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High-throughput cell-based assays
Wolfgang HuberEuropean Molecular Biology Laboratory
European Bioinformatics Institute
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Signaling pathways
Drosophila antibacterial signalling
Drosophila Toll/antifungalsignaling
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Small compounds
Full-length cDNA (over-)expression
RNAi
Interference/Perturbation tools
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He and Hannon, 2004
Initiation
Execution
RNAi
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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
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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
transcription
![Page 7: High-throughput cell-based assays Wolfgang Huber European Molecular Biology Laboratory European Bioinformatics Institute.](https://reader035.fdocuments.us/reader035/viewer/2022062421/56649d3a5503460f94a14f14/html5/thumbnails/7.jpg)
RNAi as a loss of function perturbator
gene-sequence specific
reagents (eg siRNAs)
easy to make for any gene (there are caveats...)
living cells
mRNA
gene
transcription
![Page 8: High-throughput cell-based assays Wolfgang Huber European Molecular Biology Laboratory European Bioinformatics Institute.](https://reader035.fdocuments.us/reader035/viewer/2022062421/56649d3a5503460f94a14f14/html5/thumbnails/8.jpg)
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
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Differential Expression vs Signaling Function
RNAiphenotypes
Differentially regulatedgenes
~ 70 280 genes
RIP/IMDpathways
RIP
Tak1
IKK
Rel
R
Targets
Michael Boutros
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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
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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
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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
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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
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cellHTS2
workflows for analysing a cell-based assay experiment
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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
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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
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Between plate effects
kth wellith plate
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packagesplots
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"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
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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
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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
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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.
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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
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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
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Channel summarisationx
ijki = wells (e.g. 20,000)
j = different reporters, dyes
k = different assays
i2ki1k i2k
i1k
xx ,x
x
(log-)ratio
collapses the second dimension of x
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Replicate Summarisationx
ijki = wells (e.g. 20,000)
j = different reporters, dyes
k = different assays
...ijk ijk ijr1 nx , ,x y
replicate summarization
changes third dimension of x
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Processing steps
xijk
i = wells (e.g. 20,000)
j = different reporters, dyes
k = different assays
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)
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' 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
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show example report
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Thanks
Ligia Bras
Florian Hahne
Michael Boutros
Thomas Horn, Tina Büchling, Dorothee Nickles, Dierk Ingelfinger
Elin Axelsson
Gregoire Pau
Martin Morgan