HTS data analysis

VTT MEDICAL BIOTECHNOLOGY

Analysis of HTS data

FIMM & Biomedicum Medical Bioinformatics Day,March 13, 2008, 12.30—17.30

Pekka KohonenVTT Medical Biotechnology

FIMMFIMM


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


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


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


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


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


Functional screens are used to define the effects of thesiRNAs on cell proliferation

Raw data

CTB

Normaliseddata

Cell proliferation hits from thescreens


In red: siRNAs that cause growth inhibition

-7

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Cell Line 2

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Cell Line 1Z

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row

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Cell Line 1(Z score: Growth inhibition)

Cel

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Common Anti-proliferative hits


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


• 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


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


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


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

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ffere

nce

One cell line: GE+siRNA+aCGHTwo cell lines: GE+siRNA


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


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


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

HTS data analysis

Technology

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