Post on 03-Jun-2020
transcript
Metodi e tecniche di laboratorio II. GenomicaMetodi e tecniche di laboratorio II. Genomica
INTRO INTRO –– REALREAL--TIME PCRTIME PCR
1.1. 22 Novembre 1859 22 Novembre 1859 – viene pubblicata l’opera di Darwin On the origin of
Species [L’origine della specie]
TimelineTimeline (1)(1)
2.2. 18601860 – Pasteur pubblica la scoperta che la muffa Penicillium glaucum
metabolizza di preferenza la forma L (levogira) dell’ac. Tartarico (viene
per la prima volta introdotto in Biologia il concetto di struttura molecolare
tridimensionale e con esso il connubio struttura-funzione)
3.3. 8 Marzo 1865 8 Marzo 1865 – Mendel legge per la prima volta il suo articolo sugli
Esperimenti sugli ibridi delle piante – nasce la teoria sulla ereditarietà
TimelineTimeline (2)(2)
4.4. 1869 1869 – Friederick Miescher scopre una sostanza acida all’interno del
Nucleo delle cellule che definì Acido DesossiriboNucleico (DNA)
5.5. 19311931 – Griffith definì per la prima volta il principio trasformante
(ceppi virulenti di S. pneumoniae, anche se uccisi contenevano una sostanza
in grado di trasformare ceppi avirulenti in virulenti)
6.6. 1944 1944 – Avery, MacLeod e McCarty dimostrarono che il principio
trasformante era il DNA – oggi tale processo viene definito “trasferimento
genico orizzontale”
TimelineTimeline (3)(3)
7.7. 25 Aprile 1953 25 Aprile 1953 – viene pubblicato l’articolo di J Watson e F Crick
in cui per la prima volta viene descritta la struttura del DNA
8.8. 19621962 – viene assegnato il Nobel per la Fisiologia e la Medicina a Watson,
Crick e Wilkins
9.9. 19861986 – Renato Dulbecco propone l’inizio dell’ HGP (Human Genome Project)
10.10. 2000 2000 – viene completata la prima bozza della sequenza del Genoma Umano.
…Ma è solo la fine dell’inizio!
INTRODUCTIONINTRODUCTION
Genetics: studies mechanisms of how characters inherit from an individual to anotherfrom an individual to another
Genomics: studies the structure, evolution and functions of entire genomes
In classical genetics, the genome of a diploid organism including eukarya refers to a
full set of chromosomes or genes in a gamete; thereby, a typical somatic cell contains
GENOME (1)GENOME (1)
full set of chromosomes or genes in a gamete; thereby, a typical somatic cell contains
two full sets of genomes. In haploid organisms, including bacteria, archaea, viruses,
and mitochondria, a cell contains only a single set of the genome,
usually in a single circular or contiguous linear DNA (or RNA for retroviruses).
In modern molecular biology the genome of an organism is its hereditary
information encoded in DNA (or, for retroviruses, RNA).
GENOME (2)GENOME (2)
The genome includes both the genes and the non-coding sequences of the
DNA. The term was adapted in 1920 by Hans Winkler, Professor of Botany
at the University of Hamburg, Germany. The Oxford English Dictionaryat the University of Hamburg, Germany. The Oxford English Dictionary
suggests the name to be a portmanteau of the words gene and chromosome;
however, many related -ome words already existed, such as transcriptome,
proteome, interactome forming (as we will see later) a vocabulary into which
genome fits systematically. More More preciselyprecisely, the , the genomegenome ofof anan organismorganism isis
a complete a complete geneticgenetic sequencesequence on on oneone set set ofof chromosomeschromosomes
GENOME PROJECTS (1)GENOME PROJECTS (1)
Genome projects are scientific endeavours that ultimately aim to determine
the complete genome sequence of an organismthe complete genome sequence of an organism
(be it an animal, a plant, a fungus, a bacterium, an archaean, a protist or a virus).
The genome sequence for any organism requires the DNA sequences for each of the
chromosomes in an organism to be determined. For bacteria, which usually have
just one chromosome, a genome project will aim to map the sequence of
that chromosome.
Humans, with 22 pairs of autosomes and 2 sex
chromosomes, will require 46 separate chromosome sequences in order to
represent the completed genome. The Human Genome Project was a
GENOME PROJECTS (2)GENOME PROJECTS (2)
represent the completed genome. The Human Genome Project was a
landmark genome project and some have argued that the era of
genomics is one of the more fundamental advances
in human history.
INSTITUTIONS INVOLVED IN THE INSTITUTIONS INVOLVED IN THE HUMAN GENOME PROJECTHUMAN GENOME PROJECT
• Haploid Human Genome = 3.2 X 109 nt
• Only about 25% of the genome is transcribed into RNA
• < 2%-3% codes for proteins
THE HUMAN GENOME THE HUMAN GENOME PROJECT (1)PROJECT (1)
• 45% of sequences derive from transposable elements (in a region of chrchr XX 89% of DNA is made of Tns)
●13-20% of the genome is made of repetitive elements
• Chr 17, 19 and 22 have the major gene density
• Chr 4, 13, 18, X and Y have the minor gene density
• 30-35,000 is the estimated number of human genes
• Due to isoforms and post-translational modifications the human genome is expected to generate more than 100,000 proteins
• Avarage n° of exons in a human gene = 8.8
• Avarage exon length = 145 bp (the gene coding forthe protein titin contains 234 exons!)
THE HUMAN GENOME THE HUMAN GENOME PROJECT (2)PROJECT (2)
the protein titin contains 234 exons!)
• Avarage intron length = 3,365 bp
• Avarage 5’UTR length = 300 bp
• Avarage 3’UTR length = 770 bp
• Total avarage gene length = 27,000 bp
A TYPICAL HUMAN GENE LOCUSA TYPICAL HUMAN GENE LOCUS
50 Kbp SEGMENT OF THE HUMAN CHROMOSOME 7 BELONGING TO T-CELL ββββ HUMAN RECEPTOR
GENE SEGMENTSCODING
FOR A PARTOF THE β T-CELL
LINEs; SINEs;
MICROSATELLITES (STR)
GENE OF TRIPSINOGEN
OF THE β T-CELLRECEPTOR PROTEIN
PSEUDOGENE(RELATED TO THE
FUNCTIONAL MEMBERSOF THE TRIPSINOGEN
GENE FAMILY)
LINEs; SINEs;
LTRs AND TRANSPOSONS
MICROSATELLITES (STR)
A TYPICAL EUCARYOTIC GENE STRUCTUREA TYPICAL EUCARYOTIC GENE STRUCTURE
5’UTR5’UTR 3’UTR3’UTR
AN EXAMPLE OF HOW COMPLEX IS THEAN EXAMPLE OF HOW COMPLEX IS THEGENE EXPRESSION: GENE EXPRESSION:
SPATIOSPATIO--TEMPORAL PATTERNSTEMPORAL PATTERNS
FetalFetal HbHb((GGγγγγγγγγ and and AAγγγγγγγγ))[[LiverLiver]]
AdultAdult HbHb((δδδδδδδδ and and ββββββββ))[[BoneBone MarrowMarrow]]
ATGGAGGAGGACATGTACGTGGACATTTTCCTGGACCCTTATACCTTCCAGATGGAGGAGGACATGTACGTGGACATTTTCCTGGACCCTTATACCTTCCAGGATGACTTTCCTCCAGCTACGTCTCAACTATTCAGCCCAGGAGCGCCTTTAGATGTGCACCCACTTAATCCATCCAATCCAGAGACTGTATTTCATTCACATCTTGGTGCAGTCAAAAAGGCACCCAGTGACTTTTCATCTGTGGATCTAAGCTTCTTACCAGATGAACTTACCCAAGAAAATAAAGACCGAACTGTCACTGGAAACAAAGTCACAAATGAGGAAAGCTTTAGGACTCAAGATTGGCAAAGTCAGTTGCAGTTGCCTGATGAACAAGGCAGTGGGCTGAACTTGAATAGCAACAGTTCACCAGATACCCAGTCATGTCTGTGCTCTCATGATGCTGACTCCAACCAGCTCTCTTCAGAAACACCAAATTCCAATGCCTTACCTGTGGTATTGATATCATCCATGACACCAATGAACCCTGTTACAGAATGTTCTGGAATTGTGCCTCAATTACAGAATGTAGTTTCCACTGCAAATCTGGCCTGTAAATTGGATCTGAGAAAAATAGCTTTGAATGCCAAAAACACAGAATATAATCCAAAGAGGTTTGCTGCAGTCATAATGAGGATCCGAGAGCCAAGGACCACAGCTCTTATATTTAGCTCTGGGAAAGTGGTCTGTACAGGAGCCAAAAGTGAAGACGAGTCTCGGCTGGCAGCAAGAAAGTATGCTCGCGTGGTGCAGAAGCTGGGGTTCCCCGTCAGATTCTTCAATTTTAAAATTCAGAACATGGTTGCAAGCTGTGATGTGAAATTTCCCATCAGGCTGGAGATTTTGGCACTAACCCATCGGCAGTTCAGTAGTTATGAGCCTGAACTGTTCCCTGGCCTTATTTATAAGATGGTGAAACCGCAGGTTGTGCTGCTCATCTTTGCATCTGGAAAGGTTGTACTGACAGGTGCCAAAGAGCGTTCTGAGATCTACGAAGCATTTGAAAACATGTATCCTATTCTAGAAAGTTTTAAGAAAGTCTGAATGGAGGAGGACATATACCTGGACCTCTTCCTGGATCCTTATACCATCCAGGATGACTTTCCTCCAGCTATGTCTCAACTGTTCAGCCCAGGAGTGCCTTTAGACATGCACTCACTTCCATCTAATCCAGAGACTGTGTTTCATCCACATCTTGGTGGAGTCAAAAAGGCATCCACTGACTTTTCATCTGTGGATCTAAGCTTCTTACCAGATGAACTTACCCAAGAAAATAGAGACCAAACTGTCACTGGAAACAAGCTGGCAAGTGAGGAAAGCTGTAGGACTCGAGATCGACAAAGTCAGTTGCAGTTGCCCGATGAACATGGCAGTGAGCTGAACTTGAATAGCAACAGTTCACCAGATCCCCAGTCATGCCTGTGCTTTGATGATGCTCACTCCAACCAGCCCTCTCCAGAAACACCAAACTCCAATGCCTTACCTGTGGCATTGATAGCATCCATGATGCCAATGAACCCTGTTCCAGGATTTTCTGGAATTGTGCCTCAATTACAGAATGTAGTTTCCACTGCAAATCTGGCCTGTAAATTGGATCTGAGAAAAATAGCCCTGAATGCCAAAAACACAGAATATAACCCAAAGAGGTTTGCTGCAGTAATAATGAGGATCCGAGAGCCAAGGACAACAGCTCTCATCTTTAGCTCTGGGAAAGTGGTCTGTACAGGAGCCAAAAGTGAAGAGGAGTCTCGGCTGGCAGCGAGAAAGTATGCTCGTGTGGTGCAGAAGCTCGGGTTCCCTGTCAGATTCTTCAATTTTAAAATTCAGAACATGGTTGGAAGCTGTGATGTGAAATTTCCCATCAGGCTGGAGATTTTGGCACTAACCCATCGGCAGTTCAGTAGTTATGAACCTGAACTTTTCCCCGGCCTTATTTATAAGATGGTAAAACCACAGGTTGTGTTGCTAATCTTTGCATCTGGAAAAGTTGTGTTAACAGGTGCCAAAGAGCGTTCTGAGATCTATGAAGCATTTGAAAACATGTATCCTATTCTAGAAAGTTTTAAGAAAGTCTGAATGGAGCAGGAGGAGACCTACCTGGAGCTCTACCTGGACCAGTGCGCCGCTCAGGATGGCCTTGCCCCACCCAGGTCTCCCCTGTTCAGCCCAGTTGTACCTTATGATATGTACATACTGAATGCATCCAATCCGGATACTGCATTTAATTCGAACCCTGAAGTCAAAGAAACATCTGGTGATTTCTCATCTGTGGATCTTAGCTTCCTACCAGATGAAGTTACCCAGGAAAATAAAGACCAGCCTGTCATTAGCAAACACGAAACTGAAGAAAATTCTGAAAGCCAAAGTCCACAAAGTAGGTTGCCATCACCCAGCGAACAGGACGTTGGGCTGGGCTTAAACAGCAGCAGTTTGTCAAATTCCCATTCACAGCTGCACCCTGGTGATACTGACTCAGTCCAGCCCTCTCCTGAGAAACCAAACTCCGACTCCTTGTCTCTGGCATCCATAACTCCCATGACACCAATGACCCCTATTTCAGAATGTTGTGGAATTGTACCTCAACTACAGAATATAGTTTCCACTGTAAACCTGGCCTGTAAGTTGGATCTGAAGAAAATAGCTTTGCATGCAAAAAATGCAGAATATAACCCAAAGAGGTTTGCTGCTGTCATAATGAGGATCCGAGAGCCCAGGACAACAGCCCTTATATTTAGCTCTGGGAAGATGGTCTGCACGGGAGCCAAAAGTGAAGAGCAGTCTCGACTTGCAGCAAGAAAATATGCTCGTGTGGTGCAGAAGCTTGGGTTCCCTGCCAGATTCCTCGATTTTAAAATTCAGAACATGGTTGGAAGCTGTGATGTGAGATTTCCCATCAGGCTGGAAGGTTTGGTGCTAACCCATCAGCAGTTCAGTAGTTACGAGCCTGAACTGTTTCCTGGTCTTATTTATAGAATGGTAAAACCACGAATTGTGTTGCTTATCTTTGTATCTGGAAAAGTTGTGTTGACAGGTGCCAAAGAACGTTCTGAGATCTATGAAGCATTTGAAAACATCTATCCTATTCTAAAAGGTTTTAAAAAAGCCTGAGAAGTCCCCTGGGTAACTTCCAGGCAGCTTCATTTCTGAAGAGTCCAAACTGCAGCATAGAGGACTTATGAAAAACTGTAAAAAATTGGTTTTAAGTGTTCCATTAAACCCAAAGAAAACAGTCACACAACAAAGCCAGACACAGAAAATTAGGGTGACATGTTTCCTGTCATATGTGGAGCCTAGAGAACATAGAGATGATGTGAAAGCAGAAGGAGCTATCAAGAAAAAGGAAAGCAGATGGGGCAGCAGATCCATGGGAATACTGGCAGAACTGTATAATGGAAGAATGTCGTATGCACATATGAACATGTCATAATGAAACCTAGTATTTTGTACAGTTAATATGGACTAGACAATAGCACAAAGAAATTAGAGATTAGTCTAGCTATATGAAGAGGCTACATCAAAGATCACTCCTTTTTGATGGACAAATTTAATTCCTTATAACTGTAGAGCTGAGATATTCACTTGCTTGTCAGACATTAAATGTATCCCACTCTTAGGGTCTAGAAGTTACCCAGACTTCTTGTACCATGGTCCCATCTATCTTCAAAGTCAGCAGTGACGACTCTGCCTTATGACAAGGTCATCTCCTTCACTTGCTTGTCAGACATTAAATGTATCCCACTCTTAGGGTCTAGAAGTTACCCAGACTTCTTGTACCATGGTCCCATCTATCTTCAAAGTCAGCAGTGACGACTCTGCCTTATGACAAGGTCATCTCCTGCTTTCAAATCCCTCCCAAAGAGTGGCCAATTCCTCCTTGGCTGCTCAGTCAGTAAGGGCAGGCTTGGATCCTTTCCCTTTCCTAACAATGGACTTGGAATTTTAATTACATCTTCAAAACCCAAGAGCATTTGGTTTTTTTTAGATAACTGGGAGATACATTTGGAGATAGGGATTTGGGGAGCCACCGAAACATTCTACCTACCATAGGAAATAGTTATAAATCTATTTTACTGGCTGGAGAGATGGCCAAGCAGTTAAGAATACTTTCTGCTTTTTCAAAGGATAGAAATTCTGTTCCTAGCACCCACACTGGGCTTCTTAGTGATTCCAACTCTACAGGACCTGATGCCTCCTTCTCTCTGGCTTCCTTAGATACCAGTTTGTACTGGCACATGCATATGCACAGGAGAAGGCTCTCTCTCTCTCTCTCCCCCCCCCCCCTCTCTCTCTCTCACACACACACACAAGATGGTGAGATATAATTAATAAAATAAAGTAAAATTTGGATCTGTTTTAGTCAGTTTGGGATGCCATAATAAAACACCACAAACTGGGCAGTTTAAACCACAGAAATTTCCTTCATAGTTCTGAAGGCTGGAGATCTAAGATCAAGGTCCCTGCAGATTTGGTCTCTCCTGTAGCAATCCTCCATCTTTCCTTTTAGGTAGCTGCCTTAATGTTGCTCTTTTTACAGCTTTTTCTTTGTATTTCTATGAAAACATCAGACATATTGGATTGGGGCTTCTACACATGATCTTCATGGGATAAGCAATAACCATAGTTACTGATCTGTGAGGCTGGTTCTGAGTGTGCAGCTCAGTAGGCTGTCTCATTTACAGACACTATGACATTACATCACACATCACTATATAAATCCCAGATTTTTCAAAAGGATCCCCCTATTTTTATTGGAATGTCTGACTCTAGTGCAGGTTATCCAAGCTCCATTCTCAGGTTCGTTTTATCCACCAAGACTGAGCAGATGAGCTGGGCACAGAGACATGATGATGAATAATTTAAATTGTTCCTTTTAAACAGTAGAATCAAGTAAGGAAGATTTAAAAATACATTTTGCAATCTCTTACATCAAAGTGTCTTCTTCTAGAACAGTTCAATACAGTTAAGCTAAGACATTTGAATTAAAGCGTTTAAGAAAGAAAAGCTTCTCTGGATATTTGGTTTTACATTAACTTCTTGAGTTGTCTGAACCCTAACTGTGGAATTTGCACAGCTGTAGGCAAATTCTCTGTAATAGGTGAAAATCTACCTGGGGTGTGAAGGTGAAGAATAATTACAGAAATATCACATCTGAATAGATGAGGGGATTCAGCGGGCAAGGGTGCTTGCCACCAAGCCTGACACTCTGGGTTTGATCCTTGTGTTTCTTCCAGAGCTGGAAGGAGAGAACCTACTCCTGAAAATTGTCTTCTGACCATAACATGAGCTCTGCACTGTGCATGTGTCCATGCACACATGCCAATGAAGATAAATCAATATTAGAAATATCACATCTAAGAATCTGGGTATGGTGATGCTCATGCATGTTGTAACCCCAGAACTTAGGAGCTGGAGGATATACAAGTTTGTGGCTAGCCTGGACTACATGAGAAGAGAAGGGGGAAGGGAAAGAGAAGGAAAAGAAGAAAAGAAAAGGAAAAGGATAAGGATAAAGGCAGAAGAGAAAAGCATTCTTTTCTCACTTGCACAATGAGAAAACCTTATCATGCTACTCTACTGGAAGCACTAGTCTCGGCCCTCCTCTTCTTCTGGGTGCCACCAGCTGTGTCTTGCCTGGCTCATCAACTCCTTCTCTGCTTCTCACCTGACTCCTCAGCTCATTCACAGCATCTGTGCAAGGCAGCAGAGCTGGTCCCGCCTCACTGCGTGCTCCCTGAGGCTGATAAAAGGTATCTGCTCCCACAGCCAGACTGGTACTAACAAAGCTTCTTCCACTTGCCTGGACGCTGATTCCTTTGCTTGTCCTCAGCTCTACGATGACTTTCCTCCAGCTATGTCTCAACTGTTCAGCCCAGGAGTGCCTTTAGACATGCACTCACTTCCATCTAATCCAGAGACTGTGTTTCATCCACATCTTGGTGGAGTCAAAAAGGCATCCACTGACTTTTCATCTGTGGATCTAAGCTTCTTACCAGATGAACTTACCCAAGAAAATAGAGACCAAACTGTCACTGGAAACAAGCTGGCAAGTGAGGAAAGCTGTAGGACTCGAGATCGACAAAGTCAGTTGCAGTTGCCCGATGAACATGGCAGTGAGCTGAACTTGAATAGCAACAGTTCACCAGATCCCCAGTCATGCCTGTGCTTTGATGATGCTCACTCCAACCAGCCCTCTCCAGAAACACCAAACTCCAATGCCTTACCTGTGGCATTGATAGCATCCATGATGCCAATGAACCCTGTTCCAGGATTTTCTGGAATTGTGCCTCAATTACAGATGACTTTCCTCCAGCTATGTCTCAACTGTTCAGCCCAGGAGTGCCTTTAGACATGCACTCACTTCCATCTAATCCAGAGACTGTGTTTCATCCACATCTTGGTGGAGTCAAAAAGGCATCCACTGACTTTTCATCTGTGGATCTAAGCTTCTTACCAGATGAACTTACCCAAGAAAATAGAGACCAAACTGTCACTGGAAACAAGCTGGCAAGTGAGGAAAGCTGTAGGACTCGAGATCGACAAAGTCAGTTGCAGTTGCCCGATGAACATGGCAGTGAGCTGAACTTGAATAGCAACAGTTCACCAGATCCCCAGTCATGCCTGTGCTTTGATGATGCTCACTCCAACCAGCCCTCTCCAGAAACACCAAACTCCAATGCCTTACCTGTGGCATTGATAGCATCCATGATGCCAATGAACCCTGTTCCAGGATTTTCTGGAATTGTGCCTCAATTACAAGAACTTAGGAGCTGGAGGATATACAAGTTTGTGGCTAGCCTGGACTACATGAGAAGAGAAGGGGGAAGGGAAAGAGAAGGAAAAGAAGAAAAGAAAAGATAATGAGGATCCGAGAGCCCAGGACAACAGCCCTTATATTTAGCTCTGGGAAGATGGTCTGCACGGGAGCCAAAAGTGAAGAGCAGTCTCGACTTGCAGCAAGAAAATATAATGAGGATCCGAGAGCCCAGGACAACAGCCCTTATATTTAGCTCTGGGAAGATGGTCTGCACGGGAGCCAAAAGTGAAGAGCAGTCTCGACTTGCAGCAAGAAAATATAATGAGGATCCGAGAGCCCAGGACAACAGCCCTTATATTTAGCTCTGGGAAGATGGTCTGCACGGGAGCCAAAAGTGAAGAGCAGTCTCGACTTGCAGCAAGAAAATATAATGAGGATCCGAG
GENOMICSGENOMICS
GenomicsGenomics
Structural Genomics
Functional Genomics
Comparative Genomics
STRUCTURAL GENOMICSSTRUCTURAL GENOMICS
From Genome Sequencing (I°, II° and III° generation)
…To determination of the primaryand tertiary structures of all proteins of a given organism
Structural genomics (SG) Structural genomics (SG) is an internationalis an international
effort toeffort to determine the threedetermine the three--dimensionaldimensionalproteins of a given organismeffort toeffort to determine the threedetermine the three--dimensionaldimensional
shapes of all important biological macromolecules,shapes of all important biological macromolecules,
with a with a primary focus on proteinsprimary focus on proteins
FUNCTIONAL GENOMICSFUNCTIONAL GENOMICS
TranscriptomicsTranscriptomics ProteomicsProteomics
InteractomicsInteractomics… with the help of
BioInformatics
COMPARATIVE GENOMICSCOMPARATIVE GENOMICS
Molecular phlylogenesis
TECHNIQUES USED IN STRUCTURAL TECHNIQUES USED IN STRUCTURAL GENOMICSGENOMICS
oo SequencingSequencing
Large scale cloning, expression Large scale cloning, expression oo Large scale cloning, expression Large scale cloning, expression
and purificationand purification
oo XX--rayray crystallographycrystallography
oo NMR NMR spectroscopyspectroscopy
oo Computational approaches such as homology Computational approaches such as homology modellingmodelling
This is performed in dedicated This is performed in dedicated centers of structural genomicscenters of structural genomics
STRUCTURAL GENOMICS (SG) CENTERS (1)STRUCTURAL GENOMICS (SG) CENTERS (1)
STRUCTURAL GENOMICS (SG) CENTERS (2)STRUCTURAL GENOMICS (SG) CENTERS (2)
TECHNIQUES USED IN FUNCTIONALTECHNIQUES USED IN FUNCTIONALGENOMICSGENOMICS
oo CloningCloning
oo PCRPCR
oo RTRT--PCRPCR
oo RealReal--Time RTTime RT--PCRPCR
oo MicroarrayMicroarray and DNA and DNA ChipsChips
oo RNAiRNAi
oo TransgeneticsTransgenetics organismsorganisms
oo KnockKnock--out out animalsanimals
TECHNIQUES USED IN COMPARATIVETECHNIQUES USED IN COMPARATIVEGENOMICSGENOMICS
oo SequencingSequencing
oo BioInformaticsBioInformaticsoo BioInformaticsBioInformatics
THEORY THEORY
AND BASIS OF AND BASIS OF
QUANTITATIVE QUANTITATIVE
REAL TIME PCRREAL TIME PCR
qRT-PCR EVOLUTION 1985 Mullis and co-workers invented the polymerase chain reaction (PCR).
PCR PHASES
The amplification of any template is defined by four phases: 1 –
baseline; 2 – exponential; 3 – linear and 4 – plateau.
Cycle
All reagents are
present
Some of the
reagents start
wearing out
All the reagents
finished
PCR PHASES IN LINEAR VIEW
PCR PHASES IN Log VIEW
LINEAR AND Log VIEW OF 96 REPLICATES
NOTENOTE: ONLY IN THE EXPONENTIAL PHASE TWO PCR REACTIONS ARE
COMPARABLE
Rn = Normalized Reporter Signal, ie: The fluorescence emission intensity of the reporter dye divided by the fluorescence emission intensity of the passive reference dye.
∆∆∆∆Rn = The magnitude of the fluorescence signal generated during the PCR at each time point
“Real-time PCR is the continuous collection of
fluorescent signal from one or more
polymerase chain reactions over a range of
cycles.”
“Quantitative real-time PCR is the conversion “Quantitative real-time PCR is the conversion
of the fluorescent signals from
each reaction into a numerical value for each
sample.”
From “Real Time PCR”, edited by M T Dorak , 2007.
One-step RT-PCR performs RT as well as PCR in a single buffer system
Two-step RT-PCR is performed in two separate reactions
Primers for one- and two-step RT-PCR
Blue
Blue
Blue/Green
Green
Green
Orange
Red
SYBR GREEN AS AN SYBR GREEN AS AN
EXAMPLE EXAMPLE EXAMPLE EXAMPLE
OF A FREE DYEOF A FREE DYE
Pros:
1) Low cost;
2) Ease of assay development;
SYBR Green: an example of incorporation of a free dye into
the newly formed double-stranded DNA product
DNA-dye complex results in a dramatic
increase in fluorescence output 2) Ease of assay development;
3) The same detection mechanism
can be used for every assay
Cons:
1) Not specificity;
2) Toxic for reaction;
3) No Multiplex reaction
fluorescence outputof roughly 2,000 times the initial,
unbound, fluorescent signal
SYBR® Green NEEDS DISSOCIATION CURVEThe PCR product Tm is 87.5°C (curves indicated by +).
Complete absence of primer-dimer is rarely achieved in the PCR negative control (curve indicated by –). As seen in this
example, 1 out of 3 negative triplicates shows dimers (with a Tm of 78.5°C).
The two sets of curves are usually clearly separated with a 10°C shift between Tm of primer-dimers and the specific PCR
product.
dRFU
/dT
The SYBR Green I dye chemistry can be
used for quantification assay types
including:
• One-step RT-PCR for RNA
The SYBR Green I dye chemistry
• One-step RT-PCR for RNA
quantification
• Two-step RT-PCR for RNA
quantification
• DNA quantification
REPORTERREPORTER
DYESDYES
LUX™ (Light Upon EXtension) primers: an example of dye-primer based signaling systems
5’
3’
Usually FAM or JOEWhen the primer anneals and the extension goes up, there is a significant, as much as 5’is a significant, as much as
ten-fold, increase in fluorescence
TaqMan® Probe: an example of 5′ fluorogenic nuclease assay probes
There is a direct and inverse correlation between probe length and
quenching efficiency. For this Hydrolysis probes are The TaqMan® name comes from this quenching efficiency. For this reason, TaqMan® probes are kept to
less than30 bases in length.
Hydrolysis probes are designed to have a Tm 9–10˚C higher than their
matched primers
hydrolysis step in an analogy to the
action of the old computer game
character, Pacman.
TaqMan® Probe: an example of 5′ fluorogenic nuclease assay probes
5’-R 3’-Q
The TaqMan Probe-based chemistry can be
used for the following assay types:
• Quantification, including:
– One-step RT-PCR for RNA quantification
TaqMan Probe-based chemistry
– One-step RT-PCR for RNA quantification
– Two-step RT-PCR for RNA quantification
– DNA quantification
• Allelic Discrimination
• Plus/Minus
FRET (fluorescence resonance energy transfer)
TaqMan Probes: how do they function
TaqMan® Gene Expression Assays are a comprehensive collection of predesigned
primer and probe sets, which help researchers quickly and easily perform quantitative
gene expression studies on human, mouse, rat, Arabidopsis, and Drosophila genes.
1. FRET depends on the donor and acceptor
molecules being in close proximity (10–100 Å)
and falls off with the sixth power base 10 of
the distance between the two molecules.
DELTA ASSAY RULES
the distance between the two molecules.
2. The other major requirement is that the
excitation wavelength of the acceptor be
close to the emitted wavelength of the
acceptor dye (Didenko, 2001).
SYBR GREEN vs TaqMan PROBES
Minor Groove Binding (MGB) protein bearing PROBES
• MGB molecule is added to one end of the nucleic acid
sequence;
• Increased affinity and higher
MGB are used primarily for SNP (single nucleotide
Non Fluorescent Quencher(NFQ)
affinity and higher Tm due to MGB
moiety;
•Applied Biosystems and Nanogen are the major MGB Probes
manufacturers
SNP (single nucleotide
polymorphism) and allelic
discrimination assays
MOLECULAR BEACONS• 4–6 base self-complementary
sequence extension on each end;
• Perfect stem structure bringing the reporter and quencher dyes close together dyes close together forming a close FRET
association;
• During the annealing step, the probe
becomes unfolded.
UNIVERSAL THERMAL CYCLING PROTOCOL
Taq Polymerase
UNG Activation
Polymerase Activation Denaturation and
Annealing/Extension
Time about 1h30’
UNIVERSAL FAST THERMAL CYCLING PROTOCOL
Initial DenaturationDenaturation
Denaturation andAnnealing/Extension
Time about 40’
Choosing the Assay Type
Primer and probe design guidelines for quantitative assays
G determines quenching at 5’
At the end select At the end select
the primer the primer
concentrations that concentrations that
Optimization of Primer Concentration
concentrations that concentrations that
provide the lowest provide the lowest
Ct and highest Ct and highest
∆Rn for a fixed ∆Rn for a fixed
amount of target amount of target
templatetemplate
Optimization of Probe Concentration By using a 250 nM By using a 250 nM
concentration, probe concentration, probe limitation is avoided and limitation is avoided and
largelargevalues are ensured. values are ensured. Large ∆Rn values Large ∆Rn values Large ∆Rn values Large ∆Rn values
indicate a robust assay indicate a robust assay that is performing atthat is performing athigh efficiency, giving high efficiency, giving high product yield and high product yield and allowing more accurate allowing more accurate
peakpeakmeasurement.measurement.
PRELIMINARY DATA ANALYSIS
Overestimated quantity, degraded template
y = mx + b with: y = Ct
m = slope
x = log10 (quantity of template)
b = y-intercept
Efficiency (εεεε) = [10(-1/slope)]-1
Integrity = r2
Sensitivity = y-intercept
Ct
Quantity oftemplate (molecules)
1 10 102 103 104 105
33
29.7
26.4
23.1
19.8
16.5
Standard Curve
Sensitivity = y-intercept
IF ε = ε = ε = ε = −−−−3.3 3.3 3.3 3.3 and FAM is used as reporter dye:
100 101 102 103 104 105 106 107 108 109 1010 N° of Molecules
33 29.7 26.4 23.1 19.8 16.5 13.2 9.9 6.6 3.3 0 Cycles
If diluitions of factor 10 are considered, and if maximum efficiency is imagined,
(during exp phase)the number of cycles necessary to go from a diluition to the
following will be 2n = 10 n = log210 = 3.32
(molecules)
EFFECT OF
BASELINE
SETTINGS
BASELINE IS THE
ORIZONTAL SYSTEM
OF “CLEANING”
Baseline
Threshold
PRELIMINARY DATA
ANALYSIS: A
GENERAL
FLOWCHART
SECONDARY ANALYSIS
RELATIVE QUANTIFICATION
Relative standard Relative standard curve methodcurve method
Comparative CtComparative Ctmethod (∆∆Ct)method (∆∆Ct)
Performing the Run
Determining the Relative Values
Relative standard curve methodRelative standard curve method
Standard NormalizedMean Value
Standard Deviation ofthe Ratio betwwenc-myc mean value andGAPDH mean value
Mean ValueStandard Deviation
NormalizedMean Value
GAPDH mean value (see CV)
Calculating the Coefficient of Variation (CV)
RELATIVE QUANTIFICATION
Relative standard Relative standard curve methodcurve method
Comparative CtComparative Ctmethod (∆∆Ct)method (∆∆Ct)
The comparative CT method is similar to the
relative standard curve method, except
Comparative Ct method (∆∆Ct)Comparative Ct method (∆∆Ct)
NOTENOTE: For the ∆∆Ct calculation to
be valid, the efficiency of the
target amplification andthat it uses an arithmetic formula rather than a
standard curve to achieve the same
result for relative quantification.
target amplification and
the efficiency of the reference
amplification must be approximately
equal.
Due to the inverse proportional relationship between the threshold
cycle (Ct) and the original gene expression level, and the doubling
of the amount of product with every cycle, the original expression
level (L) for each gene of interest is expressed as:
L=2-Ct
To normalize the expression level of a gene of interest (GOI) to a
housekeeping gene (HKG), the expression levels of the two genes are
Derivation of the 2-∆∆∆∆∆∆∆∆Ct formula
housekeeping gene (HKG), the expression levels of the two genes are
divided:
2-Ct(GOI)/2-Ct(HGK) = 2 -[Ct(GOI)-Ct(HGK)] = 2-∆∆∆∆Ct
To determine fold change in gene expression, the normalized expression
of the GOI in the experimental sample is divided by the normalized
expression of the same GOI in the control sample:
2-∆∆∆∆Ct(exp)/2-∆∆∆∆Ct(ctrl) = 2-∆∆∆∆∆∆∆∆Ct
The complete calculation is as follows:
[2-Ct (GOI) Exp / 2-Ct (HGK) Exp] / [2-Ct (GOI) Ctrl / 2-Ct (HGK) Ctrl] =
= 2-[Ct (GOI) - Ct (HGK)] Exp / 2-[Ct (GOI) - Ct (HGK)] Ctrl =
= 2-∆∆∆∆Ct Exp / 2-∆∆∆∆Ct Ctrl = 2-∆∆∆∆∆∆∆∆Ct
An example of Comparative Ct Method
Example of a Custom MicroFluidic Card Map
24 Genes
4 Samples or 4 Samples or 1 Sample and 4 Replicates