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[email protected] GenXPro GmbH, Frankfurt am Main www.genxpro.de. Nucleotide based information:. mRNA SuperSAGE qRT -PCR, Taq -Man assays, Real-Time PCR service - 3‘- and 5‘- RACE - Normalization of cDNA libraries ( qual . Information) 2) non coding RNA ( microRNA )

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slide1

[email protected]

GenXPro GmbH, Frankfurt am Main

www.genxpro.de

slide2

Nucleotide based information:

            • mRNA
            • SuperSAGE
            • qRT-PCR, Taq-Man assays, Real-Time PCR service
  • - 3‘- and 5‘- RACE
  • - NormalizationofcDNAlibraries (qual. Information)
  • 2) non coding RNA (microRNA)
  • Genomic DNA: - Digital karyotyping (DK)
  • - MethylationspecificDK (MSDK)
  • - Genotyping
  • - Identificationof SNPs
  • - molecularmarkers

Transcription :

slide3

Our Service Portfolio

  • Digital Gene Expression Service:
  • from cells/tissues to annotated/BLASTed libraries in one to three month
  • Normalization of cDNA, sequencing and assembly
  • - RNA seq, microRNAs
  • - Taq-Man assays, Real-Time PCR service
  • Identification of SNPs, molecular (genetic) markers
  • Copy number variations (CNVs)
  • - Epigenetics
slide4

Transcriptome Analysis & Gene Discovery

SuperTag Digital Gene Expression Profiling

(ST-DGE)

An Improved version of SuperSAGE, applying second generation sequencing and a bias free PCR technology for optimal tag-to-gene association and quantification.

slide5

Digital Gene expression Profiling

Principle

What Gene is expressed and how often ?

Anchoring Enzyme

Streptavidin-Beads

Tagging Enzyme

AAAAAAA-3’

TTTTTTT-5’

5’

3’

cDNA

5’

3’

AAAAAAA-3’

TTTTTTT-5’

cDNA

5’

3’

AAAAAAA-3’

TTTTTTT-5’

cDNA

AAAAAAA-3’

TTTTTTT-5’

5’

3’

cDNA

Sequencing of Millions of 26 bp SuperTags

Counting, BLAST

slide6

Digital Gene expression Profiling

Principle

Streptavidin-Beads

1.Digestion with Anchoring Enzyme

AAAAAAA-3’

TTTTTTT-5’

5’

3’

cDNA

5’

3’

AAAAAAA-3’

TTTTTTT-5’

cDNA

5’

3’

AAAAAAA-3’

TTTTTTT-5’

cDNA

AAAAAAA-3’

TTTTTTT-5’

5’

3’

cDNA

slide7

Digital Gene Expression Profiling

Principle

What Gene is expressed and how often ?

Streptavidin-Beads

1.Digestion with Anchoring Enzyme

AAAAAAA-3’

TTTTTTT-5’

5’

3’

cDNA

5’

3’

AAAAAAA-3’

TTTTTTT-5’

cDNA

5’

3’

AAAAAAA-3’

TTTTTTT-5’

cDNA

AAAAAAA-3’

TTTTTTT-5’

5’

3’

cDNA

slide8

Digital Gene Expression Profiling

Principle

What Gene is expressed and how often ?

Streptavidin-Beads

1.Digestion with Anchoring Enzyme

AAAAAAA-3’

TTTTTTT-5’

Linker 1

cDNA

2. First Linker Ligation

3. Digestion with Tagging Enzyme

AAAAAAA-3’

TTTTTTT-5’

Linker 1

cDNA

4. Recovery of Linker-Tags

AAAAAAA-3’

TTTTTTT-5’

Linker 1

cDNA

AAAAAAA-3’

TTTTTTT-5’

Linker 1

cDNA

Highly specific 26bp “SuperTags“

slide9

Digital Gene Expression Profiling

Principle

What Gene is expressed and how often ?

Streptavidin-Beads

1.Digestion with Anchoring Enzyme

AAAAAAA-3’

TTTTTTT-5’

Linker 1

Linker 2

2. First Linker Ligation

3. Digestion with Tagging Enzyme

AAAAAAA-3’

TTTTTTT-5’

Linker 1

Linker 2

4. Recovery of Linker-Tags

5. Second Linker Ligation

AAAAAAA-3’

TTTTTTT-5’

Linker 1

Linker 2

5. PCR

AAAAAAA-3’

TTTTTTT-5’

Linker 1

Linker 2

slide10

Digital Gene Expression Profiling

Principle

What Gene is expressed and how often ?

Streptavidin-Beads

1.Digestion with Anchoring Enzyme

AAAAAAA-3’

TTTTTTT-5’

Linker 1

Linker 2

2. First Linker Ligation

Linker 1

Linker 2

Linker 1

Linker 2

3. Digestion with Tagging Enzyme

AAAAAAA-3’

TTTTTTT-5’

Linker 1

Linker 2

Linker 1

Linker 2

4. Recovery of Linker-Tags

Linker 1

Linker 2

Sequencing of Millions of Tags

5. Second Linker Ligation

AAAAAAA-3’

TTTTTTT-5’

Linker 1

Linker 2

Linker 1

Linker 2

5. PCR

Linker 1

Linker 2

6. Next-Generation Sequencing

AAAAAAA-3’

TTTTTTT-5’

Linker 1

Linker 2

Linker 1

Linker 2

7. Counting of Tags, Bioinformatics

Linker 1

Linker 2

Counting, BLAST

slide11

Digital Gene expression Profiling

Principle

Anchoring Enzyme

Streptavidin-Beads

Tagging Enzyme

AAAAAAA-3’

TTTTTTT-5’

5’

3’

cDNA

5’

3’

AAAAAAA-3’

TTTTTTT-5’

cDNA

5’

3’

AAAAAAA-3’

TTTTTTT-5’

cDNA

AAAAAAA-3’

TTTTTTT-5’

5’

3’

cDNA

Sequencing of Millions of 26 bpSuperTags

Counting, BLAST

quality of digital gene expression data depends on

Digital Gene Expression Profiling

Quality

Quality of digital gene expression data depends on:

1. Quality ofthe Tag (whatgeneisexpressed?)

2. Quantityofthe Tags (howoftenisthegeneexpressed?)

slide13

Tag-Quality

The Tagging Enzyme determines Quality of Tags:

LongSAGE, other DGE platforms

MmeI:

18-20 bp

5’- GGGACNNNNNNNNNNNNNNNNNNNN -3’

3’- CCCTGNNNNNNNNNNNNNNNNNN -5’

SuperSAGE, SuperTAG-DGE

EcoP15I : 26 bp (=SuperTAG)‏

5’-CAGCAGNNNNNNNNNNNNNNNNNNNNNNNN -3’

3’-GTCGTCNNNNNNNNNNNNNNNNNNNNNNNNNN -5’

slide14

Tag Quality

What gene?

SuperTAGs allow unequivocal Identification

of the corresponding Gene

slide15

Tag Quality

Advantages of the SuperTAG

20 bp versus 26 bp

18-20bp (MmeI, LongSAGE)

26 bp (Ecop15I, SuperTAG)

Only the 26 bp tag can differentiate between the transcripts !

slide16

Problem of PCR-introduced BIAS

Certain tags are preferentially amplified during PCR

biased quantification

The Solution: GenXPro’s bias-proof adapters (patent pending)

secure quantification

slide17

Downstream applications &

Advantages of the SuperTAG

26 bp SuperTAGs can:

  • Directly be used as highly specific primer for PCR

3‘- and 5‘- RACE, in vitro PCR, qRT-PCR: new genes & non-model organisms can be analyzed.

  • Serve as specific probes: identification of genomic or cDNA clones
  • Be directly spotted on a microarray for HT analysis1
  • Be used for the simultaneous analysis of two or more organisms (pathogen/host)2

Matsumura et al. (2006) Nature Methods 3:469-474

2. Matsumura et al. (2003) PNAS 100: 15718-15723

slide18

RNAseq vs. ST-DGE (SuperSAGE)

Mean transcript size : 2 500 bp

Tag size: ( ) 26 bp

AAAAAAA-3’

TTTTTTT-5’

5’

3’

cDNA

For the same depth of analysis, about

(50-)100 times more sequencing is required

slide19

Digital Gene Expression vs. Microarrays

Major Advantages of SuperTAG-DGE versus Microarrays

  • No false positives, no cross hybridisation
  • Open architecture platform: any gene detected, novel genes, unexpected transcripts, antisense transcripts
  • Reliable quantification of the transcriptome:
  • counts vs. semi-quantitative light signal intensities
  • Higher dynamic range: log2>6 vs. log2<3
  • Rare transcripts are exactly quantified
slide20

Digital Gene Expression vs. Microarrays

SuperTAG-DGE includes rare Transcripts

About 80–95% of all mRNA species are present in five or fewer copies per cell. These rare transcripts make up 35–50% of all the mRNAs.

slide21

SuperSAGE-Analysis: Transcript Frequencies

Example: 3.455.653 Tags from Mouse Spleen (Mus musculus)

More than 75 % rare transcripts:

This information

is lost on microarrays !

Only this part is visible for microarrays

>18.000 different transcripts excluding the singletons

* >13.000 Singletons with distinct matches to the NCBI-DB

slide22

SuperTAG vs. Micro-arrays

Comparable data:

Exact number for every transcript vs. semiquantitative values (Microarrays, RT-PCR)‏

slide23

Detection of antisense RNAs

Stress-regulation of expression of peroxidase antisense transcripts in

Cicer arietinum (chickpea)

2-fold

regulation

slide24

Normalization of cDNA libraries:

Frequent transcripts are strongly reduced

cDNA before normalization

cDNA after normalization

slide25

Normalization of cDNA libraries

Transcript frequencies

Frequencies of transcript species

Total transcript distribution

Frequent transcripts make up 50 % of all transcripts.

Most of the transcript species are expressed at low levels (below 10 copies per million).

Normalization is useful for qualitative whole transcriptome sequencing

slide26

Analysis of normalized cDNA ends:

Lower costs, sufficient for genotyping!

cDNA before normalisation

Normalized cDNA-Ends:

slide27

microRNAs and the degradome

microRNA

mRNA-ends

AAAAAAA-3’

AAAAAAA-3’

mRNA

AAAAAAA-3’

AAAAAAA-3’

Next-Gen-Sequencing, counting, BLAST

slide28

microRNAs and the degradome

microRNA-sequencing

Sequencing of Millions of microRNAs

Counting, BLAST, analysis of differential expression

PARE: parallel analysis of RNA ends

Sequencing of Millions of uncapped 5‘ ends

Counting, BLAST, analysis of differential expression

slide29

Functional annotation

Function ?

superTags

cDNA

cDNA Ends

nBLAST

nBLAST

nBLAST

nBLAST

BLASTx

BLASTx

  • Closest related organism
  • Lesser related organism
  • Lesser related organism
  • Etc.

Swissprot, Trembl, NCBI

slide30

Digital Karyotyping (DK)

Quantification of short fragments of genomic DNA

to identify chromosomal changes, amplifications, deletions, and the presence of foreign DNA sequences.

First enzyme digestion (methylation insensitive)

1.

3‘

5‘

DNA

5‘

3‘

2.

First Linker Ligation, binding to matrix

5‘

3‘

Biotin

slide31

Digital Karyotyping (DK)

3.

Second enzyme digestion (methylation sensitive)

5‘

3‘

Biotin

4.

Second linker ligation, Ecop15I digestion

5‘

3‘

Biotin

Counting,

Annotation

SuperTag 26bp

Sequencing

slide32

Methylation specific Digital Karyotyping (MSDK)

Genome-wide DNA methylation analysis

Methylation sensitive enzyme

1.

3‘

5‘

DNA

5‘

3‘

2.

First Linker Ligation, binding to matrix

5‘

3‘

Biotin

slide33

Methylation specific Digital Karyotyping (MSDK)

3.

Second enzyme digestion

5‘

3‘

Biotin

4.

Second linker ligation, Ecop15I digestion

5‘

3‘

Biotin

Counting,

Annotation

SuperTag 26bp

Sequencing

Ditagformation

slide34

References

Unravelling the interaction of HCMV with dendritic cells using SuperSAGE

M.J. Raftery, E. M. Buchner, H.Matsumura, T.Giese, A. Winkelmann, M. Reuter, R.Terauchi, G.Schönrich and D. H Krüger

J  Gen Virol (2009), DOI 10.1099/vir.0.010538-0

Molecular signatures of apomictic and sexual ovules in the Boecheraholboellii complex

Timothy F. Sharbel, Marie-Luise Voigt, Jose´ Maria Corral, Thomas Thiel, AlokVarshney, Jochen Kumlehn,

Heiko Vogel and Björn Rotter (2009) The Plant Journal, doi: 10.1111/j.1365-313X.2009.03826.x

Long-Short-Long Games in mRNA Identification: The Length Matters

Wang . S. M. (2008) Current Pharmaceutical Biotechnology, 9, 362-367

SuperSAGE: thedrought stress-responsivetranscriptomeofchickpearoots

Molina C.M., Rotter B., Horres R., Udupa S., Besser B., Bellarmino L., Baum M., Matsumura H., Terauchi R., Kahl G. and Winter P. (2008) BMC Genomics , 9:553doi:10.1186/1471-2164-9-553

Sperminesignalingplays a significantrole in thedefenseresponseofArabidopsisthalianatocucumbermosaicvirus.

Mitsuya Y, Takahashi Y, Berberich T, Miyazaki A, Matsumura H, Takahashi H, Terauchi R, Kusano T. (2008)

J Plant Physiol. Oct 13.

SuperSAGE: a modern platform for genome-wide quantitative transcript profiling.

Matsumura H, Krüger DH, Kahl G, Terauchi R.

CurrPharmBiotechnol. 2008 Oct;9(5):368-74.

SuperSAGE array: the direct use of 26-base-pair transcript tags in oligonucleotide arrays. Matsumura H, Bin Nasir KH, Yoshida K, Ito A, Kahl G, Kruger DH, Terauchi R. (2006) Nat Methods 3:469-474.

Gene expression analysis of plant host-pathogen interactions by SuperSAGE. Matsumura H, Reich S, Ito A, Saitoh H, Kamoun S, Winter P, Kahl G, Reuter M, Kruger DH, Terauchi R. 2003 Proc NatlAcadSci U S A. 100:15718-1523.

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