Targets => Genes to analyse References => Do not vary, normalization => Fluoi Target EffTarget-Ct Target EffReference-Ct ref = Fluoi ref

Software StepOne Plus Software Export in Excel R_Grofit

Experimental Design

QPCR and communities Project:
1. Synthetic community composed of 9 species 2. Community evolution - over time ? - stressed with metals ? => Abundance of each species inside the community… + Time +/- stress

How to proceed Experimental Design: Targets & References
Probing vs. Priming DNA extraction Relative vs. Absolute Quantification

Genes to analyse May be sensitive to condition No variation Species specific Universal

- Specific/metabolic genes/specific sequence => Select those sequences => Fully sequenced genome => Seems tedious

- Specific/metabolic genes/specific sequence => Select those sequences => Fully sequenced genome - 16S RNA gene => Constant and variable regions Suggested: Wang Y, Qian P-Y (2009) Conservative Fragments in Bacterial 16S rRNA Genes and Primer Design for 16S Ribosomal DNA Amplicons in Metagenomic Studies. PLoS ONE 4(10): e7401.

- Specific/metabolic genes/specific sequence => Select those sequences => Fully sequenced genome - 16S RNA gene => Constant and variable regions => Targets and Reference on the same gene ! => 16S are sequenced But… => # of 16S ? => HGT => rpoB ? Suggested: Wang Y, Qian P-Y (2009) Conservative Fragments in Bacterial 16S rRNA Genes and Primer Design for 16S Ribosomal DNA Amplicons in Metagenomic Studies. PLoS ONE 4(10): e7401.

How to proceed Experimental Design:
Targets & References = 16S RNA gene variable & constant region Probing vs. Priming DNA extraction Relative vs. Absolute Quantification

<10 freeze-thaw cycles
Probing vs. Priming TaqMan SYBR Green TaqMan SYBR Green Specific probing Specific priming # of targets/well up to 4 1 Stability <10 freeze-thaw cycles OK Price probes 150 €/40nmol x Price primers 60 €/40 nmol Price master mix 9 €/mL 40-80 €/mL Price plates + membrane 1-2 € + tips (filtered of not…) Price added/target 150 +/-60 € 60 € Price added/plate 10 € 41 € (image from wikipedia)

Targets & References = 16S RNA gene variable & constant region Probing vs. Priming = SYBR Green Primer design Primer specificity and sensitivity DNA extraction Relative vs. Absolute Quantification

Primer design 1. Check in the literature for Specific and Universal primers Suggested: Wang Y, Qian P-Y (2009) Conservative Fragments in Bacterial 16S rRNA Genes and Primer Design for 16S Ribosomal DNA Amplicons in Metagenomic Studies. PLoS ONE 4(10): e7401.

Primer design 1. Check in the literature for Specific and Universal primers 2. Select the variable regions: Alignement 16S DNA sequences: (Database) RDB, NCBI… (Alignment) ClustalW/MEGA6…

Primer design 1. Check in the literature for Specific and Universal primers 2. Select the variable regions: Alignement 16S DNA sequences: (Database) RDB, NCBI… (Alignment) ClustalW/MEGA6… 3. Primer design: Home-made, softwares (Primer3…), servers:

Primer design 1. Check in the literature for Specific and Universal primers 2. Select the variable regions: Alignement 16S DNA sequences: (Database) RDB, NCBI… (Alignment) ClustalW/MEGA6… 3. Primer design: Home-made, software (Primer3), servers: Specific settings for QPCR primers: - about b - Amplicon size = about bp - No GC or clamp in 3'/not too much GC in 5' - No stretch of more than 3 bases - No self- or cross- annealing - GC% (40-60) 4. Check Quality and parameters ThermoFischer Scientific Multiple Primer Analyzer (free): Tm, size, self and cross- annealing BLAST

Primer Specificity Specificity: only 1 amplicons + no amplicons with the other species Sensitivity Specificity and Sensitivity inside the community For each primer = - via Normal PCR or QPCR 1 2 3 4 5 1 = gDNA X_21ng 2 = gDNA X_2.1ng 3 = No DNA Control 4 = gDNA Community 5 = gDNA X_2.1ng inside the gDNA Community gDNA Community = 5 bacteria, between 0.5 and 1 ng each 1 2 3 4 5

Targets & References = 16S RNA gene variable & constant region Probing vs. Priming = SYBR Green Primer design Primer specificity and sensitivity DNA extraction Relative vs. Absolute Quantification

DNA extraction Community growth in SSM8 = Marine medium +/- Zn 0.5 mM
= > Humic acids, salts ++ and metals => PCR contaminants DNA extraction using kits /phenol extraction (ajustable) Against humic acid = During extraction Aluminium Ammonium Sulfate/PVP/PEG Further purification steps Gel electrophoresis/size exclusion chromatography/silica based DNA binding columns (kits) Us: Phenol extraction with 2 washing steps CHCl3-AIA steps DNA extraction lead to about 2 µg of DNA from 1.5 ml OD 0.4. For gram + Mycobacterium vanbaalenii => only 200 ng. DNA quantification : Nanodrop or picogreen Suggested: Goyer C, Dandie CE. (2012). Quantification of Microorganisms Targeting Conserved Genes in Complex Environmental Targets using Quantitative Polymerase Chain Reaction. In: Quantitative Real-Time PCR in Applied Microbiology. Martin Filion, pp 87–106.

Targets & References = 16S RNA gene variable & constant region Probing vs. Priming = SYBR Green Primer design Primer specificity and sensitivity DNA extraction = Phenol-Chloroform extraction Relative vs. Absolute Quantification

Relative vs. Absolute Quantification
Relative Quantification => Relative to a Reference e.g., 1 species compared to all species Absolute Quantification => Using a Calibration curve i.e., need a standard Output = Ratios Output = # of copies Fluoi Target EffTarget-Ct Target EffReference-Ct ref = Fluoi ref Ct Ctx Qx Log10 Quantity Suggested: Pfaffl MW. (2001). A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29: e45. Brankatschk R, Bodenhausen N, Zeyer J, Burgmann H. (2012). Simple Absolute Quantification Method Correcting for Quantitative PCR Efficiency Variations for Microbial Community Targets. Applied and Environmental Microbiology 78: 4481–4489.

Relative Quantification => Relative to a Reference e.g., 1 species compared to all species Absolute Quantification => Using a Calibration curve i.e., need a standard Output = Ratios Output = # of copies Less space on the plate No need to have a calibrant Necessary when Eff. are not OK Suggested: Pfaffl MW. (2001). A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29: e45. Brankatschk R, Bodenhausen N, Zeyer J, Burgmann H. (2012). Simple Absolute Quantification Method Correcting for Quantitative PCR Efficiency Variations for Microbial Community Targets. Applied and Environmental Microbiology 78: 4481–4489.

Targets & References = 16S RNA gene variable & constant region Probing vs. Priming = SYBR Green Primer design Primer specificity and sensitivity DNA extraction = Phenol-Chloroform extraction Relative vs. Absolute Quantification = Absolute Quantification Standard/Control

Control DNA for calibration curve
Standard/Control for calibration curve has to been quantifiable => known copy number Ct Eff.1 = 10-1/slope Log10 Quantity/Dilution/# of copies Suggested: Rebrikov DV, Trofimov DI. (2006). [Real-time PCR: approaches to data analysis (a review)]. Prikl Biokhim Mikrobiol 42: 520–528.

Standard/Control for calibration curve has to been quantifiable => known copy number - Gene of interest inserted in a plasmid E. coli cell Gene of interest Known size Plasmid Known size Plasmid miniprep Known concentration Known copy number

Standard/Control for calibration curve has to been quantifiable => known copy number - Gene of interest inserted in a plasmid - Genomic DNA of known concentration… Bacterial cell gDNA Known size gDNA extraction Known concentration Known copy number Gene of interest Known size

Standard/Control for calibration curve has to been quantifiable => known copy number - Gene of interest inserted in a plasmid - Genomic DNA of known concentration… => But in our case… complexity of the Targets: Several species “Environmental” Targets, metals… not totally clean

Standard/Control for calibration curve has to been quantifiable => known copy number - Gene of interest inserted in a plasmid - Genomic DNA of known concentration… Using genomic DNA of E. coli 5x 3-fold dilutions in water or community of 0,25ng/µl or 0,5 ng/µl 3 replicates + + + Water

Standard/Control for calibration curve has to been quantifiable => known copy number - Gene of interest inserted in a plasmid - Genomic DNA of known concentration… Using genomic DNA of E. coli 5x 3-fold dilutions in water or community of 0,25ng/µl or 0,5 ng/µl 3 replicates For Ct = 18 Background Eff. 1 log10[DNA]Ec [DNA]Ec 1.743 -1.566 0.027 0.25 1.901 -1.369 0.043 0.50 1.871 -1.370

Community Calibration curve
Using a gDNA community: - gDNA extracted in the same conditions => same contaminants - Community of gDNA close to real one => same species => same influence on Efficiency => same influence on Efficiency # 16S/genome DNA size (bp) ng/µl stock I want 500µl # 16S in Std 1 Pseudomonas putida 7 94.81 1 5.3 Shewanella frigidimarina 8 103.27 0.5 2.4 764840 Shewanella baltica 10 81.55 3.1 867003 Burkholderia glumae 67.1 3.7 92131 Burkholderia xenovorans 6 105.05 4.8 571235 Methylibium petroleiphilum 125.35 2.0 99756 Mycobacterium vanbaalenii 2 18.44 13.6 142711 Cupriavidus metallidurans 4 45.55 11.0 536042 Escherichia coli 55.44 9.0 Community 6.5ng 54.8 + Water 445.2 Suggested: DNAcopy number calculator

Targets & References = 16S RNA gene variable & constant region Probing vs. Priming = SYBR Green Primer design Primer specificity and sensitivity DNA extraction = Phenol-Chloroform extraction Relative vs. Absolute Quantification = Absolute Quantification Standard/Control => Community of gDNA of known concentration => # 16S RNA gene (copy number)

1 species analysis

Qualtity check QPCR Analysis :
Software available with the QPCR machine Sheng Zhao, Russell D. Fernald. Comprehensive algorithm for quantitative real-time polymerase chain reaction. J. Comput. Biol. 2005 Oct;12(8): QPCR package for R qpcR : Ritz Spiess,2008

Plate Layout 1. Open “161216-Data sets 1 species.xls”
2. “Plate Layout” tab Biological replicates 3 communites without Zn A, B, C 3 communities with Zn AZ, BZ, CZ Technical replicates Diluted 4 x 3 times + Technical replicates of the first 2 dilutions Primer Specific and Universal

Fast Quality check 1. “How to proceed” tab 2. Errors/dilutions…
Increasing dilutions_3 replicates each

Fast Quality check ? 1. “How to proceed” tab 2. Errors/dilutions…
3. Melting curves SYBR Green specific of double stranded DNA 1 2 3 4 5 ?

Fast Quality check 1. “How to proceed” tab 2. Errors/dilutions…
3. Melting curves SYBR Green specific of double stranded DNA If several amplicons = several sequences = several patterns of denaturation

Quality check 1. Melting 2.Eff. 2 => per well
3. Eff.1 => per sample Eff.1=5.79

Quantification 1. Deal with dilutions 2. Normalization with UNI:

Quantification 1. Deal with dilutions 2. Normalization with UNI:
𝑄 𝑥 𝑄 𝑐 => But Ratios !

Quantification 1. Deal with dilutions 2. Normalization with UNI:
3. Visualization and statistical analyses: Script: “ RMarkdown-QPCR.Rmd” 𝑄 𝑥 𝑄 𝑐 => But Ratios ! 𝑐𝑜𝑒𝑓𝑓.𝐴= 𝑄 𝐶 𝐴 𝑄 𝐶 𝐴 =1, 𝑐𝑜𝑒𝑓𝑓.𝐵= 𝑄 𝐶 𝐵 𝑄 𝐶 𝐴 , 𝑐𝑜𝑒𝑓𝑓.𝐶= 𝑄 𝐶 𝐶 𝑄 𝐶 𝐴 , 𝑐𝑜𝑒𝑓𝑓.𝐴𝑍= 𝑄 𝐶 𝐴𝑍 𝑄 𝐶 𝐴 , 𝑐𝑜𝑒𝑓𝑓.𝐵𝑍= 𝑄 𝐶 𝐵𝑍 𝑄 𝐶 𝐴 , 𝑐𝑜𝑒𝑓𝑓.𝐶𝑍= 𝑄 𝐶 𝐶𝑍 𝑄 𝐶 𝐴 𝑄 𝐵 ′ = 𝑄 𝐵 𝑐𝑜𝑒𝑓𝑓.𝐵 =>

Community analysis

Species abundance inside the community
1. Quality check (Norm coeff., Universal primers, Efficiency 1 and 2) 2. Transform 16S RNA copy # in cell # 3. Sum and %

EFFICIENCY !!!!! Take home message - DNA prep - Targets and References
- Threshold - Do not follow blindely the proposed methods

Aknowledgement Proteomic and Microbiology lab, UMONS Ruddy Wattiez
David C. Gillan Mélanie Beraud Guiseppe Giambarresi

Target and References Targets
- Specific genes for each one of the species => Select those sequences => Fully sequenced genome - 16S RNA gene => Constant and variable regions => Targets and Reference on the same gene !  replication Suggested: Wang Y, Qian P-Y (2009) Conservative Fragments in Bacterial 16S rRNA Genes and Primer Design for 16S Ribosomal DNA Amplicons in Metagenomic Studies. PLoS ONE 4(10): e7401.

Relative Quantification: Relative to a reference e.g., 1 gene compared to 16S / 1 species on all species… 𝑄 𝑠𝑎𝑚𝑝𝑙𝑒 𝑄 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 = 𝐸𝑓𝑓 𝑇𝑎𝑟𝑔𝑒𝑡 −𝐶𝑡 𝑠𝑎𝑚𝑝𝑙𝑒 𝐸𝑓𝑓 𝑇𝑎𝑟𝑔𝑒𝑡 −𝐶𝑡 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 = 𝐸𝑓𝑓 𝑇𝑎𝑟𝑔𝑒𝑡 −𝐶 𝑡 5𝑎𝑚𝑝𝑙𝑒 −− 𝐶𝑡 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 = 𝐸𝑓𝑓 𝑇𝑎𝑟𝑔𝑒𝑡 𝐶 𝑡 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 − 𝐶𝑡 5𝑎𝑚𝑝𝑙𝑒 = 𝑬𝒇𝒇 𝑻𝒂𝒓𝒈𝒆𝒕 𝑪 𝒕 𝒄𝒐𝒏𝒕𝒓𝒐𝒍 − 𝑪𝒕 𝟓𝒂𝒎𝒑𝒍𝒆 𝑬𝒇𝒇 𝑹𝒆𝒇𝒆𝒓𝒆𝒏𝒄𝒆 𝑪 𝒕 𝒄𝒐𝒏𝒕𝒓𝒐𝒍 − 𝑪𝒕 𝟓𝒂𝒎𝒑𝒍𝒆 Usually simplified 𝜟𝜟𝐂𝐭= 𝟐 𝜟𝑪 𝒕 𝑻𝒂𝒓𝒈𝒆𝒕 −𝜟 𝑪𝒕 𝑹𝒆𝒇𝒆𝒓𝒆𝒏𝒄𝒆 = Just ratios at the end ~ EffTarget=EffReference= 2 Suggested: Schmittgen TD, Livak KJ. (2008). Analyzing real-time PCR data by the comparative CT method. Nature Protocols 3: 1101–1108.

PCR => Exponential DNA amplification 1 2 3 Base 2 # of copies Cycle # copies = 2Cycle And it is exponential with base 2. Meaning if you have 2 copies at the beginning, you have 4 after 1 cycle, 8 after 2 cycles… If you plot the # of copies at each cycle, you should then have a straight line with slope being 2 to the power of cycles

PCR => Exponentiel DNA amplification 1 2 3 # of copies Cycle y=a2x Something is limiting Of course, this is not totally what is happening, what you have is actually more like this, 3 zones you actually have 3 zones:

PCR => Exponentiel DNA amplification 1 2 3 1 2 3 # of copies Cycle y=a2x Something is limiting Of course, this is not totally what is happening, what you have is actually more like this, 3 zones you actually have 3 zones:

Baseline = Minimum concentration/Fluorescence detectable = LOD Fluo Cycle y=a2x The Baseline is the minimum Fluorescence at which you can see an amplification. This Baseline is due to sensitivity: Fluorescence reading… This baseline should theoretically be the same, whatever the concentration is… Baseline

Baseline = Minimum concentration/Fluorescence detectable = LOD Fluo Cycle LOD y=a2x This baseline is actually just a limit of detection. Let’s say that if you have less than 8 amplicons, it is undetectable, the baseline will be corresponding to the fluorescence of those 8 amplicons… Baseline

Delay in the curve depends on DNA concentration Decreasing initial [DNA] Fluo Cycle LOD y=a2x The delay in the amplification is due to DNA concentration. Baseline

Delay in the curve depends on DNA concentration Decreasing [DNA]i Decreasing initial [DNA] 1 1 2 Fluo Cycle 1 2 3 LOD y=a2x 2 3 4 The less DNA you have, the more time you need to reach a detectable fluorescence. Historically, the DNA initial concentration was calculated using the baseline: Baseline 2 3 4 3 4 5

[DNA]i Decreasing initial [DNA] [DNA] = f(Fluo) [DNA]i = f(Fluoi) Fluo Cycle y=a2x The idea was that if you had no LOD, you could directly read the initial fluorescence at Cycle 0 Baseline [DNA]i [DNA]i [DNA]i

[DNA]i Decreasing initial [DNA] [DNA] = f(Fluo) [DNA]i = f(Fluoi) Fluo Cycle y=a2x Then a initial difference in the initial fluorescence would give you the difference in initial concentration of DNA. Baseline [DNA]i x [DNA]i y [DNA]i

Amplification curve: Cycle = f(LogFluo) Decreasing initial [DNA] [DNA] = f(Fluo) [DNA]i = f(Fluoi) Log(Fluo) Cycle D([DNA]iA- [DNA]iB) = 2 (CtB-CtA) y=a2x The difference you should see in the initial fluorescence can be seen on the x axis of the amplification curve… Baseline x y [DNA]i x [DNA]i y [DNA]i

Amplification curve: Cycle = f(LogFluo) Decreasing initial [DNA] [DNA] = f(Fluo) [DNA]i = f(Fluoi) Log(Fluo) Cycle D([DNA]iA- [DNA]iB) = 2 (CtB-CtA) y=a2x IF… you use a similar Fluo threshold. The difference you should see in the initial fluorescence can be seen on the x axis of the amplification curve… If you use a threshold… you have to be on a straigth line or it won’t work. Historically, it was the baseline, but actually, the amplifications curves looks more like this… Baseline x y [DNA]i x [DNA]i y [DNA]i

Amplification curve: Cycle = f(LogFluo) The baseline is difficult to use, because a bit messy… so now, we use

Threshold: Threshold used to recover the Ct/Cq Threshold The baseline is difficult to use, because a bit messy… so now, we use a threshold usualy chosen by the software so it match all the amplification curves… The more it is at the beginning, the better it is, because the less error is made with the efficiency (if the efficiency is not perfect).

[DNA]i = f[Fluo]i = f(Ct/Cq) Threshold The baseline is difficult to use, because a bit messy… so now, we use a threshold usualy chosen by the software so it match all the amplification curves… The more it is at the beginning, the better it is, because the less error is made with the efficiency (if the efficiency is not perfect). The point is: This Ct or Cq is directly correlated to the initial concentration of DNA, meaning the more you have DNA at the beginning, the lower will be the Ct, the less DNA you have at the beginning the higher your Ct will be… This correlation is logarithmic… With the number of cycle, the fluorescence and the Ct, you can theoretically recover your [DNA]i. Cq11 Cq1 Cq3 Cq5 Cq8 Cq10

PCR => DNA amplification 1 2 3 Specific => 1 selected DNA sequence Amplification => Visualisation OK, just to be very fas, to be sure everyone is following, I will remind you the principle of the QPCR… Really fast, don’t worry. It is an amplification which is specifc, meanng your are choosing the DNA you want to amplify Quantitative or Real Time

Background situation Why QPCR ? + Allow specificity
+ Allow quantitation - More time and money consuming

How to publish MiQE Efficiency, check the primers…

Alternatives 16S and metagenomic, transcriptomique
New droplet QPCR = no efficiency, but more expensive (= single molecule PCR)

PCR => Exponential DNA amplification 1 PCR cycle: 1 DNA 2b => 2 DNA 2b Exponential amplification You already know this, butI really want to be sure that noone missed something importnat: You have a double stranded DNA, you mae it single stranded using DNA denaturation, then you select the gene to amplify using primers (which are specific single strand DNA), the polymerase will bind to the duplex and then start polymerisation until it falls out. you finaly have, after one cycle 2 copies of your initial DNA. for the specific region. Those 2 copies will be the start of a new PCR cycle and you will finish having a lot of DNA of this specific little region you selected with your primers. This amplification is exponential

QPCR for quantification of synthetic ecosystems: hurdles and solutions

Similar presentations

Presentation on theme: "QPCR for quantification of synthetic ecosystems: hurdles and solutions"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

QPCR for quantification of synthetic ecosystems: hurdles and solutions

Similar presentations

Presentation on theme: "QPCR for quantification of synthetic ecosystems: hurdles and solutions"— Presentation transcript:

Similar presentations

About project

Feedback