Post on 26-May-2018
FFlexible lexible Microarray Platform from Microarray Platform from Agilent TechnologiesAgilent Technologies
Welgene Biotech.YiYi--ShingShing Lin Ph.DLin Ph.D
2007/Dec2007/Dec
yslin@welgene.com.twyslin@welgene.com.tw
Clinical LabOffice Area
microarray services
Authorized distributor
Welgene Biotech. Welgene Biotech. Distributor & Service ProviderDistributor & Service Provider
GeneExpressionmRNA LevelaCGH Chromosomal AberrationmiRNA post-transcriptional regulationChIP-on-chip epigenomics, transcription factor
HumanMouseMouse DevelopmentPig (Custom Array)RatArabidopsisC. elegansDog
Species M. griseaMonkey Rice Xenopus laevisYeastZebrafishCustom Build
Agilent Technologies60-mer Oligo Microarray
Application
InIn--situsitu Synthesis of Agilent ArraysSynthesis of Agilent Arrays
Nature Biotechnology Vol 19 April 2001 P342
A C T G
2.
3.
1.
6060--mer mer OligoOligo arrayarray
High Sensitivity in 60mer OligoHigh Sensitivity in 60mer Oligo
Average LLD*60mers: 0.004pM25mers: 0.032pM
*LLD:Lower Limit of Detection
Spike-in Concentration
BG
Sub
stra
cted
Sig
nal
Inkjet Technology for High DensityInkjet Technology for High Density
Back toPerformance
244,000
Accuracy drop volume drop placement jet spacing
Speed and throughput Reliability
Pin-spotting
Ink jet
Resistor Off
LiquidVaporizes
GasExpands
Resistor On Resistor Off
DropBreaks Off Reservoir
Refills
FillReservoir
< 1 msec
To Explore Gene RegulationTo Explore Gene Regulation
DNA level
RNA level
DNA copy number
uclesome / TFs binding
DNA methylation
Gene expression
microRNA regulation
CH3
Trend: More Microarray ApplicationsTrend: More Microarray Applications
Chromosome DNA Gene promoters mRNA mRNA variants microRNAs
aCGH SNPs miRNA
DNA RNA
SNPs ChIP/LA miRNAGene
ExpressionAlternateSplicing
Specificity
Sensitity
Key requirements
Informatics
Flexibility
Concept of ArrayConcept of Array--based CGHbased CGHReference DNA Test DNA
Log2
ratio
=0
Trend: More Microarray ApplicationsTrend: More Microarray Applications
Chromosomes DNA Gene promoters mRNA mRNA variants microRNAs
aCGH SNPs miRNA
DNA RNA
SNPs ChIP/LA miRNAGene
ExpressionAlternateSplicing
Specificity
Sensitity
Key requirements
Informatics
Flexibility
ChIPChIP--onon--chipchip(chromatin (chromatin immunoprecipitationimmunoprecipitation--onon--chip)chip)
To answer:
which genes are regulated by a known TF/DNA binding protein
High Accuracy of ChIPHigh Accuracy of ChIP--onon--chipchipStart with optimal probe designStart with optimal probe design
Methodology Tile 60-mers at 1-bp spacing across non-RepeatMasked genome (1.3B probes). Reduce 10-fold by thermodynamic scores (130M probes). Homology search against the genome using ProbeSpec (custom homology
search tool designed for probe matching). Reduce 10-fold using homology scores (13M probes). Re-score homology using MegaBlast (catches gapped alignments).
GENE XGENE YTF
Probe optimization criteria: Uniqueness (homology) Tm Self-structure
CpG island
CpG Island Array
95 bp
probe design target interval
probes
Array Design StrategyArray Design Strategy
Human and Mouse CpG island array available now!!!
~250 bp
Probe ProbeProbeProbe
gene
Transcription Start Site
8 kb promoter region 5.5 kb upstream, 2.5 kb downstreamPromoter Array
ChIPChIP--onon--chip processchip process
ChIP ChIP from A Local Vantage Pointfrom A Local Vantage PointGENE XTF
Chromatin broken intosmall fragments
Genomic Region:
TF
TF
TF
TF
TF
TF
TF
TF
TF
TFTF
Antibody-coatedMagnetic Bead
UNTREATED Anti-TF TREATED
IP-enriched DNA fragments
GENE XGENE YTF
ChIPChIP--enriched DNA enriched DNA vs.vs. total DNA inputtotal DNA input
Total DNA input (WCE)
Enriched DNA (IP)
Note: chromatin DNA fragments
are ~100 - 500 bp
244K randomized 60-mer
Richard A. Young, PhDWhitehead Institute
MIT
Details
Application: ChIP-on-chip
Comprehensive and genome-wide localization of 18,000 putative binding events
Co-occupancy OCT4, SOX2, NANOG help understand the mechanisms of pluripotency
Identification of autoregulatory(feed-forward) circuits
Stem Cell Transcriptional Regulation Stem Cell Transcriptional Regulation NetworksNetworks
Trend: More Microarray ApplicationsTrend: More Microarray Applications
Chromosomes DNA Gene promoters mRNA mRNA variants microRNAs
aCGH SNPs miRNA
DNA RNA
SNPs ChIP/LA miRNAGene
ExpressionAlternateSplicing
Specificity
Sensitity
Key requirements
Informatics
Flexibility
miRNAmiRNAScientific Background and Scientific Background and ImportanceImportance
Cancer Genomics: Small RNAs with big impactsfrom Nature 435: 745-746 (9 June 2005)
*Source: NIH CRISP database at http://crisp.cit.nih.gov/
Definition: 19-30 nucleotide long single-stranded RNAs
that post-transcriptionally regulate gene expression
Key regulators of gene expression, development, proliferation, differentiation, and apoptosis
May regulate >30% of human genes Current Sanger miRBASE Release 9.1 has
4361 entries, 474 identified in humans Projected $100M to be awarded by the NIH for
miRNA-related research in 2008*Discovery of new miRNAs is ongoing
Challenges in Challenges in miRNAmiRNA ProfilingProfiling Small size High sequence homology Expressed with large dynamic range Growing & changing database
http://www.ipmc.cnrs.fr/images/equipes/honore/mirna_steps.jpg
Add T-tilt to increase miRNA hybridization accessibility
G-addition to stabilize the hyb. duplex
Adjust Tm by shortening the probe at the 5end (5-end is conserved among miRNA, I.e less specifific)
Add 5-hat to repellthe longer RNA, such as mRNA
Probe design of Probe design of miRNAmiRNA arrayarray
Figure 2bmiRNAs
hsa-let-7ahsa-let-7bhsa-let-7chsa-let-7dhsa-let-7ehsa-let-7fhsa-let-7ghsa-let-7i
UGAGGUAGUAGGUUGUAUAGUUUGAGGUAGUAGGUUGUGUGGUUUGAGGUAGUAGGUUGUAUGGUUAGAGGUAGUAGGUUGCAUAGUUGAGGUAGGAGGUUGUAUAGUUGAGGUAGUAGAUUGUAUAGUUUGAGGUAGUAGUUUGUACAGUUGAGGUAGUAGUUUGUGCUGU
2222222121222121
miRNA Sequence Length (nts)
0
85 - 100%70 - 84%55 - 69%40 - 54%25 - 39%10 - 24%5 - 9%0 - 4% Grey Number0
0000000
1
0000
11
4
33
2
00
00
00
00
12
75
5
30
1
1
1
1
11
11 00
0
1 24
3
6
Data ComparisonData Comparison
32 (58%)32 (70%)Overlap number (%)
5546 Down-regulated miRNAs (3-fold)
47 (70%)47 (78%)Overlap number (%)
6760Up-regulated miRNAs(3-fold)
203 (90%)313 (67%)Detectable miRNAs (%)
225470Total miRNAs
qRT-PCRMicroarray
Trend: More Microarray ApplicationsTrend: More Microarray Applications
Chromosomes DNA Gene promoters mRNA mRNA variants microRNAs
aCGH SNPs miRNA
DNA RNA
SNPs ChIP/LA miRNAGene
ExpressionAlternateSplicing
Specificity
Sensitity
Key requirements
Informatics
Flexibility
Agilent Complete Design FlexibilityAgilent Complete Design Flexibility
105K
105K
244K
44K 44K 44K 44K
For genome wide design
Lower Cost with high resolution
Lower cost per experiment
15K 15K 15K 15K
15K 15K 15K 15K
High-throughput sampling
Data Driven
Hypothesis Driven
Probe cost by home-brew spotting:244000 x 60 x 8 = 117,120,000
105000 x 60 x 8 = 50,400,000
44000 x 60 x 8 = 21,120,000
15000 x 60 x 8 = 7,200,000
Agilent Custom Array ProgramAgilent Custom Array Program
Create Probe
Groups
Upload Probes
Search Agilent Probes
Create Microarray Designs
Submit to Manufactu
ring
Order Arrays
Upload genome
sequence
Custom Probe Design
Computational Probe SelectionComputational Probe Selection
Rosetta/Agilent probe selection algorithms
Validated selection criteriai. Vector maskingii. Repeat maskingiii. Tmiv. Base compositionv. BLAST for specificityvi. Folding characteristics
Probe locationi. Adjacent to 3-endii. Evenly distributed
Gene
Transcript 1
Transcript 2
Transcript 3
Transcript 4
Merged into oneconsensus region
Cant merge
I II III
Merged ConsensusRegion
CandidateProbes
Real Design Case by WelgeneReal Design Case by Welgene
Custom Array DesignCustom Array Design
Probe content ofProbe content of MagnaportheMagnaporthe griseagriseaMicroarrayMicroarray
15,170 predicted genes by the Fungal Genomics Laboratory at North Carolina State University
Rice ESTs were derived from cDNA generated from uninfected as well as infected rice tissues
Array Design fromArray Design from dede--novonovo SequencingSequencing
cDNA Libraries Construction
ESTs Sequencing
Sequences Assemble
Gene ClusterGene ClusterCustom Array Design
Annotation
Example:Medaka Microarray
Annotation: Gene InformationAnnotation: Gene Information
ZebraFish O
Total 12,429 clusters->8,091 clusters to Agilent custom array
Leveraging Leveraging AgilentAgilentss Flexible Printing PlatformFlexible Printing Platform
We will manufacture: What you want When you want it In the quantity you need With no or low set up fees With or without professional
bioinformatics support Customers dont have to: Adapt your research needs to standard
product designs Wait for complex setups or oligo sets Amortize large design or oligo
investments over large volumes
is the engine that drives Agilents microarray
End of Section I.End of Section I.
Any Question?Any Question?
Glass
Substrate
Microarray
Quarantine
Process
Dice/Package
Not for sale!
IndustrialIndustrial--Scale InkScale Ink--jet Printingjet PrintingQuality Control and Process MonitoringQuality Control and Process Monitoring
Microarray Manufacture WorkflowMicroarray Manufacture Workflow
Define Genetic Content
Probe Design and Selection(Computational & Empirical)
Design File Creation
Substrate Applied to Glass Wafers
Nucleic Acids & ReagentsPrepared for Printing
In-situ Chemical Synthesis via Inkjet Printing Technology
Quality Assessment ofRepresentative Microarrays
Microarrays Packaged for Shipping
On-line eArray Design Microarray Print
QC
QC
QC
QC
QC
Introduction of Microarray Data AnalysisIntroduction of Microarray Data Analysis--using using GeneSpringGeneSpring as an exampleas an example
Welgene Biotech.YiYi--ShingShing Lin Ph.DLin Ph.D
2007/Dec2007/Dec
yslin@welgene.com.twyslin@welgene.com.tw
Information storage
Data storage
execution
Idea: measure the amount of mRNA to see which genes are being expressed in (used by) the cell.
Measuring protein directly might be better, but is currently harder.
Microarray principle (two color system)Microarray principle (two color system)Sample A
(cells,tissue,etc.)Extract total RNA
Label withfluorescent Cy5
in RT rxn
AAAAAAAAAA
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Sample B(cells,tissue,etc.)
AAAAAAAAAA
AAAAAAAAAA
Label withfluorescent Cy3
in RT rxn
Pool
Hyb target - spotted DNA
Competitive hybridization
Microarray
Laser scan forCy5
Laser scan forCy3
Cy5 signalCy3 signal
ExpressionRatio
DiseaseNormal
1 hourt=0
TreatedUntreated
Examples:
Microarray principleMicroarray principle(single color system)(single color system)
Sample A(cells,tissue,etc.)
Extract total RNA
Label withfluorescent Cy3
in RT rxn
AAAAAAAAAA
AAAAAAAAAA
Sample B(cells,tissue,etc.)
AAAAAAAAAA
AAAAAAAAAA
Label withfluorescent Cy3
in RT rxn
Hyb target - spotted DNA
hybridization
Microarray (A)
hybridization
Microarray (B)
Laser scan forCy3
Laser scan forCy3
Cy3 signal(A)Cy3 signal(B)
ExpressionRatio
Microarray (A) Microarray (B)
From Raw Expression Data to Biological Knowledge
Go from this
prostaglandinsynthesisIn TGF-b treatment
...To this!
Agilent GeneSpring GXAgilent GeneSpring GX
Start with the Raw
expression values
Load the data into
GeneSpring
Use powerful but easy to use
statistics to filter the list
Find biological relevance of the genes
GenomeGenome
Annotation updateAnnotation update
Analysis StepsAnalysis Steps
Untreated Treated with ILbeta
Data normalization
Quality filtering
Find DE genes
Find GO category & Gene function of DE genes
Data QC
Analysis stepsAnalysis steps
Untreated Treated with ILbeta
Data normalization
Quality filtering
Find DE genes
Find GO category & Gene function of DE genes
Data QC
Scatter Plot Scatter Plot
Check the Distribution of your Check the Distribution of your Signals with Box PlotsSignals with Box Plots
Analysis StepsAnalysis Steps
Untreated Treated with ILbeta
Data normalization
Quality filtering
Find DE genes
Find GO category & Gene function of DE genes
Data QC
Global (Per Chip) NormalizationGlobal (Per Chip) NormalizationNo Normalization Global Normalization
IntensityIntensity--dependent LOWESS dependent LOWESS NormalizationNormalization
Reality
Plot of log intensity Cy3 vs. log intensity Cy5
Ideal scenario
Cy3
Cy5
Analysis StepsAnalysis Steps
Untreated Treated with ILbeta
Data normalization
Quality filtering
Find DE genes
Find GO category & Gene function of DE genes
Data QC
Quality Quality FFilterilter
Data Filtering Data Filtering by Flagby Flag
Array Signal DistributionArray Signal Distribution
Highly expressed
moderately expressed
Low expressed
No expressed
50
Analysis StepsAnalysis Steps
Untreated Treated with ILbeta
Data normalization
Quality filtering
Find DE genes
Find GO category & Gene function of DE genes
Data QC
Defining parameter setupDefining parameter setup
Untreated Treated with ILbeta
Enter the parameters for replicate samples
Find Differentially Expressed Genes with Find Differentially Expressed Genes with ANOVAANOVA
Finding The Biological Significance Finding The Biological Significance
Function Category Gene Ontology
Molecular function Biological process Cellular component
Pathway Analysis KEGG Biocarta GeneMap
Full Gene Ontology BrowserFull Gene Ontology Browser
Allows you to more easily determine the function of your genes, by calculating the enrichment of your gene list with genes from a particular GO category
GO Ontology Browser: TableGO Ontology Browser: Table Genes in Category total
number of genes in the genome that have been assigned to this category
% of Genes in Category percentage of the total genome that has been assigned to this category.
Genes in List in Category the total number of genes that are both in the selected gene list and in this category
% of Genes in List in Category percentage of the selected gene list that falls into this category.
p-value the hypergeometric p-value (without multiple testing corrections). A measure of the statistical significance of the overlap between the selected gene list and this category.
KEGGKEGG
KEGG: Kyoto Encyclopedia of Genes and Genomes
http://www.genome.jp/kegg/
A grand challenge in the post-genomic era is a complete computer representation of the cell and the organism, which will enable computational prediction of higher-level complexity of cellular processes and organism behaviors from genomic information. Towards this end we have been developing a bioinformatics resource named KEGG, Kyoto Encyclopedia of Genes and Genomes, as part of the research projects in the KanehisaLaboratory of Kyoto University Bioinformatics Center.
Pathway ListPathway List
Example: ApoptosisExample: Apoptosis
BiocartaBiocarta
www.biocarta.com
Pathway Information in GeneSpring GXPathway Information in GeneSpring GX
From Raw Expression Data to From Raw Expression Data to Biological KnowledgeBiological Knowledge
Go from this
prostaglandinsynthesisIn TGF-b treatment
...To this!
Integrate with Ingenuity Pathway Integrate with Ingenuity Pathway Analysis (IPA)Analysis (IPA)
Run the analysis in IPA
Gene List from IPA to Gene List from IPA to GeneSpringGeneSpring
Save selected
My List for GeneSpring
Genes from IPA analysis.zip
Drag & Drop Gene List from IPA to Drag & Drop Gene List from IPA to GeneSpringGeneSpring
Genes from IPA analysis.zip
Thank You for Your ListeningThank You for Your ListeningHave a nice day and Goodbye!
Agilent platform NTU 2007m DecGeneSpring GX introduction 2007 NTU