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