High-throughput cell-based assays

High-throughput cell-based assays

Wolfgang HuberEuropean Molecular Biology Laboratory

European Bioinformatics Institute

Signaling pathways

Drosophila antibacterial signalling

Drosophila Toll/antifungalsignaling

Small compounds

Full-length cDNA (over-)expression

RNAi

Interference/Perturbation tools

He and Hannon, 2004

Initiation

Execution

RNAi

RNAi as a loss of function perturbator

gene-sequence specific

reagents (eg siRNAs)

easy to make for any gene (there are caveats...)

protein

living cells

mRNA

gene

degradation

translation

transcription





protein

living cells

mRNA

gene

degradation

transcription





living cells

mRNA

gene

transcription

T7

Precursor dsRNA

siRNAs

Degradation of target message

C. elegans Drosophila Mammals

Injection and soaking

Feeding bacteria

WormsCell-

culture

Bathing Transfection

> 200bp> 200bp 21bp

Cell-culture

dsRNA dsRNA siRNAE. coli

RNAi experiments in different organisms

Dicer

Differential Expression vs Signaling Function

RNAiphenotypes

Differentially regulatedgenes

~ 70 280 genes

RIP/IMDpathways

RIP

Tak1

IKK

Rel

R

Targets

Michael Boutros

Most pathway targets are not required for pathway function

RNAiphenotypes

Differentially regulatedgenes

~ 70 280 genes

3

RIP/IMDpathways

RIP

Tak1

IKK

Rel

R

Targets

Michael Boutros

Any cellular process can be probed.- (de-)activation of a signaling pathway- cell differentiation- changes in the cell cycle dynamics- morphological changes- activation of apoptosisSimilarly, for organisms (e.g. fly embryos, worms)

Phenotypes can be registered at various levels of detail- yes/no alternative- single quantitative variable- tuple of quantitative variables- image- time course

What is a phenotype: it all depends on the assay

Plate reader 96 or 384 well plates, 1…4 measurements per well

FACS; Acumen Explorer ca. 2000 x 4…8 measurements per well

Automated Microscopy practically unlimited. many MB

Monitoring tools for automated phenotyping

RNAi by reverse-transfection

Cells are seeded on-top of pre-aliquoted siRNA pools

Computational analysisSecondary assays

72h

2x 68 384-well plates

Cell viability(‘CellTiterGlo’ Assay)

+/- Compound treatment (48h pt)

Plate reader Assays

cellHTS2

workflows for analysing a cell-based assay experiment

NChannelSetassayData can contain N=0, 1, 2, ..., matrices of the same size

feat

ure

Dat

a (A

nn

ota

ted

Dat

afra

me)

D

Target gene ID

Sequence

Physical coordinates

Phy

sica

lco

ordi

nate

sS

eque

nce

Tar

get g

ene

ID

“ph

eno

”Dat

a (A

nn

ota

ted

Dat

afra

me)

Sam

ple-

ID r

edS

ampl

e-ID

gre

en

Arr

ay ID

BSample-ID blue

_ALL_

G

R

Array-ID

Sample-ID green

Sample-ID red

Sam

ple-

ID b

lue

varMetaData

labelDescriptio

n labelDescriptio

n

channelDescriptio

n

The dataNumeric values x

ijk

i = wells (e.g. 20,000)

j = different reporters (e.g. 2)

k = different assays (e.g. 5)

Metadata about wells

pi = plate in which is well i

ri = row (within plate) of well i

ci = column (within plate) of well I

siRNA sequence, target gene, ....

Metadata about reporters

Fluc, Rluc, ...

Metadata about assays k

replicate number

different variants of the assay

date it was done

Between plate effects

kth wellith plate

packagesplots

"Normalisation"x

ijki = wells (e.g. 20,000)

j = different reporters, dyes

k = different assays

medianijk

ijk

hjk h i

xx

x | p = p

Plate median normalisation

- can use other estimators of location, e.g. mean, midpoint of shorth;

or shift and scale according to values of positive and negative

controls

- maintains the dimensions of x

Normalization: Plate effects

Percent of control

Normalized percent

inhibition

z-score

k-th welli-th plate100' ki

ki posi

xx =

μ

100pos

' i kiki pos neg

i i

μ xx =

μ μ

' ki iki

i

xx =

σ

Spatial normalization

B-score:

two-way medianpolish

rth rowcth columnith plate

ˆˆˆrci i ri ci'

rcii

x μ +R +Cx =

MAD

after

fitted row and column effects

before

Malo et al., Nat. Biotech. 2006

How to estimate the normalization parameters?

From which data points:

• Based on the intensities of the controls

if they work uniformly well across all plates

• Based on the intensities of the samples

invoke assumptions such as "most genes have no effect", or "same distribution of effect sizes"

Which estimator:

mean vs median vs shorth

standard deviation vs MAD vs IQR

In the best case, it doesn't matter.No universally optimal answer, it depends on the data.

Estimators of location

Histogram of x

x

Fre

qu

en

cy

-2 0 2 4 6 8 10

02

04

06

08

01

00

12

0

meanmedianshorthhalf.range.mode

mean

Estimators of scale

2

1

75 25

1standard deviation : ( )

1

median absolute deviation: median | median{ }|

interquartile range: Q ({ }) Q ({ })

shorth

{ }

n

ii

i j

i i

x xn

x x

x x

Channel summarisationx




i2ki1k i2k

i1k

xx ,x

x

(log-)ratio

collapses the second dimension of x

Replicate Summarisationx




...ijk ijk ijr1 nx , ,x y

replicate summarization

changes third dimension of x

Processing steps

xijk

i = wells (e.g. 20,000)



normalisation of whole plate effects dim unchanged

normalisation of within plate effects dim unchanged

summarization of channels change 2nd dim

replicate summarization change 3rd dim

scoring (transformation into z-scores) can change 1st dim

contrasts (as in linear models) change 3rd dim

NchannelSet provides a robust and powerful infrastructure for these

operations (and keeping the metadata aligned and intact)

' 1 3 p n

p n

Z

Zhang JH, Chung TD, Oldenburg KR, "A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays." J Biomol Screen. 1999;4(2):67-73.

2 2p n NB: would be more efficient

show example report

Thanks

Ligia Bras

Florian Hahne

Michael Boutros

Thomas Horn, Tina Büchling, Dorothee Nickles, Dierk Ingelfinger

Elin Axelsson

Gregoire Pau

Martin Morgan

High-throughput cell-based assays

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