1. Verna Vu & Timothy Abreo

2. Study the transcriptome of transgenic mice vs wild type
δC-doublecortin-like kinase (Dclk1)
Transgenic mice express a constitutively
active splice variant of
doublecortin-like kinase 1 (Dclk1) gene.
Goals:
Study the transcriptome of
wild type vs transgenic mice
Compare microarray with DGE
δC-DCLK-short mice spend less time, move less in the open arms of the elevated plus maze→ δC-DCLK-short mice display a more anxious behavioral phenotype.

3. Microarray Problems: 5 different microarray high back ground
measures only relative abundances of transcripts
only predefined sequences can be detected
transcripts expressed at low levels can’t be detected
5 different microarray
Affymetrix
Agilent
Illumina
Applied Biosystems
Home spotted
dnachips-microarrays.wikispaces.com/

4. Quantitative PCR analysis
Cycle Based
Sybr Green: Binds to all ds PCR products
Gold standard: used to validate
Problems
Can’t Multiplex
Many cycles to detect low frequency
RNA transcripts, can be lost in background
noise
Can’t use on all transcripts
real time PCR
gold standard. can’t qPCR all genes but used to validate in this study
a lot of work

5. Helpful Nomenclature
Canonical sequence: sequences that reflects the most common choice of base at each position
Noncanonical sequence: tags in the genome that map to any known exon either strand
Transcripts per million: t.p.m.

6. Origins of Digital Gene Expression (DGE): SAGE
Problems:
Laborious
Sequencing steps
100 k canonical tags would take up to a year
Considerable financial investment needed

7. Digital Gene Expression (DGE) tag profiling
Procedure
1st and 2nd strand cDNA synthesis using oligo-dT beads to capture polyadenylated RNA
While still on beads, NlaIII was used to digest the DNA, makes cDNA fragment with 3’ most CATG
GEX adapter 1 is ligated to the free 5’ end
MmeI then cuts 17bp downstream of CATG site
Gex adapter 2 ligate to free 3’ end
PCR conducted using primers complementary to adapters, then sent for deep sequencing using Solexa/Illumina Whole Genome Sequencer
NO CLONING
First purify RNA, make cDNA, digest with first restriction enzyme, ligate first adapter to 3’ end, digest with other enzyme and ligate 5’ adapter

8. ENSEMBL transcripts
To enable comparison, all canonical sequence tags and microarray probe sequences were put in FASTA format and then aligned to the ENSEMBL transcript database.
Same samples used for DGE and microarray.

9. Alternative Polyadenylation
Transcripts with different 3’ ends that are separated by at least one restriction site can be differentiated with DGE
47% of detected ENSEMBL transcripts were discovered by more than one tag, most likely a result
of alternative polyadenylation
in 3’UTR
29% estimated previously
based on EST sequences

10. Antisense Transcription
Found evidence of bidirectional transcription in 51% of gene clusters when considering canonical and non-canonical tags of abundance >2 t.p.m.
Antisense transcripts were expressed at high levels, although sense transcripts were still most abundant
Importance: there could be gene transcripts in the opposite direction that we don’t know the function
antisense tags that are found were not due to reverse transcriptase artifacts

11. Differentially Expressed Genes
Sequencing of pooled samples caused problems; blood contamination in one sample
Used Bayesian statistics to attempt to exclude genes that were not truly differentially expressed
abundance of transcripts found to be highly expressed in blood

12. Biological Implications
Used Gene Ontology consortium and DGE data to identify differentially regulated gene sets
Most affected pathway:
Disturbances of microtubule guided transport of SNARE containing synaptic vesicles due to changes in gene expression
CaMK pathway was the second most affected pathway, possibly via feedback mechanisms
involved in behavioral pathways in brain

13. Dynamic Range
The ratio between the largest and smallest possible values of a changeable quantity
3 to 4 orders of magnitude
Lowest frequency but most consistently detected was 2 t.p.m. (0.3 copies per cell)
limit of chips at lower range. due to noise. low signal
linear portion of sigmoidal curve =DR

14. Comparison to SAGE and Microarray
The effect of sequencing depth on detection of differentially expressed genes
Simulated SAGE: randomly took 1/60 of DGE reads.
Detected a decrease in 15 fold from 3179 to 200 reads
Simulated SAGE:
lowest: 91 t.p.m.
DGE:
average: >2 t.p.m.
median: 4 t.p.m
lowest: 0.8 t.pm.
Microarray platforms
median: 106 t.p.m
Affymetrix had the most transcripts
in common with DGE
11 different probes per transcript

15. Validation with qPCR 29 significant genes from DGE, 33 from microarray
43/62 showed some sort of variation between the wt and transgenic same direction
only 5 were considered to be significant by both platforms
To test for accuracy
log 2 ratio: expression level of transgenic/wild type

16. Reproducibility
The correlation between the normalized number of counts from the summed individual samples in their laboratory and the pool analyzed in the other laboratory were
0.98 wild-type
0.96 transgenic
To test for precision
Technique allows collaboration
with other labs.
False discovery rate was 8.5%
determined the distribution of the differences between independent replicate measurements of log ratios between wild-type and transgenic samples

17. Why use DGE over Microarrays?
unbiased view of the transcriptome
detects high levels of differential polyadenylation and antisense transcription
data are more precise & accurate than microarray data
data analysis requires a lower number of preprocessing  facilitates interlaboratory comparisons
high interlaboratory comparability of DGE data, probably due to the avoidance of hybridization processes (notoriously difficult to standardize)
more sensitive in the detection of low-abundant transcripts and of small changes in gene expression.
Absence of background signal and saturation effects
unbiased view of the transcriptome
detects high levels of differential polyadenylation and antisense transcription (undetectable with standard microarrays)
data are more precise & accurate than microarray data
data analysis requires a lower number of preprocessing steps (like background correction and normalization), which facilitates interlaboratory comparisons
high interlaboratory comparability of DGE data, probably due to the avoidance of hybridization processes, which are notoriously difficult to standardize
more sensitive in the detection of low-abundant transcripts and of small changes in gene expression.
Absence of background signal and saturation effects (major causes of ratio compression on microarrays)

18. Implications:
Enhancements in sequencing depth to improve accuracy in particular for low abundant transcripts
Improvements in sensitivity, resolution, interlaboratory consistency
Boost field of expression profiling
Basic research and comparative genomics fields will benefit from major improvement of data portability
Once held back by extensive and lengthy standardization issues

19. What the Future has in Store
Deep sequencing→ robustness, comparability and richness of expression profiling data.
Will boost collaborative, comparative and integrative genomics studies
RNAseq
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