hts data analysis
DESCRIPTION
A talk on high-throughput RNai and compound screening data analysis given at Finnish Institute for Molecular medicine (FIMM) March 13, 2008, 12.30—17.30.TRANSCRIPT
VTT MEDICAL BIOTECHNOLOGY
Analysis of HTS data
FIMM & Biomedicum Medical Bioinformatics Day,March 13, 2008, 12.30—17.30
Pekka KohonenVTT Medical Biotechnology
FIMMFIMM
VTT MEDICAL BIOTECHNOLOGY
Presentation overview1. The high-throughput screening workflow2. Design considerations in the screens
• Which genes to assay: biological question at hand• Sources of error in the screens:
• Biological/technical variance (negative controls)• Transfectability of the cells (positive controls)• Off-target effects (redundancy and replication)
3. RNAi screening data normalization4. Hit picking and prioritization5. New technologies: Cell Arrays and Lysate Arrays6. Integration of data from other sources7. Hight Throughput screening database (HTSdb)
• Combines multiple assays and platforms• plate based, lysate arrays, cell arrays, supporting data(GE, aCGH)
• Based on R/MySQL• "First Light" recently
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Screening work-flow
Biological question
Primary screens
Replicating hits
Biological assay Reagents: Libraries of siRNAs, miRNAs,compounds
Secondary screening
Data integration with gene expression,aCGH, other screens (cancer/normal)
Investigation of pathways targeted,literature mining
Prioritized hits for further validation
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3) 35 ul of trypsinizedcell suspension
1) Pipet diluted siRNAs
2) Add transfectionagent
Flow-through of a High-throughput screenin 384 wells
4) Incubate72 hrs
5) Add cell phenotypestains & incubate
6) Fluorescencemeasurement
& data analysis
384 well plates
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Design considerations: Off-target effects
• Non-sequencespecific off-targeteffects:– Interferon
response– siRNA causing
miRNAmachinerysaturation
– Lipid toxicity• Specific:
– Effects onrelated mRNAs
– miRNAmechanismbased off-targeteffects
Off-target effects are usually cell line and siRNA specific
The best way to mitagate them is to have 2-4 siRNAs per gene
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Edge-effects and B-score normalization
• Edge effect is seen especially with the Cell Titer Blue (CTB) reagent• Edge effect causes a lowered signal intensity at the edges• In the B-score normalization estimates of row/column effects are obtained
using a two-way median polish. (Brideau et al., J Biomol Screen. 2003)
Raw data showing anedge effect
B-score normalized dataafter removal of the edge
effect
RNAi screening data normalization
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Functional screens are used to define the effects of thesiRNAs on cell proliferation
Raw data
CTB
Normaliseddata
Cell proliferation hits from thescreens
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In red: siRNAs that cause growth inhibition
-7
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Cell Line 2
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0 50 100 150 200 250 300 350 400 450
Cell Line 1Z
scor
e: g
row
th in
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Cell Line 1(Z score: Growth inhibition)
Cel
l Lin
e 2
(Z s
core
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wth
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bitio
n)
Common Anti-proliferative hits
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Pan-hitting siRNAs
siRNAs hitting theparental cell line
siRNAs hitting thevariant_1 cell line
siRNAs hittingpreferentially the parentcell line
Parental Variant_1 Variant_2
Cell Titer Blue (CTB) growth inhibition screens (Blue means growth inhibited)
by Pasi Halonen
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• Up to 46 000 spots with different individual siRNA transfections in single assay plate.• Arrays with cells growing only on arrayed spots.• System allows low cost uHTS with minimal infastructure requirements.• Has five measurement channels for visualization of different antibodies and stains
I TECHNOLOGY INTRODUCTION - TRANSFECTION CELL ARRAYS
by Juha Rantala
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2. Automated image analysis• image based cytometry
3. Result classification by morphology, intensity, localisation, number etc.
• Analysis of antibody staining/ organelle stains
1. Imaging
DNA ACTIN Antibody 1. Antibody 2. + Antibody 3. ?
Image analysis will be a bioinformatics challenge for theImage analysis will be a bioinformatics challenge for thecell array technologycell array technology
10,000s ofimages fromeach experiment- requiringterabytes forstorage
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Lysates from cultured cell lines
Pre-miR transfections
siRNA transfections
Multiple proteinmicroarray slides
Signal quantification and analysis of functional effects
II Cell lysate microarrays for multiple end-point analysis
Phenotype markers
EMT: E-cadherin, Vimentin, Beta-catenin
Targets & pathways: p53, c-Myc, Met
Proliferation: Ki-67, Cyclin E, Histone H3
Cell cycle: Cyclins D, E, A, B1, p-HistoneH3(Ser10)
Apoptosis: Caspase-3, PARP, Histone H2AX
Protein lysates
by Rami Mäkelä
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Integration of data from other sources
Increased gene expressionand greater siRNA growthinhibition
Gene amplification, siRNAgrowth inhibition and geneexpression increase
by Henrik Edgren
Expression ratio to parental
sirN
Agr
owth
inhi
bitio
n di
ffere
nce
One cell line: GE+siRNA+aCGHTwo cell lines: GE+siRNA
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High Throughput Screening Database:Multiple Assays of the same Model System
HTSdbPlate based:- CTB- CellTiter-Glo™- ApoOne™- luciferase assays
Lysate arrays:- up to 3 channels- multiple endpoints- use of ratios
Cell Arrays:- up to 5 channels- uHTS (10000's)- improved repeatability- use ratios for normalization
Supporting Data:
- gene expression
- aCGH
- miRNA expression
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HTSdb Design Principles• Pragmatic - focused on analysis needs• Extensible to new data sources, normalizations and sample
annotation terms• Different assays done on same biological samples can be
combined (eg. CTB, ApoOne, Lysate Arrays)• Other data sources (gene expression, miRNA expression)
can be combined with screening datas• MySQL open source database• R statistical programming language is used to access the
database and to analyze the datas• Bioconductor R-libraries are used when applicable• Ensembl: all identifiers are linked to ensembl genes
"First Light" recently - data input,normalization and retrieval
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Database Structure
Annotations of reagentsAnnotations of reagentssiRNA, miRNA, compounssiRNA, miRNA, compouns
Datas: raw and normalizedDatas: raw and normalized
Screen AnnotationsScreen Annotations
VTT MEDICAL BIOTECHNOLOGY CONFIDENTIALCONFIDENTIAL
Canceromics• Matthias Nees•• Elmar BucherElmar Bucher•• HenrikHenrik EdgrenEdgren•• Kalle OjalaKalle Ojala•• SamiSami KilpinenKilpinen•• JohnJohn--Patrick MpindiPatrick Mpindi•• TommiTommi PistoPisto•• PekkaPekka TiikkainenTiikkainen•• Henri SaraHenri Sara•• Maija WolfMaija Wolf
Biochips• Petri Saviranta•• RamiRami MMääkelkelää•• JuhaJuha RantalaRantala
High-throuput screening• Merja Perälä• Pekka KohonenPekka Kohonen•• Arttu HeinonenArttu Heinonen•• Niko SahlbergNiko Sahlberg•• Pasi HalonenPasi Halonen•• SuviSuvi--Katri LeivonenKatri Leivonen•• Saija HaapaSaija Haapa--PaananenPaananen•• Vidal FeyVidal Fey
Harri SiitariHarri Siitari
VTT Medical BiotechnologyMedical Biotechnology,, Turku, FinlandTurku, Finland
Olli KallioniemiOlli Kallioniemi